Reviewerly/exHarmony huggingface dataset:
Twin-Finder: Integrated Reasoning Engine for Pointer-Related Code Clone Detection

Detecting code clones is crucial in various software engineering tasks. In particular, code clone detection can have significant uses in the context of analyzing and fixing bugs in large scale applications. However, prior works, such as machine learning-based clone detection, may cause a considerable amount of false positives. In this paper, we propose Twin-Finder, a novel, closed-loop approach for pointer-related code clone detection that integrates machine learning and symbolic execution techniques to achieve precision. Twin-Finder introduces a clone verification mechanism to formally verify if two clone samples are indeed clones and a feedback loop to automatically generated formal rules to tune machine learning algorithm and further reduce the false positives. Our experimental results show that Twin-Finder can swiftly identify up 9× more code clones comparing to a tree-based clone detector, Deckard and remove an average 91.69% false nositives.
<i>Google Translate</i> Gets Voltaire: Literary Translation and the Age of Artificial Intelligence

In the fall of 2016, users of Google Translate working from French to English and English to French saw a marked improvement in the quality of the translated texts. Google Translate was beginning to develop from a purely phrase-based statistical machine translation system into a system that uses deep learning and artificial neural networks that can adapt and learn in reaction to all they encounter. The Google Translate development team has been working on expanding and strengthening the system’s capability of pragmatic analysis. This capability entails extracting useful information from a text so that its meaning can be accurately inferred in an attempt to overcome the vagueness and ambiguity inherent in language, which have remained the greatest hurdles to effective machine translation. This article explores the potential of Google Translate to provide increasingly competent translations of literary texts. Using passages from the works of Voltaire translated by Google Translate into English, this article explores both the successes and the ongoing problems faced by new generations of Google Translate, and of machine translation systems in general, as their developers seek to overcome the hurdles of identifying and rendering style, nuance, and ambiguity in language.
Efficient and Secure Delegation of Exponentiation in General Groups to a Single Malicious Server

Group exponentiation is an important and relatively expensive operation used in many public-key cryptosystems and, more generally, cryptographic protocols. To expand the applicability of these solutions to computationally weaker devices, it has been advocated that this operation is delegated from a computationally weaker client to a computationally stronger server. In the case of a single, possibly malicious, server, this problem has remained open since the introduction of a formal model. In previous work we have proposed practical and secure solutions applicable to two classes of specific groups, related to well-known cryptosystems. In this paper, we investigate this problem in a general class of multiplicative groups, possibly going beyond groups currently subject to quantum cryptanalysis attacks. Our main results are efficient delegation protocols for exponentiation in these general groups. The main technique in our results is a reduction of the protocol’s security probability (i.e., the probability that a malicious server convinces a client of an incorrect exponentiation output) that is more efficient than by standard parallel repetition. The resulting protocols satisfy natural requirements such as correctness, security, privacy and efficiency, even if the adversary uses the full power of quantum computers. In particular, in our protocols the client performs a number of online group multiplications smaller by 1–2 orders of magnitude than in a non-delegated computation.
Intelligent and Immersive Visual Analytics of Health Data

Massive amounts of health data have been created together with the advent of computer technologies and next generation sequencing technologies. Analytical techniques can significantly aid in the processing, integration and interpretation of the complex data. Visual analytics field has been rapidly evolving together with the advancement in automated analysis methods such as data mining, machine learning and statistics, visualization, and immersive technologies. Although automated analysis processes greatly support the decision making, conservative domains such as medicine, banking, and insurance need trusts on machine learning models. Explainable artificial intelligence could open the black boxes of the machine learning models to improve the trusts for decision makers. Immersive technologies allow the users to engage naturally with the blended reality in where they can look the information in different angles in addition to traditional screens. This chapter reviews and discusses the intelligent visualization, artificial intelligence and immersive technologies in health domain. We also illustrate the ideas with various case studies in genomic data visual analytics.
A classification of attacks to In-Vehicle Components (IVCs)

The rapid adoption of technology for the purpose of connecting all things to a global network can be witnessed in those ranging from simple applications, such as toasters, to more complex ones, such as vehicles. In particular, the connected vehicles which have emerged from this adoption utilize various sensors and external connections to either better allow its user to make a more informed decision, or make that decision autonomously. As a majority of the population relies on some sort of vehicular transportation, this means connected vehicles have access to an unprecedented amount of data, some of which is private, and some of which is critical in allowing the vehicle to perform as expected. Although we agree that this data-driven approach can allow for better decisions to be made, the fact remains that the connection between the In-Vehicle Networks (IVNs) and the Internet increases the number of ways the IVNs can now be targeted. In this paper, we focus on the security of common In-Vehicle Components (IVCs); that is, IVNs and the interfaces that interact in some way with such networks. Possible attacks to IVNs and their interfaces are identified and classified in a novel way, along with potential causes for each attack. The novel attack classification scheme can influence the development of new types of countermeasures to attacks, or the adoption of new and existing countermeasures to attacks that fall under the same classification.
Non-convex non-local reactive flows for saliency detection and segmentation

We propose and numerically solve a new variational model for automatic saliency detection and segmentation in digital images. Using a non-local framework we consider a family of edge preserving functions combined with a new quadratic saliency detection term. Such a term defines a constrained bilateral obstacle problem for image classification driven by p-Laplacian operators, including the so-called hyper-Laplacian case (0<p<1). As an application the related non-convex non-local reactive flows are considered for glioblastoma segmentation in magnetic resonance fluid-attenuated inversion recovery (MRI-Flair) images. A fast convolutional kernel based approximated solution is computed. The numerical experiments show that the non-convexity related to the hyper-Laplacian operators provokes sparseness of the non-local gradients and provides better results in terms of the standard metrics when the parameter p decreases.
Distributed sentiment analysis of an agglutinative language via Spark by applying machine learning methods

Currently there are a large amount of studies conducted on the field of sentiment analysis worldwide. Due to continuous drastic increase of amount of data, all natural language processing systems require special approach oriented to it. Implementing and evaluating such mechanism was a strong incentive to conduct this research. In this paper, sentiment analysis was implemented on Kazakh language via Spark on the basis of data set taken from Kazakh books. The data was initially unbalanced. There was achieved training F1 measure over 90 %.
Secure and Efficient Outsourcing of Matrix Multiplication based on Secret Sharing Scheme using only One Server

In this paper, we propose secure and efficient outsourcing of matrix multiplication based on secret sharing scheme and using only one server. Matrix multiplication has been widely used in the field of machine learning, including deep learning. However, as the size of a matrix becomes larger, the computational complexity becomes more enormous (e.g., matrix multiplication between two m × <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> matrices can be done with O( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) complexity). Because of the limited performance or storage on the device, it is impossible for an individual to perform such large-scale computations. To solve this problem, the technology that enables the consumer as a client to outsource computation securely to a high-performance server in the cloud, namely, secure outsourcing computation is studied. Our method is realized by multiplying some shares of a secret by different random numbers. The proposed method removes the disadvantage of secret sharing scheme that needs multiple servers to perform operations of less computational complexity than those of the method based on homomorphic encryption. We prove the security of our method against not only passive but also active adversaries. In addition, we show that our method is more efficient than existing methods of secure outsourcing computation.
Automation of Team Formation in Software Development Projects in an Enterprise: What Needs to Improve?

With the increasing advancement of economic globalization, optimization of human resource allocation plays a significant role in the development of software development enterprise as software development is a human-intensive activity. Major contributors that affect the outcome of software projects are mainly personnel assignment decisions. There is an increasing need to assign the right team to a project from the very beginning of the project. The task of formation of an optimal team for a software development project is a crucial and a very complex task to be handled manually. If not handled properly, it may result in failure of processes, strategies and plans which may finally lead to the failure of complete software development project. The rationale of this study is to gather knowledge on the previous research that have been carried out in this problem domain and to analyze them in order to identify their benefits and limitations in addressing the problem. This is the initial phase of an ongoing research and the knowledge acquired would be utilized to propose a new solution that fills in the gaps in existing approaches in the literature.
Improving Histopathological Image Segmentation and Classification using Graph Convolution Network

In this paper, we present a system for segmentation and classification of breast cancer ROI images by integrating the idea of hierarchical processing of segmentation and classification tasks. The system is composed of a segmentation module and a GCN module, where the GCN module is designed to improve the performance of the classification result. The segmentation module is used to obtain the segmentation masks of the image patches of the ROI image, which are spliced to generate the segmenion result of the ROI image. The GCN module is used to capture the spatial and semantic dependencies among image patches of the ROI image, by constructing the graph using the segmentation masks of the image patches. Based on the learned features by the GCN module, the classification result of the ROI image can be obtained. Experimental results on the grand challenge on BreAst Cancer Histology images (BACH) 2018 dataset [17] show that, the proposed segmentation and classification method outperforms the winner of BACH 2018 significantly, which demonstrates the effectiveness of the proposed method.
Efficient Load balancing to serve Heterogeneous Requests in Clustered Systems using Kubernetes

Load balancing is an important part of a distributed computing environment which ensures that all devices or processors perform the same amount of work in an equal amount of time. Most load balancing algorithms assume similar processing power and workload for all the processors. However, now systems have become more complex and can have processors of different capabilities, workload, and configurations. In this paper, we propose an alternative algorithm for scheduling tasks. We configure the clusters dedicated to a particular type of task(real-time, dataintensive, etc.). Labels have been defined for each job to classify them into these categories. Then we modify the algorithm to introduce load balancing techniques using task migration.
Test Case Generation Algorithms and Tools for Specifications in Natural Language

Nowadays, most consumer products are equipped with methods of network communications, and nondeterministic tests, which are originated from random message exchanges via the network, should be carried out. Therefore, the tests of the consumer products with network have obliged us to consume much time to design and conduct. For reducing the labor of designing test cases, algorithms and tools, which help test engineers to convert specifications written in a natural language into semiformal descriptions, and to generate test cases including deterministic and nondeterministic test cases as decision tables, are proposed in the paper. The algorithms and tools were applied to a tiny example for evaluation and confirmed that they have succeeded in generating test cases from documents in a natural language.
Resource-Aware Cache Management for In-Memory Data Analytics Frameworks

Continuously increasing amounts of data have led to the emergence of distributed in-memory computing systems for higher data processing speeds. Finding an efficient memory management method has become key to improving the performance of these systems because of the confined memory resource. Recent research in dependency-aware caching, e.g., Least Reference Count (LRC) and Least Composition Reference Count (LCRC), has enabled significant improvements in efficiency by profiling the application's Directed Acyclic Graph (DAG). However, these methods do not consider the dynamic occupancy mechanism of the Unified Memory Manager (UMM) in Spark, which could cause heavily referenced and costly data blocks to be evicted and produce high recomputing overhead. To alleviate this defect, we propose a resource-aware cache management approach that uses both runtime resource metrics and dependency information. By applying an adaptive approach, we can retain the data blocks that have greater contributions to obtain the final results. We demonstrate the effectiveness of our cache management approach through a series of widely used benchmarks. The experimental results show that compared with current DAG-aware implementations, our approach improves performance by an average of 15% and up to 24%. Compared with the default Least Recently Used (LRU) strategy in Spark, our implementation reduces job runtime by 28% on average and by up to 61%.
Bitcoin Crypto–Bounties for Quantum Capable Adversaries

With the advances in quantum computing taking place over the last few years, researchers have started considering the implications on cryptocurrencies. As most digital signature schemes would be impacted, it is somewhat reassuring that transition schemes to quantum resistant signatures are already being considered for Bitcoin. In this work, we stress the danger of public key reuse, as it prevents users from recovering their funds in the presence of a quantum enabled adversary despite any transition scheme the developers decide to implement. We emphasize this threat by quantifying the damage a functional quantum computer could inflict on Bitcoin (and Bitcoin Cash) by breaking exposed public keys.
Knowledge Deduction and Reuse Application to the Products’ Design Process

In this paper, we introduce a framework for knowledge reuse and deduction in mechanical products design and development. The proposed system effectively exploits the capitalized and inferred knowledge. To this end, we settled up an ontology dealing with the design process of mechanical products such as “the car”. The ontology-based framework is supported by a software tool that brings an automatic and personalized assistance to correspondent actors using the deduction process. Indeed, the systems provides the relevant knowledge to the suitable users in order to facilitate their professional tasks considering their roles and collaboration. Experimental results have demonstrated the effectiveness of reusing knowledge during product development lifecycle.
Global Explanations with Local Scoring

Artificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these “black box” models grows, it is our responsibility to understand their inner working and formulate them in human-understandable explanations. To this end, we propose a rule-based model-agnostic explanation method that follows a local-to-global schema: it generalizes a global explanation summarizing the decision logic of a black box starting from the local explanations of single predicted instances. We define a scoring system based on a rule relevance score to extract global explanations from a set of local explanations in the form of decision rules. Experiments on several datasets and black boxes show the stability, and low complexity of the global explanations provided by the proposed solution in comparison with baselines and state-of-the-art global explainers.
Learning Graph Processes with Multiple Dynamical Models

Network-science related applications frequently deal with inference of spatio-temporal processes. Such inference tasks can be aided by a graph whose topology contributes to the underlying spatio-temporal dependencies. Contemporary approaches extrapolate dynamic processes relying on a fixed dynamical model, that is not adaptive to changes in the dynamics. Alleviating this limitation, the present work adopts a candidate set of graph-adaptive dynamical models with one active at any given time. Given partially observed nodal samples, a scalable Bayesian tracker is leveraged to infer the graph processes and learn the active dynamical model simultaneously in a data-driven fashion. The resulting algorithm is termed graph-adaptive interacting multiple dynamical models (Grad-IMDM). Numerical tests with synthetic and real data corroborate that the proposed Grad-IMDM is capable of tracking the graph processes and adapting to the dynamical model that best fits the data.
Virtual Reality in Health Care

This paper presents a prime aspect of Augmented and Virtual Reality development in the field of healthcare. We explored several recent works and articles and a comparison between generic application development and immersive technology-based application is included. The paper talks about more practical approaches that can be taken to enhance the effectiveness of the application. The resources (infrastructure) to complete this study are provided by the University of Cincinnatis Center for Simulation and Virtual Environment Research (UCSIM). And several experiments and projects in the field of health care are used as a reference to make conclusions.
Value of Eye-Tracking Data for Classification of Information Processing–Intensive Handling Tasks: Quasi-Experimental Study on Cognition and User Interface Design

In order to give a wide range of people the opportunity to ensure and support home care, one approach is to develop medical devices that are as user-friendly as possible. This allows nonexperts to use medical devices that were originally too complicated to use. For a user-centric development of such medical devices, it is essential to understand which user interface design best supports patients, caregivers, and health care professionals.Using the benefits of mobile eye tracking, this work aims to gain a deeper understanding of the challenges of user cognition. As a consequence, its goal is to identify the obstacles to the usability of the features of two different designs of a single medical device user interface. The medical device is a patient assistance device for home use in peritoneal dialysis therapy.A total of 16 participants, with a subset of seniors (8/16, mean age 73.7 years) and young adults (8/16, mean age 25.0 years), were recruited and participated in this study. The handling cycle consisted of seven main tasks. Data analysis started with the analysis of task effectiveness for searching for error-related tasks. Subsequently, the in-depth gaze data analysis focused on these identified critical tasks. In order to understand the challenges of user cognition in critical tasks, gaze data were analyzed with respect to individual user interface features of the medical device system. Therefore, it focused on the two dimensions of dwell time and fixation duration of the gaze.In total, 97% of the handling steps for design 1 and 96% for design 2 were performed correctly, with the main challenges being task 1 insert, task 2 connect, and task 6 disconnect for both designs. In order to understand the two analyzed dimensions of the physiological measurements simultaneously, the authors propose a new graphical representation. It distinguishes four different patterns to compare the eye movements associated with the two designs. The patterns identified for the critical tasks are consistent with the results of the task performance.This study showed that mobile eye tracking provides insights into information processing in intensive handling tasks related to individual user interface features. The evaluation of each feature of the user interface promises an optimal design by combining the best found features. In this way, manufacturers are able to develop products that can be used by untrained people without prior knowledge. This would allow home care to be provided not only by highly qualified nurses and caregivers, but also by patients themselves, partners, children, or neighbors.
Sustainable blockchain technology in the maritime shipping industry

With the advent of blockchain-facilitated transactions, it is anticipated that major structural changes will occur in the maritime field. The impact of these changes is global; however, major transaction issues and standards still must be agreed upon and enacted. This chapter investigates the use of blockchain technology, its limitations, and the potential impact of blockchain on the maritime industry. We suggest that sustainability issues in the form of transaction attributes will need to be defined and accepted worldwide before blockchain can be fully realized as a significant step in development of the future shipping industry.