Union and Difference of Models, 10 years later

This paper contains a summary of the talk given by the author on the occasion of the MODELS 2013 most influential paper award. The talk discussed the original paper as published in 2003, the research work done by others afterwards and the author’s personal reflection on the award. 1 Version Control of Software and System Models There are two main usage scenarios for design models in software and system development: models as sketches, that serve as a communication aid in informal discussions, and models as formal artifacts, to be analyzed, transformed into other artifacts, maintained and evolved during the whole software and system development process. In this second scenario, models are valuable assets that should be kept in a trusted repository. In a complex development project, these models will be updated often and concurrently by different developers. Therefore, there is a need for a version control system for models with optimistic locking. This is a system to compare, merge and store all versions of all models created within a development project. We can illustrate the use of a version control system for models as follows. Let us assume that the original model shown at the top of Figure 1 is edited simultaneously by two developers. One developer has decided that the subclass B is no longer necessary in the model. Simultaneously, the other developer has decided that class C should have a subclass D. The problem is to combine the contributions of both developers into a single model. This is the model shown at the bottom of Fig. 1. We presented the basic algorithms to solve this problem in the original paper published in the proceedings of the UML 2003 conference [1]. The proposed solution is based on calculating the final model as the merge of the differences between the original and the edited models. Figure 2 shows an example of the difference of two models, in this case the difference between the models edited by the developers and the original model. The result of the difference is not always a model, in a similar way that the difference between two natural numbers is not a natural number but a negative one. An example of this is shown in the bottom B C A Original Model


Stakeholder Identification in the Requirements Engineering Process

Adequate, timely and effective consultation of relevant stakeholders is of paramount importance in the requirements engineering process. However, the thorny issue of making sure that all relevant stakeholders are consulted has received less attention than other areas which depend on it, such as scenario-based requirements, involving users in development, negotiating between different viewpoints and so on. The literature suggests examples of stakeholders, and categories of stakeholder, but does not provide help in identifying stakeholders for a specific system. In this paper, we discuss current work in stakeholder identification, propose an approach to identifying relevant stakeholders for a specific system, and propose future directions for the work. 1. What is a ÔstakeholderÕ? There is a large body of literature in the strategic management area which discusses organisations in terms of a stakeholder model. Stakeholder analysis, it is claimed, can be used to analyse an organisationÕs performance and determine its future strategic direction. An oft-quoted definition of ÔstakeholderÕ, taken from a key reference in this literature is: ÔA stakeholder in an organisation is (by definition) any group or individual who can affect or is affected by the achievement of the organisationÕs objectives.Õ [10] A much broader definition, which has also been attributed to Freeman, is that a stakeholder is Ôanything influencing or influenced byÕ the firm, but it has been claimed that this definition is problematic because it leads to the identification of a very broad set of stakeholders. It is important to distinguish between influencers and stakeholders because while some potential stakeholders may indeed be both stakeholders and influencers, some who have a real stake in an enterprise may have no influence, e.g. a job applicant, while some influencers may have no stake, e.g. the media [8]. Information systems (IS) researchers have also taken up the idea of stakeholders: ÔWe define stakeholders as these participants <in the development process> together with any other individuals, groups or organisations whose actions can influence or be influenced by the development and use of the system whether directly or indirectly.Õ [22] In software engineering, stakeholders have been defined as: ÔThe people and organisations affected by the applicationÕ [3] ÔSystem stakeholders are people or organisations who will be affected by the system and who have a direct or indirect influence on the system requirementsÕ [16] ÔStakeholders are people who have a stake or interest in the projectÕ [4] A more explicit refinement of this definition is: ÔÉ anyone whose jobs will be altered, who supplies or gains information from it, or whose power or influence within the organisation will increase or decrease.Õ [7] They go on to say that ÔIt will frequently be the case that the formal ÔclientÕ who orders the system falls very low on the list of those affected. Be very wary of changes which take power, influence or control from some stakeholders without returning something tangible in its place.Õ [7] When faced with the practical problem of how to identify the set of stakeholders relevant to a specific project, these definitions are not particularly helpful. The main concern is that, although such definitions are usually accompanied by example groups of stakeholders, they are vague and may lead to consideration of inappropriate or incomplete groups of stakeholders. Categories of stakeholder include end-users, managers and others involved in the organisational processes influenced by the system, engineers responsible for system development and maintenance, customers of the Sharp, Finkelstein & Galal 11/12/99 2 organisation who will use the system to provide a service, external bodies such as regulators, domain experts, and so on. They will each have different goals, and will try to satisfy their own without recourse to others [16]. Cotterell and Hughes [4] suggest that stakeholders might be in one of three categories: internal to the project team; external to the project team, but internal to the organisation; and external to both the project team and the organisation. Newman and Lamming [20] suggest a different division: into those who will use the system directly or indirectly, and those who will be involved in developing the system. This distinction is also taken up with respect to the development of knowledge-based systems [22]. The set of stakeholders in the knowledge acquisition process and the set of stakeholders in the use of a knowledge-based system, are not necessarily identical; they are likely to vary in membership, and for those members in common, the type and level of stake they have is likely to vary. The IS literature suggests a different division again, into ÔhubsÕ or ÔsponsorsÕ and ÔspokesÕ or ÔadaptorsÕ, where the former are those initiating and sustaining the system, while the latter are those participating [21]. Macaulay [19] identifies four categories of stakeholder in any computer system: 1. Those responsible for design and development; 2. Those with a financial interest, responsible for its sale or purchase; 3. Those responsible for introduction and maintenance; 4. Those who have an interest in its use. But again, she offers no guidelines for identifying specific stakeholders for a given system. So far, we have not distinguished between individuals or groups and roles. As with other situations, the mapping between stakeholders and individuals or groups is not oneto-one. It is therefore more appropriate to think of stakeholders as roles rather than as specific people. In subsequent discussion, we shall use ÔstakeholderÕ to mean Ôstakeholder roleÕ. 2. Identifying stakeholders There has been little focus on the participants of the requirements engineering (RE) process, for example in terms of how to trace participants in the RE process and how to identify stakeholders [13]. All of the references cited above emphasise the importance of identifying stakeholders, and although they provide examples, or broad guidance for identifying them, none describes a model or a concrete approach for identifying stakeholders for a specific project. This deficiency has been noted in the management literature, and in the IS literature [21], where the approaches have been criticised for either assuming that stakeholders are ÔobviousÕ, or for providing broad categories which are too generic to be of practical use. Expert identification for knowledge-based systems development has similarities with stakeholder identification in RE, although here too there has been an assumption that experts are readily identifiable [22]. Pouloudi and Whitley [21] suggest four principles of stakeholder identification, and describe an approach which is based on these principles, and which they used to identify stakeholders in the drug use management domain. Lyytinen and Hirschheim [17] also provides some guidance for stakeholder identification for IS, while acknowledging that Ôthe identification of the set of all stakeholders is far from trivialÕ. Methods for requirements engineering, e.g. KAOS [5], do not directly support stakeholder identification; it seems to be assumed that it is straightforward. The task of identifying actors for use case development [15] has similarities with stakeholder identification, but is targeted only at a fraction of system stakeholders. Stakeholders are related to each other and interact with each other [22,11,17]. Interactions between them include: exchanging information, products, or instructions, or providing supporting tasks. Information about the stakeholders and the nature of their relationships and interactions needs to be identified and recorded. Dimensions of importance are: relationships between stakeholders, the relationship of each stakeholder to the system, and the priority to be given to each stakeholderÕs view. This information is needed to manage, interpret, balance and process stakeholder input. Reconciling different stakeholder views is beyond this paper, but is considered in [12]. 3. An approach to identifying stakeholders for requirements engineering Here, we propose an approach to discovering all relevant stakeholders of a specific system that we believe is domain-independent, effective and pragmatic. We draw on the literature cited above, and on aspects of ethical decision-making in software systems [18]. Our starting point is a set of stakeholders that we refer to as ÔbaselineÕ stakeholders. From these, we can recognise ÔsupplierÕ stakeholders and ÔclientÕ stakeholders: the former provides information or supporting tasks to the baseline, and the latter processes or inspects the products of the baseline. Other stakeholders that we call ÔsatelliteÕs interact with the baseline in a variety of ways. ÔInteractionÕ may involve communicating, reading a set of rules or guidelines, searching for information and so on. Our approach focuses on interactions between stakeholders rather than relationships between the system and the stakeholder, Sharp, Finkelstein & Galal 11/12/99 3 because they are easier to follow. Figure 1 illustrates the main elements of stakeholder identification. Figure 1. The main elements of stakeholder identification addressed by our approach 3.1 Baseline stakeholders We have identified four groups of baseline stakeholder: users, developers, legislators and decision-makers. We have dubbed these ÔbaselineÕ because the web of stakeholders and their relationships can be identified from them. The nature of each group is explored below. 3.1.1 Users. The term ÔuserÕ has many interpretations. For example, Holtzblatt and Jones [14] include in their definition of ÔusersÕ those who manage direct users, those who receive products from the system, those who test the system, those who make the purchasing decision, and those who use competitive products. Eason [9] identifies three categories of user: primary, secondary and te


A Smart Cloud Robotic System Based on Cloud Computing Services

In this paper, we present a smart service robotic system based on cloud computing services. The design and implementation of infrastructure, computation components and communication components are introduced. The proposed system can alleviate the complex computation and storage load of robots to cloud and provide various services to the robots. The computation components can dynamically allocate resources to the robots. The communication components allow easy access of the robots and provide flexible resource management. Furthermore, we modeled the task-scheduling problem and proposed a max-heaps algorithm. The simulation results demonstrate that the proposed algorithm minimized the overall task costs.


The spread of behavior in an online social network experiment.

How do social networks affect the spread of behavior? A popular hypothesis states that networks with many clustered ties and a high degree of separation will be less effective for behavioral diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the social space. A competing hypothesis argues that when behaviors require social reinforcement, a network with more clustering may be more advantageous, even if the network as a whole has a larger diameter. I investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities. Individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network. The behavior spread farther and faster across clustered-lattice networks than across corresponding random networks.


Voltage buffer compensation using Flipped Voltage Follower in a two-stage CMOS op-amp

In Miller and current buffer compensation techniques, the compensation capacitor often loads the output node. If a voltage buffer is used in feedback, the compensation capacitor obviates the loading on the output node. In this paper, we introduce an implementation of a voltage buffer compensation using a Flipped Voltage Follower (FVF) for stabilizing a two-stage CMOS op-amp. The op-amps are implemented in a 180-nm CMOS process with a power supply of 1.8V while operating with a quiescent current of 110μA. Results indicate that the proposed voltage buffer compensation using FVF improves the Unity Gain Frequency from 5.5MHz to 12.2MHz compared to Miller compensation. Also, the proposed technique enhances the transient response while lowering the compensation capacitance by 47% and 17.7% compared to Miller and common-drain compensation topologies. Utilization of FVF or its variants as a voltage buffer in a feedback compensation network has wide potential applications in the analog design space.


Dynamic question generation system for web-based testing using particle swarm optimization

One aim of testing is to identify weaknesses in students’ knowledge. Computerized tests are now one of the most important ways to judge learning, and, selecting tailored questions for each learner is a significant part of such tests. Therefore, one current trend is that computerized adaptive tests (CATs) not only assist teachers in estimating the learning performance of students, but also facilitate understanding of problems in their learning process. These tests, must effectively and efficiently select questions from a large-scale item bank, and to cope with this problem we propose a dynamic question generation system for web-based tests using the novel approach of particle swarm optimization (PSO). The dynamic question generation system is built to select tailored questions for each learner from the item bank to satisfy multiple assessment requirements. Furthermore, the proposed approach is able to efficiently generate near-optimal questions that satisfy multiple assessment criteria. With a series of experiments, we compare the efficiency and efficacy of the PSO approach with other approaches. The experimental results show that the PSO approach is suitable for the selection of near-optimal questions from large-scale item banks. 2007 Elsevier Ltd. All rights reserved.


Deep Embedding for Spatial Role Labeling

This paper introduces the visually informed embedding of word (VIEW), a continuous vector representation for a word extracted from a deep neural model trained using the Microsoft COCO data set to forecast the spatial arrangements between visual objects, given a textual description. The model is composed of a deep multilayer perceptron (MLP) stacked on the top of a Long Short Term Memory (LSTM) network, the latter being preceded by an embedding layer. The VIEW is applied to transferring multimodal background knowledge to Spatial Role Labeling (SpRL) algorithms, which recognize spatial relations between objects mentioned in the text. This work also contributes with a new method to select complementary features and a fine-tuning method for MLP that improves the F1 measure in classifying the words into spatial roles. The VIEW is evaluated with the Task 3 of SemEval-2013 benchmark data set, SpaceEval.


To recognize shapes, first learn to generate images.

The uniformity of the cortical architecture and the ability of functions to move to different areas of cortex following early damage strongly suggest that there is a single basic learning algorithm for extracting underlying structure from richly structured, high-dimensional sensory data. There have been many attempts to design such an algorithm, but until recently they all suffered from serious computational weaknesses. This chapter describes several of the proposed algorithms and shows how they can be combined to produce hybrid methods that work efficiently in networks with many layers and millions of adaptive connections.


Design and optimization of thermo-mechanical reliability in wafer level packaging

Article history: Received 4 July 2009 Received in revised form 16 November 2009 Available online 29 January 2010 0026-2714/$ see front matter 2009 Elsevier Ltd. A doi:10.1016/j.microrel.2009.11.010 * Corresponding author. Address: Department of M University, P.O. Box 10028, Beaumont, TX 77710, USA +1 409 880 8121. E-mail address: xuejun.fan@lamar.edu (X.J. Fan). In this paper, a variety of wafer level packaging (WLP) structures, including both fan-in and fan-out WLPs, are investigated for solder joint thermo-mechanical reliability performance, from a structural design point of view. The effects of redistribution layer (RDL), bump structural design/material selection, polymer-cored ball application, and PCB design/material selection are studied. The investigation focuses on four different WLP technologies: standard WLP (ball on I/O WLP), ball on polymer WLP without under bump metallurgy (UBM) layer, ball on polymer WLP with UBM layer, and encapsulated copper post WLP. Ball on I/O WLP, in which solder balls are directly attached to the metal pads on silicon wafer, is used as a benchmark for the analysis. 3-D finite element modeling is performed to investigate the effects of WLP structures, UBM layer, polymer film material properties (in ball on polymer WLP), and encapsulated epoxy material properties (in copper post WLP). Both ball on polymer and copper post WLPs have shown great reliability improvement in thermal cycling. For ball on polymer WLP structures, polymer film between silicon and solder balls creates a ‘cushion’ effect to reduce the stresses in solder joints. Such cushion effect can be achieved either by an extremely compliant film or a ‘hard’ film with a large coefficient of thermal expansion. Encapsulated copper post WLP shows the best thermo-mechanical performance among the four WLP structures. Furthermore, for a fan-out WLP, it has been found that the critical solder balls are the outermost solder balls under die-area, where the maximum thermal mismatch takes place. In a fan-out WLP package, chip size, other than package size, determines the limit of solder joint reliability. This paper also discusses the polymer-cored solder ball applications to enhance thermo-mechanical reliability of solder joints. Finally, both experimental and finite element analysis have demonstrated that making corner balls non-electrically connected can greatly improve the WLP thermomechanical reliability. 2009 Elsevier Ltd. All rights reserved.


Introducing Expected Returns into Risk Parity Portfolios : A New Framework for Tactical and Strategic Asset Allocation

Risk parity is an allocation method used to build diversified portfolios that does not rely on any assumptions of expected returns, thus placing risk management at the heart of the strategy. This explains why risk parity became a popular investment model after the global financial crisis in 2008. However, risk parity has also been criticized because it focuses on managing risk concentration rather than portfolio performance, and is therefore seen as being closer to passive management than active management. In this article, we show how to introduce assumptions of expected returns into risk parity portfolios. To do this, we consider a generalized risk measure that takes into account both the portfolio return and volatility. However, the trade-off between performance and volatility contributions creates some difficulty, while the risk budgeting problem must be clearly defined. After deriving the theoretical properties of such risk budgeting portfolios, we apply this new model to asset allocation. First, we consider long-term investment policy and the determination of strategic asset allocation. We then consider dynamic allocation and show how to build risk parity funds that depend on expected returns.


wradlib – An Open Source Library for Weather Radar Data Processing

Observation data from weather radars is deemed to have great potential in hydrology and meteorology for forecasting of severe weather or floods in small and urban catchments by providing high resolution measurements of precipitation. With the time series from operational weather radar networks attaining lengths suitable for the long term calibration of hydrological models, the interest in using this data is growing. There are, however, several challenges impeding a widespread use of weather radar data. The first being a multitude of different file formats for data storage and exchange. Although the OPERA project [1] has taken steps towards harmonizing the data exchange inside the European radar network [2], different dialects still exist in addition to a large variety of legacy formats. The second barrier is what we would like to describe as the product dilemma. A great number of potential applications also implies a great number of different and often competing requirements as to the quality of the data. As an example, while one user might be willing to accept more false alarms in a clutter detection filter, e.g. to avoid erroneous data assimilation results, another might want a more conservative product in order not to miss the small scale convection that leads to a hundred year flood in a small head catchment. A single product from a radar operator, even if created with the best methods currently available, will never be able to accommodate all these needs simultaneously. Thus the product will either be a compromise or it will accommodate one user more than the other. Often the processing chain needs to be in a specific order, where a change of a certain processing step is impossible without affecting the results of all following steps. Sometimes some post-processing of the product might be possible, but if not, the user’s only choice is to either take the product as is or leave it. If a user should decide that he would take a raw radar product and try to do customized corrections, he is faced with basically reinventing the wheel, writing routines for data I/O, error corrections, georeferencing and visualization, trying to find relevant literature and to extract algorithms from publications, which, often enough, do not offer all implementation details. This takes a lot of time and effort, which could be used much more effectively, if standard algorithms were available in a well documented and easy to use manner. wradlib is intended to provide this collection of routines and algorithms in order to facilitate and foster the use of weather radar data in as many disciplines as possible including research, application development and teaching.


A survey of hardware Trojan threat and defense

Hardware Trojans (HTs) can be implanted in security-weak parts of a chip with various means to steal the internal sensitive data or modify original functionality, which may lead to huge economic losses and great harm to society. Therefore, it is very important to analyze the specific HT threats existing in the whole life cycle of integrated circuits (ICs), and perform protection against hardware Trojans. In this paper, we elaborate an IC market model to illustrate the potential HT threats faced by the parties involved in the model. Then we categorize the recent research advances in the countermeasures against HT attacks. Finally, the challenges and prospects for HT defense are illuminated. & 2016 Elsevier B.V. All rights reserved.


An overview of Fog computing and its security issues

Fog computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage and application services to end users. In this article, we elaborate the motivation and advantages of Fog computing and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state of the art of Fog computing and similar work under the same umbrella. Distinguished from other reviewing work of Fog computing, this paper further discloses the security and privacy issues according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of system security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device. In addition, we discuss the authentication and authorization techniques that can be used in Fog computing. An example of authentication techniques is introduced to address the security scenario where the connection between Fog and Cloud is fragile. Copyright © 2015 John Wiley & Sons, Ltd.


Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications

Transfer learning allows practitioners to recognize and apply knowledge learned in previous tasks (source task) to new tasks or new domains (target task), which share some commonality. The two important factors impacting the performance of transfer learning models are: (a) the size of the target dataset, and (b) the similarity in distribution between source and target domains. Thus far, there has been little investigation into just how important these factors are. In this paper, we investigate the impact of target dataset size and source/target domain similarity on model performance through a series of experiments. We find that more data is always beneficial, and model performance improves linearly with the log of data size, until we are out of data. As source/target domains differ, more data is required and fine tuning will render better performance than feature extraction. When source/target domains are similar and data size is small, fine tuning and feature extraction renders equivalent performance. Our hope is that by beginning this quantitative investigation on the effect of data volume and domain similarity in transfer learning we might inspire others to explore the significance of data in developing more accurate statistical models.


Determining the effectiveness of prompts for self-regulated learning in problem-solving scenarios

Cognitive scientists have studied internal cognitive structures, processes, and systems for decades in order to understand how they function in human learning. In order to solve challenging tasks in problem situations, learners not only have to perform cognitive activities, e.g., activating existing cognitive structures or organizing new information, they also have to set specific goals, plan their activities, monitor their performance during the problem-solving process, and evaluate the efficiency of their actions. This paper reports an experimental study with 98 participants where effective instructional interventions for self-regulated learning within problemsolving processes are investigated. Furthermore, an automated assessment and analysis methodology for determining the quality of learning outcomes is introduced. The results indicate that generic prompts are an important aid for developing cognitive structures while solving problems.


Synchronising movements with the sounds of a virtual partner enhances partner likeability

Previous studies have demonstrated that synchronising movements with other people can influence affiliative behaviour towards them. While research has focused on synchronisation with visually observed movement, synchronisation with a partner who is heard may have similar effects. We replicate findings showing that synchronisation can influence ratings of likeability of a partner, but demonstrate that this is possible with virtual interaction, involving a video of a partner. Participants performed instructed synchrony in time to sounds instead of the observable actions of another person. Results show significantly higher ratings of likeability of a partner after moving at the same time as sounds attributed to that partner, compared with moving in between sounds. Objectively quantified synchrony also correlated with ratings of likeability. Belief that sounds were made by another person was manipulated in Experiment 2, and results demonstrate that when sounds are attributed to a computer, ratings of likeability are not affected by moving in or out of time. These findings demonstrate that interaction with sound can be experienced as social interaction in the absence of genuine interpersonal contact, which may help explain why people enjoy engaging with recorded music.


KeyGraph: Automatic Indexing by Co-Occurrence Graph Based on Building Construction Metaphor

In this paper, we present an algorithm for extracting keywords representing the asserted main point in a document, without relying on external devices such as natural language processing tools or a document corpus. Our algorithm KeyGraph is based on the segmentation of a graph, representing the co-occurrence between terms in a document, into clusters. Each cluster corresponds to a concept on which author's idea is based, and top ranked terms by a statistic based on each term's relationship to these clusters are selected as keywords. This strategy comes from considering that a document is constructed like a building for expressing new ideas based on traditional concepts. The experimental results show that thus extracted terms match author's point quite accurately, even though KeyGraph does not use each term's average frequency in a corpus, i.e., KeyGraph is a contentsensitive, domain independent device of indexing.


Community detection using a neighborhood strength driven Label Propagation Algorithm

Studies of community structure and evolution in large social networks require a fast and accurate algorithm for community detection. As the size of analyzed communities grows, complexity of the community detection algorithm needs to be kept close to linear. The Label Propagation Algorithm (LPA) has the benefits of nearly-linear running time and easy implementation, thus it forms a good basis for efficient community detection methods. In this paper, we propose new update rule and label propagation criterion in LPA to improve both its computational efficiency and the quality of communities that it detects. The speed is optimized by avoiding unnecessary updates performed by the original algorithm. This change reduces significantly (by order of magnitude for large networks) the number of iterations that the algorithm executes. We also evaluate our generalization of the LPA update rule that takes into account, with varying strength, connections to the neighborhood of a node considering a new label. Experiments on computer generated networks and a wide range of social networks show that our new rule improves the quality of the detected communities compared to those found by the original LPA. The benefit of considering positive neighborhood strength is pronounced especially on real-world networks containing sufficiently large fraction of nodes with high clustering coefficient.


Fast Supervised Discrete Hashing

Learning-based hashing algorithms are “hot topics” because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called “fast supervised discrete hashing” (FSDH) based on “supervised discrete hashing” (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm. To the best of our knowledge, this strategy has not previously been used for hashing. Traditional SDH decomposes the optimization into three sub-problems, with the most critical sub-problem - discrete optimization for binary hash codes - solved using iterative discrete cyclic coordinate descent (DCC), which is time-consuming. However, FSDH has a closed-form solution and only requires a single rather than iterative hash code-solving step, which is highly efficient. Furthermore, FSDH is usually faster than SDH for solving the projection matrix for least squares regression, making FSDH generally faster than SDH. For example, our results show that FSDH is about 12-times faster than SDH when the number of hashing bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than FastHash when the number of hashing bits is 64 on the MNIST data-base. Our experimental results show that FSDH is not only fast, but also outperforms other comparative methods.


Vascular structures in dermoscopy*

Dermoscopy is an aiding method in the visualization of the epidermis and dermis. It is usually used to diagnose melanocytic lesions. In recent years, dermoscopy has increasingly been used to diagnose non-melanocytic lesions. Certain vascular structures, their patterns of arrangement and additional criteria may demonstrate lesion-specific characteristics. In this review, vascular structures and their arrangements are discussed separately in the light of conflicting views and an overview of recent literature.


Audio-visual emotion recognition : A dynamic , multimodal approach

Designing systems able to interact with students in a natural manner is a complex and far from solved problem. A key aspect of natural interaction is the ability to understand and appropriately respond to human emotions. This paper details our response to the continuous Audio/Visual Emotion Challenge (AVEC'12) whose goal is to predict four affective signals describing human emotions. The proposed method uses Fourier spectra to extract multi-scale dynamic descriptions of signals characterizing face appearance, head movements and voice. We perform a kernel regression with very few representative samples selected via a supervised weighted-distance-based clustering, that leads to a high generalization power. We also propose a particularly fast regressor-level fusion framework to merge systems based on different modalities. Experiments have proven the efficiency of each key point of the proposed method and our results on challenge data were the highest among 10 international research teams. Key-words Affective computing, Dynamic features, Multimodal fusion, Feature selection, Facial expressions. ACM Classification


Lane detection using Fourier-based line detector

In this paper a new approach to lane marker detection problem is introduced as a significant complement for a semi/fully autonomous driver assistance system. The method incorporates advance line detection using multilayer fractional Fourier transform (MLFRFT) and the state-of-the-art advance lane detector (ALD). Experimental results have shown a considerable reduction in computational complexity.


Directed diffusion: a scalable and robust communication paradigm for sensor networks

Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.


Adaptive Protocols for Information Dissemination in Wireless Sensor Networks

In this paper, we present a family of adaptive protocols, called SPIN (Sensor Protocols for Information via Negotiation), that efficiently disseminates information among sensors in an energy-constrained wireless sensor network. Nodes running a SPIN communication protocol name their data using high-level data descriptors, called metadata. They use meta-data negotiations to eliminate the transmission of redundant data throughout the network. In addition, SPIN nodes can base their communication decisions both upon application-specific knowledge of the data and upon knowledge of the resources that are available to them. This allows the sensors to efficiently distribute data given a limited energy supply. We simulate and analyze the performance of two specific SPIN protocols, comparing them to other possible approaches and a theoretically optimal protocol. We find that the SPIN protocols can deliver 60% more data for a given amount of energy than conventional approaches. We also find that, in terms of dissemination rate and energy usage, the SPIN protocols perform close to the theoretical optimum.


Dynamic source routing in ad hoc wireless networks

An ad hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any established infrastructure or centralized administration. In such an environment, it may be necessary for one mobile host to enlist the aid of other hosts in forwarding a packet to its destination, due to the limited range of each mobile host’s wireless transmissions. This paper presents a protocol for routing in ad hoc networks that uses dynamic source routing. The protocol adapts quickly to routing changes when host movement is frequent, yet requires little or no overhead during periods in which hosts move less frequently. Based on results from a packet-level simulation of mobile hosts operating in an ad hoc network, the protocol performs well over a variety of environmental conditions such as host density and movement rates. For all but the highest rates of host movement simulated, the overhead of the protocol is quite low, falling to just 1% of total data packets transmitted for moderate movement rates in a network of 24 mobile hosts. In all cases, the difference in length between the routes used and the optimal route lengths is negligible, and in most cases, route lengths are on average within a factor of 1.01 of optimal.


Epidemic Algorithms for Replicated Database Maintenance

When a database is replicated at many sites, maintaining mutual consistency among the sites in the face of updates is a significant problem. This paper describes several randomized algorithms for distributing updates and driving the replicas toward consistency. The algorithms are very simple and require few guarantees from the underlying communication system, yet they ensure that the effect of every update is eventually reflected in all replicas. The cost and performance of the algorithms are tuned by choosing appropriate distributions in the randomization step. The algorithms are closely analogous to epidemics, and the epidemiology literature aids in understanding their behavior. One of the algorithms has been implemented in the Clearinghouse servers of the Xerox Corporate Internet, solving long-standing problems of high traffic and database inconsistency. An earlier version of this paper appeared in the Proceedings of the Sixth Annual ACM Symposium on Principles of Distributed Computing, Vancouver, August 1987, pages 1-12. CR


The Chemical Basis of Morphogenesis

The paper discussed is by Alan Turing. It was published in 1952 and presents an idea of how periodic patterns could be formed in nature. Looking on periodic structures – like the stripes on tigers, the dots on leopards or the whirly leaves on woodruff – it is hard to imagine those patterns are formated by pure chance. On the other hand, thinking of the unbelievable multitude of possible realizations, the patterns can not all be exactly encoded in the genes. The paper “The Chemical Basis of Morphogenesis” proposes a possible mechanism due to an interaction of two “morphogenes” which react and diffuse through the tissue. Fulfilling some constrains regarding the diffusibilities and the behaviour of the reactions, this mechanism – called Turing mechanism – can lead to a pattern of concentrations defining the structure we see.


An Overview of 3D Software Visualization

Software visualization studies techniques and methods for graphically representing different aspects of software. Its main goal is to enhance, simplify and clarify the mental representation a software engineer has of a computer system. During many years, visualization in 2D space has been actively studied, but in the last decade, researchers have begun to explore new 3D representations for visualizing software. In this article, we present an overview of current research in the area, describing several major aspects like: visual representations, interaction issues, evaluation methods and development tools. We also perform a survey of some representative tools to support different tasks, i.e., software maintenance and comprehension, requirements validation and algorithm animation for educational purposes, among others. Finally, we conclude identifying future research directions.


Ultra-wideband, short-pulse ground-penetrating radar: simulation and measurement

Ultra-wideband (UWB), short-pulse (SP) radar is investigated theoretically and experimentally for the detection and identification of targets buried in and placed atop soil. The calculations are performed using a rigorous, three-dimensional (3-D) Method of Moments algorithm for perfectly conducting bodies of revolution. Particular targets investigated theoretically include anti-personnel mines, anti-tank mines, and a 55-gallon drum, for which we model the time-domain scattered fields and the buried-target late-time resonant frequencies. With regard to the latter, the computed resonant frequencies are utilized to assess the feasibility of resonance-based buried-target identification for this class of targets. The measurements are performed using a novel UWB, SP synthetic aperture radar (SAR) implemented on a mobile boom. Experimental and theoretical results are compared.


Negotiation as a Metaphor for Distributed Problem Solving

We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques. We present a framework called the contract net that specifies communication and control in a distributed problem solver. Task distribution is viewed as an interactive process, a discussion carried on between a node with a task to be executed and a group of nodes that may be able to execute the task. We describe the kinds of information that must be passed between nodes during the discussion in order to obtain effective problem-solving behavior. This discussion is the origin of the negotiation metaphor: Task distribution is viewed as a form of contract negotiation. We emphasize that protocols for distributed problem solving should help determine the content of the information transmitted, rather than simply provide a means of sending bits from one node to another. The use of the contract net framework is demonstrated in the solution of a simulated problem in area surveillance, of the sort encountered in ship or air traffic control. We discuss the mode of operation of a distributed sensing system, a network of nodes extending throughout a relatively large geographic area, whose primary aim is the formation of a dynamic map of traffic in the area. From the results of this preliminary study we abstract features of the framework applicable to problem solving in general, examining in particular transfer of control. Comparisons with PLANNER, CONNIVER, HEARSAY-n, and PUP6 are used to demonstrate that negotiation--the two-way transfer of information--is a natural extension to the transfer of control mechanisms used in earlier problem-


An application of analytic hierarchy process ( AHP ) to investment portfolio selection in the banking sector of the Nigerian capital market

The importance of investment to the individual, a nation and the world economy cannot be over emphasized. Investment involves the sacrifice of immediate consumption to achieve greater consumption in the future. The Nigerian banking sector has made tremendous success in the recent past. All the banks that approached the capital market through public offers and right issues to raise their capital base recorded huge success. Investors in bank stocks have enjoyed high returns on investment, despite the slow growth in the nation’s economy. However, the recent financial crisis that started in America, which has caused economy meltdown in many nations of the world and sudden fall in share prices, has brought about a higher risk than envisaged on investors, particularly those investing in bank stocks. This study underlines the importance of different criteria, factors and alternatives that are essential to successful investment decisions by applying the Analytic Hierarchy Process (AHP) in the selection of investment in bank stocks in the circumstances of financial crisis. The study provides recommendation on strategic investment decision options.


Suggestibility of the child witness: a historical review and synthesis.

The field of children's testimony is in turmoil, but a resolution to seemingly intractable debates now appears attainable. In this review, we place the current disagreement in historical context and describe psychological and legal views of child witnesses held by scholars since the turn of the 20th century. Although there has been consistent interest in children's suggestibility over the past century, the past 15 years have been the most active in terms of the number of published studies and novel theorizing about the causal mechanisms that underpin the observed findings. A synthesis of this research posits three "families" of factors--cognitive, social, and biological--that must be considered if one is to understand seemingly contradictory interpretations of the findings. We conclude that there are reliable age differences in suggestibility but that even very young children are capable of recalling much that is forensically relevant. Findings are discussed in terms of the role of expert witnesses.


The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?

A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent robotic grasping. However, in order to fully evaluate the value of touch sensing for grasp outcome prediction, we must understand how touch sensing can influence outcome prediction accuracy when combined with other modalities. Doing so using conventional model-based techniques is exceptionally difficult. In this work, we investigate the question of whether touch sensing aids in predicting grasp outcomes within a multimodal sensing framework that combines vision and touch. To that end, we collected more than 9,000 grasping trials using a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger, and evaluated visuo-tactile deep neural network models to directly predict grasp outcomes from either modality individually, and from both modalities together. Our experimental results indicate that incorporating tactile readings substantially improve grasping performance.


Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

In recent years, the interest in unsupervised learning of disentangled representations has significantly increased. The key assumption is that real-world data is generated by a few explanatory factors of variation and that these factors can be recovered by unsupervised learning algorithms. A large number of unsupervised learning approaches based on auto-encoding and quantitative evaluation metrics of disentanglement have been proposed; yet, the efficacy of the proposed approaches and utility of proposed notions of disentanglement has not been challenged in prior work. In this paper, we provide a sober look on recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12 000 models covering the six most prominent methods, and evaluate them across six disentanglement metrics in a reproducible large-scale experimental study on seven different data sets. On the positive side, we observe that different methods successfully enforce properties “encouraged” by the corresponding losses. On the negative side, we observe that in our study (1) “good” hyperparameters seemingly cannot be identified without access to ground-truth labels, (2) good hyperparameters neither transfer across data sets nor across disentanglement metrics, and (3) that increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. These results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.


Contraction of the abdominal muscles associated with movement of the lower limb.

BACKGROUND AND PURPOSE
Activity of the trunk muscles is essential for maintaining stability of the lumbar spine because of the unstable structure of that portion of the spine. A model involving evaluation of the response of the lumbar multifidus and abdominal muscles to leg movement was developed to evaluate this function.

SUBJECTS
To examine this function in healthy persons, 9 male and 6 female subjects (mean age = 20.6 years, SD = 2.3) with no history of low back pain were studied.

METHODS
Fine-wire and surface electromyography electrodes were used to record the activity of selected trunk muscles and the prime movers for hip flexion, abduction, and extension during hip movements in each of those directions.

RESULTS
Trunk muscle activity occurring prior to activity of the prime mover of the limb was associated with hip movement in each direction. The transversus abdominis (TrA) muscle was invariably the first muscle that was active. Although reaction time for the TrA and oblique abdominal muscles was consistent across movement directions, reaction time for the rectus abdominis and multifidus muscles varied with the direction of limb movement.

CONCLUSION AND DISCUSSION
Results suggest that the central nervous system deals with stabilization of the spine by contraction of the abdominal and multifidus muscles in anticipation of reactive forces produced by limb movement. The TrA and oblique abdominal muscles appear to contribute to a function not related to the direction of these forces.


Low-degree Graph Partitioning via Local Search with Applications to Constraint Satisfaction, Max Cut, and Coloring

We present practical algorithms for constructing partitions of graphs into a fixed number of vertex-disjoint subgraphs that satisfy particular degree constraints. We use this in particular to find k-cuts of graphs of maximum degree ∆ that cut at least a k−1 k (1 + 1 2∆+k−1 ) fraction of the edges, improving previous bounds known. The partitions also apply to constraint networks, for which we give a tight analysis of natural local search heuristics for the maximum constraint satisfaction problem. These partitions also imply efficient approximations for several problems on weighted bounded-degree graphs. In particular, we improve the best performance ratio for the weighted independent set problem to 3 ∆+2 , and obtain an efficient algorithm for coloring 3-colorable graphs with at most 3∆+2 4 colors. Communicated by M. Fürer: submitted February 1996; revised March 1997. Halldórsson, Lau, Low-degree Graph Partitioning, JGAA, 1(3) 1–13 (1997) 2


New cardinality estimation algorithms for HyperLogLog sketches

This paper presents new methods to estimate the cardinalities of data sets recorded by HyperLogLog sketches. A theoretically motivated extension to the original estimator is presented that eliminates the bias for small and large cardinalities. Based on the maximum likelihood principle a second unbiased method is derived together with a robust and efficient numerical algorithm to calculate the estimate. The maximum likelihood approach can also be applied to more than a single HyperLogLog sketch. In particular, it is shown that it gives more precise cardinality estimates for union, intersection, or relative complements of two sets that are both represented by HyperLogLog sketches compared to the conventional technique using the inclusion-exclusion principle. All the new methods are demonstrated and verified by extensive simulations.


Analysis of YouTube’s traffic adaptation to dynamic environments

The popular Internet service, YouTube, has adopted by default the HyperText Markup Language version 5 (HTML5). With this adoption, YouTube has moved to Dynamic Adaptive Streaming over HTTP (DASH) as Adaptive BitRate (ABR) video streaming technology. Furthermore, rate adaptation in DASH is solely receiver-driven. This issue motivates this work to make a deep analysis of YouTube’s particular DASH implementation. Firstly, this article provides a state of the art about DASH and adaptive streaming technology, and also YouTube traffic characterization related work. Secondly, this paper describes a new methodology and test-bed for YouTube’s DASH implementation traffic characterization and performance measurement. This methodology and test-bed do not make use of proxies and, moreover, they are able to cope with YouTube traffic redirections. Finally, a set of experimental results are provided, involving a dataset of 310 YouTube’s videos. The depicted results show a YouTube’s traffic pattern characterization and a discussion about allowed download bandwidth, YouTube’s consumed bitrate and quality of the video. Moreover, the obtained results are cross-validated with the analysis of HTTP requests performed by YouTube’s video player. The outcomes of this article are applicable in the field of Quality of Service (QoS) and Quality of Experience (QoE) management. This is valuable information for Internet Service Providers (ISPs), because QoS management based on assured download bandwidth can be used in order to provide a target end-user’s QoE when YouTube service is being consumed.


Analyzing the Effectiveness and Applicability of Co-training

Recently there has been signi cant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting [1] applies to datasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not. When a natural split does not exist, co-training algorithms that manufacture a feature split may out-perform algorithms not using a split. These results help explain why co-training algorithms are both discriminative in nature and robust to the assumptions of their embedded classi ers.


Exact finite-state machine identification from scenarios and temporal properties

Finite-state models, such as finite-state machines (FSMs), aid software engineering in many ways. They are often used in formal verification and also can serve as visual software models. The latter application is associated with the problems of software synthesis and automatic derivation of software models from specification. Smaller synthesized models are more general and are easier to comprehend, yet the problem of minimum FSM identification has received little attention in previous research. This paper presents four exact methods to tackle the problem of minimum FSM identification from a set of test scenarios and a temporal specification represented in linear temporal logic. The methods are implemented as an open-source tool. Three of them are based on translations of the FSM identification problem to SAT or QSAT problem instances. Accounting for temporal properties is done via counterexample prohibition. Counterexamples are either obtained from previously identified FSMs, or based on bounded model checking. The fourth method uses backtracking. The proposed methods are evaluated on several case studies and on a larger number of randomly generated instances of increasing complexity. The results show that the Iterative SAT-based method is the leader among the proposed methods. The methods are also compared with existing inexact approaches, i.e., the ones which do not necessarily identify the minimum FSM, and these comparisons show encouraging results.


Real-Time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor

We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments. Existing methods typically fail for hand-object interactions in cluttered scenes imaged from egocentric viewpoints—common for virtual or augmented reality applications. Our approach uses two subsequently applied Convolutional Neural Networks (CNNs) to localize the hand and regress 3D joint locations. Hand localization is achieved by using a CNN to estimate the 2D position of the hand center in the input, even in the presence of clutter and occlusions. The localized hand position, together with the corresponding input depth value, is used to generate a normalized cropped image that is fed into a second CNN to regress relative 3D hand joint locations in real time. For added accuracy, robustness and temporal stability, we refine the pose estimates using a kinematic pose tracking energy. To train the CNNs, we introduce a new photorealistic dataset that uses a merged reality approach to capture and synthesize large amounts of annotated data of natural hand interaction in cluttered scenes. Through quantitative and qualitative evaluation, we show that our method is robust to self-occlusion and occlusions by objects, particularly in moving egocentric perspectives.


Enhanced belief propagation decoding of polar codes through concatenation

The bit-channels of finite-length polar codes are not fully polarized, and a proportion of such bit-channels are neither completely “noiseless” nor completely “noisy”. By using an outer low-density parity-check code for these intermediate channels, we show how the performance of belief propagation (BP) decoding of the overall concatenated polar code can be improved. A simple example reports an improvement in Eb /N0 of 0.3 dB with respect to the conventional BP decoder.


From "Bonehead" to "cLoNehEAd": Nicknames, Play and Identity on Internet Relay Chat


Same-day genomic and epigenomic diagnosis of brain tumors using real-time nanopore sequencing

Molecular classification of cancer has entered clinical routine to inform diagnosis, prognosis, and treatment decisions. At the same time, new tumor entities have been identified that cannot be defined histologically. For central nervous system tumors, the current World Health Organization classification explicitly demands molecular testing, e.g., for 1p/19q-codeletion or IDH mutations, to make an integrated histomolecular diagnosis. However, a plethora of sophisticated technologies is currently needed to assess different genomic and epigenomic alterations and turnaround times are in the range of weeks, which makes standardized and widespread implementation difficult and hinders timely decision making. Here, we explored the potential of a pocket-size nanopore sequencing device for multimodal and rapid molecular diagnostics of cancer. Low-pass whole genome sequencing was used to simultaneously generate copy number (CN) and methylation profiles from native tumor DNA in the same sequencing run. Single nucleotide variants in IDH1, IDH2, TP53, H3F3A, and the TERT promoter region were identified using deep amplicon sequencing. Nanopore sequencing yielded ~0.1X genome coverage within 6 h and resulting CN and epigenetic profiles correlated well with matched microarray data. Diagnostically relevant alterations, such as 1p/19q codeletion, and focal amplifications could be recapitulated. Using ad hoc random forests, we could perform supervised pan-cancer classification to distinguish gliomas, medulloblastomas, and brain metastases of different primary sites. Single nucleotide variants in IDH1, IDH2, and H3F3A were identified using deep amplicon sequencing within minutes of sequencing. Detection of TP53 and TERT promoter mutations shows that sequencing of entire genes and GC-rich regions is feasible. Nanopore sequencing allows same-day detection of structural variants, point mutations, and methylation profiling using a single device with negligible capital cost. It outperforms hybridization-based and current sequencing technologies with respect to time to diagnosis and required laboratory equipment and expertise, aiming to make precision medicine possible for every cancer patient, even in resource-restricted settings.


TUT: a statistical model for detecting trends, topics and user interests in social media

The rapid development of online social media sites is accompanied by the generation of tremendous web contents. Web users are shifting from data consumers to data producers. As a result, topic detection and tracking without taking users' interests into account is not enough. This paper presents a statistical model that can detect interpretable trends and topics from document streams, where each trend (short for trending story) corresponds to a series of continuing events or a storyline. A topic is represented by a cluster of words frequently co-occurred. A trend can contain multiple topics and a topic can be shared by different trends. In addition, by leveraging a Recurrent Chinese Restaurant Process (RCRP), the number of trends in our model can be determined automatically without human intervention, so that our model can better generalize to unseen data. Furthermore, our proposed model incorporates user interest to fully simulate the generation process of web contents, which offers the opportunity for personalized recommendation in online social media. Experiments on three different datasets indicated that our proposed model can capture meaningful topics and trends, monitor rise and fall of detected trends, outperform baseline approach in terms of perplexity on held-out dataset, and improve the result of user participation prediction by leveraging users' interests to different trends.


Synchronous neural oscillations and cognitive processes

The central problem for cognitive neuroscience is to describe how cognitive processes arise from brain processes. This review summarizes the recent evidence that synchronous neural oscillations reveal much about the origin and nature of cognitive processes such as memory, attention and consciousness. Memory processes are most closely related to theta and gamma rhythms, whereas attention seems closely associated with alpha and gamma rhythms. Conscious awareness may arise from synchronous neural oscillations occurring globally throughout the brain rather than from the locally synchronous oscillations that occur when a sensory area encodes a stimulus. These associations between the dynamics of the brain and cognitive processes indicate progress towards a unified theory of brain and cognition.


Medicinal strategies in the treatment of obesity

When prevention fails, medicinal treatment of obesity may become a necessity. Any strategic medicinal development must recognize that obesity is a chronic, stigmatized and costly disease that is increasing in prevalence. Because obesity can rarely be cured, treatment strategies are effective only as long as they are used, and combined therapy may be more effective than monotherapy. For a drug to have significant impact on body weight it must ultimately reduce energy intake, increase energy expenditure, or both. Currently approved drugs for long-term treatment of obesity include sibutramine, which inhibits food intake, and orlistat, which blocks fat digestion.


Identity Authentication and Capability Based Access Control (IACAC) for the Internet of Things

In the last few years the Internet of Things (IoT) has seen widespread application and can be found in each field. Authentication and access control are important and critical functionalities in the context of IoT to enable secure communication between devices. Mobility, dynamic network topology and weak physical security of low power devices in IoT networks are possible sources for security vulnerabilities. It is promising to make an authentication and access control attack resistant and lightweight in a resource constrained and distributed IoT environment. This paper presents the Identity Authentication and Capability based Access Control (IACAC) model with protocol evaluation and performance analysis. To protect IoT from man-in-the-middle, replay and denial of service (Dos) attacks, the concept of capability for access control is introduced. The novelty of this model is that, it presents an integrated approach of authentication and access control for IoT devices. The results of other related study have also been analyzed to validate and support our findings. Finally, the proposed protocol is evaluated by using security protocol verification tool and verification results shows that IACAC is secure against aforementioned attacks. This paper also discusses performance analysis of the protocol in terms of computational time compared to other Journal of Cyber Security and Mobility, Vol. 1, 309–348. c © 2013 River Publishers. All rights reserved. 310 P.N. Mahalle et al. existing solutions. Furthermore, this paper addresses challenges in IoT and security attacks are modelled with the use cases to give an actual view of IoT networks.


A magnetostatic 2-axis MEMS scanner with I-section rib-reinforcement and slender permanent magnet patterns

This study demonstrates the 2-axis epitaxial silicon scanner driven by the coil-less magnetostatic force using electroplated permanent magnet (CoNiMnP) film. The present approach has four merits: (1) the process employs the cheap silicon wafer with epitaxial layer; and the electrochemical etching stop technique is used to precisely control the thickness of scanner; (2) the I-section rib-reinforced structure is implemented to provide high stiffness of the mirror plate; (3) the magnetostatic driving force on scanner is increased by electroplated permanent magnet film with slender patterns; (4) the size of packaged scanner is reduced since the assembled permanent magnets are not required.


Improvements in children's speech recognition performance

There are several reasons why conventional speech recognition systems modeled on adult data fail to perform satisfactorily on children's speech input. For instance, children's vocal characteristics differ signi cantly from those of adults. In addition, their choices of vocabulary and sentence construction modalities usually do not conform to adult patterns. We describe comparative studies demonstrating the performance gain realized by adopting to children's acoustic and language model data to construct a children's speech recognition system.


Social Media and Firm Equity Value

Companies have increasingly advocated social media technologies and platforms to improve and ultimately transform business performance. This study examines whether social media has a predictive relationship with firm equity value, which metric of social media has the strongest relationship, and the dynamics of the relationship. The results derived from the vector autoregressive models suggest that social media-based metrics (blogs and reviews) are significant leading indicators of firm stock market performance. We also find a faster predictive value of social media, i.e., a shorter “wear-in” effect compared with conventional online behavior metrics, such as web traffic and search. Interestingly, conventional digital user metrics (Google search and web traffic), which have been widely adopted to measure online consumer behavior, are found to have significant yet substantially weaker predictive relationships with firm equity value than social media. The results provide new insights for social media managers and firm equity valuations.


Byte Rotation Encryption Algorithm through parallel processing and multi-core utilization

Securing digital data has become tedious task as the technology is increasing. Existing encryption algorithms such as AES,DES and Blowfish ensure information security but consume lot of time as the security level increases. In this paper, Byte Rotation Encryption Algorithm (BREA) has implemented using parallel processing and multi-core utilization. BREA divides data into fixed size blocks. Each block is processed parallely using random number key. So the set of blocks are executed in parallel by utilizing all the available CPU cores. Finally, from the experimental analysis, it is observed that the proposed BREA algorithm reduces execution time when the number of cores has increased.


Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques

We present Sentiment Analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles (“digital camera” and “music” reviews), and more general documents including general webpages and news articles.


Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis

Many approaches to automatic sentiment analysis begin with a large lexicon of words marked with their prior polarity (also called semantic orientation). However, the contextual polarity of the phrase in which a particular instance of a word appears may be quite different from the word's prior polarity. Positive words are used in phrases expressing negative sentiments, or vice versa. Also, quite often words that are positive or negative out of context are neutral in context, meaning they are not even being used to express a sentiment. The goal of this work is to automatically distinguish between prior and contextual polarity, with a focus on understanding which features are important for this task. Because an important aspect of the problem is identifying when polar terms are being used in neutral contexts, features for distinguishing between neutral and polar instances are evaluated, as well as features for distinguishing between positive and negative contextual polarity. The evaluation includes assessing the performance of features across multiple machine learning algorithms. For all learning algorithms except one, the combination of all features together gives the best performance. Another facet of the evaluation considers how the presence of neutral instances affects the performance of features for distinguishing between positive and negative polarity. These experiments show that the presence of neutral instances greatly degrades the performance of these features, and that perhaps the best way to improve performance across all polarity classes is to improve the system's ability to identify when an instance is neutral.


Recognizing subjectivity: a case study in manual tagging

In this paper, we describe a case study of a sentence-level categorization in which tagging instructions are developed and used by four judges to classify clauses from the Wall Street Journal as either subjective or objective. Agreement among the four judges is analyzed, and, based on that analysis, each clause is given a nal classiication. To provide empirical support for the classiications, correlations are assessed in the data between the subjective category and a basic semantic class posited by Quirk et al. (1985).


Determining the Sentiment of Opinions

Identifying sentiments (the affective parts of opinions) is a challenging problem. We present a system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion. The system contains a module for determining word sentiment and another for combining sentiments within a sentence. We experiment with various models of classifying and combining sentiment at word and sentence levels, with promising results.


The Penn Arabic Treebank : Building a Large-Scale Annotated Arabic Corpus

From our three year experience of developing a large-scale corpus of annotated Arabic text, our paper will address the following: (a) review pertinent Arabic language issues as they relate to methodology choices, (b) explain our choice to use the Penn English Treebank style of guidelines, (requiring the Arabic-speaking annotators to deal with a new grammatical system) rather than doing the annotation in a more traditional Arabic grammar style (requiring NLP researchers to deal with a new system); (c) show several ways in which human annotation is important and automatic analysis difficult, including the handling of orthographic ambiguity by both the morphological analyzer and human annotators; (d) give an illustrative example of the Arabic Treebank methodology, focusing on a particular construction in both morphological analysis and tagging and syntactic analysis and following it in detail through the entire annotation process, and finally, (e) conclude with what has been achieved so far and what remains to be done.


Algorithms for continuous queries: A geometric approach

Algorithms for Continuous Queries: A Geometric


Mapping the U.S. Political Blogosphere: Are Conservative Bloggers More Prominent?

Weblogs are now a key part of online culture, and social scientists are interested in characterising the networks formed by bloggers and measuring their extent and impact in areas such as politics. However, researchers wishing to conduct quantitative social science analysis of the blogging phenomenon are faced with the challenge of using new methods of data collection and analysis largely derived from fields outside of the social sciences, such as the information sciences. This paper presents an overview of one new approach for collecting and analysing weblog data, and illustrates this approach in the context of a preliminary quantitative analysis of online networks formed by a sample of North-American “A-list” political bloggers. There are two aims to this paper. First is to assess (using different data and methods) the conclusion of Adamic and Glance (2005) that there are significant differences in the behaviour of liberal and conservative bloggers, with the latter forming more dense patterns of linkages. We find broad support for this conclusion, and empirically assess the implications of differences in conservative/liberal linking behaviour for the online visibility of different political messages or ideologies. The second aim is to highlight the role of web mining and data visualisation in the analysis of weblogs, and the opportunities and challenges inherent in this new field of research.


Semantic Facial Expression Editing using Autoencoded Flow

High-level manipulation of facial expressions in images — such as changing a smile to a neutral expression — is challenging because facial expression changes are highly non-linear, and vary depending on the appearance of the face. We present a fully automatic approach to editing faces that combines the advantages of flow-based face manipulation with the more recent generative capabilities of Variational Autoencoders (VAEs). During training, our model learns to encode the flow from one expression to another over a low-dimensional latent space. At test time, expression editing can be done simply using latent vector arithmetic. We evaluate our methods on two applications: 1) single-image facial expression editing, and 2) facial expression interpolation between two images. We demonstrate that our method generates images of higher perceptual quality than previous VAE and flow-based methods.


Reliability of measurements of muscle tone and muscle power in stroke patients.

OBJECTIVES
to establish the reliability of the modified Ashworth scale for measuring muscle tone in a range of muscle groups (elbow, wrist, knee and ankle; flexors and extensors) and of the Medical Research Council scale for measuring muscle power in the same muscle groups and their direct antagonists.

DESIGN
a cross-sectional study involving repeated measures by two raters. We estimated reliability using the kappa statistic with quadratic weights (Kw).

SETTING
an acute stroke ward, a stroke rehabilitation unit and a continuing care facility.

SUBJECTS
people admitted to hospital with an acute stroke-35 patients, median age 73 (interquartile range 65-80), 20 men and 15 women.

RESULTS
inter- and intra-rater agreement for the measurement of power was good to very good for all tested muscle groups (Kw = 0.84-0.96, Kw = 0.70-0.96). Inter- and intra-rater agreement for the measurement of tone in the elbow, wrist and knee flexors was good to very good (Kw = 0.73-0.96, Kw = 0.77-0.94). Inter- and intra-rater agreement for the measurement of tone in the ankle plantarflexors was moderate to good (Kw = 0.45-0.51, Kw = 0.59-0.64).

CONCLUSIONS
the Medical Research Council scale was reliable in the tested muscle groups. The modified Ashworth scale demonstrated reliability in all tested muscle groups except the ankle plantarflexors. If reliable measurement of tone at the ankle is required for a specific purpose (e.g. to measure the effect of therapeutic intervention), further work will be necessary.


Microservice Ambients: An Architectural Meta-Modelling Approach for Microservice Granularity

Isolating fine-grained business functionalities byboundaries into entities called microservices is a core activityunderlying microservitization. We define microservitization asthe paradigm shift towards microservices. Determining theoptimal microservice boundaries (i.e. microservice granularity) is among the key microservitization design decisions thatinfluence the Quality of Service (QoS) of the microservice applicationat runtime. In this paper, we provide an architecturecentricapproach to model this decision problem. We build onambients — a modelling approach that can explicitly capturefunctional boundaries and their adaptation. We extend the aspect-oriented architectural meta-modelling approach of ambients—AMBIENT-PRISMA — with microservice ambients. A microservice ambient is a modelling concept that treatsmicroservice boundaries as an adaptable first-class entity. Weuse a hypothetical online movie subscription-based systemto capture a microservitization scenario using our aspectorientedmodelling approach. The results show the ability ofmicroservice ambients to express the functional boundary of amicroservice, the concerns of each boundary, the relationshipsacross boundaries and the adaptations of these boundaries. Additionally, we evaluate the expressiveness and effectivenessof microservice ambients using criteria from ArchitectureDescription Language (ADL) classification frameworkssince microservice ambients essentially support architecturedescription for microservices. The evaluation focuses on thefundamental modelling constructs of microservice ambientsand how they support microservitization properties such asutility-driven design, tool heterogeneity and decentralised governance. The evaluation highlights how microservice ambientssupport analysis, evolution and mobility/location awarenesswhich are significant to quality-driven microservice granularityadaptation. The evaluation is general and irrespective of theparticular application domain and the business competenciesin that domain.


Action Anticipation with RBF Kernelized Feature Mapping RNN

We introduce a novel Recurrent Neural Network-based algorithm for future video feature generation and action anticipation called feature mapping RNN . Our novel RNN architecture builds upon three effective principles of machine learning, namely parameter sharing, Radial Basis Function kernels and adversarial training. Using only some of the earliest frames of a video, the feature mapping RNN is able to generate future features with a fraction of the parameters needed in traditional RNN. By feeding these future features into a simple multilayer perceptron facilitated with an RBF kernel layer, we are able to accurately predict the action in the video. In our experiments, we obtain 18% improvement on JHMDB-21 dataset, 6% on UCF101-24 and 13% improvement on UT-Interaction datasets over prior stateof-the-art for action anticipation.


Chip Level Techniques for EMI Reduction in LCD Panels

This paper presents chip level techniques to improve electro-magnetic interference (EMI) characteristics of LCD-TV panels. A timing controller (TCON) uses over-driving algorithms to improve the response time of liquid crystals (LC). Since this algorithm needs previous frame data, external memory such as double data rate synchronous DRAM (DDR SDRAM) is widely used as a frame buffer. A TTL interface between the TCON and memory is used for read and write operations, generating EMI noise. For reduction of this EMI, three methods are described. The first approach is to reduce the driving current of data I/O buffers. The second is to adopt spread spectrum clock generation (SSCG), and the third is to apply a proposed algorithm which minimizes data transitions. EMI measurement of a 32" LCD-TV panel shows that these approaches are very effective for reduction of EMI, achieving 20dB reduction at 175MHz.


A Two-Stage Iterative Decoding of LDPC Codes for Lowering Error Floors

In iterative decoding of LDPC codes, trapping sets often lead to high error floors. In this work, we propose a two-stage iterative decoding to break trapping sets. Simulation results show that the error floor performance can be significantly improved with this decoding scheme.


Strategic Directions in Artificial Intelligence

—constructing intelligent machines, whether or not these operate in the same way as people do; —formalizing knowledge and mechanizing reasoning, both commonsense and refined expertise, in all areas of human endeavor; —using computational models to understand the psychology and behavior of people, animals, and artificial agents; and —making working with computers as easy and as helpful as working with skilled, cooperative, and possibly expert people.


The digital platform: a research agenda

As digital platforms are transforming almost every industry today, they are slowly finding their way into the mainstream information systems (IS) literature. Digital platforms are a challenging research object because of their distributed nature and intertwinement with institutions, markets and technologies. New research challenges arise as a result of the exponentially growing scale of platform innovation, the increasing complexity of platform architectures, and the spread of digital platforms to many different industries. This paper develops a research agenda for digital platforms research in IS. We recommend researchers seek to (1) advance conceptual clarity by providing clear definitions that specify the unit of analysis, degree of digitality and the sociotechnical nature of digital platforms; (2) define the proper scoping of digital platform concepts by studying platforms on different architectural levels and in different industry settings; and (3) advance methodological rigour by employing embedded case studies, longitudinal studies, design research, data-driven modelling and visualization techniques. Considering current developments in the business domain, we suggest six questions for further research: (1) Are platforms here to stay?; (2) How should platforms be designed?; (3) How do digital platforms transform industries?; (4) How can data-driven approaches inform digital platforms research?; (5) How should researchers develop theory for digital platforms?; and (6) How do digital platforms affect everyday life?


Theory and Application of Magnetic Flux Leakage Pipeline Detection

Magnetic flux leakage (MFL) detection is one of the most popular methods of pipeline inspection. It is a nondestructive testing technique which uses magnetic sensitive sensors to detect the magnetic leakage field of defects on both the internal and external surfaces of pipelines. This paper introduces the main principles, measurement and processing of MFL data. As the key point of a quantitative analysis of MFL detection, the identification of the leakage magnetic signal is also discussed. In addition, the advantages and disadvantages of different identification methods are analyzed. Then the paper briefly introduces the expert systems used. At the end of this paper, future developments in pipeline MFL detection are predicted.


Automatic detection of incomplete requirements via symbolic analysis

The usefulness of a system specification depends in part on the completeness of the requirements. However, enumerating all necessary requirements is difficult, especially when requirements interact with an unpredictable environment. A specification built with an idealized environmental view is incomplete if it does not include requirements to handle non-idealized behavior. Often incomplete requirements are not detected until implementation, testing, or worse, after deployment. Even when performed during requirements analysis, detecting incomplete requirements is typically an error prone, tedious, and manual task. This paper introduces Ares, a design-time approach for detecting incomplete requirements decomposition using symbolic analysis of hierarchical requirements models. We illustrate our approach by applying Ares to a requirements model of an industry-based automotive adaptive cruise control system. Ares is able to automatically detect specific instances of incomplete requirements decompositions at design-time, many of which are subtle and would be difficult to detect, either manually or with testing.


Internet-based peer support for parents: a systematic integrative review.

OBJECTIVES
The Internet and social media provide various possibilities for online peer support. The aim of this review was to explore Internet-based peer-support interventions and their outcomes for parents.

DESIGN
A systematic integrative review.

DATA SOURCES
The systematic search was carried out in March 2014 in PubMed, Cinahl, PsycINFO and Cochrane databases.

REVIEW METHODS
Two reviewers independently screened the titles (n=1793), abstracts and full texts to decide which articles should be chosen. The inclusion criteria were: (1) an Internet-based community as an intervention, or at least as a component of an intervention; (2) the participants in the Internet-based community had to be mothers and/or fathers or pregnant women; (3) the parents had to interact and communicate with each other through the Internet-based community. The data was analysed using content analysis. When analysing peer-support interventions only interventions developed by researchers were included and when analysing the outcomes for the parents, studies that focused on mothers, fathers or both parents were separated.

RESULTS
In total, 38 publications met the inclusion criteria. Most of the studies focused on Internet-based peer support between mothers (n=16) or both parents (n=15) and seven focused on fathers. In 16 studies, the Internet-based interventions had been developed by researchers and 22 studies used already existing Internet peer-support groups, in which any person using the Internet could participate. For mothers, Internet-based peer support provided emotional support, information and membership in a social community. For fathers, it provided support for the transition to fatherhood, information and humorous communication. Mothers were more active users of Internet-based peer-support groups than fathers. In general, parents were satisfied with Internet-based peer support. The evidence of the effectiveness of Internet-based peer support was inconclusive but no harmful effects were reported in these reviewed studies.

CONCLUSIONS
Internet-based peer support provided informational support for parents and was accessible despite geographical distance or time constraints. Internet-based peer support is a unique form of parental support, not replacing but supplementing support offered by professionals. Experimental studies in this area are needed.


A Rule-Based Framework of Metadata Extraction from Scientific Papers

Most scientific documents on the web are unstructured or semi-structured, and the automatic document metadata extraction process becomes an important task. This paper describes a framework for automatic metadata extraction from scientific papers. Based on a spatial and visual knowledge principle, our system can extract title, authors and abstract from scientific papers. We utilize format information such as font size and position to guide the metadata extraction process. The experiment results show that our system achieves a high accuracy in header metadata extraction which can effectively assist the automatic index creation for digital libraries.


Battery-Draining-Denial-of-Service Attack on Bluetooth Devices

We extend the Xen virtual machine monitor with the ability to host a hundred of virtual machines on a single physical node. Similarly to a demand paging of virtual memory, we page out idle virtual machines making them available on demand. Paging is transparent. An idle virtual machine remains fully operational. It is able to respond to external events with a delay comparable to a delay under a medium load. To achieve desired degree of consolidation, we identify and leave in memory only a minimal working set of pages required to maintain the illusion of running VM and respond to external events. To keep the number of active pages small without harming performance dramatically, we build a correspondence between every event and its working set. Reducing a working set further, we implement a copy-on-write page sharing across virtual machines running on the same host. To decrease resources occupied by a virtual machine’s file system, we implement a copy-on-write storage and golden imaging.


Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

Currently autonomous or self-driving vehicles are at the heart of academia and industry research becauseof itsmulti-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a givenorigin to agivendestination,motionplanningmethods incorporate searching for apath to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have beenproposed in the literature. Thepurpose of this paper is to reviewexisting approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1)findingapath, (2) searching for the safestmanoeuvre and (3)determining themost feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existingmethods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performanceofplanningalgorithmsat all three levels. Somepromising areasof future focushave been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical reviewon planning techniques presented in this paper, along with theassociateddiscussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research. Crown Copyright 2015 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).


MonoPerfCap : Human Performance Capture from Monocular Video WEIPENGXU , AVISHEKCHATTERJEE , MICHAELZOLLHÖFER , HELGERHODIN , DUSHYANTMEHTA

We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.


A linguistic theory of translation : an essay in applied linguistics

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A theory of formal synthesis via inductive learning

Formal synthesis is the process of generating a program satisfying a high-level formal specification. In recent times, effective formal synthesis methods have been proposed based on the use of inductive learning. We refer to this class of methods that learn programs from examples as formal inductive synthesis. In this paper, we present a theoretical framework for formal inductive synthesis. We discuss how formal inductive synthesis differs from traditional machine learning. We then describe oracle-guided inductive synthesis (OGIS), a framework that captures a family of synthesizers that operate by iteratively querying an oracle. An instance of OGIS that has had much practical impact is counterexample-guided inductive synthesis (CEGIS). We present a theoretical characterization of CEGIS for learning any program that computes a recursive language. In particular, we analyze the relative power of CEGIS variants where the types of counterexamples generated by the oracle varies. We also consider the impact of bounded versus unbounded memory available to the learning algorithm. In the special case where the universe of candidate programs is finite, we relate the speed of convergence to the notion of teaching dimension studied in machine learning theory. Altogether, the results of the paper take a first step towards a theoretical foundation for the emerging field of formal inductive synthesis.