Reviewerly/exHarmony huggingface dataset:
Blockchain Technology and its Impact on Future of Internet of Things (IoT) and Cyber Security

Due to Bitcoin's innovative block structure, it is both immutable and decentralized, making it a valuable tool or instrument for changing current financial systems. However, the appealing features of Bitcoin have also drawn the attention of cybercriminals. The Bitcoin scripting system allows users to include up to 80 bytes of arbitrary data in Bitcoin transactions, making it possible to store illegal information in the blockchain. This makes Bitcoin a powerful tool for obfuscating information and using it as the command-and-control infrastructure for blockchain-based botnets. On the other hand, Blockchain offers an intriguing solution for IoT security. Blockchain provides strong protection against data tampering, locks Internet of Things devices, and enables the shutdown of compromised devices within an IoT network. Thus, blockchain could be used both to attack and defend IoT networks and communications.
CNN based Approach for Sign Recognition in the Indian Sign language

Vocal communication is a way to express thoughts, opinions, and emotions. There are some special people who find it difficult to communicate vocally as they suffer from Nonverbal Autism. Communication between people with speech impairment and non-signers becomes difficult as they tend to use Sign Language. Each individual is not able to understand Sign Language. In this paper, a model is proposed which converts the gestures into text and furthermore to audio format. The main aim is to bridge the communication gap between speech impairment people and non-signers. By taking video input from the camera, AlexNet, which is a Convolution Neural Network architecture, can classify real-time hand gestures. During training, the model has been trained on more than 400 images of 10 gestures consisting of alphabets, numbers and phrases, which are then preprocessed with canny edge filtering in order to improve accuracy. Along with it, the recognized gesture is converted into audio. The model has achieved an accuracy of 95.31 % with a 90:10 training-testing ratio.
Systolic Array Based Convolutional Neural Network Inference on FPGA

Convolutional Neural Networks (CNNs) possess a particular edge over its predecessor, the Multi-Layer Perceptron (MLP). This is due to its weight sharing features that allows the CNN to use less parameters for the same number of outputs as compared to the MLP. Systolic arrays capitalize on the weight sharing property of CNNs to do data reuse while performing convolutional operations, in order to reduce the power consumption from the memory accesses. A kernel fitting systolic processing element array was designed with only positive multiplication to increase the throughput and power efficiency of the CNN accelerator, while using weight stationary dataflow to achieve data reuse in the systolic array. A cost-optimized lightweight solution is implemented through low-cost FPGA hardware so as to allow for greater accessibility. The CNN accelerator consumes 0.363 W power at 100 MHz operating frequency. A peak throughput of 10.98 GOps/s was achieved with peak performance density of 0.200 GOps/s/DSP and peak power efficiency of 30.26 GOps/s/W. Even with the added support for additional functions, proposed design achieved up to 1.59x better power efficiency compared to other systolic implementations and up to 6.17x better power efficiency compared to non-systolic implementations.
Secure Data Access Management for Cyber Threats using Artificial Intelligence

Artificial intelligence (AI) research, which aims to imitate intelligent behaviour in artificial systems, has long drawn inspiration from human cognition. The most critical component of artificial intelligence is repetitive learning. The impact of AI on security is a major issue for the entire world. Future method in social and natural sciences should also take this into account, as it is important. Security safeguards are critical, especially because many services are delivered via online devices. Security and proper data governance should be included in the outcomes. The industrial and digital revolutions are significantly impacted by the use of Artificial Intelligence in society. Precautions to maintain accessibility when using electronic devices are essential in many applications. Applications like data mining, data analytics, bitcoin, and blockchain technology must be shielded from online risks when accessing data. More cyber threats might materialise as a result of their use of the Internet and uncontrolled data access for their applications. As a means of reducing these risks and maximising the advantages of the wide spread application of AI advancements, the analysis offers high-security safe data access management for dealing with cyber risks utilizing Artificial Intelligence. Therefore, it offers a solution for those who are ready to take an enterprising risk to create innovative goods or services that may be used globally for commerce. The performance of this method is calculated on different aspects such as Accuracy, Recall and Specificity. Though the accuracy for different methods varies, this analysis will give the best results.
Image Deconvolution using Deep Learning-based Adam Optimizer

The image deconvolution eliminates the image blur using the manual methods in which the kernels are applied, and the neural network method uses the optimizers. The optimizers are designed from the examples, or the predetermined manual instructions are given. The inverse problems of the blur images using manual methods have made it difficult for the deconvolution, hence the optimized techniques are designed to get the deconvolution images. Different optimizers are designed to the deconvolution of the images, but the quality of restoration is not as expected. The existing algorithms use the Gradient Descent optimizer for the deconvolution, which is designed for the removal of the Gaussian Noise but not to the other types of noises. The proposed algorithm uses Adam optimizer to remove all the other types of noises like poison and speckle noise. The proposed method is based on deep learning, in which residual convolutional neural network is used for training. The proposed network is trained with Adam optimizer, which increases the Peak Signal to Noise Ratio and Structural Similarity Index Measurement than the previously existing algorithms. The Adam optimizer can also be applied for the real time problems of the image deconvolution.
Blockchain based Trusted Secure Philanthropy Platform: Crypto-GoCharity

Blockchain is one of the significant innovations and a disruptive technology. It is becoming a primary choice for solving many problems in the field of data security. Blockchain technology is also gaining popularity in the domain of crowdfunding and philanthropy. The paper describes the flaws in the charitable giving ecosystem and how blockchain can be used to address these issues through the use of blockchain building blocks such as distributed ledger technology, smart contracts, and cryptography. Through distributed ledger technology, the entire ledger copy is shared with all nodes in the blockchain, bringing transparency to the charity ecosystem. The immutability of the ledger transaction records ensures tamper resistance and fosters trust in the ecosystem. Cryptography also makes transactions more secure. The study aims to address the issue of charity donation monitoring in the philanthropic sector by removing the middleman and employing crypto-currency. This paper proposes philanthropy services through cryptocurrency via a web-based platform. The proposed system is a decentralized system which is built on Ethereum Blockchain using Solidity Smart Contract, which will provide complete transparency and donors will be able to track their donations. This paper proposes philanthropy services through cryptocurrency via a web-based platform.
Prevention of Phishing attacks using AI Algorithm

A particularly successful type of cybercrime that allows criminals to trick people and steal crucial data is phishing. Phishing is currently one of the most common online scams. Phishing assaults may result in significant losses for sensitive information, identity theft, businesses, and the government are just some of their victims. Phishing websites are widespread entrance points for social engineering attacks carried out online, including countless website frauds. When such attacks occur, by copying the acts of reputable websites, the attacker(s) create website pages and send the URL(s) to the targeted recipients via social networking, texting, or spam messaging. However, the number of victims is rising exponentially as a result of inadequate security technologies. Studies have categorized phishing attacks by basic phishing methods and defenses, ignoring the significance of the end-to-end phishing lifecycle. This article offers a fresh, in-depth model of phishing that takes into account attack stages, different types of attackers, threats, targets, attack channels, and attack tactics. The proposed anatomy will also make it easier for readers to understand how long a phishing effort lasts, increasing knowledge of these attacks and the techniques used as well as aiding in the creation of a comprehensive anti-phishing system. Due to the anonymity and lack of regulations on the Internet, phishing attacks are more likely to be successful. The effectiveness of the phishing detection system is limited, according to existing research. To protect consumers from cyberattacks, a clever strategy is needed. A detection method used in this work is based on artificial intelligence-based LSTM which has produced satisfactory performance and accuracy.
Management of Lifelong Education Projects for Sustainable Development

The modern world needs the inspiration of socially responsible leaders for a sustainable future and the promotion of sustainable lifestyles. The purpose of the article is to identify the results of the project «Strengthening University Social Responsibility for Sustainable Development». Research methods: case studies, sociological survey, and graphic method. Results of the research: promoting ethical behaviour and social responsibility in the academic environment; involvement of students to make decisions at the university; promoting dialogue and cooperation; promoting the development of a culture of creation and implementation of university projects on social responsibility.
AIF-LFNet: All-in-Focus Light Field Super-Resolution Method Considering the Depth-Varying Defocus

As an aperture-divided computational imaging system, microlens array (MLA) -based light field (LF) imaging is playing an increasingly important role in computer vision. As the trade-off between the spatial and angular resolutions, deep learning (DL) -based image super-resolution (SR) methods have been applied to enhance the spatial resolution. However, in existing DL-based methods, the depth-varying defocus is not considered both in dataset development and algorithm design, which restricts many applications such as depth estimation and object recognition. To overcome this shortcoming, a super-resolution task that reconstructs all-in-focus high-resolution (HR) LF images from low-resolution (LR) LF images is proposed by designing a large dataset and proposing a convolutional neural network (CNN) -based SR method. The dataset is constructed by using Blender software, consisting of 150 light field images used as training data, and 15 light field images used as validation and testing data. The proposed network is designed by proposing the dilated deformable convolutional network (DCN) -based feature extraction block and the LF subaperture image (SAI) Deblur-SR block. The experimental results demonstrate that the proposed method achieves more appealing results both quantitatively and qualitatively.
A Lightweight Block With Information Flow Enhancement for Convolutional Neural Networks

Convolutional neural networks (CNNs) have demonstrated excellent capability in various visual recognition tasks but impose an excessive computational burden. The latter problem is commonly solved by utilizing lightweight sparse networks. However, such networks have a limited receptive field in a few layers, and the majority of these networks face a severe information barrage due to their sparse structures. Spurred by these deficiencies, this work proposes a Squeeze Convolution block with Information Flow Enhancement (SCIFE), comprising a Divide-and-Squeeze Convolution and an Information Flow Enhancement scheme. The former module constructs a multi-layer structure through multiple squeeze operations to increase the receptive field and reduce computation. The latter replaces the affine transformation with the point convolution and dynamically adjusts the activation function’s threshold, enhancing information flow in both channels and layers. Moreover, we reveal that the original affine transformation may harm the network’s generalization capability. To overcome this issue, we utilize a point convolution with a zero initial mean. SCIFE can serve as a plug-and-play replacement for vanilla convolution blocks in mainstream CNNs, while extensive experimental results demonstrate that CNNs equipped with SCIFE compress benchmark structures without sacrificing performance, outperforming their competitors.
Reputation Consensus-Based Scheme for Information Sharing in Internet of Vehicles

Low latency and low energy consumption are prerequisites for Internet of Vehicles (IoV) research; nevertheless, consensus mechanisms of legacy blockchain applied to IoV typically incur high delay and serious energy consumption due to the computational puzzle. In this paper, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to implement a novel reputation consensus mechanism for IoV based on vehicle reputation values and traffic environment, which can not only reduce the computational pressure but also ensure the fairness of consensus. Firstly, the information transmission vehicles (ITVs) would be picked following the reputation value and its associated environmental data that makes the choice more comprehensive and objective than other mechanisms. Secondly, the authenticity of sharing information is determined with the help of Bayesian inference, aided by the reputation value of message reporting vehicles (MRVs). Finally, simulation results show that the proposed scheme can improve consensus efficiency by picking ITVs in a short time and avoid the spreading of false information effectively in the IoV system.
Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network

Automatic modulation recognition (AMR) plays an essential role in modern communication systems. In recent years, various modulation recognition algorithms based on deep learning have been emerging, but the problem of low recognition accuracy has not been solved well. To solve this problem, based on the existing MCLDNN algorithm, in this paper, we proposed an improved spatiotemporal multi-channel network (IQ-related features Multi-channel Convolutional Bi-LSTM with Gaussian noise, IQGMCL). Firstly, dividing the input IQ signals into three channels, time sequence feature extraction is carried out for route I, route Q, and route IQ, respectively. For route IQ, convolution kernel (2,1) is first used to extract relevant features. Two layers of the small convolution kernel (1,3) are used to extract time sequence features further, and the three channels are used to extract features further. Then, a two-layer short-length memory network is used to extract features from time and space more effectively. Through comparison experiments, Bi-LSTM is introduced to replace one layer of LSTM, and a fully connected layer is removed to prevent overfitting. Finally, multiplicative Gaussian noise is introduced to naturally corrode the feature parameters, further improving the robustness and accuracy of the model. Experiments are carried out on three public datasets RML2016.10a, RML2016.10b, and RML2016.04C. The experiments show that the IQGMCL network has higher recognition accuracies on all datasets, especially on the RML2016.10a dataset. When the SNR is 4 dB, the recognition accuracy reaches 93.52%. When the SNR is greater than 0 dB, the average recognition accuracy reaches 92.3%, 1.31%, and 1.2% higher than the original MCLDNN network, respectively.
Cooperative coevolutionary differential evolution with linkage measurement minimization for large-scale optimization problems in noisy environments

Abstract Many optimization problems suffer from noise, and the noise combined with the large-scale attributes makes the problem complexity explode. Cooperative coevolution (CC) based on divide and conquer decomposes the problems and solves the sub-problems alternately, which is a popular framework for solving large-scale optimization problems (LSOPs). Many studies show that the CC framework is sensitive to decomposition, and the high-accuracy decomposition methods such as differential grouping (DG), DG2, and recursive DG (RDG) are extremely sensitive to sampling accuracy, which will fail to detect the interactions in noisy environments. Therefore, solving LSOPs in noisy environments based on the CC framework faces unprecedented challenges. In this paper, we propose a novel decomposition method named linkage measurement minimization (LMM). We regard the decomposition problem as a combinatorial optimization problem and design the linkage measurement function (LMF) based on Linkage Identification by non-linearity check for real-coded GA (LINC-R). A detailed theoretical analysis explains why our proposal can determine the interactions in noisy environments. In the optimization, we introduce an advanced optimizer named modified differential evolution with distance-based selection (MDE-DS), and the various mutation strategy and distance-based selection endow MDE-DS with strong anti-noise ability. Numerical experiments show that our proposal is competitive with the state-of-the-art decomposition methods in noisy environments, and the introduction of MDE-DS can accelerate the optimization in noisy environments significantly.
Posterior Representations for Bayesian Context Trees: Sampling, Estimation and Convergence

We revisit the Bayesian Context Trees (BCT) modelling framework for discrete time series, which was recently found to be very effective in numerous tasks including model selection, estimation and prediction. A novel representation of the induced posterior distribution on model space is derived in terms of a simple branching process, and several consequences of this are explored in theory and in practice. First, it is shown that the branching process representation leads to a simple variable-dimensional Monte Carlo sampler for the joint posterior distribution on models and parameters, which can efficiently produce independent samples. This sampler is found to be more efficient than earlier MCMC samplers for the same tasks. Then, the branching process representation is used to establish the asymptotic consistency of the BCT posterior, including the derivation of an almost-sure convergence rate. Finally, an extensive study is carried out on the performance of the induced Bayesian entropy estimator. Its utility is illustrated through both simulation experiments and real-world applications, where it is found to outperform several state-of-the-art methods.
Large Field-Size Elliptic Curve Processor for Area-Constrained Applications

This article has proposed an efficient area-optimized elliptic curve cryptographic processor architecture over GF(2409) and GF(2571). The proposed architecture employs Lopez-Dahab projective point arithmetic operations. To do this, a hybrid Karatsuba multiplier of 4-split polynomials is proposed. The proposed multiplier uses general Karatsuba and traditional schoolbook multiplication approaches. Moreover, the multiplier resources are reused to implement the modular squares and addition chains of the Itoh-Tsujii algorithm for inverse computations. The reuse of resources reduces the overall area requirements. The implementation is performed in Verilog (HDL). The achieved results are provided on Xilinx Virtex 7 device. In addition, the performance of the proposed design is evaluated on ASIC 65 nm process technology. Consequently, a figure-of-merit is constructed to compare the FPGA and ASIC implementations. An exhaustive comparison to existing designs in the literature shows that the proposed architecture utilizes less area. Therefore, the proposed design is the right choice for area-constrained cryptographic applications.
Detection and Cross-domain Evaluation of Cyberbullying in Facebook Activity Contents for Turkish

Cyberbullying refers to bullying and harassment of defenseless or vulnerable people such as children, teenagers, and women through any means of communication (e.g., e-mail, text messages, wall posts, tweets) over any online medium (e.g., social media, blogs, online games, virtual reality environments). The effect of cyberbullying may be severe and irreversible and it has become one of the major problems of cyber-societies in today’s electronic world. Prevention of cyberbullying activities as well as the development of timely response mechanisms require automated and accurate detection of cyberbullying acts. This study focuses on the problem of cyberbullying detection over Facebook activity content written in Turkish. Through extensive experiments with the various machine and deep learning algorithms, the best estimator for the task is chosen and then employed for both cross-domain evaluation and profiling of cyber-aggressive users. The results obtained with fivefold cross-validation are evaluated with an average-macro F1 score. These results show that BERT is the best estimator with an average macro F1 of 0.928, and employing it on various datasets collected from different OSN domains produces highly satisfying results. This article also reports detailed profiling of cyber-aggressive users by providing even more information than what is visible to the naked eye.
Deep Reinforcement Learning to Automate Cryptocurrency Trading

This research produces a deep reinforcement learning model for algorithmic trading of cryptocurrencies. The model aims to help traders earn greater profits than using traditional strategies. Although traditional strategies can generate profits, they require considerable knowledge, experience, and time to generate optimal profits. Models are trained to trade on the cryptocurrency market. Model inputs are 1 minute interval candlestick data and technical indicators for the BTC/USDT cryptocurrency pair. The model produces an output in the form of a buy, hold, or sell signal. Models are created with the PPO algorithm and a custom environment that follows the gym interface. The performance of the model is compared to the Buy and Hold strategy. The tests that have been carried out show that the model produced in this study still cannot beat the Buy and Hold strategy.
Explaining Bias in Deep Face Recognition via Image Characteristics

In this paper, we propose a novel explanatory framework aimed to provide a better understanding of how face recognition models perform as the underlying data characteristics (protected attributes: gender, ethnicity, age; nonprotected attributes: facial hair, makeup, accessories, face orientation and occlusion, image distortion, emotions) on which they are tested change. With our framework, we evaluate ten state-of-the-art face recognition models, comparing their fairness in terms of security and usability on two data sets, involving six groups based on gender and ethnicity. We then analyze the impact of image characteristics on models performance. Our results show that trends appearing in a single-attribute analysis disappear or reverse when multi-attribute groups are considered, and that performance disparities are also related to non-protected attributes. Source code: https://cutt.1y/2XwRLiA.