title: Prediction of Financial Time Series Using Hidden Markov Models

abstract: Hidden Markov Models (HMM) is a powerful machine learning model. HMM’s main usage has been in solving classification and pattern recognition problems in biology, speech and voice recognition. In recent days, attempts have been made to use HMM for prediction in general and prediction of time series in particular. However, this is not straightforward. To overcome the challenges in predicting time series with HMM some hybrid approaches have been applied. This paper has two main objectives. The first, is to compare HMM with other models when used for prediction of financial time series. We will show comparison between HMM and other models and also between different types of HMM’s as unique contribution of this work. In recent years, prediction of stock market behavior became a field of great interest to many scientists. Hence, as a case study, we will use our implementation and examine HMM with Multivariate Normal Distribution and Gaussian Mixture Model in hidden states. We use nearest likelihood prediction algorithm and compare results with MAPE. As training dataset, we will use some popular stock from NASDAQ and S&P500 indexes historical data. The second, is to showcase a working system with generic HMM, advanced training and prediction algorithms implementation in C#. All the code is open sourced as contribution to the community. The system allows researchers from different disciplines to use HMM and other models to solve classification, pattern recognition and prediction on various series represented by k-dimensional vectors arrays and a way to compare and reuse the models under test.


title: Optimal control of building HVAC&R systems using complete simulation-based sequential quadratic programming (CSB-SQP)

abstract: This paper presents a general and systematic methodology, termed complete simulation-based sequential quadratic programming (CSB-SQP), for determining the optimal control of building HVAC&R systems. This approach allows the coupling of a detailed simulation program with an efficient optimization method, namely the sequential quadratic programming (SQP) algorithm. This approach allows the use of accurate component models of the system as against empirical models as currently used, while providing efficient optimal solutions to be determined. We develop the mathematical basis of the methodology and apply it to a simple cooling plant system to illustrate the accuracy, efficiency and robustness of this method. The issue of implementing such an optimization under real-time control is also discussed.


title: Real-time forest fire detection with wireless sensor networks

abstract: In this paper, we propose a wireless sensor network paradigm for real-time forest fire detection. The wireless sensor network can detect and forecast forest fire more promptly than the traditional satellite-based detection approach. This paper mainly describes the data collecting and processing in wireless sensor networks for real-time forest fire detection. A neural network method is applied to in-network data processing. We evaluate the performance of our approach by simulations.


title: The neural response to facial attractiveness.

abstract: What are the neural correlates of attractiveness? Using functional MRI (fMRI), the authors addressed this question in the specific context of the apprehension of faces. When subjects judged facial beauty explicitly, neural activity in a widely distributed network involving the ventral occipital, anterior insular, dorsal posterior parietal, inferior dorsolateral, and medial prefrontal cortices correlated parametrically with the degree of facial attractiveness. When subjects were not attending explicitly to attractiveness, but rather were judging facial identity, the ventral occipital region remained responsive to facial beauty. The authors propose that this region, which includes the fusiform face area (FFA), the lateral occipital cortex (LOC), and medially adjacent regions, is activated automatically by beauty and may serve as a neural trigger for pervasive effects of attractiveness in social interactions.


title: Category Information Collection in RFID Systems

abstract: In RFID-enabled applications, when a tag is put into use and associated with a specific object, the category-related information (e.g., the brands of clothes) about this object might be preloaded into the tag's memory as required. Since such information reflects the category attributes, all tags in the same category carry the identical category information. To collect this information, we do not need to repeatedly interrogate each tag; one tag's response in a category is sufficient. In this paper, we investigate the new problem of category information collection in a multi-category RFID system, which is referred to as information sampling. We propose an efficient two-phase sampling protocol (TPS). By quickly zooming into a category and isolating a tag from this category, TPS is able to sample a category by broadcasting only 7.5-bit polling vector (very efficient when compared to the 96-bit tag ID). We theoretically analyze the protocol performance and discuss the optimal parameter settings that minimize the overall execution time. Extensive simulations show that TPS outperforms the benchmark, greatly improving the sampling performance.


title: Identify the Benefits of the Different Steps in an i-Vector Based Speaker Verification System

abstract: This paper focuses on the analysis of the i-vector paradigm, a compact representation of spoken utterances that is used by most of the state of the art speaker verification systems. This work was mainly motivated by the need to quantify the impact of their steps on the final performance, especially their ability to model data according to a theoretical Gaussian framework. These investigations allow to highlight the key points of the approach, in particular a core conditioning procedure, that lead to the success of the i-vector paradigm.


title: Feasibility, safety, acceptability, and functional outcomes of playing Nintendo Wii Fit Plus™ for frail elderly: study protocol for a feasibility trial

abstract: BACKGROUND ::: Frailty can be defined as a medical syndrome with multiple causes and contributors, characterized by diminished strength and endurance and reduced physiological function that increases the vulnerability to develop functional dependency and/or death. Studies have shown that the most commonly studied exercise protocol for frail older adults is the multimodal training. Interactive video games (IVGs) involve tasks in virtual environments that combine physical and cognitive demands in an attractive and challenging way. The aim of this study will be to evaluate the feasibility, safety, acceptability, and functional outcomes of playing Nintendo Wii Fit PlusTM (NWFP) for frail older adults. ::: ::: ::: METHODS/DESIGN ::: The study is a randomized controlled, parallel group, feasibility trial. Participants will be randomly assigned to the experimental group (EG) and control group (CG). The EG will participate in 14 training sessions, each lasting 50 min, twice a week. In each training session, the participants will play five games, with three attempts at each game. The first attempt will be performed with the assistance of a physical therapist to correct the movements and posture of the patients and subsequent attempts will be performed independently. Scores achieved in the games will be recorded. The participants will be evaluated by a blinded physical therapist at three moments: before and after intervention and 30 days after the end of the intervention (follow-up). We will assess the feasibility, acceptability, safety, and clinical outcomes (postural control, gait, cognition, quality of life, mood, and fear of falling). ::: ::: ::: DISCUSSION ::: Due to the deficiencies in multiple systems, studies have shown that multimodal interventions including motor-cognitive stimulation can improve the mobility of frail elderly adults. IVGs, among them the NWFP, are considered as a multimodal motor-cognitive intervention that can potentially improve motor and cognitive functions in the frail elderly. However, there is still no evidence in the literature that proves the feasibility, safety, acceptability, and functional outcomes of this intervention in frail elderly individuals. ::: ::: ::: TRIAL REGISTRATION ::: Brazilian Registry of Clinical Trials (RBR-823rst). World Health Organization Trial Registration Data Set (Additional file 1).


title: Risk-based adaptive security for smart IoT in eHealth

abstract: Emerging Internet of Things (IoTs) technologies provide many benefits to the improvement of eHealth. The successful deployment of IoTs depends on ensuring security and privacy that need to adapt to their processing capabilities and resource use. IoTs are vulnerable to attacks since communications are mostly wireless, unattended things are usually vulnerable to physical attacks, and most IoT components are constrained by energy, communications, and computation capabilities necessary for the implementation of complex security-supporting schemes. This paper describes a risk-based adaptive security framework for IoTs in eHealth that will estimate and predict risk damages and future benefits using game theory and context-awareness techniques. The paper also describes the validation case study.


title: Removal of Partial Occlusion from Single Images

abstract: This paper examines large partial occlusions in an image which occur near depth discontinuities when the foreground object is severely out of focus. We model these partial occlusions using matting, with the alpha value determined by the convolution of the blur kernel with a pinhole projection of the occluder. The main contribution is a method for removing the image contribution of the foreground occluder in regions of partial occlusion, which improves the visibility of the background scene. The method consists of three steps. First, the region of complete occlusion is estimated using a curve evolution method. Second, the alpha value at each pixel in the partly occluded region is estimated. Third, the intensity contribution of the foreground occluder is removed in regions of partial occlusion. Experiments demonstrate the method's ability to remove the effects of partial occlusion in single images with minimal user input.


title: Postnatal depression and infant growth and development in low income countries: a cohort study from Goa, India

abstract: Background: Postnatal depression is a recognised cause of delayed cognitive development in infants in developed countries. Being underweight is common in South Asia. ::: ::: Aims: To determine whether postnatal depression contributes to poor growth and development outcomes in Goa, India. ::: ::: Methods: Cohort study for growth outcomes with nested case-control study for developmental outcomes. A total of 171 babies were weighed and measured at 6–8 weeks following birth. The following measures were used: Edinburgh Postnatal Depression Scale for maternal mood, and sociodemographic and infant health variables. Outcome measures were: weight (<5th centile), length (<5th centile), and Developmental Assessment Scale for Indian Infants scores at six months. ::: ::: Results: Postnatal depression was a strong, and independent, predictor of low weight and length and was significantly associated with adverse mental development quotient scores. ::: ::: Conclusions: This study provides evidence for the first time that postnatal depression, a potentially treatable disorder, is a cause of poor growth and development in South Asia.


title: Vision-Based Offline-Online Perception Paradigm for Autonomous Driving

abstract: Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in real-time. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community.


title: Query Expansion Using Term Distribution and Term Association

abstract: Good term selection is an important issue for an automatic query expansion (AQE) technique. AQE techniques that select expansion terms from the target corpus usually do so in one of two ways. Distribution based term selection compares the distribution of a term in the (pseudo) relevant documents with that in the whole corpus / random distribution. Two well-known distribution-based methods are based on Kullback-Leibler Divergence (KLD) and Bose-Einstein statistics (Bo1). Association based term selection, on the other hand, uses information about how a candidate term co-occurs with the original query terms. Local Context Analysis (LCA) and Relevance-based Language Model (RM3) are examples of association-based methods. Our goal in this study is to investigate how these two classes of methods may be combined to improve retrieval effectiveness. We propose the following combination-based approach. Candidate expansion terms are first obtained using a distribution based method. This set is then refined based on the strength of the association of terms with the original query terms. We test our methods on 11 TREC collections. The proposed combinations generally yield better results than each individual method, as well as other state-of-the-art AQE approaches. En route to our primary goal, we also propose some modifications to LCA and Bo1 which lead to improved performance.


title: A Survey on Hardware Implementations of Visual Object Trackers

abstract: Visual object tracking is an active topic in the computer vision domain with applications extending over numerous fields. The main sub-tasks required to build an object tracker (e.g. object detection, feature extraction and object tracking) are computation-intensive. In addition, real-time operation of the tracker is indispensable for almost all of its applications. Therefore, complete hardware or hardware/software co-design approaches are pursued for better tracker implementations. This paper presents a literature survey of the hardware implementations of object trackers over the last two decades. Although several tracking surveys exist in literature, a survey addressing the hardware implementations of the different trackers is missing. We believe this survey would fill the gap and complete the picture with the existing surveys of how to design an efficient tracker and point out the future directions researchers can follow in this field. We highlight the lack of hardware implementations for state-of-the-art tracking algorithms as well as for enhanced classical algorithms. We also stress the need for measuring the tracking performance of the hardware-based trackers. Additionally, enough details of the hardware-based trackers need to be provided to allow reasonable comparison between the different implementations.


title: Personality as manifest in word use: Correlations with self-report, acquaintance report, and behavior

abstract: The use of words is one of the most direct means of expressing thoughts and feelings. However, past studies have had limited success in correlating word use with personality. The purpose of the present study was to identify categories of word use relevant to personality using a broad range of personality data. Using data from 181 participants, the present study correlated word use within a 1-hr life history interview with self-judgments of personality, judgments of personality provided by close acquaintances (who were not exposed to the language sample), and behavioral ratings based on direct observation from a context entirely separate from that from which the language sample was derived. Several categories of word use yielded a large number of correlates with self- and acquaintance personality ratings and behavior. It is suggested that word use is related to personality to a larger degree than previously observed and deserves increased attention as a source of data in personality assessment.


title: Social Choice Theory and Recommender Systems: Analysis of the Axiomatic Foundations of Collaborative Filtering

abstract: The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the behavior of multiple users to recommend items of interest to individual users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed several variations of the technology. We take the perspective of CF as a methodology for combining preferences. The preferences predicted for the end user is some function of all of the known preferences for everyone in a database. Social Choice theorists, concerned with the properties of voting methods, have been investigating preference aggregation for decades. At the heart of this body of work is Arrow’s result demonstrating the impossibility of combining preferences in a way that satisfies several desirable and innocuous-looking properties. We show that researchers working on CF algorithms often make similar assumptions. We elucidate these assumptions and extend results from Social Choice theory to CF methods. We show that only very restrictive CF functions are consistent with desirable aggregation properties. Finally, we discuss practical implications of these results.


title: Link prediction in directed social networks

abstract: In today’s online social networks, it becomes essential to help newcomers as well as existing community members to find new social contacts. In scientific literature, this recommendation task is known as link prediction. Link prediction has important practical applications in social network platforms. It allows social network platform providers to recommend friends to their users. Another application is to infer missing links in partially observed networks. The shortcoming of many of the existing link prediction methods is that they mostly focus on undirected graphs only. This work closes this gap and introduces link prediction methods and metrics for directed graphs. Here, we compare well-known similarity metrics and their suitability for link prediction in directed social networks. We advance existing techniques and propose mining of subgraph patterns that are used to predict links in networks such as GitHub, GooglePlus, and Twitter. Our results show that the proposed metrics and techniques yield more accurate predictions when compared with metrics not accounting for the directed nature of the underlying networks.


title: PCF vs DCF: a performance comparison

abstract: Wireless LANs are gaining importance at a very rapid pace. The idea of being mobile and connected to the Internet is driving new innovations in this area. With the recent innovations in the field of multimedia networks, the requirements of QoS support over wireless LANs are becoming more stringent. Quality of service in a wireless LAN is affected by a number of parameters like channel access method, physical/environmental conditions, number of nodes, distance etc. A proper selection of protocols/topology helps in maintaining/improving the QoS support of a wireless network. In this paper, the authors analyze the effect of channel access methods on the multimedia (voice) traffic. Two channel access methods, namely point coordinate function (PCF) and distributed coordinated function are considered for their support for QoS. The simulation results indicate that using PCF for multimedia traffic results in better performance.


title: A Survey of Frameworks and Game Engines for Serious Game Development

abstract: Given the sparsity of standard game engines and frameworks for serious game development, developers of serious games typically rely on entertainment-based game development tools. However, given the large number of game engines and frameworks dedicated to entertainment game development, deciding on which tool to employ may be difficult. A literature review that examined the frameworks and game engines used to develop serious games was recently conducted. Here, a list of the most commonly identified frameworks and game engines and a summary of their features is provided. The results presented provide insight to those seeking tools to develop serious games.


title: Cloud RAN challenges and solutions

abstract: In this paper, we take an overall look at key technical challenges in the evolution of the radio access network (RAN) architecture towards Cloud RAN and solutions to overcome them. To address fronthaul limitations, we examine the implications and tradeoffs enabled by functional splits on fronthaul needs, system performance, and centralization scale. We examine the architecture of algorithms for multi-cell coordination and implications in a Cloud RAN environment. To maximize the use of general-purpose processors (GPP) and operating systems such as Linux for Cloud RAN, we propose methods of achieving real-time performance suitable for RAN functions. To enable right-sizing the amount of compute used for various RAN functions based on the workload, we propose methods of pooling and elastic scaling for RAN functions that exploit the fact that certain RAN functions perform per-user operations while others perform per-cell operations. Cloud RAN also aims to use cloud management technologies such as virtualized infrastructure management (VIM) and orchestration for automating the instantiation and scaling of RAN functions. We identify special needs for RAN arising from real-time constraints and a mix of GPP and non-GPP hardware. In the evolution towards 5G, we propose the use of Cloud-RAN-based multi-connectivity anchoring to address processing bottlenecks in a scalable manner. We identify opportunities for optimization across RAN and other network layers enabled by the distributed edge cloud architecture.


Potential Adverse Effects of Violent Video Gaming: Interpersonal- Affective Traits Are Rather Impaired Than Disinhibition in Young Adults


title: Neural Mechanisms of Extinction Learning and Retrieval

abstract: Emotional learning is necessary for individuals to survive and prosper. Once acquired, however, emotional associations are not always expressed. Indeed, the regulation of emotional expression under varying environmental conditions is essential for mental health. The simplest form of emotional regulation is extinction, in which conditioned responding to a stimulus decreases when the reinforcer is omitted. Two decades of research on the neural mechanisms of fear conditioning have laid the groundwork for understanding extinction. In this review, we summarize recent work on the neural mechanisms of extinction learning. Like other forms of learning, extinction occurs in three phases: acquisition, consolidation, and retrieval, each of which depends on specific structures (amygdala, prefrontal cortex, hippocampus) and molecular mechanisms (receptors and signaling pathways). Pharmacological methods to facilitate consolidation and retrieval of extinction, for both aversive and appetitive conditioning, are setting the stage for novel treatments for anxiety disorders and addictions.


title: A Probabilistic View of Neighborhood-Based Recommendation Methods

abstract: Probabilistic graphic model is an elegant framework to compactly present complex real-world observations by modeling uncertainty and logical flow (conditionally independent factors). In this paper, we present a probabilistic framework of neighborhood-based recommendation methods (PNBM) in which similarity is regarded as an unobserved factor. Thus, PNBM leads the estimation of user preference to maximizing a posterior over similarity. We further introduce a novel multi-layer similarity descriptor which models and learns the joint influence of various features under PNBM, and name the new framework MPNBM. Empirical results on real-world datasets show that MPNBM allows very accurate estimation of user preferences.


title: A variable look-ahead controller for lateral guidance of four wheeled vehicles

abstract: A variable look-ahead controller for the lateral guidance of vehicles for automated highway systems is proposed. The control objective is to make the lateral error at a certain point ahead of the vehicle zero. The distance of this point from the vehicle is called the look-ahead distance. An input-output linearization controller to achieve this objective is proposed. It is shown that the yaw internal dynamics can be damped at all longitudinal velocities if the look-ahead distance is a certain quadratic function of the longitudinal velocity.


title: Monitoring of Cardiorespiratory Signal: Principles of Remote Measurements and Review of Methods

abstract: Physiological signs can be remotely observed from the physiological and physical effects caused by a cardiorespiratory activity. A wide range of research on remote cardiorespiratory monitoring systems has been done using different methods, including methods based on Doppler effect, thermal imaging, and video camera imaging. The aim of this paper was to review and compare the newest and most promising of such remote measuring methods, introducing their merits and limitations under different circumstances. In addition, this paper summarizes the performance of these methods in a table regarding the noise artifacts, subject movement, the number of regions of interest, generalization to multiple subjects, detection range (distance), biological effects, and cost. This is a thorough general overview of the remote measurement of cardiorespiratory methods.


title: False Confessions Causes, Consequences, and Implications for Reform

abstract: Despite the commonsense belief that people do not confess to crimes they did not commit, 20 to 25% of all DNA exonerations involve innocent prisoners who confessed. After distinguishing between voluntary, compliant, and internalized false confessions, this article suggests that a sequence of three processes is responsible for false confessions and their adverse consequences. First, police sometimes target innocent people for interrogation because of erroneous judgments of truth and deception. Second, innocent people sometimes confess as a function of certain interrogation tactics, dispositional suspect vulnerabilities, and the phenomenology of innocence. Third, jurors fail to discount even those confessions they see as coerced. At present, researchers are seeking ways to improve the accuracy of confession evidence and its evaluation in the courtroom.


title: Topic Models For Feature Selection in Document Clustering.

abstract: We investigate the idea of using a topic model such as the popular Latent Dirichlet Allocation model as a feature selection step for unsupervised document clustering, where documents are clustered using the proportion of the various topics that are present in each document. One concern with using “vanilla” LDA as a feature selection method for input to a clustering algorithm is that the Dirichlet prior on the topic mixing proportions is too smooth and well-behaved. It does not encourage a “bumpy” distribution of topic mixing proportion vectors, which is what one would desire as input to a clustering algorithm. As such, we propose two variant topic models that are designed to do a better job of producing topic mixing proportions that have a good clustering structure.


title: Quantitave measures of cluster quality for use in extracellular recordings

abstract: While the use of multi-channel electrodes (stereotrodes and tetrodes) has allowed for the simultaneous recording and identification of many neurons, quantitative measures of the quality of neurons in such recordings are lacking. In multi-channel recordings, each spike waveform is discriminated in a high-dimensional space, making traditional measures of unit quality inapplicable. We describe two measures of unit isolation quality, Lratio and Isolation Distance, and evaluate their performance using simulations and tetrode recordings. Both measures quantified how well separated the spikes of one cluster (putative neuron) were from other spikes recorded simultaneously on the same multi-channel electrode. In simulations and tetrode recordings, both Lratio and Isolation Distance discriminated well- and poorly-separated clusters. In data sets from the rodent hippocampus in which neurons were simultaneously recorded intracellularly and extracellularly, values of Isolation Distance and Lratio were related to the correct identification of spikes.


title: Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks

abstract: One key challenging issue of facial expression recognition is to capture the dynamic variation of facial physical structure from videos. In this paper, we propose a part-based hierarchical bidirectional recurrent neural network (PHRNN) to analyze the facial expression information of temporal sequences. Our PHRNN models facial morphological variations and dynamical evolution of expressions, which is effective to extract “temporal features” based on facial landmarks (geometry information) from consecutive frames. Meanwhile, in order to complement the still appearance information, a multi-signal convolutional neural network (MSCNN) is proposed to extract “spatial features” from still frames. We use both recognition and verification signals as supervision to calculate different loss functions, which are helpful to increase the variations of different expressions and reduce the differences among identical expressions. This deep evolutional spatial-temporal network (composed of PHRNN and MSCNN) extracts the partial-whole, geometry-appearance, and dynamic-still information, effectively boosting the performance of facial expression recognition. Experimental results show that this method largely outperforms the state-of-the-art ones. On three widely used facial expression databases (CK+, Oulu-CASIA, and MMI), our method reduces the error rates of the previous best ones by 45.5%, 25.8%, and 24.4%, respectively.


title: Artificial Neural Networks that Learn to Satisfy Logic Constraints

abstract: Logic-based problems such as planning, theorem proving, or puzzles, typically involve combinatoric search and structured knowledge representation. Artificial neural networks are very successful statistical learners, however, for many years, they have been criticized for their weaknesses in representing and in processing complex structured knowledge which is crucial for combinatoric search and symbol manipulation. Two neural architectures are presented, which can encode structured relational knowledge in neural activation, and store bounded First Order Logic constraints in connection weights. Both architectures learn to search for a solution that satisfies the constraints. Learning is done by unsupervised practicing on problem instances from the same domain, in a way that improves the network-solving speed. No teacher exists to provide answers for the problem instances of the training and test sets. However, the domain constraints are provided as prior knowledge to a loss function that measures the degree of constraint violations. Iterations of activation calculation and learning are executed until a solution that maximally satisfies the constraints emerges on the output units. As a test case, block-world planning problems are used to train networks that learn to plan in that domain, but the techniques proposed could be used more generally as in integrating prior symbolic knowledge with statistical learning


title: OMIR: Ontology-Based Multimedia Information Retrieval System for Web Usage Mining

abstract: Information extraction relevant to the user queries is the challenging task in the ontology environment due to data varieties such as image, video, and text. The utilization of appropriate semantic entities enables the content-based search on annotated text. Recently, the automatic extraction of textual content in the audio-visual content is an advanced research area in a multimedia MM environment. The annotation of the video includes several tags and comments. This paper proposes the Collaborative Tagging CT model based on the Block Acquiring Page Segmentation BAPS method to retrieve the tag-based information. The information extraction in this model includes the Ontology-Based Information Extraction OBIE based on the single ontology utilization. The semantic annotation phase in the proposed work inserts the metadata with limited machine-readable terms. The insertion process is split into two major processes such as database uploading to server and extraction of images/web pages based on the results of semantic phase. Novel weight-based novel clustering algorithms are introduced to extract knowledge from MM contents. The ranking based on the weight value in the semantic annotation phase supports the image/web page retrieval process effectively. The comparative analysis of the proposed BAPS-CT with the existing information retrieval IR models regarding the average precision rate, time cost, and storage space rate assures the effectiveness of BAPS-CT in OMIR.


title: Building Understanding of Smart City Initiatives

abstract: This study presents the first results of an analysis primarily based on semi-structured interviews with government officials and managers who are responsible for smart city initiatives in four North American cities—Philadelphia and Seattle in the United States, Quebec City in Canada, and Mexico City in Mexico. With the reference to the Smart City Initiatives Framework that we suggested in our previous research, this study aims to build a new understanding of smart city initiatives. Main findings are categorized into eight aspects including technology, management and organization, policy context, governance, people and communities, economy, built infrastructure, and natural environment.


title: NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer's Disease

abstract: This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimer's disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) for feature selection and extraction of the most relevant features. The resulting NMF-transformed sets of data, which contain a reduced number of features, are classified by means of a SVM-based classifier with bounds of confidence for decision. The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification.


title: An Analysis and Comparison of CDN-P2P-hybrid Content Delivery System and Model

abstract: In order to fully utilize the stable edge transmission capability of CDN and the scalable last-mile transmission capability of P2P, while at the same time avoiding ISP-unfriendly policies and unlimited usage of P2P delivery, some researches have begun focusing on CDN-P2P-hybrid architecture and ISP-friendly P2P content delivery technology in recent years. In this paper, we first survey CDN-P2P-hybrid architecture technology, including current industry efforts and academic efforts in this field. Second, we make comparisons between CDN and P2P. And then we explore and analyze main issues, including overlay route hybrid issues, and playing buffer hybrid issues. After that we focus on CDN-P2P-hybrid model analysis and design, we compare the tightly-coupled hybrid model with the loosely-coupled hybrid model, and we propose that there are some main common models which need further study. At last, we analyze the prospective research direction and propose our future work.


title: Optimizing mining rates under financial uncertainty in global mining complexes

abstract: This paper presents a distributed and dynamic programming framework to the mining production rate target tracking of multiple metal mines under financial uncertainty. A single mine׳s target tracking is stated as a stochastic optimization problem and the solution is obtained by solving the dynamic program which gives the optimal production rate schedule of each mine as a Markovian feedback control on the price process. The global solution is distributed on multiple mines by a policy iteration method, and this iterative method is shown to provide the unique equilibrium among Markovian strategies. Numerical results confirm the efficacy of the proposed global method when compared to individual optimization of mining rate target tracking.


Improving Liver Lesion Detection using CT-to-PET FCN and GAN Networks


title: Distance Measurement Model Based on RSSI in WSN

abstract: The relationship between RSSI (Received Signal Strength Indication) values and distance is the foundation and the key of ranging and positioning technologies in wireless sensor networks. Log-normal shadowing model (LNSM), as a more general signal propagation model, can better describe the relationship between the RSSI value and distance, but the parameter of variance in LNSM is depended on experiences without self-adaptability. In this paper, it is found that the variance of RSSI value changes along with distance regu- larly by analyzing a large number of experimental data. Based on the result of analysis, we proposed the relationship function of the variance of RSSI and distance, and established the log-normal shadowing model with dynamic variance (LNSM-DV). At the same time, the method of least squares(LS) was selected to es- timate the coefficients in that model, thus LNSM-DV might be adjusted dynamically according to the change of environment and be self-adaptable. The experimental results show that LNSM-DV can further reduce er- ror, and have strong self-adaptability to various environments compared with the LNSM.


title: A smartphone SP10T T/R switch in 180-nm SOI CMOS with 8kV+ ESD protection by co-design

abstract: This paper reports the first 8kV+ ESD-protected SP10T transmit/receive (T/R) antenna switch for quad-band (0.85/0.9/1.8/1.9-GHz) GSM and multiple W-CDMA smartphones fabricated in an 180-nm SOI CMOS. A novel physics-based switch-ESD co-design methodology is applied to ensure full-chip optimization for a SP10T test chip and its ESD protection circuit simultaneously.


title: Pricing models and Motivations for MMO play

abstract: The purpose of this study is to investigate and conceptualize the relationship between pricing models and motivations for MMO play. After the review of previous studies, we conduct an empirical research in the Japanese MMO industry to find determinants of pricing models. As a result, we found that (1) relationship between monthly fixed fee, continuous play (play period) and social motivation, (2) relationship between per-item billing, relatively short play period and high immersion.


title: A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling

abstract: We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and only using local inference. However, when automatically predicted part-of-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL-2009 dataset. We also consider Chinese, Czech and Spanish where our approach also achieves competitive results. Syntactic parsers are unreliable on out-of-domain data, so standard (i.e., syntactically-informed) SRL models are hindered when tested in this setting. Our syntax-agnostic model appears more robust, resulting in the best reported results on standard out-of-domain test sets.


title: Controversies in Gender Diagnoses

abstract: Abstract This article presents the author's thoughts on gender diagnosis controversies during his tenure at the DSM-5 Workgroup on Sexual and Gender Identity Disorders and the ICD-11 Working Group on the Classification of Sexual Disorders and Sexual Health. The work summarizes some of the published conclusions of the DSM-5 and ICD-11 revision processes regarding three particular controversies: (1) stigma versus access to care; (2) the retention of a child gender diagnosis; and (3) the treatment of prepubescent transgender children. Both the DSM and ICD work groups decided that despite the stigma associated with a diagnosis, retaining an adolescent and adult gender diagnosis is necessary to maintain access to care. As for the child gender diagnosis, given the heterogeneity of this clinical population and that gender dysphoria does not persist in most children, a child diagnosis of Gender Dysphoria (DSM) and Gender Incongruence (ICD) should be retained to facilitate ongoing evaluation and management in chil...


title: A Novel Quantitative Approach For Measuring Network Security

abstract: Evaluation of network security is an essential step in securing any network. This evaluation can help security professionals in making optimal decisions about how to design security countermeasures, to choose between alternative security architectures, and to systematically modify security configurations in order to improve security. However, the security of a network depends on a number of dynamically changing factors such as emergence of new vulnerabilities and threats, policy structure and network traffic. Identifying, quantifying and validating these factors using security metrics is a major challenge in this area. In this paper, we propose a novel security metric framework that identifies and quantifies objectively the most significant security risk factors, which include existing vulnerabilities, historical trend of vulnerability of the remotely accessible services, prediction of potential vulnerabilities for any general network service and their estimated severity and finally policy resistance to attack propagation within the network. We then describe our rigorous validation experiments using real- life vulnerability data of the past 6 years from National Vulnerability Database (NVD) [10] to show the high accuracy and confidence of the proposed metrics. Some previous works have considered vulnerabilities using code analysis. However, as far as we know, this is the first work to study and analyze these metrics for network security evaluation using publicly available vulnerability information and security policy configuration.


title: Simplification and optimization of i-vector extraction

abstract: This paper introduces some simplifications to the i-vector speaker recognition systems. I-vector extraction as well as training of the i-vector extractor can be an expensive task both in terms of memory and speed. Under certain assumptions, the formulas for i-vector extraction—also used in i-vector extractor training—can be simplified and lead to a faster and memory more efficient code. The first assumption is that the GMM component alignment is constant across utterances and is given by the UBM GMM weights. The second assumption is that the i-vector extractor matrix can be linearly transformed so that its per-Gaussian components are orthogonal. We use PCA and HLDA to estimate this transform.


title: How to write a systematic review.

abstract: Evidence-based medicine (EBM) is the combination of the best available research evidence with clinical experience and patient needs. The concept of EBM as a part of clinical decision making has become increasingly popular over the last decade. In the hierarchy of studies meta-analysis and systematic reviews occupy the highest levels. A systematic review of a clinical question can be performed by following a relatively standard form. These techniques as described here can be performed without formal training. Systematic reviews conducted in this fashion can be used as a higher form of current concepts or as review articles and replace the traditional expert opinion narrative review.


title: Association rules mining for name entity recognition

abstract: We propose a new name entity class extraction method based on association rules. We evaluate and compare the performance of our method with the state of the art maximum entropy method. We show that our method consistently yields a higher precision at a competitive level of recall. This result makes our method particularly suitable for tasks whose requirements emphasize the quality rather than the quantity of results.


THU_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model


Wasserstein GAN: Technical Report


title: Suitable drying model for far infrared drying of taegeuk ginseng

abstract: In this study we have elucidated the suitable drying model for the far infrared drying of taegeuk ginseng. Thin layer drying tests of taegeuk ginseng were conducted at 3 temperature levels of 45, 55, and 65°C, and 3 taegeuk ginseng size levels of large, medium, and small. Four thin layer drying models were used to estimate drying curves. The goodness-of-fit of the drying models were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean relative deviation modulus (Pe), and reduced chi-square (χ2). In the mean time, the influences of drying temperature and taegeuk ginseng size on drying behavior were also determined. The results revealed that the Page model, which can describe the far infrared drying behavior of taegeuk ginseng with a great accuracy, was the best model to fit adequately most of the drying conditions for the far infrared drying of taegeuk ginseng.


title: Embedded systems and software challenges in electric vehicles

abstract: The design of electric vehicles require a complete paradigm shift in terms of embedded systems architectures and software design techniques that are followed within the conventional automotive systems domain. It is increasingly being realized that the evolutionary approach of replacing the engine of a car by an electric engine will not be able to address issues like acceptable vehicle range, battery lifetime performance, battery management techniques, costs and weight, which are the core issues for the success of electric vehicles. While battery technology has crucial importance in the domain of electric vehicles, how these batteries are used and managed pose new problems in the area of embedded systems architecture and software for electric vehicles. At the same time, the communication and computation design challenges in electric vehicles also have to be addressed appropriately. This paper discusses some of these research challenges.


title: Your botnet is my botnet: analysis of a botnet takeover

abstract: Botnets, networks of malware-infected machines that are controlled by an adversary, are the root cause of a large number of security problems on the Internet. A particularly sophisticated and insidious type of bot is Torpig, a malware program that is designed to harvest sensitive information (such as bank account and credit card data) from its victims. In this paper, we report on our efforts to take control of the Torpig botnet and study its operations for a period of ten days. During this time, we observed more than 180 thousand infections and recorded almost 70 GB of data that the bots collected. While botnets have been "hijacked" and studied previously, the Torpig botnet exhibits certain properties that make the analysis of the data particularly interesting. First, it is possible (with reasonable accuracy) to identify unique bot infections and relate that number to the more than 1.2 million IP addresses that contacted our command and control server. Second, the Torpig botnet is large, targets a variety of applications, and gathers a rich and diverse set of data from the infected victims. This data provides a new understanding of the type and amount of personal information that is stolen by botnets.


title: Hybrid detection of Black hole and gray hole attacks in MANET

abstract: MANET performance is degraded as a result of Denial-of-service (DoS) attacks such as Black hole and gray hole attacks causing loss of packets. Though many researchers have found different approaches for the effective detection and elimination of these attacks, a single technique to detect both the attacks simultaneously is generally rare. In this paper, a novel hybrid black/gray hole detection approach is proposed for detecting both the black and gray hole attacks in Dynamic Source Routing (DSR) protocol for MANET by using the same technique. DSR protocol dynamically discovers a source route across multiple network hops to any destination in the MANET. In this hybrid approach, the initialized monitor nodes collect the packet flow informations about the neighboring nodes. Then the information distance metric is computed using which two detection thresholds are determined. Then distance metric for all the nodes are compared with the first threshold. If the information distance metric of a node is greater than the first detection threshold, then the node is considered to be malicious nodes. If the information distance metric of the nodes are below the second threshold but not less than the first threshold, the nodes are marked as gray hole attackers while if they are greater than the second threshold, the nodes are marked as black hole attackers. Experimental results show that the proposed hybrid black/gray hole detection approach detects and eliminates the attacks effectively with better throughput, packet drop rate, packet delivery ratio and routing overhead.


title: A Lyapunov functional for a SIRI model with nonlinear incidence of infection and relapse

abstract: We analyze the dynamics of a disease propagation model with relapse under the assumption that the incidence of infection is given in an abstract, possibly bi-nonlinear form. Sufficient conditions for the local stability of equilibria are obtained by means of Lyapunov's second method and it is shown that global stability can be attained under suitable monotonicity conditions. The persistence of the system is then investigated and it is established that the basic reproduction number R"0 is a threshold parameter for the stability of the system. Alternate Lyapunov functionals are also introduced, being observed that the originating functional template generalizes both quadratic and Volterra functionals.


title: Mathematical Backdoors in Symmetric Encryption Systems - Proposal for a Backdoored AES-like Block Cipher

abstract: Recent years have shown that more than ever governments and intelligence agencies try to control and bypass the cryptographic means used for the protection of data. Backdooring encryption algorithms is considered as the best way to enforce cryptographic control. Until now, only implementation backdoors (at the protocol/implementation/management level) are generally considered. In this paper we propose to address the most critical issue of backdoors: mathematical backdoors or by-design backdoors, which are put directly at the mathematical design of the encryption algorithm. While the algorithm may be totally public, proving that there is a backdoor, identifying it and exploiting it, may be an intractable problem. We intend to explain that it is probably possible to design and put such backdoors. Considering a particular family (among all the possible ones), we present BEA-1, a block cipher algorithm which is similar to the AES and which contains a mathematical backdoor enabling an operational and effective cryptanalysis. The BEA-1 algorithm (80-bit block size, 120-bit key, 11 rounds) is designed to resist to linear and differential cryptanalyses. A challenge will be proposed to the cryptography community soon. Its aim is to assess whether our backdoor is easily detectable and exploitable or not.


Geometric Control of Quadrotor Attitude in Wind With Flow Sensing and Thrust Constraints


title: Method for Developing Consistent Future Energy Scenarios as Input for Transmission Expansion Planning

abstract: The power supply faces new challenges due to the integration of a high amount of renewable energy sources (RES), the reduction of conventional power plants (CPP) and the introduction of new technologies like electric vehicles, power-to-x applications etc. in the power grid. These developments are uncertain and depend mainly on technological progress and political framework. To ensure secure, reliable and economic power supply in the future, the transmission grid needs to handle the altered requirements. Designing, constructing and commissioning of new transmission lines is an elaborate process. Hence, transmission system operators (TSOs) need to take investment decisions for new equipment under uncertainty. Consequently, these uncertainties need to be considered adequately in the planning process. This paper proposes a method to develop consistent future scenarios for generation and load. The approach takes into account the actual generation park including the localization of all sources and sinks. For each scenario, the occurrence probability is determined.


title: Automatic Detection of Vague Words and Sentences in Privacy Policies

abstract: Website privacy policies represent the single most important source of information for users to gauge how their personal data are collected, used and shared by companies. However, privacy policies are often vague and people struggle to understand the content. Their opaqueness poses a significant challenge to both users and policy regulators. In this paper, we seek to identify vague content in privacy policies. We construct the first corpus of human-annotated vague words and sentences and present empirical studies on automatic vagueness detection. In particular, we investigate context-aware and context-agnostic models for predicting vague words, and explore auxiliary-classifier generative adversarial networks for characterizing sentence vagueness. Our experimental results demonstrate the effectiveness of proposed approaches. Finally, we provide suggestions for resolving vagueness and improving the usability of privacy policies.


title: Modeling Task Control of Eye Movements

abstract: In natural behavior, visual information is actively sampled from the environment by a sequence of gaze changes. The timing and choice of gaze targets, and the accompanying attentional shifts, are intimately linked with ongoing behavior. Nonetheless, modeling of the deployment of these fixations has been very difficult because they depend on characterizing the underlying task structure. Recently, advances in eye tracking during natural vision, together with the development of probabilistic modeling techniques, have provided insight into how the cognitive agenda might be included in the specification of fixations. These techniques take advantage of the decomposition of complex behaviors into modular components. A particular subset of these models casts the role of fixation as that of providing task-relevant information that is rewarding to the agent, with fixation being selected on the basis of expected reward and uncertainty about environmental state. We review this work here and describe how specific examples can reveal general principles in gaze control.


title: M.: A Bayesian networks approach for predicting proteinprotein interactions from genomic data

abstract: We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.


title: CamK: A camera-based keyboard for small mobile devices

abstract: Due to the smaller size of mobile devices, on-screen keyboards become inefficient for text entry. In this paper, we present CamK, a camera-based text-entry method, which uses an arbitrary panel (e.g., a piece of paper) with a keyboard layout to input text into small devices. CamK captures the images during the typing process and uses the image processing technique to recognize the typing behavior. The principle of CamK is to extract the keys, track the user's fingertips, detect and localize the keystroke. To achieve high accuracy of keystroke localization and low false positive rate of keystroke detection, CamK introduces the initial training and online calibration. Additionally, CamK optimizes computation-intensive modules to reduce the time latency. We implement CamK on a mobile device running Android. Our experiment results show that CamK can achieve above 95% accuracy of keystroke localization, with only 4.8% false positive keystrokes. When compared to on-screen keyboards, CamK can achieve 1.25X typing speedup for regular text input and 2.5X for random character input.


City Crime Profiling Using Cluster Analysis


title: Fused one-vs-all mid-level features for fine-grained visual categorization

abstract: As an emerging research topic, fine-grained visual categorization has been attracting growing attentions in recent years. Due to the large inter-class similarity and intra-class variance, recognizing objects in fine-grained domains is extremely challenging, and sometimes even humans can not recognize them accurately. Traditional bag-of-words model could obtain desirable results for basic-level category classification by weak alignment using spatial pyramid matching model, but may easily fail in fine-grained domains since the discriminative features are not only subtle but also extremely localized. The fine differences often get swamped by those irrelevant features, and it is virtually impossible to distinguish them. To address the problems above, we propose a new framework for fine-grained visual categorization. We strengthen the spatial correspondence among parts by including foreground segmentation and part localization. Based on the part representations of the images, we learn a large set of mid-level features which are more suitable for fine-grained tasks. Comparing with the low level features directly extracted from the images, the learned one-vs-all mid-level features enjoy the following advantages. First, the dimension of the mid-level features is relatively small. In order to obtain high classification accuracy, the dimension of the low level features usually reaches several thousand to tens of thousand, and becomes even larger when introducing spatial pyramid model. However, the dimension of our mid-level features is related to the number of classes, which is far less. Second, each entry of the proposed mid-level features is meaningful, which forms a more compact representation of the image. Third, the mid-level features are more robust than the low level ones, which is helpful for classification. Fourth, the learning process of the mid-level features is independent and can be easily combined with other techniques to boost the performance. We evaluate the proposed approach on the extensive fine-grained dataset CUB 200-2011 and Stanford Dogs, by learning the mid-level features based on the popular Fisher vectors and convolutional neural network, we boost the classification accuracy by a considerable margin and advance the state-of-the-art performance in fine-grained visual categorization.


title: Marginal Structural Models versus Structural Nested Models as Tools for Causal Inference

abstract: Robins (1993, 1994, 1997, 1998ab) has developed a set of causal or counterfactual models, the structural nested models (SNMs). This paper describes an alternative new class of causal models — the (non-nested) marginal structural models (MSMs). We will then describe a class of semiparametric estimators for the parameters of these new models under a sequential randomization (i.e., ignorability) assumption. We then compare the strengths and weaknesses of MSMs versus SNMs for causal inference from complex longitudinal data with time-dependent treatments and confounders. Our results provide an extension to continuous treatments of propensity score estimators of an average treatment effect.


title: Cognitive linguistics

abstract: Cognitive linguistics is one of the fastest growing and influential perspectives on the nature of language, the mind, and their relationship with sociophysical (embodied) experience. It is a broad theoretical and methodological enterprise, rather than a single, closely articulated theory. Its primary commitments are outlined. These are the Cognitive Commitment-a commitment to providing a characterization of language that accords with what is known about the mind and brain from other disciplines-and the Generalization Commitment-which represents a dedication to characterizing general principles that apply to all aspects of human language. The article also outlines the assumptions and worldview which arises from these commitments, as represented in the work of leading cognitive linguists. WIREs Cogn Sci 2012, 3:129-141. doi: 10.1002/wcs.1163 For further resources related to this article, please visit the WIREs website.


title: Multi-organ plant identification with multi-column deep convolutional neural networks

abstract: Automatically identifying plants from images is a hot research topic due to its importance in production and science popularization. This process attempts to automatically identify the name of a plant with a known taxon from a given image. The majority of existing studies on automatic plant identification focus on identifying plants with a single organ, such as flower, leaf, or fruits. Plant identification using a single organ is not sufficiently reliable because different plants many have similar organs. To overcome this problem, this paper is devoted to automatically identifying plants by combining multiple organs of plants. Specifically, we propose a multi-column deep convolutional neural networks (MCDCNN) model to combine multiple organs for efficient plant identification. Extensive experiments demonstrate the effectiveness of our model, and the plant identification performance is greatly improved.


title: Deep Saliency Hashing for Fine-grained Retrieval

abstract: In recent years, hashing methods have been proved to be effective and efficient for the large-scale Web media search. However, the existing general hashing methods have limited discriminative power for describing fine-grained objects that share similar overall appearance but have subtle difference. To solve this problem, we for the first time introduce the attention mechanism to the learning of fine-grained hashing codes. Specifically, we propose a novel deep hashing model, named deep saliency hashing (DSaH), which automatically mines salient regions and learns semantic-preserving hashing codes simultaneously. DSaH is a two-step end-to-end model consisting of an attention network and a hashing network. Our loss function contains three basic components, including the semantic loss, the saliency loss, and the quantization loss. As the core of DSaH, the saliency loss guides the attention network to mine discriminative regions from pairs of images. We conduct extensive experiments on both fine-grained and general retrieval datasets for performance evaluation. Experimental results on fine-grained datasets, including Oxford Flowers-17, Stanford Dogs-120, and CUB Bird demonstrate that our DSaH performs the best for fine-grained retrieval task and beats the strongest competitor (DTQ) by approximately 10% on both Stanford Dogs-120 and CUB Bird. DSaH is also comparable to several state-of-the-art hashing methods on general datasets, including CIFAR-10 and NUS-WIDE.


title: The Cubli: A reaction wheel based 3D inverted pendulum

abstract: The Cubli is a 15×15×15 cm cube with reaction wheels mounted on three of its faces. By applying controlled torques to the reaction wheels the Cubli is able to balance on its corner or edge. This paper presents the development of the Cubli. First, the mechatronic design of the Cubli is presented. Then the multi-body system dynamics are derived. The parameters of the nonlinear system are identified using a frequency domain based approach while the Cubli is balancing on its edge with a nominal controller. Finally, the corner balancing using a linear feedback controller is presented along with experimental results.


title: Modeling and Analysis of K-Tier Downlink Heterogeneous Cellular Networks

abstract: Cellular networks are in a major transition from a carefully planned set of large tower-mounted base-stations (BSs) to an irregular deployment of heterogeneous infrastructure elements that often additionally includes micro, pico, and femtocells, as well as distributed antennas. In this paper, we develop a tractable, flexible, and accurate model for a downlink heterogeneous cellular network (HCN) consisting of K tiers of randomly located BSs, where each tier may differ in terms of average transmit power, supported data rate and BS density. Assuming a mobile user connects to the strongest candidate BS, the resulting Signal-to-Interference-plus-Noise-Ratio (SINR) is greater than 1 when in coverage, Rayleigh fading, we derive an expression for the probability of coverage (equivalently outage) over the entire network under both open and closed access, which assumes a strikingly simple closed-form in the high SINR regime and is accurate down to -4 dB even under weaker assumptions. For external validation, we compare against an actual LTE network (for tier 1) with the other K-1 tiers being modeled as independent Poisson Point Processes. In this case as well, our model is accurate to within 1-2 dB. We also derive the average rate achieved by a randomly located mobile and the average load on each tier of BSs. One interesting observation for interference-limited open access networks is that at a given \sinr, adding more tiers and/or BSs neither increases nor decreases the probability of coverage or outage when all the tiers have the same target-SINR.


title: Comparison of graph-based and logic-based multi-relational data mining

abstract: We perform an experimental comparison of the graph-based multi-relational data mining system, Subdue, and the inductive logic programming system, CProgol, on the Mutagenesis dataset and various artificially generated Bongard problems. Experimental results indicate that Subdue can significantly outperform CProgol while discovering structurally large multi-relational concepts. It is also observed that CProgol is better at learning semantically complicated concepts and it tends to use background knowledge more effectively than Subdue. An analysis of the results indicates that the differences in the performance of the systems are a result of the difference in the expressiveness of the logic-based and the graph-based representations. The ability of graph-based systems to learn structurally large concepts comes from the use of a weaker representation whose expressiveness is intermediate between propositional and first-order logic. The use of this weaker representation is advantageous while learning structurally large concepts but it limits the learning of semantically complicated concepts and the utilization background knowledge.


title: Compile-time composition of run-time data and iteration reorderings

abstract: Many important applications, such as those using sparse data structures, have memory reference patterns that are unknown at compile-time. Prior work has developed run-time reorderings of data and computation that enhance locality in such applications.This paper presents a compile-time framework that allows the explicit composition of run-time data and iteration-reordering transformations. Our framework builds on the iteration-reordering framework of Kelly and Pugh to represent the effects of a given composition. To motivate our extension, we show that new compositions of run-time reordering transformations can result in better performance on three benchmarks.We show how to express a number of run-time data and iteration-reordering transformations that focus on improving data locality. We also describe the space of possible run-time reordering transformations and how existing transformations fit within it. Since sparse tiling techniques are included in our framework, they become more generally applicable, both to a larger class of applications, and in their composition with other reordering transformations. Finally, within the presented framework data need be remapped only once at runtime for a given composition thus exhibiting one example of overhead reductions the framework can express.


title: Visually Indicated Sounds

abstract: Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual scene. We present an algorithm that synthesizes sound from silent videos of people hitting and scratching objects with a drumstick. This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions.


title: The benefits of noise in neural systems: bridging theory and experiment

abstract: Although typically assumed to degrade performance, random fluctuations, or noise, can sometimes improve information processing in non-linear systems. One such form of 'stochastic facilitation', stochastic resonance, has been observed to enhance processing both in theoretical models of neural systems and in experimental neuroscience. However, the two approaches have yet to be fully reconciled. Understanding the diverse roles of noise in neural computation will require the design of experiments based on new theory and models, into which biologically appropriate experimental detail feeds back at various levels of abstraction.


title: RFID Mutual Authentication Protocol based on Synchronized Secret

abstract: Radio Frequency Identification (RFID) tags, due to their ability to uniquely identify every individual item and low cost, are well suited for supply chain management and are expected to replace barcodes in the near future. However, unlike barcodes, these tags have a longer range in which they are allowed to be scanned, subjecting them to unauthorized scanning by malicious readers and to various other attacks, including cloning attacks. Privacy and security concerns inhibit the fast adaption of RFID technology for many applications. A number of authentication protocols that address these concerns have been proposed but realworld solutions that are secure and maintain low communication cost are still needed and being investigated. Recently, Cho et al. proposed a hash-based RFID mutual authentication protocol using a secret value. However, this paper shows that Cho et al.’s protocol is weak against desynchronization attack and proposes a remedy mutual authentication protocol, which offers a high level of security based on hash operation with synchronized secret. The protocol is applicable to resource, power and computationally constraint platforms such as RFID tags. Our investigation shows that it can provide mutual authentication and untraceability as well as resistance to replay, denial-of-service and man-in-the-middle attacks, while retaining a competitive computation cost.


title: Projection-Based Metal-Artifact Reduction for Industrial 3D X-ray Computed Tomography

abstract: Multi-material components, which contain metal parts surrounded by plastic materials, are highly interesting for inspection using industrial 3D X-ray computed tomography (3DXCT). Examples of this application scenario are connectors or housings with metal inlays in the electronic or automotive industry. A major problem of this type of components is the presence of metal, which causes streaking artifacts and distorts the surrounding media in the reconstructed volume. Streaking artifacts and dark-band artifacts around metal components significantly influence the material characterization (especially for the plastic components). In specific cases these artifacts even prevent a further analysis. Due to the nature and the different characteristics of artifacts, the development of an efficient artifact-reduction technique in reconstruction-space is rather complicated. In this paper we present a projection-space pipeline for metal-artifacts reduction. The proposed technique first segments the metal in the spatial domain of the reconstructed volume in order to separate it from the other materials. Then metal parts are forward-projected on the set of projections in a way that metal-projection regions are treated as voids. Subsequently the voids, which are left by the removed metal, are interpolated in the 2D projections. Finally, the metal is inserted back into the reconstructed 3D volume during the fusion stage. We present a visual analysis tool, allowing for interactive parameter estimation of the metal segmentation. The results of the proposed artifact-reduction technique are demonstrated on a test part as well as on real world components. For these specimens we achieve a significant reduction of metal artifacts, allowing an enhanced material characterization.


title: Physical modeling of communication systems in information theory

abstract: It is common in information theoretic channel models to rely on the average squared Euclidean norm of the channel input as being proportional to transmit power. Likewise, it is common to assume noise that is additive, Gaussian, and white. It is a legitimate question to ask, whether such a modeling approach has enough degrees of freedom to capture the physical constraints that are imposed on implementations of a communication system. In this paper, we show that in many, though not all, situations it is indeed possible to obtain a complete physical model, while nevertheless sticking with average squared Euclidean norm as power, and white Gaussian noise. Our systematic approach works in two steps. First, all channel inputs and outputs are replaced by ports, which are de.ned by two conjugated variables (like voltage and current). By this multi-port modeling approach, we can obtain a complete physical model. Secondly, we introduce linear transformations between the inputs and outputs of the information theoretic channel model on the one hand, and the physical inputs and physical outputs of the communication system, on the other. This approach gives us enough degrees of freedom to obtain a complete information theoretic model, which correctly reflects the physical constraints that are imposed upon the communication system by its environment. We apply the proposed approach to a multi-antenna communication system, and show that it is indeed possible that the channel capacity of multi-antenna systems can grow super-linearly with the number of antennas for large signal to noise ratios.


title: Inferring ontology graph structures using OWL reasoning

abstract: BackgroundOntologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies’ semantic content remains a challenge.ResultsWe developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies’ semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph.ConclusionsOnto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.


title: Droid-AntiRM: Taming Control Flow Anti-analysis to Support Automated Dynamic Analysis of Android Malware

abstract: While many test input generation techniques have been proposed to improve the code coverage of dynamic analysis, they are still inefficient in triggering hidden malicious behaviors protected by anti-analysis techniques. In this work, we design and implement Droid-AntiRM, a new approach seeking to tame anti-analysis automatically and improve automated dynamic analysis. Our approach leverages three key observations: 1) Logic-bomb based anti-analysis techniques control the execution of certain malicious behaviors; 2) Anti-analysis techniques are normally implemented through condition statements; 3) Anti-analysis techniques normally have no dependence on program inputs. Based on these observations, Droid-AntiRM uses various techniques to detect anti-analysis in malware samples, and rewrite the condition statements in anti-analysis cases through bytecode instrumentation, thus forcing the hidden behavior to be executed at runtime. Through a study of 3187 malware samples, we find that 32.50% of them employ various anti-analysis techniques. Our experiments demonstrate that Droid-AntiRM can identify anti-analysis instances from 30 malware samples with a true positive rate of 89.15% and zero false negative. By taming the identified anti-analysis, Droid-AntiRM can greatly improve the automated dynamic analysis, successfully triggering 44 additional hidden malicious behaviors from the 30 samples. Further performance evaluation shows that Droid-AntiRM has good efficiency to perform large-scale analysis.


title: Design and calibration of an innovative ultrasonic, arduino based anemometer

abstract: A precise estimation of wind intensity and direction is important for many applications. Authors, thanks to a consolidated experience in marine robotics, are building a sail propelled marine drone for a sustainable monitoring of wide sea areas. In particular, sail propulsion assures a near to infinite autonomy since limited energy demand of on board electronic can be assured by renewable sources such as solar cells. However, to correctly manage a sail system, a correct estimation of wind intensity and its direction is essential. Conventional anemometers make use of delicate mechanical moving parts, are therefore not suitable to work autonomously in difficult environments such as the marine one. Commercial ultrasonic anemometers represent a valid alternative but their cost, weight and size need to be optimized for the proposed application which is a cheap, light and relatively small drone. In this work the authors redesigned both acoustic and electronic system to meet the required specifications. Innovations have been suggested in terms of materials, components and introducing a smart measurement algorithm that allowed the authors to simplify the design, construction and calibration of the instrument. Finally, the system has been tested and calibrated in a wind tunnel, introducing a simple and effective calibration algorithm able to drastically reject disturbances arising from interference between mechanical system and the wind flow under measurement.


title: Collaborative matrix factorization mechanism for group recommendation in big data-based library systems

abstract: Purpose ::: ::: ::: ::: ::: Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient. ::: ::: ::: ::: ::: Social implications ::: ::: ::: ::: ::: The proposed methods have potential value to improve scientific collaboration and research innovation. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.


title: Phrase-based Compressive Cross-Language Summarization

abstract: The task of cross-language document summarization is to create a summary in a target language from documents in a different source language. Previous methods only involve direct extraction of automatically translated sentences from the original documents. Inspired by phrasebased machine translation, we propose a phrase-based model to simultaneously perform sentence scoring, extraction and compression. We design a greedy algorithm to approximately optimize the score function. Experimental results show that our methods outperform the state-of-theart extractive systems while maintaining similar grammatical quality.


title: Integrated Ontology Learner: Towards Generic Semantic Annotation Framework

abstract: To achieve goal of Semantic Web, existing Web documents have to be tagged with semantic information using ontology as a formal conceptualization of a particular domain. However, the manual ontology creation is tedious, expensive, biased, and complex task which can easily result in a knowledge acquisition bottleneck. This paper presents generic and automatic ontology learning approach that uses data from both unstructured and semi structured sources. The approach relies on statistical and neural network techniques (word2vec) to transform implicit knowledge in unstructured text into explicit machine-processable domain knowledge with feature of adaptability into other domains and languages. We experiment the feasibility of the proposed approach for tourism domain using Amharic news collected from Walta news agency and Amharic Wikipedia dump. The experimental result exhibits 78.75% of precision in candidate term extraction, 79.59% of precision in taxonomy induction and 55.00% of precision in specific semantic relation extraction for a morphologically complex Amharic language with limited size corpus.


title: A Precoding-Based Multicarrier Non-Orthogonal Multiple Access Scheme for 5G Cellular Networks

abstract: Non-orthogonal multiple access (NOMA) has become one of the desirable schemes for the 5G cellular network standard due to its better cell coverage capability, higher data rate, and massive connectivity. Orthogonal frequency division multiplexing (OFDM) can be combined with NOMA to get the higher spectral efficiency. The main drawback of OFDM-based NOMA (OFDM-NOMA) scheme is high peak-to-average power ratio (PAPR). Therefore, this paper presents a new discrete-cosine transform matrix precoding-based uplink OFDM-NOMA scheme for PAPR reduction. Additionally, the proposed precoding-based uplink multicarrier NOMA scheme takes advantage of information spreading over the entire signal spectrum; thus, bit-error rate is also reduced. Simulation results show that the proposed precoding-based NOMA scheme outperforms as compared with the non-precoding-based NOMA schemes available in the literature.


title: Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification

abstract: Neuronal power attenuation or enhancement in specific frequency bands over the sensorimotor cortex, called Event-Related Desynchronization (ERD) or Event-Related Synchronization (ERS), respectively, is a major phenomenon in brain activities involved in imaginary movement of body parts. However, it is known that the nature of motor imagery-related electroencephalogram (EEG) signals is non-stationary and highly variable over time and frequency. In this paper, we propose a novel method of finding a discriminative time- and frequency-dependent spatial filter, which we call 'non-homogeneous filter.' We adaptively select bases of spatial filters over time and frequency. By taking both temporal and spectral features of EEGs in finding a spatial filter into account it is beneficial to be able to consider non-stationarity of EEG signals. In order to consider changes of ERD/ERS patterns over the time-frequency domain, we devise a spectrally and temporally weighted classification method via statistical analysis. Our experimental results on the BCI Competition IV dataset II-a and BCI Competition II dataset IV clearly presented the effectiveness of the proposed method outperforming other competing methods in the literature.


title: Building virtual cities, inspiring intelligent citizens: Digital games for developing students' problem solving and learning motivation

abstract: This study investigates the effectiveness digital game-based learning (DGBL) on students' problem solving, learning motivation, and academic achievement. In order to provide substantive empirical evidence, a quasi-experimental design was implemented over the course of a full semester (23 weeks). Two ninth-grade Civics and Society classes, with a total of 44 students (15-16 years old), were randomly assigned to one of two conditions: an experimental group (incorporating DGBL) and a comparison group (taught using traditional instruction). Two-way mixed ANOVA was employed to evaluate changes in problem solving ability and compare the effectiveness the two strategies, while ANCOVA was used to analyze the effects on learning motivation and academic achievement. The results of this study are summarized as follows: (1) The DGBL strategy was clearly effective in promoting students' problem solving skills, while the control group showed no improvement. Additionally, data from the mid-test and post-test demonstrate that, as a higher order thinking skill, problem-solving requires a full semester to develop. (2). DGBL resulted in better learning motivation for students in the experimental group as compared to learners receiving TI. (3) Contrary to some suggestions that digital games could inhibit academic achievement, no statistically significant difference was found between the two groups. Most importantly, the quantitative improvement in problem-solving and learning motivation suggest that DGBL can be exploited as a useful and productive tool to support students in effective learning while enhancing the classroom atmosphere. Future research in DGBL should emphasize the evaluation of other higher order elements of the cognitive domain in terms of academic achievement outcomes and skills, such as critical and creative thinking.


title: Surrogate-assisted evolutionary computation: Recent advances and future challenges

abstract: Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single- or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.


title: Bandwidth-efficient indoor optical wireless communications with white light-emitting diodes

abstract: With white-light LEDs one can combine lighting and wireless communication functionalities in one source. We investigate feasible transmission rates in a moderate-size office room illuminated with commercially available phosphor-based LEDs. Due to prevalent high signal-to-noise ratios and negligible inter-symbol-interference maximum data rates of 300 Mbit/s are shown to be achievable with the use of pulse amplitude modulation (PAM) or discrete multi-tone modulation (DMT). Also, we present first experiments with white-light LEDs and demonstrate wireless net data rates of up to 101 Mbit/s.


title: Design and Characterization of High-Voltage 4H-SiC p-IGBTs

abstract: High-voltage p-channel 4H-SiC insulated gate bipolar transistors (IGBTs) have been fabricated and characterized. The devices have a forward voltage drop of 7.2 V at 100 A/cm2 and a -16 V gate bias at 25degC, corresponding to a specific on-resistance of 72 mOmega ldr cm2 and a differential on-resistance of 26 mmOmega ldr cm2. Hole mobility of 12 cm2/V ldr s in the inversion channel with a threshold voltage of -6 V was achieved by optimizing the n+ well doping profile and gate oxidation process. A novel current enhancement layer was adopted to reduce the JFET resistance and enhance conductivity modulation by improving hole current spreading and suppressing the electron current conduction through the top n-p-n transistor. Inductive switching results have shown that the p-IGBT exhibited a turn-off time of ~1 mus and a turn-off energy loss of 12 m J at 4-kV dc-link voltage and 6-A load current at 25degC. The turn-off trajectory from the measured inductive load switching waveforms and numerical simulations shows that the p-IGBT had a near-square reverse bias safe operating area. Numerical simulations have been conducted to achieve an improved tradeoff between forward voltage drop and switching off energy by investigating the effects of drift layer lifetime and p-buffer layer parameters. The advantages of SiC p-IGBTs, such as the potential of very low ON-state resistance, slightly positive temperature coefficient, high switching speed, small switching losses, and large safe operating area, make them suitable and attractive for high-power high-frequency applications.


title: EBEM's Enhanced Capabilities Facilitate the Navy's Emerging Operational Requirements and Enable Bandwidth Efficient Communications over IP

abstract: The MD-1366 Enhanced Bandwidth Efficient Modem (EBEM) with Ethernet Service Expansion Module (ESEM) is the STANAG 4486 ed. 3 compliant FDMA modem used by the Department of Defense (DoD) for Satellite Communications (SATCOM). Recently, features of the EBEM such as embedded Transmission Security (TRANSEC) and Antenna Handover (AH) have been enhanced to meet the US Navy's emerging operational requirements. EBEM's FIPS 140-2 level 2 certified TRANSEC capabilities have been enhanced to support simplex link establishment, allowing the Navy to support split IP operations, combining transponded SATCOM paths (WGS) with partially processed SATCOM paths (AEHF) to effectively produce a duplex path for user terminal sessions. EBEM's Antenna Handover capabilities have been extended to provide low processing delay while maintaining Bit Count Integrity (BCI) through handover events. These upgrades, along with existing features, to be deployed in conjunction with Advanced Digital Network System (ADNS) Increment IV will allow the Navy to smoothly transition to bandwidth efficient IP-based data. Most notably, the Navy will be able to leverage Adaptive Coding and Modulation (ACM) in conjunction with EBEM's flow control capabilities to feed information back to the router as the data rate adapts to the dynamic satellite channel conditions. This paper discusses the new capabilities in detail as well as recent results related to the Navy's effort to integrate the new features into ADNS.


title: Consensus protocols for blockchain-based data provenance: Challenges and opportunities

abstract: Blockchain has recently attracted tremendous interest due to its ability to enhance security and privacy through a immutable shared distributed ledger. Blockchain's ability to detect integrity violations are particularly key in providing assured data provenance in cloud platform. The practical adoption of blockchain will largely hinge on consensus protocols meeting performance and security guarantees. In this paper, we present the design issues for consensus protocols for blockchain based cloud provenance. We present the blockchain based data provenance framework for cloud. We find that there are performance and security challenges in adopting proof-of-work consensus protocol within this framework. We present unique design challenges and opportunities in developing proof-of-stake for data provenance in cloud platform.


title: Networks, Network Governance, and Networked Networks

abstract: Theories of democratic governance have undergone significant changes over the last two decades with the spread of ideas and popular practices associated with New Public Management (NPM) and New Governance. In particular, inter-ministerial and inter-societal networking is becoming important because of the capacity to regulate complex transactional interdependency in modern administrative states. This paper examined various forms of networks and identified factors that influence the form of governance including asset-specificity, task complexity, transaction continuity, uncertainty, the degree of differentiation, and the Intensity of inter-organizational interdependence. In addition, the role of public institutions in different network governance varies considerably as they tend to play more active roles in vertical networks while a more symmetric relationship arise in horizontal networks among private and public actors. Managing and coordinating complex networks requires a skillful manager of the networked...