title: Predictive Network Representation Learning for Link Prediction

abstract: In this paper, we propose a predictive network representation learning (PNRL) model to solve the structural link prediction problem. The proposed model defines two learning objectives, i.e., observed structure preservation and hidden link prediction. To integrate the two objectives in a unified model, we develop an effective sampling strategy to select certain edges in a given network as assumed hidden links and regard the rest network structure as observed when training the model. By jointly optimizing the two objectives, the model can not only enhance the predictive ability of node representations but also learn additional link prediction knowledge in the representation space. Experiments on four real-world datasets demonstrate the superiority of the proposed model over the other popular and state-of-the-art approaches.


title: Improving the Robustness of Deep Neural Networks via Stability Training

abstract: In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such as compression, rescaling, and cropping. We validate our method by stabilizing the state of-the-art Inception architecture [11] against these types of distortions. In addition, we demonstrate that our stabilized model gives robust state-of-the-art performance on largescale near-duplicate detection, similar-image ranking, and classification on noisy datasets.


title: News topics categorization using latent Dirichlet allocation and sparse representation classifier

abstract: Recently, subscribing news from websites has become a new trend for many Internet users. In a news reading browser, it is essential all the news documents are properly categorized. For automatically categorizing the news topics, this paper presents a news categorization method using latent Dirichlet allocation (LDA) and sparse representation classifier (SRC). In our work, the LDA is used as the feature learning method. The multinomial distribution of the news topics is regarded as the feature of the document. These features are stacked as an over-complete dictionary, permitting us to perform SRC-based categorization. The experimental results show that the proposed method outperforms the traditional method.


The significance of disc lesions.


title: Effects of weight gain and weight loss on regional fat distribution

abstract: Background: Normal-weight adults gain lower-body fat via adipocyte hyperplasia and upper-body subcutaneous (UBSQ) fat via adipocyte hypertrophy. Objectives: We investigated whether regional fat loss mirrors fat gain and whether the loss of lower-body fat is attributed to decreased adipocyte number or size. Design: We assessed UBSQ, lower-body, and visceral fat gains and losses in response to overfeeding and underfeeding in 23 normalweight adults (15 men) by using dual-energy X-ray absorptiometry and abdominal computed tomography scans. Participants gained w5% of weight in 8 wk and lost w80% of gained fat in 8 wk. We measured abdominal subcutaneous and femoral adipocyte sizes and numbers after weight gain and loss. Results: Volunteers gained 3.1 6 2.1 (mean 6 SD) kg body fat with overfeeding and lost 2.4 6 1.7 kg body fat with underfeeding. Although UBSQ and visceral fat gains were completely reversed after 8 wk of underfeeding, lower-body fat had not yet returned to baseline values. Abdominal and femoral adipocyte sizes, but not numbers, decreased with weight loss. Decreases in abdominal adipocyte size and UBSQ fat mass were correlated (r = 0.76, P = 0.001), as were decreases in femoral adipocyte size and lower-body fat (r = 0.49, P = 0.05). Conclusions: UBSQ and visceral fat increase and decrease proportionately with a short-term weight gain and loss, whereas a gain of lower-body fat does not relate to the loss of lower-body fat. The loss of lower-body fat is attributed to a reduced fat cell size, but not number, which may result in long-term increases in fat cell numbers. Am J Clin Nutr 2012;96:229–33.


title: A new radical-based approach to online handwritten Chinese character recognition

abstract: This paper proposes a new radical-based approach for online handwritten chinese character recognition. The approach is novel in three respects: statistical classification of radicals, over-segmentation of characters into candidate radicals, and lexicon-driven recognition of characters. Currently, we have applied the approach to Chinese characters of left-right structure and are extending to other structures. Preliminary results on a sample set of 4,284 characters consisting of 1,118 radicals demonstrate the superiority of the proposed approach.


The Feet in HCI : A Survey of Foot-Based Interaction


title: Appearance normalization of histology slides

abstract: This paper presents a method for automatic color and intensity normalization of digitized histology slides stained with two different agents. In comparison to previous approaches, prior information on the stain vectors is used in the plane estimation process, resulting in improved stability of the estimates. Due to the prevalence of hematoxylin and eosin staining for histology slides, the proposed method has significant practical utility. In particular, it can be used as a first step to standardize appearance across slides and is effective at countering effects due to differing stain amounts and protocols and counteracting slide fading. The approach is validated against non-prior plane-fitting using synthetic experiments and 13 real datasets. Results of application of the method to adjustment of faded slides are given, and the effectiveness of the method in aiding statistical classification is shown.


title: A survey of wireless sensor technologies applied to precision agriculture

abstract: This paper gives a state-of-art of wireless sensor network (WSN) technologies and solutions applied to precision agriculture. The paper first considers applications and existing experiences that show how WSN technologies have been introduced in to agricultural applications. Then, a survey in hardware and software solutions is related with special emphasis on technological aspects. Finally, the paper shows how five networking and technological solutions may impact the next generation of sensors. These are: (1) scalar wireless sensor networks; (2) wireless multimedia sensor networks; (3) mobility of nodes; (4) tag-based systems; and (5) smart-phone applications.


title: Multi-resolution terrain rendering with GPU tessellation

abstract: GPU tessellation is very efficient and is reshaping the terrain-rendering paradigm. We present a novel terrain-rendering algorithm based on GPU tessellation. The planar domain of the terrain is partitioned into a set of tiles, and a coarse-grained quadtree is constructed for each tile using a screen-space error metric. Then, each node of the quadtree is input to the GPU pipeline together with its own tessellation factors. The nodes are tessellated and the vertices of the tessellated mesh are displaced by filtering the displacement maps. The multi-resolution scheme is designed to optimize the use of GPU tessellation. Further, it accepts not only height maps but also geometry images, which displace more vertices toward the higher curvature feature parts of the terrain surface such that the surface detail can be well reconstructed with a small number of vertices. The efficiency of the proposed method is proven through experiments on large terrain models. When the screen-space error threshold is set to a pixel, a terrain surface tessellated into 8.5 M triangles is rendered at 110 fps on commodity PCs.


Predicting Haskell Type Signatures From Names


title: Grassmannian beamforming for multiple-input multiple-output wireless systems

abstract: Multiple-input multiple-output (MIMO) wireless systems provides capacity much larger than that provided by traditional single-input single-output (SISO) wireless systems. Beamforming is a low complexity technique that increases the receive signal-to-noise ratio (SNR), however, it requires channel knowledge. Since in practice channel knowledge at the transmitter is difficult to realize, we propose a technique where the receiver designs the beamforming vector and sends it to the transmitter by transmitting a label in a finite set, or codebook, of beamforming vectors. A codebook design method for quantized versions of maximum ratio transmission, equal gain transmission, and generalized selection diversity with maximum ratio combining at the receiver is presented. The codebook design criterion exploits the quantization problem's relationship with Grassmannian line packing. Systems using the beamforming codebooks are shown to have a diversity order of the product of the number of transmit and the number of receive antennas. Monte Carlo simulations compare the performance of systems using this new codebook method with the performance of systems using previously proposed quantized and unquantized systems.


title: A Comparative Study of Log-Structured Merge-Tree-Based Spatial Indexes for Big Data

abstract: The proliferation of GPS-enabled mobile devices has generated geo-tagged data at an unprecedented rate over the past decade. Data-processing systems that aim to ingest, store, index, and analyze Big Data must deal with such geo-tagged data efficiently. In this paper, among representative, disk-resident spatial indexing methods that have been adopted by major SQL and NoSQL systems, we implement five variants of these methods in the form of Log-Structured Merge-tree-based (LSM) spatial indexes in order to evaluate their pros and cons for dynamic geo-tagged Big Data. We have implemented the alternatives, including LSM-based B-tree, R-tree, and inverted index variants, in Apache AsterixDB, an open source Big Data management system. This implementation enabled comparison in terms of real end-to-end performance, including logging and locking overheads, in a full-function, query-based system setting. Our evaluation includes both static and dynamic workloads, ranging from a "load once, query many" case to a case where continuous concurrent incremental inserts are mixed with concurrent queries. Based on the results, we discuss the pros and cons of the five index variants.


title: Atherosclerosis: process, indicators, risk factors and new hopes.

abstract: Background: Atherosclerosis is the major cause of morbidities and mortalities worldwide. In this study we aimed to review the mechanism of atherosclerosis and its risk factors, focusing on new findings in atherosclerosis markers and its risk factors. Furthermore, the role of antioxidants and medicinal herbs in atherosclerosis and endothelial damage has been discussed and a list of important medicinal plants effective in the treatment and prevention of hyperlipidemia and atherosclerosis is presented. Methods: The recently published papers about atherosclerosis pathogenesis and herbal medicines effective in the treatment and prevention of hyperlipidemia and atherosclerosis were searched. Results: Inflammation has a crucial role in pathogenesis of atherosclerosis. The disease is accompanied by excessive fibrosis of the intima, fatty plaques formation, proliferation of smooth muscle cells, and migration of a group of cells such as monocytes, T cells, and platelets which are formed in response to inflammation. The oxidation of low density lipoprotein (LDL) to Ox‑LDL indicates the first step of atherosclerosis in cardiovascular diseases. Malondialdehyde factor shows the level of lipoperoxidation and is a sign of increased oxidative pressure and cardiovascular diseases. In special pathological conditions such as severe hypercholesterolemia, peroxynitrite concentration increases and atherosclerosis and vascular damage are intensified. Medicinal plants have shown to be capable of interacting these or other pathogenesis factors to prevent atherosclerosis.


title: Plagiarism checker for Persian (PCP) texts using hash-based tree representative fingerprinting

abstract: With due respect to the authors’ rights, plagiarism detection is one of the critical problems in the field of text-mining, in which many researchers are interested. This issue has been considered as a serious one in high academic institutions. There exist language-free tools that do not yield any reliable results since the special features of every language are ignored in them. Considering the paucity of works in the field of Persian language due to the lack of reliable plagiarism checkers in Persian, there is a need for a method to improve the accuracy of detecting plagiarized Persian phrases. An attempt is made in this work to present the PCP solution. This solution is a combinational method, in which, in addition to the meaning and stem of words, synonyms and pluralization are dealt with by applying the document tree representation based on manner fingerprinting the text in the 3-grams words. The grams obtained are eliminated from the text, hashed through the BKDR hash function, and stored as the fingerprint of a document in fingerprints of the reference document repositories in order to check the suspicious documents. The proposed PCP method here is evaluated by eight experiments on seven different sets, which include the suspicions documents and the reference document from the Hamshahri newspaper website. The results obtained indicate that the accuracy of this proposed method in detecting similar texts, in comparison with the "Winnowing" localized method, has a 21.15% average improvement. The accuracy of the PCP method in detecting the similarities, in comparison with the language-free tool, reveals a 31.65% average improvement.


title: When second wave HCI meets third wave challenges

abstract: This paper surveys the current status of second generation HCI theory, faced with the challenges brought to HCI by the so-called third wave. In the third wave, the use context and application types are broadened, and intermixed, relative to the focus of the second wave on work. Technology spreads from the workplace to our homes and everyday lives and culture. Using these challenges the paper specifically addresses the topics of multiplicity, context, boundaries, experience and participation in order to discuss where second wave theory and conceptions can still be positioned to make a contribution as part of the maturing of our handling of the challenges brought on by the third wave.


title: Bias in algorithmic filtering and personalization

abstract: Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal with the growing amount of information on the social web and the burden it brings on the average user, these gatekeepers recently started to introduce personalization features, algorithms that filter information per individual. In this paper we show that these online services that filter information are not merely algorithms. Humans not only affect the design of the algorithms, but they also can manually influence the filtering process even when the algorithm is operational. We further analyze filtering processes in detail, show how personalization connects to other filtering techniques, and show that both human and technical biases are present in today's emergent gatekeepers. We use the existing literature on gatekeeping and search engine bias and provide a model of algorithmic gatekeeping.


title: Twitter news classification using SVM

abstract: With the development of web blogs, Social Networks, many news providers used to share their news headlines in various web sites and web blogs. Now-a-days in Sri Lanka, there are many news groups whom share their news headlines in micro blogging services such as Twitter. These data may carry out much valuable information which will relevant to many social research areas. Thus, the purpose of this research is to classify news into different groups so that the user could identify the most popular news group in a given country for a given time. The short messages were extracted from Twitter micro blog. Several active news groups were chosen to extract the short messages. Each short message was classified manually into 12 groups. These classified data were used to train the machine learning techniques. Words of each short message was considered as features and a feature vector was created using bag-of-words approach in order to create the instances. The data were trained using SVM (Support Vector Machine) machine learning techniques. The main reason of using SVM for the current study is, SVM supports high dimensional data. Current research is a high dimensional problem as a large number of features will be collected using short messages. Cross validation was done in order to avoid the biasness of data. The performance of the system will be the effectiveness of the system. Thus precision and recall values are calculated to measure the performance of the system. Fβ was calculated to obtain a single value measurement. The results show that the system provides high performance for most groups. However, the group development-government does not show much performance using SVM.


title: Recommender systems: from algorithms to user experience

abstract: Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.


title: Using Thought-Provoking Children's Questions to Drive Artificial Intelligence Research

abstract: We propose to use thought-provoking children's questions (TPCQs), namely Highlights BrainPlay questions, as a new method to drive artificial intelligence research and to evaluate the capabilities of general-purpose AI systems. These questions are designed to stimulate thought and learning in children, and they can be used to do the same thing in AI systems, while demonstrating the system's reasoning capabilities to the evaluator. We introduce the TPCQ task, which which takes a TPCQ question as input and produces as output (1) answers to the question and (2) learned generalizations. We discuss how BrainPlay questions stimulate learning. We analyze 244 BrainPlay questions, and we report statistics on question type, question class, answer cardinality, answer class, types of knowledge needed, and types of reasoning needed. We find that BrainPlay questions span many aspects of intelligence. Because the answers to BrainPlay questions and the generalizations learned from them are often highly open-ended, we suggest using human judges for evaluation.


title: Flower menus: a new type of marking menu with large menu breadth, within groups and efficient expert mode memorization

abstract: This paper presents Flower menu, a new type of Marking menu that does not only support straight, but also curved gestures for any of the 8 usual orientations. Flower menus make it possible to put many commands at each menu level and thus to create as large a hierarchy as needed for common applications. Indeed our informal analysis of menu breadth in popular applications shows that a quarter of them have more than 16 items. Flower menus can easily contain 20 items and even more (theoretical maximum of 56 items). Flower menus also support within groups as well as hierarchical groups. They can thus favor breadth organization (within groups) or depth organization (hierarchical groups): as a result, the designers can lay out items in a very flexible way in order to reveal meaningful item groupings. We also investigate the learning performance of the expert mode of Flower menus. A user experiment is presented that compares linear menus (baseline condition), Flower menus and Polygon menus, a variant of Marking menus that supports a breadth of 16 items. Our experiment shows that Flower menus are more efficient than both Polygon and Linear menus for memorizing command activation in expert mode.


Ten Lectures on Wavelets (Ingrid Daubechies)


title: Neutralizing Antibodies to Human Rhinovirus Produced in Laboratory Animals and Humans that Recognize a Linear Sequence from VP2

abstract: Synthetic peptides representing amino acids 156 to 170 of virus capsid protein VP2 from human rhinovirus (HRV) type 2 have previously been used to elicit a neutralizing antibody response; polyclonal antisera against this peptide have been compared with a virus-neutralizing monoclonal antibody (8F5) which recognizes the same linear sequence. The neutralizing activity of both antibodies against virus is abolished by an amino acid substitution in VP2 at position 163 but only the peptide antiserum neutralizing activity is affected by a change in VP2 at position 158. The minimal binding site of 8F5 has been mapped, using overlapping peptides, to a sequence TRLNPD covering VP2 residues 160 to 165. Using affinity purification it has been shown that 8 to 10% of total neutralizing activity of polyclonal rabbit or guinea-pig antivirus antiserum is due to recognition of this linear VP2 156 to 170 determinant. Furthermore, a similar population of neutralizing antibodies appears to be associated with an immune response to recent HRV infection in humans. These results confirm the existence of linear determinants on the surface of HRV which may be mimicked by suitable synthetic peptides.


title: Evidence-based practice for the busy nurse practitioner: Part two: Searching for the best evidence to clinical inquiries

abstract: Purpose: The purpose of this four-part evidence-based practice (EBP) series is to enhance the nurse practitioner’s (NP’s) EBP skills by reviewing the process of developing a clinical question, searching for the best evidence, and critically appraising and applying the findings. Part two of the series focuses on how to search the published scientific literature for the most relevant studies that will answer a specific clinical question of importance to the NP. Data sources: Scientific literature review, gray searching, PubMed and other online literature databases and resources, and online EBP websites. Conclusions: Technology has allowed multiple healthcare resources to be available at one’s fingertips enabling both NPs and their patients to find answers to clinical questions. EBP databases can be categorized as synthesized/filtered, unfiltered, and background information/expert opinion resources. Learning which database can best answer the clinical inquiry can streamline the search process. Implications for practice: For the busy NP, EBP has emerged as an important strategy to maintain valid, accurate, and relevant clinical knowledge. It is expected that this part of the series will enable NPs to identify appropriate databases to answer clinical inquires while refining their search strategy skills, which takes both time and practice.


title: Privacy-preserving Machine Learning in Cloud

abstract: Machine learning algorithms based on deep neural networks (NN) have achieved remarkable results and are being extensively used in different domains. On the other hand, with increasing growth of cloud services, several Machine Learning as a Service (MLaaS) are offered where training and deploying machine learning models are performed on cloud providers' infrastructure. However, machine learning algorithms require access to raw data which is often privacy sensitive and can create potential security and privacy risks. To address this issue, we develop new techniques to provide solutions for applying deep neural network algorithms to the encrypted data. In this paper, we show that it is feasible and practical to train neural networks using encrypted data and to make encrypted predictions, and also return the predictions in an encrypted form. We demonstrate applicability of the proposed techniques and evaluate its performance. The empirical results show that it provides accurate privacy-preserving training and classification.


title: Cascaded Random Forest for Fast Object Detection

abstract: A Random Forest consists of several independent decision trees arranged in a forest. A majority vote over all trees leads to the final decision. In this paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage with minimal computational effort leading to a massively speeded-up detection framework. Three different cascade voting strategies are implemented and evaluated. Additionally, the training and classification speed-up is analyzed. Several experiments on public available datasets for pedestrian detection, lateral car detection and unconstrained face detection demonstrate the benefit of our contribution.


Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning


UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity


title: Reinforcement learning for control: Performance, stability, and deep approximators

abstract: Abstract Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain. This review mainly covers artificial-intelligence approaches to RL, from the viewpoint of the control engineer. We explain how approximate representations of the solution make RL feasible for problems with continuous states and control actions. Stability is a central concern in control, and we argue that while the control-theoretic RL subfield called adaptive dynamic programming is dedicated to it, stability of RL largely remains an open question. We also cover in detail the case where deep neural networks are used for approximation, leading to the field of deep RL, which has shown great success in recent years. With the control practitioner in mind, we outline opportunities and pitfalls of deep RL; and we close the survey with an outlook that – among other things – points out some avenues for bridging the gap between control and artificial-intelligence RL techniques.


title: A Novel Wide Duty Cycle Range Wide Band High Frequency Isolated Gate Driver for Power Converters

abstract: This paper presents a novel single source fed isolated gate driver circuit based on the dual-forward converter topology. The two forward converters are paralleled at the secondary side to achieve 0%–100 $\%$ duty cycle range together with galvanic isolation. The gate driver switching frequency is independent of the main pulse-width modulation frequency (PWM). As a result, the gate driver switching devices and magnetics are designed for high frequencies independent of the main PWM frequency. At high frequencies, problems of resonance due to parasitics become significant. This paper discusses this problem and proposes a novel solution to mitigate it. In addition, this paper also presents a novel solution and circuit to produce negative gate pulse for miller clamp. The proposed circuit can be used for any application, irrespective of switching frequency, thus making it a wide band generalized gate driver. The circuit is analyzed and verified experimentally.


Precision-Recall versus Accuracy and the Role of Large Data Sets


title: ASSERT: A Physician-in-the-loop Content-Based Retrieval System for HRCT Image Databases

abstract: It is now recognized in many domains that content-based image retrieval from a database of images cannot be carried out by using completely automated approaches. One such domain is medical radiology for which the clinically useful information in an image typically consists of gray level variations in highly localized regions of the image. Currently, it is not possible to extract these regions by automatic image segmentation techniques. To address this problem, we have implemented a human-in-the-loop (a physician-in-the-loop, more specifically) approach in which the human delineates the pathology bearing regions (PBR) and a set of anatomical landmarks in the image when the image is entered into the database. To the regions thus marked, our approach applies low-level computer vision and image processing algorithms to extract attributes related to the variations in gray scale, texture, shape, etc. In addition, the system records attributes that capture relational information such as the position of a PBR with respect to certain anatomical landmarks. An overall multidimensional index is assigned to each image based on these attribute values.


title: Recognizing Causality in Verb-Noun Pairs via Noun and Verb Semantics

abstract: Several supervised approaches have been proposed for causality identification by relying on shallow linguistic features. However, such features do not lead to improved performance. Therefore, novel sources of knowledge are required to achieve progress on this problem. In this paper, we propose a model for the recognition of causality in verb-noun pairs by employing additional types of knowledge along with linguistic features. In particular, we focus on identifying and employing semantic classes of nouns and verbs with high tendency to encode cause or non-cause relations. Our model incorporates the information about these classes to minimize errors in predictions made by a basic supervised classifier relying merely on shallow linguistic features. As compared with this basic classifier our model achieves 14.74% (29.57%) improvement in F-score (accuracy), respectively.


title: High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and Its Applications to High-Level Vision

abstract: Most of the current boundary detection systems rely exclusively on low-level features, such as color and texture. However, perception studies suggest that humans employ object-level reasoning when judging if a particular pixel is a boundary. Inspired by this observation, in this work we show how to predict boundaries by exploiting object-level features from a pretrained object-classification network. Our method can be viewed as a "High-for-Low" approach where high-level object features inform the low-level boundary detection process. Our model achieves state-of-the-art performance on an established boundary detection benchmark and it is efficient to run. Additionally, we show that due to the semantic nature of our boundaries we can use them to aid a number of high-level vision tasks. We demonstrate that using our boundaries we improve the performance of state-of-the-art methods on the problems of semantic boundary labeling, semantic segmentation and object proposal generation. We can view this process as a "Low-for-High'" scheme, where low-level boundaries aid high-level vision tasks. Thus, our contributions include a boundary detection system that is accurate, efficient, generalizes well to multiple datasets, and is also shown to improve existing state-of-the-art high-level vision methods on three distinct tasks.


title: A Hierarchical MdMC Approach to 2D Video Game Map Generation.

abstract: In this paper we describe a hierarchical method for procedurally generating 2D game maps using multi-dimensional Markov chains (MdMCs). Our method takes a collection of 2D game maps, breaks them into small chunks and performs clustering to find a set of chunks that correspond to high-level structures (high-level tiles) in the training maps. This set of high-level tiles is then used to re-represent the training maps, and to fit two sets of MdMC models: a high-level model captures the distribution of high-level tiles in the map, and a set of low-level models capture the internal structure of each highlevel tile. These two sets of models can then be used to hierarchically generate new maps. We test our approach using two classic games, Super Mario Bros. and Loderunner, and compare the results against other existing map generators.


title: Rail Inspection Meets Big Data: Methods and Trends

abstract: Rail inspection is one of the most important tasks for rail industry, in order to guarantee the safety of railway systems and control their cost. Rail are systematically inspected for defects using various non-destructive evaluation (NDE) techniques, which include ultrasonic inspection, visual detection, magnetic flux leakage method, acoustic emission inspection etc. The data obtained by these NDE devices are going to increase in both quality and quantity, therefore big data has emerged as a potential challenge for rail inspection. This paper reviews the advanced NDE techniques for rail inspection, and brings forward a new framework of rail inspection based on big data, according to the characteristics of inspection data.


title: Towards reliable traffic sign recognition

abstract: The demand for reliable traffic sign recognition (TSR) increases with the development of safety driven advanced driver assistance systems (ADAS). Emerging technologies like brake-by-wire or steer-by-wire pave the way for collision avoidance and threat identification systems. Obviously, decision making in such critical situations requires high reliability of the information base. Especially for comfort systems, we need to take into account that the user tends to trust the information provided by the ADAS [1]. In this paper, we present a robust system architecture for the reliable recognition of circular traffic signs. Our system employs complementing approaches for the different stages of current TSR systems. This introduces the application of local SIFT features for content-based traffic sign detection along with widely applied shape-based approaches. We further add a technique called contracting curve density (CCD) to refine the localization of the detected traffic sign candidates and therefore increase the performance of the subsequent classification module. Finally, the recognition stage based on SIFT and SURF descriptions of the candidates executed by a neural net provides a robust classification of structured image content like traffic signs. By applying these steps we compensate the weaknesses of the utilized approaches, and thus, improve the system's performance.


title: Understanding and Combating Link Farming in the Twitter Social Network

abstract: Recently, Twitter has emerged as a popular platform for discovering real-time information on the Web, such as news stories and people's reaction to them. Like the Web, Twitter has become a target for link farming, where users, especially spammers, try to acquire large numbers of follower links in the social network. Acquiring followers not only increases the size of a user's direct audience, but also contributes to the perceived influence of the user, which in turn impacts the ranking of the user's tweets by search engines. In this paper, we first investigate link farming in the Twitter network and then explore mechanisms to discourage the activity. To this end, we conducted a detailed analysis of links acquired by over 40,000 spammer accounts suspended by Twitter. We find that link farming is wide spread and that a majority of spammers' links are farmed from a small fraction of Twitter users, the social capitalists, who are themselves seeking to amass social capital and links by following back anyone who follows them. Our findings shed light on the social dynamics that are at the root of the link farming problem in Twitter network and they have important implications for future designs of link spam defenses. In particular, we show that a simple user ranking scheme that penalizes users for connecting to spammers can effectively address the problem by disincentivizing users from linking with other users simply to gain influence.


title: Grasp quality measures: review and performance

abstract: The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures. This article reviews the quality measures proposed in the literature to evaluate grasp quality. The quality measures are classified into two groups according to the main aspect they evaluate: location of contact points on the object and hand configuration. The approaches that combine different measures from the two previous groups to obtain a global quality measure are also reviewed, as well as some measures related to human hand studies and grasp performance. Several examples are presented to illustrate and compare the performance of the reviewed measures.


title: On detecting spatial categorical outliers

abstract: Spatial outlier detection is an important research problem that has received much attentions in recent years. Most existing approaches are designed for numerical attributes, but are not applicable to categorical ones (e.g., binary, ordinal, and nominal) that are popular in many applications. The main challenges are the modeling of spatial categorical dependency as well as the computational efficiency. This paper presents the first outlier detection framework for spatial categorical data. Specifically, a new metric, named as Pair Correlation Ratio (PCR), is measured for each pair of category sets based on their co-occurrence frequencies at specific spatial distance ranges. The relevances among spatial objects are then calculated using PCR values with regard to their spatial distances. The outlierness for each object is defined as the inverse of the average relevance between an object and its spatial neighbors. Those objects with the highest outlier scores are returned as spatial categorical outliers. A set of algorithms are further designed for single-attribute and multi-attribute spatial categorical datasets. Extensive experimental evaluations on both simulated and real datasets demonstrated the effectiveness and efficiency of our proposed approaches.


title: Amphetamine-Induced Increases in Extracellular Dopamine, Drug Wanting, and Novelty Seeking: A PET/[11C]Raclopride Study in Healthy Men

abstract: Eight healthy men underwent two positron emission tomography (PET) [11C]raclopride scans, one following placebo, the second following d-amphetamine (0.30 mg/kg, p.o.). PET data were analyzed using: (1) brain parametric maps to statistically generate regions of significant change; and (2) a priori identified regions of interest (ROI) manually drawn on each individual's co-registered magnetic resonance (MR) images. Compared with placebo, d-amphetamine decreased [11C]raclopride binding potential (BP) with significant effects in ventral but not dorsal striatum. Change in BP in the statistically generated cluster correlated with self-reported drug-induced ‘drug wanting’ (r = 0.83, p = .01) and the personality trait of Novelty Seeking-Exploratory Excitability (r = 0.79, p = .02). The same associations were seen in the manually drawn ROI in ventral striatum but not in dorsal putamen or caudate. Changes in extracellular dopamine (DA) did not correlate with mood. Mesolimbic DA might mediate interest in obtaining reward rather than reward, per se. Individual differences in amphetamine-induced DA release might be related to predispositions to drug and novelty seeking.


Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models


title: Autonomous inverted helicopter flight via reinforcement learning

abstract: Helicopters have highly stochastic, nonlinear, dynamics, and autonomous helicopter flight is widely regarded to be a challenging control problem. As helicopters are highly unstable at low speeds, it is particularly difficult to design controllers for low speed aerobatic maneuvers. In this paper, we describe a successful application of reinforcement learning to designing a controller for sustained inverted flight on an autonomous helicopter. Using data collected from the helicopter in flight, we began by learning a stochastic, nonlinear model of the helicopter’s dynamics. Then, a reinforcement learning algorithm was applied to automatically learn a controller for autonomous inverted hovering. Finally, the resulting controller was successfully tested on our autonomous helicopter platform.


title: Accessing CUDA Features in the OpenGL Rendering Pipeline: A Case Study Using N-Body Simulation

abstract: The advances of the graphics programing unit (GPU) architecture and its rapidly evolving towards general purpose GPU make a series of applications adopt a general purpose (GPGPU) and a graphics computing interoperability approach in which the first is used for heavy calculations and the second for 3D graphics rendering. Because GPGPU exposes several hardware features, such as shared memory and thread synchronization mechanism, it allows a developer to write more efficient code. Nevertheless, we conjecture that such hardware features are also available in the graphics computing interface OpenGL 4.5 or later through the graphics concepts: blending, transform feedback, tessellation and instancing. In this paper we assess our conjecture by implementing an N-body simulation with both approaches. We indeed devise a novel non-graphics application to the tessellation hardware and the instanced rendering circuit. Instead of refining a mesh, we use the abstract patch for gaining direct accesses to shared memory. In the place of drawing multiple objects, we apply the instanced rendering technology for improving sequential data accesses. Comparative timing analysis is provided. We believe that these results provide better understanding of the graphics features that are useful for closing the performance gap between OpenGL and a GPGPU architecture, and open a new perspective on implementing solely with the OpenGL graphics applications that require both intense, but pre-specified, memory accesses and 3D graphics rendering.


title: Image flower recognition based on a new method for color feature extraction

abstract: In this paper, we present, first, a new method for color feature extraction based on SURF detectors. Then, we proved its efficiency for flower image classification. Therefore, we described visual content of the flower images using compact and accurate descriptors. These features are combined and the learning process is performed using a multiple kernel framework with a SVM classifier. The proposed method has been tested on the dataset provided by the university of oxford and achieved better results than our implementation of the method proposed by Nilsback and Zisserman (Nilsback and Zisserman, 2008) in terms of classification rate and execution time.


title: Home automation in the wild: challenges and opportunities

abstract: Visions of smart homes have long caught the attention of researchers and considerable effort has been put toward enabling home automation. However, these technologies have not been widely adopted despite being available for over three decades. To gain insight into this state of affairs, we conducted semi-structured home visits to 14 households with home automation. The long term experience, both positive and negative, of the households we interviewed illustrates four barriers that need to be addressed before home automation becomes amenable to broader adoption. These barriers are high cost of ownership, inflexibility, poor manageability, and difficulty achieving security. Our findings also provide several directions for further research, which include eliminating the need for structural changes for installing home automation, providing users with simple security primitives that they can confidently configure, and enabling composition of home devices.


title: Cache Valley virus infection in Texas sheep flocks.

abstract: Cache Valley virus (CVV), an arbovirus indigenous to the United States, has been implicated as an important teratogenic agent in sheep. The prevalence and distribution of Texas sheep with CVV-specific antibody were investigated. In 1981, 19.1% of 366 sheep located in 22 counties of Texas had antibodies specific for CVV. Of 50 flocks examined in the major sheep-producing counties in Texas, 34 had sheep with antibodies that reacted with CVV, including all sheep tested in 6 flocks that were seropositive. Sera obtained from sheep at the Texas Agricultural Experiment Station at San Angelo between 1986 and 1989 were also examined for CVV-specific antibody because this flock was the subject of the episode of CVV-associated congenital malformations during the 1986 and 1987 lambing season. Approximately 8.6% of 104 sheep in 1986, 63.4% of 164 in 1987, 11.3% of 44 in 1988, and 71.9% of 89 in 1989 from the Texas Agricultural Experiment Station at San Angelo tested were seropositive. The data indicate that CVV infections in sheep were widespread in Texas in 1981 and that the virus is enzootic in sheep at the Texas Agricultural Experiment Station in San Angelo, where the episode of congenital malformations had initially been reported in 1987.


title: Distally Based Sural Artery Adipofascial Flap based on a Single Sural Nerve Branch: Anatomy and Clinical Applications

abstract: BACKGROUND: The distally based sural artery flap is a reliable, local reconstructive option for small soft tissue defects of the distal third of the leg. The purpose of this study is to describe an adipofascial flap based on a single sural nerve branch without sacrificing the entire sural nerve, thereby preserving sensibility of the lateral foot. METHODS: The posterior aspect of the lower limb was dissected in 15 cadaveric limbs. Four patients with soft tissue defects over the tendo-achilles and ankle underwent reconstruction using the adipofascial flap, which incorporated the distal peroneal perforator, short saphenous vein, and a single branch of the sural nerve. RESULTS: From the anatomical study, the distal peroneal perforator was situated at an average of 6.2 cm (2.5-12 cm) from the distal tip of the lateral malleolus. The medial and lateral sural nerve branches ran subfascially and pierced the muscle fascia 16 cm (14-19 cm) proximal to the lateral malleolus to enter the subcutaneous plane. They merged 1-2 cm distal to the subcutaneous entry point to form the common sural nerve at a mean distance of 14.5 cm (11.5-18 cm) proximal to the lateral malleolus. This merging point determined the pivot point of the flap. In the clinical cases, all patients reported near complete recovery of sensation over the lateral foot six months after surgery. All donor sites healed well with a full range of motion over the foot and ankle. CONCLUSIONS: The distally based sural artery adipofascial flap allowed for minimal sensory loss, a good range of motion, an aesthetically acceptable outcome and can be performed by a single surgeon in under 2 hours.


title: MutantX-S: Scalable Malware Clustering Based on Static Features

abstract: The current lack of automatic and speedy labeling of a large number (thousands) of malware samples seen everyday delays the generation of malware signatures and has become a major challenge for anti-virus industries. In this paper, we design, implement and evaluate a novel, scalable framework, called MutantX-S, that can efficiently cluster a large number of samples into families based on programs' static features, i.e., code instruction sequences. MutantX-S is a unique combination of several novel techniques to address the practical challenges of malware clustering. Specifically, it exploits the instruction format of ×86 architecture and represents a program as a sequence of opcodes, facilitating the extraction of N-gram features. It also exploits the hashing trick recently developed in the machine learning community to reduce the dimensionality of extracted feature vectors, thus significantly lowering the memory requirement and computation costs. Our comprehensive evaluation on a MutantX-S prototype using a database of more than 130,000 malware samples has shown its ability to correctly cluster over 80% of samples within 2 hours, achieving a good balance between accuracy and scalability. Applying MutantX-S on malware samples created at different times, we also demonstrate that MutantX-S achieves high accuracy in predicting labels for previously unknown malware.


title: Entropy-SGD: Biasing Gradient Descent Into Wide Valleys

abstract: This paper proposes a new optimization algorithm called Entropy-SGD for training deep neural networks that is motivated by the local geometry of the energy landscape. Local extrema with low generalization error have a large proportion of almost-zero eigenvalues in the Hessian with very few positive or negative eigenvalues. We leverage upon this observation to construct a local-entropy-based objective function that favors well-generalizable solutions lying in large flat regions of the energy landscape, while avoiding poorly-generalizable solutions located in the sharp valleys. Conceptually, our algorithm resembles two nested loops of SGD where we use Langevin dynamics in the inner loop to compute the gradient of the local entropy before each update of the weights. We show that the new objective has a smoother energy landscape and show improved generalization over SGD using uniform stability, under certain assumptions. Our experiments on convolutional and recurrent networks demonstrate that Entropy-SGD compares favorably to state-of-the-art techniques in terms of generalization error and training time.


title: Machine Learning Techniques for Anomaly Detection: An Overview

abstract: Intrusion detection has gain a broad attention and become a fertile field for several researches, and still being the subject of widespread interest by researchers. The intrusion detection community still confronts difficult problems even after many years of research. Reducing the large number of false alerts during the process of detecting unknown attack patterns remains unresolved problem. However, several research results recently have shown that there are potential solutions to this problem. Anomaly detection is a key issue of intrusion detection in which perturbations of normal behavior indicates a presence of intended or unintended induced attacks, faults, defects and others. This paper presents an overview of research directions for applying supervised and unsupervised methods for managing the problem of anomaly detection. The references cited will cover the major theoretical issues, guiding the researcher in interesting research directions.


title: Light-Induced Transformation of Defect Structures in Photochromic Liquid-Crystal Emulsions

abstract: We investigated photoresponsive behavior of azobenzene-containing liquid crystals dispersed with glycerol or water droplets. At an initial state, Saturn ring and hedgehog defects were formed around the glycerol and water droplets, respectively, indicating that homeotropic anchoring was induced by adsorption of the azobenzene molecules onto the droplets. For the glycerol droplets, we observed structural transformation between Saturn ring and boojums on irradiation with ultra-violet and visible light. For the water droplets, an inter-droplet distance varied by changes of the defect size on the irradiation. These phenomena would result from modulation of anchoring conditions by the photoisomerization of the adsorbed dyes.


title: Fiscal Volatility Shocks and Economic Activity

abstract: We study the effects of changes in uncertainty about future fiscal policy on aggregate economic activity. Fiscal deficits and public debt have risen sharply in the wake of the financial crisis. While these developments make fiscal consolidation inevitable, there is considerable uncertainty about the policy mix and timing of such budgetary adjustment. To evaluate the consequences of this increased uncertainty, we first estimate tax and spending processes for the U.S. that allow for time-varying volatility. We then feed these processes into an otherwise standard New Keynesian business cycle model calibrated to the U.S. economy. We find that fiscal volatility shocks have an adverse effect on economic activity that is comparable to the effects of a 25-basis-point innovation in the federal funds rate.


title: Sub-GAN: An Unsupervised Generative Model via Subspaces

abstract: The recent years have witnessed significant growth in constructing robust generative models to capture informative distributions of natural data. However, it is difficult to fully exploit the distribution of complex data, like images and videos, due to the high dimensionality of ambient space. Sequentially, how to effectively guide the training of generative models is a crucial issue. In this paper, we present a subspace-based generative adversarial network (Sub-GAN) which simultaneously disentangles multiple latent subspaces and generates diverse samples correspondingly. Since the high-dimensional natural data usually lies on a union of low-dimensional subspaces which contain semantically extensive structure, Sub-GAN incorporates a novel clusterer that can interact with the generator and discriminator via subspace information. Unlike the traditional generative models, the proposed Sub-GAN can control the diversity of the generated samples via the multiplicity of the learned subspaces. Moreover, the Sub-GAN follows an unsupervised fashion to explore not only the visual classes but the latent continuous attributes. We demonstrate that our model can discover meaningful visual attributes which is hard to be annotated via strong supervision, e.g., the writing style of digits, thus avoid the mode collapse problem. Extensive experimental results show the competitive performance of the proposed method for both generating diverse images with satisfied quality and discovering discriminative latent subspaces.


title: GPS Spoofing Detection via Dual-Receiver Correlation of Military Signals

abstract: Cross-correlation of unknown encrypted signals between two Global Navigation Satellite System (GNSS) receivers is used for spoofing detection of publicly-known signals. This detection technique is one of the strongest known defenses against sophisticated spoofing attacks if the defended receiver has only one antenna. The attack strategy of concern overlays false GNSS radio-navigation signals on top of the true signals. The false signals increase in power, lift the receiver tracking loops off of the true signals, and drag the loops and the navigation solution to erroneous but consistent results. Hypothesis testing theory is used to develop a codeless cross-correlation detection method for use in inexpensive, narrowband civilian GNSS receivers. The detection method is instantiated by using the encrypted military Global Positioning System (GPS) P(Y) code on the L1 frequency in order to defend the publicly-known civilian GPS C/A code. Successful detection of spoofing attacks is demonstrated by off-line processing of recorded RF data from narrowband 2.5 MHz RF front-ends, which attenuate the wideband P(Y) code by 5.5 dB. The new technique can detect attacks using correlation intervals of 1.2 s or less.


title: Multi-Class Latent Concept Pooling for Computer-Aided Endoscopy Diagnosis

abstract: Successful computer-aided diagnosis systems typically rely on training datasets containing sufficient and richly annotated images. However, detailed image annotation is often time consuming and subjective, especially for medical images, which becomes the bottleneck for the collection of large datasets and then building computer-aided diagnosis systems. In this article, we design a novel computer-aided endoscopy diagnosis system to deal with the multi-classification problem of electronic endoscopy medical records (EEMRs) containing sets of frames, while labels of EEMRs can be mined from the corresponding text records using an automatic text-matching strategy without human special labeling. With unambiguous EEMR labels and ambiguous frame labels, we propose a simple but effective pooling scheme called Multi-class Latent Concept Pooling, which learns a codebook from EEMRs with different classes step by step and encodes EEMRs based on a soft weighting strategy. In our method, a computer-aided diagnosis system can be extended to new unseen classes with ease and applied to the standard single-instance classification problem even though detailed annotated images are unavailable. In order to validate our system, we collect 1,889 EEMRs with more than 59K frames and successfully mine labels for 348 of them. The experimental results show that our proposed system significantly outperforms the state-of-the-art methods. Moreover, we apply the learned latent concept codebook to detect the abnormalities in endoscopy images and compare it with a supervised learning classifier, and the evaluation shows that our codebook learning method can effectively extract the true prototypes related to different classes from the ambiguous data.


title: On the capacity of multi-antenna Gaussian channels

abstract: We investigate the use of multi-antennas at both ends of a point-to-point communication system over the additive Gaussian channel. We consider a system with t transmit antennas and r receive antennas in which the received vector v/spl isin/C/sup /spl tau// depends on the transmitted vector u/spl isin/C/sup /spl tau// via: v=Hu+w where H/spl isin/C/sup r/spl times/t/ is the channel transfer matrix and w is zero-mean complex circular symmetric Gaussian noise. We assume that E[ww]=/spl sigma//sup 2/I/sub r/. The transmitter is constrained in its total power, i.e., E[uu]/spl les/E/sub s/. We assume that the channel matrix H is known at both ends of the communication system, and that the waveform channel is flat over the bandwidth of interest.


title: Design concept of a high power high frequency power supply for feeding 500 kV, 100 mA cockcroft-walton generator

abstract: This paper presents a basic design concept and simulation of a 106 kVA high-power high-frequency (HPHF) power supply for feeding a transformer coupled symmetrical Cockcroft — Walton (CW) voltage multiplier producing 500 kV, 100 mA as the rated output for particle accelerators. Key technology includes high power converter with single-phase inverter that runs at 20 kHz and yields voltage of low THD and maximum dv/dt of 2 kV/ μs to prevent partial discharge in the transformer. The HPHF system is designed for varying the rated output voltage of 500 kV from 5% to 100 % of its value and to restrict the voltage rise time below 100 ms. At first, the Cockcroft-Walton circuit is simulated and its output parameters are obtained. Then, the simulation results of the designed HPHF system supplying the CW circuit has been presented and compared with design theory.


title: PAMS: A new position-aware multi-sensor dataset for human activity recognition using smartphones

abstract: Nowadays smartphones are ubiquitous in various aspects of our lives. The processing power, communication bandwidth, and the memory capacity of these devices have surged considerably in recent years. Besides, the variety of sensor types, such as accelerometer, gyroscope, humidity sensor, and bio-sensors, which are embedded in these devices, opens a new horizon in self-monitoring of physical daily activities. One of the primary steps for any research in the area of detecting daily life activities is to test a detection method on benchmark datasets. Most of the early datasets limited their work to collecting only a single type of sensor data such as accelerometer data. While some others do not consider age, weight, and gender of the subjects who have participated in collecting their activity data. Finally, part of the previous works collected data without considering the smartphone's position. In this paper, we introduce a new dataset, called Position-Aware Multi-Sensor (PAMS). The dataset contains both accelerometer and gyroscope data. The gyroscope data boosts the accuracy of activity recognition methods as well as enabling them to detect a wider range of activities. We also take the user information into account. Based on the biometric attributes of the participants, a separate learned model is generated to analyze their activities. We concentrate on several major activities, including sitting, standing, walking, running, ascending/descending stairs, and cycling. To evaluate the dataset, we use various classifiers, and the outputs are compared to the WISDM. The results show that using aforementioned classifiers, the average precision for all activities is above 88.5%. Besides, we measure the CPU, memory, and bandwidth usage of the application collecting data on the smartphone.


title: Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia

abstract: Abstract Background When studying learning, researchers directly observe only the participants’ choices, which are often assumed to arise from a unitary learning process. However, a number of separable systems, such as working memory (WM) and reinforcement learning (RL), contribute simultaneously to human learning. Identifying each system’s contributions is essential for mapping the neural substrates contributing in parallel to behavior; computational modeling can help to design tasks that allow such a separable identification of processes and infer their contributions in individuals. Methods We present a new experimental protocol that separately identifies the contributions of RL and WM to learning, is sensitive to parametric variations in both, and allows us to investigate whether the processes interact. In experiments 1 and 2, we tested this protocol with healthy young adults (n = 29 and n = 52, respectively). In experiment 3, we used it to investigate learning deficits in medicated individuals with schizophrenia (n = 49 patients, n = 32 control subjects). Results Experiments 1 and 2 established WM and RL contributions to learning, as evidenced by parametric modulations of choice by load and delay and reward history, respectively. They also showed interactions between WM and RL, where RL was enhanced under high WM load. Moreover, we observed a cost of mental effort when controlling for reinforcement history: participants preferred stimuli they encountered under low WM load. Experiment 3 revealed selective deficits in WM contributions and preserved RL value learning in individuals with schizophrenia compared with control subjects. Conclusions Computational approaches allow us to disentangle contributions of multiple systems to learning and, consequently, to further our understanding of psychiatric diseases.


Playing Subaltern: Videogames and Postcolonialism


title: AFFECTIVE CONSEQUENCES OF IMPOSING THE INTENSITY OF PHYSICAL ACTIVITY: DOES THE LOSS OF PERCEIVED AUTONOMY MATTER?

abstract: There is a common belief that, when allowed to select their exercise intensity, most people choose levels too low to accrue substantial benefits. Thus, one of the presumed func- tions of exercise practitioners is to "push" participants. A previous study showed that, if the imposed intensity exceeds the self-selected by even 10%, this suffices to reduce pleasure. Here, we isolated the effect of autonomy loss by having participants (a) exercise at a self- selected intensity and (b) exercise at an intensity set by the experimenter, which was iden- tical to the self-selected. This reduced perception of autonomy and choice, and also attenu- ated increases in energy and levels of interest/enjoyment after the controlled condition. These effects could not be accounted for by differences in intensity, perceived competence, or self-efficacy. Thus, consistent with self-determination theory, loss of perceived autonomy in setting one's level of exercise intensity can negatively impact affect, with potentially neg- ative implications for adherence.


title: Source Localization in Wireless Sensor Networks From Signal Time-of-Arrival Measurements

abstract: Recent advances in wireless sensor networks have led to renewed interests in the problem of source localization. Source localization has broad range of applications such as emergency rescue, asset inventory, and resource management. Among various measurement models, one important and practical source signal measurement is the received signal time of arrival (TOA) at a group of collaborative wireless sensors. Without time-stamp at the transmitter, in traditional approaches, these received TOA measurements are subtracted pairwise to form time-difference of arrival (TDOA) data for source localization, thereby leading to a 3-dB loss in signal-to-noise ratio (SNR). We take a different approach by directly applying the original measurement model without the subtraction preprocessing. We present two new methods that utilize semidefinite programming (SDP) relaxation for direct source localization. We further address the issue of robust estimation given measurement errors and inaccuracy in the locations of receiving sensors. Our results demonstrate some potential advantages of source localization based on the direct TOA data over time-difference preprocessing.


How Microarrays Can Improve Our Understanding of Immune Responses and Vaccine Development


title: Optimizing F-measure: A Tale of Two Approaches

abstract: F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then predicts labels with maximum expected F-measure. In this paper, we investigate the theoretical justifications and connections for these two approaches, and we study the conditions under which one approach is preferable to the other using synthetic and real datasets. Given accurate models, our results suggest that the two approaches are asymptotically equivalent given large training and test sets. Nevertheless, empirically, the EUM approach appears to be more robust against model misspecification, and given a good model, the decision-theoretic approach appears to be better for handling rare classes and a common domain adaptation scenario. Copyright 2012 by the author(s)/owner(s).


title: DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware

abstract: With more than two million applications, Android marketplaces require automatic and scalable methods to efficiently vet apps for the absence of malicious threats. Recent techniques have successfully relied on the extraction of lightweight syntactic features suitable for machine learning classification, but despite their promising results, the very nature of such features suggest they would unlikely--on their own--be suitable for detecting obfuscated Android malware. To address this challenge, we propose DroidSieve, an Android malware classifier based on static analysis that is fast, accurate, and resilient to obfuscation. For a given app, DroidSieve first decides whether the app is malicious and, if so, classifies it as belonging to a family of related malware. DroidSieve exploits obfuscation-invariant features and artifacts introduced by obfuscation mechanisms used in malware. At the same time, these purely static features are designed for processing at scale and can be extracted quickly. For malware detection, we achieve up to 99.82% accuracy with zero false positives; for family identification of obfuscated malware, we achieve 99.26% accuracy at a fraction of the computational cost of state-of-the-art techniques.


title: Epidemiology of Constipation in North America: A Systematic Review

abstract: OBJECTIVE ::: The aim of this study was to systematically review the published literature regarding prevalence, risk factors, incidence, natural history, and the effect on quality of life of constipation in North America. ::: ::: ::: METHODS ::: A computer-assisted search of MEDLINE, EMBASE, and Current Contents databases was performed independently by two investigators. Study selection criteria included the following: (1) North American population-based sample of adults with constipation; (2) publication in full manuscript form in English; and (3) report on the prevalence, incidence, and natural history of constipation or impact of constipation on quality of life. Eligible articles were reviewed in a duplicate, independent manner. Data extracted were compiled in tables and presented in descriptive form. ::: ::: ::: RESULTS ::: The estimates of the prevalence of constipation in North America ranged from 1.9% to 27.2%, with most estimates from 12% to 19%. Prevalence estimates by gender support a female-to-male ratio of 2.2:1. Constipation appears to increase with increasing age, particularly after age 65. No true population-based incidence studies or natural history studies were identified. In one cohort, 89% of patients with constipation still reported constipation at 14.7 months follow-up. From limited data, quality of life appears to be diminished by constipation, but the clinical significance of this is unclear. ::: ::: ::: CONCLUSIONS ::: Constipation is very common, as approximately 63 million people in North America meet the Rome II criteria for constipation. Minimal data are available regarding incidence, natural history, and quality of life in patients with constipation. Effort should be expended toward the study of these topics, particularly in the elderly, who are disproportionately affected by this condition.


title: Knowledge Discovery in Textual Databases (KDT)

abstract: The information age is characterized by a rapid growth in the amount of information available in electronic media. Traditional data handling methods are not adequate to cope with this information flood. Knowledge Discovery in Databases (KDD) is a new paradigm that focuses on computerized exploration of large amounts of data and on discovery of relevant and interesting patterns within them. While most work on KDD is concerned with structured databases, it is clear that this paradigm is required for handling the huge amount of information that is available only in unstructured textual form. To apply traditional KDD on texts it is necessary to impose some structure on the data that would be rich enough to allow for interesting KDD operations. On the other hand, we have to consider the severe limitations of current text processing technology and define rather simple structures that can be extracted from texts fairly automatically and in a reasonable cost. We propose using a text categorization paradigm to annotate text articles with meaningful concepts that are organized in hierarchical structure. We suggest that this relatively simple annotation is rich enough to provide the basis for a KDD framework, enabling data summarization, exploration of interesting patterns, and trend analysis. This research combines the KDD and text categorization paradigms and suggests advances to the state of the art in both areas.


title: A context-aware personalized travel recommendation system based on geotagged social media data mining

abstract: The proliferation of digital cameras and the growing practice of online photo sharing using social media sites such as Flickr have resulted in huge volumes of geotagged photos available on the Web. Based on users' traveling preferences elicited from their travel experiences exposed on social media sites by sharing geotagged photos, we propose a new method for recommending tourist locations that are relevant to users i.e., personalization in the given context i.e., context awareness. We obtain user-specific travel preferences from his/her travel history in one city and use these to recommend tourist locations in another city. Our technique is illustrated on a sample of publicly available Flickr dataset containing photos taken in various cities of China. Results show that our context-aware personalized method is able to predict tourists' preferences in a new or unknown city more precisely and generate better recommendations compared to other state-of-the-art landmark recommendation methods.


title: The Relationship Between Dimensions of Love, Personality, and Relationship Length

abstract: The present study examined the associations among participant demographics, personality factors, love dimensions, and relationship length. In total, 16,030 participants completed an internet survey assessing Big Five personality factors, Sternberg’s three love dimensions (intimacy, passion, and commitment), and the length of time that they had been involved in a relationship. Results of structural equation modeling (SEM) showed that participant age was negatively associated with passion and positively associated with intimacy and commitment. In addition, the Big Five factor of Agreeableness was positively associated with all three love dimensions, whereas Conscientiousness was positively associated with intimacy and commitment. Finally, passion was negatively associated with relationship length, whereas commitment was positively correlated with relationship length. SEM results further showed that there were minor differences in these associations for women and men. Given the large sample size, our results reflect stable associations between personality factors and love dimensions. The present results may have important implications for relationship and marital counseling. Limitations of this study and further implications are discussed.


title: Delay-dependent robust H ∞ filtering of uncertain stochastic genetic regulatory networks with mixed time-varying delays

abstract: In this paper, the robust H ∞ filtering problem for uncertain stochastic genetic regulatory networks (GRNs) with mixed time-varying delays is involved. The uncertain stochastic GRNs under consideration are extended to involve Ito-type stochastic disturbance, norm-bounded parameter uncertainties, and time-varying discrete and distributed delays. By constructing a proper Lyapunov-Krasovskii functional and using reciprocal convex technique, sufficient conditions in terms of linear matrix inequalities were presented to guarantee that the filtering error systems are mean-square robustly asymptotically stable with disturbance attenuation level γ. In the end, two numerical examples are given to illustrate the effectiveness of the proposed approach.


title: Application of an improved Apriori algorithm in a mobile e-commerce recommendation system

abstract: The purpose of this paper is to make the mobile e-commerce shopping more convenient and avoid information overload by a mobile e-commerce recommendation system using an improved Apriori algorithm.,Combined with the characteristics of the mobile e-commerce, an improved Apriori algorithm was proposed and applied to the recommendation system. This paper makes products that are recommended to consumers valuable by improving the data mining efficiency. Finally, a Taobao online dress shop is used as an example to prove the effectiveness of an improved Apriori algorithm in the mobile e-commerce recommendation system.,The results of the experimental study clearly show that the mobile e-commerce recommendation system based on an improved Apriori algorithm increases the efficiency of data mining to achieve the unity of real time and recommendation accuracy.,The improved Apriori algorithm is applied in the mobile e-commerce recommendation system solving the limitation of the visual interface in a mobile terminal and the mass data that are continuously generated. The proposed recommendation system provides greater prediction accuracy than conventional systems in data mining.


title: A Task-Oriented Approach for Cost-Sensitive Recognition

abstract: With the recent progress in visual recognition, we have already started to see a surge of vision related real-world applications. These applications, unlike general scene understanding, are task oriented and require specific information from visual data. Considering the current growth in new sensory devices, feature designs, feature learning methods, and algorithms, the search in the space of features and models becomes combinatorial. In this paper, we propose a novel cost-sensitive task-oriented recognition method that is based on a combination of linguistic semantics and visual cues. Our task-oriented framework is able to generalize to unseen tasks for which there is no training data and outperforms state-of-the-art cost-based recognition baselines on our new task-based dataset.


title: Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases

abstract: Much work in recent years has gone into the construction of large knowledge bases (KBs), such as Freebase, DBPedia, NELL, and YAGO. While these KBs are very large, they are still very incomplete, necessitating the use of inference to fill in gaps. Prior work has shown how to make use of a large text corpus to augment random walk inference over KBs. We present two improvements to the use of such large corpora to augment KB inference. First, we present a new technique for combining KB relations and surface text into a single graph representation that is much more compact than graphs used in prior work. Second, we describe how to incorporate vector space similarity into random walk inference over KBs, reducing the feature sparsity inherent in using surface text. This allows us to combine distributional similarity with symbolic logical inference in novel and effective ways. With experiments on many relations from two separate KBs, we show that our methods significantly outperform prior work on KB inference, both in the size of problem our methods can handle and in the quality of predictions made.


title: 5D Covariance tracing for efficient defocus and motion blur

abstract: The rendering of effects such as motion blur and depth-of-field requires costly 5D integrals. We accelerate their computation through adaptive sampling and reconstruction based on the prediction of the anisotropy and bandwidth of the integrand. For this, we develop a new frequency analysis of the 5D temporal light-field, and show that first-order motion can be handled through simple changes of coordinates in 5D. We further introduce a compact representation of the spectrum using the covariance matrix and Gaussian approximations. We derive update equations for the 5 × 5 covariance matrices for each atomic light transport event, such as transport, occlusion, BRDF, texture, lens, and motion. The focus on atomic operations makes our work general, and removes the need for special-case formulas. We present a new rendering algorithm that computes 5D covariance matrices on the image plane by tracing paths through the scene, focusing on the single-bounce case. This allows us to reduce sampling rates when appropriate and perform reconstruction of images with complex depth-of-field and motion blur effects.


title: Bio-inspired knee joint mechanism for a hydraulic quadruped robot

abstract: Over the last few decades, legged robots are becoming a promising solution for rough terrain navigation, however, existing legged machines often lack versatility to perform a wide range of different gaits. To build a highly dynamic and versatile legged robot, it is essential to have lightweight legs with optimized design and suitable actuators for the desired robot performance and tasks. The design goals are to achieve 1) a wide range of motion for bigger foot workspace which will increase rough terrain walking performance by increasing the number of reachable footholds for each step, 2) optimized joint torque curve since torque output is related to joint angle if linear actuators like pistons are used. In this paper, we focus on the knee joint and propose the adaptation and optimization of the so-called isogram mechanism. It exhibits a changeable instantaneous center of rotation (CICR), similar to a human knee joint. We will show how an optimization of design parameters lead to a knee joint design that satisfies the above-mentioned goals. The main contributions of this paper are the kinematic and torque analysis of the isogram mechanism that is actuated by a linear actuator; the optimization of the mechanism's design parameters; a comparison between the proposed knee joint with the hinge-type knee joint of the quadruped robot HyQ; and experimental results of a proof-of-concept prototype leg featuring the proposed mechanism.


title: Novel lightweight engineering artifacts for modeling non-functional requirements in agile processes

abstract: Agile software development methodologies, such as Scrum, have gained tremendous popularity and proven successful in quickly delivering quality Functional Requirements (FRs). However, agile methodologies have not adequately modeled Non-Functional Requirements (NFRs) and their potential solutions (operationalizations) with FRs in early development phases. This research proposes three fundamental agile artifacts to model FRs, NFRs, and their potential solutions in a visual environment. First, FRs are modeled through Agile Use Cases (AUCs). Second, NFRs are modeled through Agile Loose Cases (ALCs). Third, NFRs potential solutions are modeled through Agile Choose Cases (ACCs). AUCs are newly proposed hybrid of use cases and agile user stories. ALCs are proposed loosely-defined agile NFRs. ACCs are proposed potential solutions (operationalizations) for ALCs. The three artifacts are combined in a visual framework to promote agile modeling of NFRs (primarily) and how they are linked to FRs. The artifacts are the building blocks of a more comprehensive framework for modeling NFRs in agile software development processes.


title: Classification of text documents supervised by domain ontologies

abstract: The research objective is to establish an approach for supporting the classification of text documents referring to a specified domain. The focus is on the preliminary topic assignment to the documents used for training the model. The method implements domain ontology as background knowledge. The idea consists in extracting the preliminary topics for training the classifier by means of unsupervised machine learning on a text corpus and further alignment of the document vectors to concepts of the ontology. The results obtained by classification of new documents supervised by e-governance ontology with several machine learning algorithms showed sufficient match of their content to the ontology concepts. A conclusion is drawn that the approach can support the automatic extraction of documents relevant to any domain described by ontology.


title: Finding and tracking people from the bottom up

abstract: We describe a tracker that can track moving people in long sequences without manual initialization. Moving people are modeled with the assumption that, while configuration can vary quite substantially from frame to frame, appearance does not. This leads to an algorithm that firstly builds a model of the appearance of the body of each individual by clustering candidate body segments, and then uses this model to find all individuals in each frame. Unusually, the tracker does not rely on a model of human dynamics to identify possible instances of people; such models are unreliable, because human motion is fast and large accelerations are common. We show our tracking algorithm can be interpreted as a loopy inference procedure on an underlying Bayes net. Experiments on video of real scenes demonstrate that this tracker can (a) count distinct individuals; (b) identify and track them; (c) recover when it loses track, for example, if individuals are occluded or briefly leave the view; (d) identify the configuration of the body largely correctly; and (e) is not dependent on particular models of human motion.


title: A High-Performance HOG Extractor on FPGA

abstract: Pedestrian detection is one of the key problems in emerging self-driving car industry. And HOG algorithm has proven to provide good accuracy for pedestrian detection. There are plenty of research works have been done in accelerating HOG algorithm on FPGA because of its low-power and high-throughput characteristics. In this paper, we present a high-performance HOG architecture for pedestrian detection on a low-cost FPGA platform. It achieves a maximum throughput of 526 FPS with 640x480 input images, which is 3.25 times faster than the state of the art design. The accelerator is integrated with SVM-based prediction in realizing a pedestrian detection system. And the power consumption of the whole system is comparable with the best existing implementations.


Universities in a competitive global marketplace : A systematic review of the literature on higher education marketing


title: Can I Recognize My Body's Weight? The Influence of Shape and Texture on the Perception of Self

abstract: The goal of this research was to investigate women's sensitivity to changes in their perceived weight by altering the body mass index (BMI) of the participants' personalized avatars displayed on a large-screen immersive display. We created the personalized avatars with a full-body 3D scanner that records the participants' body geometry and texture. We altered the weight of the personalized avatars to produce changes in BMI while keeping height, arm length, and inseam fixed and exploited the correlation between body geometry and anthropometric measurements encapsulated in a statistical body shape model created from thousands of body scans. In a 2 × 2 psychophysical experiment, we investigated the relative importance of visual cues, namely shape (own shape vs. an average female body shape with equivalent height and BMI to the participant) and texture (own photo-realistic texture or checkerboard pattern texture) on the ability to accurately perceive own current body weight (by asking the participant, “Is it the same weight as you?”). Our results indicate that shape (where height and BMI are fixed) had little effect on the perception of body weight. Interestingly, the participants perceived their body weight veridically when they saw their own photo-realistic texture. As compared to avatars with photo-realistic texture, the avatars with checkerboard texture needed to be significantly thinner in order to represent the participants' current weight. This suggests that in general the avatars with checkerboard texture appeared bigger. The range that the participants accepted as their own current weight was approximately a 0.83% to − 6.05% BMI change tolerance range around their perceived weight. Both the shape and the texture had an effect on the reported similarity of the body parts and the whole avatar to the participant's body. This work has implications for new measures for patients with body image disorders, as well as researchers interested in creating personalized avatars for games, training applications, or virtual reality.


title: A Look at Parsing and Its Applications

abstract: This paper provides a brief introduction to recent work in statistical parsing and its applications. We highlight successes to date, remaining challenges, and promising future work.


title: The competitive advantage of nations: An application to academia

abstract: Within the field of bibliometrics, there is sustained interest in how nations “compete” in terms of academic disciplines, and what determinants explain why countries may have a specific advantage in one discipline over another. However, this literature has not, to date, presented a comprehensive structured model that could be used in the interpretation of a country's research profile and academic output. In this paper, we use frameworks from international business and economics to present such a model.


title: Study of design specification of three-dimensional product for locomotive based on TOP-DOWN design methodology

abstract: By studying the information-based technique, and analyzing the product design state at Dalian Locomotive Rolling Stock Co., Ltd., in connection with locomotive industry characteristics, the Top-Down design methodology-based design specification of product was put forward. By popularization of the design specification, designers can improve the structure of product in time according design requirements, perform the design of new product and improve the design of existing product as soon as possible, and realize the cooperative work between designer and technologist.


title: A Comprehensive and Harmonized Digital Forensic Investigation Process Model

abstract: Performing a digital forensic investigation (DFI) requires a standardized and formalized process. There is currently neither an international standard nor does a global, harmonized DFI process (DFIP) exist. The authors studied existing state-of-the-art DFIP models and concluded that there are significant disparities pertaining to the number of processes, the scope, the hierarchical levels, and concepts applied. This paper proposes a comprehensive model that harmonizes existing models. An effort was made to incorporate all types of processes proposed by the existing models, including those aimed at achieving digital forensic readiness. The authors introduce a novel class of processes called concurrent processes. This is a novel contribution that should, together with the rest of the model, enable more efficient and effective DFI, while ensuring admissibility of digital evidence. Ultimately, the proposed model is intended to be used for different types of DFI and should lead to standardization.


title: Power analysis and determination of sample size for covariance structure modeling.

abstract: A framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented. We emphasize the value of confidence intervals for fit indices, and we stress the relationship of confidence intervals to a framework for hypothesis testing. The approach allows for testing null hypotheses of not-good fit, reversing the role of the null hypothesis in conventional tests of model fit, so that a significant result provides strong support for good fit. The approach also allows for direct estimation of power, where effect size is defined in terms of a null and alternative value of the root-mean-square error of approximation fit index proposed by J. H. Steiger and J. M. Lind (1980). It is also feasible to determine minimum sample size required to achieve a given level of power for any test of fit in this framework. Computer programs and examples are provided for power analyses and calculation of minimum sample sizes.


title: Design and implementation of efficient three-phase interleaved DC-DC converter

abstract: An efficient three phase interleaved DC-DC converter is proposed with inductor current and voltage control. The use of multiple sensors for each phase current is eliminated using current reconstruction. This reduces the number of current sensors and improves the reliability. The inductor current control is achieved using single current sensor which measures DC-link current. The proposed converter is designed to maintain the output voltage constant under various loading conditions. The parameters of design are presented with the selection of components required for a converter. This efficient converter with minimum sensor has low cost digital controller. The implementation of three phase interleaved converter with its performance is also presented.


title: Interaction gestalt and the design of aesthetic interactions

abstract: Although there has been a drastic increase in the research of aesthetics of interaction, we still lack well-defined practical knowledge of how to design aesthetic interactions. In order to develop such knowledge, we adapt three important ways of thinking in designing interactions influenced by traditional design disciplines, namely, 1) understanding what it is that is designed---i.e. interaction, 2) knowing what is possible to be manipulated when designing interactions---i.e. attributes of interaction, and 3) mastering how to manipulate the attributes to shape the interactions. We explain our approach by arguing from the somaesthetic perspective. We propose the concept of interaction gestalt, as a way to achieve those three ways of thinking in design. We then propose a set of interaction gestalt attributes that can be used in designing aesthetic interactions. We end with a discussion of the implications and benefits of this approach in interaction design.


title: A survey on mobile edge computing

abstract: Mobile Edge Computing is an emerging technology that provides cloud and IT services within the close proximity of mobile subscribers. Traditional telecom network operators perform traffic control flow (forwarding and filtering of packets), but in Mobile Edge Computing, cloud servers are also deployed in each base station. Therefore, network operator has a great responsibility in serving mobile subscribers. Mobile Edge Computing platform reduces network latency by enabling computation and storage capacity at the edge network. It also enables application developers and content providers to serve context-aware services (such as collaborative computing) by using real time radio access network information. Mobile and Internet of Things devices perform computation offloading for compute intensive applications, such as image processing, mobile gaming, to leverage the Mobile Edge Computing services. In this paper, some of the promising real time Mobile Edge Computing application scenarios are discussed. Later on, a state-of-the-art research efforts on Mobile Edge Computing domain is presented. The paper also presents taxonomy of Mobile Edge Computing, describing key attributes. Finally, open research challenges in successful deployment of Mobile Edge Computing are identified and discussed.


Training Data Enrichment for Infrequent Discourse Relations


title: A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams

abstract: Topological data analysis is becoming a popular way to study high dimensional feature spaces without any contextual clues or assumptions. This paper concerns itself with one popular topological feature, which is the number of $d-$dimensional holes in the dataset, also known as the Betti$-d$ number. The persistence of the Betti numbers over various scales is encoded into a persistence diagram (PD), which indicates the birth and death times of these holes as scale varies. A common way to compare PDs is by a point-to-point matching, which is given by the $n$-Wasserstein metric. However, a big drawback of this approach is the need to solve correspondence between points before computing the distance; for $n$ points, the complexity grows according to $\mathcal{O}($n$^3)$. Instead, we propose to use an entirely new framework built on Riemannian geometry, that models PDs as 2D probability density functions that are represented in the square-root framework on a Hilbert Sphere. The resulting space is much more intuitive with closed form expressions for common operations. The distance metric is 1) correspondence-free and also 2) independent of the number of points in the dataset. The complexity of computing distance between PDs now grows according to $\mathcal{O}(K^2)$, for a $K \times K$ discretization of $[0,1]^2$. This also enables the use of existing machinery in differential geometry towards statistical analysis of PDs such as computing the mean, geodesics, classification etc. We report competitive results with the Wasserstein metric, at a much lower computational load, indicating the favorable properties of the proposed approach.


title: A resource-based theory of sustainable rents

abstract: This paper summarizes and comments on Conner (1991) that contributes to the strategic management area by providing an historical comparison of resource-based theory and five schools of thought within industrial organization economics. Conner (1991) argues that the fundamental distinction between resource-based theory and transaction costs theory is that resource-based theory focuses on the deployment and combination of specific inputs while transaction costs theory focuses on the avoidance of opportunism. I offer three responses to this claim. First, Conner’s distinction was not central to the resource-based literature at the time the article was published. Second, I raise concerns about building a resource-based theory of the firm that assumes away the problems of opportunistic behavior. Third, I offer an alternative view of the fundamental similarities and differences between resource-based theory and transaction costs theory.