Reviewerly/exHarmony huggingface dataset:
Design and testing of silicon photonic 4F system for convolutional neural networks

Convolution Neural Networks have raised as the key technology for most of the novel applications that appear in the last years. Convolution, the main operation that CNN has to perform, has a high computational cost, raising power consumption and latency, especially for large matrices. Optics and photonics can perform the same operation at virtual O(1) cost and speed-of-light latency, thanks to the properties of Fourier optics. In this paper, we will show the implementation of the main components and the modeling for non-idealities that might occur.
Raven solver: From perception to reasoning

A higher level of machine intelligence is expected after the prosperity of deep learning in computer vision. The Raven's progressive matrices (RPM), for example, raises the requirements of learning from perception to reasoning. Previous solutions for RPM tend to build monolithic end-to-end models, whose excellent performances are of no comfort given that their interpretability and generalizability are far from satisfactory. With hindsight, we formalize RPM as a stratified problem, and propose an eclectic solver. To recapitulate, we explore the possibility of deep contrastive learning for interpretable feature extraction, deep regression with rule representation, and information pooling for reasoning and generalization. Our model achieves 94.63% and 92.90% in RAVEN and I-RAVEN datasets, and demonstrates interpretability in visual attribute learning. More than that, we also discover one potential defect of the original RAVEN dataset.
Identifying effective criteria for author matching in bioinformatics

With the increasing development of information and scientific databases, scientific collaboration has expanded in health sciences. This study aims to prioritize the criteria that affect finding potential author matches in bioinformatics using fuzzy Multiple Criteria Decision Making (MCDM) methods such as Analytical Hierarchy Process (AHP), Fuzzy Delphi Method (FDM), and Triangular Fuzzy Numbers (TFN). To answer the research questions, a mix of documentary analysis and fuzzy methods is utilized. The documentary analysis stage involves collecting relevant documents and resources using the purposive sampling approach and ranking the effective criteria. The subsequent step involves experts determining the priorities of the effective criteria using pairwise comparisons and the Delphi questionnaire. The final weights are obtained based on the research purpose. The study shows that 79 criteria related to the research purpose can be grouped into three general categories: behavioral, topological, and content-based criteria. The most effective criteria in finding and recommending a potential author match are “journal titles”, “citations”, “paper titles”, “affiliations”, “keywords”, and “abstracts”. Among these criteria, citation and paper titles have a higher priority compared to others. The results indicate that content-based criteria have the most significant impact on finding potential author matches in static scholar networks and networks with text information. Furthermore, among the content-based criteria, the number of publications in common specialized journals and the number of common citations are the most sought-after criteria for finding a potential author match with the highest similarity.
Formal Estimation of Collision Risks for Autonomous Vehicles: A Compositional Data-Driven Approach

In this work, we propose a compositional data-driven approach for the formal estimation of collision risks for autonomous vehicles (AVs) with black-box dynamics while acting in a stochastic multi-agent framework. The proposed approach is based on the construction of sub-barrier certificates for each stochastic agent via a set of data collected from its trajectories while providing an a priori guaranteed confidence on the data-driven estimation. In our proposed setting, we first cast the original collision risk problem for each agent as a robust optimization program (ROP). Solving the acquired ROP is not tractable due to an unknown model that appears in one of its constraints. To tackle this difficulty, we collect finite numbers of data from trajectories of each agent and provide a scenario optimization program (SOP) corresponding to the original ROP. We then establish a probabilistic bridge between the optimal value of SOP and that of ROP, and accordingly, we formally construct a sub-barrier certificate for each unknown agent based on the number of data and a required level of confidence. We then propose a compositional technique based on small-gain reasoning to quantify the collision risk for multi-agent AVs with some desirable confidence based on sub-barrier certificates of individual agents constructed from data. For the case that the proposed compositionality conditions are not satisfied, we provide a relaxed version of compositional results without requiring any compositionality conditions but at the cost of providing a potentially conservative collision risk. Eventually, we also present our approaches for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-stochastic</i> multi-agent AVs. We demonstrate the effectiveness of our proposed results by applying them to a vehicle platooning consisting of 100 vehicles with 1 leader and 99 followers. We formally estimate the collision risk for the whole network by collecting sampled data from trajectories of each agent.
Why is zero trust so difficult?

A zero-trust architecture offers significant security benefits and has become one of the most-discussed topics in information security. It's proven to offer high levels of breach prevention and has been shown to greatly reduce the costs of those organisations that are breached. So why is full adoption so slow, even among those organisations that have embraced the concept?
Ion trap quantum computing using integrated photonics

Atomic ions controlled by laser light are among the leading candidates for large scale quantum computing. However operational systems today require vast scaling to reach levels capable of useful computational tasks. The integration of light delivery will be an essential component of this for the trapped-ion approach. I will describe results in which the use of photonics integrated into ion trap chips has allowed us to perform high fidelity two-qubit gates, which are an essential building block for quantum computers. These systems have now been extended to the operation of multiple trap zones, and the creation of novel optical fields for ion trap control while a new generation of chips allowing integration of all required wavelengths as well as optimised light delivery offers further enhancements in performance and scaling.
A research landscape on software defect prediction

Abstract Software defect prediction is the process of identifying defective files and modules that need rigorous testing. In the literature, several secondary studies including systematic reviews, mapping studies, and review studies have been reported. However, no research work such as a tertiary study that combines secondary studies has focused on providing a landscape of software defect prediction useful to understand the body of knowledge. Motivated by this, we intend to perform a tertiary study by following a systematic literature review protocol to provide a research landscape of the targeted domain. We synthesize the quality of the secondary studies and investigate the employed techniques and the performance evaluation measures for evaluating the software defect prediction model. Furthermore, this study aims at exploring different datasets employed in the reported experimentation. Moreover, the current study intends at highlighting the research trends, gaps, and opportunities in the targeted research domain. The results indicate that none of the reported defect prediction techniques can be regarded as the best; however, the reported techniques performed better in different testing situations. In addition, machine learning (ML)‐based techniques perform better than traditional statistical techniques mainly due to the potential of discovering the defects and generating generalized results. Moreover, the obtained results highlight the need for further work in the domain of ML‐based techniques. Furthermore, publicly available datasets should be considered for experimentation or replication purposes. The potential future work can focus on data quality, ethical ML, cross‐project defect prediction, early defect prediction process, class imbalance problem, and model overfitting.
Talk like me: Exploring the feedback speech rate regulation strategy of the voice user interface for elderly people

Voice user interface (VUI) is widely used in developing intelligent products due to its low learning cost. However, most of such products do not consider the cognitive and language ability of elderly people, which leads to low interaction efficiency, poor user experience, and unfriendliness to them. Firstly, the paper analyzes the factors which influence the voice interaction behavior of elderly people: speech rate of elderly people, dialog task type, and feedback word count. And then, the voice interaction simulation experiment was designed based on the wizard of Oz testing method. Thirty subjects (M = 61.86 years old, SD = 7.16; 15 males and 15 females) were invited to interact with the prototype of a voice robot through three kinds of dialog tasks and six configurations of the feedback speech rate. Elderly people's speech rates at which they speak to a person and to a voice robot, the feedback speech rates they expected for three dialog tasks were collected. The correlation between subjects' speech rate and the expected feedback speech rate, the influence of dialog task type, and feedback word count on elderly people's expected feedback speech rate were analyzed. The results show that elderly people speak to a voice robot with a lower speech rate than they speak to a person, and they expected the robot feedback speech rate to be lower than the rate they speak to the robot. There is a positive correlation between subjects' speech rate and the expected speech rate, which implies that elderly people with faster speech rates expected a faster feedback speech rate. There is no significant difference between the elderly people's expected speech rate for non-goal-oriented and goal-oriented dialog tasks. Meanwhile, a negative correlation between the feedback word count and the expected feedback speech rate is found. This study extends the knowledge boundaries of VUI design by investigating the influencing factors of voice interaction between elderly people and VUI. These results also provide practical implications for developing suitable VUI for elderly people, especially for regulating the feedback speech rate of VUI.
Transfer learning on stratified data: joint estimation transferred from strata

This paper studies the target model with the help of auxiliary models from different but possibly related groups. Inspired by transfer learning, we propose a method called joint estimation transferred from strata (JETS). To obtain a sparse solution, JETS constructs a penalized framework combining a term that penalizes the target model and an additional term that penalizes the differences between auxiliary models and the target model. In this way, JETS overcomes the challenge caused by the limited samples in high-dimensional study, and obtains stable and accurate estimates regardless of whether auxiliary samples contain noisy information. We demonstrate that this method enjoys the computational advantage of the traditional methods such as the lasso. During simulations and applications, the proposed method is compared with several existing methods and JETS outperforms others.
An adaptive loss backward feature elimination method for class-imbalanced and mixed-type data in medical diagnosis

Feature selection or elimination methods have been widely used to mine high-dimensional data from many fields such as biomedicine. However, classical feature selection methods usually focus on class-balanced and numerical data, and the classification performance is poor when processing class-imbalanced or mixed-type data. Moreover, existing studies hardly consider the co-occurrence of both class-imbalance and mixture of numerical and nominal features. In this paper, we propose an adaptive loss backward feature elimination method for class-imbalanced and mixed-type data (FEIM). In FEIM, a technique to convert and decompose nominal features is employed to process mixed-type data. Meanwhile, an adaptive loss function is proposed to make the classifier pay more attention to the minority class. In experiments, the effectiveness and stability of FEIM are synthetically evaluated against seven medical diagnostic datasets in terms of the measurements of F measurement and precision, and the results of feature selection are analyzed in detail. The experimental results show that, compared with three common methods (Recursive Feature Elimination SVM (RFE-SVM), Relief, and Holdout Algorithm for Backward Feature Elimination (HO-BFE)), FEIM can achieve a higher classification accuracy and is more stable when the parameters are changed. On the other hand, the feature selection results of FEIM are more meaningful by processing class-imbalanced and mixed-type data. Therefore, FEIM is a competitive and effective method to process feature elimination for class-imbalanced and mixed-type biomedical data for in medical diagnosis. The supporting information is available in the electronic supplementary material.
Multilingual Knowledge Graph Completion Based on Structure Features of the Dual-Branch

With the development of information fusion, knowledge graph completion tasks have received a lot of attention.​ some studies investigate the broader underlying problems of linguistics, while embedding learning has a narrow focus.​This poses significant challenges due to the heterogeneity of coarse-graining patterns. Then, to settle the whole matter,​ a framework for completion is designed, named Triple Encoder-Scoring Module (TEsm). The model employs an alternating two-branch structure that fuses local features into the interaction pattern of the triplet itself by perfectly combining distance and structure models. Moreover, it is mapped to a uniform shared space. Upon completion, an ensemble inference method is proposed to query multiple predictions from different graphs using a weight classifier. Experiments show that the experimental dataset used for the completion task is DBpedia, which contains five different linguistic subsets..​Our extensive experimental results demonstrate that TEsm can efficiently and smoothly solve the optimal completion task, validating the performance of the proposed model.​
Efficient design of a quantum absolute-value circuit using Clifford+T gates

Abstract Current quantum computers have a limited number of resources and are heavily affected by internal and external noise. Therefore, small, noise-tolerant circuits are of great interest. With regard to circuit size, it is especially important to reduce the number of required qubits. Concerning to fault-tolerance, circuits entirely built with Clifford+T gates allow the use of error correction codes. However, the T-gate has an excessive cost, so circuits with a high number of T-gates should be avoided. This work focuses on optimising in such terms an operation that is widely used in larger circuits and algorithms: the calculation of the absolute-value of two’s complement encoded integers. The proposed circuit halves the number of required T gates with respect to the best circuit currently available in the literature. Moreover, our circuit requires at least 2 qubits less than the other circuits for such an operation.
Bounds on the differential uniformity of the Wan-Lidl polynomials

We study the differential uniformity of the Wan-Lidl polynomials over finite fields. A general upper bound, independent of the order of the field, is established. Additional bounds are established in settings where one of the parameters is restricted. In particular, we establish a class of permutation polynomials which have differential uniformity at most 5 over fields of order 3 mod 4, irrespective of the field size. Computational results are also given.
Calibrating single-qubit gates by a two-dimensional Rabi oscillation

We present and analyze a simple scheme to calibrate single–qubit gates. It determines the amplitude and phase difference between a quadrature pair of drives, as well as their common detuning from the qubit resonance. The method is based on a two–dimensional Rabi oscillation, a sequence of two pulses of varying length sourced from the drive pair. We demonstrate error diagnosis using this scheme on an ensemble of nitrogen-vacancy centers in diamond and point out subtle pitfalls in its implementation.
Brace for an uptick in insider fraud

Computer Fraud & SecurityVol. 2023, No. 3 FeaturesBrace for an uptick in insider fraudOmri KletterOmri KletterBottomlineSearch for more papers by this authorOmri KletterPublished Online:17 Mar 2023https://doi.org/10.12968/S1361-3723(23)70012-2AboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References 1. ‘2022 Cost of Insider Threats’. Ponemon Institute/Proofpoint. Accessed Mar 2023. www.proofpoint.com/us/resources/threat-reports/cost-of-insider-threats. Google Scholar2. Grotens, Ruud. ‘Themis report finds technology gap in fight against insider fraud’. Bottomline, 10 Mar 2022. Accessed Mar 2023. www.bottomline.com/thought-leadership/fraud-and-financial-crime/themis-report-finds-technology-gap-fight-against-insider-fraud. Google Scholar FiguresReferencesRelatedDetails 1 March 2023Volume 2023Issue 3ISSN (print): 1361-3723ISSN (online): 1873-7056 Metrics History Published online 17 March 2023 Published in print 1 March 2023 Information© MA Business Ltd.
Now is the time to invest in cyber

Cyber threats are not new, but the scale of the attacks we're seeing and the damage they are causing is unprecedented. If you haven't already put in place cyber defences, your organisation is at risk and may be facing an existential threat. This means that everyone who has been putting off investing in information security solutions needs to reconsider – and quickly.
Getting ahead of the attackers

Security is often reactive – responding to new forms of attack and trying to patch vulnerabilities before threat actors exploit them. It often seems difficult to get ahead of the curve, to secure the organisation in a considered and systematic way, rather than just firefighting the latest disaster.
Analysis and Synthesis of Technology for Textual Information Classification

The task of developing effective text information classification systems requires the thoughtful analysis and synthesis of variable components of technology. These components strongly affect the practical efficiency and the requirements to the data. For this purpose, a typical technology was discussed, comparing the regular “learning from features” approach versus the more advanced “deep learning” approach, that studies from data. In order to implement the technology, the first approach was tested, which included the means (methods, algorithms) for analysis of the features of the source text, by applying the dimensionality transformation, and building model solutions that allow the correct classification of data by a set of features. As a result, all the steps of the technology are described, which allowed to determine the way of presenting data in terms of hidden features in data, their presentation in a standard visual form and evaluate the solution, as well as its practical efficiency, based on this set of features. In a depth study, the informational core of the document was studied, using the regression and T-stochastic grouping of features for dimensionality reduction.The separate results contain estimation of practical efficiency of the algorithms in terms of time and relative performance for each step of the proposed technology. This estimation gives a possibility to obtain the best algorithm of intelligent data processing that is useful for a given dataset and application. In order to estimate the best suited algorithm for separation in reduced dimension an experiment was carried out which allowed the selection of the best range of data classification algorithms, in particular boosting methods. As a result of the analysis of the technology, the necessary steps of this technology were discussed and the classification on real text data was conducted, which allowed to identify the most important stages of the technology for text classification.
Concatenated deleting machines and their characteristics

Abstract In this work, we study the performance of one shot and concatenated deleting machines (DMs). We show that the output state of one shot DMs are mixed inseparable, and do not violate Bell’s inequality but can be used as a teleportation channel for all values of the input state parameters. On the other hand, we observe in the concatenation of different DMs that the output states are mixed inseparable and do not violate Bell’s inequality, and cannot be used as a teleportation channel. Further, some important attributes such as inseparability, violation of Bell’s inequality, and teleportation fidelity of the DMs remain unchanged under the order of concatenation. In this context of a teleportation channel, one shot DMs are useful when compared to concatenated DMs.
Making Practices in Pursuit of Ecological Ethos: Learnings from Three Ecovillages in Australia

Ecovillages, communities in which people integrate self-built houses, shared facilities, organic farming, composting, and self-governance into everyday practices, serve as laboratories for ongoing experiments in sustainable living. While topics like sustainability, simple living, and making are well discussed in the HCI community, we aim to investigate what socio-environmental issues are identified in ecovillages and how and with which ethos makers address these issues in their making practices. In this paper, we present findings from an ethnographic investigation around sustainable making by focusing on the endeavours of three ecovillages and highlighting their ethos to draw out lessons for HCI researchers. We discuss the relationship between making practices and sustainability from four aspects: space, material, network, and meaning, and provide lessons and implications for the HCI community.
On the Ecological and Internal Rationality of Bayesian Conditionalization and Other Belief Updating Strategies

Previous articleNext article No AccessOn the Ecological and Internal Rationality of Bayesian Conditionalization and Other Belief Updating StrategiesOlav Benjamin VassendOlav Benjamin Vassend Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by The British Journal for the Philosophy of Science Just Accepted Society: The British Society for the Philosophy of Science Views: 5Total views on this site Views: 5Total views on this site Article DOIhttps://doi.org/10.1086/724447 Views: 5Total views on this site HistoryAccepted December 11, 2022 © 2023 The British Society for the Philosophy of Science. All Rights reserved.PDF download Crossref reports no articles citing this article.