Reviewerly/exHarmony huggingface dataset:
Bitcoin Price Forecasting using Time-series Architectures

The cryptocurrency market has shown an imperfect market property due to volatility and erratic behavior. Cryptocurrencies“ prices largely fluctuate because of the 24/7 trading policy that weakens the forecasting power of thetraditional models. However, research works reveal that cryptocurrency prices cannot be truly random in nature. In thispaper, traditional methods of time-series forecasting such as the ARIMA and SARIMA were presented and compared with the recent deep learning models such as the GRU, RNN, LSTM, and Facebook Prophet. Historical data for Bitcoin was acquired consisting of 2770 samples from 2018 to 2022. The study yielded the lowest MSE and RMSE of 2159166.25 and 1469.41 respectively for the GRU model. FB Prophet yielded the highest RMSE and MSE among all the experimented models.
A Route Planning Method of UAV Based on Multi-aircraft Cooperation

In modern warfare, cooperative operations of multiple UAVs has become a trend and been valued by various countries. However, as the numbers of UAVs increases, the cost of computing resources for the existing cooperative planning algorithm of multiple UAVs will increase exponentially, which is a heavy burden for hardware. In order to utilize the existing military electronic information system resources to complete the cooperative planning of multiple UAVs, this paper firstly proposes a dynamic configurable cooperative planning architecture and parallel processing method to meet the requirement of cooperative route planning. Then a dynamic load balancing route planning processing algorithm is designed, which can solve the problem of unbalanced load of each processing nodes in multi-computer system when the routing points are unevenly distributed in battlefield space. In the end, the experimental results show that the proposed method can significantly reduce the processing delay of multi-UAV route planning and improve the acceleration ratio of parallel processing.
Statistical approach for diagnosing oncological diseases by blood parameters

A mathematical method is proposed for the preliminary diagnosis of human oncological diseases of various localization by blood parameters. The authors have developed a statistical classifier based on a polynomial-regression approach and having probabilistic estimates. Training was carried out on databases of analysis of peripheral blood of cancer patients in various body systems.
Their Mere Existence Is Resistance: Undocu/DACAmented Collegians’ Resisting Subordination in Turbulent Times

This critical ethnography examines the resistance efforts of undocu/ DACAmented collegians during the Trump campaign and presidency era (2015–2021). Guided by the matrix of resistance, this study sought to highlight the various methods and intentions these collegians use to resist subordination amidst an anti-im/migrant sociopolitical climate. Findings reveal how their efforts are demonstrated via (a) participation in civic engagement in local and national advocacy organizations, (b) protests against border patrol agents’ presence on campus, (c) coming out with their liminal legal status, and (d) their professional aspirations. Exigent recommendations are offered for higher education institutional agents at all levels.
Dense Siamese Network for Dense Unsupervised Learning

This paper presents Dense Siamese Network (DenseSiam), a simple unsupervised learning framework for dense prediction tasks. It learns visual representations by maximizing the similarity between two views of one image with two types of consistency, i.e., pixel consistency and region consistency. Concretely, DenseSiam first maximizes the pixel level spatial consistency according to the exact location correspondence in the overlapped area. It also extracts a batch of region embeddings that correspond to some sub-regions in the overlapped area to be contrasted for region consistency. In contrast to previous methods that require negative pixel pairs, momentum encoders or heuristic masks, DenseSiam benefits from the simple Siamese network and optimizes the consistency of different granularities. It also proves that the simple location correspondence and interacted region embeddings are effective enough to learn the similarity. We apply DenseSiam on ImageNet and obtain competitive improvements on various downstream tasks. We also show that only with some extra task-specific losses, the simple framework can directly conduct dense prediction tasks. On an existing unsupervised semantic segmentation benchmark, it surpasses state-of-the-art segmentation methods by 2.1 mIoU with 28% training costs. Code and models are released at https://github.com/ZwwWayne/DenseSiam .
Stablecoins: Reducing the Volatility of Cryptocurrencies

In the wake of financial crises, stablecoins are gaining adoption among digital currencies. We discuss how stablecoins help reduce the volatility of cryptocurrencies by surveying different types of stablecoins and their stability mechanisms. We classify different approaches to stablecoins in three main categories (i) fiat or asset backed, (ii) crypto-collateralized, and (iii) algorithmic stablecoins, giving examples of concrete projects in each class. We assess the relative tradeoffs between the different approaches. We also discuss challenges associated with the future of stablecoins and their adoption, their adoption and point out future research directions.
The Hull Structure and Defect Detection Based on Improved YOLOv5 for Mobile Platform

Hull inspection is of great significance to ensure the safety of ships for ocean transportation. Improving the intelligent performance of inspection can benefit the efficiency of hull inspection. In this paper, we propose a detection method for hull structure and defect using improved YOLOv5. Residual connections and weighted feature fusion are adopted to learn the importance of different input features, which enhance feature expression and improve the efficiency. Coupled with transformer block, we further capture global information and abundant contextual information. For the convenience of detection, we convert the detection model and deploy it on mobile devices. The detection model is packaged into apk, which is installed on the mobile platform to realize the hull image detection. Extensive experiments show that our improved YOLOv5 achieves better performance than the original algorithm on our hull structure and defect datasets. The results of mAP are 82.5% and 65.8%, which are improved by 3.7% and 2.8% respectively. Based on mobile deployment, the high accuracy of mobile detection demonstrates that our work is of great significance for the research of ship inspection.
SAR image registration based on KECA-SAR-SIFT operator

Synthetic aperture radar (SAR) image registration is a key technology in SAR image processing. The accuracy and efficiency of registration directly affect the quality of subsequent image processing. In order to further improve the accuracy and computational efficiency of the traditional SAR-SIFT (SAR-scale invariant feature transform, SAR-SIFT) image registration algorithm, an improved SAR-SIFT algorithm based on Kernel entropy component analysis (KECA) is proposed. Firstly, the SAR-Harris scale space is established, the extreme points in the space are selected and the main directions of the key points are calculated; then, the SAR-SIFT descriptor is generated, and the KECA algorithm is used for further feature extraction, and the KECA-SAR-SIFT descriptors is obtained after dimensionality reduction; finally, feature points of two or more pictures are matched by comparing the similarity of the extracted descriptors. The experimental results showed that the proposed method effectively improved the matching accuracy, shortened the matching time, and had certain robustness.
FLEXIBLE PEDAGOGICAL RESPONSES TO HARD PROFESSIONAL CHALLENGES

The article considers the challenges for the national professional school, especially for the teachers in a new BANI world that is replacing the VUCA world. The most important feature of the BANI world is the incomprehensibility of what is happening and, most importantly, what is created by a person. One of the response options is to rely on the Lean-Agile methodology that allows one to reorganize the educational process adequately and optimally, to change the nature of interaction of all the participants and stakeholders and to update many traditional, including Russian, forms and values of work organization. The article justifies expediency of applying effective and promising principles of the Lean-Agile approach in conditions of Russian professional education
Solving K-MDPs

Markov Decision Processes (MDPs) are employed to model sequential decision-making problems under uncertainty. Traditionally, algorithms to solve MDPs have focused on solving large state or action spaces. With increasing applications of MDPs to human-operated domains such as conservation of biodiversity and health, developing easy-to-interpret solutions is of paramount importance to increase uptake of MDP policies. Here, we define the problem of solving K-MDPs, i.e., given an original MDP and a constraint on the number of states (K), generate a reduced state space MDP that minimizes the difference between the original optimal MDP value function and the reduced optimal K-MDP value function. Building on existing non-transitive and transitive approximate state abstraction functions, we propose a family of three algorithms based on binary search with sub-optimality bounded polynomially in a precision parameter: ϕQ*εK-MDP-ILP, ϕQ*dK-MDP and ϕa*dK-MDP. We compare these algorithms to a greedy algorithm (ϕQ*ε Greedy K-MDP) and clustering approach (k-means++ K-MDP). On randomly generated MDPs and two computational sustainability MDPs, ϕa*dK-MDP outperformed all algorithms when it could find a feasible solution. While numerous state abstraction problems have been proposed in the literature, this is the first time that the general problem of solving K-MDPs is suggested. We hope that our work will generate future research aiming at increasing the interpretability of MDP policies in human-operated domains.
POTENTIALITIES AND DIFFICULTIES OF TECHNOLOGICAL MEDIATION IN THE WORK OF NURSE MANAGERS IN HOSPITALS

ABSTRACT Objective to describe the potentialities and difficulties mentioned by nurse managers in the use of technologies in hospitals. Method qualitative, descriptive-exploratory study, conducted in the period of one year, 2019 to 2020, in Brazil and Portugal, in four hospitals. Data were collected through interviews, following a semi-structured script, involving 71 nurse managers. For data analysis, resources of the ATLAS.ti software were used, following the precepts of thematic content analysis and work process theory. Results nurse managers consider that technologies contribute to the improvement of institutional processes, information recording, time management, data storage and patient safety. Among the difficulties, problems related to the work instruments themselves are highlighted, such as lack of equipment/computers, slowness and lack of systems integration; and related to the workforce, such as time management, cultural adaptation, lack of knowledge and training to use technology. Conclusion innovative technologies contribute to management work, but their effectiveness depends on training, adequate number of professionals, in addition to efficient and integrated equipment and information systems.
Suborn Channels: Incentives Against Timelock Bribes

As the Bitcoin mining landscape becomes more competitive, analyzing potential attacks under the assumption of rational miners becomes increasingly relevant. In the rational setting, blockchain users can bribe miners to reap an unfair benefit. Established protocols such as Duplex Micropayment Channels and Lightning Channels are susceptible to bribery, which upends their financial guarantees. Indeed, we prove that in a two-party contract in which the honest party can spend an output right away, whereas the malicious can only spend the same output after a timelock, the latter party can promise a high fee to the miners, who then intentionally ignore the transaction of the honest party in anticipation of the higher fee. This effectively prevents a valid transaction from ever entering the blockchain, resulting in potentially severe financial losses for the honest and considerable gains for the malicious party. We expand previous results on timelock bribes to more realistic blockchains, proving that a general class of contracts are susceptible. We then apply our results to Duplex Micropayment Channels and Lightning Channels, providing exact bounds on their safe operating region. Furthermore, we enhance the Bitcoin Script of Duplex Micropayment Channels so that the coins of a party that attempts to bribe are given to the miners as fees, therefore effectively disincentivizing bribes. Our solution, named Suborn channels, is implemented as a proof-of-concept. We also propose a small change to Lightning Channels that achieves a similar effect. Moreover, we formally express the exact circumstances under which our two proposals ensure alignment of miner incentives with the prescribed protocol outcome.
Design A Novel Image Encryption Algorithm Based on A PRNG with High Performance

This paper proposes a novel two-dimensional logistic map (2D-LM). Relative to the classical 2D logistic modulation map (2D-LMM), it has larger chaotic range and better chaos performance. In comparison with some 2D chaos maps, the proposed new 2D-LM also has unique advantages in the complexity of sequence. To further study its application, the novel image encryption algorithm is designed based on the proposed 2D-LM. The security tests show the higher anti-decryption ability of the design algorithm. Finally, we develop a FPGA test platform to realize the Pseudo-Random Number Generator (PRNG) based on the proposed 2D-LM. The experiment results show the feasibility of its industrial applications.
Hybrid renewable energy harvesting device for street lightning in the Kingdom of Bahrain

The need for an affordable and reliable power supply for traffic monitoring systems and street lightening remains demanding and is a challenge. A gust of wind when a car rush could be harvested into renewable energy as well as the other available ambient energy sources. This study aims to develop a sustainable alternative solution of on-site energy to this critical challenge, by harvesting the energy from the road using a smart device mechanism utilizing car passing wind and solar energy. Hence, a hybrid renewable energy device for energy generation to supply King Fahad Causeway Street lights in the Kingdom of Bahrain was proposed in this study. The proposed design of the device included a vertical axis wind turbine to utilize the air coming from the passing cars on the causeway and a solar system to convert the solar radiation into electricity to maximize the output power since the Kingdom of Bahrain has a great potential of solar energy. Simulations are carried out using two different software’s (QBlade and PVsyst) to validate the theoretically calculated results. The total power generated by the device was approximately determined to be 10 kWh/day. The total energy output and the percentage error between the theoretical and simulation results were calculated, they ranged between 19% to 28%. This study reveals the effectiveness of the proposed device for on-site sources of energy for traffic monitoring systems and streets lighting, the device could be also implemented in other areas in the cities where there is rabid and constant vehicle traffic.
A Web-Based Graphical User Interface for Programming Optimal Control Based Robot Motion Tasks

The programming of complex tasks with robot manipulators faces the challenges of complying to constraints and needing to account for several, sometimes conflicting performance objectives. Optimal control problems (OCP) are able to explicitly account for these challenges by solving constrained optimization problems to find optimal trajectories that realize such tasks. The wide adoption of OCPs, however, is restricted by the high engineering cost and the high computational complexity associated to its implementation. To that end, in this paper we present a web application and graphical user interface (GUI) that acts as a front-end of Tasho, a toolbox for constrained-based task specification of robot motion tasks. This web application aims to lower the engineering complexity of setting, simulating and deploying a robot task by realizing a direct workflow from task definition on the GUI to deployment on a real robot using ROS. By interfacing state-of-the-art solvers and libraries through Tasho, the web application delivers computationally efficient solutions to the OCPs associated to the task.
Pedagogical Conditions and Mechanisms of Formation of Children’s Value-Semantic Orientations in an Educational Organization

The implementation of educational programs aimed at the formation of value-semantic orientations of children is an urgent task of modern education, for the solution of which it is necessary to develop modeling ideas about pedagogical conditions and mechanisms for the formation of value-semantic orientations in an educational organization. The theoretical provisions and methodological principles of the anthropology of education are formulated, which made it possible to consider an educational organization as an integral humanitarian-organic system that provides age conditions for the formation of value-semantic orientations of children at different levels of education. The conditions and mechanisms of their formation are considered. The hypothesis is put forward that the formation of value-semantic orientations should be considered in the context of the development of personal and subjective positions, the integration of which provides the possibility of self-determination of a growing person. A complex of system-wide, intra-system and intersystem conditions for their formation in educational organizations has been developed. Differences in their characteristics are presented. The main mechanisms and related processes of the formation of value-semantic orientations are considered. It is concluded that the theoretical and methodological justification carried out is the basis for designing new technologies of educational work in modern socio-cultural conditions.
Towards an automatic requirements classification in a new Spanish dataset

Machine Learning (ML) algorithms have become a powerful instrument in software requirements classification. Nevertheless, most of the research focusing on requirements is in English, with less attention to other languages. Given a lack of datasets in Spanish, we created a new dataset from a collection of requirements from final degree projects from the University of A Coruña. In this paper, we investigate which combinations of text vectorization techniques with ML algorithms perform best for requirements classification in a Spanish dataset. We found that SVM with TF-IDF gives the highest f1-score (0.95 and 0.79 for functional and non-functional classification).
Privacy-Friendly Peer-to-Peer Energy Trading: A Game Theoretical Approach

In this paper, we propose a decentralized, privacy-friendly energy trading platform (PFET) based on game theoretical approach - specifically Stackelberg competition. Unlike existing trading schemes, PFET provides a competitive market in which prices and demands are determined based on competition, and computations are performed in a decentralized manner which does not rely on trusted third parties. It uses homomorphic encryption cryptosystem to encrypt sensitive information of buyers and sellers such as sellers' prices and buyers' demands. Buyers calculate total demand on particular seller using an encrypted data and sensitive buyer profile data is hidden from sellers. Hence, privacy of both sellers and buyers is preserved. Through privacy analysis and performance evaluation, we show that PFET preserves users' privacy in an efficient manner.
BEMSS- Blockwise Encryption based Multi Secret Sharing scheme for Securing Visual Content

In recent years, the significant research area in the domain of multimedia security is image information security. This can be provided by converting it into a secret code through the process of encryption or hiding its true significance or a combination of both. It is required in many domains like defence, military, banking, education, etc, to keep these sensitive images from being hacked or destroyed. In this paper, BEMSS, an (n,n) multi-secret image encryption with a secret sharing scheme is proposed using blockwise encryption. In the proposed technique two-level encryption, modulo and inter blockwise encryption, are performed to the n secret images, and a two-level share generation process is performed to the encrypted images. BEMSS recovers n lossless the secret images by modulo and blockwise decryption process. Experimental results obtained show the effectiveness of the proposed BEMSS which provide a computationally efficient security scheme.
Entangled States Are Harder to Transfer than Product States

The distribution of entangled states is a key task of utmost importance for many quantum information processing protocols. A commonly adopted setup for distributing quantum states envisages the creation of the state in one location, which is then sent to (possibly different) distant receivers through some quantum channels. While it is undoubted and, perhaps, intuitively expected that the distribution of entangled quantum states is less efficient than that of product states, a thorough quantification of this inefficiency (namely, of the difference between the quantum-state transfer fidelity for entangled and factorized states) has not been performed. To this end, in this work, we consider n-independent amplitude-damping channels, acting in parallel, i.e., each, locally, on one part of an n-qubit state. We derive exact analytical results for the fidelity decrease, with respect to the case of product states, in the presence of entanglement in the initial state, for up to four qubits. Interestingly, we find that genuine multipartite entanglement has a more detrimental effect on the fidelity than two-qubit entanglement. Our results hint at the fact that, for larger n-qubit states, the difference in the average fidelity between product and entangled states increases with increasing single-qubit fidelity, thus making the latter a less trustworthy figure of merit.
Combining Transformer Embeddings with Linguistic Features for Complex Word Identification

Identifying which words present in a text may be difficult to understand by common readers is a well-known subtask in text complexity analysis. The advent of deep language models has also established the new state-of-the-art in this task by means of end-to-end semi-supervised (pre-trained) and downstream training of, mainly, transformer-based neural networks. Nevertheless, the usefulness of traditional linguistic features in combination with neural encodings is worth exploring, as the computational cost needed for training and running such networks is becoming more and more relevant with energy-saving constraints. This study explores lexical complexity prediction (LCP) by combining pre-trained and adjusted transformer networks with different types of traditional linguistic features. We apply these features over classical machine learning classifiers. Our best results are obtained by applying Support Vector Machines on an English corpus in an LCP task solved as a regression problem. The results show that linguistic features can be useful in LCP tasks and may improve the performance of deep learning systems.