Reviewerly/exHarmony huggingface dataset:
Mapping of Newcomer Clients in Federated Learning Based on Activation Strength

Federated learning is a collaborative machine learning approach that allows multiple parties to train a model without exchanging sensitive data. In manufacturing, where different parties may have proprietary or sensitive data that cannot be shared, this is especially useful. However, traditional federated learning approaches (as proposed by McMahan et al.) do not consider the differences in data and computing resources across different parties, leading to sub-optimal model performance. Personalized federated learning addresses this issue by allowing each party to contribute to the model training according to its specific data and resources. Furthermore, most common approaches only consider a limited set of data and a short period of time, without considering the system’s long-term usefulness. It is important to consider the integration of new clients and the continuous change of data, which could result in the addition of new classes. This paper will explore the potential of federated learning in manufacturing and present a flexible and expandable approach, focusing on mapping newcomer clients based on activation strength of weights.
A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data

The growing prevalence of artificial decision systems has prompted a keen interest in their efficiency, yet this progress is accompanied by their inherent complexity. This poses a significant challenge for various domains, including operational research, where decisions hold crucial influence over outcomes and thus must not remain undisclosed. Counterfactual explanations are greatly remarked as a simple (to understand) yet efficient way to explain the decisions made by a machine learning model by finding a minimal set of changes required to change the prediction outcome for a specific instance. We, then, present a novel algorithmic approach, called CFNOW, which implements a modular, fast, two-step process using tabu search, a well-known metaheuristic framework, to find counterfactuals for multiple data types (tabular, image, and text) with high efficiency. We run an extensive benchmark study with more than 5,000 factual points from 25 datasets to demonstrate that CFNOW can generate high-quality counterfactual results in terms of metrics such as speed, coverage, distance, and sparsity, surpassing the state-of-the-art. These characteristics, associated with the simple code implementation, may aid embedding explainability to complex models which are often necessary for compliance requirements.
Emerging Exploration Strategies of Knowledge Graphs

The utilization of semantic web technologies has led to the development of knowledge graphs represented as triples that allow for the exploration of specific and cross-domains. Despite the advantages of semantic links between entities in facilitating user exploration, they can also lead to an overwhelming number of exploration choices that can cause confusion, frustration, uncertainty, and a sense of being lost in the abundant graph, particularly for users who are not familiar with the domain. Thus, identifying exploration strategies is critical to improving user exploration and increasing exploration utility. This study aims to identify exploration strategies that promote knowledge utility (i.e., increase users’ domain knowledge) and exploration experience (i.e., provide users with a positive and pleasant feeling). To accomplish this goal, an experimental user study was conducted, involving lay users in the musical instrument domain, where they were presented with an exploration task and then allowed to freely explore musical instruments. Parameters related to exploration paths were used to analyze the exploration patterns that users follow during their exploration. The findings reveal exploration strategies that promote knowledge utility and exploration experience. This research contributes to the literature on intelligent methods of guiding user exploration through knowledge graphs to enhance exploration effectiveness, which can have broad applications in knowledge graph utilization.
An Adaptive LDA Optimal Topic Number Selection Method in News Topic Identification

Nowadays, news text information is exploding, and people need more and more heterogeneous news content.Therefore, news text topic identification is needed to help viewers quickly and accurately screen and filter news related to their interests to save time and energy.The Latent Dirichlet Allocation(LDA) model is the most commonly used method for text topic identification.The optimal number of topics must be specified in advance when using the LDA model to extract topics in previous studies.However, the selection of the too-large or the too-small number of topics significantly impacts the final results of LDA topic models, which directly determines the quality of topic extraction.Moreover, the news text datasets from social media are very time-sensitive, and the combination of temporal and semantic modelling has not been considered in past studies of news topic identification.This paper proposes an adaptive optimal topic number determination method for the fusion of semantic and temporal information in news datasets to address the existing problems.Semantic and temporal are first extracted in this method as two different views.Then, density peak clustering of multi-view information fusion is performed based on the two obtained feature vectors.The clustering results are used as the final optimal number of topics.To demonstrate the effectiveness of the proposed method, this paper compares the performance of four traditional methods for determining the optimal number of topics with the performance of this paper's method on the public datasets.The results show that the optimal number of topics considering semantic and temporal factors is significantly better than the other four traditional methods regarding F-value, PMI scores, MI scores.It performs well in other indicators as well.The above experimental results show that the method proposed in this paper combines the temporal and semantic of news data to determine the optimal number of topics of news text, which can improve the accuracy of selecting the optimal number of topics in the LDA model and the effectiveness of the topic identification of news text to some extent.It can help viewers better understand and utilize the massive news text information.In addition, the method also broadens the idea of identifying and mining unique datasets from multiple perspectives.
Towards a Practice-Led Research Agenda for User Interface Design of Recommender Systems

The design of recommender systems’ graphical user interfaces (GUIs) is critical for a user's experience with these systems. However, most research into recommenders focuses on algorithms, overlooking the design of their interfaces. Additionally, the studies on the design of recommender interfaces that do exist do not always manage to cross the research-practice gap. This disconnect may be due to a lack of alignment between academic focus and the most pressing needs of practitioners, as well as the way research findings are communicated. To address these issues, this paper presents the results of a comprehensive study involving 215 designers worldwide, aiming to identify the primary challenges in designing recommender GUIs and the resources practitioners need to tackle those challenges. Building on these findings, this paper proposes a practice-led research agenda for the human-computer interaction community on designing recommender interfaces and suggestions for more accessible and actionable ways of disseminating research results in this domain.
Effects of Prior Experience, Gender, and Age on Trust in a Banking Chatbot With(Out) Breakdown and Repair

Trust is an attitudinal construct that can be sensitive to prior experience, gender, and age. In our study, we explored how trust in a banking chatbot might be shaped by these user characteristics. Statistical analysis of 251 participants, who interacted with one of six chatbots defined by humanlikeness (high/low) and conversational performance (no breakdown, breakdown with repaired, breakdown without repair), showed that the user characteristics of gender and age did not significantly impact trust, but prior experience did. Trust resilience was found across the gender and age groups. The effect of users’ prior experience on their trust in a chatbot which they have never used holds implications for research and practice. Future studies on the effect of cultural context, longer interaction episodes, and more diverse application contexts on trust in chatbots are recommended.
Sick in the Car, Sick in VR? Understanding How Real-World Susceptibility to Dizziness, Nausea, and Eye Strain Influences VR Motion Sickness

A substantial number of Virtual Reality (VR) users (studies report 30–80%) suffer from cyber sickness, a negative experience caused by a sensory mismatch of real and virtual stimuli. Prior research proposed different mitigation strategies. Yet, it remains unclear how effectively they work, considering users’ real-world susceptibility to motion sickness. We present a lab experiment, in which we assessed 146 users’ real-world susceptibility to nausea, dizziness, and eye strain before exposing them to a roller coaster ride with low or high visual resolution. We found that nausea is significantly lower for higher resolution but real-world motion susceptibility has a much stronger effect on dizziness, nausea, and eye strain. Our work points towards a need for research investigating the effectiveness of approaches to mitigate motion sickness so as not to include them from VR use and access to the metaverse.
Epoxy: ACID Transactions across Diverse Data Stores

Developers are increasingly building applications that incorporate multiple data stores, for example to manage heterogeneous data. Often, these require transactional safety for operations across stores, but few systems support such guarantees. To solve this problem, we introduce Epoxy, a protocol for providing transactions across heterogeneous data stores. We make two contributions. First, we adapt multi-version concurrency control to a cross-data store setting, storing versioning information in record metadata and filtering reads with predicates on metadata so they only see record versions in a global transaction snapshot. Second, we show our design enables an atomic commit protocol that does not require data stores implement the participant protocol of two-phase commit, requiring only durable writes. We implement Epoxy for five data stores: Postgres, Elasticsearch, MongoDB, Google Cloud Storage, and MySQL. We evaluate it by adapting TPC-C and microservice workloads to a multi-data store environment. We find it has comparable performance to the distributed transaction protocol XA on TPC-C while providing stronger guarantees like isolation, and has overhead of &lt;10% compared to a non-transactional baseline on read-mostly microservice workloads and 72% on write-heavy workloads.
Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching

Entity matching (EM) is a challenging problem studied by different communities for over half a century. Algorithmic fairness has also become a timely topic to address machine bias and its societal impacts. Despite extensive research on these two topics, little attention has been paid to the fairness of entity matching. Towards addressing this gap, we perform an extensive experimental evaluation of a variety of EM techniques in this paper. We generated two social datasets from publicly available datasets for the purpose of auditing EM through the lens of fairness. Our findings underscore potential unfairness under two common conditions in real-world societies: (i) when some demographic groups are over-represented, and (ii) when names are more similar in some groups compared to others. Among our many findings, it is noteworthy to mention that while various fairness definitions are valuable for different settings, due to EM's class imbalance nature, measures such as positive predictive value parity and true positive rate parity are, in general, more capable of revealing EM unfairness.
Saibot: A Differentially Private Data Search Platform

Recent data search platforms use ML task-based utility measures rather than metadata-based keywords, to search large dataset corpora. Requesters submit a training dataset, and these platforms search for augmentations ---join or union-compatible datasets---that, when used to augment the requester's dataset, most improve model (e.g., linear regression) performance. Although effective, providers that manage personally identifiable data demand differential privacy (DP) guarantees before granting these platforms data access. Unfortunately, making data search differentially private is nontrivial, as a single search can involve training and evaluating datasets hundreds or thousands of times, quickly depleting privacy budgets. We present Saibot , a differentially private data search platform that employs Factorized Privacy Mechanism (FPM), a novel DP mechanism, to calculate sufficient semi-ring statistics for ML over different combinations of datasets. These statistics are privatized once, and can be freely reused for the search. This allows Saibot to scale to arbitrary numbers of datasets and requests, while minimizing the amount that DP noise affects search results. We optimize the sensitivity of FPM for common augmentation operations, and analyze its properties with respect to linear regression. Specifically, we develop an unbiased estimator for many-to-many joins, prove its bounds, and develop an optimization to redistribute DP noise to minimize the impact on the model. Our evaluation on a real-world dataset corpus of 329 datasets demonstrates that Saibot can return augmentations that achieve model accuracy within 50--90% of non-private search, while the leading alternative DP mechanisms (TPM, APM, shuffling) are several orders of magnitude worse.
<scp>DeepCens</scp>: A deep learning‐based system for real‐time image and video censorship

Abstract The popularity of social networks and video‐on‐demand platforms has increased the importance of image and video censorship. These platforms contain various content such as violence, explicit content, drug use and smoking that may be offensive or harmful to certain viewers. As a result, censorship is employed to filter or remove content that is unsuitable for particular audiences such as children and teenagers. However, policies for censoring harmful content in digital environments are either limited or nonexistent. This underscores the need for automated systems that can detect and censor harmful content in real time. To address these challenges, we developed the first of our knowledge systems using deep learning techniques to censor harmful content. We propose two novel, YOLO‐based real‐time censorship algorithms. Our approaches employ a pipeline‐based architecture that parallelizes the operations with subprocesses. In our experiments, the proposed algorithms performed faster and with higher accuracies compared to traditional approaches. Specifically, its content‐based accuracies were 98% for explicit content, 97% for alcohol, 98% for cigarettes and 97% for violence. Our research highlights the importance of developing effective and efficient solutions for censoring harmful content on digital media platforms. Our deep learning‐based system represents a promising approach to this challenge and has the potential to enhance user safety and protect vulnerable groups from harmful and offensive content. Future research will continue to refine and improve such systems to better address the evolving landscape of digital media and the challenges posed by harmful content.
Generate Descriptions of Medical Dialogues Through Two-Layers Transformer-Based Summarization

This paper presents a two-layer transformer-based summarization approach for generating descriptions of medical dialogues, with a focus on the concept that using small amounts of training data can produce promising results and avoid the need for high computing resources. Previous research has primarily utilized the full or most of the HealthCareMagic dataset, comprising 225,872 medical dialogues for training, while this study uses much smaller training datasets of 5000 and 7000 instances. Despite the smaller training sets, our approach yields promising results, with a challenging improvement of ROUGE evaluation metric up to 0.44 compared to previous work. The two-layer summarization technique involves the BART pre-trained transformer model for generating medical dialogue summaries, followed by fine-tuning the T5 pre-trained model to generate one-statement descriptions for each summary. This research has practical implications for improving medical dialogue summarization, enabling healthcare professionals to review and assess patient information more quickly.
A Comprehensive Survey on the IoT - Based Electronic Healthcare Records Security, Privacy Issues, and Countermeasures Using Blockchain Technology

The Internet of Things (IoT) is currently a widespread developing technology. Nowadays many fields are increasing their usage of IoT environments, for example, implementing it in agriculture, space, healthcare, manufacturing, construction, mining, smart homes, surveillance, and in almost every field. A great amount of data will be produced, transmitted, collected, stored, and processed in the cloud. Consequently, several threats and attacks will be increased. Security and Privacy issues have been the main cause of concern. To make it harder for attackers, IoT devices must be heavily secured. Privacy and security for everything to manage personalized data to make personalization. The IoT effectively communicates data using sensing data from remote locations to communicate to share private information. The main problem is that unauthorized users or attackers may affect the data at the time of Personal health care records, leading to the stealing of data. This paper presents a detailed survey of IoT Security, Privacy issues and countermeasures in the healthcare industry, and how to protect sensitive data. This review study intends to identify and evaluate research studies that have either been conceptualized or executed into action for EHRs management by utilizing blockchain technology. The survey looked at different research papers taken from a variety of different types of publications. The accessibility, privacy, and scalability of publications are the primary objectives of the in-depth analysis.
gCore: Exploring Cross-Layer Cohesiveness in Multi-Layer Graphs

As multi-layer graphs can give a more accurate and reliable picture of the complex relationships between entities, cohesive subgraph mining, a fundamental task in graph analysis, has been studied on multi-layer graphs in the literature. However, existing cohesive subgraph models are designated for special multi-layer graphs such as multiplex networks and heterogeneous information networks. In this paper, we propose generalized core (gCore), a new notion of cohesive subgraph on general multi-layer graphs without any predefined constraints on the interconnections between vertices. The gCore model considers both the intra-layer and cross-layer cohesiveness of vertices. Three related problems are studied in this paper including gCore search (GCS), gCore decomposition (GCD), and gCore indexing (GCI). A polynomial-time algorithm based on the peeling paradigm is proposed to solve the GCS problem. By considering the containment among gCores, a "tree of trees" data structure called KP-tree is designed for efficiently solving the GCD problem and serving as a compact storage and index of all gCores. Several advanced lossless compaction techniques including node/subtree elimination, subtree transplant, and subtree merge are proposed to help reduce the storage overhead of the KP-tree and speed up the process of solving GCD and GCI. Besides, a KP-tree-based GCS algorithm is designed, which can retrieve any gCore in linear time in the size of the gCore and the height of the KP-tree. The experiments on 10 real-world graphs verify the effectiveness of the gCore model and the efficiency of the proposed algorithms.
Stress Embodied: Developing Multi-sensory Experiences for VR Police Training

VR applications primarily rely on audio-visual stimuli, limiting the sense of immersion. Multi-sensory stimuli show promise in enhancing presence, realistic behavior, and overall experience. Existing approaches are either stationary or wearable, and movement-intensive. Multi-user VR police training requires a mobile device for intensive multi-sensory stimuli. This paper presents the design and development of a mobile platform for multi-sensory feedback, introducing heat, wind, mist, and pain to improve immersion. Preliminary evaluations indicate promising effects on stress in VR. The paper concludes with lessons learned for designing multi-sensory experiences in police VR training.
Playing with Data: An Augmented Reality Approach to Interact with Visualizations of Industrial Process Tomography

Industrial process tomography (IPT) is a specialized imaging technique widely used in industrial scenarios for process supervision and control. Today, augmented/mixed reality (AR/MR) is increasingly being adopted in many industrial occasions, even though there is still an obvious gap when it comes to IPT. To bridge this gap, we propose the first systematic AR approach using optical see-through (OST) head mounted displays (HMDs) with comparative evaluation for domain users towards IPT visualization analysis. The proof-of-concept was demonstrated by a within-subject user study ( $$n=20$$ ) with counterbalancing design. Both qualitative and quantitative measurements were investigated. The results showed that our AR approach outperformed conventional settings for IPT data visualization analysis in bringing higher understandability, reduced task completion time, lower error rates for domain tasks, increased usability with enhanced user experience, and a better recommendation level. We summarize the findings and suggest future research directions for benefiting IPT users with AR/MR.
Fitts’ Throughput Vs Empirical Throughput: A Comparative Study

Every time a user taps on an element on a screen, she provides some “information”. Classically, Fitts’ law accounts for the speed accuracy trade-off in this operation, and Fitts’ throughput provides the “rate of information transfer” from the human to the device. However, Fitts’ throughput is a theoretical construct, and it is difficult to interpret it in the practical design of interfaces. Our motivation is to compare this theoretical rate of information transfer with the empirical values achieved in typical, realistic pointing tasks. To do so, we developed four smartphone-based interfaces - a 1D and a 2D interface for a typical Fitts’ study and a 1D and a 2D interface for an empirical study. In the Fitts’ study, participants touched the target bar or circle as quickly as possible. In the empirical study, participants typed seven 10-digit phone numbers ten times each. We conducted a systematic, within-subjects study with 20 participants and report descriptive statistics for the Fitts’ throughput and the empirical throughput values. We also carried out statistical significance tests, the results of which are as follows. As we had expected, the Fitts’ throughput for 1D task was significantly higher than the empirical throughput for the number typing task in 1D. Surprisingly, the difference was in the opposite direction for the 2D tasks. Further, we found that throughputs for both the 2D tasks were higher than their 1D counterparts, which too is an unusual result. We compare our values with those reported in key Fitts’ law literature and propose potential explanations for these surprises, which need to be evaluated in future research.