Reviewerly/exHarmony huggingface dataset:
Memory-efficient multi-scale residual dense network for single image rain removal

Outdoor photography is greatly affected by bad rainfall, which degrades the quality of captured images and hinders practical application. In response to the above problems, we propose a memory-efficient multi-scale residual dense network (MMRD-Net) for single image rain removal. Firstly, we use a combination or fusion of extended convolution blocks of different sizes to accurately extract multi-scale features of rain streaks and raindrops while retaining rich local detail. Secondly, we add attention modules to the residual block, which performs adaptive operations on rainfall and non-rainfall areas to extract more and more accurate image features and details of rain streaks and raindrops. During this period, we opened up a space for shared use by all the concatenation layers in the network, which reduces the memory consumption of the network. It allows for better aggregation of stormwater features at multiple levels and also allows for faster convergence of the network. Finally, we tested on both synthetic and real datasets to obtain subjective results and objective evaluations. The experimental results show that the performance of our algorithm is good and robust for joint rain removal tasks of different densities. Our algorithm outperforms twelve other different classical algorithms in terms of its ability to remove rain streaks. It can produce visually sharper deraining images and the enhanced visibility of the images is effective for computer vision applications (Google Vision API).
A comprehensive soft security model for Cognitive Internet of Things

The Cognitive Internet of Things (CIoT) is a rapidly evolving field that combines artificial intelligence (AI) with the Internet of Things (IoT) ecosystem. By augmenting IoT with AI, objects can sense, perceive, think, and make decisions independently with minimal initial knowledge. These cognitive objects form a society and work toward achieving their goals through cooperation. However, trust is a significant security challenge in such a society. To address this challenge, this paper proposes a soft security approach to model trust in CIoT using Collaborative Multi-Agent Systems (CMAS). Our model introduces an interactive, autonomous, and self-taught agent that can move toward a secure situation without needing a supervisor. We use a state machine to model an insecure ecosystem where agents can behave honestly, adversarially, neutrally, or hypocritically. We define inner trust as a combination of direct and indirect experiences and observations and global trust as the weighted average of inner trust and recommendations. Additionally, we employ the reinforcement learning algorithm to train agents. To evaluate our model, we developed a proprietary tool called ASSOCIATE and assessed our model using data from the Santander Smart City dataset and simulator-generated data. Our evaluations encompass three aspects: the success rate in achieving the goal, the recognition of agents’ behavior, and the quality of the diagnosis.
A Deep Neural Network Based on Prior-Driven and Structural Preserving for SAR Image Despeckling

Remarkable effectiveness has been demonstrated by deep neural networks in the despeckling task for Synthetic Aperture Radar (SAR) images. However, blurring and loss of fine details can result from many despeckling models due to up-sampling and mean square error (MSE) loss. Additionally, existing degradation models and prior information are ignored by existing despeckling models, which directly learn the mapping from degraded to clear images. To address these issues, an optimization algorithm for the SAR despeckling task based on the integral-Newton method is proposed in this article. Then, a prior-driven despeckling network is proposed, which can automatically capture the implicit priors in SAR images to replace traditional manually-made priors. Furthermore, to make the network focus more on learning the structural prior information of images, a structure-preserving loss function based on MSE and the Canny edge detection operator is designed, which improves the detail of the network retention ability and speeds up convergence. Outstanding results on both simulated datasets and real SAR images are achieved by the proposed method, as shown by a large number of experimental results. Moreover, significant advantages of the proposed method both visually and quantitatively are revealed by comparison with classical and state-of-the-art despeckling algorithms.
Predicting Future Eye Gaze Using Inertial Sensors

Eye tracking is a technology that is in high demand, especially for next-generation virtual reality (VR), because it enables foveated rendering, which significantly reduces computational costs by rendering only the area at which a user is gazing at a high resolution and the rest at a lower resolution. However, the conventional eye-tracking technique requires per-eye camera hardware attached near the eyes within a VR headset. Moreover, the detected eye gaze follows the actual eye gaze with a finite delay because of the camera latency, the need for image processing, and the VR system’s native latency. This paper proposes an eye-tracking solution that predicts a user’s future eye gaze using only the inertial sensors that are already built into VR headsets for head tracking. To this end, we formulate three time-series regression problems to predict (1) the current eye gaze using past head orientation data, (2) the future eye gaze using past head orientation and eye gaze data, and (3) the future eye gaze using past head orientation data only. We solve the first and second problems using machine learning models and develop two solutions for the final problem: two-stage and single-stage approaches. The two-stage approach for the final problem relies on two machine learning models connected in series, one for the first problem and the other for the second problem. The single-stage approach uses a single model to predict the future eye gaze directly from past head orientation data. We evaluate the proposed solutions based on real eye-tracking traces captured from a VR headset for multiple test players, considering various combinations of machine learning models. The experimental results show that the proposed solutions for the final problem reduce the error for a center-fixed gaze by up to 50% and 20% for anticipation times of 50 and 150 ms, respectively.
Understanding Flash-Loan-based Wash Trading

Flash Loan, a popular lending service in the decentralized finance (DeFi) ecosystem, allows users to borrow a large number of virtual assets without any collateral. It can be leveraged to support many financial activities (such as arbitrage, collateral swap, self-liquidation, etc.), but unfortunately, it is often abused for malicious intent. One example of abusing flash loan servicing is to simultaneously sell and buy the same crypto currency on the same exchange to mislead the market, aka wash trading. It can manipulate the crypto currency market at a very low cost (anecdotally average around 0.033 ETH gas fee for each transaction on Ethereum mainnet), thereby dramatically damaging the stability and fairness of the market. More seriously, attackers can amplify the market impact by borrowing more assets from Flash Loan platforms. Until now, there has been little attention paid to Flash-Loan-based wash trading, but meanwhile, we have started to witness significant wash trading activities using Flash Loan. In this research, we analyze the properties of Flash-Loan-based wash trading in detail and propose a heuristic-based detection method. The real-world Flash Loan transaction data from Ethereum is used to verify our proposed detection method and more than 6,000 wash transactions were found. Moreover, we analyze the relationship between wash transactions and fluctuations in the price and volume of targeted assets. Finally, we evaluate the cost difference between traditional wash trading and Flash-Loan-based wash trading to reveal the attackers' motivation.
Adoption and Actual Privacy of Decentralized CoinJoin Implementations in Bitcoin

We present a first measurement study on the adoption and actual privacy of two popular decentralized CoinJoin implementations, Wasabi and Samourai, in the broader Bitcoin ecosystem. By applying highly accurate (¿ 99%) algorithms we can effectively detect 30,251 Wasabi and 223,597 Samourai transactions within the block range 530,500 to 725,348 (2018-07-05 to 2022-02-28). We also found a steady adoption of these services with a total value of mixed coins of ca. 4.74 B USD and average monthly mixing amounts of ca. 172.93 M USD) for Wasabi and ca. 41.72 M USD for Samourai. Furthermore, we could trace ca. 322 M USD directly received by cryptoasset exchanges and ca. 1.16 B USD indirectly received via two hops. Our analysis further shows that the traceability of addresses during the pre-mixing and post-mixing narrows down the anonymity set provided by these coin mixing services. It also shows that the selection of addresses for the CoinJoin transaction can harm anonymity. Overall, this is the first paper to provide a comprehensive picture of the adoption and privacy of distributed CoinJoin transactions. Understanding this picture is particularly interesting in the light of ongoing regulatory efforts that will, on the one hand, affect compliance measures implemented in cryptocurrency exchanges and, on the other hand, the privacy of end-users.
FUSE – Flexible File Format and Intermediate Representation for Secure Multi-Party Computation

Secure Multi-Party Computation (MPC) is continuously becoming more and more practical. Many optimizations have been introduced, making MPC protocols more suitable for solving real-world problems. However, the MPC protocols and optimizations are usually implemented as a standalone proof of concept or in an MPC framework and are tightly coupled with special-purpose circuit formats, such as Bristol Format. This makes it very hard and time-consuming to re-use algorithmic advances and implemented applications in a different context. Developing generic algorithmic optimizations is exceptionally hard because the available MPC tools and formats are not generic and do not provide the necessary infrastructure.
BinWrap: Hybrid Protection against Native Node.js Add-ons

Modern applications, written in high-level programming languages, enjoy the security benefits of memory and type safety. Unfortunately, even a single memory-unsafe library can wreak havoc on the rest of an otherwise safe application, nullifying all the security guarantees offered by the high-level language and its managed runtime. We perform a study across the Node.js ecosystem to understand the use patterns of binary add-ons. Taking the identified trends into account, we propose a new hybrid permission model aimed at protecting both a binary add-on and its language-specific wrapper. The permission model is applied all around a native add-on and is enforced through a hybrid language-binary scheme that interposes on accesses to sensitive resources from all parts of the native library. We infer the add-on's permission set automatically over both its binary and JavaScript sides, via a set of novel program analyses. Applied to a wide variety of native add-ons, we show that our framework, BinWrap, reduces access to sensitive resources, defends against real-world exploits, and imposes an overhead that ranges between 0.71%–10.4%.
Active learning for Arabic sentiment analysis

Sentiment analysis becomes an essential part of every social network, as it enables decision-makers to know more about users' opinions. Arabic sentiment analysis has a small number of datasets relative to English. This paper introduces active learning to the Arabic NLP community and demonstrates its power. Active learning is the process of selecting the data before labelling it to get the best benefits from the labelled data and improve performance. As it takes much time and effort to label large datasets, a better way is to label selected data and get higher performance. This paper applies active learning to the ArSarcasm-v2 dataset, which is labelled for sentiment, sarcasm, and dialect, by using the SAIDS "Sentiment Analysis Informed of Dialect and Sarcasm" model that predicts sarcasm and dialect and then uses them to predict the sentiment of the text. The paper runs multiple active learning experiments with different setups. The paper achieves state-of-the-art performance on ArSarcasm-v2 for sentiment analysis by achieving a 76.71 F1-score using 95% of the training dataset which means that adding some data points makes the performance worse. The paper also demonstrates that by using active learning, we achieve 97%, 99%, and 100% of the highest F1-score reached by all training data using only 10%, 20%, and 27% of the training data respectively. This means that using about one-quarter of the training data attains the same F1-score as using the full training data. As per our knowledge, this is the first paper to introduce active learning to the Arabic NLP community.
LoDen: Making Every Client in Federated Learning a Defender Against the Poisoning Membership Inference Attacks

Federated learning (FL) is a widely used distributed machine learning framework. However, recent studies have shown its susceptibility to poisoning membership inference attacks (MIA). In MIA, adversaries maliciously manipulate the local updates on selected samples and share the gradients with the server (i.e., poisoning). Since honest clients perform gradient descent on samples locally, an adversary can distinguish whether the attacked sample is a training sample based on observation of the change of the sample’s prediction. This type of attack exacerbates traditional passive MIA, yet the defense mechanisms remain largely unexplored.
Artificial intelligence in steering the digital transformation of collaborative technical education

In recent years, the United Arab Emirates (UAE) has been a leader in the global adoption of AI and online schooling. Despite using the traditional educational structure, military colleges have embraced this new technology. This research analyzed the present adoption rate, difficulties, and solutions for implementing an AI-based online education system. The results demonstrate that digital technology has a tangible impact on all facets of higher education if it is supported by the institution. The results also indicate that the organization plays a crucial role in the integration of digital technology into teaching and learning, and that an examination of the materiality already present in the Collaborative Technical Education (CTE) organization is necessary for comprehending the potential effects of new digital technology. Finally, this paper addressed how AI can make e-learning more interesting, efficient, and tailored to each individual learner, all of which contribute to better learning outcomes and wider access to technical education. The details of how artificial intelligence can be used to promote diversity and equity in the classroom are laid out. To motivate educators to create mixed-reality artifacts and conduct further research to support collaborative educational environments, this article discusses current works and visualizes the current state of the field.
Alternating attention Transformer for single image deraining

Recently, Transformer-based network architectures have achieved significant improvements over convolutional neural networks (CNNs) in the field of single image deraining, due to the powerful ability of modeling non-local information. However, these approaches employ all token similarities between queries and keys to aggregate global features based on a dense self-attention mechanism, which may neglect to focus on the most relevant information and induce blurry effect by the irrelevant representations. To alleviate the above issues, we propose an effective alternating attention Transformer (called AAT) for boosting image deraining performance. Specifically, we only select the most useful similarity values based on top-k approximate calculation to achieve sparse self-attention. In our framework, the representational capability of Transformer is significantly improved by alternately applying dense and sparse self-attention blocks. In addition, we insert a multi-dilconv feed-forward network to replace the native MLP into our proposed AAT, in order to better characterize the multi-scale rain streaks distribution. To compensate for the lack of modeling of Transformer backbone on local features, we introduce the local feature refinement block for achieving high-quality derained results. Extensive experiments on benchmark datasets demonstrate the effectiveness of our proposed method. The source codes will be released.
The revision of the product liability directive: a key piece in the artificial intelligence liability puzzle

Abstract The revision of the Product Liability Directive plays a key role in the policy strategy of resolving what might be called the artificial intelligence liability jigsaw puzzle. By accommodating the Product Liability Directive to the challenges of artificial intelligence systems, several policy quandaries have been aptly addressed and frontally solved. Two bridges have been built. First, a bridge between the Artificial Intelligence Act and the national fault-based liability rules (draft Artificial Intelligence Liability Directive), and secondly, a complementariness bridge between the Product Liability Directive and fault-based liability rules (draft Revised Product Liability Directive). The second interplay largely revolves around a generously expanded and invigorated product liability regime. These policy choices do not ensure that the jigsaw puzzle produces a fully harmonised image of liability in respect of artificial intelligence, insofar as the system of liability is still partially dependent upon national laws on fault-based liability. Nonetheless, the new measures signal in a pragmatic way a desirable and expected trend towards an increasing unification of liability regimes.
An object detection method for the work of an unmanned sweeper in a noisy environment on an improved YOLO algorithm

Efficient and accurate object detection is crucial for the widespread use of low-cost unmanned sweepers. This paper focuses on the low-cost sweeper in practical working scenarios and proposes a traffic participant detection method based on an enhanced YOLO-v5 model. To train the model on noise knowledge, three types of noise are added to the data set in the offline phase, according to the vibration response of the mathematical model, and the impact of the low-cost camera. The loss function was optimized to balance detection accuracy and real-time performance while focusing on traffic participant detection using YOLO-v5. CTDS and BFSA modules were proposed based on the attention mechanism to enhance the YOLO-v5 model. Comparative experiments demonstrated the effectiveness of the proposed method, with the enhanced YOLO-v5 model achieving a 4.5% higher mean average precision than the traditional YOLO-v5 network. Moreover, the proposed method can process images at a frame per second of 89 while ensuring real-time performance, meeting the object detection requirements of actual sweeper.
Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

The likelihood of surface water and groundwater contamination is higher in regions close to landfills due to the possibility of leachate percolation, which is a potential source of pollution. Therefore, proposing a reliable framework for monitoring leachate and groundwater parameters is an essential task for the managers and authorities of water quality control. For this purpose, an efficient hybrid artificial intelligence model based on grey wolf metaheuristic optimization algorithm and extreme learning machine (ELM-GWO) is used for predicting landfill leachate quality (COD and BOD5) and groundwater quality (turbidity and EC) at the Saravan landfill, Rasht, Iran. In this study, leachate and groundwater samples were collected from the Saravan landfill and monitoring wells. Moreover, the concentration of different physico-chemical parameters and heavy metal concentration in leachate (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, Ca, Na, NO3, Cl, K, COD, and BOD5) and in groundwater (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, EC, TDS, pH, Cl, Na, NO3, and K). The results obtained from ELM-GWO were compared with four different artificial intelligence models: multivariate adaptive regression splines (MARS), extreme learning machine (ELM), multilayer perceptron artificial neural network (MLPANN), and multilayer perceptron artificial neural network integrated with grey wolf metaheuristic optimization algorithm (MLPANN-GWO). The results of this study confirm that ELM-GWO considerably enhanced the predictive performance of the MLPANN-GWO, ELM, MLPANN, and MARS models in terms of the root-mean-square error, respectively, by 43.07%, 73.88%, 74.5%, and 88.55% for COD; 23.91%, 59.31%, 62.85%, and 77.71% for BOD5; 14.08%, 47.86%, 53.43%, and 57.04% for turbidity; and 38.57%, 59.64%, 67.94%, and 74.76% for EC. Therefore, ELM-GWO can be applied as a robust approach for investigating leachate and groundwater quality parameters in different landfill sites.
Research Status, Hotspots, and Trend Analysis of the Rural Living Environment Upgrade in China from 1992 to 2022: A Bibliometric and Narrative Review Analysis

The rural living environment is the foundation of rural ecology and rural residents’ life, and the quality of the rural living environment is directly related to the green and low-carbon development of rural areas and the physical and mental health of rural residents. This article aims to grasp the current situation and of rural living environment improvement research and its various research hotspots, explore research frontiers and trends, and provide crucial theoretical guidance for rural living environment improvement research by searching and analyzing China’ national knowledge infrastructure database (CNKI). The article adopts CiteSpace6.2 bibliometric software and the narrative review method to analyze 1349 papers on rural living environment upgrading in CNKI from 1992 to 2022. Among them, the narrative review collects and analyzes the relevant information on rural living environment improvement to provide the background and content for the relevant arguments. It is found that (1) the rural living environment governance policies can be divided into four stages, namely, the initial stage, policy exploration, policy enhancement, and policy deepening, with the policy system continuously maturing and the governance content becoming clearer; (2) research hot topics can be summarized as research on the connotation and governance model of the rural living environment, research on development status, research on effective evaluation and influential factors, and research on treatment model and technology involving interdisciplinary integration development; (3) the rural living environment upgrade research history can be divided into three periods: before 2004, which was the start-up stage, thus the number of publications was relatively small, and core topics included the transformation of rural toilets and rural domestic sewage treatment; 2004–2017, which is the expansion stage, thus the total amount of literature is increasing yearly, and the research topics are gradually enriched; after 2018, which is the the mature development stage, further clarifying the key tasks of rural living environment upgrades; (4) in the future, it is necessary to keep the focus on research in the four key areas, deepen the research on governance connotation, influential factors, and effect evaluation, explore the typical models of rural living environment construction in China, and provide research guidelines and practical decisions for rural living environment governance.