Reviewerly/exHarmony huggingface dataset:
Smartphone Applications for Monitoring Physical Activities

Society always tends to use devices that make life easier and more comfortable. Smartphone is one of the most usable devices, which have penetrated the daily life of people. Apart from the main use of smartphones (calling and texting), many other applications of smartphones are also very important. The smartphone is embedded with a variety of sensors like an accelerometer, gyroscope, magnetometer, and camera along with communication modules like Wi-Fi, Bluetooth, and NFC. For the past decade, the smartphone is also being used for monitoring physical activities. It is possible with the usage of sensors, communication modules, and computing facilities in the smartphone. Human activity recognition (HAR) seeks to recognize the actions of a person using the information obtained from many sources, such as the environment or body sensors. Smartphone sensors like accelerometers and gyroscopes are also being used for activity recognition. This book chapter discusses the use of smartphones for activity recognition, enabling sensors, fusion of sensors for activity recognition, artificial intelligence methods of activity recognition, and implementation of activity recognition methods in IoT/fog computing scenarios.
Improving the Quality of Teacher Training for the Organization of Dialogic Education of Primary School Students: Methodical Aspect

The article presents organizational and methodological aspects of professional training of future primary school teachers for dialogic learning of students. Dialogic learning is a kind of interactive organization of the educational process, based on mutual understanding and interaction, considered by the author as an effective factor of improving professional future primary school teachers’ skills. The organizational aspects of the effective practical solution of the researched problem are outlined, such as dialogization of the content of education; introduction of various forms of productive dialogues and a system of methods of problem-based learning; dialogization of communication as a means of creation a certain psycho-emotional educational microenvironment; formation of future specialists’ skills of critical reflection as an internal dialogue for self-improvement and self-development. The pedagogical potential of the disciplines of psycho-pedagogical and professional-methodical orientation on the dialogue of the educational process is determined. It has been proved that effective forms and methods of dialogic learning in teacher training are: training, group activities, problem lectures, interactive classes, discussions, didactic games, business- and role-playing games, etc. The basic principles of pedagogical ethics and culture of the organization of dialogic training within the study of an elective course “Communicative Pedagogy” are emphasized. It was found that the quality of dialogic learning is determined by the level of formation of participants’ communicative competence in pedagogical interaction, especially their mastery of the basic techniques of listening. The author’s materials from the dissertation “Preparation of primary school teachers for the organization of dialogic training” for PhD (K. Fomin, 2020) are partially used in the article.
Domain expertise extraction for finding rising stars

Abstract The field of expertise extraction utilizes published research enabling communities to highlight and identify the skills of researchers within specific scientific domains. This can be useful for evaluating research performance, and in the case of rising stars, in identifying top scientific talent. Previous research has harvested a range of publication indicators in an effort to identify expertise and talent. These include content indicators, citation metrics, and also the position of a researcher within a full collaboration network of scientists. The existing mechanism of expertise extraction utilizes all papers attributed to a scientific author, thereby potentially neglecting their specific or specialized expertise. Here we show that a tensor decomposition technique when applied to the problem addresses a number of useful problems. This includes better identification of individual expertise, as well as an integrated appraisal of an author’s role in an extended scientific network. The technique will afford new analyses of knowledge production which consider specialisation and diversity as core elements for further analysis. More generally the tensor decomposition techniques presented in this paper can be applied to a range of scientometric problems where multi-modal data is encountered.
Swin-MFA: A Multi-Modal Fusion Attention Network Based on Swin-Transformer for Low-Light Image Human Segmentation

In recent years, image segmentation based on deep learning has been widely used in medical imaging, automatic driving, monitoring and security. In the fields of monitoring and security, the specific location of a person is detected by image segmentation, and it is segmented from the background to analyze the specific actions of the person. However, in low-illumination conditions, it is a great challenge to the traditional image-segmentation algorithms. Unfortunately, a scene with low light or even no light at night is often encountered in monitoring and security. Given this background, this paper proposes a multi-modal fusion network based on the encoder and decoder structure. The encoder, which contains a two-branch swin-transformer backbone instead of the traditional convolutional neural network, fuses the RGB and depth features with a multiscale fusion attention block. The decoder is also made up of the swin-transformer backbone and is finally connected via the encoder with several residual connections, which are proven to be beneficial in improving the accuracy of the network. Furthermore, this paper first proposes the low light-human segmentation (LLHS) dataset of portrait segmentation, with aligned depth and RGB images with fine annotation under low illuminance, by combining the traditional monocular camera and a depth camera with active structured light. The network is also tested in different levels of illumination. Experimental results show that the proposed network has good robustness in the scene of human segmentation in a low-light environment with varying illumination. The mean Intersection over Union (mIoU), which is often used to evaluate the performance of image segmentation model, of the Swin-MFA in the LLHS dataset is 81.0, is better than those of ACNet, 3DGNN, ESANet, RedNet and RFNet at the same level of depth in a mixed multi-modal network and is far ahead of the segmentation algorithm that only uses RGB features, so it has important practical significance.
Reported User-Generated Online Hate Speech: The ‘Ecosystem’, Frames, and Ideologies

The spread of hate speech challenges the health of democracy and media systems in contemporary societies. This study aims to contribute to a better understanding of user-generated online hate speech reported by Internet users to national monitoring organizations, in particular its ‘ecosystem‘, discursive elements, and links to political discourses. First, we analyzed the main characteristics of the reported statements (source, removal rate, and targets) to reveal the media and political context of reported user-generated online hate speech. Next, we focused on hate speech statements against migrants and analyzed their discursive elements with the method of critical frame analysis (frames, actors, metaphors, and references) to understand the corresponding discourse. The main discursive feature of these statements is the prognosis, which calls for death and violence, so we could label this communication as ‘executive speech.’ Other key features are references to weapons and Nazi crimes from WWII, indicating the authors’ extreme-right ideological convictions, and the metaphors, employed to provoke disgust from migrants, present them as culturally inferior and raise fears about their supposed violent behavior. The corresponding diagnoses frame migrants as a threat in a similar way to populist political discourses of othering and complement these in providing ‘final’ solutions in prognoses.
Impact of COVID-19 on Digital Business Models—Focus on Sustainability

The COVID-19 pandemic has accelerated the digital transformation worldwide and is challenging many companies across a wide range of industries. As a result, companies are subject to digital transformation and must improve their sustainability in the long term to remain competitive in the future. In this context, countries are at different stages of development. Therefore, the impact of COVID-19 on digital business models in different countries (Germany, Japan and Georgia) with a focus on sustainability has been investigated. A qualitative model was developed based on a structured literature review. Further hypotheses have been established based on empirical data from international experts using Grounded Theory. Important influencing factors related to digitalization include transparency, efficiency, data protection, digital mindset and sustainability aspects. Regarding sustainability, a distinction can be made between the two determinants “Sustainable office” and “The idea of sustainability”.
Verifying the SHA-3 Implementation from OpenSSL with the Software Analysis Workbench

This paper discusses a proof of SHA-3 in OpenSSL. SHA-3 is the new standard from NIST for computing digests. OpenSSL is one of the most widely used implementations of cryptographic protocols. Cryptol and the Software Analysis Workbench (SAW) are a language and verification engine to specify cryptographic primitives and prove equivalence to implementations. As digests are the basis for all cryptographic protocols, the research in this paper is an automated SAW proof of equivalence between the SHA-3 standard and its OpenSSL implementation in C. The research contributes Cryptol specifications for the standard. The equivalence proof shows the importance of overrides in SAW that replace models from C code with those from Cryptol to reduce the complexity of the proof obligations that must be accomplished relative to the implementation in C. The research establishes the viability of verifying modern cryptographic primitives and the importance of modularity in their definitions, and implementations, for overrides to manage complexity.
Using the Knowledge of Competent Salespeople: Modeling Business Negotiation Topics to Obtain Customers’ Real Needs

This study focuses on the business negotiations conducted by competent salespeople to elicit the true needs of their customers. Herein, we devise a system wherein the knowledge embedded in the negotiation process is stored as a network of negotiation topics, and an agent will be able to recommend appropriate topics during the new negotiation based on the network structure. We assume that the comprehensive topic network is a result of superimposing typical negotiation cases. Under this assumption, if the importance information of individual partial networks, meaning typical negotiation cases, is known, it can be combined to calculate the importance of each node (topic). The agent can recommend topics with reference to the importance of each node. The analytic hierarchy process is proposed as a method for measuring the importance of each business negotiation case. Then, we take up a B2B transaction example, that is, a sale with consulting regarding a security camera system to another company, to show the results when the proposed method is applied.
Temporal Logic in Multi-agent Environment

This paper studies temporal multi-agent’s relational models with distinct time accessibility relations for agents. Distinct valuations of truth values for agents’ are also allowed, and a global valuation is the one which in a sense summarizes the opinion of agents. Some illustrating examples are provided (cf. displayed in paper below formulas (1.2), (1.3)). From mathematical point of view, we deal with satisfiability problem for formulas, and we construct a mathematical algorithm (cf. Theorem 1.3) verifying satisfiability. Also we prove that the problem of admissibility for inference rules in some such logics is decidable. Open problems from the area are proposed.
Automated Consistency Analysis for Legal Contracts

Contracts in business life, and in particular company purchase agreements, often comprise a large number of provisions and are correspondingly long and complex. In practice, it is therefore a great challenge to keep track of their regulatory context and to identify and avoid inconsistencies in such contracts. Against this background, we propose a semi-formal as well as a formal logical modeling of this type of contracts, using decidable first-order theories. We also present the tool ContractCheck, which performs fully automated inconsistency analyses on the considered contracts using Satisfiability Modulo Theories (SMT) solving.
A Systematic Review About Requirements Elicitation for Multi-Agent Systems

Requirements elicitation is the first stage of the requirements engineering, and it is essential to discover the stakeholders’ needs for any software, including multi-agent systems. This particular kind of software is composed by agents, autonomous and pro-active entities, which can collaborate among themselves to achieve a given goal. However, multi-agent systems have some particular requirements that are not normally found in other software. Taking this into consideration, this paper aims to determine the actual state of the elicitation techniques for multi-agent systems by means of a systematic review, highlighting how these techniques work, their strengths and weaknesses and its support to the BDI model.
Anti-recompression video watermarking algorithm without bit-rate increase

Watermarking technology based on high-efficiency video coding (HEVC) is evolving such that it can be used in various scenarios, such as copyright protection, ownership certification, tamper detection, and authentication. However, video watermarking algorithms based on HEVC still have poor resistance to recompression attack, which seriously affects the practicability of watermarking algorithms. To solve this limitation, we propose a robust HEVC watermarking algorithm based on the analysis of the characteristics of HEVC recompression. The influencing factors of choosing embedding carrier are discussed, and the calculation formula of texture complexity is then deduced. According to the texture complexity, the watermark is embedded into the stable 4 × 4 luma blocks. Specifically, the watermark information is completely embedded after modulating the signs of the quantized discrete sinusoidal transform coefficients of the carrier. Experimental results show that the algorithm is robust to the recompression attack with constant quantization parameter. There is no obvious artifact after watermark embedding, which reflects the proposed algorithm has good imperceptibility. In addition, the embedding and extraction process of the algorithm is simple and fast, and the embedding of the watermark does not increase the video bit rate.
Trusted Cloud Service System Based on Block Chain Technology

With the development and popularization of cloud computing technology, more and more users choose cloud services to build their application systems. With the improvement of users’ understanding of software systems, in addition to functionality, trustworthiness has become another key issue concerned by users. Based on the existing research on cloud service trustworthiness measurement and evaluation, this paper proposes a cloud service-trusted delivery model based on asymmetric encryption and hash function and a trusted runtime model of cloud service system based on block chain technology. The proposed models will effectively solve the untrusted problems such as denial and tampering in the process of cloud service acquisition, as well as the system anomaly at runtime. Finally, through targeted experiments to verify the effectiveness and feasibility of the proposed models, and through experimental analysis, this paper expounds on the principle and mechanism of model operation and trustworthiness guarantee.
Evaluation and Analysis of English Teaching Ability Based on Nonlinear Random Matrix Model

With the sustained, rapid, and steady development of China’s economy, the development of higher education is also faster and faster. With economic development and social progress, the demand for talents is also growing. Colleges and universities are the cradle of cultivating excellent talents and undertake the important task of cultivating reserve forces for the country. As an engineer of human soul, English teachers shoulder the responsibility of cultivating excellent talents and the hope of countless parents. For a long time, there have been many problems in the evaluation of College English teachers in China. For example, the assessment objective is too simple, only related to salary promotion and professional title evaluation. In terms of the content of evaluation, they often only pay attention to the quantity and level of scientific research results, ignoring the evaluation of potential, attitude, and other aspects, especially the social responsibility of teachers. Based on the theory of nonlinear random matrix and the theory of competence, this study establishes the quality evaluation model of university teachers in China by combining qualitative and quantitative methods. On this basis, it plays an important role in the selection, training, promotion, and further development of university teachers in China. First of all, according to a large number of domestic and foreign literature on competency, this study summarizes the competency theory and competency model. Using the hypothetical model, a questionnaire was designed and distributed to build a competency model of university teachers from four aspects: professional knowledge, related skills, work attitude, and personality motivation. Finally, the weight of each index in the model is calculated. This study not only constructs the competency evaluation model but also uses the principal component analysis method to calculate it in detail, which provides a good quantitative basis for the selection and performance evaluation of college teachers, so as to prevent the situation beyond its control. It paves an endless bright road for further stable and healthy development. Therefore, the overall research results of this study have greater practical application value and rich connotation.
An equivalence based classification of [6, 3, 4] error correcting codes over <i>GF</i>(5)

We were able to classify all 1-error correcting [5, 3] codes over GF(5) in paper [1] and [5, 2] ternary codes in paper [2]. Now, our focus is an equivalence based classification of error correcting linear codes of length 6 over GF(5). In paper [3], we investigated the equivalence of [6,3,4] codes of length 6 and dimension 3 over GF(4). In this paper we classify the [6,3,4] codes over GF(5). We explore the weight distribution of these codes as well as their duals.
Three-dimensional feature extraction using local reference frame for detecting human–object interaction

To understand the visual world, a device knows not only the instances but also how they interact. Humans are at the center of such interactions. Detection of human–object interaction (HOI) is one of the growing research fields in computer vision. However, identifying HOIs due to the large label space of verbs and their interaction with various object types still needs much research. We focus on HOIs in images, which is necessary for a deeper understanding of the scene. In addition to two-dimensional (2D) information, such as the appearance of humans and objects and their spatial location, three-dimensional (3D) status, especially in the configuration of the human body and object as well as their location and spatial, can play an important role in learning HOI. The mapping of 2D to 3D world adds depth information to the problem. These issues led us to collect 3D information along with the 2D features of the images to provide more accurate results. We show 3D attributes, such as face transformation, the viewing angle, the position of an object, and its related location to the human face, can improve HOI learning. The results of experiments on large-scale data show that our method has been able to improve the outcome of interactions.
Single frame color fringe projection profilometry based on empirical mode decomposition imbalance correction algorithm for fast three-dimensional reconstruction

Phase-shifting profilometry (PSP) is considered to be the most accurate technique for phase retrieval with fringe projection profilometry (FPP) systems. However, PSP requires that multiple phase-shifted fringe patterns be acquired, usually sequentially, which limits PSP to static or quasistatic imaging. We introduce color FPP (CFPP), which provides three-dimensional (3D) imaging using a single acquisition, for the application of real-time profiling. A single frame acquisition provides all three phase-shifted fringe patterns needed for the PSP phase retrieval algorithm. However, some problems must be overcome in CFPP, namely, color coupling and imbalance. Following up on the study, we propose a fixed coefficient decoupling method based on point-to-point correction and then introduce the empirical mode decomposition algorithm to decompose a color fringe pattern into three high-frequency components and three low-frequency components for imbalance correction, which improves measurement accuracy and realizes a single frame measurement for complex and discontinuous objects. After correction by the proposed method, the shape of the tested object can be recovered by one a single frame color fringe pattern, which meets the need for dynamic measurement, and the accuracy is not limited by the movement speed of object. The simulations and experimental results show that the proposed method can significantly reduce the influence of color coupling and imbalance in CFPP, and thus realize dynamic and high-precision 3D reconstruction.
Shortest path with acceleration constraints: complexity and approximation algorithms

Abstract We introduce a variant of the Shortest Path Problem (SPP), in which we impose additional constraints on the acceleration over the arcs, and call it Bounded Acceleration SPP (BASP). This variant is inspired by an industrial application: a vehicle needs to travel from its current position to a target one in minimum-time, following pre-defined geometric paths connecting positions within a facility, while satisfying some speed and acceleration constraints depending on the vehicle position along the currently traveled path. We characterize the complexity of BASP, proving its NP-hardness. We also show that, under additional hypotheses on problem data, the problem admits a pseudo-polynomial time-complexity algorithm. Moreover, we present an approximation algorithm with polynomial time-complexity with respect to the data of the original problem and the inverse of the approximation factor $$\epsilon$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> . Finally, we present some computational experiments to evaluate the performance of the proposed approximation algorithm.
Volleyball Movement Object Detection and Behavior Recognition Method of Artificial Neural Network

Movement object detection method is the basis and key of the modem intelligent video surveillance system. It combines advanced technologies in many fields, such as artificial intelligence, image processing, and pattern recognition, and is the research area of computer vision technology. Therefore, it is important to explore the motion detection and target recognition algorithms. The data collection method has concluded that T &gt; 60 is absent from four different videos. The T value is traversed from 0–255 to select the largest T value as the split threshold. Through the analysis, the range of the threshold selection can be reduced to 0-60, thus improving the operational efficiency.