Reviewerly/exHarmony huggingface dataset:
Vehicle Damage Detection and Classification Using Image Processing

Vehicle have an impact on people’s daily safety, and because there are so many different types and sizes of materials, it can be challenging to distinguish and detect the conditions around the vehicle. In this project, we looked into the matter of car damage classification and detection, which insurance providers can utilize to quickly automates the handling of vehicle insurance disputes. Deep convolutional networks can be used to detect car damage and with recent developments in computer vision, which are largely attributable to the implementation of quick, scalable, and entire trainable CNN. We manually gathered and annotated pictures of numerous online sources that showed various kinds of car damage. By analyzing the deep learning-based YOLO (you only look once) series target detection method, a recognition approach that relies on YOLOS is provided to achieve timely and efficient identification of the damage in the vehicle. The COCO dataset's base weights are used to train the model. 35-90 epochs are used to process the photos. The region of damage is highlighted in the final image using a color splash technique after processing. The approach would increase customer satisfaction while assisting in lowering the cost of processing insurance claims. Vendors of automobiles can do away with the labor-intensive manual damage assessment process. Additional vehicles will be priced accurately and transparently, along with any necessary repairs. It is also able to decrease misleading vehicle insurance claims.
Automated Auditing of Price Gouging TOD Vulnerabilities in Smart Contracts

With the emergence of decentralized finance, smart contracts and their users become more and more susceptible to expensive exploitations. This paper investigates the price gouging transaction order dependency vulnerabilities in smart contracts. A static analysis based approach is proposed to automatically locate and rectify such vulnerabilities, and a prototype tool using Slither, a static analyzer for Solidity, is also developed. All in all, empirical results on a benchmark suite containing 51 Solidity smart contracts show that the proposed methodology can be used successfully to both detect such vulnerabilities and rectify them, or to certify that a Solidity smart contract under question does not contain such vulnerabilities.
The potential of emotive language to influence the understanding of textual information in media coverage

Abstract Online media attempts to influence how people think. The promotion of online discourses and the use of extra-linguistic factors enable a tilt towards a desired way of thinking. Internet users seek, make decisions, and act not only according to their beliefs but also according to the ideas propagated by the media. This paper focuses on public relations formation in a media discourse and an emotive response to news coverage. The paper aims to analyze the use of emotive linguistic means at the level of Kazakh media discourse. Through contextual analysis of emotive vocabulary used in media discourse, the paper explores the cognitive perception of media coverage by readers. This method allows for an in-death study of emotivity. The scientific novelty of the study is that it examines the emotive aspect of the Internet media discourse. The results show that emotivity of the Kazakh media texts is expressed using lexical and syntactic means and is crucial for building public relations and influencing the audience. The studied corpus includes media texts from the three largest online media sources in Kazakhstan. It appears that the culture of Internet media readers plays a decisive role in how they perceive products of communication, even if there are other variables involved in the equation. The use of emotive items in online media debates was found to depend on discourse content and writer’s intentions. A sample of emotive items was used. Content published on politics-oriented online media (Zakon.kz) channeled negative emotions, namely sadness and fear. Online media sources with entertainment content (Kazinform and Sputnik Kazakhstan) were characterized by the presence of such universal emotions as fear, joy, and hope. Positive emotions prevail. In general, the potential of Internet media content to influence readers and manipulation tactics vary depending on the content of the coverage.
Probabilistic programming with stochastic variational message passing

Stochastic approximation methods for variational inference have recently gained popularity in the probabilistic programming community since these methods are amenable to automation and allow online, scalable, and universal approximate Bayesian inference. Unfortunately, common Probabilistic Programming Languages (PPLs) with stochastic approximation engines lack the efficiency of message passing-based inference algorithms with deterministic update rules such as Belief Propagation (BP) and Variational Message Passing (VMP). Still, Stochastic Variational Inference (SVI) and Conjugate-Computation Variational Inference (CVI) provide principled methods to integrate fast deterministic inference techniques with broadly applicable stochastic approximate inference. Unfortunately, implementation of SVI and CVI necessitates manually driven variational update rules, which does not yet exist in most PPLs. In this paper, we cast SVI and CVI explicitly in a message passing-based inference context. We provide an implementation for SVI and CVI in ForneyLab, which is an automated message passing-based probabilistic programming package in the open source Julia language. Through a number of experiments, we demonstrate how SVI and CVI extends the automated inference capabilities of message passing-based probabilistic programming.
Encryption and Decryption of Marker Based 3-Dimensional Augmented Reality Image Using Modified Hill Cipher Technique for Secure Transfer

The Mobility of digital data has increased over time, but securing them is a major task to do. Research has been conducted to help shield data from unauthorized intruders and attacks that includes disclosure of data, masquerade, content modification and repudiation, though safeguarding Augmented Reality media remains an unfulfilled issue. This research takes a step towards encrypting augmented reality content by implementing the modified Hill cipher encryption approach. This is acquired by offering marker-based mobility of immersive 3 Dimensional media, that is scanned by the user to extract it; furthermore, users first verify their identity by entering the passcode or key to the modified Hill cipher to decrypt the data. Coupling cryptography and augmented reality will lead to a safe and secured meaningful engagement of the digital media over the networks.
Self-supervised multi-scale pyramid fusion networks for realistic bokeh effect rendering

Images with visual pleasing bokeh effect are often unattainable for mobile cameras with compact optics and tiny sensors. To balance the aesthetic requirements on photo quality and expensive high-end SLR cameras, synthetic bokeh effect rendering has emerged as an attractive machine learning topic for engineering applications on imaging systems. However, most of bokeh rendering models either heavily relied on prior knowledge such as scene depth or were topic-irrelevant data-driven networks without task-specific knowledge, which restricted models' training efficiency and testing accuracy. Since bokeh is closely related to a phenomenon called "circle of confusion", therefore, in this paper, following the principle of bokeh generation, a novel self-supervised multi-scale pyramid fusion network has been proposed for bokeh rendering. During the pyramid fusion process, structure consistencies are employed to emphasize the importance of respective bokeh components. Task-specific knowledge which mimics the "circle of confusion" phenomenon through disk blur convolutions is utilized as self-supervised information for network training. The proposed network has been evaluated and compared with several state-of-the-art methods on a public large-scale bokeh dataset- the "EBB!" Dataset. The experiment performance demonstrates that the proposed network has much better processing efficiency and can achieve better realistic bokeh effect with much less parameters size and running time. Related source codes and pre-trained models of the proposed model will be available soon on https://github.com/zfw-cv/MPFNet.
An Automated CAPTCHA for Website Protection Based on User Behavioral Model

Abstract-CAPTCHA (Completely Automated Public Test to tell Computers and Humans Apart) currently is the standard security technology and has been used widely in applications on commercial websites. It is an automated method with human maintenance and intervention and it is used for protecting the websites from automated attackers. There exist different types of CAPTCHA, ranging from difficult/easy to complex/simple ones. The problem with current CAPTCHAs is most of them use one single layout and simple user interface to distinguish the human from the attacker. This mechanism will help the attacker to easily bypass by using an appropriate bot program. In this paper, the main objective is to make a survey regarding the available security technique and evaluate the existing ones according to a set of characteristics for defending against attackers. Upon the evaluation result, a new user behavioral model has been proposed based on the user activity. In the proposed method the user behavior is scored according to the characteristics needed by their web applications. Finally, the model is implemented by building a web application and validated using an experimental setup and achieved a score of 70.26 for the usability of the model and the proposed method compared with other CAPTCHA tests and with the experimental evaluations, the proposed method is easier to solve and more user-friendly.
An Innovative Approach Towards Violence Recognition Based on Deep Belief Network

This paper provides an informative overview about the structure, the operating principle, and the mathematical model of the Deep Belief Network (DBN) which is one of the best known and trusted deep learning models as well as a brief outline of the well-known and, likewise, the suggested pattern recognition applications that make wide use of this specific kind of neural network based on probability model. The application particularly targeted in this paper is the recognition of actions of violence. Hence, various experimentations have been conducted by varying several parameters using DeeBNet object-oriented MATLAB toolbox and the proposed approach returned an accuracy of 65.5 %.
The market of vocational professional education in Russia in the new economic conditions

Since the end of 2014, the Russian economy has undergone major changes. The crisis continued until 2018, followed by stabilization and short-term growth in 2018-2019. The emerging positive trends were interrupted by the pandemic. All this has had a signifcant impact on the vocational professional eduation market, both in quantitative and structural terms. The modern world is changing too fast, it is necessary to form the creative potential of a person. The article states that specialists’ adaptation mechanisms to new conditions should change with the help of advanced training, obtaining new knowledge and skills in all forms of education, including the system of vocational professional education. This means that the market of educational services formed by state, departmental and corporate educational institutions must respond to the demands of the economy and consumers of these services. Such advanced technologies and methods as online studying should be used along with and in combination with ofine classes. The conducted research has shown that large corporations allocate signifcant funds for the training and retraining of specialists corresponding to the level of technological development, capable of making eﬀective management decisions. The authors of the article note that in the system of vocational professional education consumers of educational services are most interested in training programs related to management, marketing, information and computer technologies, which is quite consistent with the digital economy and the knowledge economy
Digitalisation of learning: a driving force for higher education modernisation or a humanitarian threat?

The article discusses the problems of modernisation of the system of higher education in the Republic of Belarus related to the implementation of digital forms and methods of education. The inextricable link between the digitalisation of education and the ongoing fourth industrial revolution is revealed. On the basis of empirical data from studies carried out at Belarusian State University, it is shown that Belarusian and foreign students have increased their level of knowledge, skills and competencies in the use of information and communication technologies during a year of study in a pandemic period. Students appreciate the transfer to distance methods of a significant proportion of classes. The author noted that effective digitalisation of education contributes to the growth of the competitiveness of educational institutions. It is concluded that the active use of digitalisation is consistent with the goals of modernisation of the higher education system and does not pose a significant threat to the self-development of students. The results obtained can be used in research of the digitalisation of higher education in Belarus and other EAEU countries, and in teaching the Belarusian and foreign students studying in Belarusian universities.
SM4 Chaotic Masking Scheme Against Power Analysis Based on FPGA

SCA is one of the biggest threats to the security of encryption chip. It can crack the unprotected encryption chip at lower cost and faster speed, which weakens the security of the SM4 encryption algorithm circuit without any protection. In this paper, the SM4 encryption algorithm is implemented on FPGA. The pseudo-random sequence generated by discrete chaotic system is used to randomly mask the intermediate value in the SM4 encryption process. This will disrupt the power consumption in the encryption process, thus preventing power analysis. Experimental results show that the proposed chaotic mask scheme can effectively prevent intermediate value leakage and protect the SM4 encryption system from power analysis attack.
A Dynamic Adaptive Weighted Differential Evolutionary Algorithm

This study proposed a dynamic adaptive weighted differential evolution (DAWDE) algorithm to solve the problems of differential evolution (DE) algorithm such as long search time, easy stagnation, and local optimal solution. First, adaptive adjustment strategies of scaling factor and crossover factor are proposed, which are utilized to dynamically balance the global and local search, and avoid premature convergence. Second, an adaptive mutation operator based on population aggregation degree is proposed, which takes population aggregation degree as the amplitude coefficient of the basis vector to determine the influence degree of the optimal individual on the mutation direction. Finally, the Gauss perturbation operator is introduced to generate random disturbance and accelerate premature individuals to jump out of the local optimum. The simulation results show that the DAWDE algorithm can obtain better optimization results and has the characteristics of stronger global optimization ability, faster convergence, higher solution accuracy, and stronger stability compared with other optimization algorithms.
Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction

On e-commerce platforms, predicting if two products are compatible with each other is an important functionality to achieve trustworthy product recommendation and search experience for consumers. However, accurately predicting product compatibility is difficult due to the heterogeneous product data and the lack of manually curated training data. We study the problem of discovering effective labeling rules that can enable weakly-supervised product compatibility prediction. We develop AMRule, a multi-view rule discovery framework that can (1) adaptively and iteratively discover novel rulers that can complement the current weakly-supervised model to improve compatibility prediction; (2) discover interpretable rules from both structured attribute tables and unstructured product descriptions. AMRule adaptively discovers labeling rules from large-error instances via a boosting-style strategy, the high-quality rules can remedy the current model's weak spots and refine the model iteratively. For rule discovery from structured product attributes, we generate composable high-order rules from decision trees; and for rule discovery from unstructured product descriptions, we generate prompt-based rules from a pre-trained language model. Experiments on 4 real-world datasets show that AMRule outperforms the baselines by $5.98%$ on average and improves rule quality and rule proposal efficiency.
A new sampling strategy to improve the performance of mobile robot path planning algorithms

The path planning of a mobile robot using one of the sampling-based algorithms such as RRT(Rapidly-exploring random tree) and their variation RRT*, has received enormous attention especially in the last decade due to the ability of these algorithms to solve complex high-dimensional problems, as well as their completeness in probability i.e., they find a solution, if any, with infinite execution time. However, these algorithms have some limitations such as the low convergence speed for RRT* or producing a path far from optimal for RRT. This paper presents an improvement inspired by the A* algorithm, which can be applied to RRT and RRT* to overcome their shortcomings such as path cost and convergence time. Several simulation experiments were performed in different scenarios to compare improved RRT (A-RRT) and basic RRT, similarly for RRT*, these experiments showed a considerable reduction in run time and path cost.
An Irregularly Dropped Garbage Detection Method Based on Improved YOLOv5s

Waste sorting and recycling play a significant role in carbon neutrality, and the government has promoted waste sorting stations in various cities while the stations have limited efficiency due to the absence of intelligent surveillance systems to monitor and analyze the scene in waste stations, especially to detect the irregularly dropped garbage. To take the most advantage of these stations, this study proposes an improved YOLO (You Only Look Once) v5s detector named YOLOv5s-Garbage to monitor waste sorting stations in real-time. This study enhances its ability to detect garbage by introducing CBAM (Convolutional Block Attention Module) and using EIoU (Efficient Intersection over Union) to accelerate the convergence of the bonding box loss. According to experiments, the mAP of YOLOv5s-Garbage on the waste sorting dataset reaches 89.7%, which is 3.3% higher than the classical YOLOv5s. This study then combines the DeepSort tracking algorithm and re-filter process to filter the target garbage to distinguish the irregularly dropped garbage and normal one, which reduces the false alarm significantly.
Photovoltaic Energy Prediction for New-Generation Cells with Limited Data: A Transfer Learning Approach

Photovoltaic (PV) energy systems are receiving increasing attention, given their relative ease of installation, with 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> generation technologies promising even simpler fabrication processes and less-intrusive installation possibilities. Therefore, methods for predicting the PV energy output are important to balance the production of other types of renewable sources and avoid wasting energy, with approaches based on machine learning models being especially studied in recent applications. In the case of new-generation cells, limited data is available to train such models, making the use of transfer learning a viable approach to increase prediction accuracy. However, no work in the literature has considered a transfer learning approach studying how much knowledge can be transferred between 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> and 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> generation PV technologies. In this paper, we propose the first approach in the literature based on machine learning and transfer learning for the PV energy prediction, in the case of new-generation PV technologies for which limited training data is available. We tested our method on data collected from several locations throughout the world, with results confirming the validity of the approach.
Frequency-aware Camouflaged Object Detection

Camouflaged object detection (COD) is important as it has various potential applications. Unlike salient object detection (SOD), which tries to identify visually salient objects, COD tries to detect objects that are visually very similar to the surrounding background. We observe that recent COD methods try to fuse features from different levels using some context aggregation strategies originally developed for SOD. Such an approach, however, may not be appropriate for COD as these existing context aggregation strategies are good at detecting distinctive objects while weakening the features from less discriminative objects. To address this problem, we propose in this article to exploit frequency learning to suppress the confusing high-frequency texture information, to help separate camouflaged objects from their surrounding background, and a frequency-based method, called FBNet, for camouflaged object detection. Specifically, we design a frequency-aware context aggregation (FACA) module to suppress high-frequency information and aggregate multi-scale features from a frequency perspective, an adaptive frequency attention (AFA) module to enhance the features of the learned important frequency components, and a gradient-weighted loss function to guide the proposed method to pay more attention to contour details. Experimental results show that our model outperforms relevant state-of-the-art methods.
ProvotuMN: Decentralized, Mix-Net-based, and Receipt-free Voting System

Recent years saw an increase in voting systems using public permissionless blockchains. Although public blockchains offer transparency and immutability, permissioned consensus is better suited for voting systems’ requirements, because an initial level of trust in authorities is always required. Hence, a permissioned Distributed Ledger (DL) immutably storing the voting system’s audit trail satisfies demands measurably.ProvotuMN 3.0 is a decentralized and receipt-Free (RF) voting system based on an end-to-end verifiable Re-Encryption Mixnet (RMN). RMNs allow for flexible votes and elections and decouple the ballot structure from the cryptographic voting protocol. Thus, ProvotuMN decentralizes trust (i) through the use of cryptographic shuffles and Non-Interactive Zero-Knowledge Proofs (NIZKP) in an RMN executed among DL nodes, (ii) by employing a distributed key generation for election keys, and (iii) by offering a decentralized re-encryption service assuring RF. Performance evaluations performed indicate that the voting scheme is scalable for large-scale voting.