title: A Language-Based Approach to Security

abstract: Language-based security leverages program analysis and program rewriting to enforce security policies. The approach promises efficient enforcement of fine-grained access control policies and depends on a trusted computing base ofon ly modest size. This paper surveys progress and prospects for the area, giving overviews of in-lined reference monitors, certifying compilers, and advances in type theory.


Mandatory Licensure for Sonographers


Gender Differences in Goal Orientation between High School Students in Physical Education Classes


title: Exploiting a parallel TEXT - DATA corpus

abstract: In this paper, we describe SUMTIME-METEO, a parallel corpus of naturally occurring weather forecast texts and their corresponding forecast data; data that the human authors inspected while writing the forecast texts. We have analysed the corpus to acquire knowledge needed to build a text generator for automatically producing textual weather forecasts from numerical weather prediction data. Although parallel corpora are commonly used for the development and evaluation of machine translation technology, it is fairly novel in the text generation community. Our analyses of the corpus, in some cases, produced ambiguous results that are not useful and reflected inconsistencies in the underlying corpus. Despite the internal inconsistencies, the text-data parallel corpus was helpful in generating initial hypotheses, which were then tested with knowledge from other sources. We also describe how we have used the corpus for evaluating our prototype forecast text generator.


title: Image Segmentation by Cascaded Region Agglomeration

abstract: We propose a hierarchical segmentation algorithm that starts with a very fine over segmentation and gradually merges regions using a cascade of boundary classifiers. This approach allows the weights of region and boundary features to adapt to the segmentation scale at which they are applied. The stages of the cascade are trained sequentially, with asymetric loss to maximize boundary recall. On six segmentation data sets, our algorithm achieves best performance under most region-quality measures, and does it with fewer segments than the prior work. Our algorithm is also highly competitive in a dense over segmentation (super pixel) regime under boundary-based measures.


title: Ultrasensitive multi-analyte electrochemical immunoassay based on GNR-modified heated screen-printed carbon electrodes and PS@PDA-metal labels for rapid detection of MMP-9 and IL-6

abstract: Abstract An ultrasensitive electrochemical immunoassay was developed for rapid detection of interleukin-6 (IL-6) and matrix metallopeptidase-9 (MMP-9); the method utilized PS@PDA-metal nanocomposites based on graphene nanoribbon (GNR)-modified heated screen-printed carbon electrode (HSPCE). Because of the good hydrophilicity and low toxicity, GNRs were used to immobilize antibodies (Ab) and amplify the electrochemical signal. PS@PDA-metal was used to label antibodies and generate a strong electrochemical signal in acetic buffer. A sandwich strategy was adopted to achieve simultaneous detection of MMP-9 and IL-6 based on HSPCE without cross-talk between adjacent electrodes in the range of 10 −5 to 10 3 ng mL −1 with detection limits of 5 fg mL −1 and 0.1 pg mL −1 (S/N=3), respectively. The proposed method showed wide detection range, low detection limit, acceptable stability and good reproducibility. Satisfactory results were also obtained in the practical samples, thus showing this is a promising technique for simultaneous clinical detection of biocomponent proteins.


title: Compact Tapered Slot Antenna Array for 5G Millimeter-Wave Massive MIMO Systems

abstract: A low-complexity metallic tapered slot antenna (TSA) array for millimeter-wave multibeam massive multiple-input multiple-output communication is proposed in this paper. Good beamforming performance can be achieved by the developed antenna array because the element spacing can easily meet the requirement of half-wavelength in the H-plane. The antenna element is fed by a substrate-integrated waveguide, which can be directly integrated with the millimeter-wave circuits. The proposed TSA is fabricated and measured. Measured results show that the reflection coefficient is lower than −15 dB Voltage Standing Wave Ratio ((VSWR) ≤ 1.45) within the frequency range from 22.5 to 32 GHz, which covers the 24.25–27.5-GHz band proposed by International Telecommunications Union (ITU) and the 27.5–28.35-GHz band proposed by Federal Communications Commission (FCC) for 5G. The gain of the antenna element varies from 8.2 to 9.6 dBi over the frequency range of 24–32 GHz. The simulated and measured results also illustrate good radiation patterns across the wide frequency band (24–32 GHz). A $1\times 4$ H-plane array integrated with the multichannel millimeter-wave transceivers on one PCB is demonstrated and excellent performance is achieved.


title: An 8V 50μW 1.2MHz CMOS-MEMS oscillator

abstract: We report the design of a 50 μW 1.2 MHz monolithic MEMS oscillator embedded in a standard 0.35 μm CMOS with dc-bias lower than 8V. This is the first-time demonstration for a sub-100 μW single-chip CMOS-MEMS oscillator operated in MHz range. To overcome the motional impedance R m > 8 MΩ under sub-10V dc-bias (VP), an ultra-low input referred noise ( p of 8V and 11V, the phase noise (PN) FOM of 146 dB and 177 dB is attained at 1-kHz offset, respectively, for sub-100μW operations. Moreover, the power FOM of 40.6 nW/kHz (V p = 8V) and 52.8 nW/kHz (V p = 11V) is also comparable with state-of-the-art low power CMOS and MEMS oscillators.


title: The Effects of Shopping Orientations, Online Trust and Prior Online Purchase Experience toward Customers’ Online Purchase Intention

abstract: The advancement of the World Wide Web has resulted in the creation of a new form of retail transactionselectronic retailing (e-tailing) or web-shopping. Thus, customers’ involvements in online purchasing have become an important trend. As such, it is vital to identify the determinants of the customer online purchase intention. The aim of this research is to evaluate the impacts of shopping orientations, online trust and prior online purchase experience to the customer online purchase intention. A total of 242 undergraduate information technology students from a private university in Malaysia participated in this research. The findings revealed that impulse purchase intention, quality orientation, brand orientation, online trust and prior online purchase experience were positively related to the customer online purchase intention.


title: A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data

abstract: Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.


title: IoT based interactive shopping ecosystem

abstract: Internet of Things aims at integrating networked information systems to real world entities. It connects objects such as smart phones, sensors, LED(Light Emitting Diodes) displays and even clothes through the internet allowing them to interact and exchange information among themselves. In today's times IoT has found its application in practically every walk of life and inventory management is no exception. At present, inventory management is becoming increasingly complex, where there is an exponential rise in the diversity and number of both products and customers. The biggest problem faced by store owners is to optimize the experience of inventory management along with increased sales and reduced operational costs. With such enormous product lines it becomes extremely difficult for store owners to track and monitor the performance of a product in terms of its sales, shelf life, cost and customer response. IoT acts as a solution to this problem as it facilitates the use of Wireless Sensor Networks (WSN) in order to interconnect all the various actors in a logistic system. This humongous network of interconnected devices generate massive amounts of data which is difficult to store and process. Cloud computing here plays a role of a facilitator and provides great help in addressing challenges related to storage and processing capabilities. In this paper, we present an Interactive Shopping Model along with an Automated Inventory Intelligent Management System (AIMS) that benefits from the amalgamation of IoT and Cloud and provides real time monitoring, tracking and management of products. We also propose an algorithm that depicts the working of our system. The proposed system along with the algorithm are simulated using the iFogSim simulator. To this end, we illustrate the working of our proposed system along with the simulated results.


title: Artificial Intelligence in Radiology: Decision Support Systems

abstract: Computer-based systems that incorporate artificial intelligence techniques can help physicians make decisions about their patients' care. In radiology, systems have been developed to help physicians choose appropriate radiologic procedures and to formulate accurate diagnoses. These decision support systems use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning. This article reviews these artificial intelligence techniques, describes their application in radiology, and discusses the role that decision support systems may play in radiology's future.


title: LDA-based keyword selection in text categorization

abstract: Text categorization is the task of automatically assigning unlabeled text documents to some predefined category labels by means of an induction algorithm. Since the data in text categorization are high-dimensional, feature selection is broadly used in text categorization systems for reducing the dimensionality. In the literature, there are some widely known metrics such as information gain and document frequency thresholding. Recently, a generative graphical model called latent dirichlet allocation (LDA) that can be used to model and discover the underlying topic structures of textual data, was proposed. In this paper, we use the hidden topic analysis of LDA for feature selection and compare it with the classical feature selection metrics in text categorization. For the experiments, we use SVM as the classifier and tf∗idf weighting for weighting the terms. We observed that almost in all metrics, information gain performs best at all keyword numbers while the LDA-based metrics perform similar to chi-square and document frequency thresholding.


title: Depth-aware CNN for RGB-D Segmentation

abstract: Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure. The availability of depth data enables progress in RGB-D semantic segmentation with CNNs. State-of-the-art methods either use depth as additional images or process spatial information in 3D volumes or point clouds. These methods suffer from high computation and memory cost. To address these issues, we present Depth-aware CNN by introducing two intuitive, flexible and effective operations: depth-aware convolution and depth-aware average pooling. By leveraging depth similarity between pixels in the process of information propagation, geometry is seamlessly incorporated into CNN. Without introducing any additional parameters, both operators can be easily integrated into existing CNNs. Extensive experiments and ablation studies on challenging RGB-D semantic segmentation benchmarks validate the effectiveness and flexibility of our approach.


title: Update on Facial Aging

abstract: Facial aging was once thought to be the result of the relentless downward pull of gravity on skin and underlying fat. In turn, facial fat was believed to be a contiguous sheet of tissue. However, over the past four decades, a number of investigators have examined more closely the causes of facial aging, leading to a better understanding of age-related changes, and have confirmed and further explored the proposal by Gonzalez-Ulloa and Flores in 1965 that facial aging involves changes in muscle and bone, as well as skin and fat. Further, the recent work of Rohrich and Pessa (and other authors) has demonstrated that facial fat is not a sheet of tissue, but rather is compartmentalized throughout the face. This discovery has allowed the evolution of improved techniques for facial rejuvenation.


title: Partially Generative Neural Networks for Gang Crime Classification with Partial Information

abstract: More than 1 million homicides, robberies, and aggravated assaults occur in the United States each year. These crimes are often further classified into different types based on the circumstances surrounding the crime (e.g., domestic violence, gang-related). Despite recent technological advances in AI and machine learning, these additional classification tasks are still done manually by specially trained police officers. In this paper, we provide the first attempt to develop a more automatic system for classifying crimes. In particular, we study the question of classifying whether a given violent crime is gang-related. We introduce a novel Partially Generative Neural Networks (PGNN) that is able to accurately classify gang-related crimes both when full information is available and when there is only partial information. Our PGNN is the first generative-classification model that enables to work when some features of the test examples are missing. Using a crime event dataset from Los Angeles covering 2014-2016, we experimentally show that our PGNN outperforms all other typically used classifiers for the problem of classifying gang-related violent crimes.


title: Political Polarization in the American Public

abstract: For more than two decades political scientists have discussed rising elite polarization in the United States, but the study of mass polarization did not receive comparable attention until fairly recently. This article surveys the literature on mass polarization. It begins with a discussion of the concept of polarization, then moves to a critical consideration of different kinds of evidence that have been used to study polarization, concluding that much of the evidence presents problems of inference that render conclusions problematic. The most direct evidence—citizens' positions on public policy issues—shows little or no indication of increased mass polarization over the past two to three decades. Party sorting—an increased correlation between policy views and partisan identification—clearly has occurred, although the extent has sometimes been exaggerated. Geographic polarization—the hypothesized tendency of like-minded people to cluster together—remains an open question. To date, there is no conclusive e...


title: Driven to distraction: Extraneous events and underreaction to earnings news

abstract: Psychological evidence indicates that it is hard to process multiple stimuli and perform multiple tasks at the same time. This paper tests the INVESTOR DISTRACTION HYPOTHESIS, which holds that the arrival of extraneous news causes trading and market prices to react sluggishly to relevant news about a firm. Our test focuses on the competition for investor attention between a firm's earnings announcements and the earnings announcements of other firms. We find that the immediate stock price and volume reaction to a firm's earnings surprise is weaker, and post-earnings announcement drift is stronger, when a greater number of earnings announcements by other firms are made on the same day. Distracting news has a stronger effect on firms that receive positive than negative earnings surprises. Industry-unrelated news has a stronger distracting effect than related news. A trading strategy that exploits post-earnings announcement drift is unprofitable for announcements made on days with little competing news.


title: Full body gait analysis with Kinect

abstract: Human gait is an important indicator of health, with applications ranging from diagnosis, monitoring, and rehabilitation. In practice, the use of gait analysis has been limited. Existing gait analysis systems are either expensive, intrusive, or require well-controlled environments such as a clinic or a laboratory. We present an accurate gait analysis system that is economical and non-intrusive. Our system is based on the Kinect sensor and thus can extract comprehensive gait information from all parts of the body. Beyond standard stride information, we also measure arm kinematics, demonstrating the wide range of parameters that can be extracted. We further improve over existing work by using information from the entire body to more accurately measure stride intervals. Our system requires no markers or battery-powered sensors, and instead relies on a single, inexpensive commodity 3D sensor with a large preexisting install base. We suggest that the proposed technique can be used for continuous gait tracking at home.


title: Wide-angle reflection migration technique with an example from the POLAR profile (northern Scandinavia)

abstract: Abstract The 440 km long POLAR profile runs approximately SW to NE across the Baltic Shield from the Karelian Province through the Lapland Granulite Belt and the Kola Peninsula Province in northern Finland and northern Norway. The migration method developed by V.N. Pilipenko (Physics of the Earth, N1, 1983, 36–42) was applied to the wide-angle reflection data obtained along the profile. To prepare the data for the migration, P- and S-waves were analysed with more attention to the distribution of real reflectors in the crust and on the relation between the P- and S-wave velocities. Depth-migrated images of the crustal structure were obtained for both the P- and S-wave fields. This new analysis revealed structural features of the crust which had not before been imaged. The most impressive features are several inclined boundaries in the upper crust dipping northeast beneath the Lapland Granulite Belt. Another newly found element is a lower crustal boundary at a depth of 35 km in the Karelian Province which is well traced in the P-wave migration image but is not observed in the S-wave image. This boundary corresponds to a strong change in the V P / V S ratio in the lower crust which may be interpreted in terms of mafic crustal composition. We have found that this migration technique can be applied to deep seismic sounding data resulting in new opportunities for deep crustal seismic studies. Nevertheless, more and denser observations are necessary to continue the improvement of the resolution and the reliability of the structure images.


Towards a Stable Quantized Convolutional Neural Networks: An Embedded Perspective


title: Worst-Case Optimal Join Algorithms: Techniques, Results, and Open Problems

abstract: Worst-case optimal join algorithms are the class of join algorithms whose runtime match the worst-case output size of a given join query. While the first provably worse-case optimal join algorithm was discovered relatively recently, the techniques and results surrounding these algorithms grow out of decades of research from a wide range of areas, intimately connecting graph theory, algorithms, information theory, constraint satisfaction, database theory, and geometric inequalities. These ideas are not just paperware: in addition to academic project implementations, two variations of such algorithms are the work-horse join algorithms of commercial database and data analytics engines. ::: This paper aims to be a brief introduction to the design and analysis of worst-case optimal join algorithms. We discuss the key techniques for proving runtime and output size bounds. We particularly focus on the fascinating connection between join algorithms and information theoretic inequalities, and the idea of how one can turn a proof into an algorithm. Finally, we conclude with a representative list of fundamental open problems in this area.


title: Facial Expression Recognition Using a Large Out-of-Context Dataset

abstract: We develop a method for emotion recognition from facial imagery. This problem is challenging in part because of the subjectivity of ground truth labels and in part because of the relatively small size of existing labeled datasets. We use the FER+ dataset [8], a dataset with multiple emotion labels per image, in order to build an emotion recognition model that encompasses a full range of emotions. Since the amount of data in the FER+ dataset is limited, we explore the use of a much larger face dataset, MS-Celeb-1M [41], in conjunction with the FER+ dataset. Specific layers within an Inception-ResNet-v1 [13, 38] model trained for facial recognition are used for the emotion recognition problem. Thus, we leverage the MS-Celeb-1M dataset in addition to the FER+ dataset and experiment with different architectures to assess the overall performance of neural networks to recognize emotion using facial imagery.


Extracting Collocations from Text Corpora


title: 3-D Map Merging on Pose Graphs

abstract: In this letter, we propose an approach for merging three-dimensional maps represented as pose graphs of point clouds. Our method can effectively deal with typical distortions affecting simultaneous localization and mapping-generated maps. Traditional map merging techniques that use a single rigid body transformation to relate the reference frames of different maps. Instead, our approach achieves more accurate results by eliminating the inconsistencies resulting from distortions affecting the inputs, and can succeed in those situations where traditional approaches fail for substantial deformations. The core idea behind our solution is to localize the robot in a reference map by using the data from another map as observations. We validated our approach on publicly available datasets, and provide quantitative results that confirm its effectiveness on challenging instances of the merging problem.


Corpus-Based Approaches to Semantic Interpretation in Natural Language Processing


Hybrid Sample-based Surface Rendering


title: Learning ontology from Big Data through MongoDB database

abstract: Big Data are collections of data sets so large and complex to process using classical database management tools. NOSQL databases are in the base of storage of Big Data. Learning ontology from these databases will extract their hidden semantics. This paper illustrates an approach to learn ontology from document oriented NOSQL database. We choose to deal with MongoDB. We present main steps of our approach and detail transformation rules.


title: A hybrid Sentiment Classification method using Neural Network and Fuzzy Logic

abstract: Neural Network(NN) and fuzzy systems are suitable for determining the input-output relationships. NN contend with numeric and quantitative information whereas fuzzy systems can handle symbolic and qualitative information. Coupling of Neural Network and Fuzzy Logic results in an intelligent crossbreed system widely referred to as Neuro-fuzzy system (NFS) that exploits the most effective qualities of these two approaches expeditiously. The coupled system combines the human alike logical reasoning of fuzzy systems with the training and connectedness structure of neural network. In this paper, we propose a method for performing Sentiment Classification using an NN and fuzzy set theory. In this method input reviews are fuzzified by using Gaussian membership function and fuzzification matrix is build. This matrix is transposed and passed to Multilayer Perceptron Backpropagation Network(MLPBPN).


title: Optimization of Frequent Itemset Mining on Multiple-Core Processor

abstract: Multi-core processors are proliferated across different domains in recent years. In this paper, we study the performance of frequent pattern mining on a modern multi-core machine. A detailed study shows that, even with the best implementation, current FP-tree based algorithms still under-utilize a multi-core system due to poor data locality and insufficient parallelism expression. We propose two techniques: a cache-conscious FP-array (frequent pattern array) and a lock-free dataset tiling parallelization mechanism to address this problem. The FP-array efficiently improves the data locality performance, and makes use of the benefits from hardware and software prefetching. The result yields an overall 4.0 speedup compared with the state-of-the-art implementation. Furthermore, to unlock the power of multi-core processor, a lock-free parallelization approach is proposed to restructure the FP-tree building algorithm. It not only eliminates the locks in building a single FP-tree with fine-grained threads, but also improves the temporal data locality performance. To summarize, with the proposed cache-conscious FP-array and lock-free parallelization enhancements, the overall FP-tree algorithm achieves a 24 fold speedup on an 8-core machine. Finally, we believe the presented techniques can be applied to other data mining tasks as well with the prevalence of multi-core processor.


SimpleScience: Lexical Simplification of Scientific Terminology


title: Guaranteed safe online learning of a bounded system

abstract: For some time now machine learning methods have been widely used in perception for autonomous robots. While there have been many results describing the performance of machine learning techniques with regards to their accuracy or convergence rates, relatively little work has been done on developing theoretical performance guarantees about their stability and robustness. As a result, many machine learning techniques are still limited to being used in situations where safety and robustness are not critical for success. One way to overcome this difficulty is by using reachability analysis, which can be used to compute regions of the state space, known as reachable sets, from which the system can be guaranteed to remain safe over some time horizon regardless of the disturbances. In this paper we show how reachability analysis can be combined with machine learning in a scenario in which an aerial robot is attempting to learn the dynamics of a ground vehicle using a camera with a limited field of view. The resulting simulation data shows that by combining these two paradigms, one can create robotic systems that feature the best qualities of each, namely high performance and guaranteed safety.


Topic Extraction from Scientific Literature for Competency Management


title: TextOntoEx: Automatic ontology construction from natural English text

abstract: Most of existing ontologies construction tools support construction of ontological relations (e.g., taxonomy, equivalence, etc.) but they do not support construction of domain relations, non-taxonomic conceptual relationships (e.g., causes, caused by, treat, treated by, has-member, contain, material-of, operated-by, controls, etc.). Domain relations are found mainly in text sources. TextOntoEx constructs ontology from natural domain text using semantic pattern-based approach. TextOntoEx is a chain between linguistic analysis and ontology engineering. TextOntoEx analyses natural domain text to extract candidate relations and then maps them into meaning representation to facilitate constructing ontology. The paper explains this approach in more details and discusses some experiments on deriving ontology from natural text.


title: Acridine Orange as a Fluorescent Probe for Lysosomal Proton Pump1

abstract: Acridine orange was found to accumulate in pure lysosomal particles (tritosomes) in vitro, and the quenching of its fluorescence correlated well with the delta pH (inside acid) across the lysosomal membrane. Use of this dye showed that Mg-ATP caused lysosomal acidification. This acidification was sensitive to N,N'-dicyclohexylcarbodiimide, N-ethylmaleimide, and azide, but not to oligomycin, ouabain or vanadate. These results supported the idea of the existence of a lysosomal H+-pump, suggested in a previous paper (Ohkuma et al. (1982) Proc. Natl. Acad. Sci. U.S. 79, 2758-2762).


title: Sliding Window Trend Analysis: A Method for Short and Open Circuit Detection in Copper Electrorefining

abstract: This work presents a system to detect short and open circuits suitable for Optibar® inter-cell bars in copper electrorefining processes. The system is based in Optibar's voltage connecting segments. Voltages are sampled and transmitted via WI-FI to a compatible SQL database every five minutes. The technique proposed performs sliding window analysis on anode-cathode voltages slopes to detect short and open circuits. The system was implemented in a refinery section of 20 cells with 1,100 cathodes.


title: Simplified Extended Kalman Filter Observer for SOC Estimation of Commercial Power-Oriented LFP Lithium Battery Cells

abstract: The lithium iron phosphate (LFP) cell chemistry is finding wide acceptance for energy storage on-board hybrid electric vehicles (HEVs) and electric vehicles (EVs), due to its high intrinsic safety, fast charging, and long cycle life. However, three main challenges need to be addressed for the accurate estimation of their state of charge (SOC) at runtime:  Long voltage relaxation time to reach its open circuit voltage (OCV) after a current pulse  Time-, temperature- and SOC-dependent hysteresis  Very flat OCV-SOC curve for most of the SOC range In view of these problems, traditional SOC estimation techniques such as coulomb counting with error correction using the SOC-OCV correlation curve are not suitable for this chemistry. This work addressed these challenges with a novel combination of the extended Kalman filter (EKF) algorithm, a two-RC-block equivalent circuit and the traditional coulomb counting method. The simplified implementation of the EKF algorithm offers a computationally efficient option for runtime SOC evaluation on-board vehicles. The SOC estimation was validated with experimental data of a current profile contaminated with pseudo-random noise and with an offset in the initial condition. The model rapidly converged to within 4% of the true SOC even with imposed errors of 40% to initial SOC, 24% to current measurement and 6% to voltage measurement.


title: A relevance-based topic model for news event tracking

abstract: Event tracking is the task of discovering temporal patterns of popular events from text streams. Existing approaches for event tracking have two limitations: scalability and inability to rule out non-relevant portions in text streams. In this study, we propose a novel approach to tackle these limitations. To demonstrate the approach, we track news events across a collection of weblogs spanning a two-month time period.


title: Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

abstract: Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain.


title: Object-Based Multiple Foreground Segmentation in RGBD Video

abstract: We present an RGB and Depth (RGBD) video segmentation method that takes advantage of depth data and can extract multiple foregrounds in the scene. This video segmentation is addressed as an object proposal selection problem formulated in a fully-connected graph, where a flexible number of foregrounds may be chosen. In our graph, each node represents a proposal, and the edges model intra-frame and inter-frame constraints on the solution. The proposals are selected based on an RGBD video saliency map in which depth-based features are utilized to enhance the identification of foregrounds. Experiments show that the proposed multiple foreground segmentation method outperforms related techniques, and the depth cue serves as a helpful complement to RGB features. Moreover, our method provides performance comparable to the state-of-the-art RGB video segmentation techniques on regular RGB videos with estimated depth maps.


title: A Review on Shape based Descriptors for Image Retrieval

abstract: In the age of information technology, a large number of images are generated at 24/7 which leads to a growing interest for searching out similar images from the large databases/ data warehouses. For searching an image from the database, images need to be described by certain features. The most important feature to describe an image is its shape. Now-adays, shape is used for image retrieval. Description of Shape is denoted by various techniques which are generally divided into two broad categoriesRegion based descriptor and Contour based descriptor. Contour based descriptor considers the whole area of the image while region based descriptor considers the boundary lines of the image. In this paper, shape based descriptors are reviewed. Some important shape descriptors have been identified for image retrieval according to the standard principles.


title: Road course estimation using deep learning on radar data

abstract: One of the most fundamental tasks in autonomous driving is the recognition of the road ahead. Using radar data, this is usually done via rule based algorithms. This paper proposes a deep learning approach to estimate the course of the ego lane based on occupancy grids generated by radar sensors. The method is also able to simultaneously give a reliability measurement of the predicted driving path. An automatic labeling process is engaged by utilizing the known ego pose of the vehicle obtained by a high precision positioning sensor. Due to its automated labeling process, learning data can be built up very cost efficiently.


title: An electronically guided walking stick for the blind

abstract: Mobility for the blind is always a great problem. Just like a sighted, blind also needs to travel around inside a closed premises like house, factory, office, school etc. They may also like to go for shopping, visiting friends and other places of their interest. Presently available electronic travelling aids like sonic path finder, sonic torch etc. are not suitable for using inside a closed premises such as school, factory, office etc. In this paper an electronically guided walking stick that can be used conveniently inside a closed premises has been discussed.


Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning


title: A Blockchain Connected Gateway for BLE-Based Devices in the Internet of Things

abstract: Recently, the popularity of the Internet of Things (IoT) has led to a rapid development and significant advancement of ubiquitous applications seamlessly integrated within our daily life. Owing to the accompanying growth of the importance of privacy, a great deal of attention has focused on the issues of secure management and robust access control of IoT devices. In this paper, we propose the design of a blockchain connected gateway which adaptively and securely maintains user privacy preferences for IoT devices in the blockchain network. Individual privacy leakage can be prevented because the gateway effectively protects users’ sensitive data from being accessed without their consent. A robust digital signature mechanism is proposed for the purposes of authentication and secure management of privacy preferences. Furthermore, we adopt the blockchain network as the underlying architecture of data processing and maintenance to resolve privacy disputes.


title: COSY: An Energy-Efficient Hardware Architecture for Deep Convolutional Neural Networks Based on Systolic Array

abstract: Deep convolutional neural networks (CNNs) show extraordinary abilities in artificial intelligence applications, but their large scale of computation usually limits their uses on resource-constrained devices. For CNN’s acceleration, exploiting the data reuse of CNNs is an effective way to reduce bandwidth and energy consumption. Row-stationary (RS) dataflow of Eyeriss is one of the most energy-efficient state-of-the-art hardware architectures, but has redundant storage usage and data access, so the data reuse has not been fully exploited. It also requires complex control and is intrinsically unable to skip over zero-valued inputs in timing. In this paper, we present COSY (CNN on Systolic Array), an energy-efficient hardware architecture based on the systolic array for CNNs. COSY adopts the method of systolic array to achieve the storage sharing between processing elements (PEs) in RS dataflow at the RF level, which reduces low-level energy consumption and on-chip storage. Multiple COSY arrays sharing the same storage can execute multiple 2-D convolutions in parallel, further increasing the data reuse in the low-level storage and improving throughput. To compare the energy consumption of COSY and Eyeriss running actual CNN models, we build a process-based energy consumption evaluation system according to the hardware storage hierarchy. The result shows that COSY can achieve an over 15% reduction in energy consumption under the same constraints, improving the theoretical Energy-Delay Product (EDP) and Energy-Delay Squared Product (ED^2P) by 1.33 x on average. In addition, we prove that COSY has the intrinsic ability for zero-skipping, which can further increase the improvements to 2.25× and 3.83× respectively.


title: The Location of High-technology Manufacturing in Great Britain: Changes in the Late 1980s

abstract: Positive feedback processes, set in the context of all-industry location factors, provide the theoretical background to the statistical analysis of the shift-share residual component which is used to identify the localisation and delocalisation by county of high-technology manufacturing in Great Britain. Delocalisation from London appears to be well established, but apart from the Welsh countries, where the stimulus seems to be largely policy effects, none of the other shift-share residuals are significant enough to be distinguishable from random variation. However, this is after the elimination of an 'aerospace effect' which significantly disturbs the otherwise random spatial variation. The techniques used in the paper separate signal from noise in the residual component of the shift-share method, which by itself offers no 'explanation' of the residual.


title: Twitter Heron: Stream Processing at Scale

abstract: Storm has long served as the main platform for real-time analytics at Twitter. However, as the scale of data being processed in real-time at Twitter has increased, along with an increase in the diversity and the number of use cases, many limitations of Storm have become apparent. We need a system that scales better, has better debug-ability, has better performance, and is easier to manage -- all while working in a shared cluster infrastructure. We considered various alternatives to meet these needs, and in the end concluded that we needed to build a new real-time stream data processing system. This paper presents the design and implementation of this new system, called Heron. Heron is now the de facto stream data processing engine inside Twitter, and in this paper we also share our experiences from running Heron in production. In this paper, we also provide empirical evidence demonstrating the efficiency and scalability of Heron.


title: Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

abstract: This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system ﬁrstly extracts approximate entropy (ApEn)featuresfromﬁxedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat, this system linearizes the ApEn features series through an adaptive piecewise linear ﬁtting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classiﬁer. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: “wake” and “drowsy”. The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the “awake” state, and 15.15% false detections of the “drowsy” state. The results also conﬁrm that the proposed method based on SWA signal is valuable for applications in preventing trafﬁc accidents caused by driver fatigue.


title: High-Speed Parallel CRC Implementation Based on Unfolding, Pipelining, and Retiming

abstract: This brief presents a high-speed parallel cyclic redundancy check (CRC) implementation based on unfolding, pipelining, and retiming algorithms. CRC architectures are first pipelined to reduce the iteration bound by using novel look-ahead pipelining methods and then unfolded and retimed to design high-speed parallel circuits. A comparison on commonly used generator polynomials between the proposed design and previously proposed parallel CRC algorithms shows that the proposed design can increase the speed by up to 25% and control or even reduce hardware cost


abstract: INTRODUCTION ::: For many patients with gender dysphoria, gender-confirmation surgery (GCS) helps align their physical characteristics with their gender identity and is a fundamental element of comprehensive treatment. This article is the 2nd in a 3-part series about the treatment of gender dysphoria. Non-operative management was covered in part 1. This section begins broadly by reviewing surgical eligibility criteria, benefits of GCS, and factors associated with regret for transgender men and women. Then, the scope narrows to focus on aspects of feminizing genital GCS, including a discussion of vaginoplasty techniques, complications, and sexual function outcomes. Part 3 features operative considerations for masculinizing genital GCS. ::: ::: ::: AIM ::: To summarize the World Professional Association for Transgender Health's (WPATH) surgical eligibility criteria and describe how patients with gender dysphoria benefit from GCS, provide an overview of genital and non-genital feminizing gender-confirmation procedures, and review vaginoplasty techniques, preoperative considerations, complications, and outcomes. ::: ::: ::: METHODS ::: A review of relevant literature through April 2017 was performed using PubMed. ::: ::: ::: MAIN OUTCOME MEASURES ::: Review of literature related to surgical eligibility criteria for GCS, benefits of GCS, and surgical considerations for feminizing genitoplasty. ::: ::: ::: RESULTS ::: Most transgender men and women who satisfy WPATH eligibility criteria experience improved quality of life, overall happiness, and sexual function after GCS; regret is rare. Penile inversion vaginoplasty is the preferred technique for feminizing genital GCS according to most surgeons, including the authors whose surgical technique is described. Intestinal vaginoplasty is reserved for certain scenarios. After vaginoplasty most patients report overall high satisfaction with their sexual function even when complications occur, because most are minor and easily treatable. ::: ::: ::: CONCLUSION ::: GCS alleviates gender dysphoria for appropriately selected transgender men and women. Preoperative, intraoperative, and postoperative considerations of feminizing genital gender-confirmation procedures were reviewed. Hadj-Moussa M, Ohl DA, Kuzon WM. Feminizing Genital Gender-Confirmation Surgery. Sex Med Rev 2018;6:457-468.


title: Modeling Individual Subtests of the WAIS IV with Multiple Latent Factors

abstract: Performance on a cognitive test can be viewed either as measuring a unitary function or as reflecting the operation of multiple factors. Individual subtests in batteries designed to measure human abilities are commonly modeled as a single latent factor. Several latent factors are then used to model groups of subtests. However these latent factors are not independent as they are related through hierarchical or oblique structures. As a result, the simple structure of subtest performance results in complex latent factors. The present study used structural equation modeling to evaluate several multidimensional models of the Wechsler Adult Intelligence Scales- fourth edition (WAIS-IV) subtests. Multidimensional models of subtest performance provided better model fit as compared to several previously proposed one dimensional models. These multidimensional models also generalized well to new samples of populations differing in age from that used to estimate the model parameters. Overall these results show that models that describe subtests as multidimensional functions of uncorrelated factors provided a better fit to the WAIS-IV correlations than models that describe subtests as one dimensional functions of correlated factors. There appears to be a trade-off in modeling subtests as one dimensional and modeling with homogeneous latent traits. More consideration should be given to models that include multiple uncorrelated latent factors as determinants of the performance on a given subtest. These results support the view that performance on any given cognitive test is potentially the result of multiple factors. Simple structure may be too simple.


title: Keypoint signatures for fast learning and recognition

abstract: Statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the training itself is usually relatively slow and performed offline. Although methods have recently been proposed to train the classifier online, they can only learn a very limited number of new keypoints. This represents a handicap for real-time applications, such as Simultaneous Localization and Mapping (SLAM), which require incremental addition of arbitrary numbers of keypoints as they become visible. ::: ::: In this paper, we overcome this limitation and propose a descriptor that can be learned online fast enough to handle virtually unlimited numbers of keypoints. It relies on the fact that if we train a Randomized Tree classifier to recognize a number of keypoints extracted from an image database, all other keypoints can be characterized in terms of their response to these classification trees. This signature is fast to compute and has a discriminative power that is comparable to that of the much slower SIFT descriptor.


title: Simulation-based performance assessment of production planning formulations for semiconductor wafer fabrication

abstract: In this paper we compare two production planning formulations in a rolling horizon setting. The first is based on fixed lead times that are a multiple of the period length, while the second uses non-linear clearing functions. A scaled-down simulation model of a wafer fab is used to assess the performance of the two formulations. We examine the impact of the planning window and period length on the performance of the production planning formulations. The performance advantage of clearing functions that is observed in a static setting can be also observed in a rolling horizon setting.


title: Joint Transceiver Optimization of MIMO SWIPT Systems for Harvested Power Maximization

abstract: This letter studies a single-user power splitting-based multiple-input multiple-output system for simultaneous wireless information and power transfer. We aim to maximize the harvested power by joint design of transmit signal covariance matrix and receive power splitting factor under both a system rate constraint and a total power constraint. The harvested power maximization problem is difficult to solve due mainly to the nonconcave objective and the nonlinear coupling of design variables in the constraints. To tackle these challenges, we first derive a good approximation of the problem by ignoring some negligible noise terms and then further simplify it to a more tractable form by well exploiting the problem structure. Based on the Frank–Wolfe algorithm, we propose a simple yet efficient iterative algorithm to address the resulting problem. Numerical results validate the efficiency of the proposed algorithm.


title: Low delay MPEG DASH streaming over the WebRTC data channel

abstract: Dynamic Adaptive Streaming over HTTP (MPEG-DASH) is becoming the de-facto data format and streaming solution for over-the-top (OTT) video on-demand streaming. The underlying HTTP transport has its limitations and known penalties in slow start, under-utilization, and other inefficiency. An alternate approach to DASH is the new transport schemes like HTTP/2.0 and WebSocket. In this work, we explore WebRTC as a streaming transport to carry DASH data and demonstrate that it is competitive in serving low delay streaming applications with fast channel switching, with proper video data coding and streaming signaling/control solutions.


title: Face Recognition and Verification Using Artificial Neural Network

abstract: Automatic recognition of human faces is a significant problem in the development and application of pattern recognition. In this paper, we introduce a simple technique for identification of human faces in cluttered scenes based on neural nets. In detection phase, neural nets are used to test whether a window of 18x27 pixels contains a face or not. A major difficulty in learning process comes from the large database required for face / nonface images. We solve this problem by dividing these data into two groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. The proposed face recognition technique consists of three parts; preprocessing, feature extraction, and recognition steps. Gradient Vector method is used for facial feature extraction. A face recognition system based on recent method which concerned with both representation and recognition using artificial neural networks is presented. It then evaluates the performance of the system by applying two photometric normalization techniques: histogram equalization and homomorphic filtering. The system produces promising results for face verification and face recognition. The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at editor@ijcaonline.org


title: Personalization of Context-Dependent Applications Through Trigger-Action Rules

abstract: Our life is characterized by the presence of a multitude of interactive devices and smart objects exploited for disparate goals in different contexts of use. Thus, it is impossible for application developers to predict at design time the devices and objects users will exploit, how they will be arranged, and in which situations and for which objectives they will be used. For such reasons, it is important to make end users able to easily and autonomously personalize the behaviour of their Internet of Things applications, so that they can better comply with their specific expectations. In this paper, we present a method and a set of tools that allow end users without programming experience to customize the context-dependent behaviour of their Web applications through the specification of trigger-action rules. The environment is able to support end-user specification of more flexible behaviour than what can be done with existing commercial tools, and it also includes an underlying infrastructure able to detect the possible contextual changes in order to achieve the desired behaviour. The resulting set of tools is able to support the dynamic creation and execution of personalized application versions more suitable for users’ needs in specific contexts of use. Thus, it represents a contribution to obtaining low threshold/high ceiling environments. We also report on an example application in the home automation domain, and a user study that has provided useful positive feedback.


title: Probabilistic author-topic models for information discovery

abstract: We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer.


title: Stealth Attacks and Protection Schemes for State Estimators in Power Systems

abstract: State estimators in power systems are currently used to, for example, detect faulty equipment and to route power flows. It is believed that state estimators will also play an increasingly important role in future smart power grids, as a tool to optimally and more dynamically route power flows. Therefore security of the estimator becomes an important issue. The estimators are currently located in control centers, and large numbers of measurements are sent over unencrypted communication channels to the centers. We here study stealthy false-data attacks against these estimators. We define a security measure tailored to quantify how hard attacks are to perform, and describe an efficient algorithm to compute it. Since there are so many measurement devices in these systems, it is not reasonable to assume that all devices can be made encrypted overnight in the future. Therefore we propose two algorithms to place encrypted devices in the system such as to maximize their utility in terms of increased system security. We illustrate the effectiveness of our algorithms on two IEEE benchmark power networks under two attack and protection cost models.


Soft Material Characterization for Robotic Applications


title: A finite field cosine transform-based image processing scheme for color image encryption

abstract: In this paper, we propose a color image processing method based on a finite field cosine transform (FFCT). Basically, the approach consists in applying a 32-point FFCT over GF(224) to blocks of a color image. Since RGB images are considered, each pixel of the corresponding image blocks can be represented as a binary 24-tuple and then treated as an element of GF(224). By means of computer experiments, we show that such a joint three-channel processing leads to the uniformization of the histograms of an image and decorrelates adjacent pixels. This makes our method useful to provide robustness against statistical attacks in image encryption schemes.


title: Evaluation of a non-point source pollution model, AnnAGNPS, in a tropical watershed

abstract: Impaired water quality caused by human activity and the spread of invasive plant and animal species has been identified as a major factor of degradation of coastal ecosystems in the tropics. The main goal of this study was to evaluate the performance of AnnAGNPS (Annualized Non-Point Source Pollution Model), in simulating runoff and soil erosion in a 48km^2 watershed located on the Island of Kauai, Hawaii. The model was calibrated and validated using 2years of observed stream flow and sediment load data. Alternative scenarios of spatial rainfall distribution and canopy interception were evaluated. Monthly runoff volumes predicted by AnnAGNPS compared well with the measured data (R^2=0.90, P<0.05); however, up to 60% difference between the actual and simulated runoff were observed during the driest months (May and July). Prediction of daily runoff was less accurate (R^2=0.55, P<0.05). Predicted and observed sediment yield on a daily basis was poorly correlated (R^2=0.5, P<0.05). For the events of small magnitude, the model generally overestimated sediment yield, while the opposite was true for larger events. Total monthly sediment yield varied within 50% of the observed values, except for May 2004. Among the input parameters the model was most sensitive to the values of ground residue cover and canopy cover. It was found that approximately one third of the watershed area had low sediment yield (0-1tha^-^1y^-^1), and presented limited erosion threat. However, 5% of the area had sediment yields in excess of 5tha^-^1y^-^1. Overall, the model performed reasonably well, and it can be used as a management tool on tropical watersheds to estimate and compare sediment loads, and identify ''hot spots'' on the landscape.


title: Efficient methods for topic model inference on streaming document collections

abstract: Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of training documents requires approximate inference techniques that are computationally expensive. With today's large-scale, constantly expanding document collections, it is useful to be able to infer topic distributions for new documents without retraining the model. In this paper, we empirically evaluate the performance of several methods for topic inference in previously unseen documents, including methods based on Gibbs sampling, variational inference, and a new method inspired by text classification. The classification-based inference method produces results similar to iterative inference methods, but requires only a single matrix multiplication. In addition to these inference methods, we present SparseLDA, an algorithm and data structure for evaluating Gibbs sampling distributions. Empirical results indicate that SparseLDA can be approximately 20 times faster than traditional LDA and provide twice the speedup of previously published fast sampling methods, while also using substantially less memory.


title: Robust and Effective Malware Detection through Quantitative Data Flow Graph Metrics

abstract: We present a novel malware detection approach based on metrics over quantitative data flow graphs. Quantitative data flow graphs QDFGs model process behavior by interpreting issued system calls as aggregations of quantifiable data flows. Due to the high abstraction level we consider QDFG metric based detection more robust against typical behavior obfuscation like bogus call injection or call reordering than other common behavioral models that base on raw system calls. We support this claim with experiments on obfuscated malware logs and demonstrate the superior obfuscation robustness in comparison to detection using n-grams. Our evaluations on a large and diverse data set consisting of about 7000 malware and 500 goodware samples show an average detection rate of 98.01i?ź% and a false positive rate of 0.48i?ź%. Moreover, we show that our approach is able to detect new malware i.e. samples from malware families not included in the training set and that the consideration of quantities in itself significantly improves detection precision.


title: LIMSI$@$WMT'16: Machine Translation of News

abstract: This paper describes LIMSI’s submissions to the shared WMT’16 task “Translation of News”. We report results for Romanian-English in both directions, for English to Russian, as well as preliminary experiments on reordering to translate from English into German. Our submissions use mainly NCODE and MOSES along with continuous space models in a post-processing step. The main novelties of this year’s participation are the following: for the translation into Russian and Romanian, we have attempted to extend the output of the decoder with morphological variations and to use a CRF model to rescore this new search space; as for the translation into German, we have been experimenting with source-side pre-ordering based on a dependency structure allowing permutations in order to reproduce the tar- get word order.


title: Investigating Facebook and Google usage in Taiwan’s college students

abstract: This study proposes and tests a research model that was developed based on the uses-and-gratifications theory, personal traits, and intimacy. The aim of this study was to investigate whether selected factors have differential predicting power with respect to the use of Facebook and Google services in Taiwan. This study employed seven constructs: purposive value, hedonic value, social identity, social support, interpersonal relationship, personality traits, and intimacy as the factors predicting Facebook and Google usage. An electronic survey technique was used to collect data from the internet. The results showed that hedonic value and social identity constructs can significantly predict Facebook usage and that purposive value has significant predicting power on Google usage. The construct intimacy is the most significant factor in both Google and Facebook usage. Our findings lead to the suggestion that, for social network sites (SNSs) providers to differentiate the quality of their SNSs from others’ Web ...


title: IoT-Based Smart Garbage System for Efficient Food Waste Management

abstract: Owing to a paradigm shift toward Internet of Things (IoT), researches into IoT services have been conducted in a wide range of fields. As a major application field of IoT, waste management has become one such issue. The absence of efficient waste management has caused serious environmental problems and cost issues. Therefore, in this paper, an IoT-based smart garbage system (SGS) is proposed to reduce the amount of food waste. In an SGS, battery-based smart garbage bins (SGBs) exchange information with each other using wireless mesh networks, and a router and server collect and analyze the information for service provisioning. Furthermore, the SGS includes various IoT techniques considering user convenience and increases the battery lifetime through two types of energy-efficient operations of the SGBs: stand-alone operation and cooperation-based operation. The proposed SGS had been operated as a pilot project in Gangnam district, Seoul, Republic of Korea, for a one-year period. The experiment showed that the average amount of food waste could be reduced by 33%.


title: Wood defect detection method with PCA feature fusion and compressed sensing

abstract: We used principal component analysis (PCA) and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as a classifier, improves identification accuracy. We extracted 25 features, including geometry and regional features, gray-scale texture features, and invariant moment features, from wood board images and then integrated them using PCA, and selected eight principal components to express defects. After the fusion process, we used the features to construct a data dictionary, and realized the classification of defects by computing the optimal solution of the data dictionary in l1 norm using the least square method. We tested 50 Xylosma samples of live knots, dead knots, and cracks. The average detection time with PCA feature fusion and without were 0.2015 and 0.7125 ms, respectively. The original detection accuracy by SOM neural network was 87 %, but after compressed sensing, it was 92 %.


title: The airport gate assignment problem: mathematical model and a tabu search algorithm

abstract: Considers an airport gate assignment problem that dynamically assigns airport gates to scheduled flights based on passengers' daily origin and destination flow data. The objective of the problem is to minimize the overall connection times during which passengers walk to catch their connection flights. We formulate this problem as a mixed 0-1 quadratic integer programming problem and then reformulate it as a mixed 0-1 integer problem with a linear objective function and constraints. We design a simple tabu search meta-heuristic to solve the problem. The algorithm exploits the special properties of different types of neighborhood moves, and create highly effective candidate list strategies. We also address issues of tabu short-term memory, dynamic tabu tenure, aspiration rules, and various intensification and diversification strategies. Preliminary computational experiments are conducted, and the results are presented and analyzed.


title: Feature-based opinion mining through ontologies

abstract: Abstract The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public’s opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users’ opinions. However, current opinion mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative opinion mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based opinion mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches.


title: PARLOG: parallel programming in logic

abstract: PARLOG is a logic programming language in the sense that nearly every definition and query can be read as a sentence of predicate logic. It differs from PROLOG in incorporating parallel modes of evaluation. For reasons of efficient implementation, it distinguishes and separates and-parallel and or-parallel evaluation. PARLOG relations are divided into two types: single-solution relations and all-solutions relations. A conjunction of single-solution relation calls can be evaluated in parallel with shared variables acting as communication channels for the passing of partial bindings. Only one solution to each call is computed, using committed choice nondeterminism. A conjunction of all-solutions relation calls is evaluated without communication of partial bindings, but all the solutions may be found by an or-parallel exploration of the different evaluation paths. A set constructor provides the main interface between single-solution relations and all-solutions relations. This paper is a tutorial introduction to PARLOG. It assumes familiarity with logic programming. Categories and Subject Descriptors: D.l.l [Programming Techniques]: Applicative (Functional)


Marahel : A Language for Constructive Level Generation


title: A Better Way to Attend: Attention with Trees for Video Question Answering

abstract: We propose a new attention model for video question answering. The main idea of the attention models is to locate on the most informative parts of the visual data. The attention mechanisms are quite popular these days. However, most existing visual attention mechanisms regard the question as a whole. They ignore the word-level semantics where each word can have different attentions and some words need no attention. Neither do they consider the semantic structure of the sentences. Although the Extended Soft Attention (E-SA) model for video question answering leverages the word-level attention, it performs poorly on long question sentences. In this paper, we propose the heterogeneous tree-structured memory network (HTreeMN) for video question answering. Our proposed approach is based upon the syntax parse trees of the question sentences. The HTreeMN treats the words differently where the visual words are processed with an attention module and the verbal ones not. It also utilizes the semantic structure of the sentences by combining the neighbors based on the recursive structure of the parse trees. The understandings of the words and the videos are propagated and merged from leaves to the root. Furthermore, we build a hierarchical attention mechanism to distill the attended features. We evaluate our approach on two datasets. The experimental results show the superiority of our HTreeMN model over the other attention models especially on complex questions.


title: Probr - A Generic and Passive WiFi Tracking System

abstract: WiFi-enabled devices broadcast a vast amount of data without being associated to any access points. To study and analyze this data, a generic passive WiFi tracking system called Probr was developed. Probr manages various types of WiFi capturing devices, collects captured WiFi traces, processes collected WiFi traces, and visualizes WiFi activities via its Web interface. Probr supports several on-line analysis use cases and is extensible with respect to custom storage solutions to fit further use cases. Thus, Probr is the first system of that kind known, enabling full device administration and provided completely as Open Source. A case study conducted demonstrates the capabilities of Probr for use cases such as room utilization estimation, indoor device localization, tracking a person's presence between multiple Probr-equipped locations, and analysis of device vendor preferences.


title: RC3E: Provision and Management of Reconfigurable Hardware Accelerators in a Cloud Environment

abstract: Heterogeneous systems consisting of general-purpose processors and different types of hardware accelerators are becoming more and more common in HPC systems. Especially FPGAs provide a promising opportunity to improve both performance and energy efficiency of such systems. Adding FPGAs to clouds or data centers allows easy access to such reconfigurable resources. In this paper we present our cloud service models and cloud hypervisor called RC3E, which integrates virtualized FPGA-based hardware accelerators into a cloud environment. With our hardware and software framework, multiple (virtual) user designs can be executed on a single physical FPGA device. We demonstrate the performance of our approach by implementing up to four virtual user cores on a single device and present future perspectives for FPGAs in cloud-based data environments.


title: Position and orientation control of passive wire-driven motion support system using servo brakes

abstract: Wire-driven haptic devices can easily achieve highspeed operation because of the low inertia of light and thin wires. Therefore, it is expected to be widely used as a motion support system in fields such as sports training. However, usually these wires are driven by controlling each wire's tension using servo motors, which raises concerns about the safety in human-robot interaction. Thus, we propose to support the user by controlling servo brakes instead of motors, which can ensure safety. In this paper, we introduce a passive wire-driven system without any motors for realistic motion support in sports training. The system can measure the user motion and support the user while following a target form by a novel passive control method considering the orientation as well as the position using 7 wires' lengths and the brake tensions. We also conduct error evaluation experiments and show that a tennis beginner using this system can accurately follow the target form.


title: Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition

abstract: In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set-based face recognition, where each training and testing example contains a set of face images, which were captured from different variations of pose, illumination, expression, resolution, and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set-based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa. To address this, we propose a SFDL method to learn discriminative features and dictionaries simultaneously from raw face pixels so that discriminative information from facial image sets can be jointly exploited by a one-stage learning procedure. To better exploit the nonlinearity of face samples from different image sets, we propose a deep SFDL (D-SFDL) method by jointly learning hierarchical non-linear transformations and class-specific dictionaries to further improve the recognition performance. Extensive experimental results on five widely used face data sets clearly shows that our SFDL and D-SFDL achieve very competitive or even better performance with the state-of-the-arts.


V for Vocab: An Intelligent Flashcard Application


title: Rospeex: A cloud robotics platform for human-robot spoken dialogues

abstract: To build conversational robots, roboticists are required to have deep knowledge of both robotics and spoken dialogue systems. Although they can use existing cloud services that were built for other services, e.g., voice search, it will be difficult to share robotics-specific speech corpora obtained as server logs, because they will get buried in non-robotics-related logs. Building a cloud platform especially for the robotics community will benefit not only individual robot developers but also the robotics community since we can share the log corpus collected by it. This is challenging because we need to build a wide variety of functionalities ranging from a stable cloud platform to high-quality multilingual speech recognition and synthesis engines. In this paper, we propose “rospeex,” which is a cloud robotics platform for multilingual spoken dialogues with robots. We analyze the logs we have collected by operating rospeex for more than a year. Our key contribution lies in building a cloud robotics platform and allowing the robotics community to use it without payment or authentication.


title: The Fleet Size and Mix Vehicle Routing Problem with Backhauls: Formulation and Set Partitioning-based Heuristics

abstract: In this paper we present a new variant of the classical Vehicle Routing Problem – the Fleet Size and Mix Vehicle Routing Problem with Backhauls (FSMVRPB). An ILP formulation of the FSMVRPB is presented. Optimal solutions for small size instances are produced and upper and lower bounds are generated for larger ones. In this paper we also propose a Set Partitioning Problem (SPP) based heuristic. Three frameworks are developed and tested on a set of new FSMVRPB data instances which we generated. Computational results are presented which can be used for future benchmarking.


title: Generalized measuring-worm algorithm: high-accuracy mapping and movement via cooperating swarm robots

abstract: Recently, many extensive studies have been conducted on robot control via self-positioning estimation techniques. In the simultaneous localization and mapping (SLAM) method, which is one approach to self-positioning estimation, robots generally use both autonomous position information from internal sensors and observed information on external landmarks. SLAM can yield higher accuracy positioning estimations depending on the number of landmarks; however, this technique involves a degree of uncertainty and has a high computational cost, because it utilizes image processing to detect and recognize landmarks. To overcome this problem, we propose a state-of-the-art method called a generalized measuring-worm (GMW) algorithm for map creation and position estimation, which uses multiple cooperating robots that serve as moving landmarks for each other. This approach allows problems of uncertainty and computational cost to be overcome, because a robot must find only a simple two-dimensional marker rather than feature-point landmarks. In the GMW method, the robots are given a two-dimensional marker of known shape and size and use a front-positioned camera to determine the marker distance and direction. The robots use this information to estimate each other's positions and to calibrate their movement. To evaluate the proposed method experimentally, we fabricated two real robots and observed their behavior in an indoor environment. The experimental results revealed that the distance measurement and control error could be reduced to less than 3 %.


title: Privacy Aspects of Recommender Systems

abstract: The popularity of online recommender systems has soared; they are deployed in numerous websites and gather tremendous amounts of user data that are necessary for recommendation purposes. This data, however, may pose a severe threat to user privacy, if accessed by untrusted parties or used inappropriately. Hence, it is of paramount importance for recommender system designers and service providers to find a sweet spot, which allows them to generate accurate recommendations and guarantee the privacy of their users. In this chapter we overview the state of the art in privacy enhanced recommendations. We analyze the risks to user privacy imposed by recommender systems, survey the existing solutions, and discuss the privacy implications for the users of recommenders. We conclude that a considerable effort is still required to develop practical recommendation solutions that provide adequate privacy guarantees, while at the same time facilitating the delivery of high-quality recommendations to their users.


title: A light-weight distributed scheme for detecting ip prefix hijacks in real-time

abstract: As more and more Internet IP prefix hijacking incidents are being reported, the value of hijacking detection services has become evident. Most of the current hijacking detection approaches monitor IP prefixes on the control plane and detect inconsistencies in route advertisements and route qualities. We propose a different approach that utilizes information collected mostly from the data plane. Our method is motivated by two key observations: when a prefix is not hijacked, 1) the hop count of the path from a source to this prefix is generally stable; and 2) the path from a source to this prefix is almost always a super-path of the path from the same source to a reference point along the previous path, as long as the reference point is topologically close to the prefix. By carefully selecting multiple vantage points and monitoring from these vantage points for any departure from these two observations, our method is able to detect prefix hijacking with high accuracy in a light-weight, distributed, and real-time fashion. Through simulations constructed based on real Internet measurement traces, we demonstrate that our scheme is accurate with both false positive and false negative ratios below 0.5%.


title: Coherent-to-diffuse power ratio estimation for dereverberation

abstract: The estimation of the time- and frequency-dependent coherent-to-diffuse power ratio (CDR) from the measured spatial coherence between two omnidirectional microphones is investigated. Known CDR estimators are formulated in a common framework, illustrated using a geometric interpretation in the complex plane, and investigated with respect to bias and robustness towards model errors. Several novel unbiased CDR estimators are proposed, and it is shown that knowledge of either the direction of arrival (DOA) of the target source or the coherence of the noise field is sufficient for unbiased CDR estimation. The validity of the model for the application of CDR estimates to dereverberation is investigated using measured and simulated impulse responses. A CDR-based dereverberation system is presented and evaluated using signal-based quality measures as well as automatic speech recognition accuracy. The results show that the proposed unbiased estimators have a practical advantage over existing estimators, and that the proposed DOA-independent estimator can be used for effective blind dereverberation.


title: An elegant sufficiency: load-aware differentiated scheduling of data transfers

abstract: We investigate the file transfer scheduling problem, where transfers among different endpoints must be scheduled to maximize pertinent metrics. We propose two new algorithms that exploit the fact that the aggregate bandwidth obtained over a network or at a storage system tends to increase with the number of concurrent transfers---but only up to a certain limit. The first algorithm, SEAL, uses runtime information and data-driven models to approximate system load and adapt transfer schedules and concurrency so as to maximize performance while avoiding saturation. We implement this algorithm using GridFTP as the transfer protocol and evaluate it using real transfer logs in a production WAN environment. Results show that SEAL can improve average slowdowns and turnaround times by up to 25% and worst-case slowdown and turnaround times by up to 50%, compared with the best-performing baseline scheme. Our second algorithm, STEAL, further leverages user-supplied categorization of transfers as either "interactive" (requiring immediate processing) or "batch" (less time-critical). Results show that STEAL reduces the average slowdown of interactive transfers by 63% compared to the best-performing baseline and by 21% compared to SEAL. For batch transfers, compared to the best-performing baseline, STEAL improves by 18% the utilization of the bandwidth unused by interactive transfers. By elegantly ensuring a sufficient, but not excessive, allocation of concurrency to the right transfers, we significantly improve overall performance despite constraints.


title: Safety implications of cultural and cognitive issues in nuclear power plant operation

abstract: This research project was designed to investigate cultural and cognitive issues related to the work of nuclear power plant operators during their time on the job in the control room and during simulator training (emergency situations), in order to show how these issues impact on plant safety. The modeling of the operators work deals with the use of operational procedures, the constant changes in the focus of attention and the dynamics of the conflicting activities. The paper focuses on the relationships between the courses of action of the different operators and the constraints imposed by their working environment. It shows that the safety implications of the control room operators' cognitive and cultural issues go far beyond the formal organizational constructs usually implied. Our findings indicate that the competence required for the operators are concerned with developing the possibility of constructing situation awareness, managing conflicts, gaps and time problems created by ongoing task procedures, and dealing with distractions, developing skills for collaborative work.


title: Hybrid Attention based Multimodal Network for Spoken Language Classification

abstract: We examine the utility of linguistic content and vocal characteristics for multimodal deep learning in human spoken language understanding. We present a deep multimodal network with both feature attention and modality attention to classify utterance-level speech data. The proposed hybrid attention architecture helps the system focus on learning informative representations for both modality-specific feature extraction and model fusion. The experimental results show that our system achieves state-of-the-art or competitive results on three published multimodal datasets. We also demonstrated the effectiveness and generalization of our system on a medical speech dataset from an actual trauma scenario. Furthermore, we provided a detailed comparison and analysis of traditional approaches and deep learning methods on both feature extraction and fusion.


title: Semi-Markov model for simulating MOOC students

abstract: Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale experiment. To this end, we employ generative models to draw independent samples of artificial students in Monte Carlo simulations. We use Semi-Markov Chains for modeling student's activities and Expectation-Maximization algorithm for fitting the model. From the fitted model, we generate simulated students whose processes of weekly activities are similar to these of the real students.


title: Controlled parking for self-driving cars

abstract: We explored the problems which are soon to be faced while parking autonomous cars in parking lots. Like where is the closest parking slot available to the autonomous car? How to navigate to that location? What kind of parking structures could be good for autonomous cars? We also provide an initial solution which uses a central server and the graph of the parking lot to guide the cars to the closest parking slots. With experiments, we have shown that our proposed method should be effective for the controlled parking for self-driving cars.


title: Prediction of flow behavior of micro-particles in risers in the presence of van der Waals forces

abstract: Abstract Flow behavior of gas and micro-particles in riser is predicted within the framework of the classical Euler–Lagrange approach. The Newtonian equation of motion considering the effect of van der Waals forces is solved for each simulated particle in the system. Particle collisional dynamics is modeled by means of the direct simulation Monte Carlo (DSMC) method. The influence of van der Waals forces on the particle collisions is investigated. The effect of interparticle collisions on the particle concentration and velocity distributions is presented. These numerical results allow one to understand the effect of the considered parameters on the flow behavior of micro-particles agglomerations.


title: Moving Network Protection from Software to Hardware: An Energy Efficiency Analysis

abstract: Software-based network security is constantly challenged by the increase in network speeds and number of attacks. At the same time, mobile network access underscores the need for energy efficiency. In this paper, we present a new way to improve the throughput and to reduce the energy consumption of an anomaly-based intrusion detection system for probing attacks. Our framework implements the same classifier algorithm in software (C++) and in hardware (synthesizable VHDL), and then compares the energy efficiency of the two approaches. Our results for a decision tree classifier show that the hardware version consumed only 0.03% of the energy used by the same algorithm in software, even though the hardware version operates with a throughput that is 15 times that of the software version.


title: Fast and Accurate Metadata Authoring Using Ontology-Based Recommendations.

abstract: In biomedicine, high-quality metadata are crucial for finding experimental datasets, for understanding how experiments were performed, and for reproducing those experiments. Despite the recent focus on metadata, the quality of metadata available in public repositories continues to be extremely poor. A key difficulty is that the typical metadata acquisition process is time-consuming and error prone, with weak or nonexistent support for linking metadata to ontologies. There is a pressing need for methods and tools to speed up the metadata acquisition process and to increase the quality of metadata that are entered. In this paper, we describe a methodology and set of associated tools that we developed to address this challenge. A core component of this approach is a value recommendation framework that uses analysis of previously entered metadata and ontology-based metadata specifications to help users rapidly and accurately enter their metadata. We performed an initial evaluation of this approach using metadata from a public metadata repository.


title: Technology Aspects of E-Government Readiness in Developing Countries: A Review of the Literature

abstract: The rapid global growth of the Internet and information technology has inspired many governments to transform their traditional services into electronic ones. Many governments are now developing, implementing and improving their strategies to transform government services using information and communication technologies (ICTs). E-Government, as it is known, has become a popular focus of government efforts in many developed countries and, more recently, in several developing countries. Further, e-government services have become a significant and active means for interaction among government, citizens and businesses. E-government comprises several dimensions, one of the main ones being e-government readiness. To put technology to effective use, a government must be “ready”. E-government readiness helps a government to measure its stages of readiness, identify its gaps, and then redesign its government strategy. One of the aspects of e- government readiness is that of technological readiness, which plays an important role in implementing an effective and efficient e- government project. This paper explores the gaps in current knowledge relating to the technological aspects of e-government readiness through the conduct of a literature review. In particular, the review focuses on the models and frameworks that have been developed to assess e-government readiness.


title: Automatic irrigation system using soil moisture sensor and temperature sensor with microcontroller AT89S52

abstract: Food production techniques have to be improved because of rapid demand in food. Since India has agriculture as the main source of production, proper irrigation schemes are to be employed for an efficient outcome. This design automates the irrigation process thereby reducing the manual intervention and the water losses. It is more helpful in the places where water scarcity is seen more. It consists of 2 sensors which takes the values of temperature of surroundings and moisture level of soil. Output of these sensors are given to ADC and then to microcontroller. Microcontroller compares the values with the threshold values and drives the relay which controls the motor. LCD display is used to display data in the field. GSM modem is also a part of design which helps in transmitting SMS to farmer's phone number which contains the status of motor in field. The design is cost effective and also affordable.


title: An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images

abstract: Reversible data hiding in encrypted images (RDHEI) is an effective technique to embed data in the encrypted domain. An original image is encrypted with a secret key and during or after its transmission, it is possible to embed additional information in the encrypted image, without knowing the encryption key or the original content of the image. During the decoding process, the secret message can be extracted and the original image can be reconstructed. In the last few years, RDHEI has started to draw research interest. Indeed, with the development of cloud computing, data privacy has become a real issue. However, none of the existing methods allow us to hide a large amount of information in a reversible manner. In this paper, we propose a new reversible method based on MSB (most significant bit) prediction with a very high capacity. We present two approaches, these are: high capacity reversible data hiding approach with correction of prediction errors and high capacity reversible data hiding approach with embedded prediction errors. With this method, regardless of the approach used, our results are better than those obtained with current state of the art methods, both in terms of reconstructed image quality and embedding capacity.


Achievements in Space Robotics Expanding the Horizons of Service and Exploration BY


title: Dominance hierarchy and spatial distribution in captive red-capped mangabeys (Cercocebus torquatus torquatus): Testing Hemelrijk’s agent-based model

abstract: We empirically tested Hemelrijk’s agent-based model (Hemelrijk 1998), in which dyadic agonistic interaction between primate-group subjects determines their spatial distribution and whether or not the dominant subject has a central position with respect to the other subjects. We studied a group of captive red-capped mangabeys (Cercocebus torquatus torquatus) that met the optimal conditions for testing this model (e.g. a linear dominance hierarchy). We analyzed the spatial distribution of the subjects in relation to their rank in the dominance hierarchy and the results confirmed the validity of this model. In accordance with Hemelrijk’s model (Hemelrijk 1998), the group studied showed an ambiguity-reducing strategy that led to non-central spatial positioning on the part of the dominant subject, thus confirming the model indirectly. Nevertheless, for the model to be confirmed directly, the group has to adopt a risk-sensitive strategy so that observers can study whether dominant subjects develop spatial centrality. Our study also demonstrated that agent-based models are a good tool for the study of certain complex behaviors observed in primates because these explanatory models can help formulate suggestive hypotheses for exploring new lines of research in primatology. Keywords: Dominance-hierarchy rank; spatial distribution; Cercocebus torquatus; agent-based models


title: Cluster analysis of weighted bipartite networks: a new copula-based approach

abstract: In this work we are interested in identifying clusters of "positional equivalent" actors, i.e. actors who play a similar role in a system. In particular, we analyze weighted bipartite networks that describes the relationships between actors on one side and features or traits on the other, together with the intensity level to which actors show their features. We develop a methodological approach that takes into account the underlying multivariate dependence among groups of actors. The idea is that positions in a network could be defined on the basis of the similar intensity levels that the actors exhibit in expressing some features, instead of just considering relationships that actors hold with each others. Moreover, we propose a new clustering procedure that exploits the potentiality of copula functions, a mathematical instrument for the modelization of the stochastic dependence structure. Our clustering algorithm can be applied both to binary and real-valued matrices. We validate it with simulations and applications to real-world data.