Reviewerly/exHarmony huggingface dataset:
Evaluation of the Distributed Strategies for Data Parallel Deep Learning Model in TensorFlow

Distributed deep learning is a branch of machine intelligence in which the runtime of deep learning models may be dramatically lowered by using several accelerators. Most of the past research reports the performance of the data parallelism technique of DDL. Nevertheless, additional parallelism solutions in DDL must be investigated, and their performance modeling for specific applications and application stacks must be reported. Such efforts may aid other researchers in making more informed judgments while creating a successful DDL algorithm. Distributed deep learning strategies are becoming increasingly popular as they allow for training complex models on large datasets in a much shorter time than traditional training methods. TensorFlow, a popular open-source framework for building and training machine learning models, provides several distributed training strategies. This chapter provides a detailed evaluation of the different TensorFlow strategies for medical data. The TensorFlow distribution strategy API is utilized to perform distributed training in TensorFlow.
A Chatbot-Based Strategy for Regional Language-Based Train Ticket Ordering Using a novel ANN Model

Chatbots are becoming increasingly crucial in modern society. Typically, a large group of individuals will purchase train tickets together. This requires considerable effort and time. Multiple inquiries from a user are part of the booking procedure. In this research, the authors create an intelligent, user-friendly chatbot for booking train tickets in the native language. In this study, a Tamil-speaking chatbot is developed to assist with train ticket purchases. The authors employed NLP techniques to create an effective and user-friendly conversational interface. The above poll indicates that chatbots have been used in a variety of contexts with positive results. This method will make purchasing tickets much less of a burden for residents of remote areas, who will appreciate it. The ANN model is used to train the chatbot to discern the consumer's desires and respond accordingly. The proposed method has a success rate of 85% and will benefit consumers by expediting and simplifying ticket transactions.
Identifying the barriers faced by medical students using virtual reality simulation

Abstract Background Virtual reality technology is increasingly used in medical education to facilitate immersive experiences allowing users to interact with a simulated environment to foster learning. However, the potential barriers to this experience from a user perspective have not been fully explored. Methods Twenty final-year medical students participated in answering a pre- and post-simulation questionnaire as part of a facilitated virtual reality simulation session. The questionnaire explored participants’ expectations and experiences of virtual reality, in relation to adverse effects, interest and usefulness through 0–10 rating scales. Free-text questioning was used for further discussion. Results Ratings of all assessed adverse symptoms experienced by students were significantly lower than their expected ratings (p &lt; 0.01), and ratings of reported usefulness and interest in virtual reality significantly increased post-simulation (p &lt; 0.01). The median ratings given for all symptoms experienced by students were either 0 or 1/10 (none/minimal). Free-text comments raised problems of visual focussing in those who wear glasses. Discussion Our results suggest that virtual reality is an appropriate simulation modality which is acceptable to students. Potential barriers such as cybersickness and discomfort were minimally experienced and did not impact reported usefulness. We promote the generalisability of these findings by demonstrating them on commonly used consumer equipment and a common use case.
Adaptive weighted wild horse optimizer based on backward learning and small-hole imaging strategy

Wild Horse Optimizer (WHO) is a population-based metaheuristic algorithm inspired by animal behavior, which mainly imitates the decent behavior, grazing behavior, mating behavior and leadership dominance behavior of wild horses in nature to find the optimal. The initialization of the population by imitating the behavior of wild horses is prone to uneven distribution of population positions, and its position updating method is prone to local optimal problems while improving the efficiency of the search. In order to enhance the population diversity and to break out of the local optimum, an adaptive weighted wild horse optimizer based on backward learning and small-hole imaging strategy is proposed. The backward learning strategy is used to enhance the population diversity and improve the uneven distribution of individuals; The adaptive weight and small-hole imaging strategy are added to the local search strategy to improve the global search ability and jump out of the local optimum. To verify the effectiveness of the proposed algorithm, simulation experiments were conducted by using 23 benchmark test functions to test the search ability and Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Rat Swarm Optimizer (RSO) and Multi-Verse Optimizer (MVO) algorithms are compared in terms of their search performance, and finally four real engineering design problems are solved. The simulation results indicate that the proposed FHPWHO has excellent merit-seeking capability.
DWAEF: a deep weighted average ensemble framework harnessing novel indicators for sarcasm detection1

Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers.
Decidability in argumentation semantics

Much of the formal study of algorithmic concerns with respect to semantics for abstract argumentation frameworks has focused on the issue of computational complexity. In contrast matters regarding computability have been largely neglected. Recent trends in semantics have, however, started to concentrate not so much on the formulation of novel semantics but more on identifying common properties: for example, from basic ideas such as conflict-freeness through to quite sophisticated ideas such as serializability. The aim of this paper is to look at the implications these more recent studies have for computability rather than computational complexity.
Experiences with Student Projects Focusing on Chemistry Shows in Undergraduate Chemistry Teacher Education

The article is devoted to the preparation of future chemistry teachers for their motivational influence on pupils. It analyses the experience of the course Means of Motivation in Chemistry Teaching, which is designed as an elective course in the undergraduate preparation of future chemistry teachers at our university. It looks closely at the analysis of student projects that focus on chemistry, showing preparation and implementation, and that are part of the course.
An Exploratory Study Gathering Security Requirements for the Software Development Process

Software development stands out as one of the most rapidly expanding markets due to its pivotal role in crafting applications across diverse sectors like healthcare, transportation, and finance. Nevertheless, the sphere of cybersecurity has also undergone substantial growth, underscoring the escalating significance of software security. Despite the existence of different secure development frameworks, the persistence of vulnerabilities or software errors remains, providing potential exploitation opportunities for malicious actors. One pivotal contributor to subpar security quality within software lies in the neglect of cybersecurity requirements during the initial phases of software development. In this context, the focal aim of this study is to analyze the importance of integrating security modeling by software developers into the elicitation processes facilitated through the utilization of abuse stories. To this end, the study endeavors to introduce a comprehensive and generic model for a secure software development process. This model inherently encompasses critical elements such as new technologies, human factors, and the management of security for the formulation of abuse stories and their integration within Agile methodological processes.
Cloud–Edge Collaborative Inference with Network Pruning

With the increase in model parameters, deep neural networks (DNNs) have achieved remarkable performance in computer vision, but larger DNNs create a bottleneck for deploying DNNs on resource-constrained edge devices. The cloud–edge collaborative inference based on network pruning provides a solution for the deployment of DNNs on edge devices. However, the pruning methods adopted by existing frameworks are locally effective, and the compressed models are over-sparse. In this paper, we design a cloud–edge collaborative inference framework based on network pruning to make full use of the limited computing resources on edge devices. In our framework, we propose a sparsity-aware feature bias minimization pruning method to reduce the feature bias that happens during network pruning and prevent the pruned model from being over-sparse. To further reduce the inference latency, we consider the difference in computing resources between edge devices and the cloud, then design a task-oriented asymmetric feature coding to reduce the communication overhead of transmitting intermediate data. With comprehensive experiments, our framework can reduce end-to-end latency by 82% to 84% with less than 1% accuracy loss, compared to the cloud–edge collaborative inference framework with traditional methods, and our framework has the lowest end-to-end latency and accuracy loss compared to other frameworks.
Fraction Execution Resolver Using a Hybrid Multi-CPU/GPU Encoding Scheme

Modern video coding standards make use of sub-pixel motion estimation to improve the video quality and reduce the bitrate. It is known that the fraction motion estimation (FME) part follows the integer motion estimation (IME) and adds an extra computational overhead due to the interpolation and the additional motion searches. In this paper, we propose a fraction execution resolver (FER) algorithm that lets the encoder skip the fraction part when specific criteria are met by introducing a preliminary fast test decision point (pFTDP) function for the IME part. If the pFTDP returns zero motion vectors (MVs) and the displacement search area center is also zero, then the fraction part is skipped. The pFTDP decision maker is executed only once, when a 2N × 2N block is first met, while all subsequent blocks follow this initial decision either by receiving the necessary MVs and RD from the pFTDP function or by using the precalculated IME values from the GPU kernel. For our experiments, we use a multithreaded CPU environment that also makes use of GPUs only for the integer part. Our evaluations provide a greater than 1600% encoding time saving at its peak in comparison with the default HEVC sequential mode and ideally a saving of greater than 2286% for still video frame sequences. The total average speedup for both Class A and Class B video sequences is ×13.45. The gain of the FER itself is more than ×3.9 compared with the same multithreaded setup environment. The PSNR and bitrate overhead observed are proportional to the tiling scheme used and are more related to the way CABAC works internally. The FER’s negative effects on coding efficiency are proven to be negligible. A balance between speed and quality achieved by using a lower tiling pattern is shown to minimize the negative effects of the encoding scheme pattern. The experimental results confirm the validity of our motivation, namely, that we can benefit from a software fraction execution resolver without any extra hardware costs. The gain is further increased when video sequences have more static blocks than others.
Converged Security and Information Management System as a Tool for Smart City Infrastructure Resilience Assessment

Current research on smart cities is primarily focused on the area of applicability of information and communication technologies. However, in the context of a multidisciplinary approach, it is also necessary to pay attention to the resilience and converged security of individual infrastructures. Converged security represents a particular security type based on a selected spectrum of certain convergent security types of, assuming the creation of a complementary whole. Considering the outputs of the analysis of security breaches manifestations, this kind of security makes it possible to detect emerging security breaches earlier (still in the symptom stage), thus providing a more efficient and targeted solution suitable for building smart city infrastructure. In its essence, the article refers to the practical application of the converged security theoretical principles presented in the publication to a functional sample, deployed and tested in practical conditions in context of selected smart city infrastructure protection and resilience. Considering the nature of the practical application, the convergence of a wider spectrum of smart security alarm systems in the resilience assessment context is defined. In the beginning, the general principles of security/safety and the need for their convergence are presented. In this context, the mathematical model called Converged Resilience Assessment (CRA) method is presented for better understanding. Subsequently, Physical Security Information Management (PSIM) and Security Information and Event Management (SIEM) systems are described as a technological concept that can be used for resilience assessment. The most beneficial part is the structural, process, and functional description of the Converged Security and Information Management System (CSIM) using the concept of smart security alarm systems converged security.
Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding

Modern, commonly used cryptosystems based on encryption keys require that the length of the stream of encrypted data is approximately the length of the key or longer. In practice, this approach unnecessarily complicates strong encryption of very short messages commonly used for example in ultra-low-power and resource-constrained wireless network sensor nodes based on microcontrollers (MCUs). In such cases, the data payload can be as short as a few bits of data while the typical length of the key is several hundred bits or more. The article proposes an idea of employing a complex of two algorithms, initially applied for data compression, acting as a standard-length encryption key algorithm to increase the transmission security of very short data sequences, even as short as one or a few bytes. In this article, we present and evaluate an approach that uses LZW and Huffman coding to achieve data transmission obfuscation and a basic level of security.
Exploring the Impact of Mobile Exams on Saudi Arabian Students: Unveiling Anxiety and Behavioural Changes across Majors and Gender

Students’ anxiety and behavioural changes while using different examination methods (paper, PC, and mobile exam platforms (MEPs)) were estimated. The influence of academic majors and gender was also determined by answering the following questions: How do anxiety and behavioural changes vary among students using different exam methods? How do students’ anxiety levels vary according to academic majors and gender while using different exam methods? A survey was conducted with 826 students enrolled in eight colleges at King Faisal University in Saudi Arabia. The results revealed less anxiety and fewer harmful behavioural changes among students using MEPs compared with other methods. Furthermore, less anxiety and fewer behavioural changes were observed among health and science majors than humanities and social science majors and among female students than male students while using MEPS. Therefore, MEPs should be gradually adopted by higher education institutions in Saudi Arabia, especially for humanities and social science majors and for male students. Furthermore, researchers and decision-makers should find unique solutions to reduce the positive correlation between anxiety and behavioural changes among health and science majors and female students toward MEPs. This can be achieved by identifying obstacles and introducing modern solutions, such as AI-generated exams, among others.
The ASPIRE Project: Atmospheric Parameters Affecting Solar Irradiance and Solar Energy in Athens, Greece—Overview and Results

The study presents results of the research project ASPIRE (atmospheric parameters affecting spectral solar irradiance and solar energy). A new dataset has been created for Athens, Greece, for one year (December 2020–November 2021), consisting of detailed measurements of spectral solar irradiance (SSI) from 300 to 1020 nm and its influencing parameters (clouds, aerosols, ozone, nitrogen dioxide, sulfur dioxide, water vapor). The new dataset is explored, and major results are presented. The datasets are available to scientists from interdisciplinary scientific communities for research and education purposes. Here, we present an overview of different studies within ASPIRE, dealing with effects of different aerosol types on solar measurements and PV-related outputs and also the evaluation of a nowcasting solar model under different atmospheric conditions.
Can Same-right-and-different-left Gestures Be Recognized with Only Right-hand Signals?

Sign language serves as a bridge between the hearing-impaired and other people. Existing sensor-based approaches tend to only collect data from the dominant hand. Does this signal collection method affect the accuracy of gesture recognition, especially gestures where the dominant hand has the same movement while the non-dominant hand has different movements? The specific gestures are called same-right-and-different-left (SRDL) where the right hand is dominant. This article is the first to propose an SRDL-aware sign language recognition system. First, an SRDL discriminator based on an autoencoder and range classifier is designed to determine whether the gesture is SRDL. Second, an SRDL feature selector based on clustering relationship is presented. Multivariate variational mode decomposition and fast fourier transform are used to obtain the feature expression. Moreover, a clustering relationship algorithm is proposed to dynamically select features for every group of SRDL gestures in the feature expression. Finally, the experimental results show that the average word error rate is 14.3% and decreases by 8.5% and 12.1% compared with Signspeaker and MyoSign, respectively.
Error Codes in and for Network Steganography

We illustrate the inter-relationship between network steganography and error coding through examples where error codes (correction or erasure codes) are used in steganographic channels and examples where steganographic channels are established in data on which error codes are applied. In particular, we experimentally investigate an existing approach of a steganographic channel in a transmission with error correction code with respect to bandwidth, robustness and detectability, and expand this construction to provide another example of multi-level steganography, i.e., a steganographic channel within a steganographic channel.
Predicting Physical Disturbances in Organic Computing Systems Using Automated Machine Learning

Robustness against internal or external disturbances is a key competence of Organic Computing Systems. Hereby, a rarely discussed aspect are physical disturbances, therefore, failures or breakdowns that affect a systems physical components. Before experiencing such a disturbance, physical components may show various measurable signs of deterioration that might be assessed through sensor data. If interpreted correctly, it would be possible to predict future physical disturbances and act appropriately in order to prevent them from possibly harming the overall system. As the actual structure of such data as well as the behaviour that disturbances produce might not be known a priori, it is of interest to equip Organic Computing Systems with the ability to learn to predict them autonomously. We utilize the Automated Machine Learning Framework TPOT for an online-learning-inspired methodology for learning to predict physical disturbances in an iterative manner. We evaluate our approach using a freely available dataset from the broader domain of Predictive Maintenance research and show that our approach is able to build predictors with reasonable prediction quality autonomously.
Recent advances and future challenges in federated recommender systems

Abstract Recommender systems are an integral part of modern-day user experience. They understand their preferences and support them in discovering meaningful content by creating personalized recommendations. With governmental regulations and growing users’ privacy awareness, capturing the required data is a challenging task today. Federated learning is a novel approach for distributed machine learning, which keeps users’ privacy in mind. In federated learning, the participating peers train a global model together, but personal data never leave the device or silo. Recently, the combination of recommender systems and federated learning gained a growing interest in the research community. A new recommender type named federated recommender system was created. This survey presents a comprehensive overview of current research in that field, including federated algorithms, architectural designs, and privacy mechanisms in the federated setting. Furthermore, it points out recent challenges and interesting future directions for further research.