title: Entropy-guided Retinex anisotropic diffusion algorithm based on partial differential equations (PDE) for illumination correction

abstract: This report describes the experimental results obtained using a proposed variational Retinex algorithm for controlled illumination correction. Two colour restoration and enhancement schemes of the algorithm are presented for drastically improved results. The algorithm modifies the reflectance image using global and local contrast enhancement approaches and gradually removes the residual illumination to yield highly pleasing results. The proposed algorithms are optimized by way of simultaneous perceptual quality metric (PQM) stabilization and entropy maximization for fully automated processing solving the problem of determination of stopping time. The usage of the HSI or HSV colour space ensures a unique solution to the optimization problem unlike in the RGB space where there is none (forcing manual selection of number of iteration. The proposed approach preserves and enhances details in both bright and dark regions of underexposed images in addition to eliminating the colour distortion, over-exposure in bright image regions, halo effect and grey-world violations observed in Retinex-based approaches. Extensive experiments indicate consistent performance as the proposed approach exploits and augments the advantages of PDE-based formulation, performing illumination correction, colour enhancement correction and restoration, contrast enhancement and noise suppression. Comparisons shows that the proposed approach surpasses most of the other conventional algorithms found in the literature.


title: Deep Appearance Models for Face Rendering

abstract: We introduce Deep Appearance Models for capturing human facial appearance with a multi-view camera system, real-time realistic rendering in VR, and performance-driven animation from HMD-mounted cameras.


title: Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval

abstract: This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words and entity annotations. The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks. The two components are learned end-to-end, making EDRM a natural combination of entity-oriented search and neural information retrieval. Our experiments on a commercial search log demonstrate the effectiveness of EDRM. Our analyses reveal that knowledge graph semantics significantly improve the generalization ability of neural ranking models.


title: Gender differences in seasonal affective disorder (SAD)

abstract: 146 women and 44 men (out- and inpatients; treatment sample) with Seasonal Affective Disorder (SAD; winter type) were tested for gender differences in demographic, clinical and seasonal characteristics. Sex ratio in prevalence was (women : men) 3.6 : 1 in unipolar depressives and 2.4 : 1 in bipolars (I and II). Sex ratios varied also between different birth cohorts and men seemed to underreport symptoms. There was no significant difference in symptom-profiles in both genders, however a preponderance of increased eating and different food selection on a trend level occured in women. The female group suffered significantly more often from thyroid disorders and from greater mood variations because of dark and cloudy weather. Women referred themselves to our clinic significantly more frequently as compared to men. In summary gender differences in SAD were similar to those of non-seasonal depression: the extent of gender differences in the prevalence of affective disorders appears to depend on case criteria such as diagnosis (unipolar vs. bipolar), birth cohort and number of symptoms as minimum threshold for diagnosis. We support the idea of applying sex-specific diagnostic criteria for diagnosing depression on the basis of our data and of the literature.


title: Big Data, Big Knowledge: Big Data for Personalized Healthcare

abstract: The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the “physiological envelope” during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.


title: Torque, Power, Losses, and Heat Calculation of a Transverse Flux Reluctance Machine With Soft Magnetic Composite Materials and Disk-Shaped Rotor

abstract: In this paper, the authors introduce a type of transverse flux reluctance machines. These machines work without permanent magnets or electric rotor excitation and hold several advantages, including a high power density, high torque, and compact design. Disadvantages are a high fundamental frequency and a high torque ripple that complicates the control of the motor. The device uses soft magnetic composites (SMCs) for the magnetic circuit, which allows complex stator geometries with 3-D magnetic flux paths. The winding is made from hollow copper tubes, which also form the main heat sink of the machine by using water as a direct copper coolant. Models concerning the design and computation of the magnetic circuit, torque, and the power output are described. A crucial point in this paper is the determination of hysteresis and eddy-current losses in the SMC and the calculation of power losses and current displacement in the copper winding. These are calculated with models utilizing a combination of analytic approaches and finite-element method simulations. Finally, a thermal model based on lumped parameters is introduced, and calculated temperature rises are presented.


title: Fucoxanthin, a Marine Carotenoid Present in Brown Seaweeds and Diatoms: Metabolism and Bioactivities Relevant to Human Health

abstract: The marine carotenoid fucoxanthin can be found in marine brown seaweeds, the macroalgae, and diatoms, the microalgae, and has remarkable biological properties. Numerous studies have shown that fucoxanthin has considerable potential and promising applications in human health. In this article, we review the current available scientific literature regarding the metabolism, safety, and bioactivities of fucoxanthin, including its antioxidant, anti-inflammatory, anticancer, anti-obese, antidiabetic, antiangiogenic and antimalarial activities, and its protective effects on the liver, blood vessels of the brain, bones, skin, and eyes. Although some studies have shown the bioavailability of fucoxanthin in brown seaweeds to be low in humans, many studies have suggested that a dietary combination of fucoxanthin and edible oil or lipid could increase the absorption rate of fucoxanthin, and thus it might be a promising marine drug.


title: The relation between alcohol and aggression: An integrated biopsychosocial conceptualization

abstract: The relation between acute alcohol consumption and aggressive behavior is a complex phenomenon that has been studied from a variety of different disciplines. This article reviews findings from both survey and experimental research. The influence of both situational and individual difference variables on the alcohol-aggression relation is discussed and the strengths and weaknesses of various methodological approaches are highlighted. Current theoretical perspectives of the alcohol-aggression relation are reviewed. An integrated heuristic framework of the alcohol-aggression relation also is outlined. This conceptualization involves both distal and proximal risk factors for problems with alcohol and violence, which include biological, psychological, interpersonal, and contextual influences. Research and treatment implications of this framework are also discussed. It is recommended that researchers attempt to measure variables from a variety of domains in order to obtain a better understanding of this complex phenomenon. Furthermore, it is emphasized that there is a clear need to further implement and assess primary and secondary prevention efforts and to design integrated and flexible approaches for individuals with alcohol and violence problems. Language: en


title: Music improves verbal memory encoding while decreasing prefrontal cortex activity: an fNIRS study

abstract: Listening to music engages the whole brain, thus stimulating cognitive performance in a range of non-purely musical activities such as language and memory tasks. This article addresses an ongoing debate on the link between music and memory for words. While evidence on healthy and clinical populations suggests that music listening can improve verbal memory in a variety of situations, it is still unclear what specific memory process is affected and how. This study was designed to explore the hypothesis that music specifically benefits the encoding part of verbal memory tasks, by providing a richer context for encoding and therefore less demand on the dorsolateral prefrontal cortex (DLPFC). Twenty-two healthy young adults were subjected to functional near-infrared spectroscopy (fNIRS) imaging of their bilateral DLPFC while encoding words in the presence of either a music or a silent background. Behavioral data confirmed the facilitating effect of music background during encoding on subsequent item recognition. fNIRS results revealed significantly greater activation of the left hemisphere during encoding (in line with the HERA model of memory lateralization) and a sustained, bilateral decrease of activity in the DLPFC in the music condition compared to silence. These findings suggest that music modulates the role played by the DLPFC during verbal encoding, and open perspectives for applications to clinical populations with prefrontal impairments, such as elderly adults or Alzheimer’s patients.


Artificial Intelligence and Literary Creativity: Inside the Mind of BRUTUS, a Storytelling Machine


title: An optimal algorithm for approximate nearest neighbor searching fixed dimensions

abstract: Consider a set of S of n data points in real d -dimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching, we preprocess S into a data structure, so that given any query point q ∈ R d , is the closest point of S to q can be reported quickly. Given any positive real e, data point p is a (1 +e)- approximate nearest neighbor of q if its distance from q is within a factor of (1 + e) of the distance to the true nearest neighbor. We show that it is possible to preprocess a set of n points in R d in O(dn log n ) time and O(dn) space, so that given a query point q ∈ R d , and e > 0, a (1 + e)-approximate nearest neighbor of q can be computed in O ( c d , e log n ) time, where c d,e ≤ d 1 + 6d/ e ; d is a factor depending only on dimension and e. In general, we show that given an integer k ≥ 1, (1 + e)-approximations to the k nearest neighbors of q can be computed in additional O(kd log n ) time.


title: Seamless Image Stitching in the Gradient Domain

abstract: Image stitching is used to combine several individual images having some overlap into a composite image. The quality of image stitching is measured by the similarity of the stitched image to each of the input images, and by the visibility of the seam between the stitched images.


Abstractive Summarization for Event News Tweets


title: Effective resistance and reactance of a solid cylindrical conductor placed in a semi-closed slot

abstract: The paper is concerned with the investigation of the current distribution over the cross-section of a solid cylindrical conductor lying in a slot with a narrow opening. It is found that the current density at any point is expressible as the sum of an infinite series of Bessel functions, each including an arbitrary constant which is determinable by making a reasonable assumption regarding the boundary conditions. By means of this result it is possible to determine the complex impedance of the conductor, and hence its equivalent resistance and internal reactance per unit length.


title: Evaluating the importance of multi-sensory input on memory and the sense of presence in virtual environments

abstract: 322 subjects participated in an experimental study to investigate the effects of tactile, olfactory, audio and visual sensory cues on a participant's sense of presence in a virtual environment and on their memory for the environment and the objects in that environment. Results strongly indicate that increasing the modalities of sensory input in a virtual environment can increase both the sense of presence and memory for objects in the environment. In particular, the addition of tactile, olfactory and auditory cues to a virtual environment increased the user's sense of presence and memory of the environment. Surprisingly, increasing the level of visual detail did not result in an increase in the user's sense of presence or memory of the environment.


title: Semantics-based Access Control Approach for Web Service

abstract: Due to the open and distributed characteristics of web service, its access control becomes a challenging problem which has not been addressed properly. In this paper, we show how semantic web technologies can be used to build a flexible access control system for web service. We follow the Role-based Access Control model and extend it with credential attributes. The access control model is represented by a semantic ontology, and specific semantic rules are constructed to implement such as dynamic roles assignment, separation of duty constraints and roles hierarchy reasoning, etc. These semantic rules can be verified and executed automatically by the reasoning engine, which can simplify the definition and enhance the interoperability of the access control policies. The basic access control architecture based on the semantic proposal for web service is presented. Finally, a prototype of the system is implemented to validate the proposal.


title: Comparison of path loss models for indoor 30 GHz, 140 GHz, and 300 GHz channels

abstract: This paper compares performance of the single-frequency floating-intercept (FI) model, the single-frequency close-in (CI) model, the multi-frequency alpha-beta-gamma (ABG) model, and the multi-frequency close-in frequency-dependant (CIF) model at 30 GHz, 140 GHz, and 300 GHz. For comparison purposes, extensive propagation measurements at 30 GHz (26.5–40 GHz), D-band (110–170 GHz), and 300 GHz (300–316 GHz) are conducted in the indoor line-of-sight (LoS) environments. The results show that if no measurement error is present in the channel impulse response, all four models have very similar performance and the model with the smallest number of parameters would be the optimal choice. Furthermore, results show that multi-frequency models have higher stability than single-frequency models. Finally, the results show that in the presence of measurement errors or lack of detailed antenna gain characterization, models without physical anchor (i.e. FI and ABG models) outperform models with physical anchor and correctly predict the reason for path loss mismatch between model and theoretical values.


A Review of Convolutional Neural Networks for Inverse Problems in Imaging


title: Building an argumentation platform for 3D reconstruction using CIDOC-CRM and Drupal

abstract: This paper describes a practical implementation of a CIDOC-CRM modeled virtual argumentation reconstruction model using the open-source DB Drupal. It describes a workflow for entering virtual reconstruction paradata, metadata and data in order to allow the tracing of knowledge provenance in reconstruction in CH. A case-study is presented of the documentation of a reconstruction of the Lioness Lintel (Hephaisteion, Athens).


title: Clinical Association Between Pharmacogenomics and Adverse Drug Reactions

abstract: Adverse drug reactions (ADRs) are a major public health concern and cause significant patient morbidity and mortality. Pharmacogenomics is the study of how genetic polymorphisms affect an individual’s response to pharmacotherapy at the level of a whole genome. This article updates our knowledge on how genetic polymorphisms of important genes alter the risk of ADR occurrence after an extensive literature search. To date, at least 244 pharmacogenes identified have been associated with ADRs of 176 clinically used drugs based on PharmGKB. At least 28 genes associated with the risk of ADRs have been listed by the Food and Drug Administration as pharmacogenomic biomarkers. With the availability of affordable and reliable testing tools, pharmacogenomics looks promising to predict, reduce, and minimize ADRs in selected populations.


title: GPU-Accelerated Large-Scale Distributed Sorting Coping with Device Memory Capacity

abstract: Splitter-based parallel sorting algorithms are known to be highly efficient for distributed sorting due to their low communication complexity. Although using GPU accelerators could help to reduce the computation cost in general, their effectiveness in distributed sorting algorithms remains unclear. We investigate applicability of using GPU devices to the splitter-based algorithms and extend HykSort, an existing splitter-based algorithm by offloading costly computation phases to GPUs. To cope with the volumes of data exceeding the GPU memory capacity, out-of-core local sort is used with small overhead about 7.5 percent when the data size is tripled. We evaluate the performance of our implementation on the TSUBAME2.5 supercomputer that comprises over 4,000 NVIDIA K20x GPUs. Weak scaling analysis shows 389 times speedup with 0.25 TB/s throughput when sorting 4 TB of 64 bit integer values on 1,024 nodes compared to running on one node; this is 1.40 times faster than the reference CPU implementation. Detailed analysis however reveals that the performance is mostly bottlenecked by the CPU-GPU host-to-device bandwidth. With orders of magnitude improvements announced for next generation GPUs, the performance boost will be tremendous in accordance with other successful GPU accelerations.


title: Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud

abstract: Cloud computing provides new opportunities to deploy scalable application in an efficient way, allowing enterprise applications to dynamically adjust their computing resources on demand. In this paper we analyze and test the microservice architecture pattern, used during the last years by large Internet companies like Amazon, Netflix and LinkedIn to deploy large applications in the cloud as a set of small services that can be developed, tested, deployed, scaled, operated and upgraded independently, allowing these companies to gain agility, reduce complexity and scale their applications in the cloud in a more efficient way. We present a case study where an enterprise application was developed and deployed in the cloud using a monolithic approach and a microservice architecture using the Play web framework. We show the results of performance tests executed on both applications, and we describe the benefits and challenges that existing enterprises can get and face when they implement microservices in their applications.


title: Next generation 600V CSTBT™ with an advanced fine pattern and a thin wafer process technologies

abstract: In this paper, we present the characteristics of a fabricated 600V CSTBT™ as the next generation IGBT. The techniques applied this novel device include about half-size shrinkage of the transistor unit cell with a fine pattern process and an LPT (Light Punch Through) structure utilizing an advanced thin wafer process technology. As a result, these techniques brought a significant reduction of the Vce(sat) and the Eoff. The Vce(sat)-Eoff trade-off relationship of the proposed CSTBT has been improved by approximately 20% compared to the conventional one possessing wide SOA (Safe Operating Area) enough to device applications.


title: OBST-based segmentation approach to financial time series

abstract: Financial time series data are large in size and dynamic and non-linear in nature. Segmentation is often performed as a pre-processing step for locating technical patterns in financial time series. In this paper, we propose a segmentation method based on Turning Points (TPs). The proposed method selects TPs from the financial time series in question based on their degree of importance. A TP's degree of importance is calculated on the basis of its contribution to the preservation of the trends and shape of the time series. Algorithms are also devised to store the selected TPs in an Optimal Binary Search Tree (OBST) and to reconstruct the reduced sample time series. Comparison with existing approaches show that the time series reconstructed by the proposed method is able to maintain the shape of the original time series very well and preserve more trends. Our approach also ensures that the average retrieval cost is kept at a minimum.


title: Systems biology approaches for advancing the discovery of effective drug combinations

abstract: Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations.


title: Power–duration relationship: Physiology, fatigue, and the limits of human performance

abstract: AbstractThe duration that exercise can be maintained decreases as the power requirements increase. In this review, we describe the power–duration (PD) relationship across the full range of attainable power outputs in humans. We show that a remarkably small range of power outputs is sustainable (power outputs below the critical power, CP). We also show that the origin of neuromuscular fatigue differs considerably depending on the exercise intensity domain in which exercise is performed. In the moderate domain (below the lactate threshold, LT), fatigue develops slowly and is predominantly of central origin (residing in the central nervous system). In the heavy domain (above LT but below CP), both central and peripheral (muscle) fatigue are observed. In this domain, fatigue is frequently correlated with the depletion of muscle glycogen. Severe-intensity exercise (above the CP) is associated with progressive derangements of muscle metabolic homeostasis and consequent peripheral fatigue. To counter these effec...


title: Time Series ANN Approach for Weather Forecasting

abstract: Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting.


title: Adapting Engineering Education to Industrie 4.0 Vision

abstract: Industry 4.0 is originally a future vision described in the high-tech strategy of the German government that is conceived upon information and communication technologies like Cyber-Physical Systems, Internet of Things, Physical Internet, and Internet of Services to achieve a high degree of flexibility in production (individualized mass production), higher productivity rates through real-time monitoring and diagnosis, and a lower wastage rate of material in production. An important part of the tasks in the preparation for Industry 4.0 is the adaption of the higher education to the requirements of this vision, in particular the engineering education. In this work, we introduce a road map consisting of three pillars describing the changes/enhancements to be conducted in the areas of curriculum development, lab concept, and student club activities. We also report our current application of this road map at the Turkish German University, Istanbul.


title: A Comprehensive Study on the Internet of Underwater Things: Applications, Challenges, and Channel Models †

abstract: The Internet of Underwater Things (IoUT) is a novel class of Internet of Things (IoT), and is defined as the network of smart interconnected underwater objects. IoUT is expected to enable various practical applications, such as environmental monitoring, underwater exploration, and disaster prevention. With these applications, IoUT is regarded as one of the potential technologies toward developing smart cities. To support the concept of IoUT, Underwater Wireless Sensor Networks (UWSNs) have emerged as a promising network system. UWSNs are different from the traditional Territorial Wireless Sensor Networks (TWSNs), and have several unique properties, such as long propagation delay, narrow bandwidth, and low reliability. These unique properties would be great challenges for IoUT. In this paper, we provide a comprehensive study of IoUT, and the main contributions of this paper are threefold: (1) we introduce and classify the practical underwater applications that can highlight the importance of IoUT; (2) we point out the differences between UWSNs and traditional TWSNs, and these differences are the main challenges for IoUT; and (3) we investigate and evaluate the channel models, which are the technical core for designing reliable communication protocols on IoUT.


title: Factorial Multi-Task Learning: A Bayesian Nonparametric Approach Sunil Kumar Gupta

abstract: Multi-task learning is a paradigm shown to improve the performance of related tasks through their joint learning. However, for real-world data, it is usually difficult to assess the task relatedness and joint learning with unrelated tasks may lead to serious performance degradations. To this end, we propose a framework that groups the tasks based on their relatedness in a subspace and allows a varying degree of relatedness among tasks by sharing the subspace bases across the groups. This provides the flexibility of no sharing when two sets of tasks are unrelated and partial/total sharing when the tasks are related. Importantly, the number of task-groups and the subspace dimensionality are automatically inferred from the data. To realize our framework, we introduce a novel Bayesian nonparametric prior that extends the traditional hierarchical beta process prior using a Dirichlet process to permit potentially infinite number of child beta processes. We apply our model for multi-task regression and classification applications. Experimental results using several synthetic and real datasets show the superiority of our model to other recent multi-task learning methods.


title: A tendon skeletal finger model for evaluation of pinching effort

abstract: In this paper, we propose a tendon skeletal finger model and discuss which finger postures the human feels easy to pinch based on the tendon forces and the human experimental results. The finger model mimics a human tendon skeletal structure. The tendon forces during the pinching motion were simulated using the finger model. Simulation results show that the tendon forces closely mirror the human muscle activity. Sensory evaluation of subjective pinching effort was conducted with five subjects. The subject pinched five kinds of cylinders, from 20 [mm] to 100 [mm]. The pinching force and the surface EMGs were simultaneously measured in the experiment. Based on the human questionnaire tests, we investigated which finger postures the human feels easy to pinch a cylinder. The results show that the pattern of the EMGs measured by the experiment is very similar to that of the tendon forces calculated by the finger model simulation. This indicates that the tendon force is a useful index of the subjective pinching effort and it can be used for the quantitative evaluation instead of EMGs.


title: A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

abstract: The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.


title: Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning

abstract: Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportation efficiency. An efficient fleet management strategy not only can significantly improve the utilization of transportation resources but also increase the revenue and customer satisfaction. It is a challenging task to design an effective fleet management strategy that can adapt to an environment involving complex dynamics between demand and supply. Existing studies usually work on a simplified problem setting that can hardly capture the complicated stochastic demand-supply variations in high-dimensional space. In this paper we propose to tackle the large-scale fleet management problem using reinforcement learning, and propose a contextual multi-agent reinforcement learning framework including two concrete algorithms, namely contextual deep Q-learning and contextual multi-agent actor-critic, to achieve explicit coordination among a large number of agents adaptive to different contexts. We show significant improvements of the proposed framework over state-of-the-art approaches through extensive empirical studies.


title: Backpage and Bitcoin: Uncovering Human Traffickers

abstract: Sites for online classified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unscalable. In addition, discerning whether an ad is advertising a trafficked victim or an independent sex worker is a very difficult task. Very little concrete ground truth (i.e., ads definitively known to be posted by a trafficker) exists in this space. In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad). Specifically, we develop a machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 90% TPR and 1% FPR. We also design a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions. Finally, we demonstrate via a 4-week proof of concept using Backpage as the sex ad site, how an analyst can use these automated approaches to potentially find human traffickers.


title: Personalised Information Retrieval: survey and classification

abstract: Information Retrieval (IR) systems assist users in finding information from the myriad of information resources available on the Web. A traditional characteristic of IR systems is that if different users submit the same query, the system would yield the same list of results, regardless of the user. Personalised Information Retrieval (PIR) systems take a step further to better satisfy the user's specific information needs by providing search results that are not only of relevance to the query but are also of particular relevance to the user who submitted the query. PIR has thereby attracted increasing research and commercial attention as information portals aim at achieving user loyalty by improving their performance in terms of effectiveness and user satisfaction. In order to provide a personalised service, a PIR system maintains information about the users and the history of their interactions with the system. This information is then used to adapt the users' queries or the results so that information that is more relevant to the users is retrieved and presented. This survey paper features a critical review of PIR systems, with a focus on personalised search. The survey provides an insight into the stages involved in building and evaluating PIR systems, namely: information gathering, information representation, personalisation execution, and system evaluation. Moreover, the survey provides an analysis of PIR systems with respect to the scope of personalisation addressed. The survey proposes a classification of PIR systems into three scopes: individualised systems, community-based systems, and aggregate-level systems. Based on the conducted survey, the paper concludes by highlighting challenges and future research directions in the field of PIR.


title: Modeling of Wide Bandgap Power Semiconductor Devices—Part I

abstract: Wide bandgap power devices have emerged as an often superior alternative power switch technology for many power electronic applications. These devices theoretically have excellent material properties enabling power device operation at higher switching frequencies and higher temperatures compared with conventional silicon devices. However, material defects can dominate device behavior, particularly over time, and this should be strongly considered when trying to model actual characteristics of currently available devices. Compact models of wide bandgap power devices are necessary to analyze and evaluate their impact on circuit and system performance. Available compact models, i.e., models compatible with circuit-level simulators, are reviewed. In particular, this paper presents a review of compact models for silicon carbide power diodes and MOSFETs.


title: Desalination by Membrane Distillation

abstract: Desalination of seawater is the technology predominantly used to alleviate the problem of water scarcity in coastal regions. The sustainability of all desalination processes depends mainly on the reduction of energy costs, the increase of water recovery and the enhancement in recovery and reuse of the generated waste. Integrated/hybrid membrane processes have attracted much interest in the desalination field. Membrane distillation (MD) is a membrane process that can be used in combination with other processes. The objective of this chapter is to review the capability of MD in treating highly concentrated aqueous solutions derived from other desalination processes. This potential application is attracting an increasing interest due to the problem of brine discharges to the environment. Various benefits can be obtained with the utilisation of MD in integrated/hybrid membrane processes, e.g. enhanced quality of the water produced, brine concentration up to zero discharge and energy savings.


title: Slicing: A New Approach for Privacy Preserving Data Publishing

abstract: Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the l-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.


title: Does Consciousness Disappear in Dreamless Sleep?

abstract: Consciousness is often said to disappear in deep, dreamless sleep. We argue that this assumption is oversimplified. Unless dreamless sleep is defined as unconscious from the outset there are good empirical and theoretical reasons for saying that a range of different types of sleep experience, some of which are distinct from dreaming, can occur in all stages of sleep. We introduce a novel taxonomy for describing different kinds of dreamless sleep experiences and suggest research methods for their investigation. Future studies should focus on three areas: memory consolidation, sleep disorders, and sleep state (mis)perception. Our proposal suggests new directions for sleep and dream science, as well as for the neuroscience of consciousness, and can also inform the diagnosis and treatment of sleep disorders.


title: Dynamic resource allocation using reinforcement learning for LTE-U and WiFi in the unlicensed spectrum

abstract: The growing demand for spectrum resources and the limited licensed spectrum have led to widespread concern about the coexistence of LTE and WiFi in the unlicensed spectrum. Since LTE and WiFi are two different systems, many solutions have been proposed to solve their coexistence problems, including the coexistence scheme based on blank subframe allocation. This paper presents a scheme that uses Q-learning algorithm to dynamically allocate blank subframes so that both LTE-U and WiFi system can successfully coexist. To support the proposed scheme, we introduce a new LTE-U frame structure, which can not only allocate blank subframes but also reduce the LTE delay. Simulation results show that the proposed approach can effectively improve the overall system performance in terms of the utilization of spectrum.


title: Vezzani: A multi-camera vision system for fall detection and alarm generation

abstract: In-house video surveillance can represent an excellent support for people with some difficulties (e.g. elderly or disabled people) living alone and with a limited autonomy. New hardware technologies and in particular digital cameras are now affordable and they have recently gained credit as tools for (semi-) automatically assuring people's safety. In this paper a multi-camera vision system for detecting and tracking people and recognizing dangerous behaviours and events such as a fall is presented. In such a situation a suitable alarm can be sent, e.g. by means of an SMS. A novel technique of warping people's silhouette is proposed to exchange visual information between partially overlapped cameras whenever a camera handover occurs. Finally, a multi-client and multi-threaded transcoding video server delivers live video streams to opera- tors=remote users in order to check the validity of a received alarm. Semantic and event-based transcoding algorithms are used to optimize the bandwidth usage. A two-room setup has been created in our laboratory to test the performance of the overall system and some of the results obtained are reported.


title: Increasing pre-kindergarten early literacy skills in children with developmental disabilities and delays

abstract: Abstract Two hundred and nine children receiving early childhood special education services for developmental disabilities or delays who also had behavioral, social, or attentional difficulties were included in a study of an intervention to increase school readiness, including early literacy skills. Results showed that the intervention had a significant positive effect on children's literacy skills from baseline to the end of summer before the start of kindergarten ( d = .14). The intervention also had significant indirect effects on teacher ratings of children's literacy skills during the fall of their kindergarten year (β = .09). Additionally, when scores were compared to standard benchmarks, a greater percentage of the children who received the intervention moved from being at risk for reading difficulties to having low risk. Overall, this study demonstrates that a school readiness intervention delivered prior to the start of kindergarten may help increase children's early literacy skills.


title: RTL implementation and analysis of fixed priority, round robin, and matrix arbiters for the NoC's routers

abstract: Networks-on-Chip (NoC) is an emerging on-chip interconnection centric platform that influences modern high speed communication infrastructure to improve the performance of many-core System-on-Chip (SoCs) designs. The core of each NoCs router involves arbiter and multiplier pairs that need to be carefully co-optimized in order to achieve an overall efficient implementation. Low transmission latency design is one of the most important parameters of NoC design. This paper uses parametric Verilog HDL to implement the designs and compares the performance in terms of power, area, and delay of different types of arbiters using for NoCs routers. The RTL implementation is performed using parametric Verilog HDL and analysis in term of power, area and delay is performed using Xilinx ISE 14.7 and Xpower Analyzer (XPA) with Xpower Estimator (XPE). The target device uses for these implementation is Vertex 6.


title: Mobile Quad-Controlled wireless Robotic Arm

abstract: In this paper, we develop a remotely controlled robotic arm with 4 degree of freedom (D.O.F) that is wirelessly controlled using four control mechanisms, i.e, Voice Control, Smart Phone-Tilt Control, Remote control and Hand Gesture Control. Wireless technologies such as Bluetooth and Wi-Fi are used to access the Quad-Controlled Robotic Arm (QCRA). A prototype QCRA is developed. The QCRA can be used to pick and place objects from one place to another on receiving the commands from distance, thereby reducing the human effort. An Android application is developed for convenience of the user in operating this QCRA, using different control mechanisms. Performance evaluation results are encouraging. Potential applications of the QCRA in homes, industries and for physically challenged/aged people are also discussed.


title: Application of Dimensionality Reduction in Recommender System -- A Case Study

abstract: Abstract : We investigate the use of dimensionality reduction to improve performance for a new class of data analysis software called "recommender systems" Recommender systems apply knowledge discovery techniques to the problem of making product recommendations during a live customer interaction. These systems are achieving widespread success in E-commerce nowadays, especially with the advent of the Internet. The tremendous growth of customers and products poses three key challenges for recommender systems in the E-commerce domain. These are: producing high quality recommendations, performing many recommendations per second for millions of customers and products, and achieving high coverage in the face of data sparsity. One successful recommender system technology is collaborative filtering, which works by matching customer preferences to other customers in making recommendations. Collaborative filtering has been shown to produce high quality recommendations, but the performance degrades with the number of customers and products. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very largescale problems. This paper presents two different experiments where we have explored one technology called Singular Value Decomposition (SVD) to reduce the dimensionality of recommender system databases. Each experiment compares the quality of a recommender system using SVD with the quality of a recommender system using collaborative filtering. The first experiment compares the effectiveness of the two recommender systems at predicting consumer preferences based on a database of explicit ratings of products. The second experiment compares the effectiveness of the two recommender systems at producing Top-N lists based on a real-life customer purchase database from an E-Commerce site. Our experience suggests that SVD has the potential to meet many of the challenges of recommender systems, under certain conditions.


title: Real-Time Grasping Planning for Robotic Bin-Picking and Kitting Applications

abstract: Multi-fingered robotic hands can potentially offer a higher degree of dexterity for grasping a variety of parts than a traditional parallel gripper or a custom end of arm tool. However, real-time grasp planning for complex robotic hands is a challenging task. In this paper, we present real-time grasping planning algorithms that utilize two simple yet effective dimension reduction strategies. The first strategy is the “intersected volume” computation which places the robotic palm relative to the object as well as its environment. The second strategy is the “finger curling planes” which decomposes a grasp contact computation problem in high-dimensional configuration space into several independent grasp contact computation problems in low-dimensional configuration space. Our algorithms have been demonstrated with simulation, physical experiments, and robotic bin-picking and kitting applications on two robotic platforms with three different robotic hands.


title: Impact of Persuasion Processes on Consumer Attitude Towards Online Shopping: The Moderating Role of Product Involvement and Consumer Self-Efficacy

abstract: Online vendors expand significant efforts in attracting consumers to their websites. However, once a consumer lands on a vendor’s website, a more challenging task is to convince her/him to actually transact with the website by purchasing products. This research-in-progress study draws on the elaboration-likelihood model (ELM) to develop a theoretical model for the impacts of different persuasion processes (peripheral and central routes) on consumer attitude towards purchasing products online. We also propose motivational and ability factors that are likely to moderate the effects of these different persuasion processes on attitude. A survey-based methodology is outlined to empirically validate the proposed research model using structural equation modelling techniques. Potential contributions from this research to both theory and practice are also outlined.


title: A hybrid architectural approach to understanding and appropriately generating indirect speech acts

abstract: Current approaches to handling indirect speech acts (ISAs) do not account for their sociolinguistic underpinnings (i.e., politeness strategies). Deeper understanding and appropriate generation of indirect acts will require mechanisms that integrate natural language (NL) understanding and generation with social information about agent roles and obligations, which we introduce in this paper. Additionally, we tackle the problem of understanding and handling indirect answers that take the form of either speech acts or physical actions, which requires an inferential, plan-reasoning approach. In order to enable artificial agents to handle an even wider-variety of ISAs, we present a hybrid approach, utilizing both the idiomatic and inferential strategies. We then demonstrate our system successfully generating indirect requests and handling indirect answers, and discuss avenues of future research.


title: A Systematic Topology Evaluation Methodology for High-Density Three-Phase PWM AC-AC Converters

abstract: This paper presents a systematic evaluation approach of three-phase pulsewidth-modulated (PWM) AC-AC converter topologies for high-density applications. All major components and subsystems in a converter are considered and the interdependence of all the constraints and design parameters is systematically studied. The key design parameters, including switching frequency, modulation scheme, and passive values, are selected by considering their impacts on loss, harmonics, electromagnetic interference (EMI), control dynamics and stability, and protection. The component selection criteria as well as the physical design procedures are developed from the high-density standpoint. The concept of using the same inductor for harmonic suppression and EMI filtering is introduced in the design. With the proposed methodology, four converter topologies, a back-to-back voltage source converter (BTB-VSC), a nonregenerative three-level boost (Vienna-type) rectifier plus voltage source inverter (NTR-VSI), a back-to-back current source converter (BTB-CSC), and a 12-switch matrix converter, are analyzed and compared for high specific power using SiC devices. The evaluation results show that with the conditions specified in this paper, BTB-VSC and NTR-VSI have considerably lower loss, resulting in higher specific power than BTB-CSC and the matrix converter. The proposed methodology can be applied to other topologies with different comparison metrics and can be a useful tool for high-density topology selection.


title: Emotion Classification Using Massive Examples Extracted from the Web

abstract: In this paper, we propose a data-oriented method for inferring the emotion of a speaker conversing with a dialog system from the semantic content of an utterance. We first fully automatically obtain a huge collection of emotion-provoking event instances from the Web. With Japanese chosen as a target language, about 1.3 million emotion provoking event instances are extracted using an emotion lexicon and lexical patterns. We then decompose the emotion classification task into two sub-steps: sentiment polarity classification (coarsegrained emotion classification), and emotion classification (fine-grained emotion classification). For each subtask, the collection of emotion-proviking event instances is used as labelled examples to train a classifier. The results of our experiments indicate that our method significantly outperforms the baseline method. We also find that compared with the single-step model, which applies the emotion classifier directly to inputs, our two-step model significantly reduces sentiment polarity errors, which are considered fatal errors in real dialog applications.


title: The Socio-Technical Dimension of Inertia in Digital Transformations

abstract: When organizations undertake large transformation initiatives enabled by information technology, these efforts ::: are often hampered by inertia. The literature suggests that inertia plays a dual role in organizations: it is both required for organizational efficiency and an antecedent of resistance to change. While traditionally inertia is believed to reside in human actors, we suggest that inertia is rooted in multiple facets – in routines, resources such as social agents, and also technology – and plays on multiple levels – at individual, group, and organizational ones. In this essay, we propose a new conceptualization of inertia that encompasses and integrates these elements. Our model suggests that inertia occurs as path-dependent rigidity in organizational behavior through the coalescence of social entities with technology artifacts. We illustrate our new understanding of inertia by revisiting two case vignettes of inertia and impeded digital transformations.


title: Image Based Forest Fire Detection Using Dynamic Characteristics with Artificial Neural Networks

abstract: In this paper, we propose a real-time forest fire detection algorithm using artificial neural networks based on dynamic characteristics of fire regions segmented from video images. Fire region is obtained from image with the help of threshold values in HSV color space. Area, roundness and contour are computed for fire regions from each 5 continuous frames. The average and mean square deviation of them are used as dynamic characteristics, and taken as input of the artificial neural network. The trained BP network can help identify forest fire, even distinguish it from moving car or flying flag with red color. Experimental results of our method prove its value in forest fire surveillance.


title: CaseStudy Visualization and Information Retrieval

abstract: We describe our efforts to analyze network intrusion detection data using information retrieval and visualization tools. By regarding Telnet sessions as documents, which may or may not include attacks, a session that contains a certain type of attack can be used as a query, allowing us to search the data for other instances of that same type of attack. The use of information visualization techniques allows us to quickly and clearly find the attacks and also find similar, potentially new types of attacks.


title: Image aesthetic predictors based on weighted CNNs

abstract: Convolutional Neural Networks (CNNs) have been widely adopted for many imaging applications. For image aesthetics prediction, state-of-the-art algorithms train CNNs on a recently-published large-scale dataset, AVA. However, the distribution of the aesthetic scores on this dataset is extremely unbalanced, which limits the prediction capability of existing methods. We overcome such limitation by using weighted CNNs. We train a regression model that improves the prediction accuracy of the aesthetic scores over state-of-the-art algorithms. In addition, we propose a novel histogram prediction model that not only predicts the aesthetic score, but also estimates the difficulty of performing aesthetics assessment for an input image. We further show an image enhancement application where we obtain an aesthetically pleasing crop of an input image using our regression model.


title: Juvenile Gigantomastia in a 12-Year-Old Girl: A Case Report

abstract: Juvenile gigantomastia is a rare, massive breast enlargement. It appears in adolescence and can affect one or both breasts of otherwise healthy girls. This report describes a case of a bilateral and symmetric gigantomastia in a 12-year-old girl.


title: Contribution to the characterization of Opuntia spp. juices by LC–DAD–ESI-MS/MS

abstract: Opuntia spp. fruits are considered as health promoting foods due to the diversity of bioactive molecules found in these fruits. The composition in organic acids, flavonols and betalains in the Opuntia ficus-indica juice from a region of Portugal was accomplished for the first time by liquid chromatography and tandem mass spectrometry using an electrospray ionization source operating in negative and positive mode. The methodology used allowed the detection of 44 compounds, from which 32 were identified. Isorhamnetin derivatives were the dominant flavonol glycosides. A total of 9 betalains including 6 betaxanthins and 3 betacyanin were also detected in the fruit juice samples and indicaxanthin, betanin and isobetanin were the major pigments. Phenolic acid and phenylpyruvic acid derivatives were also identified. To our knowledge, it is the first time derivative compounds from piscidic acid, phenolic compounds and betalains are characterized in cactus pear juice using a single LC–DAD–ESI-MS/MS method.


title: Graphical Perception of Continuous Quantitative Maps: the Effects of Spatial Frequency and Colormap Design

abstract: Continuous 'pseudocolor' maps visualize how a quantitative attribute varies smoothly over space. These maps are widely used by experts and lay citizens alike for communicating scientific and geographical data. A critical challenge for designers of these maps is selecting a color scheme that is both effective and aesthetically pleasing. Although there exist empirically grounded guidelines for color choice in segmented maps (e.g., choropleths), continuous maps are significantly understudies, and their color-coding guidelines are largely based on expert opinion and design heuristics--many of these guidelines have yet to be verified experimentally. We conducted a series of crowdsourced experiments to investigate how the perception of continuous maps is affected by colormap characteristics and spatial frequency (a measure of data complexity). We find that spatial frequency significantly impacts the effectiveness of color encodes, but the precise effect is task-dependent. While rainbow schemes afforded the highest accuracy in quantity estimation irrespective of spatial complexity, divergent colormaps significantly outperformed other schemes in tasks requiring the perception of high-frequency patterns. We interpret these results in relation to current practices, and devise new and more granular guidelines for color mapping in continuous maps.


title: Object Proposal Generation With Fully Convolutional Networks

abstract: Object proposal generation, as a preprocessing technique, has been widely used in current object detection pipelines to guide the search of objects and avoid exhaustive sliding window search across images. Current object proposals are mostly based on low-level image cues, such as edges and saliency. However, objectness is possibly a high-level semantic concept showing whether one region contains objects. This paper presents a framework utilizing fully convolutional networks (FCNs) to produce object proposal positions and bounding box location refinement with Support Vector Machine (SVM) to further improve proposal localization. Experiments on the PASCAL VOC 2007 show that using high-level semantic object proposals obtained by FCN, the object recall can be improved. An improvement in detection mean average precision is also seen when using our proposals in the Fast R-convolutional neural network framework. In addition, we also demonstrate that our method shows stronger robustness when introduced to image perturbations, e.g., blurring, JPEG compression, and salt and pepper noise. Finally, the generalization capability of our model (trained on the PASCAL VOC 2007) is evaluated and validated by testing on PASCAL VOC 2012 validation set, ILSVRC 2013 validation set, and MS COCO 2014 validation set.


A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion


title: Discrete Variational Autoencoders

abstract: Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We present a novel method to train a class of probabilistic models with discrete latent variables using the variational autoencoder framework, including backpropagation through the discrete latent variables. The associated class of probabilistic models comprises an undirected discrete component and a directed hierarchical continuous component. The discrete component captures the distribution over the disconnected smooth manifolds induced by the continuous component. As a result, this class of models efficiently learns both the class of objects in an image, and their specific realization in pixels, from unsupervised data, and outperforms state-of-the-art methods on the permutation-invariant MNIST, Omniglot, and Caltech-101 Silhouettes datasets.


title: Data formats for high efficiency coding of Lytro-Illum light fields

abstract: Light fields captured by the Lytro-Illum camera are the first to appear in the consumer market, capable of providing refocused pictures at acceptable spatial resolution and quality. Since this is partially due to sampling of a huge number of light rays, efficient compression methods are required to store and exchange light field data. This paper presents a performance study of HEVC-compatible coding of Lytro-Illum light fields using different data formats for standard coding. The efficiency of 5 different light field data formats are evaluated using a data set of 12 light field images and the standard HEVC coding configurations of Still-Image Profile, All-Intra, Low Delay B and P and Random Access. Unexpectedly, the results show that relative performance is not consistent across all coding configurations, raising new research questions regarding standard coding of Lytro-Illum light fields using HEVC. Most importantly, the proposed data formats greatly increase HEVC performance.


title: Influence of dipolar interactions on the magnetic properties of superparamagnetic particle systems

abstract: Magnetic effects caused by dipolar interactions in single-domain magnetic ensembles at finite temperatures are described. A modified superparamagnetic approach based on the mean field theory and random anisotropy model has been developed to describe the magnetization curves of nanoparticle assemblies. The model was used to fit experimental zero-field-cooled and field-cooled magnetization curves in Fe 3O 4 nanoparticles embedded in paraffin. The fitting parameters were based on structural properties of the materials and the strength of the interactions between nanoparticles. The model provides a quantitative description of the effects of the nanoparticle interaction with good agreement with the experiment. In addition, the model was adapted to describe magnetic properties of a NiFe thin film patterned into a nanodot array, showing potential to be used as a framework to predict magnetic interaction effects in high-density 2D arrays such as bit patterned media.Magnetic effects caused by dipolar interactions in single-domain magnetic ensembles at finite temperatures are described. A modified superparamagnetic approach based on the mean field theory and random anisotropy model has been developed to describe the magnetization curves of nanoparticle assemblies. The model was used to fit experimental zero-field-cooled and field-cooled magnetization curves in Fe 3O 4 nanoparticles embedded in paraffin. The fitting parameters were based on structural properties of the materials and the strength of the interactions between nanoparticles. The model provides a quantitative description of the effects of the nanoparticle interaction with good agreement with the experiment. In addition, the model was adapted to describe magnetic properties of a NiFe thin film patterned into a nanodot array, showing potential to be used as a framework to predict magnetic interaction effects in high-density 2D arrays such as bit patterned media.


title: PointNet: A 3D Convolutional Neural Network for real-time object class recognition

abstract: During the last few years, Convolutional Neural Networks are slowly but surely becoming the default method solve many computer vision related problems. This is mainly due to the continuous success that they have achieved when applied to certain tasks such as image, speech, or object recognition. Despite all the efforts, object class recognition methods based on deep learning techniques still have room for improvement. Most of the current approaches do not fully exploit 3D information, which has been proven to effectively improve the performance of other traditional object recognition methods. In this work, we propose PointNet, a new approach inspired by VoxNet and 3D ShapeNets, as an improvement over the existing methods by using density occupancy grids representations for the input data, and integrating them into a supervised Convolutional Neural Network architecture. An extensive experimentation was carried out, using ModelNet - a large-scale 3D CAD models dataset - to train and test the system, to prove that our approach is on par with state-of-the-art methods in terms of accuracy while being able to perform recognition under real-time constraints.


title: An Empirical Study on Detecting and Fixing Buffer Overflow Bugs

abstract: Buffer overflow is one of the most common types of software security vulnerabilities. Although researchers have proposed various static and dynamic techniques for buffer overflow detection, buffer overflow attacks against both legacy and newly-deployed software systems are still quite prevalent. Compared with dynamic detection techniques, static techniques are more systematic and scalable. However, there are few studies on the effectiveness of state-of-the-art static buffer overflow detection techniques. In this paper, we perform an in-depth quantitative and qualitative study on static buffer overflow detection. More specifically, we obtain both the buggy and fixed versions of 100 buffer overflow bugs from 63 real-world projects totalling 28 MLoC (Millions of Lines of Code) based on the reports in Common Vulnerabilities and Exposures (CVE). Then, quantitatively, we apply Fortify, Checkmarx, and Splint to all the buggy versions to investigate their false negatives, and also apply them to all the fixed versions to investigate their false positives. We also qualitatively investigate the causes for the false-negatives and false-positives of studied techniques to guide the design and implementation of more advanced buffer overflow detection techniques. Finally, we also categorized the patterns of manual buffer overflow repair actions to guide automated repair techniques for buffer overflow. The experiment data is available at http://bo-study.github.io/Buffer-Overflow-Cases/.


A study on fuzzy labeling graphs


title: Design considerations for fractional-slot winding configurations of synchronous machines

abstract: This paper presents some design considerations for synchronous machines characterized by a fractional number of slots per pole per phase. The main advantage of this configuration is a smooth torque, which is due to the elimination of periodicity between slots and poles. A second advantage is a higher fault-tolerant capability, making the machine able to work even in faulty conditions. However, the fractional-slot configuration presents a high content of MMF harmonics that may cause an unbalanced saturation and thus an unbearable torque ripple. A method to design fractional-slot machines is illustrated in this paper, including double-layer and single-layer windings. The analytical computation is extended to determine the harmonics of MMF distribution. Their effect is highlighted in isotropic as well as anisotropic machines. Finally, some considerations are reported to avoid unsuitable configurations


title: Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLens

abstract: Prior work relevant to incorporating personality into recommender systems falls into two categories: social science studies and algorithmic ones. Social science studies of preference have found only small relationships between personality and category preferences, whereas, algorithmic approaches found a little improvement when incorporating personality into recommendations. As a result, despite good reasons to believe personality assessments should be useful in recommenders, we are left with no substantial demonstrated impact. In this work, we start with user data from a live recommender system, but study category-by-category variations in preference (both rating levels and distribution) across different personality types. By doing this, we hope to isolate specific areas where personality is most likely to provide value in recommender systems, while also modeling an analytic process that can be used in other domains. After controlling for the family-wise error rate, we find that High Agreeableness users rate at least 0.5 stars higher on a 5-star scale compared to low Agreeableness users. We also find differences in consumption in four different personality types between people who manifested high and low levels of that personality.


title: Tax planning and corporate effective tax rates

abstract: This study attempts to investigate the tax planning activities of Malaysian public companies listed on Bursa Malaysia during the official assessment system tax regime (OAS) (whereby the tax computation is prepared by tax authorities) and the self assessment system tax regime (OAS) (the tax computation is prepared by taxpayers). The objective is to examine the level of corporate effective tax rates (ETRs) experienced by companies during both tax regimes. This study uses pooled sample data of 316 companies for the years 1993 to 2006. In investigating the aggresiveness of the tax planning activites during the two tax regimes, the investigation period is classified into two: the period from 1993 to 2000 represents the official assessment system tax regime, and the period from 2001 to 2006 represents the self assessment system tax regime. This study finds that corporate ETRs are below the statutory tax rate (STR) in both tax regimes. The current study reveals that ETRs during the self assessment system tax regime is lower than the official assessment system tax regime. Additionally, this study examines the determinants of corporate ETRs during both tax regimes. The result supports political cost theory which suggests that larger companies endure higher ETRs. Further, the statistical results provide evidence that lower ETRs are significantly related to highly leverage companies, greater investment in fixed assets and lower investment in inventory. This study finds that companies with higher return on assets face lower ETRs. In addition, sector analysis are carried out to provide the evidence for the variability of ETRs across sectors. The results indicate that companies from trading and services, properties and construction sectors face higher ETRs. Overall, this study explains the impacts of tax provisions on corporate ETRs and determinants of corporate ETRs.


title: The design of a squarer-ring slot antenna for circular polarization

abstract: A new design of a circularly polarized square-ring slot antenna is proposed. The circular polarization is achieved by a series microstrip-line-feed configuration coupling to two orthogonal sides of the square-ring slot. A parametric study is conducted using a commercial simulation software based on the method of moments, and the design procedure is established. The required ground-plane size of the proposed design for a given frequency is also indicated. Three antenna prototypes respectively operated at distinct frequency bands are constructed and measured for providing the simulation validation. Experimental results show that the proposed antenna has good circularly polarized radiations and the 3 dB axial-ratio bandwidth is more than 6%.


title: Bicycling for transportation and health: The role of infrastructure

abstract: This paper aims to provide insight on whether bicycling for everyday travel can help US adults meet the recommended levels of physical activity and what role public infrastructure may play in encouraging this activity. The study collected data on bicycling behavior from 166 regular cyclists in the Portland, Oregon metropolitan area using global positioning system (GPS) devices. Sixty percent of the cyclists rode for more than 150 minutes per week during the study and nearly all of the bicycling was for utilitarian purposes, not exercise. A disproportionate share of the bicycling occurred on streets with bicycle lanes, separate paths, or bicycle boulevards. The data support the need for well-connected neighborhood streets and a network of bicycle-specific infrastructure to encourage more bicycling among adults. This can be accomplished through comprehensive planning, regulation, and funding.


title: Improving optimal control of grid-connected lithium-ion batteries through more accurate battery and degradation modelling

abstract: The increased deployment of intermittent renewable energy generators opens up many new opportunities for grid-connected energy storage. Batteries offer significant flexibility compared with most other storage technologies, but are relatively expensive. The lifetime of the battery is a key factor in the business case. The lifetime depends on how the battery is used, but most techno-economic analyses do not account for this. Here, three lithium-ion battery dynamic and degradation models of increasing realism are formulated, and the predicted degradation of each is compared with a large-scale experimental degradation data set (Mat4Bat). A respective improvement in RMS capacity prediction error from 11% to 5% is found by increasing the model accuracy. The three models are then used within an optimal control algorithm to perform price arbitrage over one year, including degradation. Results show that the revenue can be increased substantially while degradation can be reduced by using more realistic models. The estimated best case profit using a sophisticated model is a 175% improvement compared with the simplest model. This illustrates that using a simplistic battery model in a techno-economic assessment of grid-connected batteries might substantially underestimate the business case and lead to erroneous conclusions.


title: Anomaly Detection Analysis of Intrusion Data Using Supervised & Unsupervised Approach

abstract: Anomaly based network intrusion detection (ANID) is an important problem that has been researched within diverse research areas and various application domains. Several anomaly based network intrusion detection systems (ANIDS) can be found in the literature. Most ANIDSs employ supervised algorithms, whose performances highly depend on attack-free training data. However, this kind of training data is difficult to obtain in real world network environment. Moreover, with changing network environment or services, patterns of normal traffic will be changed. This leads to high false positive rate of supervised ANIDSs. Using unsupervised anomaly detection techniques, however, the system can be trained with unlabeled data and is capable of detecting previously unseen attacks. We have categorized the existing ANIDSs based on its type, class, nature of detection/ processing, level of security, etc. We also enlist some proximity measures for intrusion data analysis and detection. We also report some experimental results for detection of attacks over the KDD’99 dataset.


title: Subpixel-based down-sampling via Min-Max Directional Error

abstract: Subpixel-based down-sampling is a method that can potentially improve the apparent resolution of a down-scaled image by controlling individual subpixels rather than pixels. However, the increased luminance resolution often comes at the expense of chrominance distortion. In this paper, we formulate the subpixel-based down-sampling as a Min-Max problem (MinMax Directional Error) which we call MMDE. Unfortunately, the solution of MMDE is computational intensive, especially for large images. We thus relax the MMDE by determining the maximum error based on HVS, which largely reduces the number of constraints. We call such relaxation as MMDE-VR (Visual Relaxation). Simulation results illustrate that MMDE-VR can effectively reduce visible color fringing artifacts while still maintaining sharpness.


title: Strategies for successful CRM implementation

abstract: Purpose – Customer relationship management (CRM) is an information system that tracks customers' interactions with the firm and allows employees to instantly pull up information about the customers such as past sales, service records, outstanding records and unresolved problem calls. This paper aims to put forward strategies for successful implementation of CRM and discusses barriers to CRM in e‐business and m‐business.Design/methodology/approach – The paper combines narrative with argument and analysis.Findings – CRM stores all information about its customers in a database and uses this data to coordinate sales, marketing, and customer service departments so as to work together smoothly to best serve their customers' needs.Originality/value – The paper demonstrates how CRM, if used properly, could enhance a company's ability to achieve the ultimate goal of retaining customers and gain strategic advantage over its competitors.


title: Paving the Way Towards Reactive Planar Spanner Construction in Wireless Networks

abstract: A spanner is a subgraph of a given graph that supports the original graph’s shortest path lengths up to a constant factor. Planar spanners and their distributed construction are of particular interest for geographic routing, which is an efficient localized routing scheme for wireless ad hoc and sensor networks. Planarity of the network graph is a key criterion for guaranteed delivery, while the spanner property supports efficiency in terms of path length. We consider the problem of reactive local spanner construction, where a node’s local topology is determined on demand. Known message-efficient reactive planarization algorithms do not preserve the spanner property, while reactive spanner constructions with a low message overhead have not been described so far. We introduce the concept of direct planarization which may be an enabler of efficient reactive spanner construction. Given an edge, nodes check for all incident intersecting edges a certain geometric criterion and withdraw the edge if this criterion is not satisfied. We use this concept to derive a generic reactive topology control mechanism and consider two geometric criteria. Simulation results show that direct planarization increases the performance of localized geographic routing by providing shorter paths than existing reactive approaches.


title: Periodic resource model for compositional real-time guarantees

abstract: We address the problem of providing compositional hard real-time guarantees in a hierarchy of schedulers. We first propose a resource model to characterize a periodic resource allocation and present exact schedulability conditions for our proposed resource model under the EDF and RM algorithms. Using the exact schedulability conditions, we then provide methods to abstract the timing requirements that a set of periodic tasks demands under the EDF and RM algorithms as a single periodic task. With these abstraction methods, for a hierarchy of schedulers, we introduce a composition method that derives the timing requirements of a parent scheduler from the timing requirements of its child schedulers in a compositional manner such that the timing requirement of the parent scheduler is satisfied, if and only if the timing requirements of its child schedulers are satisfied.


title: An Efficient Tracking System by Orthogonalized Templates

abstract: Sparse representation (SR)-based tracking systems have become popular in the past recent years for its effectiveness. However, the underlying assumption of these tracking systems is that the target appearance can be linearly represented by a sparse approximation over a set of templates and a residual term, which usually needs to solve $\ell_1$ norm minimization for many times and brings a heavy computational cost. This paper introduces an efficient tracking system by discovering orthogonalized templates. By orthogonalizing templates from previous frames and removing their correlation, we show that the sparsity of template weights is not necessary in target appearance modeling and thus a least squares regularization can be employed. We also decompose the residual term into two components in observation model to take occlusion cases into consideration. We demonstrate that, in comparison with the SR-based tracking systems that use $\ell_1$ learning, our tracking system is much more computationally efficient while getting an even better performance. Experiments on a variety of challenging video sequences demonstrate both the effectiveness and efficiency of the system.


title: JuMP: A Modeling Language for Mathematical Optimization

abstract: JuMP is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a high-level, algebraic syntax. JuMP takes advantage of advanced features of the Julia programming language to offer unique functionality while achieving performance on par with commercial modeling tools for standard tasks. In this work we will provide benchmarks, present the novel aspects of the implementation, and discuss how JuMP can be extended to new problem classes and composed with state-of-the-art tools for visualization and interactivity.


title: Detecting stress during real-world driving tasks using physiological sensors

abstract: This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97% across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers.


title: User-centered design methods in practice: a survey of the state of the art

abstract: This paper reports the results of a recent survey involving over one hundred leading professionals of user-centered design (UCD). The survey covered a broad range of issues ranging from the profile of a typical UCD project including the percentage of total budget on UCD, organizational impact of UCD, measures of UCD success, and the most widely used methods and techniques. Results show that cost-benefit tradeoffs are a key consideration in the adoption of UCD methods. Measures of UCD effectiveness are lacking and rarely applied. There is a major discrepancy between the commonly cited measures and what were actually applied.


title: Dual Band and Dual Polarized Microstrip Patch Antenna

abstract: In today’s modern communication industry, antennas are the most important components required to create a communication link. Microstrip antennas are the most suited for aerospace and mobile applications because of their low profile, light weight and low power handling capacity. They can be designed in a variety of shapes in order to obtain enhanced gain and bandwidth, dual band and circular polarization to even ultra wideband operation. The thesis provides a detailed study of the design of probe-fed Rectangular Microstrip Patch Antenna to facilitate dual polarized, dual band operation. The design parameters of the antenna have been calculated using the transmission line model and the cavity model. For the simulation process IE3D electromagnetic software which is based on method of moment (MOM) has been used. The effect of antenna dimensions and substrate parameters on the performance of antenna have been discussed. ::: The antenna has been designed with embedded spur lines and integrated reactive loading for dual band operation with better impedance matching. The designed antenna can be operated at two frequency band with center frequencies 7.62 (with a bandwidth of 11.68%) and 9.37 GHz (with a bandwidth of 9.83%). A cross slot of unequal length has been inserted so as to have dual polarization. This results in a minor shift in the central frequencies of the two bands to 7.81 and 9.28 GHz. At a frequency of 9.16 GHz, circular polarization has been obtained. So the dual band and dual frequency operation has successfully incorporated into a single patch.


title: Reduction Otoplasty: Correction of the Large or Asymmetric Ear

abstract: Reduction otoplasty is an uncommon procedure performed for macrotia and ear asymmetry. Techniques described in the literature for this procedure are few. The authors present their ear reduction approach that not only achieves the desired reduction effectively and accurately, but also addresses and creates the natural anatomic proportions of the ear, leaving a scar well hidden within the fold of the helix.


title: The Effects of Interrupting Behavior on Interpersonal Attitude and Engagement in Dyadic Interactions

abstract: Interruptions frequently occur in dyadic human interaction. In addition to serve as turn-taking mechanism, they may lead to different perceptions of both the interruptee and interrupter's interpersonal attitude, engagement and involvement. We present an empirical study to investigate whether different interruption types (i.e. amount of overlap between speakers and utterance completeness) and strategies (disruptive vs. cooperative) in agent-agent interaction have an impact on perceived agents' interpersonal attitude, engagement and involvement. We found that the interruption type has more influence on the perceived attitudes of both agents, whereas by using a cooperative strategy (as opposed to a disruptive one) an interrupter is perceived as more engaged and more involved in the interaction.


title: Automatically Tagging Constructions of Causation and Their Slot-Fillers

abstract: This paper explores extending shallow semantic parsing beyond lexical-unit triggers, using causal relations as a test case. Semantic parsing becomes difficult in the face of the wide variety of linguistic realizations that causation can take on. We therefore base our approach on the concept of constructions from the linguistic paradigm known as construction grammar (CxG). In CxG, a construction is a form/function pairing that can rely on arbitrary linguistic and semantic features. Rather than codifying all aspects of each construction’s form, as some attempts to employ CxG in NLP have done, we propose methods that offload that problem to machine learning. We describe two supervised approaches for tagging causal constructions and their arguments. Both approaches combine automatically induced pattern-matching rules with statistical classifiers that learn the subtler parameters of the constructions. Our results show that these approaches are promising: they significantly outperform naive baselines for both construction recognition and cause and effect head matches.


title: Detecting Signal Structure from Randomly-Sampled Data

abstract: Recent theoretical results in Compressive Sensing (CS) show that sparse (or compressible) signals can be accurately reconstructed from a reduced set of linear measurements in the form of projections onto random vectors. The associated reconstruction consists of a nonlinear optimization that requires knowledge of the actual projection vectors. This work demonstrates that random time samples of a data stream could be used to identify certain signal features, even when no time reference is available. since random sampling suppresses aliasing a small (sub-Nyquist) set of samples can represent high-bandwidth signals. Simulations were carried out to explore the utility of such a procedure for detecting and classifying signals of interest.


title: Modeling of ground-penetrating Radar for accurate characterization of subsurface electric properties

abstract: The possibility to estimate accurately the subsurface electric properties from ground-penetrating radar (GPR) signals using inverse modeling is obstructed by the appropriateness of the forward model describing the GPR subsurface system. In this paper, we improved the recently developed approach of Lambot et al. whose success relies on a stepped-frequency continuous-wave (SFCW) radar combined with an off-ground monostatic transverse electromagnetic horn antenna. This radar configuration enables realistic and efficient forward modeling. We included in the initial model: 1) the multiple reflections occurring between the antenna and the soil surface using a positive feedback loop in the antenna block diagram and 2) the frequency dependence of the electric properties using a local linear approximation of the Debye model. The model was validated in laboratory conditions on a tank filled with a two-layered sand subject to different water contents. Results showed remarkable agreement between the measured and modeled Green's functions. Model inversion for the dielectric permittivity further demonstrated the accuracy of the method. Inversion for the electric conductivity led to less satisfactory results. However, a sensitivity analysis demonstrated the good stability properties of the inverse solution and put forward the necessity to reduce the remaining clutter by a factor 10. This may partly be achieved through a better characterization of the antenna transfer functions and by performing measurements in an environment without close extraneous scatterers.


title: Aligning Sentences from Standard Wikipedia to Simple Wikipedia

abstract: This work improves monolingual sentence alignment for text simplification, specifically for text in standard and simple Wikipedia. We introduce a method that improves over past efforts by using a greedy (vs. ordered) search over the document and a word-level semantic similarity score based on Wiktionary (vs. WordNet) that also accounts for structural similarity through syntactic dependencies. Experiments show improved performance on a hand-aligned set, with the largest gain coming from structural similarity. Resulting datasets of manually and automatically aligned sentence pairs are made available.


title: A gateway system for an automotive system: LIN, CAN, and FlexRay

abstract: In the automotive industry, the usage of microcontrollers has been increasing rapidly. Many mechanical parts of automobiles are being replaced with microcontrollers, and these microcontrollers are controlling various parts of the automobile, so communications between microcontrollers must be reliable. Until now, several protocols for automotive communications have been introduced, and LIN, CAN, and FlexRay are the most widely known protocols. Different vendors of automobiles use different protocols and each protocol possess different features. This paper presents a network gateway system between LIN, low-speed CAN, high-speed CAN, and FlexRay.


title: FPGA acceleration of hyperspectral image processing for high-speed detection applications

abstract: Recent advances in photonics and imaging technology allow the development of cutting-edge, lightweight hyperspectral sensors, both push-broom/line-scanning and snapshot/frame. At the same time, emerging applications in robotics, food inspection, medicine and earth observation are posing critical challenges on real-time processing and computational efficiency, both in terms of accuracy and power consumption. In this direction, in the current paper, we accelerate hyperspectral processing kernels by utilizing FPGAs, i.e., Zynq-7000 SoC, to perform similarity-based matching of spectral signatures. We propose a custom HW architecture based on multi-level parallelization, modularity, and parametric VHDL coding, which allows for in-depth design space exploration and trade-off analysis. Depending on configuration, our implementation processes 22–107 Megapixels per second providing an acceleration of 40–355x vs Intel-i3 CPU and 360-104x vs the embedded ARM Cortex A9, whereas the overall detection quality ranges from 56% to 97% when evaluated with multiple objects and images of 285 spectral channels.


title: DOI 10.3758/s13423-011-0181-y BRIEF REPORT Study strategies of college students: Are self-testing and scheduling related to achievement?

abstract: Previous studies, such as those by Kornell and Bjork (Psychonomic Bulletin & Review, 14:219–224, 2007) and Karpicke, Butler, and Roediger (Memory, 17:471–479, 2009), have surveyed college students’ use of various study strategies, including self-testing and rereading. These studies have documented that some students do use self-testing (but largely for monitoring memory) and rereading, but the researchers did not assess whether individual differences in strategy use were related to student achievement. Thus, we surveyed 324 undergraduates about their study habits as well as their college grade point average (GPA). Importantly, the survey included questions about self-testing, scheduling one’s study, and a checklist of strategies commonly used by students or recommended by cognitive research. Use of self-testing and rereading were both positively associated with GPA. Scheduling of study time was also an important factor: Low performers were more likely to engage in late-night studying than were high performers; massing (vs. spacing) of study was associated with the use of fewer study strategies overall; and all students—but especially low performers—were driven by impending deadlines. Thus, self-testing, rereading, and scheduling of study play important roles in real-world student achievement.


title: Conversational Speech Transcription Using Context-Dependent Deep Neural Networks

abstract: Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, combine the classic artificial-neural-network HMMs with traditional context-dependent acoustic modeling and deep-belief-network pre-training. CD-DNN-HMMs greatly outperform conventional CD-GMM (Gaussian mixture model) HMMs: The word error rate is reduced by up to one third on the difficult benchmarking task of speaker-independent single-pass transcription of telephone conversations.


title: IT ALIGNMENT: WHAT HAVE WE LEARNED?

abstract: We provide a review of the alignment literature in IT, addressing questions such as: What have we learned? What is disputed? Who are contributors to the debate? The article is intended to be useful to faculty and graduate students considering conducting research on alignment, instructors preparing lectures, and practitioners seeking to assess the ‘state-of-play’. It is both informational and provocative. Challenges to the value of alignment research, divergent views, and new perspectives on alignment are presented. It is hoped that the article will spark helpful conversation on the merits of continued investigation of IT alignment.


title: A Blind Baud-Rate ADC-Based CDR

abstract: ADC-based receivers process the received data in the digital domain, eliminating the need for much of the analog front end. In addition, a feed-forward blind architecture [1,2] eliminates the feedback loop between digital and analog domains so that the ADC and digital CDR can be designed and simulated independently. Previous works [1,2] sampled the incoming data at 2 samples per UI and at 1.45 samples per UI to achieve 5Gb/s and 6.875Gb/s, respectively. To further increase the data rate to 10Gb/s, we sample at baud rate (1 sample per UI). Existing baud-rate architectures [3] rely on a phase-tracking clock to sample at the middle of the data eye. This paper presents a blind baud-rate CDR, fabricated in 65nm CMOS. At 10Gb/s, the CDR demonstrates a high-frequency jitter tolerance of 0.19UI with ±300ppm of frequency offset. Also, the CDR demonstrates successful operation with 1000ppm offset, which amounts to sub-baud-rate sampling.