Reviewerly/exHarmony huggingface dataset:
Distributed new energy system access based on blockchain Consensus Algorithm

Abstract With the development of society, non-renewable energy such as oil and natural gas are gradually consumed and exhausted, and the energy crisis has become the biggest resistance to the development. Therefore, the development of New Energy (NE) has become an important issue for the Sustainable Development (SD) of modern society. Distributed NE system is the new direction of modern energy development. Therefore, higher requirements are put forward to improve the performance of distributed NE systems. The traditional NE system is relatively scattered and cannot be used intensively. There is no consensus among various NE systems, and the energy utilization is insufficient. It leads to a great waste of energy in the process of developing and using NE. The introduction of Blockchain Technology (BT) has contributed to solving many practical problems. BT can not only ensure the authenticity of data but also meet the requirements of high quality by taking advantage of its own characteristics of data tamperability and traceability mechanism. The Consensus Algorithm (hereinafter indicated by CA) in the blockchain ensures that the final results received by all participants are consistent and error-free through data synchronization, and distributed, consistency cooperation. The access of distributed NE solves the disadvantage that the traditional NE system cannot be used in a centralized way, to access NE more efficiently, conveniently, and quickly. In this paper, the distributed NE system was accessed based on the blockchain CA. By adopting BT and CA, a distributed NE system that was easy to access was established. The system could systematically deploy the existing developed NE and then make centralized use of it. This paper compares the efficiency, stability, and loss of access of the distributed NE system using the blockchain CA with that of the traditional distributed NE system. The results showed that the use of blockchain CA to access distributed NE could effectively utilize NE, and the efficiency of distributed NE access after using blockchain CA increased by about 28.8 %. At the same time, the experimental results showed that the access stability was high and the access loss was low. With the continuous development of NE, the blockchain CA would gradually become an important technical support in the energy field, thus promoting the overall development of the energy industry.
Large graph layout optimization based on vision and computational efficiency: a survey

Abstract Graph layout can help users explore graph data intuitively. However, when handling large graph data volumes, the high time complexity of the layout algorithm and the overlap of visual elements usually lead to a significant decrease in analysis efficiency and user experience. Increasing computing speed and improving visual quality of large graph layouts are two key approaches to solving these problems. Previous surveys are mainly conducted from the aspects of specific graph type, layout techniques and layout evaluation, while seldom concentrating on layout optimization. The paper reviews the recent works on the optimization of the visual and computational efficiency of graphs, and establishes a taxonomy according to the stage when these methods are implemented: pre-layout, in-layout and post-layout. The pre-layout methods focus on graph data compression techniques, which involve graph filtering and graph aggregation. The in-layout approaches optimize the layout process from computing architecture and algorithms, where deep learning techniques are also included. Visual mapping and interactive layout adjustment are post-layout optimization techniques. Our survey reviews the current research on large graph layout optimization techniques in different stages of the layout design process, and presents possible research challenges and opportunities in the future.
A Modified Enhanced Method of Audio – Video Steganography for High Security Data Transmission

Steganography is an art of convert communication, which offers secrete and secure way of communication. It has many application areas such as audio-video synchronization,in defense forces etc… The audio data is converted into binary format and encrypted using a XOR encryption algorithm. Then the encrypted audio data converted into XL data form. The video frames are divided into non-overlapping blocks, and the difference between the adjacent pixels in each block is computed to obtain a reference matrix. Each block is then iterated through, if the difference between adjacent pixels is greater than or equal to the average difference in the reference matrix, the MPVD (Modified Pixel Value Differencing) based LSB of the pixel value is modified to embed the encrypted audio data bit. The embedded bit is then XORed with the corresponding LSB of adjacent pixel in the same block. The modified video frames can be sent over an unsecured channel without revealing the embedded encrypted audio data. To extract the encrypted audio message from the stego video, the embedded bits are XORed with the corresponding LSB of the adjacent pixel in the same block, and the modified LSBs are extracted and decrypted using the same encryption algorithm and key.
A Ding Ontology of Chinese Bronze

Ding is a significant type of Chinese bronze that holds key cultural value. Traditional humanists have primarily focused on dating and classifying Ding. However, in the context of Digital Humanities, the research perspective of humanities scholars is gradually shifting towards data-driven research, with linked data emerging as a popular topic. A well-defined and standard ontology representing the complete domain knowledge is essential for linked Ding data. Unfortunately, most existing ontology cannot represent fine-grained knowledge of Ding or is too restrictive to represent partial knowledge of bronze Ding. In this context, we propose a fine-grained Ding ontology to represent the bronze Ding knowledge. In this paper, we present in detail the Ding ontology of Chinese bronze during the Shang and Zhou dynasties (from 1600 BC to 256 BC). We provide a detailed exposition of the Ding ontology and evaluate its effectiveness using OOPS!, OntoMetrics, and by answering competency questions in SPARQL. The building methodology of Ding ontology follows the ISO principles (ISO 1087 and ISO 704). The objective of this paper is to develop an open ontology of Ding during the Shang and Zhou dynasties, which can serve as a valuable resource for bilingual terminology dictionaries. The Ding ontology was published at http://www.dhontology.com/ChineseCulture/data/bronze.owl .
Understanding How National CSIRTs Evaluate Cyber Incident Response Tools and Data: Findings from Focus Group Discussions

National Computer Security Incident Response Teams (CSIRTs) have been established worldwide to coordinate responses to computer security incidents at the national level. While it is known that national CSIRTs routinely use different types of tools and data from various sources in their cyber incident investigations, limited studies are available about how national CSIRTs evaluate and choose which tools and data to use for incident response. Such an evaluation is important to ensure that these tools and data are of good quality and, consequently, help to increase the effectiveness of the incident response process and the quality of incident response investigations. Seven online focus group discussions with 20 participants (all staff members) from 15 national CSIRTs across Africa, Asia Pacific, Europe, and North and South America were carried out to address this gap. Results from the focus groups led to four significant findings: (1) there is a confirmed need for a systematic evaluation of tools and data used in national CSIRTs, (2) there is a lack of a generally accepted standard procedure for evaluating tools and data in national CSIRTs, (3) there is a general agreement among all focus group participants regarding the challenges that impinge a systematic evaluation of tools and data by national CSIRTs, and (4) we identified a list of candidate criteria that can help inform the design of a standard procedure for evaluating tools and data by national CSIRTs. Based on our findings, we call on the cyber security community and national CSIRTs to develop standard procedures and criteria for evaluating tools and data that CSIRTs, in general, can use.
Addressing Emotions and Beliefs for Vulnerable Jobseekers with Virtual Reality

Abstract Virtual reality (VR) technologies have been shown to impact the affective and motivational dimensions of learning, for example increasing learners’ enjoyment, confidence, and self-efficacy beliefs. While VR is increasingly being used for workforce development, research on these dimensions of learning in authentic workforce development contexts remains thin. This mixed-method case study addressed the need for more research on VR’s affordances for engaging affective dimensions of learning. We investigated the impact of a job interview VR simulation on the emotions, confidence, and self-efficacy beliefs of jobseekers who have been impacted by the criminal justice system. By observing the implementation of this application in an authentic workforce development context, we also addressed questions about how VR use and facilitation vary among participants. We find compelling evidence that a VR job interview simulation tailored to the experiences of people impacted by the criminal justice system can alleviate some of the emotional toll the job search takes on this vulnerable population, but we did not find evidence of changes in their self-efficacy beliefs. We discuss ways participants described the simulation as authentic practice, and how its facilitation and use varied, emphasizing the importance of VR design as a tool within a broader instructional context.
Non-line-of-sight target tracking with improved recurrent extreme learning machine

Abstract Target tracking provides important location-based services in many applications. The main challenge of target tracking is to combat the severe degradation problem in Non-Line-of-Sight (NLOS) scenario. Most Deep Learning algorithms available in literature to address this issue belong to batch learning with high complexity. This paper proposes a novel online sequential learning algorithm, Improved Recurrent Extreme Learning Machine (IRELM), to solve the NLOS target tracking problem as a position series prediction task. IRELM is able to train Recurrent Neural Network (RNN) inputs one-by-one and adaptively update the input weight, hidden weight, feedback weight and output weight. Extensive simulations and experiments prove the superior tracking performance and feasible complexity of IRELM over the state-of-the-art Deep Learning methods.
Lightweight and Elegant Data Reduction Strategies for Training Acceleration of Convolutional Neural Networks

Due to industrial demands to handle increasing amounts of training data, lower the cost of computing one model at a time, and lessen the ecological effects of intensive computing resource consumption, the job of speeding the training of deep neural networks becomes exceedingly challenging. Adaptive Online Importance Sampling and IDS are two brand-new methods for accelerating training that are presented in this research. On the one hand, Adaptive Online Importance Sampling accelerates neural network training by lowering the number of forward and backward steps depending on how poorly a model can identify a given data sample. On the other hand, Intellectual Data Selection accelerates training by removing semantic redundancies from the training dataset and subsequently lowering the number of training steps. The study reports average 1.9x training acceleration for ResNet50, ResNet18, MobileNet v2 and YOLO v5 on a variety of datasets: CIFAR-100, CIFAR-10, ImageNet 2012 and MS COCO 2017, where training data are reduced by up to five times. Application of Adaptive Online Importance Sampling to ResNet50 training on ImageNet 2012 results in 2.37 times quicker convergence to 71.7% top-1 accuracy, which is within 5% of the baseline. Total training time for the same number of epochs as the baseline is reduced by 1.82 times, with an accuracy drop of 2.45 p.p. The amount of time required to apply Intellectual Data Selection to ResNet50 training on ImageNet 2012 is decreased by 1.27 times with a corresponding decline in accuracy of 1.12 p.p. Applying both methods to ResNet50 training on ImageNet 2012 results in 2.31 speedup with an accuracy drop of 3.5 p.p.
End-to-End Post-Quantum Cryptography Encryption Protocol for Video Conferencing System Based on Government Public Key Infrastructure

Owing to the expansion of non-face-to-face activities, security issues in video conferencing systems are becoming more critical. In this paper, we focus on the end-to-end encryption (E2EE) function among the security services of video conferencing systems. First, the E2EE-related protocols of Zoom and Secure Frame (SFrame), which are representative video conferencing systems, are thoroughly investigated, and the two systems are compared and analyzed from the overall viewpoint. Next, the E2EE protocol in a Government Public Key Infrastructure (GPKI)-based video conferencing system, in which the user authentication mechanism is fundamentally different from those used in commercial sector systems such as Zoom and SFrame, is considered. In particular, among E2EE-related protocols, we propose a detailed mechanism in which the post-quantum cryptography (PQC) key encapsulation mechanism (KEM) is applied to the user key exchange process. Since the session key is not disclosed to the central server, even in futuristic quantum computers, the proposed mechanism, which includes the PQC KEM, still satisfies the E2EE security requirements in the quantum environment. Moreover, our GPKI-based mechanism induces the effect of enhancing the security level of the next-generation video conferencing systems up to a quantum-safe level.
Linguistic Communication Channels Reveal Connections between Texts: The New Testament and Greek Literature

We studied two fundamental linguistic channels—the sentences and the interpunctions channels—and showed they can reveal deeper connections between texts. The applied theory does not follow the actual paradigm of linguistic studies. As a study case, we considered the Greek New Testament, with the purpose of determining mathematical connections between its texts and possible differences in the writing style (mathematically defined) of the writers and in the reading skill required of their readers. The analysis was based on deep-language parameters and communication/information theory. To set the New Testament texts in the larger Greek classical literature, we considered texts written by Aesop, Polybius, Flavius Josephus, and Plutarch. The results largely confirmed what scholars have found about the New Testament texts, therefore giving credibility to the theory. The Gospel according to John is very similar to the fables written by Aesop. Surprisingly, the Epistle to the Hebrews and Apocalypse are each other’s “photocopies” in the two linguistic channels and not linked to all other texts. These two texts deserve further study by historians of the early Christian church literature at the level of meaning, readers, and possible Old Testament texts that might have influenced them. The theory can guide scholars to study any literary corpus.
Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos

In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming, which has caused much alarm and controversy over recent years. Our intention is to recommend informative YouTube videos to those seeking a balanced viewpoint of this area and the key arguments/issues. To this end we analyze Twitter data; Reddit comments and posts; user comments, view statistics and likes/dislikes of YouTube videos. The combination of sentiment analysis with raw statistics and linking users with their posts gives deeper insights into their needs and quest for quality information. Sentiment analysis provides the insights into user likes and dislikes, graph theory provides the linkage patterns and relationships between users, posts, and sentiment.
Chatbots for Cultural Venues: A Topic-Based Approach

Digital assistants—such as chatbots—facilitate the interaction between persons and machines and are increasingly used in web pages of enterprises and organizations. This paper presents a methodology for the creation of chatbots that offer access to museum information. The paper introduces an information model that is offered through the chatbot, which subsequently maps the museum’s modeled information to structures of DialogFlow, Google’s chatbot engine. Means for automating the chatbot generation process are also presented. The evaluation of the methodology is illustrated through the application of a real case, wherein we developed a chatbot for the Archaeological Museum of Tripolis, Greece.
A Blockchain-Based Auction Framework for Location-Aware Services

As a critical factor in ensuring the growth of the electronic auction (e-auction) domain, the privacy and security of the participants (sellers and buyers) must always be guaranteed. Traditionally, auction data, including participant details, are stored in a third party (auctioneer) database. This leads to a high risk of a single point of failure in terms of privacy and security. Toward this end, blockchain technology has emerged as a promising decentralized communication paradigm to address such risks. This paper presents a blockchain-based auction framework as a decentralized e-auctioning framework for location-aware services. In particular, the framework consists of three components: pre-auctioning, main auctioning, and post-auctioning processes with four algorithms. Our primary focus is on location-aware services, such as storage space rental, apartment rental, transport hire, and parking space rental. The trading volumes are expected to be high; hence, simplifying the design is a crucial requirement. In addition to the benefits of eliminating the centralized entity (the auctioneer), fees are redistributed among participants as rewards. Further, we incorporate a service quality monitoring method that ranks the services provided by participants. This ranking allows participants to determine the rank of other participants with whom they wish to trade. We have conducted several experiments to evaluate the proposed framework’s cost feasibility and to ensure the ease of the business flow.
Developing a Sustainable Online Platform for Language Learning across Europe

In this paper, we present a sustainable approach for addressing the language skills gap among EU citizens, which significantly hinders their mobility across the EU and their participation in education, in training, as well as in youth programmes. Our approach is based on the sustainable design of the OpenLang Network platform, which provides an open and collaborative online learning environment for language learners and teachers across Europe, and addresses the limitations of existing computer-assisted language learning approaches. The OpenLang Network platform is bringing together educators and Erasmus+ mobility participants to improve their language skills and cultural knowledge. To this end, the OpenLang Network platform offers a collection of multilingual Open Educational Resources and language learning services. The paper presents the results from the user evaluation of the platform, which has been conducted with members of its community of language teachers and learners. A mixed methods approach has been adopted in order to collect and analyse both qualitative and quantitative data from users about the sustainable design of the OpenLang Network platform, as well as to measure the user satisfaction levels of the platform’s language learning services. According to the user evaluation results, the platform offers a sustainable online environment and a positive user experience for language learning. The user evaluation has also helped us identify a set of best practices and challenges associated with the long-term sustainability of an online language learning community.
Construction and Research of Ultra-Short Term Prediction Model of Solar Short Wave Irradiance Suitable for Qinghai–Tibet Plateau

The Qinghai–Tibet Plateau region has abundant solar energy, which presents enormous potential for the development of solar power generation. Accurate prediction of solar radiation is crucial for the safe and cost-effective operation of the power grid. Therefore, constructing a suitable ultra-short-term prediction model for the Tibetan Plateau region holds significant importance. This study was based on the autoregressive integrated moving average model (ARIMA), random forest model (RF), and long short-term memory model (LSTM) to construct a prediction model for forecasting the average irradiance for the next 10 min. By locally testing and optimizing the model parameter, the study explored the applicability of each model in different seasons and investigates the impact of factors such as training dataset and prediction time range on model accuracy. The results showed that: (1) the accuracy of the ARIMA model was lower than the persistence model used as a reference model, while both the RF model and LSTM model had higher accuracy than the persistence model; (2) the sample size and distribution of the training dataset significantly affected the accuracy of the models. When both the season (distribution) and sample size were the same, RF achieved the highest accuracy. The optimal sample sizes for ARIMA, RF, and LSTM models in each season were as follows: spring (3564, 1980, 4356), summer (2772, 4752, 2772), autumn (3564, 3564, 4752), and winter (3168, 3168, 4752). (3) The prediction forecast horizon had a significant impact on the model accuracy. As the forecast horizon increased, the errors of all models gradually increased, reaching a peak between 80 and 100 min before slightly decreasing and then continuing to rise. When both the season and forecast horizon were the same, RF had the highest accuracy, with an RMSE lower than ARIMA by 65.6–258.3 W/m2 and lower than LSTM by 3.7–83.3 W/m2. Therefore, machine learning can be used for ultra-short-term forecasting of solar irradiance in the Qinghai–Tibet Plateau region to meet the forecast requirements for solar power generation, providing a reference for similar studies.
CoSOV1Net: A Cone- and Spatial-Opponent Primary Visual Cortex-Inspired Neural Network for Lightweight Salient Object Detection

Salient object-detection models attempt to mimic the human visual system's ability to select relevant objects in images. To this end, the development of deep neural networks on high-end computers has recently achieved high performance. However, developing deep neural network models with the same performance for resource-limited vision sensors or mobile devices remains a challenge. In this work, we propose CoSOV1net, a novel lightweight salient object-detection neural network model, inspired by the cone- and spatial-opponent processes of the primary visual cortex (V1), which inextricably link color and shape in human color perception. Our proposed model is trained from scratch, without using backbones from image classification or other tasks. Experiments on the most widely used and challenging datasets for salient object detection show that CoSOV1Net achieves competitive performance (i.e., Fβ=0.931 on the ECSSD dataset) with state-of-the-art salient object-detection models while having a low number of parameters (1.14 M), low FLOPS (1.4 G) and high FPS (211.2) on GPU (Nvidia GeForce RTX 3090 Ti) compared to the state of the art in lightweight or nonlightweight salient object-detection tasks. Thus, CoSOV1net has turned out to be a lightweight salient object-detection model that can be adapted to mobile environments and resource-constrained devices.