Reviewerly/exHarmony huggingface dataset:
A Tale of Two Contact-Tracing Apps – Comparing Australia’s COVIDSafe and New Zealand’s NZ COVID Tracer

The Australian and New Zealand governments have recently released smartphone-based apps to complement contact tracing in the event that they face a resurgence of COVID-19 infections. The apps form part of both countries' policies to support a return to social and economic engagement following extended lockdowns. Using process mapping and analysis, we evaluate the potential of the two apps to improve the performance of existing contact tracing systems across a range of efficiency and effectiveness criteria. We find that the Bluetooth-based Australian app appears likely to assist that country's contact tracing system to perform performing more efficiently and effectively in the event of a resurgence of the virus, and should increase confidence in re-engagement. The New Zealand QR code-based app, however, is not well-aligned with these objectives. Its interaction with a range of other regulations and obligations, combined with the high operational costs it imposes on its users, is likely to militate against its use. Its focus on business locations rather than contact between individuals risks information overload for contact tracers if a resurgence should arise, while it fails to capture information from personal interactions between strangers taking place in locations where QR codes cannot be acquired or displayed, but which are likely to be high-risk in the event of a resurgence. The latter may significantly constrain the development of individual confidence to re-engage. Our analysis suggests that in the international context, Bluetooth-based apps based on interactions between individuals likely better support these population-based objectives than QR code-based location-specific apps.
APIzation: Generating Reusable APIs from StackOverflow Code Snippets

Developer forums like StackOverflow have become essential resources to modern software development practices. However, many code snippets lack a well-defined method declaration, and thus they are often incomplete for immediate reuse. Developers must adapt the retrieved code snippets by parameterizing the variables involved and identifying the return value. This activity, which we call APIzation of a code snippet, can be tedious and time-consuming. In this paper, we present APIZATOR to perform APIzations of JAVA code snippets automatically. APIZATOR is grounded by four common patterns that we extracted by studying real APIzations in GitHub. APIZATOR presents a static analysis algorithm that automatically extracts the method parameters and return statements. We evaluated APIZATOR with a ground-truth of 200 APIzations collected from 20 developers. For 113 (56.50 %) and 115 (57.50 %) APIzations, APIZATOR and the developers extracted identical parameters and return statements, respectively. For 163 (81.50 %) APIzations, either the parameters or the return statements were identical.
A study on soft computing optimizing techniques

Soft Computing is the study of thinking, propositioning and derivations. The thought behind soft computing is to display psychological conduct of human brain. Soft computing is establishment of reasonable insight in machines. In contrast to hard registering, soft computing is open minded toward imprecision, vulnerability, incomplete truth and estimation. The job model for soft computing is the human brain. The core value of soft computing is: Exploit the capacity to bear imprecision, vulnerability and fractional truth to accomplish manageability, strength and low arrangement cost. Soft computing techniques are now been utilized as a tool for solving real-life day-to-day problems. Soft computing procedures are being preferred because of the unpredictability and vulnerability of the various processes. Major soft computing tools applied for this object are genetic algorithm, ant colony optimization, swarm intelligence, and particle swarm optimization. The primary point of this paper is to sum up a portion of the streamlining methods for acknowledging and using its application to dominate insightful work.
Towards Robust Multi-Tenant Clouds Through Multi-Constrained VM Placement

More and more tenants (enterprises and personal users) migrate their tasks to clouds since it is a simple and low-cost way to obtain enough computing resources. However, due to potential node failures and malicious tenants, the modern cloud encounters one critical challenge, i.e., robustness. Conventionally, the cloud vendors deploy auxiliary systems to protect the cloud, which requires additional resource cost and increases the network complexity. To enhance the system robustness, this paper proposes a complementary scheme to improve the cloud robustness through efficient VM placement. Specifically, to alleviate the impact of malicious tenants and node failures on the cloud, when deploying VMs, we limit the number of pods (or service nodes) that each tenant can access, and the number of tenants hosted by each pod (or service node). Though there are a lot of works on VM placement, it is very challenging when the robustness issue is taken into consideration. To solve this problem, we formulate an integer linear programming and propose a rounding-based algorithm with a logarithmic approximation ratio. The simulation results show the high efficiency of the proposed algorithm. For example, our algorithm can improve the network throughput by 150% with other alternatives.
CLAP: A Contract-Based Incentive Mechanism for Cooperative Localization Balancing Localization Accuracy and Location Privacy

In cooperative localization, the sharing of location information has raised the risk of the privacy breach. Geo-indistinguishability, as a formal notion of location privacy, can protect the user’s location privacy by adding random noise into his true location, while it degrades the localization accuracy inevitably. This article considers the tradeoff between the cooperative nodes’ privacy preservation level and the target’s localization accuracy. Since it incurs privacy cost for the cooperative nodes to report their location information, an incentive mechanism for the cooperative nodes to contribute their data is necessary. In this article, we propose a feasible incentive mechanism, named CLAP, based on contract theory to reward the cooperative nodes and ensure the expected localization accuracy. Specifically, we establish an optimization problem of minimizing the payment from the target, while guaranteeing the expected localization accuracy. By simplifying the constraints and transforming the original problem into a convex optimization problem, a closed-form solution is given. Extensive simulations evaluate the performance and validate the feasibility of our proposed contract-based incentive mechanism.
Stolen at the pump: an empirical analysis of risk factors on gas pump skimmer fraud attacks

Purpose The purpose of this study is to examine the characteristics of gas stations where skimmer attacks occurred in a sample of Texas gas stations between 2019 and 2021. This paper seeks to contribute to the literature related to payment card fraud at the gas pump by providing one of the first examinations of the gas stations where gas pump skimming is known to have occurred. Design/methodology/approach Using data collected from a Texas state regulatory agency between 2021, the authors examine characteristics of gas stations where a gas pump skimming was detected. Findings Results suggest that the presence of a surveillance camera system was significantly related to gas pump skimmer detection for gas stations in both urban and rural areas. Europay chip readers were not present in any of the pumps where a skimmer attack was detected. Originality/value Gas pump skimming is a form of payment card fraud that costs upwards of US$11bn a year in the USA alone. Gas pump skimming occurs when electronic devices are illegally installed fuel pumps to capture data or record cardholders’ personal identification numbers. This is among the first studies to use data obtained from a state agency with specific information regarding each individual occurrence of gas pump skimming. As such, the paper makes a unique contribution by exploring specific characteristics of gas stations where skimming and ultimately payment card fraud, occurred.
MIDV-LAIT: A Challenging Dataset for Recognition of IDs with Perso-Arabic, Thai, and Indian Scripts

In this paper, we present a new dataset for identity documents (IDs) recognition called MIDV-LAIT. The main feature of the dataset is the textual fields in Perso-Arabic, Thai, and Indian scripts. Since open datasets with real IDs may not be published, we synthetically generated all the images and data. Even faces are generated and do not belong to any particular person. Recently some datasets have appeared for evaluation of the IDs detection, type identification, and recognition, but these datasets cover only Latin-based and Cyrillic-based languages. The proposed dataset is to fix this issue and make it easier to evaluate and compare various methods. As a baseline, we process all the textual field images in MIDV-LAIT with Tesseract OCR. The resulting recognition accuracy shows that the dataset is challenging and is of use for further researches.
BlockFLow: Decentralized, Privacy-Preserving, and Accountable Federated Machine Learning

Federated machine learning enables multiple clients to collectively train a machine learning model without sharing sensitive data. However, without proper accountability mechanisms, adversarial clients can weaken the collective model. BlockFLow is a fully decentralized, privacy-preserving, and accountable federated learning system. It introduces an Ethereum blockchain smart contract to coordinate a federated learning experiment and to hold clients accountable. BlockFLow rewards clients proportional to the quality of their individual contributions, does not reveal the underlying datasets, and is resilient to a minority of adversarial clients. Unlike existing systems, BlockFLow does not require a centralized test dataset, sharing of datasets between the clients, or any trusted entities. We evaluated BlockFLow on logistic regression models. Our results illustrate that BlockFLow successfully rewards honest clients and identifies adversarial clients. These results, along with blockchain costs that do not scale with model complexity, demonstrate the effectiveness of BlockFLow as an accountable federated learning system.
Design of computerized operator support system for technical specification monitoring

A critical but difficult task performed by the operators of nuclear power plants (NPPs) is monitoring their operation in compliance with their technical specification (Tech Spec). The volume of the document, complexity of the contents, high workload, diversity of the interpretation, and time dependence are the representative challenges encountered by operators. To address these challenges, several researchers have attempted to develop operator support systems that can assist operators to perform Tech-Spec-related activities. However, complying with a Tech Spec requires various types of monitoring functions, and a rule-based system is not typically an efficient method. Therefore, this paper proposes a computerized operator support system for monitoring a Tech Spec. First, this study analyzes the structure and the surveillance requirements in a Tech Spec. Second, artificial intelligence techniques are reviewed. Third, the architecture of the system is proposed. Finally, this paper introduces a prototype for monitoring a Tech Spec.
Instruction Extension of RV32I and GCC Back End for Ascon Lightweight Cryptography Algorithm

Lightweight cryptography is useful to provide security and privacy in resource constraint embedded devices. Latency and memory consumption are the key elements in performance metrics for lightweight cryptography algorithm implementations. Ascon lightweight cryptography algorithm is one of the finalists in CEASAR competition. In this study, special cryptographic non-standard RISC-V instructions have been developed in order to reduce the required number of clock cycles and instruction memory for the execution of the algorithm on RV32I based processors. A profiling methodology has been developed to choose the best special instruction for achieving the highest benefit in performance. An end-to-end test environment has been formed by extending the GNU Compiler Collection and Spike RISC-V ISA Simulator for the special cryptographic instruction extensions of RISC-V processors. New intrinsic functions and instruction patterns for the new instructions have been integrated into the GCC RISC-V back end. Spike has been modified with the new instructions to run the program. The algorithm has been analysed with the proposed instructions and different optimization flags and improvement results have been shown in this study.
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples

The pervasiveness of neural networks (NNs) in critical computer vision and image processing applications makes them very attractive for adversarial manipulation. A large body of existing research thoroughly investigates two broad categories of attacks targeting the integrity of NN models. The first category of attacks, commonly called Adversarial Examples, perturbs the model’s inference by carefully adding noise into input examples. In the second category of attacks, adversaries try to manipulate the model during the training process by implanting Trojan backdoors. Researchers show that such attacks pose severe threats to the growing applications of NNs and propose several defenses against each attack type individually. However, such one-sided defense approaches leave potentially unknown risks in real-world scenarios when an adversary can unify different attacks to create new and more lethal ones bypassing existing defenses.In this work, we show how to jointly exploit adversarial perturbation and model poisoning vulnerabilities to practically launch a new stealthy attack, dubbed AdvTrojan. AdvTrojan is stealthy because it can be activated only when: 1) a carefully crafted adversarial perturbation is injected into the input examples during inference, and 2) a Trojan backdoor is implanted during the training process of the model. We leverage adversarial noise in the input space to move Trojan-infected examples across the model decision boundary, making it difficult to detect. The stealthiness behavior of AdvTrojan fools the users into accidentally trusting the infected model as a robust classifier against adversarial examples. AdvTrojan can be implemented by only poisoning the training data similar to conventional Trojan backdoor attacks. Our thorough analysis and extensive experiments on several benchmark datasets show that AdvTrojan can bypass existing defenses with a success rate close to 100% in most of our experimental scenarios and can be extended to attack federated learning as well as high-resolution images.
RepliComment: Identifying clones in code comments

Code comments are the primary means to document implementation and facilitate program comprehension. Thus, their quality should be a primary concern to improve program maintenance. While much effort has been dedicated to detecting bad smells, such as clones in code, little work has focused on comments. In this paper we present our solution to detect clones in comments that developers should fix. RepliComment can automatically analyze Java projects and report instances of copy-and-paste errors in comments, and can point developers to which comments should be fixed. Moreover, it can report when clones are signs of poorly written comments. Developers should fix these instances too in order to improve the quality of the code documentation. Our evaluation of 10 well-known open source Java projects identified over 11K instances of comment clones, and over 1,300 of them are potentially critical. We improve on our own previous work Blasi and Gorla (2018), which could only find 36 issues in the same dataset. Our manual inspection of 412 issues reported by RepliComment reveals that it achieves a precision of 79% in reporting critical comment clones. The manual inspection of 200 additional comment clones that RepliComment filters out as being legitimate, could not evince any false negative.
Application Agnostic Container Migration and Failover

A key aspect of the cloud is its flexibility and abstraction of the underlying hardware. Historically, virtual machines have been the backbone of the cloud industry, allowing cloud providers to offer virtualized multi-tenant solutions. Today, virtualization by lightweight process containers continue to increase in popularity and make up a larger portion of the cloud, often replacing virtual machines, especially in fog and edge computing. Virtualization can enhance flexibility by enabling support for live migration and failover. Live migration is the process of moving a running instance of a virtual machine or container from one host to another and failover ensures that failures will be automatically detected and the instance restarted, possibly on another host.This paper presents an overview of current migration techniques, and metrics that can be used for comparison. We also present a proof-of-concept implementation and description of a system that enables support for both live migration and failover for containers by extending current container migration techniques. It is able to offer this to any OCI-compliant container, and could therefore potentially be integrated into current container frameworks. In addition, measurements are provided and used to compare the proof-of-concept implementation to the pre-copy migration technique. We achieve a downtime equal to, and total migration time lower than that of pre-copy migration at the cost of an increased amount of data needed to be transferred.
Assassin

The increasing complexity of data analysis tasks makes it dependent on human expertise and challenging for non-experts. One of the major challenges faced in data analysis is the selection of the proper algorithm for given tasks and data sets. Motivated by this, we develop Assassin, aiming at helping users without enough expertise to automatically select optimal algorithms for classification tasks. By embedding meta-learning techniques and reinforced policy, our system can automatically extract experiences from previous tasks and train a meta-classifier to implement algorithm recommendations. Then we apply genetic search to explore hyperparameter configuration for the selected algorithm. We demonstrate Assassin with classification tasks from OpenML. The system chooses an appropriate algorithm and optimal hyperparameter configuration for them to achieve a high-level performance target. The Assassin has a user-friendly interface that allows users to customize the parameters during the search process.
Performance Evaluation of Ground AR Anchor with WebXR Device API

Recently, the development of 3D graphics technology has led to various technologies being combined with reality, where a new reality is defined or studied; they are typically named by combining the name of the technology with “reality”. Representative “reality” includes Augmented Reality, Virtual Reality, Mixed Reality, and eXtended Reality (XR). In particular, research on XR in the web environment is actively being conducted. The Web eXtended Reality Device Application Programming Interface (WebXR Device API), released in 2018, allows instant deployment of XR services to any XR platform requiring only an active web browser. However, the currently released tentative version has poor stability. Therefore, in this study, the performance evaluation of WebXR Device API is performed using three experiments. A camera trajectory experiment is analyzed using ground truth, we checked the standard deviation between the ground truth and WebXR for the X, Y, and Z axes. The difference image experiment is conducted for the front, left, and right directions, which resulted in a visible difference image for each image of ground truth and WebXR, small mean absolute error, and high match rate. In the experiment for measuring the 3D rendering speed, a frame rate similar to that of real-time is obtained.
Differentially Private Coded Federated Linear Regression

Federated Learning (FL) is a distributed learning method, where participants learn a model collaboratively by exchanging information without sharing their ‘raw’ databases. This is of great importance in areas where databases hold private information of individuals, for e.g., healthcare information. In this paper we address two major challenges in FL for Linear Regression models when used on private databases in a Mobile Edge Computing (MEC) environment: 1) Providing provable privacy guarantees and 2) Reduction in communication and computational overheads at the client. We propose our scheme Differentially Private-Coded Federated Linear Regression (DP-CFL) in which each client generates “coded” data by perturbed random linear combinations of its data and shares them with a MEC Server. The MEC Server combines coded data from multiple clients and performs gradient descent in the coded domain without any further communication from clients. We analytically prove (∊, δ)-Differential Privacy guarantee for our scheme and use a combination of analysis and simulations to show the computation and communication savings of our approach with respect to a Federated Differentially Private-Stochastic Gradient Descent (FDP-SGD).
Improve enterprise knowledge management with internet of things: a case study from auto insurance industry

The Internet of Things (IoT) technology has been increasingly used in industries. However, studies on IoT in knowledge management (KM) are still very limited. This research contributes to understanding the role of IoT-based technologies in KM, specifically acting as a knowledge creation mediator. By investigating a case study in the automobile insurance industry, this paper reveals IoT-based technologies' supporting role and impacts on the insurer's decision-making process – risk assessment and pricing, business process performance – claim accuracy and efficiency, and the role of an IoT system's functionalities in improving drivers' attitude (i.e., higher satisfaction) and behaviours (i.e., main resources for Data Analysis using the socialisation-externalisation-combination-internalisation (SECI) model), the research offers practitioners an overview of the value of IoT concerning organisational KM and demonstrates the role of IoT technologies in enhancing business performance, leading to broader social good.
CheckSim: A Reference-Based Identity Document Verification by Image Similarity Measure

This paper presents a generic deep-learning based framework for identity document verification, which we call CheckSim. We tackle the document verification problem as a similarity measure between two aligned images: a query document image and a predefined reference representing the document model. To this end, we explore the use of two particular architectures of Convolutional Neural Network (CNN) models that are well adapted to find similarity or relationship between two comparable inputs, namely siamese and triplet CNN. The models are trained using specific loss function, then used for extracting high-dimensional features from the query and the reference images. The distance between these two feature vectors is used to decide if the two images are similar or different based on a threshold optimized by minimizing a custom cost function. An experimental comparison of these two models as well as traditional approaches based on handcrafted features is conducted on a real-world data set of patches extracted from identity documents. The obtained results show that our approach achieves good results and outperforms handcrafted features based methods.