Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Continued Integrity Service for IoT Big Data Management: A Comprehensive Design

The state-of-the-art centralized Internet of Things (IoT) data flow pipeline has started aging since it cannot cope with the vast number of newly connected IoT devices. As a result, the community begins the transition to a decentralized pipeline to encourage data and resource sharing. However, the move is not trivial. With many instances allocating data or service arbitrarily, how can we guarantee the correctness of IoT data or processes that other parties offer. Furthermore, in case of dispute, how can the IoT data assist in determining which party is guilty of faulty behavior. Finally, the number of Service Level Agreement (SLA) increases as the number of sharing grows. The problem then becomes how we can provide a natural SLA generation and verification that we can automate instead of going through a manual and tedious legalization process through a trusted third party. In this paper, we explore blockchain solutions to answer those issues and propose continued data integrity services for IoT big data management. Specifically, we design five integrity protocols across three phases of IoT operations—during the transmission of IoT data (data in transit), when we physically store the data in the database (data at rest), and at the time of data processing (data in process). In each phase, we first lay out our motivations and survey the related blockchain solutions from the literature. We then use curated papers from our surveys as building blocks in designing the protocol. Using our proposal, we augment the overall value of IoT data and commands, generated in the IoT system, as they are now tamper-proof, verifiable, non-repudiable, and more robust.
A Type-2 Fuzzy Logic Approach to Explainable AI for regulatory compliance, fair customer outcomes and market stability in the Global Financial Sector

The field of Artificial Intelligence (AI) is enjoying unprecedented success and is dramatically transforming the landscape of the financial services industry. However, there is a strong need to develop an accountability and explainability framework for AI in financial services, based on a risk-based assessment of appropriate explainability levels and techniques by use case and domain. This paper proposes a risk management framework for the implementation of AI in banking with consideration of explainability and outlines the implementation requirements to enable AI to achieve positive outcomes for financial institutions and the customers, markets and societies they serve. The work presents the evaluation of three algorithmic approaches (Neural Networks, Logistic Regression and Type 2 Fuzzy Logic with evolutionary optimisation) for nine banking use cases. We review the emerging regulatory and industry guidance on ethical and safe adoption of AI from key markets worldwide and compare leading AI explainability techniques. We will show that the Type-2 Fuzzy Logic models deliver very good performance which is comparable to or lagging marginally behind the Neural Network models in terms of accuracy, but outperform all models for explainability, thus they are recommended as a suitable machine learning approach for use cases in financial services from an explainability perspective. This research is important for several reasons: (i) there is limited knowledge and understanding of the potential for Type-2 Fuzzy Logic as a highly adaptable, high performing, explainable AI technique; (ii) there is limited cross discipline understanding between financial services and AI expertise and this work aims to bridge that gap; (iii) regulatory thinking is evolving with limited guidance worldwide and this work aims to support that thinking; (iv) it is important that banks retain customer trust and maintain market stability as adoption of AI increases.
Trust management of services (<scp>TMoS</scp>): Investigating the current mechanisms

Abstract Generally, service interactions in dynamic environments comprising cloud computing, Internet of Things (IoT), and etc. occur in an unspecified situation causing trust management a significant aspect. The widespread usage of services in today's vastly high‐tech world reveals the rising necessity of trust management in terms of services. In dynamic environments, there are not enough confidences for users to recognize trustworthy service providers. Hence, evaluating and managing the trustworthiness of services is a vital challenge for enabling customers to select trustworthy resources in dynamic environments. To the extent of our knowledge, in spite of the vital role of Trust Management of Services (TMoS), there is not any thorough and systematic work in this scope with a specific focus on services. Thus, this research investigates the current methods of trust management in terms of services, which are published up to February 2020. We have identified 68 papers that are diminished to 22 mostly qualified papers through the article selection process. Also, we have examined and compared the selected papers in terms of their merits and demerits, considering the important parameters in this field. The investigation results point out that the security, scalability, and dynamicity are essential to almost all research. Furthermore, privacy and reliability are the most essential parameters in the field of TMoS. Moreover, the statistical results and information can contribute to future works; also, we have conducted some open issues and future work suggestions that could be an effective roadmap for future researchers in this area.
Watch Out for Extrinsic Bugs! A Case Study of Their Impact in Just-In-Time Bug Prediction Models on the OpenStack Project

Intrinsic bugs are bugs for which a bug introducing change can be identified in the version control system of a software. In contrast, extrinsic bugs are caused by external changes to a software, such as errors in external APIs; thereby they do not have an explicit bug introducing change in the version control system. Although most previous research literature has assumed that all bugs are of intrinsic nature, in a previous study, we show that not all bugs are intrinsic. This paper shows an example of how considering extrinsic bugs can affect software engineering research. Specifically, we study the impact of extrinsic bugs in Just In Time bug prediction by partially replicating a recent study by McIntosh and Kamei on JIT models. These models are trained using properties of earlier bug-introducing changes. Since extrinsic bugs do not have bug introducing changes in the version control system, we manually curate McIntosh and Kamei's dataset to distinguish between intrinsic and extrinsic bugs. Then, we address their original research questions, this time removing extrinsic bugs, to study whether bug-introducing changes are a moving target in Just-In-Time bug prediction. Finally, we study whether characteristics of intrinsic and extrinsic bugs are different. Our results show that intrinsic and extrinsic bugs are of different nature. When removing extrinsic bugs the performance is different up to 16 % Area Under the Curve points. This indicates that our JIT models obtain a more accurate representation of the real world. We conclude that extrinsic bugs negatively impact Just-In-Time models. Furthermore, we offer evidence that extrinsic bugs should be further investigated, as they can significantly impact how software engineers understand bugs.
A Novel Center-based Differential Evolution Algorithm

Differential Evolution (DE) algorithm has been shown notable performance in solving complex optimization problems. In recent years, some variants of the DE algorithm have been proposed based on the concept of center-based sampling strategy. To the best of our knowledge, the related papers employed center-based sampling for population initialization or as the base vector in mutation operator. In fact, they were operation-level approaches applied during the optimization process, and none of them was about proposing a population-level approach to utilize center-based sampling to accelerate convergence rate of algorithms. This paper proposes a novel center-based sampling scheme for the DE algorithm that utilizes center-based sampling as a member of the population. In our scheme, one candidate solution is the center of the best candidate solutions, while other individuals in the population behave similarly to the standard DE algorithm. The center-based candidate solution is not updated using standard operators and is set to the center in each iteration. To validate our scheme, we benchmark our algorithm on CEC-2017 benchmark functions with three dimensions of 30, 50, and 100. Also, we design some experiments to analyze the behavior of the proposed center-based scheme. Our experiments demonstrate a significant improvement of the proposed algorithm on the majority of benchmark functions.
Enhanced synchronization-inspired clustering for high-dimensional data

Abstract The synchronization-inspired clustering algorithm (Sync) is a novel and outstanding clustering algorithm, which can accurately cluster datasets with any shape, density and distribution. However, the high-dimensional dataset with high dimensionality, high noise, and high redundancy brings some new challenges for the synchronization-inspired clustering algorithm, resulting in a significant increase in clustering time and a decrease in clustering accuracy. To address these challenges, an enhanced synchronization-inspired clustering algorithm, namely SyncHigh, is developed in this paper to quickly and accurately cluster the high-dimensional datasets. First, a PCA-based (Principal Component Analysis) dimension purification strategy is designed to find the principal components in all attributes. Second, a density-based data merge strategy is constructed to reduce the number of objects participating in the synchronization-inspired clustering algorithm, thereby speeding up clustering time. Third, the Kuramoto Model is enhanced to deal with mass differences between objects caused by the density-based data merge strategy. Finally, extensive experimental results on synthetic and real-world datasets show the effectiveness and efficiency of our SyncHigh algorithm.
Simultaneous measurement of three-dimensional deformation based on digital speckle pattern interferometry technology

In the present study, a brief, efficient and synchronous 3D (three-dimensional) deformation measurement method based on digital speckle pattern interferometry (DSPI) and digital shear speckle interference (DSSPI) was proposed. Comparing with traditional multi-laser and multi-CCD DSPI methods, this method used a single CCD and two lasers to capture the speckle patterns from three sensitive directions simultaneously, which simplified the optical arrangement, reduced the system cost, and improved the detection efficiency effectively. Based on the proposed method, the phase maps could be obtained by space carrier method, and the phase maps included both out-of-plane deformation and in-plane strain information (vertical in-plane, horizontal in-plane and out-of-plane). According to the deformation and strain information, the 3D deformation distribution could be obtained through an integral operation. In the present study, the measuring algorithm and optical arrangement were described in detail, and experiments were carried out to verify the validity.
Learning From Weakly Labeled Data Based on Manifold Regularized Sparse Model

In multilabel learning, each training example is represented by a single instance, which is relevant to multiple class labels simultaneously. Generally, all relevant labels are considered to be available for labeled data. However, instances with a full label set are difficult to obtain in real-world applications, thus leading to the weakly multilabel learning problem, that is, relevant labels of training data are partially known and many relevant labels are missing, and even abundant training data are associated with an empty label set. To address the problem, we propose a new multilabel method to learn from weakly labeled data. To be specific, an optimization framework is constructed based on the manifold regularized sparse model, in which the correlations among labels and feature structure are considered to model global and local label correlations, thereby achieving discriminative feature analysis for mapping training data to ground-truth label space. Moreover, the proposed method has an excellent mechanism to conduct semisupervised multilabel learning by exploiting training data with the predicted label set of the unlabeled. Experiments on various real-world tasks reveal that the proposed method outperforms some state-of-the-art methods.
A systematic mapping study of developer social network research

Developer social networks (DSNs) are a tool for the analysis of community structures and collaborations between developers in software projects and software ecosystems. Within this paper, we present the results of a systematic mapping study on the use of DSNs in software engineering research. We identified 255 primary studies on DSNs. We mapped the primary studies to research directions, collected information about the data sources and the size of the studies, and conducted a bibliometric assessment. We found that nearly half of the research investigates the structure of developer communities. Other frequent topics are prediction systems build using DSNs, collaboration behavior between developers, and the roles of developers. Moreover, we determined that many publications use a small sample size regarding the number of projects, which could be problematic for the external validity of the research. Our study uncovered several open issues in the state of the art, e.g., studying inter-company collaborations, using multiple information sources for DSN research, as well as general lack of reporting guidelines or replication studies.
Usability and user experience evaluation of natural user interfaces: a systematic mapping study

Natural user interface (NUI) is considered a recent topic in human–computer interaction (HCI) and provides innovative forms of interaction, which are performed through natural movements of the human body like gestures, voice, and gaze. In the software development process, usability and user eXperience (UX) evaluations are a relevant step, since they evaluate several aspects of the system, such as efficiency, effectiveness, user satisfaction, and immersion. Thus, the goal of the authors’ systematic mapping study (SMS) is to identify usability and UX evaluation technologies used by researchers and developers in software with NUIs. Their SMS selected 56 papers containing evaluation technologies for NUI. Overall, the authors identified 30 different usability and UX evaluation technologies for NUI. The analysis of these technologies reveals most of them are used to evaluate software in general, without considering the specificities of NUI. Besides, most technologies evaluate only one aspect, Usability or UX. In other words, these technologies do not consider Usability and UX together. For future work, they intend to develop an evaluation technology for NUIs that fills the gaps identified in their SMS and combining Usability and UX.
Adaptive Generalized Measurement for Unambiguous State Discrimination of Quaternary Phase-Shift-Keying Coherent States

Generalized quantum measurements identifying nonorthogonal states without ambiguity often play an indispensable role in various quantum applications. For such an unambiguous state-discrimination scenario, we have a finite probability of obtaining inconclusive results and minimizing the probability of inconclusive results is of particular importance. In this paper, we experimentally demonstrate an adaptive generalized measurement that can unambiguously discriminate the quaternary phase-shift-keying coherent states with a near-optimal performance. Our scheme is composed of displacement operations, single-photon detections and adaptive control of the displacements dependent on a history of photon-detection outcomes. Our experimental results show a clear improvement of both a probability of conclusive results and a ratio of erroneous decision caused by unavoidable experimental imperfections over conventional static generalized measurements.2 MoreReceived 9 September 2020Revised 10 March 2021Accepted 25 March 2021DOI:https://doi.org/10.1103/PRXQuantum.2.020305Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptical quantum information processingQuantum communicationQuantum information processingQuantum Information
Comprehensive Evaluation of Machine Learning Countermeasures for Detecting Microarchitectural Side-Channel Attacks

Microarchitectural Side-Channel Attacks (SCAs) have posed serious threats to the security of modern computing systems. Such attacks exploit side-channel vulnerabilities stemming from fundamental performance-enhancing components such as cache memories. The existing works on detection of SCAs based on low-level microarchitectural features have considered collecting both victim and attack applications' hardware events that are captured from processors' hardware performance counter (HPC) registers. However, in such techniques the attack HPCs data can be easily manipulated and/or corrupted resulting in misleading the SCAs detection mechanism. In addition, the prior studies have explored the suitability of a limited number of Machine Learning (ML) algorithms in detecting microarchitectural SCAs. In response, in this paper, we conduct a comprehensive evaluation of various machine learning-based countermeasures for real-time side-channel attack detection based on low-level microarchitectural features. For this purpose, the victim applications' behavior is collected using the HPC features and analyzed under no attack and attack conditions to avoid potential manipulation of attackers' HPCs. We further explore the HPCs monitoring overhead when microarchitectural features are sampled at different intervals to find out the appropriate sampling interval for SCAs detection. For the purpose of thorough analysis, various types of ML classifiers are implemented and precisely compared across different evaluation metrics including detection accuracy, F-measure, robustness (Area Under the ROC Curve), and computational latency to identify the most efficient ML classifiers for real-time microarchitectural SCAs detection
Capacity of Quantum Private Information Retrieval With Multiple Servers

We study the capacity of quantum private information retrieval (QPIR) with multiple servers. In the QPIR problem with multiple servers, a user retrieves a classical file by downloading quantum systems from multiple servers each of which contains the copy of a classical file set while the identity of the downloaded file is not leaked to each server. The QPIR capacity is defined as the maximum rate of the file size over the whole dimension of the downloaded quantum systems. When the servers are assumed to share prior entanglement, we prove that the QPIR capacity with multiple servers is 1 regardless of the number of servers and files. We construct a rate-one protocol only with two servers. This capacity-achieving protocol outperforms its classical counterpart in the sense of capacity, server secrecy, and upload cost. The strong converse bound is derived concisely without using any secrecy condition. We also prove that the capacity of multi-round QPIR is 1.
Psychiatric Interventions in Virtual Reality: Why We Need an Ethical Framework

Recent improvements in virtual reality (VR) allow for the representation of authentic environments and multiple users in a shared complex virtual world in real time. These advances have fostered clinical applications including in psychiatry. However, although VR is already used in clinical settings to help people with mental disorders (e.g., exposure therapy), the related ethical issues require greater attention. Based on a thematic literature search the authors identified five themes that raise ethical concerns related to the clinical use of VR: (1) reality and its representation, (2) autonomy, (3) privacy, (4) self-diagnosis and self-treatment, and (5) expectation bias. Reality and its representation is a theme that lies at the heart of VR, but is also of specific significance in a clinical context when perceptions of reality are concerned, for example, during psychosis. Closely associated is the autonomy of VR users. Although autonomy is a much-considered topic in biomedical ethics, it has not been sufficiently discussed when it comes to applications of VR in psychiatry. In this review, the authors address the different themes and recommend the development of an ethical framework for the clinical use of VR.
Deep Learning for Intake Gesture Detection From Wrist-Worn Inertial Sensors: The Effects of Data Preprocessing, Sensor Modalities, and Sensor Positions

Wrist-worn inertial measurement units have emerged as a promising technology to passively capture dietary intake data. State-of-the-art approaches use deep neural networks to process the collected inertial data and detect characteristic hand movements associated with intake gestures. In order to clarify the effects of data preprocessing, sensor modalities, and sensor positions, we collected and labeled inertial data from wrist-worn accelerometers and gyroscopes on both hands of 100 participants in a semi-controlled setting. The method included data preprocessing and data segmentation, followed by a two-stage approach. In Stage 1, we estimated the probability of each inertial data frame being intake or non-intake, benchmarking different deep learning models and architectures. Based on the probabilities estimated in Stage 1, we detected the intake gestures in Stage 2 and calculated the F1 score for each model. Results indicate that top model performance was achieved by a CNN-LSTM with earliest sensor data fusion through a dedicated CNN layer and a target matching technique (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> = .778). As for data preprocessing, results show that applying a consecutive combination of mirroring, removing gravity effect, and standardization was beneficial for model performance, while smoothing had adverse effects. We further investigate the effectiveness of using different combinations of sensor modalities (i.e., accelerometer and/or gyroscope) and sensor positions (i.e., dominant intake hand and/or non-dominant intake hand).
AMR Similarity Metrics from Principles

Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical Smatch metric (Cai and Knight, 2013 ) aligns the variables of two graphs and assesses triple matches. The recent SemBleu metric (Song and Gildea, 2019 ) is based on the machine-translation metric Bleu (Papineni et al., 2002 ) and increases computational efficiency by ablating the variable-alignment. In this paper, i) we establish criteria that enable researchers to perform a principled assessment of metrics comparing meaning representations like AMR; ii) we undertake a thorough analysis of Smatch and SemBleu where we show that the latter exhibits some undesirable properties. For example, it does not conform to the identity of indiscernibles rule and introduces biases that are hard to control; and iii) we propose a novel metric S 2 match that is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. We assess its suitability and show its advantages over Smatch and SemBleu.