Reviewerly/exHarmony huggingface dataset:
Fast Modular Exponentiation Methods for Public-Key Cryptography

Modular exponentiation (ME) is a complex operation for several public-key cryptosystems (PKCs). Moreover, ME is expensive for resource-constrained devices in terms of computation time and energy consumption, especially when the exponent is large. ME is defined as the task of raising an integer <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> to power <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> and reducing the result modulo some integer <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> . Several methods to calculate ME have been proposed. In this paper, we present the efficient ME methods. We then implement the methods using different security levels of RSA keys on a Raspberry Pi. Finally, we give the fast ME method.
Uneven Bi-Classifier Learning for Domain Adaptation

The bi-classifier paradigm is widely adopted as an adversarial method to address domain shift challenge in unsupervised domain adaptation (UDA) by evenly training two classifiers. In this paper, we report that although the generalization ability of the feature extractor can be strengthened by the two even classifiers, the decision boundaries of the two classifiers would be shrank to the source domain in the adversarial process, which weakens the discriminative ability of the learned model. To tame this dilemma, we disentangle the function of the two classifiers and introduce uneven bi-classifier learning for domain adaptation. Specifically, we leverage the F-norm (Frobenius Norm) of classifier predictions instead of the classifier disagreement to achieve adversarial learning. By this way, our feature extractor can be adversarially trained with a single classifier and the other classifier is used for preserving the target-specific decision boundaries. The proposed uneven bi-classifier learning protocol can simultaneously enhance the generalization ability of the feature extractor and expand the decision boundary of the target classifier. Extensive experiments on large-scale datasets prove that our method can significantly surpass previous domain adaptation methods, even with only a single classifier being involved.
Autonomous Drone Swarm Navigation in Complex Environments

Swarm intelligence is the behavior exhibited by a group of organisms performing simple actions in unison which leads to complex global behavior. This learnt behavior promotes formation of artificial swarm systems for accomplishing complex tasks and in this paper, the behavior learnt by a swarm of drones is used to accomplish multiple tasks related to complex environment navigation, obstacle avoidance and single-target tracking. In the past, manual formulation of artificial swarm systems has been attempted. The swarms usually have to be flexible entities that need to adapt to changing environments but manually crafted swarms can’t adapt to newer operating conditions thus they are not feasible. To tackle these challenges, an autonomous drone swarm navigation (ADSN) system is presented by introducing a customized architecture based on Truly Proximal Policy Optimization (TPPO) with the addition of memory cells. Furthermore, suitable 3D environments are designed for conductive learning with the addition of stability factors for drones to mimic real-life environments. Measures like Mean Cumulative Reward (MCR), Value Loss (VL), and Entropy are used to measure the performance of the presented ADSN system and other models with and without LSTM and found that incorporating memory does enhance the performance of the models although in some cases increase might not be that significant. TPPO with memory seems to have least loss and almost always performs best. Significant improvements were achieved compared to existing methodologies in convergence speed and enhanced stability of the model by customizing the architecture and hyper- parameter optimization. It is observed that Soft Actor Critic (SAC) is highly dependent on the values of the hyper-parameters and its results vary greatly with different values of hyper-parameters. The presented model has applications in maze navigation, target tracking, hover drones and other real-time scenarios.
An Energy-Efficient Accelerator for Medical Image Reconstruction From Implicit Neural Representation

This work presents an energy-efficient accelerator for medical image reconstruction from implicit neural representation (INR). The accelerator implements an INR-based algorithm to deliver high-quality medical image reconstruction with arbitrary resolution from a compact implicit format. In particular, we propose a dedicated hardware architecture based on an optimized computation flow for the INR-based reconstruction algorithm, which co-designs data reuse and computation load. The proposed architecture takes in the coordinate of the intersection of three scans and outputs all the voxel intensities, minimizing the data movement between on-chip and off-chip. To validate the proposed accelerator, we build a proof-of-concept prototype demonstration system using field programmable gate array (FPGA). We also map our design to 40nm CMOS technology to measure the performance of the proposed accelerator. The implementation results show that, running at 400MHz, the proposed accelerator is capable of processing medical images with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula> resolution in real-time at 26.3 frames per second (FPS), with a power consumption of only 795 mW. Comparison results show that the performance, as well as the energy efficiency of the proposed accelerator, outperforms the central processing unit (CPU)-based and graphic processing unit (GPU)-based implementations.
Cellular Binary Neural Network for Accurate Image Classification and Semantic Segmentation

This paper presents the Cellular Binary Neural Network (CBNN), which is an efficient deep neural network with binary weights and activations. To address the challenge of performance drop caused by low-precision representation, the CBNN adopts multiple subnets which are connected via learnable global lateral paths. The introduced lateral connections are assumed to be sparse and grouped with respect to different source layers. The inter-network lateral connections and inner-network parameters are simultaneously optimized by the distributional loss, classification loss and the group sparse regularization term. Experiments on the CIFAR-10 and ImageNet datasets showed that, by incorporating optimized group-sparse lateral paths, the CBNN outperformed many state-of-the-art binary neural networks in terms of classification accuracy. Besides, to verify the generalization of the proposed binary model, we extended the CBNN on semantic segmentation task. CBNN takes advantage of the multiple subnets to derive the more informative feature maps which are computed by the parallel aggregation in the last convolution block. Experiments on PASCAL VOC segmentation dataset demonstrated that, under the same segmentation settings, the proposed method achieved the superior performance over other compared networks and even the full-precision counterpart.
Reinforcement Learning Toolkits for Gaming: A Comparative Qualitative Analysis

Historically viewed as a niche economic sector, gaming is now projected to exceed a global annual revenue of $218.7 billion in 2024, taking advantage of recent Artificial Intelligence (AI) advances.In recent years, specific AI techniques namely; Machine Learning (ML) and Reinforcement Learning (RL), have seen impressive progress and popularity.Techniques developed within these two fields are now able to analyze and learn from gameplay experiences enabling more interactive, immersive, and engaging games.While the number of ML and RL algorithms is growing, their implementations through frameworks and toolkits are also extensive too.Moreover, the game design and development community lacks a framework for informed evaluation of available RL toolkits.In this paper, we present a comprehensive survey of RL toolkits for games using a qualitative evaluation methodology.
A Cyber Security Threats, Vulnerability, Challenges and Proposed Solution

Internet security is one of the most pressing concerns in the twenty-first century when taken into consideration with other online concerns. This is because technological progress, the growth of communications, and the development of the cyber-Internet have all led to a revolution in the way information and data is sent, as well as in e-commerce. Because of this, it was important to find ways to protect information and data on the internet, as well as electronic money accounts and electronic wallets. Information security on the Internet, security of electronic operations, network security, and application security are all included under the umbrella term "cybersecurity." Cybersecurity refers to the measures that are taken to protect data and information stored on all electronic devices that are connected to the Internet from malicious attacks, hacking operations, data theft, and sabotage. All electronic activities and applications require some kind of cyber security, including websites, financial institutions (including banks, banks, and bank accounts), satellite operations (including military missions), and drone management.
Sentiment Analysis of Social Media Content in Pashto Language using Deep Learning Algorithms

&lt;p&gt;Sentiment Analysis (SA) has become an active research area due to introduction of social media as it provides content generation facility of its users. Thanks to social media platforms, common people can share their views, opinions and experiences. The main focus of the researchers has been to carry out sentiment analysis in the English language content. Minimal work has been done in the field of SA in content in Pashto language which is the national language of Afghanistan and widely spoken in Pakistan as well. In this research study, our aim is to perform SA in Pashto text of social media content using machine learning and state of the art deep learning algorithms. We exploit various text feature engineering techniques like Term Frequency-Inverse Document frequency, bag-of-words, n-gram, as well as deep features of word2vec, and GloVe. We perform three sets of test subjectivity analysis, binary and tertiary level sentiment classification. Being a pioneer work, we received satisfactory results on self-prepared datasets which is extracted from social media sources. The empirical analysis-based results are evaluated using standard performance evaluation measures such as accuracy, precision, recall and f-measure. Among numerous applied algorithms, Random Forest obtained better results as compared to other algorithms.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Introducing CSP Dataset: A Dataset Optimized for the Study of the Cold Start Problem in Recommender Systems

Recommender systems are tools that help users in the decision-making process of choosing items that may be relevant for them among a vast amount of other items. One of the main problems of recommender systems is the cold start problem, which occurs when either new items or new users are added to the system and, therefore, there is no previous information about them. This article presents a multi-source dataset optimized for the study and the alleviation of the cold start problem. This dataset contains info about the users, the items (movies), and ratings with some contextual information. The article also presents an example user behavior-driven algorithm using the introduced dataset for creating recommendations under the cold start situation. In order to create these recommendations, a mixed method using collaborative filtering and user-item classification has been proposed. The results show recommendations with high accuracy and prove the dataset to be a very good asset for future research in the field of recommender systems in general and with the cold start problem in particular.
Cybersecurity Risk Assessment: A Systematic Mapping Review, Proposal, and Validation

Incorporating technologies across all sectors has meant that cybersecurity risk assessment is now a critical step in cybersecurity risk management. However, risk assessment can be a complicated process for organizations. Therefore, many authors have attempted to automate this step using qualitative and quantitative tools. The problems with the tools and the risk assessment stage in general are (1) not considering all the sub-steps of risk assessment and (2) not identifying the variables necessary for an accurate risk calculation. To address these issues, this article presents a systematic mapping review (SMR) of tools that automate the cybersecurity risk assessment stage based on studies published in the last decade. As a result, we identify and describe 35 tools from 40 primary studies. Most of the primary studies were published between 2012 and 2020, indicating an upward trend of cyber risk assessment tool publication in recent years. The main objectives of this paper are to: (I) identify the differences (reference models and applications) and coverage of the main qualitative and quantitative models, (II) identify relevant risk assessment variables, (III) propose a risk assessment model (qualitative and quantitative) that considers the main variables and sub-stages of risk assessment stage, and (IV) obtain an assessment of the proposed model by experts in the field of cybersecurity. The proposal was sent to a group of 28 cybersecurity experts who approved the proposed variables and their relevance in the cybersecurity risk assessment stage, identifying a majority use of qualitative tools but a preference of experts for quantitative tools.
Communication Channels and their Challenges

Software development teams depend on the constant and varied use of technological tools that contribute to the fluidity of development activities. The communication channels provided by these tools contribute to the participatory culture of software development, where requirements include getting involved, learning and co-producing the code. However, the choice of channels to support development is still an open question in the scientific community. Through an investigation of closed remote software teams from a large public educational institution in Brazil during the COVID-19 Pandemic, we identified their preferred communication channels and analyzed them to learn how to improve collaboration. We also drew some comparison with social developers and took a look at the challenges that these channels impose and solutions adopted to mitigate these problems.
Examining Personal and Altruistic Fear of Ransomware

Despite the growth in literature examining fear of cybercrime, few studies have examined altruistic fear online. Additionally, few studies have examined ransomware fear, potentially limiting the effectiveness of fear reduction policies. Using randomly assigned vignettes administered to online samples (n = 428; 432), findings indicate that perceptions of disorder were associated with increased personal fear, yet were not associated with altruistic ransomware fear. Social cohesion was associated with altruistic fear, yet was not associated with personal fear. Findings demonstrate the need for fear reduction policies to acknowledge the multifaceted nature of fear and adapt explanatory models to online contexts.
Fuzzy switching function-based sliding mode controller design for T-S fuzzy descriptor systems

The design of switching functions is extremely important in the design of sliding mode controllers in that it closely relates to the solvability of the sliding mode control problems. This paper is concerned with the design problems of switching functions and fuzzy sliding mode controllers for T-S fuzzy descriptor systems. First, a generalized regular form of T-S fuzzy descriptor systems is defined. Then the design method of the linear switching function-based fuzzy sliding mode controller is developed and the result obtained indicates a relationship between the design parameter in the switching function and the gain matrix in the controller of T-S fuzzy systems. From this relationship, a new framework of the switching function design for T-S fuzzy descriptor systems is formulated. Moreover, the design method of the fuzzy switching function-based sliding mode controllers is proposed to exemplify the proposed framework. It is shown that the proposed fuzzy switching function-based method is less conservative than the linear switching function-based method from the viewpoint of the solvability of the sliding mode control problems. Finally, the results obtained are verified through four illustrative examples.
Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

Friend recommendation plays a key role in promoting user experience in online social networks (OSNs). However, existing studies usually neglect users' fine-grained interest as well as the evolving feature of interest, which may cause unsuitable recommendation. In particular, some OSNs, such as the online learning community, even have little work on friend recommendation. To this end, we strive to improve friend recommendation with fine-grained evolving interest in this paper. We take the online learning community as an application scenario, which is a special type of OSNs for people to learn courses online. Learning partners can help improve learners' learning effect and improve the attractiveness of platforms. We propose a learning partner recommendation framework based on the evolution of fine-grained learning interest (LPRF-E for short). We extract a sequence of learning interest tags that changes over time. Then, we explore the time feature to predict evolving learning interest. Next, we recommend learning partners by fine-grained interest similarity. We also refine the learning partner recommendation framework with users' social influence (denoted as LPRF-F for differentiation). Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy (i.e., approximate 50% improvements on the precision and the recall) and can recommend learning partners with high quality (e.g., more experienced and helpful).The online version contains supplementary material available at 10.1007/s11390-021-2124-z.
Improved Quantum Analysis of SPECK and LowMC

As the prevalence of quantum computing is growing in leaps and bounds over the past few years, there is an ever-growing need to analyze the symmetric-key ciphers against the upcoming threat. Indeed, we have seen a number of research works dedicated to this. Our work delves into this aspect of block ciphers, with respect to the SPECK family and LowMC family. The SPECK family received two quantum analysis till date (Jang et al., Applied Sciences, 2020; Anand et al., Indocrypt, 2020). We revisit these two works, and present improved benchmarks SPECK (all 10 variants). Our implementations incur lower full depth compared to the previous works. On the other hand, the quantum circuit of LowMC was explored earlier in Jaques et al.'s Eurocrypt 2020 paper. However, there is an already known bug in their paper, which we patch. On top of that, we present two versions of LowMC (on L1, L3 and L5 variants) in quantum, both of which incur significantly less full depth than the bug-fixed implementation.
Stronger Security and Generic Constructions for Adaptor Signatures

Adaptor signatures have seen wide applications in layer-2 and peer-to-peer blockchain applications such as atomic swaps and payment channels. We first identify two shortcomings of previous literature on adaptor signatures. (1) Current aim of “script-less” adaptor signatures restricts instantiability, limiting designs based on BLS or current NIST PQC candidates. (2) We identify gaps in current formulations of security. In particular, we show that current notions do not rule out a class of insecure schemes. Moreover, a natural property concerning the on-chain unlinkability of adaptor signatures has not been formalized. We then address these shortcomings by providing new and stronger security notions, as well as new generic constructions from any signature scheme and hard relation. On definitions: On constructions:
An Efficient and Secure Authentication Protocol with Deep Learning Based Key Generation toward Securing Healthcare Data in IoT

Due to the emergence of technologies in the growing world, the IoT is termed as a novel concept. The combination of internet with smart devices provides convenient user-related services. The attack toward confidential data may leak data and can cause serious security issues. Meanwhile, the classical security techniques cannot be utilized in a direct manner. Thus, novel techniques are needed for safeguarding the IoT from security issues. This paper designs secure authentication model for IoT in the healthcare. Several entities, such as gateway node, sensor node and medical professional are included for performing the secure authentication. The proposed protocol is devised by providing a mathematical model that makes usage of hashing function, encryption algorithm, XOR, Chebyshev polynomial, passwords, secret keys, and other security operations. Here, Secret key is generated with the deep neuro fuzzy network (DNFN) using Extreme Learning Machine (ELM) based sub-key generation. The proposed technique outperformed with less computation time of 33,484,154,235 ns, smallest memory of 51.750 MB, and highest detection rate of 94.9%.
The Application Effect of Remote Sensing Technology in Hydrogeological Investigation under Big Data Environment

The hydrogeological investigation is a work carried out by comprehensive utilization of various exploration methods to identify hydrogeological conditions in the target area, and develop and utilize groundwater resources. There are great differences in hydrogeological conditions in different regions. Hence, it is necessary to take exploration technology according to local conditions to master hydrogeological information as much as possible. Among them, the remote sensing (RS) technology can reflect the ground surveying and mapping results with high efficiency and precision through the analysis of satellite or aerial photographs, which is a commonly used method in the current hydrogeological investigation. According to satellite RS data, this work evaluates the distribution of groundwater levels in the study area and explores the geological and hydrogeological conditions of the groundwater system in the affected area. Firstly, the human-computer interactive interpretation method is used to analyze the topography and geomorphology conditions. Secondly, the spectral characteristic curve analysis method is used to extract the spectral characteristics of regional stratum lithology, and analyze and determine the lithology composition and structure of the aquifer. Thirdly, the single-band and multiband models of soil moisture RS estimation of groundwater level are implemented. Finally, the measured data are employed to verify and analyze the estimated value of the model. The results are in line with the actual value, and good results have been achieved.