Reviewerly/exHarmony huggingface dataset:
Exploring the Structure of Proteins and Other Biomolecules with a VR Museum: Lessons in Classroom Integration

The three-dimensional nature of macromolecules is often difficult for undergraduate students to grasp. This leads to difficulties in understanding key concepts in Biochemistry, such as protein function and conformational change. Virtual reality (VR) technologies, which can aid students in three-dimensional visualizations, have been shown to increase student motivation, but published reports do not universally agree about whether VR improves student comprehension. Here we present the implementation of a VR experience that was designed to complement existing biochemistry experiments and an analysis of both student engagement with and understanding of the material presented in VR. Results indicate that students enjoyed this interactive, immersive activity and suggest evidence of increased understanding. However, the effectiveness of the VR experience─and effective assessment of such an experience─may depend on a number of factors.
From Explanation to Understanding

Understanding has usually been seen as a method of hermeneutics. Until recently, philosophers of science paid little attention to the topic of scientific understanding because they came to the conclusion that understanding can be nothing more than a psychological by-product of scientific activity. However, many scientists believed that understanding was an important aim of science. The article states that understanding is a universal cognitive phenomenon applicable to the knowledge of not only cultural and historical phenomena, but also natural objects and processes. Understanding does not oppose explanation; some types of the latter produce scientific understanding. In the course of criticism of the standard model of scientific explanation, the first concepts of “scientific understanding” arose – unifying and causal-mechanistic. A pragmatic approach to scientific understanding is also considered, linking it to the cognitive skills of a scientist or a group of scientists.
Faster Montgomery multiplication and Multi-Scalar-Multiplication for SNARKs

The bottleneck in the proving algorithm of most of elliptic-curve-based SNARK proof systems is the Multi-Scalar-Multiplication (MSM) algorithm. In this paper we give an overview of a variant of the Pippenger MSM algorithm together with a set of optimizations tailored for curves that admit a twisted Edwards form. We prove that this is the case for SNARK-friendly chains and cycles of elliptic curves, which are useful for recursive constructions. Our contribution is twofold: first, we optimize the arithmetic of finite fields by improving on the well-known Coarsely Integrated Operand Scanning (CIOS) modular multiplication. This is a contribution of independent interest that applies to many different contexts. Second, we propose a new coordinate system for twisted Edwards curves tailored for the Pippenger MSM algorithm.Accelerating the MSM over these curves is critical for deployment of recursive proof&lt; systems applications such as proof-carrying-data, blockchain rollups and blockchain light clients. We implement our work in Go and benchmark it on two different CPU architectures (x86 and arm64). We show that our implementation achieves a 40-47% speedup over the state-of-the-art implementation (which was implemented in Rust). This MSM implementation won the first place in the ZPrize competition in the open division “Accelerating MSM on Mobile” and will be deployed in two real-world applications: Linea zkEVM by ConsenSys and probably Celo network.
Just-in-time defect prediction for mobile applications: using shallow or deep learning?

Abstract Just-in-time defect prediction (JITDP) research is increasingly focused on program changes instead of complete program modules within the context of continuous integration and continuous testing paradigm. Traditional machine learning-based defect prediction models have been built since the early 2000s, and recently, deep learning-based models have been designed and implemented. While deep learning (DL) algorithms can provide state-of-the-art performance in many application domains, they should be carefully selected and designed for a software engineering problem. In this research, we evaluate the performance of traditional machine learning algorithms and data sampling techniques for JITDP problems and compare the model performance with the performance of a DL-based prediction model. Experimental results demonstrated that DL algorithms leveraging sampling methods perform significantly worse than the decision tree-based ensemble method. The XGBoost-based model appears to be 116 times faster than the multilayer perceptron-based (MLP) prediction model. This study indicates that DL-based models are not always the optimal solution for software defect prediction, and thus, shallow, traditional machine learning can be preferred because of better performance in terms of accuracy and time parameters.
Vulnerable smart contract function locating based on Multi-Relational Nested Graph Convolutional Network

The immutable and trustable characteristics of blockchain enable smart contracts to be applied in various fields. Unfortunately, smart contracts are subject to various vulnerabilities, which are frequently exploited by attackers, causing financial damage to users. Therefore, it is extremely important to perform effective vulnerability detection and locating to ensure the security of smart contracts. Deep learning has shown great advantages in smart contract vulnerability detection due to its powerful end-to-end feature learning. The previous deep learning based approaches to smart contract vulnerability detection focus on identifying whether there are vulnerabilities in a smart contract. However, this kind of detection cannot achieve fine-grained vulnerability detection, i.e., locating which function in the smart contract is vulnerable. In this paper, we study the problem of vulnerable smart contract function locating. We construct a novel Multi-Relational Nested contract Graph (MRNG) to better characterize the rich syntactic and semantic information in the smart contract code, including the relationships between data and instructions. An MRNG represents a smart contract, where each node represents a function in the smart contract and each edge describes the calling relationship between the functions. In addition, we create a Multi-Relational Function Graph (MRFG) for each function, which characterizes the corresponding function code. That is, each function is characterized as an MRFG, which corresponds to a node in the MRNG. Each MRFG uses different types of edges to represent the different control and data relationships between nodes within a function. We also propose a Multi-Relational Nested Graph Convolutional Network (MRN-GCN) to process the MRNG. MRN-GCN first extracts and aggregates features from each MRFG, using the edge-enhanced graph convolution network and self-attention mechanism. The extracted feature vector is then assigned to the corresponding node in the MRNG to obtain a new Featured Contract Graph (FCG) for the smart contract. Graph convolution is used to further extract features from the FCG. Finally, a feed forward network with a Sigmoid function is used to locate the vulnerable functions. Experimental results on the real-world smart contract datasets show that model MRN-GCN can effectively improve the accuracy, precision, recall and F1-score performance of vulnerable smart contract function locating.
A Fragile Watermarking Method for Content-Authentication of H.264-AVC Video

This work proposes a blind fragile GOP-based watermarking technique that ensures the H.264-AVC video's content authenticity. In the H.264 video, the self-authentication code is created by securely hashing a number of intra prediction modes of I-frames in addition to motion vectors of P-frames and B-frames. The resulting authentication code is subsequently injected into the IPCM-Least block's Significant Bits (LSB) of some I-frames' luma and chroma pixel samples. To manage and regulate authentication data and watermark processes (embedding, detection, and verification), a key based on video footage and a secret key has been created. Intra prediction modes and motion vectors from the bitstream can be partially decoded to detect and confirm the hidden watermark data without the need for the original movie or complete video decoding. Several experiments were conducted to assess the sensitivity of the proposed method against signal processing, rate control and transcoding, conventional GOP-based and Frame-based attacks. Experimental simulations demonstrate that the implanted fragile watermark is sensitive to several low to harmful and content-preserving attacks. With only a modest bit-rate increase and hardly any perceptual quality deterioration, a large payload capacity is achieved. Experimental results show that the proposed method is suburb in term of imperceptibility, and lower effect on bit-rate. Applications like tamper-proofing, content-authentication, and covert communication are ideally suited for this technique. Based on achieved results, the devised method is capable of detecting any form of spatial and/or temporal manipulations which make it ideal for real-time applications.
Truth Social Dataset

Formally announced to the public following former President Donald Trump’s bans and suspensions from mainstream social networks in early 2022 following his role in the January 6 Capitol Riots, Truth Social was launched as an ``alternative'' social media platform that claims to be a refuge for free speech, offering a platform for those disaffected by the content moderation policies of then existing, mainstream social networks. The subsequent rise of Truth Social has been driven largely by hard-line supporters of the former president as well as those affected by the content moderation of other social networks. These distinct qualities combined with the its status as the main mouthpiece of the former president positions Truth Social as a particularly influential social media platform and give rise to several research questions. However, outside of a handful of news reports, little is known about the new social media platform partially due to a lack of well-curated data. In the current work, we describe a dataset of over 823,000 posts to Truth Social and and social network with over 454,000 distinct users. In addition to the dataset itself, we also present some basic analysis of its content, certain temporal features, and its network.
Mental Workload Evaluation for PMM Outbound Student in X University (Unix) Using NASA-TLX Method

Program Mahasiswa Merdeka (PMM) is one of the MBKM program. This program allows student to study in different university and majors. It was attended by students of Engineering Faculty from UNIX in State and Private University throughout Indonesia. By participating PMM, the student have overload credits because they have to finish double credit from homebased and destination university. This condition affects the burden on students both physically and mentally. This mental burden of students has an impact on student achievement academically, so the mental burden analysis is needed. In this study, an evaluation of the mental workload was carried out using the NASA-TLX approach. The result found that in general 75% of students experienced a very high workload. The percentage of students who take PMM in State University as much as 50% is in the category of high mental workload and 50% is very high category. The percentage of students who take PMM in Private University is 25% in the very high category and 75% is in the high category. From this study, UNIX can have some consideration about providing the brief (tips and tricks) before PMM for student mental preparation and choosing the Private University for destination university.
The influence of soft and hard quality management practices on quality improvement and performance in UAE higher education

The present research examines the debated relationship between quality management and innovation using a multidimensional quality management perspective. The quality performance that is supposed to result from the adoption of quality management is investigated further as a possible mediator between quality management and innovation in the higher education sector. The data needed to test the hypotheses was gathered by sending a survey via the internet to the faculty members at universities in the United Arab Emirates. Applying the approach of structural equation modelling with partial least squares, the hypothesised associations between 175 respondents are evaluated. According to the findings, implementing rigorous quality management has a direct as well as indirect impact on innovation performance via its impact on quality performance. The impacts of soft quality management on hard quality management have indirect consequences on innovation performance. The association between rigorous quality management and innovation performance is moderated in part by quality performance. This study provides one of the initial studies to apply the multidimensional method of quality management in higher education and has the potential to assist directors in better comprehending the interdependencies between soft and hard quality practices.
The effects of the internal and the external factors affecting artificial intelligence (AI) adoption in e-innovation technology projects in the UAE? Applying both innovation and technology acceptance theories

This study has examined factors, such as technology and employee influence on artificial intelligence (AI) adoption of e-innovative projects in the United Arab Emirates. The present study revealed the success or failure of e-innovation adoption in the public sector of the UAE and hinted at potential e-innovative projects to consider essential factors before adopting it. The study's sample covered the government sector, and the data collection method was a survey questionnaire with a sample size of 1037 responses made up of government employees. This paper was mainly built upon the diffusion of innovation and technology acceptance theories. The analysis findings showed that technology (an external factor) significantly and positively contributed to adopting AI e-innovation technology. Further analysis revealed that employee (internal factor) proxies directly influenced the adoption of AI e-innovation technology. Overall, internal and external factors contributed to adopting e-innovation technology in the United Arab Emirates. For future directions, additional factors related to the market should be considered to explore their contribution.
Patent Analysis on Artificial Intelligence in Food Industry: Worldwide Present Scenario

The advancement in technology is nowadays largely based on Artificial Intelligence (AI). AI has been actively gaining prominence over the last few years in Food Industry. Food Industry is leveraging the power of AI. Patent analysis on ‘AI and Food Industry’ in global databases indicates huge applications of AI specially in upgrading the method of Food Sorting, Food Safety, Food Nutrition, Food Delivery as well as Supply Chain and Food Processor. The present study aims to analyze Patent documents during (2000–19) which reveals significant research on AI reshaping the Food Industry. The major findings in the present patent analysis have been performed through the patent search in different paid and unpaid databases. The findings in this domain establishes that the major contributing jurisdictions for patent filing in this particular domain are China, USA, Korea and Japan. The study indicates that the most of the patents (26%) on AI in food industry come from application of AI in (A) cooking device or kitchen appliances followed by AI and health monitor (15%), Restaurant sector (13%) and Agriculture sector (10%). The statistics regarding domestic and international patent application, contributions from companies and Universities along with current research trend have also been analyzed.
FEATURES OF CONDUCTING SOCIO-PSYCHOLOGICAL TRAININGS IN AN ONLINE FORMAT

The article deals with the topical issue of modern vocational education - the use of current methods and methods of teaching university students, in particular future psychologists. The article is devoted to the analysis of the peculiarities of the preparation and conduct of socio-psychological trainings in an online format. The authors reveal the role of training sessions in the formation of professional competencies of practical psychologists, systematize the experience of organizational and technical preparation of online training, provide a system of methods and digital tools for activating group work, reveal the features of the selection of exercises for training in online format, and also give examples of specific exercises.
Vacuum provides quantum advantage to otherwise simulatable architectures

We consider a computational model composed of ideal Gottesman-Kitaev-Preskill stabilizer states, Gaussian operations including all rational symplectic operations and all real displacements, and homodyne measurement. We prove that such architecture is classically efficiently simulatable by explicitly providing an algorithm to calculate the probability density function of the measurement outcomes of the computation. We also provide a method to sample when the circuits contain conditional operations. This result is based on an extension of the celebrated Gottesman-Knill theorem, via introducing proper stabilizer operators for the code at hand. We conclude that the resource enabling quantum advantage in the universal computational model considered by Baragiola et al. [Phys. Rev. Lett. 123, 200502 (2019)], composed of a subset of the elements given above augmented with a provision of vacuum states, is indeed the vacuum state.
Unravelling the application of machine learning in cancer biomarker discovery

&lt;p&gt;Machine learning is playing an increasingly important role in the healthcare industry by transforming the way cancer is diagnosed and treated. By analyzing patient data, genomic data, and imaging data, machine learning algorithms can identify molecular signatures that distinguish cancer patients from healthy patients. Biomarkers that can accurately detect and diagnose cancer can be identified through analysis of these data sources. Additionally, personalized cancer therapies can be developed by identifying the most effective treatments based on individual patient characteristics and cancer type. Some of the machine learning techniques used for cancer biomarker discovery include deep learning and support vector machines, which can respectively identify complex patterns in data and classify data to identify relevant biomarkers. The benefits of using machine learning for cancer biomarker discovery are significant, including more precise and personalized treatments, improved patient outcomes, and the potential to transform cancer diagnosis and treatment. However, there are also challenges associated with using machine learning for cancer biomarker discovery, such as data collection and privacy issues, as well as the need for more powerful computational resources. This article explores the potential of machine learning in cancer biomarker discovery and argues that ongoing research in this field has the potential to revolutionize cancer diagnosis and treatment. Future research directions should focus on further developing machine learning algorithms and effective data collection and privacy protocols.&lt;/p&gt;
Development of a Software System for Realtime Management of Crime Reports in Southwestern Nigeria: The Administrative Approach

One of the major challenges of managing crime data is lack of robust and adequate management system.In this work, a comprehensive administrative website was built for effective management of crime reports/data.The crime data which was collected from a dedicated reporting mobile application was rendered into different categories on the admin dashboard.The admin dashboard incorporates features such as multilevel accessibility (which includes the generic admin and superadmin), an option to create reports, view reports, add a specific security outfit and so on.Unified Modeling Language (UML) and the waterfall software development technique were used in building the administrative website.The client side of the website was developed with client-side technologies, namely HTML, CSS, Vanilla JavaScript, and React, while the server side of the app was built using Node JS and MongoDB database.The application project files were hosted on MVPS virtual private cloud server, which is characterized by virtualization of resources and provision of backups.The developed software system was evaluated using the Lighthouse chrome developer tool and beta testing.The results of the Lighthouse analysis showed that the website is responsive and easily accessible.On the other hand, the results obtained from the beta test showed that the users' experience was not hampered while the web application was being put to use.
OmniLive: Super-Resolution Enhanced 360° Video Live Streaming for Mobile Devices

The live streaming of omnidirectional video (ODV) on mobile devices demands considerable network resources; thus, current mobile networks are incapable of providing users with high-quality ODV equivalent to conventional flat videos. We observe that mobile devices, in fact, underutilize graphics processing units (GPUs) while processing ODVs; hence, we envisage an opportunity exists in exploiting video super-resolution (VSR) for improved ODV quality. However, the device-specific discrepancy in GPU capability and dynamic behavior of GPU frequency in mobile devices create a challenge in providing VSR-enhanced ODV streaming. In this paper, we propose OmniLive, an on-device VSR system for mobile ODV live streaming. OmniLive addresses the dynamicity of GPU capability with an anytime inference-based VSR technique called Omni SR. For Omni SR, we design a VSR deep neural network (DNN) model with multiple exits and an inference scheduler that decides on the exit of the model at runtime. OmniLive also solves the performance heterogeneity of mobile GPUs using the Omni neural architecture search (NAS) scheme. Omni NAS finds an appropriate DNN model for each mobile device with Omni SR-specific neural architecture search techniques. We implemented OmniLive as a fully functioning system encompassing a streaming server and Android application. The experiment results show that our anytime VSR model provides four times upscaled videos while saving up to 57.15% of inference time compared with the previous super-resolution model showing the lowest inference time on mobile devices. Moreover, OmniLive can maintain 30 frames per second while fully utilizing GPUs on various mobile devices.
A spatiotemporal intelligent framework and experimental platform for urban digital twins

This work emphasizes the current research status of the urban Digital Twins to establish an intelligent spatiotemporal framework. A Geospatial Artificial Intelligent (GeoAI) system is developed based on the Geographic Information System and Artificial Intelligence. It integrates multi-video technology and Virtual City in urban Digital Twins. Besides, an improved small object detection model is proposed: YOLOv5-Pyramid, and Siamese network video tracking models, namely MPSiam and FSSiamese, are established. Finally, an experimental platform is built to verify the georeferencing correction scheme of video images. The experimental results show that the Multiply-Accumulate value of MPSiam is 0.5B, and that of ResNet50-Siam is 4.5B. Besides, the model is compressed by 4.8 times. The inference speed has increased by 3.3 times, reaching 83 Frames Per Second. 3% of the Average Expectation Overlap is lost. Therefore, the urban Digital Twins-oriented GeoAI framework established here has excellent performance for video georeferencing and target detection problems.