title: Fan-out wafer-level packaging for 3D IC heterogeneous integration

abstract: Two 3D IC heterogeneous integrations by fan-out wafer-level packaging (FOWLP) technology are investigated in this study. The emphasis of the first such method is on the design, and of the other method, the emphasis is on the manufacturing process.


title: Application Crash Consistency and Performance with CCFS

abstract: Recent research has shown that applications often incorrectly implement crash consistency. We present the Crash-Consistent File System (ccfs), a file system that improves the correctness of application-level crash consistency protocols while maintaining high performance. A key idea in ccfs is the abstraction of a stream. Within a stream, updates are committed in program order, improving correctness; across streams, there are no ordering restrictions, enabling scheduling flexibility and high performance. We empirically demonstrate that applications running atop ccfs achieve high levels of crash consistency. Further, we show that ccfs performance under standard file-system benchmarks is excellent, in the worst case on par with the highest performing modes of Linux ext4, and in some cases notably better. Overall, we demonstrate that both application correctness and high performance can be realized in a modern file system.


title: The systemizing quotient: an investigation of adults with Asperger syndrome or high-functioning autism, and normal sex differences

abstract: Systemizing is the drive to analyse systems or construct systems. A recent model of psychological sex differences suggests that this is a major dimension in which the sexes differ, with males being more drawn to systemize than females. Currently, there are no self-report measures to assess this important dimension. A second major dimension of sex differences is empathizing (the drive to identify mental states and respond to these with an appropriate emotion). Previous studies find females score higher on empathy measures. We report a new self-report questionnaire, the Systemizing Quotient (SQ), for use with adults of normal intelligence. It contains 40 systemizing items and 20 control items. On each systemizing item, a person can score 2, 1 or 0, so the SQ has a maximum score of 80 and a minimum of zero. In Study 1, we measured the SQ of n = 278 adults (114 males, 164 females) from a general population, to test for predicted sex differences (male superiority) in systemizing. All subjects were also given the Empathy Quotient (EQ) to test if previous reports of female superiority would be replicated. In Study 2 we employed the SQ and the EQ with n = 47 adults (33 males, 14 females) with Asperger syndrome (AS) or high-functioning autism (HFA), who are predicted to be either normal or superior at systemizing, but impaired at empathizing. Their scores were compared with n = 47 matched adults from the general population in Study 1. In Study 1, as predicted, normal adult males scored significantly higher than females on the SQ and significantly lower on the EQ. In Study 2, again as predicted, adults with AS/HFA scored significantly higher on the SQ than matched controls, and significantly lower on the EQ than matched controls. The SQ reveals both a sex difference in systemizing in the general population and an unusually strong drive to systemize in AS/HFA. These results are discussed in relation to two linked theories: the 'empathizing-systemizing' (E-S) theory of sex differences and the extreme male brain (EMB) theory of autism.


title: A Study On Convolutional Neural Network Based End-To-End Replay Anti-Spoofing

abstract: The second Automatic Speaker Verification Spoofing and Countermeasures challenge (ASVspoof 2017) focused on"replay attack"detection. The best deep-learning systems to compete in ASVspoof 2017 used Convolutional Neural Networks (CNNs) as a feature extractor. In this paper, we study their performance in an end-to-end setting. We find that these architectures show poor generalization in the evaluation dataset, but find a compact architecture that shows good generalization on the development data. We demonstrate that for this dataset it is not easy to obtain a similar level of generalization on both the development and evaluation data. This leads to a variety of open questions about what the differences are in the data; why these are more evident in an end-to-end setting; and how these issues can be overcome by increasing the training data.


title: The case of GWAS of obesity: does body weight control play by the rules?

abstract: As yet, genome-wide association studies (GWAS) have not added much to our understanding of the mechanisms of body weight control and of the etiology of obesity. This shortcoming is widely attributed to the complexity of the issues. The appeal of this explanation notwithstanding, we surmise that (i) an oversimplification of the phenotype (namely by the use of crude anthropometric traits) and (ii) a lack of sound concepts of body weight control and, thus, a lack of a clear research focus have impeded better insights most. The idea of searching for polygenetic mechanisms underlying common forms of obesity was born out of the impressive findings made for monogenetic forms of extreme obesity. In the case of common obesity, however, observational studies on normal weight and overweight subjects never provided any strong evidence for a tight internal control of body weight. In addition, empirical studies of weight changes in normal weight and overweight subjects revealed an intra-individual variance that was similar to inter-individual variance suggesting the absence of tight control of body weight. Not least, this lack of coerciveness is reflected by the present obesity epidemic. Finally, data on detailed body composition highlight that body weight is too heterogeneous a phenotype to be controlled as a single entity. In summary GWAS of obesity using crude anthropometric traits have likely been misled by popular heritability estimates that may have been inflated in the first place. To facilitate more robust and useful insights into the mechanisms of internal control of human body weight and, consequently, the genetic basis of obesity, we argue in favor of a broad discussion between scientists from the areas of integrative physiologic and of genomics. This discussion should aim at better conceived studies employing biologically more meaningful phenotypes based on in depth body composition analysis. To advance the scientific community—including the editors of our top journals—needs a re-launch of future GWAS of obesity.


title: Dual-Band Dual-Polarized Full-Wave Rectenna Based on Differential Field Sampling

abstract: A dual-band rectenna for radio frequency (RF) energy harvesting is presented in this letter. The proposed antenna has two concentric square patches electrically connected with a small microstrip line connection. Four ports are located in the inner patch. The configuration of the ports enables a differential field sampling scheme and dual polarization. The antenna operates for the WiFi frequency bands of 2.4 and 5.5 GHz with 7.52 and 7.26 dBi gain, respectively, for each frequency. A full-wave Greinacher voltage doubler rectifier for each polarization has been employed for RF-to-dc conversion. The proposed novel topology utilizes the differential field sampling for each polarization and quadruples the overall output voltage by the rectification process. The differential output voltage source from the rectenna can directly act as a power source as typically electronics require differential source for their operation.


Multilevel Modeling - A Methodology for Computer System Design


title: Partially labeled classification with Markov random walks

abstract: To classify a large number of unlabeled examples we combine a limited number of labeled examples with a Markov random walk representation over the unlabeled examples. The random walk representation exploits any low dimensional structure in the data in a robust, probabilistic manner. We develop and compare several estimation criteria/algorithms suited to this representation. This includes in particular multi-way classification with an average margin criterion which permits a closed form solution. The time scale of the random walk regularizes the representation and can be set through a margin-based criterion favoring unambiguous classification. We also extend this basic regularization by adapting time scales for individual examples. We demonstrate the approach on synthetic examples and on text classification problems.


title: Arabic text classification using K-nearest neighbour algorithm

abstract: Many algorithms have been implemented to the problem of Automatic Text Categorization (ATC). Most of the work in this area has been carried out on English texts, with only a few researchers addressing Arabic texts. We have investigated the use of the K-Nearest Neighbour (K-NN) classifier, with an Inew , cosine, jaccard and dice similarities, in order to enhance Arabic ATC. We represent the dataset as un -stemmed and stemmed data; with the use of TREC-2002, in order to remove prefixes and suffixes. However, for statistical text representation, Bag-Of-Words (BOW) and character-level 3 (3-Gram) were used. In order to, reduce the dimensionality of feature space; we used several feature selection methods. Experiments conducted with Arabic text showed that the K-NN classifier, with the new method similarity Inew 92.6% Macro-F1, had better performance than the K-NN classifier with cosine, jaccard and dice similarities. Chi-square feature selection, with representation by BOW, led to the best performance over other feature selection methods using BOW and 3-Gram.


Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning


title: Operations Research (OR) in Service Industries: A Comprehensive Review

abstract: The share of gross domestic product from the service industry reflects the competitiveness of a nation; the service industry in the USA accounts for around 80% of its gross domestic product, and it has been increasing gradually. Continual innovations and advances in enabling technologies for the service industry are crucial for developed countries to sustain their leading positions in the globalized economy. To clarify future research directions of operations research (OR) in the service industry, the state of art of OR has been examined systematically, the new requirements of OR are identified for its applications in service industries in comparison with those in manufacturing industries, and the limitations of existing methodologies and tools have been discussed. This paper was intended to provide an updated review on how OR has been applied in the service sector in recent years and what directions the study of OR will be carried forward in the near future. Under a proposed research framework, recent OR-related articles were collected from 17 leading OR journals and classified into the five most active sectors, that is, transportation and warehousing, information and communication, human health and social assistance, retails and wholesales, and financial and insurance services. The conclusions on the limitations of existing studies and the demanding ORs in the service have been drawn from our summaries and observations from a comprehensive review in this field. Copyright © 2013 John Wiley & Sons, Ltd.


title: Stereo-based all-terrain obstacle detection using visual saliency

abstract: This paper proposes a hybridization of two well-known stereo-based obstacle detection techniques for all-terrain environments. While one of the techniques is employed for the detection of large obstacles, the other is used for the detection of small ones. This combination of techniques opportunistically exploits their complementary properties to reduce computation and improve detection accuracy. Being particularly computation intensive and prone to generate a high false-positive rate in the face of noisy three-dimensional point clouds, the technique for small obstacle detection is further extended in two directions. The goal of the first extension is to reduce both problems by focusing the detection on those regions of the visual field that detach more from the background and, consequently, are more likely to contain an obstacle. This is attained by means of spatially varying the data density of the input images according to their visual saliency. The second extension refers to the use of a novel voting mechanism, which further improves robustness. Extensive experimental results confirm the ability of the proposed method to robustly detect obstacles up to a range of 20 m on uneven terrain. Moreover, the model runs at 5 Hz on 640 × 480 stereo images. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.


title: Co-constrained handles for deformation in shape collections

abstract: We present a method for learning custom deformation handles for an object, from a co-analysis of similar objects. Our approach identifies the geometric and spatial constraints among the different parts of an object, and makes this information available through abstract shape handles. These handles allow the user to prescribe arbitrary deformation directives including free-form surface deformations. However, only a subset of admissible deformations is enabled to the user as learned from the constraint space. Example applications are presented in shape editing, co-deformation and style transfer.


title: Itone: a japanese text input method for a dual joystick game controller

abstract: In this paper, we report the design and evaluation of a Japanese text input method called IToNe. It uses a dual joystick game controller to input Japanese text. The left and right joysticks are respectively assigned to the left and right halves of the Japanese syllabary chart. The combination of left and right joystick signals selects a character. IToNe is theoretically faster than the Japanese selection chart. Results of a preliminary user study show that users prefer IToNe to EGCONVERT: its smoothness during operation and accuracy measures are significantly higher. In addition, the learning curve of IToNe suggests that it can be learned easily and that it potentially provides higher performance.


title: A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques

abstract: This paper presents a critical review of the most important evolutionary-based multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search capabilities of evolutionary algorithms. Each technique is briefly described with its advantages and disadvantages, its degree of applicability and some of its known applications. Finally, the future trends in this discipline and some of the open areas of research are also addressed.


hybrid group decision support system for supplier selection using nalytic hierarchy process , fuzzy set theory and neural network rpan


title: TwinFusion: High Framerate Non-rigid Fusion through Fast Correspondence Tracking

abstract: Real time non-rigid reconstruction pipelines are extremely computationally expensive and easily saturate the highest end GPUs currently available. This requires careful strategic choices to be made about a set of highly interconnected parameters that divide up the limited compute. At the same time, offline systems, prove the value of increasing voxel resolution, more iterations, and higher frame rates. To this end, we demonstrate a set of remarkably simple but effective modifications to these algorithms that significantly reduce the average per-frame computation cost allowing these parameters to be increased. Specifically, we divide the depth stream into sub-frames and fusion-frames, disabling both model accumulation (fusion) and non-rigid alignment (model tracking) on the former. Instead, we efficiently track point correspondences across neighboring sub-frames. We then leverage these correspondences to initialize the standard non-rigid alignment to a fusion-frame where data can then be accumulated into the model. As a result, compute resources in the modified non-rigid reconstruction pipeline can be immediately re-purposed to increase voxel resolution, use more iterations or to increase the frame rate. To demonstrate the latter, we leverage recent high frame rate depth algorithms to build a novel “twin” sensor consisting of a low-res/highfps sub-frame camera and a second low-fps/high-res fusion camera.


title: Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding

abstract: Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames by watching unlabeled videos via deep convolutional network has made significant progress recently. Current state-of-the-art (SoTA) methods treat the two tasks independently. One important assumption of the existing depth estimation methods is that the scenes contain no moving object. In this paper, we propose to address the two tasks as a whole, i.e. to jointly understand per-pixel 3D geometry and motion. This eliminates the need of static scene assumption and enforces the inherent geometrical consistency during the learning process, yielding significantly improved results for both tasks. We call our method as "Every Pixel Counts++" or "EPC++". Various loss terms are formulated to jointly supervise the learning across geometrical cues and effective adaptive training strategy is proposed to achieve better performance. Comprehensive experiments were conducted on datasets with different scenes, including driving scenario (KITTI 2012 and KITTI 2015 datasets), mixed outdoor/indoor scenes (Make3D) and synthetic animation (MPI Sintel dataset). Performance on the five tasks of depth estimation, optical flow estimation, odometry, moving object segmentation and scene flow estimation shows that our approach outperforms other SoTA methods, demonstrating the effectiveness of each module of our proposed method.


title: This time it’s personal: the memory benefit of hearing oneself

abstract: ABSTRACTThe production effect is the memory advantage of saying words aloud over simply reading them silently. It has been hypothesised that this advantage stems from production featuring distinctive information that stands out at study relative to reading silently. MacLeod (2011) (I said, you said: The production effect gets personal. Psychonomic Bulletin & Review, 18, 1197–1202. doi:10.3758/s13423-011-0168-8) found superior memory for reading aloud oneself vs. hearing another person read aloud, which suggests that motor information (speaking), self-referential information (i.e., “I said it”), or both contribute to the production effect. In the present experiment, we dissociated the influence on memory of these two components by including a study condition in which participants heard themselves read words aloud (recorded earlier) – a first for production effect research – along with the more typical study conditions of reading aloud, hearing someone else speak, and reading silently. There was a gradient ...


title: Error characteristics and calibration-free techniques for wireless LAN-based location estimation

abstract: Using wireless LAN technology for location estimation provides alternate means to enable location-based applications without investment in sensor network infrastructure and special hardware. However, the main drawback of wireless LAN-based location systems is calibration of signal strength as a function of location in spatially high-density, which consumes manual labor and needs to be carried out repeatedly. In this paper, we analyze empirical error characteristics of calibration-based location algorithms such as triangulation in various spatial densities of calibration, using commercially available wireless LAN products. Then, we propose Triangular Interpolation and eXtrapolation (TIX), a calibration-free location algorithm, and present empirical performance evaluation. TIX can achieve mean distance error within 5.4 m, which is comparable to within 4.7 m errors of the calibration-based algorithms. We also present theoretical analysis on error characteristics of the location algorithms deriving accuracy limits and quantifying the effect of RF measurement and calibration.


title: Depth with Nonlinearity Creates No Bad Local Minima in ResNets

abstract: Abstract In this paper, we prove that depth with nonlinearity creates no bad local minima in a type of arbitrarily deep ResNets with arbitrary nonlinear activation functions, in the sense that the values of all local minima are no worse than the global minimum value of corresponding classical machine-learning models, and are guaranteed to further improve via residual representations. As a result, this paper provides an affirmative answer to an open question stated in a paper in the conference on Neural Information Processing Systems 2018. This paper advances the optimization theory of deep learning only for ResNets and not for other network architectures.


HGsuspector : Scalable Collective Fraud Detection in Heterogeneous Graphs


A noise resistant dependency measure for rough set-based feature selection


title: Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks

abstract: As 3D movie viewing becomes mainstream and the Virtual Reality (VR) market emerges, the demand for 3D contents is growing rapidly. Producing 3D videos, however, remains challenging. In this paper we propose to use deep neural networks to automatically convert 2D videos and images to a stereoscopic 3D format. In contrast to previous automatic 2D-to-3D conversion algorithms, which have separate stages and need ground truth depth map as supervision, our approach is trained end-to-end directly on stereo pairs extracted from existing 3D movies. This novel training scheme makes it possible to exploit orders of magnitude more data and significantly increases performance. Indeed, Deep3D outperforms baselines in both quantitative and human subject evaluations.


title: CrowdOTA: An Online Task Assignment System in Crowdsourcing

abstract: Crowdsourcing is widely accepted as a means for resolving tasks that are hard for computers, e.g., entity resolution. Unfortunately, Crowdsourcing may yield relatively low-quality results if there is no proper quality control. Although previous studies attempt to eliminate workers by estimating workers' qualities via qualification tests or hidden tests, the qualities estimated may not be accurate, because workers may have diverse qualities across tasks. Thus, the quality of the results could be further improved by wisely assigning tasks to the workers who are specialized in the tasks and online task assignment is an effective way to achieve this goal. However, existing crowdsourcing platforms either do not support online task assignment (e.g., CrowdFlower) or are not user-friendly because they require to write complicated codes (e.g., Amazon MTurk). To address these limitations, we develop an online task assignment system, which can on-the-fly assign workers with appropriate tasks. We have deployed our system on top of MTurk. We demonstrate the following scenarios using our system. Firstly, requesters can easily utilize our system to enable online task assignment in order to improve answer quality. Moreover, requesters do not need to write any code. Secondly, our system can infer the quality of workers, and requesters can design and test their own task assignment algorithms using our proposed information. Thirdly, our system can monitor tasks and workers in real time, and the requesters can eliminate bad workers or terminate the crowdsourcing process to reduce the unnecessary cost.


title: Simulation of micro-bump interconnections failure analysis for 2.5D IC packaging

abstract: This paper provides micro-bump fracture analysis in the context of a 2.5D IC package under reflow process. With the increasing demands for product functionality, the pitch size and diameter of micro-bumps have become smaller, as a means of achieving higher input/output counts in microelectronic packages. However, by decreasing micro-bump diameter, integrity of the microelectronic package is becoming compromised. The majority of research on the system in package (SiP) has focused on the Coefficient of Thermal Expansion (CTE) mismatch and heat junctions. The primary problems arising due to CTE mismatch and heat dissipation are failures or fatigues in 2.5D IC package, which can escalate to critical reliability issues. However, thermo-mechanical stress induced by temperature loading has a significant effect on material strength, causing, for example, interfacial cracking or micro-bump failure. Thus, 2.5D IC package modeling needs to be developed in order to identify factors that can mitigate micro-bump failure under reflow process. In this paper, we discuss the different insights pertaining to physics of thermo-mechanical loading for 2.5D IC package.


title: Zero‐field NMR and NQR spectrometer

abstract: In comparison to high‐field NMR, zero‐field techniques offer advantages in terms of spectral interpretability in studies of polycrystalline or amorphous solids. This article describes a technique and apparatus for time‐domain measurements of nuclear magnetism in the absence of applied fields (Fourier transform zero‐field NMR and NQR). Magnetic field cycling and high field detection are employed to enhance sensitivity. The field cycling is accomplished with an air‐driven shuttle system which moves the sample between regions of high and low magnetic field, in combination with switchable electromagnets in the low‐field region. Sudden field steps or pulses are used to initiate coherent nuclear spin evolution in zero field and to monitor such evolution as a function of time. Experimental results are shown and analyzed. Possible variations on the basic method are described and their relative advantages are discussed.


title: Tutorial review of synthetic-aperture radar (SAR) with applications to imaging of the ocean surface

abstract: A synthetic aperture radar (SAR) can produce high-resolution two-dimensional images of mapped areas. The SAR comprises a pulsed transmitter, an antenna, and a phase-coherent receiver. The SAR is borne by a constant velocity vehicle such as an aircraft or satellite, with the antenna beam axis oriented obliquely to the velocity vector. The image plane is defined by the velocity vector and antenna beam axis. The image orthogonal coordinates are range and cross range (azimuth). The amplitude and phase of the received signals are collected for the duration of an integration time after which the signal is processed. High range resolution is achieved by the use of wide bandwidth transmitted pulses. High azimuth resolution is achieved by focusing, with a signal processing technique, an extremely long antenna that is synthesized from the coherent phase history. The pulse repetition frequency of the SAR is constrained within bounds established by the geometry and signal ambiguity limits. SAR operation requires relative motion between radar and target. Nominal velocity values are assumed for signal processing and measurable deviations are used for error compensation. Residual uncertainties and high-order derivatives of the velocity which are difficult to compensate may cause image smearing, defocusing, and increased image sidelobes. The SAR transforms the ocean surface into numerous small cells, each with dimensions of range and azimuth resolution. An image of a cell can be produced provided the radar cross section of the cell is sufficiently large and the cell phase history is deterministic. Ocean waves evidently move sufficiently uniformly to produce SAR images which correlate well with optical photographs and visual observations. The relationship between SAR images and oceanic physical features is not completely understood, and more analyses and investigations are desired.


title: Comparison of different POS Tagging Techniques (N-Gram, HMM and Brill’s tagger) for Bangla

abstract: There are different approaches to the problem of assigning each word of a text with a parts-of-speech tag, which is known as Part-Of-Speech (POS) tagging. In this paper we compare the performance of a few POS tagging techniques for Bangla language, e.g. statistical approach (n-gram, HMM) and transformation based approach (Brill’s tagger). A supervised POS tagging approach requires a large amount of annotated training corpus to tag properly. At this initial stage of POS-tagging for Bangla, we have very limited resource of annotated corpus. We tried to see which technique maximizes the performance with this limited resource. We also checked the performance for English and tried to conclude how these techniques might perform if we can manage a substantial amount of annotated corpus.


title: A NEW E-LEARNİNG PARADİGM: TOOLS AND TECHNİQUES

abstract: Technology we believe can place a greater range of tools and resources at the disposal of teachers and students and one of the by-products of the speed with which things change in the era of the ICT revolution is that there is a lot of new paradigms which is explored in this work. Several of the tools and techniques available for the delivery of electronic learning are presented. Also, basic IT tools are shown to possess considerable capabilities alongside the discussions of specialized tools such as symbolic, computer algebra and graphical simulation packages and their contributions to learning. Presented in this paper also is the adaptation of hardware which was originally designed for recreation and entertainment to e-learning.


title: A firewall for Internet of Things

abstract: With the advent of the Internet of Things (IoT), privacy and security of sensitive data has become a major concern. In general, sensors which are the enablers for IoT, send the sensed data to a cloud database over the internet. The communication to the cloud database may be compromised by an adversary, or the database maybe accessed by a curious database administrator, thereby raising security concerns. To solve this issue, we demonstrate a solution to safeguard IoT devices in a home network scenario from potential attacks. A firewall is set up using a Raspberry Pi as a gateway which secures their communication with the cloud database. Furthermore, we plan to build a location-aware (physical location in the home network scenario) heuristics and a signature based traffic detection dashboard running on the Raspberry Pi, in order to control the IoT devices and log their network behavior.


title: The Good, the Bad, and the Ugly: An Empirical Study of Implicit Type Conversions in JavaScript

abstract: Most popular programming languages support situations where a value of one type is converted into a value of another type without any explicit cast. Such implicit type conversions, or type coercions, are a highly controversial language feature. Proponents argue that type coercions enable writing concise code. Opponents argue that type coercions are error-prone and that they reduce the understandability of programs. This paper studies the use of type coercions in JavaScript, a language notorious for its widespread use of coercions. We dynamically analyze hundreds of programs, including real-world web applications and popular benchmark programs. We find that coercions are widely used (in 80.42% of all function executions) and that most coercions are likely to be harmless (98.85%). Furthermore, we identify a set of rarely occurring and potentially harmful coercions that safer subsets of JavaScript or future language designs may want to disallow. Our results suggest that type coercions are significantly less evil than commonly assumed and that analyses targeted at real-world JavaScript programs must consider coercions.


title: Exploring the Impact of IT Service Management Process Improvement Initiatives: A Case Study Approach

abstract: IT companies worldwide have started to improve their service management processes based on best practice frameworks, such as IT Infrastructure Library (ITIL). However, many of these companies face difficulties in demonstrating the positive outcomes of IT service management (ITSM) process improvement. This has led us to investigate the research problem: What positive impacts have resulted from IT service management process improvement? The main contributions of this paper are 1) to identify the ITSM process improvement outcomes in two IT service provider organizations and 2) provide advice as lessons learnt.


Extending the Use of Adaptor Grammars for Unsupervised Morphological Segmentation of Unseen Languages


title: © 2005 Production and Operations Management Society Sustainable Operations Management

abstract: Operations management researchers and practitioners face new challenges in integrating issues of sustainability with their traditional areas of interest. During the past 20 years, there has been growing pressure on businesses to pay more attention to the environmental and resource consequences of the products and services they offer and the processes they deploy. One symptom of this pressure is the movement towards triple bottom line reporting (3BL) concerning the relationship of profit, people, and the planet. The resulting challenges include integrating environmental, health, and safety concerns with green-product design, lean and green operations, and closed-loop supply chains. We review these and other “sustainability” themes covered in the first 50 issues of Production and Operations Management and conclude with some thoughts on future research challenges in sustainable operations management.


title: Capacitive power transfer for contactless charging

abstract: The simplicity and low cost of capacitive interfaces makes them very attractive for wireless charging stations. Major benefits include low electromagnetic radiation and the amenability of combined power and data transfer over the same interface. We present a capacitive power transfer circuit using series resonance that enables efficient high frequency, moderate voltage operation through soft-switching. An included analysis predicts fundamental limitations on the maximum achievable efficiency for a given amount of coupling capacitance and is used to find the optimum circuit component values and operating point. Automatic tuning loops ensure the circuit operates at the optimum frequency and maximum efficiency over a wide range of coupling capacitance and load conditions. An example interface achieves near 80% efficiency at 3.7 W with only 63pF of coupling capacitance. An automatic tuning loop adjusts the frequency from 4.2 MHz down to 4MHz to allow for 25% variation in the nominal coupling capacitance. The duty cycle is also automatically adjusted to maintain over 70% efficiency for light loads down to 0.3 W.


title: SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again

abstract: We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover the full 6D pose space and train on synthetic model data only. Our approach competes or surpasses current state-of-the-art methods that leverage RGB-D data on multiple challenging datasets. Furthermore, our method produces these results at around 10Hz, which is many times faster than the related methods. For the sake of reproducibility, we make our trained networks and detection code publicly available.


Robotic Sorting using Machine Learning


title: Cardiac coherence, self-regulation, autonomic stability, and psychosocial well-being

abstract: The ability to alter one’s emotional responses is central to overall well-being and to effectively meeting the demands of life. One of the chief symptoms of events such as trauma, that overwhelm our capacities to successfully handle and adapt to them, is a shift in our internal baseline reference such that there ensues a repetitive activation of the traumatic event. This can result in high vigilance and over-sensitivity to environmental signals which are reflected in inappropriate emotional responses and autonomic nervous system dynamics. In this article we discuss the perspective that one’s ability to self-regulate the quality of feeling and emotion of one’s moment-to-moment experience is intimately tied to our physiology, and the reciprocal interactions among physiological, cognitive, and emotional systems. These interactions form the basis of information processing networks in which communication between systems occurs through the generation and transmission of rhythms and patterns of activity. Our discussion emphasizes the communication pathways between the heart and brain, as well as how these are related to cognitive and emotional function and self-regulatory capacity. We discuss the hypothesis that selfinduced positive emotions increase the coherence in bodily processes, which is reflected in the pattern of the heart’s rhythm.This shift in the heart rhythm in turn plays an important role in facilitating higher cognitive functions, creating emotional stability and facilitating states of calm. Over time, this establishes a new inner-baseline reference, a type of implicit memory that organizes perception, feelings, and behavior. Without establishing a new baseline reference, people are at risk of getting “stuck” in familiar, yet unhealthy emotional and behavioral patterns and living their lives through the automatic filters of past familiar or traumatic experience.


title: Tactile roughness: neural codes that account for psychophysical magnitude estimates

abstract: Hypothetical neural codes underlying the sensation of tactile roughness were investigated in a combined psychophysical and neurophysiological study. The stimulus set consisted of plastic surfaces embossed with dot arrays of varying dot diameter and center-to-center spacing. Human subjects explored each surface with the pad of the index finger and reported their subjective sense of roughness magnitude. The same surfaces were scanned across the receptive fields of cutaneous mechanoreceptive afferents in monkeys while recording the evoked action potentials. Hypothetical neural codes for roughness magnitude were computed from the neural response patterns and tested for their ability to account for the psychophysical data. The psychophysical results showed that subjective roughness magnitude is an inverted U-shaped function of dot spacing that peaks near 3.0 mm spacing, and that increased dot diameter produces decreased roughness sensations at all dot spacings. Hypothetical neural codes that do not bear a consistent relationship to roughness magnitude across all of these stimulus conditions can be rejected as the code for roughness. Four types of neural codes were considered. They were based on (1) mean firing rate, (2) general variation in firing rate, (3) short-term temporal variation in firing rate, and (4) local spatial variation in firing rate. Mean firing rate failed to explain the psychophysical results: surfaces that evoked the same firing rate often evoked very different roughness judgments. In contrast, neural codes based on firing-rate variation, especially in slowly adapting afferents, account for the psychophysical results.


title: Unbounded verification, falsification, and characterization of security protocols by pattern refinement

abstract: We present a new verification algorithm for security protocols that allows for unbounded verification, falsification, and complete characterization. The algorithm provides a number of novel features, including: (1) Guaranteed termination, after which the result is either unbounded correctness, falsification, or bounded correctness. (2) Efficient generation of a finite representation of an infinite set of traces in terms of patterns, also known as a complete characterization. (3) State-of-the-art performance, which has made new types of protocol analysis feasible, such as multi-protocol analysis.


title: Automatic TFT-LCD mura detection based on image reconstruction and processing

abstract: Automatic inspection of Mura defects is a challenging task in thin-film transistor liquid crystal display (TFT-LCD) defect detection, which is critical for LCD manufacturers to guarantee high standard quality control. In this paper, we propose a set of automatic procedures to detect mura defects by using image processing and computer vision techniques. Singular Value Decomposition (SVD) and Discrete Cosine Transformation(DCT) techniques are employed to conduct image reconstruction, based on which we are able to obtain the differential image of LCD Cells. In order to detect different types of mura defects accurately, we then design a method that employs different detection modules adaptively, which can overcome the disadvantage of simply using a single threshold value. Finally, we provide the experimental results to validate the effectiveness of the proposed method in mura detection.


title: BVDSS (drain to source breakdown voltage) instability in shielded gate trench power MOSFETs

abstract: In this paper, we develop a constant current avalanche injection stress method for evaluating BV DSS instability in shielded gate trench MOSFETs, a low/medium voltage power transistor technology used in a broad range of applications. We show this test is very sensitive to the BVDSS instability in charge balance devices whereas traditional HTRB (high temperature reverse bias) reliability test cannot capture the BVDSS instability issue. The new stress methodology allows us to study the root cause for BVDSS instability, suggesting ways for stabilizing the breakdown voltage for this important class of power transistors.


title: Evaluating Fluency in Human–Robot Collaboration

abstract: Collaborative fluency is the coordinated meshing of joint activities between members of a well-synchronized team. In recent years, researchers in human-robot collaboration have been developing robots to work alongside humans aiming not only at task efficiency, but also at human-robot fluency. As part of this effort, we have developed a number of metrics to evaluate the level of fluency in human-robot shared-location teamwork. While these metrics are being used in existing research, there has been no systematic discussion on how to measure fluency and how the commonly used metrics perform and compare. In this paper, we codify subjective and objective human-robot fluency metrics, provide an analytical model for four objective metrics, and assess their dynamics in a turn-taking framework. We also report on a user study linking objective and subjective fluency metrics and survey recent use of these metrics in the literature.


E-Learning : strategies for delivering knowledge in the digital age


title: Human gut microbes impact host serum metabolome and insulin sensitivity

abstract: Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.


title: Analysis of SEER Dataset for Breast Cancer Diagnosis using C4.5 Classification Algorithm

abstract: Medical professionals need a reliable prediction methodology to diagnose cancer and distinguish between the different stages in cancer. Classification is a data mining function that assigns items in a collection to target groups or classes. C4.5 classification algorithm has been applied to SEER breast cancer dataset to classify patients into either "Carcinoma in situ" (beginning or pre-cancer stage) or "Malignant potential" group. Pre-processing techniques have been applied to prepare the raw dataset and identify the relevant attributes for classification. Random test samples have been selected from the pre-processed data to obtain classification rules. The rule set obtained was tested with the remaining data. The results are presented and discussed. Breast cancer occurs due to an uncontrolled growth of cells in the breast tissues (1). Tumor is an abnormal cell growth that can be either benign or malignant. Benign tumors are non invasive while malignant tumors are cancerous and spread to other parts of the body. Early diagnosis and treatment helps to prevent the spread of cancer. Breast cancer begins in the cells of the lobules or the ducts (2). 5-10% of cancers are due to an abnormality which is inherited from the parents and about 90% of breast cancers are due to genetic abnormalities that happen as a result of the aging process (3).


title: Horton tables: fast hash tables for in-memory data-intensive computing

abstract: Hash tables are important data structures that lie at the heart of important applications such as key-value stores and relational databases. Typically bucketized cuckoo hash tables (BCHTs) are used because they provide high-throughput lookups and load factors that exceed 95%. Unfortunately, this performance comes at the cost of reduced memory access efficiency. Positive lookups (key is in the table) and negative lookups (where it is not) on average access 1.5 and 2.0 buckets, respectively, which results in 50 to 100% more table-containing cache lines to be accessed than should be minimally necessary. ::: ::: To reduce these surplus accesses, this paper presents the Horton table, a revamped BCHT that reduces the expected cost of positive and negative lookups to fewer than 1.18 and 1.06 buckets, respectively, while still achieving load factors of 95%. The key innovation is remap entries, small in-bucket records that allow (1) more elements to be hashed using a single, primary hash function, (2) items that overflow buckets to be tracked and rehashed with one of many alternate functions while maintaining a worst-case lookup cost of 2 buckets, and (3) shortening the vast majority of negative searches to 1 bucket access. With these advancements, Horton tables outperform BCHTs by 17% to 89%.


title: Self-schema and self-discrepancy mediate the influence of Instagram usage on body image satisfaction among youth

abstract: This study investigated the direct effect of Instagram usage on body satisfaction, whether this effect is mediated by appearance self-schema and self-discrepancy, and whether self-esteem moderates this effect. A total of 273 university students using Instagram were conveniently recruited to do the online survey on demographic characteristics, Instagram usage, self-schema, self-discrepancy, body satisfaction and self-esteem. Partial Least Squares Structural Equation Modeling was used to test the research hypotheses. As predicted, the results revealed that appearance self-schema and self-discrepancy mediated the effect of Instagram usage on body satisfaction. Moreover, this mediation effect varied depending on the Instagram users' self-esteem level, such that the negative effect of Instagram usage on body satisfaction through self-schema and self-discrepancy became stronger for those with a lower level of self-esteem. Instagram usage has a negative effect on body satisfaction.Appearance self-schema has a significant positive influence on self-discrepancy.Self-schema and self-discrepancy mediate the effect of Instagram usage on body satisfaction.The Instagram usage on self-schema is stronger for with a lower level of self-esteem.Self-esteem moderates the influence of Instagram usage on body satisfaction.


permission. Roofline: An Insightful Visual Performance Model for Floating-Point Programs and Multicore Architectures*


title: CoRAL: A Transparent Fault-Tolerant Web Service

abstract: The Web is increasingly used for critical applications and services. We present a client-transparent mechanism, called CoRAL, that provides high reliability and availability for Web service. CoRAL provides fault tolerance even for requests being processed at the time of server failure. The scheme does not require deterministic servers and can thus handle dynamic content. CoRAL actively replicates the TCP connection state while maintaining logs of HTTP requests and replies. In the event of a primary server failure, active client connections fail over to a spare, where their processing continues seamlessly. We describe key aspects of the design and implementation as well as several performance optimizations. Measurements of system overhead, failover performance, and preliminary validation using fault injection are presented.


title: Phasic firing in dopaminergic neurons is sufficient for behavioral conditioning

abstract: Natural rewards and drugs of abuse can alter dopamine signaling, and ventral tegmental area (VTA) dopaminergic neurons are known to fire action potentials tonically or phasically under different behavioral conditions. However, without technology to control specific neurons with appropriate temporal precision in freely behaving mammals, the causal role of these action potential patterns in driving behavioral changes has been unclear. We used optogenetic tools to selectively stimulate VTA dopaminergic neuron action potential firing in freely behaving mammals. We found that phasic activation of these neurons was sufficient to drive behavioral conditioning and elicited dopamine transients with magnitudes not achieved by longer, lower-frequency spiking. These results demonstrate that phasic dopaminergic activity is sufficient to mediate mammalian behavioral conditioning.


title: Comparison of lumbar spine stabilization exercise versus general exercise in young male patients with lumbar disc herniation after 1 year of follow-up.

abstract: OBJECTIVE ::: The safest and most effective conservative treatment for patients with lumbar disc herniation (LDH) has not been established. The purpose of this study was to evaluate the effect of lumbar spine stabilization exercise (LSSE) and general exercise (GE) on pain intensity and functional capacity in young male patients with LDH. ::: ::: ::: METHODS ::: Sixty-three young male adults aged 20 to 29 years with the diagnosis of LDH were enrolled and divided into an LSSE group (n=30) and a GE group (n=33). Patients in both groups received low-power laser (LPL) therapy during the first week of the onset of LDH. Patients in the GE group underwent a GE program. Patients in the LSSE group followed an LSSE program for 3 months. All of the patients were subjected to pain intensity and functional capacity evaluations four times: at pre-and post-LPL therapy, and at 3 months and 1 year post-exercise. Pain intensity of the lower back and legs was evaluated with the visual analogue scale (VAS), and functional capacity was evaluated with the Oswestry Disability Index (ODI). ::: ::: ::: RESULTS ::: Both groups showed a significant reduction in VAS and ODI scores at 3 and 12 months post-exercise compared with before treatment (P<0.001). The LSSE group showed a significant reduction in the average score of the VAS for low back pain (P=0.012) and the ODI (P=0.003) at 12 months post-exercise compared with the GE group. ::: ::: ::: CONCLUSIONS ::: LSSE and GE are considered as effective interventions for young male patients with LDH. Moreover, LSSE is more effective than GE, and physical therapy, such as LPL, is required during acute LDH.


title: Gecko inspired micro-fibrillar adhesives for wall climbing robots on micro/nanoscale rough surfaces

abstract: This paper presents the fabrication, characterization and testing of bio-inspired synthetic dry adhesive fiber arrays. Fibers were fabricated via micromolding followed by spatula tip formation via dipping. Arrays of fibers with diameters between 28 mum and 57 mum and a height of 114 mum were fabricated with high uniformity on 6.25 cm2 areas with up to 95% yield. Adaptation to uneven surfaces was observed with fiber elongations over 6 times the original height of the fiber. The unstructured sample exhibited 1.6 times as much adhesion as the fiber array sample for the flat punch indenter. However, fibrillar samples demonstrated up to 5.3 times as much adhesion as the unstructured sample for the hemispherical indenter. The fibrillar adhesive sample was implemented on a wall climbing robot which was able to carry itself and climb a distance on a painted wall and wood door.


title: Joint laxity and the benign joint hypermobility syndrome in student and professional ballet dancers.

abstract: OBJECTIVE: To ascertain the prevalence of hypermobility and the benign joint hypermobility syndrome (BJHS) in male and female student and professional ballet dancers, and explore whether BJHS has any effect on a dance career. METHODS: Students from the Royal Ballet School and professional dancers from the Royal Ballet Company, London, were compared with a control group of teenagers and adults from a local secondary school and The Royal Opera House, respectively. The data, examined by variance analysis, included anthropometric variables, the Beighton score, and clinical features constituting BJHS. Odds ratios for hypermobility and BJHS in dancers were calculated, and the prevalence and distribution of BJHS was examined. RESULTS: Hypermobility and BJHS were common in male and female dancers compared with controls. An OR of 11.0 (95% CI 3.3-31.8) was found for hypermobility in dancers for both the ballet school and the professional company. The prevalence of BJHS was found to decline both from student to professional and within the ballet company from corps de ballet to Principal. Odds ratios for BJHS in student dancers were significant, OR = 3.9 (95% CI 1.3-11.3), but not so in professional dancers: OR = 1.7 (95% CI 0.6-4.7). Arthralgia was common in dancers and was reported more often in males than females. In females, pain was reported most by dancers with other features of BJHS, in particular stretchy skin. CONCLUSION: Hypermobility and BJHS are common in both male and female student and professional ballet dancers. The fall in prevalence, and the greater reporting of arthralgia with other features of BJHS in young female dancers, suggests that BJHS may have an important negative influence, and this may have implications for training. The same pattern was not observed in males, suggesting that their pain-reporting and injury are related to factors other than BJHS.


title: Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment.

abstract: The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and more specifically on the analysis of the connectivity of a NeuCube model trained with electroencephalography (EEG) data. The case study data used to illustrate this method is EEG data collected from three groups-subjects with opiate addiction, patients undertaking methadone maintenance treatment, and non-drug users/healthy control group. The proposed method classifies more accurately the EEG data than traditional statistical and artificial intelligence (AI) methods and can be used to predict response to treatment and dose-related drug effect. But more importantly, the method can be used to compare functional brain activities of different subjects and the changes of these activities as a result of treatment, which is a step towards a better understanding of both the EEG data and the brain processes that generated it. The method can also be used for a wide range of applications, such as a better understanding of disease progression or aging.


title: Alcohol, marijuana, and tobacco use patterns among youth in Canada

abstract: The authors characterized changes in the prevalence of alcohol, tobacco, and marijuana use over time, and examined age of onset, co-morbid use and sociodemographic factors associated with ever using alcohol, tobacco, or marijuana in a nationally representative sample of Canadian youth. Data were collected from students in grades 7–9 as part of the Canadian Youth Smoking Survey (n = 19,018 in 2002; n = 29,243 in 2004). Descriptive analyses examined age of onset, co-morbid substance use and changes over time. Logistic regression models were used to examine factors associated with ever trying alcohol, tobacco, or marijuana with the 2004 data. Alcohol was the most prevalent substance used by youth and it was also the only substance which exhibited increased rates of use between 2002 and 2004. Co-morbid substance use was common, and it was rare to find youth who had used marijuana or tobacco without also having tried alcohol. As expected, youth who had poorer school performance were more likely to drink and smoke marijuana or tobacco, as were youth with more disposable income. Such timely and relevant data are important for guiding future policy, programing, and surveillance activities.


title: Privacy Prediction of Images Shared on Social Media Sites Using Deep Features

abstract: Online image sharing in social media sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. With the exponential increase in the number of images that are shared online every day, the development of effective and efficient prediction methods for image privacy settings are highly needed. The performance of models critically depends on the choice of the feature representation. In this paper, we present an approach to image privacy prediction that uses deep features and deep image tags as feature representations. Specifically, we explore deep features at various neural network layers and use the top layer (probability) as an auto-annotation mechanism. The results of our experiments show that models trained on the proposed deep features and deep image tags substantially outperform baselines such as those based on SIFT and GIST as well as those that use "bag of tags" as features.


title: CARTMAP: a neural network method for automated feature selection in financial time series forecasting

abstract: In the past two decades, there has been much interest in applying neural networks to financial time series forecasting. Yet, there has been relatively little attention paid to selecting the input features for training these networks. This paper presents a novel CARTMAP neural network based on Adaptive Resonance Theory that incorporates automatic, intuitive, transparent, and parsimonious feature selection with fast learning. On average, over three separate 4-year simulations spanning 2004–2009 of Dow Jones Industrial Average stocks, CARTMAP outperformed related and classical alternatives. The alternatives were an industry standard random walk, a regression model, a general purpose ARTMAP, and ARTMAP with stepwise feature selection. This paper also discusses why the novel feature selection scheme outperforms the alternatives and how it can represent a step toward more transparency in financial modeling.


title: Query log driven web search results clustering

abstract: Different important studies in Web search results clustering have recently shown increasing performances motivated by the use of external resources. Following this trend, we present a new algorithm called Dual C-Means, which provides a theoretical background for clustering in different representation spaces. Its originality relies on the fact that external resources can drive the clustering process as well as the labeling task in a single step. To validate our hypotheses, a series of experiments are conducted over different standard datasets and in particular over a new dataset built from the TREC Web Track 2012 to take into account query logs information. The comprehensive empirical evaluation of the proposed approach demonstrates its significant advantages over traditional clustering and labeling techniques.


Designing a Complete Model of Role-based Access Control System for Distributed Networks


title: NetSTAT: a network-based intrusion detection approach

abstract: Network-based attacks have become common and sophisticated. For this reason, intrusion detection systems are now shifting their focus from the hosts and their operating systems to the network itself. Network-based intrusion detection is challenging because network auditing produces large amounts of data, and different events related to a single intrusion may be visible in different places on the network. This paper presents NetSTAT, a new approach to network intrusion detection. By using a formal model of both the network and the attacks, NetSTAT is able to determine which network events have to be monitored and where they can be monitored.


title: The syntactical image of the city:a reciprocal definition of spatial elements and spatial syntaxes

abstract: This paper presents a study of the relationship between city elements, as defined byLynch, and the spatial descriptors commonly used in space syntax research, leadingto a proposed relationship between the hitherto unrelated concepts of intelligibilityand imageability. The paper starts by demonstrating how each of Lynch?s five cityelements (the node, path, district, edge and landmark) may be redefined using aselection of spatial notations, primarily the axial line and the isovist. Furthermore,by precisely defining the relationship between the axial line and the isovist, it can beshown that all of Lynch?s elements may be redefined using a single, coherent familyof tightly-related spatial entities. A case study of Boston, circa 1950, is used to testan application of these redefinitions and the relationships between the various spatialdescriptors and Lynch?s elements. In turn, this leads to a hypothesis concerning therelationship between the concepts of intelligibility and imageabilty, concepts thatwere previously considered to be independent. Finally, the paper concludes bybuilding upon the relationship between intelligibility and imageabilty to concludethat this relationship provides strong evidence for an underlying cognitive basis tospace syntax.


title: LLC resonant inverter for induction heating with asymmetrical voltage-cancellation control

abstract: This paper proposes a high efficiency LLC resonant inverter for induction heating applications by using asymmetrical voltage cancellation control. The proposed control method is implemented in a full-bridge topology for induction heating application. The operating frequency is automatically adjusted to maintain a small constant lagging phase angle under load parameter variation. The output power is controlled using the asymmetrical voltage cancellation technique. The LLC resonant tank is designed without the use of output transformer. This results in an increase of the net efficiency of the induction heating system. The validity of the proposed method is verified through computer simulation and hardware experiment at the operating frequency of 93 to 96 kHz.


title: A Study of Feature Construction for Text-based Forecasting of Time Series Variables

abstract: Time series are ubiquitous in the world since they are used to measure various phenomena (e.g., temperature, spread of a virus, sales, etc.). Forecasting of time series is highly beneficial (and necessary) for optimizing decisions, yet is a very challenging problem; using only the historical values of the time series is often insufficient. In this paper, we study how to construct effective additional features based on related text data for time series forecasting. Besides the commonly used n-gram features, we propose a general strategy for constructing multiple topical features based on the topics discovered by a topic model. We evaluate feature effectiveness using a data set for predicting stock price changes where we constructed additional features from news text articles for stock market prediction. We found that: 1) Text-based features outperform time series-based features, suggesting the great promise of leveraging text data for improving time series forecasting. 2) Topic-based features are not very effective stand-alone, but they can further improve performance when added on top of n-gram features. 3) The best topic-based feature appears to be a long-term aggregation of topics over time with high weights on recent topics.


title: Audio style transfer

abstract: “Style transfer” among images has recently emerged as a very active research topic, fuelled by the power of convolution neural networks (CNNs), and has become fast a very popular technology in social media. This paper investigates the analogous problem in the audio domain: How to transfer the style of a reference audio signal to a target audio content? We propose a flexible framework for the task, which uses a sound texture model to extract statistics characterizing the reference audio style, followed by an optimization-based audio texture synthesis to modify the target content. In contrast to mainstream optimization-based visual transfer method, the proposed process is initialized by the target content instead of random noise and the optimized loss is only about texture, not structure. These differences proved key for audio style transfer in our experiments. In order to extract features of interest, we investigate different architectures, whether pre-trained on other tasks, as done in image style transfer, or engineered based on the human auditory system. Experimental results on different types of audio signal confirm the potential of the proposed approach.


title: QUIC and TCP: A Performance Evaluation

abstract: Current Internet transport protocols are being revisited in an attempt to respond to traffic growth in size, diversity and the emergence of new applications. Designed to reduce Web TCP latency and connection establishment time, QUIC uses UDP and defines its own congestion control. To verify some of QUIC’s performance claims, we carried out an extensive set of controlled experiments reflecting Internet traffic conditions by configuring various parameters, such as the round trip time (RTT), the packet loss ratio, web pages, and caching. Surprisingly, our experimental results show that QUIC presented worst page load time (PLT) results than TCP. We next present some of our interpretations of these results.


title: Novel Three-Phase AC–AC Sparse Matrix Converters

abstract: A novel three-phase ac-ac sparse matrix converter having no energy storage elements and employing only 15 IGBTs, as opposed to 18 IGBTs of a functionally equivalent conventional ac-ac matrix converter, is proposed. It is shown that the realization effort could be further reduced to only nine IGBTs in an ultra sparse matrix converter (USMC) in the case where only unidirectional power flow is required and the fundamental phase displacement at the input and at the output is limited to plusmnpi/6. The dependency of the voltage and current transfer ratios of the sparse matrix converters on the operating parameters is analyzed and a space vector modulation scheme is described in combination with a zero current commutation method. Finally, the sparse matrix concept is verified by simulation and experimentally using a 6.8-kW/400-V very sparse matrix converter, which is implemented with 12 IGBT switches, and USMC prototypes.


title: Adaptive Learning Environments and eLearning Standards

abstract: This paper examines the sufficiency of existing eLearning standards for facilitating and supporting the introduction of adaptive techniques in computer-based learning systems. To that end, the main representational and operational requirements of adaptive learning environments are examined and contrasted against current eLearning standards. The motivation behind this preliminary analysis is attainment of: interoperability between adaptive learning systems; reuse of adaptive learning materials; and, the facilitation of adaptively supported, distributed learning activities.


title: The Pain Catastrophizing Scale: Development and Validation

abstract: In Study 1, the Pain Catastrophizing Scale (PCS) was administered to 425 undergraduates. Analyses yielded a three component solution comprising (a) rumination, (b) magnification, and (c) helplessness. In Study 2, 30 undergraduate participants were classified as catastrophizers (n = 15) or noncatastrophizers (n = 15) on the basis of their PCS scores and participated in an cold pressor procedure. Catastrophizers reported significantly more negative pain-related thoughts, greater emotional distress, and greater pain intensity than noncatastrophizers. Study 3 examined the relation between PCS scores, negative pain-related thoughts, and distress in 28 individuals undergoing an aversive electrodiagnostic medical procedure. Catastrophizers reported more negative pain-related thoughts, more emotional distress, and more pain than noncatastrophizers. Study 4 examined the relation between the PCS and measures of depression, trait anxiety, negative affectivity, and fear of pain. Analyses revealed moderate correlations among these measures, but only the PCS contributed significant unique variance t o the prediction of pain intensity.


title: Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

abstract: Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to improve its accuracy while ensuring that it can run at big data scales. Many approaches use acoustic measures based on spectrogram-type data, such as the Mel-frequency cepstral coefficient (MFCC) features which represent a manually-designed summary of spectral information. However, recent work in machine learning has demonstrated that features learnt automatically from data can often outperform manually-designed feature transforms. Feature learning can be performed at large scale and “unsupervised”, meaning it requires no manual data labelling, yet it can improve performance on “supervised” tasks such as classification. In this work we introduce a technique for feature learning from large volumes of bird sound recordings, inspired by techniques that have proven useful in other domains. We experimentally compare twelve different feature representations derived from the Mel spectrum (of which six use this technique), using four large and diverse databases of bird vocalisations, classified using a random forest classifier. We demonstrate that in our classification tasks, MFCCs can often lead to worse performance than the raw Mel spectral data from which they are derived. Conversely, we demonstrate that unsupervised feature learning provides a substantial boost over MFCCs and Mel spectra without adding computational complexity after the model has been trained. The boost is particularly notable for single-label classification tasks at large scale. The spectro-temporal activations learned through our procedure resemble spectro-temporal receptive fields calculated from avian primary auditory forebrain. However, for one of our datasets, which contains substantial audio data but few annotations, increased performance is not discernible. We study the interaction between dataset characteristics and choice of feature representation through further empirical analysis.


title: Neural network based visualization of collaborations in a citizen science project

abstract: Citizen science projects are those in which volunteers are asked to collaborate in scientific projects, usually by volunteering idle computer time for distributed data processing efforts or by actively labeling or classifying information - shapes of galaxies, whale sounds, historical records are all examples of citizen science projects in which users access a data collecting system to label or classify images and sounds. ::: In order to be successful, a citizen science project must captivate users and keep them interested on the ::: project and on the science behind it, increasing therefore the time the users spend collaborating with the ::: project. Understanding behavior of citizen scientists and their interaction with the data collection systems ::: may help increase the involvement of the users, categorize them accordingly to different parameters, facilitate their collaboration with the systems, design better user interfaces, and allow better planning and deployment of similar projects and systems. ::: Users behavior can be actively monitored or derived from their interaction with the data collection systems. Records of the interactions can be analyzed using visualization techniques to identify patterns and outliers. In this paper we present some results on the visualization of more than 80 million interactions of almost 150 thousand users with the Galaxy Zoo I citizen science project. Visualization of the attributes extracted from their behaviors was done with a clustering neural network (the Self-Organizing Map) and a selection of icon- and pixel-based techniques. These techniques allows the visual identification of groups of similar behavior in several different ways.


title: SAFELI: SQL injection scanner using symbolic execution

abstract: This paper presents the current progress, main algorithm, and the open problems of a tool set called "SAFELI," for detecting SQL Injection vulnerabilities resident in Web applications. SAFELI instruments the bytecode of Java Web applications and utilizes symbolic execution to statically inspect security vulnerabilities. At each location that submits SQL query, an equation is constructed to find out the initial values of Web controls that lead to the breach of database security. The equation is solved by a hybrid string solver where the solution obtained is used to construct test cases. SQL injection attacks are replayed by SAFELI to designers, step by step. We also raise open problems on more powerful string solver techniques that work at the semantics level.


title: Aging in Place: Self-Care in Smart Home Environments

abstract: Older adults prefer living independently at home, in their familiar environment, over a longer period of time (Vermeulen, 2006). Facilitating aging in place helps increasing older adults’ quality of life. It enables actively participating in the community and maintaining social networks, increases personal security, and limits the negative effect of relocation, such as confusion, alienation and developing speeding up mental and physical decline. Illustratively, aging in place can contribute to preservation of cognition, decrease in depression, and prevention of incontinence (e.g., Bassuk, et al., 1999; Berg-Warman, 2006; Marek et al., 2005). Maybe most importantly, aging in place offers the possibility to live in an environment which helps older adults to relive old memories dear to them. In addition, aging in place can contribute to levelling the imbalance in care supply and demand due to the population’s aging. The global population is ageing, i.e., the median age rises, and the number of older adults who are requiring more health care increases1. Illustratively, in the Netherlands, it is estimated that in 2030, over 4 million people, almost a fourth of the population, will be over 65 years old (Blokstra, 2007). As a result, the number of people with chronic conditions is unceasingly rising. Due to aging, people become more prone to chronic diseases, such as diabetes, heart failure and dementia, often suffer from multimorbidity (i.e., multiple, correlated diseases), over an extended period of time. Also, due to aging, there will be a decrease in the number of potential care professionals, which adds to their increasing workload (Bakker et al., 2005). Furthermore, the shortage in the medical workforce will lead to higher care prices and an increase in the economic burden for patients, insurance companies, government and, thus, for society (Druss et al., 2001). The possible impact of this demographic development raises serious concerns and there is urgency in reducing the gap between demand and supply in health care. An important requirement for aging in place is the ability for people to perform self-care. Self-care is undertaken by individuals, together with families, and communities with the intention of preventing disease, limiting illness, and restoring health (Leventhal et al., 2004).


title: Fall detection using ceiling-mounted 3D depth camera

abstract: This paper proposes an algorithm for fall detection using a ceiling-mounted 3D depth camera. The lying pose is separated from common daily activities by a k-NN classifier, which was trained on features expressing head-floor distance, person area and shape's major length to width. In order to distinguish between intentional lying postures and accidental falls the algorithm also employs motion between static postures. The experimental validation of the algorithm was conducted on realistic depth image sequences of daily activities and simulated falls. It was evaluated on more than 45000 depth images and gave 0% error. To reduce the processing overload an accelerometer was used to indicate the potential impact of the person and to start an analysis of depth images.


title: Design of degrees of distribution of LDS-OFDM

abstract: In this paper, we study the design of low density signature-orthogonal frequency division multiple access (LDS-OFDM). Compare to the conventional OFDMA system, LDS system has some desirable properties that allows system operating at overloaded conditions. LDS-OFDM system has been shown that outperforms the corresponding OFDMA system. In this paper, we focus on the design of the LDS degrees distribution, which has not been addressed in the literature. The goal is to achieve the optimum system load. We propose an iterative optimization procedure by utilizing the EXIT chart. Optimized load has been given in this paper with corresponding LDS spreading parameters. The performance of the designed LDS-OFDM system is evaluated. Monte Carlo based simulations further confirm that the designed LDS-OFDM outperforms the corresponding OFDMA system by up to 10 dB.


Deep Coattention Networks for Reading Comprehension


title: Automatic person annotation of family photo album

abstract: Digital photographs are replacing tradition films in our daily life and the quantity is exploding. This stimulates the strong need for efficient management tools, in which the annotation of “who” in each photo is essential. In this paper, we propose an automated method to annotate family photos using evidence from face, body and context information. Face recognition is the first consideration. However, its performance is limited by the uncontrolled condition of family photos. In family album, the same groups of people tend to appear in similar events, in which they tend to wear the same clothes within a short time duration and in nearby places. We could make use of social context information and body information to estimate the probability of the persons' presence and identify other examples of the same recognized persons. In our approach, we first use social context information to cluster photos into events. Within each event, the body information is clustered, and then combined with face recognition results using a graphical model. Finally, the clusters with high face recognition confidence and context probabilities are identified as belonging to specific person. Experiments on a photo album containing over 1500 photos demonstrate that our approach is effective.


title: Statistical learning theory

abstract: A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.


title: MIMIC-III, a freely accessible critical care database

abstract: MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework. Machine-accessible metadata file describing the reported data (ISA-Tab format)


title: Corpora for sentiment analysis of Arabic text in social media

abstract: Different Natural Language Processing (NLP) applications such as text categorization, machine translation, etc., need annotated corpora to check quality and performance. Similarly, sentiment analysis requires annotated corpora to test the performance of classifiers. Manual annotation performed by native speakers is used as a benchmark test to measure how accurate a classifier is. In this paper we summarise currently available Arabic corpora and describe work in progress to build, annotate, and use Arabic corpora consisting of Facebook (FB) posts. The distinctive nature of these corpora is that they are based on posts written in Dialectal Arabic (DA) not following specific grammatical or spelling standards. The corpora are annotated with five labels (positive, negative, dual, neutral, and spam). In addition to building the corpora, the paper illustrates how manual tagging can be used to extract opinionated words and phrases to be used in a lexicon-based classifier.


title: Evaluation with Informational and Navigational Intents

abstract: Given an ambiguous or underspecified query, search result diversification aims at accomodating different user intents within a single "entry-point" result page. However, some intents are informational, for which many relevant pages may help, while others are navigational, for which only one web page is required. We propose new evaluation metrics for search result diversification that considers this distinction, as well as a simple method for comparing the intuitiveness of a given pair of metrics quantitatively. Our main experimental findings are: (a) In terms of discriminative power which reflects statistical reliability, the proposed metrics, DIN#-nDCG and P+Q#, are comparable to intent recall and D#-nDCG, and possibly superior to α-nDCG; (b) In terms of preference agreement with intent recall, P+Q# is superior to other diversity metrics and therefore may be the most intuitive as a metric that emphasises diversity; and (c) In terms of preference agreement with effective precision, DIN#-nDCG is superior to other diversity metrics and therefore may be the most intuitive as a metric that emphasises relevance. Moreover, DIN#-nDCG may be the most intuitive as a metric that considers both diversity and relevance. In addition, we demonstrate that the randomised Tukey's Honestly Significant Differences test that takes the entire set of available runs into account is substantially more conservative than the paired bootstrap test that only considers one run pair at a time, and therefore recommend the former approach for significance testing when a set of runs is available for evaluation.


title: Mobility Issues in Selected Regions of Poland and Slovakia – Outcomes of International Project SOL (Save Our Lives) Survey

abstract: The paper briefly describes the methodology and procedure for carrying out the public opinion survey which was focused on selected mobility issues. The survey was realized in 10 regions of Central Europe. Paper also sums up the outcomes of the evaluation of the travel preferences and public attitudes towards problems and risk factors of road safety and potential effectiveness of authorities in the Warmia-Mazury region in Poland and Zilina region in Slovakia. The survey was implemented with the support of a single methodology developed by partner organizations of international project SOL - Save Our Lives.


Network Intrusion Detection Using a Hidden Naïve Bayes Binary Classifier


title: Augmenting the learning experience in primary and secondary school education: a systematic review of recent trends in augmented reality game-based learning

abstract: There is a significant body of research relating to augmented reality (AR) uses for learning in the primary and the secondary education sectors across the globe. However, there is not such a substantial amount of work exploring the combination of AR with game-based learning (ARGBL). Although ARGBL has the potential to enable new forms of teaching and transform the learning experience, it remains unclear how ARGBL applications can impact students’ motivation, achievements, and learning performance. This study reports a systematic review of the literature on ARGBL approaches in compulsory education considering the advantages, disadvantages, instructional affordances, and/or effectiveness of ARGBL across various primary and secondary education subjects. In total, 21 studies published between 2012 and 2017 in 11 indexed journals were analysed, with 14 studies focusing on primary education and 7 on secondary. The main findings from this review provide the current state of the art research in ARGBL in compulsory education. Trends and the vision towards the future are also discussed, as ARGBL can potentially influence the students’ attendance, knowledge transfer, skill acquisition, hands-on digital experience, and positive attitude towards their learning. This review aims to lay the groundwork for educators, technology developers, and other stakeholders involved in the development of literacy programmes for young children by offering new insights with effective advice and suggestions on how to increase student motivation and improve learning outcomes and the learning experience by incorporating ARGBL into their teaching.


title: Structure Analysis Based Parking Slot Marking Recognition for Semi-automatic Parking System

abstract: Semi-automatic parking system is a driver convenience system automating steering control required during parking operation. This paper proposes novel monocular-vision based target parking-slot recognition by recognizing parking-slot markings when driver designates a seed-point inside the target parking-slot with touch screen. Proposed method compensates the distortion of fisheye lens and constructs a bird’s eye view image using homography. Because adjacent vehicles are projected along the outward direction from camera in the bird’s eye view image, if marking line-segment distinguishing parking-slots from roadway and front-ends of marking line-segments dividing parking-slots are observed, proposed method successfully recognizes the target parking-slot marking. Directional intensity gradient, utilizing the width of marking line-segment and the direction of seed-point with respect to camera position as a prior knowledge, can detect marking line-segments irrespective of noise and illumination variation. Making efficient use of the structure of parking-slot markings in the bird’s eye view image, proposed method simply recognizes the target parking-slot marking. It is validated by experiments that proposed method can successfully recognize target parking-slot under various situations and illumination conditions.


title: Blind estimators for reverberation time and direct-to-reverberant energy ratio using subband speech decomposition

abstract: This paper describes algorithms for estimating two important features associated with the reverberation effect on speech signals: the reverberation time and direct-to-reverberant energy ratio. Both methods are referred to as blind algorithms in the sense that they are entirely based on the reverberant signal itself, not depending on the knowledge of the clean original signal. Proposed schemes use subband analysis to generate more and more reliable information, which is post-processed using basic statistical analysis to provide the desired estimate for each particular feature. Modifications on the original estimation algorithms are introduced to cope with lower SNRs. Performance of both algorithms is assessed under the ACE Challenge scope, which included a set of 288 speech signals for training and 4500 signals for final test. Results indicate the effectiveness of both techniques particularly in high-SNR situations.


title: Finding Communities in Dynamic Social Networks

abstract: Communities are natural structures observed in social networks and are usually characterized as "relatively dense" subsets of nodes. Social networks change over time and so do the underlying community structures. Thus, to truly uncover this structure we must take the temporal aspect of networks into consideration. Previously, we have represented framework for finding dynamic communities using the social cost model and formulated the corresponding optimization problem [33], assuming that partitions of individuals into groups are given in each time step. We have also presented heuristics and approximation algorithms for the problem, with the same assumption [32]. In general, however, dynamic social networks are represented as a sequence of graphs of snapshots of the social network and the assumption that we have partitions of individuals into groups does not hold. In this paper, we extend the social cost model and formulate an optimization problem of finding community structure from the sequence of arbitrary graphs. We propose a semi definite programming formulation and a heuristic rounding scheme. We show, using synthetic data sets, that this method is quite accurate on synthetic data sets and present its results on a real social network.


title: Automated Security Test Generation with Formal Threat Models

abstract: Security attacks typically result from unintended behaviors or invalid inputs. Security testing is labor intensive because a real-world program usually has too many invalid inputs. It is highly desirable to automate or partially automate security-testing process. This paper presents an approach to automated generation of security tests by using formal threat models represented as Predicate/Transition nets. It generates all attack paths, i.e., security tests, from a threat model and converts them into executable test code according to the given Model-Implementation Mapping (MIM) specification. We have applied this approach to two real-world systems, Magento (a web-based shopping system being used by many online stores) and FileZilla Server (a popular FTP server implementation in C++). Threat models are built systematically by examining all potential STRIDE (spoofing identity, tampering with data, repudiation, information disclosure, denial of service, and elevation of privilege) threats to system functions. The security tests generated from these models have found multiple security risks in each system. The test code for most of the security tests can be generated and executed automatically. To further evaluate the vulnerability detection capability of the testing approach, the security tests have been applied to a number of security mutants where vulnerabilities are injected deliberately. The mutants are created according to the common vulnerabilities in C++ and web applications. Our experiments show that the security tests have killed the majority of the mutants.


title: Cross-Domain Sentiment Classification by Capsule Network With Semantic Rules

abstract: Sentiment analysis is an important but challenging task. Remarkable success has been achieved on domains where sufficient labeled training data is available. Nevertheless, annotating sufficient data is labor-intensive and time-consuming, establishing significant barriers for adapting the sentiment classification systems to new domains. In this paper, we introduce a Capsule network for sentiment analysis in domain adaptation scenario with semantic rules (CapsuleDAR). CapsuleDAR exploits capsule network to encode the intrinsic spatial part-whole relationship constituting domain invariant knowledge that bridges the knowledge gap between the source and target domains. Furthermore, we also propose a rule network to incorporate the semantic rules into the capsule network to enhance the comprehensive sentence representation learning. Extensive experiments are conducted to evaluate the effectiveness of the proposed CapsuleDAR model on a real world data set of four domains. Experimental results demonstrate that CapsuleDAR achieves substantially better performance than the strong competitors for the cross-domain sentiment classification task.


title: A self-powered ultra-fast DC solid state circuit breaker using a normally-on SiC JFET

abstract: This paper introduces a new self-powered solid state circuit breaker (SSCB) concept using a normally-on SiC JFET as the main static switch and a fast-starting isolated DC/DC converter as the protection driver. The new SSCB detects short circuit faults by sensing its drain-source voltage rise, and draws power from the fault condition to turn and hold off the SiC JFET. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or additional wiring. A unique low power isolated DC/DC converter is designed and optimized to provide a fast reaction to a short circuit event. The SSCB prototypes have experimentally demonstrated a fault current interruption capability up to 180 amperes at a DC bus voltage of 400 volts within 0.8 microseconds. DC circuit protection applications provide a unique market opportunity for wide bandgap power semiconductor devices outside the conventional focus on power electronic converter applications.


title: Securing Docker Containers from Denial of Service (DoS) Attacks

abstract: The concept of containerization and virtualization is getting traction in the cloud based IT environments. Docker engine is popular implementation for simplifying and streamlining containerization technology. IT industry realizes numerous automation and acceleration features and facilities through the embracement of the Docker-sponsored containerization paradigm, which is an operating system (OS)-level and lightweight virtualization. However the security issues affect the widespread and confident usage of Docker platform. In this paper, we have discussed important security issues of the Docker containers as well as the related work that is being carried out in this area. Also we have proposed security algorithms and methods to address DoS attacks related issues in the Docker container technology. The preliminary experiments and testing of the security methods are promising.


title: Universal Coating for Programmable Matter

abstract: The idea behind universal coating is to have a thin layer of a specific substance covering an object of any shape so that one can measure a certain condition (like temperature or cracks) at any spot on the surface of the object without requiring direct access to that spot. We study the universal coating problem in the context of self-organizing programmable matter consisting of simple computational elements, called particles, that can establish and release bonds and can actively move in a self-organized way. Based on that matter, we present a worst-case work-optimal universal coating algorithm that uniformly coats any object of arbitrary shape and size that allows a uniform coating. Our particles are anonymous, do not have any global information, have constant-size memory, and utilize only local interactions.


title: WGS Capacity using the DoD Joint IP Modem (DVB-S2, RCS)

abstract: The DoD joint internet protocol modem is based on the digital video broadcast (DVB) standard. In a star network topology the forward traffic is DVB-Satellite 2 (DVB-S2) and the return traffic is DVB-return channel by satellite (DVB-RCS). The joint modem adds an embedded TRANSEC capability and a mesh overlay allowing user terminals to communicate without passing through the hub. The analysis will take advantage of the variable coding and modulation (VCM) feature [as well as the constant coding and modulation (CCM) feature of DVB-S]. First, the DVB-S2 capacity will be determined for wideband gapfiller satellite (WGS) located at 175deg east using a set of reference links located in Korea using CCM and VCM in the forward direction. Then the capacity of WGS will be determined in the return direction using DVB-RCS. Finally, the mesh capacity will be determined using WGS with VSAT terminals (1m) in Korea for voice over internet protocol (VoIP) traffic


Chunk-based Decoder for Neural Machine Translation


title: Rare Disease Physician Targeting: A Factor Graph Approach

abstract: In rare disease physician targeting, a major challenge is how to identify physicians who are treating diagnosed or underdiagnosed rare diseases patients. Rare diseases have extremely low incidence rate. For a specified rare disease, only a small number of patients are affected and a fractional of physicians are involved. The existing targeting methodologies, such as segmentation and profiling, are developed under mass market assumption. They are not suitable for rare disease market where the target classes are extremely imbalanced. The authors propose a graphical model approach to predict targets by jointly modeling physician and patient features from different data spaces and utilizing the extra relational information. Through an empirical example with medical claim and prescription data, the proposed approach demonstrates better accuracy in finding target physicians. The graph representation also provides visual interpretability of relationship among physicians and patients. The model can be extended to incorporate more complex dependency structures. This article contributes to the literature of exploring the benefit of utilizing relational dependencies among entities in healthcare industry.