Reviewerly/exHarmony huggingface dataset:
Cryptanalysis of HALFLOOP Block Ciphers

HALFLOOP is a family of tweakable block ciphers that are used for encrypting automatic link establishment (ALE) messages in high frequency radio, a technology commonly used by the military, other government agencies and industries which require high robustness in long-distance communications. Recently, it was shown in [DDLS22] that the smallest version of the cipher, HALFLOOP-24, can be attacked within a practical time and memory complexity. However, in the real-word ALE setting, it turns out that this attack require to wait more than 500 years to collect the necessary amount of plaintext-tweak-ciphertext pairs fulfilling the conditions of the attack.In this paper, we present real-world practical attacks against HALFLOOP-24 which are based on a probability-one differential distinguisher. In our attacks, we significantly reduce the data complexity to three differential pairs in the chosen-plaintext (CPA) setting which is optimal in the sense that even a brute force attack needs at least six plaintext-tweak-ciphertext pairs to uniquely identify the correct key. Considering the same ALE setting as [DDLS22], this translates to a reduction from 541 years to 2 hours worth of intercepted traffic.Besides, we provide the first, non generic, public cryptanalysis of HALFLOOP-48 and HALFLOOP-96. More precisely, we present Demirci-Selçuk meet-in-the-middle attacks against full-round HALFLOOP-48 and round-reduced HALFLOOP-96 to recover the complete master key in a CPA setting. However, unlike the attacks on HALFLOOP-24, our attacks on the larger versions are only theoretical. Moreover for HALFLOOP-96 the known generic time-memory trade-off attack, based on a flawed tweak handling, remains the strongest attack vector.In conclusion, we iterate what was already stated in [DDLS22]: HALFLOOP does not provide adequate protection and should not be used.
Inside the Jaynes–Cummings sum

Abstract It is shown that the atomic inversion in the Jaynes–Cummings model has an exact representation as an integral over the Hankel contour. For a field in a coherent state, the integral is evaluated using the saddle point method. The trajectories of saddle points as a function of time are on the branches of the multi-valued Lambert function. All of them start at the initial moment of time, but make the maximum contribution to the inversion at different times. If the collapse and the first revival are clearly distinguished, then subsequent revivals are determined by the comparable contributions of several trajectories.
Towards functional requirements for automated ground transport vehicles operating under harsh weather conditions

An automated vehicle (AV) needs to translate all human driver capabilities into specific functionalities by using a large number of systems. An AV also depends on external systems which are capable of communicating with it. In the AWARD project, the concept of System of Systems (SoS) is used to denote a set of four interoperable systems belonging to the Automated Ground goods Transport System (AGTS). This paper proposes a new methodology for defining the Functional Requirements (FR) needed for the AGTS setup. This methodology defines FR concerning Operational Design Domain (ODD)- and AGTS-elements. We first provide a study about the most commonly used sensor systems in AVs. Then, we describe the SoS and an example of the ODD definition. Finally, we illustrate how a list of FR is obtained from the relationship between an ODD and the different elements of the SoS.
Theoretical and Practical Aspects of Modern Methods of Teaching English at the Ganja State University

The purpose of the research is to identify the key components of the modern methodology of teaching English in the example of Ganja State University in Azerbaijan. Research is significant due to the global importance of English as the primary international language and its role in modernizing education systems worldwide. The main question of the research is to find out the impact of the key components of the modern methodology of teaching English on the formation of the necessary competencies in young specialists. For this purpose, the main tasks were solved: the key requirements for modern methods of teaching English were analyzed, the current state of education in Azerbaijan as a whole was characterized, and the methods for improving the modern educational process were proposed. The main directions of research are predetermined by the lack of theoretical and methodological development of this issue as well as its increasing practical significance. The leading method of studying this issue is the analysis of the most popular and effective methods of teaching English in modern conditions, determining the need for their synthesis in the formation of competent young professionals. Also, ways of further development of the most effective methods were modeled. The study examined foreign experts’ materials to comprehensively assess the significance of learning foreign languages in contemporary settings. It identified vital English teaching methods based on current trends and expert research. These findings hold practical value for improving English instruction at Ganja State University in Azerbaijan.
Impact of a DMGA Scaffolding-Based Module on Improving the EFL Speaking Skills among Vietnamese ESP Learners

This paper aims to investigate whether the scaffolding model named “Diagnosing, Modelling and Sharing, Guiding, and Applying” enhances the EFL speaking skills among ESP learners at public university in Vietnam. It also attempts to explore these ESP learners’ perceptions towards this scaffolding-based module. The study employed The study employed a mixed method research approach with an explanatory sequential research design. The participants were a total of 50 first-year undergraduate students of the ESP Department at a public university in Vietnam, who were equally divided into two intact groups, namely the intervention group and the control group. The scaffolding model was implemented in the teaching module during the 16-week intervention. The results of the Wilcoxon test of the pretest and post-test of the intervention group revealed a significant difference in the EFL speaking performance among the Vietnamese ESP learners with a p value of 0.00, and an increase in the mean score of the pre-test from 5.5 to 6.7 in the post-test, and with a large effect size (r =.42). On the other hand, no significant result was reported from the control group (p=0.20, the pretest mean score= 5.80, and the post-test mean score=5.89). It is hoped that this study could provide a scaffolding framework that is compatible with the emerging needs of teaching innovation. This research also suggests a long-term strategy for innovating English teaching and learning, which is a core goal of Vietnam National Foreign Language Project 2030.
General Purpose Deep Learning Accelerator Based On Bit Interleaving

Along with the rapid evolution of deep neural networks, the ever-increasing complexity imposes formidable computation intensity on the hardware accelerator. In this paper, we propose a novel computing philosophy called “bit interleaving” and the associate accelerator couple called “Bitlet” and Bitlet-X to maximally exploit the bit-level sparsity. Apart from the existing bit-serial/parallel accelerators, Bitlet leverages the abundant “sparsity parallelism” in the parameters to enforce the inference acceleration. Bitlet is versatile by supporting diverse precisions on a single platform, including floating-point 32 and fixed-point from 1b to 24b. The versatility enables Bitlet feasible for both efficient inference and training. Besides, by updating the key compute engine in the accelerator, Bitlet-X could furthermore improve the peak power consumption and efficiency for the inference-only scenario, with competitive accuracy. Empirical studies on 12 domain-specific deep learning applications highlight the following results: (1) up to 81×/21× energy efficiency improvement for training/inference over recent high-performance GPUs; (2) up to 15×/8× higher speedup/efficiency over state-of-the-art fixed-point accelerators; (3) 1.5mm2 area and scalable power consumption from 570mW (fp32) to 432mW (16b) and 365mW (8b) @28nm TSMC; (4) 1.3× improvement of the peak power efficiency for the Bitlet-X over Bitlet; (5) highly configurable justified by the ablation and sensitivity studies.
Decoding sEMG Under Non-Ideal Conditions Toward Robust Muscle-Machine Interface Control

The evaluation of systems under non-ideal conditions is a research problem, particularly in robotic applications for the rehabilitation of people with disabilities. Accordingly, the evaluation of algorithmic strategies for robustness validation under different non-ideal conditions is a current challenge for the scientific community. Therefore, in this study, a computational methodology based on Extreme Learning Machine (ELM) was evaluated for the recognition of seven hand gestures using sEMG under five non-ideal conditions. The shift of eight sEMG electrodes, three upper-limb postures, increased muscle fatigue, and inter-subject and inter-day variabilities were evaluated. The results indicate that the proposed methodology performs well under specific conditions in comparison with previous strategies reported in the literature using Machine Learning classifiers. Therefore, the findings of this study are potentially important for the field of robotics; however, more efforts are still needed to develop more robust computational methods to obtain higher accuracy under non-ideal conditions, with the aim of implementing more controllable, usable, and reliable systems.
Accelerating Reinforcement Learning for Autonomous Driving Using Task-Agnostic and Ego-Centric Motion Skills

Efficient and effective exploration in continuous space is a central problem in applying reinforcement learning (RL) to autonomous driving. Skills learned from expert demonstrations or designed for specific tasks can benefit the exploration, but they are usually costly-collected, unbalanced/suboptimal, or failing to transfer to diverse tasks. However, human drivers can adapt to varied driving tasks without demonstrations by taking efficient and structural explorations in the entire skill space rather than a limited space with task-specific skills. Inspired by the above fact, we propose an RL algorithm exploring all feasible motion skills instead of a limited set of task-specific and object-centric skills. Without demonstrations, our method can still perform well in diverse tasks. First, we build a task-agnostic and ego-centric (TaEc) motion skill library in a pure motion perspective, which is diverse enough to be reusable in different complex tasks. The motion skills are then encoded into a low-dimension latent skill space, in which RL can do exploration efficiently. Validations in various challenging driving scenarios demonstrate that our proposed method, TaEc-RL, outperforms its counterparts significantly in learning efficiency and task performance.
Fine-tuned BERT Language Model for Efficient Nuclear Medicine Data Retrieval

Efficient data retrieval is crucial in medical databases, where quick access to relevant patient information can significantly impact data management. In this work, we propose an Natural language processing (NLP)-based approach for predicting the type of scan from nuclear medicine reports to enable efficient data retrieval. We utilized a deep learning approach, using the fine-tuned BERT model, to classify the scan type based on the finding. The scan types we predicted include 51 different classes, such as MPI SPECT, Whole-Body Bone Scans, Thyroid Scans, and Lung Perfusion. To train and evaluate the model, we used a 75/10/15 split for the training, evaluation, and test phases, respectively. The radiology reports were generated by different nuclear medicine physicians with varying years of experience, ensuring a high variability of the dataset. We evaluated our model on various types of scans and achieved an accuracy of 98% on a dataset consisting of 36,000 nuclear medicine reports from different centers. Our proposed approach has the potential to significantly improve the efficiency of data retrieval from medical databases by automating the process of extracting scan names from radiology reports. This will enable faster and more accurate retrieval of relevant information for medical professionals, thus improving the quality of patient care.
On a Two-Layer Modular Arithmetic

Two-layer organization of modular arithmetic is considered. Lower layer uses many moduli at hardware precision and simultaneous conversion to/from RNS as described in [2]. Upper layer uses specially selected large moduli allowing for fast reduction and/or reconstruction. Implementation of two different strategies for selecting moduli on the upper layer confirms practicality of proposed approach.
Corruption and its Impact on Socio-Economic Development in Selected Countries of Africa

Africa is indeed a continent blessed with an abundance of natural resources, ranging from valuable minerals like gold, diamonds, copper, and oil. These resources hold immense potential for fostering economic growth and development across the continent. However, Africa continues to grapple with extreme poverty and underdevelopment, with corruption being identified as a major impediment to progress. This paper focuses on the pervasive issue of corruption in Africa, with Nigeria, Uganda, and Zimbabwe as case studies. The research employs a descriptive and analytical method of historical research, grounded in the Prebendal Theory as its theoretical framework. The findings of this study reveal the staggering impact of corruption on Africa's development, with annual losses of approximately $60 to $100 billion due to illicit financial flows. These flows divert resources away from political and socio-economic advancement, exacerbating widespread impoverishment. In conclusion, the paper underscores the corrosive effect of corruption on Africa's development and offers several recommendations to address this issue. These include promoting merit-based leadership selection, enforcing stronger legal actions against corruption, empowering anti-corruption agencies, engaging citizens and civil society in the fight against corruption, and strengthening the rule of law and judicial independence. These recommendations aim to pave the way for a more transparent and accountable future in Africa.
Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection

Machine learning is a science of computer algorithms that enable systems to automatically learn actions and adjust them without explicit programming and improve from experience using pattern recognition. This work offers a practical introduction to the core concepts and principles of bagging decision trees used for breast cancer diagnosis. In this article, three main algorithms, viz. linear regression (LR), decision tree (DT), and random forest, were used. The random forest method used bagging techniques for selecting data points, and feature optimization was also carried out. Through our experiments, it has been found that the results obtained with the bagging trees algorithm outperform the result obtained with the best decision tree parameters. A feature optimization scheme was also introduced in the selection of data points during the training phase, which effectively increased accuracy.
Next-generation Digital Forensics Challenges and Evidence Preservation Framework for IoT Devices

The proliferation of the Internet of Things devices in today’s environment generates huge amount of information about users and surroundings. Data produced by IoT devices attracts cybercriminals to perform malicious activity. The technologies like cloud and fog computing are emerging as the next-generation infrastructure for Internet of Things which may be challenging for digital investigation. In this paper, IoT and fog-based frameworks for digital forensics of IoT devices are explained and tools used in different levels of IoT such as physical level, cloud level, network level, and mobile application level are briefly discussed. The process of evidence collection and challenges in IoT forensics paradigms are well studied. For securing the extracted artifacts IoT evidence preservation framework is proposed (IoT-EvPF). Furthermore, the forensic challenges in a cloud computing environment and anti-forensics techniques used by cybercriminals to hide their identity and malicious activity are discussed. We have identified research gaps and provided a framework to encourage more thought and conversation about the difficulties of retrieving digital evidence from Fog Computing systems.
An Application of Blockchain to Secure the Conventional Product Authentication Management System

Products counterfeit is a multi-billion dollars market, and the growth of this field is high. There havebeen many technological enhancements like Radio Frequency Identification (RFID) and Encrypted QuickResponse (EQR) code. These technologies have been used to track down those products in the supply chaindownstream and identify products for authenticity. Under these circumstances, all existing solutions must rely ona central database for operating. The matter of this subject points out the drawback of single point of failure. As aresult, these systems are more prone to cyber-attacks. In some cases, supply chain insiders play as a bad actor ofthe system too. To prevent this, tracing concrete proof of provenance of transactions is needed. In addition,business operations require contract and consensus. These can be solved using blockchain technology. Asbusiness requires privacy and cannot rely on public network. Thus, the solution of this need requires to be builtenterprise blockchain environment.
Prediction of Age Loss on 160 KVA Transformer PT. PLN ULP Kenjeran Surabaya using The Linear Regression Method

Distribution transformer is one of the important equipment in the distribution of electricity to consumers. The electricity demand increases every year, so that the transformer works optimally, it must also be considered regarding the loading. According to IEC 354 if the transformer is loaded stable 80% with conditions around 20℃ and the winding temperature is 98℃, but if the ambient temperature is more than 20℃ then the age of the transformer will decrease. With an average loading of 71.75%, based on the calculation that the predicted useful age of the 160 KVA distribution transformer in 2027 will have an age loss index of 1.1 p.u, so it is estimated that the remaining age of the distribution transformer is 12 years and 7 months. Analysis using a statistical approach assisted by SPSS software was also carried out to see how much influence the loading has on the age loss of the transformer.
Error of Dummy Variable Treatment of Death Penalty in Regression Model —— From the Perspective of Execution and Evolution

With the evolution of the empirical law methodology, a large number of statistical models have been applied in the field of law. When dealing with regression models, if the application of death penalty occurs, empirical law papers tend to deal with virtual variables, that is, to assign a value of 0 or 1. From the perspective of the evolution of execution, gun execution and injection are to abandon the cruel execution out of humanitarian care, rather than think that the standard above the death penalty is the equal evil, so it is not reasonable to treat the virtual variable of the death penalty. Through empirical research, it is found that whether the penalty amount is virtual variable treatment has a significant impact on the regression coefficient of the final regression equation, and the empirical research results are consistent with the prediction.
Algorithms of Liquid Level Control Based on Fuzzy Logic Controllers

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Application of emerging technologies for research support in Nigerian academic libraries: Trends, problems and prospects

Academic libraries in the modern era are often asked to justify their existence in tertiary institutions by showing how the institution and society at large have benefited from the library services. One main area that librarians often point to is the research productivity of members of academic institutions. However, studies have shown that research productivity among Nigerian scholars is low, which means that academic libraries have to be more innovative in supporting researchers in their domains. Emerging technologies offer innovative ways of supporting research activities by providing tools and resources that streamline the research process and ensure proper visibility for research outputs of academic library clients. This article, which is based on a review of previous studies, explores various areas where academic libraries in Nigeria can apply emerging technologies, the likely challenges, and strategies that can be adopted to ensure sustainable use of emerging technologies in academic libraries. It has been found that emerging technologies can enhance existing library services and create new ones, such as data mining, data management, and scholarly communication, among others. However, although steps are being taken by academic librarians in Nigerian tertiary institutions to leverage technology in providing the needed support for researchers, the pace of technology adoption is still slow and the range of technologies being adopted is limited compared to available options. This state of affairs has been attributed to challenges such as lack of infrastructure, librarians’ skills, and a negative attitude towards change. The study recommends a multidimensional approach to the application of emerging technologies in Nigerian academic libraries
Security and preservation of election data in Nigeria in the fourth industrial revolution

A fraud-free and credible election is a necessary ingredient to the growth of democracy. Election malpractices and violence, from 1959 till date, have offered major challenges to the Nigerian political system. To achieve a sustainable democracy in Nigeria, it is important to build public trust by ensuring the security and preservation of electoral data. The world has gradually moved into the Fourth Industrial Revolution (4IR), an era where artificial intelligence, big data, internet of things, robotics, blockchain, cloud computing and 3-D printing technologies dictate the pace of activities in all walks of life. This paper suggests specific 4IR technologies solutions to electoral data security and preservation challenges. It also suggests that the Nigerian Government announce policies to serve as catalysts for the Independent National Electoral Commission and stakeholders to harness these developments to ensure that electoral processes benefit from these technologies.