title: Learning to Act Using Real-Time Dynamic Programming

abstract: Learning methods based on dynamic programming (DP) are receiving increasing attention in artificial intelligence. Researchers have argued that DP provides the appropriate basis for compiling planning results into reactive strategies for real-time control, as well as for learning such strategies when the system being controlled is incompletely known. We introduce an algorithm based on DP, which we call Real-Time DP (RTDP), by which an embedded system can improve its performance with experience. RTDP generalizes Korf''s Learning-Real-Time-A algorithm to problems involving uncertainty. We invoke results from the theory of asynchronous DP to prove that RTDP achieves optimal behavior in several different classes of problems. We also use the theory of asynchronous DP to illuminate aspects of other DP-based reinforcement learning methods such as Watkins'' Q-Learning algorithm. A secondary aim of this article is to provide a bridge between AI research on real-time planning and learning and relevant concepts and algorithms from control theory. This research was supported by grants to A.G. Barto from the National Science Foundation (ECS-8912623 and ECS-9214866) and the Air Force Office of Scientific Research, Bolling AFB (AFOSR-89-0526).


title: SOM-TC: Self-Organizing Map for Hierarchical Trajectory Clustering

abstract: Trajectory clustering techniques help discover interesting insights from moving object data, including common routes for people and vehicles, anomalous sub-trajectories, etc. Existing trajectory clustering techniques fail to take in to account the uncertainty present in location data. In this paper, we investigate the problem of clustering trajectory data and propose a novel algorithm for clustering similar full and sub-trajectories together while modeling uncertainty in this data. We describe the necessary pre-processing techniques for clustering trajectory data, namely techniques to discretize raw location data using Possible World semantics to capture the inherent uncertainty in location data, and to segment full trajectories in to meaningful sub-trajectories. As a baseline, we extend the well known K-means algorithm to cluster trajectory data. We then describe and evaluate a new trajectory clustering algorithm, SOM-TC (Self-Organizing Map Based Trajectory Clustering), that is inspired from the self-organizing map technique and is at least 4x faster than the baseline K-means and current density based clustering approaches.


title: Attacking Machine Learning models as part of a cyber kill chain

abstract: Machine learning is gaining popularity in the network security domain as many more network-enabled devices get connected, as malicious activities become stealthier, and as new technologies like Software Defined Networking emerge. Compromising machine learning model is a desirable goal. In fact, spammers have been quite successful getting through machine learning enabled spam filters for years. While previous works have been done on adversarial machine learning, none has been considered within a defense-in-depth environment, in which correct classification alone may not be good enough. For the first time, this paper proposes a cyber kill-chain for attacking machine learning models together with a proof of concept. The intention is to provide a high level attack model that inspire more secure processes in research/design/implementation of machine learning based security solutions.


title: High Performance Text Recognition Using a Hybrid Convolutional-LSTM Implementation

abstract: Optical character recognition (OCR) has made great progress in recent years due to the introduction of recognition engines based on recurrent neural networks, in particular the LSTM architecture. This paper describes a new, open-source line recognizer combining deep convolutional networks and LSTMs, implemented in PyTorch and using CUDA kernels for speed. Experimental results are given comparing the performance of different combinations of geometric normalization, 1D LSTM, deep convolutional networks, and 2D LSTM networks. An important result is that while deep hybrid networks without geometric text line normalization outperform 1D LSTM networks with geometric normalization, deep hybrid networks with geometric text line normalization still outperform all other networks. The best networks achieve a throughput of more than 100 lines per second and test set error rates on UW3 of 0.25%.


title: Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System

abstract: This paper investigates the problem of bootstrapping a statistical dialogue manager without access to training data and proposes a new probabilistic agenda-based method for simulating user behaviour. In experiments with a statistical POMDP dialogue system, the simulator was realistic enough to successfully test the prototype system and train a dialogue policy. An extensive study with human subjects showed that the learned policy was highly competitive, with task completion rates above 90%.


title: CMOS T/R Switch Design: Towards Ultra-Wideband and Higher Frequency

abstract: This paper presents the comprehensive considerations of CMOS transmit/receive (T/R) switch design towards ultra-wideband and over 15-GHz frequencies. Techniques for minimizing parasitics and increasing linearity are discussed. A customized transistor layout is proposed for T/R switch design and its effects on insertion loss and isolation are studied. The analysis shows that a series-only architecture using the customized transistor layout achieves better insertion loss and reasonable isolation. A double-well body-floating technique is proposed and its effects are discussed. A differential switch architecture without shunt arms is designed and verified by experimental results. Fabricated in 0.13-mum triple-well CMOS, the T/R switch exhibits less than 2 dB insertion loss and higher than 21 dB isolation up to 20 GHz. With resistive body floating and differential architecture, the high linearity is of ultra-wideband characteristic, more than 30-dBm power 1-dB compression point (P1dB) is obtained up to 20 GHz in only 0.03 mm2 active die area.


title: Learned perceptual image enhancement

abstract: Learning a typical image enhancement pipeline involves minimization of a loss function between enhanced and reference images. While L1 and L2 losses are perhaps the most widely used functions for this purpose, they do not necessarily lead to perceptually compelling results. In this paper, we show that adding a learned no-reference image quality metric to the loss can significantly improve enhancement operators. This metric is implemented using a CNN (convolutional neural network) trained on a large-scale dataset labelled with aesthetic preferences of human raters. This loss allows us to conveniently perform back-propagation in our learning framework to simultaneously optimize for similarity to a given ground truth reference and perceptual quality. This perceptual loss is only used to train parameters of image processing operators, and does not impose any extra complexity at inference time. Our experiments demonstrate that this loss can be effective for tuning a variety of operators such as local tone mapping and dehazing.


title: Adolescents’ Exposure to Sexually Explicit Material on the Internet

abstract: Drawing on a survey of 745 Dutch adolescents ages 13 to 18, the authors investigated (a) the occurrence and frequency of adolescents’ exposure to sexually explicit material on the Internet and (b) the correlates of this exposure. Seventy-one percent of the male adolescents and 40% of the female adolescents had been exposed to some kind of online sexually explicit material in the 6 months prior to the interview. Adolescents were more likely to be exposed to sexually explicit material online if they were male, were high sensation seekers, were less satisfied with their lives, were more sexually interested, used sexual content in other media more often, had a fast Internet connection, and had friends that were predominantly younger. Among male adolescents, a more advanced pubertal status was also associated with more frequent exposure to online sexually explicit material. Among female adolescents, greater sexual experience decreased exposure to online sexually explicit material.


title: Neurofeedback-based functional near-infrared spectroscopy upregulates motor cortex activity in imagined motor tasks

abstract: Abstract. Neurofeedback is a method for using neural activity displayed on a computer to regulate one’s own brain function and has been shown to be a promising technique for training individuals to interact with brain–machine interface applications such as neuroprosthetic limbs. The goal of this study was to develop a user-friendly functional near-infrared spectroscopy (fNIRS)-based neurofeedback system to upregulate neural activity associated with motor imagery, which is frequently used in neuroprosthetic applications. We hypothesized that fNIRS neurofeedback would enhance activity in motor cortex during a motor imagery task. Twenty-two participants performed active and imaginary right-handed squeezing movements using an elastic ball while wearing a 98-channel fNIRS device. Neurofeedback traces representing localized cortical hemodynamic responses were graphically presented to participants in real time. Participants were instructed to observe this graphical representation and use the information to increase signal amplitude. Neural activity was compared during active and imaginary squeezing with and without neurofeedback. Active squeezing resulted in activity localized to the left premotor and supplementary motor cortex, and activity in the motor cortex was found to be modulated by neurofeedback. Activity in the motor cortex was also shown in the imaginary squeezing condition only in the presence of neurofeedback. These findings demonstrate that real-time fNIRS neurofeedback is a viable platform for brain–machine interface applications.


title: Single image tree modeling

abstract: In this paper, we introduce a simple sketching method to generate a realistic 3D tree model from a single image. The user draws at least two strokes in the tree image: the first crown stroke around the tree crown to mark up the leaf region, the second branch stroke from the tree root to mark up the main trunk, and possibly few other branch strokes for refinement. The method automatically generates a 3D tree model including branches and leaves. Branches are synthesized by a growth engine from a small library of elementary subtrees that are pre-defined or built on the fly from the recovered visible branches. The visible branches are automatically traced from the drawn branch strokes according to image statistics on the strokes. Leaves are generated from the region bounded by the first crown stroke to complete the tree. We demonstrate our method on a variety of examples.


title: Towards an ICF core set for ADHD: a worldwide expert survey on ability and disability

abstract: This is the second in a series of four empirical studies designed to develop International Classification of Functioning, Disability and Health (ICF and Children and Youth version, ICF-CY) core sets for attention deficit hyperactivity disorder (ADHD). The objective of this stage was to gather the opinions from international experts on which ability and disability concepts were considered relevant to functioning in ADHD. An email-based survey was carried out amongst international experts in ADHD. Relevant functional ability and disability concepts were extracted from their responses and linked to the ICF/-CY categories by two independent researchers using a standardised linking procedure. 174 experts from 11 different disciplines and 45 different countries completed the survey. Meaningful concepts identified in their responses were linked to 185 ICF/-CY categories. Of these, 83 categories were identified by at least 5 % of the experts and considered the most relevant to ADHD: 30 of these were related to Body functions (most identified: attention functions, 85 %), 30 to Activities and Participation (most identified: school education, 52 %), 20 to Environmental factors (most identified: support from immediate family, 61 %), and 3 to Body structures (most identified: structure of brain, 83 %). Experts also provided their views on particular abilities related to ADHD, naming characteristics such as high-energy levels, flexibility and resiliency. Gender differences in the expression of ADHD identified by experts pertained mainly to females showing more internalising (e.g. anxiety, low self-esteem) and less externalising behaviours (e.g. hyperactivity), leading to a risk of late- and under-diagnosis in females. Results indicate that the impact of ADHD extends beyond the core symptom domains, into all areas of life and across the lifespan. The current study in combination with three additional preparatory studies (comprehensive scoping review, focus groups, clinical study) will provide the scientific basis to define the ADHD ICF/-CY core sets for multi-purpose use in basic and applied research and every day clinical practice.


title: Designing Intelligent Tutoring Systems: A Bayesian Approach

abstract: This paper proposes a model and an architecture for designing intelligent tutoring system using Bayesian Networks. The design model of an intelligent tutoring system is directed towards the separation between the domain knowledge and the tutor shell. The architecture is composed by a user model, a knowledge base, an adaptation module, a pedagogical module and a presentation module. Bayesian Networks are used to assess user’s state of knowledge and preferences, in order to suggest pedagogical options and recommend future steps in the tutor. The proposed architecture is implemented in the Internet, enabling its use as an e-learning tool. An example of an intelligent tutoring system is shown for illustration purposes.


Internet of Things : Perspectives , Challenges and Opportunities


title: Adaptive GPU cache bypassing

abstract: Modern graphics processing units (GPUs) include hardware- controlled caches to reduce bandwidth requirements and energy consumption. However, current GPU cache hierarchies are inefficient for general purpose GPU (GPGPU) comput- ing. GPGPU workloads tend to include data structures that would not fit in any reasonably sized caches, leading to very low cache hit rates. This problem is exacerbated by the design of current GPUs, which share small caches be- tween many threads. Caching these streaming data struc- tures needlessly burns power while evicting data that may otherwise fit into the cache. We propose a GPU cache management technique to im- prove the efficiency of small GPU caches while further re- ducing their power consumption. It adaptively bypasses the GPU cache for blocks that are unlikely to be referenced again before being evicted. This technique saves energy by avoid- ing needless insertions and evictions while avoiding cache pollution, resulting in better performance. We show that, with a 16KB L1 data cache, dynamic bypassing achieves sim- ilar performance to a double-sized L1 cache while reducing energy consumption by 25% and power by 18%. The technique is especially interesting for programs that do not use programmer-managed scratchpad memories. We give a case study to demonstrate the inefficiency of current GPU caches compared to programmer-managed scratchpad memories and show the extent to which cache bypassing can make up for the potential performance loss where the effort to program scratchpad memories is impractical.


title: Regional and global concerns over wetlands and water quality.

abstract: Water quality in many stream catchments and river basins is severely impacted by nutrient enrichment as a result of agriculture. Water-resource managers worldwide are considering the potential role of riparian zones and floodplain wetlands in improving stream-water quality, as there is evidence at the site scale that such wetlands are efficient at removing nutrients from through-flowing water. However, recent studies have highlighted disadvantages of such use of wetlands, including emissions of greenhouse gases and losses of biodiversity that result from prolonged nutrient loading. Here, we discuss the water purification function of wetlands at the site and catchment scale and suggest ways in which these disadvantages could be overcome.


title: Clinical relevance of host immunity in breast cancer: from TILs to the clinic

abstract: The clinical relevance of the host immune system in breast cancer has long been unexplored. Studies developed over the past decade have highlighted the biological heterogeneity of breast cancer, prompting researchers to investigate whether the role of the immune system in this malignancy is similar across different molecular subtypes of the disease. The presence of high levels of lymphocytic infiltration has been consistently associated with a more-favourable prognosis in patients with early stage triple-negative and HER2-positive breast cancer. These infiltrates seem to reflect favourable host antitumour immune responses, suggesting that immune activation is important for improving survival outcomes. In this Review, we discuss the composition of the immune infiltrates observed in breast cancers, as well as data supporting the clinical relevance of host antitumour immunity, as represented by lymphocytic infiltration, and how this biomarker could be used in the clinical setting. We also discuss the rationale for enhancing immunity in breast cancer, including early data on the efficacy of T-cell checkpoint inhibition in this setting.


title: Bayesian reconstruction of 3D shapes and scenes from a single image

abstract: It is common experience for human vision to perceive full 3D shape and scene from a single 2D image with the occluded parts "filled-in" by prior visual knowledge. We represent prior knowledge of 3D shapes and scenes by probabilistic models at two levels - both are defined on graphs. The first level model is built on a graph representation for single objects, and it is a mixture model for both man-made block objects such as trees and grasses. It assumes surface and boundary smoothness, 3D angle symmetry etc. The second level model is built on the relation graph of all objects in a scene. It assumes that objects should be supported for maximum stability with global bounding surfaces, such as ground, sky and walls. Given an input image, we extract the geometry and photometric structures through image segmentation and sketching, and represent them in a big graph. Then we partition the graph into subgraphs each being an object, infer the 3D shape and recover occluded surfaces, edges and vertices in each subgraph, and infer the scene structures between the recovered 3D sub-graphs. The inference algorithm samples from the prior model under the constraint that it reproduces the observed image/sketch under projective geometry.


Old Image Denoising and Auto-Colorization Using Linear Regression , Multilayer Perceptron and Convolutional Nearual Network


Are Windows and Views Really Better ? A Quantitative Analysis of the Economic and Psychological Value of Views


title: Recurrent knowledge graph embedding for effective recommendation

abstract: Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation on real-world datasets shows the superiority of RKGE against state-of-the-art methods. Furthermore, we show that RKGE provides meaningful explanations for recommendation results.


title: Striking a difference? The effect of knapping techniques on blade attributes

abstract: Studies of lithic blade technology offer an important step towards explanations of technological diversification among Stone Age hunter-gatherers, and for tracking continuity and change in cultural traits through time and space. One prominent example is the many efforts to map the spatiotemporal diffusion of pressure blade technology. In this context, a key concern is to distinguish the various knapping techniques, applied by prehistoric knappers. Specific observable blade attributes, found by experimental work is proposed to provide essential information, to determine the technique used. To date, however, the causal relationship between blade knapping techniques and postulated technique-related attributes remains largely untested in quantitative terms. With the purpose of contributing to a better understanding of how various knapping techniques, and in this case the indentor type used for blade removal, effect particular aspects of blade morphology, statistical analysis of experimental data is used, and subsequently applied as a basis for predicting knapping techniques in blade assemblages from Early and Middle Mesolithic (ca. 9500–6300 cal. BC) Southern Norway. The results clearly indicate a considerable overlap in the distributions of the majority of the attributes with regards to technique, and that their causal relationship should be viewed with considerable caution. The discriminate capability increases, however, when specific composite attributes are considered. Importantly, what is also shown is that at the blade population level, results from statistical analysis of experimental data contribute to predict general tendencies in knapping technique variability in archaeological blade assemblages, while simultaneously formalising the discriminating characteristics that differentiate those assemblages. Taken together, these results have implications when investigating variation and change in blade technology in time and space.


title: An Automated Teamwork Infrastructure for Heterogeneous Software Agents and Humans

abstract: Agent integration architectures enable a heterogeneous, distributed set of agents to work together to address problems of greater complexity than those addressed by the individual agents themselves. Unfortunately, integrating software agents and humans to perform real-world tasks in a large-scale system remains difficult, especially due to three main challenges: ensuring robust execution in the face of a dynamic environment, providing abstract task specifications without all the low-level coordination details, and finding appropriate agents for inclusion in the overall system. To address these challenges, our Teamcore project provides the integration architecture with general-purpose teamwork coordination capabilities. We make each agent team-ready by providing it with a proxy capable of general teamwork reasoning. Thus, a key novelty and strength of our framework is that powerful teamwork capabilities are built into its foundations by providing the proxies themselves with a teamwork model.Given this teamwork model, the Teamcore proxies addresses the first agent integration challenge, robust execution, by automatically generating the required coordination actions for the agents they represent. We can also exploit the proxies' reusable general teamwork knowledge to address the second agent integration challenge. Through team-oriented programming, a developer specifies a hierarchical organization and its goals and plans, abstracting away from coordination details. Finally, KARMA, our Knowledgeable Agent Resources Manager Assistant, can aid the developer in conquering the third agent integration challenge by locating agents that match the specified organization's requirements. Our integration architecture enables teamwork among agents with no coordination capabilities, and it establishes and automates consistent teamwork among agents with some coordination capabilities. Thus, team-oriented programming provides a level of abstraction that can be used on top of previous approaches to agent-oriented programming. We illustrate how the Teamcore architecture successfully addressed the challenges of agent integration in two application domains: simulated rehearsal of a military evacuation mission and facilitation of human collaboration.


title: Tracking mm-Wave channel dynamics: Fast beam training strategies under mobility

abstract: In order to cope with the severe path loss, millimeter-wave (mm-wave) systems exploit highly directional communication. As a consequence, even a slight beam mis-alignment between two communicating devices (for example, due to mobility) can generate a significant signal drop. This leads to frequent invocations of time-consuming mechanisms for beam re-alignment, which deteriorate system performance. In this paper, we propose smart beam training and tracking strategies for fast mm-wave link establishment and maintenance under node mobility. We leverage the ability of hybrid analog-digital transceivers to collect channel information from multiple spatial directions simultaneously and formulate a probabilistic optimization problem to model the temporal evolution of the mm-wave channel under mobility. In addition, we present for the first time a beam tracking algorithm that extracts information needed to update the steering directions directly from data packets, without the need for spatial scanning during the ongoing data transmission. Simulation results, obtained by a custom simulator based on ray tracing, demonstrate the ability of our beam training/tracking strategies to keep the communication rate only 10% below the optimal bound. Compared to the state of the art, our approach provides a 40% to 150% rate increase, yet requires lower complexity hardware.


title: Language re-entrance and the 'inner voice'

abstract: As soon as we stop talking aloud, we seem to experience a kind of 'inner voice', a steady stream of verbal fragments expressing ongoing thoughts. What kind of information processing structures are required to explain such a phenomenon? Why would an 'inner voice' be useful? How could it have arisen? This paper explores these questions and reports briefly some computational experiments to help elucidate them.


A possible mechanism of horseback riding on dynamic trunk alignment


title: Blur vs. Block: Investigating the Effectiveness of Privacy-Enhancing Obfuscation for Images

abstract: Computer vision can lead to privacy issues such as unauthorized disclosure of private information and identity theft, but it may also be used to preserve user privacy. For example, using computer vision, we may be able to identify sensitive elements of an image and obfuscate those elements thereby protecting private information or identity. However, there is a lack of research investigating the effectiveness of applying obfuscation techniques to parts of images as a privacy enhancing technology. In particular, we know very little about how well obfuscation works for human viewers or users' attitudes towards using these mechanisms. In this paper, we report results from an online experiment with 53 participants that investigates the effectiveness two exemplar obfuscation techniques: "blurring" and "blocking", and explores users' perceptions of these obfuscations in terms of image satisfaction, information sufficiency, enjoyment, and social presence. Results show that although "blocking" is more effective at de-identification compared to "blurring" or leaving the image "as is", users' attitudes towards "blocking" are the most negative, which creates a conflict between privacy protection and users' experience. Future work should explore alternative obfuscation techniques that could protect users' privacy and also provide a good viewing experience.


title: Study protocol: a mixed methods feasibility study for a loaded self-managed exercise programme for patellofemoral pain

abstract: Patellofemoral pain (PFP) is one of the most common forms of knee pain in adults under the age of 40, with a prevalence of 23% in the general population. The long-term prognosis is poor, with only one third of people pain-free 1 year after diagnosis. The biomedical model of pain in relation to persistent PFP has recently been called into question. It has been suggested that interventions for chronic musculoskeletal conditions should consider alternative mechanisms of action, beyond muscles and joints. Modern treatment therapies should consider desensitising strategies, with exercises that target movements and activities patients find fearful and painful. High-quality research on exercise prescription in relation to pain mechanisms, not directed at specific tissue pathology, and dose response clearly warrants further investigation. Our primary aim is to establish the feasibility and acceptability of conducting a definitive RCT which will evaluate the clinical and cost-effectiveness of a loaded self-managed exercise programme for people with patellofemoral pain. This is a single-centred, multiphase, sequential, mixed-methods trial that will evaluate the feasibility of running a definitive large-scale randomised controlled trial of a loaded self-managed exercise programme versus usual physiotherapy. Initially, 8–10 participants with a minimum 3-month history of PFP will be recruited from an NHS physiotherapy waiting list and interviewed. Participants will be invited to discuss perceived barriers and facilitators to exercise engagement, and the meaning and impact of PFP. Then, 60 participants will be recruited in the same manner for the main phase of the feasibility trial. A web-based service will randomise patients to a loaded self-managed exercise programme or usual physiotherapy. The loaded self-managed exercise programme is aimed at addressing lower limb knee and hip weakness and is positioned within a framework of reducing fear/avoidance with an emphasis on self-management. Baseline assessment will include demographic data, average pain within the last week (VAS), fear avoidance behaviours, catastrophising, self-efficacy, sport and leisure activity participation, and general quality of life. Follow-up will be 3 and 6 months. The analysis will focus on descriptive statistics and confidence intervals. The qualitative components will follow a thematic analysis approach. This study will evaluate the feasibility of running a definitive large-scale trial on patients with patellofemoral pain, within the NHS in the UK. We will identify strengths and weaknesses of the proposed protocol and the utility and characteristics of the outcome measures. The results from this study will inform the design of a multicentre trial. ISRCTN35272486.


LSTM ENCODER – DECODER FOR DIALOGUE RESPONSE GENERATION


title: Exploring the use of a mobile robot as an imitation agent with children with lowfunctioning autism, submitted to Autonomous Robots, Special Issue on Socially Assistive Robots

abstract: Unpredictability and complexity of social interactions are important challenges for a low functioning autistic child. The objective of this research is to study how a mobile robot can, by appearing more predictable, appealing and simple than a human being, facilitate reciprocal interaction such as imitative play. By conducting an exploratory study involving four children, we found that forms of shared conventions such as imitation of body movements and of familiar actions are higher with two children paired with a human mediator, compared to two children paired with a robot mediator. However, the two children paired with the robot mediator demonstrated increased shared attention (visual contact, physical proximity) and imitate facial expressions (smile) more than the children paired with the human mediator.


Hierarchical Portfolio Search: Prismata's Robust AI Architecture for Games with Large Search Spaces.


title: SemCiR: A Citation Recommendation System based on a Novel Semantic Distance Measure

abstract: Purpose ::: ::: ::: ::: – The purpose of this paper is to propose a novel citation recommendation system that inputs a text and recommends publications that should be cited by it. Its goal is to help researchers in finding related works. Further, this paper seeks to explore the effect of using relational features in addition to textual features on the quality of recommended citations. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: – In order to propose a novel citation recommendation system, first a new relational similarity measure is proposed for calculating the relatedness of two publications. Then, a recommendation algorithm is presented that uses both relational and textual features to compute the semantic distances of publications of a bibliographic dataset from the input text. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: – The evaluation of the proposed system shows that combining relational features with textual features leads to better recommendations, in comparison with relying only on the textual features. It also demonstrates that citation context plays an important role among textual features. In addition, it is concluded that different relational features have different contributions to the proposed similarity measure. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: – A new citation recommendation system is proposed which uses a novel semantic distance measure. This measure is based on textual similarities and a new relational similarity concept. The other contribution of this paper is that it sheds more light on the importance of citation context in citation recommendation, by providing more evidences through analysis of the results. In addition, a genetic algorithm is developed for assigning weights to the relational features in the similarity measure.


title: Learning Deep Policies for Physics-Based Manipulation in Clutter

abstract: Uncertainty in modeling real world physics makes transferring traditional open-loop motion planning techniques from simulation to the real world particularly challenging. Available closed-loop policy learning approaches, for physics-based manipulation tasks, typically either focus on single object manipulation, or rely on imitation learning, which inherently constrains task generalization and performance to the available demonstrations. In this work, we propose an approach to learn a policy for physics-based manipulation in clutter, which enables the robot to react to the uncertain dynamics of the real world. We start with presenting an imitation learning technique which compiles demonstrations from a sampling-based planner into an action-value function encoded as a deep neural network. We then use the learned action-value function to guide a look-ahead planner, giving us a control policy. Lastly, we propose to refine the deep action-value function through reinforcement learning, taking advantage of the look-ahead planner. We evaluate our approach in a physics-enabled simulation environment with artificially injected uncertainty, as well as in a real world task of manipulation in clutter.


title: Breast Cancer Diagnosis Via Data Mining: Performance Analysis of Seven Different Algorithms

abstract: According to World HealthOrganization (WHO), breast cancer is the top cancer in women both in the developed and the developing world. Increased life expectancy, urbanization and adoption of western lifestyles trigger the occurrence of breast cancer in the developing world. Most ca ncer events are diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer outcome and survival is very crucial. In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis onbreast cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other patients are in trouble with breast cancer ito be predicted under cover of those data. Predictions of the other patients are realized through seven different algorithms and the accuracies of those have been given. The data about the patients have been taken from UCI Machine Learning Reposito ry thanks to Dr. William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process, RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.


title: Clustering with Same-Cluster Queries

abstract: We propose a framework for Semi-Supervised Active Clustering framework (SSAC), where the learner is allowed to interact with a domain expert, asking whether two given instances belong to the same cluster or not. We study the query and computational complexity of clustering in this framework. We consider a setting where the expert conforms to a center-based clustering with a notion of margin. We show that there is a trade off between computational complexity and query complexity; We prove that for the case of $k$-means clustering (i.e., when the expert conforms to a solution of $k$-means), having access to relatively few such queries allows efficient solutions to otherwise NP hard problems. In particular, we provide a probabilistic polynomial-time (BPP) algorithm for clustering in this setting that asks $O\big(k^2\log k + k\log n)$ same-cluster queries and runs with time complexity $O\big(kn\log n)$ (where $k$ is the number of clusters and $n$ is the number of instances). The success of the algorithm is guaranteed for data satisfying margin conditions under which, without queries, we show that the problem is NP hard. We also prove a lower bound on the number of queries needed to have a computationally efficient clustering algorithm in this setting.


title: Deep learning in pharmacy: The prediction of aqueous solubility based on deep belief network

abstract: The aqueous solubility of a drug is a significant factor for its bioavailability. Since many drugs on the market are the oral drugs, their absorption and metabolism in organisms are closely related to its aqueous solubility. As one of the most important properties of drug, the molecule aqueous solubility has received increasing attentions in drug discovery field. The methods of shallow machine learning have been applied to the field of pharmacy, with some success. In this paper, we established a multilayer deep belief network based on semi-supervised learning model to predict the aqueous solubility of compounds. This method can be used for recognizing whether compounds are soluble or not. Firstly, we discussed the influence of feature dimension to predict accuracy. Secondly, we analyzed the parameters of model in predicting aqueous solubility of drugs and contrasted the shallow machine learning with the similar deep architecture. The results showed that the model we proposed can predict aqueous solubility accurately, the accuracy of DBN reached 85.9%. The stable performance on the evaluation metrics confirms the practicability of our model. Moreover, the DBN model could be applied to reduce the cost and time of drug discovery by predicting aqueous solubility of drugs.


title: Protocol Oblivious Forwarding (POF): Software-Defined Networking with Enhanced Programmability

abstract: Software-defined networking separates the control and forwarding planes of a network to make it more programmable and application- aware. As one of the initial implementations of SDN, OpenFlow abstracts a forwarding device as a flow table and realizes flow processing by applying the "match-and-act" principle. However, the protocol-dependent nature of OpenFlow still limits the programmability of the forwarding plane. Hence, in this article, we discuss how to leverage protocol-oblivious forwarding (POF) to further enhance the network programmability such that the forwarding plane becomes protocol-independent and can be dynamically reprogrammed to support new protocol stacks seamlessly. We first review the development of OpenFlow and explain the motivations for introducing POF. Then we explain the working principle of POF, discuss our efforts on realizing the POF development ecosystem, and show our implementation of POF-based source routing as a novel use case. Finally, we elaborate on the first WAN-based POF network testbed that includes POF switches located in two cities in China.


title: Cannabis improves night vision: a case study of dark adaptometry and scotopic sensitivity in kif smokers of the Rif mountains of northern Morocco.

abstract: Previous reports have documented an improvement in night vision among Jamaican fishermen after ingestion of a crude tincture of herbal cannabis, while two members of this group noted that Moroccan fishermen and mountain dwellers observe an analogous improvement after smoking kif, sifted Cannabis sativa mixed with tobacco (Nicotiana rustica). Field-testing of night vision has become possible with a portable device, the LKC Technologies Scotopic Sensitivity Tester-1 (SST-1). This study examines the results of double-blinded graduated THC administration 0-20 mg (as Marinol) versus placebo in one subject on measures of dark adaptometry and scotopic sensitivity. Analogous field studies were performed in Morocco with the SST-1 in three subjects before and after smoking kif. In both test situations, improvements in night vision measures were noted after THC or cannabis. It is believed that this effect is dose-dependent and cannabinoid-mediated at the retinal level. Further testing may assess possible clinical application of these results in retinitis pigmentosa or other conditions.


title: A quantitative comparison of NIRS and fMRI across multiple cognitive tasks

abstract: Near infrared spectroscopy (NIRS) is an increasingly popular technology for studying brain function. NIRS presents several advantages relative to functional magnetic resonance imaging (fMRI), such as measurement of concentration changes in both oxygenated and deoxygenated hemoglobin, finer temporal resolution, and ease of administration, as well as disadvantages, most prominently inferior spatial resolution and decreased signal-to-noise ratio (SNR). While fMRI has become the gold standard for in vivo imaging of the human brain, in practice NIRS is a more convenient and less expensive technology than fMRI. It is therefore of interest to many researchers how NIRS compares to fMRI in studies of brain function. In the present study we scanned participants with simultaneous NIRS and fMRI on a battery of cognitive tasks, placing NIRS probes over both frontal and parietal brain regions. We performed detailed comparisons of the signals in both temporal and spatial domains. We found that NIRS signals have significantly weaker SNR, but are nonetheless often highly correlated with fMRI measurements. Both SNR and the distance between the scalp and the brain contributed to variability in the NIRS/fMRI correlations. In the spatial domain, we found that a photon path forming an ellipse between the NIRS emitter and detector correlated most strongly with the BOLD response. Taken together these findings suggest that, while NIRS can be an appropriate substitute for fMRI for studying brain activity related to cognitive tasks, care should be taken when designing studies with NIRS to ensure that: 1) the spatial resolution is adequate for answering the question of interest and 2) the design accounts for weaker SNR, especially in brain regions more distal from the scalp.


title: Quantification of abdominal and pelvic floor muscle synergies in response to voluntary pelvic floor muscle contractions.

abstract: The relative levels of pelvic floor muscle (PFM) activation and pressure generated by maximum voluntary PFM contractions were investigated in healthy continent women. The normal sequence of abdominal and PFM activation was determined. Fifteen women performed single and repeated maximum voluntary PFM contractions in supine, sitting and standing. PFM electromyographic (EMG) signals and associated intra-vaginal pressure data were recorded simultaneously. Surface EMG data were recorded from rectus abdominus (RA), external obliques (EO), internal obliques (IO) and transversus abdominus (TA). Abdominal and PFM EMG and intra-vaginal pressure amplitudes generated during voluntary PFM contractions were not different among the positions. Muscle activation sequence differed by position. In supine, EO activation preceded all other muscles by 27 ms (p = 0.043). In sitting, all of the muscles were activated simultaneously. In standing, RA and EO were activated 11 and 17 ms, respectively, prior to the PFMs and TA and IO were activated 10 and 12 ms, respectively, after the PFMs (p<<0.001). The results suggest that women are able to perform equally strong PFM contractions in supine, sitting and standing, however the pattern of abdominal and PFM activation varies by position. These differences may be related to position-dependent urine leakage in women with stress incontinence.


title: Electromagnetic characteristics of body area network using magnetically-coupled wearable coils worn on bent arm

abstract: We have proposed body area networks using magnetic coupling between coils worn on an arm, and confirmed that the transmission characteristics were improved more than 20 dB compared with those of the conventional human body communication using electrodes in contact with the body. However, the arm will be bent with various angles in daily life. In this paper, electromagnetic characteristics such as transmission characteristics between the wearable coils worn on the bent arm and magnetic field distributions around the coils and arm were investigated. As a result, the transmission characteristics were stable and hardly influenced against bending the human arm with various angles, and sufficient biocompatibility with specific absorption rate less than 2 W/kg was confirmed.


title: Nitrogen-doped hollow carbon spheres as a support for platinum-based electrocatalysts

abstract: Platinum and platinum alloys supported on carbon materials are the state of the art electrocatalysts for the essential oxygen reduction reaction (ORR) in low-temperature fuel cells. The limited stability of such materials under the often detrimental operation conditions of fuel cells still remains a critical issue to improve. In this work, we explore the impact of nitrogen-doped carbon supports on the activity and stability of platinum-based fuel cell catalysts. We present a nitrogen-doped mesostructured carbon material, nitrogen-doped hollow carbon spheres (NHCS), as a support for platinum-based electrocatalysts. A detailed study of the electrochemical activity and stability was carried out for two Pt@NHCS materials i.e., as-made material (Pt@NHCS) with a Pt particle size smaller than 2 nm and the corresponding material after thermal treatment at 850 °C (Pt@NHCSΔT) with a Pt particle size of ca. 2–3 nm. Activity in the ORR was studied by rotating disc electrode (RDE) thin-film measurements, and electroca...


title: Feature Selection in Imbalance data sets

abstract: Feature selection methods have been used these days in the various fields. Like information retrieval and filtering, text classification, risk management, web categorization, medical diagnosis and the detection of credit card fraud. In this paper we focus on feature selection for imbalanced problems. One of the greatest challenges in machine learning and data mining research is the class imbalance problems. Imbalance problems can appear in two different types of data sets: binary problems, where one of the two classes comprises considerably more samples than the other, and multiclass problems, where each class only contains a tiny fraction of the samples. In this paper we want to explain a prior knowledge for an expert system which can tell us which feature selection metrics perform best based on our data characteristics and regardless of the classifier used.


title: The role of high-frequency oscillatory activity in reward processing and learning

abstract: Abstract Oscillatory activity has been proposed as a key mechanism in the integration of brain activity of distant structures. Particularly, high frequency brain oscillatory activity in the beta and gamma range has received increasing interest in the domains of attention and memory. In addition, a number of recent studies have revealed an increase of beta–gamma activity (20–35 Hz) after unexpected or relevant positive reward outcomes. In the present manuscript we review the literature on this phenomenon and we propose that this activity is a brain signature elicited by unexpected positive outcomes in order to transmit a fast motivational value signal to the reward network. In addition, we hypothesize that beta–gamma oscillatory activity indexes the interaction between attentional and emotional systems, and that it directly reflects the appearance of unexpected positive rewards in learning-related contexts.


title: Resource Management in the Internet of Things: Clustering, Synchronisation and Software Agents

abstract: The objects of the Internet of Things will be empowered by embedded devices whose constrained resources will need to be managed efficiently. It is envisioned that these devices will be able to form ad-hoc networks, and that the connection from these networks to the Internet of Things infrastructure will not always be possible. In this chapter we propose the use of clustering, software agents and synchronisation techniques in order to overcome the challenges of managing the resources of the Internet of Things objects. We argue that clustering will be beneficial to reduce the energy expenditure and improve the scalability and robustness of the object networks. Software agents will aide in the automation of task, both for the objects and the Internet of Things users. Finally, synchronisations techniques will be necessary to address the various challenges of harmonising plenty of copies of object data with potentially partially disconnected Internet of Things architecture components.


title: Trace Elements in Different Brands of Yerba Mate Tea

abstract: Yerba mate (Ilex paraguariensis), a widely consumed beverage in South America, contains various biochemically active substances, among them are several minerals. This paper reports on the results of a survey of trace elements in the yerba mate infusions. Three different commercially available trademarks of I. paraguariensis were evaluated, simulating the popular mode of preparation. Atomic absorption analyses for cadmium, lead, copper, zinc, aluminum, iron, chromium, manganese, molybdenum, and silver were performed using a graphite furnace. The levels ranged from 0.03 to 0.06 mg/L for copper, from 0.41 to 1.0 mg/L for zinc, from 0.32 to 1.7 mg/L for aluminum, from 0.12 to 0.23 mg/L for iron, from 2.3 to 7.0 mg/L for manganese, and from 0.01 to 0.03 mg/L for silver. The levels of chromium did not exceed 0.005 mg/L, while molybdenum, cadmium, and lead were lower than <0.01 mg/L. Metal content in mate tea infusions depends on a number of factors, some of which are controllable and others not, but the differences among various sources are admissible. Trace elements in mate plants seem to be weakly bounded to the substrate. The concentration of biometals does not exceed the limits accepted by Brazilian and international legislation when available.


title: Early Drift Detection Method

abstract: An emerging problem in Data Streams is the detection of concept drift. This problem is aggravated when the drift is gradual over time. In this work we deflne a method for detecting concept drift, even in the case of slow gradual change. It is based on the estimated distribution of the distances between classiflcation errors. The proposed method can be used with any learning algorithm in two ways: using it as a wrapper of a batch learning algorithm or implementing it inside an incremental and online algorithm. The experimentation results compare our method (EDDM) with a similar one (DDM). Latter uses the error-rate instead of distance-error-rate.


title: Developing an underwater glider for educational purposes

abstract: Buoyancy-driven underwater gliders (UGs) have proven to be quite effective for long-range, long-term oceanographic sampling. However, current off-the-shelf UGs are large, heavy, expensive, and difficult to modify, both in hardware and software, which prevents their frequent use for lake observations and limits researchers' ability to perform multi-vehicle coordination experiments. Our current research goal is to develop UGs that would share the buoyancy-driven concept with the first generation of gliders, but are smaller in size, lighter in weight, and lower in price. Our future research goal is to design and develop an underwater glider fleet that will result in novel and transformative research capabilities in coordinated control. Along with advancing research and broadening data collection ability, UGs provide a hands-on tool for engaging students in sophisticated STEM learning. This paper describes in detail the design, manufacturing, and modeling of our inexpensive Glider for Underwater Problem-solving and Presentation in Education (GUPPIE). GUPPIE was developed using practical components such as syringes for buoyancy control and a hull made of acrylic for easy analysis. The design is both affordable and easy-to-duplicate. GUPPIE's pedagogical platform provides hands-on learning applications that demonstrate glider mechanics, mechatronics, hydrodynamics, trimming, diving and surfacing performance, and mobility in water.


title: Early Detection of Malicious Flux Networks via Large-Scale Passive DNS Traffic Analysis

abstract: In this paper, we present FluxBuster, a novel passive DNS traffic analysis system for detecting and tracking malicious flux networks. FluxBuster applies large-scale monitoring of DNS traffic traces generated by recursive DNS (RDNS) servers located in hundreds of different networks scattered across several different geographical locations. Unlike most previous work, our detection approach is not limited to the analysis of suspicious domain names extracted from spam emails or precompiled domain blacklists. Instead, FluxBuster is able to detect malicious flux service networks in-the-wild, i.e., as they are "accessed” by users who fall victim of malicious content, independently of how this malicious content was advertised. We performed a long-term evaluation of our system spanning a period of about five months. The experimental results show that FluxBuster is able to accurately detect malicious flux networks with a low false positive rate. Furthermore, we show that in many cases FluxBuster is able to detect malicious flux domains several days or even weeks before they appear in public domain blacklists.


title: Internet Gambling: Issues, Concerns, and Recommendations

abstract: The influence of technology in the field of gambling innovation continues to grow at a rapid pace. After a brief overview of gambling technologies and deregulation issues, this review examines the impact of technology on gambling by highlighting salient factors in the rise of Internet gambling (i.e., accessibility, affordability, anonymity, convenience, escape immersion/dissociation, disinhibition, event frequency, asociability, interactivity, and simulation). The paper also examines other factors in relation to Internet gambling including the relationship between Internet addiction and Internet gambling addiction. The paper ends by overviewing some of the social issues surrounding Internet gambling (i.e., protection of the vulnerable, Internet gambling in the workplace, electronic cash, and unscrupulous operators). Recommendations for Internet gambling operators are also provided.


title: Realistic animation of rigid bodies

abstract: The theoretical background and implementation for a computer animation system to model a general class of three dimensional dynamic processes for arbitrary rigid bodies is presented. The simulation of the dynamic interaction among rigid bodies takes into account various physical characteristics such as elasticity, friction, mass, and moment of inertia to produce rolling and sliding contacts. If a set of bodies is statically unstable, the system dynamically drives it toward a stable configuration while obeying the geometric constraints of the system including general non-holonomic constraints. The system also provides a physical environment with which objects animated using more traditional techniques can interact. The degree of interaction is easily controlled by the animator. A computationally efficient method to merge kinematics and dynamics for articulated rigid bodies to produce realistic motion is presented.


title: A user-centered approach to evaluating human interaction with Web search engines: an exploratory study

abstract: A growing body of studies is developing approaches to evaluating human interaction with Web search engines, including the usability and effectiveness of Web search tools. This study explores a user-centered approach to the evaluation of the Web search engine Inquirus - a Web meta-search tool developed by researchers from the NEC Research Institute. The goal of the study reported in this paper was to develop a user-centered approach to the evaluation including: (1) effectiveness: based on the impact of users' interactions on their information problem and information seeking stage, and (2) usability: including screen layout and system capabilities for users. Twenty-two volunteers searched Inquirus on their own personal information topics. Data analyzed included: (1) user pre- and post-search questionnaires and (2) Inquirus search transaction logs. Key findings include: (1) Inquirus was rated highly by users on various usability measures, (2) all users experienced some level of shift/change in their information problem, information seeking, and personal knowledge due to their Inquirus interaction, (3) different users experienced different levels of change/shift, and (4) the search measure precision did not correlate with other user-based measures. Some users experienced major changes/shifts in various user-based variables, such as information problem or information seeking stage with a search of low precision and vice versa. Implications for the development of user-centered approaches to the evaluation of Web and information retrieval (IR) systems and further research are discussed.


title: Connected domination number on Cartesian product of simple fuzzy graphs

abstract: A dominating set D of a fuzzy graph G=(V,σ,m) is connected dominating set if the induced subgraph is connected. The minimum cardinality of a connected dominating set of G is called the connected domination number of G and is denoted by γ c (G). The Cartesian product of simple fuzzy graphs is also a fuzzy graph. In this paper we introduce the connected domination number on Cartesian product of simple fuzzy graphs and obtain the results for this parameter in fuzzy graph.


title: Caring for Clients and Families With Anxiety

abstract: This study elucidated Japanese home care nurses’ experiences of supporting clients and families with anxiety. We interviewed 10 registered nurses working in home care agencies and analyzed the data using grounded theory to derive categories pertaining to the nurses’ experiences of providing care. We conceptualized nurses’ approaches to caring for anxiety into three categories: First, they attempted to reach out for anxiety even when the client/family did not make it explicit; second, they tried to alter the outlook of the situation; and third, they created comfort in the lives of the client/family. The conceptualizations of nurses’ strategies to alleviate client/family anxiety may reflect Japanese/Eastern cultural characteristics in communication and their view of the person and social care system, but these conceptualizations may also inform the practice of Western nurses by increasing awareness of skills they may also have and use.


Analysis of commercial and free and open source solvers for linear optimization problems 1


title: Acute paraplegia after general anesthesia.

abstract: Acute paraplegia is a rare but catastrophic complication of surgeries performed on aorta and corrective operations of vertebral column. Trauma to spinal cord after spinal anesthesia and ischemia of spinal cord also may lead to acute paraplegia. Acute paraplegia as a complication of general anesthesia in surgeries performed on sites other than aorta and vertebral column is very rare. Here we present a 56 year old woman with acute paraplegia due to spinal cord infarction after laparoscopic cholecystectomy under general anesthesia probably caused by atherosclerosis of feeding spinal arteries and ischemia of spinal cord after reduction of blood flow possibly due to hypotension during general anesthesia.


title: Using the Job Demands-Resources model to predict burnout and performance.

abstract: The job demands-resources (JD-R) model was used to examine the relationship between job characteristics, burnout, and (other-ratings of) performance (N = 146). We hypothesized that job demands (e.g., work pressure and emotional demands) would be the most important antecedents of the exhaustion component of burnout, which, in turn, would predict in-role performance (hypothesis 1). In contrast, job resources (e.g., autonomy and social support) were hypothesized to be the most important predictors of extra-role performance, through their relationship with the disengagement component of burnout (hypothesis 2). In addition, we predicted that job resources would buffer the relationship between job demands and exhaustion (hypothesis 3), and that exhaustion would be positively related to disengagement (hypothesis 4). The results of structural equation modeling analyses provided strong support for hypotheses 1, 2, and 4, but rejected hypothesis 3. These findings support the JD-R model's claim that job demands and job resources initiate two psychological processes, which eventually affect organizational outcomes. © 2004 Wiley Periodicals, Inc.


title: Beyond the G-spot: clitourethrovaginal complex anatomy in female orgasm

abstract: Jannini et al. describe the evidence supporting the concept of the clitourethrovaginal (CUV) complex, and the overlap with the properties of the elusive G-spot. Consideration of the intricate anatomical relationships involved in female sexual arousal and orgasm could help to avoid damage to the CUV complex during surgery.


Towards Perceptual Image Dehazing by Physics-based Disentanglement and Adversarial Training


title: Genie: A Secure, Transparent Sharing and Services Platform for Genetic and Health Data

abstract: Artificial Intelligence (AI) incorporating genetic and medical information have been applied in disease risk prediction, unveiling disease mechanism, and advancing therapeutics. However, AI training relies on highly sensitive and private data which significantly limit their applications and robustness evaluation. Moreover, the data access management after sharing across organization heavily relies on legal restriction, and there is no guarantee in preventing data leaking after sharing. Here, we present Genie, a secure AI platform which allows AI models to be trained on medical data securely. The platform combines the security of Intel Software Guarded eXtensions (SGX), transparency of blockchain technology, and verifiability of open algorithms and source codes. Genie shares insights of genetic and medical data without exposing anyone's raw data. All data is instantly encrypted upon upload and contributed to the models that the user chooses. The usage of the model and the value generated from the genetic and health data will be tracked via a blockchain, giving the data transparent and immutable ownership.


title: Attitudes toward suicide among college students in South Korea and the United States

abstract: BackgroundSouth Korea (hereafter, Korea) has witnessed a rapid increase in its suicide rate over the past few decades and currently reports the highest rate among Organization for Economic Cooperation and Development (OECD) countries. Conversely, the United States has maintained its suicide rate near the OECD average. The present study examines and compares attitudes toward suicide among college students in either country to explain the higher prevalence of suicide in Korea.FindingsNon-Korean students in the United States, Korean students in the United States, and Korean students in Korea completed a web-based questionnaire on Attitudes Toward Suicide (ATTS). A series of two-way 3 × 2 between subjects Analysis of Variance (ANOVA) tests of the participants’ group and gender, as well as post-hoc comparisons, were conducted to examine differences across various attitude domains. As expected, the results revealed group differences in the majority of attitude areas. Most notably, students in Korea reported more permissive attitudes toward suicide and were less likely to believe in the right to prevent others’ suicide. Gender did not have an effect on any attitudes except on the right to prevent suicide and there were no interactions between group and gender.ConclusionsThe results suggest the importance of addressing public attitudes toward suicide in future suicide prevention efforts in Korea.


title: Mean shift analysis and applications

abstract: A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatial-range (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven. The proposed filtering method associates with each pixel in the image the closest local mode in the density distribution of the joint domain. Segmentation into a piecewise constant structure requires only one more step, fusion of the regions associated with nearby modes. The proposed technique has two parameters controlling the resolution in the spatial and range domains. Since convergence is guaranteed, the technique does not require the intervention of the user to stop the filtering at the desired image quality. Several examples, for gray and color images, show the versatility of the method and compare favorably with results described in the literature for the same images.


title: Highly-scalable searchable symmetric encryption with support for boolean queries. Cryptology ePrint Archive, Report 2013/169

abstract: This work presents the design and analysis of the first searchable symmetric encryption (SSE) protocol that supports conjunctive search and general Boolean queries on outsourced symmetrically- encrypted data and that scales to very large databases and arbitrarily-structured data including free text search. To date, work in this area has focused mainly on single-keyword search. For the case of conjunctive search, prior SSE constructions required work linear in the total number of documents in the database and provided good privacy only for structured attribute-value data, rendering these solutions too slow and inflexible for large practical databases.


title: A Kinect-Based System for Physical Rehabilitation: Utilizing Tai Chi Exercises to Improve Movement Disorders in Patients with Balance Ability

abstract: In this paper, we present a Kinect-based rehabilitation system to assist patients with movement disorders by performing the "seated Tai Chi" exercises at home. With this physical rehabilitation system, we can evaluate whether patients achieve rehabilitation results correctly or not. By using skeletal tracking of Microsoft Kinect sensor, each gesture of the patient while performing Tai Chi is recognized and validated. The experiment was carried out according to an ABAB sequence in which A represents the baseline and B represents intervention phases. Experimental results show that the participants significantly increased their motivation for physical rehabilitation, thus improving exercise performance during the intervention phases.


title: A novel approach of mobility management for the D2D communications in 5G mobile cellular network system

abstract: The concept of 5G is regarded to gain 1000× data speed with 20 Billion devices (IoT) connected to the network which will literary connect everything. From the network point of view, lower latency combined with high flexibility is not just limited to the concept of 5G but already on the way to be implemented in real network by 2020. In a cellular network, Device to Device (D2D) communication has been viewed as a promising technology to overcome many existed problems especially the capacity and quality considering the scarce spectrum resource. However, this comes at the price of huge interference and complex mobility issues although it was initially proposed as a new paradigm to enhance the network performance. Cellular devices without smooth mobility readiness are hardly acceptable. A smart mobility system in cellular in-band underlay D2D communication should have lower latency, lower power consumption, and higher data rates. My proposition is to review the existing mobility management system for the LTE-Advanced technology and propose a unique algorithm over the existing proposal so that lower signalling overhead and lower delay along with uninterrupted D2D communication are guaranteed for the 5G cellular communication system by 2020.


title: Achieving High Code Coverage in Android UI Testing via Automated Widget Exercising

abstract: Automated functional user interface (UI) testing of mobile apps is a challenging task due to the highly interactive nature of the app UIs, and thus it commonly fails to provide high code coverage. In this paper, we present DroidDEV, an automated UI exerciser which aims to achieve high code coverage for real-world Android apps. DroidDEV dynamically builds a finite UI flow graph, generates and injects UI-context-aware inputs to exercise all the widgets on all the UI screens through a best-first search path finding algorithm. We have evaluated DroidDEV on 20 real-world open-source Android apps and compared it with manual UI testing and two other prevalent automated approaches including MobiGUITAR and Monkey. On these subjects, DroidDEV reports code coverage similar to manual UI testing, and it outperforms MobiGUITAR and Monkey in terms of code coverage and exercising time.


title: Design of dipole beam-steering antenna array for 5G handset applications

abstract: In this manuscript, a novel design of millimeter-wave (mm-Wave) dipole phased array antenna for the fifth generation (5G) handset applications is proposed. The proposed antenna is designed to cover some of the candidate bands for 5G communications from 24 to 50 GHz, and has dimension of 8×5.5mm2. The antenna exhibits small and wide bandwidth. The beam steering characteristics in the operation band are presented for a linear array of this antenna.


title: Compact thermal modeling of spin transfer torque magnetic tunnel junction

abstract: Magnetic tunnel junction (MTJ) with spin transfer torque (STT) switching method features fast speed, low power, great scalability and high compatibility with conventional CMOS process. Nevertheless, its magnetic and electrical properties can be easily influenced by operation temperature and self-heating effect, which further results in performance degradation and reliability issues of MTJ based memories and logic circuits. This paper investigates the behaviors of MTJ under different temperatures and further proposes a model in consideration of temperature impact on performance of MTJ, which can be used to optimize the design of STT-MRAM in terms of dynamic operations and temperature tolerance.


title: Incremental tensor analysis: Theory and applications

abstract: How do we find patterns in author-keyword associations, evolving over timeq Or in data cubes (tensors), with product-branchcustomer sales informationq And more generally, how to summarize high-order data cubes (tensors)q How to incrementally update these patterns over timeq Matrix decompositions, like principal component analysis (PCA) and variants, are invaluable tools for mining, dimensionality reduction, feature selection, rule identification in numerous settings like streaming data, text, graphs, social networks, and many more settings. However, they have only two orders (i.e., matrices, like author and keyword in the previous example). We propose to envision such higher-order data as tensors, and tap the vast literature on the topic. However, these methods do not necessarily scale up, let alone operate on semi-infinite streams. Thus, we introduce a general framework, incremental tensor analysis (ITA), which efficiently computes a compact summary for high-order and high-dimensional data, and also reveals the hidden correlations. Three variants of ITA are presented: (1) dynamic tensor analysis (DTA); (2) streaming tensor analysis (STA); and (3) window-based tensor analysis (WTA). In paricular, we explore several fundamental design trade-offs such as space efficiency, computational cost, approximation accuracy, time dependency, and model complexity. We implement all our methods and apply them in several real settings, such as network anomaly detection, multiway latent semantic indexing on citation networks, and correlation study on sensor measurements. Our empirical studies show that the proposed methods are fast and accurate and that they find interesting patterns and outliers on the real datasets.


title: Evaluation of factors affecting customer loyalty in the restaurant industry

abstract: The aim of the present research was to investigate the factors affecting customer loyalty in the restaurant industry. Data was collected using questionnaire distributed in 10 randomly selected branches of Boof Chain Restaurant in Tehran. In each branch, 40 customers were selected for the study. Ultimately, the research sample consisted of 268 customers. Structured equation modeling was used for data analysis and hypothesis testing. The obtained results show that food quality, service quality, restaurant environment, and perception of price fairness had a positive impact on customer satisfaction, but the impact of restaurant location on customer satisfaction was not confirmed. Also, food quality, service quality, and perception of price fairness had a positive effect on customer trust. The results show that food quality is the most important factor affecting customer satisfaction and trust in Boof Chain Restaurants. Customer satisfaction had a positive impact on customer loyalty, but the effect of customer trust on customer loyalty was not confirmed. ::: ::: ::: ::: Key words: Restaurant industry, loyalty, customer satisfaction, customer trust.


Question Answering Using Deep Learning


title: Random Projection Based Representations for Learning Policies in Deterministic Atari Games

abstract: Recent advances in sample efficient reinforcement learning algorithms in quasi-deterministic environments highlight the requirement for computationally inexpensive visual representations. Here we investigate non-parametric dimensionality reduction techniques based on random linear transformations and we provide empirical evidence on the importance of high-variance projections using sparse random matrices in the context of episodic controllers learning deterministic policies. We also propose a novel Maximum-Variance Random Projection and improve on the performance of the original Model-Free Episodic Control results with respect to both sample efficiency and final average score.


Drowsy Detection On Eye Blink Duration Using Algorithm


title: Cooperative Wideband Spectrum Sensing for the Centralized Cognitive Radio Network

abstract: Various primary user (PU) radios have been allocated into fixed frequency bands in the whole spectrum. A cognitive radio network (CRN) should be able to perform the wideband spectrum sensing (WSS) to detect temporarily unoccupied frequency bands. We summarize four occupancy features for the frequency bands. 1. The occupancy is sparse; 2. The frequency band allocation information is fixed and common; 3. There are three categories for the frequency band usages; 4. The occupied frequency bands are common in the CRN. For the first time, we consider all features as the prior knowledge in the compressed sensing based cooperative WSS (CWSS) algorithm design for a centralized CRN. We propose a modified orthogonal matching pursuit (Mod-OMP) algorithm and a modified simultaneous orthogonal matching pursuit (Mod-SOMP) algorithm for the CWSS. We compare the CWSS performance of Mod-OMP/Mod-SOMP with the original OMP/SOMP and show the performance improvements.


title: Extrastriate body area underlies aesthetic evaluation of body stimuli

abstract: Humans appear to be the only animals to have developed the practice and culture of art. This practice presumably relies on special processing circuits within the human brain associated with a distinct subjective experience, termed aesthetic experience, and preferentially evoked by artistic stimuli. We assume that positive or negative aesthetic judgments are an important function of neuroaesthetic circuits. The localisation of these circuits in the brain remains unclear, though neuroimaging studies have suggested several possible neural correlates of aesthetic preference. We applied repetitive transcranial magnetic stimulation (rTMS) over candidate brain areas to disrupt aesthetic processing while healthy volunteers made aesthetic preference judgments between pairs of dance postures, or control non-body stimuli. Based on evidence from visual body perception studies, we targeted the ventral premotor cortex (vPMC) and extrastriate body area (EBA), in the left and right hemispheres. rTMS over EBA reduced aesthetic sensitivity for body stimuli relative to rTMS over vPMC, while no such difference was found for non-body stimuli. We interpret our results within the framework of dual routes for visual body processing. rTMS over either EBA or vPMC reduced the contributions of the stimulated area to body processing, leaving processing more reliant on the unaffected route. Disruption of EBA reduces the local processing of the stimuli and reduced observers’ aesthetic sensitivity. Conversely, disruption of the global route via vPMC increased the relative contribution of the local route via EBA and thus increased aesthetic sensitivity. In this way, we suggest a complementary contribution of both local and global routes to aesthetic processing.


title: Physically Unclonable Functions: A Study on the State of the Art and Future Research Directions

abstract: The idea of using intrinsic random physical features to identify objects, systems, and people is not new. Fingerprint identification of humans dates at least back to the nineteenth century [21] and led to the field of biometrics. In the 1980s and 1990s of the twentieth century, random patterns in paper and optical tokens were used for unique identification of currency notes and strategic arms [2, 8, 53]. A formalization of this concept was introduced in the very beginning of the twenty-first century, first as physical one-way functions [41, 42], physical random functions [13], and finally as physical(ly) unclonable functions or PUFs.1 In the years following this introduction, an increasing number of new types of PUFs were proposed, with a tendency toward more integrated constructions. The practical relevance of PUFs for security applications was recognized from the start, with a special focus on the promising properties of physical unclonability and tamper evidence.


title: Inductive microtransformer exploiting the magnetoelastic effect

abstract: Microtransformers built up in thin film technology lend themselves to be used as micro-strain gauges. The basic concept is to utilize the magnetoelastic effect for modulating a microtransformer coupling as a function of the strain the core is exposed to. The microtransformer consists of a pair of dual layer planar coils, coupled by two soft magnetic yokes. The microtransformer is located in the support area of a cantilever beam. The main emphasis is put on investigating the technological challenges involved in building such a device. While neither the transformer design nor the flux guide material have been yet optimized for the intended application, the investigations reveal that the basic concept is very promising. The initial test samples are reaching strain gauge k-factors substantially greater than for classic strain gauges.


title: Multi-linear polarization reconfigurable center-fed circular patch antenna with shorting posts

abstract: In this paper, a novel multi-linear polarization reconfigurable antenna with shorting posts, which can achieve four linear polarizations (0°, 45°, 90°, 135°), has been proposed. By switching the diodes between two groups of shorting posts, four linear polarizations can be realized. The dimensions of the proposed antenna are about 0.56λ× 0.56λ× 0.07λ at 2.4 GHz. The measured results agree well with the simulated ones.


title: The effects of a player's network centrality on resource accessibility, game enjoyment, and continuance intention: A study on online gaming communities

abstract: This study applies social capital theory to investigate how a player's network centrality in an online gaming community (i.e., a guild) affects his/her attitude and continuance intention toward a Massive Multiplayer Online Game (MMOG). Analysis of 347 usable responses shows that players' network centrality has a negative impact on their ties to players who belong to other guilds (i.e., non-guild interaction), but a positive effect on players' access to resources. However, players' network centrality fails to increase their perceived game enjoyment directly. Players' resource accessibility and perceived game enjoyment play mediating roles in the relationship between network centrality and attitude toward playing an MMOG, which in turn influences game continuance intention. The results also show that although players' non-guild interaction is negatively related to their resource accessibility from the networks, it is positively associated with perceived game enjoyment. The article concludes with implications and limitations of the study.


title: Cognitive Diagnostic Analysis of Reading Comprehension Items: The Case of English Proficiency Assessment in Saudi Arabia

abstract: This paper deals with cognitive diagnostic analysis of the reading comprehension part of the Standardized Test of English Proficiency (STEP-RC) developed and administered by the National Center for Assessment in Saudi Arabia. The Least Squares Distance Model (LSDM) was used to obtain probabilities of correct performance on cognitive attributes of STEP-RC items across levels of the underlying ability measured by the test. Among other results, cutting scores on the scale for correct performance on targeted attributes are provided for the purposes of criterion-based diagnostic decisions. Aspects of the validity of the cognitive attributes are also examined in the LSDM framework. The methodology and procedures illustrated in this study can be applied in different areas of assessment and cognitive analysis.


title: On the Mathematics of Flat Origamis

abstract: Origami is the art of folding pieces of paper into works of sculpture without the aid of scissors or glue. Modern advancements in the complexity of origami (e.g., the work of Montroll and Maekawa) reveal a rich geometric structure governing the possibilities of paperfolding. In this paper we initiate a mathematical study of this “origami geometry” and explore the possibilities of a graph theoretic model. In particular, we study the properties of origami models which fold flat (i.e., can be pressed in a book without crumpling). Necessary and sufficient conditions are given for an origami model to locally fold flat, and the problems encountered in trying to extend these results globally are discussed.


title: A Double-Sided LCLC-Compensated Capacitive Power Transfer System for Electric Vehicle Charging

abstract: A double-sided LCLC -compensated capacitive power transfer (CPT) system is proposed for the electric vehicle charging application. Two pairs of metal plates are utilized to form two coupling capacitors to transfer power wirelessly. The LCLC -compensated structure can dramatically reduce the voltage stress on the coupling capacitors and maintain unity power factor at both the input and output. A 2.4-kW CPT system is designed with four 610-mm × 610-mm copper plates and an air gap distance of 150 mm. The experimental prototype reaches a dc–dc efficiency of 90.8% at 2.4-kW output power. At 300-mm misalignment case, the output power drops to 2.1 kW with 90.7% efficiency. With a 300-mm air gap distance, the output power drops to 1.6 kW with 89.1% efficiency.


title: Barriers to physical activity in older adults in Germany: a cross-sectional study

abstract: Background ::: Data on barriers to physical activity in older adults in Germany are scarce. The aim of this study was to analyse barriers to physical activity in a cohort of older adults, allowing comparisons between men and women, and age groups.


title: Fault ride through capability for solar inverters

abstract: AbstractA solar inverter for utility scale has been developed in this paper, and the inverter has fault ride through (FRT) capability, which is now discussed in Japan and similar to requirement in U.S.A and Europe. This solar inverter consists of a boost chopper and a three-phase 2-level inverter, and the capacity covers from 20 kW to 600 kW. This paper first describes the FRT capability. Second, how to control the boost chopper and the inverter is shown. Especially, a new inverter current control to achieve FRT capability and dynamic voltage support (DVS) during a grid fault is proposed. After that, several results using an experimental model (5-kW solar inverter) are shown. Finally, results of a prototype model (20 kW), which has been installed in an actual grid, are presented.


title: Systematic review of the research evidence examining the effectiveness of interventions using a sensory integrative approach for children.

abstract: Twenty-seven studies were systematically reviewed to identify, evaluate, and synthesize the research literature on the effectiveness of sensory integration (SI) intervention on the ability of children with difficulty processing and integrating sensory information to engage in desired occupations and to apply these findings to occupational therapy practice. Results suggest the SI approach may result in positive outcomes in sensorimotor skills and motor planning; socialization, attention, and behavioral regulation; reading-related skills; participation in active play; and achievement of individualized goals. Gross motor skills, self-esteem, and reading gains may be sustained from 3 mo to 2 yr. Findings may be limited by Type II error because of small sample sizes, variable intervention dosage, lack of fidelity to intervention, and selection of outcomes that may not be meaningful to clients and families or may not change with amount of treatment provided. Replication of findings with methodologically and theoretically sound studies is needed to support current findings


title: Airborne fungi spores distribution in various locations in Lagos, Nigeria

abstract: Exposure to outside air microorganisms especially fungi has been linked with illness such as allergic respiratory symptoms, rhinitis, asthma, and infection such as mycosis. Airborne fungal composition was sampled from five locations in Lagos State, Nigeria, between May 2014 and April 2016. Fungi spores were collected using the sedimentation plate method with the Petri dishes of dichloran-glycerol 18 (DG-18) and potato dextrose agar (PDA) media. Fungi sporulated faster on DG-18 agar plate as compared with PDA. The abundances of fungal spores collected monthly at the locations varied. The most abundant spores came from the fungi were Aspergillus niger (14.47%), Aspergillus sydowii (10.37%), and Aspergillus flavus (7.93%). Additional species were present in the collections including Ascomycetes: Penicillium funiculosum (5.49%), Neurospora crassa (5.32%), Penicillium oxalicum (4.71%), Penicillium pinophilum (2.88%), Fusarium verticillioides (3.05%), Penicillium simplicissimum (1.83%), Aphaderanum sp. (0.22%), Curvularia sp. (0.22%), Aspergillus oryzae (0.22%), and Paecilomyces sp. (0.61%) and the Mucoromycotina Zygomycetes: Rhizopus oryzae (4.10%) and Mucor sp. (3.44%). Fungal concentrations were significantly higher (P ≤ 0.05) during the rainy season compared with the dry season. Aspergillus and Penicillium were the most predominant airborne fungal genera while Mucor, Alternaria, and Cladosporium were some of the least observed. Generally, abundance of fungi was significantly high during the wet season in all the studied locations.


title: Can we still avoid automatic face detection?

abstract: After decades of study, automatic face detection and recognition systems are now accurate and widespread. Naturally, this means users who wish to avoid automatic recognition are becoming less able to do so. Where do we stand in this cat-and-mouse race? We currently live in a society where everyone carries a camera in their pocket. Many people willfully upload most or all of the pictures they take to social networks which invest heavily in automatic face recognition systems. In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition? If so, how? Must evasion techniques be obvious to be effective, or are there still simple measures that users can use to protect themselves? In this work, we find ways to evade face detection on Facebook, a representative example of a popular social network that uses automatic face detection to enhance their service. We challenge widely-held beliefs about evading face detection: do our old techniques such as blurring the face region or wearing "privacy glasses" still work? We show that in general, state-of-the-art detectors can often find faces even if the subject wears occluding clothing or even if the uploader damages the photo to prevent faces from being detected.


title: Synthesis of semiaromatic polyimides from aromatic diamines containing adamantyl units and alicyclic dianhydrides

abstract: We prepared new semiaromatic polyimides from alicyclic dianhydrides and aromatic diamines containing adamantyl and biadamantyl units. Polycondensations were performed in 1-methyl-2-pyrrolidinone at room temperature for 7 h and then 80 °C for 23 h, giving poly(amic acid)s with inherent viscosities up to 0.58 dL/g. Poly(amic acid)s were converted to corresponding poly(imide)s by thermal treatment. Poly(imide)s showed relatively high thermal stability (5% weight loss around 450 °C) and low dielectric constants (2.69–2.79). © 2003 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 42: 144–150, 2004


title: Classification of Research Citations (CRC)

abstract: Research is a continuous phenomenon. It is recursive in nature. Every research is based on some earlier research outcome. A general approach in reviewing the literature for a problem is to categorize earlier work for the same problem as positive and negative citations. In this paper, we propose a novel automated technique, which classifies whether an earlier work is cited as sentiment positive or sentiment negative. Our approach first extracted the portion of the cited text from citing paper. Using a sentiment lexicon we classify the citation as positive or negative by picking a window of at most five (5) sentences around the cited place (corpus). We have used Na\"ive-Bayes Classifier for sentiment analysis. The algorithm is evaluated on a manually annotated and class labelled collection of 150 research papers from the domain of computer science. Our preliminary results show an accuracy of 80%. We assert that our approach can be generalized to classification of scientific research papers in different disciplines.


title: An EMG Interface for the Control of Motion and Compliance of a Supernumerary Robotic Finger

abstract: In this paper, we present an electromyographic (EMG) control interface for a supernumerary robotic finger. This novel wearable robot can be used to compensate the missing grasping abilities in chronic stroke patients or to augment human healthy hand so to enhance its grasping capabilities and workspace. The proposed EMG interface controls the motion of the robotic extra finger and its joint compliance. In particular, we use a commercial EMG armband for gesture recognition to be associated with the motion control of the robotic device and surface one channel EMG electrodes interface to regulate the compliance of the robotic device. We also present an updated version of a robotic extra finger where the adduction/abduction motion is realized through ball bearing and spur gears mechanism. We validated the proposed interface with two sets of experiments related to compensation and augmentation. In the first set of experiments, different bi-manual tasks have been performed with the help of the robotic device and simulating a paretic hand. In the second set, the robotic extra finger is used to enlarge the workspace and manipulation capability of healthy hands. In both the sets, the same EMG control interface has been used. The obtained results demonstrate that the proposed control interface is intuitive and can successfully be used for both compensation and augmentation purposes. The proposed approach can be exploited also for the control of different wearable devices that has to actively cooperate with the human limbs.


title: Modeling, Analysis and Testing of Autonomous Operation of an Inverter-Based Microgrid

abstract: The analysis of the small-signal stability of conventional power systems is well established, but for inverter based microgrids there is a need to establish how circuit and control features give rise to particular oscillatory modes and which of these have poor damping. This paper develops the modeling and analysis of autonomous operation of inverter-based microgrids. Each sub-module is modeled in state-space form and all are combined together on a common reference frame. The model captures the detail of the control loops of the inverter but not the switching action. Some inverter modes are found at relatively high frequency and so a full dynamic model of the network (rather than an algebraic impedance model) is used. The complete model is linearized around an operating point and the resulting system matrix is used to derive the eigenvalues. The eigenvalues (termed "modes") indicate the frequency and damping of oscillatory components in the transient response. A sensitivity analysis is also presented which helps identifying the origin of each of the modes and identify possible feedback signals for design of controllers to improve the system stability. With experience it is possible to simplify the model (reduce the order) if particular modes are not of interest as is the case with synchronous machine models. Experimental results from a microgrid of three 10-kW inverters are used to verify the results obtained from the model


title: Estimating 6D Pose From Localizing Designated Surface Keypoints

abstract: In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image. The core of our approach is that we first designate a set of surface points on target object model as keypoints and then train a keypoint detector (KPD) to localize them. Finally a PnP algorithm can recover the 6D pose according to the 2D-3D relationship of keypoints. Different from recent state-of-the-art CNN-based approaches that rely on a time-consuming post-processing procedure, our method can achieve competitive accuracy without any refinement after pose prediction. Meanwhile, we obtain a 30% relative improvement in terms of ADD accuracy among methods without using refinement. Moreover, we succeed in handling heavy occlusion by selecting the most confident keypoints to recover the 6D pose. For the sake of reproducibility, we will make our code and models publicly available soon.


title: Affective News: The Automated Coding of Sentiment in Political Texts

abstract: An increasing number of studies in political communication focus on the “sentiment” or “tone” of news content, political speeches, or advertisements. This growing interest in measuring sentiment coincides with a dramatic increase in the volume of digitized information. Computer automation has a great deal of potential in this new media environment. The objective here is to outline and validate a new automated measurement instrument for sentiment analysis in political texts. Our instrument uses a dictionary-based approach consisting of a simple word count of the frequency of keywords in a text from a predefined dictionary. The design of the freely available Lexicoder Sentiment Dictionary (LSD) is discussed in detail here. The dictionary is tested against a body of human-coded news content, and the resulting codes are also compared to results from nine existing content-analytic dictionaries. Analyses suggest that the LSD produces results that are more systematically related to human coding than are results ...