Reviewerly/exHarmony huggingface dataset:
Effects of Augmented Reality Object and Texture Presentation on Walking Behavior

Wearable devices that display visual augmented reality (AR) are now on the market, and we are becoming able to see AR displays on a daily basis. By being able to use AR displays in everyday environments, we can benefit from the ability to display AR objects in places where it has been difficult to place signs, to change the content of the display according to the user or time of day, and to display video. However, there has not been sufficient research on AR displays’ effect on users in everyday environments. In this study, we investigate how users are affected by AR displays. In this paper, we report our research results on the AR displays’ effect on the user’s walking behavior. We conducted two types of experiments—one on the effects of displaying AR objects on the user’s walking path, and the other on the effects of changing the floor texture by AR on the user’s walking behavior. As a result of the experiments, we found that the AR objects/textures affected the user’s walking behavior.
The dynamics simulation of knowledge potentials of agents including the feedback

Abstract The conceptual and mathematical models of the agents’ knowledge potential redistribution considering their constituent components are constructed in the paper. A nonlinear model describing the dynamics of agents’ knowledge potentials is developed taking into account the feedback effects (agent – student) on the source of knowledge (teacher / lecturer). In particular, the procedure of constructing a multicomponent two-dimensional array of discrete values of the knowledge potential constituent components for generating procedures to enhance (improve) the professional competences of knowledge sources, is generalized and specified.
A Dynamic Sharding Protocol Design for Consortium Blockchains

The transaction efficiency of blockchains has been a painful point haunting researchers all the time. The metrics of Transaction per Second (TPS) present a low value, especially as the number of network nodes increases. In order to improve the blockchain's TPS, numerous articles explored the solutions from multiple aspects, such as: using a new block system based on the Directed Acyclic Graph (DAG), adjusting the block structure, optimizing the consensus algorithm or adopting a sharding protocol. The sharding protocols play an extremely important role among these schemes, while the existing ones suffer from secure risks due to their static sharding. In this paper, we proposed a dynamic sharding protocol for consortium blockchains, employing random values to randomly partition the network. Our protocol aims for improving the throughput of the blockchain system as well as ensuring its security. The OMNET++ simulation results showed that, in the case of a large-scale consortium blockchain network of 400 nodes which is divided into 16 shards, the performance of our scheme is able to attain up to 50 times promotion, compared to the system without sharding.
Enhanced Context Attention Network for Image Super Resolution

The performance of image super-resolution (SR) have been greatly improved with deep convolution neural network (CNN). Despite image SR targets at recovering high-frequency details, most SR methods still focus on generating high-level features via a deep and wide network. They lack the discriminative ability of high-frequency information hidden in the abundant CNN features, thus hindering CNNs to yield better SR results. To tackle this issue, we propose two new attention mechanism: context weighted channel attention (CWCA) and persistent spatial attention (PSA). They modulate abundant features by suppressing the useless features and enhancing the interested ones in a channel-and-spatial manner. The network is then enabled to concentrate more on informative features closely related to the high-frequency components of an image. Furthermore, we propose enhanced attention residual groups with dense connection (EARG-D) to capture not only short-term information but also long-term information to maintain more useful features. Finally, we construct a deep enhanced context attention super resolution network (EASR) for better image reconstruction. Quantitative and qualitative experiments well demonstrate that our proposed method performs better than existing state-of-the-art SR methods.
The Level Set Kalman Filter for State Estimation of Continuous-Discrete Systems

We propose a new extension of Kalman filtering for continuous-discrete systems with nonlinear state-space models that we name as the level set Kalman filter (LSKF). The LSKF assumes the probability distribution can be approximated as a Gaussian, and updates the Gaussian distribution through a time-update step and a measurement-update step. The LSKF improves the time-update step when compared to existing methods, such as the continuous-discrete cubature Kalman filter (CD-CKF) by reformulating the underlying Fokker-Planck equation as an ordinary differential equation for the Gaussian, thereby avoiding expansion in time. Together with a carefully picked measurement-update method, numerical experiments show that the LSKF has a consistent performance improvement over CD-CKF for a range of parameters, while also simplifies the implementation, as no user-defined timestep subdivision between measurements is required, and the spatial derivatives of the drift function are not explicitly needed.
Architectural and Technological Dialogue: Development of “New City” Generation

Abstract The article considers urban concepts of new generation cities based on comparison of their architectural and technological features. Analysis of approaches to the design of modern urban areas allowed to determine the dominant principles of urban planning as a synergy of “Creative + Comfort + Smart + Green”. In this context, the author identifies a group of futuristic cities whose projects are based on the synthesis of technological and architectural innovations and eco-friendliness. The definition “neo-city” is introduced and examples are given that are already being built. Population density, lack of territories and environmental problems are reasons for foundation of neo-cities in South-Eastern countries. It is noted that neo-cities are represented in concepts “city without cars”, “city without cars and skyscrapers”, “energy-saving”, “garbage-free” and “high-tech city”. Architectural appearance of each city has its own innovation. Using of bionics principles in architecture of cities is aimed at creating a comfortable environment. Neo-cities are quite ambitious projects in terms of design and financial and technical characteristics. Risk of their operating is technical failures due to the concentration of technical devices and sensors. It is actualized, that the attractiveness of such cities is determined by a comfortable social infrastructure and effective urban management.
Research on English Movie Resource Information Mining Based on Dynamic Data Stream Classification

Massive English film and television resources will provide people with a rich content, while it has become more and more difficult for people to find the resources and information they need. Through the analysis of massive amounts of film and television data, the application system can effectively push the works that users may like. In response to this problem, we can build an English film and television resource information mining model by combining fuzzy neural network algorithms and dynamic data stream classification technology. Firstly, we use dynamic data stream classification technology to preprocess and screen English film and television resource information. Secondly, we use the fuzzy neural network algorithm to conduct data mining on related film and television resource information. The experimental results verify the superior performance of the English film and television resource information mining model established in this paper. This model can help people to find the resources and information they need.
AutoSpace: Neural Architecture Search with Less Human Interference

Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction. In this paper, we consider automating the search space design to minimize human interference, which however faces two challenges: the ex-plosive complexity of the exploration space and the expensive computation cost to evaluate the quality of different search spaces. To solve them, we propose a novel differentiable evolutionary framework named AutoSpace, which evolves the search space to an optimal one with following novel techniques: a differentiable fitness scoring function to efficiently evaluate the performance of cells and a reference architecture to speedup the evolution procedure and avoid falling into sub-optimal solutions. The frame-work is generic and compatible with additional computational constraints, making it feasible to learn specialized search spaces that fit different computational bud-gets. With the learned search space, the performance of recent NAS algorithms can be improved significantly compared with using previously manually designed spaces. Remarkably, the models generated from the new search space achieve 77.8% top-1 accuracy on ImageNet under the mobile setting (MAdds 500M), outperforming previous SOTA EfficientNet-B0 by≤0.7%. https://github.com/zhoudaquan/AutoSpace.git.
Application of Climatic Characteristics Statistical Assessments and GIS Technologies for Solar Energy Systems Development

Abstract The problems of optimising the placement of photovoltaic panels to ensure the most stable power generation are considered. Method for assessing the risks of solar power plants operation instability in the based on taking into account the climatic characteristics of the variability of the insolation and snow cover using GIS technologies is presented. This approach is especially important for Russia, a country with a wide variety of natural and climatic conditions. To test this technique, we calculated the intra-annual and year-to year variation of the statistical characteristics of the total insolation incident for regions of Russia. The use of GIS technologies made it possible to assess the distribution of the features of the variability of solar radiation over the area. This paper presents the results of calculations and constructed maps for the Republic of Altai, the region of Russia with heterogeneous insolation conditions, where solar energy has been actively developing recent years. In these territories, zones with the lowest indicators of variability in the input of solar radiation have been identified where a high level of stability of electricity generation on photovoltaic power plants can be predicted.
Polytech WebQuest as an Organization Form of Students Project Activities

Project activities are an important component of the innovative development trend. Skillful project team building influences the efficiency of all work, so it is critical to understand roles of team members. This understanding will help put together an efficient group and improve the quality of decision-making on group management. Roles can be defined through a number of tests, but actual practical activities represent a more reliable source of information. A WebQuest is an umbrella term for various online and offline events in this format based on game mechanics. A number of solutions within the WebQuest allow defining students' roles in the team in the course of practical activities with a high level of engagement in the game. This approach has been studied in this article. The article presents assessments of roles with the help of the following indicators: the player's speed of response as compared with other team members; differences between answers within the group; the order of answers given, etc. Additionally, the project allows testing during the game, and that makes it possible to find out the participants' (players') opinions about the current distribution of roles in the team. Aside from studying the results of the event and collecting data on the aforementioned hypotheses, it is planned to compare the obtained results with other generally accepted team role tests, and the actual results of student team project activities in the framework of a special university course – Introduction to Project Management. Therefore, the main focus of the study is to check the hypothesis that roles of participants in university project teams can be selected through student games in a specially arranged short-term playing environment at the university.
Internet of Things as tool for cognitive process in Computer Science study program

IoT devices are becoming a common thing in everyday life. Students also encounter them at home or at school. The inclusion of IoT devices in teaching can make the learning process more interesting and interactive, having a greater impact on the cognitive process. Learning with IoT devices better demonstrates the application domain to students and brings the problem closer to their daily lives. The article demonstrates how one IoT device can be used for teaching in different areas of the Computer Science field. The Fibaro Starter Kit with sensors was used for the case study and its applicability to three different CS subjects was shown.
Revisiting Self-supervised Monocular Depth Estimation

Self-supervised learning of depth map prediction and motion estimation from monocular video sequences is of vital importance—since it realizes a broad range of tasks in robotics and autonomous vehicles. A large number of research efforts have enhanced the performance by tackling illumination variation, occlusions, and dynamic objects, to name a few. However, each of those efforts targets individual goals and endures as separate works. Moreover, most of previous works have adopted the same CNN weights for initialization, not reaping recent advances in self-supervised feature learning. Therefore, the need to investigate the inter-dependency of the previous methods and the effect of different initial features remains. To achieve these objectives, we revisit numerous previously proposed self-supervised methods for joint learning of depth and motion, perform a comprehensive empirical study, and unveil multiple crucial insights.
On-line learning the graph edit distance costs

This paper presents the first on-line learning method to automatically deduce the insertion, deletion and substitution edit costs of the graph edit distance. The learning method is based on embedding the substitution and deletion operations into a Euclidean space. The points in this space are classified into the ones that represent substitution edit operations and the ones that represent deletion edit operations. Thus, the learning strategy is based on deducing the hyper-plane in this space that best splits these two types of points. Any linear classifier can be used to deduce this hyper-plane, for instance LDA or SVM. The on-line method has the advantage that learning the edit costs and computing the graph edit distance with the new updated costs can be done simultaneously. Experimental validation shows that the matching accuracy is competitive with the off-line methods but without the need of the whole learning set.
Senti_ALSTM: Sentiment Analysis of Movie Reviews Using Attention-Based-LSTM

Association with the customers is one of the principal elements for businesses. It is essential for these firms to recognize exactly what clients’ opinions about the latest as well as established products and services. One such business is film industry. Movies are a dominant source of entertainment. Yield of a movie relies on a great extent to the reviews on social media, blogs, forums, IMDB, and movie viewing platforms. In this paper, to achieve revolutionary performance in sentiment analysis, attention-based long short-term memory, ‘Senti_ALSTM,’ model is proposed which improves the neural networks by assigning memory to it and further reducing the vanishing gradient problem of recurrent neural network. Senti_ALSTM experiment with attention in long short-term memory model which takes into account the information reflected by the input sequence, thus remembering the context, and establishes the relation between input and output sequence. Further, the success of the benchmark data confirms the efficiency of the proposed model over the other models.
Frame Semantics guided network for Abstractive Sentence Summarization

Abstractive Text Summarization is an important and practical task, aiming to rephrase the input text into a short version summary, while preserving its same and important semantics. In this paper, we propose a novel Frame Semantics guided network for Abstractive Sentence Summarization (FSum), which is able to learn a better text semantic representation by selecting more relevant Frame semantics from text, and integrating Frame semantic representation with text representation effectively. Extensive experiments demonstrate that our proposed FSum model performs significantly better than existing state-of-the-art techniques on both Gigaword and DUC 2004 benchmark datasets.
Regression-based clustering network via combining prior information

Despite the promising performance, existing regression-based clustering methods still have the following limitations. (1) They only extract the shallow discriminant features, resulting in unstable clustering performance on data with complex underlying subspaces. (2) It is difficult to optimize the objective due to the discretization of the elements in the cluster indicator matrix, resulting in suboptimal solution. (3) They fail to employ the structure prior information embedded in the clustering label matrix, resulting in suboptimal clustering performance. Targeting at above problems, we propose a novel Regression-based Clustering network via Combining Prior Information (RC2PI), which consists of a convolutional auto-encoder, a priori information encoder, and a discriminator. Specifically, the auto-encoder is used to generate the ideal distribution to relax discrete cluster indicator matrix, which can help obtain optimal solution. The prior information encoder is employed to exploit the structure prior knowledge embedded in clustering label matrix, thereby boosting clustering via a self-supervised manner. The discriminator, as a connector of the above two sub-networks, is used for verifying the embedding process of prior information that will guide the auto-encoder to generate a more reliable actual distribution. Extensive experiments demonstrate the effectiveness of RC2PI over state-of-the-art methods.
Using K-Means and Variable Neighborhood Search for Automatic Summarization of Scientific Articles

This work presents a method for summarizing scientific articles from the arXive dataset using Variable Neighborhood Search (VNS) heuristics to automatically find the best summaries in terms of ROUGE-1 score we could assemble from scientific article text sentences. Then vectorizing the sentences using BERT pre-trained language model and augmenting the vectors with topic embeddings obtained by applying the K-means algorithm. Finally, training the Random Forest classification model to find sentences suitable for the summary and compile a summary from the selected sentences. The described algorithm produced summaries with high ROUGE-1 scores (0.45 on average), so we are heading for further developments on a larger dataset.
Artificial Intelligence for Public Sector: Chatbots as a Customer Service Representative

Many governments began to give customer service a major priority until they became competitive in some of the services provided by its various ministries, global profit companies, in terms of the way and quality of the service provided, depending on the integration of government systems, continuous training for customer service employees, as well as electronic transformation and the use of the latest technologies. This chapter addresses the technologies used in government service centers, in particular artificial intelligence techniques, and on the feasibility of using the Chatbots and its benefits, how it works best to ensure its effectiveness in providing services, also about the role that can be played in providing its services in light of the Coronavirus pandemic outbreak, finally will try to answer the most important question whether can this technology replace the human customer services representative?
Quality Inference in Federated Learning With Secure Aggregation

Federated learning algorithms are developed both for efficiency reasons and to ensure the privacy and confidentiality of personal and business data, respectively.Despite no data being shared explicitly, recent studies showed that the mechanism could still leak sensitive information.Hence, secure aggregation is utilized in many real-world scenarios to prevent attribution to specific participants.In this paper, we focus on the quality (i.e., the ratio of correct labels) of individual training datasets and show that such quality information could be inferred and attributed to specific participants even when secure aggregation is applied.Specifically, through a series of image recognition experiments, we infer the relative quality ordering of participants.Moreover, we apply the inferred quality information to stabilize training performance, measure the individual contribution of participants, and detect misbehavior.
Detecting Anomalous Transactions via an IoT Based Application: A Machine Learning Approach for Horse Racing Betting

During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Government agencies and local authorities have placed strict regulations regarding the location and amount allowed for gambling. These efforts are made to prevent gambling addictions and monitor fraudulent activities. The revenue earned from gambling provides a considerable amount of tax revenue. The inception of internet gambling have allowed professional gamblers to par take in unlawful acts. However, the lack of studies on the technical inspections and systems to prohibit unlawful internet gambling has caused incidents such as the Walkerhill Hotel incident in 2016, where fraudsters placed bets abnormally by modifying an Internet of Things (IoT)-based application called “MyCard”. This paper investigates the logic used by smartphone IoT applications to validate the location of users and then confirm continuous threats. Hence, our research analyzed transactions made on applications that operated using location authentication through IoT devices. Drawing on gambling transaction data from the Korea Racing Authority, this research used time series machine learning algorithms to identify anomalous activities and transactions. In our research, we propose a method to detect and prevent these anomalies by conducting a comparative analysis of the results of existing anomaly detection techniques and novel techniques.