title: Research on metadata-driven data quality assessment architecture

abstract: So far there have never been systematic evaluation criteria and entire assessment and evaluation system in Data quality internationally. Based on the research on the relative content of both international and domestic data quality, this article analyzes the requests of data quality for the large enterprises. First of all, this paper raises and builds a complete data quality assessment system. Second, the definitions and specific algorithms of data quality assessment indicators and poses data quality analysis are built to evaluate the architecture and processes. A frame structure of data quality meta-model is presented in this paper. In addition, this paper also designs an evaluation system. This system includes the classification and definition of data quality and the algorithm in evaluation index and the system and process of data quality evaluation. This paper provides credibility basis for enterprises in evaluation of data quality.


title: Multi-modal Dimensional Emotion Recognition using Recurrent Neural Networks

abstract: Emotion recognition has been an active research area with both wide applications and big challenges. This paper presents our effort for the Audio/Visual Emotion Challenge (AVEC2015), whose goal is to explore utilizing audio, visual and physiological signals to continuously predict the value of the emotion dimensions (arousal and valence). Our system applies the Recurrent Neural Networks (RNN) to model temporal information. We explore various aspects to improve the prediction performance including: the dominant modalities for arousal and valence prediction, duration of features, novel loss functions, directions of Long Short Term Memory (LSTM), multi-task learning, different structures for early feature fusion and late fusion. Best settings are chosen according to the performance on the development set. Competitive experimental results compared with the baseline show the effectiveness of the proposed methods.


title: Light at the End of the Tunnel: High-Speed LiDAR-Based Train Localization in Challenging Underground Environments

abstract: In this paper, we present an infrastructure-free mapping and localization framework for rail vehicles using only a lidar sensor. Our method is designed to handle the pathological environment found in modern underground tunnels: narrow, parallel, and relatively smooth concrete walls with very little infrastructure to break up the empty spaces in the tunnel. By using an RQE-based, point-cloud alignment approach, we are able to implement a sliding-window algorithm, used for both mapping and localization. We demonstrate the proposed method with datasets gathered on a subway train travelling at high speeds (up to 70 km/h) in an underground tunnel for a total of 20 km across 6 runs. Our method is capable of mapping the tunnel with less than 0.6% error over the total length of the generated map. It is capable of continuously localizing, relative to the generated map, to within 10 cm in stations and at crossovers, and 1.8 m in pathological sections of tunnel. This method improves rail-based localization in a tunnel, which can be used to increase capacity on existing railways and for automated trains.


title: An overview of text steganography

abstract: Steganography embeds a secret message inside an innocent looking cover medium, stealthily, without creating any attention. The cover medium used can be a text, image, audio, video, network packets, etc. To embed the secret, steganographic techniques rely on the redundant information of the used cover medium or the properties which human perceptual system fails to differentiate. Hence the choice of using text document as a cover medium is the most difficult one as they have less redundant information. However, as text documents are widely used in organizations, using text document as a cover medium might be a preferable choice in such environment. Considering this, this paper presents an overview of the existing techniques of text steganography and its classification. A comparison of the existing techniques is also provided.


title: Exploiting Process Cubes, Analytic Workflows and Process Mining for Business Process Reporting: A Case Study in Education

abstract: Business Process Intelligence (BPI) is an emerging topic that has gained popularity in the last decade. It is driven by the need for analysis techniques that allow businesses to understand and improve their processes. One of the most common applications of BPI is reporting, which consists on the struc-tured generation of information (i.e., reports) from raw data. In this article, state-of-the-art process mining techniques are used to periodically produce automated reports that relate the actual performance of students of Eindhoven University of Technology to their studying behavior. To avoid the tedious manual repetition of the same process mining procedure for each course, we have designed a work-flow calling various process mining techniques using RapidProM. To ensure that the actual students' behavior is related to their actual performance (i.e., grades for courses), our analytic workflows approach leverages on process cubes, which enable the dataset to be sliced and diced based on courses and grades. The article discusses how the approach has been operationalized and what is the structure and concrete results of the reports that have been automatically generated. The reports were sent to lecturers and feedback was collected through an evaluation form. Also, we analyzed an example report to show the range of insights that they provide.


title: Algorithms for quantum computation: discrete logarithms and factoring

abstract: A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a cost in computation time of at most a polynomial factor: It is not clear whether this is still true when quantum mechanics is taken into consideration. Several researchers, starting with David Deutsch, have developed models for quantum mechanical computers and have investigated their computational properties. This paper gives Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number of steps which is polynomial in the input size, e.g., the number of digits of the integer to be factored. These two problems are generally considered hard on a classical computer and have been used as the basis of several proposed cryptosystems. We thus give the first examples of quantum cryptanalysis. >


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

abstract: 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.


title: Factors Influencing Consumer Behavior: A Study among University Students in Malaysia

abstract: The objective of this study is to examine how personal factors such as lifestyle, personality, and economic situations affect the consumer behavior of Malaysian university students. A quantitative approach was adopted and a self-administered questionnaire was distributed to collect data from university students. Findings illustrate that ‘personality’ influences the consumer behavior among Malaysian university student. This study also noted that the economic situation had a negative relationship with consumer behavior. Findings of this study improve our understanding of consumer behavior of Malaysian University Students. The findings of this study provide valuable insights in identifying and taking steps to improve on the services, ambience, and needs of the student segment of the Malaysian market.


title: Viral Misinformation: The Role of Homophily and Polarization

abstract: The spreading of unsubstantiated rumors on online social networks (OSN) either unintentionally or intentionally (e.g., for political reasons or even trolling) can have serious consequences such as in the recent case of rumors about Ebola causing disruption to health-care workers. Here we show that indicators aimed at quantifying information consumption patterns might provide important insights about the virality of false claims. In particular, we address the driving forces behind the popularity of contents by analyzing a sample of 1.2M Facebook Italian users consuming different (and opposite) types of information (science and conspiracy news). We show that users' engagement across different contents correlates with the number of friends having similar consumption patterns (homophily), indicating the area in the social network where certain types of contents are more likely to spread. Then, we test diffusion patterns on an external sample of 4,709 intentional satirical false claims showing that neither the presence of hubs (structural properties) nor the most active users (influencers) are prevalent in viral phenomena. Instead, we found out that in an environment where misinformation is pervasive, users' aggregation around shared beliefs may make the usual exposure to conspiracy stories (polarization) a determinant for the virality of false information.


title: Is semitransparency useful for navigating virtual environments?

abstract: A relevant issue for any Virtual Environment (VE) is the navigational support provided to users who are exploring it. Semitransparency is sometimes exploited as a means to see through occluding surfaces with the aim of improving user navigation abilities and awareness of the VE structure. Designers who make this choice assume that it is useful, especially in the case of VEs with many levels of occluding surfaces, e.g. virtual buildings or cities. This paper is devoted to investigate this assumption with a proper experimental evaluation on users. First, we discuss possible ways for improving navigation, and focus on implementation choices for semitransparency as a navigation aid. Then, we present and discuss the experimental evaluation we carried out. We compared subjects' performance in three conditions: local exploitation of semitransparency inside the VE, a more global exploitation provided by a bird's-eye-view, and a control condition where neither of the two features was available.


title: Cyber-Physical Security Testbeds: Architecture, Application, and Evaluation for Smart Grid

abstract: The development of a smarter electric grid will depend on increased deployments of information and communication technology (ICT) to support novel communication and control functions. Unfortunately, this additional dependency also expands the risk from cyber attacks. Designing systems with adequate cyber security depends heavily on the availability of representative environments, such as testbeds, where current issues and future ideas can be evaluated. This paper provides an overview of a smart grid security testbed, including the set of control, communication, and physical system components required to provide an accurate cyber-physical environment. It then identifies various testbed research applications and also identifies how various components support these applications. The PowerCyber testbed at Iowa State University is then introduced, including the architecture, applications, and novel capabilities, such as virtualization, Real Time Digital Simulators (RTDS), and ISEAGE WAN emulation. Finally, several attack scenarios are evaluated using the testbed to explore cyber-physical impacts. In particular, availability and integrity attacks are demonstrated with both isolated and coordinated approaches, these attacks are then evaluated based on the physical system's voltage and rotor angle stability.


title: Topic facet modeling: semantic visual analytics for online discussion forums

abstract: In this paper, we propose a novel Topic Facet Model (TFM), a probabilistic topic model that assumes all words in single sentence are generated from one topic facet. The model is applied to automatically extract forum posts semantics for uncovering the content latent structures. We further prototype a visual analytics interface to present online discussion forum semantics. We hypothesize that the semantic modeling through analytics on open online discussion forums can help users examine the post content by viewing the summarized topic facets. Our preliminary results demonstrated that TFM can be a promising method to extract topic specificity from conversational and relatively short texts in online programming discussion forums.


A case with Poland’s syndrome and athelia


title: MIMO rate adaptation in 802.11n wireless networks

abstract: This paper studies MIMO based rate adaptation (RA) in 802.11n wireless networks. Our case study shows that existing RA algorithms offer much lower throughput than even a fixed-rate scheme. The fundamental problem is that, all such algorithms are MIMO oblivious; they do not consider the characteristics of diversity-oriented, single-stream mode and the spatial multiplexing driven, double-stream mode. We propose MiRA, a novel MIMO RA scheme that zigzags between intra- and inter-mode rate options. Our experiments show that MiRA consistently outperforms three representative RA algorithms, SampleRate, RRAA and Atheros MIMO RA, in static, mobility and collision settings.


title: A rank sum test method for informative gene discovery

abstract: Finding informative genes from microarray data is an important research problem in bioinformatics research and applications. Most of the existing methods rank features according to their discriminative capability and then find a subset of discriminative genes (usually top k genes). In particular, t-statistic criterion and its variants have been adopted extensively. This kind of methods rely on the statistics principle of t-test, which requires that the data follows a normal distribution. However, according to our investigation, the normality condition often cannot be met in real data sets.To avoid the assumption of the normality condition, in this paper, we propose a rank sum test method for informative gene discovery. The method uses a rank-sum statistic as the ranking criterion. Moreover, we propose using the significance level threshold, instead of the number of informative genes, as the parameter. The significance level threshold as a parameter carries the quality specification in statistics. We follow the Pitman efficiency theory to show that the rank sum method is more accurate and more robust than the t-statistic method in theory.To verify the effectiveness of the rank sum method, we use support vector machine (SVM) to construct classifiers based on the identified informative genes on two well known data sets, namely colon data and leukemia data. The prediction accuracy reaches 96.2% on the colon data and 100% on the leukemia data. The results are clearly better than those from the previous feature ranking methods. By experiments, we also verify that using significance level threshold is more effective than directly specifying an arbitrary k.


title: Understanding training and generalization in deep learning by Fourier analysis

abstract: Background: It is still an open research area to theoretically understand why Deep Neural Networks (DNNs)---equipped with many more parameters than training data and trained by (stochastic) gradient-based methods---often achieve remarkably low generalization error. Contribution: We study DNN training by Fourier analysis. Our theoretical framework explains: i) DNN with (stochastic) gradient-based methods often endows low-frequency components of the target function with a higher priority during the training; ii) Small initialization leads to good generalization ability of DNN while preserving the DNN's ability to fit any function. These results are further confirmed by experiments of DNNs fitting the following datasets, that is, natural images, one-dimensional functions and MNIST dataset.


title: A Framework for Computing the Privacy Scores of Users in Online Social Networks

abstract: A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. Most of this work focuses on how to share social networks owned by organizations without revealing the identities or the sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their daily information-sharing activities. In this article, we approach the privacy issues raised in online social networks from the individual users’ viewpoint: we propose a framework to compute the privacy score of a user. This score indicates the user’s potential risk caused by his or her participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive information a user discloses, the higher his or her privacy risk. Also, the more visible the disclosed information becomes in the network, the higher the privacy risk. We develop mathematical models to estimate both sensitivity and visibility of the information. We apply our methods to synthetic and real-world data and demonstrate their efficacy and practical utility.


title: From Keywords to Semantic Queries — Incremental Query Construction on the Semantic Web

abstract: Constructing semantic queries is a demanding task for human users, as it requires mastering a query language as well as the schema which has been used for storing the data. In this paper, we describe QUICK, a novel system for helping users to construct semantic queries in a given domain. QUICK combines the convenience of keyword search with the expressivity of semantic queries. Users start with a keyword query and then are guided through a process of incremental refinement steps to specify the query intention. We describe the overall design of QUICK, present the core algorithms to enable efficient query construction, and finally demonstrate the effectiveness of our system through an experimental study.


title: Agent-Based Cloud Computing

abstract: Agent-based cloud computing is concerned with the design and development of software agents for bolstering cloud service discovery, service negotiation, and service composition. The significance of this work is introducing an agent-based paradigm for constructing software tools and testbeds for cloud resource management. The novel contributions of this work include: 1) developing Cloudle: an agent-based search engine for cloud service discovery, 2) showing that agent-based negotiation mechanisms can be effectively adopted for bolstering cloud service negotiation and cloud commerce, and 3) showing that agent-based cooperative problem-solving techniques can be effectively adopted for automating cloud service composition. Cloudle consists of 1) a service discovery agent that consults a cloud ontology for determining the similarities between providers' service specifications and consumers' service requirements, and 2) multiple cloud crawlers for building its database of services. Cloudle supports three types of reasoning: similarity reasoning, compatibility reasoning, and numerical reasoning. To support cloud commerce, this work devised a complex cloud negotiation mechanism that supports parallel negotiation activities in interrelated markets: a cloud service market between consumer agents and broker agents, and multiple cloud resource markets between broker agents and provider agents. Empirical results show that using the complex cloud negotiation mechanism, agents achieved high utilities and high success rates in negotiating for cloud resources. To automate cloud service composition, agents in this work adopt a focused selection contract net protocol (FSCNP) for dynamically selecting cloud services and use service capability tables (SCTs) to record the list of cloud agents and their services. Empirical results show that using FSCNP and SCTs, agents can successfully compose cloud services by autonomously selecting services.


title: Retrieval of clothing images based on relevance feedback with focus on collar designs

abstract: The content-based image retrieval methods are developed to help people find what they desire based on preferred images instead of linguistic information. This paper focuses on capturing the image features representing details of the collar designs, which is important for people to choose clothing. The quality of the feature extraction methods is important for the queries. This paper presents several new methods for the collar-design feature extraction. A prototype of clothing image retrieval system based on relevance feedback approach and optimum-path forest algorithm is also developed to improve the query results and allows users to find clothing image of more preferred design. A series of experiments are conducted to test the qualities of the feature extraction methods and validate the effectiveness and efficiency of the RF-OPF prototype from multiple aspects. The evaluation scores of initial query results are used to test the qualities of the feature extraction methods. The average scores of all RF steps, the average numbers of RF iterations taken before achieving desired results and the score transition of RF iterations are used to validate the effectiveness and efficiency of the proposed RF-OPF prototype.


title: Psychophysiologic Effects of Hatha Yoga on Musculoskeletal and Cardiopulmonary Function: A Literature Review

abstract: Yoga has become increasingly popular in Western cultures as a means of exercise and fitness training; however, it is still depicted as trendy as evidenced by an April 2001 Time magazine cover story on "The Power of Yoga." There is a need to have yoga better recognized by the health care community as a complement to conventional medical care. Over the last 10 years, a growing number of research studies have shown that the practice of Hatha Yoga can improve strength and flexibility, and may help control such physiological variables as blood pressure, respiration and heart rate, and metabolic rate to improve overall exercise capacity. This review presents a summary of medically substantiated information about the health benefits of yoga for healthy people and for people compromised by musculoskeletal and cardiopulmonary disease.


title: Semiotic Fitting and the Nativeness of Community

abstract: The concept of ‘semiotic fitting’ is what we provide as a model for the description and analysis of the diversity dynamics and nativeness in semiotic systems. One of its sources is the concept of ‘ecological fitting’ which was introduced by Daniel Janzen as the mechanism for the explanation of diversity in tropical ecosystems and which has been shown to work widely over the communities of various types. As different from the neo-Darwinian concept of fitness that describes reproductive success, ‘fitting’ describes functional (sign) relations and aboutness. Diversity of a semiotic system is strongly dependent on the mutual fitting of agents of which the semiotic system consists. The focus on semiotic fitting means that, in the analysis of diversity, we pay particular attention to decision making (choice), functional plasticity, recognition windows, the depth of interpretation of the agents, and the categories responsible for the structure of the semiotic system. The concept of semiotic fitting has an early analogue in Jakob von Uexkull’s concept of ‘Einpassung’ (as different from ‘Anpassung’, meaning ‘evolutionary adaptation’). The close concepts of ‘semiotic fitness’, introduced by Jesper Hoffmeyer and by Stephanie Walsh Matthews, ‘semiotic selection’, introduced by Timo Maran and Karel Kleisner, and ‘semiotic niche’, introduced by Hoffmeyer, provide different versions of the same model. If community is constructing itself on the basis of (relational) fitting, then nativeness of the community is a product of fitting, not vice versa. Nativeness is a feature that deepens in the course of community succession. The concept of ‘semiotic fitting’ demonstrates the possibility to analyse the role of both indigenous and alien species or other agents in a community on the basis of a single model.


title: Combined SPR and SERS Microscopy in the Kretschmann Configuration

abstract: A novel hybrid spectroscopic technique is proposed, combining surface plasmon resonance (SPR) with surface-enhanced Raman scattering (SERS) microscopy. A standard Raman microscope is modified to accommodate the excitation of surface plasmon-polaritons (SPPs) on flat metallic surfaces in the Kretschmann configuration, while retaining the capabilities of Raman microscopy. The excitation of SPPs is performed as in standard SPR-microscopy; namely, a beam with TM-polarization traverses off-axis a high numerical aperture oil immersion objective, illuminating at an angle the metallic film from the (glass) substrate side. The same objective is used to collect the full Kretschmann cone containing the SERS emission on the substrate side. The angular dispersion of the plasmon resonance is measured in reflectivity for different coupling conditions and, simultaneously, SERS spectra are recorded from Nile Blue (NB) molecules adsorbed onto the surface. A trade-off is identified between the conditions of optimum coupling to SPPs and the spot size (which is related to the spatial resolution). This technique opens new horizons for SERS microscopy with uniform enhancement on flat surfaces.


title: Pose Estimation from Line Correspondences using Direct Linear Transformation.

abstract: This work is concerned with camera pose estimation from correspondences of 3D/2D lines, i.e. with the Perspective-n-Line (PnL) problem. We focus on large line sets, which can be efficiently solved by methods using linear formulation of PnL. We propose a novel method 'DLT-Combined-Lines' based on the Direct Linear Transformation (DLT) algorithm, which benefits from a new combination of two existing DLT methods for pose estimation. The method represents 2D structure by lines, and 3D structure by both points and lines. The redundant 3D information reduces the minimum of required line correspondences to 5. A cornerstone of the method is a combined projection matrix estimated by the DLT algorithm. It contains multiple estimates of camera rotation and translation, which can be recovered after enforcing constraints of the matrix. Multiplicity of the estimates is exploited to improve accuracy of the proposed method. For large line sets (10 and more), the method achieves state-of-the-art accuracy on synthetic data even under strong image noise. Moreover, it is the most accurate method on real world data, outperforming the state-of-the-art by a large margin. The proposed method is also highly computationally effective, estimating the pose of 1000 lines in 12 ms on a desktop computer.


title: Crowding by a repeating pattern

abstract: Theinability to recognize a peripheral target among flankers is called crowding. For a foveal target, crowding can be distinguished from overlap masking by its sparing of detection, linear scaling with eccentricity, and invariance with target size.Crowding depends on the proximity and similarity of the flankers to the target. Flankers that are far from or dissimilar to the target do not crowd it. On a gray page, text whose neighboring letters have different colors, alternately black and white, has enough dissimilarity that it might escape crowding. Since reading speed is normally limited by crowding, escape from crowding should allow faster reading. Yet reading speed is unchanged (Chung & Mansfield, 2009). Why? A recent vernier study found that using alternating-color flankers produces strong crowding (Manassi, Sayim, & Herzog, 2012). Might that effect occur with letters and reading? Critical spacing is the minimum center-to-center target–flanker spacing needed to correctly identify the target. We measure it for a target letter surrounded by several equidistant flanker letters of the same polarity, opposite polarity, or mixed polarity: alternately white and black. We find strong crowding in the alternating condition, even though each flanker letter is beyond its own critical spacing (as measured in a separate condition). Thus a periodic repeating pattern can produce crowding even when the individual elements do not. Further, in all conditions we find that, once a periodic pattern repeats (two cycles), further repetition does not affect critical spacing of the innermost flanker.


title: Distributed user modelling for universal information access.

abstract: Abstract In a distributed multi-agent based software environment, the traditional monolithic user model ceases to exist and is replaced by user model fragments, developed by the various software agents populating the environment. These model fragments have been developed in a variety of specific contexts to help achieve various goals. User models are thus fragmented, relativized, local, and often quite shallow. They are inherently inconsistent with one another and reflect not only characteristics of the users, but also certain social relationships among them. With the arising proliferation of models and data, the user modelling problem transforms into retrieval and integration of the available user model fragments "just in time" by a particular computational agent to the breadth and depth needed for a specific purpose. In this paper we explore the implications of distributed user models, drawing examples from I-Help, a collaborative system for peer help. 1. INTRODUCTION The next generation of mobile, distributed, autonomous computer applications will allow future computing environments to be accessible from everywhere: not only on desk-top and lap-tops, but also on palm-tops, cell phones; these environments will be worn (data glasses, watches, etc.) and be embedded in everyday devices, home appliances, in the environment. People with various cultural background, goals, knowledge, and disabilities will be interacting with these mobile and ubiquitous computing environments in all imaginable contexts: in vehicles, in meetings, on public transport, while shopping, relaxing, eating, cooking etc… Meeting user needs adequately is crucial for future software applications, since the variety of contexts and devices in which software will be used implies a huge diversity of user needs to be met. The only way of achieving adaptation in distributed software environments is by designing systems that are no longer static stand-alone applications, but dynamic integrative environments that configure themselves according to the individual needs of the user, the context of use, and the platform requirements. Such systems should be able to operate independently of a person's location. In order to achieve such environments, the application functions have to be packaged into small atomic units or agents, which can re-assemble themselves dynamically depending on situational variables. Thus the atomic units will be reused in an appropriate way depending on the current hosting environment, e.g. uniformity and compatibility is achieved. These two qualities are important for two reasons: first, to allow the benefits for interaction and interoperability of software in the networked world, and second, to reduce the load on users learning how to operate a variety of software on different devices. User modelling is crucial in order to track the context of the user. Due to the variety of contexts ensured by the mobility and ubiquity of the computing devices, a lot of information is needed to capture the aspects that are important for the user’s needs, for example, about her exact location, state etc. The more information is available, the more adequate the user model and therefore, the better the adaptation of the functionality towards the individual user will be. Tracking the user's movement, location, state is especially important since the users are mobile. We believe that conventional methods for software design even though appropriate for distributed applications design are not sufficient for applications supporting universal user access. These methods assume providing a fixed functionality defined at their design time independently of the conditions of their use, the experience or typical tasks of the user. There are techniques ensuring interoperability in distributed software, for example, CORBA, Jini and DCOM. However, with distributed objects, even though objects may run on different platforms, applications generally form a single monolithic entity of tightly bound objects, with hand-coded calls to known methods of pre-existing objects. The problem that the applications may require different functionality in a wide variety of contexts (platforms, locations, user needs etc.) cannot be addressed. There is research on methods allowing software applications to, for example, dynamically constrain the offered functionality when the parameters of the environment do not allow the provision of full functionality (Noble & Satayanaranyanan, 1999). For example, if a


Research on Security Issues of the Internet of Things


title: The “Physics” of Notations: Toward a Scientific Basis for Constructing Visual Notations in Software Engineering

abstract: Visual notations form an integral part of the language of software engineering (SE). Yet historically, SE researchers and notation designers have ignored or undervalued issues of visual representation. In evaluating and comparing notations, details of visual syntax are rarely discussed. In designing notations, the majority of effort is spent on semantics, with graphical conventions largely an afterthought. Typically, no design rationale, scientific or otherwise, is provided for visual representation choices. While SE has developed mature methods for evaluating and designing semantics, it lacks equivalent methods for visual syntax. This paper defines a set of principles for designing cognitively effective visual notations: ones that are optimized for human communication and problem solving. Together these form a design theory, called the Physics of Notations as it focuses on the physical (perceptual) properties of notations rather than their logical (semantic) properties. The principles were synthesized from theory and empirical evidence from a wide range of fields and rest on an explicit theory of how visual notations communicate. They can be used to evaluate, compare, and improve existing visual notations as well as to construct new ones. The paper identifies serious design flaws in some of the leading SE notations, together with practical suggestions for improving them. It also showcases some examples of visual notation design excellence from SE and other fields.


title: Organization of space perception: neural representation of three-dimensional space in the posterior parietal cortex

abstract: The representation of perceptual space in the posterior parietal cortex can be divided into at least two categories: far space, beyond arm's reach, and peripersonal space, within arm's reach. These are encoded by different groups of neurons that are closely related to the control of gaze and the guidance of arm and hand movement, respectively.


title: GPU optimization of the SGM stereo algorithm

abstract: GPU hardware architectures have evolved into a suitable platform for the hardware acceleration of complex computing tasks. Stereo vision is one such task where acceleration is desirable for robotic and automotive systems. Much research was invested in developing stereo vision algorithms with increased quality, but real-time implementations are still lacking. In this work we focus on creating a real-time dense stereo reconstruction system. We selected the Semi-global Matching method as the basis of our system due to its high quality and reduced computational complexity. The Census transform is selected as the matching metric because our results show that it can reduce the matching errors for traffic images compared to classical solutions. We also present two modifications to the original Semi-Global algorithm to improve the sub-pixel accuracy and the execution time. The system was implemented and evaluated on a current generation GPU with a running time of 19ms for image having the resolution 512×383.


title: Reliability analysis of healthcare information systems: State of the art and future directions

abstract: Testing and verification of healthcare information systems is a challenging and important issue since faults in these critical systems may lead to loss of lives, and in the best cases, loss of money and reputations. However, due to the complexity of these systems, and the increasing demand for new products and new technologies in this domain, there are several methods and technologies being used for testing these systems. In this paper, we review the state of the art on testing and verification of healthcare information systems, and then we identify several open issues and challenges in the area. We divide the exiting methods into three categories: simulation based methods, formal methods, and other techniques such as semi-formal methods. Then, we discuss challenging and open issues in the domain.


title: Sarcasm Detection in Twitter: "All Your Products Are Incredibly Amazing!!!" - Are They Really?

abstract: Sarcasm is a special form of irony by which the person conveys implicit information, usually the opposite of what is said, within the message he transmits. Sarcasm is largely used in social networks and microblogging websites, where people mock or criticize in a way that makes it difficult even for humans to tell if what is said is what is meant. Recognizing sarcastic statements can be very useful when it comes to improving automatic sentiment analysis of data collected from social networks. It helps also enhance the efficiency of after-sales services or consumer assistance through understanding the intentions and real opinions of consumers when browsing their feedbacks or complaints. In this paper we propose a method to detect sarcasm in Twitter that makes use of the different components of the tweet. We propose four sets of features that cover different types of sarcasm we defined, and that will be used to classify tweets into sarcastic and non-sarcastic. We evaluate the performances of our approach. We study the importance of each of the proposed sets of features and evaluate its added value to the classification.


title: Intra-abdominal pressure and abdominal wall muscular function: Spinal unloading mechanism.

abstract: article i nfo Article history: Received 22 January 2010 Accepted 28 June 2010 Background: The roles of antagonistic activation of abdominal muscles and of intra-abdominal pressurization remain enigmatic, but are thought to be associated with both spinal unloading and spinal stabilization in activities such as lifting. Biomechanical analyses are needed to understand the function of intra-abdominal pressurization because of the anatomical and physiological complexity, but prior analyses have been over- simplified. Methods: To test whether increased intra-abdominal pressure was associated with reduced spinal compression forces for efforts that generated moments about each of the principal axis directions, a previously published biomechanical model of the spine and its musculature was modified by the addition of anatomically realistic three-layers of curved abdominal musculature connected by fascia to the spine. Published values of muscle cross-sectional areas and the active and passive stiffness properties were assigned. The muscle activations were calculated assuming minimized muscle stress and stretch for the model loaded with flexion, extension, lateral bending and axial rotation moments of up to 60 Nm, along with intra-abdominal pressurization of 5 or 10 kPa (37.5 or 75 mm Hg) and partial bodyweight (340 N). Findings: The analysis predicted a reduction in spinal compressive force with increase in intra-abdominal pressurization from 5 to 10 kPa. This reduction at 60 Nm external effort was 21% for extension effort, 18% for flexion effort, 29% for lateral bending and 31% for axial rotation. Interpretation: This analysis predicts that intra-abdominal pressure produces spinal unloading, and shows likely muscle activation patterns that achieve this.


title: Gaussian Process Prior Variational Autoencoders

abstract: Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is the prior assumption that latent sample representations are independent and identically distributed. However, for many important datasets, such as time-series of images, this assumption is too strong: accounting for covariances between samples, such as those in time, can yield to a more appropriate model specification and improve performance in downstream tasks. In this work, we introduce a new model, the Gaussian Process (GP) Prior Variational Autoencoder (GPPVAE), to specifically address this issue. The GPPVAE aims to combine the power of VAEs with the ability to model correlations afforded by GP priors. To achieve efficient inference in this new class of models, we leverage structure in the covariance matrix, and introduce a new stochastic backpropagation strategy that allows for computing stochastic gradients in a distributed and low-memory fashion. We show that our method outperforms conditional VAEs (CVAEs) and an adaptation of standard VAEs in two image data applications.


title: Integrated circuit security: new threats and solutions

abstract: In this paper, we present new threats to the security of integrated circuits (ICs) and outline a new solution.


title: Output constraint control on path following of four-wheel independently actuated autonomous vehicles

abstract: This paper investigates the path following problem for four-wheel independently actuated (FWIA) autonomous vehicles (AVs). A novel output constraint controller is proposed to deal with the lateral offset control in path following and maintain the vehicle lateral stability in the presence of tire sliding effects. The innovation of this work includes the following two aspects: (1) A novel output constraint control strategy, hyperbolic projection method, is proposed to strictly bound the lateral offset to prevent vehicle transgressing the safety bound in the path following; (2) A robust LQR controller, is adopted to obtain the optimal active front steering (AFS) and direct yaw moment (DYC) control inputs, with the vehicle stability consideration, and to eliminate the parameter uncertainties and external disturbances. CarSim-Simulink joint simulation results indicate that the proposed controller can compactly bound the lateral offset to avoid transgressing the safe boundary during path following process, especially in extreme driving condition, in presence of tire sliding effects, system uncertainties and disturbances.


Online Self-Disclosure Behaviors


title: The game, the player, the world: looking for a heart of gameness

abstract: This paper proposes a definition of games. I describe the classic game model, a list of six features that are necessary and sufficient for something to be a game. The definition shows games to be transmedial: There is no single game medium, but rather a number of game media, each with its own strengths. The computer is simply the latest game medium to emerge. While computer games1 are therefore part of the broader area of games, they have in many cases evolved beyond the classic game model.


Incorporating a cooperative design model in a computer-aided design improvement system.


title: Accelerating Science: A Computing Research Agenda

abstract: Author(s): Honavar, Vasant G; Hill, Mark D; Yelick, Katherine | Abstract: The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine natural phenomena through the computational lens, i.e., using algorithmic or information processing abstractions of the underlying processes; and our ability to acquire, share, integrate and analyze disparate types of data. However, there is a huge gap between our ability to acquire, store, and process data and our ability to make effective use of the data to advance discovery. Despite successful automation of routine aspects of data management and analytics, most elements of the scientific process currently require considerable human expertise and effort. Accelerating science to keep pace with the rate of data acquisition and data processing calls for the development of algorithmic or information processing abstractions, coupled with formal methods and tools for modeling and simulation of natural processes as well as major innovations in cognitive tools for scientists, i.e., computational tools that leverage and extend the reach of human intellect, and partner with humans on a broad range of tasks in scientific discovery (e.g., identifying, prioritizing formulating questions, designing, prioritizing and executing experiments designed to answer a chosen question, drawing inferences and evaluating the results, and formulating new questions, in a closed-loop fashion). This calls for concerted research agenda aimed at: Development, analysis, integration, sharing, and simulation of algorithmic or information processing abstractions of natural processes, coupled with formal methods and tools for their analyses and simulation; Innovations in cognitive tools that augment and extend human intellect and partner with humans in all aspects of science.


title: Solid waste management in the hospitality industry: a review.

abstract: Solid waste management is a key aspect of the environmental management of establishments belonging to the hospitality sector. In this study, we reviewed literature in this area, examining the current status of waste management for the hospitality sector, in general, with a focus on food waste management in particular. We specifically examined the for-profit subdivision of the hospitality sector, comprising primarily of hotels and restaurants. An account is given of the causes of the different types of waste encountered in this sector and what strategies may be used to reduce them. These strategies are further highlighted in terms of initiatives and practices which are already being implemented around the world to facilitate sustainable waste management. We also recommended a general waste management procedure to be followed by properties of the hospitality sector and described how waste mapping, an innovative yet simple strategy, can significantly reduce the waste generation of a hotel. Generally, we found that not many scholarly publications are available in this area of research. More studies need to be carried out on the implementation of sustainable waste management for the hospitality industry in different parts of the world and the challenges and opportunities involved.


title: Strategic Plan to Eliminate Health Disparities in New Jersey

abstract: In September 2004, the New Jersey Legislature authorized the Commissioner of the Department of Health and Senior Services (the Department) to establish the Eliminating Health Disparities Initiative in the Office on Minority and Multicultural Health (OMMH). Public Law 2004, c. 137, required the OMMH to develop and implement a comprehensive, coordinated plan to improve the health of racial and ethnic minorities within the State. In New Jersey, like throughout the nation, health disparities are deep, persistent and complex. The Eliminating Health Disparities Initiative provides an opportunity to examine the Department’s programs that target specific minority populations, identifies strengths of the Department’s programs and proposes new strategies. The NJDHSS Plan to Eliminate Health Disparities (the Plan) is the Department’s strategy book on a comprehensive and targeted effort to address some of the major issues that contribute to racial and ethnic health disparities in New Jersey.


title: Combining neural networks and tree search for task and motion planning in challenging environments

abstract: Task and motion planning subject to Linear Temporal Logic (LTL) specifications in complex, dynamic environments requires efficient exploration of many possible future worlds. Model-free reinforcement learning has proven successful in a number of challenging tasks, but shows poor performance on tasks that require long-term planning. In this work, we integrate Monte Carlo Tree Search with hierarchical neural net policies trained on expressive LTL specifications. We use reinforcement learning to find deep neural networks representing both low-level control policies and task-level “option policies” that achieve high-level goals. Our combined architecture generates safe and responsive motion plans that respect the LTL constraints. We demonstrate our approach in a simulated autonomous driving setting, where a vehicle must drive down a road in traffic, avoid collisions, and navigate an intersection, all while obeying rules of the road.


Chapter 1 Recommender Systems : Introduction and Challenges


title: Analysis and optimization of software requirements prioritization techniques

abstract: Prioritizing requirements helps the project team to understand which requirements are most important and most urgent. Based on this finding a software engineer can decide what to develop/implement in the first release and what on the coming releases. Prioritization is also a useful activity for decision making in other phases of software engineering like development, testing, and implementation. There are a number of techniques available to prioritize the requirements with their associated strengths and limitations. In this paper we will examine state of the art techniques and analyze their applicability on software requirements domain. At the end we present a framework that will help the software engineer of how to perform prioritization process by combining existing techniques and approaches.


title: On the Limitations of Representing Functions on Sets

abstract: Recent work on the representation of functions on sets has considered the use of summation in a latent space to enforce permutation invariance. In particular, it has been conjectured that the dimension of this latent space may remain fixed as the cardinality of the sets under consideration increases. However, we demonstrate that the analysis leading to this conjecture requires mappings which are highly discontinuous and argue that this is only of limited practical use. Motivated by this observation, we prove that an implementation of this model via continuous mappings (as provided by e.g. neural networks or Gaussian processes) actually imposes a constraint on the dimensionality of the latent space. Practical universal function representation for set inputs can only be achieved with a latent dimension at least the size of the maximum number of input elements.


title: Efficient Large-Scale Multi-Modal Classification

abstract: While the incipient internet was largely text-based, the modern digital world is becoming increasingly multi-modal. Here, we examine multi-modal classification where one modality is discrete, e.g. text, and the other is continuous, e.g. visual representations transferred from a convolutional neural network. In particular, we focus on scenarios where we have to be able to classify large quantities of data quickly. We investigate various methods for performing multi-modal fusion and analyze their trade-offs in terms of classification accuracy and computational efficiency. Our findings indicate that the inclusion of continuous information improves performance over text-only on a range of multi-modal classification tasks, even with simple fusion methods. In addition, we experiment with discretizing the continuous features in order to speed up and simplify the fusion process even further. Our results show that fusion with discretized features outperforms text-only classification, at a fraction of the computational cost of full multi-modal fusion, with the additional benefit of improved interpretability.


AN EFFICIENT HEURISTIC BASED TREE CLASSIFIER MODEL FOR MEDICAL DISEASE DIAGNOSIS


title: Disentangling Correlated Speaker and Noise for Speech Synthesis via Data Augmentation and Adversarial Factorization

abstract: To leverage crowd-sourced data to train multi-speaker text-to-speech (TTS) models that can synthesize clean speech for all speakers, it is essential to learn disentangled representations which can independently control the speaker identity and background noise in generated signals. However, learning such representations can be challenging, due to the lack of labels describing the recording conditions of each training example, and the fact that speakers and recording conditions are often correlated, e.g. since users often make many recordings using the same equipment. This paper proposes three components to address this problem by: (1) formulating a conditional generative model with factorized latent variables, (2) using data augmentation to add noise that is not correlated with speaker identity and whose label is known during training, and (3) using adversarial factorization to improve disentanglement. Experimental results demonstrate that the proposed method can disentangle speaker and noise attributes even if they are correlated in the training data, and can be used to consistently synthesize clean speech for all speakers. Ablation studies verify the importance of each proposed component.


title: Combining analyses, combining optimizations

abstract: Modern optimizing compilers use several passes over a program's intermediate representation to generate good code. Many of these optimizations exhibit a phase-ordering problem. Getting the best code may require iterating optimizations until a fixed point is reached. Combining these phases can lead to the discovery of more facts about the program, exposing more opportunities for optimization. This article presents a framework for describing optimizations. It shows how to combine two such frameworks and how to reason about the properties of the resulting framework. The structure of the frame work provides insight into when a combination yields better results. To make the ideas more concrete, this article presents a framework for combining constant propagation, value numbering, and unreachable-code elimination. It is an open question as to what other frameworks can be combined in this way.


title: Axon overproduction and elimination in the corpus callosum of the developing rhesus monkey

abstract: We have studied the cytological and quantitative aspects of axon addition and elimination in the corpus callosum of the developing rhesus monkey. Electron microscopic analysis reveals that during fetal development the number of callosal axons increases from 4 million at embryonic day 65 (E65) to 188 million at birth (E 165). Thus, the number of callosal axons in newborn monkeys exceeds the number present in the adult (an average of 56 million; LaMantia and Rakic, 1990a) by at least 3.5 times. Although there is some variability among the 11 fetal and newborn monkeys examined, there appears to be a progressive increase in the total number of callosal axons from midgestation through birth. The presence and numbers of growth cones from E65 through birth suggests that axon addition occurs exclusively during this period. There is no ultrastructural or quantitative indication of postnatal axon addition. After birth, about 70% of the axons in the callosum are eliminated in 2 phases. During the first phase, which includes the first 3 postnatal weeks, approximately 80 million axons are lost at an estimated rate of 4.4 million/d or 50/sec. During the second phase, which continues for the following 3 months, an additional 50 million axons are eliminated at a rate of 0.5 million/d or 5/sec until the adult value is reached. A discontinuous distribution of different classes of axons along the anterior-posterior axis of the tract reminiscent of the pattern seen in the adult is detectable before the onset of the first phase of axon elimination. Since the basic topography and terminal field patterns of callosal projections are well established before birth in all regions of the monkey cortex examined so far (Goldman-Rakic et al., 1983; Killackey and Chalupa, 1986; Dehay et al., 1988; Schwartz and Goldman-Rakic, 1990), we conclude that the massive postnatal elimination of callosal axons described here is unlikely to play a significant role in the development of discretely patterned callosal projection zones or their columnar terminations. The coincidence of axon elimination and the increase in synaptic density throughout the primate cerebral cortex during the first 6 postnatal months (Rakic et al., 1986), however, suggests that supernumerary axons may be lost during a process that results in the local proliferation of synapses from a subset of initial interhemispheric projections.


title: VEEVVIE: Visual Explorer for Empirical Visualization, VR and Interaction Experiments

abstract: Empirical, hypothesis-driven, experimentation is at the heart of the scientific discovery process and has become commonplace in human-factors related fields. To enable the integration of visual analytics in such experiments, we introduce VEEVVIE, the Visual Explorer for Empirical Visualization, VR and Interaction Experiments. VEEVVIE is comprised of a back-end ontology which can model several experimental designs encountered in these fields. This formalization allows VEEVVIE to capture experimental data in a query-able form and makes it accessible through a front-end interface. This front-end offers several multi-dimensional visualization widgets with built-in filtering and highlighting functionality. VEEVVIE is also expandable to support custom experimental measurements and data types through a plug-in visualization widget architecture. We demonstrate VEEVVIE through several case studies of visual analysis, performed on the design and data collected during an experiment on the scalability of high-resolution, immersive, tiled-display walls.


title: Developing a Maturity Model for Product-Service Systems in Manufacturing Enterprises

abstract: The constantly rising fraction of industrial services reveals the structural economic change that is changing the manufacturing industry landscape. Manufacturing firms have more and more problems to find the appropriate information technology (IT) solution for planning and execution as they are confronted with the strategic challenge of reducing operating costs while at the same time meeting ever-increasing industrial service demands. Despite the existence of some standardization efforts dealing with product-related services, there is no common understanding of product-service systems and the corresponding processes and IT systems among manufacturing companies. This paper addresses this need by developing a maturity model capturing the key requirements for the information systems (IS) support of product-service systems based on a multiple case study. Further, we confronted these requirements with scientifically recognized maturity models and standard specifications developed by the German Standards Institute. The development of the maturity model is grounded in the design science research approach and is evaluated by managerial experts.


title: Proposed embedded security framework for Internet of Things (IoT)

abstract: IoT is going to be an established part of life by extending the communication and networking anytime, anywhere. Security requirements for IoT will certainly underline the importance of properly formulated, implemented, and enforced security policies throughout their life-cycle. This paper gives a detailed survey and analysis of embedded security, especially in the area of IoT. Together with the conventional security solutions, the paper highlights the need to provide in-built security in the device itself to provide a flexible infrastructure for dynamic prevention, detection, diagnosis, isolation, and countermeasures against successful breaches. Based on this survey and analysis, the paper defines the security needs taking into account computational time, energy consumption and memory requirements of the devices. Finally, this paper proposes an embedded security framework as a feature of software/hardware co-design methodology.


title: Exploring Think-Alouds in Usability Testing: An International Survey

abstract: Research problem: The study explored think-aloud methods usage within usability testing by examining the following questions: How, and why is the think-aloud method used? What is the gap between theory and practice? Where does this gap occur? Literature review: The review informed the survey design. Usability research based on field studies and empirical tests indicates that variations in think-aloud procedures may reduce test reliability. The guidance offered on think-aloud procedures within a number of handbooks on usability testing is also mixed. This indicates potential variability in practice, but how much and for what reasons is unknown. Methodology: An exploratory, qualitative survey was conducted using a web-based questionnaire (during November-December 2010). Usability evaluators were sought via emails (sent to personal contacts, usability companies, conference attendees, and special interest groups) to be cascaded to the international community. As a result we received 207 full responses. Descriptive statistics and thematic coding were used to analyze the data sets. Results: Respondents found the concurrent technique particularly suited usability testing as it was fast, easy for users to relate to, and requires limited resources. Divergent practice was reported in terms of think-aloud instructions, practice, interventions, and the use of demonstrations. A range of interventions was used to better understand participant actions and verbalizations, however, respondents were aware of potential threats to test reliability, and took steps to reduce this impact. Implications: The reliability considerations underpinning the classic think-aloud approach are pragmatically balanced against the need to capture useful data in the time available. A limitation of the study is the focus on the concurrent method; other methods were explored but the differences in application were not considered. Future work is needed to explore the impact of divergent use of think-aloud instructions, practice tasks, and the use of demonstrations on test reliability.


Designing Mazes for 2 D Games by Artificial Ant Colony Algorithm


title: Convergence Problems with Generative Adversarial Networks (GANs)

abstract: Generative adversarial networks (GANs) are a novel approach to generative modelling, a task whose goal it is to learn a distribution of real data points. They have often proved difficult to train: GANs are unlike many techniques in machine learning, in that they are best described as a two-player game between a discriminator and generator. This has yielded both unreliability in the training process, and a general lack of understanding as to how GANs converge, and if so, to what. The purpose of this dissertation is to provide an account of the theory of GANs suitable for the mathematician, highlighting both positive and negative results. This involves identifying the problems when training GANs, and how topological and game-theoretic perspectives of GANs have contributed to our understanding and improved our techniques in recent years.


title: Refractive Epipolar Geometry for Underwater Stereo Matching

abstract: A fundamental component of stereo vision is that of epipolar geometry. It tells us that the corresponding point of a pixel in one image is restricted to a line in another image. This constraint reduces both the complexity of finding stereo correspondences and also the chance of making incorrect matches. When a refractive surface is introduced, which is in underwater imaging, this constraint no longer holds. Instead, the corresponding point of a pixel in one image is now restricted to a curve, not a line. In this paper we investigate the impact of a planar refractive interface has on underwater imaging and stereo matching. We address the issue of 3D point projection in a refractive medium, including cases where the refractive interface is not parallel with the camera's imaging plane. A novel method for calibrating the parameters of a planar refractive interface in the local image space is proposed. We show how to compute the refractive epipolar curve for a pixel, which allows us to generate a matching cost volume that compensates for the effects of refraction. Our experimental results show that our new approach can significantly improve the results of underwater stereo matching over previous approaches.


title: Perceived value, transaction cost, and repurchase-intention in online shopping: A relational exchange perspective

abstract: This study proposes a framework to understand the impacts of both value-related and transaction cost-related factors on repurchase intention from online shoppers' perspective. In particular, this study defines the construct of cost as a three-component conceptualization (i.e., information searching cost, moral hazard cost, and specific asset investment). Based upon empirical evidence on a survey of 887 online shoppers, this study shows that consumers' perceived value and each cost component are positively related to repurchase intention. Importantly, information searching cost exerts the most significant influence on repurchase intentions among the four relational factors. The result suggests that providing sufficient cues to reduce consumers' information searching cost is the core element of repurchase intention formation. This article closes with theoretical and practical implications.


title: Fast and Space-Efficient Entity Linking for Queries

abstract: Entity linking deals with identifying entities from a knowledge base in a given piece of text and has become a fundamental building block for web search engines, enabling numerous downstream improvements from better document ranking to enhanced search results pages. A key problem in the context of web search queries is that this process needs to run under severe time constraints as it has to be performed before any actual retrieval takes place, typically within milliseconds. In this paper we propose a probabilistic model that leverages user-generated information on the web to link queries to entities in a knowledge base. There are three key ingredients that make the algorithm fast and space-efficient. First, the linking process ignores any dependencies between the different entity candidates, which allows for a O(k2) implementation in the number of query terms. Second, we leverage hashing and compression techniques to reduce the memory footprint. Finally, to equip the algorithm with contextual knowledge without sacrificing speed, we factor the distance between distributional semantics of the query words and entities into the model. We show that our solution significantly outperforms several state-of-the-art baselines by more than 14% while being able to process queries in sub-millisecond times---at least two orders of magnitude faster than existing systems.


2-D shape blending: an intrinsic solution to the vertex path problem


title: Manipulating mindset to positively influence introductory programming performance

abstract: Introductory programming classes are renowned for their high dropout rates. The authors propose that this is because students learn to adopt a fixed mindset towards programming. This paper reports on a study carried out with an introductory programming class, based on Dweck's mindset research. Combinations of three interventions were carried out: tutors taught mindset to students; growth mindset feedback messages were given to students on their work; and, when stuck, students were encouraged to use a crib sheet with pathways to solve problems. The study found that the mixture of teaching mindset and giving mindset messages on returned work resulted in a significant change in mindset and a corresponding significant change in test scores - improvements in test scores were found in a class test given immediately after the six-week intervention and at the end-of-year exam. The authors discuss the results and the strengths and weaknesses of the study.


title: Doppio: A Reconfigurable Dual-Face Smartwatch for Tangible Interaction

abstract: Doppio is a reconfigurable smartwatch with two touch sensitive display faces. The orientation of the top relative to the base and how the top is attached to the base, creates a very large interaction space. We define and enumerate possible configurations, transitions, and manipulations in this space. Using a passive prototype, we conduct an exploratory study to probe how people might use this style of smartwatch interaction. With an instrumented prototype, we conduct a controlled experiment to evaluate the transition times between configurations and subjective preferences. We use the combined results of these two studies to generate a set of characteristics and design considerations for applying this interaction space to smartwatch applications. These considerations are illustrated with a proof-of-concept hardware prototype demonstrating how Doppio interactions can be used for notifications, private viewing, task switching, temporary information access, application launching, application modes, input, and sharing the top.


title: Feedback learning particle swarm optimization

abstract: Abstract: In this paper, a feedback learning particle swarm optimization algorithm with quadratic inertia weight (FLPSO-QIW) is developed to solve optimization problems. The proposed FLPSO-QIW consists of four steps. Firstly, the inertia weight is calculated by a designed quadratic function instead of conventional linearly decreasing function. Secondly, acceleration coefficients are determined not only by the generation number but also by the search environment described by each particle's history best fitness information. Thirdly, the feedback fitness information of each particle is used to automatically design the learning probabilities. Fourthly, an elite stochastic learning (ELS) method is used to refine the solution. The FLPSO-QIW has been comprehensively evaluated on 18 unimodal, multimodal and composite benchmark functions with or without rotation. Compared with various state-of-the-art PSO algorithms, the performance of FLPSO-QIW is promising and competitive. The effects of parameter adaptation, parameter sensitivity and proposed mechanism are discussed in detail.


title: A game-based abstraction-refinement framework for Markov decision processes

abstract: In the field of model checking, abstraction refinement has proved to be an extremely successful methodology for combating the state-space explosion problem. However, little practical progress has been made in the setting of probabilistic verification. In this paper we present a novel abstraction-refinement framework for Markov decision processes (MDPs), which are widely used for modelling and verifying systems that exhibit both probabilistic and nondeterministic behaviour. Our framework comprises an abstraction approach based on stochastic two-player games, two refinement methods and an efficient algorithm for an abstraction-refinement loop. The key idea behind the abstraction approach is to maintain a separation between nondeterminism present in the original MDP and nondeterminism introduced during the abstraction process, each type being represented by a different player in the game. Crucially, this allows lower and upper bounds to be computed for the values of reachability properties of the MDP. These give a quantitative measure of the quality of the abstraction and form the basis of the corresponding refinement methods. We describe a prototype implementation of our framework and present experimental results demonstrating automatic generation of compact, yet precise, abstractions for a large selection of real-world case studies.


title: An Unusual Electrochemical Reductive Cleavage of Azo Dye into Highly Redox Active Copolymeric Aniline Derivatives on a MWCNT Modified Electrode Surface at Neutral pH and Its Electroanalytical Features

abstract: Developments of new decomposition or degradation methods of environmentally hazardous azo dyes from textile industries are very important. Usually, strong acid-based chemical/electrochemical and neutral pH-based bacterial decomposition methods were widely used. Here, we report a mild, simple, and facile electrochemical method for decomposition of azo dye (Sudan yellow; SY) into a highly redox active copolymer of polyanilines via aniline derivatives as intermediates on a MWCNT modified glassy carbon electrode (GCE/MWCNT) surface unusually in a neutral pH using phosphate buffer solution (PBS) (GCE/MWCNT@SY-CoPANIpH7). One of the intermediate products, aniline (Mw = 93 mol g–1, calculated) was identified by an in situ cyclic voltammetry-electrochemical quartz crystal balance experiment. No such SY electrochemical reaction was observed on a naked GCE surface. Physico-chemical characterizations by TEM, Raman, IR, and UV–vis spectroscopic methods supported the formation of polymeric product on MWCNT surface (GC...


Nursing Theory in Holistic Nursing Practice


title: Survival horror games - an uncanny modality

abstract: This study investigates the relationship between the perceived eeriness of a virtual character with the perception of human-likeness for some attributes of motion and sound. 100 participants were asked to rate 13 video clips of 12 different virtual characters and one human. The results indicate that attributes of motion and sound do exaggerate the uncanny phenomenon and how frightening that character is perceived to be. Strong correlations were identified for the perceived eeriness for a character with how human-like a character's voice sounded, how human-like facial expression appeared and the synchronization of the character's sound with lip movement; characters rated as the least synchronized were perceived to be the most frightening. Based on the results of this study, this paper seeks to define an initial set of hypotheses for the fear-evoking aspects of character facial rendering and vocalization in survival horror games that can be used by game designers seeking to increase the fear factor in the genre and that will form the basis of further experiments which, it is hoped, will lead to a conceptual framework for the uncanny.


title: A multiple resampling method for learning from imbalanced data sets

abstract: Resampling methods are commonly used for dealing with the class-imbalance problem. Their advantage over other methods is that they are external and thus, easily transportable. Although such approaches can be very simple to implement, tuning them most effectively is not an easy task. In particular, it is unclear whether oversampling is more effective than undersampling and which oversampling or undersampling rate should be used. This paper presents an experimental study of these questions and concludes that combining different expressions of the resampling approach is an effective solution to the tuning problem. The proposed combination scheme is evaluated on imbalanced subsets of the Reuters-21578 text collection and is shown to be quite effective for these problems.


title: Touchjacking attacks on web in android, ios, and windows phone

abstract: To make it easy for applications to interact with the Web, most mobile platforms, including Android, iOS, and Windows Phone, provide a mechanism that allows applications to embed a small but powerful browser component inside. This mechanism is called WebView in Android (it is called different names in other platforms). WebView implements a number of APIs that can be used by applications to interact with the web contents inside WebView. It has been pointed out by the previous work that malicious applications can use these APIs to attack the web contents inside WebView. Proposals are made by the previous work to fix the problems of those APIs. We have discovered that by fixing those APIs, WebView is still not secure. This is because the previous work only focuses on the APIs specifically designed for WebView; they have overlooked the APIs that WebView inherits from its super classes. These APIs are designed for the general-purposed user interface (UI) components, and they seem to pose no risk to those components; however, the combination of these APIs with the Web has led to new risks. We have identified several attacks based on these APIs. Our attacks are called Touchjacking attacks. They treat WebView as a blackbox, i.e., they do not use the APIs that are designed specifically for WebView; instead, they only use the inherited APIs. Through these APIs, malicious applications can attack the web contents inside WebView. The impact of the attacks is quite significant, as all the platforms that we have studied, including Android, iOS, and Windows Phone, are vulnerable to these attacks.


title: DiedricAR: a mobile augmented reality system designed for the ubiquitous descriptive geometry learning

abstract: This article presents a mobile Augmented Reality system, called DiedricAR, aimed at the learning of Descriptive Geometry. Thanks to its ability to recreate virtual models in real space, Augmented Reality is a technology suitable for making Descriptive Geometry comprehension and interpretation easier. The DiedricAR application allows students to learn in autonomously way by using their own mobile devices (smartphones and tablets), that work as Augmented Reality displays over training material (DiedricAR exercise workbook) specially designed for the new learning model defined by the European Higher Education System. Compared to some of the existing Augmented Reality systems used to learn Descriptive Geometry, DiedricAR offers the advantage of being specifically developed for mobile devices giving the students the possibility of using ubiquitous learning to its ultimate extent by interacting with the didactical content (i.e. showing the desired intermediate step when solving dihedral exercises). The presentation of DiedricAR is completed by exploring some key items such as the potential benefits for students' spatial ability, the relationship between application design and user experience, and software performance on several mobile devices.


title: Mechanical Buckling: Mechanics, Metrology, and Stretchable Electronics

abstract: Mechanical buckling usually means catastrophic failure in structural mechanics systems. However, controlled buckling of thin films on compliant substrates has been used to advantage in diverse fields such as micro-/nanofabrication, optics, bioengineering, and metrology as well as fundamental mechanics studies. In this Feature Article, a mechanical buckling model is presented, which sprang, in part, from the buckling study of high-quality, single-crystalline nanomaterials. To check the mechanical-buckling phenomenon down to the nano-/molecular scale, well-aligned single-walled carbon nanotube arrays and cross linked carbon-based monolayers are transferred from growth substrate onto elastomeric substrate and then they are buckled into well-defined shapes that are amenable to quantitative analysis. From this nano- or molecular-scale buckling, it is shown that the mechanical moduli of nanoscale materials can easily be determined, even using a model based on continuum mechanics. In addition, buckling phenomena can be utilized for the determination of mechanical moduli of organic functional materials such as poly(3-hexylthiophene) (P3HT) and P3HT/6,6-phenyl-C61-butyric acid methyl ester (PCBM) composite, which are widely used for organic transistors and organic photovoltaics. The results provide useful information for the realization of flexible and/or stretchable organic electronics. Finally, the fabrication and applications of “wavy, stretchable” single-crystal Si electronics on elastomeric substrates are demonstrated.


title: Effects of Contrasting Climate and Paclobutrazol on the Growth and Water Use of Two Container-Grown Landscape Plants

abstract: Abstract A study was conducted under contrasting climates [southwestern Georgia (humid, temperate) and central Arizona (arid, Sonoran desert)] to evaluate the effects of paclobutrazol on the growth and water use of container-grown Feijoa Sellowiana O. Berg and Ligustrum japonicum Thunb. Plants grown in Georgia were taller, had approximately twice as many leaves and five times more canopy leaf area, a lower shoot:root ratio, and generally higher water use compared to plants grown in Arizona. Differences in meteorological factors such as higher maximum and minimum temperatures and increased solar radiation in Arizona compared to Georgia were coincident with smaller plant size and lower water use of plants grown in Arizona compared to those grown in Georgia. Paclobutrazol inhibited height growth of plants grown in Arizona; however, this growth inhibition was not affected by application concentration. In Georgia, paclobutrazol inhibited height growth of all plants; however, Ligustrum treated with 250 ppm pacl...


LED BASED VISIBLE LIGHT COMMUNICATION: TECHNOLOGY, APPLICATIONS AND CHALLENGES – A SURVEY


title: Evaluation of Docker containers based on hardware utilization

abstract: Docker is an open platform for developers and system administrators to build, ship, and run distributed applications using Docker Engine, a portable, lightweight runtime and packaging tool, and Docker Hub, a cloud service for sharing applications and automating workflows. The main advantage is that, Docker can get code tested and deployed into production as fast as possible. Different applications can be run over Docker containers with language independency. In this paper the performance of these Docker containers are evaluated based on their system performance. That is based on system resource utilization. Different benchmarking tools are used for this. Performance based on file system is evaluated using Bonnie++. Other system resources such as CPU utilization, memory utilization etc. are evaluated based on the benchmarking code (using psutil) developed using python. Detail results obtained from all these tests are also included in this paper. The results include CPU utilization, memory utilization, CPU count, CPU times, Disk partition, network I/O counter etc.


title: Cognitive sophistication does not attenuate the bias blind spot.

abstract: The so-called bias blind spot arises when people report that thinking biases are more prevalent in others than in themselves. Bias turns out to be relatively easy to recognize in the behaviors of others, but often difficult to detect in one’s own judgments. Most previous research on the bias blind spot has focused on bias in the social domain. In 2 studies, we found replicable bias blind spots with respect to many of the classic cognitive biases studied in the heuristics and biases literature (e.g., Tversky & Kahneman, 1974). Further, we found that none of these bias blind spots were attenuated by measures of cognitive sophistication such as cognitive ability or thinking dispositions related to bias. If anything, a larger bias blind spot was associated with higher cognitive ability. Additional analyses indicated that being free of the bias blind spot does not help a person avoid the actual classic cognitive biases. We discuss these findings in terms of a generic dual-process theory of cognition.


A Hybrid Approach for Music Recommendation


title: Auto-tuning active queue management

abstract: Active queue management (AQM) algorithms preemptively drop packets to prevent unnecessary delays through a network while keeping utilization high. Many AQM ideas have been proposed, but none have been widely adopted because these rely on pre-specification or pre-tuning of parameters and thresholds that do not necessarily adapt to dynamic network conditions. We develop an AQM algorithm that relies only on network runtime measurements and a natural threshold, the knee on the delay-utilization curve. We call our AQM algorithm Delay Utilization Knee (DUK) based on its key characteristic of keeping the system operating at the knee of the delay-utilization curve. We implement and evaluate DUK in the Linux kernel in a testbed, that we build, and in the ns-3 network simulator. We find that DUK can attain reduced queuing delay and reduced flow completion times compared to other algorithms with virtually no reduction in link utilization under varying network conditions.


title: Marketers Understanding Engineers and Engineers Understanding Marketers: The Opportunities and Constraints of a Cross-Discipline Course Using 3D Printing to Develop Marketable Innovations.

abstract: Marketers are criticized for not understanding the steps in the engineering research and development process and the challenges of manufacturing a new product at a profit. Engineers are criticized for not considering the marketability of and customer interest in such a product during the planning stages. With the development of 3D printing, rapid prototyping at a reasonable cost may help to overcome the “sound barriers” between the two disciplines. We report on how a cross-discipline class team, using the stage gate model taught by an engineering professor and a marketing professor, helped both groups of students acquire cross-functional knowledge of marketing and engineering design to hear the voice of the customer while creating and prototyping an innovative new product. We provide in detail the work students completed at each stage and explain how developing rough prototypes and testing customer reaction to them lead to 3D printing of an alpha prototype. With an alpha prototype, which looks and functio...


title: Violations of Implicit Theories and the Sense of Prediction and Control: Implications for Motivated Person Perception

abstract: Beginning with the assumption that implicit theories of personality are crucial tools for understanding social behavior, the authors tested the hypothesis that perceivers would process person information that violated their predominant theory in a biased manner. Using an attentional probe paradigm (Experiment 1) and a recognition memory paradigm (Experiment 2), the authors presented entity theorists (who believe that human attributes are fixed) and incremental theorists (who believe that human attributes are malleable) with stereotype-relevant information about a target person that supported or violated their respective theory. Both groups of participants showed evidence of motivated, selective processing only with respect to theory-violating information. In Experiment 3, the authors found that after exposure to theory-violating information, participants felt greater anxiety and worked harder to reestablish their sense of prediction and control mastery. The authors discuss the epistemic functions of implicit theories of personality and the impact of violated assumptions.


title: Accurate Area, Time and Power Models for FPGA-Based Implementations

abstract: This paper presents accurate area, time, power estimation models for implementations using FPGAs from the Xilinx Virtex-2Pro family (Deng et al. 2008). These models are designed to facilitate efficient design space exploration in an automated algorithm-architecture codesign framework. Detailed models for estimating the number of slices, block RAMs and 18×18-bit multipliers for fixed point and floating point IP cores have been developed. These models are also utilized to develop power models that consider the effect of logic power, signal power, clock power and I/O power. Timing models have been developed to predict the latency of the fixed point and floating point IP cores. In all cases, the model coefficients have been derived by using curve fitting or regression analysis. The modeling error is quite small for single IP cores; the error for the area estimate, for instance, is on the average 0.95%. The error for fairly large examples such as floating point implementation of 8-point FFTs is also quite small; it is 1.87% for estimation of number of slices and 3.48% for estimation of power consumption. The proposed models have also been integrated into a hardware-software partitioning tool to facilitate design space exploration under area and time constraints.


title: A New Subtype of Lichen Planopilaris Affecting Vellus Hairs and Clinically Mimicking Androgenetic Alopecia.

abstract: BACKGROUND ::: Lichen planopilaris (LPP) is a follicular variant of lichen planus. A new subtype of LPP mimicking androgenetic alopecia (AGA) may be misdiagnosed. Inappropriate medical therapy or hair transplantation may exacerbate this subtype. ::: ::: ::: OBJECTIVE ::: To introduce clinicopathologic findings of a new subtype of LPP that selectively affects vellus hair in the pattern of AGA. ::: ::: ::: MATERIALS AND METHODS ::: In a cross-sectional study, 433 (66.6%) men and 217 (33.4%) women with alopecia who were candidates of medical treatment or hair transplantation were included. Gross and microscopic attributes of their diseases were investigated. ::: ::: ::: RESULTS ::: Among the total of 650 patients, 58 (8.9%; 95% confidence interval, 6.7%-11.1%) patients, including 52 women and 6 men, had LPP. We identified a distinct category of LPP presenting with diffuse hair loss in the pattern of AGA with predominant terminal hair, significant decrease in vellus hair, and minute punctuate scars in histopathology. Vellus hair follicles were the main sites of involvement. Perifollicular fibrosis and mild fibrosis with lichenoid lymphocytic infiltration around infundibular area of vellus hair follicles were present. ::: ::: ::: CONCLUSION ::: There is a new form of LPP with clinical features similar to AGA. This form is histopathologically similar to LPP but selectively affects vellus hair follicles.


title: Terahertz Silicon–BCB–Quartz Dielectric Waveguide: An Efficient Platform for Compact THz Systems

abstract: A silicon–BCB–quartz (SBQ) terahertz (THz) dielectric waveguide is proposed, investigated, fabricated, and verified experimentally. In this structure, silicon and quartz are bonded using a thin adhesive layer, benzocyclobutene (BCB). The fabrication process of this new waveguide, which is very simple and can be performed using basic clean room facilities, is presented. To demonstrate the performance of the proposed waveguide structure, a number of samples are designed, fabricated, and measured for the 500–580 GHz frequency range. Using the proposed structure, a THz waveguide with losses as low as 0.026 $\frac{\text{dB}}{\lambda _{0}}$ is achieved for the mentioned frequency range. Moreover, an SBQ bend is designed, fabricated, and the measurement results are presented. The proposed structure is low loss, efficient, easy-to-fabricate, and presents a platform for numerous dielectric-based THz passive and active devices.


net Wireless Fetal Monitoring


title: STRESS FRACTURE OF THE PUBIC RAMUS IN FEMALE RECRUITS

abstract: During a four-month period we observed 12 stress fractures of the inferior pubic ramus in 11 military recruits undergoing basic training. Eleven of these were in women. This high number was considered to be caused by the introduction of mixed training of male and female recruits; this forces women to increase their stride length when marching. The presenting symptom was chronic groin pain which failed to settle with rest, and the clinical diagnoses were confirmed by radiographs in all but one patient who was diagnosed by 99m Tc bone scanning. Since the required stride length has been reduced there have been no new cases of stress fracture of the pelvis.


Detection and Recognition of Text Embedded in Online Images via Neural Context Models


title: The What, How, Why, and Where of Self-Construal

abstract: Since the publication of Markus and Kitayama’s pivotal article on culture and the self, the concepts of independent, relational, and interdependent self-construal have become important constructs in cultural psychology and research on the self. The authors review the history of these constructs, their measurement and manipulation, and their roles in cognition, emotion, motivation, and social behavior. They make suggestions for future research and point to problems still to be sorted out. Researchers interested in these constructs have many opportunities to make important contributions to the literature in a variety of fields, including health psychology, education, counseling, and international relations.


title: Literature Survey on Interplay of Topics, Information Diffusion and Connections on Social Networks

abstract: Researchers have attempted to model information diffusion and topic trends and lifecycle on online social networks. They have investigated the role of content, social connections and communities, familiarity and behavioral similarity in this context. The current article presents a survey of representative models that perform topic analysis, capture information diffusion, and explore the properties of social connections in the context of online social networks. The article concludes with a set of outlines of open problems and possible directions of future research interest. This article is intended for researchers to identify the current literature, and explore possibilities to improve the art.


title: Various stem cells in acupuncture meridians and points and their putative roles

abstract: Traditional Chinese and Korean medicine uses various manipulations on acupuncture points/acupoints that are located along imaginary lines on the surface of a human body, which are called 'meridians'. Acupuncture has been used from the ancient time till now to cure various diseases, including for the purpose of regenerative medicine. In various studies, meridians are alternatively called as Bong-Han ducts, primo vessels, or hyaluronic-acid rich ducts, while acupoints are called Bong-Han corpuscles, primo nodes, or hyaluronic-acid rich nodes. Meridians and acupuncture points form a system that is now called primo vascular system (PVS), which is claimed to contain various kinds of stem cells. The stem cell size is between 1-5 microns. The smallest is the primo microcells that have a putative role in regeneration. Other stem cells are adult pluripotent and hematopoietic stem cells that play a role in extra bone marrow hematopoiesis. The presence of PVS has been reproduced by many studies. However, the various stem cells need further studies to prove their existence and function, and harvesting PVS to isolate the stem cells might harm the health of the donor.


title: Constrained band selection for hyperspectral imagery

abstract: Constrained energy minimization (CEM) has shown effective in hyperspectral target detection. It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures. This paper explores this idea for band selection and develops a new approach to band selection, referred to as constrained band selection (CBS) for hyperspectral imagery. It interprets a band image as a desired target signature vector while considering other band images as unknown signature vectors. As a result, the proposed CBS using the concept of the CEM to linearly constrain a band image, while also minimizing band correlation or dependence provided by other band images, is referred to as CEM-CBS. Four different criteria referred to as Band Correlation Minimization (BCM), Band Correlation Constraint (BCC), Band Dependence Constraint (BDC), and Band Dependence Minimization (BDM) are derived for CEM-CBS.. Since dimensionality resulting from conversion of a band image to a vector may be huge, the CEM-CBS is further reinterpreted as linearly constrained minimum variance (LCMV)-based CBS by constraining a band image as a matrix where the same four criteria, BCM, BCC, BDC, and BDM, can be also used for LCMV-CBS. In order to determine the number of bands required to select p, a recently developed concept, called virtual dimensionality, is used to estimate the p. Once the p is determined, a set of p desired bands can be selected by the CEM/LCMV-CBS. Finally, experiments are conducted to substantiate the proposed CEM/LCMV-CBS four criteria, BCM, BCC, BDC, and BDM, in comparison with variance-based band selection, information divergence-based band selection, and uniform band selection.


title: A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer

abstract: Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.


title: Interactive Dynamics for Large Virtual Reality Applications

abstract: Recent commercial virtual reality shows have become very demanding in terms of interactive credibility and visual realism. In an effort to push further the immersive quality and the sense of ’being there’ in the most recent VR production of the Foundation of the Hellenic World “A Walk through Ancient Olympia”, the user, apart from virtually visiting the historical site, becomes an interacting part of the edutainment activity. Described in this paper is a new script-controlled generic dynamics system devised for heavy interactive VR applications and used in the above commercial production, which utilises the notion of force-fields and also simulates aerodynamic effects in a real-time, computationally efficient manner. The impact of the interactive dynamics system on the usability and virtual presence is also discussed, in the context of a fully immersive stereoscopic surround-screen virtual reality environment and a role-playing interactive show.