Reviewerly/exHarmony huggingface dataset:
The use of augmented reality technology to improve the efficiency of teaching

The article is devoted to the study of foreign and domestic experience in using augmented reality applications in classes in various disciplines, including tools that a teacher can use in everyday activities to increase the effectiveness of teaching school curriculum subjects. The review of modern mobile applications with augmented reality elements suitable for use in the educational process is presented. For example, the article describes the applications Quiver and Animal AR 3D Safari for kids; Surface math AR, Geometry — Augmented Reality, AR Geometry applications for studying mathematics; AR Human Atlas, Anatomy AR Book for studying medicine; Mondly AR for studying foreign languages. The new technology Merge Cube (a hologram in pupils' hands), which is used in applications Mr. Body and AR Medical to study anatomy, Galaxy Explorer to study the solar system, Museum Viewer to visit a virtual museum and study history is described in the article. The availability of augmented reality technology for use in the educational process is demonstrated on the basis of constructors such as HP Reveal, CoSpaces Edu and EV Toolbox. For example, HP Reveal and CoSpaces Edu were used to create interactive content such as video content and a dynamic animal gallery to explore the world around. CoSpaces Edu was used for creating interactive dialogs in English based on elements of block visual programming. EV Toolbox was used to create the simple application that was the project based on marker technology to study the history of the V. I. Vernadsky Crimean Federal University.
Logistic regression based in-service assessment of mobile web browsing service quality acceptability

Abstract In this paper, we presented a logistic regression model that we applied for assessment of the users’ quality of experience with web browsing service over mobile network. With this regard, we chose the Average-Time-to-Connect-TCP network service quality parameter as an independent predictor, obtained by passive monitoring of live traffic data, captured by a passive probe on the mobile network Gn interface, and related to detailed records of the Transport Control Protocol. In parallel with in-service measuring the selected network parameter, we conducted simultaneous subjective tests of the quality of experience acceptability to users, specifically for web browsing service. Particularly, it was found that the model provided correct acceptability classification in 84.5% of cases, while reducing the chosen independent predictor for 100 ms implied increasing the chance of the service acceptability by factor of 1.65. Based on the obtained results, it comes out that the applied logistic regression model provides satisfactory estimation of the web browsing service quality experience acceptability.
HBP: Hotness Balanced Partition for Prioritized Iterative Graph Computations

Existing graph partition methods are designed for round-robin synchronous distributed frameworks. They balance workload without discrimination of vertex importance and fail to consider the characteristics of priority-based scheduling, which may limit the benefit of prioritized graph computation. To accelerate prioritized iterative graph computations, we propose Hotness Balanced Partition (HBP) and a stream-based partition algorithm Pb-HBP. Pb-HBP partitions graph by distributing vertices with discrimination according to their hotness rather than blindly distributing vertices with equal weights, which aims to evenly distribute the hot vertices among workers. Our results show that our proposed partition method outperforms the state-of-the-art partition methods, Fennel and HotGraph. Specifically, Pb-HBP can reduce 40-90% runtime of that by hash partition, 5-75% runtime of that by Fennel, and 22-50% runtime of that by HotGraph.
Target tracking algorithm combined part-based and redetection for UAV

Abstract In the process of target tracking for UAV video images, the performance of the tracking algorithm declines or even the tracking fails due to target occlusion and scale variation. This paper proposes an improved target tracking algorithm based on the analysis of the tracking framework of the kernel correlation filter. First, four subblocks around the center of the target center are divided. A correlation filter fusing Histogram of Oriented Gradient (HOG) feature and Color Name (CN) feature tracks separately each target subblocks. According to the spatial structure characteristics in the subblocks, the center location and scale of the target are estimated. Secondly, the correct center location of target is determined by the global filter. Then, a tracking fault detection method is proposed. When tracking fails, the target redetection module which uses the normalized cross-correlation algorithm (NCC) to obtain the candidate target set in the re-detection area is started. Besides, this algorithm uses the global filter to obtain real target from the candidate set. In the meanwhile, this algorithm adjusts sectionally the learning rate of the classifiers according to detection results. Lastly, the performance of this algorithm is verified on the UAV123 dataset. The results show that compared with several mainstream methods, that of this algorithm is significantly improved when dealing with target scale variation and occlusion.
Comparing a Polling and Push-Based Approach for Live Open Data Interfaces

There are two mechanisms for publishing live changing resources on the Web: a client can pull the latest state of a resource or the server pushes updates to the client. In the state of the art, it is clear that pushing delivers a lower latency compared to pulling, however, this has not been tested for an Open Data usage scenario where 15 k clients are not an exception. Also, there are no general guidelines when to use a polling or push-based approach for publishing live changing resources on the Web. We performed (i) a field report of live Open datasets on the European and U.S. Open Data portal and (ii) a benchmark between HTTP polling and Server-Sent Events (SSE) under a load of 25 k clients. In this article, we compare the scalability and latency of updates on the client between polling and pushing. For the scenario where users want to receive an update as fast as possible, we found that SSE excels above polling in three aspects: lower CPU usage on the server, lower latency on the client and more than double the number of clients that can be served. However, considering that users can perceive a certain maximum latency on the client (MAL) of an update acceptable, we describe in this article at which MAL point a polling interface can be able to serve a higher number of clients than pushing. Open Data publishers can use these insights to determine which mechanism is the most cost-effective for the usage scenario they foresee of their live updating resources on the Web.
An Online Platform for Enhancing Learning Experiences with Web-Based Augmented Reality and Pictorial Bar Code

Lacking the teaching materials is one of the common problems of the education system, especially in developing countries. The teachers attempt to give as much theoretical knowledge as possible to students while forgetting to train them with practical activities and ways of self-thinking. Augmented Reality (AR) provides the capability to overlay virtual information onto educational textbooks which makes them more attractive; hence, motivates students to learn. In this chapter, we present an online AR study platform which is capable of running on an everyday handset without requiring any software installation. It could provide a stress-free, low-cost, portable and promising solutio to use within the educational environment. In addition, they introduce a new transparent AR marker concept that could combine the advantages of the original template markers and barcode markers.
Pooling region learning of visual word for image classification using bag-of-visual-words model

In the problem where there is not enough data to use Deep Learning, Bag-of-Visual-Words (BoVW) is still a good alternative for image classification. In BoVW model, many pooling methods are proposed to incorporate the spatial information of local feature into the image representation vector, but none of the methods devote to making each visual word have its own pooling regions. The practice of designing the same pooling regions for all the words restrains the discriminability of image representation, since the spatial distributions of the local features indexed by different visual words are not same. In this paper, we propose to make each visual word have its own pooling regions, and raise a simple yet effective method for learning pooling region. Concretely, a kind of small window named observation window is used to obtain its responses to each word over the whole image region. The pooling regions of each word are organized by a kind of tree structure, in which each node indicates a pooling region. For each word, its pooling regions are learned by constructing a tree with its labelled coordinate data. The labelled coordinate data consist of the coordinates of responses and image class labels. The effectiveness of our method is validated by observing if there is an obvious classification accuracy improvement after applying our method. Our experimental results on four small datasets (i.e., Scene-15, Caltech-101, Caltech-256 and Corel-10) show that, the classification accuracy is improved by about 1% to 2.5%. We experimentally demonstrate that the practice of making each word have its own pooling regions is beneficial to image classification task, which is the significance of our work.
Emoticons and non-verbal communications across Arabic, English, and Korean Tweets

Purpose The purpose of this study is to examine how tweeters drawn from three different languages and cultural boundaries manage the lack of contextual cues through an analysis of Arabic, English and Korean tweets. Design/methodology/approach Data for this study is drawn from a corpus of tweets ( n = 1,200) streamed using Python through Twitter API. Using the language information, the authors limited the number of tweets to 400 randomly selected tweets from each language, totaling 1,200 tweets. Final coding taxonomy was derived through interactive processes preceded by literature and a preliminary analysis based on a small subset ( n = 150) by isolating nonverbal communication devices and emoticons. Findings The results of the study present that there is great commonality across these tweets in terms of strategies and creativity in compensating for the constraints imposed by the tweet platform. The language-specific characteristics are also shown in the form of different usage of devices. Research limitations/implications Emoticon usage indicates that the communication mode influences online social interaction; the restriction of 140 maximum characters seems to engender a frequent usage of emoticons across tweets regardless of language differences. The results of the study bring forth implications into the design of social media technologies that reflect affective aspects of communication and language-/culture-specific traits and characteristics. Originality/value To the best of the authors’ knowledge, there are no qualitative studies examining paralinguistic nonverbal communication cues in the Twitter platform across language boundaries.
Entity Synonym Discovery via Multipiece Bilateral Context Matching

Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization. Existing works either only utilize entity features, or rely on structured annotations from a single piece of context where the entity is mentioned. To leverage diverse contexts where entities are mentioned, in this paper, we generalize the distributional hypothesis to a multi-context setting and propose a synonym discovery framework that detects entity synonyms from free-text corpora with considerations on effectiveness and robustness. As one of the key components in synonym discovery, we introduce a neural network model SynonymNet to determine whether or not two given entities are synonym with each other. Instead of using entities features, SynonymNet makes use of multiple pieces of contexts in which the entity is mentioned, and compares the context-level similarity via a bilateral matching schema. Experimental results demonstrate that the proposed model is able to detect synonym sets that are not observed during training on both generic and domain-specific datasets: Wiki+Freebase, PubMed+UMLS, and MedBook+MKG, with up to 4.16% improvement in terms of Area Under the Curve and 3.19% in terms of Mean Average Precision compared to the best baseline method.
Analysis of Variant Data Mining Methods for Depiction of Fraud

Financial fraud is a growing problem with far-reaching concerns in the financial sector. Online transaction is the basic problem that raises many fraudulent quires around the world which cause loss of money to the people. These transactions generated huge volume of complex data in daily life. The depiction of fraud from credit card is still a key challenge due to two main reasons: firstly, profiles of ordinary and fraudulent behavior changes with the Passage of time, and secondly highly skewed credit card fraud records. Therefore, this study considered this challenge and proposed the solution to identify the fraudulent transactions through the credit cards using data mining techniques. Data mining has played a significant role in identifying credit card fraud from online transactions. Dataset collected from the publically available source and refine it. The employed classifiers are Naive Bayes, Bayes net, Logistic regression, Random forest, Decision tree, support vector machine, Decision stump, K- Nearest Neighbor, J48 and Binary Classification Technique. These techniques are applied on the preprocessed data. This data consists of 284,785 credit card transactions. Extensive experiments were conducted. The accuracy of each classifier was recorded in order to perform comparison. Our empirical analysis spotlights that K-NN outperforms in term of accuracy which is 99.95% than other classifiers. The findings of this study would be useful for the banking sector.
Genetic Learning Particle Swarm Optimization with Interlaced Ring Topology

Genetic learning particle swarm optimization (GL-PSO) is a hybrid optimization method based on particle swarm optimization (PSO) and genetic algorithm (GA). The GL-PSO method improves the performance of PSO by constructing superior exemplars from which individuals of the population learn to move in the search space. However, in case of complex optimization problems, GL-PSO exhibits problems to maintain appropriate diversity, which leads to weakening an exploration and premature convergence. This makes the results of this method not satisfactory. In order to enhance the diversity and adaptability of GL-PSO, and as an effect of its performance, in this paper, a new modified genetic learning method with interlaced ring topology and flexible local search operator has been proposed. To assess the impact of the introduced modifications on performance of the proposed method, an interlaced ring topology has been integrated with GL-PSO only (referred to as GL-PSOI) as well as with a flexible local search operator (referred to as GL-PSOIF). The new strategy was tested on a set of benchmark problems and a CEC2014 test suite. The results were compared with five different variants of PSO, including GL-PSO, GGL-PSOD, PSO, CLPSO and HCLPSO to demonstrate the efficiency of the proposed approach.
GCN-IA: User Profile Based on Graph Convolutional Network with Implicit Association Labels

Inferring multi-label user profile plays a significant role in providing individual recommendations and exact-marketing, etc. Current researches on multi-label user profile either ignore the implicit associations among labels or do not consider the user and label semantic information in the social networks. Therefore, the user profile inferred always does not take full advantage of the global information sufficiently. To solve above problem, a new insight is presented to introduce implicit association labels as the prior knowledge enhancement and jointly embed the user and label semantic information. In this paper, a graph convolutional network with implicit associations (GCN-IA) method is proposed to obtain user profile. Specifically, a probability matrix is first designed to capture the implicit associations among labels for user representation. Then, we learn user embedding and label embedding jointly based on user-generated texts, relationships and label information. On four real-world datasets in Weibo, experimental results demonstrate that GCN-IA produces a significant improvement compared with some state-of-the-art methods.
Quantum system compression: A Hamiltonian guided walk through Hilbert space

We present a systematic study of quantum system compression for the evolution of generic many-body problems. The necessary numerical simulations of such systems are seriously hindered by the exponential growth of the Hilbert space dimension with the number of particles. For a \emph{constant} Hamiltonian system of Hilbert space dimension $n$ whose frequencies range from $f_{\min}$ to $f_{\max}$, we show via a proper orthogonal decomposition, that for a run-time $T$, the dominant dynamics are compressed in the neighborhood of a subspace whose dimension is the smallest integer larger than the time-bandwidth product $\delf=(f_{\max}-f_{\min})T$. We also show how the distribution of initial states can further compress the system dimension. Under the stated conditions, the time-bandwidth estimate reveals the \emph{existence} of an effective compressed model whose dimension is derived solely from system properties and not dependent on the particular implementation of a variational simulator, such as a machine learning system, or quantum device. However, finding an efficient solution procedure \emph{is} dependent on the simulator implementation{\color{black}, which is not discussed in this paper}. In addition, we show that the compression rendered by the proper orthogonal decomposition encoding method can be further strengthened via a multi-layer autoencoder. Finally, we present numerical illustrations to affirm the compression behavior in time-varying Hamiltonian dynamics in the presence of external fields. We also discuss the potential implications of the findings for machine learning tools to efficiently solve the many-body or other high dimensional Schr{\"o}dinger equations.
Flight Path Planning for Small UAV Low Altitude Flights

Abstract Recent days there are many projects of UAV urban applications, moreover, although in different habited area. Several concepts like urban drone taxi, flying boards, flying bikes, etc. are in phase of prototype system test and evaluation. As part of the modern robotization, UAVs are in the focus of attention of numerous scientists. UAVs, as special platforms are widely used by the Military in different scenarios and different flight missions. Capabilities of UAVs used in non-military applications (i.e. Police applications, disaster management applications, border control, firefighter applications, etc.) will serve well the idea of UAV 3D (Dirty-Dull-Dangerous) application. The purpose of the author is to derive those low altitude UAV flight regimes when low altitude flight automation in necessary due to flight safety aspects, or, due to any existing regulations, and, finally, find and propose flight paths of the UAV better fitting given mission of the UAV.
End-to-End Memory Networks: A Survey

Constructing a dialog system which can speak naturally with a human is considered as a major challenge of artificial intelligence. End-to-end dialog system is taken to be a primary research topic in the area of conversational systems. Since an end-to-end dialog system is structured based on learning a dialog policy from transactional dialogs in a defined extent, therefore, useful datasets are required for evaluating the learning procedures. In this paper, different deep learning techniques are applied to the Dialog bAbI datasets. On this dataset, the performance of the proposed techniques is analyzed. The performance results demonstrate that all the proposed techniques attain decent precisions on the Dialog bAbI datasets. The best performance is obtained utilizing end-to-end memory network with a unified weight tying scheme (UN2N).
Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations

Abstract This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution to the optimization problem will converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results.
Theorizing about the Syntax of Human Language

Linguists standardly assume that a grammar is a formal system that ‘generates’ a set of derivations. But this is not the only way to formalize grammars. I sketch a different basis for syntactic theory: model-theoretic syntax (MTS). It defines grammars as finite sets of statements that are true (or false) in certain kinds of structure (finite labeled graphs such as trees). Such statements provide a direct description of syntactic structure. Generative grammars do not do this; they are strikingly ill-suited to accounting for certain familiar properties of human languages, like the fact that ungrammaticality is a matter of degree. Many aspects of linguistic phenomena look radically different when viewed in MTS terms. I pay special attention to the fact that sentences containing invented nonsense words (items not in the lexicon) are nonetheless perceived as sentences. I also argue that the MTS view dissolves the overblown controversy about whether the set of sentences in a human language is always infinite: many languages (both Brazilian indigenous languages and others) appear not to employ arbitrarily iterative devices for embedding or coordination, but under an MTS description this does not define them as radically distinct in typological terms.