title: MigCEP: operator migration for mobility driven distributed complex event processing

abstract: A recent trend in communication networks --- sometimes referred to as fog computing --- offers to execute computational tasks close to the access points of the networks. This enables real-time applications, like mobile Complex Event Processing (CEP), to significantly reduce end-to-end latencies and bandwidth usage. Most work studying the placement of operators in such an environment completely disregards the migration costs. However, the mobility of users requires frequent migration of operators, together with possibly large state information, to meet latency restrictions and save bandwidth in the infrastructure. This paper presents a placement and migration method for providers of infrastructures that incorporate cloud and fog resources. It ensures application-defined end-to-end latency restrictions and reduces the network utilization by planning the migration ahead of time. Furthermore, we present how the application knowledge of the CEP system can be used to improve current live migration techniques for Virtual Machines to reduce the required bandwidth during the migration. Our evaluations show that we safe up to 49% of the network utilization with perfect knowledge about a users mobility pattern and up to 27% of the network utilization when considering the uncertainty of those patterns.


title: Applying Data Mining to Customer Churn Prediction in an Internet Service Provider

abstract: A business incurs much higher charges when attempting to win new customers than to retain existing ones. As a result, much research has been invested into new ways of identifying those customers who have a high risk of churning. However, customer retention efforts have also been costing organizations large amounts of resources. Same is the situation in ISP industry in I.R.Iran. Exploiting the use of demographic, billing and usage data, this study tends to identify the best churn predictors on the one hand and evaluates the accuracy of different data mining techniques on the other. Clustering users as per their usage features and incorporating that cluster membership information in classification models is another aspect which has been addressed in this study.


Artificial Intelligence in Hypertension Diagnosis : A Review


title: Desire for amputation of a limb : paraphilia, psychosis, or a new type of identity disorder

abstract: Background. The objective of this paper is to describe and conceptualize an unusual and probably rare condition : the intense longstanding desire to have an amputation. Method. Structured interviews were conducted by telephone of 52 subjects (mean age: 48 . 6, range 23–77 years ; 47 male, 4 female, 1 intersexed) self-identified as having had a desire to have an amputation. Results. Seventeen per cent (n=9) had an arm or leg amputated with two-thirds using methods that put the subject at risk of death and one-third enlisting a surgeon to amputate their healthy limb. The most common reported reason for wanting an amputation was the subject’s feeling that it would correct a mismatch between the person’s anatomy and sense of his or her ‘true ’ self (identity). None were delusional. For all but one subject age at onset was during childhood or early adolescence. For those who had psychotherapy or medication there was no change in the intensity of the desire for amputation. The six subjects who had an amputation at their desired site reported that following the amputation they felt better than they ever had and no longer had a desire for an amputation. Conclusions. These preliminary results suggest the existence of an extremely unusual clinically distinct condition characterized by a lifelong desire to have an amputation of a particular limb. The condition is associated with serious negative consequences : amputation attempts, impairment and marked distress. Reflecting similarities between Gender Identity Disorder and this condition, the author suggests that it may be conceptualized as an unusual dysfunction in the development one’s fundamental sense of anatomical (body) identity.


title: PeopleNet: engineering a wireless virtual social network

abstract: People often seek information by asking other people even when they have access to vast reservoirs of information such as the Internet and libraries. This is because people are great sources of unique information, especially that which is location-specific, community-specific and time-specific. Social networking is effective because this type of information is often not easily available anywhere else. In this paper, we conceive a wireless virtual social network which mimics the way people seek information via social networking. PeopleNet is a simple, scalable and low-cost architecture for efficient information search in a distributed manner. It uses the infrastructure to propagate queries of a given type to users in specific geographic locations, called bazaars. Within each bazaar, the query is further propagated between neighboring nodes via peer-to-peer connectivity until it finds a matching query. The PeopleNet architecture can overlay easily on existing cellular infrastructure and entails minimal software installation. We identify three metrics for system performance: (i) probability of a match, (ii) time to find a match and (iii) number of matches found by a query. We describe two simple models, called the swap and spread models, for query propagation within a bazaar. We qualitatively argue that the swap model is better with respect to the performance metrics identified and demonstrate this via simulations. Next, we compute analytically the probability of match for the swap model. We show that the probability of match can be significantly improved if, prior to swapping queries, the nodes exchange some limited information about their buffer contents. We propose a simple greedy algorithm which uses this limited information to decide which queries to swap. We show via simulation that this algorithm achieves significantly better performance. Overall our results demonstrate that PeopleNet, with its bazaar concept and peer-to-peer query propagation, can provide a simple and efficient mechanism for seeking information.


Object detection in high resolution satellite images


title: Developmental experiences during extracurricular activities and Australian adolescents’ self-concept: particularly important for youth from disadvantaged schools

abstract: Extracurricular activities provide adolescents with a number of positive personal and interpersonal developmental experiences. This study investigated whether developmental experiences that occurred during extracurricular activities were linked to a more positive self-concept for Australian adolescents, and whether this link was particularly salient for youth from disadvantaged schools. Adolescents (N = 1,504, 56% Female) from 26 diverse high schools across Western Australia were surveyed. The findings revealed that adolescents from low socio-economic status schools who participated in extracurricular activities had a more positive general self-worth and social self-concept than adolescents from similar socio-economic schools who did not participate in any extracurricular activities. Furthermore, the positive developmental experiences that occurred during extracurricular activities predicted a more positive general self-worth and social and academic self-concept, and this link was stronger for youth from low SES schools. These findings suggest that the developmental experiences afforded by extracurricular activities may foster positive adolescent development.


ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics


title: A semisupervised learning method to merge search engine results

abstract: The proliferation of searchable text databases on local area networks and the Internet causes the problem of finding information that may be distributed among many disjoint text databases (distributed information retrieval). How to merge the results returned by selected databases is an important subproblem of the distributed information retrieval task. Previous research assumed that either resource providers cooperate to provide normalizing statistics or search clients download all retrieved documents and compute normalized scores without cooperation from resource providers.This article presents a semisupervised learning solution to the result merging problem. The key contribution is the observation that information used to create resource descriptions for resource selection can also be used to create a centralized sample database to guide the normalization of document scores returned by different databases. At retrieval time, the query is sent to the selected databases, which return database-specific document scores, and to a centralized sample database, which returns database-independent document scores. Documents that have both a database-specific score and a database-independent score serve as training data for learning to normalize the scores of other documents. An extensive set of experiments demonstrates that this method is more effective than the well-known CORI result-merging algorithm under a variety of conditions.


title: Fuzzy rough classifiers for class imbalanced multi-instance data

abstract: In multi-instance learning, each learning object consists of many descriptive instances. In the corresponding classification problems, each training object is labeled, but its constituent instances are not. The classification objective is to predict the class label of unseen objects. As in traditional single-instance classification, when the class sizes of multi-instance data are imbalanced, classification is degraded. Many multi-instance classifiers have been proposed, but few take into account the possibility of class imbalance, which causes them to fail in this situation. In this paper, we propose a new type of classifier that embodies a solution to the multi-instance class imbalance problem. Our proposal relies on the use of fuzzy rough set theory. We present two families of classifiers respectively based on information extracted at bag-level and at instance-level. We experimentally show that our algorithms outperform state-of-the-art solutions to multi-instance imbalanced data classification, evaluated by the popular metrics AUC and geometric mean. HighlightsWe propose a new type of classifier for imbalanced multi-instance data.Our classification method is based on fuzzy rough set theory.We develop a framework consisting of two classifier families.Our proposal is experimentally shown to outperform the state-of-the-art.


title: Design and analysis of quad-band grid array microstrip antenna at UWB and ISM channel frequencies for WBAN operations

abstract: In this paper, we present design and simulation of a compact grid array microstrip patch antenna. In the design of antenna a RT/duroid 5880 substrate having relative permittivity, thickness and loss tangent of 2.2, 1.57 mm and 0.0009 respectively, has been used. The simulated antenna performance was obtained by Computer Simulation Technology Microwave Studio (CST MWS). The antenna performance was investigated by analyzing its return loss (S11), radiation pattern, voltage standing wave ratio (VSWR) parameters. The simulated S11 parameter has shown that antenna operates for Industrial, Scientific and Medical (ISM) band and Wireless Body Area Network (WBAN) applications at 2.45 GHZ ISM, 6.25 GHZ, 8.25 GHZ and 10.45 GHZ ultra-wideband (UWB) four resonance frequencies with bandwidth > 500MHz (S11 < −10dB). The antenna directivity increased towards higher frequencies. The VSWR of resonance frequency bands is also achieved succesfully less than 2. It has been observed that the simulation result values of the antenna are suitable for WBAN applications.


title: Low-Latency Analytics on Colossal Data Streams with SummaryStore

abstract: SummaryStore is an approximate time-series store, designed for analytics, capable of storing large volumes of time-series data (~1 petabyte) on a single node; it preserves high degrees of query accuracy and enables near real-time querying at unprecedented cost savings. SummaryStore contributes time-decayed summaries, a novel abstraction for summarizing data streams, along with an ingest algorithm to continually merge the summaries for efficient range queries; in conjunction, it returns reliable error estimates alongside the approximate answers, supporting a range of machine learning and analytical workloads. We successfully evaluated SummaryStore using real-world applications for forecasting, outlier detection, and Internet traffic monitoring; it can summarize aggressively with low median errors, 0.1 to 10%, for different workloads. Under range-query microbenchmarks, it stored 1PB synthetic stream data (10241TB streams), on a single node, using roughly 10 TB (100x compaction) with 95%-ile error below 5% and median cold-cache query latency of 1.3s (worst case latency under 70s).


title: Can machine learning be secure?

abstract: Machine learning systems offer unparalled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. However, machine learning algorithms themselves can be a target of attack by a malicious adversary. This paper provides a framework for answering the question, "Can machine learning be secure?" Novel contributions of this paper include a taxonomy of different types of attacks on machine learning techniques and systems, a variety of defenses against those attacks, a discussion of ideas that are important to security for machine learning, an analytical model giving a lower bound on attacker's work function, and a list of open problems.


title: Depth hole filling using the depth distribution of neighboring regions of depth holes in the Kinect sensor

abstract: This paper proposes a depth hole filling method for RGBD images obtained from the Microsoft Kinect sensor. First, the proposed method labels depth holes based on 8-connectivity. For each labeled depth hole, the proposed method fills depth hole using the depth distribution of neighboring pixels of the depth hole. Then, we refine the hole filling result with cross-bilateral filtering. In experiments, by simply using the depth distribution of neighboring pixels, the proposed method improves the acquired depth map and reduces false filling caused by incorrect depth-color fusion.


title: DeepMath - Deep Sequence Models for Premise Selection

abstract: We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics. We propose a two stage approach for this task that yields good results for the premise selection task on the Mizar corpus while avoiding the hand-engineered features of existing state-of-the-art models. To our knowledge, this is the first time deep learning has been applied to theorem proving on a large scale.


title: Multi-scale Deep Learning Architectures for Person Re-identification

abstract: Person Re-identification (re-id) aims to match people across non-overlapping camera views in a public space. It is a challenging problem because many people captured in surveillance videos wear similar clothes. Consequently, the differences in their appearance are often subtle and only detectable at the right location and scales. Existing re-id models, particularly the recently proposed deep learning based ones match people at a single scale. In contrast, in this paper, a novel multi-scale deep learning model is proposed. Our model is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching. The importance of different spatial locations for extracting discriminative features is also learned explicitly. Experiments are carried out to demonstrate that the proposed model outperforms the state-of-the art on a number of benchmarks


title: Noisy Matrix Completion under Sparse Factor Models

abstract: This paper examines a general class of noisy matrix completion tasks, where the goal is to estimate a matrix from observations obtained at a subset of its entries, each of which is subject to random noise or corruption. Our specific focus is on settings where the matrix to be estimated is well-approximated by a product of two ( a priori unknown) matrices, one of which is sparse. Such structural models—referred to here as sparse factor models—have been widely used, for example, in subspace clustering applications, as well as in contemporary sparse modeling and dictionary learning tasks. Our main theoretical contributions are estimation error bounds for sparsity-regularized maximum likelihood estimators for the problems of this form, which are applicable to a number of different observation noise or corruption models. Several specific implications are examined, including scenarios where observations are corrupted by additive Gaussian noise or additive heavier-tailed (Laplace) noise, Poisson-distributed observations, and highly quantized (e.g., 1 b) observations. We also propose a simple algorithmic approach based on the alternating direction method of multipliers for these tasks, and provide experimental evidence to support our error analyses.


title: SMM-Based Hypervisor Integrity Measurement

abstract: Hypervisors play an important role in the virtualised environment and consequently are a prime target for attacks. Different kinds of attacks have been reported and a great deal of research has been done to address vulnerabilities in hypervisors. Recently, after successful defeat of integrity measurement tools, a new class of measurement tools have been developed capitalising on the SMM to measure the integrity of hypervisors and other system components. Although those new tools are successful in their tasks, they do not take full advantage of the main benefits of SMM: isolation and stealth. We argue that this is due to the architecture those tools employ. Thus, in this paper, we establish a set of requirements and propose a generic architecture to build and deploy an SMM-based hypervisor integrity measurement tool. We believe that such an architecture might be applied to any SMM-based tool.


title: Automatic Irrigation System with Data Log Creation

abstract: Water being a crucial resource should be judiciously used, especially when it comes to agriculture. It has been seen that right amount of water helps the farmer to have a good yield and the quality is also better. The paper proposes a technique to automate the irrigation system using Arduino Mega 2560 along with GSM 800L which is controlled through a mobile application module and create a log of the sensor data collected from the field on a webpage.


title: Integrating agile practices into software engineering courses

abstract: Agile software development methodologies claim to be superior for adapting to the changing needs of customers and projects and to the people on the team. As a result, these methodologies are steadily gaining interest and popularity in industry. Some examples of agile methodologies are Extreme Programming, Feature-Driven Development, Scrum, and Win-Win Spiral. Each of these processes comprises a set of practices, some of which are currently considered best practices and are consistent with what is taught in software engineering courses today. Other practices, however, are controversial and run contrary to the focus of most curricular materials. As educators, we must assess the academic and technical values of emerging technologies and, if convinced of their worthiness, we owe it to our students to integrate them into our curricula.


title: Applications of Graph Theory in Computer Science

abstract: Graphs are among the most ubiquitous models of both natural and human-made structures. They can be used to model many types of relations and process dynamics in computer science, physical, biological and social systems. Many problems of practical interest can be represented by graphs. In general graphs theory has a wide range of applications in diverse fields. This paper explores different elements involved in graph theory including graph representations using computer systems and graph-theoretic data structures such as list structure and matrix structure. The emphasis of this paper is on graph applications in computer science. To demonstrate the importance of graph theory in computer science, this article addresses most common applications for graph theory in computer science. These applications are presented especially to project the idea of graph theory and to demonstrate its importance in computer science.


title: Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion

abstract: This article presents the results of video-based Human Robot Interaction (HRI) trials which investigated people's perceptions of different robot appearances and associated attention-seeking features and behaviors displayed by robots with different appearance and behaviors. The HRI trials studied the participants' preferences for various features of robot appearance and behavior, as well as their personality attributions towards the robots compared to their own personalities. Overall, participants tended to prefer robots with more human-like appearance and attributes. However, systematic individual differences in the dynamic appearance ratings are not consistent with a universal effect. Introverts and participants with lower emotional stability tended to prefer the mechanical looking appearance to a greater degree than other participants. It is also shown that it is possible to rate individual elements of a particular robot's behavior and then assess the contribution, or otherwise, of that element to the overall perception of the robot by people. Relating participants' dynamic appearance ratings of individual robots to independent static appearance ratings provided evidence that could be taken to support a portion of the left hand side of Mori's theoretically proposed `uncanny valley' diagram. Suggestions for future work are outlined.


title: BigStation: enabling scalable real-time signal processingin large mu-mimo systems

abstract: Multi-user multiple-input multiple-output (MU-MIMO) is the latest communication technology that promises to linearly increase the wireless capacity by deploying more antennas on access points (APs). However, the large number of MIMO antennas will generate a huge amount of digital signal samples in real time. This imposes a grand challenge on the AP design by multiplying the computation and the I/O requirements to process the digital samples. This paper presents BigStation, a scalable architecture that enables realtime signal processing in large-scale MIMO systems which may have tens or hundreds of antennas. Our strategy to scale is to extensively parallelize the MU-MIMO processing on many simple and low-cost commodity computing devices. Our design can incrementally support more antennas by proportionally adding more computing devices. To reduce the overall processing latency, which is a critical constraint for wireless communication, we parallelize the MU-MIMO processing with a distributed pipeline based on its computation and communication patterns. At each stage of the pipeline, we further use data partitioning and computation partitioning to increase the processing speed. As a proof of concept, we have built a BigStation prototype based on commodity PC servers and standard Ethernet switches. Our prototype employs 15 PC servers and can support real-time processing of 12 software radio antennas. Our results show that the BigStation architecture is able to scale to tens to hundreds of antennas. With 12 antennas, our BigStation prototype can increase wireless capacity by 6.8x with a low mean processing delay of 860μs. While this latency is not yet low enough for the 802.11 MAC, it already satisfies the real-time requirements of many existing wireless standards, e.g., LTE and WCDMA.


title: Investigating Mobile Social Media Users? Behaviors in Tourism Collaborative Information Seeking

abstract: Leisure travelers most often travel in groups with family, relatives or friends. It is argued that tourist information search tends to be more collaborative than a completely individual decision-making exercise. Collaborative information seeking (CIS) deals with collaboration in searching for information. In recent years, social media has been steadily increasing as an important information source for tourists who access social media frequently through their mobile devices. Research has shown that travelers rely more on the suggestions and reviews provided by experienced tourists for their travel planning and decision-making. Tourists undertake social search during which they exploit their social networks/relationships to improve the search process. However, limited research has investigated social media users' behaviors in collaborative search for tourism information. What is the role of social media in supporting of collaborative tourism information search, including collaborative search patterns and strategies in social environments, has remained unknown. This research aims to investigate mobile social media users' behaviors in collaborative search for tourism information. Using the Grounded Theory approach, a conceptual framework of mobile social media users' tourism CIS behavior will be built. Semi-structured in-depth interviews will be employed to collect users' search experiences.


Recurrent Neural Network Embedding for Knowledgebase Completion


title: Optimized simultaneous conflict-free task assignment and path planning for multi-AGV systems

abstract: In this paper we address the problem of assigning a set of tasks to a set of Automated Guided Vehicles (AGVs), in a conflict-free manner. Specifically, we consider a system of multiple AGVs, moving along a predefined roadmap, and utilized for transportation of goods in automated warehouses. Sequential application of task assignment and path planning often gives rise to pathological situations, such as deadlocks, in which AGVs block each other, thus preventing tasks completion. In this paper we propose a method for assigning tasks while taking into account the subsequent path planning, encoding possible conflicts into a conflict graph, that is subsequently utilized for defining constraints of an optimization problem. Simulations are performed on maps of real industrial environments, to compare the proposed method with traditional task assignment.


title: Piezoelectric Pressure Sensing Device Using Top-Gate Effect of Dual-Gate a-IGZO TFT

abstract: We propose a pressure sensor composed of single-gate and dual-gate amorphous-indium–gallium–zinc–oxide thin-film transistors (TFTs) that are integrated with a P(VDF-TrFE)/PZT composite piezo-capacitor. The TFTs are connected in an inverter configuration, where the piezo-capacitor is connected to the floating top-gate of the dual-gate TFT (driving TFT). When the pressure is applied by finger pressing, the potential of the top-gate changes, and thus, the threshold voltage of the driving TFT shifts, leading to the generation of an output signal that is typically in the range of 200–300 mV.


title: Heart Disease Diagnosis Using PredictiveData mining

abstract: Heart disease is a major health problem and it affects a large number of people. Cardiovascular Disease (CVD) is one such threat. Unless detected and treated at an early stage it will lead to illness and causes death. There is no adequate research focus on effective analysis tools to discover relationships and trends in data especially in the medical sector. Health care industry today generates large amounts of complex clinical data about patients and other hospital resources. Data mining techniques are used to analyze this rich collection of data from different perspectives and deriving useful information. This project intends to design and develop diagnosis and prediction system for heart diseases based on predictive mining. Number of experiments has been conducted to compare the performance of various predictive data mining techniques including Decision tree and Naive Bayes algorithms. In this proposed work, a 13 attribute structured clinical database from UCI Machine Learning Repository has been used as a source data. Decision tree and Naive Bayes have been applied and their performance on diagnosis has been compared. Naive Bayes outperforms when compared to Decision tree.


title: Purchase occasion influence on the role of music in advertising

abstract: Abstract The role of background music in audience responses to commercials (and other marketing elements) has received increasing attention in recent years. This article extends the discussion of music's influence in two ways: (1) by using music theory to analyze and investigate the effects of music's structural profiles on consumers' moods and emotions and (2) by examining the relationship between music's evoked moods that are congruent versus incongruent with the purchase occasion and the resulting effect on purchase intentions. The study reported provides empirical support for the notion that when music is used to evoke emotions congruent with the symbolic meaning of product purchase, the likelihood of purchasing is enhanced.


title: Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling

abstract: In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations. The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages. Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks. The proposed model has favorable generalization properties as it retains over 89.8% of its average POS tagging accuracy when trained at 1.2% of the total available training data, i.e.~150 sentences per language.


title: Nodular progression of lentigo malignant melanoma during a treatment with tocilizumab: cause or coincidence?

abstract: Tocilizumab is an anti-interleukin (IL)-6 receptor monoclonal antibody, used since 2010 for the treatment of severe rheumatoid arthritis (RA). It is known to induce infection, similarly to other biotherapies which modulate immune response and cytokines. Few cases of malignancy, however, have as yet been reported. We describe here the case of a patient with severe RA, previously treated with prednisolone, methotrexate, leflunomide, etanercept, and rituximab, who, after 8 months of treatment with tocilizumab, developed rapidly progressive nodular melanoma on a preexisting pigmented lesion on her left cheek. Recently, another case of nodular melanoma under tocilizumab has been published. The possible causative role of tocilizumab and other immunomodulatory agents in the development of this malignancy is discussed. Based on the present case, dermatologic screening is recommended before initiation of tocilizumab.


title: Modeling Cyber-Physical Vulnerability of the Smart Grid With Incomplete Information

abstract: This paper addresses the attack modeling using vulnerability of information, communication and electric grid network. Vulnerability of electric grid with incomplete information has been analyzed using graph theory based approach. Vulnerability of information and communication (cyber) network has been modeled utilizing concepts of discovery, access, feasibility, communication speed and detection threat. Common attack vector based on vulnerability of cyber and physical system have been utilized to operate breakers associated with generating resources to model aurora-like event. Real time simulations for modified IEEE 14 bus test case system and graph theory analysis for IEEE 118 bus system have been presented. Test case results show the possible impact on smart grid caused by integrated cyber-physical attack.


A Change for the Better: Realizing Business-IT Alignment Through Organizational Change


title: Drowsiness detection using heart rate variability

abstract: It is estimated that 10-30 % of road fatalities are related to drowsy driving. Driver's drowsiness detection based on biological and vehicle signals is being studied in preventive car safety. Autonomous nervous system activity, which can be measured noninvasively from the heart rate variability (HRV) signal obtained from surface electrocardiogram, presents alterations during stress, extreme fatigue and drowsiness episodes. We hypothesized that these alterations manifest on HRV and thus could be used to detect driver's drowsiness. We analyzed three driving databases in which drivers presented different sleep-deprivation levels, and in which each driving minute was annotated as drowsy or awake. We developed two different drowsiness detectors based on HRV. While the drowsiness episodes detector assessed each minute of driving as "awake" or "drowsy" with seven HRV derived features (positive predictive value 0.96, sensitivity 0.59, specificity 0.98 on 3475 min of driving), the sleep-deprivation detector discerned if a driver was suitable for driving or not, at driving onset, as function of his sleep-deprivation state. Sleep-deprivation state was estimated from the first three minutes of driving using only one HRV feature (positive predictive value 0.80, sensitivity 0.62, specificity 0.88 on 30 drivers). Incorporating drowsiness assessment based on HRV signal may add significant improvements to existing car safety systems.


title: Handbook of Nursing Diagnosis

abstract: SECTION 1: Nursing Diagnoses Activity Intolerance Activity Planning, Ineffective Adaptive Capacity, Decreased Intracranial Anxiety Body Temperature, Risk for Imbalanced Bowel Incontinence Breastfeeding, Effective Breastfeeding, Ineffective Breastfeeding, Interrupted Cardiac Output, Decreased Caregiver Role Strain Comfort, Impaired Communication, Impaired Confusion Constipation Contamination: Individual Contamination: Family Contamination: Community Coping, Ineffective Coping, Ineffective Community Compromised Family Coping Disabled Family Coping Decisional Conflict Diarrhea Dignity, Risk for Compromised Human Risk for Disuse Syndrome Deficient Diversional Activity Dysreflexia, Autonomic Electrolyte Imbalance , Risk for Energy Field, Disturbed Environmental Interpretation Syndrome, Impaired Family Processes, Interrupted Fatigue Fear Fluid Volume, Deficient Fluid Volume, Excess Fluid Volume Imbalance, Risk for Gastrointestinal Mobility , Dysfunctional Gastrointestinal Mobility , Risk for Dysfunctional Gastrointestinal Perfusion, Risk for Ineffective Glucose, Risk for Unstable Blood Grieving Growth and Development, Delayed Health Behaviors, Risk-Prone Health Maintenance, Ineffective Health Seeking Behaviors (Specify) Home Maintenance, Impaired Hopelessness Human Dignity, Risk for Compromised Infant Behavior, Disorganized Infection, Risk for Injury, Risk for Perioperative Positioning Deficient Knowledge Latex Allergy Liver Function, Risk for Impaired Loneliness, Risk for Maternal Infant Dyad, Risk for Disturbed Mobility, Impaired Physical Moral Stress Neonatal Jaundice Neurovascular Dysfunction , Risk for Ineffective Peripheral Noncompliance Nutrition, Imbalanced: Less Than Body Requirements Nutrition, Imbalanced: More Than Body Requirements Nutrition, Imbalanced: Potential for More Than Body Requirements Parenting, Impaired Peripheral Neurovascular Dysfunction, Risk for Post-Trauma Response Powerlessness Protection, Ineffective Relocation Stress (Syndrome) Resilience, Impaired Individual Respiratory Function, Risk for Impaired Role Performance, Ineffective Sedentary Lifestyle Self-Care Deficit Syndrome Self-Concept, Disturbed Self-Harm , Risk for Disturbed Sensory Perception Sexuality Patterns, Ineffective Injury, Risk for Perioperative Positioning Disturbed Sleep Patterns Social Interaction, Impaired Social Isolation Sorrow, Chronic Spiritual Distress Stress Overload Sudden Infant Death Syndrome, Risk for Surgical Recovery, Delayed Self-Health Management, Ineffective Family Self-Health Management, Ineffective Community Thought Processes, Disturbed leave in Tissue Perfusion, Ineffective Unilateral Neglect Impaired Urinary Elimination Violence, Risk for Other-Directed Abbreviated Table of Contents


title: Bearing Fault Diagnosis Using Fully-Connected Winner-Take-All Autoencoder

abstract: Intelligent fault diagnosis of bearings has been a heated research topic in the prognosis and health management of rotary machinery systems, due to the increasing amount of available data collected by sensors. This has given rise to more and more business desire to apply data-driven methods for health monitoring of machines. In recent years, various deep learning algorithms have been adapted to this field, including multi-layer perceptrons, autoencoders, convolutional neural networks, and so on. Among these methods, autoencoder is of particular interest for us because of its simple structure and its ability to learn useful features from data in an unsupervised fashion. Previous studies have exploited the use of autoencoders, such as denoising autoencoder, sparsity aotoencoder, and so on, either with one layer or with several layers stacked together, and they have achieved success to certain extent. In this paper, a bearing fault diagnosis method based on fully-connected winner-take-all autoencoder is proposed. The model explicitly imposes lifetime sparsity on the encoded features by keeping only $k$ % largest activations of each neuron across all samples in a mini-batch. A soft voting method is implemented to aggregate prediction results of signal segments sliced by a sliding window to increase accuracy and stability. A simulated data set is generated by adding white Gaussian noise to original signals to test the diagnosis performance under noisy environment. To evaluate the performance of the proposed method, we compare our methods with some state-of-the-art bearing fault diagnosis methods. The experiments result show that, with a simple two-layer network, the proposed method is not only capable of diagnosing with high precision under normal conditions, but also has better robustness to noise than some deeper and more complex models.


title: 1 Teacher Efficacy: Its Meaning and Measure

abstract: The theoretical and empirical underpinnings of teacher efficacy are examined to bring coherence to the construct and its measurement. First, we explore the correlates of teacher efficacy revealed using various instruments and search for patterns that suggest a better understanding of the construct. Next, we introduce a model of teacher efficacy that reconciles two competing conceptual strands found in the literature. Then we examine implications of the research on teacher efficacy for teacher preparation and suggest strategies for improving the efficacy of inservice teachers. Finally, we propose new directions for research in light of the proposed model.


title: Convolutional Neural Networks

abstract: Convolution Neural Networks (CNNs) in essence are neural networks that employ the convolution operation (instead of a fully connected layer) as one of its layers.


title: A modular architecture for transparent computation in Recurrent Neural Networks

abstract: Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Godelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments.


title: Solid State Drives: The Beginning of the End for Current Practice in Digital Forensic Recovery?

abstract: Digital evidence is increasingly relied upon in computer forensic examinations and legal proceedings in the modern courtroom. The primary storage technology used for digital information has remained constant over the last two decades, in the form of the magnetic disc. Consequently, investigative, forensic, and judicial procedures are well-established for magnetic disc storage devices (Carrier, 2005). However, a paradigm shift has taken place in technology storage and complex, transistor-based devices for primary storage are now increasingly common. Most people are aware of the transition from portable magnetic floppy discs to portable USB transistor flash devices, yet the transition from magnetic hard drives to solid-state drives inside modern computers has so far attracted very little attention from the research community. ::: ::: ::: Here we show that it is imprudent and potentially reckless to rely on existing evidence collection processes and procedures, and we demonstrate that conventional assumptions about the behaviour of storage media are no longer valid. In particular, we demonstrate that modern storage devices can operate under their own volition in the absence of computer instructions. Such operations are highly destructive of traditionally recoverable data. This can contaminate evidence; can obfuscate and make validation of digital evidence reports difficult; can complicate the process of live and dead analysis recovery; and can complicate and frustrate the post recovery forensic analysis. ::: ::: Our experimental findings demonstrate that solid-state drives (SSDs) have the capacity to destroy evidence catastrophically under their own volition, in the absence of specific instructions to do so from a computer.


title: A holistic framework for the fostering of an information security sub-culture in organizations

abstract: Modern businesses operates in an emerging global information society. In this information society it is imperative for modern organizations to take the protection of their information resources seriously. This protection of information resources is to a large extent dependent on human co-operated behavior. This human factor is the weakest link in information security, and consists of two interrelated dimensions. Firstly, employees must have sufficient knowledge about information security in order to effectively implement, and maintain, the various information security controls. Secondly, the employees must have the correct attitude towards information security. These two dimensions to the human factor in information security are closely related, and to a degree co-dependent upon each other. It would thus make sense to address these dimensions holistically. This paper combines previously proposed principles and methodologies into a single holistic framework that addresses both the dimensions to this human factor in information security.


title: Measurement and analysis of online social networks

abstract: Online social networking sites like Orkut, YouTube, and Flickr are among the most popular sites on the Internet. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks. This paper presents a large-scale measurement study and analysis of the structure of multiple online social networks. We examine data gathered from four popular online social networks: Flickr, YouTube, LiveJournal, and Orkut. We crawled the publicly accessible user links on each site, obtaining a large portion of each social network's graph. Our data set contains over 11.3 million users and 328 million links. We believe that this is the first study to examine multiple online social networks at scale. Our results confirm the power-law, small-world, and scale-free properties of online social networks. We observe that the indegree of user nodes tends to match the outdegree; that the networks contain a densely connected core of high-degree nodes; and that this core links small groups of strongly clustered, low-degree nodes at the fringes of the network. Finally, we discuss the implications of these structural properties for the design of social network based systems.


title: Effective Recommendation with Category Hierarchy

abstract: Although flat item category structure where categories are independent in a same level has been well studied to enhance recommendation performance, in many real applications, item category is often organized in hierarchies to reflect the inherent correlations among categories. In this paper, we propose a novel matrix factorization model by exploiting category hierarchy from the perspectives of users and items for effective recommendation. Specifically, a user (an item) can be influenced (characterized) by her preferred categories (the categories it belongs to) in the hierarchy. We incorporate how different categories in the hierarchy co-influence a user and an item. Empirical results show the superiority of our approach against other counterparts.


title: 3D Reconstruction of Interior Wall Surfaces under Occlusion and Clutter

abstract: Laser scanners are often used to create 3D models of buildings for civil engineering applications. The current manual process is time-consuming and error-prone. This paper presents a method for using laser scanner data to model predominantly planar surfaces, such as walls, floors, and ceilings, despite the presence of significant amounts of clutter and occlusion, which occur frequently in natural indoor environments. Our goal is to recover the surface shape, detect and model any openings, and fill in the occluded regions. Our method identifies candidate surfaces for modeling, labels occluded surface regions, detects openings in each surface using supervised learning, and reconstructs the surface in the occluded regions. We evaluate the method on a large, highly cluttered data set of a building consisting of forty separate rooms.


title: Automatic navigation and landing of an indoor AR. drone quadrotor using ArUco marker and inertial sensors

abstract: Within the next few years, unmanned quadrotors are likely to become an important vehicle in humans' daily life. However, their automatic navigation and landing in indoor environments are among the commonly discussed topics in this regard. In fact, the quadrotor should be able to automatically find the landing point from the nearby position, navigate toward it, and finally, land on it accurately and smoothly. In this paper, we proposed a low-cost and thorough solution to this problem by using both bottom-facing and front-facing cameras of the drone. In addition, in the case that vision data were unavailable, inertial measurements alongside a Kalman filter were used to navigate the drone to achieve the promising continuity and reliability. An AR.Drone 2.0 quadrotor, as well as an ArUco marker, were employed in order to test the proposed method experimentally. The results indicated that the drone successfully landed on the predefined position with an acceptable time and accuracy.


title: Gaining confidence in the software development process using expert systems

abstract: Software safety standards recommend techniques to use throughout the software development lifecycle. These recommendations are a result of consensus building amongst software safety experts. Thus the reasoning underpinning compliance to these standards tends to be quite subjective. In addition, there are factors such as the size of the project, the effect of a review process on earlier phases of the development lifecycle, the complexity of the design and the quality of the staff, that arguably influence the assessment process but are not formally addressed by software safety standards. In this paper we present an expert system based on Bayesian Belief networks that take into account these and other factors when assessing the integrity at which the software was developed. This system has been reviewed by engineers working with software safety standard IEC61508. In this paper we illustrate some arguments that can be supported using the proposed system. ::: ::: This paper and the work it describes were partly funded by the Health and Safety Executive. The opinions or conclusions expressed are those of the authors alone and do not necessarily represent the views of the Health and Safety Executive.


title: Visual Processing of Contour Patterns under Conditions of Inattentional Blindness

abstract: An inattentional blindness paradigm was adapted to measure ERPs elicited by visual contour patterns that were or were not consciously perceived. In the first phase of the experiment, subjects performed an attentionally demanding task while task-irrelevant line segments formed square-shaped patterns or random configurations. After the square patterns had been presented 240 times, subjects' awareness of these patterns was assessed. More than half of all subjects, when queried, failed to notice the square patterns and were thus considered inattentionally blind during this first phase. In the second phase of the experiment, the task and stimuli were the same, but following this phase, all of the subjects reported having seen the patterns. ERPs recorded over the occipital pole differed in amplitude from 220 to 260 msec for the pattern stimuli compared with the random arrays regardless of whether subjects were aware of the patterns. At subsequent latencies (300-340 msec) however, ERPs over bilateral occipital-parietal areas differed between patterns and random arrays only when subjects were aware of the patterns. Finally, in a third phase of the experiment, subjects viewed the same stimuli, but the task was altered so that the patterns became task relevant. Here, the same two difference components were evident but were followed by a series of additional components that were absent in the first two phases of the experiment. We hypothesize that the ERP difference at 220-260 msec reflects neural activity associated with automatic contour integration whereas the difference at 300-340 msec reflects visual awareness, both of which are dissociable from task-related postperceptual processing.


title: Effective online software anomaly detection

abstract: While automatic online software anomaly detection is crucial for ensuring the quality of production software, current techniques are mostly inefficient and ineffective. For online software, its inputs are usually provided by the users at runtime and the validity of the outputs cannot be automatically verified without a predefined oracle. Furthermore, some online anomalous behavior may be caused by the anomalies in the execution context, rather than by any code defect, which are even more difficult to detect. Existing approaches tackle this problem by identifying certain properties observed from the executions of the software during a training process and using them to monitor online software behavior. However, they may require a large execution overhead for monitoring the properties, which limits the applicability of these approaches for online monitoring. We present a methodology that applies effective algorithms to select a close to optimal set of anomaly-revealing properties, which enables online anomaly detection with minimal execution overhead. Our empirical results show that an average of 76.5% of anomalies were detected by using at most 5.5% of execution overhead.


title: The DASH Diet and Insulin Sensitivity

abstract: Lifestyle modifications, including adoption of the Dietary Approaches to Stop Hypertension (DASH) dietary pattern, weight loss in individuals who are overweight or obese, and physical activity, are effective in the prevention and treatment of hypertension. A healthy lifestyle may also have beneficial effects on metabolic abnormalities, such as insulin resistance, that are associated with high blood pressure. This review examines the independent and combined effects of the DASH diet and weight loss plus exercise on blood pressure and insulin sensitivity, with a focus on recently published results from the ENCORE study. Our data suggest that the DASH eating plan alone lowers blood pressure in overweight individuals with higher than optimal blood pressure, but significant improvements in insulin sensitivity are observed only when the DASH diet is implemented as part of a more comprehensive lifestyle modification program that includes exercise and weight loss.


title: Biomechtronic design of a wearable upper dual-arm exoskeleton robot for lifting tasks

abstract: Robot technology is increasingly widely applied in medical field for rehabilitation, especially for the paralysis of stroke patients. In this paper, a new design concept of upper limb rehabilitation robot has been developed. The model is expected to perform three degrees of freedom that is rotation in horizontal and vertical planes. This system is of advantages in terms of light weight, dexterity size, and friendly interactive performance. Upper limb rehabilitative robot is able to simulate the motion of normal humans arm movements and to provide patients rehabilitative training to help them recover from illness. The proposed robot allows great freedom of movements of the operator.


title: From quantum foundations via natural language meaning to a theory of everything

abstract: In this paper we argue for a paradigmatic shift from ‘reductionism’ to ‘togetherness’. In particular, we show how interaction between systems in quantum theory naturally carries over to modelling how word meanings interact in natural language. Since meaning in natural language, depending on the subject domain, encompasses discussions within any scientific discipline, we obtain a template for theories such as social interaction, animal behaviour, and many others.


title: The Role of Soil Conservation on Mean Crop Yield and Variance of Yield - Evidence from the Ethiopian Highlands

abstract: Land degradation has been one of the major areas of concern in Ethiopia. Governments and development agencies have invested substantial resources to promote land management technologies and reduce land degradation. However, there is little understanding of the impacts that land management technologies have on yield and yield variability. This paper investigates the impact of stone bunds on mean yield and variance of yield, using multiple plot observations per household in low- and highrainfall areas of the Ethiopian highlands. Our analysis incorporated the propensity score matching method, stochastic dominance analysis, and exogenous and endogenous switching regression methods. We found statistically significant and positive impact of stone bunds on yield in low-rainfall areas. This did not hold in high-rainfall areas. We did not find a statistically significant stone-bund impact on production risk in either high- or low-rainfall areas. The results were robust to both parametric and nonparametric analysis. The overall conclusion from the analysis is that the performance of stone bunds varies by agro-ecology type. This implies the need for designing and implementing appropriate technologies that enhance productivity and are better adapted to local conditions.


title: Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec

abstract: Distributed dense word vectors have been shown to be effective at capturing token-level semantic and syntactic regularities in language, while topic models can form interpretable representations over documents. In this work, we describe lda2vec, a model that learns dense word vectors jointly with Dirichlet-distributed latent document-level mixtures of topic vectors. In contrast to continuous dense document representations, this formulation produces sparse, interpretable document mixtures through a non-negative simplex constraint. Our method is simple to incorporate into existing automatic differentiation frameworks and allows for unsupervised document representations geared for use by scientists while simultaneously learning word vectors and the linear relationships between them.


title: Improving the Investment Climate in China

abstract: This report is based on an investment climate survey conducted in 2002 in five Chinese cities (Beijing, Chengdu, Guangzhou, Shanghai, and Tianjin). We gratefully acknowledge financial support from the United Kingdom’s Department for International Development (DFID). A grant from DFID supported the collaboration of the Enterprise Survey Organization in this survey and fellowships for two ESO staff, Mr. Yang Yumin and Ms. Li Hui, to visit Washington, DC, for analysis and preparation of this report. This project is part of a larger effort in the World Bank Group to help countries assess their investment climates and to identify reforms that will lead to higher productivity, more efficient investment, and ultimately more job creation and growth.


title: Scheduling Precedence Graphs in Systems with Interprocessor Communication Times

abstract: The problem of nonpreemptively scheduling a set of m partially ordered tasks on n identical processors subject to interprocessor communication delays is studied in an effort to minimize the makespan. A new heuristic, called Earliest Task First (ETF), is designed and analyzed. It is shown that the makespan $\omega _{{\text{ETF}}} $ generated by ETF always satisfies $\omega _{{\text{ETF}}} \leqq ({{2 - 1} / n})\omega _{{\text{opt}}}^{(i)} + C$, where $\omega _{{\text{opt}}}^{(i)} $ is the optimal makespan without considering communication delays and C is the communication requirements over some immediate predecessor-immediate successor pairs along one chain. An algorithm is also provided to calculate C. The time complexity of Algorithm ETF is $O(nm^2 )$.


title: Following Devil's Footprints: Cross-Platform Analysis of Potentially Harmful Libraries on Android and iOS

abstract: It is reported recently that legitimate libraries are repackaged for propagating malware. An in-depth analysis of such potentially-harmful libraries (PhaLibs), however, has never been done before, due to the challenges in identifying those libraries whose code can be unavailable online (e.g., removed from the public repositories, spreading underground, etc.). Particularly, for an iOS app, the library it integrates cannot be trivially recovered from its binary code and cannot be analyzed by any publicly available anti-virus (AV) systems. In this paper, we report the first systematic study on PhaLibs across Android and iOS, based upon a key observation that many iOS libraries have Android versions that can potentially be used to understand their behaviors and the relations between the libraries on both sides. To this end, we utilize a methodology that first clusters similar packages from a large number of popular Android apps to identify libraries, and strategically analyze them using AV systems to find PhaLibs. Those libraries are then used to search for their iOS counterparts within Apple apps based upon the invariant features shared cross platforms. On each discovered iOS PhaLib, our approach further identifies its suspicious behaviors that also appear on its Android version and uses the AV system on the Android side to confirm that it is indeed potentially harmful. Running our methodology on 1.3 million Android apps and 140,000 popular iOS apps downloaded from 8 markets, we discovered 117 PhaLibs with 1008 variations on Android and 23 PhaLibs with 706 variations on iOS. Altogether, the Android PhaLibs is found to infect 6.84% of Google Play apps and the iOS libraries are embedded within thousands of iOS apps, 2.94% among those from the official Apple App Store. Looking into the behaviors of the PhaLibs, not only do we discover the recently reported suspicious iOS libraries such as mobiSage, but also their Android counterparts and 6 other back-door libraries never known before. Those libraries are found to contain risky behaviors such as reading from their host apps' keychain, stealthily recording audio and video and even attempting to make phone calls. Our research shows that most Android-side harmful behaviors have been preserved on their corresponding iOS libraries, and further identifies new evidence about libraries repackaging for harmful code propagations on both sides.


title: Band Gap Fluorescence from Individual Single-Walled Carbon Nanotubes

abstract: Fluorescence has been observed directly across the band gap of semiconducting carbon nanotubes. We obtained individual nanotubes, each encased in a cylindrical micelle, by ultrasonically agitating an aqueous dispersion of raw single-walled carbon nanotubes in sodium dodecyl sulfate and then centrifuging to remove tube bundles, ropes, and residual catalyst. Aggregation of nanotubes into bundles otherwise quenches the fluorescence through interactions with metallic tubes and substantially broadens the absorption spectra. At pH less than 5, the absorption and emission spectra of individual nanotubes show evidence of band gap–selective protonation of the side walls of the tube. This protonation is readily reversed by treatment with base or ultraviolet light.


title: Neural Melody Composition from Lyrics

abstract: In this paper, we study a novel task that learns to compose music from natural language. Given the lyrics as input, we propose a melody composition model that generates lyrics-conditional melody as well as the exact alignment between the generated melody and the given lyrics simultaneously. More specifically, we develop the melody composition model based on the sequence-to-sequence framework. It consists of two neural encoders to encode the current lyrics and the context melody respectively, and a hierarchical decoder to jointly produce musical notes and the corresponding alignment. Experimental results on lyrics-melody pairs of 18,451 pop songs demonstrate the effectiveness of our proposed methods. In addition, we apply a singing voice synthesizer software to synthesize the “singing” of the lyrics and melodies for human evaluation. Results indicate that our generated melodies are more melodious and tuneful compared with the baseline method.


title: Movie review mining and summarization

abstract: With the flourish of the Web, online review is becoming a more and more useful and important information resource for people. As a result, automatic review mining and summarization has become a hot research topic recently. Different from traditional text summarization, review mining and summarization aims at extracting the features on which the reviewers express their opinions and determining whether the opinions are positive or negative. In this paper, we focus on a specific domain - movie review. A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis and movie knowledge. The experimental results show the effectiveness of the proposed approach in movie review mining and summarization.


title: Mapping applications to the RaPiD configurable architecture

abstract: The goal of the RaPiD (Reconfigurable Pipelined Datapath) architecture is to provide high performance configurable computing for a range of computationally-intensive applications that demand special-purpose hardware. This is accomplished by mapping the computation into a deep pipeline using a configurable array of coarse-grained computational units. A key feature of RaPiD is the combination of static and dynamic control. While the underlying computational pipelines are configured statically, a limited amount of dynamic control is provided which greatly increases the range and capability of applications that can be mapped to RaPiD. This paper illustrates this mapping and configuration for several important applications including a FIR filter, 2-D DCT, motion estimation, and parametric curve generation; it also shows how static and dynamic control are used to perform complex computations.


title: SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories

abstract: Predicting the next location a user tends to visit is an important task for applications like location-based advertising, traffic planning, and tour recommendation. We consider the next location prediction problem for semantic trajectory data, wherein each GPS record is attached with a text message that describes the user's activity. In semantic trajectories, the confluence of spatiotemporal transitions and textual messages indicates user intents at a fine granularity and has great potential in improving location prediction accuracies. Nevertheless, existing methods designed for GPS trajectories fall short in capturing latent user intents for such semantics-enriched trajectory data. We propose a method named semantics-enriched recurrent model (SERM). SERM jointly learns the embeddings of multiple factors (user, location, time, keyword) and the transition parameters of a recurrent neural network in a unified framework. Therefore, it effectively captures semantics-aware spatiotemporal transition regularities to improve location prediction accuracies. Our experiments on two real-life semantic trajectory datasets show that SERM achieves significant improvements over state-of-the-art methods.


title: Impedance-ground modulated coplanar waveguide matching transformers with applications to miniaturized wilkinson power dividers

abstract: We propose a novel design concept to continuously vary the widths of the signal trace and grounds of a conventional coplanar waveguide (CPW) to achieve a predefined matching characteristic of a transmission line transformer. Theoretical analysis is established, optimized and evaluated for a compact transformer. Then, a miniaturized 1 GHz CPW Wilkinson power divider (WPD) is designed and simulated to validate the design procedure. Simulations show input port matching below −30 dB, output ports matching/isolation below −15 dB, and transmission losses of −3.1 dB at the design frequency.


title: Pulse Shape Discrimination Algorithms, Figures of Merit, and Gamma-Rejection for Liquid and Solid Scintillators

abstract: Pulse shape discrimination (PSD) in scintillators is useful to distinguish between signals from neutrons and gamma rays. A standard algorithm is to divide the integral of the tail end of the pulse by the total pulse integral, which itself yields the deposited energy. When the PSD ratio is plotted against the energy, two bands emerge for scintillators that respond differently to neutrons and gammas. Often, a figure of merit (FOM) is defined as the distance between the two bands in a particular energy range, divided by the sum of the full-widths at half-maximum of the PSD ratio bands. A high FOM is usually interpreted as a good indicator of how well a certain scintillator device can distinguish between neutrons and gammas. This, however, ignores the actual shape of the two bands, which may not have a Gaussian shape in the direction of the PSD ratio axis, especially in the presence of pulse pileup. Thus, the FOM may not say much about the gamma rejection capability of the detector, and can therefore be a little misleading. Here, we describe the results of research and development performed on a stilbene detector borrowed from the Lawrence Livermore National Laboratory, but the algorithms described were also successfully used on a number of plastic and liquid scintillation detectors. The standard algorithm described above was compared with a newly developed wavelet-based algorithm that is also used to reject pileup, as well as a pulse shape fitting approach which is shown to be capable of significantly improving the gamma-rejection ratio.


title: Pairwise-covariance linear discriminant analysis

abstract: In machine learning, linear discriminant analysis (LDA) is a popular dimension reduction method. In this paper, we first provide a new perspective of LDA from an information theory perspective. From this new perspective, we propose a new formulation of LDA, which uses the pairwise averaged class covariance instead of the globally averaged class covariance used in standard LDA. This pairwise (averaged) covariance describes data distribution more accurately. The new perspective also provides a natural way to properly weigh different pairwise distances, which emphasizes the pairs of class with small distances, and this leads to the proposed pairwise covariance properly weighted LDA (pcLDA). The kernel version of pcLDA is presented to handle nonlinear projections. Efficient algorithms are presented to efficiently compute the proposed models.


Neural Darwinism and Consciousness 1 RUNNING HEAD : Neural Darwinism and Consciousness Neural Darwinism and Consciousness


title: Enriching textbooks with images

abstract: Textbooks have a direct bearing on the quality of education imparted to the students. Therefore, it is of paramount importance that the educational content of textbooks should provide rich learning experience to the students. Recent studies on understanding learning behavior suggest that the incorporation of digital visual material can greatly enhance learning. However, textbooks used in many developing regions are largely text-oriented and lack good visual material. We propose techniques for finding images from the web that are most relevant for augmenting a section of the textbook, while respecting the constraint that the same image is not repeated in different sections of the same chapter. We devise a rigorous formulation of the image assignment problem and present a polynomial time algorithm for solving the problem optimally. We also present two image mining algorithms that utilize orthogonal signals and hence obtain different sets of relevant images. Finally, we provide an ensembling algorithm for combining the assignments. To empirically evaluate our techniques, we use a corpus of high school textbooks in use in India. Our user study utilizing the Amazon Mechanical Turk platform indicates that the proposed techniques are able to obtain images that can help increase the understanding of the textbook material.


title: Broadband Circularly Polarized Slot Antenna Array Using a Compact Sequential-Phase Feeding Network

abstract: A broadband circularly polarized (CP) slot antenna array fed by an asymmetric coplanar waveguide (CPW) with stepped and inverted T-shaped strips is proposed. Using four square slot antenna elements with sequential rotation oblique feed and a modifled sequential-phase (SP) feeding network, broadband CP can be achieved. The measured i10dB re∞ection coe-cient bandwidth and 3dB axial ratio (AR) bandwidth are 55.4% (1.63{2.88GHz) and 58% (1.65{3GHz), respectively. Good radiation characteristics with gain more than 6dBic over the operating band are obtained by the proposed antenna array with a compact size of 155 £ 155 £ 0:8mm 3 . Details of the proposed antenna array design and experimental results are presented and discussed.


title: Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction

abstract: We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus. We show that the usage of a domain-specific corpus is vital. We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.


title: An end-to-end system for designing mechanical structures for print-and-fold robots

abstract: This work presents a script-based development environment aimed at allowing users to easily design and create mechanical bodies for folded plastic robots. The origami-inspired fabrication process is inexpensive and widely accessible, and the tools developed in this work allow for open source design sharing and modular reuse. Designs are generated by recursively combining mechanical components – from primitive building blocks, through mechanisms and assemblies, to full robots – in a flexible yet well-defined manner. This process was used to design robotic elements of increasing complexity up to a multi-degree-of-freedom compliant manipulator arm, demonstrating the power of this system. The developed system is extensible, opening avenues for further research ultimately leading to the development of a complete robot compiler.


title: Cardiac Dysrhythmia Detection with GPU-Accelerated Neural Networks

abstract: Cardiac dysrhythmia is responsible for over half a million deaths in the United States annually. In this work, we evaluate the performance of neural networks on classifying electrocardiogram (ECG) sequences as normal or abnormal (arrhythmia). Using neural networks as our primary learning model, we explain our model’s performance and discuss hyperparameter tuning. Comparing the results of our model to SVMs, random forests, and logistic regression, we find that our neural network outperforms the other three models with a binary classification accuracy of 91.9%. For the multi-class classification task, we achieve an accuracy of 75.7%. The use of GPUs accelerates the neural network training process up to an order of magnitude over CPUs.


5 Microwave Beamforming Networks for Intelligent Transportation Systems Ardavan Rahimian


title: Architectural description languages (ADLs) vs UML: a review

abstract: The field of software architecture which is considered as a subfield of software engineering is now about two decades old. During this period a number of software Architecture Description Languages (ADLs) emerged and vanished. But none of the ADLs became much popular amongst the practitioners except a few, that too only in a specific domain. On the other hand Unified Modeling Language (UML) which some times is not even accepted as an ADL or accepted with a some hesitation has become an industry de facto standard notation for documenting software architectures. This paper makes an attempt to find an answer to this question as to what went wrong with the ADLs that they did not become much popular beyond their place of origin. Is UML really an Architecture Description Language.


title: Biological monitoring of workers exposed to ethylbenzene and co-exposed to xylene

abstract: Objective: Ethylbenzene is an important constituent of widely used solvent mixtures in industry. The objective of the present study was to provide information about biological monitoring of occupational exposure to ethylbenzene, and to review the biological limit values corresponding to the threshold limit value of ethylbenzene. Methods: A total of 20 male workers who had been exposed to a mixture of ethylbenzene and xylene, through painting and solvent mixing with commercial xylene in a metal industry, were recruited into this study. Environmental and biological monitoring were performed during an entire week. The urinary metabolites monitored were mandelic acid for ethylbenzene and methylhippuric acid for xylene. Correlations were analyzed between urinary metabolites and environmental exposure for ethylbenzene and xylene. The interaction effects of a binary exposure to ethylbenzene and xylene were also investigated using a physiologically based pharmacokinetic (PBPK) model. Results: The average environmental concentration of organic solvents was 12.77 ppm for xylene, and 3.42 ppm for ethylbenzene. A significant correlation (R2=0.503) was found between environmental xylene and urinary methylhippuric acid. Urinary level of methylhippuric acid corresponding to 100 ppm of xylene was 1.96 g/g creatinine in the worker study, whereas it was calculated as 1.55 g/g creatinine by the PBPK model. Urinary level of mandelic acid corresponding to 100 ppm of ethylbenzene was found to be 0.7 g/g creatinine. PBPK results showed that the metabolism of ethylbenzene was highly depressed by co-exposure to high concentrations of xylene leading to a non-linear behavior. Conclusions: At low exposures, both methylhippuric acid and mandelic acid can be used as indicators of commercial xylene exposures. However at higher concentrations mandelic acid cannot be recommended as a biological indicator due to the saturation of mandelic acid produced by the co-exposure to xylene.


title: A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy

abstract: Time Series Clustering is an important subroutine in many higher-level data mining analyses, including data editing for classifiers, summarization, and outlier detection. It is well known that for similarity search the superiority of Dynamic Time Warping (DTW) over Euclidean distance gradually diminishes as we consider ever larger datasets. However, as we shall show, the same is not true for clustering. Clustering time series under DTW remains a computationally expensive operation. In this work, we address this issue in two ways. We propose a novel pruning strategy that exploits both the upper and lower bounds to prune off a very large fraction of the expensive distance calculations. This pruning strategy is admissible and gives us provably identical results to the brute force algorithm, but is at least an order of magnitude faster. For datasets where even this level of speedup is inadequate, we show that we can use a simple heuristic to order the unavoidable calculations in a most-useful-first ordering, thus casting the clustering into an anytime framework. We demonstrate the utility of our ideas with both single and multidimensional case studies in the domains of astronomy, speech physiology, medicine and entomology. In addition, we show the generality of our clustering framework to other domains by efficiently obtaining semantically significant clusters in protein sequences using the Edit Distance, the discrete data analogue of DTW.


title: Rumor source detection with multiple observations: fundamental limits and algorithms

abstract: This paper addresses the problem of a single rumor source detection with multiple observations, from a statistical point of view of a spreading over a network, based on the susceptible-infectious model. For tree networks, multiple sequential observations for one single instance of rumor spreading cannot improve over the initial snapshot observation. The situation dramatically improves for multiple independent observations. We propose a unified inference framework based on the union rumor centrality, and provide explicit detection performance for degree-regular tree networks. Surprisingly, even with merely two observations, the detection probability at least doubles that of a single observation, and further approaches one, i.e., reliable detection, with increasing degree. This indicates that a richer diversity enhances detectability. For general graphs, a detection algorithm using a breadth-first search strategy is also proposed and evaluated. Besides rumor source detection, our results can be used in network forensics to combat recurring epidemic-like information spreading such as online anomaly and fraudulent email spams.


title: Exploring User Purpose Writing Single Tweets ∗

abstract: Microblogging is a new way of communication among people ::: which allows them to disseminate messages via web, mobile ::: phone, email or instant messaging. In 2010, microbloggers ::: generated 65 million messages a day on Twitter alone. Our ::: hypothesis is that tweeting is an activity which its users per- ::: form in order to stills some needs. In this paper, we describe ::: an approach for analysing user purposes in writing single ::: tweets and organize these purposes to taxonomy. We nd ::: that people use microblogging for eight dierent purpose, ::: e.g. promotion, social interaction and expressing emotions. ::: We aim to classify the tweet into categories of purposes.


title: Image Co-segmentation via Saliency Co-fusion

abstract: Most existing high-performance co-segmentation algorithms are usually complex due to the way of co-labeling a set of images as well as the common need of fine-tuning few parameters for effective co-segmentation. In this paper, instead of following the conventional way of co-labeling multiple images, we propose to first exploit inter-image information through co-saliency, and then perform single-image segmentation on each individual image. To make the system robust and to avoid heavy dependence on one single saliency extraction method, we propose to apply multiple existing saliency extraction methods on each image to obtain diverse salient maps. Our major contribution lies in the proposed method that fuses the obtained diverse saliency maps by exploiting the inter-image information, which we call saliency co-fusion. Experiments on five benchmark datasets with eight saliency extraction methods show that our saliency co-fusion-based approach achieves competitive performance even without parameter fine-tuning when compared with the state-of-the-art methods.


title: Factors in software quality for knowledge-based systems

abstract: Abstract The paper considers the need for quality knowledge-based software systems. The paper defines quality in terms of a manufacturing process and then relates the creation of software to manufacturing. It then considers the aspects that go towards the creation of a quality knowledge-based system: the specification, the development methodology, and the validation and verification processes that check that a product meets its specification. The paper does this by considering a set of benchmarks against which the level of quality can be measured and then how each of the aspects contributes to heighten each of the factors that contribute to the level of quality. The paper proposes that the specification, methodology, and validation criteria can all heighten the level of correctness and that if they are used collectively then the correctness of a system and hence the quality level can be raised significantly.


title: Mining association rules between sets of items in large databases

abstract: We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.


title: Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform

abstract: We introduce a new low-distortion embedding of l2d into lpO(log n) (p=1,2), called the Fast-Johnson-Linden-strauss-Transform. The FJLT is faster than standard random projections and just as easy to implement. It is based upon the preconditioning of a sparse projection matrix with a randomized Fourier transform. Sparse random projections are unsuitable for low-distortion embeddings. We overcome this handicap by exploiting the "Heisenberg principle" of the Fourier transform, ie, its local-global duality. The FJLT can be used to speed up search algorithms based on low-distortion embeddings in l1 and l2. We consider the case of approximate nearest neighbors in l2d. We provide a faster algorithm using classical projections, which we then further speed up by plugging in the FJLT. We also give a faster algorithm for searching over the hypercube.


title: Investigation of fNIRS Brain Sensing as Input to Information Filtering Systems

abstract: Today's users interact with an increasing amount of information, demanding a similar increase in attention and cognition. To help cope with information overload, recommendation engines direct users' attention to content that is most relevant to them. We suggest that functional near-infrared spectroscopy (fNIRS) brain measures can be used as an additional channel to information filtering systems. Using fNIRS, we acquire an implicit measure that correlates with user preference, thus avoiding the cognitive interruption that accompanies explicit preference ratings. We explore the use of fNIRS in information filtering systems by building and evaluating a brain-computer movie recommender. We find that our system recommends movies that are rated higher than in a control condition, improves recommendations with increased interaction with the system, and provides recommendations that are unique to each individual.


title: Crowdsourcing and language studies: the new generation of linguistic data

abstract: We present a compendium of recent and current projects that utilize crowdsourcing technologies for language studies, finding that the quality is comparable to controlled laboratory experiments, and in some cases superior. While crowdsourcing has primarily been used for annotation in recent language studies, the results here demonstrate that far richer data may be generated in a range of linguistic disciplines from semantics to psycholinguistics. For these, we report a number of successful methods for evaluating data quality in the absence of a 'correct' response for any given data point.


title: Estimating Defect Density Using Test Coverage

abstract: Defect density is one of the most important factors that allow one to decide if a piece of software is ready to be released. In theory, one can find all the defects and count them, however it is impossible to find all the defects within any reasonable amount of time. Estimating defect density can become difficult for high reliability software, since the remaining defects can be extremely hard to test. Defect seeding will work only if the distribution of seeded defects is similar to the existing defects. One possible way is to apply the exponential SRGM and thus estimate the total number of defects present at the beginning of testing. Here we show the problems with this approach and present a new approach based on software test coverage. Software test coverage directly measures the thoroughness of testing avoiding the problem of variations of test effectiveness. Here we present interpretations of the parameters of the coveragedefect-density model presented by Malaiya et al. We apply this model to actual test data to project the residual defect density. The results show that this method results in estimates that are more stable than the existing methods. This method is easier to understand and the convergence to the estimate can be visually observed.


title: A Survey of Intrusion Detection Systems for Mobile Ad Hoc Networks

abstract: Tactical Mobile Ad-hoc Networks (MANETs) are widely deployed in military organizations that have critical needs of communication even with the absence of fixed infrastructure. The lack of communication infrastructure and dynamic topology makes MANETs vulnerable to a wide range attacks. Tactical military operations are among the prime users of ad-hoc networks. The military holds higher standards of security requirements and thus require special intrusion detection applications. Conventional Intrusion Detection System (IDS) require central control and monitoring entities and thus cannot be applied to MANETs. Solutions to secure these networks are based on distributed and cooperative security. This paper presents a survey of IDS specifically for MANETs and also highlights the strengths and weakness of each model. We present a unique evaluation matrix for measuring the effectiveness of IDS for MANETs in an emergency response scenario.


title: On the Length of Programs for Computing Finite Binary Sequences

abstract: The use of Turing machines for calculating finite binary sequences is studied from the point of view of information theory and the theory of recursive functions. Various results are obtained concerning the number of instructions in programs. A modified form of Turing machine is studied from the same point of view. An application to the problem of defining a patternless sequence is proposed in terms of the concepts here developed.


title: A Global Linear Method for Camera Pose Registration

abstract: We present a linear method for global camera pose registration from pair wise relative poses encoded in essential matrices. Our method minimizes an approximate geometric error to enforce the triangular relationship in camera triplets. This formulation does not suffer from the typical `unbalanced scale' problem in linear methods relying on pair wise translation direction constraints, i.e. an algebraic error, nor the system degeneracy from collinear motion. In the case of three cameras, our method provides a good linear approximation of the trifocal tensor. It can be directly scaled up to register multiple cameras. The results obtained are accurate for point triangulation and can serve as a good initialization for final bundle adjustment. We evaluate the algorithm performance with different types of data and demonstrate its effectiveness. Our system produces good accuracy, robustness, and outperforms some well-known systems on efficiency.


pileptic seizure detection using multiwavelet transform based approximate ntropy and artificial neural networks


title: Evaluating and modelling Hanabi-playing agents

abstract: Agent modelling involves considering how other agents will behave, in order to influence your own actions. In this paper, we explore the use of agent modelling in the hidden-information, collaborative card game Hanabi. We implement a number of rule-based agents, both from the literature and of our own devising, in addition to an Information Set-Monte Carlo Tree Search (IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new, predictor version that uses a model of the agents with which it is paired. We observe a significant improvement in game-playing strength from this agent in comparison to IS-MCTS, resulting from its consideration of what the other agents in a game would do. In addition, we create a flawed rule-based agent to highlight the predictor's capabilities with such an agent.


title: An incremental deployment algorithm for mobile robot teams

abstract: This paper describes an algorithm for deploying the members of a mobile robot team into an unknown environment. The algorithm deploys robots one-at-a-time, with each robot making use of information gathered by the previous robots to determine the next deployment location. The deployment pattern is designed to maximize the area covered by the robots' sensors, while simultaneously ensuring that the robots maintain line-of-sight contact with one another This paper describes the basic algorithm and presents results obtained from a series of experiments conducted using both real and simulated robots.


title: Content-Based Music Information Retrieval (CB-MIR) and Its Applications toward the Music Industry: A Review

abstract: A huge increase in the number of digital music tracks has created the necessity to develop an automated tool to extract the useful information from these tracks. As this information has to be extracted from the contents of the music, it is known as content-based music information retrieval (CB-MIR). In the past two decades, several research outcomes have been observed in the area of CB-MIR. There is a need to consolidate and critically analyze these research findings to evolve future research directions. In this survey article, various tasks of CB-MIR and their applications are critically reviewed. In particular, the article focuses on eight MIR-related tasks such as vocal/non-vocal segmentation, artist identification, genre classification, raga identification, query-by-humming, emotion recognition, instrument recognition, and music clip annotation. The fundamental concepts of Indian classical music are detailed to attract future research on this topic. The article elaborates on the signal-processing techniques to extract useful features for performing specific tasks mentioned above and discusses their strengths as well as weaknesses. This article also points to some general research issues in CB-MIR and probable approaches toward their solutions so as to improve the efficiency of the existing CB-MIR systems.


Defining the Roles of IFN-γ and IL-17A in Inflammation and Protection against Helicobacter pylori Infection


title: Law, Finance and Firm Growth

abstract: We investigate how differences in legal and financial systems affect firms' use of external financing to fund growth. We show that in countries whose legal systems score high on an efficiency index, a greater proportion of firms use long-term external financing. An active, though not necessarily large, stock market and a large banking sector are also associated with externally financed firm growth. The increased reliance on external financing occurs in part because established firms in countries with well-functioning institutions have lower profit rates. Government subsidies to industry do not increase the proportion of firms relying on external financing.


title: Adaptive knowledge transfer for multiple instance learning in image classification

abstract: Multiple Instance Learning (MIL) is a popular learning technique in various vision tasks including image classification. However, most existing MIL methods do not consider the problem of insufficient examples in the given target category. In this case, it is difficult for traditional MIL methods to build an accurate classifier due to the lack of training examples. Motivated by the empirical success of transfer learning, this paper proposes a novel approach of Adaptive Knowledge Transfer for Multiple Instance Learning (AKT-MIL) in image classification. The new method transfers cross-category knowledge from source categories under multiple instance setting for boosting the learning process. A unified learning framework with a data-dependent mixture model is designed to adaptively combine the transferred knowledge from sources with a weak classifier built in the target domain. Based on this framework, an iterative coordinate descent method with Constraint Concave-Convex Programming (CCCP) is proposed as the optimization procedure. An extensive set of experimental results demonstrate that the proposed AKT-MIL approach substantially outperforms several state-of-the-art algorithms on two benchmark datasets, especially in the scenario when very few training examples are available in the target domain.


title: Parallel-coupled line filters with enhanced stopband performances

abstract: A new class of parallel-coupled line filters with broad stopband response is introduced. The design is based on the synthesis of bandpass prototypes with pre-defined upper stopband characteristics. The new filters have uniform- and stepped-impedance resonators, some of which are loaded by open-circuited stubs at their open-circuited ends. A seventh-order filter implementation is presented with a fundamental passband centered at 1 GHz. The measured wide-band transmission characteristic of the filter demonstrated a broad upper stopband and was in agreement with simulations. The performance of the new filter is also compared with the characteristic of a conventionally designed filter to highlight the advantages of the proposed design method.


title: A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing

abstract: Evaluation of blood smear is a commonly clinical test these days. Most of the time, the hematologists are interested on white blood cells (WBCs) only. Digital image processing techniques can help them in their analysis and diagnosis. For example, disease like acute leukemia is detected based on the amount and condition of the WBC. The main objective of this paper is to segment the WBC to its two dominant elements: nucleus and cytoplasm. The segmentation is conducted using a proposed segmentation framework that consists of an integration of several digital image processing algorithms. Twenty microscopic blood images were tested, and the proposed framework managed to obtain 92% accuracy for nucleus segmentation and 78% for cytoplasm segmentation. The results indicate that the proposed framework is able to extract the nucleus and cytoplasm region in a WBC image sample.


title: A Critical View of Driver Behavior Models: What Do We Know, What Should We Do?

abstract: There appears to be a lack of new ideas in driver behavior modeling. Although behavioral research is under some pressure, it seems too facile to attribute this deplorable state of affairs only to a lack of research funds. In my opinion the causal chain may well run in the opposite direction. An analysis of what is wrong has led me to the conclusion that human factors research in the area of driver behavior has hardly been touched by the “cognitive revolution” that swept psychology in the past fifteen years. A more cognitive approach might seem advisable and the “promise of progress” of such an approach should be assessed.