Criminal multilation of the human body in Sweden--a thirty-year medico-legal and forensic psychiatric study.

During the 30-year period 1961-1990, a total of 22 deaths with criminal multilation/dismemberment of the human body were registered in Sweden. The multilations occurred in time clusters, mostly during the summer and winter periods, and increased during the three decades, with incidence rates of 0.05, 0.1, and 0.125 per million inhabitants and year, respectively. Multilation was noted 6.6 times more often in large urban areas than in the rest of Sweden. Defensive mutilation, in order to get rid of the corpse or make its identity more difficult, was noted in ten instances, aggressive mutilation following outrageous overkilling in four, offensive mutilation (lust murder) in seven, and necromanic multilation in one instance. In the last-mentioned case the cause of death was natural, while all deaths in the first three groups were homicidal, or homicide was strongly suspected. All perpetrators were males, in six instances assisted by other persons. In more than half of the cases the perpetrator's occupation was associated with application of anatomical knowledge, e.g., butcher, physician, veterinary assistant, or hunter. The perpetrators of the defensive and aggressive mutilations were mostly disorganized, i.e., alcoholics or drug users with previous psychiatric contacts and criminal histories, while the lust murderers were mostly organized, with a history of violent crimes (including the "serial killing" type), drug abuse and mental disorders with anxiety and schizophrenia, in that order to a diminishing degree. There were differences in mode of mutilation, depending on whether the mutilation was carried out by a layman, a butcher, or a physician. In only one case was the perpetrator convicted for the mutilation act itself; in the remaining instances the manslaughter, as a more serious crime, assimilated the mutilation. When the mutilation made it impossible to establish the cause of death, the perpetrators, despite strong circumstantial evidence indicating murder, were acquitted.


The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.


Leaving so soon?: understanding and predicting web search abandonment rationales

Users of search engines often abandon their searches. Despite the high frequency of Web search abandonment and its importance to Web search engines, little is known about why searchers abandon beyond that it can be for good or bad reasons. In this paper, we ex-tend previous work by studying search abandonment using both a retrospective survey and an in-situ method that captures aban-donment rationales at abandonment time. We show that although satisfaction is a common motivator for abandonment, one-in-five abandonment instances does not relate to satisfaction. We also studied the automatic prediction of the underlying reason for ob-served abandonment. We used features of the query and the results, interaction with the result page (e.g., cursor movements, scrolling, clicks), and the full search session. We show that our classifiers can learn to accurately predict the reasons for observed search abandonment. Such accurate predictions help search providers estimate user satisfaction for queries without clicks, affording a more complete understanding of search engine performance.


A state-of-the-art 3D sensor for robot navigation

This paper relates first experiences using a state-of-the-art, time-of-flight sensor that is able to deliver 3D images. The properties and capabilities of the sensor make it a potential powerful tool for applications within mobile robotics especially for real-time tasks, as the sensor features a frame rate of up to 30 frames per second. Its capabilities in terms of basic obstacle avoidance and local path-planning are evaluated and compared to the performance of a standard laser scanner.


IEC 61850 impact on substation design

IEC 61850 communication networks and systems for utility automation is a standard for communications that creates an environment that will allow dramatic changes in the design and operation of substations. The paper describes the different components of such systems and the distribution of functions between the devices included in it. Multiple Merging Units communicating over a 100 Mb Ethernet with multiple devices and/or a central computer that receives and processes current and voltage samples with a rate of 256 samples/cycle allow the implementation of all typical substation protection, automation, control, monitoring and recording functions in a very efficient way. The concepts also allow to change the design of layout of the high voltage switchgear.


OPC Unified Architecture: A Service-oriented Architecture for Smart Grids

In this paper, the OPC UA is introduced as a key technology for realizing a variety of Smart Grid use cases enabling relevant tasks of automation and control. OPC UA is the successor of the established Classic OPC specification and state of the art regarding information exchange in the industrial automation branch. One of its key improvements over the Classic OPC is that the area of application is no longer restricted to industrial automation but OPC UA can be applied almost in every domain facing challenges in automated control. This improvement stems from a more generic and object-oriented approach. For the adoption of OPC UA to Smart Grids, two of the most important data models -- the Common Information Model (CIM) and the IEC 61850 -- have been identified to be integrated into OPC UA communication. In this contribution, basic OPC UA features and functionalities (information modeling, communication services, and information security) are introduced and discussed in the context of Smart Grids.


The social psychology of stigma.

This chapter addresses the psychological effects of social stigma. Stigma directly affects the stigmatized via mechanisms of discrimination, expectancy confirmation, and automatic stereotype activation, and indirectly via threats to personal and social identity. We review and organize recent theory and empirical research within an identity threat model of stigma. This model posits that situational cues, collective representations of one's stigma status, and personal beliefs and motives shape appraisals of the significance of stigma-relevant situations for well-being. Identity threat results when stigma-relevant stressors are appraised as potentially harmful to one's social identity and as exceeding one's coping resources. Identity threat creates involuntary stress responses and motivates attempts at threat reduction through coping strategies. Stress responses and coping efforts affect important outcomes such as self-esteem, academic achievement, and health. Identity threat perspectives help to explain the tremendous variability across people, groups, and situations in responses to stigma.


Gait support for complete spinal cord injury patient by synchronized leg-swing with HAL

Biped walking improves the circulation of blood as well as bone density of the lower limbs, thereby enhancing the quality of life (QOL). It is significant not only to healthy people but also to physically challenged persons such as complete spinal cord injury (SCI) patients. The purpose of this paper is to propose an estimation algorithm that infers the intention related to the forward leg-swing in order to support the gait for complete SCI patients wearing an exoskeleton system called a Hybrid Assistive Limb (HAL), and to verify the effectiveness of the proposed algorithm through a clinical trial. The proposed algorithm infers the patient's intention in synchronization with the deviation of the center of the ground reaction force (CoGRF) that is observed immediately before a person starts walking. The patient conveys this intention by inducing the deviation of the CoGRF, using crutches or handrails with both of his/her arms. In the clinical trial, we confirmed that the algorithm inferred the patient's intention to swing the leg forward, and achieved a smooth gait in synchronization with it. As a result, the gait speed and cadence of the SCI patient with HAL during the 10-meter walking test increased to 6.67 [m/min] and 20 [steps/min], respectively after several trials.


A Web Scraping Methodology for Bypassing Twitter API Restrictions

Retrieving information from social networks is the first and primordial step many data analysis fields such as Natural Language Processing, Sentiment Analysis and Machine Learning. Important data science tasks relay on historical data gathering for further predictive results. Most of the recent works use Twitter API, a public platform for collecting public streams of information, which allows querying chronological tweets for no more than three weeks old. In this paper, we present a new methodology for collecting historical tweets within any date range using web scraping techniques bypassing for Twitter API restrictions.


Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Most tasks in natural language processing can be cast into question answering (QA) problems over language input. We introduce the dynamic memory network (DMN), a unified neural network framework which processes input sequences and questions, forms semantic and episodic memories, and generates relevant answers. Questions trigger an iterative attention process which allows the model to condition its attention on the result of previous iterations. These results are then reasoned over in a hierarchical recurrent sequence model to generate answers. The DMN can be trained end-to-end and obtains state of the art results on several types of tasks and datasets: question answering (Facebook’s bAbI dataset), sequence modeling for part of speech tagging (WSJ-PTB), coreference resolution (Quizbowl dataset) and text classification for sentiment analysis (Stanford Sentiment Treebank). The model relies exclusively on trained word vector representations and requires no string matching or manually engineered features.


Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.


Lifted Relational Neural Networks

We propose a method combining relational-logic representations with neural network learning. A general lifted architecture, possibly reflecting some background domain knowledge, is described through relational rules which may be handcrafted or learned. The relational rule-set serves as a template for unfolding possibly deep neural networks whose structures also reflect the structures of given training or testing relational examples. Different networks corresponding to different examples share their weights, which co-evolve during training by stochastic gradient descent algorithm. The framework allows for hierarchical relational modeling constructs and learning of latent relational concepts through shared hidden layers weights corresponding to the rules. Discovery of notable relational concepts and experiments on 78 relational learning benchmarks demonstrate favorable performance of the method.


TensorLog: A Differentiable Deductive Database

Large knowledge bases (KBs) are useful in many tasks, but it is unclear how to integrate this sort of knowledge into “deep” gradient-based learning systems. To address this problem, we describe a probabilistic deductive database, called TensorLog, in which reasoning uses a differentiable process. In TensorLog, each clause in a logical theory is first converted into certain type of factor graph. Then, for each type of query to the factor graph, the message-passing steps required to perform belief propagation (BP) are “unrolled” into a function, which is differentiable. We show that these functions can be composed recursively to perform inference in non-trivial logical theories containing multiple interrelated clauses and predicates. Both compilation and inference in TensorLog are efficient: compilation is linear in theory size and proof depth, and inference is linear in database size and the number of message-passing steps used in BP. We also present experimental results with TensorLog and discuss its relationship to other first-order probabilistic logics.


Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition

This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.


Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks

Distant supervision is an efficient approach that automatically generates labeled data for relation extraction (RE). Traditional distantly supervised RE systems rely heavily on handcrafted features, and hence suffer from error propagation. Recently, a neural network architecture has been proposed to automatically extract features for relation classification. However, this approach follows the traditional expressed-at-least-once assumption, and fails to make full use of information across different sentences. Moreover, it ignores the fact that there can be multiple relations holding between the same entity pair. In this paper, we propose a multi-instance multi-label convolutional neural network for distantly supervised RE. It first relaxes the expressed-at-least-once assumption, and employs cross-sentence max-pooling so as to enable information sharing across different sentences. Then it handles overlapping relations by multi-label learning with a neural network classifier. Experimental results show that our approach performs significantly and consistently better than state-of-the-art methods.


Predictors of loneliness among older women and men in Sweden: A national longitudinal study.

OBJECTIVES
Longitudinal research on loneliness in old age has rarely considered loneliness separately for men and women, despite gender differences in life experiences. The objective of this study was to examine the extent to which older women and men (70+) report feelings of loneliness with a focus on: (a) changes in reported loneliness as people age, and (b) which factors predict loneliness.

METHOD
Data from the 2004 and 2011 waves of SWEOLD, a longitudinal national survey, was used (n = 587). The prediction of loneliness in 2011 by variables measured in 2004 and 2004-2011 variable change scores was examined in three logistic regression models: total sample, women and men. Variables in the models included: gender, age, education, mobility problems, depression, widowhood and social contacts.

RESULTS
Older people moved into and out of frequent loneliness over time, although there was a general increase in loneliness with age. Loneliness at baseline, depression increment and recent widowhood were significant predictors of loneliness in all three multivariable models. Widowhood, depression, mobility problems and mobility reduction predicted loneliness uniquely in the model for women; while low level of social contacts and social contact reduction predicted loneliness uniquely in the model for men.

CONCLUSION
This study challenges the notion that feelings of loneliness in old age are stable. It also identifies important gender differences in prevalence and predictors of loneliness. Knowledge about such differences is crucial for the development of effective policy and interventions to combat loneliness in later life.


Research on high-frequency induction heating load-matched based on LLC resonant circuit

Modern high-frequency induction heating technology puts forward higher requirements for load matching of the induction heating power supply. LLC resonant load circuit can replace the traditional induction heating load matching transformer, and increase power density. This paper analyzes the load matching characteristics of LLC load resonant circuit in high frequency induction heating power supply, and gives the parameter selection method of matching components. At last, combining with phase-shifting pulse-width modulation (PWM) control method, the phase-shifting induction heating experiment platform based on LLC load resonant circuit is established. Experimental results show that the performance of LLC circuit in high frequency induction heating power supply load matching is excellent.


An 800-MHz–6-GHz Software-Defined Wireless Receiver in 90-nm CMOS

A software-defined radio receiver is designed from a low-power ADC perspective, exploiting programmability of windowed integration sampler and clock-programmable discrete-time analog filters. To cover the major frequency bands in use today, a wideband RF front-end, including the low-noise amplifier (LNA) and a wide tuning-range synthesizer, spanning over 800 MHz to 6 GHz is designed. The wideband LNA provides 18-20 dB of maximum gain and 3-3.5 dB of noise figure over 800 MHz to 6 GHz. A low 1/f noise and high-linearity mixer is designed which utilizes the passive mixer core properties and provides around +70 dBm IIP2 over the bandwidth of operation. The entire receiver circuits are implemented in 90-nm CMOS technology. Programmability of the receiver is tested for GSM and 802.11g standards


Towards Bi-Directional Communication in Human-Swarm Teaming: A Survey

Swarm systems consist of large numbers of robots that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from search-and-rescue situations to Cyber defence. The two decision making cycles of swarms and humans operate on two different time-scales, where the former is normally orders of magnitude faster than the latter. Closing the loop at the intersection of these two cycles will create fast and adaptive humanswarm teaming networks. This paper brings desperate pieces of the ground work in this research area together to review this multidisciplinary literature. We conclude with a framework to synthesize the findings and summarize the multi-modal indicators needed for closed-loop human-swarm adaptive systems.


Opportunities and advances in ultra-wideband electronically scanned arrays

This paper discusses bandwidth and polarization challenges in modern dual-polarized ultra-wideband (UWB) electronically scanned array (ESA) aperture design and two recent solution advances aiming to remedy such issues: a 6:1 Planar Ultrawideband Modular Antenna (PUMA) array and a 10:1 Sliced Notch Antenna (SNA) array. The proposed arrays enable wide bandwidth, low cross-polarization, and high efficiency from both low-profile and high-profile array research directions to appeal to a wider variety of applications and design embodiments. The proposed UWB-ESAs are analyzed alongside a conventional 10:1 all-metal Vivaldi array to shed light upon modern design trends and implications.


Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it’s often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. * Corresponding author


Incremental dense multi-modal 3D scene reconstruction

Aquiring reliable depth maps is an essential prerequisite for accurate and incremental 3D reconstruction used in a variety of robotics applications. Depth maps produced by affordable Kinect-like cameras have become a de-facto standard for indoor reconstruction and the driving force behind the success of many algorithms. However, Kinect-like cameras are less effective outdoors where one should rely on other sensors. Often, we use a combination of a stereo camera and lidar, however, process the acquired data in independent pipelines which generally leads to sub-optimal performance since both sensors suffer from different drawbacks. In this paper, we propose a probabilistic model that efficiently exploits complementarity between different depth-sensing modalities for incremental dense scene reconstruction. Our model uses a piecewise planarity prior assumption which is common in both the indoor and outdoor scenes. We demonstrate the effectiveness of our approach on the KITTI dataset, and provide qualitative and quantitative results showing high-quality dense reconstruction of a number of scenes.


An Algebraic Theory of Graph Reduction

Wc show how membership in classes of graphs definable m monwhc second-order ]oglc and of bounded treewldth can be decided by finite sets of terminating reduction rules. The method is constructive in the sense that wc describe an algorlthm that wdl produce, from J formula in monxhc second-order Ioglc and an mleger k such that the class dcfmed by the formul~ IS of treewidth s k, a set of rewrite rules that rcducxs any member of the elms to one of’ firrltely many graphs, in a number of steps bounded by the size c~f the graph. This rcductmn syjtem ymlds an algorlthm that runs m time linear m the size of the graph. We illustrate our results with rcductlon systems that recognux some families of outerplanar and planar graphs. Categories .ind Subject Descriptors F.2 2 [Analysis of Algorithms and Problem Complexity], Nonnurnerical Algorithms and Pr[>blems—cotyz/] ~trczt~o/zs O;Zdzscrctc strut tztrcs; F.-1 3 [Mathematical Logic and Formal Languages]; Formal Languages-ckmscs defu~ed by ~runzn~ars c~ruutomafa. G 2.2 [Discrete Mathematics]; Graph Thco~–grup/~ alcqortrhms, trees General Terms Algorithm\, Languages, Perform~nce. Theory Additlonai


The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models

MOTIVATION
Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively.

RESULTS
We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others.

AVAILABILITY
The specification of SBML Level 1 is freely available from http://www.sbml.org/


A lightweight symbolic virtual machine for solver-aided host languages

Solver-aided domain-specific languages (SDSLs) are an emerging class of computer-aided programming systems. They ease the construction of programs by using satisfiability solvers to automate tasks such as verification, debugging, synthesis, and non-deterministic execution. But reducing programming tasks to satisfiability problems involves translating programs to logical constraints, which is an engineering challenge even for domain-specific languages.
 We have previously shown that translation to constraints can be avoided if SDSLs are implemented by (traditional) embedding into a host language that is itself solver-aided. This paper describes how to implement a symbolic virtual machine (SVM) for such a host language. Our symbolic virtual machine is lightweight because it compiles to constraints only a small subset of the host's constructs, while allowing SDSL designers to use the entire language, including constructs for DSL embedding. This lightweight compilation employs a novel symbolic execution technique with two key properties: it produces compact encodings, and it enables concrete evaluation to strip away host constructs that are outside the subset compilable to constraints. Our symbolic virtual machine architecture is at the heart of Rosette, a solver-aided language that is host to several new SDSLs.


Learning to Collaborate for Question Answering and Asking

Question answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in literature. In this paper, we give a systematic study that seeks to leverage the connection to improve both QA and QG. We present a training algorithm that generalizes both Generative Adversarial Network (GAN) and Generative Domain-Adaptive Nets (GDAN) under the question answering scenario. The two key ideas are improving the QG model with QA through incorporating additional QA-specific signal as the loss function, and improving the QA model with QG through adding artificially generated training instances. We conduct experiments on both document based and knowledge based question answering tasks. We have two main findings. Firstly, the performance of a QG model (e.g in terms of BLEU score) could be easily improved by a QA model via policy gradient. Secondly, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model. Learning when to regard generated questions as positive instances could bring performance boost.


Developing Citizens ’ Trust towards Successful Adoption of E-Government : an Empirical Study from Saudi Arabia

Despite the significant influence of citizens’ trust towards successful adoption of e-government, the majority of research studies investigating trust in e-government focus only on technology and government agencies. This research provides a deep understanding of the concept of trust in e-government by investigating the influence of technology, government agencies, risk, citizens’ characteristic. Structure Equation Modelling PLS-SEM is utilized to analyse the collected data and to test the proposed hypotheses. The findings of the study reveals the positive and significant impacts of both technical factors and citizens’ characteristic while factors related to government agencies and risk provide negative impacts in trust in egovernment. Also, this paper identifies effective strategies that government need to develop citizens’ trust on their online services. Research contributions and limitations are also discussed in the end of the paper.


A ground level causal learning algorithm

Open domain causal learning involves learning and establishing causal connections between events directly from sensory experiences. It has been established in psychology that this often requires background knowledge. However, background knowledge has to be built from first experiences, which we term ground level causal learning, which basically involves observing temporal correlations. Subsequent knowledge level causal learning can then be based on this ground level causal knowledge. The causal connections between events, such as between lightning and thunder, are often hard to discern based on simple temporal correlations because there might be noise - e.g., wind, headlights, sounds of vehicles, etc. - that intervene between lightning and thunder. In this paper, we adopt the position that causal learning is inductive and pragmatic, and causal connections exist on a scale of graded strength. We describe a method that is able to filter away noise in the environment to obtain likely causal connections between events.


A study on the effects of personalization and task information on implicit feedback performance

While Implicit Relevance Feedback (IRF) algorithms exploit users' interactions with information to customize support offered to users of search systems, it is unclear how individual and task differences impact the effectiveness of such algorithms. In this paper we describe a study on the effect on retrieval performance of using additional information about the user and their search tasks when developing IRF algorithms. We tested four algorithms that use document display time to estimate relevance, and tailored the threshold times (i.e., the time distinguishing relevance from non-relevance) to the task, the user, a combination of both, or neither. Interaction logs gathered during a longitudinal naturalistic study of online information-seeking behavior are used as stimuli for the algorithms. The findings show that tailoring display time thresholds based on task information improves IRF algorithm performance, but doing so based on user information worsens performance. This has implications for the development of effective IRF algorithms.


Neural Program Synthesis from Diverse Demonstration Videos

Interpreting decision making logic in demonstration videos is key to collaborating with and mimicking humans. To empower machines with this ability, we propose a neural program synthesizer that is able to explicitly synthesize underlying programs from behaviorally diverse and visually complicated demonstration videos. We introduce a summarizer module as part of our model to improve the network’s ability to integrate multiple demonstrations varying in behavior. We also employ a multi-task objective to encourage the model to learn meaningful intermediate representations for end-to-end training. We show that our model is able to reliably synthesize underlying programs as well as capture diverse behaviors exhibited in demonstrations. The code is available at https://shaohua0116.github.io/demo2program.


Does the application of constraint-induced movement therapy during acute rehabilitation reduce arm impairment after ischemic stroke?

BACKGROUND AND PURPOSE
Motor dysfunction after unilateral deafferentation in primates can be overcome by restraining the unaffected limb. We asked whether a constraint-induced movement (CIM) program could be implemented within 2 weeks after stroke and whether CIM is more effective than traditional upper-extremity (UE) therapies during this period.

METHODS
Twenty-three persons were enrolled in a pilot randomized, controlled trial that compared CIM with traditional therapies. A blinded observer rated the primary end point, the Action Research Arm Test (ARA). Inclusion criteria were the following: ischemic stroke within 14 days, persistent hemiparesis, evidence of preserved cognitive function, and presence of a protective motor response. Differences between the groups were compared by using Student's t tests, ANCOVA, and Mann-Whitney U: tests.

RESULTS
Twenty subjects completed the 14-day treatment. Two adverse outcomes, a recurrent stroke and a death, occurred in the traditional group; 1 CIM subject met rehabilitation goals and was discharged before completing 14 inpatient days. The CIM treatment group had significantly higher scores on total ARA and pinch subscale scores (P:<0.05). Differences in the mean ARA grip, grasp, and gross movement subscale scores did not reach statistical significance. UE activities of daily living performance was not significantly different between groups, and no subject withdrew because of pain or frustration.

CONCLUSIONS
A clinical trial of CIM therapy during acute rehabilitation is feasible. CIM was associated with less arm impairment at the end of treatment. Long-term studies are needed to determine whether CIM early after stroke is superior to traditional therapies.


Controllability of complex networks

The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.


Evaluation of alternative glyph designs for time series data in a small multiple setting

We present the results of a controlled experiment to investigate the performance of different temporal glyph designs in a small multiple setting. Analyzing many time series at once is a common yet difficult task in many domains, for example in network monitoring. Several visualization techniques have, thus, been proposed in the literature. Among these, iconic displays or glyphs are an appropriate choice because of their expressiveness and effective use of screen space. Through a controlled experiment, we compare the performance of four glyphs that use different combinations of visual variables to encode two properties of temporal data: a) the position of a data point in time and b) the quantitative value of this data point. Our results show that depending on tasks and data density, the chosen glyphs performed differently. Line Glyphs are generally a good choice for peak and trend detection tasks but radial encodings are more effective for reading values at specific temporal locations. From our qualitative analysis we also contribute implications for designing temporal glyphs for small multiple settings.


Learning to Segment Instances in Videos with Spatial Propagation Network

We propose a deep learning-based framework for instance-level object segmentation. Our method mainly consists of three steps. First, We train a generic model based on ResNet-101 for foreground/background segmentations. Second, based on this generic model, we fine-tune it to learn instance-level models and segment individual objects by using augmented object annotations in first frames of test videos. To distinguish different instances in the same video, we compute a pixel-level score map for each object from these instance-level models. Each score map indicates the objectness likelihood and is only computed within the foreground mask obtained in the first step. To further refine this per frame score map, we learn a spatial propagation network. This network aims to learn how to propagate a coarse segmentation mask spatially based on the pairwise similarities in each frame. In addition, we apply a filter on the refined score map that aims to recognize the best connected region using spatial and temporal consistencies in the video. Finally, we decide the instance-level object segmentation in each video by comparing score maps of different instances.


A Longitudinal Assessment of the Persistence of Twitter Datasets

With social media datasets being increasingly shared by researchers, it also presents the caveat that those datasets are not always completely replicable. Having to adhere to requirements of platforms like Twitter, researchers cannot release the raw data and instead have to release a list of unique identifiers, which others can then use to recollect the data from the platform themselves. This leads to the problem that subsets of the data may no longer be available, as content can be deleted or user accounts deactivated. To quantify the impact of content deletion in the replicability of datasets in a long term, we perform a longitudinal analysis of the persistence of 30 Twitter datasets, which include over 147 million tweets. Having the original datasets collected between 2012 and 2016, and recollecting them later by using the tweet IDs, we look at four different factors that quantify the extent to which recollected datasets resemble original ones: completeness, representativity, similarity and changingness. Even though the ratio of available tweets keeps decreasing as the dataset gets older, we find that the textual content of the recollected subset is still largely representative of the whole dataset that was originally collected. The representativity of the metadata, however, keeps decreasing over time, both because the dataset shrinks and because certain metadata, such as the users’ number of followers, keeps changing. Our study has important implications for researchers sharing and using publicly shared Twitter datasets in their research.


Transaction Repair for Multi-Version Concurrency Control

The optimistic variants of Multi-Version Concurrency Control (MVCC) avoid blocking concurrent transactions at the cost of having a validation phase. Upon failure in the validation phase, the transaction is usually aborted and restarted from scratch. The "abort and restart" approach becomes a performance bottleneck for use cases with high contention objects or long running transactions. In addition, restarting from scratch creates a negative feedback loop in the system, because the system incurs additional overhead that may create even more conflicts.
 In this paper, we propose a novel approach for conflict resolution in MVCC for in-memory databases. This low overhead approach summarizes the transaction programs in the form of a dependency graph. The dependency graph also contains the constructs used in the validation phase of the MVCC algorithm. Then, when encountering conflicts among transactions, our mechanism quickly detects the conflict locations in the program and partially re-executes the conflicting transactions. This approach maximizes the reuse of the computations done in the initial execution round, and increases the transaction processing throughput.


Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments

We present an extended analysis of our previous work on the HydroSense technology, which is a low-cost and easily installed single-point sensor of pressure for automatically disaggregating water usage activities in the home (Froehlich et al., 2009 [53]). We expand upon this work by providing a survey of existing and emerging water disaggregation techniques, a more comprehensive description of the theory of operation behind our approach, and an expanded analysis section that includes hot versus coldwater valve usage classification and a comparisonbetween two classification approaches: the template-based matching scheme used in Froehlich et al. (2009) [53] and a new stochastic approach using a Hidden Markov Model. We show that both are successful in identifying valveand fixturelevel water events with greater than 90% accuracies. We conclude with a discussion of the limitations in our experimental methodology and open problems going forward. © 2010 Elsevier B.V. All rights reserved.


Estimating and Interpreting The Instantaneous Frequency of a Signal-Part 1 : Fundamentals

The frequency of a sinusoidal signal is a well defined quantity. However, often in practice, signals are not truly sinusoidal, or even aggregates of sinusoidal components. Nonstationary signals in particular do not lend themselves well to decomposition into sinusoidal components. For such signals, the notion of frequency loses its effectiveness, and one needs to use a parameter which accounts for the time-varying nature of the process. This need has given rise to the idea of instantaneous frequency. The instantaneous frequency (IF) of a signal is a parameter which is often of significant practical importance. In many situations such as seismic, radar, sonar, communications, and biomedical applications, the IF is a good descriptor of some physical phenomenon. This paper discusses the concept of instantaneous frequency, its definitions, and the correspondence between the various mathematical models formulated for representation of IF. The paper also considers the extent to which the IF corresponds to our intuitive expectation of reality. A historical review of the successive attempts to define the IF is presented. Then the relationships between the IF and the group-delay, analytic signal, and bandwidth-time (BT) product are explored, as well as the relationship with time-frequency distributions. Finally, the notions of monocomponent and multicomponent signals, and instantaneous bandwidth are discussed. It is shown that all these notions are well described in the context of the theory presented.


Nonintrusive Load Monitoring and Diagnostics in Power Systems

This paper describes a transient event classification scheme, system identification techniques, and implementation for use in nonintrusive load monitoring. Together, these techniques form a system that can determine the operating schedule and find parameters of physical models of loads that are connected to an AC or DC power distribution system. The monitoring system requires only off-the-shelf hardware and recognizes individual transients by disaggregating the signal from a minimal number of sensors that are installed at a central location in the distribution system. Implementation details and field tests for AC and DC systems are presented.


Inferring activities from interactions with objects

A key aspect of pervasive computing is using computers and sensor networks to effectively and unobtrusively infer users' behavior in their environment. This includes inferring which activity users are performing, how they're performing it, and its current stage. Recognizing and recording activities of daily living is a significant problem in elder care. A new paradigm for ADL inferencing leverages radio-frequency-identification technology, data mining, and a probabilistic inference engine to recognize ADLs, based on the objects people use. We propose an approach that addresses these challenges and shows promise in automating some types of ADL monitoring. Our key observation is that the sequence of objects a person uses while performing an ADL robustly characterizes both the ADL's identity and the quality of its execution. So, we have developed Proactive Activity Toolkit (PROACT).


NAWMS: nonintrusive autonomous water monitoring system

Water is nature's most precious resource and growing demand is pushing fresh water supplies to the brink of non-renewability. New technological and social initiatives that enhance conservation and reduce waste are needed. Providing consumers with fine-grained real-time information has yielded benefits in conservation of power and gasoline. Extending this philosophy to water conservation, we introduce a novel water monitoring system, NAWMS, that similarly empowers users.
 The goal of our work is to furnish users with an easy-to-install self-calibrating system that provides information on when, where, and how much water they are using. The system uses wireless vibration sensors attached to pipes and, thus, neither plumbing nor special expertise is necessary for its installation. By implementing a non-intrusive, autonomous, and adaptive system using commodity hardware, we believe it is cost-effective and widely deployable.
 NAWMS makes use of the existing household water flow meter, which is considered accurate, but lacks spatial granularity, and adds vibration sensors on individual water pipes to estimate the water flow to each individual outlet. Compensating for manufacturing, installation, and material variabilities requires calibration of these low cost sensors to achieve a reasonable level of accuracy. We have devised an adaptive auto-calibration procedure, which attempts to solve a two phase linear programming and mixed linear geometric programming problem.
 We show through experiments on a three pipe testbed that such a system is indeed feasible and adapts well to minimize error in the water usage estimate. We report an accuracy, over likely domestic flow-rate scenarios, with long-term stability and a mean absolute error of 7%.


Face recognition based on the uncorrelated discriminant transformation

The extraction of discriminant features is the most fundamental and important problem in face recognition. This paper presents a method to extract optimal discriminant features for face images by using the uncorrelated discriminant transformation and KL expansion. Experiments on the ORL database and the NUST603 database have been performed. Experimental results show that the uncorrelated discriminant transformation is superior to the Foley}Sammon discriminant transformation and the new method to extract uncorrelated discriminant features for face images is very e!ective. An error rate of 2.5% is obtained with the experiments on the ORL database. An average error rate of 1.2% is obtained with the experiments on the NUST603 database. Experiments show that by extracting uncorrelated discriminant features, face recognition could be performed with higher accuracy on lower than 16 16 resolution mosaic images. It is suggested that for the uncorrelated discriminant transformation, the optimal face image resolution can be regarded as the resolution m n which makes the dimensionality N"mn of the original image vector space be larger and closer to the number of known-face classes. 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.


Tracking multiple rigid symmetric and non-symmetric objects in real-time using depth data

In this paper, a robust, real-time object tracking approach capable of dealing with multiple symmetric and non-symmetric objects in a real-time requirement setting is proposed. The approach relies only on depth data to track multiple objects in a dynamic environment and uses random-forest based learning to deal with problems like object occlusion, motion-blur due to camera motion and clutter. We show that the relation between object motion and the corresponding change in its 3D point cloud data can be learned using only 6 random forests. A framework that unites object pose estimation and object pose tracking to efficiently track multiple objects in 3D space is presented. The approach is robust in tracking objects even in presence of motion blur that causes noisy depth data and is capable of real-time performance with 1.8ms per frame. The experimental evaluations demonstrate the performance of the approach against robustness, accuracy and speed and compare the approach with the state of the art. A publicly available dataset with real-world data is also provided for future benchmarking.


Smart home care network using sensor fusion and distributed vision-based reasoning

A wireless sensor network employing multiple sensing and event detection modalities and distributed processing is proposed for smart home monitoring applications. Image sensing and vision-based reasoning are employed to verify and further analyze events reported by other sensors. The system has been developed to address the growing application domain in caregiving to the elderly and persons in need of monitored living, who care to live independently while enjoying the assurance of timely access to caregivers when needed. An example of sensed events is the accidental fall of the person under care. A wireless badge node acts as a bridge between the user and the network. The badge node provides user-centric event sensing functions such as detecting falls, and also provides a voice communication channel between the user and the caregiving center when the system detects an alert and dials the center. The voice connection is carried over an IEEE 802.15.4 radio link between the user badge and another node in the network that acts as a modem. Using signal strength measurements, the network nodes keep track of the approximate location of the user in the monitoring environment.The network also includes wall-mounted image sensor nodes, which are triggered upon detection of a fall to analyze their field-of-view and provide the caregiving center with further information about the user 's status. A description of the developed network and several examples of the vision-based reasoning algorithm are presented in the paper.


A viability theory for digital businesses: Exploring the evolutionary changes of revenue mechanisms to support managerial decisions

The ongoing integration of Information Technology (IT) into various areas of our lives has led to a plethora of digital products and services. To survive competition in the long run, these offerings not only have to keep up with constant technological developments, but also have to adapt from a business point of view. Managers of these digital businesses have to especially focus on the design and evolution of their business’ revenue mechanisms to ensure the viability of their offerings. The related decisions are not trivial, as managers have to be aware of the relevant contextual factors and have to react quickly to changes in the environment. This paper proposes a viability theory for digital businesses described by 17 propositions that may guide managers in the design of revenue mechanisms and thereby support the evolution as well as the viability of a digital business.


Efficient Zero-Knowledge Contingent Payments in Cryptocurrencies Without Scripts

One of the most promising innovations offered by the cryptographic currencies (like Bitcoin) are the so-called smart contracts, which can be viewed as financial agreements between mutually distrusting participants. Their execution is enforced by the mechanics of the currency, and typically has monetary consequences for the parties. The rules of these contracts are written in the form of so-called “scripts”, which are pieces of code in some “scripting language”. Although smart contracts are believed to have a huge potential, for the moment they are not widely used in practice. In particular, most of Bitcoin miners allow only to post standard transactions (i.e.: those without the non-trivial scripts) on the blockchain. As a result, it is currently very hard to create non-trivial smart contracts in Bitcoin. Motivated by this, we address the following question: “is it possible to create non-trivial efficient smart contracts using the standard transactions only?” We answer this question affirmatively, by constructing efficient Zero-Knowledge Contingent Payment protocol for a large class of NP-relations. This includes the relations for which efficient sigma protocols exist. In particular, our protocol can be used to sell a factorization (p, q) of an RSA modulus n = pq, which is an example that we implemented and tested its efficiency in practice. As another example of the “smart contract without scripts” we show how our techniques can be used to implement the contract called “trading across chains”.


Survey of Knowledge Representation and Reasoning Systems

As part of the information fusion task we wish to automatically fuse information derived from the text extraction process with data from a structured knowledge base. This process will involve resolving, aggregating, integrating and abstracting information via the methodologies of Knowledge Representation and Reasoning into a single comprehensive description of an individual or event. This report surveys the key principles underlying research in the field of Knowledge Representation and Reasoning. It represents an initial step in deciding upon a Knowledge Representation and Reasoning system for our information fusion task. APPROVED FOR PUBLIC RELEASE


Proving the Incompatibility of Efficiency and Strategyproofness via SMT Solving

Two important requirements when aggregating the preferences of multiple agents are that the outcome should be economically efficient and the aggregation mechanism should not be manipulable. In this article, we provide a computer-aided proof of a sweeping impossibility using these two conditions for randomized aggregation mechanisms. More precisely, we show that every efficient aggregation mechanism can be manipulated for all expected utility representations of the agents’ preferences. This settles an open problem and strengthens several existing theorems, including statements that were shown within the special domain of assignment. Our proof is obtained by formulating the claim as a satisfiability problem over predicates from real-valued arithmetic, which is then checked using a satisfiability modulo theories (SMT) solver. To verify the correctness of the result, a minimal unsatisfiable set of constraints returned by the SMT solver was translated back into a proof in higher-order logic, which was automatically verified by an interactive theorem prover. To the best of our knowledge, this is the first application of SMT solvers in computational social choice.


Measurement of return on marketing investment : A conceptual framework and the future of marketing metrics

There is growing recognition that firms in the contemporary business environment derive substantial and sustained competitive advantage from a bundle of intangible assets such as knowledge, networks and innovative capability. Measuring the return on such intangible assets has now become imperative for managers. The present manuscript focuses on the measurement of the return on marketing. We first discuss the conditions that make this task a high managerial priority. We then discuss measurement efforts to date, both in general management and marketing. We then offer a conceptual framework that places measurement efforts in a historical perspective. We conclude with a discussion on where the future of marketing metrics lies. © 2006 Elsevier Inc. All rights reserved.


Proactive avoidance of moving obstacles for a service robot utilizing a behavior-based control

A main challenge in the application of service robotics is safe and reliable navigation of robots in human everyday environments. Supermarkets, which are chosen here as an example, pose a challenging scenario because they usually have a cluttered and nested character. The robot has to avoid collisions with static and even with moving obstacles while interacting with nearby humans or a dedicated user respectively. This paper presents a hierarchical approach for the proactive avoidance of moving objects as it is used on the robot shopping trolley InBOT. The behavior-based control (bbc) of InBOT is extended by a reflex and a reactive behavior to ensure adequate reaction times when confronted with a possible collision. On top of the bbc a spatio-temporal planner is situated which is able to predict environmental changes and therefore can generate a safe movement sequence accordingly.


Mobile app conceptual browser: Online marketplaces information extraction

Online marketplaces are e-commerce websites where thousands of products are provided by multiple third parties. There are dozens of these differently structured marketplaces that need to be visited by the end users to reach their targets. This searching process consumes a lot of time and effort; moreover it negatively affects the user experience. In this paper, extensive analysis and evaluation of the existing e-marketplaces are performed to improve the end-users experience through a Mobile App. The main goal of this study is to find a solution that is capable of integrating multiple heterogeneous hidden data sources and unify the received responses into one single, structured and homogeneous source. Furthermore, the user can easily choose the desired product or reformulate the query through the interface. The proposed Android Mobile App is based on the multi-level conceptual analysis and modeling discipline, in which, data are analyzed in a way that helps in discovering the main concepts of any unknown domain captured from the hidden web. These concepts discovered through information extraction are then structured into a tree-based interface for easy navigation and query reformulation. The application has been evaluated through substantial experiments and compared to other existing mobile applications. The results showed that analyzing the query results and re-structuring the output before displaying to the end-user in a conceptual multilevel mechanism are reasonably effective in terms of number of clicks, time taken and number of navigation screens. Based on the proposed intelligent application, the interface is minimized to only two navigation screens, and the time needed to browse products from multiple marketplaces is kept reasonable in order to reach the target product.


CRISPR/Cas9 for genome editing: progress, implications and challenges.

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) protein 9 system provides a robust and multiplexable genome editing tool, enabling researchers to precisely manipulate specific genomic elements, and facilitating the elucidation of target gene function in biology and diseases. CRISPR/Cas9 comprises of a nonspecific Cas9 nuclease and a set of programmable sequence-specific CRISPR RNA (crRNA), which can guide Cas9 to cleave DNA and generate double-strand breaks at target sites. Subsequent cellular DNA repair process leads to desired insertions, deletions or substitutions at target sites. The specificity of CRISPR/Cas9-mediated DNA cleavage requires target sequences matching crRNA and a protospacer adjacent motif locating at downstream of target sequences. Here, we review the molecular mechanism, applications and challenges of CRISPR/Cas9-mediated genome editing and clinical therapeutic potential of CRISPR/Cas9 in future.


Cross Domain Regularization for Neural Ranking Models using Adversarial Learning

Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training on large datasets. Their ability to learn new features directly from the data, however, may come at a price. Without any special supervision, these models learn relationships that may hold only in the domain from which the training data is sampled, and generalize poorly to domains not observed during training. We study the effectiveness of adversarial learning as a cross domain regularizer in the context of the ranking task. We use an adversarial discriminator and train our neural ranking model on a small set of domains. The discriminator provides a negative feedback signal to discourage the model from learning domain specific representations. Our experiments show consistently better performance on held out domains in the presence of the adversarial discriminator---sometimes up to 30% on precision$@1$.


Occlusion-Free Visual Servoing for the Shared Autonomy Teleoperation of Dual-Arm Robots

Visual servoing in telerobotics provides information to the operator about the remote location and assists in the task execution to reduce stress on the user. Occlusions in this scenario can, on the one hand, lead to the visual servoing failure, and, on the other hand, degrade the user experience and navigation performance due to the obstructed vision of elements of interest. In this letter, we consider a teleoperation system composed of two robot arms where one is remotely operated, while the other is autonomous and equipped with an eye-in-hand camera sensor. We propose a reactive unified convex optimization based controller that allows the execution of occlusion-free tasks by autonomously adjusting the camera robot, so as to keep the teleoperated one in the field of view. The occlusion avoidance is formulated inside the optimization as a constraint in the image space, it is formally derived from a collision avoidance analogy and made robust against noisy measurements and dynamic environment. A state machine is used to switch the control policy whenever an occlusion might occur. We validate our approach with experiments on a 14 d.o.f. dual-arm ABB YuMi robot equipped with an red, green, blue (RGB) camera and teleoperated by a 3 d.o.f. Novint Falcon device.


Large-scale brain networks in cognition: emerging methods and principles

An understanding of how the human brain produces cognition ultimately depends on knowledge of large-scale brain organization. Although it has long been assumed that cognitive functions are attributable to the isolated operations of single brain areas, we demonstrate that the weight of evidence has now shifted in support of the view that cognition results from the dynamic interactions of distributed brain areas operating in large-scale networks. We review current research on structural and functional brain organization, and argue that the emerging science of large-scale brain networks provides a coherent framework for understanding of cognition. Critically, this framework allows a principled exploration of how cognitive functions emerge from, and are constrained by, core structural and functional networks of the brain.


The Rosenberg Self-Esteem Scale: translation and validation in university students.

The aim of this study was to translate into Spanish and to validate the Rosenberg Self-Esteem Scale (RSES), completed by 420 university students. Confirmatory factor analysis revealed that the model that best fit the data, both in the total sample and in the male and female subsamples, was the one-factor structure with method effects associated with positively worded items. The results indicated high, positive correlations between self-esteem and the five dimensions of self-concept. The scale showed satisfactory levels of internal consistency and temporal stability over a four-week period. Lastly, gender differences were obtained. These findings support the use of the RSES for the assessment of self-esteem in higher education.


Teaching Computational Thinking Using Agile Software Engineering Methods: A Framework for Middle Schools

Computational Thinking (CT) has been recognized as one of the fundamental skills that all graduates should acquire. For this reason, motivational concerns need to be addressed at an early age of a child, and reaching students who do not consider themselves candidates for science, technology, engineering, and mathematics disciplines is important as well if the broadest audience possible is to be engaged. This article describes a framework for teaching and assessing CT in the context of K-12 education. The framework is based on Agile software engineering methods, which rely on a set of principles and practices that can be mapped to the activities of CT. The article presents as well the results of an experiment applying this framework in two sixth-grade classes, with 42 participants in total. The results show that Agile software engineering methods are effective at teaching CT in middle schools, after the addition of some tasks to allow students to explore, project, and experience the potential product before using the software tools at hand. Moreover, according to the teachers’ feedback, the students reached all the educational objectives of the topics involved in the multidisciplinary activities. This result can be taken as an indicator that it is possible to use computing as a medium for teaching other subjects, besides computer science.


1 Pavement distress detection methods : a review 2

The road pavement condition affects safety and comfort, traffic and travel times, vehicles 8 operating cost and emission levels. In order to optimize the road pavement management and 9 guarantee satisfactory mobility conditions for all the road users, the Pavement Management 10 System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of 11 pavement distress data, the possibility of data maintenance and updating, in order to evaluate the 12 best maintenance program. In the last decade, many researches have been focused on pavement 13 distress detection, using a huge variety of technological solutions for both data collection and 14 information extraction and qualification. This paper presents a literature review of data collection 15 systems and processing approach aimed at the pavement condition evaluation. Both commercial 16 solutions and research approaches have been included. The main goal is to draw a framework of 17 the actual existing solutions, considering them from a different point of view in order to identify 18 the most suitable for further research and technical improvement, also considering the automated 19 and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the 20 data collection and extraction to the type of distress, considering the distress detection, 21 classification and quantification phases of the procedure. 22


A Unified Local and Global Model for Discourse Coherence

We present a model for discourse coherence which combines the local entitybased approach of (Barzilay and Lapata, 2005) and the HMM-based content model of (Barzilay and Lee, 2004). Unlike the mixture model of (Soricut and Marcu, 2006), we learn local and global features jointly, providing a better theoretical explanation of how they are useful. As the local component of our model we adapt (Barzilay and Lapata, 2005) by relaxing independence assumptions so that it is effective when estimated generatively. Our model performs the ordering task competitively with (Soricut and Marcu, 2006), and significantly better than either of the models it is based on.


A knowledge-based table recognition method for Chinese bank statement images

Automatic processing of large volume scanned Chinese bank statements is a urgent demand recently. Conventional methods can not well handle the following challenges of this problem: various layout styles, noises, and especially requirement of fast speed for large Chinese character set. This paper proposes a knowledge based table recognition method to meet fast speed requirement with good accuracy. Two kinds of knowledge are utilized to accelerate the identification of digit columns and the cell recognition: i) geometric knowledge about column alignment and quasi equal digit width, and ii) semantic knowledge about prior format based on the results from an optical character recognition (OCR) engine of digits. Experimental results on a real dataset show the effectiveness of our method.


Artificial Neural Network Method for Solution of Boundary Value Problems With Exact Satisfaction of Arbitrary Boundary Conditions

A method for solving boundary value problems (BVPs) is introduced using artificial neural networks (ANNs) for irregular domain boundaries with mixed Dirichlet/Neumann boundary conditions (BCs). The approximate ANN solution automatically satisfies BCs at all stages of training, including before training commences. This method is simpler than other ANN methods for solving BVPs due to its unconstrained nature and because automatic satisfaction of Dirichlet BCs provides a good starting approximate solution for significant portions of the domain. Automatic satisfaction of BCs is accomplished by the introduction of an innovative length factor. Several examples of BVP solution are presented for both linear and nonlinear differential equations in two and three dimensions. Error norms in the approximate solution on the order of 10-4 to 10-5 are reported for all example problems.


Promoting tourism destinations : A strategic marketing approach

Introduction This paper outlines the main topics of a report prepared for the Tourism Promotion Committee (TPC) in Heraklion District, Crete. This body is responsible for coordinating marketing activities and promoting its area. Its membership is composed of the main public and private sector organizations involved in providing tourism services in Crete. The report was compiled for TPC by the first author, who has been closely involved in the development of tourism marketing and planning on Crete for over ten years. It was commissioned with the aim of improving the destination’s marketing effectiveness and efficiency. The task assigned was to provide a report (i) reviewing the marketing activities undertaken by the TPC; (ii) providing the appropriate approach to be adopted and implemented in promoting Heraklion as tourism destination; and (iii) giving a set of recommendations for key areas requiring improvement. Marios D. Soteriades and Vasiliki A. Avgeli


Zoomorphic design

Zoomorphic shapes are man-made shapes that possess the form or appearance of an animal. They have desirable aesthetic properties, but are difficult to create using conventional modeling tools. We present a method for creating zoomorphic shapes by merging a man-made shape and an animal shape. To identify a pair of shapes that are suitable for merging, we use an efficient graph kernel based technique. We formulate the merging process as a continuous optimization problem where the two shapes are deformed jointly to minimize an energy function combining several design factors. The modeler can adjust the weighting between these factors to attain high-level control over the final shape produced. A novel technique ensures that the zoomorphic shape does not violate the design restrictions of the man-made shape. We demonstrate the versatility and effectiveness of our approach by generating a wide variety of zoomorphic shapes.


Differential Cryptanalysis of the Data Encryption Standard


This paper discusses some aspects of testing random and pseudorandom number generators. A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications (NIST Special Publication) is presented. This paper also demonstrates the software implementation of the test suite with output protocols and presents experiences from testing some random and pseudorandom generators.


A New Iterative Secret Key Cryptosystem Based on Reversible and Irreversible Cellular Automata

Many cryptosystems have been released to secure electronic data on internet. Some data are very critical to be transmitted as plaintext. Thus, to ensure the data confidentiality and integrity, a list of cryptosystems have been elaborated. The most important ones are divided into two categories: symmetric algorithms encrypting and decrypting data in blocks using a single secret key; and asymmetric algorithms using public keys to cipher texts and secret keys to reconstruct plaintexts. The of the present work is the design and implement a new secret key cryptosystem encrypting and decrypting data in blocks according to a number of iterations. Each plaintext block is encrypted using cellular automata and a list of sub keys deduced from a secret key through cellular automata. To demonstrate the feasibility, the proposed scheme is compared with AES algorithm, the well-known symmetric block cipher. We prove that our algorithm resists against statistical attacks and it is faster than AES-256 achieving good confusion and diffusion tests.


Differential Cryptanalysis of Feal and N-Hash

In 1,2] we introduced the notion of diierential cryptanalysis and described its application to DESS11] and several of its variants. In this paper we show the applicability of diierential cryptanalysis to the Feal family of encryption algorithms and to the N-Hash hash function. In addition, we show how to transform diierential cryptanalytic chosen plaintext attacks into known plaintext attacks.


A new kind of science

The distinctive idea is easy to state, though it needs some examples to appreciate. It is that simple rules can generate complex very complex outcomes when they are repeatedly applied. Isolated examples of this phenomenon have been known for centuries. For example, the number p has a simple definition the ratio of a circle's circumference to its diameter and there is a quite simple formula to calculate its digits. The result begins: 3.14159265358979323846264338327950288419716939937510582097494459230781640 6286208998628034825342117068...


Fixed-point algorithms for learning determinantal point processes

Determinantal point processes (DPPs) offer an elegant tool for encoding probabilities over subsets of a ground set. Discrete DPPs are parametrized by a positive semidefinite matrix (called the DPP kernel), and estimating this kernel is key to learning DPPs from observed data. We consider the task of learning the DPP kernel, and develop for it a surprisingly simple yet effective new algorithm. Our algorithm offers the following benefits over previous approaches: (a) it is much simpler; (b) it yields equally good and sometimes even better local maxima; and (c) it runs an order of magnitude faster on large problems. We present experimental results on both real and simulated data to illustrate the numerical performance of our technique.


Stock Market Prediction based on Time Series Data and Market Sentiment

In this project, we would like to create a system that predicts stock market movements on a given day, based on time series data and market sentiment analysis. We will use Twitter data on that day to predict the market sentiment and S&P 500 values to perform analysis on historical data.


An Automated Approach for Digital Forensic Analysis of Heterogeneous Big Data

The major challenges with big data examination and analysis are volume, complex interdependence across content, and heterogeneity. The examination and analysis phases are considered essential to a digital forensics process. However, traditional techniques for the forensic investigation use one or more forensic tools to examine and analyse each resource. In addition, when multiple resources are included in one case, there is an inability to cross-correlate findings which often leads to inefficiencies in processing and identifying evidence. Furthermore, most current forensics tools cannot cope with large volumes of data. This paper develops a novel framework for digital forensic analysis of heterogeneous big data. The framework mainly focuses upon the investigations of three core issues: data volume, heterogeneous data and the investigators cognitive load in understanding the relationships between artefacts. The proposed approach focuses upon the use of metadata to solve the data volume problem, semantic web ontologies to solve the heterogeneous data sources and artificial intelligence models to support the automated identification and correlation of artefacts to reduce the burden placed upon the investigator to understand the nature and relationship of the artefacts.


Application of Katharine Kolkaba Comfort theory to nursing care of patient

aring is in practice since existence but caring as profession named Nursing was scientifically Coined by the founder of modern nursing who in her writings “what it is and what it is not”declared the boundaries along with its Constitutional elements. Those elements with appropriate association not only revealed the nature of caring dogma it also shows that it is doing something base on thinking something or in simple words this is practice rely on theory. According to McCrae, (2011) For the sake of assuming valuable status ofprofession in society nursing struggle to legitimate its position by generation and application of theory. Nursing literature is rich regarding theory practice association. although constant body of argument, counter argument and lack of agreed conclusion regarding nursing theory creates a bit confusion among nurses but ideally nursing theory should set the code of every day practice of nurses(Giltinane, 2013) as lack of a valued unique contribution nurses make to health care based on a specialized body of knowledge will portrayed nursing role as subordinate to doctor (Mccrae, 2011) Eminent nurses’ theorist, scientist, Researchers, Clinicians, Educators and intellectual intelligentsia aid evidence to different perspectives of theory practice link. Some unanimous convincing narratives argue that the relationship is reciprocal and according to (Mckenna, 2005) Nursing theory takes its origin from practice, than reverse to practice for testing and at the end become set to guide the practice.


The impact of social and conventional media on firm equity value: A sentiment analysis approach

Available online 30 December 2012


Applying autonomous sensor systems in logistics — Combining sensor networks , RFIDs and software agents

New sensor, communication and software technologies are used to broaden the facilities of tracing and tracing systems for food transports. n embedded assessing unit detects from sensor data collected by a wireless network potential risks for the freight quality. The estimation of he current maturing state of agricultural products will be supported by measurements of the gaseous hormone ethylene as an indicator for the ipening processes. A miniaturized high-resolution gas chromatography is under construction. The system autonomously configures itself to a roduct specific supervision task based on data scanned by an RFID reader during freight loading. Mobile software agents accompany the freight long the supply chain. They pre-process the vast sensor data and submit only substantial changes to the freight owner. 2006 Elsevier B.V. All rights reserved.


Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless sensor networks (WSNs) are the main components of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in machine-to-machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.


Near-Duplicates Detection and Elimination Based on Web Provenance for Effective Web Search

Users of World Wide Web utilize search engines for information retrieval in web as search engines play a vital role in finding information on the web. However, the performance of a web search is greatly affected by flooding of search results with information that is redundant in nature i.e., existence of nearduplicates. Such near-duplicates holdup the other promising results to the users. Many of these near-duplicates are from distrusted websites and/or authors who host information on web. Such nearduplicates may be eliminated by means of Provenance. Thus, this paper proposes a novel approach to identify such near-duplicates based on provenance. In this approach a provenance model has been built using web pages which are the search results returned by existing search engine. The proposed model combines both content based and trust based factors for classifying the results as original or near-duplicates. Keywords— Web search, Near-duplicates, Provenance, Semantics,


Schemaless NoSQL Data Stores - Object-NoSQL Mappers to the Rescue?

NoSQL data stores are becoming increasingly popular in application development. These systems are attractive for developers due to their ability to handle large volumes of data, as well as data with a high degree of structural variety. Typically, NoSQL data stores are accessed programmatically. Due to the imminent lack of standardized query languages, building applications against the native interfaces of NoSQL data stores creates an unfortunate technical lock-in. To re-gain platform independence, developers turn to object mapper libraries as an additional level of abstraction when accessing NoSQL data stores. The current market for Java object mappers that support NoSQL data stores is still volatile, with commercial and open source products competing for adoption. In this paper, we give an overview on the state-of-the-art in Object-Relational Mappers that can handle also NoSQL data stores, as well as dedicated Object-NoSQL Mappers. We are able to show that choosing the right object mapper library is a strategic decision with far reaching consequences: Current mappers diverge in the NoSQL data stores that they support, in their features, their robustness, their truthfulness to the documentation and query standards, and ultimately, in the runtime overhead that they introduce. Especially in web development, runtime overhead is a crucial aspect contributing to the application latency, and ultimately, the user experience. By shedding light on the current market, we intend to provide software architects with the necessary information to make informed decisions.


Efficient Image Dehazing with Boundary Constraint and Contextual Regularization

Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L1-norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details. Experimental results on a variety of haze images demonstrate the effectiveness and efficiency of the proposed method.


Trust in Online Shopping: The Korean Student Experience

E-commerce has become an important part of business. In South Korea, the market size of online shopping malls was 13,460 billion Korean Won in 2006, and this figure keeps growing. Thus, gaining loyal customers has become a rising concern. In this study, we adopted Lewicki and Bunker's three different types of trust, namely, calculus-based trust, knowledge-based trust, and identification-based trust, in order to investigate their hierarchical relationships in e-commerce and their impacts on customer satisfaction and loyalty. A total of 104 responses from university students were analyzed to test the proposed model and its hypotheses using PLS. The results showed that hierarchical relationships between different types of trust exist in the online environment, and among them, knowledge-based trust has the strongest impact on customer satisfaction. This finding implied that practitioners should focus on developing an appropriate online strategy in terms of how to build up trust-based relationships with online customers.


Music-based respiratory biofeedback in visually-demanding tasks

Biofeedback tools generally use visualizations to display physiological information to the user. As such, these tools are incompatible with visually demanding tasks such as driving. While auditory or haptic biofeedback may be used in these cases, the additional sensory channels can increase workload or act as a nuisance to the user. A number of studies, however, have shown that music can improve mood and concentration, while also reduce aggression and boredom. Here, we propose an intervention that combines the benefits of biofeedback and music to help users regulate their stress response while performing a visual task (driving a car simulator). Our approach encourages slow breathing by adjusting the quality of the music in response to the user’s breathing rate. We evaluate the intervention on a design with music and auditory biofeedback as independent variables. Our results indicate that our music-biofeedback intervention leads to lower arousal (reduced electrodermal activity and increased heart rate variability) than music alone, auditory biofeedback alone and a control condition.


Graph databases: A survey

In the era of big data, data analytics, business intelligence database management plays a vital role from technical business management and research point of view. Over many decades, database management has been a topic of active research. There are different type of database management system have been proposed over a period of time but Relational Database Management System (RDBMS) is the one which has been most popularly used in academic research as well as industrial setup[1]. In recent years, graph databases regained interest among the researchers for certain obvious reasons. One of the most important reasons for such an interest in a graph database is because of the inherent property of graphs as a graph structure. Graphs are present everywhere in the data structure, which represents the strong connectivity within the data. Most of the graph database models are defined in which data-structure for schema and instances are modeled as graph or generalization of a graph. In such graph database models, data manipulations are expressed by graph-oriented operations and type constructors [9]. Now days, most of the real world applications can be modeled as a graph and one of the best real world examples is social or biological network. This paper gives an overview of the different type of graph databases, applications, and comparison between their models based on some properties.


Fundamentals of Wearable Computers and Augmented Reality, Second Edition

The best ebooks about Fundamentals Of Wearable Computers And Augmented Reality Second Edition that you can get for free here by download this Fundamentals Of Wearable Computers And Augmented Reality Second Edition and save to your desktop. This ebooks is under topic such as fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality 11 location-based mixed and augmented reality storytelling fundamentals of wearable computers and augmented reality forum book review mit press journals the vietnam war revised 2nd edition ebook | slangsurfing dicom to 3d holograms: use case for augmented reality in pensees de j j rousseau citoyen de geneve v1 2 1785 ebook click here to access this book : free download amendment xivequal protection constitutional amendments solutions manual to fundamentals of engineering document about distant mirrors america as a foreign regeneration of peasants slangsurfing the cell ebook | esmajorelojes il buddhismo contemporaneo italian edition ebook | slanggeek proceedings of a court of inquiryconvened at the navy answer coming soonmore blog postings on arts letters the travel and tropical medicine manual 4e ebook fundamentals of sql server 2012 replication


The State of Public Infrastructure-as-a-Service Cloud Security

The public Infrastructure-as-a-Service (IaaS) cloud industry has reached a critical mass in the past few years, with many cloud service providers fielding competing services. Despite the competition, we find some of the security mechanisms offered by the services to be similar, indicating that the cloud industry has established a number of “best-practices,” while other security mechanisms vary widely, indicating that there is also still room for innovation and experimentation. We investigate these differences and possible underlying reasons for it. We also contrast the security mechanisms offered by public IaaS cloud offerings and with security mechanisms proposed by academia over the same period. Finally, we speculate on how industry and academia might work together to solve the pressing security problems in public IaaS clouds going forward.


Researching Mental Health Disorders in the Era of Social Media: Systematic Review

BACKGROUND
Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.

OBJECTIVE
The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.

METHODS
We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.

RESULTS
The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.

CONCLUSIONS
Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.


Advances in geometric modeling and feature extraction on pots

This paper outlines progress on the 3DK Knowledge and Distributed Intelligence project undertaken by the Partnership for Research In Stereo Modeling (PRISM) at Arizona State University. Three of the six 3DK pilot projects of this National Science Foundation funded project involve archaeological and biological material, namely ceramic vessels, lithic artifacts, and bones. These projects are: (1) “3D Morphology of Ceramic Vessels” which aims to learn about vessel uniformity and proportionality as indicators of developing craft specialization and complex social organization among prehistoric cultures; (2) “Lithic Refitting” which intends to develop the algorithms required to (partially) automate the refitting process through 3D scanning and surface modeling; and finally (3) “3D Topography of Joint Surfaces” which investigates the ability of human ancestors to make tools and walk upright by developing software that will automate segmentation of osteological features and quantify the surface area and curvature of joint surfaces and the congruency between reciprocal joint surfaces, allowing for the development of biomechanical models. This paper discusses the links between these three unique projects and explores the underlying research aspects common to all: geometric modeling, feature recognition, and the development of a database structure aimed at making 3D models of these artifacts available online for query.


A Comparison of Knives for Bread Slicing

Vertical partitioning is a crucial step in physical database design in row-oriented databases. A number of vertical partitioning algorithms have been proposed over the last three decades for a variety of niche scenarios. In principle, the underlying problem remains the same: decompose a table into one or more vertical partitions. However, it is not clear how good different vertical partitioning algorithms are in comparison to each other. In fact, it is not even clear how to experimentally compare different vertical partitioning algorithms. In this paper, we present an exhaustive experimental study of several vertical partitioning algorithms. We categorize vertical partitioning algorithms along three dimensions. We survey six vertical partitioning algorithms and discuss their pros and cons. We identify the major differences in the use-case settings for different algorithms and describe how to make an apples-to-apples comparison of different vertical partitioning algorithms under the same setting. We propose four metrics to compare vertical partitioning algorithms. We show experimental results from the TPC-H and SSB benchmark and present four key lessons learned: (1) we can do four orders of magnitude less computation and still find the optimal layouts, (2) the benefits of vertical partitioning depend strongly on the database buffer size, (3) HillClimb is the best vertical partitioning algorithm, and (4) vertical partitioning for TPC-H-like benchmarks can improve over column layout by only up to 5%.


Large-scale PV system based on the multiphase isolated DC/DC converter

The large-scale photovoltaic (PV) systems have already been reached 200 MW power level and they will continue to grow in size in the upcoming years. This trend will challenge the existing PV system architectures by requiring power converters with a higher power rating and a higher voltage level at the point of common coupling (PCC). The cascaded H-bridge (CHB) multilevel converter is one of the solutions which could deal with the aforementioned challenges. However, the topology based on the CHB converter faces the issue of leakage current that flows through the solar panel parasitic capacitance to ground which could damage the PV panels and pose safety problems. This paper proposes a multiphase isolated DC/DC converter for the CHB topology for a large-scale PV system which eliminates the leakage current issue. At the same time, the multiphase structure of the DC/DC converter helps to increase the power rating of the converter and to reduce the PV voltage and current ripples. A 0.54 MW rated seven-level CHB converter using multiphase isolated DC/DC converters has been modeled and simulated using MATLAB/Simulink and PLECS Blockset. Simulation results of different case studies are presented to evaluate the performance of the proposed PV system configuration.


Word Sense Induction for Novel Sense Detection

We apply topic modelling to automatically induce word senses of a target word, and demonstrate that our word sense induction method can be used to automatically detect words with emergent novel senses, as well as token occurrences of those senses. We start by exploring the utility of standard topic models for word sense induction (WSI), with a pre-determined number of topics (=senses). We next demonstrate that a non-parametric formulation that learns an appropriate number of senses per word actually performs better at the WSI task. We go on to establish state-of-the-art results over two WSI datasets, and apply the proposed model to a novel sense detection task.