Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior

Researchers have highlighted how tracking learners’ eye-gaze can reveal their reading behaviors and strategies, and this provides a framework for developing personalized feedback to improve learning and problem solving skills. In this paper, we describe analyses of eye-gaze data collected from 16 middle school students who worked with Betty’s Brain, an open-ended learning environment, where students learn science by building causal models to teach a virtual agent. Our goal was to test whether newly available consumer-level eye trackers could provide the data that would allow us to probe further into the relations between students’ reading of hypertext resources and building of graphical causal maps. We collected substantial amounts of gaze data and then constructed classifier models to predict whether students would be successful in constructing correct causal links. These models predicted correct map-building actions with an accuracy of 80% (F1 = 0.82; Cohen’s kappa κ = 0.62). The proportions of correct link additions are in turn directly related to learners’ performance in Betty's Brain. Therefore, students’ gaze patterns when reading the resources may be good indicators of their overall performance. These findings can be used to support the development of a real-time eye gaze analysis system, which can detect students reading patterns, and when necessary provide support to help them become better readers.


A Wireless Sensor Network-Based Ubiquitous Paprika Growth Management System

Wireless Sensor Network (WSN) technology can facilitate advances in productivity, safety and human quality of life through its applications in various industries. In particular, the application of WSN technology to the agricultural area, which is labor-intensive compared to other industries, and in addition is typically lacking in IT technology applications, adds value and can increase the agricultural productivity. This study attempts to establish a ubiquitous agricultural environment and improve the productivity of farms that grow paprika by suggesting a 'Ubiquitous Paprika Greenhouse Management System' using WSN technology. The proposed system can collect and monitor information related to the growth environment of crops outside and inside paprika greenhouses by installing WSN sensors and monitoring images captured by CCTV cameras. In addition, the system provides a paprika greenhouse environment control facility for manual and automatic control from a distance, improves the convenience and productivity of users, and facilitates an optimized environment to grow paprika based on the growth environment data acquired by operating the system.


A conditional approach to dispositional constructs: the local predictability of social behavior.

A conditional approach to dispositions is developed in which dispositional constructs are viewed as clusters of if-then propositions. These propositions summarize contingencies between categories of conditions and categories of behavior rather than generalized response tendencies. A fundamental unit for investigating dispositions is therefore the conditional frequency of acts that are central to a given behavior category in circumscribed situations, not the overall frequency of behaviors. In an empirical application of the model, we examine how people's dispositional judgments are linked to extensive observations of targets' behavior in a range of natural social situations. We identify categories of these social situations in which targets' behavior may be best predicted from observers' dispositional judgements, focusing on the domains of aggression and withdrawal. One such category consists of subjectively demanding or stressful situations that tax people's performance competencies. As expected, children judged to be aggressive or withdrawn were variable across situations in dispositionally relevant behaviors, but they diverged into relatively predictable aggressive and withdrawn actions in situations that required the social, self-regulatory, and cognitive competencies they lacked. Implications of the conditional approach for personality assessment and person perception research are considered.


System structure for software fault tolerance

The paper presents, and discusses the rationale behind, a method for structuring complex computing systems by the use of what we term “recovery blocks”, “conversations” and “fault-tolerant interfaces”. The aim is to facilitate the provision of dependable error detection and recovery facilities which can cope with errors caused by residual design inadequacies, particularly in the system software, rather than merely the occasional malfunctioning of hardware components.


Explainable PCGML via Game Design Patterns

Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.


Learning grammatical structure with Echo State Networks

Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. However, their performance on natural language tasks has been largely unexplored until now. Simple Recurrent Networks (SRNs) have a long history in language modeling and show a striking similarity in architecture to ESNs. A comparison of SRNs and ESNs on a natural language task is therefore a natural choice for experimentation. Elman applies SRNs to a standard task in statistical NLP: predicting the next word in a corpus, given the previous words. Using a simple context-free grammar and an SRN with backpropagation through time (BPTT), Elman showed that the network was able to learn internal representations that were sensitive to linguistic processes that were useful for the prediction task. Here, using ESNs, we show that training such internal representations is unnecessary to achieve levels of performance comparable to SRNs. We also compare the processing capabilities of ESNs to bigrams and trigrams. Due to some unexpected regularities of Elman's grammar, these statistical techniques are capable of maintaining dependencies over greater distances than might be initially expected. However, we show that the memory of ESNs in this word-prediction task, although noisy, extends significantly beyond that of bigrams and trigrams, enabling ESNs to make good predictions of verb agreement at distances over which these methods operate at chance. Overall, our results indicate a surprising ability of ESNs to learn a grammar, suggesting that they form useful internal representations without learning them.


Cooperation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: possible implications for cognition, consciousness, intelligence and creativity

It is suggested that the anatomical structures which mediate consciousness evolved as decisive embellishments to a (non-conscious) design strategy present even in the simplest unicellular organisms. Consciousness is thus not the pinnacle of a hierarchy whose base is the primitive reflex, because reflexes require a nervous system, which the single-celled creature does not possess. By postulating that consciousness is intimately connected to self-paced probing of the environment, also prominent in prokaryotic behavior, one can make mammalian neuroanatomy amenable to dramatically straightforward rationalization. Muscular contraction is the nervous system's only externally directed product, and the signaling routes which pass through the various brain components must ultimately converge on the motor areas. The function of several components is still debatable, so it might seem premature to analyze the global operation of the circuit these routes constitute. But such analysis produces a remarkably simple picture, and it sheds new light on the roles of the individual components. The underlying principle is conditionally permitted movement, some components being able to veto muscular contraction by denying the motor areas sufficient activation. This is true of the basal ganglia (BG) and the cerebellum (Cb), which act in tandem with the sensory cerebrum, and which can prevent the latter's signals to the motor areas from exceeding the threshold for overt movement. It is also true of the anterior cingulate, which appears to play a major role in directing attention. In mammals, the result can be mere thought, provided that a second lower threshold is exceeded. The veto functions of the BG and the Cb stem from inhibition, but the countermanding disinhibition develops at markedly different rates in those two key components. It develops rapidly in the BG, control being exercised by the amygdala, which itself is governed by various other brain regions. It develops over time in the Cb, thereby permitting previously executed movements that have proved advantageous. If cognition is linked to overt or covert movement, intelligence becomes the ability to consolidate individual motor elements into more complex patterns, and creativity is the outcome of a race-to-threshold process which centers on the motor areas. Amongst the ramifications of these ideas are aspects of cortical oscillations, phantom limb sensations, amyotrophic lateral sclerosis (ALS) the difficulty of self-tickling and mirror neurons.


Incentive mechanism for peer-to-peer media streaming

We propose a rank-based peer-selection mechanism for peer-to-peer media streaming systems. The mechanism provides incentives for cooperation through service differentiation. Contributors to the system are rewarded with flexibility and choice in peer selection, resulting in high quality streaming sessions. Free-riders are given limited options in peer selection, if any, and hence receive low quality streaming. Through simulation and wide-area measurement studies, we verify that the mechanism can provide near optimal streaming quality to the cooperative users until the bottleneck shifts from the sources to the network.


Model-based Q-learning for humanoid robots

This paper is proposal with regarding reinforcement learning and robotics. It contains our study so far about reinforcement learning problem and Q-learning — one of the methods to solve it. The method is tested both by running on a simulation and on NAO robot. Both are written in high programming language. Since the testing are also done on NAO robot. This paper also includes our understanding about NAO robot and Robotic Operating System (ROS), and our approach to apply Q-learning on NAO robot. In the end, we have been successfully tested Q-learning method and apply it to NAO robot in real-world environment.


Community detection and visualization in social networks: Integrating structural and semantic information

Due to the explosion of social networking and the information sharing among their users, the interest in analyzing social networks has increased over the recent years. Two general interests in this kind of studies are community detection and visualization. In the first case, most of the classic algorithms for community detection use only the structural information to identify groups, that is, how clusters are formed according to the topology of the relationships. However, these methods do not take into account any semantic information which could guide the clustering process, and which may add elements to conduct further analyses. In the second case most of the layout algorithms for clustered graphs have been designed to differentiate the groups within the graph, but they are not designed to analyze the interactions between such groups. Identifying these interactions gives an insight into the way different communities exchange messages or information, and allows the social network researcher to identify key actors, roles, and paths from one community to another.
 This article presents a novel model to use, in a conjoint way, the semantic information from the social network and its structural information to, first, find structurally and semantically related groups of nodes, and second, a layout algorithm for clustered graphs which divides the nodes into two types, one for nodes with edges connecting other communities and another with nodes connecting nodes only within their own community. With this division the visualization tool focuses on the connections between groups facilitating deep studies of augmented social networks.


C4: the continuously concurrent compacting collector

C4, the Continuously Concurrent Compacting Collector, an updated generational form of the Pauseless GC Algorithm [7], is introduced and described, along with details of its implementation on modern X86 hardware. It uses a read barrier to support concur- rent compaction, concurrent remapping, and concurrent incremental update tracing. C4 differentiates itself from other generational garbage collectors by supporting simultaneous-generational concurrency: the different generations are collected using concurrent (non stop-the-world) mechanisms that can be simultaneously and independently active. C4 is able to continuously perform concurrent young generation collections, even during long periods of concurrent full heap collection, allowing C4 to sustain high allocation rates and maintain the efficiency typical to generational collectors, without sacrificing response times or reverting to stop-the-world operation. Azul systems has been shipping a commercial implementation of the Pauseless GC mechanism, since 2005. Three successive generations of Azul's Vega series systems relied on custom multi-core processors and a custom OS kernel to deliver both the scale and features needed to support Pauseless GC. In 2010, Azul released its first software-only commercial implementation of C4 for modern commodity X86 hardware, using Linux kernel enhancements to support the required feature set. We discuss implementa- tion details of C4 on X86, including the Linux virtual and physical memory management enhancements that were used to support the high rate of virtual memory operations required for sustained pauseless operation. We discuss updates to the collector's manage- ment of the heap for efficient generational collection and provide throughput and pause time data while running sustained workloads.


A study of normative and informational social influences upon individual judgement.

By NOW, many experimental studies (e.g., 1, 3, 6) have demonstrated that individual psychological processes are subject to social influences. Most investigators, however, have not distinguished among different kinds of social influences; rather, they have carelessly used the term "group" influence to characterize the impact of many different kinds of social factors. In fact, a review of the major experiments in this area—e.g., those by Sherif (6), Asch (1), Bovard (3)—would indicate that the subjects (5s) in these experiments as they made their judgments were not functioning as members of a group in any simple or obvious manner. The S, in the usual experiment in this area, made perceptual judgments hi the physical presence of others after hearing their judgments. Typically, the S was not given experimental instructions which made him feel that he was a member of a group faced with a common task requiring cooperative effort for its most effective solution. If "group" influences were at work in the foregoing experiments, they were subtly and indirectly created rather than purposefully created by the experimenter.


T REE-STRUCTURED DECODING WITH DOUBLY-RECURRENT NEURAL NETWORKS

We propose a neural network architecture for generating tree-structured objects from encoded representations. The core of the method is a doubly recurrent neural network model comprised of separate width and depth recurrences that are combined inside each cell (node) to generate an output. The topology of the tree is modeled explicitly together with the content. That is, in response to an encoded vector representation, co-evolving recurrences are used to realize the associated tree and the labels for the nodes in the tree. We test this architecture in an encoderdecoder framework, where we train a network to encode a sentence as a vector, and then generate a tree structure from it. The experimental results show the effectiveness of this architecture at recovering latent tree structure in sequences and at mapping sentences to simple functional programs.


Extreme Learning Machine for Multilayer Perceptron

Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via ℓ1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.


Motivational Leadership: Tips From the Business World.

It is an important task for leadership to identify the motivating factors for employees and motivate them to fulfill their individual and organizational goals. Although there are several motivational factors (extrinsic and intrinsic), intrinsic motivational factors such as autonomy, mastery, and purpose are more important for deeper lasting job satisfaction and higher performance. In this article, the authors discuss how an understanding of these factors that influence motivation has the potential to transform an organization.


Stronger Baselines for Trustable Results in Neural Machine Translation

Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectural and algorithmic improvements that lead to significant gains over “vanilla” NMT implementations. However, these new techniques are rarely evaluated in the context of previously published techniques, specifically those that are widely used in state-of-theart production and shared-task systems. As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use. In this work, we recommend three specific methods that are relatively easy to implement and result in much stronger experimental systems. Beyond reporting significantly higher BLEU scores, we conduct an in-depth analysis of where improvements originate and what inherent weaknesses of basic NMT models are being addressed. We then compare the relative gains afforded by several other techniques proposed in the literature when starting with vanilla systems versus our stronger baselines, showing that experimental conclusions may change depending on the baseline chosen. This indicates that choosing a strong baseline is crucial for reporting reliable experimental results.


TMCoI-SIOT: A trust management system based on communities of interest for the social Internet of Things

We propose a trust management system (TMS) for the social Internet of things (SIOT) called TMCoI-SIOT. The proposed system integrates numerous factors such as direct and indirect trust; transaction factors and social modeling of trust. The novelty of our approach can be summed up in three aspects. The first aspect concerns the network architecture which is seen as a set of nodes groups called community of interest (CoI). Each community nodes shares the same interests and is headed by an administrator (admin. The second aspect is the trust management system that prevents On-Off attacks. The third aspect concerns the trust prediction method that uses the Kalman filter technique in order to estimate nodes behaviors and prevent possible attacks. We prove the effectiveness of the proposed system by simulation against TMS attacks.


Two comparison-alternative high temperature PCB-embedded transformer designs for a 2 W gate driver power supply

With fast power semiconductor devices based on GaN and SiC becoming more common, there is a need for improved driving circuits. Transformers with smaller inter-winding capacitance in the isolated gate drive power supply helps in reducing the conducted EMI emission from the power converter to auxiliary sources. This paper presents a transformer with a small volume, a low power loss and a small inter-capacitance in a gate drive power supply to fast switching devices, such as GaN HEMT and SiC MOSFET. The transformer core is embedded into PCB to increase the integration density. Two different transformer designs, the coplanar-winding PCB embedded transformer and the toroidal PCB embedded transformer, are presented and compared. The former has a 0.8 pF inter-capacitance and the latter has 85% efficiency with 73 W/in3 power density. Both designs are dedicated to a 2 W gate drive power supply for wide-band-gap device, which can operate at 200 °C ambient temperature.


Towards Effective Log Summarization

Database access logs are the canonical go-to resource for tasks ranging from performance tuning to security auditing. Unfortunately, they are also large, unwieldy, and it can be difficult for a human analyst to divine the intent behind typical queries in the log. With an eye towards creating tools for ad-hoc exploration of queries by intent, we analyze techniques for clustering queries by intent. Although numerous techniques have already been developed for log summarization, they target specific goals like query recommendation or storage layout optimization rather than the more fuzzy notion of query intent. In this paper, we first survey a variety of log summarization techniques, focusing on a class of approaches that use query similarity metrics. We then propose DCABench, a benchmark that evaluates how well query similarity metrics capture query intent, and use it to evaluate three similarity metrics. DCABench uses student answers to query construction assignments to capture a wide range of distinct SQL queries that all have the same intent. Next, we propose and evaluate a query regularization process that standardizes query representations, significantly improving the effectiveness of the three similarity metrics tested. Finally, we propose an entirely new similarity metric based on the Weisfeiler-Lehman (WL) approximate graph isomorphism algorithm, which identifies salient features of a graph — or in our case, of the abstract syntax tree of a query. We show experimentally that distances in WL-feature space capture a meaningful notion of similarity, while still retaining competitive performance.


Guided-wave and leakage characteristics of substrate integrated waveguide

The substrate integrated waveguide (SIW) technique makes it possible that a complete circuit including planar circuitry, transitions, and rectangular waveguides are fabricated in planar form using a standard printed circuit board or other planar processing techniques. In this paper, guided wave and modes characteristics of such an SIW periodic structure are studied in detail for the first time. A numerical multimode calibration procedure is proposed and developed with a commercial software package on the basis of a full-wave finite-element method for the accurate extraction of complex propagation constants of the SIW structure. Two different lengths of the SIW are numerically simulated under multimode excitation. By means of our proposed technique, the complex propagation constant of each SIW mode can accurately be extracted and the electromagnetic bandstop phenomena of periodic structures are also investigated. Experiments are made to validate our proposed technique. Simple design rules are provided and discussed.


Low-loss ultra-wideband transition between conductor-backed coplanar waveguide and substrate integrated waveguide

A novel transition between conductor-backed coplanar waveguide (CBCPW) and substrate integrated waveguide (SIW) is presented for microwave and millimeter-wave integrated circuit design. The proposed integrated transition that can provide simultaneous field and impedance matching, exhibits outstanding low-loss performances over an ultra-wideband range (entire Ka-band in our case). In this work, a generalized impedance inverter whose parameters are accurately extracted by the use of a numerical Thru-Reflection-Line (TRL) calibration technique is utilized to design the transition. Measured results for the fabricated transition in back-to-back configuration show that the insertion loss is better than 0.4 dB while the return loss is better than 20 dB over the entire Ka-band.


A pseudo-class-AB telescopic-cascode operational amplifier

A class-AB architecture for single-stage operational amplifiers is presented. The structure employs a switched-capacitor level shifter to apply the signal to both sink and source output transistors to create the class-AB behavior. Using this structure, the current mirror circuit of traditional class-AB structures can be eliminated. Thus some power can be saved and the operation frequency can be increased. Simulation results of a fast-settling telescopic-cascode op-amp employing this pseudo-class-AB technique confirm the effectiveness of the approach to reduce the power consumption. This approach can be applied to the first stage of a two-stage op-amp as well, to increase the slew rate. The simulated 12-bit 100-Ms/s sample-and-hold amplifier employing the proposed pseudo-class-AB telescopic-cascode op-amp consumes only 16 mW from 3.3 V power supply.


Flux-Weakening Control of Electric Starter–Generator Based on Permanent-Magnet Machine

This paper presents the control analysis and design for a permanent-magnet machine (PMM) operated in the flux-weakening (FW) mode for an aircraft electric starter–generator (SG) application. Previous literature has focused on FW control of PMMs in the motoring (starting) mode; however, the system stability and control in the generating mode have been inadequately studied. This paper reports detailed, rigorous control analysis and design for a PMM-based aircraft electric SG operated in the FW mode. It is shown that an unstable area of operation exists. A novel control scheme which eliminates this instability is proposed. The key analytical findings of the paper are verified by experimental investigation. This paper therefore concludes that the presented technique is able to ensure system stability under all modes of operation. Furthermore, it is noted that the findings of this work are also valuable for any two-quadrant PMM drive with frequent change between starting and generating regimes under current-limiting operation.


Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings

In multi-sense word embeddings, contextual variations in corpus may cause a univocal word to be embedded into different sense vectors. Shi et al. (2016) show that this kind of pseudo multi-senses can be eliminated by linear transformations. In this paper, we show that pseudo multi-senses may come from a uniform and meaningful phenomenon such as subjective and sentimental usage, though they are seemingly redundant. In this paper, we present an unsupervised algorithm to find a linear transformation which can minimize the transformed distance of a group of sense pairs. The major shrinking direction of this transformation is found to be related with subjective shift. Therefore, we can not only eliminate pseudo multi-senses in multisense embeddings, but also identify these subjective senses and tag the subjective and sentimental usage of words in the corpus automatically.


UNDERSTANDING THE EFFECT OF INTERDEPENDENCY AND VULNERABILITY ON THE PERFORMANCE OF CIVIL INFRASTRUCTURE

Vulnerability is a measure ofthe extent to which a community, structure, service s or geographic area is likely to be damaged or disrupted by the im pact of particular hazard. Current asset management practices focuses on studying factors that affect p rformance of isolated infrastructure networks and model a set of actions to control the expected performanc e of these networks. This approach ignores the underlying spatial and functional interdependencies among these infrastructure networks and their vulnerability. The purpose of this paper is to intr oduce a new method that recognizes the effect of sp atial and functional interdependencies on vulnerability r ating of water, sewer and road networks. The propos ed method consists of: 1) risk assessment model, 2) in terdependency assessment model and 3) vulnerability assessment model. The risk model is composed of two modules: 1) water and sewer risk module and 2) road infrastructure module. The water and sewer ris k module will cluster these assets into three risk categories based on environmental, social, operatio nal and economical factors. The road infrastructure module will cluster road assets into three risk cat egories by using rational factorial technique based on road type, serviceability index, traffic load, and freez and thaw index. The interdependency model will de ploy the risk ranking to perform geospatial analysis in ArcGIS which results in determining the interdepend t layers of waters, sewers and roads. The vulnerabili ty model will deploy fuzzy neural networks techniqu e to determine the vulnerability rating based on spatial and functional interdependencies. The fuzzy neural networks are utilized to overcome the lack of histo r cal data and incorporate experts’ preferences for establishing the knowledge base for vulnerability a ssessment. The expected contribution of this framew ork is to aid decision makers in understanding the inte rdependencies between civil infrastructure assets a nd to which extent such interdependencies can compromise as ts performance.


PREDICTING HELPFUL POSTS IN OPEN-ENDED DISCUSSION FORUMS: A NEURAL ARCHITECTURE

Users participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or sharing their new findings on a certain topic. We find that, unlike Community Question Answering, where questions are mostly factoid based, the threads in a forum are often open-ended (e.g., asking for recommendations from others) without a single correct answer. In this paper, we address the task of identifying helpful posts in a forum thread, to aid users comprehend long running discussion threads, which often contain repetitive or even irrelevant posts. We propose a recurrent neural network based architecture to model (i) the relevance of a post regarding the original post starting the thread and (ii) the novelty it brings to the discussion, compared to the previous posts in the thread. Experimental results on five different types of online forum datasets show that our model significantly outperforms the state-of-the-art neural network models for text classification.


Process reengineering in the public sector : learning some private sector lessons

The possible applicability of business process reengineering (BPR) to organisations in the public sector is explored through analysis of the central issues in BPR and the emerging experience of organisations which have recently implemented it. In particular, the paper suggests that success of reengineering may depend critically on the strategic capability of the organisation prior to undertaking the effort. For that reason well-performing organisations are more likely to improve performance by means of BPR than are weak ones. Yet, in the public sector, it tends to be badly performing agencies which are most encouraged to undertake BPR. Knowing and understanding the reasons for success or failure of BPR in private organisations can prepare public sector managers for undertaking the effort, but each reengineering initiative must be tailored to the specific needs and circumstances of the individual agency. Public sector managers should use the widest possible definition of 'value' when analysing value-added in process reengineering and should be especially sensitive to the way in which 'value' in the public sector is differently interpreted by major stakeholders. During this learning process, public sector agencies would be well advised to be conservative in estimating gains from BPR. © 1997 Elsevier Science Ltd


An Efficient and Secure Automotive Wireless Software Update Framework

Future vehicles will be wirelessly connected to nearby vehicles, to the road infrastructure, and to the Internet, thereby becoming an integral part of the Internet of Things. New comfort features, safety functions, and a number of new vehicle-specific services will be integrated in future smart vehicles. These include a fast, secure, and reliable way to diagnose and reconfigure a vehicle, as well as the installation of new software (SW) on its integrated electronic control units (ECUs). Such wireless SW updates are beneficial for both automotive carmakers and customers, as they allow us to securely enable new features on the vehicle and to fix SW bugs by installing a new SW version over the air. A secure and dependable wireless SW update process is valuable in the entire lifetime of a modern vehicle as it can be used already during vehicle development and manufacturing process on the assembly line, as well as during vehicle maintenance in a service center. Additionally, future vehicles will allow us to remotely download up-to-date SW on the ECUs. To support this process over the entire vehicle's lifetime, a generic framework is needed. In this paper, SecUp, a generic framework enabling secure and efficient wireless automotive SW updates is proposed. SecUp utilizes IEEE 802.11s as wireless medium to interconnect vehicles and diagnostic devices in a dependable and fast way. Additionally, SecUp is enabling beneficial wireless SW update features such as parallel and partial SW updates to increase the efficiency, and comprises advanced security mechanisms to prevent abuse and attacks.


DeepWear: a Case Study of Collaborative Design between Human and Artificial Intelligence

Deep neural networks (DNNs) applications are now increasingly pervasive and powerful. However, fashion designers are lagging behind in leveraging this increasingly common technology. DNNs are not yet a standard part of fashion de sign practice, either clothes patterns or prototyping tools. In this paper, we present DeepWear, a method using deep convolutional generative adversarial networks for clothes design. The DNNs learn the feature of specific brand clothes and generate images then patterns instructed from the images are made, and an author creates clothes based on that. We evaluated this system by evaluating the credibility of the actual sold clothes on market with our clothes. As the result, we found it is possible to make clothes look like actual products from the generated images. Our findings have implications for collaborative design between machine and human intelligence.


IIT ( BHU ) : System Description for LSDSem ’ 17 Shared Task

This paper describes an ensemble system submitted as part of the LSDSem Shared Task 2017 the Story Cloze Test. The main conclusion from our results is that an approach based on semantic similarity alone may not be enough for this task. We test various approaches and compare them with two ensemble systems. One is based on voting and the other on logistic regression based classifier. Our final system is able to outperform the previous state of the art for the Story Cloze test. Another very interesting observation is the performance of sentiment based approach which works almost as well on its own as our final en-


Digital forensic framework using feedback and case history keeper

Cyber crime investigation is the integration of two technologies named theoretical methodology and second practical tools. First is the theoretical digital forensic methodology that encompasses the steps to investigate the cyber crime. And second technology is the practically development of the digital forensic tool which sequentially and systematically analyze digital devices to extract the evidence to prove the crime. This paper explores the development of digital forensic framework, combine the advantages of past twenty five forensic models and generate a algorithm to create a new digital forensic model. The proposed model provides the following advantages, a standardized method for investigation, the theory of model can be directly convert into tool, a history lookup facility, cost and time minimization, applicable to any type of digital crime investigation.


Toward Adversarial Online Learning and the Science of Deceptive Machines

Intelligent systems rely on pattern recognition and signaturebased approaches for a wide range of sensors enhancing situational awareness. For example, autonomous systems depend on environmental sensors to perform their tasks and secure systems depend on anomaly detection methods. The availability of large amount of data requires the processing of data in a “streaming” fashion with online algorithms. Yet, just as online learning can enhance adaptability to a non-stationary environment, it introduces vulnerabilities that can be manipulated by adversaries to achieve their goals while evading detection. Although human intelligence might have evolved from social interactions, machine intelligence has evolved as a human intelligence artifact and been kept isolated to avoid ethical dilemmas. As our adversaries become sophisticated, it might be time to revisit this question and examine how we can combine online learning and reasoning leading to the science of deceptive and counter-deceptive machines.


Adaptive Multimodal Fusion by Uncertainty Compensation With Application to Audiovisual Speech Recognition

While the accuracy of feature measurements heavily depends on changing environmental conditions, studying the consequences of this fact in pattern recognition tasks has received relatively little attention to date. In this paper, we explicitly take feature measurement uncertainty into account and show how multimodal classification and learning rules should be adjusted to compensate for its effects. Our approach is particularly fruitful in multimodal fusion scenarios, such as audiovisual speech recognition, where multiple streams of complementary time-evolving features are integrated. For such applications, provided that the measurement noise uncertainty for each feature stream can be estimated, the proposed framework leads to highly adaptive multimodal fusion rules which are easy and efficient to implement. Our technique is widely applicable and can be transparently integrated with either synchronous or asynchronous multimodal sequence integration architectures. We further show that multimodal fusion methods relying on stream weights can naturally emerge from our scheme under certain assumptions; this connection provides valuable insights into the adaptivity properties of our multimodal uncertainty compensation approach. We show how these ideas can be practically applied for audiovisual speech recognition. In this context, we propose improved techniques for person-independent visual feature extraction and uncertainty estimation with active appearance models, and also discuss how enhanced audio features along with their uncertainty estimates can be effectively computed. We demonstrate the efficacy of our approach in audiovisual speech recognition experiments on the CUAVE database using either synchronous or asynchronous multimodal integration models.


Distributed Auction Algorithms for the Assignment Problem with Partial Information

operations centers (MOC), in which multiple DMs with partial information and partial control over assets are involved in the development of operational level plans. The MOC emphasizes standardized processes and methods, centralized assessment and guidance, networked distributed planning capabilities, and decentralized execution for assessing, planning and executing missions across a range of military operations  Abstract—Task-asset assignment is a fundamental problem paradigm in a wide variety of applications. Typical problem setting involves a single decision maker (DM) who has complete knowledge of the weight (reward, benefit, accuracy) matrix and who can control any of the assets to execute the tasks. Motivated by planning problems arising in distributed organizations, this paper introduces a novel variation of the assignment problem, wherein there are multiple DMs and each DM knows only a part of the weight matrix and/or controls a subset of the assets. We extend the auction algorithm to such realistic settings with various partial information structures using a blackboard coordination structure. We show that by communicating the bid, the best and the second best profits among DMs and with a coordinator, the DMs can reconstruct the centralized assignment solution. The auction setup provides a nice analytical framework for formalizing how team members build internal models of other DMs and achieve team cohesiveness over time. [1]. In this vein, we are developing analytical and computational models for multi-level coordinated mission planning and monitoring processes associated with MOCs, so that they can function effectively in highly dynamic, asymmetric, and unpredictable mission environments. Two key problem areas are: 1) realistic modeling of multi-level coordination structures that link tactical, operational and strategic levels of decision making; and 2) collaborative planning with partial information and partial control of assets. In the collaborating planning problem, each DM “owns” a set of assets and is responsible for planning certain tasks. Each task is characterized by a vector of resource requirements, while each asset is characterized by a vector of resource capabilities (see Fig. 1). Multiple assets (from the same DM or multiple DMs) may be required to process a task. The degree of match between the task-resource requirement vector and asset-resource capability vector determines the accuracy of task execution. In addition, the elements of


A Survey of First-Order Probabilistic Models

There has been a long standing division in Artificial Intelligence between logical and probabilistic reasoning approaches. While probabilistic models can deal well with inherent uncertainty in many real-world domains, they operate on a mostly propositional level. Logic systems, on the other hand, can deal with much richer representations, especially first-order ones, but treat uncertainty only in limited ways. Therefore, an integration of these types of inference is highly desirable, and many approaches have been proposed, especially from the 1990s on. These solutions come from many different subfields and vary greatly in language, features and (when available at all) inference algorithms. Therefore their relation to each other is not always clear, as well as their semantics. In this survey, we present the main aspects of the solutions proposed and group them according to language, semantics and inference algorithm. In doing so, we draw relations between them and discuss particularly important choices and tradeoffs.


Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications.


Information Extraction Supported Question Answering

This paper discusses the use of our information extraction (IE) system, Textract, in the questionanswering (QA) track of the recently held TREC-8 tests. One of our major objectives is to examine how IE can help IR (Information Retrieval) in applications like QA. Our study shows: (i) IE can provide solid support for QA; (ii ) low-level IE like Named Entity tagging is often a necessary component in handling most types of questions; (iii ) a robust natural language shallow parser provides a structural basis for handling questions; (iv) high-level domain independent IE, i.e. extraction of multiple relationships and general events, is expected to bring about a breakthrough in QA.


Teechain: Scalable Blockchain Payments using Trusted Execution Environments

Blockchain protocols such as Bitcoin are gaining traction for exchanging payments in a secure and decentralized manner. Their need to achieve consensus across a large number of participants, however, fundamentally limits their performance. We describe Teechain, a new off-chain payment protocol that utilizes trusted execution environments (TEEs) to perform secure, efficient and scalable fund transfers on top of a blockchain, with asynchronous blockchain access. Teechain introduces secure payment chains to route payments across multiple payment channels. Teechain mitigates failures of TEEs with two strategies: (i) backups to persistent storage and (ii) a novel variant of chain-replication. We evaluate an implementation of Teechain using Intel SGX as the TEE and the operational Bitcoin blockchain. Our prototype achieves orders of magnitude improvement in most metrics compared to existing implementations of payment channels: with replicated Teechain nodes in a trans-atlantic deployment, we measure a throughput of over 33, 000 transactions per second with 0.1 second latency.


A 2-D Electronically Steered Phased-Array Antenna With 2$\,\times\,$ 2 Elements in LC Display Technology

For the first time, a 2-D electronically steered phased-array antenna with a liquid-crystal (LC)-based variable delay line is presented. The structure, which is designed at 17.5 GHz, consists of a 2 × 2 microstrip patch antenna array, continuously variable delay lines with a novel geometry, RF feeding, and biasing networks. The expected insertion loss of the variable delay line is less than 4 dB with a maximum differential phase shift of 300°. During the measurements, the antenna is steered by applying an appropriate dc biasing in the range of 0-15 V to the variable delay lines. It is also shown that the return loss is always better than 15 dB at the operating frequency when the antenna is steered.


Current Evidence and Opportunities for Expanding the Role of Occupational Therapy for Adults With Musculoskeletal Conditions.

Musculoskeletal conditions are the second greatest cause of disability worldwide, and chronic musculoskeletal conditions affect nearly the same percentage of the general population as chronic circulatory and respiratory conditions combined. Moreover, people with musculoskeletal conditions experience a significant decline in independence with daily activities and occupational performance, key areas targeted by occupational therapy interventions. This special issue of the American Journal of Occupational Therapy provides comprehensive summaries of evidence for the care of common musculoskeletal conditions, highlights important implications that support evidence-informed practice, and proposes ways to advance the practice of occupational therapy to improve the lives of people with musculoskeletal conditions.


Agile Model-Driven Modernization to the Service Cloud

Migration of legacy systems to more advanced technologies and platforms is a current issue for many software organizations. Model-Driven Modernization combined with Software as a Service delivery model is a very promising approach, which possesses a lot of advant ges, including reduced costs, automation of migration ac tivities and reuse of system functionality. However, a drawback of such an innovative modernization approach is that it lacks mature software process models to guide its adoption. Thus , a methodology for seamless execution of different mig ration and deployment activities is quite needed. On the other hand, agile development methods have been successfully adopted in various projects, which partly or thoroughly use the engineering and delivery models exploited in the modernization process. This paper presents how a pa rticular methodology for Model-Driven Modernization with deployment to the Cloud is enriched with agile tech niques to address different challenging issues. The extended agile methodology could be used by organizations which ha ve already applied agile software development as well as by organizations that plan to introduce it in their work. KeywordsCloud computing; Agile Methodology; Modeldriven Modernization; Software as a service


Organoids as an in vitro model of human development and disease

The in vitro organoid model is a major technological breakthrough that has already been established as an essential tool in many basic biology and clinical applications. This near-physiological 3D model facilitates an accurate study of a range of in vivo biological processes including tissue renewal, stem cell/niche functions and tissue responses to drugs, mutation or damage. In this Review, we discuss the current achievements, challenges and potential applications of this technique.


Toward a Maturity Model Development Process for Information Systems ( MMDePSI )

Maturity models are significant tools to ensure continuous improvement of systems and activities. They allow selfassessment and provide a means benchmark of these activities in relation to best practices. This article proposes the MMDePSI process for the development of new models of maturity in information systems. It begins by presenting the existing methods and approaches. Then he describes the MMDePSI process and the corresponding meta-model. Before concluding and presenting prospects of this work, it evaluates the proposed process.


Intelligent Car Braking System with Collision Avoidance and ABS

This paper provides an efficient way to design an automatic car braking system using Fuzzy Logic. The system could avoid accidents caused by the delays in driver reaction times at critical situations. The proposed Fuzzy Logic Controller is able to brake a car when the car approaches for an obstacle in the very near range. Collision avoidance is achieved by steering the car if the obstacle is in the tolerable range and hence there is no necessity to apply the brakes. Another FLC (which is cascaded with the first FLC for collision avoidance) implements the Anti-lock Braking capability during heavy braking condition. Thus the system is made intelligent since it could take decisions automatically depending upon the inputs from ultrasonic sensors. A simulative study is done using MATLAB and LabVIEW software. The results obtained by the simulation model are compared with the existing system and the proposed model conveys a satisfactory result which has high consumer acceptance. ATMega controller is used for implementation of the proposed system.


3D textureless object detection and tracking: An edge-based approach

This paper presents an approach to textureless object detection and tracking of the 3D pose. Our detection and tracking schemes are coherently integrated in a particle filtering framework on the special Euclidean group, SE(3), in which the visual tracking problem is tackled by maintaining multiple hypotheses of the object pose. For textureless object detection, an efficient chamfer matching is employed so that a set of coarse pose hypotheses is estimated from the matching between 2D edge templates of an object and a query image. Particles are then initialized from the coarse pose hypotheses by randomly drawing based on costs of the matching. To ensure the initialized particles are at or close to the global optimum, an annealing process is performed after the initialization. While a standard edge-based tracking is employed after the annealed initialization, we employ a refinement process to establish improved correspondences between projected edge points from the object model and edge points from an input image. Comparative results for several image sequences with clutter are shown to validate the effectiveness of our approach.


6-DoF model-based tracking of arbitrarily shaped 3D objects

Image-based 6-DoF pose estimation of arbitrarily shaped 3D objects based on their shape is a rarely studied problem. Most existing image-based methods for pose estimation either exploit textural information in form of local features or, if shape-based, rely on the extraction of straight line segments or other primitives. Straight-forward extensions of 2D approaches are potentially more general, but in practice assume a limited range of possible view angles. The general problem is that a 3D object can potentially produce completely different 2D projections depending on its relative pose to the observing camera. One way to reduce the solution space is to exploit temporal information, i.e. perform tracking. Again, existing model-based tracking approaches rely on relatively simple object geometries. In this paper, we propose a particle filter based tracking approach that can deal with arbitrary shapes and arbitrary or even no texture, i.e. it offers a general solution to the rigid object tracking problem. As our approach can deal with occlusions, it is in particular of interest in the context of goal-directed imitation learning involving the observation of object manipulations. Results of simulation experiments as well as real-world experiments with different object types prove the practical applicability of our approach.


On sequential Monte Carlo sampling methods for Bayesian filtering

In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.


Architectural Support for Mitigating Row Hammering in DRAM Memories

DRAM scaling has been the prime driver of increasing capacity of main memory systems. Unfortunately, lower technology nodes worsen the cell reliability as it increases the coupling between adjacent DRAM cells, thereby exacerbating different failure modes. This paper investigates the reliability problem due to Row Hammering, whereby frequent activations of a given row can cause data loss for its neighboring rows. As DRAM scales to lower technology nodes, the threshold for the number of row activations that causes data loss for the neighboring rows reduces, making Row Hammering a challenging problem for future DRAM chips. To overcome Row Hammering, we propose two architectural solutions: First, Counter-Based Row Activation (CRA), which uses a counter with each row to count the number of row activations. If the count exceeds the row hammering threshold, a dummy activation is sent to neighboring rows proactively to refresh the data. Second, Probabilistic Row Activation (PRA), which obviates storage overhead of tracking and simply allows the memory controller to proactively issue dummy activations to neighboring rows with a small probability for all memory access. Our evaluations show that these solutions are effective at mitigating Row hammering while causing negligible performance loss (<; 1 percent).


A Dynamic Approach for MPE and Weighted MAX-SAT

The problem of Most Probable Explanation (MPE) arises in the scenario of probabilistic inference: finding an assignment to all variables that has the maximum likelihood given some evidence. We consider the more general CNF-based MPE problem, where each literal in a CNF-formula is associated with a weight. We describe reductions between MPE and weighted MAX-SAT, and show that both can be solved by a variant of weighted model counting. The MPE-SAT algorithm is quite competitive with the state-of-the-art MAX-SAT, WCSP, and MPE solvers on a variety of problems.


Model selection in ecology and evolution.

Recently, researchers in several areas of ecology and evolution have begun to change the way in which they analyze data and make biological inferences. Rather than the traditional null hypothesis testing approach, they have adopted an approach called model selection, in which several competing hypotheses are simultaneously confronted with data. Model selection can be used to identify a single best model, thus lending support to one particular hypothesis, or it can be used to make inferences based on weighted support from a complete set of competing models. Model selection is widely accepted and well developed in certain fields, most notably in molecular systematics and mark-recapture analysis. However, it is now gaining support in several other areas, from molecular evolution to landscape ecology. Here, we outline the steps of model selection and highlight several ways that it is now being implemented. By adopting this approach, researchers in ecology and evolution will find a valuable alternative to traditional null hypothesis testing, especially when more than one hypothesis is plausible.


Asynchronous Exceptions in Haskell

Asynchronous exceptions, such as timeouts are important for robust, modular programs, but are extremely difficult to program with — so much so that most programming languages either heavily restrict them or ban them altogether. We extend our earlier work, in which we added synchronous exceptions to Haskell, to support asynchronous exceptions too. Our design introduces scoped combinators for blocking and unblocking asynchronous interrupts, along with a somewhat surprising semantics for operations that can suspend. Uniquely, we also give a formal semantics for our system.


Cannabis use and psychosis: a longitudinal population-based study.

Cannabis use may increase the risk of psychotic disorders and result in a poor prognosis for those with an established vulnerability to psychosis. A 3-year follow-up (1997-1999) is reported of a general population of 4,045 psychosis-free persons and of 59 subjects in the Netherlands with a baseline diagnosis of psychotic disorder. Substance use was assessed at baseline, 1-year follow-up, and 3-year follow-up. Baseline cannabis use predicted the presence at follow-up of any level of psychotic symptoms (adjusted odds ratio (OR) = 2.76, 95% confidence interval (CI): 1.18, 6.47), as well as a severe level of psychotic symptoms (OR = 24.17, 95% CI: 5.44, 107.46), and clinician assessment of the need for care for psychotic symptoms (OR = 12.01, 95% CI: 2.24, 64.34). The effect of baseline cannabis use was stronger than the effect at 1-year and 3-year follow-up, and more than 50% of the psychosis diagnoses could be attributed to cannabis use. On the additive scale, the effect of cannabis use was much stronger in those with a baseline diagnosis of psychotic disorder (risk difference, 54.7%) than in those without (risk difference, 2.2%; p for interaction = 0.001). Results confirm previous suggestions that cannabis use increases the risk of both the incidence of psychosis in psychosis-free persons and a poor prognosis for those with an established vulnerability to psychotic disorder.


A Modification of LambdaMART to Handle Noisy Crowdsourced Assessments

We consider noisy crowdsourced assessments and their impact on learning-to-rank algorithms. Starting with EM-weighted assessments, we modify LambdaMART in order to use smoothed probabilistic preferences over pairs of documents, directly as input to the ranking algorithm.


Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation

Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, object positions and types, and other factors. We term this kind of imitation learning “imitation-from-observation,” and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations in the same environment configuration, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show the effectiveness of our approach in learning a wide range of real-world robotic tasks modeled after common household chores from videos of a human demonstrator, including sweeping, ladling almonds, pushing objects as well as a number of tasks in simulation.


A point-based MDP for robust single-lane autonomous driving behavior under uncertainties

In this paper, a point-based Markov Decision Process (QMDP) algorithm is used for robust single-lane autonomous driving behavior control under uncertainties. Autonomous vehicle decision making is modeled as a Markov Decision Process (MDP), then extended to a QMDP framework. Based on MDP/QMDP, three kinds of uncertainties are taken into account: sensor noise, perception constraints and surrounding vehicles' behavior. In simulation, the QMDP-based reasoning framework makes the autonomous vehicle perform with differing levels of conservativeness corresponding to different perception confidence levels. Road tests also indicate that the proposed algorithm helps the vehicle in avoiding potentially unsafe situations under these uncertainties. In general, the results indicate that the proposed QMDP-based algorithm makes autonomous driving more robust to limited sensing ability and occasional sensor failures.


Quantum Reinforcement Learning

The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state, and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is updated in parallel according to rewards. Some related characteristics of QRL such as convergence, optimality, and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speedup learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given, and the results demonstrate the effectiveness and superiority of the QRL algorithm for some complex problems. This paper is also an effective exploration on the application of quantum computation to artificial intelligence.


METRIC AND TOPO-GEOMETRIC PROPERTIES OF URBAN STREET NETWORKS : some convergences , divergences , and new results

The theory of cities, which has grown out of the use of space syntax techniques in urban studies, proposes a curious mathematical duality: that urban space is locally metric but globally topo-geometric. Evidence for local metricity comes from such generic phenomena as grid intensification to reduce mean trip lengths in live centres, the fall of movement from attractors with metric distance, and the commonly observed decay of shopping with metric distance from an intersection. Evidence for global topo-geometry come from the fact that we need to utilise both the geometry and connectedness of the larger scale space network to arrive at configurational measures which optimally approximate movement patterns in the urban network. It might be conjectured that there is some threshold above which human being use some geometrical and topological representation of the urban grid rather than the sense of bodily distance to making movement decisions, but this is unknown. The discarding of metric properties in the large scale urban grid has, however, been controversial. Here we cast a new light on this duality. We show first some phenomena in which metric and topo-geometric measures of urban space converge and diverge, and in doing so clarify the relation between the metric and topo-geometric properties of urban spatial networks. We then show how metric measures can be used to create a new urban phenomenon: the partitioning of the background network of urban space into a network of semi-discrete patches by applying metric universal distance measures at different metric radii, suggesting a natural spatial area-isation of the city at all scales. On this basis we suggest a key clarification of the generic structure of cities: that metric universal distance captures exactly the formally and functionally local patchwork properties of the network, most notably the spatial differentiation of areas, while the top-geometric measures identifying the structure which overcomes locality and links the urban patchwork into a whole at different scales. Introduction: the dual urban network The theory of cities, which has grown out of the use of space syntax techniques in urban studies, proposes that urban street networks have a dual form: a foreground network of linked centres at all scales, and a background network of primarily residential space in which the foreground network is embedded. (Hillier 2001/2) The theory also notes a mathematical duality. On the one hand, measures which express the geometric and topological properties of the network at an extended scale, such as integration and choice measures in axial maps or segment angular maps, are needed to capture structure-function relations such as natural movement patterns (Hillier & Iida 2005). We can call these measures topo-geometric. On the other, at a more localised level, an understanding of structure-function relations often requires an account of metric properties – for example the generic, but usually local, phenomenon of grid intensification to reduce mean trip lengths in live centres (Siksna 1997, Hillier 1999), the fall of movement rates with metric distance from attractors, and the commonly observed decay of shopping with metric distance from an intersection. In terms of understanding structure-function relations. urban space seems to be globally topo-geometric but locally metric. Here we propose to link these two dualities in a more thorough-going way. We show first that the large scale foreground network of space in cities, in spite of the claims of critics (Ratti 2004), really is not metric. On the contrary, the substitution of metric for topo-geometric measures in the analysis, has catastrophic effects on the ability of syntax to account for structure-function relations at this scale. At the same time, topo-geometric measures turn out to capture some interesting metric properties of the larger scale urban network. But we then show that the background network of space really is metric in a much more general sense than has been thought, in that metric measures at different radii can be used to partition the background network of urban space into a patchwork of semi-discrete areas, suggesting a natural metric area-isation of cities at all scales as a function of the placing, shaping and scaling of urban blocks. On this basis we suggest a clarification of the dual structure of cities: that metric ‘universal distance’ (distance from all points to all others – Hillier 1996) measures can capture the spatial differentiation of the background urban network into a patchwork of local areas, while the topo-geometric measures identify the structures which overcomes locality and links the urban patchwork into a whole at different scales. The patchwork theory is in effect a theory of block size and shape, picking up the local distortions in urban space induced by the placing and shaping of physical structures. More generally, we can say that the local-to-global topo-geometric structure reflects the visual, and so non-local effects of placing blocks in space, while the patchwork structure reflects metric and so local effects. The patchwork theory extends and generalises the concept of grid intensification, meaning the reduction of block size to reduce mean distance from all points to all others in a space network. As shown in (Hillier 2000), holding total land coverage and travellable distance in free space constant, a grid in which smaller blocks are placed at the centre and larger blocks at the edge has lower mean distance from all points to all others in the space network than a regular grid, while if larger blocks are placed at the centre and smaller blocks at the edge, then the mean distance from all points to others in the space network is higher than in a regular grid. This follows from the partitioning theory set out in Chapter 8 of Space is the Machine. (Hillier 1996) In general in urban grids, live centres and sub-centres (‘live’ in the sense of having movement dependent uses such as retail and catering) tend to the grid intensified form to maximise the inter-accessibility of the facilities within in the centre, residential areas tend to larger block sizes, reflecting the need to restrain and structure movement in the image of a spatial culture, while the linkages between centres tend to an even larger block size again, an effect of the directional structuring of routes, so that the network of linked centres which dominate the spatial structure of cities tend to oscillate between a relatively large and relative small block size, with the residential background occupying the middle range. This block size pattern is explained more fully in (Hillier 2001/2). In this paper we: – First review the duality of urban space in three ways: geometrically to establish its empirical existence as a key dimension of urban form, functionally to show its implications in terms of movement and land use patterns, and syntactically to show the relations between the two. – We then explore some of the suggestions that have been made about reducing the distance between syntax and more traditional metric approaches, in particular by examining the suggestion of Ratti that we should add metric weightings to the main syntax measures. We show the consequences of these suggestions for any theory which seeks to identify functionally meaningful structures in urban space – We then suggest a general method for showing the metric effect on space of block placing and shaping, both visually and in terms of patterns in scattergrams, by showing theoretical cases – We then apply this method to some cities and show its ability to identify if not natural spatial areas then at least a natural periodicity in city networks through which they tend to a natural spatial area-isation at all scales, reflecting the ways in which we talks about urban areas and regions at different scales. Metric and geometric properties of the grid First, we consider the urban duality geometrically by looking sections of metropolitan Tokyo and London, shown in Figure 1. As shown in (Hillier 2001/2) and later formalised in (Carvalho & Penn 2004), we must first remind ourselves of the fractal nature of urban least line networks: all are made up at all scales, from the local area to the city regions, of a small number of long lines and a large number of short lines. But there is more to be said. Longer and shorter lines form different kinds of geometric patterns. If we look for patterns in the section of Tokyo, the first thing the eye notes are line continuities. What we are seeing in effect is sequence of lines linked at their ends by nearly straight intersections with other lines, forming a visually dominant pattern in the network. But in general, the lines forming these nearly straight continuities as Figueredo calls them (Figueredo 2003) are longer than other nearby lines. This has the effect that if we find a locally longer line it is likely that at either end it will lead to another to which it will be connected by a nearly straight connection, and these lines will in turn be similarly connected. Probabilistically, we can say the longer the line, the more likely it is to end in a nearly straight connection, and taken together these alignments form a network of multi-directional sequences. Intuitively, the value of these in navigating urban grids is obvious, but here, following (Hillier 1999) we are making a structural point. Figure 1: sections of the Tokyo and London street networks What then of the shorter lines ? Again, in spite of a highly variable geometry, we find certain consistencies. First, shorter lines tend to form clusters, so that in the vicinity of each longer line there will be several shorter lines. These localised groups tend to form more grid-like local patterns, with lines either passing through each other, or ending on other lines, at near right angles. We can say then that the shorter the line, the more likely it is to


Current Status of Bioinks for Micro-Extrusion-Based 3D Bioprinting

Recent developments in 3D printing technologies and design have been nothing short of spectacular. Parallel to this, development of bioinks has also emerged as an active research area with almost unlimited possibilities. Many bioinks have been developed for various cells types, but bioinks currently used for 3D printing still have challenges and limitations. Bioink development is significant due to two major objectives. The first objective is to provide growth- and function-supportive bioinks to the cells for their proper organization and eventual function and the second objective is to minimize the effect of printing on cell viability, without compromising the resolution shape and stability of the construct. Here, we will address the current status and challenges of bioinks for 3D printing of tissue constructs for in vitro and in vivo applications.


Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data

Growing concern over traffic safety has led to research efforts directed towards predicting freeway crashes in Advanced Traffic Management and Information Systems (ATMIS) environment. This paper aims at developing a crash-likelihood prediction model using real-time traffic-flow variables (measured through series of underground sensors) and rain data (collected at weather stations) potentially associated with crash occurrence. Archived loop detector and rain data and historical crash data have been used to calibrate the model. This model can be implemented using an online loop and rain data to identify high crash potential in real-time. Principal component analysis (PCA) and logistic regression (LR) have been used to estimate a weather model that determines a rain index based on the rain readings at the weather station in the proximity of the freeway. A matched case-control logit model has also been used to model the crash potential based on traffic loop data and the rain index. The 5-min average occupancy and standard deviation of volume observed at the downstream station, and the 5-min coefficient of variation in speed at the station closest to the crash, all during 5-10 min prior to the crash occurrence along with the rain index have been found to affect the crash occurrence most significantly.


Data exfiltration: A review of external attack vectors and countermeasures

Context: One of the main targets of cyber-attacks is data exfiltration, which is the leakage of sensitive or private data to an unauthorized entity. Data exfiltration can be perpetrated by an outsider or an insider of an organization. Given the increasing number of data exfiltration incidents, a large number of data exfiltration countermeasures have been developed. These countermeasures aim to detect, prevent, or investigate exfiltration of sensitive or private data. With the growing interest in data exfiltration, it is important to review data exfiltration attack vectors and countermeasures to support future research in this field. Objective: This paper is aimed at identifying and critically analysing data exfiltration attack vectors and countermeasures for reporting the status of the art and determining gaps for future research. Method: We have followed a structured process for selecting 108 papers from seven publication databases. Thematic analysis method has been applied to analyse the extracted data from the reviewed papers. Results: We have developed a classification of (1) data exfiltration attack vectors used by external attackers and (2) the countermeasures in the face of external attacks. We have mapped the countermeasures to attack vectors. Furthermore, we have explored the applicability of various countermeasures for different states of data (i.e., in use, in transit, or at rest). Conclusion: This review has revealed that (a) most of the state of the art is focussed on preventive and detective countermeasures and significant research is required on developing investigative countermeasures that are equally important; (b) Several data exfiltration countermeasures are not able to respond in real-time, which specifies that research efforts need to be invested to enable them to respond in real-time (c) A number of data exfiltration countermeasures do not take privacy and ethical concerns into consideration, which may become an obstacle in their full adoption (d) Existing research is primarily focussed on protecting data in ‘in use’ state, therefore, future research needs to be directed towards securing data in ‘in rest’ and ‘in transit’ states (e) There is no standard or framework for evaluation of data exfiltration countermeasures. We assert the need for developing such an evaluation framework.


Revisiting Cryptographic Accumulators, Additional Properties and Relations to Other Primitives

Cryptographic accumulators allow to accumulate a finite set of values into a single succinct accumulator. For every accumulated value, one can efficiently compute a witness, which certifies its membership in the accumulator. However, it is computationally infeasible to find a witness for any nonaccumulated value. Since their introduction, various accumulator schemes for numerous practical applications and with different features have been proposed. Unfortunately, to date there is no unifying model capturing all existing features. Such a model can turn out to be valuable as it allows to use accumulators in a black-box fashion. To this end, we propose a unified formal model for (randomized) cryptographic accumulators which covers static and dynamic accumulators, their universal features and includes the notions of undeniability and indistinguishability. Additionally, we provide an exhaustive classification of all existing schemes. In doing so, it turns out that most accumulators are distinguishable. Fortunately, a simple, light-weight generic transformation allows to make many existing dynamic accumulator schemes indistinguishable. As this transformation, however, comes at the cost of reduced collision freeness, we additionally propose the first indistinguishable scheme that does not suffer from this shortcoming. Finally, we employ our unified model for presenting a black-box construction of commitments from indistinguishable accumulators as well as a black-box construction of indistinguishable, undeniable universal accumulators from zero-knowledge sets. Latter yields the first universal accumulator construction that provides indistinguishability.


Motion Planning : The Essentials

This is the first installment of a two-part tutorial. The goal of the first part is to give the reader a basic understanding of the technical issues and types of approaches to solving the basic path planning or obstacle avoidance problem. The second installment will cover more advanced issues, including feedback, differential constraints, and uncert ainty. Note that is a brieftutorial, rather than a comprehensive surveyof methods. For the latter, consult recent textbooks [4], [9].


Customer churn prediction for retail business

Customer churn happens when a customer discontinues his or her interaction with a company. In retail business, a customer is treated to be churned once his/her transactions outdate a particular amount of time. Once a customer becomes a churn, the loss incurred by the company is not just the lost revenue due to the lost customer but also the costs involved in additional marketing in order to attract new customer. Reducing customer churn is a key business goal of every online business. In this project, we have considered data set from UCI Machine Learning repository. This dataset contains records of transactions that occurred between December 1, 2010 and December 1, 2011. This is recorded from an online retail gift store based in United Kingdom. The customers of this company are mainly wholesalers. This data set is pre-processed by removing NAs, validating numerical values, removing erroneous data points. We then perform aggregations on the data to generate invoice based and customer based data sets. A variable churn is attached to each data point. This churn value is determined based on the customer's transactions. Three algorithms are run on this customer aggregated dataset to predict churn value. They are Random forest, Support vector machines and Extreme gradient boosting. A comparative study is done on these three algorithms.


Detection of falls using accelerometers and mobile phone technology.

OBJECTIVES
to study the sensitivity and specificity of fall detection using mobile phone technology.

DESIGN
an experimental investigation using motion signals detected by the mobile phone.

SETTING AND PARTICIPANTS
the research was conducted in a laboratory setting, and 18 healthy adults (12 males and 6 females; age = 29 ± 8.7 years) were recruited.

MEASUREMENT
each participant was requested to perform three trials of four different types of simulated falls (forwards, backwards, lateral left and lateral right) and eight other everyday activities (sit-to-stand, stand-to-sit, level walking, walking up- and downstairs, answering the phone, picking up an object and getting up from supine). Acceleration was measured using two devices, a mobile phone and an independent accelerometer attached to the waist of the participants.

RESULTS
Bland-Altman analysis shows a higher degree of agreement between the data recorded by the two devices. Using individual upper and lower detection thresholds, the specificity and sensitivity for mobile phone were 0.81 and 0.77, respectively, and for external accelerometer they were 0.82 and 0.96, respectively.

CONCLUSION
fall detection using a mobile phone is a feasible and highly attractive technology for older adults, especially those living alone. It may be best achieved with an accelerometer attached to the waist, which transmits signals wirelessly to a phone.


LiTell: robust indoor localization using unmodified light fixtures

Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near term adoption. In this paper, we propose LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high frequency ( > 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. Our experiments demonstrate LiTell's high reliability in discriminating different FLs, and its potential to achieve sub-meter location granularity. Our user study in a multi-storey office building, parking lot and grocery store further validates LiTell as an accurate, robust and ready-to-use indoor localization system.


A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment

Cloud computing, a distributed computing paradigm, enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. Workflow scheduling is one of the most challenging problems in cloud computing. Although, workflow scheduling on distributed systems like grids and clusters have been extensively studied, however, these solutions are not viable for a cloud environment. It is because, a cloud environment differs from other distributed environment in two major ways: on-demand resource provisioning and pay-as-you-go pricing model. Thus, to achieve the true benefits of workflow orchestration onto cloud resources novel approaches that can capitalize the advantages and address the challenges specific to a cloud environment needs to be developed. This work proposes a dynamic cost-effective deadline-constrained heuristic algorithm for scheduling a scientific workflow in a public cloud. The proposed technique aims to exploit the advantages offered by cloud computing while taking into account the virtual machine (VM) performance variability and instance acquisition delay to identify a just-in-time schedule of a deadline constrained scientific workflow at lesser costs. Performance evaluation on some well-known scientific workflows exhibit that the proposed algorithm delivers better performance in comparison to the current state-of-the-art heuristics.


Artificial Intelligence as an Effective Classroom Assistant

The field of artificial intelligence in education (AIED) uses techniques from AI and cognitive science to better understand the nature of learning and teaching and to build systems to help learners gain new skills or understand new concepts. This article studies metareviews and meta-analyses to make the case for blended learning, wherein the teacher can offload some work to AIED systems.


On Data Integrity Attacks Against Real-Time Pricing in Energy-Based Cyber-Physical Systems

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources and could effectively guide the participants to achieve individual welfare maximization in the system. To be specific, we develop a Lagrangian-based approach to transform the global optimization conducted by the power company into distributed optimization problems to obtain explicit energy consumption, supply, and price decisions for individual participants. Also, we show that these distributed problems derived from the global optimization by the power company are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate and formalize the vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks: Ex-ante attacks and Ex-post attacks, which are launched by the adversary before or after the decision-making process. We systematically analyze the welfare impacts of these attacks on the real-time pricing scheme. Through a combination of theoretical analysis and performance evaluation, our data shows that the real-time pricing scheme could effectively guide the participants to achieve welfare maximization, while cyber-attacks could significantly disrupt the results of real-time pricing decisions, imposing welfare reduction on the participants.


An improved boosting based on feature selection for corporate bankruptcy prediction

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability in corporate bankruptcy prediction. In this paper, a new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy. Through injecting feature selection strategy into Boosting, FS-Booting can get better performance as base learners in FS-Boosting could get more accuracy and diversity. For the testing and illustration purposes, two real world bankruptcy datasets were selected to demonstrate the effectiveness and feasibility of FS-Boosting. Experimental results reveal that FS-Boosting could be used as an alternative method for the corporate bankruptcy prediction. 2013 Elsevier Ltd. All rights reserved.


Multimodal Convolutional Neural Networks for Matching Image and Sentence

In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content and one matching CNN modeling the joint representation of image and sentence. The matching CNN composes different semantic fragments from words and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. More specifically, our proposed m-CNNs significantly outperform the state-of-the-art approaches for bidirectional image and sentence retrieval on the Flickr8K and Flickr30K datasets.


A SICK cure for the evaluation of compositional distributional semantic models

Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.


IJ CS Differences in Individualistic and Collectivistic Tendencies among College Students in Japan and the United States Emiko

It is a worldwide stereotype that Japanese, compared to Americans, are oriented more toward collectivism. But this stereotypical notion of more collectivism among Japanese, which typically stems from a view that individualism and collectivism stand at opposite ends of a continuum, has been filled with dashed empirical findings, especially in a sample of college students. In the current study, following the view that individualism and collectivism are two separate concepts rather than one with two extremes, we test and compare both individualistic and collectivistic tendencies among college students in Japan and the United States. A review of theories and research on this dimension of cultural variability across the two diverse cultures and the literature on societal pressure of collectivity and on parents as primary socialization agents of culturally expected values lead to two hypotheses: 1) Japanese college students tend less toward individualism than do Americans, and 2) Japanese college students tend less toward collectivism than do Americans. Analysis of identical survey data from college students in Japan and in the United States provides strong support for both hypotheses.


Autonomous robotic system for bridge deck data collection and analysis

Bridge deck inspection is conducted to identify bridge condition deterioration and, thus, to facilitate implementation of appropriate maintenance or rehabilitation procedures. In this paper, we report the development of a robotic system for bridge deck data collection and analysis. The robotic system accurately localizes itself and autonomously maneuvers on the bridge deck to collect visual images and conduct nondestructive evaluation (NDE) measurements. The developed robotic system can reduce the cost and time of the bridge deck data collection. Crack detection and mapping algorithm to build the deck crack maps is presented in detail. The electrical resistivity (ER), impact-echo (IE) and ultrasonic surface waves (USW) data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck. The presented robotic system has been successfully deployed to inspect numerous bridges.


Effects of Harsh and Unpredictable Environments in Adolescence on Development of Life History Strategies: A Longitudinal Test of an Evolutionary Model.

The National Longitudinal Study of Adolescent Health data were used to test predictions from life history theory. We hypothesized that (1) in young adulthood an emerging life history strategy would exist as a common factor underlying many life history traits (e.g., health, relationship stability, economic success), (2) both environmental harshness and unpredictability would account for unique variance in expression of adolescent and young adult life history strategies, and (3) adolescent life history traits would predict young adult life history strategy. These predictions were supported. The current findings suggest that the environmental parameters of harshness and unpredictability have concurrent effects on life history development in adolescence, as well as longitudinal effects into young adulthood. In addition, life history traits appear to be stable across developmental time from adolescence into young adulthood.