title: Programmer-based fault prediction

abstract: Background: Previous research has provided evidence that a combination of static code metrics and software history metrics can be used to predict with surprising success which files in the next release of a large system will have the largest numbers of defects. In contrast, very little research exists to indicate whether information about individual developers can profitably be used to improve predictions. Aims: We investigate whether files in a large system that are modified by an individual developer consistently contain either more or fewer faults than the average of all files in the system. The goal of the investigation is to determine whether information about which particular developer modified a file is able to improve defect predictions. We also continue an earlier study to evaluate the use of counts of the number of developers who modified a file as predictors of the file's future faultiness. Method: We analyzed change reports filed by 107 programmers for 16 releases of a system with 1,400,000 LOC and 3100 files. A "bug ratio" was defined for programmers, measuring the proportion of faulty files in release R out of all files modified by the programmer in release R-1. The study compares the bug ratios of individual programmers to the average bug ratio, and also assesses the consistency of the bug ratio across releases for individual programmers. Results: Bug ratios varied widely among all the programmers, as well as for many individual programmers across all the releases that they participated in. We found a statistically significant correlation between the bug ratios for programmers for the first half of changed files versus the ratios for the second half, indicating a measurable degree of persistence in the bug ratio. However, when the computation was repeated with the bug ratio controlled not only by release, but also by file size, the correlation disappeared. In addition to the bug ratios, we confirmed that counts of the cumulative number of different developers changing a file over its lifetime can help to improve predictions, while other developer counts are not helpful. Conclusions: The results from this preliminary study indicate that adding information to a model about which particular developer modified a file is not likely to improve defect predictions. The study is limited to a single large system, and its results may not hold more widely. The bug ratio is only one way of measuring the "fault-proneness" of an individual programmer's coding, and we intend to investigate other ways of evaluating bug introduction by individuals.


title: Design of a Baggage Handling System

abstract: In a previous paper we have shown how the design of an object processing system can be reduced to a graph embedding problem. Now we apply the transformations found there to a particular system, namely a Baggage Handling System (BHS) of airports, focusing especially on the sorting processors area, as one of the main challenging points. By means of an historical case study, we demonstrate how the method can be successfully applied.


title: Middleware Platform for Distributed Applications Incorporating Robots, Sensors and the Cloud

abstract: Cyber-physical systems (CPS) in the factory of thefuture will consist of cloud-hosted software governing an agileproduction process that is executed by mobile robots and thatis controlled by analyzing the data from a vast number ofsensors. CPSs thus operate on a distributed production floorinfrastructure and the set-up continuously changes with eachnew manufacturing task. In this paper, we present our OSGibasedmiddleware that abstracts the deployment of servicebasedCPS software components on a distributed platformcomprising robots, actuators, sensors and the cloud. Moreover, our middleware provides specific support to develop componentsbased on artificial neural networks, a technique that recentlybecame very popular for sensor data analytics and robot control. We demonstrate a system where a robot takes actions based onthe input from sensors in its vicinity.


title: Transcription of Arabic names into Latin

abstract: Transcription and transliteration are experiencing significant growth due to the increasingly multilingual Internet and to the exponential needs in the field of cross-lingual information retrieval. This is especially true for finding named entities (names of persons, places, companies, organizations, etc.), but these entities have a plurality of forms, spellings, and transcripts depending on languages and countries. The case of Arabic names illustrates this complex and multifaceted situation. In this article, we will briefly introduce the theoretical and practical difficulties that arise in the transcription and transliteration of Arabic names into Latin characters, as well as possible solutions and processing that can solve these difficulties.


title: Inhibition of interferon signaling by dengue virus

abstract: Dengue virus is a worldwide-distributed mosquito-borne flavivirus with a positive strand RNA genome. Its transcribed polyprotein is cleaved by host- and virus-encoded peptidases into 10 proteins, some of which are of unknown function. Although dengue virus-infected cells seem to be resistant to the antiviral action of IFN, the viral products that mediate this resistance are unknown. Therefore, we have analyzed the ability of the 10 dengue virus-encoded proteins to antagonize the IFN response. We found that expression in human A549 cells of the dengue virus nonstructural proteins NS2A, NS4A, or NS4B enhances replication of an IFN-sensitive virus. Moreover, expression of NS4B and, to a lesser extent, of NS2A and NS4A proteins results in down-regulation of IFN-beta-stimulated gene expression. Cells expressing NS4B or infected with dengue virus do not exhibit nuclear signal transducer and activator of transcription (STAT) 1 on treatment with IFN-beta or IFN-gamma, indicating that NS4B might be involved in blocking IFN signaling during dengue virus infections. This protein, encoded by a positive strand RNA virus, is implicated as an IFN-signaling inhibitor.


title: The impact of data aggregation in wireless sensor networks

abstract: Sensor networks are distributed event-based systems that differ from traditional communication networks in several ways: sensor networks have severe energy constraints, redundant low-rate data, and many-to-one flows. Data-centric mechanisms that perform in-network aggregation of data are needed in this setting for energy-efficient information flow. In this paper we model data-centric routing and compare its performance with traditional end-to-end routing schemes. We examine the impact of source-destination placement and communication network density on the energy costs and delay associated with data aggregation. We show that data-centric routing offers significant performance gains across a wide range of operational scenarios. We also examine the complexity of optimal data aggregation, showing that although it is an NP-hard problem in general, there exist useful polynomial-time special cases.


title: SQL on Structurally-Encrypted Databases

abstract: We show how to encrypt a relational database in such a way that it can efficiently support a large class of SQL queries. Our construction is based solely on structured encryption (STE) and does not make use of any property-preserving encryption (PPE) schemes such as deterministic and order-preserving encryption. As such, our approach leaks considerably less than PPE-based solutions which have recently been shown to reveal a lot of information in certain settings (Naveed et al., CCS ’15). Our construction is efficient and—under some conditions on the database and queries—can have asymptotically-optimal query complexity. We also show how to extend our solution to be dynamic while maintaining the scheme’s optimal query complexity.


title: Thinking Fast and Slow with Deep Learning and Tree Search

abstract: Sequential decision making problems, such as structured prediction, robotic control, and game playing, require a combination of planning policies and generalisation of those plans. In this paper, we present Expert Iteration (ExIt), a novel reinforcement learning algorithm which decomposes the problem into separate planning and generalisation tasks. Planning new policies is performed by tree search, while a deep neural network generalises those plans. Subsequently, tree search is improved by using the neural network policy to guide search, increasing the strength of new plans. In contrast, standard deep Reinforcement Learning algorithms rely on a neural network not only to generalise plans, but to discover them too. We show that ExIt outperforms REINFORCE for training a neural network to play the board game Hex, and our final tree search agent, trained tabula rasa, defeats MoHex, the previous state-of-the-art Hex player.


title: HeteroMed: Heterogeneous Information Network for Medical Diagnosis

abstract: With the recent availability of Electronic Health Records (EHR) and great opportunities they offer for advancing medical informatics, there has been growing interest in mining EHR for improving quality of care. Disease diagnosis due to its sensitive nature, huge costs of error, and complexity has become an increasingly important focus of research in past years. Existing studies model EHR by capturing co-occurrence of clinical events to learn their latent embeddings. However, relations among clinical events carry various semantics and contribute differently to disease diagnosis which gives precedence to a more advanced modeling of heterogeneous data types and relations in EHR data than existing solutions. To address these issues, we represent how high-dimensional EHR data and its rich relationships can be suitably translated into HeteroMed, a heterogeneous information network for robust medical diagnosis. Our modeling approach allows for straightforward handling of missing values and heterogeneity of data. HeteroMed exploits metapaths to capture higher level and semantically important relations contributing to disease diagnosis. Furthermore, it employs a joint embedding framework to tailor clinical event representations to the disease diagnosis goal. To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis. Experimental results of our study show superior performance of HeteroMed compared to prior methods in prediction of exact diagnosis codes and general disease cohorts. Moreover, HeteroMed outperforms baseline models in capturing similarities of clinical events which are examined qualitatively through case studies.


title: An architecture and implement model for Model-View-Presenter pattern

abstract: Model-View-Presenter (MVP) is a pattern which aimed at providing a cleaner separation between the View, the Model and the Presenter. The paper advances an architecture model of MVP pattern on .NET platform and a formal method of how to implement it. After that, an example of implement MVP pattern to web application or desktop application is cited.


title: An Evaluation of Distributed Concurrency Control

abstract: Increasing transaction volumes have led to a resurgence of interest in distributed transaction processing. In particular, partitioning data across several servers can improve throughput by allowing servers to process transactions in parallel. But executing transactions across servers limits the scalability and performance of these systems. ::: ::: In this paper, we quantify the effects of distribution on concurrency control protocols in a distributed environment. We evaluate six classic and modern protocols in an in-memory distributed database evaluation framework called Deneva, providing an apples-to-apples comparison between each. Our results expose severe limitations of distributed transaction processing engines. Moreover, in our analysis, we identify several protocol-specific scalability bottlenecks. We conclude that to achieve truly scalable operation, distributed concurrency control solutions must seek a tighter coupling with either novel network hardware (in the local area) or applications (via data modeling and semantically-aware execution), or both.


title: Detecting sources of computer viruses in networks: theory and experiment

abstract: We provide a systematic study of the problem of finding the source of a computer virus in a network. We model virus spreading in a network with a variant of the popular SIR model and then construct an estimator for the virus source. This estimator is based upon a novel combinatorial quantity which we term rumor centrality. We establish that this is an ML estimator for a class of graphs. We find the following surprising threshold phenomenon: on trees which grow faster than a line, the estimator always has non-trivial detection probability, whereas on trees that grow like a line, the detection probability will go to 0 as the network grows. Simulations performed on synthetic networks such as the popular small-world and scale-free networks, and on real networks such as an internet AS network and the U.S. electric power grid network, show that the estimator either finds the source exactly or within a few hops in different network topologies. We compare rumor centrality to another common network centrality notion known as distance centrality. We prove that on trees, the rumor center and distance center are equivalent, but on general networks, they may differ. Indeed, simulations show that rumor centrality outperforms distance centrality in finding virus sources in networks which are not tree-like.


title: Unsuccessful Retrieval Attempts Enhance Subsequent Learning

abstract: Taking tests enhances learning. But what happens when one cannot answer a test question—does an unsuccessful retrieval attempt impede future learning or enhance it? The authors examined this question using materials that ensured that retrieval attempts would be unsuccessful. In Experiments 1 and 2, participants were asked fictional general-knowledge questions (e.g., “What peace treaty ended the Calumet War?”). In Experiments 3–6, participants were shown a cue word (e.g., whale) and were asked to guess a weak associate (e.g., mammal); the rare trials on which participants guessed the correct response were excluded from the analyses. In the test condition, participants attempted to answer the question before being shown the answer; in the read-only condition, the question and answer were presented together. Unsuccessful retrieval attempts enhanced learning with both types of materials. These results demonstrate that retrieval attempts enhance future learning; they also suggest that taking challenging tests—instead of avoiding errors—may be one key to effective learning.


title: Predicting Recidivism Among Adult Male Child Pornography Offenders: Development of the Child Pornography Offender Risk Tool (CPORT)

abstract: In this study, we developed a structured risk checklist, the Child Pornography Offender Risk Tool (CPORT), to predict any sexual recidivism among adult male offenders with a conviction for child pornography offenses. We identified predictors of sexual recidivism using a 5-year fixed follow-up analysis from a police case file sample of 266 adult male child pornography offenders in the community after their index offense. In our 5-year follow-up, 29% committed a new offense, and 11% committed a new sexual offense, with 3% committing a new contact sexual offense against a child and 9% committing a new child pornography offense. The CPORT items comprised younger offender age, any prior criminal history, any contact sexual offending, any failure on conditional release, indication of sexual interest in child pornography material or prepubescent or pubescent children, more boy than girl content in child pornography, and more boy than girl content in other child depictions. The CPORT was significantly associated with any sexual recidivism, with moderate predictive accuracy, and thus has promise in the risk assessment of adult male child pornography offenders with further cross-validation.


title: A Framework for Batched and GPU-Resident Factorization Algorithms Applied to Block Householder Transformations

abstract: As modern hardware keeps evolving, an increasingly effective approach to developing energy efficient and high-performance solvers is to design them to work on many small size and independent problems. Many applications already need this functionality, especially for GPUs, which are currently known to be about four to five times more energy efficient than multicore CPUs. We describe the development of one-sided factorizations that work for a set of small dense matrices in parallel, and we illustrate our techniques on the QR factorization based on Householder transformations. We refer to this mode of operation as a batched factorization. Our approach is based on representing the algorithms as a sequence of batched BLAS routines for GPU-only execution. This is in contrast to the hybrid CPU-GPU algorithms that rely heavily on using the multicore CPU for specific parts of the workload. But for a system to benefit fully from the GPU’s significantly higher energy efficiency, avoiding the use of the multicore CPU must be a primary design goal, so the system can rely more heavily on the more efficient GPU. Additionally, this will result in the removal of the costly CPU-to-GPU communication. Furthermore, we do not use a single symmetric multiprocessor (on the GPU) to factorize a single problem at a time. We illustrate how our performance analysis, and the use of profiling and tracing tools, guided the development and optimization of our batched factorization to achieve up to a 2-fold speedup and a 3-fold energy efficiency improvement compared to our highly optimized batched CPU implementations based on the MKL library (when using two sockets of Intel Sandy Bridge CPUs). Compared to a batched QR factorization featured in the CUBLAS library for GPUs, we achieved up to \(5\times \) speedup on the K40 GPU.


title: Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

abstract: Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior work has focused on computing Nash equilibria in a handcrafted abstraction of the domain. In this paper we introduce the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge. Our method combines fictitious self-play with deep reinforcement learning. When applied to Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium, whereas common reinforcement learning methods diverged. In Limit Texas Holdem, a poker game of real-world scale, NFSP learnt a strategy that approached the performance of state-of-the-art, superhuman algorithms based on significant domain expertise.


title: Improving Nepali News Recommendation Using Classification Based on LSTM Recurrent Neural Networks

abstract: News classification is the process of grouping news documents into some predefined categories. Due to the increasing volume of the Nepali news content being generated every day by thousands of online news portals, appropriate classification of these news items has become a necessity for the news readers. This research was targeted to improve the Nepali news classification based on Recurrent Neural Networks, that uses deep layers of neural networks to classify the news to an appropriate category. In this research paper, five popular news portals website across eight different categories was used for the purpose of data gathering and their classification accuracies were compared among these websites as well as overall accuracy was measured. The model was compared with the Support Vector Machine based on the parameters Accuracy, Precision, Recall and F1 Score. The use of Long Short Term Memory Recurrent Neural Network has improved the precision with the use of word2vec model. The presented model in the research has achieved a good accuracy of 84.63% and precision of 89% in compared to the SVM where the accuracy was 81.41% and precision 85%. Based on the categories of the news, sports news was classified more accurately by the model and economy was least accurately classified.


title: Reciprocal collision avoidance for quadrotors using on-board visual detection

abstract: In this paper we present a collision avoidance system based on visual detection. Our hardware consists of a Hummingbird quadrotor equipped with a large red marker with two built-in fish-eye cameras. Fusion of the measurements from the two cameras is done using a Gaussian-mixture probability hypothesis density filter, which allows for tracking several aircrafts at the same time. Our collision avoidance algorithm is based on navigation functions designed to cope with cameras characterized by limited field of view. Its mathematical correctness has been proven in a former paper [1]. The collision avoidance maneuver is performed without the vehicles explicitly exchanging information via communication but instead relying solely on on-board sensors. Our system has been validated in an indoor space with four different collision scenarios. Trajectory data was recorded with an external motion capture system and demonstrate good robustness against sensing noise.


title: A Case of Diprosopus: Perinatal Counseling and Management

abstract: Diprosopus is a rare congenital malformation associated with high mortality. Here, we describe a patient with diprosopus, multiple life-threatening anomalies, and genetic mutations. Prenatal diagnosis and counseling made a beneficial impact on the family and medical providers in the care of this case.


title: Prepositions in Context

abstract: Prepositions are highly polysemous, and their variegated senses encode significant semantic information. In this paper we match each preposition's complement and attachment and their interplay crucially to the geometry of the word vectors to the left and right of the preposition. Extracting such features from the vast number of instances of each preposition and clustering them makes for an efficient preposition sense disambigution (PSD) algorithm, which is comparable to and better than state-of-the-art on two benchmark datasets. Our reliance on no external linguistic resource allows us to scale the PSD algorithm to a large WikiCorpus and learn sense-specific preposition representations -- which we show to encode semantic relations and paraphrasing of verb particle compounds, via simple vector operations.


title: Hidden style in the city: an analysis of geolocated airbnb rental images in ten major cities

abstract: In this article, we analyze geolocated Airbnb rental images in ten major cities. Airbnb is a hallmark institution in the sharing economy, allowing anyone with a bed and shelter to act like a micro-hotel, i.e. a bed-and-breakfast for other travelers. Travelers often spend less on Airbnb rentals than hotels and get a residential experience in a new place. Since hosts advertise their rentals on Airbnb, the site has a wealth of residential interior images from all over the world: from rural Africa to downtown Manhattan. As part of an ongoing project, we have downloaded over 200,000 images posted on Airbnb to ask: how do people decorate their homes in different locales? Do they use certain colors, or have a certain ornate or simple style? Here, we test ten major metropolitan areas using image rating responses from Mechanical Turk as well as automated image color predominance routines to investigate geographical differences in interior styles. We find overarching indicators of globalization and a lack of local culture in the case of color, but that different neighborhoods within cities have different levels or ornateness when decorating their properties. The results of this research can also help to identify the kinds of interiors that are more pleasant in the eyes of customers.


Factors Affecting Trust in Market


title: Behavioural beliefs of Ghanaian radiographers and reporting of child physical abuse

abstract: Abstract Introduction Radiographers are well placed to flag non accidental injury in children due to their unique position within the imaging chain. Being able to identify (or suspect) physical abuse in children and reporting the incident are, however, two different issues. This study was conducted to explore the external influences in the decision making of the Ghanaian radiographer to report suspected child physical abuse (CPA). Method This was a qualitative study which applied interpretive phenomenology. Semi-structured interviews were conducted with 20 radiographers who were selected from various hospitals throughout the ten regions of Ghana using purposive sampling. Data was thematically analysed and managed with NVivo Version 10. Themes developed formed the basis of this discussion. Results Several socio-cultural beliefs and behaviours impacted on the Ghanaian radiographers' decisions to report suspected child physical abuse. The findings of this study indicated that cultural solidarity, superstition and police frustrations were among other factors that characterised the Ghanaian radiographer's inability to report child physical abuse when it occurred. Conclusion Radiographers reported fear of both physical and spiritual attack when child physical abuse was reported. This paper argues that, to achieve the fight against child physical abuse in some African countries such as Ghana, radiographers would have to be educated and counselled against belief in superstition and adherence to some cultural values which affect child protection.


title: A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

abstract: To achieve general intelligence, agents must learn how to interact with others in a shared environment: this is the challenge of multiagent reinforcement learning (MARL). The simplest form is independent reinforcement learning (InRL), where each agent treats its experience as part of its (non-stationary) environment. In this paper, we first observe that policies learned using InRL can overfit to the other agents' policies during training, failing to sufficiently generalize during execution. We introduce a new metric, joint-policy correlation, to quantify this effect. We describe an algorithm for general MARL, based on approximate best responses to mixtures of policies generated using deep reinforcement learning, and empirical game-theoretic analysis to compute meta-strategies for policy selection. The algorithm generalizes previous ones such as InRL, iterated best response, double oracle, and fictitious play. Then, we present a scalable implementation which reduces the memory requirement using decoupled meta-solvers. Finally, we demonstrate the generality of the resulting policies in two partially observable settings: gridworld coordination games and poker.


title: Platelet-rich plasma vs hyaluronic acid to treat knee degenerative pathology: study design and preliminary results of a randomized controlled trial

abstract: Platelet Rich Plasma (PRP), a blood-derived product rich in growth factors, is a promising treatment for cartilage defects but there is still a lack of clinical evidence. The aim of this study is to show, through a randomized double blind prospective trial, the efficacy of this procedure, by comparing PRP to Hyaluronic Acid (HA) injections for the treatment of knee chondropathy or osteoarthritis (OA). 109 patients (55 treated with HA and 54 with PRP) were treated and evaluated at 12 months of follow-up. The patients were enrolled according to the following inclusion criteria: age> 18 years, history of chronic (at least 4 months) pain or swelling of the knee and imaging findings of degenerative changes of the joint (Kellgren-Lawrence Score up to 3). A cycle of 3 weekly injections was administered blindly. All patients were prospectively evaluated before and at 2, 6, and 12 months after the treatment by: IKDC, EQ-VAS, TEGNER, and KOOS scores. Range of motion and knee circumference changes were measured over time. Adverse events and patient satisfaction were also recorded. Only minor adverse events were detected in some patients, such as mild pain and effusion after the injections, in particular in the PRP group, where a significantly higher post-injective pain reaction was observed (p=0.039). At the follow-up evaluations, both groups presented a clinical improvement but the comparison between the two groups showed a not statistically significant difference in all scores evaluated. A trend favorable for the PRP group was only found in patients with low grade articular degeneration (Kellgren-Lawrence score up to 2). Results suggest that PRP injections offer a significant clinical improvement up to one year of follow-up. However, conversely to what was shown by the current literature, for middle-aged patients with moderate signs of OA, PRP results were not better than those obtained with HA injections, and thus it should not be considered as first line treatment. More promising results are shown for its use in low grade degeneration, but they still have to be confirmed.


title: Reliable image matching via modified Hausdorff distance with normalized gradient consistency measure

abstract: Reliable image matching is important to many problems in computer vision, image processing and pattern recognition. Hausdorff distance and many of its variations have been employed for image matching with success. In this paper we propose an improved image matching method based on a modified Hausdorff distance with normalized gradient consistency measure. The proposed new image matching algorithm integrates the geometric Hausdorff distance with the photometric intensity gradient information to obtain a better image similarity measure. To show the improvement of the proposed algorithm, we test it with some previous image matching methods on the problem of face recognition under lighting changes. Experimental results show the proposed method produces more accurate face recognition than the previous methods.


title: Applying multiple time series data mining to large-scale network traffic analysis

abstract: Minimize false positive and false negative is one of the difficult problems of network traffic analysis. This paper propose a large-scale communications network traffic feature analysis method using multiple time series data mining, analyze multiple traffic feature time series as a whole, produce valid association rules of abnormal network traffic feature, characterize the entire communication network security situation accurately. Experiment with Abilene network data verify this method.


title: RF power harvesting: a review on designing methodologies and applications

abstract: Wireless power transmission was conceptualized nearly a century ago. Certain achievements made to date have made power harvesting a reality, capable of providing alternative sources of energy. This review provides a summ ary of radio frequency (RF) power harvesting technologies in order to serve as a guide for the design of RF energy harvesting units. Since energy harvesting circuits are designed to operate with relatively small voltages and currents, they rely on state-of-the-art electrical technology for obtaining high efficiency. Thus, comprehensive analysis and discussions of various designs and their tradeoffs are included. Finally, recent applications of RF power harvesting are outlined.


title: Enabling deep-tissue networking for miniature medical devices

abstract: We present IVN (In-Vivo Networking), a system that enables powering up and communicating with miniature sensors implanted or injected in deep tissues. IVN overcomes fundamental challenges which have prevented past systems from powering up miniature sensors beyond superficial depths. These challenges include the significant signal attenuation caused by bodily tissues and the miniature antennas of the implantable sensors. IVN's key contribution is a novel beamforming algorithm that can focus its energy toward an implantable device, despite its inability to estimate its channel or its location. We implement a multi-antenna prototype of IVN, and perform extensive evaluations via in-vitro, ex-vivo, and in-vivo tests in a pig. Our results demonstrate that it can power up and communicate with millimeter-sized sensors at over 10 cm depths in fluids, as well as battery-free tags placed in a central organ of a swine. The implications of our new beamforming technology extend beyond miniature implantables. In particular, our results demonstrate that IVN can power up off-the-shelf passive RFIDs at distances of 38 m, i.e., 7.6X larger than the operation range of the same RFIDs.


title: Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net

abstract: There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation. Both 2D and 3D models have their advantages and disadvantages. In this paper, we present an alternative framework, which trains 2D networks on different viewpoints for segmentation, and builds a 3D Volumetric Fusion Net (VFN) to fuse the 2D segmentation results. VFN is relatively shallow and contains much fewer parameters than most 3D networks, making our framework more efficient at integrating 3D information for segmentation. We train and test the segmentation and fusion modules individually, and propose a novel strategy, named cross-cross-augmentation, to make full use of the limited training data. We evaluate our framework on several challenging abdominal organs, and verify its superiority in segmentation accuracy and stability over existing 2D and 3D approaches.


title: Photo Filter Recommendation by Category-Aware Aesthetic Learning

abstract: Nowadays, social media has become a popular platform for the public to share photos. To make photos more visually appealing, users usually apply filters on their photos without domain knowledge. However, due to the growing number of filter types, it becomes a major issue for users to choose the best filter type. For this purpose, filter recommendation for photo aesthetics takes an important role in image quality ranking problems. In these years, several works have declared that Convolutional Neural Networks (CNNs) outperform traditional methods in image aesthetic categorization, which classifies images into high or low quality. Most of them do not consider the effect on filtered images; hence, we propose a novel image aesthetic learning for filter recommendation. Instead of binarizing image quality, we adjust the state-of-the-art CNN architectures and design a pairwise loss function to learn the embedded aesthetic responses in hidden layers for filtered images. Based on our pilot study, we observe image categories (e.g., portrait, landscape, food) will affect user preference on filter selection. We further integrate category classification into our proposed aesthetic-oriented models. To the best of our knowledge, there is no public dataset for aesthetic judgment with filtered images. We create a new dataset called Filter Aesthetic Comparison Dataset (FACD). It contains 28,160 filtered images based on the AVA dataset and 42,240 reliable image pairs with aesthetic annotations using Amazon Mechanical Turk. It is the first dataset containing filtered images and user preference labels. We conduct experiments on the collected FACD for filter recommendation, and the results show that our proposed category-aware aesthetic learning outperforms aesthetic classification methods (e.g., 12% relative improvement).


title: Seismic Facies Classification And Identification By Competitive Neural Networks

abstract: We present an approach based on competitive neural networks for the classification and identification of reservoir facies from seismic data. This approach can be adapted to perform either classification of the seismic facies based entirely on the characteristics of the seismic response, without requiring the use of any well information, or automatic identification and labeling of the facies where well information is available. The former is of prime use for oil prospecting in new regions, where few or no wells have been drilled, whereas the latter is most useful in development fields, where the information gained at the wells can be conveniently extended to the interwell regions. Cross-validation tests on synthetic and real seismic data demonstrated that the method can be an effective means of mapping the reservoir heterogeneity. For synthetic data, the output of the method showed considerable agreement with the actual geologic model used to generate the seismic data; for the real data application, the predicted facies accurately matched those observed at the wells. Moreover, the resulting map corroborates our existing understanding of the reservoir and shows substantial similarity to the low-frequency geologic model constructed by interpolating the well information, while adding significant detail and enhanced resolution to that model.


title: Self-inflicted burns in patients with chronic combat-related post-traumatic stress disorder.

abstract: This study examined self-inflicted burns in case series of four patients with chronic combat-related post-traumatic stress disorder (PTSD). Those patients were hospitalized in the Burn Unit of the University Hospital of Traumatology in Zagreb because of severe burns and had a premorbid psychiatric history of PTSD. Demographic data and information regarding the circumstances surrounding the incident, burn severity, treatment and outcomes of these patients were collected. The authors have analyzed possible impacts of the sensationalistic way in which media present cases of self-inflicted burning that induce other, new cases of this suicide type, known in the literature as "Werther's syndrome". The importance of multidisciplinary approach in the treatment of burn patients is stressed with emphasis on the important role of liaison psychiatrist in treating these patients. It is necessary to educate media people to avoid sensational reporting on this kind of events. Continuous psychiatric treatment of vulnerable individuals could be useful in prevention of self-inflicted burns.


title: Getting users to pay attention to anti-phishing education: evaluation of retention and transfer

abstract: Educational materials designed to teach users not to fall for phishing attacks are widely available but are often ignored by users. In this paper, we extend an embedded training methodology using learning science principles in which phishing education is made part of a primary task for users. The goal is to motivate users to pay attention to the training materials. In embedded training, users are sent simulated phishing attacks and trained after they fall for the attacks. Prior studies tested users immediately after training and demonstrated that embedded training improved users' ability to identify phishing emails and websites. In the present study, we tested users to determine how well they retained knowledge gained through embedded training and how well they transferred this knowledge to identify other types of phishing emails. We also compared the effectiveness of the same training materials delivered via embedded training and delivered as regular email messages. In our experiments, we found that: (a) users learn more effectively when the training materials are presented after users fall for the attack (embedded) than when the same training materials are sent by email (non-embedded); (b) users retain and transfer more knowledge after embedded training than after non-embedded training; and (c) users with higher Cognitive Reflection Test (CRT) scores are more likely than users with lower CRT scores to click on the links in the phishing emails from companies with which they have no account.


title: Saliency detection based on frequency and spatial domain analyses

abstract: We propose a new saliency detection model by combining global information from frequency domain analysis and local information from spatial domain analysis. In the frequency domain analysis, instead of modeling salient regions, we model the nonsalient regions using global information; these so-called repeating patterns that are not distinctive in the scene are suppressed by using spectrum smoothing. In spatial domain analysis, we enhance those regions that are more informative by using a center-surround mechanism similar to that found in the visual cortex. Finally, the outputs from these two channels are combined to produce the saliency map. We demonstrate that the proposed model has the ability to highlight both small and large salient regions in cluttered scenes and to inhibit repeating objects. Experimental results also show that the proposed model outperforms existing algorithms in predicting objects regions where human pay more attention.


title: A Cloud-based monitoring framework for Smart Home

abstract: Today, Smart Home monitoring services have attracted much attention from both academia and industry. However, in the conventional monitoring mechanism the remote camera can not be accessed for remote monitoring anywhere and anytime. Besides, traditional approaches might have the limitation in local storage due to lack of device elasticity. In this paper, we proposed a Cloud-based monitoring framework to implement the remote monitoring services of Smart Home. The main technical issues considered include Data-Cloud storage, Local-Cache mechanism, Media device control, NAT traversal, etc. The implementation shows three use scenarios: (a) operating and controlling video cameras for remote monitoring through mobile devices or sound sensors; (b) streaming live video from cameras and sending captured image to mobile devices; (c) recording videos and images on a cloud computing platform for future playback. This system framework could be extended to other applications of Smart Home.


title: Forensic Application-Fingerprinting Based on File System Metadata

abstract: While much work has been invested in tools for aquisition and extraction of digital evidence, there are only few tools that allow for automatic event reconstruction. In this paper, we present a generic approach for forensic event reconstruction based on digital evidence from file systems. Our approach applies the idea of fingerprinting to changes made by applications in file system metadata. We present a system with which it is possible to automatically compute file system fingerprints of individual actions. Using NTFS timestamps as an example, we show that with our approach it is possible to automatically reconstruct actions performed by different applications even if the set of files accessed by those actions overlap.


title: Futhark: purely functional GPU-programming with nested parallelism and in-place array updates

abstract: Futhark is a purely functional data-parallel array language that offers a machine-neutral programming model and an optimising compiler that generates OpenCL code for GPUs. This paper presents the design and implementation of three key features of Futhark that seek a suitable middle ground with imperative approaches. First, in order to express efficient code inside the parallel constructs, we introduce a simple type system for in-place updates that ensures referential transparency and supports equational reasoning. Second, we furnish Futhark with parallel operators capable of expressing efficient strength-reduced code, along with their fusion rules. Third, we present a flattening transformation aimed at enhancing the degree of parallelism that (i) builds on loop interchange and distribution but uses higher-order reasoning rather than array-dependence analysis, and (ii) still allows further locality-of-reference optimisations. Finally, an evaluation on 16 benchmarks demonstrates the impact of the language and compiler features and shows application-level performance competitive with hand-written GPU code.


title: Tracking Interacting Objects Optimally Using Integer Programming

abstract: In this paper, we show that tracking different kinds of interacting objects can be formulated as a network-flow Mixed Integer Program. This is made possible by tracking all objects simultaneously and expressing the fact that one object can appear or disappear at locations where another is in terms of linear flow constraints. We demonstrate the power of our approach on scenes involving cars and pedestrians, bags being carried and dropped by people, and balls being passed from one player to the next in a basketball game. In particular, we show that by estimating jointly and globally the trajectories of different types of objects, the presence of the ones which were not initially detected based solely on image evidence can be inferred from the detections of the others.


title: Binary codes for tagging x-ray images via deep de-noising autoencoders

abstract: A Content-Based Image Retrieval (CBIR) system which identifies similar medical images based on a query image can assist clinicians for more accurate diagnosis. The recent CBIR research trend favors the construction and use of binary codes to represent images. Deep architectures could learn the non-linear relationship among image pixels adaptively, allowing the automatic learning of high-level features from raw pixels. However, most of them require class labels, which are expensive to obtain, particularly for medical images. The methods which do not need class labels utilize a deep autoencoder for binary hashing, but the code construction involves a specific training algorithm and an ad-hoc regularization technique. In this study, we explored using a deep de-noising autoencoder (DDA), with a new unsupervised training scheme using only backpropagation and dropout, to hash images into binary codes. We conducted experiments on more than 14,000 x-ray images. By using class labels only for evaluating the retrieval results, we constructed a 16-bit DDA and a 512-bit DDA independently. Comparing to other unsupervised methods, we succeeded to obtain the lowest total error by using the 512-bit codes for retrieval via exhaustive search, and speed up 9.27 times with the use of the 16-bit codes while keeping a comparable total error. We found that our new training scheme could reduce the total retrieval error significantly by 21.9%. To further boost the image retrieval performance, we developed Radon Autoencoder Barcode (RABC) which are learned from the Radon projections of images using a de-noising autoencoder. Experimental results demonstrated its superior performance in retrieval when it was combined with DDA binary codes.


title: Modeling Age-Friendly Environment, Active Aging, and Social Connectedness in an Emerging Asian Economy

abstract: This paper empirically tested eight key features of WHO guidelines to age-friendly community by surveying 211 informal caregivers and 402 self-care adults (aged 45 to 85 and above) in Malaysia. We examined the associations of these eight features with active aging and social connectedness through exploratory and confirmatory factor analyses. A structural model with satisfactory goodness-of-fit indices (CMIN/df = 1.11, RMSEA = 0.02, NFI = 0.97, TLI = 1.00, CFI = 1.00, and GFI = 0.96) indicates that transportation and housing, community support and health services, and outdoor spaces and buildings are statistically significant in creating an age-friendly environment. We found a statistically significant positive relationship between an age-friendly environment and active aging. This relationship is mediated by social connectedness. The results indicate that built environments such as accessible public transportations and housing, affordable and accessible healthcare services, and elderly friendly outdoor spaces and buildings have to be put into place before social environment in building an age-friendly environment. Otherwise, the structural barriers would hinder social interactions for the aged. The removal of the environmental barriers and improved public transportation services provide short-term solutions to meet the varied and growing needs of the older population.


title: The Flipped Classroom: An Opportunity to Engage Millennial Students through Active Learning

abstract: "Flipping" the classroom employs easy-to-use, readily accessihle technology in order to free class time from lecture. This allows for an expanded range of learning activities during class time. Using class time for active learning versus lecture provides opportunities for greater teacher-to-student mentoring, peer-to-peer collaboration and cross-disciplinary engagement. This review of literature addresses the challenges of engaging today's students in lecture-based classrooms and presents an argument for application of the "flipped classroom" model hy educators in the disciplines of family and consumer sciences.


title: Time-Location-Relationship Combined Service Recommendation Based on Taxi Trajectory Data

abstract: Recently, urban traffic management has encountered a paradoxical situation which is the empty carrying phenomenon for taxi drivers and the difficulty of taking a taxi for passengers. In this paper, through analyzing the quantitative relationship between passengers’ getting on and off taxis, we propose a time-location-relationship (TLR) combined taxi service recommendation model to improve taxi drivers’ profits, uncover the knowledge of human mobility patterns, and enhance passengers’ travel experience. Moreover, the TLR model uses Gaussian process regression and statistical approaches to acquire passenger volume, mean trip distance, and average trip time in functional regions during every period on weekdays and weekends, and allows drivers to pick up more passengers within a short time frame. Finally, we compare our proposed model with the autoregressive integrated moving average model, the back-propagation neural network model, the support vector machine model, and the gradient boost decision tree model by using the real taxi GPS data in Beijing. The experimental results show that our optimizing taxi service recommendation can predict more accurately than others by considering the 3-D properties.


title: Using Roget's Thesaurus for Fine-grained Emotion Recognition

abstract: Recognizing the emotive meaning of text can add another dimension to the understanding of text. We study the task of automatically categorizing sentences in a text into Ekman’s six basic emotion categories. We experiment with corpus-based features as well as features derived from two emotion lexicons. One lexicon is automatically built using the classification system of Roget’s Thesaurus, while the other consists of words extracted from WordNet-Affect. Experiments on the data obtained from blogs show that a combination of corpus-based unigram features with emotion-related features provides superior classification performance. We achieve Fmeasure values that outperform the rulebased baseline method for all emotion classes.


title: Fourth order L3C resonant converter for wide output voltage regulation

abstract: In order to fully cover the V-I plane of deep-cycle batteries, battery chargers should work under different loading conditions and output voltage, and should respond to different States of Charge (SoC). In this paper, a fourth order L3C resonant converter is introduced and employed as a battery charger. The analytical equation of the L3C voltage gain shows the possibility of regulating wide output voltage to an unprecedented degree within a narrow frequency range to cover the entire V-I charging plane of modern batteries. The topology integrates all of the parasitics elements into the resonant circuit, realizes ZVS for the inverter MOSFETs, and provides inherent over-current protection capacity. These advantages provided by the proposed L3C resonant converter make this topology an excellent candidate for use as a charger in Electric Vehicles. The experimental results extracted from a 220V DC; 550W resonant converter platform with a peak efficiency of 96:5% exhibit the excellent performance of the proposed converter for battery charger applications.


title: Modeling the high frequency behavior of the Rogowski-coil passive L/r integrator current transducer with analytical and finite element method

abstract: The Rogowski current transducers are widely used because they have several advantages over iron-cored devices: no saturation at high current, higher operating frequency and less error in wider measurement range. The main components of these current transducers are the measuring coil and integrator. Latter can be passive or active. The main advantage of passive integrator is the wider bandwidth. The simplest is the L/r integration method, where the coil itself makes the operation of integration. In this case the knowledge of the resistance and the inductance of the coil not enough to make a proper equivalent circuit model, because the upper limit of the frequency characteristic depends on the distributed capacitance of the winding. The paper [1] has showed a new analytical model, which takes the distributed capacitance of the windings into consideration. The knowledge of distributed capacitances enables to calculate the upper frequency bandwidth limit and can help to determine the phase error decrease at normal frequencies. This analytical model is based on the classical lumped parameter winding model [2], which originally designed for large power transformers and assumes continuous voltage distribution along the windings. In this paper, the authors present a finite element method for calculation of distributed capacitances of winding, where the discrete behavior of the turns is taken into consideration to make a novelty for the estimation of the upper frequency limit, which cannot be determined correctly from the analytical calculation.


title: Cost curves: An improved method for visualizing classifier performance

abstract: This paper introduces cost curves, a graphical technique for visualizing the performance (error rate or expected cost) of 2-class classifiers over the full range of possible class distributions and misclassification costs. Cost curves are shown to be superior to ROC curves for visualizing classifier performance for most purposes. This is because they visually support several crucial types of performance assessment that cannot be done easily with ROC curves, such as showing confidence intervals on a classifier's performance, and visualizing the statistical significance of the difference in performance of two classifiers. A software tool supporting all the cost curve analysis described in this paper is available from the authors.


title: SEPIC AC-DC converter for aircraft application

abstract: This paper presents an AC-DC converter design for applications where mains frequency is widely varied and low input current harmonics and output voltage regulation are required, for example, in aircraft power systems. The proposed design employs a Single Ended Primary Inductance Converter (SEPIC) at the DC-end of a single-phase bridge diode rectifier. In discontinuous conduction mode (DCM), the SEPIC converter is a good alternative to the conventional buck or boost converter, where a low input current harmonic content is achieved with proper inductor selection of the converter. Here, a 28V, 1kW converter design with variable mains frequency (360 to 800Hz) is presented. The system performance was examined through a Matlab/ Simulink model during both transient and steady state conditions.


title: Maintaining Sensing Coverage and Connectivity in Large Sensor Networks

abstract: In this paper, we address the issues of maintaining sensing coverage and connectivity by keeping a minimum number of sensor nodes in the active mode in wireless sensor networks. We investigate the relationship between coverage and connectivity by solving the following two sub-problems. First, we prove that if the radio range is at least twice the sensing range, complete coverage of a convex area implies connectivity among the working set of nodes. Second, we derive, under the ideal case in which node density is sufficiently high, a set of optimality conditions under which a subset of working sensor nodes can be chosen for complete coverage. Based on the optimality conditions, we then devise a decentralized density control algorithm, Optimal Geographical Density Control (OGDC), for density control in large scale sensor networks. The OGDC algorithm is fully localized and can maintain coverage as well as connectivity, regardless of the relationship between the radio range and the sensing range. Ns-2 simulations show that OGDC outperforms existing density control algorithms [25, 26, 29] with respect to the number of working nodes needed and network lifetime (with up to 50% improvement), and achieves almost the same coverage as the algorithm with the best result.


title: Phase-change memory technology for embedded applications

abstract: A novel p-trench phase-change memory (PCM) cell and its integration with a MOSFET selector in a standard 0.18 /spl mu/m CMOS technology are presented. The high-performance capabilities of PCM cells are experimentally investigated and their application in embedded systems is discussed. Write times as low as 10 ns and 20 ns have been measured for the RESET and SET operation, respectively, still granting a 10/spl times/ read margin. The impact of the RESET pulse on PCH cell endurance has been also evaluated. Finally, cell distributions and first statistical endurance measurements on a 4 Mbit MOS demonstrator clearly assess the feasibility of the PCM technology.


title: Output feedback assistive control of single-DOF SEA powered exoskeletons

abstract: Purpose ::: ::: ::: ::: ::: This paper aims to overcome some of the practical difficulties in assistive control of exoskeletons by developing a new assistive algorithm, called output feedback assistive control (OFAC) method. This method does not require feedbacks from force, electromyography (EMG) or acceleration signals or even their estimated values. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The presented controller uses feedbacks from position and velocity of the output link of series elastic actuators (SEAs) to increase the apparent integral admittance of the assisted systems. Optimal controller coefficients are obtained by maximizing the assistance ratio subjected to constraints of stability, coupled stability and a newly defined comfort measure. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The results confirm the effectiveness of using the inherent properties of SEAs for removing the need for extra controversial sensors in assistive control of 1 degree of freedom (1-DOF) SEA powered exoskeletons. The results also clearly indicate the successful performance of the OFAC method in reducing the external forces required for moving the assisted systems. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: As the provided experiments indicate, the proposed method can be easily applied to single DOF compliantly actuated exoskeletons to provide a more reliable assistance with lower costs. This is achieved by removing the need for extra controversial sensors. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This paper proposes a novel assistive controller for SEA-powered exoskeletons with a simple model-free structure and independent of any information about interaction forces and future paths of the system. It also removes the requirement for the extra sensors and transforms the assistive control of the compliantly actuated systems into a simpler problem of position control of the SEA motor.


title: Brain Networks of Explicit and Implicit Learning

abstract: Are explicit versus implicit learning mechanisms reflected in the brain as distinct neural structures, as previous research indicates, or are they distinguished by brain networks that involve overlapping systems with differential connectivity? In this functional MRI study we examined the neural correlates of explicit and implicit learning of artificial grammar sequences. Using effective connectivity analyses we found that brain networks of different connectivity underlie the two types of learning: while both processes involve activation in a set of cortical and subcortical structures, explicit learners engage a network that uses the insula as a key mediator whereas implicit learners evoke a direct frontal-striatal network. Individual differences in working memory also differentially impact the two types of sequence learning.


title: Retail adoption of Internet of Things: Applying TAM model

abstract: the purpose of this paper is to identify variables important to the acceptance and use of Internet of things by retail employees and to recommend management strategies for effective IoT integration in the retail stores. This study aims to extend the basic technology acceptance model (TAM) by analyzing the impact of trust, perceived behavior control and subjective norm on employee's attitude and behavioral intention towards adopting IoT in retail environments. Responses from retail employees were used to measure their perception of IoT in terms of usefulness, Ease of Use, trust, attitude towards IoT adoption, perceived behavior control and subjective norm and behavioral intention.


title: Performance investigation of selected NoSQL databases for massive remote sensing image data storage

abstract: Today's sensors are like eyes in the sky, thanks to the growth of satellite remote sensing technologies. Therefore, we see a steady evolution of the usage of different types of sensor, from airborne and satellites platforms which are generating large quantities of remote sensing image for divers applications such as; smart city, disaster management, military intelligence and others. As a result, the rate of growth in the amount of data by satellite is increasing dramatically. The velocity has exceeded 1TB per day and it will certainly increase in the future. However, it becomes crucial for these huge volume data to be stored. So, how to store and manage it efficiently becomes a real challenge because traditional ways have intensive issues; they are expensive and difficult to extend. Therefore, we need some scalable and parallel models for remote sensing data storage and processing. In this paper, we describe a scalable and distributed architecture for massive remote sensing data storage based on three No SQL databases (Apache Cassandra, Apache HBase, MongoBD). Also, a Hadoop-based framework is proposed to manage the big remote sensing data in a distributed and parallel manner.


title: Skeleton-based action recognition with convolutional neural networks

abstract: Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN). In this paper, we propose a novel convolutional neural networks (CNN) based framework for both action classification and detection. Raw skeleton coordinates as well as skeleton motion are fed directly into CNN for label prediction. A novel skeleton transformer module is designed to rearrange and select important skeleton joints automatically. With a simple 7-layer network, we obtain 89.3% accuracy on validation set of the NTU RGB+D dataset. For action detection in untrimmed videos, we develop a window proposal network to extract temporal segment proposals, which are further classified within the same network. On the recent PKU-MMD dataset, we achieve 93.7% mAP, surpassing the baseline by a large margin.


title: The genome of melon (Cucumis melo L.)

abstract: We report the genome sequence of melon, an important horticultural crop worldwide. We assembled 375 Mb of the double-haploid line DHL92, representing 83.3% of the estimated melon genome. We predicted 27,427 protein-coding genes, which we analyzed by reconstructing 22,218 phylogenetic trees, allowing mapping of the orthology and paralogy relationships of sequenced plant genomes. We observed the absence of recent whole-genome duplications in the melon lineage since the ancient eudicot triplication, and our data suggest that transposon amplification may in part explain the increased size of the melon genome compared with the close relative cucumber. A low number of nucleotide-binding site-leucine-rich repeat disease resistance genes were annotated, suggesting the existence of specific defense mechanisms in this species. The DHL92 genome was compared with that of its parental lines allowing the quantification of sequence variability in the species. The use of the genome sequence in future investigations will facilitate the understanding of evolution of cucurbits and the improvement of breeding strategies.


title: 10 Watts UHF broadband GaN based power amplifier for multi-band applications

abstract: The demand of broadband amplifier has been boosted to meet the requirements of multi-mode and multi-band wireless applications. GaN HEMT is the next generation of RF power transistor technology. In this paper, we have designed a 10W UHF broadband class-AB Power Amplifier (PA) based on GaN HEMT. The proposed amplifier has been designed and developed in the frequency range from 200–500 MHz. A maximum drain efficiency of 71% is achieved.


title: Matlab simulation on dual converter with separately excited DC motor as a load

abstract: Dual converters are commonly used to operate the dc drives where the four-quadrant operation needed. Two back to back connected full controlled converters are called as dual converter. One of the converter works as a ac to dc converter and the other one works as a dc to ac converter. Thyristors are used for the switching operation which is controlled by the pulse generator. Open loop dual converter technique proposed in this paper. The Four quadrant operation of both the single-phase dual converter and the three-phase dual converter has explained using the Simulink model. All these implements can be used to control the speed or the torque using the open or closed loop system.


The aggression theory of schizophrenia: Revisited


title: A Brain Storm Optimization With Multi-Information Interactions for Global Optimization Problems

abstract: The original brain storm optimization (BSO) fails to consider some potential information interactions in its individual update pattern, causing the premature convergence for complex problems. To address this problem, we propose a BSO algorithm with multi-information interactions (MIIBSO). First, a multi-information interaction (MII) strategy is developed, thoroughly considering various information interactions among individuals. Specially, this strategy contains three new MII patterns. The first two patterns aim to reinforce information interaction capability between individuals. The third pattern provides interactions between the corresponding dimensions of different individuals. The collaboration of the above three patterns is established by an individual stagnation feedback mechanism, contributing to preserve the diversity of the population and enhance the global search capability for MIIBSO. Second, a random grouping (RG) strategy is introduced to replace both the K-means algorithm and cluster center disruption of the original BSO algorithm, further enhancing the information interaction capability and reducing the computational cost of MIIBSO. Finally, a dynamic difference step-size (DDS), which can offer individual feedback information and improve search range, is designed to achieve an effective balance between global and local search capability for MIIBSO. By combining the MII strategy, RG, and DDS, MIIBSO achieves the effective improvement in the global search ability, convergence speed, and computational cost. MIIBSO is compared with 11 BSO algorithms and five other algorithms on the CEC2013 test suit. The results confirm that MIIBSO obtains the best global search capability and convergence speed amongst the 17 algorithms.


title: Chest X-ray Inpainting with Deep Generative Models

abstract: Generative adversarial networks have been successfully applied to inpainting in natural images. However, the current state-of-the-art models have not yet been widely adopted in the medical imaging domain. In this paper, we investigate the performance of three recently published deep learning based inpainting models: context encoders, semantic image inpainting, and the contextual attention model, applied to chest x-rays, as the chest exam is the most commonly performed radiological procedure. We train these generative models on 1.2M 128 $\times$ 128 patches from 60K healthy x-rays, and learn to predict the center 64 $\times$ 64 region in each patch. We test the models on both the healthy and abnormal radiographs. We evaluate the results by visual inspection and comparing the PSNR scores. The outputs of the models are in most cases highly realistic. We show that the methods have potential to enhance and detect abnormalities. In addition, we perform a 2AFC observer study and show that an experienced human observer performs poorly in detecting inpainted regions, particularly those generated by the contextual attention model.


title: Gender dysphoria in adolescence

abstract: AbstractAdolescents presenting with gender-related concerns are increasingly seeking support from providers from a variety of disciplines within health care settings across the world. For those treating young people who meet the criteria for the DSM 5 diagnosis of gender dysphoria (GD), complex decisions in clinical care are common. Defining best practice with this population with respect to interventions that span mental health, physical, and surgical domains can be challenging, given a relative dearth of empirical data available; yet practice guidelines have emerged from different professional organizations which can aid with this. For this review paper, a broad literature search was performed to identify relevant studies pertaining to the care of adolescents with GD. In addition, an overview of trends in clinical practice, including shifts in conceptualization of how clinicians and patients define care that is considered affirming when working with this population, is described. This paper explores the...


title: A Tutorial on Multilabel Learning

abstract: Multilabel learning has become a relevant learning paradigm in the past years due to the increasing number of fields where it can be applied and also to the emerging number of techniques that are being developed. This article presents an up-to-date tutorial about multilabel learning that introduces the paradigm and describes the main contributions developed. Evaluation measures, fields of application, trending topics, and resources are also presented.


title: Interaction for Visualization

abstract: Visualization has become a valuable means for data exploration and analysis. Interactive visualization combines expressive graphical representations and effective user interaction. Although interaction is an important component of visualization approaches, much of the visualization literature tends to pay more attention to the graphical representation than to interaction. The goal of this work is to strengthen the interaction side of visualization. Based on a brief review of general aspects of interaction, we develop an interaction-oriented view on visualization. This view comprises five key aspects: the data, the tasks, the technology, the human, as well as the implementation. Picking up these aspects individually, we elaborate several interaction methods for visualization. We introduce a multi-threading architecture for efficient interactive exploration. We present interaction techniques for different types of data e.g., multivariate data, spatio-temporal data, graphs) and different visualization tasks (e.g., exploratory navigation, visual comparison, visual editing). With respect to technology, we illustrate approaches that utilize modern interaction modalities (e.g., touch, tangibles, proxemics) as well as classic ones. While the human is important throughout this work, we also consider automatic methods to assist the interactive part. In addition to solutions for individual problems, a major contribution of this work is the overarching view of interaction in visualization as a whole. This includes a critical discussion of interaction, the identification of links between the key aspects of interaction, and the formulation of research topics for future work with a focus on interaction.


title: Human smooth and saccadic eye movements during voluntary pursuit of different target motions on different backgrounds.

abstract: Horizontal and vertical eye movements of ten human subjects were recorded with a scleral induction-coil technique during voluntary pursuit of sinusoidal, triangular and pseudo-random target motions of different frequency, amplitude and dimensionality upon a dark, diffuse or structured background. Data processing included separation of the composite eye movement into a cumulative smooth and saccadic displacement, computation of gain and phase of the composite and smooth eye movements with respect to the target movement and analysis of retinal position error. Pursuit eye movements were never completely smooth. Smooth pursuit gain was always lower than 0.95 and saccades were used to supplement the smooth eye movements in pursuing the target with the proper amplitude. The gain of composite eye movements was about unity for sinusoidal target motions and ramps; it exceeded unity for the highest frequency components in a pseudo-random motion. The gain of the smooth eye movements decreased monotonously whenever target velocity increased. It was higher for single sine waves than for a pseudo-random motion, however, with pseudo-random motion it was relatively higher for the higher frequency components. Phase lags were in general smaller for single sine waves than for pseudo-random motion, but for the latter a phase lead of the smooth component was consistently found for the lower frequency components. During pursuit of a rhomboid trajectory, the eye movements showed directional errors which are interpreted as anticipatory behaviour. The distribution of the retinal error was symmetrical around zero. Its standard deviation varied between about 0.2 and 1.3 degrees; it was about proportional to target velocity and inversely proportional to smooth pursuit gain. It was limited by the insertion of saccades which were in general corrective. The influence of a diffusely illuminated background was minimal. A structured background inhibited smooth pursuit in the horizontal direction by about 10% and in the vertical direction by about 20%. This deficit of smooth pursuit was fully compensated by the insertion of more saccades and had no consequences for the standard deviation of the retinal error. The type of structure of the background was only of marginal importance. Horizontal pursuit was in general slightly smoother and more precise than vertical pursuit.


title: Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code

abstract: The paper addresses the problem of concept location in source code by presenting an approach which combines formal concept analysis (FCA) and latent semantic indexing (LSI). In the proposed approach, LSI is used to map the concepts expressed in queries written by the programmer to relevant parts of the source code, presented as a ranked list of search results. Given the ranked list of source code elements, our approach selects most relevant attributes from these documents and organizes the results in a concept lattice, generated via FCA. The approach is evaluated in a case study on concept location in the source code of eclipse, an industrial size integrated development environment. The results of the case study show that the proposed approach is effective in organizing different concepts and their relationships present in the subset of the search results. The proposed concept location method outperforms the simple ranking of the search results, reducing the programmers' effort.


title: Mixed-integer quadrangulation

abstract: We present a novel method for quadrangulating a given triangle mesh. After constructing an as smooth as possible symmetric cross field satisfying a sparse set of directional constraints (to capture the geometric structure of the surface), the mesh is cut open in order to enable a low distortion unfolding. Then a seamless globally smooth parametrization is computed whose iso-parameter lines follow the cross field directions. In contrast to previous methods, sparsely distributed directional constraints are sufficient to automatically determine the appropriate number, type and position of singularities in the quadrangulation. Both steps of the algorithm (cross field and parametrization) can be formulated as a mixed-integer problem which we solve very efficiently by an adaptive greedy solver. We show several complex examples where high quality quad meshes are generated in a fully automatic manner.


title: Elastic stateful stream processing in storm

abstract: The advent of the Big Data era and the diffusion of Cloud computing have renewed the interest in Data Stream Processing (DSP) applications, which can timely extract useful information from distributed data sources. Due to the unpredictable rate at which the sources may produce data, DSP applications demand high dynamism. Storm has emerged as a widely adopted DSP system, which, although having many desirable features, shows some limitations due to the lack of adaptation capabilities. In this paper, we extend Storm with two mechanisms that support the run-time adaptation of DSP applications. Specifically, we introduce new components that allow automatic elasticity and stateful migration of the application components. The experimental results show the benefits of the newly introduced functionalities that, albeit equipped with proof of concept policies, allow to properly cope with workload variations while improving the resource utilization of the underlying infrastructure.


title: Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

abstract: Nonlinear dynamical systems have been used in many disciplines to model complex behaviors, including biological motor control, robotics, perception, economics, traffic prediction, and neuroscience. While often the unexpected emergent behavior of nonlinear systems is the focus of investigations, it is of equal importance to create goal-directed behavior e.g., stable locomotion from a system of coupled oscillators under perceptual guidance. Modeling goal-directed behavior with nonlinear systems is, however, rather difficult due to the parameter sensitivity of these systems, their complex phase transitions in response to subtle parameter changes, and the difficulty of analyzing and predicting their long-term behavior; intuition and time-consuming parameter tuning play a major role. This letter presents and reviews dynamical movement primitives, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques. The essence of our approach is to start with a simple dynamical system, such as a set of linear differential equations, and transform those into a weakly nonlinear system with prescribed attractor dynamics by means of a learnable autonomous forcing term. Both point attractors and limit cycle attractors of almost arbitrary complexity can be generated. We explain the design principle of our approach and evaluate its properties in several example applications in motor control and robotics.


title: The increasing burden of depression

abstract: Recent epidemiological surveys conducted in general populations have found that the lifetime prevalence of depression is in the range of 10% to 15%. Mood disorders, as defined by the World Mental Health and the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, have a 12-month prevalence which varies from 3% in Japan to over 9% in the US. A recent American survey found the prevalence of current depression to be 9% and the rate of current major depression to be 3.4%. All studies of depressive disorders have stressed the importance of the mortality and morbidity associated with depression. The mortality risk for suicide in depressed patients is more than 20-fold greater than in the general population. Recent studies have also shown the importance of depression as a risk factor for cardiovascular death. The risk of cardiac mortality after an initial myocardial infarction is greater in patients with depression and related to the severity of the depressive episode. Greater severity of depressive symptoms has been found to be associated with significantly higher risk of all-cause mortality including cardiovascular death and stroke. In addition to mortality, functional impairment and disability associated with depression have been consistently reported. Depression increases the risk of decreased workplace productivity and absenteeism resulting in lowered income or unemployment. Absenteeism and presenteeism (being physically present at work but functioning suboptimally) have been estimated to result in a loss of $36.6 billion per year in the US. Worldwide projections by the World Health Organization for the year 2030 identify unipolar major depression as the leading cause of disease burden. This article is a brief overview of how depression affects the quality of life of the subject and is also a huge burden for both the family of the depressed patient and for society at large.


title: Position-Controllable Ge Nanowires Growth on Patterned Au Catalyst Substrate

abstract: A well position-controllable single Ge nanowire array was grown on patterned Au catalysts substrate by low-pressure chemical vapor deposition. Both transmission electron microscope and X-ray diffraction results indicate that Ge nanowires are single crystalline with diamond structure. By optimizing the electron-beam lithography process, Au patterns with a diameter of 10 nm were prepared by lift-off method. The growth of Ge nanowires can be precisely controlled by adjusting the location of catalysts, which may offer the possibility of in situ fabrication of nanowire devices.


title: Simple calculator compiler using Lex and YACC

abstract: This paper contains the details of how one can develop the simple compiler for procedural lanaguage using Lex (Lexical Analyzer Generator) and YACC (Yet Another Compiler-Compiler). Lex tool helps write programs whose control flow is directed by instances of regular expressions in the input stream. It is well suited for editor-scripts type transformations and for segmenting input in preparation for a parsing routine. Lex tool source is the table of regular expressions and corresponding program fragments. The table is translated to a program which reads an input stream, copying it to an output stream and partitioning the input into strings which match the given expressions. On the other hand YACC tool receives input of the user grammar. Starting from this grammar it generates the C source code for the parser. YACC invokes Lex to scan the source code and uses the tokens returned by Lex to build a syntax tree. With the help of YACC and Lex tool one can write their own compiler.


title: Healthcare Data Analytics

abstract: At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available to solve healthcare problems. The book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent developments in healthcare computing and discusses emerging technologies that can help improve the health and well-being of patients. Written by prominent researchers and experts working in the healthcare domain, the book sheds light on many of the computational challenges in the field of medical informatics. Each chapter in the book is structured as a "survey-style" article discussing the prominent research issues and the advances made on that research topic. The book is divided into three major categories: Healthcare Data Sources and Basic Analytics -details the various healthcare data sources and analytical techniques used in the processing and analysis of such data Advanced Data Analytics for Healthcare- covers advanced analytical methods, including clinical prediction models, temporal pattern mining methods, and visual analytics Applications and Practical Systems for Healthcare -covers the applications of data analytics to pervasive healthcare, fraud detection, and drug discovery along with systems for medical imaging and decision support Computer scientists are usually not trained in domain-specific medical concepts, whereas medical practitioners and researchers have limited exposure to the data analytics area. The contents of this book will help to bring together these diverse communities by carefully and comprehensively discussing the most relevant contributions from each domain.


title: Visualizing Scholarly Publications and Citations to Enhance Author Profiles

abstract: With data on scholarly publications becoming more abundant and accessible, there exist new opportunities for using this information to provide rich author profiles to display and explore scholarly work. We present a pair of linked visualizations connected to the Microsoft Academic Graph that can be used to explore the publications and citations of individual authors. We provide an online application with which a user can manage collections of papers and generate these visualizations.


title: Review of stereo vision algorithms: From software to hardware

abstract: Stereo vision, resulting in the knowledge of deep information in a scene, is of great importance in the field of machine vision, robotics and image analysis. In this article, an explicit analysis of the existing stereo matching methods, up to date, is presented. The presented algorithms are discussed in terms of speed, accuracy, coverage, time consumption, and disparity range. Towards the direction of real-time operation, the development of stereo matching algorithms, suitable for efficient hardware implementation is highly desirable. Implementations of stereo matching algorithms in hardware for real-time applications are also discussed in details.


title: Detection of questions in Berber language using prosodic features

abstract: This paper focuses on the acquisition of the tonal and prosodic structure of affirmative and question sentences in Berber language. The study on the prosodic differences between these two types of sentences in Berber language, the detection and classification of sentence type is the main subject of this paper. We've realized a system for segmentation and automatic detection of sentence type based on both prosodic, in Berber language, a language where all studies until now are still preliminary. To this end, we developed a corpus made of 720 utterances that were extracted from 6 Berber spoken lectures. Prosodic features are, then, extracted from each sentence. These features are used as input to two different classifiers to classify each sentence into either a question or affirmative sentence. We classified questions with an accuracy of 93%. A feature-specific analysis further reveals that energy and fundamental frequency (F0) features are mainly responsible for discriminating between question and affirmative sentences.


title: A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions

abstract: Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy becomes poor. In this work, we compare the performance of DNNs with human subjects on distorted images. We show that, although DNNs perform better than or on par with humans on good quality images, DNN performance is still much lower than human performance on distorted images. We additionally find that there is little correlation in errors between DNNs and human subjects. This could be an indication that the internal representation of images are different between DNNs and the human visual system. These comparisons with human performance could be used to guide future development of more robust DNNs.


Seasonal fluctuations in the catches of salmon in the Baltic


title: Leave-one-out error and stability of learning algorithms with applications

abstract: 1,078,620. Thermal regenerators. H. BRANDT, and A. KRITZLER [trading as APPARATEBAU ROTHEMUHLE BRANDT & KRITZLER]. Sept. 28, 1965 [Sept. 29, 1964], No. 41184/65. Heading F4K. In a rotary duct type regenerator-in which a peripheral sealing frame (32) is joined to each rotating duct (20) by an expansion member (33) and also by a number of swivel joints (48) for rotating the frame with the duct, and the frame and its sealing strip (36) are held in sealing contact with the regenerator mass by resilient means, the resilient means comprising a plurality of bolts 40 spaced around the periphery of the duct and attached at one end to the frame (32), each bolt having therearound a spring 43 acting between a fixed point on the bolt and a flange 41 on the duct, the bolt being threaded in the frame and rotatable by a tommy-bar 60 to adjust the pressure of the frame on the regenerator mass-the axial movement of bolt 40, during operation of the regenerator, is limited by an adjustable nut 46 co-operating with a slide 57, 54 (also Fig. 7, not shown) inserted in a guide-way 55, 56, the slide having such thickness as to adjust the clearance a between nut 46 and slide 57, 54 to a desired value, e.g. 5A0 mm. The gap between nut 46 and flange 41 is set approximately before starting the regenerator, and when the regenerator has attained stable operating temperatures, the regenerator is stopped for the short time necessary to insert a slide of the correct thickness at each bolt.


title: New Directions in Goal-Setting Theory:

abstract: Goal-setting theory is summarized regarding the effectiveness of specific, difficult goals; the relationship of goals to affect; the mediators of goal effects; the relation of goals to self-efficacy; the moderators of goal effects; and the generality of goal effects across people, tasks, countries, time spans, experimental designs, goal sources (i.e., self-set, set jointly with others, or assigned), and dependent variables. Recent studies concerned with goal choice and the factors that influence it, the function of learning goals, the effect of goal framing, goals and affect (well-being), group goal setting, goals and traits, macro-level goal setting, and conscious versus subconscious goals are described. Suggestions are given for future research.


title: A practical evaluation of spectrum-based fault localization

abstract: Spectrum-based fault localization (SFL) shortens the test-diagnose-repair cycle by reducing the debugging effort. As a light-weight automated diagnosis technique it can easily be integrated with existing testing schemes. Since SFL is based on discovering statistical coincidences between system failures and the activity of the different parts of a system, its diagnostic accuracy is inherently limited. Using a common benchmark consisting of the Siemens set and the space program, we investigate this diagnostic accuracy as a function of several parameters (such as quality and quantity of the program spectra collected during the execution of the system), some of which directly relate to test design. Our results indicate that the superior performance of a particular similarity coefficient, used to analyze the program spectra, is largely independent of test design. Furthermore, near-optimal diagnostic accuracy (exonerating over 80% of the blocks of code on average) is already obtained for low-quality error observations and limited numbers of test cases. In addition to establishing these results in the controlled environment of our benchmark set, we show that SFL can effectively be applied in the context of embedded software development in an industrial environment.


title: Using SWRL rules for requirements engineering: Application to ERTMS/SRS specifications

abstract: ERTMS is the European Rail Traffic Management System implemented by the European Railway Agency. This project aims at making rail transport safer and more competitive. It focuses on the regularization and standardization of train and signalization control at a European level. However, the safety analysis is not included in the system specification. Consequently, it has to be done by experts in an ad-hoc way by analyzing complex, sparse and voluminous security documents. These experts spend a considerable time to look for necessary information to enter new simulation scenarios in the ERTMS platform. The aim of this work is to propose a solution for structuring security requirements for easier use and also to the extraction of implicit knowledge which is not explicitly defined in the available documents. The proposed solution is based on the use of ontologies to structure the explicit requirements and extract implicit ones from them. First, we have proposed a methodology for developing the ERTMS Ontology using the OWL language. Then, we extended this ontology by representations of explicit security requirements. After that, we defined an extensible set of semantic inference rules which automatically extract implicit knowledge allowing better use of the ERTMS platform. These rules, which are implemented using the SWRL language, can be generic or specific for certain simulation scenarios. The aim of this ontology is the formalization of these ERTMS specifications in order to obtain a data structure that can be reusable by industries to develop the ERTMS products. The future work will focus on the integration of the ontology and the associated inference library rules in the ERTMS system for automatic generation of instances, concepts and relationships defined in the ontology.


title: An Introduction to Simulated Evolutionary Optimization

abstract: Natural evolution is a population-based optimization process. Simulating this process on a computer results in stochastic optimization techniques that can often outperform classical methods of optimization when applied to difficult real-world problems. There are currently three main avenues of research in simulated evolution: genetic algorithms, evolution strategies, and evolutionary programming. Each method emphasizes a different facet of natural evolution. Genetic algorithms stress chromosomal operators. Evolution strategies emphasize behavioral changes at the level of the individual. Evolutionary programming stresses behavioral change at the level of the species. The development of each of these procedures over the past 35 years is described. Some recent efforts in these areas are reviewed. >


title: Generalized residual vector quantization for large scale data

abstract: Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and analysis. In this paper, we propose a novel vector quantization framework that iteratively minimizes quantization error. First, we provide a detailed review on a relevant vector quantization method named residual vector quantization (RVQ). Next, we propose generalized residual vector quantization (GRVQ) to further improve over RVQ. Many vector quantization methods can be viewed as the special cases of our proposed framework. We evaluate GRVQ on several large scale benchmark datasets for large scale search, classification and object retrieval. We compared GRVQ with existing methods in detail. Extensive experiments demonstrate our GRVQ framework substantially outperforms existing methods in term of quantization accuracy and computation efficiency.


Application of Machine Translation in Localization into Low-Resourced Languages


title: Peeking into the Past: Efficient Checkpoint-Assisted Time-Traveling Debugging

abstract: Debugging long-lived latent software bugs that manifest themselves only long after their introduction in the system is hard. Even state-of-the-artrecord/replay debugging techniques are of limited use to identify the rootcause of long-lived latent bugs in general and event-driven bugs in particular. We propose DeLorean, a new end-to-end solution for time-travelling debugging based on fast memory checkpointing. Our design trades off replay guarantees with efficient support for history-aware debug queries (or time-travelling introspection) and provides novel analysis tools to diagnose event-driven latent software bugs. DeLorean imposes low run-time performance and memory overhead while preserving in memory as much history information as possible by deduplicating and/or compressing the collected data. We evaluate DeLorean byextensive experimentation, exploring the performance-memory tradeoffs in different configurations and comparing our results against state-of-the-art solutions. We show that DeLorean can efficiently support high-frequency checkpoints and store millions of them in memory.


title: An enhancement to SePeCloud with improved security and efficient data management

abstract: Outsourcing data to a third-party controlled cloud computing services arises various security issues. High security schemes are required to protect data in the cloud platform. Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) method addressed these issues by improving the security and performance of a cloud environment. An enhanced version of DROPS named SePeCloud was proposed that improved the security and performance hand-in-hand with Fog based Deduplication and Privacy Preserving Online Updating. This paper improves the security and data storage with Self-Destruction mechanism by handling the applications independently built on reinforcement learning strategy. Data chunk fingerprint index and sketch index are included to support independent and parallel data destruction among multiple applications. Additionally, SePeCloud is further extended to prevent impersonation attacks from illegitimate users by introducing Modified Shamir secrete sharing scheme to handle user revocation policies with limited storage space. The experimental results prove that the final version of the SePeCloud performed better than the previous versions in terms of Replication cost savings and Computation time by improving both security and performance of the cloud system.