Reviewerly/exHarmony huggingface dataset:
Vision Sensing based People Following Robot: A Superpixel Augmented Density based Clustering Approach

This paper presents a new approach to vision based human shoe detection for its effective implementation in leader-follower modules in human-robot coexisting environments. During motion tracking, the poses of the target shoes may undergo various general transformations such as scales, rotations 2D translations, or 2D affine transformations. Thus, the shoe detection problem can be solved as a template matching problem under general conditions. Recently, a general-purpose contemporary template matching algorithm, called FAsT-Match, and an extended variant of it for RGB color domain, called CFAsT -Match, have been successfully implemented for shoe detection in successive frames captured during real-life human tracking. CFAsT-Match uses a density based clustering algorithm, namely DBSCAN, to create arbitrarily shaped clusters of the pixels of a specific template shoe image. This cluster information is required to map the color distribution from the template to the target shoe images (frames). In the present study, an advanced superpixel augmented DBSCAN based shoe detection approach is proposed, where a superpixel generation scheme in cascade with DBSCAN clustering is integrated into the CFAsT-Match for the purposes of shoe detection. Superpixel segmentation offers grouping of image pixels into perceptually meaningful entities that are more congruent with the visual system of a human being and that can act as primitives for further computation. The resulting configuration not only significantly reduces the computational complexity of the shoe detection framework, but also improves the detection accuracy. Real-life performance evaluation aptly demonstrates the supremacy of the proposed approach combining superpixel-segmentation and density based clustering.
Toward Interoperability of NFT Marketplaces

Blockchain plays a major role in the security field, it is a replicated and shared database among the network’s participants. The main feature of blockchain is that it allows untrusted individuals to securely interact and send transactions without the need for a trusted third party. Non-fungible tokens often referred to as NFTs, are blockchain-based tokens that each represents a unique asset like a piece of art, digital content, or media. An NFT can be thought of as an irrevocable digital certificate of ownership and authenticity for a given asset, whether digital or physical. It is designed to be cryptographically verifiable, unique, or scarce, and easily transferable. The large number and heterogeneity of the blockchains make NFTs interoperability management as one of the most complex and challenging tasks. The present paper provides an overview of the blockchain new projects for enabling NFTs interoperability.
Transmission Order Optimization of Coded Distributed Computing in Heterogeneous Wireless Multiple-Access Network

Coded distributed computing (CDC) has been recently proposed as a promising technique to mitigate the straggler effect in the distributed computing cluster which consists of workers with different computing capabilities, and to reduce the end-to-end task execution latency. However, the heterogeneity of computing and transmission will critically impact the latency performance, especially in the wireless multiple-access network. In this paper, we use CDC over the heterogeneous wireless multipleaccess network (HWMAN) including both computation stragglers and transmission stragglers with various capabilities. In order to reduce the computing task completion latency (computing latency and transmission latency), the optimal stop computing time of workers and the sorting order of result transmission back are obtained via two designed algorithms, namely straggler detection and ordered transmission (SDOT) and worker sorting and ordered transmission (WSOT), respectively, which not only fully utilize the computing results of stragglers, but also improve the total latency performance compared with other existing state-of-theart algorithms.
Intelligent Systems for XSS attack detection: A brief survey

Nowadays, the use of web applications is an important need for users, more than a third of critical risks found in web applications can be attributed to injections. According to OWASP, XSS is an injection attack in which an attacker inserts a malicious script within the browser’s victim in order to exercise significant activities. To resist this attack, various techniques have been designed and deployed, unfortunately, these techniques remain insufficient to perfectly protect web applications particularly, those which are based on classical tools. In this survey, we highlight the principle of XSS attack and different XSS categories, we discuss existing mechanisms especially based on intelligent systems to Figure out what is done and how the XSS attack detection problem is handled. In addition, we make a comparative study of some existing approaches and we discuss their issues.
Proposal Generation Process for Ensuring the Negotiation within the Cloud Environment

Negotiation is a decision-making process when the trading parties with conflicting aims pursue their own concerns. With the new advances of cloud technology compared with the traditional IT infrastructure, services such as discovery, elasticity and monitoring are automatically processed. In the same way, the negotiation service between clients and cloud providers can be difficult if it is performed manually. Clients need a service that allows them to negotiate automatically with the cloud providers on some parameters that are important for them. In this paper, we propose a generation process for ensuring the negotiation in the cloud environment and specifically for the infrastructure as a service (IaaS) category. Our contributions in this paper include: (1) Designing a new proposal generation process based on factors affecting the negotiation between parties in the cloud, (2) Performing simulation experiments to evaluate our strategy and compare it with the other ones.
Blockchain Based 6G Computing Power Network for SAGIN

6G will build a Space-Air-Ground Integrated Network (SAGIN) with global wide-area coverage by integrating terrestrial networks (such as mobile cellular networks) and non-terrestrial networks (such as satellite networks and high-altitude communication platforms), thus providing the all-time and all-territory broadband mobile access capabilities for various applications. In this paper, we apply federated learning (FL) to 6G SAGIN network by combining blockchain with it to prevent the adverse effects of malicious devices on models training. As an example, we further illustrate how to utilize our proposed blockchained FL platform to help 6G dynamic spectrum management. Simulation results show that the proposed system offers considerable advantages in terms of detection accuracy and obtained rewards.
Scalable Multi-Task Learning R-CNN for Object Detection in Autonomous Driving

Multi-task learning (MTL) is a rapidly growing field in the world of autonomous vehicles, particularly in the area of computer vision. Autonomous vehicles are heavily reliant on computer vision technology for tasks such as object detection, object segmentation, and object tracking. The complexity of sensor data and the multiple tasks involved in autonomous driving can make it challenging to design effective systems. MTL addresses these challenges by training a single model to perform multiple tasks simultaneously, utilizing shared representations to learn common concepts between a group of related tasks, and improving data efficiency. In this paper, we propose a scalable MTL system for object detection that can be used to construct any MTL network with different scales and shapes. The proposed system is an extension of the Mask R-CNN. It is designed to overcome the limitations of learning multiple objects in multi-label learning. We have a typical network and evaluated its performance on the Berkeley Deep Drive 100KBDD100k dataset. The experimental results demonstrate that the proposed MTL network outperforms a base single-task network, Mask RCNN, in terms of mean average precision at 50 (mAP50). Furthermore, we have also conducted a comparison with the existing representative approaches.
The Passport: A Single-Node Authentication System for Heterogeneous Voice-Controlled IoT Networks

The Passport is a robust voice biometric authentication system for heterogeneous voice-controlled IoT networks that centralizes the management of user authentication. It stores user data securely on a fog node using encryption and chaffing, making it tamper-proof and protecting user privacy by eliminating the need for outsourcing biometric data. A network of microphones is implemented to manage voice authentication across heterogeneous voice-controlled devices, and a new security protocol, The Passport Chain, is designed to verify the authenticity of the devices on the network. This protocol, inspired by blockchain technology, uses the computing time required to solve cryptographic challenges to generate unique device fingerprints and ensure the legitimacy of devices on the microphone network, preventing spoofing attacks. The system was tested and the results indicated a false acceptance rate (FAR) of 0.8 and a false rejection rate (FRR) of 1.4.
Low-Latency Internet Traffic Identification using Machine Learning with Trend-based Features

Identifying the type of network traffic has several advantages, such as detecting and preventing applications that violate an organization’s security policy or improving Quality of Service (QoS) and Quality of Experience (QoE) through traffic engineering. To enhance QoS support for Internet Service Providers (ISPs), a fine-grained classification scheme for network traffic is proposed in this paper. Statistical analysis of the throughput patterns of FTP, video conferencing, and video streaming traffic reveal that using new statistical features can be more effective at distinguishing the Internet traffic, especially from a QoS perspective, compared to the features commonly used in the literature, even for encrypted traffic. In this work, machine learning algorithms for classifying the low-latency traffic are trained using combinations of statistical features including the novel trend identification. Experiments are conducted to evaluate the proposed method using large-scale real network traffic data. Results show that our method can classify the particular type of traffic with accuracy of over 97%, and identify the low-latency traffic in the traffic mix with accuracy of 87%.
A Robust Text Information Hiding Model Based On Quick Response Code

As an important direction in the field of information hiding, steganography is a significant means to realize secret communication. With the development of artificial intelligence, researchers have tried to use Deep Learning (DL) to design automated information hiding schemes, but the existing schemes still have shortcomings in security, hiding capacity, and robustness. To solve this problem, this paper designs a set of safe and robust image information hiding schemes by using a DL network, Quick Response (QR) coding, and introducing a noise layer mechanism. In addition, this scheme makes information hiding technology get rid of the dependence on human operation and prior professional knowledge, breaks the dilemma that information hiding needs to choose the appropriate hidden carrier and modify the carrier to embed information, and also proves that it has great potential in the field of information security.
DLCCB: A Dynamic Labeling Based Covert Communication Method on Blockchain

Recently, blockchain-based covert communication has gained momentum, for the decentralization, anonymity, and immutability feature of blockchain. Nevertheless, some challenges impair its security and efficiency. Most schemes have a weak generalization ability, and can merely be applied to a specific blockchain platform. Storage-based covert transmission schemes usually have limited space for data embedding, affecting their Information delivery efficiency. Besides, static data sifting rules raise the risk of information leakage. In this paper, we design DLCCB(Dynamic Labeling based Covert Communication on Blockchain). We first split the information to be delivered into several pieces and utilize the destination address of each transaction to embed them. Then a dynamic labeling method is proposed for updating sifting rules without extra negotiation between sender and receiver. Besides, we design two kinds of sifting algorithms, namely online and offline sifting algorithm. We perform our solution on Ropsten, a test net of Ethereum. The experiment result verifies the feasibility of our scheme.
Fast Robot Hierarchical Exploration Based on Deep Reinforcement Learning

This paper investigates the use of reinforcement learning for autonomous exploration in an unknown environment. Autonomous exploration is crucial in many situations, such as urban search, security inspection, environmental mapping, etc. Traditional approaches focused on frontiers are unlikely to span a variety of enormously complex scenarios. Convergence is a little more difficult for learning-based approaches, which can adapt to many different environments. Consequently, a hierarchical exploration framework is built using frontier information. We propose a reinforcement learning-based local decision exploration model that uses deep neural networks to learn the optimal strategy from the environment. To prevent falling into local optimization, we also suggest a global rescue module to assist the robot in returning to the proper exploration track. Compared with other hierarchical methods, the framework is more effective and resilient in many contexts, greatly decreasing the total completion time and path length.
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments

Intelligent, large-scale IoT ecosystems have become possible due to recent advancements in sensing technologies, distributed learning, and low-power inference in embedded devices. In traditional cloud-centric approaches, raw data is transmitted to a central server for training and inference purposes. On the other hand, Federated Learning migrates both tasks closer to the edge nodes and endpoints. This allows for a significant reduction in data exchange while preserving the privacy of users. Trained models, though, may under-perform in dynamic environments due to changes in the data distribution, affecting the model’s ability to infer accurately; this is referred to as concept drift. Such drift may also be adversarial in nature. Therefore, it is of paramount importance to detect such behaviours promptly. In order to simultaneously reduce communication traffic and maintain the integrity of inference models, we introduce FLARE, a novel lightweight dual-scheduler FL framework that conditionally transfers training data, and deploys models between edge and sensor endpoints based on observing the model’s training behaviour and inference statistics, respectively. We show that FLARE can significantly reduce the amount of data exchanged between edge and sensor nodes compared to fixed-interval scheduling methods (over 5x reduction), is easily scalable to larger systems, and can successfully detect concept drift reactively with at least a 16x reduction in latency.
Innovations in the “Boolean Functions” Section of the Discrete Mathematics Course

This paper proposes innovations in teaching the course of discrete mathematics at universities, innovations refer to the “Boolean functions” section. A fast method for constructing the Zhegalkin polynomial for a given Boolean function is proposed. Also, for the use of Post’s theorem on the functional completeness is proposed a visual method for determining the presence of monotonicity of a Boolean function and a convenient criterion for determining whether given Boolean function is affine.
Studying Emotional Tone Transfer when Translating Literary Texts from English into Russian

The paper is devoted to the problem of analysing translations made by students specializing in translation practices. There are proposed to use bidirectional convolution neural networks to find out emotions in original texts and in translations. Used models demonstrated high accuracy and were applied to the translations of two emotionally different texts.
Self-organization of Scientific and Pedagogical Community as a Factor of Stability of the Education Informatization Processes

The article deals with the processes of digital transformation and informatization of regional education systems, which are driven by the processes of self-organization of the scientific and pedagogical community. A cluster approach for assessment of time periods of informatization in education has been highlighted. A hypothesis has been put forward about the intensive and sustainable development of clusters with the support of a set of measures aimed at the formation of new types of informal structures (scientific and pedagogical communities) in the environment of professional interaction. It has been emphasized that the self-organization of the scientific and pedagogical community is the result of building informal relationships between members of the local professional community with the purpose of solving a certain range of problems and performing functional tasks in managing the digital transformation of regional education systems. For the Khabarovsk Territory (Russia), for example, the initiators of such professional associations are the inter-regional public organization - “Academy of Informatization of Education” (AIE) and the Khabarovsk branch of the AIE, which unites scientists and specialists from universities, scientific institutions, educational institutions and education authorities. The Khabarovsk branch of the AIE has the potential to integrate the scientific and pedagogical communities based on the principles of self-organization.
Technology-Enhanced Education: Digital Media in Training Future Language Teachers/Translators/Interpreters

The current study aims at identifying students’ attitudes towards digital media presence in the classroom, their usability and likely output in educational practices. A future language worker should know how to deal with all these new epoch communication aspects and should be trained to recognize and arrange the much needed communicative mechanisms and instruments to undergird and sustain fruitful educational collaboration. Teaching-learning agenda for the students that are seeking to become teachers/translators/interpreters should be based on some non-linear discourse-oriented model. The paper further analyzes the options that can be proposed to train proper communication skills (including mediation competence) through practicing media-centered activities. A group of respondents was introduced to and involved in a set of assignments and activities based on media platforms content with built-in links to provide a vibrant speech practice discussion or a challenging self-study assignment to improve students’ communication and mediation skills. Results indicate that the web-based tasks focus on some pressing topics and can fruitfully recontextualize the academic planning either for specific linguistic disciplines (Media Discourse, Language-Culture Study) or just for speech training at language classes. The study highlights the opportunities for both teachers and students to realize their language and mediation potential.
Automatization of Tests to Assess Knowledge of Foreign Students in Teaching Mathematics

The paper is devoted to the problem of automatizing knowledge assessment. There is given the analysis of existing learning management systems and questionnaire tools to support educational process. On the example of joint bachelor program in the field of Metallurgy is presented the scheme to knowledge in Mathematics of students studying in English.
In-Materio Reservoir Devices Made of Random Network of Nanomaterials for Application to Smart Sensors and Autonomous Robotics

Reservoir computing is expected as low-power AI software or hardware systems because learning is performed only in one layer of the artificial neural network with a middle layer made of a random network with non-linear nodes. Inmaterio reservoir device is a kind of physical reservoir in which the middle layer is made of a random network of materials. Here, we would like to introduce how in-materio reservoir works and can be applied to smart sensors and autonomous robotics.
Performance Improvement of Single-Electron Reservoir Computing Circuit with Multiple-Tunnel-Junction Single Electron Oscillator

We designed the single-electron (SE) reservoir computing (RC) circuit using multiple-tunnel-junction SE oscillators (MJSEOs), which was compared to a previous SE RC circuit that utilized standard SEOs. The learning performance of the circuits was evaluated by root-mean-square error (RMSE), and it was found that the SE RC circuit with MJSEOs showed superior performance for waveform prediction than the SE RC circuit with SEOs.
The Experience of Training the Students of the Pedagogical University to the Implementation of the Blended Learning at School

The article deals with the approaches of the definition of the blended learning. There are presented the components, defining the future teachers training to the implementation of the blended learning of Computer Science. The authors describe the experience of the training of the students in the pedagogical university to the implementation of the blended learning at school.
Analysis Of Beginning Reading Ability And Difficulties Faced 4th Grade Elementary Students At MI Tarbiyatul Falah

Background: In the form of learning activities in schools, reading is one of the most essential skills that students need to master. The ability to read for students is seen as a determinant of their success in carrying out their learning activities while at school. Purpose: The purpose of this study was to find out how far the beginning reading ability of 4th grade elementary school students at MI Tarbiyatul Falah and the difficulties they face. Design and methods: This study applies a descriptive qualitative method, which is a method that is carried out by visiting data sources directly, then analyzing the data obtained as it is. Researchers collected further data using interview techniques, observation and tests on these students and class teachers to find the causal factors. Results: the researcher draws the conclusion that in students' initial reading skills, each aspect relates to and influences other aspects of reading, and this difficulty is motivated by factors that become obstacles in applying initial reading skills