Reviewerly/exHarmony huggingface dataset:
Concept drift detection based on decision distribution in inconsistent information system

Concept drift has been a widely concerned problem in data analysis and many important achievements have been obtained. However, there are few discussions on concept drift detection in inconsistent information system that is very common in various application fields. In this paper, a concept drift detection algorithm based on the decision distribution (IDP-DD) is proposed. To begin, we present the notion of decision distribution function by means of inclusion degree and delineate the decision empirical vector. Subsequently, we expound upon decision empirical distance and analyze the limits characteristics of sampling; thirdly, we display the algorithm implementation process of IDP-DD. Lastly, we evaluate the efficacy of IDP-DD with a some commonly employed datasets and a real-word example dataset. Theory analysis and experiment verification indicate that the algorithm IDP-DD can not only quantify the severity of concept drift, but also visually present the region where concept drift occurs. At the same time, combined with sampling method, this algorithm does not need to traverse all samples at one time, so the complexity and calculation cost can be effectively reduced. So IDP-DD is more stable and can effectively facilitate data-driven decision problems.
A meta-analysis on learning from 360° video

Learners are increasingly experiencing 360° videos across educational environments, including within spherical video virtual reality (SVVR) environments. Although 360° videos may offer an immersive experience, it is not yet clear if they offer any broadly generalizable learning benefits compared to other conditions. In this study, we conducted a three-level meta-analysis to examine the effects of 360° videos on learning compared to other learning conditions. Results show no significant benefit nor detriment from learning with 360° videos. We also found few factors which significantly moderated the effect. We conclude that if supporting cognitive learning outcomes is the goal, as opposed to affective or perceptive outcomes, there are likely other technologies that may be more resource efficient for supporting learning. Additional implications for research and practice are discussed.
Within- cross- consensus-view representation-based multi-view multi-label learning with incomplete data

This article develops a multi-view multi-label learning for incomplete data which are ubiquitous with the usage of three kinds of representations including within-view representation, cross-view representation, and consensus-view representation. Different from the recent learning machines, the proposed learning machine takes the feature-oriented information, label-oriented information, and associated information between features and labels in multiple representations together and exploits the hidden useful information of available instances with the usage of instance–instance correlations, feature–feature correlations, label-label correlations, and feature–label correlations. The developed learning machine is named as within- cross- consensus-view representation-based multi-view multi-label learning with incomplete data (WCC-MVML-ID). Extensive experiments on multiple multi-view and multi-label data sets with incomplete data validate the effectiveness of WCC-MVML-ID and it can be concluded that (1) WCC-MVML-ID outperforms other compared learning machines and its performances are more stable even though the missing rates of features and labels being larger; (2) compared with within-view information and consensus-view information, cross-view information is more useful for the processing problem about incomplete data; (3) WCC-MVML-ID can converge within 45 iterations.
Coping with Hate: Exploring Indian Journalists’ Responses to Online Harassment

ABSTRACTIn India, the ruling Bharatiya Janata Party (BJP) employs a digital army of right-wing supporters to harass journalists who are critical of the party's Hindu-nationalist ideology. As a result, the country's press freedom rankings have significantly declined over the past decade. While scholars have examined the discursive strategies used by online Hindu nationalists against journalists, how reporters cope with these attacks remains unknown. This study investigates how Indian media professionals perceive the personal and professional consequences of harassment for themselves and the coping strategies they use to combat digital hate. In-depth interviews with 24 journalists reveal that they employ mechanisms such as strategic social disconnection, formation of alliance networks, and working for non-profit news sites and international media organizations to overcome the effects of social media harassment. The analysis also highlights that commercial media that are subservient to the Modi government failed to protect their reporters, while independent media outlets provide much more support to their employees. The findings underscore the need for social media companies to promptly respond to content flagged by journalists and allocate additional resources to moderate hate speech in India's local languages to safeguard journalists from sustained online abuse.KEYWORDS: Narendra ModiHindu-nationalismhate speechjournalismIndiaonline harassment ‌ Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 The Sanskrit word Bhakt literally means “devotee.” The term has been used to refer to Narendra Modi’s supporters as Modi Bhakts—which implies devotees of Modi or Modi fans who blindly defend his actions and policies.2 Hindutva means Hinduness. It denotes the Hindu-nationalist ideology, which considers India a Hindu country (Hindu Rashtra).3 RSS is the most important Hindu-nationalist organization. Modi’s Bharatiya Janata Party (BJP) is the political offshoot of the RSS.4 Patrakar is a Hindi word for journalist.
Robustness of controlled Hamiltonian approaches to unitary quantum gates

We examine the effectiveness and resilience of achieving quantum gates employing three approaches stemming from quantum control methods: counterdiabatic driving, Floquet engineering, and inverse engineering. We critically analyze their performance in terms of the gate infidelity, the associated resource overhead based on energetic cost, the susceptibility to timekeeping errors, and the degradation under environmental noise. Despite significant differences in the dynamical path taken, we find a broadly consistent behavior across the three approaches in terms of the efficacy of implementing the target gate and the resource overhead. Furthermore, we establish that the functional form of the control fields plays a crucial role in determining how faithfully a gate operation is achieved. Our results are demonstrated for single-qubit gates, with particular focus on the Hadamard gate, and we discuss the extension to $N$-qubit operations.
Securing Medical Information Transmission Between IoT Devices: An Innovative Hybrid Encryption Scheme Based on Quantum Walk, DNA Encoding, and Chaos

The healthcare industry has undergone a transformation due to the widespread use of advanced communication technologies and wireless sensor networks such as the Internet of Medical Things (IoMT), Health Information Exchange Technology (HIET), Internet of Healthcare Things (IoHT) and Health IoT (HIoT). These technologies have led to an increase in the transmission of medical data, particularly medical imaging data, over various wireless communication channels. However, transmitting high-quality color medical images over insecure internet channels like the Internet and communication networks like 5G presents significant security risks that could threaten patients' data privacy. Furthermore, this process can also burden the limited bandwidth of the communication channel, leading to delayed data transmission. To address security concerns in healthcare data, researchers have focused a lot of attention on medical image encryption as a means of protecting patient data. This paper presents a color image encryption scheme that integrates multiple encryption techniques, including alternate quantum random walks, controlled Rubik's Cube transformations, and the integration of the Elliptic Curve Cryptosystem with Hill Cipher (ECCHC). The proposed scheme divides various plaintext images by creating a regular cube by layering planes of a fixed size. Each plane is rotated in an anticlockwise direction, followed by row, column and face swapping, and then DNA encoding is performed. The image cube encoded with DNA is combined with the chaotic cube through DNA addition, and a couple of random DNA sequences are chosen for DNA mutation. After undergoing DNA mutation, the encoded cube is then decoded using DNA. The proposed method has the theoretical capability of encrypting 2D images of unlimited size and number by utilizing an infinitely large cube. The proposed image encryption scheme has been rigorously tested through various experimental simulations and cyberattack analysis, which shows the efficiency and reliability of the proposed encryption scheme.
Multi-cell type and multi-level graph aggregation network for cancer grading in pathology images

In pathology, cancer grading is crucial for patient management and treatment. Recent deep learning methods, based upon convolutional neural networks (CNNs), have shown great potential for automated and accurate cancer diagnosis. However, these do not explicitly utilize tissue/cellular composition, and thus difficult to incorporate the existing knowledge of cancer pathology. In this study, we propose a multi-cell type and multi-level graph aggregation network (MMGA-Net) for cancer grading. Given a pathology image, MMGA-Net constructs multiple cell graphs at multiple levels to represent intra- and inter-cell type relationships and to incorporate global and local cell-to-cell interactions. In addition, it extracts tissue contextual information using a CNN. Then, the tissue and cellular information are fused to predict a cancer grade. The experimental results on two types of cancer datasets demonstrate the effectiveness of MMGA-Net, outperforming other competing models. The results also suggest that the information fusion of multiple cell types and multiple levels via graphs is critical for improved pathology image analysis.
Human Fall Detection Based on Posture Estimation and Infrared Thermography

Fall detection holds significant practical value in the fields of medicine and security. In this study, we propose a pose-based human fall detection system that incorporates an infrared thermal imaging camera to overcome challenges faced during nocturnal and poorly illuminated scenarios. To facilitate fall detection, we have integrated a classification function into the YOLOv7POSE network. Furthermore, the introduction of a simple attention mechanism (SimAM) to the backbone network substantially enhances its capability to discern human features. In terms of image processing, we optimized the ConvNeXt structure and integrated it into the backbone network to improve the accuracy of keypoint detection and fall classification. Our enhancements also encompass the redesign of the spatial pyramid pool structure within the head layer, resulting in improved model accuracy. We also replaced the regression function of the target detection framework with scylla intersection over union (SIOU) and correspondingly adjusted the calculation formula to enhance the accuracy of coordinate regression. Experiments show that the mean average precision (mAP) of the improved model reaches 0.964, which is 0.045 higher than that of the original YOLOv7POSE model. The accuracy of fall pose recognition and the confidence of detection are significantly increased.
Generative AI for Reengineering Variants into Software Product Lines

The migration and reengineering of existing variants into a software product line (SPL) is an error-prone and time-consuming activity. Many extractive approaches have been proposed, spanning different activities from feature identification and naming to the synthesis of reusable artefacts. In this paper, we explore how large language model (LLM)-based assistants can support domain analysts and developers. We revisit four illustrative cases of the literature where the challenge is to migrate variants written in different formalism (UML class diagrams, Java, GraphML, statecharts). We systematically report on our experience with ChatGPT-4, describing our strategy to prompt LLMs and documenting positive aspects but also failures. We compare the use of LLMs with state-of-the-art approach, BUT4Reuse. While LLMs offer potential in assisting domain analysts and developers in transitioning software variants into SPLs, their intrinsic stochastic nature and restricted ability to manage large variants or complex structures necessitate a semiautomatic approach, complete with careful review, to counteract inaccuracies.
Artificial intelligence (AI) for user experience (UX) design: a systematic literature review and future research agenda

Purpose The aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications. Design/methodology/approach This article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process. Findings The authors’ analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples. Originality/value While there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans.
VariantInc

Developers use version-control systems and software-hosting platforms to manage their software systems. They rely on the provided branching and forking mechanisms to implement new features, fix bugs, and develop customized system variants. A particular problem arises when forked variants are not re-integrated (i.e., merged), but kept and co-evolved as individual systems. This can cause maintenance overheads, due to change propagation and limitations in simultaneously managing variations in space (variants) and time (revisions). Thus, most organizations decide to integrate their set of variants into a single platform at some point, and several techniques have been proposed to semi-automate such an integration. However, existing techniques usually consider only a single revision of each variant and do not merge the revision histories, disregarding that not only variants (i.e., configuring the features of the system) but also revisions (i.e., checking out specific versions of the features) are important. We propose an automated technique, VariantInc, for analyzing, pruning, and integrating variants of a system that also merges the revision history of each variant into the resulting platform (i.e., using presence conditions). To validate VariantInc, we employed it on 160 open-source C systems of various sizes (i.e., number of forks, revisions, source code). The results show that VariantInc works as intended, and allows developers or researchers to automatically integrate variants into a platform as well as to perform software analyses.
Highly-Efficient Hardware Architecture for CRYSTALS-Kyber With a Novel Conflict-Free Memory Access Pattern

The attack on quantum computers is an enormous threat to conventional public-key cryptography. Hence, it is crucial to study quantum-resistant cryptosystems. After four rounds of evaluation, the National Institute of Standards and Technology (NIST) has decided to standardize CRYSTALS-Kyber as one of the public-key post-quantum cryptography (PQC) algorithms. In the hardware design of CRYSTALS-Kyber, the polynomial-related calculations are the most time-consuming. In this paper, we present a highly-efficient hardware architecture for CRYSTALS-Kyber. Firstly, we propose the CRYSTALS-Kyber-oriented conflict-free memory mapping scheme with two modes. Based on this scheme, we construct the mixed radix-2/4 NTT/INTT algorithm, which has no pre- or post-processing, for the first time. By using the “lazy-last-layer” trick, the available memory bandwidth of NTT is temporarily increased, and the average performance of NTT is improved. Besides, the point-wise-multiplication (PWM) is performed in a single memory bank by cooperating with the two modes of our memory mapping scheme. This avoids the waste of memory bandwidth, thus avoiding the usage of large FIFOs for the sampled data. Last, we propose an efficient modular multiplier for CRYSTALS-Kyber, and we merge the divide-by-2 operations in the finite field into modular adders and subtractors to reduce resource consumption. This design, which supports all three security levels, is implemented on Xilinx Artix-7 FPGA with 7.3k LUTs, 3.2k FFs, 2.2k Slices, 5 BRAMs, and 4 DSPs. It performs 12% better in area-time-product than other leading designs in the literature.
Flexible Remote Attestation of Pre-SNP SEV VMs Using SGX Enclaves

We propose a protocol that explores a synergy between two TEE implementations: it brings SGX-like remote attestation to SEV VMs. We use the notion of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trusted guest owner</i> , implemented as an SGX enclave, to deploy, attest, and provision an SEV VM. This machine can, in turn, rely on the trusted owner to generate SGX-like attestation proofs on its behalf. Our protocol combines the application portability of SEV with the flexible remote attestation of SGX. We formalise our protocol and prove that it achieves the intended guarantees using the Tamarin prover. Moreover, we develop an implementation for our trusted guest owner together with example SEV machines, and put those together to demonstrate how our protocol can be used in practice; we use this implementation to evaluate our protocol in the context of creating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accountable machine-learning models</i> . We also discuss how our protocol can be extended to provide a simple remote attestation mechanism for a heterogeneous infrastructure of trusted components.
Online Test-Time Adaptation for Patient-Independent Seizure Prediction

Existing domain adaptation (DA) methods typically require access to source domain data, which raises privacy concerns due to the sensitive information contained in electroencephalogram (EEG) data. Moreover, test data generally arrive in a sequential or batch manner in real-world scenarios. To this end, we adopt online test-time adaptation (OTTA) paradigm, which adapts a pretrained source model, instead of the source data, to the target data stream at test time. OTTA is very useful in the epilepsy prediction task, which can protect patient privacy and achieve online prediction for seizures. Specifically, we introduce a novel teacher–student OTTA (TSOTTA) approach based on knowledge distillation (KD) and exponential moving average (EMA) strategy. Experiments on two public seizure datasets demonstrate that TSOTTA can enhance the generalization ability of the pretrained source model in cross-patient online seizure prediction.
Product-Structuring Concepts for Automotive Platforms

The products of the automotive industry are facing one of the biggest changes: becoming digital smart devices on wheels. Driven by the rising amount of vehicle functions, electronic control units, and software, today's vehicles are becoming cyber-physical systems that are increasingly complex and hard to manage over their life cycle. To handle these challenges, the automotive industry is adopting and integrating methods like software product-line engineering, electrics/electronics platforms, and product generation. While these concepts are widely recognized in their respective research areas and various domains, there is limited research regarding the practical effectiveness of implementing these concepts in a software-driven automotive context. In this paper, we investigate existing product-structuring concepts and methods that consider both hardware and software artifacts, and their applicability to the automotive as well as other cyber-physical industries. For this purpose, we conducted a systematic mapping study to capture a comprehensive overview of existing product-structuring concepts and methods, based on which we discuss how the state-of-the-art can or cannot help solve the challenges of the automotive industry. Specifically, we analyze the practical applicability of the existing solutions to help practitioners apply them and to guide future research.
DSDGen

Developers use the forking mechanisms of modern social-coding platforms to evolve and maintain their systems. Using such mechanisms often leads to a larger number of independent variants with individual features or bug fixes that the developers may want to merge after a longer period of co-evolution. At this point, they may have forgotten (or never had) knowledge about differences between the variants. Tackling this problem, we built on the idea of on-demand documentation to develop a technique that automatically extracts and presents information for merging a class from two forks. We implemented our technique as a prototype called DSD-Gen and evaluated it through an experimental simulation with 10 students who should comprehend two real-world merge requests. Using DSDGen instead of code diffs only, more of the students could correctly comprehend the merges (6 / 10 versus 2 / 10) within a similar time. The students actively inspected the additional information provided by DSDGen and used it to comprehend the differences between the forked classes. So, DSDGen can help developers recover information for comprehending the differences caused by fork co-evolution during merges, with our results indicating opportunities for future research and improvements.
Energy-efficient virtual machine placement in distributed cloud using NSGA-III algorithm

Abstract Cloud computing is the most widely adapted computing model to process scientific workloads in remote servers accessed through the internet. In the IaaS cloud, the virtual machine (VM) is the execution unit that processes the user workloads. Virtualization enables the execution of multiple virtual machines (VMs) on a single physical machine (PM). Virtual machine placement (VMP) strategically assigns VMs to suitable physical devices within a data center. From the cloud provider's perspective, the virtual machine must be placed optimally to reduce resource wastage to aid economic revenue and develop green data centres. Cloud providers need an efficient methodology to minimize resource wastage, power consumption, and network transmission delay. This paper uses NSGA-III, a multi-objective evolutionary algorithm, to simultaneously reduce the mentioned objectives to obtain a non-dominated solution. The performance metrics (Overall Nondominated Vector Generation and Spacing) of the proposed NSGA-III algorithm is compared with other multi-objective algorithms, namely VEGA, MOGA, SPEA, and NSGA-II. It is observed that the proposed algorithm performs 7% better that the existing algorithm in terms of ONVG and 12% better results in terms of spacing. ANOVA and DMRT statistical tests are used to cross-validate the results.