Reviewerly/exHarmony huggingface dataset:
Detection &amp; tracking method for radar target based on plot‐track quality evaluation

A joint target detection and tracking method based on plot-track quality evaluation (PTQE-D&T) is proposed here. The presented method consists of three consecutive steps. First, a set of candidate plots is extracted from the Detector and Plot-Extractor, where the plot quality is calculated simultaneously based on multi-dimensional echoes constituting the plot. Then, a track-before-detect (TBD) processor based on dynamic programming (DP) is utilised to jointly elaborate the observations from multiple scans and outputs potential tracks to be confirmed. Finally, a confirmation strategy is introduced via plot-track quality evaluation. Experimental results are provided to demonstrate the effectiveness of the proposed method, where two data sets collected from a ground-based surface-search radar and a ship borne 3D radar are used. Moreover, the proposed PTQE-D&T structure is totally consistent with conventional radar data processing architecture, meaning that it is promising in engineering applications.
Theoretical Model of Computation and Algorithms for FPGA-Based Hardware Accelerators

While FPGAs have been used extensively as hardware accelerators in industrial computation [20], no theoretical model of computation has been devised for the study of FPGA-based accelerators. In this paper, we present a theoretical model of computation on a system with conventional CPU and an FPGA, based on word-RAM. We show several algorithms in this model which are asymptotically faster than their word-RAM counterparts. Specifically, we show an algorithm for sorting, evaluation of associative operation and general techniques for speeding up some recursive algorithms and some dynamic programs. We also derive lower bounds on the running times needed to solve some problems.
Research of Multi-objective Personalized Recommendation Algorithm Based on Multi-thread Concurrency

In order to strengthen the recommendation system’s ability to mine user preferences and unpopular products, a recommendation model based on three goals is proposed and the MOEA-PGMA algorithm was designed. Aiming at the large computation time of MOEA-PGMA based on this model because of the computation scale, the multi-thread concurrent execution framework Executor is introduced into the multi-core environment to achieve multi-task concurrent execution. The scheme refactors MOEA-PGMA to make it more suitable for multi-threaded environments. The experimental results show that the scheme can be effectively improve operational efficiency.
Medical Reports Analysis Using Natural Language Processing for Disease Classification

Text classification in medical domain could result in an easier way of handling large volumes of medical data. They can be segregated depending on the type of diseases, which can be determined by extracting the decisive key texts from the original document. Due to various nuances present in understanding language in general, a requirement of large volumes of text-based data is required for algorithms to learn patterns properly. The problem with existing systems such as MedScape, MedLinePlus, Wrappin, and MedHunt is that they involve human interaction and high time consumption in handling a large volume of data. By employing automation in this proposed field, the large involvement of manpower could be removed which in turn speeds up the process of classification of the medical documents by which the shortage of medical technicians in third world countries are addressed.
Mobile-Assisted Language Learning From Language Instructors' Perspectives

This chapter explores instructor use of MALL in EFL/ESL classrooms. It begins with a discussion of instructor ownership as well as personal use of mobile technology. The chapter includes a discussion of overall perceptions and/or reported educational use and benefits and barriers (not tied to specific language skills.) The chapter goes on to describe how instructors use mobile devices while teaching reading, writing, listening, and speaking skills. This is followed by a brief discussion of mobile devices in situations that integrate all four skills. For each language skill, the authors also discuss perceived and/or reported benefits and barriers. Thereafter follows a discussion of trends in the focus of research questions, research methodologies, geographic location of studies, and a brief comparison in trends between earlier and later studies. The review ends with a conclusion that provides directions for future research and reminds the reader of the utility of this literature review.
New tests of uniformity on the compact classical groups as diagnostics for weak-$^{*}$ mixing of Markov chains

This paper introduces two new families of non-parametric tests of goodness-of-fit on the compact classical groups.One of them is a family of tests for the eigenvalue distribution induced by the uniform distribution, which is consistent against all fixed alternatives.The other is a family of tests for the uniform distribution on the entire group, which is again consistent against all fixed alternatives.The construction of these tests heavily employs facts and techniques from the representation theory of compact groups.In particular, new Cauchy identities are derived and proved for the characters of compact classical groups, in order to accommodate the computation of the test statistic.We find the asymptotic distribution under the null and general alternatives.The tests are proved to be asymptotically admissible.Local power is derived and the global properties of the power function against local alternatives are explored.The new tests are validated on two random walks for which the mixing-time is studied in the literature.The new tests, and several others, are applied to the Markov chain sampler proposed by Jones, Osipov and Rokhlin [Proc.Natl.Acad.Sci.108 (2011) 15679-15686], providing strong evidence supporting the claim that the sampler mixes quickly.
The car pet in the carpet. On the interaction of computer-linguistic methodology and manual refinement in researching noun compounds

&lt;p&gt;Why does football combine productively with further nouns to form more complex expressions like football game, whereas seemingly comparable compounds like keyword only infrequently expand to more complex sequences? This project explores why some two-noun compounds are more readily available for forming triconstituent constructions than others. I hypothesize that the productivity of a two-noun compound in the formation of triconstituent sequences depends on the degree of entrenchment of that two-noun compound, assuming that only compounds that are entrenched to a certain degree are productive in forming more complex constructions. In order to test this hypothesis, a list of three-noun compounds in the English language needed to be compiled. The obvious thing to do would be to search for sequences of three nouns in POS-tagged corpora. However, since such automatized searches on the one hand do not allow the recall of all required instances and, on the other hand, often create results that are not precise enough, this requires substantial manual screening. Furthermore, in order to operationalize the concepts of entrenchment and productivity, it was necessary to count the usage frequencies of noun constructions. For this work, as well, the automatic elicitation of the data needed to be complemented by further manual selection in order to obtain correct usage frequencies. Both the complex automatic and manual work processes in the elicitation of the data will be presented in detail to give an impression of the extent of such a project.&lt;/p&gt;
How complex is a random picture?

We study the amount of information that is contained in “random pictures”, by which we mean the sample sets of a Boolean model. To quantify the notion “amount of information”, two closely connected questions are investigated: on the one hand, we study the probability that a large number of balls is needed for a full reconstruction of a Boolean model sample set. On the other hand, we study the quantization error of the Boolean model w.r.t. the Hausdorff distance as a distortion measure.
Algorithm Integration Behavior for Discovering Group Membership Rules

Information exploitation processes use different data mining algorithms for obtaining knowledge patterns from data obtained on the problem domain. One of the assumptions when working with these algorithms is that the complexity of the membership domain of the cases they use does not affect the quality of the obtained results. So, it is important to analyze the behavior of the information exploitation process through the discovery of group membership rules by using clustering and induction algorithms. This research characterizes the complexity of the domains in terms of the pieces of knowledge that describe them and information exploitation processes they seek to discover. The results of the experiments show that, in the case of the process for discovering group membership rules, the quality of the patterns differs depending on the algorithms used in the process and the complexity of the domains to which they are applied.
A Novel Hybrid Approach for Email Spam Detection based on Scatter Search Algorithm and K-Nearest Neighbors

Because cyberspace and Internet predominate in the life of users, in addition to business opportunities and time reductions, threats like information theft, penetration into systems, etc. are included in the field of hardware and software. Security is the top priority to prevent a cyber-attack that users should initially be detecting the type of attacks because virtual environments are not monitored. Today, email is the foundation of many internet attacks that have happened. The Hackers and penetrators are using email spam as a way to penetrate into computer systems junk. Email can contain viruses, malware, and malicious code. Therefore, the type of email should be detected by security tools and avoid opening suspicious emails. In this paper, a new model has been proposed based on the hybrid of Scatter Searching Algorithm (SSA) and K-Nearest Neighbors (KNN) to email spam detection. The Results of proposed model on Spambase dataset shows which our model has more accuracy with Feature Selection (FS) and in the best case, its percentage of accuracy is equal to 94.54% with 500 iterations and 57 features. Also, the comparison shows that the proposed model has better accuracy compared to the evolutionary algorithm (data mining and decision detection such as C4.5).
Information Technologies for Environmental Monitoring of Plankton Algae Distribution Based on Satellite Image Data

The paper discusses the features and specifics of using the methods of remote sensing of the Earth when monitoring the ecological and technical state of water management process systems. A technology for monitoring surface waters according to remote sensing of the Earth is proposed. A method of satellite monitoring of algal bloom intensity (monitoring of planktonic algae clusters) has been proposed and substantiated. An approach to flood risk assessment using satellite observations is proposed. The technique of quantitative assessment of water quality according to the space monitoring of surface waters is substantiated. As a result of the research, it was found that when assessing the complex effects of pollutants on the ecological state of aquatic ecosystems using aerospace technologies, it is advisable to take into account changes in biological indicators (indicators of biomass and species composition of phytoplankton and higher aquatic plants). A method has been developed for predicting the long-term risks of emergency situations of a hydrological and hydrometeorological nature based on physical and mathematical modeling and the use of satellite observations and spatially distributed data. According to the results of further studies based on the results obtained in this work, it is planned to create flood risks distribution maps, quantitative predictions of surface water quality deterioration, as well as an assessment of the risks of air and soil pollution.
Multi-frame Super Resolution Using Enhanced Papoulis-Gerchberg Method

The research work, discusses the Papoulis-Gerchberg method with re-spect to image restoration procedure called super resolution image reconstruction. This underlined work also demonstrates that Papoulis-Gerchberg performs well only in certain conditions. Modifications are proposed to the Papoulis-Gerchberg method to obtain better super resolution results. The proposed modifications overcome the restrictions of Papoulis-Gerchberg method making it possible on a wide range of images. The suggested modification not only improves the quality of the image but also reduces the computation complexity. The proposed method has greater advantage and high computation speed which is most needed for most of the applications in the real world. The results for the modified Papoulis-Gerchberg are presented to demonstrate its performance.
Long-range distribution of multiphoton entanglement

We propose a long-distance quantum communication protocol based on multiphoton bipartite entanglement and photon-number-resolved detection, which shows remarkable robustness to high transmission losses and offers near-maximally entangled states in realistic implementations.
A method for updating ontology-based user Profile in personalized Document Retrieval System using Bayesian networks

Traditional approaches to content-based recommendation and collaborative filtering do not suffer from cold-start problem, which is a challenge to recommend items for an unknown user. In this paper we present a Personalized Document Retrieval System which takes into account a social network informat ion about the users. The overall idea of the system is to cluster users into groups of similar interests based on theirs usage data and to determine a representative profile for each of the groups. When a new user joins the system, he or she is classified into one of existing group based on his or her user data and the representative profile of the group becomes a starting profile for the new user. This paper focuses on a method for updating ontology-based user profile using Bayesian network approach. We analyze some properties of proposed updating method and describe an idea of experimental evaluations.
Coherence and Salience-Based Multi-Document Relationship Mining

In today's interconnected world, there is an endless 24/7 stream of new articles appearing online. Faced with these overwhelming amounts of data, it is often helpful to consider only the key entities and concepts and their relationships. This is challenging, as relevant connections may be spread across a number of disparate articles and sources. In this paper, we propose a unified framework to aid users in quickly discerning salient connections and facts from a set of related documents, and presents the resulting information in a graph-based visualization. Specifically, given a set of relevant documents as input, we firstly extract candidate facts from above sources by exploiting Open Information Extraction (Open IE) approaches. Then, we design a Two-Stage Candidate Triple Filtering (TCTF) approach based on a self-training framework to maintain only coherent facts associated with the specified document topic from the candidates and connect them in the form of an initial graph. We further construct this graph by a heuristic to ensure the final conceptual graph only consist of facts likely to represent meaningful and salient relationships, which users may explore graphically. The experiments on two real-world datasets illustrate that our extraction approach achieves 2.4% higher on the average of F-score over several OpenIE baselines. We also further present an empirical evaluation of the quality of the final generated conceptual graph towards different topics on its coverage rate of topic entities and concepts, confidence score, and the compatibility of involved facts. Experimental results show the effectiveness of our proposed approach.
Applying Oculography and Biosignals Measurements to Gameplay Modes Analysis

In this paper we present results of an experiment designed to measure correlation between Galvanic Skin Response – GSR (also: Electrodermal Activity – EDA) and Oculographic (Eye Tracking) data. In the experiment we analyze the participants reactions on changing gameplay modes (changing dynamic and pace of user interaction with the game mechanics). The goal of the analysis is to demonstrate what influence of changing gameplay modes have on physiology (especially spontaneous body response). The gameplay modes used in experiment were chase mode (dynamic) and sneak mode (satisfying). In the experiment we have used Pupil Lab eye-tracker and EDA sensor based on BITalino platform. The game Serial Cleaner by iFun4all was used to conduct the experiment. The experiment resulted in multiple observations. Substantial correlation between participant failure and change of EDA pattern were observed as well as over 10% change in fixations durations.
Autonomous Living Building: Adapting to Occupant’s Behavior

This paper demonstrates an autonomous living building system that adapts its behavior to changing environment as they are happening. The system implements a real-time and real-world scenario at the University of Houston where multiple Microsoft Kinect sensors have been utilized to demonstrate the living building capability. The sensors in Kinect generate point clouds data representing the dynamic context; a static context is captured using the CAD tool and archived in the system to model the built environment. Both static and dynamic context information are processed and summarized to infer the interaction between the occupant (dynamic context) and the building (static context). The results are then taken through an Artificial Intelligent (AI) engine by using supervised machine learning algorithm to infer the decision the building should take to adjust the environment. The implemented smart building prototype was demonstrated in a home library setting and provides a clear evidence of the capability of such systems. The system reacted within the acceptable timeframe expected by a human activity (seconds). The prototype sets the stage of a more complicated scenario study that uses hundreds of sensors and actuators. The prototype will be used as testbed to study the distributed control and communication system for living building concept.
Social Weak-tie Assisted Cross-domain Short Video Recommendation

As a comprehensive information carrier, the short video is gaining increasing attention for a user to spread information, read the news and conduct interpersonal communication. Consequently, short video recommendation problem has been a hot spot in the field of the recommender system. However, current short video recommendation algorithms have to tackle with data sparsity and cold start problem, which is caused by the small amounts of data that the recommender system has accumulated and massive video data while limited users' access.Aiming at the problem of data sparsity and cold start, the paper proposes a short video recommendation algorithm Social Weak-tie Bayesian Personalized Ranking (SWTBPR). Social weak-tie refers to the following relation in online social network, which means they are not real-world friends, somehow it can reflect users' preference. Experimental results demonstrate that SWTBPR outperforms other existing video recommendation algorithms and solves the data sparsity and cold start problem with a real-world dataset collected from Sina Weibo.
Application of Mobile Technologies in Library Service Delivery

With the rapid advancement in technology in the last two decades, mainly because of the advent of the internet, the world no longer works the same. The advent of wireless networks and more recently mobile devices such as smart phones, tablets, etc. following the internet, contributed a lot their applicability in library's operations. As contained in extant literature, mobile devices have become a very important part of communication not only for telephone service users but also for the internet users. The two main reasons behind success of mobile technology is the benefits such as mobility and ubiquity served by mobile devices. To this end, the chapter explores the application of mobile technology on service delivery in libraries, the advantages and disadvantages. Also highlighted were the skills required by the librarians in incorporating mobile technologies to library services.
Entanglement criterion independent on observables for multipartite Gaussian states based on uncertainty principle

Quantum entanglement is one of important resources for quantum communication. Entanglement criteria help us detect entangled states. One of important criteria is the local uncertainty relation (LUR) entanglement criteria, which is studied extensively. However, all existent LUR criteria are dependent on the chosen observables. In the paper, applying the uncertainty principle, we improve the LUR criteria to obtain entanglement criteria for multipartite Gaussian states, which are independent on observalbes.
An Algorithm for Automatic Recogniton of Digital QAM Modulations

Abstract This paper proposes a new Matlab-developed algorithm for automatic recognition of digital modulations using the constellation of states. Using this technique the automatic distinction between four digital modulation schemes (8-QAM, 16-QAM, 32-QAM and 64-QAM) was made. It has been seen that the efficiency of the algorithm is influenced by the type of modulation, the value of the signal-to-noise ratio and the number of samples. In the case of an AWGN noise channel the simulation results indicated that the value of SNR (signal-to-noise ratio) has a small influence on the recognition rate for lower-order QAM (8-QAM and 16-QAM). The length of the signal may change essentially the recognition rate of this algorithm especially for modulations with a high number of bits per symbol. Consequently, for the 64-QAM modulation in a case of 25dB signal-to-noise ratio the recognition rate is doubled if the sample rate is incresed from 5400 to 80640.
WCIS 2019

The first workshop on Conversational Interaction Systems is held in Paris, France on July 25th, 2019, co-located with the ACM Special Interest Group on Information Retrieval (SIGIR). The goal of the workshop is to bring together researchers from academia and industry to discuss the challenges and future of conversational agents and interactive systems. The workshop has an exciting program that spans a number of subareas including: multi-modal conversational interfaces, dialogue accessibility, and scaling such systems. The program includes eight invited talks, a lively panel discussion on emerging topics, and presentation of original research papers.
Research on Haze Removal for Autonomous Car

To improve the safety of autonomous cars, their obstacle detection capability in bad weather must be substantially improved. Haze is a major factor that degrades outdoor images. Although various dehazing schemes have been proposed, a dehazing scheme designed to improve obstacle detection capability has not been reported. Hence, we present a dehazing algorithm that enhances the safety of an autonomous car. This algorithm should be able to work in real time, even using edge computers typically installed as car electronics. Furthermore, this algorithm should work on grayscale images, as systems dependent on color images are often unaffected by environmental color changes caused by factors such as a setting sun. The empirical results showed that our grayscale image-based proposed algorithm is comparable to the results of current cutting-edge methods, and operates in real time.