title: A survey of research on anomaly detection for time series

abstract: Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications, and anomaly detection for time series is becoming a hot research topic recently. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. In this paper, we have discussed the definition of anomaly and grouped existing techniques into different categories based on the underlying approach adopted by each technique. And for each category, we identify the advantages and disadvantages of the techniques in that category. Then, we provide a briefly discussion on the representative methods recently. Furthermore, we also point out some key issues about multivariate time series anomaly. Finally, some suggestions about anomaly detection are discussed and future research trends are also summarized, which is hopefully beneficial to the researchers of time series and other relative domains.


title: Effects of an augmented reality-based educational game on students' learning achievements and attitudes in real-world observations

abstract: Augmented reality (AR) has been recognized as a potential technology to help students link what they are observing in the real world to their prior knowledge. One of the most challenging issues of AR-based learning is the provision of effective strategy to help students focus on what they need to observe in the field. In this study, a competitive gaming approach is proposed to support AR-based learning activities conducted in real-world contexts. An experiment has been conducted on an elementary school ecology course to explore the effectiveness of the proposed approach in comparison with the conventional AR-based mobile learning approach in field trips. The experimental results show that the AR-based gaming approach can improve not only students' learning attitudes, but also their learning performance on the field trip. Accordingly, discussions and some suggestions for future work are provided.


The Six Core Elements of Business Process Management


title: Evaluating Text Segmentation using Boundary Edit Distance

abstract: This work proposes a new segmentation evaluation metric, named boundary similarity (B), an inter-coder agreement coefficient adaptation, and a confusion-matrix for segmentation that are all based upon an adaptation of the boundary edit distance in Fournier and Inkpen (2012). Existing segmentation metrics such as Pk, WindowDiff, and Segmentation Similarity (S) are all able to award partial credit for near misses between boundaries, but are biased towards segmentations containing few or tightly clustered boundaries. Despite S’s improvements, its normalization also produces cosmetically high values that overestimate agreement & performance, leading this work to propose a solution.


title: Flexible mass production concept for segmented BLDC stators

abstract: In many applications the required power density increases with a decreasing packaging size. The segmented stator topology facilitates a high filling factor and therefore an increase of the power density of the stator. To be flexible in regards of different segmented BLDC stator topologies and dimensions, product quantities and due to reduction of investment cost a shared stator production line for a wide range of different BLDC motors was developed and transferred into production. This line makes it possible to share the high quality of high degree of automation with relatively low quantities of individual stators by using it for different applications.


title: Varied Patterns of Family Resilience in Challenging Contexts

abstract: While we know much about patterns of family resilience, most of our research and clinical discussion has focused on microsystemic, intrafamilial protective processes. We have far fewer maps of the bidirectional interactions between families and other systems that contribute to successful family adaptation in challenging contexts. The purpose of this article is to address this gap in knowledge and present a map of family resilience that is both systemic and contextually and culturally responsive. Seven specific patterns of family resilience are reviewed. Combined, they account for the varied adaptational patterns families use to nurture and sustain resilience. The article concludes with reflection on how we can assess family resilience and the application of this map to family therapy. ::: ::: Video Abstract


title: Mining motifs in massive time series databases

abstract: The problem of efficiently locating previously known patterns in a time series database (i.e., query by content) has received much attention and may now largely be regarded as a solved problem. However, from a knowledge discovery viewpoint, a more interesting problem is the enumeration of previously unknown, frequently occurring patterns. We call such patterns "motifs", because of their close analogy to their discrete counterparts in computation biology. An efficient motif discovery algorithm for time series would be useful as a tool for summarizing and visualizing massive time series databases. In addition it could be used as a subroutine in various other data mining tasks, including the discovery of association rules, clustering and classification. In this paper we carefully motivate, then introduce, a nontrivial definition of time series motifs. We propose an efficient algorithm to discover them, and we demonstrate the utility and efficiency of our approach on several real world datasets.


title: Generating performance distributions via probabilistic symbolic execution

abstract: Analyzing performance and understanding the potential best-case, worst-case and distribution of program execution times are very important software engineering tasks. There have been model-based and program analysis-based approaches for performance analysis. Model-based approaches rely on analytical or design models derived from mathematical theories or software architecture abstraction, which are typically coarse-grained and could be imprecise. Program analysis-based approaches collect program profiles to identify performance bottlenecks, which often fail to capture the overall program performance. In this paper, we propose a performance analysis framework PerfPlotter. It takes the program source code and usage profile as inputs and generates a performance distribution that captures the input probability distribution over execution times for the program. It heuristically explores high-probability and low-probability paths through probabilistic symbolic execution. Once a path is explored, it generates and runs a set of test inputs to model the performance of the path. Finally, it constructs the performance distribution for the program. We have implemented PerfPlotter based on the Symbolic PathFinder infrastructure, and experimentally demonstrated that PerfPlotter could accurately capture the best-case, worst-case and distribution of program execution times. We also show that performance distributions can be applied to various important tasks such as performance understanding, bug validation, and algorithm selection.


title: Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings

abstract: Abstract Existing studies learn sentiment-specific word representations to boost the performance of Twitter sentiment classification, via encoding both n-gram and distant supervised tweet sentiment information in learning process. Pioneer efforts explicitly or implicitly assume that all words within a tweet have the same sentiment polarity as that of the whole tweet, which basically ignores the word its own sentiment polarity. To alleviate this problem, we propose to learn sentiment-specific word embedding by exploiting both the lexicon resource and distant supervised information. In particular, we develop a multi-level sentiment-enriched word embedding learning method, which employs a parallel asymmetric neural network to model n-gram, word-level sentiment, and tweet-level sentiment in the learning process. Extensive experiments on standard benchmarks demonstrate our approach outperforms state-of-the-art methods.


title: A Multi-Hop Dynamic Path-Selection (MHDP) Algorithm for the Augmented Lifetime of Wireless Sensor Networks

abstract: In Wireless Sensor Networks (WSN), the ::: lifetime of sensors is the crucial issue. Numerous schemes are proposed to ::: augment the life time of sensors based on the wide range of parameters. In majority ::: of the cases, the center of attraction will be the nodes’ lifetime enhancement ::: and routing. In the scenario of cluster based WSN, multi-hop mode of ::: communication reduces the communication cast by increasing average delay and ::: also increases the routing overhead. In this proposed scheme, two ideas are ::: introduced to overcome the delay and routing overhead. To achieve the higher ::: degree in the lifetime of the nodes, the residual energy (remaining energy) of ::: the nodes for multi-hop node choice is taken into consideration first. Then the ::: modification in the routing protocol is evolved (Multi-Hop Dynamic ::: Path-Selection Algorithm—MHDP). A dynamic path updating is initiated in ::: frequent interval based on nodes residual energy to avoid the data loss due to ::: path extrication and also to avoid the early dying of nodes due to elevation of ::: data forwarding. The proposed method improves network’s lifetime significantly. ::: The diminution in the average delay and increment in the lifetime of network ::: are also accomplished. The MHDP offers 50% delay lesser than clustering. The ::: average residual energy is 20% higher than clustering and 10% higher than ::: multi-hop clustering. The proposed method improves network lifetime by 40% than ::: clustering and 30% than multi-hop clustering which is considerably much better ::: than the preceding methods.


title: Efficient Branching Cascaded Regression for Face Alignment under Significant Head Rotation

abstract: Despite much interest in face alignment in recent years, the large majority of work has focused on near-frontal faces. Algorithms typically break down on profile faces, or are too slow for real-time applications. In this work we propose an efficient approach to face alignment that can handle 180 degrees of head rotation in a unified way (e.g., without resorting to view-based models) using 2D training data. The foundation of our approach is cascaded shape regression (CSR), which has emerged recently as the leading strategy. We propose a generalization of conventional CSRs that we call branching cascaded regression (BCR). Conventional CSRs are single-track; that is, they progress from one cascade level to the next in a straight line, with each regressor attempting to fit the entire dataset. We instead split the regression problem into two or more simpler ones after each cascade level. Intuitively, each regressor can then operate on a simpler objective function (i.e., with fewer conflicting gradient directions). Within the BCR framework, we model and infer pose-related landmark visibility and face shape simultaneously using Structured Point Distribution Models (SPDMs). We propose to learn task-specific feature mapping functions that are adaptive to landmark visibility, and that use SPDM parameters as regression targets instead of 2D landmark coordinates. Additionally, we introduce a new in-the-wild dataset of profile faces to validate our approach.


BakerSFIeld : Bringing software fault isolation to x 64


title: Degraded document image binarization using structural symmetry of strokes

abstract: Abstract This paper presents an effective approach for the local threshold binarization of degraded document images. We utilize the structural symmetric pixels (SSPs) to calculate the local threshold in neighborhood and the voting result of multiple thresholds will determine whether one pixel belongs to the foreground or not. The SSPs are defined as the pixels around strokes whose gradient magnitudes are large enough and orientations are symmetric opposite. The compensated gradient map is used to extract the SSP so as to weaken the influence of document degradations. To extract SSP candidates with large magnitudes and distinguish the faint characters and bleed-through background, we propose an adaptive global threshold selection algorithm. To further extract pixels with opposite orientations, an iterative stroke width estimation algorithm is applied to ensure the proper size of neighborhood used in orientation judgement. At last, we present a multiple threshold vote based framework to deal with some inaccurate detections of SSP. The experimental results on seven public document image binarization datasets show that our method is accurate and robust compared with many traditional and state-of-the-art document binarization approaches based on multiple evaluation measures.


title: Biobanks and personalized medicine

abstract: We provide a mini-review of how biobanks can support clinical genetics in the era of personalized medicine. We discuss types of biobanks, including disease specific and general biobanks not focused on one disease. We present considerations in setting up a biobank, including consenting and governance, biospecimens, risk factor and related data, informatics, and linkage to electronic health records for phenotyping. We also discuss the uses of biobanks and ongoing considerations, including genotype-driven recruitment, investigations of gene–environment associations, and the re-use of data generated from studies. Finally, we present a brief discussion of some of the unresolved issues, such as return of research results and sustaining biobanks over time. In summary, carefully designed biobanks can provide critical research and infrastructure support for clinical genetics in the era of personalized medicine.


title: Radiomics to predict immunotherapy-induced pneumonitis: proof of concept

abstract: We present the first reported work that explores the potential of radiomics to predict patients who are at risk for developing immunotherapy-induced pneumonitis. Despite promising results with immunotherapies, immune-related adverse events (irAEs) are challenging. Although less common, pneumonitis is a potentially fatal irAE. Thus, early detection is critical for improving treatment outcomes; an urgent need to identify biomarkers that predict patients at risk for pneumonitis exists. Radiomics, an emerging field, is the automated extraction of high fidelity, high-dimensional imaging features from standard medical images and allows for comprehensive visualization and characterization of the tissue of interest and corresponding microenvironment. In this pilot study, we sought to determine whether radiomics has the potential to predict development of pneumonitis. We performed radiomic analyses using baseline chest computed tomography images of patients who did (N = 2) and did not (N = 30) develop immunotherapy-induced pneumonitis. We extracted 1860 radiomic features in each patient. Maximum relevance and minimum redundancy feature selection method, anomaly detection algorithm, and leave-one-out cross-validation identified radiomic features that were significantly different and predicted subsequent immunotherapy-induced pneumonitis (accuracy, 100% [p = 0.0033]). This study suggests that radiomic features can classify and predict those patients at baseline who will subsequently develop immunotherapy-induced pneumonitis, further enabling risk-stratification that will ultimately lead to better treatment outcomes.


title: Estimating a Dirichlet distribution

abstract: The Dirichlet distribution and its compound variant, the Dirichlet-multinomial, are two of the most basic models for proportional data, such as the mix of vocabulary words in a text document. Yet the maximum-likelihood estimate of these distributions is not available in closed-form. This paper describes simple and efficient iterative schemes for obtaining parameter estimates in these models. In each case, a fixed-point iteration and a Newton-Raphson (or generalized Newton-Raphson) iteration is provided. 1 The Dirichlet distribution The Dirichlet distribution is a model of how proportions vary. Let p denote a random vector whose elements sum to 1, so that pk represents the proportion of item k. Under the Dirichlet model with parameter vector α, the probability density at p is p(p) ∼ D(α1, ..., αK) = Γ( ∑ k αk) ∏ k Γ(αk) ∏ k pk k (1) where pk > 0 (2)


title: Determining employee awareness using the Human Aspects of Information Security Questionnaire (HAIS-Q)

abstract: Abstract It is increasingly acknowledged that many threats to an organisation's computer systems can be attributed to the behaviour of computer users. To quantify these human-based information security vulnerabilities, we are developing the Human Aspects of Information Security Questionnaire (HAIS-Q). The aim of this paper was twofold. The first aim was to outline the conceptual development of the HAIS-Q, including validity and reliability testing. The second aim was to examine the relationship between knowledge of policy and procedures, attitude towards policy and procedures and behaviour when using a work computer. Results from 500 Australian employees indicate that knowledge of policy and procedures had a stronger influence on attitude towards policy and procedure than self-reported behaviour. This finding suggests that training and education will be more effective if it outlines not only what is expected (knowledge) but also provides an understanding of why this is important (attitude). Plans for future research to further develop and test the HAIS-Q are outlined.


title: Dense Associative Memory is Robust to Adversarial Inputs

abstract: Deep neural networks (DNNs) trained in a supervised way suffer from two known problems. First, the minima of the objective function used in learning correspond to data points (also known as rubbish...


title: Near field communication

abstract: For many years, pervasive computing research has explored the potential benefits of creating a connection between the virtual world of the Internet, and the physical world we live in. The Near Field Communication (NFC) standard might, at last, be the technology that makes this vision-sometimes referred to as the Internet of things-a ubiquitous reality.


Categorizing spelling errors to assess L2 writing Abstract


title: Vulnerabilities in hub architecture IoT devices

abstract: This paper introduces new methods for gaining sensitive information from Internet of Things (IoT) devices. Generally, manufacturers tend to forget about security when putting a new product on the market, this notion does not exclude IoT. This has been proven by current research using simple techniques and devices to obtain unencrypted information. Using similar attacks, the IoT hubs themselves was chosen as a target instead of the devices connected to them. Two different attacks will be used. First, using various methods for sniffing Hub traffic, we will attempt to gain credentials on the uplink side. Secondly, port scans will be used to gain any exploited services in order to obtain root access. After our attempts, it was found that IoT hubs can be very susceptible to tech savvy attackers using simple Man in the Middle attacks. If network access was achieved, the security of ones home is greatly compromised allowing intruders an 8–10 minute window to enter a house undetected. Using modest methodologies, consumers and companies can prevent future exploits.


title: Prototype of an intelligent system based on RFID and GPS technologies for women safety

abstract: Security for women has become a major issue in most of the countries. Survey results shows that every year around 25000 crime against women were booked across India. From the last ten years, the statistics among women abusement, sexual harassment have been steadily increasing. It has become mandatory to come up with a solution to protect the women from being a victim and to reduce the attacks. The main objective of this paper is to design and implement a highly reliable system for protecting women from being harassed. In this paper, we have developed an intelligent women safety system using Radio Frequency Identification (RFID) and Global positioning system (GPS). The main idea here is using a active RFID tag with passive RFID reader to scan the information and this information is transferred to the AT89C52 microcontroller where in the contacts of around 4 to 5 people is stored in the data base. Once the information is received by the controller, it sends the message to the contacts through GSM module and the location is tracked through the GPS. The simulation is done in ISIS proteus.


title: Applications of Structural Balance in Signed Social Networks

abstract: We present measures, models and link prediction algorithms based on the structural balance in signed social networks. Certain social networks contain, in addition to the usual 'friend' links, 'enemy' links. These networks are called signed social networks. A classical and major concept for signed social networks is that of structural balance, i.e., the tendency of triangles to be 'balanced' towards including an even number of negative edges, such as friend-friend-friend and friend-enemy-enemy triangles. In this article, we introduce several new signed network analysis methods that exploit structural balance for measuring partial balance, for finding communities of people based on balance, for drawing signed social networks, and for solving the problem of link prediction. Notably, the introduced methods are based on the signed graph Laplacian and on the concept of signed resistance distances. We evaluate our methods on a collection of four signed social network datasets.


title: Kickback noise reduction techniques for CMOS latched comparators

abstract: The latched comparator is a building block of virtually all analog-to-digital converter architectures. It uses a positive feedback mechanism to regenerate the analog input signal into a full-scale digital level. The large voltage variations in the internal nodes are coupled to the input, disturbing the input voltage-this is usually called kickback noise. This brief reviews existing solutions to minimize the kickback noise and proposes two new ones. HSPICE simulations of comparators implemented in a 0.18-/spl mu/m technology demonstrate their effectiveness.


title: Local Graph Partitioning using PageRank Vectors

abstract: A local graph partitioning algorithm finds a cut near a specified starting vertex, with a running time that depends largely on the size of the small side of the cut, rather than the size of the input graph. In this paper, we present a local partitioning algorithm using a variation of PageRank with a specified starting distribution. We derive a mixing result for PageRank vectors similar to that for random walks, and show that the ordering of the vertices produced by a PageRank vector reveals a cut with small conductance. In particular, we show that for any set C with conductance \Phi and volume k, a PageRank vector with a certain starting distribution can be used to produce a set with conductance O\left( {\sqrt {\Phi \log k} } \right). We present an improved algorithm for computing approximate PageRank vectors, which allows us to find such a set in time proportional to its size. In particular, we can find a cut with conductance at most \not o , whose small side has volume at least 2b, in time O\left( {2^b \log ^2 m/\not o^2 } \right) where m is the number of edges in the graph. By combining small sets found by this local partitioning algorithm, we obtain a cut with conductance \not o and approximately optimal balance in time O\left( {m\log ^4 m/\not o^2 } \right).


title: An incremental algorithm for updating betweenness centrality and k-betweenness centrality and its performance on realistic dynamic social network data

abstract: The increasing availability of dynamically changing digital data that can be used for extracting social networks over time has led to an upsurge of interest in the analysis of dynamic social networks. One key aspect of dynamic social network analysis is finding the central nodes in a network. However, dynamic calculation of centrality values for rapidly changing networks can be computationally expensive, with the result that data are frequently aggregated over many time periods and only intermittently analyzed for centrality measures. This paper presents an incremental betweenness centrality algorithm that efficiently updates betweenness centralities or k-betweenness centralities of nodes in dynamic social networks by avoiding re-computations through the efficient storage of information from earlier computations. In this paper, we evaluate the performance of the proposed algorithms for incremental betweenness centrality and k-betweenness centrality on both synthetic social network data sets and on several real-world social network data sets. The presented incremental algorithm can achieve substantial performance speedup (3–4 orders of magnitude faster for some data sets) when compared to the state of the art. And, incremental k-betweenness centrality, which is a good predictor of betweenness centrality, can be carried out on social network data sets with millions of nodes.


title: Reconceptualising gamification : play and pedagogy

abstract: Gamification is a complex and controversial concept. It has been both embraced as a marketing and education revolution, and dismissed as practice of exploitation. Contested within the debate around gamification has been the very concept of what a game is, what the core mechanics of games are, and whether gamification truly mobilises these core mechanics. This paper will challenge the foundation of this debate through reconceptualising gamification not as a simple set of techniques and mechanics, but as a pedagogic heritage, an alternative framework for training and shaping participant behaviour that has at its core the concepts of entertainment and engagement. In doing so it will recontextualise current practices of gamification into a longer and deeper history, and suggest potential pathways for more sophisticated gamification in the future.


title: The Psychology of Intergroup Conflict: A Review of Theories and Measures

abstract: Abstract We review psychological research on intergroup conflict. First, we outline psychological perspectives on forms and functions of groups. Second, we present the most influential psychological theories of intergroup conflict and describe their similarities and differences in predicting individual prejudice, discrimination, and conflict engagement. Third, we review popular measures of intergroup discrimination, including rating measures, behavioral measures, and allocation tasks. Furthermore, we call for a refined semantic framework to structure and differentiate between different measures of intergroup bias. Fourth, we highlight several interventions that can de-bias intergroup relations and facilitate conflict resolution. Lastly, we propose that research on the psychology of intergroup conflict may benefit from a stronger interdisciplinary orientation regarding both theoretical perspectives and methods used and point out promising avenues for future research.


title: Vehicle location finder using Global position system and Global System for Mobile

abstract: Each year, the number of stolen vehicle is on the rise. Usually, to prevent theft, a physical type countermeasure is used such as padlock, disk break lock and other more which is a preventive action but it is not enough safe. The objective of this study is to create a controllable system that can display the location of a vehicle using Global position system (GPS) to pin point the location and Global System for Mobile (GSM) as a mean for communicating with the vehicle for ease of finding after a theft attempt. The system is made to test the accuracy of the location that is send to the user when the vehicle is in motion and stationary in the city and suburb. The system is made by combining a micro controller with GPS and GSM, then comparing it with other similar device available in the market like Garmin and a reference website to find the radius of error. The study of proposed device begins by studying IEEE journal about alternative product and the vehicle itself. The hardware and program development is done by research and trial and error as the controller do not interact with both module at the same time, after successfully programming both module, it is combined into a single program with addition of interrupt program. The experiment is done in three set of tests so that the system accuracy can be determine when stationary and in motion on vehicle, output controlling is the test to determine if the controller can be made into anti-theft system. The result of the test concludes that the system can provide standard GPS coordinate when requested via Short Message Service (SMS). The system can also be used to control an actuator.


title: Story tracking: linking similar news over time and across languages

abstract: The Europe Media Monitor system (EMM) gathers and aggregates an average of 50,000 newspaper articles per day in over 40 languages. To manage the information overflow, it was decided to group similar articles per day and per language into clusters and to link daily clusters over time into stories. A story automatically comes into existence when related groups of articles occur within a 7-day window. While cross-lingual links across 19 languages for individual news clusters have been displayed since 2004 as part of a freely accessible online application (http://press.jrc.it/NewsExplorer), the newest development is work on linking entire stories across languages. The evaluation of the monolingual aggregation of historical clusters into stories and of the linking of stories across languages yielded mostly satisfying results.


title: Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

abstract: Smart cities are a future reality for municipalities around the world. Healthcare services play a vital role in the transformation of traditional cities into smart cities. In this paper, we present a ubiquitous and quality computer-aided blood analysis service for the detection and counting of white blood cells (WBCs) in blood samples. WBCs also called leukocytes or leucocytes are the cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. Analysis of leukocytes provides valuable information to medical specialists, helping them in diagnosing different important hematic diseases, such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and can be time-consuming. A mobile-cloud-assisted detection and classification of leukocytes from blood smear images can enhance accuracy and speed up the detection of WBCs. In this paper, we propose a smartphone-based cloud-assisted resource aware framework for localization of WBCs within microscopic blood smear images using a trained multi-class ensemble classification mechanism in the cloud. In the proposed framework, nucleus is first segmented, followed by extraction of texture, statistical, and wavelet features. Finally, the detected WBCs are categorized into five classes: basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on numerous benchmark databases validate the effectiveness and efficiency of the proposed system in comparison to the other state-of-the-art schemes.


title: Shreds: Fine-Grained Execution Units with Private Memory

abstract: Once attackers have injected code into a victim program's address space, or found a memory disclosure vulnerability, all sensitive data and code inside that address space are subject to thefts or manipulation. Unfortunately, this broad type of attack is hard to prevent, even if software developers wish to cooperate, mostly because the conventional memory protection only works at process level and previously proposed in-process memory isolation methods are not practical for wide adoption. We propose shreds, a set of OS-backed programming primitives that addresses developers' currently unmet needs for fine-grained, convenient, and efficient protection of sensitive memory content against in-process adversaries. A shred can be viewed as a flexibly defined segment of a thread execution (hence the name). Each shred is associated with a protected memory pool, which is accessible only to code running in the shred. Unlike previous works, shreds offer in-process private memory without relying on separate page tables, nested paging, or even modified hardware. Plus, shreds provide the essential data flow and control flow guarantees for running sensitive code. We have built the compiler toolchain and the OS module that together enable shreds on Linux. We demonstrated the usage of shreds and evaluated their performance using 5 non-trivial open source software, including OpenSSH and Lighttpd. The results show that shreds are fairly easy to use and incur low runtime overhead (4.67%).


title: Sensory Aspects of Knee Injuries

abstract: Proprioceptive ability was estimated in different groups of patients with anterior cruciate ligament (ACL) injury as well as in different groups of healthy uninjured individuals. In study I, patients with poor outcome of the ACL injury – i.e. persistent symptoms of instability – were compared with patients with few symptoms after ACL injury, as well as with a control group. The results showed that the symptomatic patients had significantly poorer proprioception than the asymptomatic patients in some of the measurements. The symptomatic patients also had poorer proprioception than the control group, whereas no difference regarding proprioception was seen between the asymptomatic patient group and the control group. The proprioceptive ability after ACL reconstruction was measured in study II where a patient group was compared with a control group. The patients had significantly poorer proprioception in, not only their injured, reconstructed, knee but also in their uninjured knee, in some of the measurements. In study III, the influence on proprioception of injury pattern – i.e. associated injuries to menisci, collateral ligaments and cartilage – and other factors such as laxity, age and gender was examined. The results showed that cartilage defects, increased laxity and higher age were related to poorer proprioception whereas higher activity level before injury was related to better proprioception after injury. The effect of short-term exercise, ergometer cycling, on proprioception was examined in a group of healthy people in study IV. After cycling, poorer proprioception was found in 2 of 4 measurements. The influence on subjective and objective knee function of the factors a) proprioception b) strength (hamstring:quadriceps ratio) and c) laxity was analysed in study V. All these factors were found to significantly affect subjective or objective knee function. These results, described in Papers I-V, show that proprioception, in groups of individuals, may be affected by ACL injury and ACL reconstruction, may differ according to injury morphology, may be affected by short-term exercise and may be related to knee function. Proprioception must therefore be considered as a factor of importance when discussing treatment and rehabilitation of patients with ACL injuries.


title: Myiasis as a Rare Complication of Male Circumcision: A Case Report and Review of Literature

abstract: Introduction. Circumcision is a common procedure carried out around the world. Due to religious reasons, it is routinely done in Bangladesh, by both traditional as well as medically trained circumcisers. Complications include excessive bleeding, loss of foreskin, infection, and injury to the glans penis. Myiasis complicating male circumcision appears to be very rare. Case Presentation. In 2010, a 10-year-old boy presented to the OPD of Dhaka Medical College Hospital with severe pain in his penile region following circumcision 7-days after. The procedure was carried out by a traditional circumciser using unsterilized instruments and dressing material. After examination, unhealthy granulation tissue was seen and maggots started coming out from the site of infestation, indicating presence of more maggots underneath the skin. An emergency operation was carried out to remove the maggots and reconstruction was carried out at the plastic surgery department. Conclusion. There is scarcity of literature regarding complications following circumcision in developing countries. Most dangerous complications are a result of procedure carried out by traditional circumcisers who are inadequately trained. Incidence of such complications can be prevented by establishing a link between the formal and informal sections of healthcare to improve the safety of the procedure.


title: Security and privacy in the Internet of Things: Current status and open issues

abstract: The Internet of Things at large will foster billions of devices, people and services to interconnect and exchange information and useful data. As IoT systems will be ubiquitous and pervasive, a number of security and privacy issues will arise. Credible, economical, efficient and effective security and privacy for IoT are required to ensure exact and accurate confidentiality, integrity, authentication, and access control, among others. In this paper, the IoT vision, existing security threats, and open challenges in the domain of IoT are discussed. The current state of research on IoT security requirements is discussed and future research directions with respect to IoT security and privacy are presented.


title: LRAGE: Learning Latent Relationships With Adaptive Graph Embedding for Aerial Scene Classification

abstract: The performance of scene classification relies heavily on the spatial and structural features that are extracted from high spatial resolution remote-sensing images. Existing approaches, however, are limited in adequately exploiting latent relationships between scene images. Aiming to decrease the distances between intraclass images and increase the distances between interclass images, we propose a latent relationship learning framework that integrates an adaptive graph with the constraints of the feature space and label propagation for high-resolution aerial image classification. To describe the latent relationships among scene images in the framework, we construct an adaptive graph that is embedded into the constrained joint space for features and labels. To remove redundant information and improve the computational efficiency, subspace learning is introduced to assist in the latent relationship learning. To address out-of-sample data, linear regression is adopted to project the semisupervised classification results onto a linear classifier. Learning efficiency is improved by minimizing the objective function via the linearized alternating direction method with an adaptive penalty. We test our method on three widely used aerial scene image data sets. The experimental results demonstrate the superior performance of our method over the state-of-the-art algorithms in aerial scene image classification.


title: Adversarial Noise Layer: Regularize Neural Network by Adding Noise

abstract: In this paper, we introduce a novel regularization method called Adversarial Noise Layer (ANL), which are able to significantly improve CNN’s generalization ability by adding carefully crafted noise into the intermediate layer activations. ANL can be easily implemented and integrated with most of the mainstream CNN-based models. We compared the effects of the different types of noise and visually demonstrate that our proposed adversarial noise instruct CNN models to learn to extract cleaner feature maps, which further reduce the risk of over-fitting. We also conclude that models trained with ANL are more robust to the adversarial examples generated by FGSM than the traditional adversarial training approaches.


title: Face spoof detection using light reflection in moderate to low lighting

abstract: Face recognition is widely viewed as an alternative means of authentication to replace traditional password methods in different applications for access control. Despite significant improvements, this form of authentication remains plagued by a number of vulnerabilities ranging from the use of printed photographs, 3D masks, and video replay attacks, prompting the need for a more robust approach in defending against such spoof attacks. Using the observation that different materials reflect light differently, we propose a system that uses light reflection patterns and night vision infrared to detect spoof attacks. We developed a system using Laplacian blur detection, Gabor filters, color moments and Local Binary Patterns, which calculates the reflection of light on different material and classifies whether the given face is real or fake. We noticed significant improvement in our results, with the system working well in a lighting controlled environment that is comparable to some existing systems. In particular, a single light source when capturing a sample for preprocessing yielded optimal results. Furthermore, we also created our own dataset comprising 40 individuals using several cameras that can serve as another source in addition to the existing CASIA-FASD and MSU MFSD public datasets.


title: Salsa20 Cryptanalysis: New Moves and Revisiting Old Styles.

abstract: In this paper, we revisit some existing techniques in Salsa20 cryptanalysis, and provide some new ideas as well. As a new result, we explain how a valid initial state can be obtained from a Salsa20 state after one round. This helps in studying the non-randomness of Salsa20 after 5 rounds. In particular, it can be seen that the 5-round bias reported by Fischer et al. (Indocrypt 2006) is a special case of our analysis. Towards improving the existing results, we revisit the idea of Probabilistic Neutral Bit (PNB) and how a proper choice of certain parameters reduce the complexity of the existing attacks. For cryptanalysis against 8-round Salsa20, we could achieve the key search complexity of 2 compared to 2 (FSE 2008) and 2 (ICISC 2012).


title: Face Recognition Using Boosted Local Features

abstract: This paper presents a new method for face recognition which learns a face similarity measure from example image pairs. A set of computationally efficient “rectangle” features are described which act on pairs of input images. The features compare regions within the input images at different locations, scales, and orientations. The AdaBoost algorithm is used to train the face similarity function by selecting features. Given a large face database, the set of face pairs is too large for effective training. We present a sampling procedure which selects a training subset based on the AdaBoost example weights. Finally, we show state of the art results on the FERET set of faces as well as a more challenging set of faces collected at our lab.


title: Peruse and Profit: Estimating the Accelerability of Loops

abstract: There exist a multitude of execution models available today for a developer to target. The choices vary from general purpose processors to fixed-function hardware accelerators with a large number of variations in-between. There is a growing demand to assess the potential benefits of porting or rewriting an application to a target architecture in order to fully exploit the benefits of performance and/or energy efficiency offered by such targets. However, as a first step of this process, it is necessary to determine whether the application has characteristics suitable for acceleration. In this paper, we present Peruse, a tool to characterize the features of loops in an application and to help the programmer understand the amenability of loops for acceleration. We consider a diverse set of features ranging from loop characteristics (e.g., loop exit points) and operation mixes (e.g., control vs data operations) to wider code region characteristics (e.g., idempotency, vectorizability). Peruse is language, architecture, and input independent and uses the intermediate representation of compilers to do the characterization. Using static analyses makes Peruse scalable and enables analysis of large applications to identify and extract interesting loops suitable for acceleration. We show analysis results for unmodified applications from the SPEC CPU benchmark suite, Polybench, and HPC workloads. For an end-user it is more desirable to get an estimate of the potential speedup due to acceleration. We use the workload characterization results of Peruse as features and develop a machine-learning based model to predict the potential speedup of a loop when off-loaded to a fixed function hardware accelerator. We use the model to predict the speedup of loops selected by Peruse and achieve an accuracy of 79%.


title: WikiWrite: generating wikipedia articles automatically

abstract: The growth of Wikipedia, limited by the availability of knowledgeable authors, cannot keep pace with the ever increasing requirements and demands of the readers. In this work, we propose Wiki-Write, a system capable of generating content for new Wikipedia articles automatically. First, our technique obtains feature representations of entities on Wikipedia. We adapt an existing work on document embeddings to obtain vector representations of words and paragraphs. Using the representations, we identify articles that are very similar to the new entity on Wikipedia. We train machine learning classifiers using content from the similar articles to assign web retrieved content on the new entity into relevant sections in the Wikipedia article. Second, we propose a novel abstractive summarization technique that uses a two-step integer-linear programming (ILP) model to synthesize the assigned content in each section and rewrite the content to produce a well-formed informative summary. Our experiments show that our technique is able to reconstruct existing articles in Wikipedia with high accuracies. We also create several articles using our approach in the English Wikipedia, most of which have been retained in the online encyclopedia.


title: Gender estimation based on supervised HOG, Action Units and unsupervised CNN feature extraction

abstract: In this paper, a deep-learning based architecture is proposed to estimate gender which includes both supervised and unsupervised facial feature extraction techniques and a deep network to fuse features and to classify them. As unsupervised techniques we have benefited deep-learning feature extraction method called convolutional neural network, and for supervised facial feature extraction we have used facial Action Units and Histogram of Oriented Gradients. Our proposed architecture has been tested on multiple datasets and for most of them the accuracy is considerably high.


title: SemEval-2007 Task-17: English Lexical Sample, SRL and All Words

abstract: This paper describes our experience in preparing the data and evaluating the results for three subtasks of SemEval-2007 Task-17 - Lexical Sample, Semantic Role Labeling (SRL) and All-Words respectively. We tabulate and analyze the results of participating systems.


title: WebSelF: a web scraping framework

abstract: We present WebSelF, a framework for web scraping which models the process of web scraping and decomposes it into four conceptually independent, reusable, and composable constituents. We have validated our framework through a full parameterized implementation that is flexible enough to capture previous work on web scraping. We conducted an experiment that evaluated several qualitatively different web scraping constituents (including previous work and combinations hereof) on about 11,000 HTML pages on daily versions of 17 web sites over a period of more than one year. Our framework solves three concrete problems with current web scraping and our experimental results indicate that composition of previous and our new techniques achieve a higher degree of accuracy, precision and specificity than existing techniques alone.


An Open-Source Implementation of a Unit Quaternion based Attitude and Trajectory Tracking for Quadrotors


title: Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data

abstract: Obtaining accurate information about current and near-term future traffic flows of all links in a traffic network has a wide range of applications, including traffic forecasting, vehicle navigation devices, vehicle routing, and congestion management. A major problem in getting traffic flow information in real time is that the vast majority of links is not equipped with traffic sensors. Another problem is that factors affecting traffic flows, such as accidents, public events, and road closures, are often unforeseen, suggesting that traffic flow forecasting is a challenging task. In this paper, we first use a dynamic traffic simulator to generate flows in all links using available traffic information, estimated demand, and historical traffic data available from links equipped with sensors. We implement an optimization methodology to adjust the origin-to-destination matrices driving the simulator. We then use the real-time and estimated traffic data to predict the traffic flows on each link up to 30 min ahead. The prediction algorithm is based on an autoregressive model that adapts itself to unpredictable events. As a case study, we predict the flows of a traffic network in San Francisco, CA, USA, using a macroscopic traffic flow simulator. We use Monte Carlo simulations to evaluate our methodology. Our simulations demonstrate the accuracy of the proposed approach. The traffic flow prediction errors vary from an average of 2% for 5-min prediction windows to 12% for 30-min windows even in the presence of unpredictable events.


title: Dual marching cubes

abstract: We present the definition and computational algorithms for a new class of surfaces which are dual to the isosurface produced by the widely used marching cubes (MC) algorithm. These new isosurfaces have the same separating properties as the MC surfaces but they are comprised of quad patches that tend to eliminate the common negative aspect of poorly shaped triangles of the MC isosurfaces. Based upon the concept of this new dual operator, we describe a simple, but rather effective iterative scheme for producing smooth separating surfaces for binary, enumerated volumes which are often produced by segmentation algorithms. Both the dual surface algorithm and the iterative smoothing scheme are easily implemented.


title: Bluetooth and Wi-Fi Wireless Protocols: A Survey and a Comparison

abstract: Bluetooth and IEEE 802.11 (Wi-Fi) are two communication protocol standards that define a physical layer and a MAC layer for wireless communications within a short range (from a few meters up to 100 m) with low power consumption (from less than 1 mW up to 100 mW). Bluetooth is oriented to connecting close devices, serving as a substitute for cables, while Wi-Fi is oriented toward computer-to-computer connections, as an extension of or substitution for cabled LANs. In this article we offer an overview of these popular wireless communication standards, comparing their main features and behaviors in terms of various metrics, including capacity, network topology, security, quality of service support, and power consumption.


title: Overcoming Variance and Process Distinctions in Information Systems Research

abstract: Most Information Systems research to date has been conducted from either a variance or a process perspective. On the one hand, some researchers argue that process and variance approaches should be kept separate, while others think they should be combined. In this paper, we argue that variance and process have more similarities than expected, and that combining them can have significant advantages for the field. We propose a ‘blended’ hybrid approach in which elements of both variance and process exist in a coherent whole, and offer a first-version tool box to start hybrid theorizing. Our paper ends with a discussion regarding the types of information systems research questions that can be appropriately approached from a hybrid standpoint.


title: Gazetiki: automatic creation of a geographical gazetteer

abstract: Geolocalized databases are becoming necessary in a wide variety of application domains. Thus far, the creation of such databases has been a costly, manual process. This drawback has stimulated interest in automating their construction, for example, by mining geographical information from the Web. Here we present and evaluate a new automated technique for creating and enriching a geographical gazetteer, called Gazetiki. Our technique merges disparate information from Wikipedia, Panoramio, and web search engines in order to identify geographical names, categorize these names, find their geographical coordinates and rank them. The information produced in Gazetiki enhances and complements the Geonames database, using a similar domain model. We show that our method provides a richer structure and an improved coverage compared to another known attempt at automatically building a geographic database and, where possible, we compare our Gazetiki to Geonames.


Syntactic Normalization of Twitter Messages


title: Impact of Security on Power Systems Operation

abstract: This paper reviews the status of security analyses in vertically integrated utilities and discusses the impact of system security on the operation and the planning of restructured power systems. The paper is focused on the static security rather than the dynamic security of power systems. The paper also discusses assumptions, functions, and calculation tools that are considered for satisfying power systems security requirements. In addition, the security coordination among time-based scheduling models is presented. In particular, real-time security analysis, short-term operation,midterm operation planning, and long-term planning are analyzed. The paper highlights issues and challenges for implementing security options in electricity markets and concludes that global analyses of security options could provide additional opportunities for seeking optimal and feasible schedules in various time scales.


title: Bayesian Hierarchical Reinforcement Learning

abstract: We describe an approach to incorporating Bayesian priors in the MAXQ framework for hierarchical reinforcement learning (HRL). We define priors on the primitive environment model and on task pseudo-rewards. Since models for composite tasks can be complex, we use a mixed model-based/model-free learning approach to find an optimal hierarchical policy. We show empirically that (i) our approach results in improved convergence over non-Bayesian baselines, (ii) using both task hierarchies and Bayesian priors is better than either alone, (iii) taking advantage of the task hierarchy reduces the computational cost of Bayesian reinforcement learning and (iv) in this framework, task pseudo-rewards can be learned instead of being manually specified, leading to hierarchically optimal rather than recursively optimal policies.


title: MSnet: Mutual Suppression Network for Disentangled Video Representations

abstract: The extraction of meaningful features from videos is important as they can be used in various applications. Despite its importance, video representation learning has not been studied much, because it is challenging to deal with both content and motion information. We present a Mutual Suppression network (MSnet) to learn disentangled motion and content features in videos. The MSnet is trained in such way that content features do not contain motion information and motion features do not contain content information; this is done by suppressing each other with adversarial training. We utilize the disentangled features from the MSnet for several tasks, such as frame reproduction, pixel-level video frame prediction, and dense optical flow estimation, to demonstrate the strength of MSnet. The proposed model outperforms the state-of-the-art methods in pixel-level video frame prediction. The source code will be publicly available.


title: Gradient Diversity: a Key Ingredient for Scalable Distributed Learning

abstract: It has been experimentally observed that distributed implementations of mini-batch stochastic gradient descent (SGD) algorithms exhibit speedup saturation and decaying generalization ability beyond a particular batch-size. In this work, we present an analysis hinting that high similarity between concurrently processed gradients may be a cause of this performance degradation. We introduce the notion of gradient diversity that measures the dissimilarity between concurrent gradient updates, and show its key role in the performance of mini-batch SGD. We prove that on problems with high gradient diversity, mini-batch SGD is amenable to better speedups, while maintaining the generalization performance of serial (one sample) SGD. We further establish lower bounds on convergence where mini-batch SGD slows down beyond a particular batch-size, solely due to the lack of gradient diversity. We provide experimental evidence indicating the key role of gradient diversity in distributed learning, and discuss how heuristics like dropout, Langevin dynamics, and quantization can improve it.


title: An Ultrasonic Transducer Interface IC With Integrated Push-Pull 40 Vpp, 400 mA Current Output, 8-bit DAC and Integrated HV Multiplexer

abstract: We present an ultrasonic transducer interface IC that includes an 8-bit, 40 Vpp, 400 mA current output DAC for arbitrary waveform transducer excitation and a ±25 V analog multiplexer. The IC was fabricated using a 0.35 μm, 50 V CMOS process. The design eliminates the need for an external power amplifier as the piezoelectric transducer is driven directly from a segmented push-pull current output DAC, which simplifies the overall system design. This approach has the advantage of simple and rapid glitch-free power-up/down, which is especially important in integrated high-output-power drivers. The DAC has been evaluated when operating at a 150 MHz sampling rate with a ±400 mA output current and a 50 Ω load. Measured performance includes 37 dB SNDR and 46 dB SFDR at 10 MHz output frequency. By implementing an additional reference DAC and extending the receiver isolation switch into an analog multiplexer, we enable on-line calibration for the purpose of reducing the driver and receiver signal path uncertainty. Measurements show a greater than ten-fold improvement in delay uncertainty to approximately 20 ps for temperature variations of 0 to 70 degrees Celsius.


title: A comparison of machine learning algorithms applied to hand gesture recognition

abstract: Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. This paper presents a comparative study of four classification algorithms for static hand gesture classification using two different hand features data sets. The approach used consists in identifying hand pixels in each frame, extract features and use those features to recognize a specific hand pose. The results obtained proved that the ANN had a very good performance and that the feature selection and data preparation is an important phase in the all process, when using low-resolution images like the ones obtained with the camera in the current work.


title: Gaze-Aware Streaming Solutions for the Next Generation of Mobile VR Experiences

abstract: This paper presents a novel approach to content delivery for video streaming services. It exploits information from connected eye-trackers embedded in the next generation of VR Head Mounted Displays (HMDs). The proposed solution aims to deliver high visual quality, in real time, around the users' fixations points while lowering the quality everywhere else. The goal of the proposed approach is to substantially reduce the overall bandwidth requirements for supporting VR video experiences while delivering high levels of user perceived quality. The prerequisites to achieve these results are: (1) mechanisms that can cope with different degrees of latency in the system and (2) solutions that support fast adaptation of video quality in different parts of a frame, without requiring a large increase in bitrate. A novel codec configuration, capable of supporting near-instantaneous video quality adaptation in specific portions of a video frame, is presented. The proposed method exploits in-built properties of HEVC encoders and while it introduces a moderate amount of error, these errors are indetectable by users. Fast adaptation is the key to enable gaze-aware streaming and its reduction in bandwidth. A testbed implementing gaze-aware streaming, together with a prototype HMD with in-built eye tracker, is presented and was used for testing with real users. The studies quantified the bandwidth savings achievable by the proposed approach and characterize the relationships between Quality of Experience (QoE) and network latency. The results showed that up to 83% less bandwidth is required to deliver high QoE levels to the users, as compared to conventional solutions.


Query expansion using local and global document analysis


title: Anonymous Two-Factor Authentication in Distributed Systems: Certain Goals Are Beyond Attainment

abstract: Despite two decades of intensive research, it remains a challenge to design a practical anonymous two-factor authentication scheme, for the designers are confronted with an impressive list of security requirements (e.g., resistance to smart card loss attack) and desirable attributes (e.g., local password update). Numerous solutions have been proposed, yet most of them are shortly found either unable to satisfy some critical security requirements or short of a few important features. To overcome this unsatisfactory situation, researchers often work around it in hopes of a new proposal (but no one has succeeded so far), while paying little attention to the fundamental question: whether or not there are inherent limitations that prevent us from designing an “ideal” scheme that satisfies all the desirable goals? In this work, we aim to provide a definite answer to this question. We first revisit two foremost proposals, i.e. Tsai et al.’s scheme and Li’s scheme, revealing some subtleties and challenges in designing such schemes. Then, we systematically explore the inherent conflicts and unavoidable trade-offs among the design criteria. Our results indicate that, under the current widely accepted adversarial model, certain goals are beyond attainment. This also suggests a negative answer to the open problem left by Huang et al. in 2014. To the best of knowledge, the present study makes the first step towards understanding the underlying evaluation metric for anonymous two-factor authentication, which we believe will facilitate better design of anonymous two-factor protocols that offer acceptable trade-offs among usability, security and privacy.


title: Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages

abstract: This paper summarizes the state-of-art particle swarm optimization (PSO) applications for resolving the economic dispatch (ED) problem, which is considered as one of the complex problems to be tackled. The PSO techniques have drawn much attention from the power system community and been successfully applied in many complex optimization problems in power systems. This paper focuses on the application of PSO techniques to the ED problems and describes their advantages and disadvantages in resolving the ED problems


title: Behavioural aspects of cerebellar function in adults with Asperger syndrome

abstract: Aside from social deficits, Asperger and autistic individuals also exhibit motor control abnormalities such as impaired gait, balance, manual dexterity and grip. One brain area that has consistently been reported on autopsy and imaging studies to be abnormal in such individuals is the cerebellum. As the cerebellum controls sensorimotor coordination and lesions here typically cause hypotonia, dysmetria and dyscoordination, we performed a series of quantitative tests aimed at investigating cerebellar function in Asperger individuals. Tests examining visually guided movement (rapid pointing), speeded complex movement (finger tapping, rapid hand turning), muscle tone (catching dropped weight), prediction, coordination and timing (balance, grip force and interval timing) were conducted on 12 Asperger subjects and 12 age and IQ matched controls. In comparison to control subjects, Asperger subject’s demonstrated: (i) decreased pointing accuracy and rate, (ii) increased postural instability, and (iii) decreased timing accuracy. IQ was found to co-vary with some parameters of each of these tasks and no further impairments were found on the remaining tests. We suggest that these specific deficits reflect impairment in the ability to integrate sensory input with appropriate motor commands and are consistent with cerebellar dysfunction in Asperger syndrome.


title: A robust scene-change detection method for video segmentation

abstract: This paper proposes a new method that combines the intensity and motion information to detect scene changes such as abrupt scene changes and gradual scene changes. Two major features are chosen as the basic dissimilarity measures, and self- and cross-validation mechanisms are employed via a static scene test. We also develop a novel intensity statistics model for detecting gradual scene changes. Experimental results show that the proposed algorithms are effective and outperform the previous approaches.


Duplicate Detection on GPUs


title: Aspect Based Sentiment Analysis with Gated Convolutional Networks

abstract: Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). Most previous approaches employ long short-term memory and attention mechanisms to predict the sentiment polarity of the concerned targets, which are often complicated and need more training time. We propose a model based on convolutional neural networks and gating mechanisms, which is more accurate and efficient. First, the novel Gated Tanh-ReLU Units can selectively output the sentiment features according to the given aspect or entity. The architecture is much simpler than attention layer used in the existing models. Second, the computations of our model could be easily parallelized during training, because convolutional layers do not have time dependency as in LSTM layers, and gating units also work independently. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models.


title: Multi-view Fusion with Deep Learning for 3D Shape Classification

abstract: 3D model retrieval is all along a difficult and hotspot in computer vision. Recently, the view-based methods make use of the advanced convolutional neural networks which achieve excellent results. However, the structural information of the 3D model was destroyed by the projection and the relevance of multiple perspectives was not considered. In order to resolve this problem, this manuscript analyzes the process of human observation of 3D models and imitates the process of human recognition of 3D models through a combination of convolutional neural networks and recurrent neural networks. Our approach can convert the relevance between perspectives into the dependency relationship between structured recurrent neural networks and restore the structural information of the 3D model. We conducted experiments on the ModelNet40 database, it shows strong performance on par or even better than state-of-the-art results.


title: A Review of Human Pose Estimation from Single Image

abstract: Human pose estimation has been an important research topic for computer vision community. It has been in focus of researchers mainly because of its significant applications in various important fields like human computer interaction, action recognition, surveillance, picture understanding, threat prediction, etc. It's difficult to cover all aspects of this domain because of the diversity of its application areas, therefore this review is focused on the most significant contributions in Human Pose Estimation methods from a single two-dimensional image. We start our study with the traditional pictorial structure, go through a discussion of the use of Deep Neural Networks that improved the human pose estimation significantly and then the most recent, more famous approach namely Stacked Hourglass. Modern methods are based on training, evaluating and comparing on some common datasets using different architectures of Deep learning modules. Hence, starting from the first practical models for estimating human pose, we provide a comprehensive study of some of the most famous deep learning methods in order to provide a concise analytical review of these most influential methods.


title: Antenna Array Synthesis for RFID-Based Electronic Toll Collection

abstract: This paper deals with the synthesis and design of reader antenna for radio-frequency identification (RFID)-based dedicated short-range communication (DSRC) systems. A method for synthesizing a planar antenna array with both the aim of stretching the communication area toward the longitudinal direction and confining it within a certain number of road lanes is described. This method can be used for any working frequency, and two synthesis examples are described taking into account requirements and system parameters of the standards electronic product code (EPC) Gen2 in the ultrahigh frequency (UHF) band, European ETSI EN 302 663 for intelligent transportation system/DSRC, and Chinese GB/T20851-2007 for electronic toll collection/DSRC at 5.8 GHz. After that, the design of a specific $4 \times 4$ planar antenna array for the case of a UHF EPC Gen2 Chinese band 920.5–924.5 MHz is reported. Microstrip technology is chosen for its advantages of being light, simple, inexpensive, and easy to manufacture. The proposed design presents good matching characteristic and bandwidth, and effectively synthesizes the required communication area (also in the presence of an external radome). Measurement results performed in an anechoic environment together with the experimental verification of the synthesized communication area confirm the validity of the proposed method.


title: A Combined Svm Ensemble Algorihm Based on KICA and KFCM

abstract: In order to improve the generalization ability of svm,the training samples are disturbed using boosting algorithm, KICA is used to get reduct features sets that have stable and good generalization ability. The svm individuals of higher accuracy and difference are generated by samples disturbance and features disturbance. The best individuals are selected by generalization error of svm in the validate set based on KFCM.Finally, the selected individuals are ensembled by simple average.The experiments show that the our algorithm have higher generalization performance and lower complexity of time and space, it is an higher effective ensemble agorithm.


title: Automatic Generation of Normalized Relational Schemas from Nested Key-Value Data

abstract: Self-describing key-value data formats such as JSON are becoming increasingly popular as application developers choose to avoid the rigidity imposed by the relational model. Database systems designed for these self-describing formats, such as MongoDB, encourage users to use denormalized, heavily nested data models so that relationships across records and other schema information need not be predefined or standardized. Such data models contribute to long-term development complexity, as their lack of explicit entity and relationship tracking burdens new developers unfamiliar with the dataset. Furthermore, the large amount of data repetition present in such data layouts can introduce update anomalies and poor scan performance, which reduce both the quality and performance of analytics over the data. In this paper we present an algorithm that automatically transforms the denormalized, nested data commonly found in NoSQL systems into traditional relational data that can be stored in a standard RDBMS. This process includes a schema generation algorithm that discovers relationships across the attributes of the denormalized datasets in order to organize those attributes into relational tables. It further includes a matching algorithm that discovers sets of attributes that represent overlapping entities and merges those sets together. These algorithms reduce data repetition, allow the use of data analysis tools targeted at relational data, accelerate scan-intensive algorithms over the data, and help users gain a semantic understanding of complex, nested datasets.


title: Efficient coflow scheduling with Varys

abstract: Communication in data-parallel applications often involves a collection of parallel flows. Traditional techniques to optimize flow-level metrics do not perform well in optimizing such collections, because the network is largely agnostic to application-level requirements. The recently proposed coflow abstraction bridges this gap and creates new opportunities for network scheduling. In this paper, we address inter-coflow scheduling for two different objectives: decreasing communication time of data-intensive jobs and guaranteeing predictable communication time. We introduce the concurrent open shop scheduling with coupled resources problem, analyze its complexity, and propose effective heuristics to optimize either objective. We present Varys, a system that enables data-intensive frameworks to use coflows and the proposed algorithms while maintaining high network utilization and guaranteeing starvation freedom. EC2 deployments and trace-driven simulations show that communication stages complete up to 3.16X faster on average and up to 2X more coflows meet their deadlines using Varys in comparison to per-flow mechanisms. Moreover, Varys outperforms non-preemptive coflow schedulers by more than 5X.


title: DGASensor: Fast Detection for DGA-Based Malwares

abstract: DNS protocol has been used by many malwares for command-and-control (C&C). To improve the resiliency of C&C communication, Domain Generation Algorithm (DGA) has been utilized by recent malwares such as Locky, Conficker and Zeus. Many detection systems have been introduced for DGA-based botnets detection. However, such botnets detection approaches suffer from several limitations, for instance, requiring a group of DGA domains, period behaviors, the presence of multiple bots, and so forth. It is very hard for them to detect an individually running DGA-based malware which leave only a few traces. In this paper, we develop DGASensor to detect DGA-based malwares immediately by identifying a single DGA domain using lexical evidence. First, DGASensor automatically analyzes the lexical patterns of the most popular domains listed in Alexa top 100,000, and then extracts two templates, namely distribution template and structure template. Second, the above two templates, pronounceable attributes, and some frequently used properties like entropy and length, are used to extract features from a single domain. Third, we train our classifier using a non-DGA dataset consisting of domains obtained from Alexa rank and a DGA dataset generated by known DGAs. At last, we provide a short word filter to decrease the false positive rate. We implement a prototype system and evaluate it using the above training dataset with 10-fold cross validation. Moreover, a set of real world DNS traffic collected from a recursive DNS server is used to measure real world performance of our system. The results show that DGASensor detects DGA domains with accuracy 93% in our training dataset and is able to identify a variety of malwares in the real world dataset with an extremely high processing capability.


title: SiC MOSFETs connected in series with active voltage control

abstract: Silicon Carbide (SiC) MOSFETs offer rapid switching and low on-state voltages. Connecting SiC MOSFETs in series will enable high voltage high frequency applications. Nonetheless, the output capacitances of SiC MOSFETs are often found to ring with the significant stray inductance inevitably found in circuits with series connected devices. The active voltage control gate drive method is used here to clamp the MOSFET voltages to ensure low and stable overshoot voltages, good voltage balancement and a near ideal turn on. It is concluded that SiC MOSFETs and SiC diodes may be connected in series with significant advantages compared to Silicon (Si) IGBTs and Si diode technologies, and the benefits may be realized with the use of active voltage control.


title: Living object detection system based on comb pattern capacitive sensor for wireless EV chargers

abstract: A living object detection (LOD) system based on a comb pattern capacitive sensor with simple and cost-effective characteristics is applied to wireless EV chargers for the first time in this paper. The proposed LOD system mounted on a transmitter (Tx) pad adopts an optimal comb shape as a sensing pattern to obtain uniform capacitance capability. Unlike general applications using a capacitive sensor, the proposed capacitive sensor, which is applied to wireless EV chargers, inherently contains significant capacitive coupling between the living object and the ground. This capacitive coupling should be carefully considered in order to accurately sense any minimal capacitance change caused by the presence of a living object. In this paper, a static circuit model of the proposed capacitive sensor with a living object considering the ground effect and an RC integrator to detect even a tiny voltage difference are established and verified by experiments. The results show that when a living object is present, the measured capacitance by the proposed sensing pattern was increased by up to 27.5% compared to when there is no living object.


title: Evaluation of positioning algorithms for wide area multilateration based alternative positioning navigation and timing (APNT) using 1090 MHz ADS-B signals

abstract: GNSS is the major technology that supports the operation of the modern air traffic management (ATM) system. Also, the positioning performance of GNSS is enhanced by many augmentation systems by providing corrections to the error sources as well as the integrity messages on the post-correction error residuals. However, unpredictable factors on the ground user environment, such as the radio frequency interference (RFI) could severely degrade or interrupt the service of GNSS receivers. In order to maintain the normal operation of the ATM system during GNSS outages, the alternative position, navigation and timing (APNT) service is essential. One of the possible approaches to provide the APNT service is the use of existing ADS-B data link at 1090 MHz to perform wide area multilateration (WAM). In consideration to use the existing ADS-B data link for APNT, the current ground based transceiver (GBT) network that is originally deployed for surveillance purpose could be used as the WAM station to measure the signal time-of-flight to estimate the time of arrival (TOA). The attractive part of this 1090 MHz ADS-B WAM approach is that there infrastructure to support reception of the signal is already or being installed. However, these stations generally do not guarantee precise (~ 50 nanoseconds) or GNSS interference robust time synchronization. Hence a challenging issue to meeting the navigation and surveillance requirements of APNT is developing an appropriate time synchronization scheme for the ground stations. In ION GNSS+ 2013 conference, we presented the post processing results of ADS-B WAM test beds as well as the time synchronization designs in order to get adequate ranging performance. In this paper, we will first investigate the positioning algorithms for this approach, and the positioning algorithms under consideration are 1) the linear iterative least squares method, 2) the non-iterative quadratic equation solution method, and 3) a hybrid of the prior 2 methods. Also, the station geometry effects on the positioning algorithms (i.e., dilution of precision (DOP)) will be evaluated. In order to keep the time for extended period when GPS is outage, the filter design to model the clock will then be studied. That is, with the model of the clocks’ characteristics we would be able to predict the clock behavior to continue the normal operation of our ADS-B WAM test bed.


title: BIM-BASED SCHEDULING OF CONSTRUCTION: A COMPARATIVE ANALYSIS OF PREVAILING AND BIM-BASED SCHEDULING PROCESSES

abstract: The implementation of BIM in construction will have far reaching implications for the organization of construction works, the roles, relations and responsibilities of the actors, business models, risk distribution, etc. At a project level, the application of BIM for management purposes facilitates the integration of management processes, e.g. integration of time and cost, which enables integrated management of labor distribution, procurement and just-in-time deliveries of building materials, on site logistics, cash-flow analysis, and other resource related aspects that connect to time scheduling. However, even though the potential of BIM is generally recognized in industry, the practical application of BIM for management purposes is still limited among contractors. Thus, the objective of this study is to explore and analyze the gap between BIM-based scheduling and the prevailing scheduling process in construction from a general contractorâ€™s point of view. The purpose of the study is to add knowledge and understanding about to what extent BIM-based scheduling can answer to the principal shortcomings of the current scheduling process. The study of the prevailing scheduling process results in five key areas of importance; Improper supply of information, Collaboration supports validation and acceptance of the schedule, Continuous improvements in scheduling is a challenge, Current scheduling methods fail to support time and resource simulation and optimization, Challenging to overview and communicate scheduling output. The study finds that BIM-based scheduling can answer to the identified shortcomings and best practice work methods in the prevailing scheduling process. However, the full use of BIM-based scheduling is dependent on an organization that foster openness, collaboration and transparency. In addition, obtaining full advantages of BIM-based scheduling presuppose standardization of building components, diffusion of the current phase divided construction process and a shift from decentralized to centralized and collaborative resource management.


title: An Introduction To Compressive Sampling

abstract: Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.


Korean Spiders of Genus Tegenaria (Araneae, Agelenidae)


title: Gamification in Fitness Apps: How Do Leaderboards Influence Exercise?

abstract: While leaderboards are a key gamification element incorporated in fitness apps to spur competition among users, various issues exist in their use. Yet, the impact of leaderboards and the social comparison they entail in these apps is under-studied and not well understood. Motivated thus, we build a theoretical model to explain their impact on the attitude and physical activity behavior of users by drawing on social comparison theory. We also propose that environmental (perceived competitive climate) and individual (self-efficacy) factors moderate the relationship between social comparison via leaderboards and users attitude. The model was tested by collecting survey and objective website data from 80 users of Nike+ Running. Our results show that users’ attitude and facilitating conditions increase their physical activity behavior. Additionally, perceived competitive climate and self-efficacy moderate the effect of social comparison on users’ attitude in opposing directions. The expected contributions and remaining research plan are described.


title: Periodontal microbial ecology

abstract: The authors have taken the liberty of presenting this manuscript in two parts. The first is a brief primer on microbial ecology, because, although the importance of microbial ecology in periodontal diseases is widely recognized, most of us do not know precisely what is meant by the term. The second section is a rather extensive overview of current studies of oral microbial ecology based almost entirely on recent in vivo studies.


title: Implementation of 3D Obstacle Compliant Mobility Models for UAV Networks in ns-3

abstract: UAV networks are envisioned to play a crucial role in the future generations of wireless networks. The mechanical degrees of freedom in the movement of UAVs provides various advantages for tactical and civilian applications. Due to the high cost of failures in system-based tests, initial analysis and refinement of designs and algorithms for UAV applications are performed through rigorous simulations. Current trend of UAV specific simulators is mainly biased towards the mechanical properties of flying. For network-centric simulations, the intended measurements on the performance of protocols in mobile scenarios are conventionally captured from general-purpose network simulators, which are not natively equipped with comprehensive models for 3D movements of UAVs. To facilitate such simulations for UAV systems, this paper presents different mobility models for emulation of the movement of a UAV. Detailed description of three mobility models (random walk, random direction, and Gauss-Markov) are presented, and their associated movement patterns are characterized. This characterization is further extended by considering the effect of large obstacles on movement patterns of nodes following the three models. The mobility models are prepared as open-source add-ons for ns-3 network simulator.


title: Broken keys to the kingdom: Security and privacy aspects of RFID-based car keys

abstract: This paper presents an overview of the current state-of-the-art of security and privacy concerns regarding RFID-based car key applications. We will first present a general overview of the technology and its evolution before moving on to an overview and discussion of the various known security weaknesses and attacks against such systems and the associated privacy risks they introduce.


title: Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management

abstract: We illuminate the myriad of opportunities for research where supply chain management intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of a SCM data scientist. Such knowledge is crucial to developing future supply chain leaders. We propose definitions of data science and predictive analytics as applied to supply chain management. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of supply chain management and DPB.


title: Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion

abstract: Over the recent years, embedding methods have attracted increasing focus as a means for knowledge graph completion. Similarly, rule-based systems have been studied for this task in the past. What is missing so far is a common evaluation that includes more than one type of method. We close this gap by comparing representatives of both types of systems in a frequently used evaluation protocol. Leveraging the explanatory qualities of rule-based systems, we present a fine-grained evaluation that gives insight into characteristics of the most popular datasets and points out the different strengths and shortcomings of the examined approaches. Our results show that models such as TransE, RESCAL or HolE have problems in solving certain types of completion tasks that can be solved by a rule-based approach with high precision. At the same time, there are other completion tasks that are difficult for rule-based systems. Motivated by these insights, we combine both families of approaches via ensemble learning. The results support our assumption that the two methods complement each other in a beneficial way.


title: Thrill: High-performance algorithmic distributed batch data processing with C++

abstract: We present the design and a first performance evaluation of Thrill -- a prototype of a general purpose big data processing framework with a convenient data-flow style programming interface. Thrill is somewhat similar to Apache Spark and Apache Flink with at least two main differences. First, Thrill is based on C++ which enables performance advantages due to direct native code compilation, a more cache-friendly memory layout, and explicit memory management. In particular, Thrill uses template meta-programming to compile chains of subsequent local operations into a single binary routine without intermediate buffering and with minimal indirections. Second, Thrill uses arrays rather than multisets as its primary data structure which enables additional operations like sorting, prefix sums, window scans, or combining corresponding fields of several arrays (zipping). We compare Thrill with Apache Spark and Apache Flink using five kernels from the HiBench suite. Thrill is consistently faster and often several times faster than the other frameworks. At the same time, the source codes have a similar level of simplicity and abstraction


title: Real-Time Classification of Twitter Trends

abstract: In this work, we explore the types of triggers that spark trends on Twitter, introducing a typology with the following 4 types: news, ongoing events, memes, and commemoratives. While previous research has analyzed trending topics over the long term, we look at the earliest tweets that produce a trend, with the aim of categorizing trends early on. This allows us to provide a filtered subset of trends to end users. We experiment with a set of straightforward language-independent features based on the social spread of trends and categorize them using the typology. Our method provides an efficient way to accurately categorize trending topics without need of external data, enabling news organizations to discover breaking news in real-time, or to quickly identify viral memes that might inform marketing decisions, among others. The analysis of social features also reveals patterns associated with each type of trend, such as tweets about ongoing events being shorter as many were likely sent from mobile devices, or memes having more retweets originating from a few trend-setters.


title: Signature schemes and anonymous credentials from bilinear maps

abstract: We propose a new and efficient signature scheme that is provably secure in the plain model. The security of our scheme is based on a discrete-logarithm-based assumption put forth by Lysyanskaya, Rivest, Sahai, and Wolf (LRSW) who also showed that it holds for generic groups and is independent of the decisional Diffie-Hellman assumption. We prove security of our scheme under the LRSW assumption for groups with bi-linear maps. We then show how our scheme can be used to construct efficient anonymous credential systems as well as group signature and identity escrow schemes. To this end, we provide efficient protocols that allow one to prove in zero-knowledge the knowledge of a signature on a committed (or encrypted) message and to obtain a signature on a committed message.


title: ShoeSoleSense: proof of concept for a wearable foot interface for virtual and real environments

abstract: ShoeSoleSense is a proof of concept, novel body worn interface - an insole that enables location independent hands-free interaction through the feet. Forgoing hand or finger interaction is especially beneficial when the user is engaged in real world tasks. In virtual environments as moving through safety training applications is often conducted via finger input, which is not very suitable. To enable a more intuitive interaction, alternative control concepts utilize gesture control, which is usually tracked by statically installed cameras in CAVE-like-installations. Since tracking coverage is limited, problems may also occur. The introduced prototype provides a novel control concept for virtual reality as well as real life applications. Demonstrated functions include movement control in a virtual reality installation such as moving straight, turning and jumping. Furthermore the prototype provides additional feedback by heating up the feet and vibrating in dedicated areas on the surface of the insole.


title: Clustering scientific publications based on citation relations: A systematic comparison of different methods

abstract: Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.


title: Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI

abstract: OBJECTIVES ::: (1) To develop a fully automated algorithm for segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), excluding intermuscular adipose tissue (IMAT) and bone marrow (BM), from axial abdominal magnetic resonance imaging (MRI) data. (2) To evaluate the algorithm accuracy and total method reproducibility using a semi-automatically segmented reference and data from repeated measurements. ::: ::: ::: BACKGROUND ::: MRI is a widely used in adipose tissue (AT) assessment. Manual analysis of MRI data is time consuming and biased by the operator. Automated analysis spares resources and increase reproducibility. Fully automated algorithms have been presented. However, reproducibility analysis has not been performed nor has methods for exclusion of IMAT and BM been presented. ::: ::: ::: METHODS ::: In total, 49 data sets from 31 subjects were acquired using a clinical 1.5 T MRI scanner. Thirteen data sets were used in the derivation of the automated algorithm and 36 were used in the validation. Common image analysis tools such as thresholding, morphological operations and geometrical models were used to segment VAT and SAT. Accuracy was assessed using a semi-automatically created reference. Reproducibility was assessed from repeated measurements. ::: ::: ::: RESULTS ::: Resulting AT volumes from the automated analysis and the reference were not found to differ significantly (2.0+/-14% and 0.84+/-2.7%, given as mean+/-s.d., for VAT and SAT, respectively). The automated analysis of the repeated measurements data significantly increased the reproducibility of the VAT measurements. One athletic subject with very small amounts of AT was considered to be an outlier. ::: ::: ::: CONCLUSIONS ::: An automated method for segmentation of VAT and SAT and exclusion of IMAT and BM from abdominal MRI data has been reported. The accuracy and reproducibility of the method has also been demonstrated using a semi-automatically segmented reference and analysis of repeated acquisitions. The accuracy of the method is limited in lean subjects.


An Integrated Framework for Opinion Mining and Summarization of Hotel Reviews


title: Single-photon 3D imaging with deep sensor fusion

abstract: We propose a data-driven method for photon-efficient 3D imaging using sensor fusion and computational reconstruction to rapidly and robustly estimate a dense depth map from low photon counts.