title: Privacy-preserving photo sharing based on a secure JPEG

abstract: Sharing photos online is a common activity on social networks and photo hosting platforms, such as Facebook, Pinterest, Instagram, or Flickr. However, after reports of citizens surveillance by governmental agencies and the scandalous leakage of celebrities private photos online, people have become concerned about their online privacy and are looking for ways to protect it. Popular social networks typically offer privacy protection solutions only in response to the public demand and therefore are often rudimental, complex to use, and provide limited degree of control and protection. Most solutions either allow users to control who can access the shared photos or for how long they can be accessed. In contrast, in this paper, we take a structured privacy by design approach to the problem of online photo privacy protection. We propose a privacy-preserving photo sharing architecture that takes into account content and context of a photo with privacy protection integrated inside the JPEG file itself in a secure way. We demonstrate the proposed architecture with a prototype mobile iOS application called ProShare that offers scrambling as the privacy protection tool for a selected region in a photo, secure access to the protected images, and secure photo sharing on Facebook.


title: A new charge pump circuit dealing with gate-oxide reliability issue in low-voltage processes

abstract: Charge pump circuits have been widely used in DRAM, EEPROM, flash memories, and in some low-voltage designs. In this paper, a new charge pump circuit is proposed. The charge transfer switches of the new proposed circuit can be turned on and turned off completely, so its pumping gain is much higher than the traditional designs. Besides, there is no gate-oxide reliability problem in the proposed charge pump circuit. The test chips have been implemented in a 3.3 V 0.35 /spl mu/m CMOS process. The measured results show that the proposed charge pump circuit has better performance than that of prior arts. The proposed circuit can be used in low-voltage process because of its high pumping gain and no overstress across the gate oxide of the devices.


title: R2 CNN: Rotational Region CNN for Arbitrarily-Oriented Scene Text Detection

abstract: Scene text detection is challenging as the input may have different orientations, sizes, font styles, lighting conditions, perspective distortions and languages. This paper addresses the problem by designing a Rotational Region CNN (R2CNN). R2CNN includes a Text Region Proposal Network (Text-RPN) to estimate approximate text regions and a multitask refinement network to get the precise inclined box. Our work has the following features. First, we use a novel multi-task regression method to support arbitrarily-oriented scene text detection. Second, we introduce multiple ROIPoolings to address the scene text detection problem for the first time. Third, we use an inclined Non-Maximum Suppression (NMS) to post-process the detection candidates. Experiments show that our method outperforms the state-of-the-art on standard benchmarks: ICDAR 2013, ICDAR 2015, COCO-Text and MSRA-TD500.


title: Nutrition transition in India

abstract: Objective: The primary objective of this review is to examine the demographic and nutrition transition in India in relation to its contribution to the emerging epidemic of chronic non-communicable diseases in this country. Setting: India, the country as a whole and its different states with a population exceeding 1 billion in 2001. Subjects: The review examines demographic changes in the population with consequent effects on the population pyramid, the rapidity and rates of urbanisation with striking variations in chronic disease patterns and the trends in obesity between rural and urban communities, attempting to relate their prevalence with the diet and lifestyle changes accompanying them. Design: The review is based largely on representative large-scale surveys in the country and other reliable documented data on population characteristics. It also includes a review of the published literature. Results: The results indicate that the demographic changes, rates of urbanisation and changes in dietary patterns are contributing to the changing trends in chronic disease in India. Conclusions: There is clear evidence of a demographic, epidemiological and nutrition transition in India that is fuelling the epidemic of chronic diseases and obesity, particularly in the urban areas.


title: Brief Papers_______________________________________________________________________________ A 2-Tuple Fuzzy Linguistic Representation Model for Computing with Words

abstract: The fuzzy linguistic approach has been applied successfully to many problems. However, there is a limitation of this approach imposed by its information representation model and the computation methods used when fusion processes are performed on linguistic values. This limitation is the loss of information; this loss of information implies a lack of precision in the final results from the fusion of linguistic information. In this paper, we present tools for overcoming this limitation. The linguistic information is expressed by means of 2-tuples, which are composed of a linguistic term and a numeric value assessed in (-0.5, 0.5). This model allows a continuous representation of the linguistic information on its domain, therefore, it can represent any counting of information obtained in a aggregation process. We then develop a computational technique for computing with words without any loss of information. Finally, different classical aggregation operators are extended to deal with the 2-tuple linguistic model.


title: Are e-books effective tools for learning? Reading speed and comprehension : iPad® vs. paper

abstract: Recently, electronic books (e-books) have become prevalent amongst the general population, as well as students, owing to their advantages over traditional books. In South Africa, a number of schools have integrated tablets into the classroom with the promise of replacing traditional books. In order to realise the potential of e-books and their associated devices within an academic context, where reading speed and comprehension are critical for academic performance and personal growth, the effectiveness of reading from a tablet screen should be evaluated. To achieve this objective, a quasi-experimental withinsubjects design was employed in order to compare the reading speed and comprehension performance of 68 students. The results of this study indicate the majority of participants read faster on an iPad, which is in contrast to previous studies that have found reading from tablets to be slower. It was also found that comprehension scores did not differ significantly between the two media. For students, these results provide evidence that tablets and e-books are suitable tools for reading and learning, and therefore, can be used for academic work. For educators, e-books can be introduced without concern that reading performance and comprehension will be hindered.


title: Team Knowledge and Coordination in Geographically Distributed Software Development

abstract: Coordination is important in software development because it leads to benefits such as cost savings, shorter development cycles, and better-integrated products. Team cognition research suggests that members coordinate through team knowledge, but this perspective has only been investigated in real-time collocated tasks and we know little about which types of team knowledge best help coordination in the most geographically distributed software work. In this field study, we investigate the coordination needs of software teams, how team knowledge affects coordination, and how this effect is influenced by geographic dispersion. Our findings show that software teams have three distinct types of coordination needs-technical, temporal, and process-and that these needs vary with the members' role; geographic distance has a negative effect on coordination, but is mitigated by shared knowledge of the team and presence awareness; and shared task knowledge is more important for coordination among collocated members. We articulate propositions for future research in this area based on our analysis.


title: Automatic Recognition of Facial Actions in Spontaneous Expressions

abstract: Spontaneous facial expressions differ from posed expressions in both which muscles are moved, and in the dynamics of the movement. Advances in the field of automatic facial expression measurement will require development and assessment on spontaneous behavior. Here we present preliminary results on a task of facial action detection in spontaneous facial expressions. We employ a user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS). The system automatically detects frontal faces in the video stream and coded each frame with respect to 20 Action units. The approach applies machine learning methods such as support vector machines and AdaBoost, to texture-based image representations. The output margin for the learned classifiers predicts action unit intensity. Frame-by-frame intensity measurements will enable investigations into facial expression dynamics which were previously intractable by human coding.


title: Neural Text Generation from Structured Data with Application to the Biography Domain

abstract: This paper introduces a neural model for concept-to-text generation that scales to large, rich domains. We experiment with a new dataset of biographies from Wikipedia that is an order of magnitude larger than existing resources with over 700k samples. The dataset is also vastly more diverse with a 400k vocabulary, compared to a few hundred words for Weathergov or Robocup. Our model builds upon recent work on conditional neural language model for text generation. To deal with the large vocabulary, we extend these models to mix a fixed vocabulary with copy actions that transfer sample-specific words from the input database to the generated output sentence. Our neural model significantly out-performs a classical Kneser-Ney language model adapted to this task by nearly 15 BLEU.


title: Repetitive transcranial magnetic stimulation for hallucination in schizophrenia spectrum disorders: A meta-analysis

abstract: Objective: ::: This study assessed the efficacy and tolerability of repetitive transcranial magnetic stimulation for treatment of auditory hallucination of patients with schizophrenia spectrum disorders.


title: Automatic Text Simplification For Handling Intellectual Property (The Case of Multiple Patent Claims)

abstract: Handling intellectual property involves the cognitive process of understanding the innovation described in the body of patent claims. In this paper we present an on-going project on a multi-level text simplification to assist experts in this complex task. Two levels of simplification procedure are described. The macro-level simplification results in the visualization of the hierarchy of multiple claims. The micro-level simplification includes visualization of the claim terminology, decomposition of the claim complex structure into a set of simple sentences and building a graph explicitly showing the interrelations of the invention elements. The methodology is implemented in an experimental text simplifying computer system. The motivation underlying this research is to develop tools that could increase the overall productivity of human users and machines in processing patent applications.


title: Towards sound detection of virtual machines

abstract: We design, implement, and evaluate a practical timing-based approach to detect virtual machine monitors (VMMs) without relying on VMM implementation details. The algorithms developed in this paper are based on fundamental properties of virtual machine monitors rather than easily modified software artifacts. We evaluate our approach against two common VMM implementations on machines with and without hardware support for virtualization in a number of remote and local experiments. We successfully distinguish between virtual and real machines in all cases even with incomplete information regarding the VMM implementation and hardware configuration of the targeted machine.


title: GraphX: Unifying Data-Parallel and Graph-Parallel Analytics

abstract: From social networks to language modeling, the growing scale and importance of graph data has driven the development of numerous new graph-parallel systems (e.g., Pregel, GraphLab). By restricting the computation that can be expressed and introducing new techniques to partition and distribute the graph, these systems can efficiently execute iterative graph algorithms orders of magnitude faster than more general data-parallel systems. However, the same restrictions that enable the performance gains also make it difficult to express many of the important stages in a typical graph-analytics pipeline: constructing the graph, modifying its structure, or expressing computation that spans multiple graphs. As a consequence, existing graph analytics pipelines compose graph-parallel and data-parallel systems using external storage systems, leading to extensive data movement and complicated programming model. ::: To address these challenges we introduce GraphX, a distributed graph computation framework that unifies graph-parallel and data-parallel computation. GraphX provides a small, core set of graph-parallel operators expressive enough to implement the Pregel and PowerGraph abstractions, yet simple enough to be cast in relational algebra. GraphX uses a collection of query optimization techniques such as automatic join rewrites to efficiently implement these graph-parallel operators. We evaluate GraphX on real-world graphs and workloads and demonstrate that GraphX achieves comparable performance as specialized graph computation systems, while outperforming them in end-to-end graph pipelines. Moreover, GraphX achieves a balance between expressiveness, performance, and ease of use.


title: ECG signal processing for recognition of cardiovascular diseases: A survey

abstract: Electrocardiogram (ECG) is nearly a periodic signal widely used for the detection and diagnosis of cardiac abnormalities. Recently with the inception of computer based techniques, automated analysis of shape and pattern of ECG waveform has facilitated physician to obtain fast and accurate diagnosis of cardiac disorders. Abnormalities related to sinus rhythms can be detected by using ECG signal beat classification, whereas Ischemic Heart Disease and Myocardial Infarction can be detected by deviation in ST segment or inversion of Τ wave in ECG signal. This paper discusses techniques proposed earlier in the literature for noise removal, feature extraction and classification of ECG signal.


title: Maximal Information Divergence from Statistical Models defined by Neural Networks

abstract: We review recent results about the maximal values of the Kullback-Leibler information divergence from statistical models defined by neural networks, including naive Bayes models, restricted Boltzmann machines, deep belief networks, and various classes of exponential families. We illustrate approaches to compute the maximal divergence from a given model starting from simple sub- or super-models. We give a new result for deep and narrow belief networks with finite-valued units.


title: Improving Speaker-Independent Lipreading with Domain-Adversarial Training

abstract: We present a Lipreading system, i.e. a speech recognition system using only visual features, which uses domain-adversarial training for speaker independence. Domain-adversarial training is integrated into the optimization of a lipreader based on a stack of feedforward and LSTM (Long Short-Term Memory) recurrent neural networks, yielding an end-to-end trainable system which only requires a very small number of frames of untranscribed target data to substantially improve the recognition accuracy on the target speaker. On pairs of different source and target speakers, we achieve a relative accuracy improvement of around 40% with only 15 to 20 seconds of untranscribed target speech data. On multi-speaker training setups, the accuracy improvements are smaller but still substantial.


title: An Evaluation of Unified Memory Technology on NVIDIA GPUs

abstract: Unified Memory is an emerging technology which is supported by CUDA 6.X. Before CUDA 6.X, the existing CUDA programming model relies on programmers to explicitly manage data between CPU and GPU and hence increases programming complexity. CUDA 6.X provides a new technology which is called as Unified Memory to provide a new programming model that defines CPU and GPU memory space as a single coherent memory (imaging as a same common address space). The system manages data access between CPU and GPU without explicit memory copy functions. This paper is to evaluate the Unified Memory technology through different applications on different GPUs to show the users how to use the Unified Memory technology of CUDA 6.X efficiently. The applications include Diffusion3D Benchmark, Parboil Benchmark Suite, and Matrix Multiplication from the CUDA SDK Samples. We changed those applications to corresponding Unified Memory versions and compare those with the original ones. We selected the NVIDIA Keller K40 and the Jetson TK1, which can represent the latest GPUs with Keller architecture and the first mobile platform of NVIDIA series with Keller GPU. This paper shows that Unified Memory versions cause 10% performance loss on average. Furthermore, we used the NVIDIA Visual Profiler to dig the reason of the performance loss by the Unified Memory technology.


title: MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer

abstract: An unprecedented booming has been witnessed in the research area of artistic style transfer ever since Gatys et al. introduced the neural method. One of the remaining challenges is to balance a trade-off among three critical aspects—speed, flexibility, and quality: (i) the vanilla optimization-based algorithm produces impressive results for arbitrary styles, but is unsatisfyingly slow due to its iterative nature, (ii) the fast approximation methods based on feed-forward neural networks generate satisfactory artistic effects but bound to only a limited number of styles, and (iii) feature-matching methods like AdaIN achieve arbitrary style transfer in a real-time manner but at a cost of the compromised quality. We find it considerably difficult to balance the trade-off well merely using a single feed-forward step and ask, instead, whether there exists an algorithm that could adapt quickly to any style, while the adapted model maintains high efficiency and good image quality. Motivated by this idea, we propose a novel method, coined MetaStyle, which formulates the neural style transfer as a bilevel optimization problem and combines learning with only a few post-processing update steps to adapt to a fast approximation model with satisfying artistic effects, comparable to the optimization-based methods for an arbitrary style. The qualitative and quantitative analysis in the experiments demonstrates that the proposed approach achieves high-quality arbitrary artistic style transfer effectively, with a good trade-off among speed, flexibility, and quality.


Omohundro’s “Basic AI Drives ” and Catastrophic Risks Carl Shulman MIRI Visiting Fellow


title: Cluster of heterogeneous computers: Using mobile agents for improving load balance

abstract: Due to the increasing interest to achieve high performance computing using cluster, which consists of heterogeneous workstations and/or personal computers (PCs) connected via a fast network. Usually programming such systems is done by applying message-passing libraries like MPI. Due to the lack of a load balancing facility, it is quite possible to overload machines on the network with tasks, and lead parallel applications to frustrated results. In this paper, the mobile agent based approach to improve the load balancing of parallel tasks in heterogeneous computing environment has been proposed. Its benefit has been demonstrated with the experimental results.


title: Real-time optical flow-based video stabilization for unmanned aerial vehicles

abstract: This paper describes the development of a novel algorithm to tackle the problem of real-time video stabilization for unmanned aerial vehicles (UAVs). There are two main components in the algorithm: (1) By designing a suitable model for the global motion of UAV, the proposed algorithm avoids the necessity of estimating the most general motion model, projective transformation, and considers simpler motion models, such as rigid transformation and similarity transformation; (2) to achieve a high processing speed, optical flow-based tracking is employed in lieu of conventional tracking and matching methods used by state-of-the-art algorithms. These two new ideas resulted in a real-time stabilization algorithm, developed over two phases. Stage I considers processing the whole sequence of frames in the video while achieving an average processing speed of 50 fps on several publicly available benchmark videos. Next, Stage II undertakes the task of real-time video stabilization using a multi-threading implementation of the algorithm designed in Stage I.


title: Effectiveness of virtual reality-based instruction on students' learning outcomes in K-12 and higher education

abstract: The purpose of this meta-analysis is to examine overall effect as well as the impact of selected instructional design principles in the context of virtual reality technology-based instruction (i.e. games, simulation, virtual worlds) in K-12 or higher education settings. A total of 13 studies (N?=?3081) in the category of games, 29 studies (N?=?2553) in the category of games, and 27 studies (N?=?2798) in the category of virtual worlds were meta-analyzed. The key inclusion criteria were that the study came from K-12 or higher education settings, used experimental or quasi-experimental research designs, and used a learning outcome measure to evaluate the effects of the virtual reality-based instruction.Results suggest games (FEM?=?0.77; REM?=?0.51), simulations (FEM?=?0.38; REM?=?0.41), and virtual worlds (FEM?=?0.36; REM?=?0.41) were effective in improving learning outcome gains. The homogeneity analysis of the effect sizes was statistically significant, indicating that the studies were different from each other. Therefore, we conducted moderator analysis using 13 variables used to code the studies. Key findings included that: games show higher learning gains than simulations and virtual worlds. For simulation studies, elaborate explanation type feedback is more suitable for declarative tasks whereas knowledge of correct response is more appropriate for procedural tasks. Students performance is enhanced when they conduct the game play individually than in a group. In addition, we found an inverse relationship between number of treatment sessions learning gains for games.With regards to the virtual world, we found that if students were repeatedly measured it deteriorates their learning outcome gains. We discuss results to highlight the importance of considering instructional design principles when designing virtual reality-based instruction. A comprehensive review of virtual reality-based instruction research.Analysis of the moderation effects of design features in a virtual environment.Using an advance statistical technique of meta-analysis to study the effects.Virtual reality environment is effective for teaching in K-12 and higher education.Results can be used by instructional designers to design the virtual environments.


title: Autonomous UAV surveillance of a ship's path with MPC for maritime situational awareness

abstract: Maritime Situational Awareness is crucial in maritime operations to identify threats and to deal with them as soon as possible. These threats can be pirates in shipping operations, icebergs when sailing in the northern sea routes, or even unknown vessels or objects that might be on the ship's path. A solution to identify these threats is the use of UAV's to overfly the ship's planned path. This solution is described in this paper, using an autonomous fixed wing UAV. Based on the provided ship's planned path, the UAV should autonomously map the area close to the ship track. To do that, an optimization problem is solved using Model Predictive Control, where the turn rate for the next time period is optimized. Based on the turn rate, the future path of the UAV is calculated and the waypoints are sent to the autopilot. This application is thoroughly tested using a Software In Loop environment, where an aircraft model is used with the autopilot's simulation. The results show that surveillance performance is improved if the UAV has information about the ship's velocity in addition to position.


title: Commercialized Practical Network Service Applications from the Integration of Network Distribution and High-Speed Cipher Technologies in Cloud Environments

abstract: Abstract — This paper presents the evaluation results of the commercialization of High Security Disaster Recovery Technology (HS-DRT) that uses network distribution and high-speed strong cipher technologies to realize efficient and secure network services. We have commercialized a disaster recovery system and evaluated the performance of the distributed engines using the hash functions, versatile spatial scrambling functions, etc., in cloud computing environments. The average processing time has been estimated in terms of the method of implementation of the engine. As for practical network applications, an automatic back-up system using an FTP server has been introduced. We have developed on-premise systems which achieve high security through the use of HS-DRT. Finally, we also propose future technologies for preventing an insider attack. Keywords-disaster recovery; backup; distributed processing; cloud; strong cipher. I. INTRODUCTION Innovative network technology, which can guarantee, as far as possible, the security of users’ or institutes' massive files of important data from any risks such as an unexpected natural disaster, a cyber attack, etc., are becoming increasingly indispensable day by day. As a means of satisfying this need, cloud computing technology is expected to provide an effective and economical backup system by making use of a very large number of data stores and processing resources which are not fully utilized. It is expected that this file data backup mechanism will be utilized by government and municipal offices, hospitals, insurance companies, etc., to guard against the occurrence of unexpected disasters such as earthquakes, large fires and storms and Tsunamis. To achieve secure back up, there is an indispensable need for prompt restoration, which may make versatile use of cellular phones, smart phones, digital signage equipment and PCs, in addition to cloud resources dispersed in multiple geographical locations. In addition to these factors, many companies and individuals involved in industry and commerce are interested in making use of public or private cloud computing facilities, provided by carriers or computer vendors as a means of achieving security and low maintenance and operation costs. In this paper we present the results of an evaluation of an innovative file backup network service, which makes use of an effective ultra-widely distributed data transfer mechanism and a high-speed strong cipher technology to realize efficient, safe data backup at an affordable maintenance and operation cost [1-6]. When a block cipher is used, the required processor and memory costs increase in an exponential manner with increasing data volume. However, with a stream cipher, the input data is simply operated on bit-by-bit, using a simple arithmetic operation, and high-speed processing becomes feasible. This is the fundamental difference between the two cipher technologies. It is possible to combine the use of technologies, specifically, the spatial scrambling of all data files, the random fragmentation of the data files, and the corresponding encryption and replication of each file fragment using a stream cipher. Figure 1 shows the concept of the proposed network service compared with a conventional back up system using the leased lines. In the data center, it is appropriate to introduce a secret sharing scheme for sending “encryption metadata” to the supervisory servers deployed in the several different locations for deciphering the original file data. To enhance security it is better to send the metadata using a Virtual Private Network(VPN). This mechanism make it quite difficult to find out any series in the “encryption metadata” itself. From a disaster recovery point of view, a secret sharing scheme with some appropriate “thresholds” should be introduced in the proposed system. If the system uses a (3,5)-threshold scheme, the system uses five supervisory servers, and can tolerate the simultaneous failure of two servers. On the other hand, from a cyber terrorism point of


title: ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements

abstract: This paper reports our submissions to the Semantic Textual Similarity (STS) task in SEM Shared Task 2013. We submitted three Support Vector Regression (SVR) systems in core task, using 6 types of similarity measures, i.e., string similarity, number similarity, knowledge-based similarity, corpus-based similarity, syntacticdependencysimilarityand machine translation similarity. Our third system with different training data and different feature sets for each test data set performs the best and ranks 35 out of 90 runs. We also submitted two systems in typed task using string based measure and Named Entity based measure. Our best system ranks 5 out of 15 runs.


title: A methodology for evaluating and selecting data mining software

abstract: As data mining evolves and matures more and more businesses are incorporating this technology into their business practices. However, currently data mining and decision support software is expensive and selection of the wrong tools can be costly in many ways. This paper provides direction and decision-making information to the practicing professional. A framework for evaluating data mining tools is presented and a methodology for applying this framework is described. Finally a case study to demonstrate the methods effectiveness is presented. This methodology represents the first-hand experience using many of the leading data mining tools against real business data at the Center for Data Insight (CDI) at Northern Arizona University (NAU). This is not a comprehensive review of commercial tools but instead provides a method and a point-of-reference for selecting the best software tool for a particular problem. Experience has shown that there is not one best data-mining tool for all purposes. This instrument is designed to accommodate differences in environments and problem domains. It is expected that this methodology will be used to publish tool comparisons and benchmarking results.


title: Distributed Multitask Learning

abstract: We consider the problem of distributed multi-task learning, where each machine learns a separate, but related, task. Specifically, each machine learns a linear predictor in high-dimensional space,where all tasks share the same small support. We present a communication-efficient estimator based on the debiased lasso and show that it is comparable with the optimal centralized method.


title: Improving production planning by utilizing stochastic programming

abstract: Abstract The effects of fluctuating demand on production and inventory levels are important in manufacturing resource planning. Thus, the focus of this presentation is on aggregate production planning of manufacturing resources in order to satisfy stochastic demand for a family of products to minimize total costs that include production and inventory holding costs over a rolling horizon. If it is assumed that, in a commercial setting, the demands are fixed, then the production plans generated by a mathematical programming procedure are not responsive to the actual fluctuations of stochastic demand in each time period. The situation discussed here considers the case where demands are normally distributed with means and variances that are sequentially revised as new observations of demand are received over time. This assumption allows the probabilistic constraint to be converted to an equivalent linearly-constrained deterministic model. Extensions to the normality assumption are discussed. Also other ideas such as optimal control theory, learning and adaptive signal processing extensions are discussed as well.


title: Pseudo-Multiple-Exposure-Based Tone Fusion With Local Region Adjustment

abstract: New generations of display technologies provide a significantly improved dynamic range compared to conventional display devices. Inverse tone mapping methods have been proposed to convert low dynamic range (LDR) images to HDR ones, and several of them require multiple exposure LDR images of the same scene as inputs. However, the vast majority of LDR images and videos available have only one single exposure. In this paper, we propose a region-based enhancement of the pseudo-exposures to generate an HDR image. First, we present an exposure dependent $S$ curve to convert one LDR image to the pseudo-multiple-exposures. Only certain regions of the pseudo-exposures contain noticeable detail information. We propose a region-based enhancement on the pseudo-exposures to boost details in the most distinct region. Thereby the region-enhanced pseudo-exposures are fused into an HDR image. The fused image thus enhances details in the bright region of the dark image and the dark region of the bright image. Compared with other inverse tone mapped methods, our method generates lower total contrast error measured under the dynamic range independent image quality assessment method in .


title: Gastrointestinal Food Allergy in Infants

abstract: ABSTRACT Food allergies are classified into three types, “IgE-mediated,” “combined IgE- and cell-mediated” and “cell-mediated/non-IgE-mediated,” depending on the involvement of IgE in their pathogenesis. Patients who develop predominantly cutaneous and/or respiratory symptoms belong to the IgE-mediated food allergy type. On the other hand, patients with gastrointestinal food allergy (GI allergy) usually develop gastrointestinal symptoms several hours after ingestion of offending foods; they belong to the cell-mediated/non-IgE-mediated or combined IgE- and cell-mediated food allergy types. GI allergies are also classified into a number of different clinical entities: food protein-induced enterocolitis syndrome (FPIES), food protein-induced proctocolitis (FPIP), food protein-induced enteropathy (Enteropathy) and eosinophilic gastrointestinal disorders (EGID). In the case of IgE-mediated food allergy, the diagnostic approaches and pathogenic mechanisms are well characterized. In contrast, the diagnostic approaches and pathogenic mechanisms of GI allergy remain mostly unclear. In this review, we summarized each type of GI allergy in regard to its historical background and updated clinical features, offending foods, etiology, diagnosis, examinations, treatment and pathogenesis. There are still many problems, especially in regard to the diagnostic approaches for GI allergy, that are closely associated with the definition of each disease. In addition, there are a number of unresolved issues regarding the pathogenic mechanisms of GI allergy that need further study and elucidation. Therefore, we discussed some of the diagnostic and research issues for GI allergy that need further investigation.


title: A minimally invasive surgery combining temporary percutaneous pedicle screw fixation without fusion and vertebroplasty with transpedicular intracorporeal hydroxyapatite blocks grafting for fresh thoracolumbar burst fractures: prospective study

abstract: BACKGROUND ::: The conventional surgical treatment for thoracolumbar burst fractures is physically invasive for the patient and also causes problems such as the sacrifice of healthy mobile segments to stabilize the fracture site. We performed a procedure for the treatment of fresh thoracolumbar burst fractures by combining percutaneous short pedicle screw fixation and vertebroplasty with transpedicular intracorporeal hydroxyapatite blocks grafting. ::: ::: ::: METHODS ::: Patients with type A3 fresh thoracolumbar burst fractures with no or mild neurological symptoms were treated using temporary posterior fixation without fusion. Consecutive 21 patients were studied, with a mean age of 45.4 years (range 23-73) and a mean follow-up period of 21.9 months (range 15-25). We evaluated operative time, estimated blood loss, low back pain on a visual analogue scale, change in the kyphotic angle, correction loss, bone union, and complications. ::: ::: ::: RESULTS ::: The average operative time was 95.7 min (range 69-143), and the average blood loss was 38.6 mL (range 10-130). The average correction angle was 9.6°. There were slight correction losses of height of the vertebral bodies. Bone union was obtained in all patients, with no instrumentation failures. Our procedure resulted in no surgery-related complications. ::: ::: ::: CONCLUSIONS ::: For the treatment of type A3 fresh thoracolumbar burst fractures, this method is less invasive and can preserve the adjacent healthy mobile segment. Our treatment is an optional therapeutic strategy for patients with thoracolumbar burst fractures and is a good option particularly for young adult patients.


title: New low-voltage class AB/AB CMOS op amp with rail-to-rail input/output swing

abstract: A new low-voltage CMOS Class AB/AB fully differential opamp with rail-to-rail input/output swing and supply voltage lower than two V/sub GS/ drops is presented. The scheme is based on combining floating-gate transistors and Class AB input and output stages. The op amp is characterized by low static power consumption and enhanced slew-rate. Moreover the proposed opamp does not suffer from typical reliability problems related to initial charge trapped in the floating-gate devices. Simulation and experimental results in 0.5-/spl mu/m CMOS technology verify the scheme operating with /spl plusmn/0.9-V supplies and close to rail-to-rail input and output swing.


title: An evaluation metric for image segmentation of multiple objects

abstract: It is important to be able to evaluate the performance of image segmentation algorithms objectively. In this paper, we define a new error measure which quantifies the performance of an image segmentation algorithm for identifying multiple objects in an image. This error measure is based on object-by-object comparisons of a segmented image and a ground-truth (reference) image. It takes into account the size, shape, and position of each object. Compared to existing error measures, our proposed error measure works at the object level, and is sensitive to both over-segmentation and under-segmentation. Hence, it can serve as a useful tool for comparing image segmentation algorithms and for tuning the parameters of a segmentation algorithm.


title: The effectiveness of staff support: evaluating Active Support training using a conditional probability approach

abstract: Active Support, a package of procedures which includes activity planning, support planning, and training on providing effective assistance, was introduced in five community residences serving 19 adults with severe mental retardation following a multiple baseline design. Real-time observational data were collected on the level of assistance residents received from staff and their engagement in activity. Active Support was shown in a companion paper (Jones et al., 1999) to increase the levels of assistance residents received and their engagement in activity. Increased assistance was particularly experienced by the behaviorally less able and the disparity in activity between the more and less able was reduced. In the analysis presented here, the effectiveness of assistance was evaluated before and after Active Support training by calculating the likelihood of engagement occurring given the occurrence of assistance. This likelihood was represented by the statistic, Yule’s Q. Yule’s Q significantly increased following Active Support training, an increase that was maintained at follow-up. The increased effectiveness of assistance was related to other research findings on the relationship between staff: resident interaction patterns and resident behavior.


title: Motor Learning Perspectives on Haptic Training for the Upper Extremities

abstract: Recent developments in neurorehabilitation have spawned numerous new robotic rehabilitation therapies. However, many of the concepts upon which these therapies are based are not fully understood and it may be necessary to explore some of the motor learning principles that apply to the use of haptics for motor learning in non-clinical scenarios/populations. We conducted a review of studies that utilized a haptic training paradigm teaching healthy participants to perform a motor skill involving the upper extremities. We discuss studies in the context of four important motor learning concepts: performance versus learning, feedback, observational learning, and functional task difficulty. Additionally, we note that the proliferation of research in haptic training has led to an extensive vocabulary of terms, some of which may be misnomers or redundant. We propose a classification of terms describing haptic training in an effort to provide clarity and further contextualize the studies. We believe that making connections to motor learning principles and clarifying meanings will facilitate a fuller understanding of the outcomes of studies in basic science research and allow for more directed applications of these training techniques to clinical populations.


title: Toward a transport model of collisionless magnetic reconnection

abstract: An absence of theoretical justification for the magnitude of resistivity is one of the major limitations of large-scale simulations of magnetic reconnection in collisionless magnetospheric plasma. We took advantage of the results of recent progress in kinetic modeling of collisionless dissipation in the vicinity of the magnetically neutral X point aiming to find ways to represent small-scale kinetic effects in large-scale models. The study was based on a combination of hybrid and particle methods and on analytical analysis. A comprehensive hybrid simulation code which incorporates the leading terms in electron dynamics responsible for breaking the frozen magnetic flux constraint (electron bulk flow inertia and nongyrotropic pressure effects) was utilized. The results of the comprehensive hybrid model were found to be in excellent quantitative agreement with the results of full particle simulations with similar setups. Both simulations demonstrated that the actual reconnection electric field is determined primarily by kinetic quasi-viscous effects and less by electron bulk flow inertia. An analytical expression for the quasi-viscous reconnection electric field averaged over the nongyrotropic region was obtained. Similar behavior of the evaluated quasi-viscous electric field and actual reconnection electric field taken from the simulations was demonstrated. Conventional hybrid simulations with simple nongyrotropic corrections to the electric field where also performed. The model was further reduced for utilization in MHD models. Analytical expressions for the time evolution of the reconnected flux evaluated from the MHD model modified by nongyrotropic corrections appeared to be in very good agreement with the results of comprehensive kinetic simulations. The evaluated averaged quasi-viscous electric field can be substituted into large-scale simulation models.


title: Analysis of context transfer in seamless IP mobility

abstract: As mobile devices roam between wireless cells, performing handovers, mobility is managed by protocols like Mobile IP and micro-mobility protocols. However, these protocols do not manage how to transfer context from cell to cell as the devices move. Context transfer mechanisms aim for performance improvements in the process of handover, so that the applications running on mobile nodes can operate with minimal disruption, thus allowing seamless mobility. In this paper we propose mechanisms that enable context transfer between access routers offering internet connectivity for mobile nodes; we use an actual implementation to test the performance of context transfer for different services and analyze the benefits that can be obtained using these mechanisms.


title: Improving performance by bridging the semantic gap between multi-queue SSD and I/O virtualization framework

abstract: Virtualization has become one of the most helpful techniques, and today it is prevalent in several computing environments including desktops, data-centers, and enterprises. However, an I/O scalability issue in virtualized environments still needs to be addressed because I/O layers are implemented to be oblivious to the I/O behaviors on virtual machines (VM). In particular, when a multi-queue solid state drive (SSD) is used as a secondary storage, each VM reveals semantic gap that degrades the overall performance of the VM by up to 74%. This is due to two key problems. First, the multi-queue SSD accelerates the possibility of lock contentions. Second, even though both the host machine and the multi-queue SSD provide multiple I/O queues for I/O parallelism, existing Virtio-Blk-Data-Plane supports only one I/O queue by an I/O thread for submitting all I/O requests. In this paper, we propose a novel approach, including the design of virtual CPU (vCPU)-dedicated queues and I/O threads, which efficiently distributes the lock contentions and addresses the parallelism issue of Virtio-Blk-Data-Plane in virtualized environments. We design our approach based on the above principle, which allocates a dedicated queue and an I/O thread for each vCPU to reduce the semantic gap. We also implement our approach based on Linux 3.17, and modify both the Virtio-Blk frontend driver of guest OS and the Virtio-Blk backend driver of Quick Emulator (QEMU) 2.1.2. Our experimental results with various I/O traces clearly show that our design improves the I/O operations per second (IOPS) in virtualized environments by up to 167% over existing QEMU.


title: Abnormal driving behavior detection for bus based on the Bayesian classifier

abstract: As the primary transportation in the urban traffic, bus plays a significant role in road safety. It would cause severe casualties if an accident occurred. To improve the safety of bus driving, we classify the specific types of latent abnormal driving behavior, which include sudden braking, lane changing casually, quick turn, fast U-turn and long time parking, and propose a method to identify the abnormal driving behavior of the bus. Firstly, we collect the acceleration, orientation and timestamp bus driving data through smartphone's accelerometer and orientation sensor. After de-nosing the collected data, we extract features in thirteen dimensions and train the naive Bayesian classifier, which is employed to detect and identify abnormal driving behaviors. The experimental results have been compared with support vector machine, and shows that the naive Bayesian classifier has an better performance than support vector machine on detecting and identifying various types of the abnormal bus driving behavior with the accuracy at 98.40%.


Sound Abstraction and Decomposition of Probabilistic Programs


title: Efficient spectral feature selection with minimum redundancy

abstract: Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and unsupervised feature selection, and has been proven to be effective in many real-world applications. One common drawback associated with most existing spectral feature selection algorithms is that they evaluate features individually and cannot identify redundant features. Since redundant features can have significant adverse effect on learning performance, it is necessary to address this limitation for spectral feature selection. To this end, we propose a novel spectral feature selection algorithm to handle feature redundancy, adopting an embedded model. The algorithm is derived from a formulation based on a sparse multi-output regression with a L2,1-norm constraint. We conduct theoretical analysis on the properties of its optimal solutions, paving the way for designing an efficient path-following solver. Extensive experiments show that the proposed algorithm can do well in both selecting relevant features and removing redundancy.


title: Technical, Experimental, and Biological Variations in Isobaric Tags for Relative and Absolute Quantitation (iTRAQ)

abstract: We assess the reliability of isobaric-tags for relative and absolute quantitation (iTRAQ), based on different types of replicate analyses taking into account technical, experimental, and biological variations. In total, 10 iTRAQ experiments were analyzed across three domains of life involving Saccharomyces cerevisiae KAY446, Sulfolobus solfataricus P2, and Synechocystis sp. PCC 6803. The coverage of protein expression of iTRAQ analysis increases as the variation tolerance increases. In brief, a cutoff point at ±50% variation (±0.50) would yield 88% coverage in quantification based on an analysis of biological replicates. Technical replicate analysis produces a higher coverage level of 95% at a lower cutoff point of ±30% variation. Experimental or iTRAQ variations exhibit similar behavior as biological variations, which suggest that most of the measurable deviations come from biological variations. These findings underline the importance of replicate analysis as a validation tool and benchmarking technique...


title: Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification

abstract: This paper proposes a new framework for deep learning that has been particularly tailored for hyperspectral image classification. We learn multiple levels of dictionaries in a robust fashion. The last layer is discriminative that learns a linear classifier. The training proceeds greedily; at a time, a single level of dictionary is learned and the coefficients used to train the next level. The coefficients from the final level are used for classification. Robustness is incorporated by minimizing the absolute deviations instead of the more popular Euclidean norm. The inbuilt robustness helps combat mixed noise (Gaussian and sparse) present in hyperspectral images. Results show that our proposed techniques outperform all other deep learning methods—deep belief network, stacked autoencoder, and convolutional neural network. The experiments have been carried out on both benchmark deep learning data sets (MNIST, CIFAR-10, and Street View House Numbers) as well as on real hyperspectral imaging data sets.


title: Design and Implementation of a FPGA-Based Controller for Resonant Inverters

abstract: Design and implementation of a PGA-based controller is presented in this paper. First general control scheme for resonant inverter is introduced and the PGA-based controller construction is given. Then the small signal model of the close loop system for a resonant inverter is analyzed. Thus compensator can be designed. Moreover, the key functional blocks within FPGA including variable frequency pulse width modulator (VFPWM), PLL and power control loop are discussed in detail. Finally experimental architecture and results are given.


title: The Effect of Leadership Leadership on Organizational Justice Justice

abstract: The effect of leadership on organizational justice was examined in this meta-analytic study. A total of 141 research studies were compiled as a result of the review activity, out of which 27 were included in the meta-analysis. The 27 research studies were collapsed to obtain a sample size of 16,639 subjects. The analytic results of the random effects model showed that leadership has a medium-magnitude, positive effect on organizational justice . Of the moderators identified, such as the publication type, year of publication , sample group /sector, leadership style /approach, leadership scale and organizational justice scale, it was found that the publication type, leadership style /approach, leadership scale and organizational justice scale were moderating variables.


title: SUMMAC : a text summarization evaluation

abstract: The TIPSTER Text Summarization Evaluation (SUMMAC) has developed several new extrinsic and intrinsic methods for evaluating summaries. It has established definitively that automatic text summarization is very effective in relevance assessment tasks on news articles. Summaries as short as 17% of full text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in accuracy. Analysis of feedback forms filled in after each decision indicated that the intelligibility of present-day machine-generated summaries is high. Systems that performed most accurately in the production of indicative and informative topic-related summaries used term frequency and co-occurrence statistics, and vocabulary overlap comparisons between text passages. However, in the absence of a topic, these statistical methods do not appear to provide any additional leverage: in the case of generic summaries, the systems were indistinguishable in accuracy. The paper discusses some of the tradeoffs and challenges faced by the evaluation, and also lists some of the lessons learned, impacts, and possible future directions. The evaluation methods used in the SUMMAC evaluation are of interest to both summarization evaluation as well as evaluation of other ‘output-related’ NLP technologies, where there may be many potentially acceptable outputs, with no automatic way to compare them.


title: Using metrics in Agile and Lean Software Development - A systematic literature review of industrial studies

abstract: ContextSoftware industry has widely adopted Agile software development methods. Agile literature proposes a few key metrics but little is known of the actual metrics use in Agile teams. ObjectiveThe objective of this paper is to increase knowledge of the reasons for and effects of using metrics in industrial Agile development. We focus on the metrics that Agile teams use, rather than the ones used from outside by software engineering researchers. In addition, we analyse the influence of the used metrics. MethodThis paper presents a systematic literature review (SLR) on using metrics in industrial Agile software development. We identified 774 papers, which we reduced to 30 primary studies through our paper selection process. ResultsThe results indicate that the reasons for and the effects of using metrics are focused on the following areas: sprint planning, progress tracking, software quality measurement, fixing software process problems, and motivating people. Additionally, we show that although Agile teams use many metrics suggested in the Agile literature, they also use many custom metrics. Finally, the most influential metrics in the primary studies are Velocity and Effort estimate. ConclusionThe use of metrics in Agile software development is similar to Traditional software development. Projects and sprints need to be planned and tracked. Quality needs to be measured. Problems in the process need to be identified and fixed. Future work should focus on metrics that had high importance but low prevalence in our study, as they can offer the largest impact to the software industry.


title: Head Pose Estimation for Driver Assistance Systems: A Robust Algorithm and Experimental Evaluation

abstract: Recognizing driver awareness is an important prerequisite for the design of advanced automotive safety systems. Since visual attention is constrained to a driver's field of view, knowing where a driver is looking provides useful cues about his activity and awareness of the environment. This work presents an identity-and lighting-invariant system to estimate a driver's head pose. The system is fully autonomous and operates online in daytime and nighttime driving conditions, using a monocular video camera sensitive to visible and near-infrared light. We investigate the limitations of alternative systems when operated in a moving vehicle and compare our approach, which integrates Localized Gradient Orientation histograms with support vector machines for regression. We estimate the orientation of the driver's head in two degrees-of-freedom and evaluate the accuracy of our method in a vehicular testbed equipped with a cinematic motion capture system.


title: Self Adversarial Training for Human Pose Estimation

abstract: This paper presents a deep learning based approach to the problem of human pose estimation. We employ generative adversarial networks as our learning paradigm in which we set up two stacked hourglass networks with the same architecture, one as the generator and the other as the discriminator. The generator is used as a human pose estimator after the training is done. The discriminator distinguishes ground-truth heatmaps from generated ones, and back-propagates the adversarial loss to the generator. This process enables the generator to learn plausible human body configurations and is shown to be useful for improving the prediction accuracy.


title: Benefits and Factors Influencing the Design of Intergenerational Digital Games: A Systematic Literature Review

abstract: The main purpose of this paper is to review the benefits and factors to be taken into consideration for the design of intergenerational digital games. We conducted a systematic in Scopus, Web of Science, PsicInfo, Pubmed and Science Direct, finally including 16 empirical studies written in English. The identified benefits were found to fall into four main categories, i.e., (1) reinforcing family bond, (2) enhancing reciprocal learning (3) increasing understanding of the other generation and (4) reducing social anxiety. According to the literature, two types of factors are important to take into consideration: player-centric and game-centric factors. We identified the nature of the interactions between older (55–81 year-olds) and younger players (4–22 year-olds), their motivations to play digital games and the difference in abilities as the main player-centric factors to take into account when designing intergenerational games. The most relevant game-centric factors were found to be goal-related and space-related forms of interaction. To gain more insight into how specific benefits of playing digital games are related to a type of game, gender or age of the participant, additional empirical studies (comparative analyses), that take these factors into account are needed.


title: 3D feature extraction from uncalibrated video clips

abstract: This paper explores the idea of extracting a dense 3D point cloud corresponding to salient features in a video. The goal is to generate the dense point cloud efficiently, in order to use the information in various other video processing tasks. We present a method that is capable of extracting 3D information of videos with no previous knowledge of the scene, while keeping computational costs low. Our method exploits the movement of the camera while robustly tracking features over time, in order to obtain multiple views of a scene and perform 3D reconstruction. Additionally, our system is able to cope with individually moving people seen in the videos, and can estimate each person's pose and fit a 3D model to it. This 3D model is inserted into the dense point cloud in order to visualize the reconstructed scenes, and does not affect the tracking of the rest of the scene


title: A HTTP cookie covert channel

abstract: This paper presents a new covert channel based on Google Analytic web cookies in HTTP protocol. The new covert channel is difficult to disrupt and is capable of reasonably high bandwidths. The Google Analytic framework is used by over half of the most popular web sites currently on the Internet; its ubiquitousness across the web implies a great impact of this covert.


title: Robust Product Design Using SOSM for Control of Shielded Metal Arc-Welding (SMAW) Process

abstract: Parametric variations, slow and fast disturbances, and nonlinearity are inherently present in abundance in all types of arc-welding processes, and shielded metal arc-welding (SMAW) process is no exception. PI has remained, for decades, a dominating control idea for control of all arc-welding processes. Its replacement by superior control concept is long overdue particularly for handling parametric variations, nonlinearity in load behavior, and several forms of disturbances present in the system. Though the work-in-progress concepts, such as sliding-mode control (SMC), are equipped to handle many of these issues, their market penetration in shaping up a complete industrial product is below expected line. In this paper, the second-order sliding mode (SOSM) control concept is practically implemented to create a market-ready product for robust control of SMAW process. Here, each aspect of the entire process such as welding at different current using different types of electrodes is detailed. It further proposes, with complete practical validation, a simple and easily implementable control function that emulates robust control features of SOSM.


Professionalism and ethics.


title: A comparison between neural-network forecasting techniques—case study: river flow forecasting

abstract: Estimating the flows of rivers can have significant economic impact, as this can help in agricultural water management and in protection from water shortages and possible flood damage. The first goal of the paper is to apply neural networks to the problem of forecasting the flow of the River Nile in Egypt. The second goal of the paper is to utilize time series as a benchmark to compare between several neural-network forecasting methods. We compare four different methods to preprocess the inputs and outputs, including a novel method proposed here based on discrete Fourier series. We also compare three different methods for the multistep ahead forecast problem: the direct method, the recursive method, and the recursive method trained using a backpropagation through time scheme. We also include a theoretical comparison between these three methods. The final comparison is between different methods to perform a longer horizon forecast, and that includes ways to partition the problem into several subproblems of forecasting K steps ahead.


Addressing the Winograd Schema Challenge as a Sequence Ranking Task


title: Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization

abstract: The aim of this paper is twofold. First, we present a thorough experimental study to show the superiority of the generalization capability of the support vector machine (SVM) approach in the automatic classification of electrocardiogram (ECG) beats. Second, we propose a novel classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the basis of ECG data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In particular, they were organized so as to test the sensitivity of the SVM classifier and that of two reference classifiers used for comparison, i.e., the k-nearest neighbor (kNN) classifier and the radial basis function (RBF) neural network classifier, with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the SVM approach as compared to traditional classifiers, and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. On an average, over three experiments making use of a different total number of training beats (250, 500, and 750, respectively), the PSO-SVM yielded an overall accuracy of 89.72% on 40438 test beats selected from 20 patient records against 85.98%, 83.70%, and 82.34% for the SVM, the kNN, and the RBF classifiers, respectively.


title: Performance Analysis of Load Balancing Architectures in Cloud Computing

abstract: The Cloud computing is a rapidly emerging distributed system paradigm that offers a huge amount of IT resources as utility services at a reduced cost and flexible schemes. The key of such flexibility is an efficient load balancer that offers better management and utilization of virtualized underlying cloud infrastructures. However, most of the existing load balancers in cloud computing are based on either centralized or fully distributed architectures while the idea of harnessing multiple load balancers in a hierarchical structure to improve the sever load and job response time is still under studied. Therefore, this paper, aims at bridging this gap by providing a comparative study between the three load balancing architectures in cloud computing: centralized, decentralized and hierarchical load balancers. The experimental results suggest that the hierarchical architecture for load balancers best suits the public cloud environment and call for further research to test whether these results can be generalized for other types of clouds.


title: V2G Parking Lot With PV Rooftop for Capacity Enhancement of a Distribution System

abstract: Vehicle-to-grid (V2G) units are gaining prominence and may dominate the auto-market in the near future. The V2G batteries require corporate parking lots for charging and discharging operations. The electric power capacity of an existing parking lot can be increased by the installation of photovoltaic (PV) rooftops. This paper describes mathematical models for estimating the electric power capacity of a V2G parking lot (VPL) system with PV canopy. The electric vehicle (EV) demand/supply model was formulated as a queuing theory problem, exhibiting stochastic characteristics. New formulae were developed to address the impacts of battery charger efficiency on the amount of power demand during battery charging, and also how the latter is effected by inverter efficiency during discharging. Mathematical models for grid gain factor were developed. The proposed models were tested using Tesla Roadster EV and Nissan leaf EV. Promising simulation results are gained leading to a conclusion that vehicle parking lots with PV facilities can potentially relieve and enhance the grid capacity. Results show that 60% gain factor is possible. The effect of weather uncertainties and energy market price were studied. The study could be useful in battery-charger control studies.


2 Vec : Learning Author Representations by Combining Content and Link Information


title: Concept Transfer Learning for Adaptive Language Understanding

abstract: Concept definition is important in language understanding (LU) adaptation since literal definition difference can easily lead to data sparsity even if different data sets are actually semantically correlated. To address this issue, in this paper, a novel concept transfer learning approach is proposed. Here, substructures within literal concept definition are investigated to reveal the relationship between concepts. A hierarchical semantic representation for concepts is proposed, where a semantic slot is represented as a composition of {\em atomic concepts}. Based on this new hierarchical representation, transfer learning approaches are developed for adaptive LU. The approaches are applied to two tasks: value set mismatch and domain adaptation, and evaluated on two LU benchmarks: ATIS and DSTC 2\&3. Thorough empirical studies validate both the efficiency and effectiveness of the proposed method. In particular, we achieve state-of-the-art performance ($F_1$-score 96.08\%) on ATIS by only using lexicon features.


title: A Survey of Data Provenance in e-Science

abstract: Data management is growing in complexity as large-scale applications take advantage of the loosely coupled resources brought together by grid middleware and by abundant storage capacity. Metadata describing the data products used in and generated by these applications is essential to disambiguate the data and enable reuse. Data provenance, one kind of metadata, pertains to the derivation history of a data product starting from its original sources.In this paper we create a taxonomy of data provenance characteristics and apply it to current research efforts in e-science, focusing primarily on scientific workflow approaches. The main aspect of our taxonomy categorizes provenance systems based on why they record provenance, what they describe, how they represent and store provenance, and ways to disseminate it. The survey culminates with an identification of open research problems in the field.


title: Deep Unsupervised Clustering Using Mixture of Autoencoders

abstract: Part of this work was done when Dejiao Zhang was doing an internship at Technicolor Research. Both Dejiao Zhang and Laura Balzano’s participations were funded by DARPA-16-43-D3M-FP-037. Both Yifan Sun and Brian Eriksson's participation occurred while also at Technicolor Research.


title: Systematic reviews in software engineering: An empirical investigation

abstract: Background Systematic Literature Reviews (SLRs) have gained significant popularity among Software Engineering (SE) researchers since 2004. Several researchers have also been working on improving the scientific and methodological infrastructure to support SLRs in SE. We argue that there is also an apparent and essential need for evidence-based body of knowledge about different aspects of the adoption of SLRs in SE. Objective The main objective of this research is to empirically investigate the adoption, value, and use of SLRs in SE research from various perspectives. Method We used mixed-methods approach (systematically integrating tertiary literature review, semi-structured interviews and questionnaire-based survey) as it is based on a combination of complementary research methods which are expected to compensate each others’ limitations. Results A large majority of the participants are convinced of the value of using a rigourous and systematic methodology for literature reviews in SE research. However, there are concerns about the required time and resources for SLRs. One of the most important motivators for performing SLRs is new findings and inception of innovative ideas for further research. The reported SLRs are more influential compared to the traditional literature reviews in terms of number of citations. One of the main challenges of conducting SLRs is drawing a balance between methodological rigour and required effort. Conclusions SLR has become a popular research methodology for conducting literature review and evidence aggregation in SE. There is an overall positive perception about this relatively new methodology to SE research. The findings provide interesting insights into different aspects of SLRs. We expect that the findings can provide valuable information to readers about what can be expected from conducting SLRs and the potential impact of such reviews.


title: A simple approach on implementing IMU sensor fusion in PID controller for stabilizing quadrotor flight control

abstract: This paper discussed about a simple approach for implementing well-known PID controller in microcontroller for quadrotor project. This controller will be used for stabilizing the quadrotor while in flight. Towards to stabilize this type of air craft, sensors must be used as the feedback for PID controller. A new method in sensor fusion and inclination angle estimation is discussed in this paper. A simple system architecture for controlling four motors also included as a reference for the flow of the programming sequence. Single axis test has been done with result to prove this method and discussed in this paper.


title: Parsing costs as predictors of reading difficulty: An evaluation using the Potsdam Sentence Corpus

abstract: The surprisal of a word on a probabilistic grammar constitutes a promising complexity metric for human sentence comprehension difficulty. Using two different grammar types, surprisal is shown to have an effect on fixation durations and regression probabilities in a sample of German readers’ eye movements, the Potsdam Sentence Corpus. A linear mixed-effects model was used to quantify the effect of surprisal while taking into account unigram frequency and bigram frequency (transitional probability), word length, and empirically-derived word predictability; the so-called “early” and “late” measures of processing difficulty both showed an effect of surprisal. Surprisal is also shown to have a small but statistically non-significant effect on empirically-derived predictability itself. This work thus demonstrates the importance of including parsing costs as a predictor of compre- hension difficulty in models of reading, and suggests that a simple identification of syntactic parsing costs with early measures and late measures with durations of post-syntactic events may be difficult to uphold. Journal of Eye Movement Research


title: A Novel 28 GHz Beam Steering Array for 5G Mobile Device With Metallic Casing Application

abstract: The design of a novel practical 28 GHz beam steering phased array antenna for future fifth generation mobile device applications is presented in this communication. The proposed array antenna has 16 cavity-backed slot antenna elements that are implemented via the metallic back casing of the mobile device, in which two eight-element phased arrays are built on the left- and right-side edges of the mobile device. Each eight-element phased array can yield beam steering at broadside and gain of >15 dBi can be achieved at boresight. The measured 10 dB return loss bandwidth of the proposed cavity-backed slot antenna element was approximately 27.5–30 GHz. In addition, the impacts of user’s hand effects are also investigated.


title: Distribution grid impact of Plug-In Electric Vehicles charging at fast charging stations using stochastic charging model

abstract: Charging PEVs (Plug-In Electric Vehicles) at public fast charging station can improve the public acceptance and increase their penetration level by solving problems related to vehicles' battery. However, the price for the impact of fast charging stations on the distribution grid has to be dealt with. The main purpose of this paper is to investigate the impacts of fast charging stations on a distribution grid using a stochastic fast charging model and to present the charging model with some of its results. The model is used to investigate the impacts on distribution transformer loading and system bus voltage profiles of the test distribution grid. Stochastic and deterministic modelling approaches are also compared. It is concluded that fast charging stations affect transformer loading and system bus voltage profiles. Hence, necessary measures such as using local energy storage and voltage conditioning devices, such as SVC (Static Var Compensator), have to be used at the charging station to handle the problems. It is also illustrated that stochastic modelling approach can produce a more sound and realistic results than deterministic approach.


title: Ant System: An Autocatalytic Optimizing Process

abstract: A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find acceptable solutions in the early stages of the search process. An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions. We report on many simulation results and discuss the working of the algorithm. Some hints about how this approach can be applied to a variety of optimization problems are also given.


title: Cooperative Intersection Management: A Survey

abstract: Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections.


title: ProvDB: Lifecycle Management of Collaborative Analysis Workflows

abstract: As data-driven methods are becoming pervasive in a wide variety of disciplines, there is an urgent need to develop scalable and sustainable tools to simplify the process of data science, to make it easier for the users to keep track of the analyses being performed and datasets being generated, and to enable the users to understand and analyze the workflows. In this paper, we describe our vision of a unified provenance and metadata management system to support lifecycle management of complex collaborative data science workflows. We argue that the information about the analysis processes and data artifacts can, and should be, captured in a semi-passive manner; and we show that querying and analyzing this information can not only simplify bookkeeping and debugging tasks but also enable a rich new set of capabilities like identifying flaws in the data science process itself. It can also significantly reduce the user time spent in fixing post-deployment problems through automated analysis and monitoring. We have implemented a prototype system, PROVDB, on top of git and Neo4j, and we describe its key features and capabilities.


title: Experimental Validation of Models Applicable to the Ultrasonic Inspection of Nuclear Components

abstract: Achieving reliable inspection of nuclear reactor components requires the development and proper field implementation, of a variety of ultrasonic techniques. Because of advances in ultrasonic technology and concerns with ever changing potential failure modes of aging reactors, new inspection techniques are constantly undergoing development and validation. The cost of a purely experimental approach to this process can be excessive due to sample fabrication, measurement and data interpretation, and destructive analysis. Consequently, research efforts have been aimed at the development and application of models which will help reduce those costs by providing theoretical guidance [1,2]. Reported here are the results of an experimental program which was undertaken with the goal of determining the accuracy of the models in predicting inspection results. Two models were considered. The first predicts the evolution of ultrasonic field patterns as a beam propagates from a transducer and into a component. The second predicts the ultrasonic inspection response of a branched crack, which is an idealization of an intergranular stress corrosion crack (IGSCC).


title: Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation

abstract: Generating scene graph to describe all the relations inside an image gains increasing interests these years. However, most of the previous methods use complicated structures with slow inference speed or rely on the external data, which limits the usage of the model in real-life scenarios. To improve the efficiency of scene graph generation, we propose a subgraph-based connection graph to concisely represent the scene graph during the inference. A bottom-up clustering method is first used to factorize the entire scene graph into subgraphs, where each subgraph contains several objects and a subset of their relationships. By replacing the numerous relationship representations of the scene graph with fewer subgraph and object features, the computation in the intermediate stage is significantly reduced. In addition, spatial information is maintained by the subgraph features, which is leveraged by our proposed Spatial-weighted Message Passing~(SMP) structure and Spatial-sensitive Relation Inference~(SRI) module to facilitate the relationship recognition. On the recent Visual Relationship Detection and Visual Genome datasets, our method outperforms the state-of-the-art method in both accuracy and speed.


title: MIMO-OFDM Radar Using a Linear Frequency Modulated Carrier to Reduce Sampling Requirements

abstract: The requirement of fast analog-to-digital (AD) and digital-to-analog (DA) converters is a challenge for high-resolution orthogonal frequency division multiplexing (OFDM) radars. This paper introduces a method to increase the modulation bandwidth of an OFDM signal by using an additional frequency modulated continuous wave carrier to reduce the requirements for the AD/DA converters while maintaining a high unambiguous velocity range. Multiplexing of transmit antennas with OFDM is still possible with the combined modulation. The presented approach is verified by simulations and measurements. A reduction of AD/DA converter bandwidth by factor eight is achieved.


title: Skin detection: a Bayesian network approach

abstract: The automated detection and tracking of humans in computer vision necessitates improved modeling of the human skin appearance. We propose a Bayesian network approach for skin detection. We test several classifiers and propose a methodology for incorporating unlabeled data. We apply the semi-supervised approach to skin detection and we show that learning the structure of Bayesian network classifiers enables learning good classifiers with a small labeled set and a large unlabeled set.


title: Media Use in School-Aged Children and Adolescents

abstract: This policy statement focuses on children and adolescents 5 through 18 years of age. Research suggests both benefits and risks of media use for the health of children and teenagers. Benefits include exposure to new ideas and knowledge acquisition, increased opportunities for social contact and support, and new opportunities to access health-promotion messages and information. Risks include negative health effects on weight and sleep; exposure to inaccurate, inappropriate, or unsafe content and contacts; and compromised privacy and confidentiality. Parents face challenges in monitoring their children's and their own media use and in serving as positive role models. In this new era, evidence regarding healthy media use does not support a one-size-fits-all approach. Parents and pediatricians can work together to develop a Family Media Use Plan (www.healthychildren.org/MediaUsePlan) that considers their children's developmental stages to individualize an appropriate balance for media time and consistent rules about media use, to mentor their children, to set boundaries for accessing content and displaying personal information, and to implement open family communication about media.


title: A Novel Three-Step Method for Correction of Type 1 Stahl Ear

abstract: BACKGROUND ::: Stahl ear, a rare congenital auricular anomaly, occurs when a third crus crosses the scaphoid fossa. Optimal elimination of the third crus and reconstruction the superior crus in type 1 Stahl ear remain undercharacterized. We present a novel 3-step technique to reliably correct a type 1 Stahl ear as a case report. ::: ::: ::: METHODS ::: A 10-year-old male with bilateral prominent type 1 Stahl ears is presented. An anterior curvilinear helical fold incision and posterior V wedge excision was used for exposure. Step 1, the third crus cartilage was excised as a full-thickness wedge and the gap approximated with posteriorly everting horizontal mattress sutures to create a flattened scaphoid fossa. Step 2, the superior crus was created using anteriorly placed horizontal mattress sutures for direct shaping. Step 3, the skin was redraped without excess trimming to minimize the visible scar. ::: ::: ::: RESULTS ::: The 3-step approach effectively eliminated the third crura and recreated the superior crura of the type 1 Stahl ears, and resulted in minimal visible anterior scarring. ::: ::: ::: DISCUSSION ::: The anterior approach provided excellent visualization and permitted tactical suture placement. The wedge excisions successfully removed the third crura. The horizontal mattress sutures to flatten the scaphoid fossa, enhance the natural antihelical curves, and form the superior crura were simple and effective.


title: TDParse - multi-target-specific sentiment recognition on Twitter

abstract: Existing target-specific sentiment recognition methods consider only a single target per tweet, and have been shown to miss nearly half of the actual targets mentioned. We present a corpus of UK election tweets, with an average of 3.09 entities per tweet and more than one type of sentiment in half of the tweets. This requires a method for multi-target specific sentiment recognition, which we develop by using the context around a target as well as syntactic dependencies involving the target. We present results of our method on both a benchmark corpus of single targets and the multi-target election corpus, showing state-of-the art performance in both corpora and outperforming previous approaches to multi-target sentiment task as well as deep learning models for single-target sentiment.


title: A review on speaker diarization systems and approaches

abstract: Speaker indexing or diarization is an important task in audio processing and retrieval. Speaker diarization is the process of labeling a speech signal with labels corresponding to the identity of speakers. This paper includes a comprehensive review on the evolution of the technology and different approaches in speaker indexing and tries to offer a fully detailed discussion on these approaches and their contributions. This paper reviews the most common features for speaker diarization in addition to the most important approaches for speech activity detection (SAD) in diarization frameworks. Two main tasks of speaker indexing are speaker segmentation and speaker clustering. This paper includes a separate review on the approaches proposed for these subtasks. However, speaker diarization systems which combine the two tasks in a unified framework are also introduced in this paper. Another discussion concerns the approaches for online speaker indexing which has fundamental differences with traditional offline approaches. Other parts of this paper include an introduction on the most common performance measures and evaluation datasets. To conclude this paper, a complete framework for speaker indexing is proposed, which is aimed to be domain independent and parameter free and applicable for both online and offline applications.


title: Moving Beyond Feature Design: Deep Architectures and Automatic Feature Learning in Music Informatics

abstract: The short history of content-based music informatics research is dominated by hand-crafted feature design, and our community has grown admittedly complacent with a few de facto standards. Despite commendable progress in many areas, it is increasingly apparent that our efforts are yielding diminishing returns. This deceleration is largely due to the tandem of heuristic feature design and shallow processing architectures. We systematically discard hopefully irrelevant information while simultaneously calling upon creativity, intuition, or sheer luck to craft useful representations, gradually evolving complex, carefully tuned systems to address specific tasks. While other disciplines have seen the benefits of deep learning, it has only recently started to be explored in our field. By reviewing deep architectures and feature learning, we hope to raise awareness in our community about alternative approaches to solving MIR challenges, new and old alike.


Energy Band Changes in Perovskites Due to Lattice Polarization


title: A Multi-modal Deep Neural Network approach to Bird-song identification

abstract: We present a multi-modal Deep Neural Network (DNN) approach for bird song identification. The presented approach takes both audio samples and metadata as input. The audio is fed into a Convolutional Neural Network (CNN) using four convolutional layers. The additionally provided metadata is processed using fully connected layers. The flattened convolutional layers and the fully connected layer of the metadata are joined and fed into a fully connected layer. The resulting architecture achieved 2., 3. and 4. rank in the BirdCLEF2017 task in various training configurations.


title: Study on bond pad damage issue in bare Cu wire bonding on SMOS8MV wafer technology

abstract: Because of lower cost, better thermal/electrical properties and reliability performances, Cu wire was replacing the traditional Au wire gradually these days in wire bonding IC connections. While Cu was harder than Au, Cu wire bonding would need higher bonding force and more ultrasonic power; which might introduce mechanical damages to bond pad and form cracking or cratering. In this paper the bond pad damage issue was found in a SOIC 32ld device with SMOS8MV wafer technology; and FIB cross section confirmed it terminated in IMD2 (Inter Metal Dielectric) layer just under the pad top metal layer. Optimizations of wire bonding parameters, including EFO (Electric Flame Off) and ball bond parameters, failed to eliminate this pad damage issue; while the experiments found the variations in a same cratering check result and the differences between 2 different cratering check results were so obvious that the improper cratering check method should be suspected as one root cause instead of unoptimized wire bonding parameters. Total 5 cratering check methods were tried on samples built with same bonding parameters and one method of two-step etching under room temperature got the least damaged pad ratio. This method cleared up cratering check noises except on the issued pad with the fixed location. Cross sections found an additional IMD2 layer between metal2 and pad top metal in the wafer structure of the issued pad, compared with that of normal pads; which might weaken the structure and introduce bond pad damages during wire bonding or cratering check. And these two metal layers, metal2 and pad top metal, were stacked together directly to clear up this bond pad damage issue of the issued pad finally. This bond pad issue was solved after tries on wire bonding parameters, cratering check method and wafer structure; and a further research would be continued to unmask the mechanism.


title: Design and implementation of a smart solid waste monitoring and collection system based on Internet of Things

abstract: Solid waste generated is an ever growing problem at local regions or at global levels. There is proper disposal of solid wastes pollute all the components of the green environment (i.e., air, land and water) at regional and global levels. Since there is rapid increase in producing or consumptions, quantity of wastes generated by the urban society has increased. The problem is more faced in the developing countries than in developed countries, as the economic growth as well as urbanization is more rapid. The continuous flow of garbage in all places where public people move around creates the unhygienic situations. It may invoke several injurious diseases among the nearby people. To avoid such a situation and to improve the cleaning, ‘smart waste management system’ is proposed. In the proposed system, the completeness of waste in the dustbins is checked with the help of Sensors used in the system, and information is sent to the required control room through GSM/GPRS system. Renesas Microcontroller is used to communicate the sensor system with GSM system. An android application is been designed to monitor the information related to the waste for different selected locations. Through this the collection of garbage can be made efficiently.


title: Design and Optimization of a 380–12 V High-Frequency, High-Current LLC Converter With GaN Devices and Planar Matrix Transformers

abstract: Data centers demand high-current, high-efficiency, and low-cost power solutions. The high-voltage dc distribution power architecture has been drawing attention due to its lower conduction loss on cables and harnesses. In this structure, the 380–12 V high output current isolated converter is the key stage. This paper presents a 1-MHz 1-kW LLC resonant converter using GaN devices and planar matrix transformers that are designed and optimized for this application. The transformer design and the optimization of the output capacitor termination are performed and verified. Finally, this cost-effective converter achieves above 97% peak efficiency and 700-W/in $^{{{3}}}$ power density.


title: The future of work: Insights for CSCW

abstract: Knowing the future of work is something that we all wish to do in order to be prepared for it. Unfortunately, humans cannot predict precisely the future of anything yet. Nevertheless, in this paper we try to get as close as we can to it by looking at some trends of the future of work. This challenge is undertaken by reviewing the scientific literature on the subject. We elaborate on some insights for CSCW based on the trends of the future of work and society.


title: Preventing Falls and Fall-Related Injuries in Hospitals

abstract: Falls are a widespread concern in hospitals settings, with whole hospital rates of between 3 and 5 falls per 1000 bed-days representing around a million inpatient falls occurring in the United States each year. Between 1% and 3% of falls in hospitals result in fracture, but even minor injuries can cause distress and delay rehabilitation. Risk factors most consistently found in the inpatient population include a history of falling, muscle weakness, agitation and confusion, urinary incontinence or frequency, sedative medication, and postural hypotension. Based on systematic reviews, recent research, and clinical and ethical considerations, the most appropriate approach to fall prevention in the hospital environment includes multifactorial interventions with multiprofessional input. There is also some evidence that delirium avoidance programs, reducing sedative and hypnotic medication, in-depth patient education, and sustained exercise programs may reduce falls as single interventions. There is no convincing evidence that hip protectors, movement alarms, or low-low beds reduce falls or injury in the hospital setting. International approaches to developing and maintaining a fall prevention program suggest that commitment of management and a range of clinical and support staff is crucial to success.


title: The critical success factors model for e-Government implementation in Indonesia

abstract: Information Technology Utilization (ITU) considered as one of success factor for public sector services in the government organization. As one of the countries that conduct bureaucratic reforms, Indonesia has sought to implement IT as an embodiment of Presidential Instruction (Inpres) in 2013, which contains the implementation of e-Government services. Through e-Government services, public service processes in Indonesia targeted to be more effective, efficient, accountable and transparent. In order to reduce the failure of e-Government implementation process, determining success factors would be an adding value thus also leads to the successful of service implementation. Several type of research have been conducted in regards to successful of e-Government implementation, especially on Critical Success Factor (CSF). Through Meta-Ethnography approach, this study revealed four critical success factors of e-Government Implementation in Indonesia, namely: organization, processes, people and technology. Further, the proposed model of this CSF will be tested in government organization across Indonesia.


title: Text summarization using enhanced MMR technique

abstract: Now a day when huge amount of documents and web contents are available, so reading of full content is somewhat difficult. Summarization is a way to give abstract form of large document so that the moral of the document can be communicated easily. Current research in automatic summarization is dominated by some effective, yet naive approaches: summarization through extraction, summarization through Abstraction and multi-document summarization. These techniques are used to building a summary of a document. Although there are a number of techniques implemented for the summarization of text for the single document or for the online web data or for any language. Here in this paper we are implemented an efficient technique for text summarization to reduce the computational cost and time and also the storage capacity.