Reviewerly/exHarmony huggingface dataset:
Covert Communication with Incumbent Channel Activities

In this paper, we investigate the throughput performance of covert communication over a channel with incumbent channel activities. Unlike existing work, we explicitly consider the interplay between channel access at the MAC layer and signal detection at the physical layer. Given the incumbent channel activities, the covertness constraint is mapped to the probability of detection at the physical layer, and the covert throughput is obtained accordingly. Our results demonstrate a significant increase in covert throughput can be achieved by exploiting the incumbent channel activities.
Coarse-to-fine fusion for language grounding in 3D navigation

We present a new network whereby an agent navigates in the 3D environment to find a target object according to a language-based instruction. Such a task is challenging because the agent has to understand the instruction correctly and takes a series of actions to locate a target among others without colliding with obstacles. The essence of our proposed network consists of a coarse-to-fine fusion model to fuse language and vision and an autoencoder to encode visual information effectively. Then, an asynchronous reinforcement learning algorithm is used to coordinate detailed actions to complete the navigation task. Extensive evaluation using three different levels of the navigation task in the 3D Vizdoom environment suggests that our model outperforms the state-of-the-art. To see if the proposed network can deal with a real-world 3D environment for the navigation task, it is combined with Rec-BERT, which is based on REVERIE. The result suggests that it performs better, especially for unseen cases, and it is also useful to visualize what and when the agent pays attention to while it navigates in a complex indoor environment.
Russian-language repository of the open clinical data “SibMED Data Clinical Repository”

The global digitalization has become one of the most significant challenges in the field of medicine and healthcare. The rapid development of digital technologies determines the growing demand for constant access to big data in real time Their use is in need for research and technological projects in the sphere of artificial intelligence technologies development. Siberian State Medical University developed the first Russian-language repository of clinical data “SibMed Data Clinical Repository” (https://dataset.ssmu.ru/). The article describes the structure, functions of the repository, and perspectives of its use.
A strengthened eCK secure identity based authenticated key agreement protocol based on the standard CDH assumption

An Authenticated Key Agreement (AKA) protocol enables two communicating parties to compute a session key with equal partnership, such that each entity is assured of the authenticity of its peer. Identity-based AKA (ID-AKA) protocols facilitate implicit authentication of the participating entities, without certificate verification. However, most of the existing ID-AKA schemes are proven secure based on the strong Gap Diffie-Hellman (GDH) assumption. Currently, there are no known implementation methods to realize the GDH assumption without using bilinear pairings. Further, none of the existing ID-AKA protocols have provable security against practical attacks due to intermediate result leakages. To this end, we propose a purely pairing-free ID-AKA protocol based on the Computational Diffie-Hellman assumption. The protocol offers provable security under the strengthened eCK (seCK) model that captures attacks resulting from intermediate result leakages. Comparative analysis with other ID-AKA protocols suggests that the proposed protocol satisfies stronger security requirements, without the gap assumption.
Enhancing IT professionals' communication skills through interactive training methods

The paper deals with the issue of enhancing communication skills among IT professionals through interactive training methods. The rapid evolution of IT education, with its constantly changing content, emerging knowledge sectors, and evolving patterns of communication and interaction, necessitates urgent attention to this matter. To identify the specific communication needs across various domains, the researchers have analyzed regulations, guidelines, and certification frameworks from European nations and Ukraine. Extensive research has been conducted on interactive teaching concepts, activities, and their advantages. The study emphasizes that interactive teaching techniques can effectively facilitate the instruction of future IT workers in both general and specialized areas. The main characteristics of several interactive teaching techniques are described. To enhance the communication abilities of IT professionals, the study suggests incorporating project-based learning, brainstorming, gamification, and discussion, with a particular emphasis on English language instruction in higher education institutions. It is crucial to keep a careful balance between interactive techniques and strategic planning to foster a suitable level of comprehension in both language use and real-world interactions.
Demographic variables do not affect the use of mobile devices for study and learning purposes.

The use of mobile devices has become an everyday occurrence, and more and more higher education institutions have started adopting this technology in their interactions with students. Previous research has confirmed that mobile devices are becoming more widely used in learning. The sphere of formal education is not static but dynamic and evolving all the time, and central to this are developments in the technologies of teaching and learning. Educational institutions, which are seen as the bastion of new thoughts and thinking, need to ensure that these innovations and developments are incorporated in their teaching philosophies and mode of delivery – mainly to be able to accommodate the new generation of learners in the Industry 4.0, which impacts on their social and personal domains. Central to this, where the focus at tertiary institutions has shifted to, is the implementation of mobile learning technologies. This study investigates the views of learners regarding the use of mobile learning devices in their study and learning environments. In order to explore this aspect, the study aimed to determine the significance of age, gender and year of study in the use of mobile learning devices. No statistically significant differences were found between the identified demographic variables (age, gender and year of study) and uses of mobile devices in a study and learning environment.
DEFIA: Evaluate defense effectiveness by fusing behavior information of cyberattacks

The existing researches point to a lack of studies addressing the quantitative evaluation of the effectiveness of cyber defense. This difficult matter has been plaguing cyber security researchers and managers. This paper provides a quantitative method to evaluate defense effectiveness, called DEFIA. DEFIA records information about attack behavior in a formatted way and evaluates the defense effectiveness based on the utility of attack behaviors. By calculating the probability features of the attack behavior in the attack sample, the physical space structure of the attack behavior information is constructed. In particular, we define the utility calculation principle of attack behaviors and regard it as the benchmark for evaluating defensive capabilities. DEFIA can quantitatively assess the defense effectiveness of defense methods deployed in computer systems. We explain how the method works by simulating some real attack scenarios and based on the information provided by Virustotal to prove that DEFIA is reasonable and feasible.
Speech scrambler with multiwavelet, Arnold Transform and particle swarm optimization

Abstract Speech scrambling aims to distort speech signals to prevent unauthorized listeners from understanding them, but conventional techniques are vulnerable to attacks. Therefore, more robust and secure speech scrambling algorithms are needed to ensure sensitive communication security. A proposed scheme uses a particle swarm optimization algorithm to generate a random key and optimize the level of noise in the scrambled signal, along with two transformations Multiwavelet and Arnold techniques to improve complexity and security. The proposed algorithm has been evaluated using various performance measurements and has demonstrated superior encryption performance than other similar audio encryption schemes with key space equal to 128 × 2.718. Further research and development in speech scrambling are essential to guarantee secure communication in sensitive contexts such as military and intelligence.
Multi-Intelligence English Teaching Model Based on Distance and Open Education

Distance education between the student and the teacher through online sessions can make it difficult for a student who does not understand a concept to ask for clarification. Lack of a physical campus or social pressure from peers can demotivate students from completing their assignments. The framework of multi-intelligence English teaching based on cloud technology (MIET-CT) is introduced to solve these kinds of issues. The method of blended learning (BL) combines in-person instruction with digital resources to improve distance and open education by examining the efficacy of a learning strategy, with an emphasis on collaborative and autonomous learning (CAL) by artificial intelligence (AI). Cloud technology can potentially encourage students' independent learning as a cognitive tool by providing a cloud platform and multimedia instruction by domain modeling. As a result, various English teaching styles have been shown to increase student's motivation to learn and provide more impressive classroom results than conventional methods.
Intelligent privacy preserving deep learning model for securing IoT healthcare system in cloud storage

Nowadays the existing legacy management-based healthcare system maintains and processes a large amount of health-related data. The widespread adoption of the Internet of Things (IoT) and its progressive development have promised the way for the development of IoT-enabled healthcare with impressive data processing and big data storage capabilities. Intelligent medical healthcare intends to offer a framework to remotely monitor users’ health-related data as the Industrial Internet of Things (IIoT) develops. Because they are stored on a cloud server, the data are still susceptible to manipulation and privacy breaches. The Keras Xception Deep Learning System (KX-DLS) with Dynamic Searchable Symmetric Encryption (DSSE) scheme is a revolutionary IoT-based deep learning intelligent privacy-preserving system that is advantageous for digital healthcare and its functionalities to handle security-related challenges. The dataset is being used to pre-train the system, and users’ personal information is kept separate in a secure location. Without knowing any personal information about the users, we analyse health-related data stored in the cloud and build a sophisticated security framework based on a deep learning model. With the most extensive collection of security features, our framework for learning intelligent privacy preservation optimizes the system to guarantee high data integrity and few privacy breaches. As a result, it may be useful in situations where users employ mobile devices with limited resources to engage a healthcare cloud system for extensive virtual health services, and the results of this research show that it has been a better-secured model in comparison with state-of-the-art previous techniques.
Computational characteristics of the random-field Ising model with long-range interaction

Ising model is a widely studied class of models in quantum computation. In this paper we investigate the computational characteristics of the random field Ising model (RFIM) with long-range interactions that decays as an inverse polynomial of distance, which can be achieved in current ion trap system. We prove that for an RFIM with long-range interaction embedded on a 2-dimensional plane, solving its ground state is NP-complete for all diminishing exponent, and prove that the 1-dimensional RFIM with long-range interaction can be efficiently approximated when the interaction decays fast enough.
Boosting Just-In-Time Code Comment Updating Via Programming Context and Refactor

Comments are summary descriptions of code snippets. When analyzing and maintaining programs, developers tend to read tidy comments rather than lengthy code. To prevent developers from misunderstanding the program or leading to potential bugs, ensuring the consistency and co-evolution of comments and the corresponding code is an integral development activity in practice. Nevertheless, when modifying code, developers sometimes neglect to update the relevant comments, resulting in inconsistency. Such comments may pose threats to the comprehension and maintenance of the software. In our study, we propose an overall approach named Context and Refactor based Comment Updater (CRCU). CRCU is a Just-In-Time (JIT) comment updater for specific commits. It takes a commit-id as input and updates all the method comments in this commit according to the code change. CRCU could be viewed as an optimization and augmentation of existing comment updaters, especially those that rely only on neural networks. Compared to the existing comment updaters, CRCU fully leverages the programming context and refactoring types of the modified methods to improve its performance. In addition, several customized enhancements in data pre-processing are introduced in CRCU to handle and filter out low-quality commits. We conduct extensive experiments to evaluate the effectiveness of CRCU. The evaluation results show that CRCU combined with the state-of-the-art approaches could improve the average Accuracy by 6.87% and reduce the developers’ edits by 0.298 on average.
Current requirements for the training of IT bachelors

The authors focus on the universal competencies of IT bachelors, which are professionally important in the development of team IT projects. The authors have revealed that the universal competencies, which IT companies interested in, are not sufficiently formed by future IT bachelors in higher education institutions, which creates problems in their further professional activities. To find the causes, the analysis of indicators of personal results and meta-subject results of secondary education and universal competencies of the Federal State Educational Standard of Higher Education 3++ was carried out. Deficiencies were identified, leading to a skew in the continuity of personal results and meta-subject results of secondary education and the development of universal competencies in higher education. The professional standards for the conjugation of work requirements with universal competencies of the Federal State Educational Standard of Higher Education 3++ have been investigated. As a result, universal competencies, which are professionally significant for effective professional activities of IT bachelors in terms of implementation of team IT projects in remote working conditions, establishment of productive communication with IT team and with the customer of the software product, as well as continuous self-development of IT specialist, were identified. The component structure of professionally significant universal competencies consisting of individual-personal, organizational, and social personal components was developed. A methodology for the development of professional significant universal competencies of future IT bachelors on the basis of team media-educational projects is proposed. This methodology takes into account the individual distribution of roles in the project team and its implemented in the first year of IT training. The results of the study may be of interest for teachers who model professional tasks, as well as serve to improve the quality of training of competitive IT specialists.
Emerging Regulations on Content Moderation and Misinformation Policies of Online Media Platforms: Accommodating the Duty of Care into Intermediary Liability Models

Abstract Disinformation, hate speech and political polarization are evident problems of the growing relevance of information and communication technologies (ICTs) in current societies. To address these issues, decision-makers and regulators worldwide discuss the role of digital platforms in content moderation and in curtailing harmful content produced by third parties. However, intermediary liability rules require a balance that avoids the risks arising from the circulation at scale of harmful content and the risks of censorship if excessive burdens force content providers to adopt a risk-averse posture in content moderation. This piece examines the trend of altering intermediary liability models to include ‘duty of care’ provisions, describing three models in Europe, North America and South America. We discuss how these models are being modified to include greater monitoring and takedown burdens on internet content providers. We conclude with a word of caution regarding this balance between censorship and freedom of expression.
Bound entanglement is not Lorentz invariant

Bound entanglement, in contrast to free entanglement, cannot be distilled into maximally entangled states by two local observers applying measurements and utilizing classical communication. In this paper we ask whether a relativistic observer classifies states according to being separable, bound or free entangled in the same manner as an unboosted observer. Surprisingly, this turns out not to be the case. And that even if the system in a given inertial frame of reference is separable with respect to the partition momenta versus spins. In detail, we show that if the spin state is initially bound entangled, some boosted observers observe their spin states to be either bound entangled, separable or free entangled. This also explains why a general measure of the entanglement property is difficult to find.
Augmented Reality in Maintenance—History and Perspectives

Augmented Reality (AR) is a technology that allows virtual elements to be superimposed over images of real contexts, whether these are text elements, graphics, or other types of objects. Smart AR glasses are increasingly optimized, and modern ones have features such as Global Positioning System (GPS), a microphone, and gesture recognition, among others. These devices allow users to have their hands free to perform tasks while they receive instructions in real time through the glasses. This allows maintenance professionals to carry out interventions more efficiently and in a shorter time than would be necessary without the support of this technology. In the present work, a timeline of important achievements is established, including important findings in object recognition, real-time operation. and integration of technologies for shop floor use. Perspectives on future research and related recommendations are proposed as well.
The effect of ICT on financial sector development in Africa: does regulatory quality matter?

The moderating role of regulatory quality in the relationship between ICT and financial development in Africa is investigated in this study. We employ data from 38 African countries from 2003 to 2020. For the analysis, a two-step system GMM is used. Our findings demonstrate that ICT and regulatory quality are essential for financial development. The net effect of ICT and regulatory quality on financial development is positive, implying that regulatory quality moderates upwards the nexus between ICT and financial development. On different financial development indicators, ICT improves financial depth and access but deteriorates financial efficiency and stability. The net effects of ICT and regulatory quality are positive for financial depth and efficiency and negative for financial access and stability. It is, therefore, imperative for the government to improve regulatory quality in Africa, given the significance of ICT and regulatory quality in supporting financial development.
A Lightweight Detection Algorithm for Unmanned Surface Vehicles Based on Multi-Scale Feature Fusion

Lightweight detection methods are frequently utilized for unmanned system sensing; however, when put in complicated water surface environments, they suffer from insufficient feature fusion and decreased accuracy. This paper proposes a lightweight surface target detection algorithm with multi-scale feature fusion augmentation in an effort to improve the poor detection accuracy of lightweight detection algorithms in the mission environment of unmanned surface vehicles (USVs). Based on the popular one-stage lightweight YOLOv7-Tiny target detection algorithms, a lightweight extraction module is designed first by introducing the multi-scale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the attention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in mAP@0.5, a 4.3% rise in mAP@0.5:0.95, and satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs better in a variety of difficult water surface circumstances.
Identifying the dialectal background of American Finnish speakers using a supervised machine-learning model

Abstract This study presents results of two experiments using supervised machine-learning models to examine individual Finnish speakers’ dialectal backgrounds. Data come from interviews conducted with heritage speakers of Finnish in northern Wisconsin and are compared to data from the Finnish Dialect Syntax Archive. The models were constructed and then, following successful validation testing, used to identify the dialectal background of five individual American Finnish speakers. Results showed individual variation in dialectal backgrounds and some correlation to speakers’ likely language input. Our approach offers a new methodological tool for examining speakers’ dialectal backgrounds in situations of language contact.
Non-Contact Global Measurement of the Engine in Working Condition

Vibration measurements are essential for studying the dynamic properties of structures, as they can better capture the global characteristics of the structure. This paper proposes a three-dimensional measurement method based on projected structured light to address the challenge of measuring the vibration information of engine pipes during operation. By using the data obtained from this method and applying the time domain analysis method for modal analysis, the modal parameters of the engine under operating conditions can be obtained. The feasibility of this method is demonstrated by comparing it with different measurement methods, and it provides an effective solution for modal identification of engines under operating conditions.