Reviewerly/exHarmony huggingface dataset:
SHA512 and MD5 Algorithm Vulnerability Testing Using Common Vulnerability Scoring System (CVSS)

This paper discusses the comparison of the results of testing the OTP (One Time Password) algorithm on two encryptions, namely SHA512 and MD5 which are applied to the Reconciliation Application of the Dinas Pemberdayaan Masyarakat dan Desa Kabupaten Sukabumi. This study uses the Vulnerability Assessment and Penetration Testing (VAPT) method, which combines two forms of vulnerability testing to achieve a much more complete vulnerability analysis by performing different tasks in the same focus area. The vulnerability assessment uses the Common Vulnerability Scoring System (CVSS) method. The results showed that the Vulnerability Assessment and Penetration Testing (VAPT) method was proven to be able to identify the level of security vulnerability in the Reconciliation Application at the Dinas Pemberdayaan Masyarakat dan Desa Kabupaten Sukabumi with a vulnerability level score of 5.3 in the SHA512 environment with a medium rating and 7.5 in the MD5 environment. with high ratings. So, it can be concluded that the best algorithm for implementing OTP is SHA512
Multiple-qubit nonadiabatic noncyclic geometric quantum computation in Rydberg atoms

Abstract Single- and two-qubit nonadiabatic noncyclic geometric quantum computation (NNGQC) have been put forward in theory (Liu B.-J. et al. , Phys. Rev. Res. 2 (2020) 043130). The features of single-qubit NNGQC have been experimentally verified and the experimental technical route of the two-qubit case has been discussed in detail (Zhang J. W. et al. , Phys. Rev. Lett. 127 (2021) 030502). The multiple-qubit quantum logic gate is critical for quantum computation. Although combining a series of single- and two-qubit NNGQC gates can form universal operations and create arbitrary multiple-qubit gates, the created multiple-qubit gate often consumes more quantum resource and requires longer evolution time, which makes it more fragile to systematic error and decoherence. In this article, we propose a scheme to directly construct the multiple-qubit NNGQC gates in Rydberg atoms. The numerical results show that the proposed quantum gates have high fidelity and are robust to systemic error and decoherence. Our scheme may be applied in the future quantum computation and many-body quantum science studies.
Augmented reality guided condylectomy

<h2>Abstract</h2> An accurate transfer of a 3D virtual planned proportional condylectomy to the patient is challenging due to the limited surgical access. A new clinical workflow that uses augmented reality to assist a condylectomy is presented step-by-step. This AR-based approach has the potential to be implemented in the clinical setting routinely.
Graph Representation Learning Beyond Node and Homophily

Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly under the node homophily assumption: connected nodes tend to have similar labels and optimize performance on node-centric downstream tasks. Their design is apparently against the task-agnostic principle and generally suffers poor performance in tasks, e.g., edge classification, that demands feature signals beyond the node-view and homophily assumption. To condense different feature signals into the embeddings, this paper proposes PairE, a novel unsupervised graph embedding method using two paired nodes as the basic unit of embedding to retain the high-frequency signals between nodes to support node-related and edge-related tasks. Accordingly, a multi-self-supervised autoencoder is designed to fulfill two pretext tasks: one retains the high-frequency signal better, and another enhances the representation of commonality. Our extensive experiments on a diversity of benchmark datasets clearly show that PairE outperforms the unsupervised state-of-the-art baselines, with up to 101.1\% relative improvement on the edge classification tasks that rely on both the high and low-frequency signals in the pair and up to 82.5\% relative performance gain on the node classification tasks.
Investigation of Cryptocurrency Enabled and Dependent Crimes

Cryptocurrency has exploded in both use in business and speculation on the stock market. Increasingly, cryptocurrency is being used in a range of criminal activities, mostly notably as payment and money-laundering tool but also in investment fraud. The speculative nature of cryptocurrencies reminds us of the dotcom boom in the 1990s where any internet related companies attracted great speculation on the stock market without having a product, business plan or any ability to make profits. Naturally, the boom was followed by the dot-com crash a few years later which ended an era of unwarranted highly speculative internet companies. And going further back further in time the gold rush where people around the world flocked to seek their fortunes in lands far away such as Australia, Canada, and the United States in the 1800s, only to discover that finding and mining a big gold stake to be a dangerous and unlikely proposition. 2021 is the year that cryptocurrency became the centre of finance, the doggy Doge meme moved from joke to reality, and where crypto became the new gold rush (alongside toilet paper). Analysing the current research and literature, the authors bring in their personal experience in industry and government investigating cybercrime, online fraud, and cryptocurrency frauds. This chapter will analyse the use of cryptocurrency in online fraud and the challenges posed in crypto investigations and crypto investment fraud.
Performance Evaluation of a Secured Framework for IoT Based on BlockChain

In the last era the number of internet-connected devices surpassed the human population. IoT integration rate into human world equals at least five times the rate of electricity and telephony. Currently in 2020 the number of IoT devices is around 50 billion smart objects. This great invasion to our live requires extensive efforts for controlling and securing those devices. BlockChain (BC) is a distributed write-only ledger that eliminates the need for third party in securing and verifying transactions between peers. BC is considered the most powerful technique for securing transactions between IoT devices. In this work, a robust and scalable blockchain-based security framework for IoT is proposed. This framework comprises clients, device gateways, and administrators. IoT devices access BC through gateways. Ethereum BlockChain is utilized in addition to Ethereum smart contracts for enforcing a set of rules defined by the system administrator. Finally metrics that fulfill both efficiency and effectiveness of the proposed framework are introduced. In the results section, the proposed work provides robust and scalable security framework for the IoT devices under different attack probabilities in addition to satisfying the conditions of lightweight, transparency, and timeliness.
Comparison of manual and automated digital image analysis systems for quantification of cellular protein expression.

Quantifying protein expression in immunohistochemically stained histological slides is an important tool for oncologic research. The use of computer-aided evaluation of IHC-stained slides significantly contributes to objectify measurements. Manual digital image analysis (mDIA) requires a user-dependent annotation of the region of interest (ROI). Others have built-in machine learning algorithms with automated digital image analysis (aDIA) and can detect the ROIs automatically. We aimed to investigate the agreement between the results obtained by aDIA and those derived from mDIA systems.We quantified chromogenic intensity (CI) and calculated the positive index (PI) in cohorts of tissue microarrays (TMA) using mDIA and aDIA. To consider the different distributions of staining within cellular sub-compartments and different tumor architecture our study encompassed nuclear and cytoplasmatic stainings in adenocarcinomas and squamous cell carcinomas.Within all cohorts, we were able to show a high correlation between mDIA and aDIA for the CI (p<0.001) along with high agreement for the PI. Moreover, we were able to show that the cell detections of the programs were comparable as well and both proved to be reliable when compared to manual counting.mDIA and aDIA show a high correlation in acquired IHC data. Both proved to be suitable to stratify patients for evaluation with clinical data. As both produce the same level of information, aDIA might be preferable as it is time-saving, can easily be reproduced, and enables regular and efficient output in large studies in a reasonable time period.
Mathematical foundations based statistical modeling of software source code for software system evolution

Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the pattern hidden in various software development processes and artifacts. This module is part of the smart requirements management system that is intended to be built. This system will have multiple modules to make the software requirements management phase more secure from vulnerabilities. Some of the critical challenges bothering the software development community are discussed. The background of Machine Learning approaches and their application in software development practices are explored. Some of the work done around modeling the source code and approaches used for vulnerabilities understanding in software systems are reviewed. Program representation is explored to understand some of the principles that would help in understanding the subject well. Further deeper dive into source code modeling possibilities are explored. Machine learning best practices are explored inline with the software source code modeling.
Framework for Enhancing the Traceability in Supply Chain Using Blockchain

Supply chain management is implementing the standards of Industry 4.0 in order to enhance the trust and safety level in the system with invention of the latest technologies. At the same time, the customers are now more focused towards the product quality. Supply chain management is the demand of the era. However, it is difficult to trace and maintain the track data throughout the process during the supply of a product. The traditional solutions were based on centralized third party solutions, where the data is inclined to various security attacks and high risk of tempering. In this paper, a solution for the management of supply of a product has been proposed which is based on IoT with an amalgamation of blockchain so as to increase the transparency of the flow during the supply of the product. The solution will help to enhance the trust as the data will be shared among all participating nodes of the network in blockchain. It also makes instant traceability of the product and this can be done by both the end of the chain. Moreover, this solution will help the consumer to check whether the product is from a reliable source or not. For the implementation and testing of smart contracts the authors have used Remix IDE, MetaMask and RinkeyBy.
Multi-source Cross Project Defect Prediction with Joint Wasserstein Distance and Ensemble Learning

Cross-Project Defect Prediction (CPDP) refers to transferring knowledge from source software projects to a target software project. Previous research has shown that the impacts of knowledge transferred from different source projects differ on the target task. Therefore, one of the fundamental challenges in CPDP is how to measure the amount of knowledge transferred from each source project to the target task. This article proposed a novel CPDP method called Multi-source defect prediction with Joint Wasserstein Distance and Ensemble Learning (MJWDEL) to learn transferred weights for evaluating the importance of each source project to the target task. In particular, first of all, applying the TCA technique and Logistic Regression (LR) train a sub-model for each source project and the target project. Moreover, the article designs joint Wassertein distance to understand the source-target relationship and then uses this as a basis to compute the transferred weights of different sub-models. After that, the transferred weights can be used to reweight these sub-models to determine their importance in knowledge transfer to the target task. We conducted experiments on 19 software projects from PROMISE, NASA and AEEEM datasets. Compared with several state-of-the-art CPDP methods, the proposed method substantially improves CPDP performance in terms of four evaluation indicators (i.e., F-measure, Balance, G-measure and MMC).
Model Averaging Prediction by K-Fold Cross-Validation

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Application of Tableau in Visual Analysis Data of a US Supermarket Sales

In today’s large and complex data background, data needs to be properly interpreted and expressed in order to convey information more clearly. In this paper, a powerful visualization tool, Tableau is used to make visual analysis of online sales data of an American supermarket, the results can better understand the information of sales situation. This can better assist decision-making and provide decision support for the managers of the supermarket.
Digital Image Watermarking at Different Levels of DWT using RGB Channels

This paper presents a digital image watermarking scheme comprising of DWT transformation. Due to the common practice of creating the copy, transmitting and spreading the data duplication of the original data occurs. Digital image watermarking has the ability to provide a solution for the unauthorized duplication problem. The scheme designed and presented in this paper comprises of mainly two modules one for embedding the watermark within the cover image and another for retrieving the watermark from the watermarked image. The process is carried out at different levels of the DWT transformation within different sub-bands of the DWT transformation. The extraction process involves the extraction of the watermark image form different channels of the RGB image mainly red, green and blue. The robustness and imperceptibility are tested. In each of the case, the corresponding PSNR and correlation values are noted and the results obtained concludes the scheme as the robust, semi-fragile and fragile digital image watermarking at different levels of the DWT transformation
Locality-Sensitive Hashing-Based Link Prediction Process on Smart Campus Education or Online Social Platform

With the development of the Internet, smart campus education and online social platforms have become the mainstream of establishing social relationships. Although many users communicate by social networks, the social networks are caught in the problem of relationship sparsity, which severely impedes users’ communication space. More fatally, in the course of building social relationships, the disclosure of sensitive information will cause users’ privacy vulnerable to be compromised by attackers. Therefore, this paper proposes a potential social relationships prediction approach based on locality-sensitive hashing (LSH) to address the above issues. Specifically, the LSH clusters similar users into the same bucket, and the fuzzy computing method is developed to predict the types of social relationships among these similar users. To further alleviate the relationship sparsity problem, the existing social network structure is utilized to predict users’ social relationships and relationship types. Furthermore, the rationality of prediction results is verified by using the social balance theory (SBT). Finally, massive experiments are executed on Epinions, and the experimental results further confirmed the efficiency and accuracy of our methodology in terms of link prediction while guaranteeing privacy-preservation.
Automatic Data Quality Assessment of Hydrographic Surveys Taking Into Account Experts’ Preferences

Data quality assessment of hydrographic surveys is a complex problem, since context dependent acquisition conditions using multiple sensors contribute to numerous data imperfections, which have different consequences depending on the intended final product. In this work we propose a generic methodology integrating experts’ preferences through multi-criteria preference models with data quality techniques to generate explainable overall quality assessments of hydrographic surveys which depend on the expected end-uses.Four hydrographic surveys of different geographic locations and contrasting characteristics were studied, according to the preferences of an acoustician, an oceanographer, and a hydrographer. The obtained results indicate that each survey was appropriately evaluated, indicating the reasons that led to the specific assessment.
The Role of Compute in Autonomous Micro Aerial Vehicles: Optimizing for Mission Time and Energy Efficiency

Autonomous and mobile cyber-physical machines are becoming an inevitable part of our future. In particular, Micro Aerial Vehicles (MAVs) have seen a resurgence in activity. With multiple use cases, such as surveillance, search and rescue, package delivery, and more, these unmanned aerial systems are on the cusp of demonstrating their full potential. Despite such promises, these systems face many challenges, one of the most prominent of which is their low endurance caused by their limited onboard energy. Since the success of a mission depends on whether the drone can finish it within such duration and before it runs out of battery, improving both the time and energy associated with the mission are of high importance. Such improvements have traditionally been arrived at through the use of better algorithms. But our premise is that more powerful and efficient onboard compute can also address the problem. In this article, we investigate how the compute subsystem, in a cyber-physical mobile machine such as a Micro Aerial Vehicle, can impact mission time (time to complete a mission) and energy. Specifically, we pose the question as what is the role of computing for cyber-physical mobile robots? We show that compute and motion are tightly intertwined, and as such a close examination of cyber and physical processes and their impact on one another is necessary. We show different “impact paths” through which compute impacts mission metrics and examine them using a combination of analytical models, simulation, and micro and end-to-end benchmarking. To enable similar studies, we open sourced MAVBench , our tool-set, which consists of (1) a closed-loop real-time feedback simulator and (2) an end-to-end benchmark suite composed of state-of-the-art kernels. By combining MAVBench, analytical modeling, and an understanding of various compute impacts, we show up to 2X and 1.8X improvements for mission time and mission energy for two optimization case studies, respectively. Our investigations, as well as our optimizations, show that cyber-physical co-design, a methodology with which both the cyber and physical processes/quantities of the robot are developed with consideration of one another, similar to hardware-software co-design, is necessary for arriving at the design of the optimal robot.
Testing for Recommendation Method in M-Health Sports Venue Recommendation System

Exercising can make the body produce more endorphins so people with regular exercise are more resistant to stress than those who have little physical activity. We can get a recommendation of the sport venue in search engine, but it can’t accommodate personal preference. The mobile application for sports venue recommendations (M-health) with specific attribute weighting that can accommodate user preference for a specific attribute can be implemented with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS was chosen as a recommendation algorithm because it has a relatively low level of algorithm complexity, so it is suitable to apply in mobile devices. To test the recommendations that are processed with TOPSIS, correlation testing is done as an alternative test besides accuracy. In general, the system takes the user's location coordinates and then recommends a Futsal court based on location, price and the number of courts. First, the user is inquired to enter the weights for each criterion. Then the user gets a recommendation for a Futsal court recommendation according to the user's current location. If the user wants detailed information about the desired futsal location, the user can click on one of the futsal places and then the detail page will be appear. After seeing the details of the selected futsal place, users can view the map to go to the relevant sports venue from the user's current location. Testing the recommendation system was based on correlation testing to see the correlation between the recommendations built by the system compared to the user's preference choices. Correlation testing was carried out to see whether there was a relationship between the results of the TOPSIS recommendation and the user's preference of sport venue. The correlation between them shows a positive correlation with a value of 0.770769231.
Measurement-Device-Independent Quantum Key Distribution of Frequency-Nondegenerate Photons

In measurement-device-independent quantum key distribution (MDI QKD), the setup of Bell state measurement (BSM) usually requires that the photons sent by different users should be frequency degenerate. It increases the complexity to implement MDI QKD since a complicated feedback system is usually required to calibrate photons' frequencies in different users. It also limits the development of MDI QKD networks since wavelength division multiplexing (WDM) technology is not compatible with this requirement. In this work, we propose a MDI QKD scheme of frequency-nondegenerate photons, in which the users send photons with different frequencies. It is based on a BSM setup with a frequency-domain beam splitter (FBS), which is realized by a phase modulator (PM). A proof-of-principle experiment is realized, showing that the BSM of frequency-nondegenerate photons could select specific users to realize MDI QKD by adjusting the modulation frequency of the PM to match the frequency difference of photons sent by the users. The results manifest that our scheme has great potential for simplifying the implementation and realizing MDI QKD networks combined with WDM.
AI Enabled Bridge Bidding Supporting Interactive Visualization

With the fast progress in perfect information game problems such as AI chess and AI Go, researchers have turned to imperfect information game problems, including Texas Hold’em and Bridge. Bridge is one of the most challenging card games that have significant research value. Bridge playing is divided into two phases: bidding and playing. This paper focuses on bridge bidding and proposes a bridge bidding service framework using deep neural networks, and supports bidding visualization for the first time. The framework consists of two parts: the bidding model (BM) with a multilayer neural network, and a visualization system. The framework predicts not only reasonable bids from the existing bidding system of humans, but also provides intuitive explanations for decisions to enable human–computer information interaction. Experimental results show that this bidding AI outperforms majority of existing systems.