Reviewerly/exHarmony huggingface dataset:
A hybrid deep learning model for Bitcoin price prediction: data decomposition and feature selection

Bitcoin has received a great deal of attention as a highly volatile asset with investors attempting to profit from its dramatic price fluctuations. We develop a hybrid deep learning model based on feature selection in different frequency domains to enrich the literature of Bitcoin price prediction. Indicators such as Technology, Economy, Green Finance and Media Attention are considered. We first decompose all the data into different frequencies through CEEMDAN approach, and then the data at the same frequency are integrated into a Random Forest model to reduce the subset of potential predictors by measuring the importance of different factors. Finally, the selected factors are put into the LSTM/GRU to make the prediction of different components of Bitcoin prices at the same frequency, and aggregate together to obtain the predicted Bitcoin prices. The empirical results show that our proposed model outperforms the benchmark models, which is verified by MCS test. The proposed hybrid method obtains much higher return on investment in simulated trading than other benchmark models. Our study inspired the investors to accurately predict Bitcoin price and dig possible relationships between different assets and its determinants in frequency domain.
Implementasi Metode Weighted Product (WP) Dalam Pengambilan Keputusan Kepala Laboraturium Komputer

In a university that has an informatics profile, a computer laboratory is needed. The laboratory is also an important facility for practicing the application of the theory that has been learned. Therefore, a laboratory head is needed who has competence in managing the laboratory so that learning is more effective. However, what happened was that the head of the laboratory who was elected did not have competence because the selection system was subjective so that it could not solve the existing problems. Therefore we need a decision support system in selecting the head of the computer laboratory. The method used in making this decision is the weighted product (WP) because this method can solve multiple withdrawals and easier calculations, and the time for calculations is shorter than other decision-making methods. after ranking with existing calculations, the results obtained from the calculation of the Weighted Product (WP) method with the highest value are in alternative A2 with the name Gery Mivano, S, T with a value of 0.374910. These results can make it easier for academics to make decisions for heads of computer laboratories who have competence in solving existing laboratory problems so that practical learning in computer laboratories can be more effective.&#x0D; boraturium komputer dapat lebih efektif.
#EndSARS Protests and the Search for Social Justice in Nigeria: Examining Activist Discourses on Police Brutality, Politics and Human Security

Abstract On October 3, 2020, members of the Special Anti-Robbery Squad ( sars ) of the Nigerian Police Force arrested a young man at Wetland Hotel, Ughelli in Delta State, and later shot and killed him, and threw him out of a moving police van. In addition to eye witnesses of what happened, the video of this incident was also trending on social media. Again, on October 5, another video went viral of sars officers’ killing a young musician popularly known as “Sleek,” in unjustifiable circumstances. This was the last straw that broke the camel’s back. What initially began as spontaneous peaceful protests soon turned bloody and by October 8, the protests had spread to almost all Nigerian cities demanding the disbandment of sars . This empirical study examines the operational structure of sars , the formation of the EndSARS movement and its first main protests in 2020 in the search for social justice. Methods in qualitative discourse analysis and multimodality, are used to analyze representational strategies in the protest discourse of the #EndSARS protesters. Other issues bordering on police brutality, injustice, corruption and state failure, which form the main narratives of the protests are analyzed.
MFF‐Net: Multiscale feature fusion semantic segmentation network for intracranial surgical instruments

In robot-assisted surgery, automatic segmentation of surgical instrument images is crucial for surgical safety. The proposed method addresses challenges in the craniotomy environment, such as occlusion and illumination, through an efficient surgical instrument segmentation network.The network uses YOLOv8 as the target detection framework and integrates a semantic segmentation head to achieve detection and segmentation capabilities. A concatenation of multi-channel feature maps is designed to enhance model generalisation by fusing deep and shallow features. The innovative GBC2f module ensures the lightweight of the network and the ability to capture global information.Experimental validation of the intracranial glioma surgical instrument dataset shows excellent performance: 94.9% MPA score, 89.9% MIoU value, and 126.6 FPS.According to the experimental results, the segmentation model proposed in this study has significant advantages over other state-of-the-art models. This provides a valuable reference for the further development of intelligent surgical robots.
A Variant of Parallel-Hybrid Genetic Algorithm for Large-Scale Traveling Salesman Problem

This research proposes a low-complexity parallel-hybrid genetic algorithm evolved in an island model to solve large instances of the traveling salesman problem. In order to increase the diversity of individuals among the islands, a variant of the 2-opt algorithm called Rnd2-opt is implemented in the first half of the islands and another variant called NN2-opt in the second half. Additionally, it incorporates a process known as the edge assembling crossover operator (EAX), along with the local search method 2-opt, to generate offspring. The resulting algorithm is implemented using an island model on a multicore processor, utilizing the POSIX thread standard. During each generation, a predefined quantity of offspring is generated from each individual. The best-quality offspring is then selected to replace the parent individual if it is superior. For individual migration, the best individual from one island is chosen to replace the worst individual from another island, employing a ring topology. The PhGA algorithm was applied in VLSI and Art TSP large instances. VLSI TSP instances have a smaller number of cities than Art TSP instances; however, their error rate is higher than Art TSP instances, both in generating the initial population and applying the EAX operator with local search. This is because VLSI instances contain cities that are arranged in a lattice pattern, and finding the best route is more difficult due to the existence of many alternative routes to the best solution.
Indonesian secular vs. Madrasah schools: assessing the discrepancy in English reading and listening tests

Abstract The greater emphasis on the significance and difference in English performance between the school types has mainly been investigated across Asian countries. However, not much is known about what language skills differentiate their overall language achievement. Using a quantitative study with comparative analysis, this study measured the reading and listening skills of 1319 Indonesian students who were selected using a stratified sample design and grouped them into secular ( Sekolah , n = 726) and Islamic ( Madrasah , n = 593) groups. The samples were selected from 9205 of the total population of secondary school students, in Bone Regency, South Sulawesi Indonesia. The three-way ANOVA results showed a significant difference ( p &lt; 0.05) in reading and listening subskills between the groups. Highly significant results of Madrasah students in reading and listening subskills indicate they are better at constructing what text means in a variety of contexts, as a literary experience in reading texts and obtaining general and specific information from listening tests compared to those attending secular schools. Poor performance of boys and students who enrolled in public secular schools may become the main explanation for achievement gaps across the groups. The main and interaction effects of the school system, sectors, and gender on the tested subskills were also explained in this study. Additionally, the result of the DIF test confirmed that the equity of the tested items between them was supported.
Which Way Does Time Go? Differences in Expert and Novice Representations of Temporal Information at Extreme Scales Interferes with Novice Understanding of Graphs

Abstract Visual representations of data are widely used for communication and understanding, particularly in science, technology, engineering, and mathematics (STEM). However, despite their importance, many people have difficulty understanding data-based visualizations. This work presents a series of three studies that examine how understanding time-based Earth-science data visualizations are influenced by scale and the different directions time can be represented (e.g., the Geologic Time Scale represents time moving from bottom-to-top, whereas many calendars represent time moving left-to-right). In Study 1, 316 visualizations from two top scholarly geoscience journals were analyzed for how time was represented. These expert-made graphs represented time in a range of ways, with smaller timescales more likely to be represented as moving left-to-right and larger scales more likely to be represented in other directions. In Study 2, 47 STEM novices were recruited from an undergraduate psychology experiment pool and asked to construct four separate graphs representing change over two scales of time (Earth’s history or a single day) and two phenomena (temperature or sea level). Novices overwhelmingly represented time moving from left-to-right, regardless of scale. In Study 3, 40 STEM novices were shown expert-made graphs where the direction of time varied. Novices had difficulty interpreting the expert-made graphs when time was represented moving in directions other than left-to-right. The study highlights the importance of considering representations of time and scale in STEM education and offers insights into how experts and novices approach visualizations. The findings inform the development of educational resources and strategies to improve students’ understanding of scientific concepts where time and space are intrinsically related.
External knowledge-assisted Transformer for image captioning

Internal relationship exploring based on the descriptive region features of image objects and grid features have contributed significantly to the development of image captioning, especially when combined with Transformer architecture. However, when conducting self-attention calculation, most of these methods only consider the relationship intra-objects and ignore the connection between entities and background. Besides, the way of exploring the relation information inside the image can also be extended. In this paper, we introduce a novel Mixed Knowledge Relation Transformer (MKRT) to explore the relationship between objects from both internal attribute relationship and external the object-verb-subject relationship. Furthermore we embed the important image background information into the relation module. In MKRT, the semantic relation obtained from the external knowledge is incorporated into the relation modeling in the novel Mixed Knowledge Relation Attention (MKRA). To validate the effectiveness of our model, we conduct extensive experiments on the most popular MSCOCO dataset, and achieve 134.5 CIDEr score on the offline test split and 133.5 CIDEr (c40) score on the official online testing server.
Image encryption algorithm based on hyperchaos and DNA coding

Abstract A novel image encryption algorithm based on hyperchaos and DNA encoding is proposed in this paper. Unlike the traditional DNA‐based encoding method, a more fine‐grained encryption approach is adopted, which enhances the security of the system. To address the inherent binary arithmetic limitation of traditional DNA computing, the authors develop a novel symmetric and reversible DNA subtraction operation that fully utilizes the encryption advantage of DNA computing. To enhance security, the authors introduce a DNA crossover mechanism based on pixel, which can generate different ciphertexts even for the same pixel values. In the algorithm, the authors propose a diffusion strategy that combines left diffusion, right diffusion, and XOR parameters. This strategy not only ensures the avalanche effect, where even a single pixel change in the plaintext image will cause a drastic change in the ciphertext image, but also greatly enhances the security of the algorithm. Experimental results and performance analysis indicate that the authors’ proposed system has good encryption performance and the ability to resist noise attacks and clipping attacks.
Blockchain for energy efficiency training in the construction industry

Abstract The construction sector faces the urgent need to prioritize energy efficiency due to an increasingly stringent regulatory landscape in response to the climate change agenda. Central to this transition is the pivotal role of education in equipping professionals with the necessary knowledge and skills. Educational solutions have emerged as powerful tools for promoting awareness and interventions to mitigate climate change. This article provides a case study that highlights the successful utilisation of computer technology in delivering digital solutions to advance energy education and promote more informed energy practices in the construction industry. The utilisation of digital technologies can enhance collaborative efforts in energy efficiency training, which is of critical significance in ensuring the security, sovereignty, transparency, immutability, and decentralisation of interventions related to energy education. This paper presents a framework that utilises Blockchain technology to facilitate training labelling and authenticity based on smart contracts and mobile passports to provide a secure and efficient solution for the delivery of training and education in the energy domain. Our research examines the challenges and opportunities related to energy efficiency training within the construction industry. By integrating industry-specific insights, exemplifications, and case studies, we provide an in-depth understanding of the interconnection between energy efficiency education and digital solutions with the unique context of the construction industry. We underscore the importance of leveraging digital platforms as educational tools to foster a deeper understanding and adoption of energy-efficient practices. We demonstrate that educational solutions play a pivotal role in driving awareness and interventions for mitigating climate change, greatly empowering individuals and organizations to adopt energy-efficient practices and to address sustainability objectives.
Assessing the socio-economic impacts of cybercrime

There are wildly different estimates of the cost of cybercrime. Often, it is not clear how the estimates were made, nor what criteria were used. The costs of cybercrime go beyond the only financial. Cybercrime has social impacts too that are rather more difficult to measure or quantify, but nevertheless are real. Individual cybercrime victims often suffer stress, anxiety, fear of being victimised again. Communities suffer from a lack of trust and social polarisation. The authors directed a study of the socio-economic impacts of cybercrime and put forward a methodology for assessing the socioeconomic impacts of cybercrime. Having a more accurate assessment of cybercrime will help policymakers and law enforcement authorities assign an appropriate priority to combatting cybercrime.
JCCM: Joint conformer and CNN model for overlapping radio signals recognition

Abstract Overlapping radio signals recognition is attracting more attention as the development and ubiquitous application of radio technologies. The traditional blind signal separation (BSS) method is mostly not effective when both radio propagation effects and low signal‐to‐noise ratio (SNR) scenarios are taken into consideration. In this letter, joint conformer and CNN model (JCCM) is proposed to separate and recognize the overlapping radio signals which are also unknown by the monitor node. JCCM utilizes the attention mechanism of conformer to encode signal spectrum into feature maps and decodes the feature maps into signal component proportions by perceiving the global and local features through convolutional neural networks (CNN). In addition, a signal preprocessing module is designed including MinPool and AvgPool layers for signal denoising. JCCM is evaluated in terms of signal separation, scaling preservation and principal signal classification experiments. The results show that JCCM has better performance especially in low SNR conditions.
WITHDRAWN: Efficient fragmentation and allocation on clustering in distributed environment (FACE)

Distributed data processing systems require a reliable storage structure that ensures consistent, high-scale, and scalable data access for all users. Data fragmentation and subsequent allocation are important aspects of a flexible distributed database system. Existing techniques may result in significant data loss while performing data allocation and fragmentation. To overcome these issues a novel fragmentation and Allocation on Clustering in Distributed Environment (FACE) is proposed for reliable storage with scalable data access for all users. Initially, the user query is given to distributed database system (DDBS), then the query is fed to the query parser. The query parser parses and converts a textual query into a query object. After parsing the query data, it is passed to the query optimizer, which optimizes the query to access the data more efficiently. In the next step, the query is fragmented using dual (horizontal and vertical) fuzzy C means clustering. In Dual fuzzy C means clustering, the vertical fragmentation focuses on clustering attribute values that are frequently accessed together, and horizontal fragmentation focuses on grouping records that meet data selection criteria. After fragmentation, the query is allocated using sequential rule mining, in which queries occur in sequential order. The allocated query is optimized using the Efficient Fragment Security (EFS) algorithm to normalize the parameters to achieve better results. The performance metrics of the proposed techniques are calculated based on Final Prediction Error (FPE), and Execution time. The proposed FACE achieves a lower execution time of the 1.56 s. The result shows that the proposed FACE decreases the overall execution time lower than the 4.70 s, 4.46 s, 3.3 Sec, and 3.14 s in MAPA, DMA, BBE, and RATS-HM respectively.
The Effects of Congruent and Incongruent Immersive Virtual Reality Modulated Exercise Environments in Healthy Individuals: A Pilot Study

High-immersive virtual reality (VR) environments can increase enjoyment and frequency of exercise participation. As VR can also be used to manipulate sensory feedback it is possible that specialist environments can modulate exercise performance and ratings of perceived exertion (RPE) and breathlessness. We aimed to (i) assess whether cycling in a “congruent” VR environment (where perceived/virtual exercise intensity and actual pedaling resistance are matched) enhances exercise performance and reduces RPE and breathlessness, and (ii) to assess whether cycling in an “incongruent” VR environment can further manipulate these perceptions. Following familiarisation, 14 healthy (7 male, 26 ± 2 years) participants repeated a series of four cycling exercise trials on a gradient adjustable ergometer under two conditions: within VR (VR condition; comprising of a custom-made VR environment in a head mounted display) and without VR (nVR condition). Within VR, the hill gradient experienced was either congruent or incongruent with the pedalling resistance. Participants could choose their power output/RPM throughout. During congruent trials participants chose to perform at a higher power output in the VR condition (+11 W ± 14, p < 0.05) with no difference in RPE or breathlessness. There was also a significant interaction between condition (VR vs nVR) and congruence for RPE and breathlessness. Specifically, when the experienced hill gradient was steeper than pedalling resistance RPE and breathlessness was greater, and when experienced hill gradient was less steep than pedalling resistance RPE and breathlessness was lower. In conclusion, we have shown that congruent VR cycling environments can modulate exercise performance. Furthermore, the novel application of incongruent VR cycling exercise manipulated exercise perceptions in either direction. This technique has potential applications in exercise training or rehabilitation modalities.
Narrativization in Information Systems Development

People see the world and convey their perception of it with narratives. In an information system context, stories are told and collected when the systems are developed. Requirements elicitation is largely dependent on communication between systems designers and users. Thus, stories have a significant impact on conceptualizing future users' needs. This paper presents a literature review on how stories and narratives have been considered in central IS literature. Narrative-theoretical parameters are used as a lens to analyze the literature. This shows that explicit discussion is non-existent, and the characteristics are considered partially. The result is a biased and narrow understanding of the informants' needs and wishes. This may be significant in the requirements because narratives are not as simple a form of communication as is usually assumed. It is proposed that better understanding narratives would equip systems analysts with an in-depth understanding about the nuances inherent in communication when communicating with users.
Construction and Optimization of Dance Curriculum Evaluation System Based on Multiple Intelligences Theory

By analyzing Howard Gardner’s Multiple Intelligences Theory and curriculum evaluation, this paper clarifies related concepts and discusses the importance of curriculum evaluation. In the construction and optimization of the dance course evaluation system based on the theory of multiple intelligences, this paper discusses the construction method and optimization strategy of the evaluation system, as well as the feasibility study of the construction and optimization of the evaluation system, and discusses the implementation and effect evaluation of the dance course evaluation system based on the theory of multiple intelligences. In this study, the advantages and potentials of each student in different intelligence fields are found by using the theory of multiple intelligences. The aim is to construct an evaluation system suitable for dance courses, so as to evaluate students’ comprehensive performance more comprehensively. This study believes that the application of multiple intelligences theory to construct the evaluation system of dance course can comprehensively evaluate students’ ability and performance in dance course from multiple dimensions. The evaluation methods involved in the research can be used to implement the evaluation system, and the effectiveness and feasibility of these evaluation methods and tools are verified through field observation and actual investigation.