Reviewerly/exHarmony huggingface dataset:
Explaining any black box model using real data

In recent years the use of complex machine learning has increased drastically. These complex black box models trade interpretability for accuracy. The lack of interpretability is troubling for, e.g., socially sensitive, safety-critical, or knowledge extraction applications. In this paper, we propose a new explanation method, SLISE, for interpreting predictions from black box models. SLISE can be used with any black box model (model-agnostic), does not require any modifications to the black box model ( post-hoc ), and explains individual predictions (local). We evaluate our method using real-world datasets and compare it against other model-agnostic, local explanation methods. Our approach solves shortcomings in other related explanation methods by only using existing data instead of sampling new, artificial data. The method also generates more generalizable explanations and is usable without modification across various data domains.
Modified Hybrid Fuzzy Algebra: MF-Algebra

The principles of modified hybrid fuzzy algebras (MF-Algebra) are introduced in this study.The notions of hybrid fuzzy algebra and several properties related to these ideas are investigated.This work investigates the connections between fuzzy algebra and fuzzy subalgebra.
Gamified experiences using 360° photography

The emergence of 360° video and photography has provided new challenges regarding the exploitation of such technologies in various contexts with the most promising one being education. Combining 360° video with VR technology and gamified elements in interaction design has been proven to be beneficial from an educational perspective. This paper provides a methodology for gamified educational experiences using 360o- media, an example of its application and preliminary evaluation findings. The implemented application is a virtual treasure hunt game taking place within a public library that allows players to explore various dimensions of their city and cultural heritage through gamified concepts. The preliminary evaluation has provided positive comments regarding the efficiency of the presented approach and the simplicity of implementation making this approach promising for further exploitation by the Cultural and Creative Industries.
Discourse Mode Categorization of Bengali Social Media Health Text

The scarcity of annotated data is a major impediment to natural language processing (NLP) research in Bengali, a language that is considered low-resource.In particular, the health and medical domains suffer from a severe paucity of annotated data.Thus, this study aims to introduce BanglaSocialHealth, an annotated social media health corpus that provides sentence-level annotations of four distinct types of expression modes, namely narrative (NAR), informative (INF), suggestive (SUG), and inquiring (INQ) modes in Bengali.We provide details regarding the annotation procedures and report various statistics, such as the median and mean length of words in different sentence modes.Additionally, we apply classical machine learning (CML) classifiers and transformer-based language models to classify sentence modes.We find that most of the statistical properties are similar in different types of sentence modes.To determine the sentence mode, the transformerbased M-BERT model provides slightly better efficacy than the CML classifiers.Our developed corpus and analysis represent a muchneeded contribution to Bengali NLP research in medical and health domains and have the potential to facilitate a range of downstream tasks, including question-answering, misinformation detection, and information retrieval.
Machine learning algorithms to predict intraoperative hemorrhage in surgical patients: a modeling study of real-world data in Shanghai, China

Prediction tools for various intraoperative bleeding events remain scarce. We aim to develop machine learning-based models and identify the most important predictors by real-world data from electronic medical records (EMRs).An established database of surgical inpatients in Shanghai was utilized for analysis. A total of 51,173 inpatients were assessed for eligibility. 48,543 inpatients were obtained in the dataset and patients were divided into haemorrhage (N = 9728) and without-haemorrhage (N = 38,815) groups according to their bleeding during the procedure. Candidate predictors were selected from 27 variables, including sex (N = 48,543), age (N = 48,543), BMI (N = 48,543), renal disease (N = 26), heart disease (N = 1309), hypertension (N = 9579), diabetes (N = 4165), coagulopathy (N = 47), and other features. The models were constructed by 7 machine learning algorithms, i.e., light gradient boosting (LGB), extreme gradient boosting (XGB), cathepsin B (CatB), Ada-boosting of decision tree (AdaB), logistic regression (LR), long short-term memory (LSTM), and multilayer perception (MLP). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance.The mean age of the inpatients was 53 ± 17 years, and 57.5% were male. LGB showed the best predictive performance for intraoperative bleeding combining multiple indicators (AUC = 0.933, sensitivity = 0.87, specificity = 0.85, accuracy = 0.87) compared with XGB, CatB, AdaB, LR, MLP and LSTM. The three most important predictors identified by LGB were operative time, D-dimer (DD), and age.We proposed LGB as the best Gradient Boosting Decision Tree (GBDT) algorithm for the evaluation of intraoperative bleeding. It is considered a simple and useful tool for predicting intraoperative bleeding in clinical settings. Operative time, DD, and age should receive attention.
Pointwise Maximal Leakage

We introduce a privacy measure called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pointwise maximal leakage</i> , generalizing the pre-existing notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maximal leakage</i> , which quantifies the amount of information leaking about a secret <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> by disclosing a single outcome of a (randomized) function calculated on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> . Pointwise maximal leakage is a robust and operationally meaningful privacy measure that captures the largest amount of information leaking about <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> to adversaries seeking to guess arbitrary (possibly randomized) functions of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> , or equivalently, aiming to maximize arbitrary gain functions. We study several properties of pointwise maximal leakage, e.g., how it composes over multiple outcomes, how it is affected by pre-and post-processing, etc. Furthermore, we propose to view information leakage as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random variable</i> which, in turn, allows us to regard privacy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guarantees</i> as requirements imposed on different statistical properties of the information leakage random variable. We define several privacy guarantees and study how they behave under pre-processing, post-processing and composition. Finally, we examine the relationship between pointwise maximal leakage and other privacy notions such as local differential privacy, local information privacy, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> -information, and so on. Overall, our paper constructs a robust and flexible framework for privacy risk assessment whose central notion has a strong operational meaning which can be adapted to a variety of applications and practical scenarios.
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning

Large-scale neural networks possess considerable expressive power. They are well-suited for complex learning tasks in industrial applications. However, large-scale models pose significant challenges for training under the current Federated Learning (FL) paradigm. Existing approaches for efficient FL training often leverage model parameter dropout. However, manipulating individual model parameters is not only inefficient in meaningfully reducing the communication overhead when training large-scale FL models, but may also be detrimental to the scaling efforts and model performance as shown by recent research. To address these issues, we propose the Federated Opportunistic Block Dropout (FedOBD) approach. The key novelty is that it decomposes large-scale models into semantic blocks so that FL participants can opportunistically upload quantized blocks, which are deemed to be significant towards training the model, to the FL server for aggregation. Extensive experiments evaluating FedOBD against four state-of-the-art approaches based on multiple real-world datasets show that it reduces the overall communication overhead by more than 88% compared to the best performing baseline approach, while achieving the highest test accuracy. To the best of our knowledge, FedOBD is the first approach to perform dropout on FL models at the block level rather than at the individual parameter level.
Toward Best Practices for Training Multilingual Dense Retrieval Models

Dense retrieval models using a transformer-based bi-encoder architecture have emerged as an active area of research. In this article, we focus on the task of monolingual retrieval in a variety of typologically diverse languages using such an architecture. Although recent work with multilingual transformers demonstrates that they exhibit strong cross-lingual generalization capabilities, there remain many open research questions, which we tackle here. Our study is organized as a “best practices” guide for training multilingual dense retrieval models, broken down into three main scenarios: when a multilingual transformer is available, but training data in the form of relevance judgments are not available in the language and domain of interest (“have model, no data”); when both models and training data are available (“have model and data”); and when training data are available but not models (“have data, no model”). In considering these scenarios, we gain a better understanding of the role of multi-stage fine-tuning, the strength of cross-lingual transfer under various conditions, the usefulness of out-of-language data, and the advantages of multilingual vs. monolingual transformers. Our recommendations offer a guide for practitioners building search applications, particularly for low-resource languages, and while our work leaves open a number of research questions, we provide a solid foundation for future work.
Decentralized quantum anonymous veto voting scheme based on measurement-device-independence

Abstract To remove any trusted third party from the voting scheme, a new primitive quantum secure multiparty exclusive OR protocol based on Measurement-Device-Independence (MDI) is presented by utilizing entanglement swapping of Bell states. Based on this primitive protocol of quantum secure multiparty protocol, a novel quantum anonymous veto voting scheme is constructed, which can meet complete security attributes. Especially, this scheme does not need any third party to count the votes, while all voters can tally the voting results by themselves. At the same time, blockchain is introduced for supervision, so that the voters can verify the validity of the voting content under extreme circumstances. The voting scheme takes Bell states as quantum resources and only performs Bell states identification based on the principle of MDI, so it is feasible to implement this scheme with the present quantum information processing technologies. Finally, the proposed voting scheme is simulated in IBM Qiskit, and the simulation results show that this scheme is correct and feasible.
Artificial Intelligence In Cyber Security: Rescue Or Challenge

Objective: The objective of this research paper is to explore the potential of artificial intelligence (AI) in enhancing and fortifying cybersecurity measures. In light of the increasing cyber threats and the limitations of traditional computer security systems, the paper aims to shed light on the concept of AI and its components. The central goal is to demonstrate how AI can be leveraged to improve cybersecurity, focusing on key areas such as machine learning, data mining, in-depth learning, and expert programs.&#x0D; Method: This research paper utilizes a literature review approach to investigate the intersection of artificial intelligence and cybersecurity. It involves analyzing existing scholarly works and studies to understand the challenges posed by evolving cyber threats and the limitations of conventional security systems. The paper identifies various components of AI that have the potential to enhance cybersecurity measures and delineates their applicability in the digital landscape.&#x0D; Results: The analysis of past literature emphasizes the growing significance of adopting artificial intelligence techniques to bolster cybersecurity practices. The results underscore the inadequacies of traditional security systems in predicting and countering the ever-evolving tactics of cyber attackers and terrorists. Through an examination of various AI components, including machine learning, data mining, in-depth learning, and expert programs, the paper demonstrates the potential benefits that these technologies can offer to the field of cybersecurity.&#x0D; Conclusions: In conclusion, this research paper highlights the escalating threats to cybersecurity in the face of rapid technological advancement. The imperative of addressing these challenges through AI becomes evident as traditional security systems prove insufficient. By harnessing the power of artificial intelligence techniques such as machine learning, data mining, in-depth learning, and expert programs, institutions can significantly enhance their cybersecurity capabilities. The paper underscores the need for a paradigm shift towards AI-driven cybersecurity strategies to effectively predict, prevent, and counteract cyber threats in the digital age.
Semi-Supervised Heterogeneous Graph Learning with Multi-Level Data Augmentation

In recent years, semi-supervised graph learning with data augmentation (DA) has been the most commonly used and best-performing method to improve model robustness in sparse scenarios with few labeled samples. However, most existing DA methods are based on the homogeneous graph, but none are specific for the heterogeneous graph. Differing from the homogeneous graph, DA in the heterogeneous graph faces greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes. Furthermore, over-squashing of information is caused by the negative curvature formed by the non-uniformity distribution and the strong clustering in a complex graph. To address these challenges, this article presents a novel method named HG-MDA (Semi-Supervised Heterogeneous Graph Learning with Multi-Level Data Augmentation). For the problem of heterogeneity of information in DA, node and topology augmentation strategies are proposed for the characteristics of the heterogeneous graph. Additionally, meta-relation-based attention is applied as one of the indexes for selecting augmented nodes and edges. For the problem of over-squashing of information, triangle-based edge adding and removing are designed to alleviate the negative curvature and bring the gain of topology. Finally, the loss function consists of the cross-entropy loss for labeled data and the consistency regularization for unlabeled data. To effectively fuse the prediction results of various DA strategies, sharpening is used. Existing experiments on public datasets (i.e., ACM, DBLP, and OGB) and the industry dataset MB show that HG-MDA outperforms current SOTA models. Additionally, HG-MDA is applied to user identification in internet finance scenarios, helping the business to add 30% key users, and increase loans and balances by 3.6%, 11.1%, and 9.8%.
Algorithm 1038: KCC: A MATLAB Package for <i>k</i> -Means-based Consensus Clustering

Consensus clustering is gaining increasing attention for its high quality and robustness. In particular, k -means-based Consensus Clustering (KCC) converts the usual computationally expensive problem to a classic k -means clustering with generalized utility functions, bringing potentials for large-scale data clustering on different types of data. Despite KCC’s applicability and generalizability, implementing this method such as representing the binary dataset in the k -means heuristic is challenging and has seldom been discussed in prior work. To fill this gap, we present a MATLAB package, KCC, that completely implements the KCC framework and utilizes a sparse representation technique to achieve a low space complexity. Compared to alternative consensus clustering packages, the KCC package is of high flexibility, efficiency, and effectiveness. Extensive numerical experiments are also included to show its usability on real-world datasets.
Transactional Cross-Chain Smart Contract Invocations

Blockchains have become increasingly important in recent years and have expanded their applicability to many domains beyond finance and cryptocurrencies. This adoption has particularly increased with the introduction of smart contracts, which are immutable, user-defined programs directly deployed on blockchain networks. However, many scenarios require business transactions to simultaneously access smart contracts on multiple, possibly heterogeneous blockchain networks while ensuring the atomicity and isolation of these transactions, which is not natively supported by current blockchain systems. Therefore, in this work, we introduce the Transactional Cross-Chain Smart Contract Invocation (TCCSCI) approach that supports such distributed business transactions while ensuring their global atomicity and serializability. The approach introduces the concept of Resource Manager Smart Contracts, and 2PC for Blockchains (2PC4BC), a client-driven Atomic Commit Protocol (ACP) specialized for blockchain-based distributed transactions. We validate our approach using a prototypical implementation, evaluate its introduced overhead, and prove its correctness.
A Secured Framework for Blockchain Technology Adoption in IoT

In networking applications, security and privacy is an important concern and it is an ongoing problem in Internet of Things (IoT) applications. Many security mechanisms like biometrics, two factor authentications etc, were recommended for IoT device. Since Security is one of the important characteristics of Blockchain (BC) technology which is familiar for Bitcoin and Ethereum, it is applied in IoT for data protection and data privacy. In an organization, the IoT data is shared between the various personnel. IoT uses non standard computing devices to send and receive the data from the sensors and other devices. In such circumstances, it is to be verified to prevent any disputes or problems among the users of the organization. The proposed study investigates how blockchain technology provides security and transparency in IoT applications and also discusses the issues and challenges.
A biologically inspired approach for recovering the trajectory of offline handwriting

Reconstructing the trajectory from the static image of handwritten ink traces is useful in many practical applications envisaging handwriting analysis and recognition from offline data, as it allows the use of methods, algorithms, and tools that deal with online data, achieving better results than those achieved on offline data. In this work, the trajectory recovery is addressed by combining a general graph traversal algorithm with knowledge about the processes involved in human learning of motor skills to perform voluntary and complex movements. The effectiveness of the proposed approach has been quantitatively and extensively evaluated on large and publicly available datasets, containing English and French multi-stroke words and isolated characters. The experimental results show that our approach outperforms the existing ones in terms of Root Mean Square Error and Dynamic Time Warping distance between the recovered trajectories and the actual ones. Furthermore, an “off-the-shelf” online recognition system provided with the trajectory recovered from offline samples showed an overall reduction of 6.8% with respect to the recognition rate achieved by the system when provided with online data; the reduction, however, drops to 2.4% once preprocessing errors are not taken into account.
Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses

Extracting information from radio-frequency (RF) signals using artificial neural networks at low energy cost is a critical need for a wide range of applications from radars to health. These RF inputs are composed of multiple frequencies. Here, we show that magnetic tunnel junctions can process analog RF inputs with multiple frequencies in parallel and perform synaptic operations. Using a backpropagation-free method called extreme learning, we classify noisy images encoded by RF signals, using experimental data from magnetic tunnel junctions functioning as both synapses and neurons. We achieve the same accuracy as an equivalent software neural network. These results are a key step for embedded RF artificial intelligence.
Identification and Management of Technical Debt

Technical debt is a concept used to describe the lack of good practices during software development, leading to several problems and costs. Identification and management strategies can help reduce these difficulties. In a previous study, Alves et al. (2016) analyzed the research landscape of such strategies from 2010 to 2014. This paper replicates and updates their study to explore the evolution of technical debt identification and management research landscape over a decade, including literature from 2010 until 2022. We analyzed 117 papers from the ACM Digital Library, IEEE Xplore, Science Direct, and Springer Link. Newly suggested strategies include automatically identifying admitted debt in comments, commits, and source code. Between 2015 and 2022, more empirical evaluations have been performed, and the general research focus has changed to a more holistic approach. Therefore, the research area evolved and reached a new level of maturity compared to previous results from Alves et al. (2016). Not only are code aspects considered for technical debt, but other aspects have also been investigated (e.g., models for the development process).
Shape-adjusting mechanism for a heliostat reflector on a target-oriented mount

Concentrated solar power with a field of heliostats has the potential to reach temperatures as high as 1500C, as required for industrial processes. We plan to accomplish this using heliostats on target-oriented mounts, with a mirror surfaces whose toroidal shape is changed throughout the day to maintain a focused disc images of the sun on a receiver. We have built a simple mechanism that deforms a rectangular mirror without the expense of dedicated motors or computer control. The heliostat’s slewbearing has a dual function of orienting the reflector to direct sunlight to the tower and also twisting the mirror shape.