Reviewerly/exHarmony huggingface dataset:
Dissecting the Terra-LUNA crash: Evidence from the spillover effect and information flow

The Terra-LUNA crash in May 2022 was triggered by the depeg of the ecosystem's stablecoin UST. It led to the unprecedented demise of a blockchain ecosystem and cost investors tens of billions of dollars. We examined the impact of the Terra-LUNA crash on the cryptocurrency market. Based on the hourly return and realized volatility from April 2022 to May 2022, we used the spillover index and effective transfer entropy to configure the interlinkage change between cryptocurrency markets. We conclude that the Terra-LUNA crash had a significant impact on the connectedness of the cryptocurrency market, investor attention, and market sentiment.
A binary Bi-phase mutation-based hybrid Equilibrium Optimizer for feature selection in medical datasets classification

With the rapid expansion in Biological Sciences, biomedical data classification has become challenging. These datasets generally consist of missing values, redundant features and irrelevant information. A novel hybrid wrapper-based feature selection method is proposed to tackle these issues effectively. In order to improve the exploration ability of the particles, the Sine factor is integrated with the Equilibrium Optimizer (EO) technique. A Bi-phase Mutation (BM) scheme is integrated to enhance the exploitation phase of the EO algorithm (BM-based Hybrid EO, BMHEO). The BMHEO method is evaluated by employing four different classifiers – KNN, SVM, Random Forest (RF) and Discriminant Analysis (DA). It is observed that the Random Forest classifier exhibits superior performance compared to the other three classifiers. Eight S-shaped and V-shaped transfer functions are integrated to convert the solutions to binary form. Based on the above enhancements, eight different versions of BMHEO are produced. The performance of these versions is assessed using twenty biomedical datasets. Experiments on these datasets demonstrate that the BMHEO-S2 version outperforms other methods in terms of fitness values and classification accuracies. This approach is also tested on a MED-NODE image dataset for classification, and higher accuracy of 97.02% is achieved.
Human–machine collaborative scoring of subjective assignments based on sequential three-way decisions

Automated scoring of subjective assignments (ASSA), one of the most representative applications in the field of artificial intelligence in education, is the process of scoring and assessing essays with the aid of natural language processing technology. Nevertheless, there are a few critical opinions regarding ASSA models. For instance, when essays of high or low quality are fed to an ASSA model, it may result in a great difference between the scores given by the model and human evaluators. To overcome these problems, this study is devoted to the exploration on human–machine collaborative scoring of subjective assignments based on sequential three-way decision. Firstly, we propose a human–machine task allocation model based on sequential three-way decisions (TA-S3WD) to establish a formal framework for human–machine collaborative scoring of subjective assignments. Secondly, we develop a human–machine collaborative subjective assignment scoring model by combining bidirectional long short-term memory (Bi-LSTM) with TA-S3WD model, called HMCS-BLTS. Finally, numerical experiments with intensive comparisons and sensitivity analysis are conducted on the automated student assessment prize (ASAP) data set to show the advantages of the proposed model. Comparative analysis shows that the HMCS-BLTS model achieves higher execution efficiency than seven well-known baseline models in terms of four common evaluation metrics. Moreover, compared with the best-performing baseline model, the HMCS-BLTS model obtains 0.8967 average quadratic weighted kappa and achieves 19.31% improvements on average using just 19.02% of the human workload.
CADEN: cellular automata and DNA based secure framework for privacy preserving in IoT based healthcare

In the present E-healthcare industry, data breaches result in substantial economic losses due to cyber-attacks and hence create a trust deficit between the industry and users. The healthcare industry has rapidly adopted IoT frameworks but the trust deficit and privacy concerns limit its utilization among the masses. Along with privacy protection, content authentication is an important requirement in a number of critical applications and fragile watermarking provides an effective solution. However, existing fragile watermarking techniques lack the accuracy of tamper detection and hence are not reliable enough in terms of security and privacy of the data. This paper presents a novel low-complexity block-based fragile watermarking technique with high security against cyber-security attacks. This is achieved by embedding a fragile watermark in the host image using pixel domain blocking approach. The security of embedded watermark has been taken care of by using Cellular Automata and DNA based ENcryption (CADEN) framework to scramble the watermark bits using various secret keys. Experimental investigations show that besides being highly secure, the proposed technique is fragile to various signal processing and geometric attacks. The comparative analysis shows that the proposed scheme, despite having lower complexity, offers better efficiency in terms of imperceptibility, tamper detection and localization compared to other state-of-the-art techniques. Besides, the fragile watermark embedding makes the system capable to preserve the secret information in case of an attack with an average BER of 40%.
Sign Language Recognition using Machine Learning

One of the ways to communicate with deaf and dumb individuals is through sign language. So, in order to speak with deaf and dumb people, one should learn sign language; yet, because not everyone can learn it, communication becomes nearly impossible. The goal of this study is to use machine learning to break through these communication hurdles. The majority of existing technologies rely on external sensors, which are out of reach for most people. We utilize OpenCV to take images and the CNN technique to train the machine, with the output being text. Many previous studies have offered methods for partial sign language identification, however this study intended for the full acceptance of American Sign Language comprises of 26 letters and 10 numbers. The majority of ASL letters are static, however few are dynamic. As a result, the goal of this research was to extract features from finger and hand motions in order to distinguish between static and dynamic gestures.
UAV path planning based on the improved PPO algorithm

In this paper, we consider the problem of unmanned aerial vehicle (UAV) path planning. The traditional path planning algorithm has the problems of low efficiency and poor adaptability, so this paper uses the reinforcement learning algorithm to complete the path planning. The classic proximal policy optimization (PPO) algorithm has problems that the samples with large rewards in the experience replay buffer will seriously affect training, this situation causes the agent’s exploration performance degradation and the algorithm has poor convergence in some path planning tasks. To solve these problems, this paper proposes a frequency decomposition-PPO algorithm (FD-PPO) based on the frequency decomposition and designs a heuristic reward function to solve the UAV path planning problem. The FD-PPO algorithm decomposes rewards into multi-dimensional frequency rewards, then calculate the frequency return to efficiently guide UAV to complete the path planning task. The simulation results show that the FD-PPO algorithm proposed in this paper can adapt to the complex environment, and has outstanding stability under the continuous state space and continuous action space. At the same time, the FD-PPO algorithm has better performance in path planning than the PPO algorithm.
Intercity Railway Risk Space Anomaly Detection Based on Train Predeparture Key Frame Extraction and IADN Network

Anomalies between the platform doors and train seriously threaten the safe operation of intercity railway train, and therefore, accurately selecting the appropriate time for anomaly detection in elaborate process of train access is vital. However, the scarcity of data samples, disadvantage of anomaly reconstruction, and unreliable reconstruction errors pose challenges to image reconstruction-based anomaly detection methods. To address these issues, this study proposes a method based on train predeparture key frame extraction and patch-level unsupervised network using image inpainting. The key frame extraction is designed to extract the key frame to assist subsequent detection by tracking the motion state of train door and converting states into the switching door signals. An unsupervised network, which named image-inpainting anomaly detection network (IADN), based on image-inpainting autoencoder (AE), local abnormal information enhancement, and global-attentive reconstruction error (GARE), is proposed for anomaly classification and localization. The network utilizes an image-inpainting AE based on masking strategy and dual-channel structure to reconstruct normal images and solve the anomaly reconstruction problem. Afterward, it proposes local abnormal information enhancement and GARE to enhance the difference between inputs and reconstructions to improve the accuracy. The proposed method is tested on the intercity railway risk space anomaly dataset, and the anomaly classification AUC, localization precision, and recall achieve 99.32%, 97.45%, and 96.29%, respectively, which outperforms state-of-the-art methods.
Student Health Detection using a Machine Learning Approach and IoT

With the development of sensor technologies, Internet of Things (IoT) technologies for creating behavioral and physiological management systems, including IoT-based pupil medical management systems, have evolved quickly. It is now required to monitor the health function status of the increasing number of students who live alone and are dispersed across large geographic areas. An IoT based methodology of management student heath is put forth in this study to continually monitor students’ vital signs and identifies biological and behavioral changes using advanced medical technology. In this concept, critical data are gathered by the IoT module, & data evaluation is done using neural network models are used to analyze the data to determine the likely risks to kids' physiological and behavioral modifications. According to the experimental findings, the suggested model is effective and accurate in determining the state of the pupils. The support vector machine had a maximum performance of 99.1% after the suggested model had been evaluated, which is a good outcome for our objectives. Algorithms for multilevel perceptron neural systems, random forests & decision trees were also beaten by the results.
Optimal Transport for Label-Efficient Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) has been a key enabler for night intelligent monitoring system. However, the extensive laboring efforts significantly limit its applications. In this paper, we raise a new label-efficient training pipeline for VI-ReID. Our observation is: RGB ReID datasets have rich annotation information and annotating infrared images is expensive due to the lack of color information. In our approach, it includes two key steps: 1) We utilize the standard unsupervised domain adaptation technique to generate the pseudo labels for visible subset with the help of well-annotated RGB datasets; 2) We propose an optimal-transport strategy trying to assign pseudo labels from visible to infrared modality. In our framework, each infrared sample owns a label assignment choice, and each pseudo label requires unallocated images. By introducing uniform sample-wise and label-wise prior, we achieve a desirable assignment plan that allows us to find matched visible and infrared samples, and thereby facilitates cross-modality learning. Besides, a prediction alignment loss is designed to eliminate the negative effects brought by the incorrect pseudo labels. Extensive experimental results on benchmarks demonstrate the effectiveness of our approach. Code will be released at https://github.com/wjm-wjm/OTLA-ReID .
Online Learning Infrastructure: Does it Strengthen the Effect of Service Quality on Student Satisfaction?

One of the main obstacles to online learning is learning infrastructure and the availability of internet facilities, i.e., internet quotas. Universitas Negeri Surabaya (UNESA) students regularly receive internet quota assistance from the Minister of Education and Culture and Universitas Negeri Surabaya. Therefore, it is expected that the online learning process will run well. The study aimed to determine 1) the impact of the online learning service quality on student satisfaction and 2) the moderation effect of Infrastructure to strengthen the effect of service quality on student satisfaction in online learning. The subjects of this study were students of the UNESA, Indonesia, with 250 students as research samples. Data collection was done by distributing online questionnaires using google forms. The data analysis technique used moderated regression analysis. The results proven that 1) the online learning service quality affects student satisfaction, 2) infrastructure as the moderator variable not proven as a moderator model, but infrastructure has a direct influence on student satisfaction in online learning. Keywords: Online Learning Infrastructure, Students Satisfaction, Learning Quality
BlobGAN: Spatially Disentangled Scene Representations

We propose an unsupervised, mid-level representation for a generative model of scenes. The representation is mid-level in that it is neither per-pixel nor per-image; rather, scenes are modeled as a collection of spatial, depth-ordered “blobs” of features. Blobs are differentiably placed onto a feature grid that is decoded into an image by a generative adversarial network. Due to the spatial uniformity of blobs and the locality inherent to convolution, our network learns to associate different blobs with different entities in a scene and to arrange these blobs to capture scene layout. We demonstrate this emergent behavior by showing that, despite training without any supervision, our method enables applications such as easy manipulation of objects within a scene (e.g. moving, removing, and restyling furniture), creation of feasible scenes given constraints (e.g. plausible rooms with drawers at a particular location), and parsing of real-world images into constituent parts. On a challenging multi-category dataset of indoor scenes, BlobGAN outperforms StyleGAN2 in image quality as measured by FID. See our project page for video results and interactive demo: http://www.dave.ml/blobgan .
Digital Watermarking-Based Cryptosystem for Cloud Resource Provisioning

Cloud computing resource privacy is important and it becomes stronger when image-based security is applied. Most cloud service providers provision resources for images to the end users. Numerous encryption strategies are applied to safeguard information of the image while empowering visual component extractions. The work uses wavelet domain technique, visual cryptography, and Arnold transformation. This scheme provides additional security features to the secret share image at the trusted authority side for copyright protection of digital images. The proposed technique is robust against some selected attacks like scaling, blurring, etc. This technique protects the copyright of digital data over the cloud. It is a need and necessary to protect and prevent the illegal copying, redistribution, and copyright infringement of multimedia. This research work aims at increasing robustness and security of the digital image copyright scheme. The proposed scheme is a hybrid authentication framework that will perform better in the watermarking system of security using simulation over MATLAB.
How Do We Fail? Stress Testing Perception in Autonomous Vehicles

Autonomous vehicles (AVs) rely on environment perception and behavior prediction to reason about agents in their surroundings. These perception systems must be robust to adverse weather such as rain, fog, and snow. However, validation of these systems is challenging due to their complexity and dependence on observation histories. This paper presents a method for characterizing failures of LiDAR-based perception systems for AVs in adverse weather conditions. We develop a methodology based in reinforcement learning to find likely failures in object tracking and trajectory prediction due to sequences of disturbances. We apply disturbances using a physics-based data augmentation technique for simulating LiDAR point clouds in adverse weather conditions. Experiments performed across a wide range of driving scenarios from a real-world driving dataset show that our proposed approach finds high likelihood failures with smaller input disturbances compared to baselines while remaining computationally tractable. Identified failures can inform future development of robust perception systems for AVs.
Mining Multi-View Information: A Strong Self-Supervised Framework for Depth-based 3D Hand Pose and Mesh Estimation

In this work, we study the cross-view information fusion problem in the task of self-supervised 3D hand pose estimation from the depth image. Previous methods usually adopt a hand-crafted rule to generate pseudo labels from multi-view estimations in order to supervise the network training in each view. However, these methods ignore the rich semantic information in each view and ignore the complex dependencies between different regions of different views. To solve these problems, we propose a cross-view fusion network to fully exploit and adaptively aggregate multi-view information. We encode diverse semantic information in each view into multiple compact nodes. Then, we introduce the graph convolution to model the complex dependencies between nodes and perform cross-view information interaction. Based on the cross-view fusion network, we propose a strong self-supervised framework for 3D hand pose and hand mesh estimation. Furthermore, we propose a pseudo multi-view training strategy to extend our framework to a more general scenario in which only single-view training data is used. Results on NYU dataset demonstrate that our method outperforms the previous self-supervised methods by 17.5% and 30.3% in multi-view and single-view scenarios. Meanwhile, our framework achieves comparable re-sults to several strongly supervised methods.
A Systematic Review of Detecting Illicit Bitcoin Transactions

In recent years, the application of virtual currency has become a part of people's life. The decentralization and anonymity of Bitcoin have made it a favorite tool for many criminals. Therefore, how to trace illegal activities in Bitcoin transactions has become one of the most important research areas. This paper systematically collects 25 research results in this field since 2018, and divides them into three areas, i.e., supervised learning, unsupervised learning, and topological analysis. The supervised learning method based on machine learning is the current mainstream in this research field. However, we believe that the model can achieve more accurate results after combining unsupervised learning and topological analysis features. Moreover, topology analysis can help to observe the entire or specific part of the Bitcoin trading network from a macro perspective so as to discover the hidden illegal activities. In addition, data visualization techniques can provide structural insights to understand the Bitcoin trading network.
SoftSpokenOT: Quieter OT Extension from Small-Field Silent VOLE in the Minicrypt Model

Given a small number of base oblivious transfers (OTs), how does one generate a large number of extended OTs as efficiently as possible? The answer has long been the seminal work of IKNP (Ishai et al., Crypto 2003) and the family of protocols it inspired, which only use Minicrypt assumptions. Recently, Boyle et al. (Crypto 2019) proposed the Silent-OT technique that improves on IKNP, but at the cost of a much stronger, non-Minicrypt assumption: the learning parity with noise (LPN) assumption. We present SoftSpokenOT, the first OT extension to improve on IKNP's communication cost in the Minicrypt model. While IKNP requires security parameter $$\lambda $$ bits of communication for each OT, SoftSpokenOT only needs $$\lambda / k$$ bits, for any k, at the expense of requiring $$2^{k-1} / k$$ times the computation. For small values of k, this tradeoff is favorable since IKNP-style protocols are network-bound. We implemented SoftSpokenOT and found that our protocol gives almost a $$5{\times }$$ speedup over IKNP in the LAN setting. Our technique is based on a novel silent protocol for vector oblivious linear evaluation (VOLE) over polynomial-sized fields. We created a framework to build maliciously secure $$\left( {\begin{array}{c}N\\ 1\end{array}}\right) $$ -OT extension from this VOLE, revisiting and improving the existing work for each step. Along the way, we found several flaws in the existing work, including a practical attack against the consistency check of Patra et al. (NDSS 2017).
Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

Refactoring is one of the most widely used techniques in practice to improve the quality of existing software. However, it is observed that refactoring does not continually improve all software quality attributes. Recent studies indicated that different refactoring techniques have significantly different, sometimes opposite, and conflicting effects on software quality attributes. In other words, there is contradictory evidence on the refactoring benefit. As a result, developers face challenges in selecting appropriate refactoring techniques when they use them to improve software quality. To the best of our knowledge, no study has investigated factors that may explain inconsistent or diverging results concerning the effect of refactoring techniques on software quality. Therefore, in this study, scenarios of using refactoring techniques factor have been identified, investigated, and thoroughly analyzed. Ten of the most commonly used refactoring techniques in practice have been chosen and individually applied in seven case studies of varying sizes (small, medium, and large). The Quality Model for Object-Oriented Design (QMOOD) is used to assess how refactoring techniques affect quality attributes. The findings provide strong evidence that this factor plays a significant role in producing the various effects of refactoring techniques on quality attributes. These findings can help software developers understand how to use refactoring techniques to improve software quality while taking this factor into account. The best scenario for using each refactoring technique to improve software system quality has been identified. The findings can provide guidelines for software developers to use refactoring techniques to improve the quality of software systems based on the best scenarios of using the refactoring techniques.