Reviewerly/exHarmony huggingface dataset:
Network Shuffling: Privacy Amplification via Random Walks

Recently, it is shown that shuffling can amplify the central differential privacy guarantees of data randomized with local differential privacy. Within this setup, a centralized, trusted shuffler is responsible for shuffling by keeping the identities of data anonymous, which subsequently leads to stronger privacy guarantees for systems. However, introducing a centralized entity to the originally local privacy model loses some appeals of not having any centralized entity as in local differential privacy. Moreover, implementing a shuffler in a reliable way is not trivial due to known security issues and/or requirements of advanced hardware or secure computation technology. Motivated by these practical considerations, we rethink the shuffle model to relax the assumption of requiring a centralized, trusted shuffler. We introduce network shuffling, a decentralized mechanism where users exchange data in a random-walk fashion on a network/graph, as an alternative of achieving privacy amplification via anonymity. We analyze the threat model under such a setting, and propose distributed protocols of network shuffling that is straightforward to implement in practice. Furthermore, we show that the privacy amplification rate is similar to other privacy amplification techniques such as uniform shuffling. To our best knowledge, among the recently studied intermediate trust models that leverage privacy amplification techniques, our work is the first that is not relying on any centralized entity to achieve privacy amplification.
Quasi-Oppositional Satin Bowerbird with Deep Learning based Content based Image Retrieval

Content-based image retrieval (CBIR) is commonly employed to retrieve images from a massive set of unlabeled images. The design of CBIR model faces several limitations, as it is mainly based on the extraction of image features to calculate the similarity amongst the query image (QI) and database images. The recent advances of deep learning (DL) models help to attain remarkable retrieval outcomes. In this view, this paper presents a novel quasi-oppositional satin bowerbird optimizer with Densely Connected Networks (QOSBO-DCN) for CBIR. The proposed QOSBO-DCN technique aims to properly retrieve the images related to the QI in an effective and automated manner. The proposed QOSBO-DCN technique derives a DenseNet-77 model as a feature extractor to derive feature vectors from the QI and database images. Besides, the QOSBO algorithm is utilized to adjust the hyperparameter values of the DenseNet-77 model in such a way that the retrieval performance can be improved. Additionally, Euclidean distance is used as a similarity measurement approach to determine the highly resembling images and retrieve them. The simulation analysis of the QOSBO-DCN technique is performed using Corel10K dataset and the results reported the betterment of the QOSBO-DCN technique over the existing techniques.
A fine‐grained and secure health data sharing scheme based on blockchain

Abstract Electronic health record (EHR) allows patients to use an open channel (ie, Internet) to control, share and manage their health records among family members, healthcare providers and other third party data users. Thus, in such an environment, privacy, confidentiality, and data consistency are the major challenges. Although cloud‐based EHR addresses the aforementioned discussions, these are prone to various malicious attacks, trust management and non‐repudiation among servers. Recently, due to the property of immutability, blockchain technology has been introduced to be as an auspicious solution for achieving EHR sharing with privacy and security preservation. Motivated by the above debates, we present BFHS, a blockchain‐based fine‐grained secure EHR sharing mechanism. On BFHS, we encrypt the EHR using ciphertext‐policy attribute‐based encryption (CP‐ABE) and upload it to the interplanetary file system (IPFS) for storage, while the matching index is encrypted via proxy re‐encryption and kept in a medical consortium blockchain. In addition, we created a credit assessment mechanism and incorporated it into the smart contract. Smart contracts, proxy re‐encryption, a credit evaluation mechanism, and IPFS all work together to give patients with a trustworthy EHR sharing environment and a dynamic access control interface. The thorough comparison and experimental analysis show that the proposed BFHS has more comprehensive security features and is more practicable.
Geometric Calibration for Cameras with Inconsistent Imaging Capabilities

Traditional calibration methods rely on the accurate localization of the chessboard points in images and their maximum likelihood estimation (MLE)-based optimization models implicitly require all detected points to have an identical uncertainty. The uncertainties of the detected control points are mainly determined by camera pose, the slant of the chessboard and the inconsistent imaging capabilities of the camera. The negative influence of the uncertainties that are induced by the two former factors can be eliminated by adequate data sampling. However, the last factor leads to the detected control points from some sensor areas having larger uncertainties than those from other sensor areas. This causes the final calibrated parameters to overfit the control points that are located at the poorer sensor areas. In this paper, we present a method for measuring the uncertainties of the detected control points and incorporating these measured uncertainties into the optimization model of the geometric calibration. The new model suppresses the influence from the control points with large uncertainties while amplifying the contributions from points with small uncertainties for the final convergence. We demonstrate the usability of the proposed method by first using eight cameras to collect a calibration dataset and then comparing our method to other recent works and the calibration module in OpenCV using that dataset.
Windmill Graph based Feature Descriptors for Facial Expression Recognition

Automatic Facial Expression Recognition (FER) is an important research area in computer vision because it has many real-time applications. However, the main issue lies in the design of a feature descriptor that could effectively capture the appearance changes in the facial images. For FER systems, graph based methods have not been much explored for feature extraction. Hence, this paper introduces novel graph based methods named Windmill Graph based Feature Descriptors (WGFD) for feature extraction. Two variants of WGFD’s (WGFDh and WGFDv) have been proposed in this work to effectively encode the relationship among neighboring pixels in a local neighborhood. For expression classification, multi-class Support Vector Machine (SVM) is utilized. The performance of the proposed feature descriptors have been evaluated on six benchmark FER datasets namely JAFFE, KDEF, MUG, CK+, TFEID and FERG with respect to seven expressions classification. The experimental results showed promising results when compared to the recent FER methods.
PAS: Performance-Aware Job Scheduling for Big Data Processing Systems

Big data analytics has become increasingly vital in many modern enterprise applications such as user profiling and business process optimization. Today’s big data processing systems, such as Hadoop MapReduce, Spark, and Hive, treat big data applications as a batch of jobs for scheduling. Existing schedulers in production systems often maintain fair allocation without considering application performance and resource utilization simultaneously. It is challenging to perform job scheduling in big data systems to achieve both low turnaround time and high resource utilization due to the high complexity in data processing logics and the dynamic variation in workloads. In this article, we propose a performance-aware scheduler, referred to as PAS, which dynamically schedules big data jobs in Hadoop YARN and Spark and autonomously adjusts scheduling policies to improve application performance and resource utilization. Specifically, PAS schedules multiple concurrent jobs using different policies based on the predicted job completion time and employs a greedy approach and a one-step lookahead strategy to opportunistically maximize the average job performance while still maintaining a satisfactory level of resource utilization. We implement PAS in Hadoop YARN and evaluate its performance with HiBench, a well-known big data processing benchmark. Experimental results show that PAS reduces the average turnaround time by 25% and the makespan by 15% in comparison with four state-of-the-art schedulers.
Progress toward practical quantum cryptanalysis by variational quantum cloning

Cryptanalysis of quantum cryptographic systems generally involves finding optimal adversarial attack strategies on the underlying protocols. The core principle of modeling quantum attacks often reduces to the ability of the adversary to clone unknown quantum states and to extract thereby meaningful secret information. Explicit optimal attack strategies typically require high computational resources due to large circuit depths or, in many cases, are unknown. Here we introduce variational quantum cloning (VarQlone), a cryptanalysis algorithm based on quantum machine learning, which allows an adversary to obtain optimal approximate cloning strategies with short depth quantum circuits, trained using hybrid classical-quantum techniques. The algorithm contains operationally meaningful cost functions with theoretical guarantees, quantum circuit structure learning and gradient-descent-based optimization. Our approach enables the end-to-end discovery of hardware-efficient quantum circuits to clone specific families of quantum states, which we demonstrate in an implementation on the Rigetti Aspen quantum hardware. We connect these results to quantum cryptographic primitives and derive explicit attacks facilitated by VarQlone. We expect that quantum machine learning will serve as a resource for improving attacks on current and future quantum cryptographic protocols.
Augmented reality for oral and maxillofacial surgery: The feasibility of a marker‐free registration method

Recognition of markers in the augmented reality system can reduce the additional cost of a guide plate required for the removal of benign tumours in oral and maxillofacial surgery, but the use of markers often has complex problems.In order to avoid the complex problem of using markers, an augmented reality system based on a marker-free registration method was proposed to track the contour of the mandible edge. Use the computer to perform preoperative planning on the jaw model, select and mark the path of maxillofacial lesion resection.This method has an error of the surface matching was 0.6453±0.2826$0.6453\pm 0.2826$ mm, and an error of the surgical resection was 0.4858±0.3712$0.4858\pm 0.3712$ mm.We demonstrate that the system can accurately enhance the display of the surgical path and provide guidance for the tradition of maxillofacial surgery.
A Graph Embedding-based Identity Inference Attack on Blockchain Systems

This paper discusses an attack scenario that infers identity in a blockchain-based smart healthcare system. There is a privacy vulnerability because blockchain data is open to all participants. Invasion of privacy is a significant problem in a blockchain-based smart healthcare system that utilizes personal information. We analyze the blockchain graph to infer a user's identity. Then, we construct account-transaction graphs using transactions on the blockchain. A graph embedding algorithm generates features of account-transaction graphs. The generated features are used to identify blockchain participants using machine learning algorithms. We evaluate our attack scenario by applying embedding algorithms in Ethereum. The results showed an inference performance of up to 0.94 in the f1-score.
Out-Of-Distribution Detection In Unsupervised Continual Learning

Unsupervised continual learning aims to learn new tasks incrementally without requiring human annotations. However, most existing methods, especially those targeted on image classification, only work in a simplified scenario by assuming all new data belong to new tasks, which is not realistic if the class labels are not provided. Therefore, to perform unsupervised continual learning in real life applications, an out-of-distribution detector is required at beginning to identify whether each new data corresponds to a new task or already learned tasks, which still remains under-explored yet. In this work, we formulate the problem for Out-of-distribution Detection in Unsupervised Continual Learning (OOD-UCL) with the corresponding evaluation protocol. In addition, we propose a novel OOD detection method by correcting the output bias at first and then enhancing the output confidence for in-distribution data based on task discriminativeness, which can be applied directly without modifying the learning procedures and objectives of continual learning. Our method is evaluated on CIFAR-100 dataset by following the proposed evaluation protocol and we show improved performance compared with existing OOD detection methods under the unsupervised continual learning scenario.
An accurate 3D augmented reality navigation system with enhanced autostereoscopic display for oral and maxillofacial surgery

Oral and maxillofacial surgery demands high precision navigation to achieve optimal surgical outcomes. Augmented reality (AR) has been a promising solution for intuitive and accurate guidance, whereas existing systems have room for improvement.We propose a high quality and accurate in situ AR navigation system. Enhanced image quality is achieved by deploying eye tracking in lenticular-based autostereoscopic display. Viewpoint tracking and optical tracking are integrated to assure accurate in situ images.The proposed system can provide in situ AR images in real-time, with a viewing angle of 59.5° × 49.1°, 3D image resolution of 0.35 × 0.21 mm, and image accuracy of 0.99 ± 0.70 mm. A maxillary drilling experiment obtains average position error of 1.12 ± 0.30 mm and localization time of 8.7 ± 3.9 s, significantly better than conventional 2D navigation system.The proposed system can display high-quality AR images and therefore reduce positioning error and operating time.
Not all who are bots are evil: A cross-platform analysis of automated agent governance

The growth of online platforms is accompanied by the increasing use of automated agents. Despite being discussed primarily in the context of opinion manipulation, agents play diverse roles within platform ecosystems that raises the need for governance approaches that go beyond policing agents’ unwanted behaviour. To provide a more nuanced assessment of agent governance, we introduce an analytical framework that distinguishes between different aspects and forms of governance. We then apply it to explore how agents are governed across nine platforms. Our observations show that despite acknowledging diverse roles of agents, platforms tend to focus on governing selected forms of their misuse. We also observe differences in governance approaches used by platforms, in particular when it comes to the agent rights/obligations and transparency of policing mechanisms. These observations highlight the necessity of advancing the algorithmic governance research agenda and developing a generalizable normative framework for agent governance.
Sign Language Recognition and Translation into Pashto Language Alphabets

Sign language is mandatory and mostly used by huge number of individuals (particularly disabled) in society and it is another way of communication; The form of the hand, the contour of the movement, and the posture of the hand, face, and body parts are all distinct in each sign language; as a result, visual sign language identification is a challenging area of computer vision research; various academics have suggested several models in recent years that have been greatly improved using deep learning approaches. This study looked at the vision-based sign language recognition models suggested utilizing deep learning approaches in the last five years. Although the suggested model&#x2019;s general trend reveals that sign language recognition accuracy has substantially increased, specific problems still have to be overcome. There are various and multiple languages in the world, people from different languages, have their alphabets and sign to communicate with each other. Many language speakers have developed a way to communicate with disabled people like Indian sign Language, French sign language several more; In this research paper, a novel Pashto sign language has been worked on. The most spoken language in southern Afghanistan is the Pashto language, and it has its grammar and history; it has approximately 40 million population in the world. The aim is to make a system by which disabled people of this language can communicate with the society. The system will take the signs and gestures of disabled people to change them into the Alphabet of Pashto language. 2500 dataset images are prepared and CNN model i s a ppl i e d to recognize the hand gestures and have executed the model with an accuracy of 98%.
Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis

Abstract Public health responses to the COVID-19 pandemic since March 2020 have led to lockdowns and social distancing in most countries around the world, with a shift from the traditional work environment to virtual one. Employees have been encouraged to work from home where possible to slow down the viral infection. The massive increase in the volume of professional activities executed online has posed a new context for cybercrime, with the increase in the number of emails and phishing websites. Phishing attacks have been broadened and extended through years of pandemics COVID-19. This paper presents a novel approach for detecting phishing Uniform Resource Locators (URLs) applying the Gated Recurrent Unit (GRU), a fast and highly accurate phishing classifier system. Comparative analysis of the GRU classification system indicates better accuracy (98.30%) than other classifier systems.
Supervised learning of COVID-19 patients' characteristics to discover symptom patterns and improve patient outcome prediction

The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and adherence to government guidelines on safety precaution measures are the most well-known strategies to prevent COVID-19 infection. In this research, we tried to examine the symptoms of COVID-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the synthetic minority over-sampling (SMOTE) method and then developed some classification models to predict the outcome of COVID-19 cases (recovery or death). Besides, we implemented a rule-based technique to identify different combinations of variables with specific ranges of their values that together affect disease severity. Our results showed that the random forest model with 95.6% accuracy, 97.1% sensitivity, 94.0% specification, 94.4% precision, 95.8% F-score, and 99.3% AUC-score outperforms state-of-the-art classification models. Finally, we identified the most significant rules that state various combinations of 6 features in certain ranges of their values lead to patients' recovery with a confidence value of 90%. In conclusion, the classification results in this study show better performance than recent studies, and the extracted rules help physicians consider other important factors to improve health services and medical decision-making for different groups of COVID-19 patients.
Research on the construction of accounting information audit quality control system based on blockchain

Abstract Audit quality control plays an irreplaceable role in improving the quality of accounting information, and it is also of great significance to the normal operation and development of enterprises. Blockchain is a security technology, which guarantees the security of data to a great extent. Information security is the basis for the quality control of accounting information auditing, and all audit quality control activities start with information data. Therefore, the integration of blockchain technology is essential for audit quality control. This article aims to build an audit quality control system, so that everyone first understands the blockchain security technology and accounting information audit quality control related theories. This article puts forward the method of setting up variables and constructing the accounting information audit quality control system to solve the current problem of low audit quality in China. The experimental results of this article show that blockchain technology is beneficial to ensure the security of audit information and can improve audit quality by 20%.
Verb form error detection in written English of Chinese EFL learners

Abstract In the past few decades, researchers have paid increasing attention to automatic error detection in natural languages, but few have focused on developing an error-checking tool for EFL learners in China. Based on the theory of Pattern Grammar, this study formalizes verb patterns through Link Grammar, a formal grammatical system developed by Sleator and Temperley (1991) , and reconstructs an Link Grammar verb dictionary to create an automatic checking tool for verb form errors in Chinese learners’ written English. The test results show that by importing more detailed pattern information of verbs in the Link Grammar dictionary, the Link Grammar parser can identify verb form errors more accurately and effectively than the original and the parsing capability of the Link Grammar parser is improved. The article shows that Pattern Grammar and Link Grammar can work together and be applied to the construction of error-checking tools for EFL learners with promising results.
How Customer Demand Reactions Impact Technology Innovation and Security

Innovation is a very important concern for both managers and governmental policy makers. There is an important interplay between security and technology innovation that is largely unrecognized in the literature. This research considers the case where technology innovation in the form of additional product features increases demand through greater functionality. However, the likelihood of a security breach increases with the number of product features as the features interact in unintended ways, thereby increasing the attack surface. Using a two-stage game, we demonstrate how potential demand changes (from direct risk or externalities) impact firm technology innovation strategy. The analysis shows that the type and extent of customer demand reaction has a significant impact on innovative feature development. This research identifies two potential impacts on the level of innovation that can be strategically managed - the impact of externalities on demand and industry risk - explaining how these forces alter the level of innovation in the product ecosystem. Additionally, high-security development is disincentivized, and leads to a type of competitive behavior where the opportunity window for high-security development for all firms is small.