title: What determines corporate transparency

abstract: We investigate corporate transparency, defined as the availability of firm-specific information to those outside publicly traded firms, and viewed as the joint output of multi-faceted systems whose components collectively produce, gather, validate and disseminate information to market participants. We factor analyze an extensive range of measures capturing countries' firm-specific information environments, and isolate two factors interpreted as financial transparency and governance transparency. We investigate whether these factors vary with the countries' legal/judicial regimes and political economies. Our main multivariate result is that the governance transparency factor is primary related to a country's legal/judicial regime, while the financial transparency factor is primarily related to political economy.


The Behavioural Paths to Wellbeing: An Exploratory Study to Distinguish Between Hedonic and Eudaimonic Wellbeing From an Activity Perspective


title: FROM INTENTION TO MOTIVATION: DEVELOPING A MOTIVATION-BASED MODEL OF IOS IMPLEMENTATION

abstract: Interorganizational systems (IOS) are increasingly being implemented throughout supply chains, attracting the attention of many researchers seeking to understand this phenomenon. We argue that the activities performed during the implementation process and the post-adoption outcomes associated with these systems can be traced back to the principle motivation underpinning the adoption decision. In developing our theory, we critique the predominant technology acceptance and diffusion of innovations based approaches as they apply to the implementation of IOS technology, and present an alternative theoretical model based on a firm’s motivational intent. We argue that differences in motivational intentions largely explain system implementation processes and outcomes. Theory-based motivational scenarios are presented to make specific predictions regarding the impact of motivations on which system investigation and development activities are performed, and their short and long-term implications for an organization.


Moral Values and The Fourth Industrial Revolution


title: Pomegranate: fast and flexible probabilistic modeling in python

abstract: We present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling encompasses a wide range of methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented in pomegranate are general mixture models, hidden Markov models, and Bayesian networks. A primary focus of pomegranate is to abstract away the complexities of training models from their definition. This allows users to focus on specifying the correct model for their application instead of being limited by their understanding of the underlying algorithms. An aspect of this focus involves the collection of additive sufficient statistics from data sets as a strategy for training models. This approach trivially enables many useful learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider how to partition data or modify the algorithms to handle these tasks themselves. pomegranate is written in Cython to speed up calculations and releases the global interpreter lock to allow for built-in multithreaded parallelism, making it competitive with---or outperform---other implementations of similar algorithms. This paper presents an overview of the design choices in pomegranate, and how they have enabled complex features to be supported by simple code.


title: Neural correlates of trust

abstract: Trust is a critical social process that helps us to cooperate with others and is present to some degree in all human interaction. However, the underlying brain mechanisms of conditional and unconditional trust in social reciprocal exchange are still obscure. Here, we used hyperfunctional magnetic resonance imaging, in which two strangers interacted online with one another in a sequential reciprocal trust game while their brains were simultaneously scanned. By designing a nonanonymous, alternating multiround game, trust became bidirectional, and we were able to quantify partnership building and maintenance. Using within- and between-brain analyses, an examination of functional brain activity supports the hypothesis that the preferential activation of different neuronal systems implements these two trust strategies. We show that the paracingulate cortex is critically involved in building a trust relationship by inferring another person's intentions to predict subsequent behavior. This more recently evolved brain region can be differently engaged to interact with more primitive neural systems in maintaining conditional and unconditional trust in a partnership. Conditional trust selectively activated the ventral tegmental area, a region linked to the evaluation of expected and realized reward, whereas unconditional trust selectively activated the septal area, a region linked to social attachment behavior. The interplay of these neural systems supports reciprocal exchange that operates beyond the immediate spheres of kinship, one of the distinguishing features of the human species.


title: Smart garbage monitoring and clearance system using internet of things

abstract: The increase in population, has led to tremendous degradation in the state of affairs of hygiene with respect to waste management system. The spillover of waste in civic areas generates the polluted condition in the neighboring areas. It may aggravate numerous severe diseases for the nearby people. This will humiliate the appraisal of the affected area. For eliminating or mitigating the garbage's and maintains the cleanness, it requires 'smartness based waste management system. This paper is proposed IOT based smart waste clean management system which checks the waste level over the dustbins by using Sensor systems. Once it detected immediately this system altered to concern authorized through GSM/GPRS. For this system used Microcontroller as an interface between the sensor system and GSM/GPRS system. To monitor and integrate an android application is developed for the desired information which is related to the various level of waste in different locations. This is ensued the greenish in the environment and support for swachh bharat for cleanness.


title: Beyond simple charts: Design of visualizations for big health data

abstract: Health data is often big data due to its high volume, low veracity, great variety, and high velocity. Big health data has the potential to improve productivity, eliminate waste, and support a broad range of tasks related to disease surveillance, patient care, research, and population health management. Interactive visualizations have the potential to amplify big data’s utilization. Visualizations can be used to support a variety of tasks, such as tracking the geographic distribution of diseases, analyzing the prevalence of disease, triaging medical records, predicting outbreaks, and discovering at-risk populations. Currently, many health visualization tools use simple charts, such as bar charts and scatter plots, that only represent few facets of data. These tools, while beneficial for simple perceptual and cognitive tasks, are ineffective when dealing with more complex sensemaking tasks that involve exploration of various facets and elements of big data simultaneously. There is need for sophisticated and elaborate visualizations that encode many facets of data and support human-data interaction with big data and more complex tasks. When not approached systematically, design of such visualizations is labor-intensive, and the resulting designs may not facilitate big-data-driven tasks. Conceptual frameworks that guide the design of visualizations for big data can make the design process more manageable and result in more effective visualizations. In this paper, we demonstrate how a framework-based approach can help designers create novel, elaborate, non-trivial visualizations for big health data. We present four visualizations that are components of a larger tool for making sense of large-scale public health data.


title: Genetic Basis of Autoantibody Positive and Negative Rheumatoid Arthritis Risk in a Multi-ethnic Cohort Derived from Electronic Health Records

abstract: Discovering and following up on genetic associations with complex phenotypes require large patient cohorts. This is particularly true for patient cohorts of diverse ancestry and clinically relevant subsets of disease. The ability to mine the electronic health records (EHRs) of patients followed as part of routine clinical care provides a potential opportunity to efficiently identify affected cases and unaffected controls for appropriate-sized genetic studies. Here, we demonstrate proof-of-concept that it is possible to use EHR data linked with biospecimens to establish a multi-ethnic case-control cohort for genetic research of a complex disease, rheumatoid arthritis (RA). In 1,515 EHR-derived RA cases and 1,480 controls matched for both genetic ancestry and disease-specific autoantibodies (anti-citrullinated protein antibodies [ACPA]), we demonstrate that the odds ratios and aggregate genetic risk score (GRS) of known RA risk alleles measured in individuals of European ancestry within our EHR cohort are nearly identical to those derived from a genome-wide association study (GWAS) of 5,539 autoantibody-positive RA cases and 20,169 controls. We extend this approach to other ethnic groups and identify a large overlap in the GRS among individuals of European, African, East Asian, and Hispanic ancestry. We also demonstrate that the distribution of a GRS based on 28 non-HLA risk alleles in ACPA+ cases partially overlaps with ACPA- subgroup of RA cases. Our study demonstrates that the genetic basis of rheumatoid arthritis risk is similar among cases of diverse ancestry divided into subsets based on ACPA status and emphasizes the utility of linking EHR clinical data with biospecimens for genetic studies.


title: Synchronization regimes in coupled noisy excitable systems.

abstract: We study synchronization regimes in a system of two coupled noisy excitable systems which exhibit excitability close to an Andronov bifurcation. The uncoupled system possesses three fixed points: a node, a saddle, and an unstable focus. We demonstrate that with an increase of coupling strength the system undergoes transitions from a desynchronous state to a train synchronization regime to a phase synchronization regime, and then to a complete synchronization regime. Train synchronization is a consequence of the existence of a saddle in the phase space. The mechanism of transitions in coupled noisy excitable systems is different from that in coupled phase-coherent chaotic systems.


title: The Impact of Structural Complexity on the Understandability of UML Statechart Diagrams

abstract: The effectiveness of current software development strategies, such as Model-Driven Development (MDD), depends largely on the quality of their primary artefacts, i.e. software models. As the standard modelling language for software systems is the Unified Modelling Language (UML), quality assurance of UML models is a major research field in Computer Science. Understandability, i.e. a model's ability to be easily understood, is one model quality property that is currently heavily under investigation. In particular, researchers are searching for the factors that determine an UML model's understandability and are looking for ways to manipulate these factors. This paper presents an empirical study investigating the effect that structural complexity has on the understandability of one particular type of UML model, i.e. the statechart diagram. Based on data collected in a family of three experiments, we have identified three dimensions of structural complexity that affect understandability: (i) the size and control flow complexity of the statechart in terms of features such as the number of states, events, guards and state transitions; (ii) the actions that are performed when entering or leaving a state; (iii) the sequence of actions that is performed while staying within a state. Based on these structural complexity dimensions we have built an understandability prediction model using a regression technique that is specifically recommended for data obtained through a repeated measures design. Our test results show that each of the underlying structural complexity dimensions has a significant impact on the understandability of a statechart diagram.


Improving Neural Networks with Dropout


Robot Arm Kinematics, Dynamics, and Control


title: Reliable method for driving events recognition

abstract: Motor vehicles greatly influence human life but are also a major cause of death and road congestion, which is an obstacle to future economic development. We believe that by learning driving patterns, useful navigation support can be provided for drivers. In this paper, we present a simple and reliable method for the recognition of driving events using hidden Markov models (HMMs), popular stochastic tools for studying time series data. A data acquisition system was used to collect longitudinal and lateral acceleration and speed data from a real vehicle in a normal driving environment. Data were filtered, normalized, segmented, and quantified to obtain the symbolic representation necessary for use with discrete HMMs. Observation sequences for training and evaluation were manually selected and classified as events of a particular type. An appropriate model size was selected, and the model was trained for each type of driving events. Observation sequences from the training set were evaluated by multiple models, and the highest probability decides what kind of driving event this sequence represents. The recognition results showed that HMMs could recognize driving events very accurately and reliably.


title: Singularity Loci of Spherical Parallel Mechanisms

abstract: This paper presents the computation and representation of the Type 2 singularity loci of symmetric spherical parallel mechanisms based on a not-well-known intuitive orientation representation. The latter, previously introduced under the name of the Tilt-and-Torsion angles, is briefly described. Then, to illustrate the approach, the two most basic spherical parallel mechanisms are considered and their Type 2 singularities are fully analyzed for various designs.


title: Shaping HTTP adaptive streams for a better user experience

abstract: With increasing audio/video service consumption through unmanaged IP networks, HTTP adaptive streaming techniques have emerged to handle bandwidth limitations and variations. But while it is becoming common to serve multiple clients in one home network, these solutions do not adequately address fine tuned quality arbitration between the multiple streams. While clients compete for bandwidth, the video suffers unstable conditions and/or inappropriate bit-rate levels. We hereby experiment a mechanism based on traffic chapping that allow bandwidth arbitration to be implemented in the home gateway, first determining desirable target bit-rates to be reached by each stream and then constraining the clients to stay within their limits. This enables the delivery of optimal quality of experience to the maximum number of users. This approach is validated through experimentation, and results are shown through a set of objective measurement criteria.


title: Data mining based analysis to explore the effect of teaching on student performance

abstract: Analysing the behaviour of student performance in classroom education is an active area in educational research. Early prediction of student performance may be helpful for both teacher and the student. However, the influencing factors of the student performance need to be identified first to build up such early prediction model. The existing data mining literature on student performance primarily focuses on student-related factors, though it may be influenced by many external factors also. Superior teaching acts as a catalyst which improves the knowledge dissemination process from teacher to the student. It also motivates the student to put more effort on the study. However, the research question, how the performance or grade correlates with teaching, is still relevant in present days. In this work, we propose a quantifiable measure of improvement with respect to the expected performance of a student. Furthermore, this study analyses the impact of teaching on performance improvement in theoretical courses of classroom-based education. It explores nearly 0.2 million academic records collected from an online system of an academic institute of national importance in India. The association mining approach has been adopted here and the result shows that confidence of both non-negative and positive improvements increase with superior teaching. This result indeed establishes the fact that teaching has a positive impact on student performance. To be more specific, the growing confidence of non-negative and positive improvements indicate that superior teaching facilitates more students to obtain either expected or better than expected grade.


title: Pervasive games in a mote-enabled virtual world using tuple space middleware

abstract: Pervasive games are a new and exciting field where the user experience benefits from the blending of real and virtual elements. Players are no longer confined to computer screens. Rather, interactions with devices embedded within the real world and physical movements become an integral part of the gaming experience. Several prototypes of pervasive games have been proposed by both industry and academia. However, in such games the issues arising from the integration of players and real world, the management of the context surrounding the players, and the need for communication and distributed coordination are often addressed in an ad-hoc fashion. Therefore, the underlying software fabric is often not reusable, ultimately slowing down the diffusion of pervasive games. In this paper we describe the design and implementation of a pervasive game on top of TinyLIME, a middleware system supporting data sharing among mobile and embedded devices. By illustrating the design of a pervasive game we developed, we argue concretely that the programming abstractions supported by TinyLIME greatly simplify the data and context management characteristics of pervasive games, and provide an effective and reusable building block for their development. TinyLIME was originally designed to support applications where mobile users collect data from sensors scattered in the physical environment. We build upon this capability to put forth a second contribution, namely, the use of wireless sensor devices (or motes) as a computing platform for pervasive games. Besides reporting physical data for the sake of the game, we use motes to store information relevant to the game plot, e.g., virtual objects. Motes are typically very small in size, and therefore can be hidden in the environment, enhancing the sense of immersion in a virtual world. To the best of our knowledge, this original use of wireless sensor devices is novel in the scientific and gaming literature. Furthermore, it is naturally supported by TinyLIME, yielding a unified programming abstraction that spans the heterogeneous gaming platform we propose.


title: A new rail-to-rail comparator with adaptive power control for low power SAR ADCs in biomedical application

abstract: In this paper, a new IV rail-to-rail comparator is presented with low noise, high speed and low power consumption. We utilize current mirrors to overcome the kickback noise. A new adaptive power control (APC) technique is also proposed to minimize the power dissipation of the comparator. Moreover, it provides an optimized and stable power dissipation irrelative to process and bias variation. A prototype IV rail-to-rail SAR ADC for biomedical application has been implemented in 0.18μm TSMC CMOS technology. It consumes 2.86 μW at 250kS/s and the figure of merit is 85.7 fJ/conversion-step. It shows that this work efficiently reduces 52% to 80% power consumption of the dynamic comparator at 500kS/s to 125kS/s.


Variations in pattern of pubertal changes in girls.


title: Applying Radio Environment Maps to Cognitive Wireless Regional Area Networks

abstract: The IEEE 802.22 wireless regional area network (WRAN) is the first worldwide commercial application of cognitive radio (CR) networks refarming the TV broadcast bands. According to US FCC's recent public notice, WRAN products are scheduled to be available for the market by February, 2009. This paper first presents a brief review of the IEEE 802.22 WRAN standardization, and then introduces the radio environment map (REM) as an innovative cost-efficient approach to developing and managing WRAN systems. REMs can provide powerful infrastructure support to the functionality of the WRAN cognitive engine (CE). The data model of the REM is presented together with an extensive discussion on how to exploit the REM for a variety of applications in WRAN systems, focusing on the REM-enabled case- and knowledge-based learning algorithms (REM-CKL) for WRAN CEs. Furthermore, REM-based radio scenario-driven testing (REM-SDT) is also presented as a viable approach to evaluating the performance of CEs. Future research topics are discussed in the final section.


title: Janus: a general purpose WebRTC gateway

abstract: This paper deals with the design and implementation of Janus, a general purpose, open source WebRTC gateway. Details will be provided on the architectural choices we took for Janus, as well as on the APIs we made available to extend and make use of it. Examples of how the gateway can be used for complex WebRTC applications are presented, together with some experimental results we collected during the development process.


title: Vesp’R: design and evaluation of a handheld AR device

abstract: This paper focuses on the design of devices for handheld spatial interaction. In particular, it addresses the requirements and construction of a new platform for interactive AR, described from an ergonomics stance, prioritizing human factors of spatial interaction. The result is a multi-configurable platform for spatial interaction, evaluated in two AR application scenarios. The user tests validate the design with regards to grip, weight balance and control allocation, and provide new insights on the human factors involved in handheld spatial interaction.


title: CiTO, the Citation Typing Ontology

abstract: CiTO, the Citation Typing Ontology, is an ontology for describing the nature of reference citations in scientific research articles and other scholarly works, both to other such publications and also to Web information resources, and for publishing these descriptions on the Semantic Web. Citation are described in terms of the factual and rhetorical relationships between citing publication and cited publication, the in-text and global citation frequencies of each cited work, and the nature of the cited work itself, including its publication and peer review status. This paper describes CiTO and illustrates its usefulness both for the annotation of bibliographic reference lists and for the visualization of citation networks. The latest version of CiTO, which this paper describes, is CiTO Version 1.6, published on 19 March 2010. CiTO is written in the Web Ontology Language OWL, uses the namespace http://purl.org/net/cito/, and is available from http://purl.org/net/cito/. This site uses content negotiation to deliver to the user an OWLDoc Web version of the ontology if accessed via a Web browser, or the OWL ontology itself if accessed from an ontology management tool such as Protégé 4 (http://protege.stanford.edu/). Collaborative work is currently under way to harmonize CiTO with other ontologies describing bibliographies and the rhetorical structure of scientific discourse.


title: Integer and Combinatorial Optimization

abstract: FOUNDATIONS. The Scope of Integer and Combinatorial Optimization. Linear Programming. Graphs and Networks. Polyhedral Theory. Computational Complexity. Polynomial-Time Algorithms for Linear Programming. Integer Lattices. GENERAL INTEGER PROGRAMMING. The Theory of Valid Inequalities. Strong Valid Inequalities and Facets for Structured Integer Programs. Duality and Relaxation. General Algorithms. Special-Purpose Algorithms. Applications of Special- Purpose Algorithms. COMBINATORIAL OPTIMIZATION. Integral Polyhedra. Matching. Matroid and Submodular Function Optimization. References. Indexes.


title: Multi-document summarization using sentence fusion for Indonesian news articles

abstract: This paper describes work on summarizing several input documents into one coherent and readable document. The steps that are discussed in this report are tokenization, POS-Tagging, building clusters of similar sentences, joining sentence by sentence fusion with word graph, extracting the result from a word graph, and in the end, sentence selection using the algorithm integer linear programming. Testing of the created system will be done manually and involves anonymous questionnaire regarding the readablility and credibility of the resulting document.


title: Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging

abstract: Motion estimation on ultrasound data is often referred to as `Speckle Tracking' in clinical environments and plays an important role in diagnosis and monitoring of cardiovascular diseases and the identification of abnormal cardiac motion. The impact of physical effects in the process of data acquisition raises many problems for conventional image processing techniques. The most significant difference to other medical data is its high level of speckle noise, which has completely different characteristics from other noise models, e.g., additive Gaussian noise. In this paper we address the problem of multiplicative speckle noise for motion estimation techniques that are based on optical flow methods and prove that the influence of this noise leads to wrong correspondences between image regions if not taken into account. To overcome these problems we propose the use of local statistics and introduce an optical flow method which uses histograms as discrete representations of local statistics for motion analysis. We show that this approach is more robust under the presence of speckle noise than classical optical flow methods.


title: An Inference-Based Approach to Recognizing Entailment

abstract: For this year's RTE challenge we have continued to pursue a (somewhat) "logical" approach to recognizing entailment, in which our system, called BLUE (Boeing Language Understanding Engine) first creates a logic-based representation of a text T and then performs simple inference (using WordNet and the DIRT inference rule database) to try and infer a hypothesis H. The overall system can be viewed as comprising of three main elements: parsing, WordNet, and DIRT, built on top of a simple baseline of bag-of-words comparison. Ablation studies suggest that WordNet substantially improves the accuracy scores, while, somewhat suprisingly, parsing and DIRT only marginally improve the accuracy scores. We illustrate and discuss these results. Overall, BLUE's reasoning is sometimes insightful but sometimes nonsensical, the primary challenges being noise in the knowledge sources, lack of world knowledge, and the difficulty of accurate syntactic and semantic analysis. Despite these challenges, we argue that forming semantic representations is a necessary first step towards the larger goal of machine reading, and worthy of further exploration. Our best scores were 61.5% (2 way), 54.7% (3 way), and F=0.29 (Search Pilot).


title: Architecture for the Internet of Things (IoT): API and Interconnect

abstract: The proposed architecture is structured into a secure API, a backbone, and separate device networks with standard interface to the backbone. The API decouples innovation of services and service logic from protocols and network elements. It also enables service portability between systems, i.e. a service may be allocated to end-systems or servers, with possible relocation and replication throughout its lifecycle. Ubiquitous service provisioning depends on interoperability, not only for provisioning of a standard QoS controlled IP bearer, but also for cross domain naming, security, mobility, multicast, location, routing and management, including fair compensation for utility provisioning. The proposed architecture not only includes these critical elements but also caters for multi-homing, mobile networks with dynamic membership, and third party persistent storage based on indirection.


title: Context-based indoor object detection as an aid to blind persons accessing unfamiliar environments

abstract: Independent travel is a well known challenge for blind or visually impaired persons. In this paper, we propose a computer vision-based indoor wayfinding system for assisting blind people to independently access unfamiliar buildings. In order to find different rooms (i.e. an office, a lab, or a bathroom) and other building amenities (i.e. an exit or an elevator), we incorporate door detection with text recognition. First we develop a robust and efficient algorithm to detect doors and elevators based on general geometric shape, by combining edges and corners. The algorithm is generic enough to handle large intra-class variations of the object model among different indoor environments, as well as small inter-class differences between different objects such as doors and elevators. Next, to distinguish an office door from a bathroom door, we extract and recognize the text information associated with the detected objects. We first extract text regions from indoor signs with multiple colors. Then text character localization and layout analysis of text strings are applied to filter out background interference. The extracted text is recognized by using off-the-shelf optical character recognition (OCR) software products. The object type, orientation, and location can be displayed as speech for blind travelers.


title: A Modified Quasi-Yagi Antenna With a New Compact Microstrip-to-Coplanar Strip Transition Using Artificial Transmission Lines

abstract: We present a modified quasi-Yagi antenna for use in WLAN access points. The antenna uses a new microstrip-to-coplanar strip (CPS) transition, consisting of a tapered microstrip input, T-junction, conventional 50-ohm microstrip line, and three artificial transmission line (ATL) sections. The design concept, mode conversion scheme, and simulated and experimental S-parameters of the transition are discussed first. It features a compact size, and a 3dB-insertion loss bandwidth of 78.6%. Based on the transition, a modified quasi-Yagi antenna is demonstrated. In addition to the new transition, the antenna consists of a CPS feed line, a meandered dipole, and a parasitic element. The meandered dipole can substantially increase to the front-to-back ratio of the antenna without sacrificing the operating bandwidth. The parasitic element is placed in close proximity to the driven element to improve impedance bandwidth and radiation characteristics. The antenna exhibits excellent end-fire radiation with a front-to-back ratio of greater than 15 dB. It features a moderate gain around 4 dBi, and a fractional bandwidth of 38.3%. We carefully investigate the concept, methodology, and experimental results of the proposed antenna.


title: Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers

abstract: Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this makes difficult to scale to longer paragraphs and larger text corpora. In this work, we focus on the task of multiple relation extraction by encoding the paragraph only once (one-pass). We build our solution on the pre-trained self-attentive (Transformer) models, where we first add a structured prediction layer to handle extraction between multiple entity pairs, then enhance the paragraph embedding to capture multiple relational information associated with each entity with an entity-aware attention technique. We show that our approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.


Möbius transitions in the dilemma of legitimacy


title: Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks

abstract: While Deep Neural Networks (DNNs) push the state-of-the-art in many machine learning applications, they often require millions of expensive floating-point operations for each input classification. This computation overhead limits the applicability of DNNs to low-power, embedded platforms and incurs high cost in data centers. This motivates recent interests in designing low-power, low-latency DNNs based on fixed-point, ternary, or even binary data precision. While recent works in this area offer promising results, they often lead to large accuracy drops when compared to the floating-point networks. We propose a novel approach to map floating-point based DNNs to 8-bit dynamic fixed-point networks with integer power-of-two weights with no change in network architecture. Our dynamic fixed-point DNNs allow different radix points between layers. During inference, power-of-two weights allow multiplications to be replaced with arithmetic shifts, while the 8-bit fixed-point representation simplifies both the buffer and adder design. In addition, we propose a hardware accelerator design to achieve low-power, low-latency inference with insignificant degradation in accuracy. Using our custom accelerator design with the CIFAR-10 and ImageNet datasets, we show that our method achieves significant power and energy savings while increasing the classification accuracy.


title: Do Energy Efficiency Investments Deliver at the Right Time

abstract: Electricity cannot be cost-effectively stored even for short periods of time. Consequently, wholesale electricity prices vary widely across hours of the day with peak prices frequently exceeding off-peak prices by a factor of ten or more. Most analyses of energy-efficiency policies ignore this variation, focusing on total energy savings without regard to when those savings occur. In this paper we demonstrate the importance of this distinction using novel evidence from a rebate program for air conditioners in Southern California. We estimate electricity savings using hourly smart-meter data and show that savings tend to occur during hours when the value of electricity is high. This significantly increases the overall value of the program, especially once we account for the large capacity payments received by generators to guarantee their availability in high-demand hours. We then compare this estimated savings profile with engineering-based estimates for this program as well as a variety of alternative energy-efficiency investments. The results illustrate a surprisingly large amount of variation in economic value across investments.


title: Ageing, neurodegeneration and brain rejuvenation

abstract: Although systemic diseases take the biggest toll on human health and well-being, increasingly, a failing brain is the arbiter of a death preceded by a gradual loss of the essence of being. Ageing, which is fundamental to neurodegeneration and dementia, affects every organ in the body and seems to be encoded partly in a blood-based signature. Indeed, factors in the circulation have been shown to modulate ageing and to rejuvenate numerous organs, including the brain. The discovery of such factors, the identification of their origins and a deeper understanding of their functions is ushering in a new era in ageing and dementia research.


title: Modeling of battery charging algorithms

abstract: This paper describes a universal computer model of charging system for accumulator batteries. Among many strategies and approaches of accumulator battery charge, the main of them all are those involving charge with constant current and voltage and combined ones with switching the constant current and/or voltage during the charge. To understand the difference of state of charge (SOC) after the charging process, we developed a mathematic model of accumulator battery-charging system with the Matlab/Simulink software [4] based on the battery cell model. The developed computer model contains a charging controller built on the base of the S-Function module in the Matlab/Simulink. The signals from voltage and temperature sensors of each cell were considered as the input information signals, and the commands for setting up the required charging current value for each cell complied with the output signals. We used the model of multi-channel oscilloscope as the measuring and recording equipment. Today, more and more scientists and engineers are being involved into the research of efficient storage and usage of electric energy. One of the important tasks in energy saving technologies is efficient charging of accumulator batteries. To solve this task, we have developed a universal computer model of charging system for accumulating batteries. The S-function module allows simplifying the research of different battery charging algorithms using the control program written with the high-level language that provides comfortable toolbox both in research activity and solving the application-oriented tasks.


title: Graphene-based Plasmonic Nano-Antenna for Terahertz Band Communication in Nanonetworks

abstract: Nanonetworks, i.e., networks of nano-sized devices, are the enabling technology of long-awaited applications in the biological, industrial and military fields. For the time being, the size and power constraints of nano-devices limit the applicability of classical wireless communication in nanonetworks. Alternatively, nanomaterials can be used to enable electromagnetic (EM) communication among nano-devices. In this paper, a novel graphene-based nano-antenna, which exploits the behavior of Surface Plasmon Polariton (SPP) waves in semi-finite size Graphene Nanoribbons (GNRs), is proposed, modeled and analyzed. First, the conductivity of GNRs is analytically and numerically studied by starting from the Kubo formalism to capture the impact of the electron lateral confinement in GNRs. Second, the propagation of SPP waves in GNRs is analytically and numerically investigated, and the SPP wave vector and propagation length are computed. Finally, the nano-antenna is modeled as a resonant plasmonic cavity, and its frequency response is determined. The results show that, by exploiting the high mode compression factor of SPP waves in GNRs, graphene-based plasmonic nano-antennas are able to operate at much lower frequencies than their metallic counterparts, e.g., the Terahertz Band for a one-micrometer-long ten-nanometers-wide antenna. This result has the potential to enable EM communication in nanonetworks.


title: Efficiency-Oriented Design of ZVS Half-Bridge Series Resonant Inverter With Variable Frequency Duty Cycle Control

abstract: The efficiency of zero voltage switching half-bridge series resonant inverter can be decreased under certain load conditions due to the high switching frequencies required. The proposed variable frequency duty cycle (VFDC) control is intended to improve the efficiency in the medium and low output power levels because of the decreased switching frequencies. The study performed in this letter includes, in a first step, a theoretical analysis of power balance as a function of control parameters. In addition, restrictions due to snubber capacitors and deadtime, and variability of the loads have been considered. Afterward, an efficiency analysis has been carried out to determine the optimum operation point. Switching and conduction losses have been calculated to examine the overall efficiency improvement. VFDC strategy efficiency improvement is achieved by means of a switching-frequency reduction, mainly at low-medium power range, and with low-quality factor loads. Domestic induction heating application is suitable for the use of VFDC strategy due to its special load characteristics. For this reason, the simulation results have been validated using an induction heating inverter with a specially designed load.


title: Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines

abstract: Time-to-failure (TTF) prognostic plays a crucial role in predicting remaining lifetime of electrical machines for improving machinery health management. This paper presents a novel three-step degradation data-driven TTF prognostics approach for rolling element bearings (REBs) in electrical machines. In the degradation feature extraction step, multiple degradation features, including statistical features, intrinsic energy features, and fault frequency features, are extracted to detect the degradation phenomenon of REBs using complete ensemble empirical mode decomposition with adaptive noise and Hilbert–Huang transform methods. In degradation feature reduction step, the degradation features, which are monotonic, robust, and correlative to the fault evolution of the REBs, are selected and fused into a principal component Mahalanobis distance health index using dynamic principal component analysis and Mahalanobis distance methods. In TTF prediction step, the degradation process and local TTF of the REBs are observed by an exponential regression-based local degradation model, and the global TTF is predicted by an empirical Bayesian algorithm with a continuous update. A practical case study involving run-to-failure experiments of REBs on PRONOSTIA platform is provided to validate the effectiveness of the proposed approach and to show a more accurate prediction of TTF than the existing major approaches.


title: Towards Real-Time Object Detection on Embedded Systems

abstract: Convolutional neural network (CNN) based methods have achieved great success in image classification and object detection tasks. However, unlike the image classification task, object detection is much more computation-intensive and energy-consuming since a large number of possible object proposals need to be evaluated. Consequently, it is difficult for object detection methods to be integrated into embedded systems with limited computing resources and energy supply. In this paper, we propose a pipelined object detection implementation on the embedded platform. We present a comprehensive analysis of state-of-the-art object detection algorithms and select Fast R-CNN as a possible solution. Additional modifications on the Fast R-CNN method are made to fit the specific platform and achieve trade-off between speed and accuracy on embedded systems. Finally, a multi-stage pipelined implementation on the embedded CPU $+$ GPU platform with duplicated module-parallelism is proposed to make full use of the limited computation resources. The proposed system is highly energy-efficient and close to real-time performance. In the first Low-Power Image Recognition Challenge (LPIRC), our system achieved the best result with mAP/Energy of 1.818e-2/(W $\cdot$ h) on the embedded Jetson TK1 CPU $+$ GPU platform.


title: Consequences of Parenting on Adolescent Outcomes

abstract: In recent years, substantial gains have been made in our understanding of the influence of parenting behaviors and styles on adolescent emotional and behavioral outcomes. Empirical work focusing on the associations between parenting and adolescent outcomes is important because the influence of parenting during adolescence continues to affect behaviors into adulthood. Additionally, there has been considerable attention paid to the mechanisms that shape parenting that then influence adolescent outcomes. For instance, researchers have found that neighborhood conditions moderated the association between parenting and adolescent development. In this paper, several covariates and contextual effects associated with parenting and adolescent outcomes will be discussed. Also, parental behaviors, parental styles and adolescent outcomes are discussed in this literature review. This review provides an assessment of the literature on parenting and adolescent outcomes from the past decade and includes advancements in parenting research. The review concludes with a summary of major research findings, as well as a consideration of future directions and implications for practice and policy.


title: A genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems

abstract: The combination of local search heuristics and genetic algorithms is a promising approach for finding near-optimum solutions to the traveling salesman problem (TSP). An approach is presented in which local search techniques are used to find local optima in a given TSP search space, and genetic algorithms are used to search the space of local optima in order to find the global optimum. New genetic operators for realizing the proposed approach are described, and the quality and efficiency of the solutions obtained for a set of symmetric and asymmetric TSP instances are discussed. The results indicate that it is possible to arrive at high quality solutions in reasonable time.


title: Breaking Habits With Implementation Intentions: A Test of Underlying Processes

abstract: Implementation intentions specifying the replacement of a habitual response with an alternative response in a critical situation can overrule habits. In three experiments the cognitive effects of such counterhabitual implementation intentions were investigated. Results showed that implementation intentions eliminated the cognitive advantage of the habitual means in the “horse race” with the alternative response. That is, in the control condition, the habitual means was more accessible than the alternative means on encountering the critical situation, but this was no longer the case when implementation intentions were formulated. However, the cognitive advantage of the habitual means was not immediately replaced by an automatic activation of the alternative means. This suggests that formulating counterhabitual implementation intentions increases individuals’ flexibility to choose which behavior to perform in the critical situation but that actual behavior will depart from their habits only to the extent that individuals have strong alternative goal intentions.


title: Online Learning: A Comprehensive Survey

abstract: Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis from a sequence of data instances one at a time. The goal of online learning is to ensure that the online learner would make a sequence of accurate predictions (or correct decisions) given the knowledge of correct answers to previous prediction or learning tasks and possibly additional information. This is in contrast to many traditional batch learning or offline machine learning algorithms that are often designed to train a model in batch from a given collection of training data instances. This survey aims to provide a comprehensive survey of the online machine learning literatures through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques. Generally speaking, according to the learning type and the forms of feedback information, the existing online learning works can be classified into three major categories: (i) supervised online learning where full feedback information is always available, (ii) online learning with limited feedback, and (iii) unsupervised online learning where there is no feedback available. Due to space limitation, the survey will be mainly focused on the first category, but also briefly cover some basics of the other two categories. Finally, we also discuss some open issues and attempt to shed light on potential future research directions in this field.


title: Volumetric imaging using single chip integrated CMUT-on-CMOS IVUS array

abstract: An intravascular ultrasound (IVUS) catheter that can provide forward viewing volumetric ultrasound images would be an invaluable clinical tool for guiding interventions. Single chip integration of front-end electronics with capacitive micromachined ultrasonic transducers (CMUTs) is highly desirable to reduce the interconnection complexity and enable miniaturization in IVUS catheters. For this purpose we use the monolithic CMUT-on-CMOS integration where CMUTs are fabricated directly on top of pre-processed CMOS wafers. This minimizes parasitic capacitances associated with connection lines. We have recently implemented a system design including all the required electronics using 0.35-µm CMOS process integrated with a 1.4-mm diameter CMUT array. In this study, we present the experimental volumetric imaging results from an ex-vivo chicken heart phantom. The imaging results demonstrate that the single-chip forward looking IVUS (FL-IVUS) system with monolithically integrated electronics has potential to visualize the front view of coronary arteries.


title: Constructing Legal Arguments with Rules in the Legal Knowledge Interchange Format (LKIF)

abstract: The Legal Knowledge Interchange Format (LKIF), being developed in the European ESTRELLA project, defines a knowledge representation language for arguments, rules, ontologies, and cases in XML. In this article, the syntax and argumentation-theoretic semantics of the LKIF rule language is presented and illustrated with an example based on German family law. This example is then applied to show how LKIF rules can be used with the Carneades argumentation system to construct, evaluate and visualize arguments about a legal case.


title: A Molecular and Cellular Theory of Depression

abstract: Recent studies have begun to characterize the actions of stress and antidepressant treatments beyond the neurotransmitter and receptor level. This work has demonstrated that long-term antidepressant treatments result in the sustained activation of the cyclic adenosine 3', 5'-monophosphate system in specific brain regions, including the increased function and expression of the transcription factor cyclic adenosine monophosphate response element-binding protein. The activated cyclic adenosine 3', 5'-monophosphate system leads to the regulation of specific target genes, including the increased expression of brain-derived neurotrophic factor in certain populations of neurons in the hippocampus and cerebral cortex. The importance of these changes is highlighted by the discovery that stress can decrease the expression of brain-derived neurotrophic factor and lead to atrophy of these same populations of stressvulnerable hippocampal neurons. The possibility that the decreased size and impaired function of these neurons may be involved in depression is supported by recent clinical imaging studies, which demonstrate a decreased volume of certain brain structures. These findings constitute the framework for an updated molecular and cellular hypothesis of depression, which posits that stressinduced vulnerability and the therapeutic action of antidepressant treatments occur via intracellular mechanisms that decrease or increase, respectively, neurotrophic factors necessary for the survival and function of particular neurons. This hypothesis also explains how stress and other types of neuronal insult can lead to depression in vulnerable individuals and it outlines novel targets for the rational design of fundamentally new therapeutic agents.


title: Question Answering as Global Reasoning over Semantic Abstractions

abstract: We propose a novel method for exploiting the semantic structure of text to answer multiple-choice questions. The approach is especially suitable for domains that require reasoning over a diverse set of linguistic constructs but have limited training data. To address these challenges, we present the first system, to the best of our knowledge, that reasons over a wide range of semantic abstractions of the text, which are derived using off-the-shelf, general-purpose, pre-trained natural language modules such as semantic role labelers, coreference resolvers, and dependency parsers. Representing multiple abstractions as a family of graphs, we translate question answering (QA) into a search for an optimal subgraph that satisfies certain global and local properties. This formulation generalizes several prior structured QA systems. Our system, SEMANTICILP, demonstrates strong performance on two domains simultaneously. In particular, on a collection of challenging science QA datasets, it outperforms various state-of-the-art approaches, including neural models, broad coverage information retrieval, and specialized techniques using structured knowledge bases, by 2%-6%.


title: CloudThings: A common architecture for integrating the Internet of Things with Cloud Computing

abstract: The Internet of Things presents the user with a novel means of communicating with the Web world through ubiquitous object-enabled networks. Cloud Computing enables a convenient, on demand and scalable network access to a shared pool of configurable computing resources. This paper mainly focuses on a common approach to integrate the Internet of Things (IoT) and Cloud Computing under the name of CloudThings architecture. We review the state of the art for integrating Cloud Computing and the Internet of Things. We examine an IoT-enabled smart home scenario to analyze the IoT application requirements. We also propose the CloudThings architecture, a Cloud-based Internet of Things platform which accommodates CloudThings IaaS, PaaS, and SaaS for accelerating IoT application, development, and management. Moreover, we present our progress in developing the CloudThings architecture, followed by a conclusion.


title: Image coding using wavelet transform

abstract: A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed. This method involves two steps. First, a wavelet transform used in order to obtain a set of biorthogonal subclasses of images: the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. It is shown that the wavelet transform is particularly well adapted to progressive transmission. >


title: Widespread underprovision on Reddit

abstract: Many online communities ask their members to do work for the good of everyone on the site. On social voting sites like Reddit, this means that users judge a stream of incoming links by voting them up or down. The links with the most up-votes bubble up to the main page, pointing everyone toward the best content. A threat to all sites designed this way, however, is underprovision: when too many people rely on others to contribute without doing so themselves. In this paper, we present findings suggesting that widespread underprovision of votes is happening on Reddit, arguably the internet's largest social voting community. Notably, Reddit overlooked 52% of the most popular links the first time they were submitted. This suggests that many potentially popular links get ignored, jeopardizing the site's core purpose. We conclude by discussing possible reasons behind it, and suggest future research on social voting sites.


title: FoToNoC: A Folded Torus-Like Network-on-Chip Based Many-Core Systems-on-Chip in the Dark Silicon Era

abstract: Dark silicon refers to the phenomenon that a fraction of a many-core chip has to become “dark” or “dim” in order to guarantee the system to be kept in a safe temperature range and allowable power budget. Techniques have been developed to selectively activate non-adjacent cores on many-core chip to avoid temperature hotspot, while resulting unexpected increase of communication overhead due to the longer average distance between active cores, and in turn affecting application performance and energy efficiency, when Network-on-Chip (NoC) is used as a scalable communication subsystem. To address the brand-new challenges brought by dark silicon, in this paper, we present FoToNoC , a Fo lded To rus-like NoC , coupled with a hierarchical management strategy for heterogeneous many-core systems. On top of it, objectives of maximizing application performance, energy efficiency and chip reliability are isolated and well achieved by hardware-software co-design in several different phases, including application mapping and scheduling, cluster management and DVFS control. Evaluations on PARSEC benchmark applications demonstrate the significance of the entire strategy. Compared with state-of-the-art approaches, the proposed FoToNoC organization can achieve on average 35.4 and 35.2 percent on communication efficiency and application performance improvement, respectively, when maintaining the safe chip temperature. The hierarchical cluster-based management strategy can further reduce an average 34.6 percent of the total energy consumption with a notable reduction on the chip peak temperature. The significant achievements on system energy efficiency and the reduction on chip temperature of H.264 decoder and DSP-stone benchmarks additionally verify the effectiveness of the proposed methods.


title: Enabling technologies for future data center networking: a primer

abstract: The increasing adoption of cloud services is demanding the deployment of more data centers. Data centers typically house a huge amount of storage and computing resources, in turn dictating better networking technologies to connect the large number of computing and storage nodes. Data center networking (DCN) is an emerging field to study networking challenges in data centers. In this article, we present a survey on enabling DCN technologies for future cloud infrastructures through which the huge amount of resources in data centers can be efficiently managed. Specifically, we start with a detailed investigation of the architecture, technologies, and design principles for future DCN. Following that, we highlight some of the design challenges and open issues that should be addressed for future DCN to improve its energy efficiency and increase its throughput while lowering its cost.


title: Location-Based Services: Back to the Future

abstract: Gainesville, Florida, 10 March 2 012. Today, the Mobile Location- Based Services Summit hosted a panel entitled "What Was Wrong with First-Generation Location-Based Services?" The panel chair, Sumi Helal of the University of Florida, invited two world-class experts in LBS history and technology to discuss the topic: Paolo Bellavista of the University of Bologna and Axel Kupper of the University of Munich. The panel discussed the popularity of today's LBSs and analyzed their distinguishing aspects in comparison with first-generation LBSs. The panel was anything but controversial, with all panelists in total agreement on what initially went wrong and why today's LBSs work. They analyzed how the failure unfolded to set the stage for a major paradigm shift in LBS business and technology and noted the milestones that shaped today's LBSs.


title: A Multidimensional Bisection Method for Unconstrained Minimization Problem

abstract: An extension of a new multidimensional bisection method for minimizing function over simplex is proposed for solving nonlinear unconstrained minimization problem. The method does not require a differentiability of function, and is guaranteed to converge to the minimizer for the class of strictly unimodal functions. The computational results demonstrating an effectiveness of algorithm for minimizing nonsmooth functions are presented.


title: Predicting Salient Updates for Disaster Summarization

abstract: During crises such as natural disasters or other human tragedies, information needs of both civilians and responders often require urgent, specialized treatment. Monitoring and summarizing a text stream during such an event remains a difficult problem. We present a system for update summarization which predicts the salience of sentences with respect to an event and then uses these predictions to directly bias a clustering algorithm for sentence selection, increasing the quality of the updates. We use novel, disaster-specific features for salience prediction, including geo-locations and language models representing the language of disaster. Our evaluation on a standard set of retrospective events using ROUGE shows that salience prediction provides a significant improvement over other approaches.


Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning


title: MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels

abstract: Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel technique of learning another neural network, called MentorNet, to supervise the training of the base deep networks, namely, StudentNet. During training, MentorNet provides a curriculum (sample weighting scheme) for StudentNet to focus on the sample the label of which is probably correct. Unlike the existing curriculum that is usually predefined by human experts, MentorNet learns a data-driven curriculum dynamically with StudentNet. Experimental results demonstrate that our approach can significantly improve the generalization performance of deep networks trained on corrupted training data. Notably, to the best of our knowledge, we achieve the best-published result on WebVision, a large benchmark containing 2.2 million images of real-world noisy labels. The code are at https://github.com/google/mentornet


A Sequence Learning Method for Domain-Specific Entity Linking


title: A Survey on the Impact of Multipath on Wideband Time-of-Arrival Based Localization

abstract: Localization using the time-of-arrival (ToA) of a wideband signal has the potential to achieve high accuracy, thereby making it a promising choice for a variety of applications. However, a major challenge impacting localization accuracy is multipath propagation, i.e., the existence of indirect paths from a target to an anchor (transceiver) via one or more obstacles present in the environment. In this paper, we provide an overview of the techniques proposed in the literature to analyze and address the effects of multipath on localization accuracy. For this purpose, we first cast the localization of one or more targets as a maximum a priori estimation problem, where the distribution of the amplitudes and ToAs of the indirect paths serves as a prior. Under this framework, we show that multipath can either be a blessing or a curse depending on the extent of prior knowledge available about the multipath statistics. Specifically, in the absence of any indirect path information, only the direct paths that go straight from a target to an anchor contain useful position information; in other words, multipath negatively impacts localization performance. Thus, in this case, it is important to detect the anchors having line-of-sight to the target(s), and we review the techniques reported in the literature to solve this problem. On the other hand, if complete indirect path information is available (i.e., the propagation paths of all the multipath components are known), then each indirect path is equivalent to a direct path from a virtual anchor, and hence, the spatial diversity offered by multipath can be exploited for improving localization accuracy.


title: Dead-Time Control System for a Synchronous Buck dc-dc Converter

abstract: The autonomy of battery-operated electronic systems directly depends on the energy conversion efficiency from the battery to the lower voltage levels required by nowadays CMOS technologies. One of the major contributions for power losses in buck dc-dc converters is explained and analyzed. The work presented in this paper focuses on optimizing buck converter efficiency (synchronous rectification) by minimizing body-diode conduction losses. A new control technique, and a drive system that regulates the dead-time to a minimum value of 2 ns, are presented. Converter losses decrease by 19 % to 32 % when compared to the fixed dead-time solution.


title: Power Attack on Small RSA Public Exponent

abstract: In this paper, we present a new attack on RSA when the public exponent is short, for instance 3 or 216+1, and when the classical exponent randomization is used. This attack works even if blinding is used on the messages. ::: ::: From a Simple Power Analysis (SPA) we study the problem of recovering the RSA private key when non consecutive bits of it leak from the implementation. We also show that such information can be gained from sliding window implementations not protected against SPA.


title: Web-page classification through summarization

abstract: Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web-page classification algorithm based on Web summarization for improving the accuracy. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then propose a new Web summarization-based classification algorithm and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that our proposed summarization-based classification algorithm achieves an approximately 8.8% improvement as compared to pure-text-based classification algorithm. We further introduce an ensemble classifier using the improved summarization algorithm and show that it achieves about 12.9% improvement over pure-text based methods.


title: Symbolic model checking without BDDs

abstract: Symbolic Model Checking [3, 14] has proven to be a powerful technique for the verification of reactive systems. BDDs [2] have traditionally been used as a symbolic representation of the system. In this paper we show how boolean decision procedures, like Stalmarck's Method [16] or the Davis & Putnam Procedure [7], can replace BDDs. This new technique avoids the space blow up of BDDs, generates counterexamples much faster, and sometimes speeds up the verification. In addition, it produces counterexamples of minimal length. We introduce a bounded model checking procedure for LTL which reduces model checking to propositional satisfiability. We show that bounded LTL model checking can be done without a tableau construction. We have implemented a model checker BMC, based on bounded model checking, and preliminary results are presented.


title: ConceptNet: A Practical Commonsense Reasoning Toolkit

abstract: ConceptNet is a freely available commonsense knowledge base and natural-language-processing tool-kit which supports many practical textual-reasoning tasks over real-world documents including topic-gisting, analogy-making, and other context oriented inferences. The knowledge base is a semantic network presently consisting of over 1.6 million assertions of commonsense knowledge encompassing the spatial, physical, social, temporal, and psychological aspects of everyday life. ConceptNet is generated automatically from the 700 000 sentences of the Open Mind Common Sense Project — a World Wide Web based collaboration with over 14 000 authors.


title: Package design of pressure sensors for high volume consumer applications

abstract: In this abstract we outline the critical design aspects for the design of high volume pressure sensor for MEMS applications. Pressure sensor designs by their nature require the active device to be in contact with the pressure to be measured, this requirement has until now restricted the possibility of applying large volume semiconductor package manufacturing techniques to pressure sensors and hence their large scale deployment in consumer applications. The current designs of pressure sensors rely on either a separate protective membrane, a Gel-Liquid to transmit the pressure to the active device or a Gel applied on top of the sensor. The active part of the sensor is normally made up of two separate chips , the active membrane which flexes in response to external pressure the movement of which is translated into electrical signals by resistive or capacitive elements and a support chip with trapped between them a reference pressure volume. In this paper the design of a novel LGA based MEMS Pressure sensor and barometer/altimeter design are demonstrated. A single chip sensor solution is outlined with no need for the trapped reference volume between the support chip and the sensor membrane. In this single chip sensor solution the reference pressure volume is trapped within the chip itself improving both the chip dimensions and overall reliability by eliminating the joint between the two chips. The Packaging for a this single chip sensor is based on the now well established LGA sensor platform, the problem of interfacing the pressure sensor to external pressure is solved by using an exposed silicon in the over moulded package which allows the pressure sensor access to the outside pressure, reducing the overall package size for the absolute pressure sensor to 3 x 3 x 1 mm. MEMS sensor devices in general depend on their mechanical performance to translate mechanical movement into an electrical signal, the interface to the outside world, the package, is a mechanical structure that has a large influence on the device performance and must be thought of a part of the device not just as protective housing. In the case of the pressure sensor the flexible silicon membrane is influenced by the thermal stresses in the package. The design aspects that have influence on the performance of the MEMS devices are outlined, the Package and sensor devices were extensively modeled using FEA thermo-mechanical simulation with the results showing a good correlation to the final device performance. Problems encountered during the development related to device manufacturability are outlined and the solutions detailed. The final performance and reliability data is also presented demonstrating the robustness of the design solution.


title: On the design of millimetre-wave antennas for 5G

abstract: This paper addresses the millimeter-wave antenna design aspect of the future 5G wireless systems. The paper reviews the objectives and requirements of millimeter-wave antennas for 5G. Recent advances in mm-wave antenna are reported and design guidelines are discussed. In particular, four different designs are identified from the recent literature based on their attractive characteristics that support 5G requirements and applications. The first design employs a dual-band slotted patch antenna operating at 28 GHz and 38 GHz. The antenna has circular polarization and is excited by a single-feed microstrip line. The present design is desirable for high-gain antenna array implementation in the mm-wave band, in order to compensate for the mm-wave propagation loss. The second design that is presented employs a compact planar inverted-F antenna (PIFA) with single layer dielectric load of a superstrate to enhance the gain and achieve a wide impedance bandwidth resulting in high efficiency. The third design that operates in the mm-wave band is a T-Shaped patch antenna. The proposed antenna a wideband range from (26.5 GHz–40 GHz) of the Ka band. The PFT substrate was used as it offers some advantages; low cost, high flexibility, harmless to human body and resistive towards environmental effects. The last mm-wave antenna design presented employs two MEMO arrays each composed of 2×2 antenna elements. The two MIMO array configurations are spatially orthogonal to each other which results in polarization diversity.


title: A CPW-fed dual band antenna based on metamaterial inspired split ring structure

abstract: A compact coplanar waveguide (CPW) fed dual band antenna is designed within a compact size of 21.57 × 25.62 × 1.6 mm3. A prototype antenna consists of an outer closed ring resonator, inner split ring structure, inner most closed ring and modified ground plane. The radiating element is employed to cover −10 dB impedance bandwidth of 110 MHz (2.29–2.4 GHz) and 2210 MHz (3.35–5.56 GHz) with center frequencies of 2.36 GHz and 4.45 GHz, respectively. A CPW-fed line with modified ground plane yields good impedance matching and broad bandwidth of the antenna. The band characteristics of metamaterial-inspired split ring structure as well as negative permeability (μ) are explained in detail. The prototype antenna has consistent Omnidirectional (H-Plane), and dipole (E-Plane) radiation pattern are obtained both resonant frequency, which is significantly useful for Industrial Scientific and Medical (ISM) radio band, Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN) applications.


title: Impact of machine‐type communications on energy and delay performance of random access channel in LTE‐advanced

abstract: Machine-type communications (MTC) are a rapidly growing technology, which is expected to generate significant revenues to mobile network operators. In particular, smart grid is predicted to become one of the key MTC use cases that involves unattended meters autonomously reporting information to a grid infrastructure. With this research, we consider a typical smart metering MTC application scenario in the context of 3GPP LTE-advanced wireless cellular system featuring a large number of devices connecting to the network near-simultaneously. The resulting overload of the random access channel requires a novel evaluation methodology based on comprehensive analysis and simulations. In this paper, we target to complement a validated evaluation framework fully compatible with the 3GPP test cases with a thorough analysis of random access channel performance in overloaded MTC scenarios. We also look at the regular MTC operation, when the devices are sending their data after initial network entry has been performed. By including energy consumption into our methodology together with the conventional performance metrics, we aim at providing a complete and unified insight into MTC device operation, including its energy efficiency. Copyright © 2013 John Wiley & Sons, Ltd.


title: Pregabalin for the discontinuation of long-term benzodiazepines use: An assessment of its effectiveness in daily clinical practice

abstract: Abstract Purpose To evaluate the effectiveness and tolerability of pregabalin in the management of the discontinuation of benzodiazepines in long-term users. Subjects and methods We performed a 12-week, prospective, uncontrolled, non-interventional, and observational study in patients aged 18 years old or above, who met DSM-IV-TR criteria for benzodiazepine dependence without other major psychiatry disorder. Evaluations included the Benzodiazepine Withdrawal Symptom Questionnaire, the Hamilton Anxiety Rating Scale, the Clinical Global Impression Scale, and the Sheehan Disability Scale. A urine drug screen for benzodiazepines was performed at baseline and every 4 weeks thereafter. The primary effectiveness variable was success rate, defined as achievement of benzodiazepine-free status at week 12 according to the urine drug screen. Results and discussion The mean dose at week 12 was 315 (±166) mg/day. The success rate of the benzodiazepine taper in the primary efficacy population (n = 282) was 52% (95% confidence interval [CI], 46–58). Success rates for women and men were 58% (95% CI, 49–67) and 46% (95% CI, 38–55), respectively. The success rates did not differ according to either the benzodiazepine of abuse or the presence of other substance use disorders. Significant and clinically relevant improvements were observed in withdrawal and anxiety symptoms, as well as in patients’ functioning. At week 12, tolerability was rated as good or excellent by 90% and 83% of the clinicians and patients, respectively. Conclusion Our results suggest that pregabalin is an efficacious and well-tolerated adjunctive treatment for benzodiazepine withdrawal.


title: A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles

abstract: Automatic matching of job offers and job candidates is a major problem for a number of organizations and job applicants that if it were successfully addressed could have a positive impact in many countries around the world. In this context, it is widely accepted that semi-automatic matching algorithms between job and candidate profiles would provide a vital technology for making the recruitment processes faster, more accurate and transparent. In this work, we present our research towards achieving a realistic matching approach for satisfactorily addressing this challenge. This novel approach relies on a matching learning solution aiming to learn from past solved cases in order to accurately predict the results in new situations. An empirical study shows us that our approach is able to beat solutions with no learning capabilities by a wide margin.


title: Attentive Crowd Flow Machines

abstract: Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow changes. In this paper, we propose a unified neural network module to address this problem, called Attentive Crowd Flow Machine~(ACFM), which is able to infer the evolution of the crowd flow by learning dynamic representations of temporally-varying data with an attention mechanism. Specifically, the ACFM is composed of two progressive ConvLSTM units connected with a convolutional layer for spatial weight prediction. The first LSTM takes the sequential flow density representation as input and generates a hidden state at each time-step for attention map inference, while the second LSTM aims at learning the effective spatial-temporal feature expression from attentionally weighted crowd flow features. Based on the ACFM, we further build a deep architecture with the application to citywide crowd flow prediction, which naturally incorporates the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks (i.e., crowd flow in Beijing and New York City) show that the proposed method achieves significant improvements over the state-of-the-art methods.


title: Ideal Female Brow Aesthetics

abstract: Abstract The concept of the ideal female eyebrow has changed over time. Modern studies examining youthful brow aesthetics are reviewed. An analysis of ideal female brow characteristics as depicted in the Western print media between 1945 and 2011 was performed. This analysis provided objective evidence that the ideal youthful brow peak has migrated laterally over time to lie at the lateral canthus. There has been a nonstatistically significant trend toward lower and flatter brows. These findings are discussed in relation to current concepts of female brow aging, with repercussions regarding endoscopic brow lift and aesthetic forehead surgery.


title: Training-Based Object Recognition in Cluttered 3D Point Clouds

abstract: Recognition of three dimensional (3D) objects is a challenging problem, especially in cluttered or occluded scenes. Many existing methods focus on a specific type of object or scene, or require prior segmentation. We describe a robust and efficient general purpose 3D object recognition method that combines machine learning procedures with 3D local features, without a requirement for a priori object segmentation. Experiments validate our method on various object types from engineering and street data scans.


title: High gain two-stage amplifier with positive capacitive feedback compensation

abstract: A novel topology for a high gain two-stage amplifier is proposed. The proposed circuit is designed in a way that the non-dominant pole is at output of the first stage. A positive capacitive feedback (PCF) around the second stage introduces a left half plane (LHP) zero which cancels the phase shift introduced by the non-dominant pole, considerably. The dominant pole is at the output node which means that increasing the load capacitance has minimal effect on stability. Moreover, a simple and effective method is proposed to enhance slew rate. Simulation shows that slew rate is improved by a factor of 2.44 using the proposed method. The proposed amplifier is designed in a 0.18um CMOS process. It consumes 0.86mW power from a 1.8V power supply and occupies 3038.5um2 of chip area. The DC gain is 82.7dB and gain bandwidth (GBW) is 88.9 MHz when driving a 5pF capacitive load. Also low frequency CMRR and PSRR+ are 127dB and 83.2dB, respectively. They are 24.8dB and 24.2dB at GBW frequency, which are relatively high and are other important properties of the proposed amplifier. Moreover, Simulations show convenient performance of the circuit in process corners and also presence of mismatch.


title: Learning to Rank Using Localized Geometric Mean Metrics

abstract: Many learning-to-rank~(LtR) algorithms focus on query-independent model, in which query and document do not lie in the same feature space, and the rankers rely on the feature ensemble about query-document pair instead of the similarity between query instance and documents. However, existing algorithms do not consider local structures in query-document feature space, and are fragile to irrelevant noise features. In this paper, we propose a novel Riemannian metric learning algorithm to capture the local structures and develop a robust LtR algorithm. First, we design a concept called ideal candidate document to introduce metric learning algorithm to query-independent model. Previous metric learning algorithms aiming to find an optimal metric space are only suitable for query-dependent model, in which query instance and documents belong to the same feature space and the similarity is directly computed from the metric space. Then we extend the new and extremely fast global Geometric Mean Metric Learning (GMML) algorithm to develop a localized GMML, namely L-GMML. Based on the combination of local learned metrics, we employ the popular Normalized Discounted Cumulative Gain~(NDCG) scorer and Weighted Approximate Rank Pairwise~(WARP) loss to optimize the ideal candidate document for each query candidate set. Finally, we can quickly evaluate all candidates via the similarity between the ideal candidate document and other candidates. By leveraging the ability of metric learning algorithms to describe the complex structural information, our approach gives us a principled and efficient way to perform LtR tasks. The experiments on real-world datasets demonstrate that our proposed L-GMML algorithm outperforms the state-of-the-art metric learning to rank methods and the stylish query-independent LtR algorithms regarding accuracy and computational efficiency.


title: Image morphing: a survey

abstract: Image morphing has received much attention in recent years. It has proven to be a powerful tool for visual effects in film and television, enabling the fluid transformation of one digital image into another. This paper surveys the growth of this field and describes recent advances in image morphing in terms of feature specification, warp generation methods, and transition control. These areas relate to the ease of use and quality of results. We describe the role of radial basis functions, thin plate splines, energy minimization, and multilevel free-form deformations in advancing the state-of-the-art in image morphing. Recent work on a generalized framework for morphing among multiple images is described.


A Synthesis of Safety Culture and Safety Climate Research


title: Attributing authorship of revisioned content

abstract: A considerable portion of web content, from wikis to collaboratively edited documents, to code posted online, is revisioned. We consider the problem of attributing authorship to such revisioned content, and we develop scalable attribution algorithms that can be applied to very large bodies of revisioned content, such as the English Wikipedia. Since content can be deleted, only to be later re-inserted, we introduce a notion of authorship that requires comparing each new revision with the entire set of past revisions. For each portion of content in the newest revision, we search the entire history for content matches that are statistically unlikely to occur spontaneously, thus denoting common origin. We use these matches to compute the earliest possible attribution of each word (or each token) of the new content. We show that this "earliest plausible attribution" can be computed efficiently via compact summaries of the past revision history. This leads to an algorithm that runs in time proportional to the sum of the size of the most recent revision, and the total amount of change (edit work) in the revision history. This amount of change is typically much smaller than the total size of all past revisions. The resulting algorithm can scale to very large repositories of revisioned content, as we show via experimental data over the English Wikipedia.


All Friends are Not Equal: Using Weights in Social Graphs to Improve Search. Social Networks


title: Towards convenient management of software clone codes in practice: an integrated approach

abstract: Software code cloning is inevitable during software development and unmanaged cloning practice can create substantial problems for software maintenance and evolution. Current research in the area software clones includes, but is not limited to: finding ways to manage clones; gaining more control over clone generation; and, studying clone evolution and its effects on the evolution of software. In this study, we investigate tools and techniques for detecting, managing, and understanding the evolution of clones, as well as design a convenient tool to make those techniques available to a developer's software development environment. Towards the goal of promoting the practical use of code clone research and to provide better support for managing clones in software systems, we first developed SimEclipse: a clone-aware software development platform, and then, using the tool, we performed a study to investigate the usefulness of using a number clone based technologies in an integrated platform rather than using those discretely. Finally, a small scale user study is performed to evaluate SimEclipse's effectiveness, usability and information management with respect to some pre-defined clone management activities. We believe that both researchers and developers would enjoy and utilize the benefits of using SimEclipse for different aspects of code clone research as well as for managing cloned code in software systems.


title: Integrating vertical and horizontal partitioning into automated physical database design

abstract: In addition to indexes and materialized views, horizontal and vertical partitioning are important aspects of physical design in a relational database system that significantly impact performance. Horizontal partitioning also provides manageability; database administrators often require indexes and their underlying tables partitioned identically so as to make common operations such as backup/restore easier. While partitioning is important, incorporating partitioning makes the problem of automating physical design much harder since: (a) The choices of partitioning can strongly interact with choices of indexes and materialized views. (b) A large new space of physical design alternatives must be considered. (c) Manageability requirements impose a new constraint on the problem. In this paper, we present novel techniques for designing a scalable solution to this integrated physical design problem that takes both performance and manageability into account. We have implemented our techniques and evaluated it on Microsoft SQL Server. Our experiments highlight: (a) the importance of taking an integrated approach to automated physical design and (b) the scalability of our techniques.


title: Dynamic Clustering in Social Networks Using Louvain and Infomap Method

abstract: Finding communities or clusters in social networks is a fa- mous topic in social network analysis. Most algorithms are limited to static snapshots, so they cannot handle dynamics within the underlying graph. In this paper we present a modification of the Louvain community detec- tion method to handle changes in the graph without rerunning the full algorithm. Also, we adapted the Louvain greedy approach to optimize the Infomap measure. The main idea is, to recalculate only a small area around the changes. Depending on the graph size and the amount of changes, this yields a massive runtime decrease. As validation data, we provide a graph generator, which produces spe- cific community structures, at given times and also intermediate steps to transform the graph from one to another specific graph. Experiments show that runtime decrease is possible without much loss of quality. These values depend on the reprocessed area inside the graph.


title: Curriculum Dropout

abstract: Dropout is a very effective way of regularizing neural networks. Stochastically"dropping out"units with a certain probability discourages over-specific co-adaptations of feature detectors, preventing overfitting and improving network generalization. Besides, Dropout can be interpreted as an approximate model aggregation technique, where an exponential number of smaller networks are averaged in order to get a more powerful ensemble. In this paper, we show that using a fixed dropout probability during training is a suboptimal choice. We thus propose a time scheduling for the probability of retaining neurons in the network. This induces an adaptive regularization scheme that smoothly increases the difficulty of the optimization problem. This idea of"starting easy"and adaptively increasing the difficulty of the learning problem has its roots in curriculum learning and allows one to train better models. Indeed, we prove that our optimization strategy implements a very general curriculum scheme, by gradually adding noise to both the input and intermediate feature representations within the network architecture. Experiments on seven image classification datasets and different network architectures show that our method, named Curriculum Dropout, frequently yields to better generalization and, at worst, performs just as well as the standard Dropout method.


title: S$A: A Shared Cache Attack That Works across Cores and Defies VM Sandboxing -- and Its Application to AES

abstract: The cloud computing infrastructure relies on virtualized servers that provide isolation across guest OS's through sand boxing. This isolation was demonstrated to be imperfect in past work which exploited hardware level information leakages to gain access to sensitive information across co-located virtual machines (VMs). In response virtualization companies and cloud services providers have disabled features such as deduplication to prevent such attacks. In this work, we introduce a fine-grain cross-core cache attack that exploits access time variations on the last level cache. The attack exploits huge pages to work across VM boundaries without requiring deduplication. No configuration changes on the victim OS are needed, making the attack quite viable. Furthermore, only machine co-location is required, while the target and victim OS can still reside on different cores of the machine. Our new attack is a variation of the prime and probe cache attack whose applicability at the time is limited to L1 cache. In contrast, our attack works in the spirit of the flush and reload attack targeting the shared L3 cache instead. Indeed, by adjusting the huge page size our attack can be customized to work virtually at any cache level/size. We demonstrate the viability of the attack by targeting an Open SSL1.0.1f implementation of AES. The attack recovers AES keys in the cross-VM setting on Xen 4.1 with deduplication disabled, being only slightly less efficient than the flush and reload attack. Given that huge pages are a standard feature enabled in the memory management unit of OS's and that besides co-location no additional assumptions are needed, the attack we present poses a significant risk to existing cloud servers.


title: Selective-area epitaxy and etching by chemical beam epitaxy

abstract: This paper reviews the unique characteristics of selective-area epitaxy by chemical beam epitaxy (CBE) using either shadow masks for dielectric masks. The beam nature permits the use of shadow masks. The high-vacuum environment results in no residual deposits on the dielectric masks. By injecting a gaseous etching beam, e.g. PCl3, directly into the CBE growth chamber, selective reactive chemical beam etching of InP was also demonstrated. This permits instant switching from etching to growth (and vice versa) in the same run for the first time in CBE, etching at high substrate temperatures, and improved morphology.


title: Image Encryption Using Chaotic Maps: A Survey

abstract: As the exchange of data over the open networks and Internet is rapidly growing, security of the data becomes a major concern. One possible solution to this problem is to encrypt the data. The data can be text, image, audio, video etc.. In today's world most of the multimedia applications involve images. Earlier image encryption techniques like AES,DES,RSA etc. exhibit low levels of security and also weak anti attack ability. This problem was overcome by using chaos based cryptography. The chaotic systems are very sensitive to initial conditions and control parameters which make them suitable for image encryption. Many works have been done in the field of chaos based image encryption. In this survey paper an attempt has been made to review the aspects and approaches of the design used for image encryption.


title: Reduction of undesired harmonic components in a steel industrial plant with DC electric arc furnaces

abstract: An electric arc furnace is a nonlinear, time varying load with stochastic behavior, which gives rise to harmonics, interharminics and voltage flicker. Since a power system has finite impedance, the current distortion caused by a DC electric arc furnace load creates a corresponding voltage distortion in the supply lines. The current and voltage harmonic distortion causes several problems in electrical power system, such as electrical, electronic, and computer equipment damage, control system errors due to electrical noise caused by harmonics, additional losses in transmission and distribution networks and etc. This paper makes an effort to display the differences between two types of DC electric arc furnace feeding system from the viewpoint of total harmonic distortion at AC side. These Types of feeding system include controlled rectifier power supply and uncontrolled rectifier chopper power supply. Simulation results show that the uncontrolled rectifier chopper power supply is more efficient than the other one.


title: SPIIMM: Toward a Model for Software Process Improvement Implementation and Management in Global Software Development

abstract: Software development organizations are globalizing their activities by adopting the phenomenon of global software development (GSD), mainly due to the significant return on investment it offers. Various challenges are associated with the software process improvement (SPI). The aim of this paper is to develop a software process improvement implementation and management model (SPIIMM) that can assist GSD organizations in assessing and improving their SPI activities. A thorough systematic literature review (SLR) study was performed to identify the critical success factors (CSFs), critical barriers (CBs), and the relevant practices of SPI. An empirical study of the industry was conducted with 111 SPI experts using a survey questionnaire to verify the outcomes of the SLR. The final CSFs and CBs were categorized into five maturity levels based on the implementation maturity model, the software outsourcing vendor readiness model, and capability maturity model integration. Each maturity level consisted of different CSFs and CBs to assess and improve the SPI-related maturity level of an organization. Three case studies were conducted to evaluate the effectiveness of the proposed model. The results revealed that SPIIMM can provide a robust framework to assess and improve SPI activities in GSD organizations.


title: Religious affiliation and suicide attempt

abstract: OBJECTIVE: Few studies have investigated the association between religion and suicide either in terms of Durkheim’s social integration hypothesis or the hypothesis of the regulative benefits of religion. The relationship between religion and suicide attempts has received even less attention. METHOD: Depressed inpatients (N=371) who reported belonging to one specific religion or described themselves as having no religious affiliation were compared in terms of their demographic and clinical characteristics. RESULTS: Religiously unaffiliated subjects had significantly more lifetime suicide attempts and more first-degree relatives who committed suicide than subjects who endorsed a religious affiliation. Unaffiliated subjects were younger, less often married, less often had children, and had less contact with family members. Furthermore, subjects with no religious affiliation perceived fewer reasons for living, particularly fewer moral objections to suicide. In terms of clinical characteristics, religiously un...