title: Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging

abstract: This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). In particular, this work sharply analyzes: (1) mini-batching, a method of averaging many samples of the gradient to both reduce the variance of a stochastic gradient estimate and for parallelizing SGD and (2) tail-averaging, a method involving averaging the final few iterates of SGD in order to decrease the variance in SGD's final iterate. This work presents the first tight non-asymptotic generalization error bounds for these schemes for the stochastic approximation problem of least squares regression. ::: Furthermore, this work establishes a precise problem-dependent extent to which mini-batching can be used to yield provable near-linear parallelization speedups over SGD with batch size one. These results are utilized in providing a highly parallelizable SGD algorithm that obtains the optimal statistical error rate with nearly the same number of serial updates as batch gradient descent, which improves significantly over existing SGD-style methods. ::: Finally, this work sheds light on some fundamental differences in SGD's behavior when dealing with agnostic noise in the (non-realizable) least squares regression problem. In particular, the work shows that the stepsizes that ensure optimal statistical error rates for the agnostic case must be a function of the noise properties. ::: The central analysis tools used by this paper are obtained through generalizing the operator view of averaged SGD, introduced by Defossez and Bach (2015) followed by developing a novel analysis in bounding these operators to characterize the generalization error. These techniques may be of broader interest in analyzing various computational aspects of stochastic approximation.


title: Stealth attacks: An extended insight into the obfuscation effects on Android malware

abstract: In order to effectively evade anti-malware solutions, Android malware authors are progressively resorting to automatic obfuscation strategies. Recent works have shown, on small-scale experiments, the possibility of evading anti-malware engines by applying simple obfuscation transformations on previously detected malware samples. In this paper, we provide a large-scale experiment in which the detection performances of a high number of anti-malware solutions are tested against two different sets of malware samples that have been obfuscated according to different strategies. Moreover, we show that anti-malware engines search for possible malicious content inside assets and entry-point classes. We also provide a temporal analysis of the detection performances of anti-malware engines to verify if their resilience has improved since 2013. Finally, we show how, by manipulating the area of the Android executable that contains the strings used by the application, it is possible to deceive anti-malware engines so that they will identify legitimate samples as malware. On one hand, the attained results show that anti-malware systems have improved their resilience against trivial obfuscation techniques. On the other hand, more complex changes to the application executable have proved to be still effective against detection. Thus, we claim that a deeper static (or dynamic) analysis of the application is needed to improve the robustness of such systems.


title: Understanding Pen and Touch Interaction for Data Exploration on Interactive Whiteboards

abstract: Current interfaces for common information visualizations such as bar graphs, line graphs, and scatterplots usually make use of the WIMP (Windows, Icons, Menus and a Pointer) interface paradigm with its frequently discussed problems of multiple levels of indirection via cascading menus, dialog boxes, and control panels. Recent advances in interface capabilities such as the availability of pen and touch interaction challenge us to re-think this and investigate more direct access to both the visualizations and the data they portray. We conducted a Wizard of Oz study to explore applying pen and touch interaction to the creation of information visualization interfaces on interactive whiteboards without implementing a plethora of recognizers. Our wizard acted as a robust and flexible pen and touch recognizer, giving participants maximum freedom in how they interacted with the system. Based on our qualitative analysis of the interactions our participants used, we discuss our insights about pen and touch interactions in the context of learnability and the interplay between pen and touch gestures. We conclude with suggestions for designing pen and touch enabled interactive visualization interfaces.


title: A Coaxial Corrugated Dual-Band Horn Feed

abstract: A compact corrugated dual-band horn feed with high power handling capacity is proposed in this letter. It is composed of an inner choke horn and an outer choke antenna, which consists of the concatenation of a corrugated circular waveguide together with a choke antenna and a spherical cover. Using mode matching theory to determine the scatter matrix of the feed, the analysis allows the return loss and transmission coefficient of each band to be computed accurately. The feeds' return loss is less than -20 dB, and a good far-field radiation pattern at each band has been achieved. All results are simulated, demonstrating the feasibility of the feed. It is suitable for feeding of prime-focus reflectors in a dual-band high-power microwave (HPM) system in the future.


title: Review: Polymeric-Based 3D Printing for Tissue Engineering

abstract: Abstract ::: Three-dimensional (3D) printing, also referred to as additive manufacturing, is a technology that allows for customized fabrication through computer-aided design. 3D printing has many advantages in the fabrication of tissue engineering scaffolds, including fast fabrication, high precision, and customized production. Suitable scaffolds can be designed and custom-made based on medical images such as those obtained from computed tomography. Many 3D printing methods have been employed for tissue engineering. There are advantages and limitations for each method. Future areas of interest and progress are the development of new 3D printing platforms, scaffold design software, and materials for tissue engineering applications.


title: Polarographic analysis of sulfur species in marine porewaters1

abstract: We describe a scheme to determine the major sulfur species found in marine porewaters. With polarographic techniques, it is possible to measure thiosulfate, sulfite, bisulfide, and polysulfide ions at a mercury electrode. Polysulfide ions, SX2--, can be considered to be composed of one sulfur in the 2- oxidation state, S(2-), and the remaining (x - 1) sulfurs in the zero-valent oxidation state, S(0). The number of sulfur atoms in each is measurable. Tetrathionate and other polythionates can be measured as well but have not been detected in porewaters studied to date. Salt marsh and subtidal porewater profiles contain significant concentrations of thiosulfate, bisulfide, and polysulfide. The ratio of S(2-) to S(0) is higher in subtidal porewaters than in salt marsh porewaters and indicates the importance of zero-valent sulfur to the biogeochemical processes prevalent in the salt marsh ecosystem. The S(0) concentrations in porewaters from salt marsh sediments are greater than those predicted from equilibrium calculations.


title: EarBit: Using Wearable Sensors to Detect Eating Episodes in Unconstrained Environments

abstract: Chronic and widespread diseases such as obesity, diabetes, and hypercholesterolemia require patients to monitor their food intake, and food journaling is currently the most common method for doing so. However, food journaling is subject to self-bias and recall errors, and is poorly adhered to by patients. In this paper, we propose an alternative by introducing EarBit, a wearable system that detects eating moments. We evaluate the performance of inertial, optical, and acoustic sensing modalities and focus on inertial sensing, by virtue of its recognition and usability performance. Using data collected in a simulated home setting with minimum restrictions on participants' behavior, we build our models and evaluate them with an unconstrained outside-the-lab study. For both studies, we obtained video footage as ground truth for participants activities. Using leave-one-user-out validation, EarBit recognized all the eating episodes in the semi-controlled lab study, and achieved an accuracy of 90.1% and an F1-score of 90.9% in detecting chewing instances. In the unconstrained, outside-the-lab evaluation, EarBit obtained an accuracy of 93% and an F1-score of 80.1% in detecting chewing instances. It also accurately recognized all but one recorded eating episodes. These episodes ranged from a 2 minute snack to a 30 minute meal.


title: Sparseness Analysis in the Pretraining of Deep Neural Networks

abstract: A major progress in deep multilayer neural networks (DNNs) is the invention of various unsupervised pretraining methods to initialize network parameters which lead to good prediction accuracy. This paper presents the sparseness analysis on the hidden unit in the pretraining process. In particular, we use the $L_{1}$ -norm to measure sparseness and provide some sufficient conditions for that pretraining leads to sparseness with respect to the popular pretraining models—such as denoising autoencoders (DAEs) and restricted Boltzmann machines (RBMs). Our experimental results demonstrate that when the sufficient conditions are satisfied, the pretraining models lead to sparseness. Our experiments also reveal that when using the sigmoid activation functions, pretraining plays an important sparseness role in DNNs with sigmoid (Dsigm), and when using the rectifier linear unit (ReLU) activation functions, pretraining becomes less effective for DNNs with ReLU (Drelu). Luckily, Drelu can reach a higher recognition accuracy than DNNs with pretraining (DAEs and RBMs), as it can capture the main benefit (such as sparseness-encouraging) of pretraining in Dsigm. However, ReLU is not adapted to the different firing rates in biological neurons, because the firing rate actually changes along with the varying membrane resistances. To address this problem, we further propose a family of rectifier piecewise linear units (RePLUs) to fit the different firing rates. The experimental results show that the performance of RePLU is better than ReLU, and is comparable with those with some pretraining techniques, such as RBMs and DAEs.


title: Why Do We Punish? Deterrence and Just Deserts as Motives for Punishment

abstract: One popular justification for punishment is the just deserts rationale: A person deserves punishment proportionate to the moral wrong committed. A competing justification is the deterrence rationale: Punishing an offender reduces the frequency and likelihood of future offenses. The authors examined the motivation underlying laypeople's use of punishment for prototypical wrongs. Study 1 (N = 336) revealed high sensitivity to factors uniquely associated with the just deserts perspective (e.g., offense seriousness, moral trespass) and insensitivity to factors associated with deterrence (e.g., likelihood of detection, offense frequency). Study 2 (N = 329) confirmed the proposed model through structural equation modeling (SEM). Study 3 (N = 351) revealed that despite strongly stated preferences for deterrence theory, individual sentencing decisions seemed driven exclusively by just deserts concerns.


Isradipine improves impaired endothelium-mediated vasodilation in patients with hyperlipidemia independent of changes in cholesterol


title: Detecting Antipatterns in Android Apps

abstract: Mobile apps are becoming complex software systems that must be developed quickly and evolve continuously to fit new user requirements and execution contexts. However, addressing these constraints may result in poor design choices, known as antipatterns, which may incidentally degrade software quality and performance. Thus, the automatic detection of antipatterns is an important activity that eases both maintenance and evolution tasks. Moreover, it guides developers to refactor their applications and thus, to improve their quality. While antipatterns are well-known in object-oriented applications, their study in mobile applications is still in their infancy. In this paper, we propose a tooled approach, called P aprika , to analyze Android applications and to detect object-oriented and Androidspecific antipatterns from binaries of mobile apps. We validate the effectiveness of our approach on a set of popular mobile apps downloaded from the Google Play Store.


title: Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research

abstract: Background Behavioral addiction research has been particularly flourishing over the last two decades. However, recent publications have suggested that nearly all daily life activities might lead to a genuine addiction. Methods and aim In this article, we discuss how the use of atheoretical and confirmatory research approaches may result in the identification of an unlimited list of “new” behavioral addictions. Results Both methodological and theoretical shortcomings of these studies were discussed. Conclusions We suggested that studies overpathologizing daily life activities are likely to prompt a dismissive appraisal of behavioral addiction research. Consequently, we proposed several roadmaps for future research in the field, centrally highlighting the need for longer tenable behavioral addiction research that shifts from a mere criteria-based approach toward an approach focusing on the psychological processes involved.


title: Natural Language Processing for Solving Simple Word Problems

abstract: This paper describes our system which solves simple arithmetic word problems. The system takes a word problem described in natural language, extracts information required for representation, orders the facts presented, applies procedures and derives the answer. It then displays this answer in natural language. The emphasis of this paper is on the natural language processing(NLP) techniques used to retrieve the relevant information from the English word problem. The system shows improvements over existing systems.


title: Quality of Service (QoS)-Guaranteed Network Resource Allocation via Software Defined Networking (SDN)

abstract: Quality of Service (QoS) -- based bandwidth allocation plays a key role in real-time computing systems and applications such as voice IP, teleconferencing, and gaming. Likewise, customer services often need to be distinguished according to their service priorities and requirements. In this paper, we consider bandwidth allocation in the networks of a cloud carrier in which cloud users' requests are processed and transferred by a cloud provider subject to QoS requirements. We present a QoS-guaranteed approach for bandwidth allocation that satisfies QoS requirements for all priority cloud users by using Open vSwitch, based on software defined networking (SDN). We implement and test the proposed approach on the Global Environment for Networking Innovations (GENI). Experimental results show the effectiveness of the proposed approach.


title: Ill-formed to well-formed question generator

abstract: Questions make tutoring interactive. They also facilitate a better learning. Response to a good quality question is a means to generate feedback about students' learning and understanding capabilities. Setting such questions is a challenge for a tutor.


title: Uncertainty Prediction of Remaining Useful Life Using Long Short-Term Memory Network Based on Bootstrap Method

abstract: The accuracy and reliability of remaining useful life (RUL) prediction have a significant influence on Condition Based Maintenance (CBM). Deep learning methods, especially Long Short-Term Memory networks (LSTM), have been proven to be effective in automatically mining the hidden features of sensors data, and then obtain a better point estimation for RUL forecast. However, it is pointless when the sensor data is full of noise. Estimated RUL often varies widely mainly due to the model parameters and the noise. In this case, it is inadequate to calculate the point estimation of RUL merely. For uncertainty prediction of RUL, this requires calculating not only the deterministic prediction value but also the estimation of the prediction interval. The reliability of deterministic RUL prediction, incorporated into the uncertainty prediction, is enhanced. In this paper, an LSTM-FNN (Feedforward Neural Network) based on bootstrap method (LSTMBS) for uncertainty prediction of RUL estimation is presented. The proposed method is tested on the benchmarking aircraft turbofan engines datasets (C-MAPSS). Different feature augmentation methods are applied to different sub-datasets of C-MAPSS due to the different operating conditions and fault modes. Results show that the proposed approach not only performs a competitive point estimation of RUL compared with other approaches but also obtains additional information on prediction interval of RUL to determine the appropriate maintenance time. In order to address this problem more universally, the unified LSTMBS model is constructed considering not discriminating the categories of new test trajectories in the practical system.


title: Domain Modelling in Computational Persuasion for Behaviour Change in Healthcare

abstract: The aim of behaviour change is to help people to change aspects of their behaviour for the better (e.g., to decrease calorie intake, to drink in moderation, to take more exercise, to complete a course of antibiotics once started, etc.). In current persuasion technology for behaviour change, the emphasis is on helping people to explore their issues (e.g., through questionnaires or game playing) or to remember to follow a behaviour change plan (e.g., diaries and email reminders). However, recent developments in computational persuasion are leading to an argument-centric approach to persuasion that can potentially be harnessed in behaviour change applications. In this paper, we review developments in computational persuasion, and then focus on domain modelling as a key component. We present a multi-dimensional approach to domain modelling. At the core of this proposal is an ontology which provides a representation of key factors, in particular kinds of belief, which we have identified in the behaviour change literature as being important in diverse behaviour change initiatives. Our proposal for domain modelling is intended to facilitate the acquisition and representation of the arguments that can be used in persuasion dialogues, together with meta-level information about them which can be used by the persuader to make strategic choices of argument to present.


title: Advanced Correlation Analysis of Operators for the Traveling Salesman Problem

abstract: An extension to the correlation analysis of Manderick et al. and Mathias and Whitley used to study TSP operators is introduced. Part correlation and partial correlation further refine the relationship between an operator's preservation of genetic information and its overall fitness. The fitness landscapes of ten genetic operators are studied in isolation and in conjunction with the 2-opt local improvement technique in a GENITOR environment. Five variables (parent fitness, child fitness, child failure rate, insertion rate and increased tour length due to failure) are considered in the analyses. Results suggest that certain operators (Edge Family, MPX and Schleuter crossover (SCHX)) maintain higher correlations over several interesting variable relations, especially the parent to child (PC) relation, than do other genetic operators.


title: Syntax 2D: An Open Source Software Platform for Space Syntax Analysis

abstract: For space syntax researchers, software is an essential tool for the analysis of spatial configuration. Unfortunately, the proprietary nature of most of this software can inhibit innovation within the field. Without the ability to examine others’ source code, we lose a potential resource for learning and an additional point of verification for peer review. Moreover, without a common base of code, researchers must constantly duplicate existing efforts when developing new software. As a result of proprietary policies, many spatial analysis programs end up limited in scope and become difficult to maintain as their authors move on to other research. The development of space syntax is hindered as long as software remains closed and fragmented. Towards this end, the University of Michigan has developed an open source platform for spatial analysis: Syntax2D. This software currently features a robust interface, combining existing measures such as isovist, graph, and axial analysis with newer features for path analysis. Our goal is for Synax2D to become a collection point for researchers, unifying different methods of analysis within a single application. With the software and source code freely available, Syntax2D is an opportunity for the space syntax community to share and build upon their work across a common framework. 1 Overview of features Our objective of the first version of Syntax2D is to lay the framework for future development. At this early stage, we are more interested in incorporating existing measures and establishing a workable interface between them than in pushing the limits with new features. Therefore, on the surface, it would seem there is not much to distinguish Syntax2D from other recent spatial analysis programs. It meets the basic needs of space syntax research with isovists, axial maps, and grid / VGA analysis. Users can import .dxf files and export data to .csv. The interface features mouse, pan, and zoom, and visualization layers that can be toggled on and off. Although it may not yet have the depth of features of existing programs, this version is fully capable. Students at the University are currently using Syntax2D for their research. One of our original contributions is the inclusion of new path-based measures. While the computer modeling techniques of spatial characteristics are being continuously refined and improved, the representation and analysis of how people move are still carried out using manual methods. Syntax2D quantifies traces of people paths so that these can be tested against spatial values as well as data obtained from observation studies. Users can load a path from within a drawing and use it to generate isovists from a series of observation points. From here, the cumulative isovist of the path is shown and the data from each of these points is analyzed according to several measures. Another option for this tool is to generate isovists with a user-specified cone of vision. Syntax2D is capable of representing data visually from the point measures, as well.


title: Protoplast transformation and regeneration of transgenic Valencia sweet orange plants containing a juice quality-related pectin methylesterase gene

abstract: Valencia orange [Citrus sinensis (L.) Osbeck] is the leading commercial citrus species in the world for processed juice products; however, the presence of thermostable pectin methylesterase (TSPME) reduces its juice quality. A long-term strategy of this work is to eliminate or greatly reduce TSPME activity in Valencia orange. Previous work resulted in the isolation of a putative TSPME gene, CsPME4, associated with a thermostable protein fraction of Valencia orange juice. To begin research designed to overexpress CsPME4 to verify the thermostability of the protein product and/or to downregulate the gene, a sense gene cassette containing a gene-specific sequence from a putative TSPME cDNA and the enhanced green fluorescent protein (GFP) as a selectable marker was constructed (M2.1). In the work reported here, M2.1 plasmid DNA was transformed (polyethylene glycol-mediated) into protoplasts isolated from an embryogenic suspension culture of Valencia somaclone line B6-68, in an effort to obtain transgenic Valencia lines. A vigorous transformed line was identified via GFP expression, physically separated from non-transformed tissue, and cultured on somatic embryogenesis induction medium. One transgenic proembryo expressing GFP was recovered and multiple shoots were regenerated. The recovery of multiple transgenic plants was expedited by in vitro grafting. Polymerase chain reaction analysis revealed the presence of the PME gene in transgenic plants, and subsequent Southern blot analysis confirmed the presence of the eGFP gene. These transgenic plants show normal growth and minor morphological variation. The thermostability of PME in these plants will be assessed after flowering and fruit set. This is the first successful transfer of a target fruit-quality gene by protoplast transformation with recovery of transgenic plants in citrus. This method of transformation has the advantage over Agrobacterium-mediated transformation in that it requires no antibiotic-resistance genes.


title: Insights into the neural basis of response inhibition from cognitive and clinical neuroscience

abstract: Neural mechanisms of cognitive control enable us to initiate, coordinate and update behaviour. Central to successful control is the ability to suppress actions that are no longer relevant or required. In this article, we review the contribution of cognitive neuroscience, molecular genetics and clinical investigations to understanding how response inhibition is mediated in the human brain. In Section 1, we consider insights into the neural basis of inhibitory control from the effects of neural interference, neural dysfunction, and drug addiction. In Section 2, we explore the functional specificity of inhibitory mechanisms among a range of related processes, including response selection, working memory, and attention. In Section 3, we focus on the contribution of response inhibition to understanding flexible behaviour, including the effects of learning and individual differences. Finally, in Section 4, we propose a series of technical and conceptual objectives for future studies addressing the neural basis of inhibition.


title: PLC security and critical infrastructure protection

abstract: Programmable Logic Controllers (PLCs) are the most important components embedded in Industrial Control Systems (ICSs). ICSs have achieved highest standards in terms of efficiency and performance. As a result of that, higher portion of infrastructure in industries has been automated for the comfort of human beings. Therefore, protection of such systems is crucial. It is important to investigate the vulnerabilities of ICSs in order to solve the threats and attacks against critical infrastructure to protect human lives and assets. PLC is the basic building block of an ICS. If PLCs are exploited, overall system will be exposed to the threat. Many believed that PLCs are secured devices due to its isolation from the external networks of the system. The attacks such as Stuxnet have proven the incorrectness of such thoughts. In this paper we have revealed the vulnerabilities of PLCs through a variety of attack vectors which could affect the related critical infrastructure. Furthermore, we have proposed solutions for such weaknesses in PLC based systems.


title: A Hybrid Multi-Population Genetic Algorithm for UAV Path Planning

abstract: This paper proposes a hybrid method to define a path planning for unmanned aerial vehicles in a non-convex environment with uncertainties. The environment becomes non-convex by the presence of no-fly zones such as mountains, cities and airports. Due to the uncertainties related to the path planning in real situations, risk of collision can not be avoided. Therefore, the planner must take into account a lower level of risk than one tolerated by the user. The proposed hybrid method combines a multi-population genetic algorithm with visibility graph. This is done by encoding all possible paths as individuals and solving a linear programming model to define the full path to be executed by the aircraft. The hybrid method is evaluated from a set of 50 maps and compared against an exact and heuristic approaches with promising results reported.


title: Interleaving Helps Students Distinguish among Similar Concepts.

abstract: When students encounter a set of concepts (or terms or principles) that are similar in some way, they often confuse one with another. For instance, they might mistake one word for another word with a similar spelling (e.g., allusion instead of illusion) or choose the wrong strategy for a mathematics problem because it resembles a different kind of problem. By one proposition explored in this review, these kinds of errors occur more frequently when all exposures to one of the concepts are grouped together. For instance, in most middle school science texts, the questions in each assignment are devoted to the same concept, and this blocking of exposures ensures that students need not learn to distinguish between two similar concepts. In an alternative approach described in this review, exposures to each concept are interleaved with exposures to other concepts, so that a question on one concept is followed by a question on a different concept. In a number of experiments that have compared interleaving and blocking, interleaving produced better scores on final tests of learning. The evidence is limited, though, and ecologically valid studies are needed. Still, a prudent reading of the data suggests that at least a portion of the exposures should be interleaved.


title: Regions-of-Interest and Spatial Layout for Content-Based Image Retrieval

abstract: To date most “content-based image retrieval” (CBIR) techniques rely on global attributes such as color or texture histograms which tend to ignore the spatial composition of the image. In this paper, we present an alternative image retrieval system based on the principle that it is the user who is most qualified to specify the query “content” and not the computer. With our system, the user can select multiple “regions-of-interest” and can specify the relevance of their spatial layout in the retrieval process. We also derive similarity bounds on histogram distances for pruning the database search. This experimental system was found to be superior to global indexing techniques as measured by statistical sampling of multiple users' “satisfaction” ratings.


title: The Aristotelian Proto-Theory of Design

abstract: In comparing deliberation to the analysis of a geometrical figure, Aristotle made a highly significant theoretical statement on design, which has largely gone unnoticed. Through an interpretation of the accounts given by Aristotle and the Greek geometer Pappus, seven features of the method of analysis can be identified, concerning the types of analysis, its stages, its start and end points, the types of reasoning involved, the relation of the two directions of reasoning, the strategy of reasoning and the targeted outcomes. This proto-theory is compared to the current theoretical landscape of design; also it is applied to clarify a current approach to conceptual design. Based on all this, the proto-theory as a theory of design is evaluated. It is concluded that the proto-theory fulfils several of the functions of a theory in a superior and fertile way. Thus, this proto-theory is not only of historical interest, but also provides a contribution to the theoretical knowledge on design.


title: Priority-select arbiter: An efficient round-robin arbiter

abstract: Round robin arbiter (RRA) is a critical block in nowadays designs. It is widely found in System-on-chips and Network-on-chips. The need of an efficient RRA has increased extensively as it is a limiting performance block. In this paper, we deliver a comparative review between different RRA architectures found in literature. We also propose a novel efficient RRA architecture. The FPGA implementation results of the previous RRA architectures and our proposed one are given, that show the improvements of the proposed RRA.


STUDY OF VISION BASED HAND GESTURE RECOGNITION USING INDIAN SIGN LANGUAGE


title: A 1.8V to 10V CMOS level shifter for RFID transponders

abstract: A 1.8V-to-10V high-voltage tolerant level shifter (HVT level shifter) is presented in this paper. This new topology of HVT level shifter makes all the transistors working in safe operating region, and consequently greatly enhances the circuit's reliability. It has been fabricated in 0.18 µm CMOS process, and successfully integrated in an embedded EEPROM memory with 10 V programming/erasing voltages in the passive UHF RFID transponders.


title: Human-body effects on the design of card-type UHF RFID tag antennas

abstract: This paper presents design considerations of a UHF-band card-type tag placed on the packet near the chest of a human body for our University-campus applications. Using FEKO simulation tool, we designed four kinds of highly platform-tolerant tag antennas and evaluated the influences of a simple human model on the power transmission coefficient, radiation pattern, and realized gain. Also, the input impedances of the tag antennas are measured by a test fixture and a two-port network analyzer to verify the simulation results and confirm the availability of the simulation tool. Finally, a better tag antenna is chosen a candidate for student-card RFID applications.


title: High-Power Density Design of a Soft-Switching High-Power Bidirectional dc–dc Converter

abstract: A bidirectional dc-dc converter typically consists of a buck and a boost converters. In order to have high-power density, the converter can be designed to operate in discontinuous conducting mode (DCM) such that the passive inductor can be minimized. The DCM operation associated current ripple can be alleviated by interleaving multiphase currents. However, DCM operation tends to increase turnoff loss because of a high peak current and its associated parasitic ringing due to the oscillation between the inductor and the device output capacitance. Thus, the efficiency is suffered with the conventional DCM operation. Although to reduce the turnoff loss a lossless capacitor snubber can be added across the switch, the energy stored in the capacitor needs to be discharged before device is turned on. This paper adopts a gate signal complimentary control scheme to turn on the nonactive switch and to divert the current into the antiparalleled diode of the active switch so that the main switch can be turned on under zero-voltage condition. This diverted current also eliminates the parasitic ringing in inductor current. For capacitor value selection, there is a tradeoff between turnon and turnoff losses. This paper suggests the optimization of capacitance selection through a series of hardware experiments to ensure the overall power loss minimization under complimentary DCM operating condition. According to the suggested design optimization, a 100-kW hardware prototype is constructed and tested. The experimental results are provided to verify the proposed design approach.


title: A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking

abstract: This paper introduces a general active-learning framework for robust on-road vehicle recognition and tracking. This framework takes a novel active-learning approach to building vehicle-recognition and tracking systems. A passively trained recognition system is built using conventional supervised learning. Using the query and archiving interface for active learning (QUAIL), the passively trained vehicle-recognition system is evaluated on an independent real-world data set, and informative samples are queried and archived to perform selective sampling. A second round of learning is then performed to build an active-learning-based vehicle recognizer. Particle filter tracking is integrated to build a complete multiple-vehicle tracking system. The active-learning-based vehicle-recognition and tracking (ALVeRT) system has been thoroughly evaluated on static images and roadway video data captured in a variety of traffic, illumination, and weather conditions. Experimental results show that this framework yields a robust efficient on-board vehicle recognition and tracking system with high precision, high recall, and good localization.


title: Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts

abstract: Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the capability of recommender systems to help users discover items they presumably do not know yet and which match their long-term preference profiles. In reality, however, recommendations can be helpful for customers also for other reasons, for example, when they remind them of items they were recently interested in or when they point site visitors to items that are currently discounted. In this work, we first adopt a systematic statistical approach to analyze what makes recommendations effective in practice and then propose ways of operationalizing these insights into novel recommendation algorithms. Our data analysis is based on log data of a large e-commerce site. It shows that various factors should be considered in parallel when selecting items for recommendation, including their match with the customer's shopping interests in the previous sessions, the general popularity of the items in the last few days, as well as information about discounts. Based on these analyses we propose a novel algorithm that combines a neighborhood-based scheme with a deep neural network to predict the relevance of items for a given shopping session.


title: Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control

abstract: This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human motion modeling. We first describe an efficient method for training an RNN model from prerecorded motion data. We implement RNNs with long short-term memory (LSTM) cells because they are capable of addressing the nonlinear dynamics and long term temporal dependencies present in human motions. Next, we train a refiner network using an adversarial loss, similar to generative adversarial networks (GANs), such that refined motion sequences are indistinguishable from real mocap data using a discriminative network. The resulting model is appealing for motion synthesis and control because it is compact, contact-aware, and can generate an infinite number of naturally looking motions with infinite lengths. Our experiments show that motions generated by our deep learning model are always highly realistic and comparable to high-quality motion capture data. We demonstrate the power and effectiveness of our models by exploring a variety of applications, ranging from random motion synthesis, online/offline motion control, and motion filtering. We show the superiority of our generative model by comparison against baseline models.


title: Deep Fusion of Multiple Semantic Cues for Complex Event Recognition

abstract: We present a deep learning strategy to fuse multiple semantic cues for complex event recognition. In particular, we tackle the recognition task by answering how to jointly analyze human actions (who is doing what), objects (what), and scenes (where). First, each type of semantic features (e.g., human action trajectories) is fed into a corresponding multi-layer feature abstraction pathway, followed by a fusion layer connecting all the different pathways. Second, the correlations of how the semantic cues interacting with each other are learned in an unsupervised cross-modality autoencoder fashion. Finally, by fine-tuning a large-margin objective deployed on this deep architecture, we are able to answer the question on how the semantic cues of who, what, and where compose a complex event. As compared with the traditional feature fusion methods (e.g., various early or late strategies), our method jointly learns the essential higher level features that are most effective for fusion and recognition. We perform extensive experiments on two real-world complex event video benchmarks, MED’11 and CCV, and demonstrate that our method outperforms the best published results by 21% and 11%, respectively, on an event recognition task.


title: Dual-Band Transmitarrays With Dual-Linear Polarization at Ka-Band

abstract: Dual-band transmitarray antennas are demonstrated at Ka-band with the capability of forming independent linearly polarized beams with a given polarization in each frequency band, while sharing the same radiating aperture. The proposed three-layer unit-cell is based on identical narrow microstrip patches printed on both receiving and transmitting layers and connected by a metallized via hole. The metal layers are printed on two identical substrates bonded with a thin film, and the designed unit-cell exhibits a 180° phase resolution (i.e., 1-b phase quantization). The dual-band dual-polarized property of the transmitarray is achieved by interleaving unit-cells operating in the down-link and up-link frequency bands. Four different prototypes are characterized to demonstrate the relevance of the proposed concepts. A good agreement is obtained between the radiation patterns, gain curves, and cross-polarization levels measured and computed in both frequency bands and polarizations.


When Learning Analytics Meets E-Learning.


title: Signal subspace approach for psychoacoustically motivated speech enhancement

abstract: In this paper we deal with the perceptually motivated signal subspace methods for speech enhancement. We focus on extended spectral-domain-constrained (SDC) estimator. It is obtained using Lagrange multipliers method. We present an algorithm for a precise computation of the Lagrange multipliers, allowing for a direct shaping the residual noise power spectrum. In addition the SDC estimator is presented in a new, possibly more effective form. As a practical implementation of the estimator we propose perceptually constrained signal subspace (PCSS) method for speech enhancement. The approach utilizes masking phenomena for residual noise shaping and is optimal for the case of coloured noise. Also, less demanding approximate version of this method is derived. Finally comparative evaluation of the most known subspace-based methods is performed using objective speech quality measures and listening tests. Results show that the PCSS method outperforms other methods providing high noise attenuation and better speech quality.


title: Proposal on Millimeter-Wave Channel Modeling for 5G Cellular System

abstract: This paper presents 28 GHz wideband propagation channel characteristics for millimeter wave (mmWave) urban cellular communication systems. The mmWave spectrum is considered as a key-enabling feature of 5G cellular communication systems to provide an enormous capacity increment; however, mmWave channel models are lacking today. The paper compares measurements conducted with a spherical scanning 28 GHz channel sounder system in the urban street-canyon environments of Daejeon, Korea and NYU campus, Manhattan, with ray-tracing simulations made for the same areas. Since such scanning measurements are very costly and time-intensive, only a relatively small number of channel samples can be obtained. The measurements are thus used to quantify the accuracy of a ray-tracer; the ray-tracer is subsequently used to obtain a large number of channel samples to fill gaps in the measurements. A set of mmWave radio propagation parameters is presented based on both the measurement results and ray-tracing, and the corresponding channel models following the 3GPP spatial channel model (SCM) methodology are also described.


title: Impedance analysis of automotive high voltage networks for EMC measurements

abstract: The increasing demand of electric power in electric vehicles cannot be supplied by conventional automotive power networks. Therefore novel power bus systems, capable to deliver the required power are under development. These bus systems have higher operating voltages, which result in lower current amplitudes for power transmission. As power efficiency is one of the main factors determining the range of an electric car with a given battery capacity, fast slew rates of the power semi-conductors are preferred. But with increasing slew rates also emitted radio frequency (RF) disturbances increase. To reduce electromagnetic interferences (EMI) of power electronics a complete shielding of the power bus system is necessary. Thus the network configuration is changed from unshielded single core multi wire harnesses into coaxial conductor layouts, and therefore also the impedance characteristics of the entire network are changed. Electromagnetic compatibility (EMC) of automotive components is measured according to CISPR 25, substituting the vehicle environment with line impedance stabilization networks (LISN). Recent research shows that LISNs, developed for low voltage networks, are not ideal to measure conducted emissions of high voltage (HV) components because of changed characteristic impedances and additional shielding. This paper deals with a method of determining the high-frequency impedances of automotive HV power networks. A Vector Network Analyzer (VNA) is used to measure Scattering parameters of different HV power cables and an automotive Li-Ion accumulator battery. Matrix conversions allow calculating an impedance network, which is able to represent an automotive HV networks.


title: Joint Computation Partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds

abstract: The proliferation of mobile devices and ubiquitous access of the wireless network enables many new mobile applications such as augmented reality, mobile gaming and so on. As the applications are latency sensitive, researchers propose to offload the complex computations of these applications to the nearby mobile edge cloud, in order to reduce the latency. Existing works mostly consider the problem of partitioning the computations between the mobile device and the traditional cloud that has abundant resources. The proposed approaches can not be applied in the context of mobile edge cloud, because both the resources in the mobile edge cloud and the wireless access bandwidth to the edge cloud are constrained. In this paper, we study joint computation partitioning and resource allocation problem for latency sensitive applications in mobile edge clouds. The problem is novel in that we combine the computation partitioning and the two-dimensional resource allocations in both the computation resources and the network bandwidth. We develop a new and efficient method, namely Multi-Dimensional Search and Adjust (MDSA), to solve the problem. We compares MDSA with the classic list scheduling method and the SearchAdjust algorithm via comprehensive simulations. The results show that MDSA outperforms the benchmark algorithms in terms of the overall application latency.


title: From Rewriting Logic, to Programming Language Semantics, to Program Verification

abstract: Rewriting logic has proven to be an excellent formalism to define executable semantics of programming languages, concurrent or not, and then to derive formal analysis tools for the defined languages with very little effort, such as model checkers. In this paper we give an overview of recent results obtained in the context of the rewriting logic semantics framework \({\mathbb {K}}\), such as complete semantics of large programming languages like C, Java, JavaScript, Python, and deductive program verification techniques that allow us to verify programs in these languages using a common verification infrastructure.


title: Synthesis of Forgiving Data Extractors

abstract: We address the problem of synthesizing a robust data-extractor from a family of websites that contain the same kind of information. This problem is common when trying to aggregate information from many web sites, for example, when extracting information for a price-comparison site. Given a set of example annotated web pages from multiple sites in a family, our goal is to synthesize a robust data extractor that performs well on all sites in the family (not only on the provided example pages). The main challenge is the need to trade off precision for generality and robustness. Our key contribution is the introduction of forgiving extractors that dynamically adjust their precision to handle structural changes, without sacrificing precision on the training set. Our approach uses decision tree learning to create a generalized extractor and converts it into a forgiving extractor, inthe form of an XPath query. The forgiving extractor captures a series of pruned decision trees with monotonically decreasing precision, and monotonically increasing recall, and dynamically adjusts precision to guarantee sufficient recall. We have implemented our approach in a tool called TREEX and applied it to synthesize extractors for real-world large scale web sites. We evaluate the robustness and generality of the forgiving extractors by evaluating their precision and recall on: (i) different pages from sites in the training set (ii) pages from different versions of sites in the training set (iii) pages from different (unseen) sites. We compare the results of our synthesized extractor to those of classifier-based extractors, and pattern-based extractors, and show that TREEX significantly improves extraction accuracy.


title: P2C: Understanding Output Data Files via On-the-Fly Transformation from Producer to Consumer Executions

abstract: In cyber attack analysis, it is often highly desirable to understand the meaning of an unknown file or network message in the absence of their consumer (i.e. the program that parses and understands the file/message). For example, a malware may stealthily collect information from a victim machine, store them as a file and later send it to a remote server. P2C is a novel technique that can parse and understand unknown files and network messages. Given a file/message that was generated in the past without the presence of any monitoring techniques, and a set of potential producers of the file/message, P2C systematically explores the execution paths in the producers without requiring any inputs. In the mean time, it tries to transform a producer execution to a consumer execution that closely resembles the ideal consumer execution that can parse the given unknown file/message. In particular, when a write operation is encountered in the original execution, P2C performs the opposite read operation on the unknown file/message and patches the original execution with the loaded value. In order to handle correlations between data fields in the file/message, P2C follows a trial-and-error approach to look for the correct transformation until the file/message can be parsed and the meaning of their fields can be disclosed. Our experiments on a set of real world applications demonstrate P2C is highly effective.


title: Time- and Wavelength-Division Multiplexed Passive Optical Network (TWDM-PON) for Next-Generation PON Stage 2 (NG-PON2)

abstract: The next-generation passive optical network stage 2 (NG-PON2) effort was initiated by the full service access network (FSAN) in 2011 to investigate on upcoming technologies enabling a bandwidth increase beyond 10 Gb/s in the optical access network. The FSAN meeting in April 2012 selected the time- and wavelength-division multiplexed passive optical network (TWDM-PON) as a primary solution to NG-PON2. In this paper, we summarize the TWDM-PON research in FSAN by reviewing the basics of TWDM-PON and presenting the world's first full-system 40 Gb/s TWDM-PON prototype. After introducing the TWDM-PON architecture, we explore TWDM-PON wavelength plan options to meet the NG-PON2 requirements. TWDM-PON key technologies and their respective level of development are further discussed to investigate its feasibility and availability. The first full-system 40 Gb/s TWDM-PON prototype is demonstrated to provide 40 Gb/s downstream and 10 Gb/s upstream bandwidth. This full prototype system offers 38 dB power budget and supports 20 km distance with a 1:512 split ratio. It coexists with commercially deployed Gigabit PON (G-PON) and 10 Gigabit PON (XG-PON) systems. The operator-vendor joint test results testify that TWDM-PON is achievable by the reuse and integration of commercial devices and components.


title: Cognitive workload classification using eye-tracking and EEG data

abstract: It has been shown that an increased mental workload in pilots could lead to a decrease in their situation awareness, which could lead, in turn, to a worse piloting performance and ultimately to critical human errors. Assessing the current pilot's psycho-physiological state is a hot topic of interest for developing advanced embedded cockpits systems capable of adapting their behavior to the state and performance of the pilot. In this work, we investigate a method to classify different levels of cognitive workload starting from synchronized EEG and eye-tracking information. The classifier object of the research is targeted to score a performance high enough to be applicable as a gauge for performance of unobtrusive monitoring systems working with data of lower quality.


title: On Energy Saving in IEEE 802.11ax

abstract: Wi-Fi access has become a fundamental technology for smart building as reliable and efficient Wi-Fi is the foundation that effectively enable indoor smart devices in smart buildings. Recently, the IEEE 802.11ax standards were introduced to facilitate high-bandwidth wireless transmission in high-density environment as the new generation wireless local area network technology. In the IEEE 802.11ax, for the sake of energy-efficient smart devices, power saving mechanisms were included to further reduce Wi-Fi power consumption beyond the existing Wi-Fi power saving mechanisms. In this paper, a power saving design designated on the uplink multi-user system in 802.11ax is proposed. Additionally, an analytical analysis is developed to evaluate the effectiveness of the proposed power saving mechanism with further computer simulations to verify.


title: Biodiversity and Ecosystem Services in Agroecosystems

abstract: Historically, agricultural systems have been managed to maximize production of food and fiber; however, agricultural landscapes can provide numerous goods and services to society. These include ecosystem services, described as those functions of ecosystems that are useful to humans or support human well-being. Agroecology is the application of ecological concepts and principles to the design and management of sustainable agricultural systems. It includes critical ecosystem services and the organisms, guilds, and ecological communities that provide them. This article explores approaches to measuring, managing, and governing agricultural landscapes and ecosystem services with a focus on agroecology and related methods.


title: Fast, Cheap, and Good: Why Animated GIFs Engage Us

abstract: Animated GIFs have been around since 1987 and recently gained more popularity on social networking sites. Tumblr, a large social networking and micro blogging platform, is a popular venue to share animated GIFs. Tumblr users follow blogs, generating a feed or posts, and choose to "like' or to "reblog' favored posts. In this paper, we use these actions as signals to analyze the engagement of over 3.9 million posts, and conclude that animated GIFs are significantly more engaging than other kinds of media. We follow this finding with deeper visual analysis of nearly 100k animated GIFs and pair our results with interviews with 13 Tumblr users to find out what makes animated GIFs engaging. We found that the animation, lack of sound, immediacy of consumption, low bandwidth and minimal time demands, the storytelling capabilities and utility for expressing emotions were significant factors in making GIFs the most engaging content on Tumblr. We also found that engaging GIFs contained faces and had higher motion energy, uniformity, resolution and frame rate. Our findings connect to media theories and have implications in design of effective content dashboards, video summarization tools and ranking algorithms to enhance engagement.


title: Self-Adaptive Hierarchical Sentence Model

abstract: The ability to accurately model a sentence at varying stages (e.g., word-phrase-sentence) plays a central role in natural language processing. As an effort towards this goal we propose a self-adaptive hierarchical sentence model (AdaSent). AdaSent effectively forms a hierarchy of representations from words to phrases and then to sentences through recursive gated local composition of adjacent segments. We design a competitive mechanism (through gating networks) to allow the representations of the same sentence to be engaged in a particular learning task (e.g., classification), therefore effectively mitigating the gradient vanishing problem persistent in other recursive models. Both qualitative and quantitative analysis shows that AdaSent can automatically form and select the representations suitable for the task at hand during training, yielding superior classification performance over competitor models on 5 benchmark data sets.


title: Manipulator Using Adaptive Neuro-Fuzzy Network

abstract: Solution of inverse kinematic equations is complex problem, the complexity comes from the nonlinearity of joint space and Cartesian space mapping and having multiple solution. In this work, four adaptive neurofuzzy networks ANFIS are implemented to solve the inverse kinematics of 4-DOF SCARA manipulator. The implementation of ANFIS is easy, and the simulation of it shows that it is very fast and give acceptable error.


title: Discovering Unknown Connections - the DBpedia Relationship Finder

abstract: The Relationship Finder is a tool for exploring connections between objects in a Semantic Web knowledge base. It offers a new way to get insights about elements in an ontology, in particular for large amounts of instance data. For this reason, we applied the idea to the DBpedia data set, which contains an enormous amount of knowledge extracted from Wikipedia. We describe the workings of the Relationship Finder algorithm and present some interesting statistical discoveries about DBpedia and Wikipedia.


title: Ninth Annual Brown Lecture in Education Research Black Educators as Educational Advocates in the Decades Before Brown v. Board of Education

abstract: This research sought to extend the historical record of advocacy for Black education by exploring the role of Black educators in the decades before the Brown v. Board of Education decision. It addressed (a) the ways the educators were involved in advocating for Black schools and (b) the relationship of the activities to the more visible accounts of the National Association for the Advancement of Colored People (NAACP). Answers to the research questions relied on historical ethnography as a methodological tool to analyze the records of the Georgia Teachers and Education Association and the NAACP. Each of these collections was also supplemented by other archival sources and interviews. Results indicate three identifiable periods of advocacy in the years before Brown. In each period, Black educators through their organizations were locally and nationally visible in advocating for education. The results reveal a co-dependent relationship with the NAACP and amplify the importance of a “connector” in establishi...


title: Key Factors for Selecting an Agile Method: A Systematic Literature Review

abstract: Agile methods have become popular in recent years because the success rate of project development using Agile methods is better than structured design methods. Nevertheless, less than 50 percent of projects implemented using Agile methods are considered successful, and selecting the wrong Agile method is one of the reasons for project failure. Selecting the most appropriate Agile method is a challenging task because there are so many to choose from. In addition, potential adopters believe that migrating to an Agile method involves taking a drastic risk. Therefore, to assist project managers and other decision makers, this study aims to identify the key factors that should be considered when selecting an appropriate Agile method. A systematic literature review was performed to elicit these factors in an unbiased manner and then content analysis was used to analyze the resultant data. It was found that the nature of project, development team skills, project constraints, customer involvement and organizational culture are the key factors that should guide decision makers in the selection of an appropriate Agile method based on the value these factors have for different organizations and/or different projects.


title: ACCLMesh: curvature-based navigation mesh generation

abstract: We propose a method to robustly and efficiently compute a navigation mesh for arbitrary and dynamic 3D environments based on curvature. This method addresses a number of known limitations in state-of-the-art techniques to produce navigation meshes that are tightly coupled to the original geometry, incorporate geometric details that are crucial for movement decisions and robustly handle complex surfaces. We integrate the method into a standard navigation and collision-avoidance system to simulate thousands of agents on complex 3D surfaces in real-time.


title: Design of Two-Stage CMOS Operational Amplifier

abstract: This paper presents a design of Two Stage CMOS operational amplifier, which operates at ±2.5V power supply using umc 2µm CMOS technology. The OP-AMP designed is a two-stage CMOS OP-AMP. The OP-AMP is designed to exhibit a unity gain frequency of 4.416MHz and exhibits a gain of 96dB with a 700 phase margin. Design and Simulation has been carried out in LT Spice tools. For this, a simple analysis with some meaningful parameters (such as gain bandwidth, phase margin, etc.) is performed. The method handles wide variety of specifications and constraints. In this, we formulate the CMOS op-amp design problem and their aspect ratios. The method we present can be applied to a wide variety of amplifier structures, but in this paper we apply the method to a specific two stage CMOS op-amp. The variation in the performance of the op-amp with variations in the width and length of the CMOS and the effect of scaling the gate oxide thickness is discussed. The simulation results have been obtained by umc 2µm CMOS technology. High gain in operational amplifiers is not the only desired figure of merit for all kind of signal processing applications. In this case, the slew rate will increase for an increase in current. Thus, we can conclude that the selection of device sizes depends on trade-offs between stability and slew-rate.


title: Remediation of chromium-contaminated water using biogenic nano-sized materials and metal-reducing bacteria.

abstract: As an environmental nanotechnology, nano-sized materials have the potential to create novel and effective in-situ and ex-situ treatments for contaminated groundwater due to its high catalytic reactivity, large surface area, and dispersibility. In this study the efficiency of Cr(VI) reduction and immobilization using biotic and abiotic nano-sized materials (NSMs) and metal-reducing bacteria (MRB) was evaluated to remediate Cr(VI)-contaminated groundwater in batch and column tests. The results of this study revealed that the combination of the mixed MRB and bio-FeS/siderite performed the highest efficiency of Cr(VI) reduction and immobilization. Cr(VI) reduction by MRB and NSMs could impact on solubility of Cr(VI) and geochemical changes favorable for precipitation and adsorption.


title: Smart BIM (Building Information Modelling)

abstract: Purpose: After a long period of international research and development, BIM has become mature. Many tools support the BIM process, or at least they claim. BIM not only offers opportunities for the Architectural Engineering and Construction industry, but also for the client. In this paper we don’t focus on the professional client, but on the client of a building assignment that act as the end-user. Involvement of the end user in the design process has been advocated by many scholars and designers, but has so far only marginally been adopted in practice. The importance of user participation is demonstrated by the lack of success of smart technologies in new housing or in renovation. Particularly elderly people resist accepting these technologies in their home environment, although they could benefit from these technologies to improve comfort and health care. As a result of poor understanding of these new technologies by both designers and end users, researchers observe that there is a mismatch between user demands and smart technology usability. Hence, this paper is an attempt to improve the role of users in the design process in two ways. Firstly, by adding the missing components of smart technology to current BIM model libraries. Secondly, by developing a virtual model in which users can interact with the smart technologies and configure their preferred layout. The final results are interesting not only for technology developers but also for housing designers who aim at improving the quality of life in future housing for aging society. Method: For a better understanding of BIM, a historical perspective is taken in this paper. The initiatives from different research institutes are discussed and how they affected each other. The up-take by the software industry is highlighted and their delicate relationship with science. In today’s design process BIM systems support spatial design that is accommodate by smart technology. Usually this smart technology is added after the spatial design in the final design stage by the installations expert. In our research we want to turn this process around; the smart technologies are accommodated by spatial design. Therefore we develop a design system with a library of smart components such as smart wall, smart kitchen and smart furniture. The difference between smart technologies and standard building components is that smart technologies interact with the building users. BIM allows for realistic visualization of designs in an early stage. In our prototype system, clients are presented a virtual space with a wide range of smart technologies. After being introduced to these technologies, the client expresses how these will fit within his/her activities. Following he/she can experience in the virtual model how smart technologies react when activities are executed. Results & Discussion A prototype system is presented that allows clients such as elderly to experience smart technologies. In contrast with traditional design it does not start from the spatial layout but from the activities that should be accommodated supported by smart technologies. We expect that fundamentally different layout will emerge from this approach. Although no experimental data are available yet, some first experiences will be discussed.


title: Indian Music Instruments Semantic Knowledge Representation

abstract: Web is an intelligent incarnation and advancement in World Wide Web to store, annotate, index and retrieve information by providing knowledge representation, classification, and categorization, a common understanding among the group of people and structuring the information in machine processable format. To structure the information in machine processable semantic models Semantic Web have introduced the concept of ontology. Ontologies are most widely used in artificial intelligence, semantic web, software engineering, information retrieval, knowledge representation, knowledge sharing, knowledge integration, knowledge reuse, and so on. Music plays a vital role in human life. A vast number of musical instruments are used to produce rhythmic sounds. The rapid growth of technology enables on line music. The instruments used and the terms used to describe the concepts varies from individual groups. To support concept based knowledge representation and semantic retrieval music files music instruments knowledge should be identified and represented. This raises the need for construction of Indian music instruments ontology. This ropes semantic web with more intelligent, capable, relevant, responsive interaction and high performance retrieval system. The Indian music instrument ontology is been constructed and the paper concludes with conclusion and future work. KeywordsInstruemtns, Ontology, Semantic Web


title: High Performance and Energy-Efficient On-Chip Cache Using Dual Port (1R/1W) Spin-Orbit Torque MRAM

abstract: This paper proposes a dual (1R/1W) port spin-orbit torque magnetic random access memory (1R/1W SOT-MRAM) for energy efficient on-chip cache applications. Our proposed dual port memory can alleviate the impact of write latency on system performance by supporting simultaneous read and write accesses. The spin-orbit device leverages the high spin current injection efficiency of spin Hall metal to achieve low critical switching current to program a magnetic tunnel junction. The low write current reduces the write power consumption, and the size of the access transistors, leading to higher integration density. Furthermore, the decoupled read and write current paths of the spin-orbit device improves oxide barrier reliability, because the write current does not flow through the oxide barrier. Device, circuit, and system level co-simulations show that a 1R/1W SOT-MRAM based L2 cache can improve the performance and energy-efficiency of the computing systems compared to SRAM and standard STT-MRAM based L2 caches.


title: Secure High-Rate Transaction Processing in Bitcoin

abstract: Bitcoin is a disruptive new crypto-currency based on a decentralized open-source protocol which has been gradually gaining momentum. Perhaps the most important question that will affect Bitcoin’s success, is whether or not it will be able to scale to support the high volume of transactions required from a global currency system. We investigate the implications of having a higher transaction throughput on Bitcoin’s security against double-spend attacks. We show that at high throughput, substantially weaker attackers are able to reverse payments they have made, even well after they were considered accepted by recipients. We address this security concern through the GHOST rule, a modification to the way Bitcoin nodes construct and re-organize the block chain, Bitcoin’s core distributed data-structure. GHOST has been adopted and a variant of it has been implemented as part of the Ethereum project, a second generation distributed applications platform.


title: A Practical Approach to Recognizing Physical Activities

abstract: We are developing a personal activity recognition system that is practical, reliable, and can be incorporated into a variety of health-care related applications ranging from personal fitness to elder care. To make our system appealing and useful, we require it to have the following properties: (i) data only from a single body location needed, and it is not required to be from the same point for every user; (ii) should work out of the box across individuals, with personalization only enhancing its recognition abilities; and (iii) should be effective even with a cost-sensitive subset of the sensors and data features. In this paper, we present an approach to building a system that exhibits these properties and provide evidence based on data for 8 different activities collected from 12 different subjects. Our results indicate that the system has an accuracy rate of approximately 90% while meeting our requirements. We are now developing a fully embedded version of our system based on a cell-phone platform augmented with a Bluetooth-connected sensor board.


title: Anomaly detection by using random projection forest

abstract: In this paper, we present a novel method for detecting anomalies from surveillance videos, which utilizes the random projection forest for evaluating the rarity of visual clues in a video frame. Given the hierarchical clustering of the data in a random projection tree and the aggregation process in the random forest, we achieve both efficient estimation of incoming samples and improved robustness against under-fitting and over-fitting under improperly selected models. Random forest is also online updatable, which is meaningful for future online anomaly detection. We designed the splitting rule for anomaly detection, the system framework and the criterion of anomaly determination. The efficiency of the proposed methods has been validated by experiments on public UCSD datasets and compared with previously reported results.


title: A-NICE-MC: Adversarial Training for MCMC

abstract: Existing Markov Chain Monte Carlo (MCMC) methods are either based on general-purpose and domain-agnostic schemes which can lead to slow convergence, or hand-crafting of problem-specific proposals by an expert. We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties. First, we propose an efficient likelihood-free adversarial training method to train a Markov chain and mimic a given data distribution. Then, we leverage flexible volume preserving flows to obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to train efficient Markov chains to sample from a prescribed posterior distribution by iteratively improving the quality of both the model and the samples. A-NICE-MC provides the first framework to automatically design efficient domain-specific MCMC proposals. Empirical results demonstrate that A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of deep neural networks, and is able to significantly outperform competing methods such as Hamiltonian Monte Carlo.


title: LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations

abstract: Image super-resolution (SR) aims to recover high-resolution images from their low-resolution counterparts for improving image analysis and visualization. Interpolation methods, widely used for this purpose, often result in images with blurred edges and blocking effects. More advanced methods such as total variation (TV) retain edge sharpness during image recovery. However, these methods only utilize information from local neighborhoods, neglecting useful information from remote voxels. In this paper, we propose a novel image SR method that integrates both local and global information for effective image recovery. This is achieved by, in addition to TV, low-rank regularization that enables utilization of information throughout the image. The optimization problem can be solved effectively via alternating direction method of multipliers (ADMM). Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolation, iterative back projection (IBP), non-local means (NLM), and TV-based up-sampling.


title: Revised Kubelka–Munk theory III A general theory of light propagation in scattering and absorptive media

abstract: A generally applicable theoretical model describing light propagating through turbid media is proposed. The theory is a generalization of the revised Kubelka–Munk theory, extending its applicability to accommodate a wider range of absorption influences. A general expression for a factor taking into account the effect of scattering on the total photon path traversed in a turbid medium is derived. The extended model is applied to systems of ink-dyed paper sheets—mixtures of wood fibers with dyes—which represent examples of systems that have thus far eluded the original Kubelka–Munk model. The results of simulations of the spectral dependence of Kubelka–Munk coefficients of absorption and scattering show that they compare very well with those derived from experimental results.


title: Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

abstract: Image restoration, including image denoising, super resolution, inpainting, and so on, is a well-studied problem in computer vision and image processing, as well as a test bed for low-level image modeling algorithms. In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers. In other words, the network is composed of multiple layers of convolution and de-convolution operators, learning end-to-end mappings from corrupted images to the original ones. The convolutional layers capture the abstraction of image contents while eliminating corruptions. Deconvolutional layers have the capability to upsample the feature maps and recover the image details. To deal with the problem that deeper networks tend to be more difficult to train, we propose to symmetrically link convolutional and deconvolutional layers with skip-layer connections, with which the training converges much faster and attains better results.


title: Analytical study on network spectrum efficiency of ultra dense networks

abstract: As radio interface approaches its practical capacity limit given limited spectrum resource, network architecture innovation/revolution would play a key role in boosting wireless communication system capacity and improving user experience. Deploying dense small cell networks overlaying the conventional macro cell networks is widely regarded as a key step towards network architecture revolution for improved spectrum and energy efficiency. In this paper, we analytically investigate the spectrum efficiency of densely deployed small cell networks in the downlink using tools from stochastic geometry. Unlike the conventional user-centric link-level spectrum efficiency analysis, we analyze network spectrum efficiency, defined as the average aggregate spectrum efficiency per cell. As the portion of cell-edge users, who would consume more spectrum resource in achieving a target rate, usually outweighs that of the cell-center users, network spectrum efficiency evaluation should count user distribution to portray the network performance as a whole. Based on the network spectrum efficiency results, we are able to find the optimal cell density and the corresponding optimal base station transmit power for achieving a high spectrum efficiency and/or energy efficiency.


title: Spatial and spatiotemporal analysis of soccer

abstract: Soccer matches consist of spatial and spatiotemporal objects including a ball, players, and a field. Players and ball are also moving objects. This means that spatial and spatiotemporal analysis on ball and players could be useful in studying soccer tactics. From this view point, we extend the application areas of spatial information science to soccer tactic analysis. First we define a feature model to specify the basic units of analysis. Second, we study the morphological properties of each feature type. Third, a set of operations are defined for each feature type. Fourth, we perform an experiment with a real data set of the 2006 World Cup final match to validate the usefulness of our framework. Even though we focus on soccer match, we expect that this framework can be applied to similar sports such as handball and basket ball.


title: Not As Smart As We Think: A Study of Collective Intelligence in Virtual Groups

abstract: AbstractOrganizations increasingly use virtual groups for many types of work, yet little research has examined factors that make groups perform better across multiple different types of tasks. Previous research has proposed that groups, like individuals, have a general factor of collective intelligence, an ability to perform consistently across multiple types of tasks. We studied groups that used computer-mediated communication (CMC) to investigate whether collective intelligence is similar or different when groups work using CMC. A collective intelligence factor did not emerge among groups using CMC, suggesting that collective intelligence manifests itself differently depending on context. This is in contrast to previous findings. Our results surface a need for more research on boundary conditions of the construct of collective intelligence. Our findings also have practical implications: managers should take care when organizing virtual group work because groups that perform well on one type of task will...


title: TanGeoMS: Tangible Geospatial Modeling System

abstract: We present TanGeoMS, a tangible geospatial modeling visualization system that couples a laser scanner, projector, and a flexible physical three-dimensional model with a standard geospatial information system (GIS) to create a tangible user interface for terrain data. TanGeoMS projects an image of real-world data onto a physical terrain model. Users can alter the topography of the model by modifying the clay surface or placing additional objects on the surface. The modified model is captured by an overhead laser scanner then imported into a GIS for analysis and simulation of real-world processes. The results are projected back onto the surface of the model providing feedback on the impact of the modifications on terrain parameters and simulated processes. Interaction with a physical model is highly intuitive, allowing users to base initial design decisions on geospatial data, test the impact of these decisions in GIS simulations, and use the feedback to improve their design. We demonstrate the system on three applications: investigating runoff management within a watershed, assessing the impact of storm surge on barrier islands, and exploring landscape rehabilitation in military training areas.


title: The project workplace for organizational learning development

abstract: Abstract This paper conceptually examines how and why projects and project teams may be conceived as highly generative episodic individual and team learning places that can serve as vehicles or agents to promote organizational learning. It draws on and dissects a broad and relevant literature concerning situated learning, organizational learning, learning spaces and project management. The arguments presented signal a movement towards a project workplace becoming more organizationally acknowledged and supported as a learning intense entity wherein, learning is a more conspicuous, deliberate and systematic social activity by project participants. This paper challenges conventional and limited organizational perceptions about project teams and their practices and discloses their extended value contributions to organizational learning development.


title: Using experimental game theory to transit human values to ethical AI

abstract: Knowing the reflection of game theory and ethics, we develop a mathematical representation to bridge the gap between the concepts in moral philosophy (e.g., Kantian and Utilitarian) and AI ethics industry technology standard (e.g., IEEE P7000 standard series for Ethical AI). As an application, we demonstrate how human value can be obtained from the experimental game theory (e.g., trust game experiment) so as to build an ethical AI. Moreover, an approach to test the ethics (rightness or wrongness) of a given AI algorithm by using an iterated Prisoner's Dilemma Game experiment is discussed as an example. Compared with existing mathematical frameworks and testing method on AI ethics technology, the advantages of the proposed approach are analyzed.


title: Fighting Accounting Fraud Through Forensic Data Analytics

abstract: Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to improve the detection of accounting fraud via the implementation of several machine learning methods to better differentiate between fraud and non-fraud companies, and to further assist the task of examination within the riskier firms by evaluating relevant financial indicators. Out-of-sample results suggest there is a great potential in detecting falsified financial statements through statistical modelling and analysis of publicly available accounting information. The proposed methodology can be of assistance to public auditors and regulatory agencies as it facilitates auditing processes, and supports more targeted and effective examinations of accounting reports.


title: PAD-Net: A Perception-Aided Single Image Dehazing Network

abstract: In this work, we investigate the possibility of replacing the $\ell_2$ loss with perceptually derived loss functions (SSIM, MS-SSIM, etc.) in training an end-to-end dehazing neural network. Objective experimental results suggest that by merely changing the loss function we can obtain significantly higher PSNR and SSIM scores on the SOTS set in the RESIDE dataset, compared with a state-of-the-art end-to-end dehazing neural network (AOD-Net) that uses the $\ell_2$ loss. The best PSNR we obtained was 23.50 (4.2% relative improvement), and the best SSIM we obtained was 0.8747 (2.3% relative improvement.)


title: Automatic generation of large-scale handwriting fonts via style learning

abstract: Generating personal handwriting fonts with large amounts of characters is a boring and time-consuming task. Take Chinese fonts as an example, the official standard GB18030-2000 for commercial font products contains 27533 simplified Chinese characters. Consistently and correctly writing out such huge amounts of characters is usually an impossible mission for ordinary people. To solve this problem, we propose a handy system to automatically synthesize personal handwritings for all characters (e.g., Chinese) in the font library by learning style from a small number (as few as 1%) of carefully-selected samples written by an ordinary person. Experiments including Turing tests with 69 participants demonstrate that the proposed system generates high-quality synthesis results which are indistinguishable from original handwritings. Using our system, for the first time the practical handwriting font library in a user's personal style with arbitrarily large numbers of Chinese characters can be generated automatically.


title: A food image recognition system with Multiple Kernel Learning

abstract: Since health care on foods is drawing people's attention recently, a system that can record everyday meals easily is being awaited. In this paper, we propose an automatic food image recognition system for recording people's eating habits. In the proposed system, we use the Multiple Kernel Learning (MKL) method to integrate several kinds of image features such as color, texture and SIFT adaptively. MKL enables to estimate optimal weights to combine image features for each category. In addition, we implemented a prototype system to recognize food images taken by cellular-phone cameras. In the experiment, we have achieved the 61.34% classification rate for 50 kinds of foods. To the best of our knowledge, this is the first report of a food image classification system which can be applied for practical use.


title: Authorship identification from unstructured texts

abstract: Authorship identification is a task of identifying authors of anonymous texts given examples of the writing of authors. The increasingly large volumes of anonymous texts on the Internet enhance the great yet urgent necessity for authorship identification. It has been applied to more and more practical applications including literary works, intelligence, criminal law, civil law, and computer forensics. In this paper, we propose a semantic association model about voice, word dependency relations, and non-subject stylistic words to represent the writing style of unstructured texts of various authors, design an unsupervised approach to extract stylistic features, and employ principal components analysis and linear discriminant analysis to identify authorship of texts. This paper provides a uniform quantified method to capture syntactic and semantic stylistic characteristics of and between words and phrases, and this approach can solve the problem of the independence of different dimensions to some extent. Experimental results on two English text corpora show that our approach significantly improves the overall performance over authorship identification.


title: Elective Affiliations: Marginal Urban Characters Negotiating Legitimacy and Autonomy in Urban Culture

abstract: ‘Elective Affiliations’ examines how walking tour guides manage to transmit cultural information, engage in the public imagination, and impart a method of urban investigation their participants while still occupying a place in-between formal institutions, social networks, and labor markets. Drawing from five-years of ethnographic data, guides are presented as living and succeeding in the ‘interstitial’ areas of cities, and are forced to negotiate the tension between structural autonomy and the legitimations arising from affiliation with cultural institutions. Walking guides are successful at their endeavors because of their ever-changing set of interrelationships, not in spite of them. ‘Elective Affiliations’ brings empirical evidence from the intersection of urbanism, tourism, and culture, and recent work on social capital and networks to recent issues of urban cultural policy.


title: Privacy Preserving Data Publishing: A Classification Perspective

abstract: The concept of privacy is expressed as release of information in a controlled way. Privacy could also be defined as privacy decides what type of personal information should be released and which group or person can access and use it. Privacy Preserving Data Publishing (PPDP) is a way to allow one to share anonymous data to ensure protection against identity disclosure of an individual. Data anonymization is a technique for PPDP, which makes sure the published data, is practically useful for processing (mining) while preserving individuals sensitive information. Most works reported in literature on privacy preserving data publishing for classification task handle numerical data. However, most real life data contains both numerical and non-numerical data. Another shortcoming is that use of distributed model called Secure Multiparty Computation (SMC). For this research, a centralized model is used for independent data publication by a single data owner. The key challenge for PPDP is to ensure privacy as well as to keep the data usable for research. Differential privacy is a technique that ensures the highest level of privacy for a record owner while providing actual information of the data set. The aim of this research is to develop a framework that satisfies differential privacy standards and to ensure maximum data usability for a classification tasks such as patient data classification in terms of blood pressure.


title: Effects of Capsaicin and Topical Anesthesia on Nonimmunologic Immediate Contact Reactions to Benzoic Acid and Methyl Nicotinate

abstract: The effects of topical capsaicin (a liberator of substance P) and topical anesthesia were studied in order to obtain information about the role of sensory nerves and substance P in the pathophysiology of nonimmunologic immediate contact reactions (NIICRs) induced by benzoic acid (BA) and methyl nicotinate (MN) in human skin. BA 500, 250, 125, 62, and 31 mM and MN 50, 25, 13, 7, 3, 1.5, and 0.75 mM in petrolatum were tested on the skin areas pretreated with capsaicin solution in 13 test subjects. BA 500, 125, 62, and 31 mM and MN 25, 10, 2, and 0.5 mM in petrolatum were tested on skin areas of 15 test subjects, anesthesized with a topical anesthetic cream (lidocaine 25 mg/g, prilocaine 25 mg/g). Prick tests with histamine dihydrochloride (10 mg/ml) were also performed on test and reference areas. Reactions were monitored 40 min after application with laser-Doppler flowmetry, and the sizes of erythema and edema reactions were measured. Capsaicin treatment inhibited erythema reactions to histamine, but did not inhibit erythema or edema from BA and MN. Topical anesthesia inhibited erythema and edema reactions to histamine as well as to BA and MN. These results suggest that neither erythema nor edema reactions to BA and MN are mediated by mechanisms sensitive to capsaicin (substance P). The mechanism by which a topical anesthetic inhibits skin reactions may result from its effect on cutaneous nerves, or it may have general antiinflammatory properties.


title: Detecting HTTP Botnet with Clustering Network Traffic

abstract: Botnet is a great threat of the Internet nowadays. For now, Botnet has transformed to the complex one based on HTTP, P2P protocols from the simple Botnets which based on IRC protocol. In this paper, we evaluate the key features of HTTP Botnet and design a new method to detect the HTTP Botnet based on feature analysis. The experiment result shows that our method is effective and efficient on detecting HTTP Botnet.


title: Depth from Edge and Intensity Based Stereo

abstract: The past few years have seen a growing interest in the application" of three-dimensional image processing. With the increasing demand for 3-D spatial information for tasks of passive navigation [7,12], automatic surveillance [9], aerial cartography [10,13], and inspection in industrial automation, the importance of effective stereo analysis has been made quite clear. A particular challenge is to provide reliable and accurate depth data for input to object or terrain modelling systems (such as [5]. This paper describes an algorithm for such stereo sensing It uses an edge-based line-by-line stereo correlation scheme, and appears to be fast, robust, and parallel implementable. The processing consists of extracting edge descriptions for a stereo pair of images, linking these edges to their nearest neighbors to obtain the edge connectivity structure, correlating the edge descriptions on the basis of local edge properties, then cooperatively removmg those edge correspondences determined to be in error - those which violate the connectivity structure of the two images. A further correlation process, using a technique similar to that used for the edges, is applied to the image intensity values over intervals defined by the previous correlation The result of the processing is a full image array disparity map of the scene viewed.


title: roberta.romano@yale.edu EVENT STUDIES AND THE LAW: PART II--Empirical Studies of Corporate Law

abstract: This paper is the second part of a review of the event study methodology, which has proved to be one of the most successful uses of econometrics in policy analysis. In this part we focus on the methodology's application to corporate law and corporate governance issues. Event studies have played an important role in the making of corporate law and in corporate law scholarship. The reason for this input is twofold. First, there is a match between the methodology and subject matter: the goal of corporate law is to increase shareholder wealth and event studies provide a metric for measurement of the impact upon stock prices of policy decisions. Second, because the participants in corporate law debates share the objective of corporate law, to adopt policies that enhance shareholder wealth, their disagreements are over the means to achieve that end. Hence, the discourse can be empirically informed. The paper concludes by sketching the methodology's use in evaluating the economic effects of regulation. While event studies' usefulness for policy analysis is now familiar in the corporate law setting, we hope that our two-part review will suggest appropriate applications to other fields of law.


title: Data Dissemination Performance in Large-Scale Sensor Networks

abstract: As the use of wireless sensor networks increases, the need for (energy-)efficient and reliable broadcasting algorithms grows. Ideally, a broadcasting algorithm should have the ability to quickly disseminate data, while keeping the number of transmissions low. In this paper we develop a model describing the message count in large-scale wireless sensor networks. We focus our attention on the popular Trickle algorithm, which has been proposed as a suitable communication protocol for code maintenance and propagation in wireless sensor networks. Besides providing a mathematical analysis of the algorithm, we propose a generalized version of Trickle, with an additional parameter defining the length of a listen-only period. This generalization proves to be useful for optimizing the design and usage of the algorithm. For single-cell networks we show how the message count increases with the size of the network and how this depends on the Trickle parameters. Furthermore, we derive distributions of inter-broadcasting times and investigate their asymptotic behavior. Our results prove conjectures made in the literature concerning the effect of a listen-only period. Additionally, we develop an approximation for the expected number of transmissions in multi-cell networks. All results are validated by simulations.


title: Potential applications of plant based derivatives as fat replacers, antioxidants and antimicrobials in fresh and processed meat products.

abstract: Growing concern about diet and health has led to development of healthier food products. In general consumer perception towards the intake of meat and meat products is unhealthy because it may increase the risk of diseases like cardiovascular diseases, obesity and cancer, because of its high fat content (especially saturated fat) and added synthetic antioxidants and antimicrobials. Addition of plant derivatives having antioxidant components including vitamins A, C and E, minerals, polyphenols, flavanoids and terpenoids in meat products may decrease the risk of several degenerative diseases. To change consumer attitudes towards meat consumption, the meat industry is undergoing major transformations by addition of nonmeat ingredients as animal fat replacers, natural antioxidants and antimicrobials, preferably derived from plant sources.


Exploring Synergies between Machine Learning and Knowledge Representation to Capture Scientific Knowledge