Reviewerly/exHarmony huggingface dataset:
Continual Horizontal Federated Learning for Heterogeneous Data

Federated learning is a promising machine learning technique that enables multiple clients to collaboratively build a model without revealing the raw data to each other. Among various types of federated learning methods, horizontal federated learning (HFL) is the best-studied category and handles homogeneous feature spaces. However, in the case of heterogeneous feature spaces, HFL uses only common features and leaves client-specific features unutilized. In this paper, we propose a HFL method using neural networks named continual horizontal federated learning (CHFL), a continual learning approach to improve the performance of HFL by taking advantage of unique features of each client. CHFL splits the network into two columns corresponding to common features and unique features, respectively. It jointly trains the first column by using common features through vanilla HFL and locally trains the second column by using unique features and leveraging the knowledge of the first one via lateral connections without interfering with the federated training of it. We conduct experiments on various real world datasets and show that CHFL greatly outperforms vanilla HFL that only uses common features and local learning that uses all features that each client has.
Europe, we have a problem! Challenges to health data-sharing in the EU

E-medical documentation is becoming an increasingly important tool that the European Commission recognizes and puts on the policy agenda, and it is expected to be created in the next few years. The main objectives of the EU Health Data Space Framework are to make the health sector more efficient and to advance scientific research in telehealth. One major hurdle is the way to ensure that it is used for the common good, but in the opinion of health professionals, this requires proper consideration of trust, ethics, and safety. For example, e-prescription will be a useful tool, but there are open questions about reimbursing cross-border costs. The potential danger and risks are that the use of eHealth data will affect the general rights of patients, especially the right to privacy and confidentiality under the European Charter of Patients' Rights and the General Data Protection Regulation. We are faced with the fact that not all countries have a national contact point for cross-border healthcare and the Regulation on Electronic Identification, Authentication and Trust Services (eIDAS). This ensures secure access to health data where patients should see who has visited their data in the hospital system or other health platforms, and in addition, may deny access to third parties. It remains challenging to receive reports from several EU countries to the Ombudsman for patients who can make visible data only to themselves through ID-certified health portals, but despite this, the hospital does not have this data and continues to use all the data.
MO-DLSCA: Deep Learning Based Non-profiled Side Channel Analysis Using Multi-output Neural Networks

Differential Deep Learning Analysis (DDLA) is the first side-channel analysis (SCA) attack using deep learning (DL) in non-profiled scenarios. However, DDLA requires many training processes to distinguish the correct key. In this paper, we propose a novel SCA technique using multi-output multi-loss neural networks, which can predict all possible hypothesis keys simultaneously in a short time of the training process. Specifically, a multi-output classification (MOC) model and a multi-output regression (MOR) model are introduced. Especially, we first suggest using identity labeling for MOR model to determine the trend of the training metric for each hypothesis key in the non-profiled SCA scenario. As a result, the correct key can be distinguished easily. The efficiency of proposed model is clarified on different SCA-protected schemes, such as masking and combined hiding-masking countermeasure methods. Significantly, our approach remarkably outperforms the DDLA model and parallel network in terms of the execution time and the success rate. In addition, by using shared layers, the proposed model achieves a higher success rate of at least 25 % in the case of combined hiding-masking countermeasure.
Segment Prefetching at the Edge for Adaptive Video Streaming

Segment prefetching is a technique that transmits the next video segments in advance closer to the user to serve con-tent with reduced latency. Due to its location and capabilities, an edge computing node is an ideal component for executing segment prefetching policies and storing/caching the prefetched segments. In this work, we study segment prefetching techniques deployed at the edge computing node for adaptive video streaming. We propose different types of segment prefetching policies and study their costs and benefits, including segment prefetching based on past segment requests, transrating, a Markov prediction model and machine learning. Besides, we analyze and discuss which segment prefetching policy is better under which circumstances and the influence of the ABR algorithm and the bitrate ladder on segment prefetching.
Boundary-Sensitive Loss Function With Location Constraint for Hard Region Segmentation

In computer-aided diagnosis and treatment planning, accurate segmentation of medical images plays an essential role, especially for some hard regions including boundaries, small objects and background interference. However, existing segmentation loss functions including distribution-, region- and boundary-based losses cannot achieve satisfactory performances on these hard regions. In this paper, a boundary-sensitive loss function with location constraint is proposed for hard region segmentation in medical images, which provides three advantages: i) our Boundary-Sensitive loss (BS-loss) can automatically pay more attention to the hard-to-segment boundaries (e.g., thin structures and blurred boundaries), thus obtaining finer object boundaries; ii) BS-loss also can adjust its attention to small objects during training to segment them more accurately; and iii) our location constraint can alleviate the negative impact of the background interference, through the distribution matching of pixels between prediction and Ground Truth (GT) along each axis. By resorting to the proposed BS-loss and location constraint, the hard regions in both foreground and background are considered. Experimental results on three public datasets demonstrate the superiority of our method. Specifically, compared to the second-best method tested in this study, our method improves performance on hard regions in terms of Dice similarity coefficient (DSC) and 95% Hausdorff distance (95%HD) of up to 4.17% and 73% respectively. In addition, it also achieves the best overall segmentation performance. Hence, we can conclude that our method can accurately segment these hard regions and improve the overall segmentation performance in medical images.
Balanced masking strategy for multi-label image classification

Data imbalance is an essential issue in multi-label image classification that may reduce the model’s generalization ability. Unlike oversampling or undersampling in single-label image classification, simply removing or repeating some images in multi-label classification will influence labels in other categories due to complex label dependencies. Current methods usually tackle this issue from either data resampling or loss function design. This paper proposes a balanced masking strategy to solve the imbalance problem in feature space for graph-based methods. By selectively manipulating the label node embedding of the graph, the model can gain the priors of the majority of samples to maintain intra-class and inter-class balance simultaneously, thus performing better and more stable. Extensive experiments verify the adaptability and effectiveness of our method, which can be easily applied to any graph-based multi-label model to achieve highly competitive performance.
Extractive Summarization of Call Transcripts

Text summarization is the process of extracting the most important information from the text and presenting it concisely in fewer sentences. Call transcript is a text that involves textual description of a phone conversation between a customer (caller) and agent(s) (customer representatives). This paper presents an indigenously developed method that combines topic modeling and sentence selection with punctuation restoration in condensing ill-punctuated or un-punctuated call transcripts to produce summaries that are more readable. Extensive testing, evaluation and comparisons have demonstrated the efficacy of this summarizer for call transcript summarization.
A fuzzy intelligent group recommender method in sparse-data environments based on multi-agent negotiation

Recommender systems have attracted numerous scholars' attention. Group recommender is a fashionable though challenging topic in the field of recommender systems. Most methods using aggregation technique performs poorly when conflict exists in members' preferences. The group recommender method based on multi-agent negotiation covers this limitation. However, existing works ignore the imperfect rationality of members during negotiation. To cover up this deficiency, we propose a fuzzy intelligent group recommender method based on multi-agent negotiation. A new concession protocol makes some compensations to the transigent member considering his imperfect rationality. In this work, the dependency structure analysis is introduced to classify opinion terms into aspects, and the fuzzy numbers are introduced to characterize a maze of contradictory opinion terms in textual reviews. Moreover, a new user similarity method is established using normal cloud models. It tackles the problem of sparse data. In addition, a case study on data in TripAdvisor.com is conducted. Its results indicate the good performance of the proposed group recommender method.
Task information types related to data gathering in media studies

Purpose The purpose of this paper is to examine what types of task information media scholars need while gathering research data to create new knowledge. Design/methodology/approach The research design is qualitative and user-oriented. A total of 25 media scholars were interviewed about their research processes and interactions with their research data. The interviews were semi-structured, complemented by critical incident interviews. The analysis focused on the activity of gathering research data. A typology of information (task, domain and task-solving information) guided the analysis of information types related to data gathering, with further analysis focusing only on task information types. Findings Media scholars needed the following task information types while gathering research data to create new knowledge: (1) information about research data (aboutness of data, characteristics of data, metadata and secondary information about data), (2) information about sources of research data (characteristics of sources, local media landscapes) and (3) information about cases and their contexts (case information, contextual information). All the task information types should be considered when building data services and tools to support media scholars' work. Originality/value The paper increases understanding of the concept of task information in the context of gathering research data to create new knowledge and thereby informs the providers of research data services about the task information types that researchers need.
An efficient privacy-preserving recommender system in wireless networks

Abstract Recommender systems have been widely used for implementing personalised content on many mobile online services to reduce computational overload and preserve wireless data for users. The underlying mechanisms used for building recommender systems analyse data collected from users to make recommendations. This poses concerns over the privacy of data from users as both service providers and the cloud will have access. Privacy-preserving recommender systems protect user information by incorporating various cryptographic mechanisms to prevent accessing the data. However, existing works are not practical due to the use of heavy cryptography. In this paper, we propose an efficient privacy-preserving recommender system that takes advantage of clustering to improve efficiency. Using a secure clustering mechanism, user data are assigned to multiple clusters before being fed into the recommendation. Our proposed protocols are privacy-preserving and do not leak information that could be used to identify a data subject. The experiments show that our system is efficient and accurate.
AraBERT and DF-GAN fusion for Arabic text-to-image generation

Current AI systems have shown impressive results in the automatic synthesis of realistic images from text descriptions tasks. In fact, Generative Adversarial Networks (GANs) are widely used in text-to-image generation tasks. The generator generates realistic images given the noise and sentence vectors, and the discriminator produces a probability of how the synthetic images are reals. In this paper, in order to generate images from Arabic text, we fuse DF-GAN as a sample and efficient text-to-image generation framework and AraBERT architecture. To achieve this purpose, firstly, we re-create new datasets matching the Arabic text-to-image generation task by applying DeepL-Translator from English to Arabic on text descriptions of original datasets. Secondly, we leverage the power of AraBERT which is trained on billions of Arabic words to produce a strong sentence embedding, and we reduce that vector’s dimension to match with DF-GAN shape. Thirdly, we inject the reduced sentence embedding into the UPBlocks sections of DF-GAN and we train the proposed architecture on two challenging datasets. Following the previous works, we use CUB and Oxford-102 flowers as original datasets. Further, we measure our framework with FID and IS. Our framework is the first that achieve much success in generating high-resolution realistic and text-matching images conditioned with Arabic text.
Learning from feedback: Evaluation of dynamic decision-making in virtual reality under various repetitive training frameworks

Dynamic decision-making involves a series of interconnected interdependent confluence of decisions to be made. Experiential training is preferred over traditional methods to train individuals in dynamic decision-making. Imparting experiential training in physical settings can be very expensive and unreliable. In virtual reality (VR), synthetic environments play a significant role in providing flexible and cost-effective training environments to enhance dynamic decision-making. However, it is still unclear how VR can be used to impart dynamic decision-making training to increase cognitive performance in complex situations. Besides, different repetitive training methods like desirable difficulty framework and heterogeneity of practice have been evaluated on generic cognitive and motor tasks. However, an evaluation of how these repetitive training methods facilitate dynamic decision-making in an individual in a virtual complex environment setting is lacking in the literature. The objective of this study is to evaluate the effect of different repetitive training methods in immersive VR on dynamic decision-making in a complex search-and-shoot environment. In a lab-based experiment, 66 healthy subjects are divided equally and randomly into three between-subject training conditions: heterogenous, difficult, and sham. On Day 1, all the participants, regardless of the condition, executed an environment of a baseline difficulty level. From Days 2 to 7, the participants alternatively executed the novice difficulty and expert difficulty versions of the environment in the heterogenous condition. In difficult conditions, the participants executed the expert difficulty version of the environment from Days 2 to 7. In the sham condition, the participants executed an unrelated VR environment from Days 2 to 7. On Day 8, the participants executed the baseline difficulty version of the environment again in all the conditions. Various performance and workload-based measures were acquired. Results revealed that the participants in the heterogenous and difficult conditions performed significantly better on Day 8 compared with Day 1. The results inferred that a combination of immersive VR environment with repetitive heterogenous training maximized performance and decreased cognitive workload at transfer. We expect to use these conclusions to create effective training environments in VR for imparting training to military personnel in dynamic decision-making scenarios.
An analysis of educational data on the correlation between learning success and students’ behavior in the university digital educational environment

Digitization is one of the key factors in the development of the modern educational system. Using end-to-end digital technologies in the educational process leads to the accumulation of a large amount of data on students' behavior and learning outcomes in the digital educational environment. The analysis of these data will enable educators to improve the efficiency and quality of students' learning. The article analyzes the correlation between learning success and students' behavior in the university digital educational environment employing the methods of educational analytics. The research relied on theoretical, empirical, and mathematical methods. We provided a definition of educational data and explain their intended purpose. Next, we described the methods used to analyze educational data and gave examples of applying models based on them in the education sector. We developed and demonstrated a model of the correlation between students' final grades and their activity during university training using the example of an online programming course hosted on the Moodle LMS platform. Impact analysis was carried out both for individual indicators and for their combination. As a result, the research has identified a student's actions that create the most significant impact on the final score. The model can be used to predict students' learning success in the digital educational environment based on their activity and learning outcomes in order to improve the managerial effectiveness of the learning process at universities.
Novel approaches to measuring the popularity inclination of users for the popularity bias problem

An efficient approach to handling the well-known popularity bias in recommendations is treating this issue by considering users' actual propensities on item popularity. This way, the recommender systems can also provide more calibrated individual recommendations. However, when estimating users' tendency on popularity, the existing methods consider all rated items in their profiles, including even negatively rated ones. In this study, we propose two novel approaches, namely Better-Than-Average (BTA) and Positively-Rated (PR), to measure the popularity inclination of individuals more correctly. Both methods aim to consider only items favored by users rather than all rated ones but differ in how they select the set of liked items. More specifically, the BTA filters out items with higher ratings than the average rating value of the user profile, while the PR considers only positively-rated ones based on the rating scale. The experiments conducted on three benchmark datasets conclude that our approaches measure the level of users' popularity inclination highly different when compared to existing methods, and such differences reach significant degrees in some cases. We also observe that the proposed BTA is more sensitive than the PR in measuring popularity inclination due to its followed mechanism for forming the set of liked items. We also conclude that the impact of our BTA and PR approaches on popularity inclination is more distinct for highly-engaged users, who are the most critical stakeholders of a recommender system.
Multimode Teaching Quality Evaluation Model of Higher Education Course Based on Improved Particle Swarm Optimization

The current classroom evaluation in colleges and universities is mostly qualitative analysis, lacking of necessary quantitative means. Although the traditional analytic hierarchy process can deal with this problem quantitatively, it is difficult to test whether the judgment matrix has consistency. A multi-mode teaching quality evaluation model design technique based on an upgraded particle swarm optimization algorithm is suggested as a solution to these issues. An appropriate assessment system index is created by studying the evaluation system of teaching quality in colleges and universities using enhanced particle swarm optimization algorithm. To deal with the qualitative problem quantitatively, we use the enhanced particle swarm optimization approach, the multi-mode teaching model of education course is established. The experimental results show that the multi-mode teaching quality evaluation model based on improved particle swarm optimization algorithm is easy to operate, and can solve the subjectivity and randomness of teaching quality evaluation.
Towards an Approach for Modeling and Architecting of Self-Adaptive Cyber-Physical Systems

Cyber-physical systems (CPS) emerge as a paradigm in which smart things actively collaborate among them, providing value-added services. The interconnection of physical things and their integration into software systems caused the CPS to be more and more complex. CPS is required to carry out a mission with minimum supervision. In fact, CPSs plan to be flexible systems that should autonomously, or with minimal manual intervention, decide how they adapt their internal behavior in response to dynamic environmental events. In this paper we discuss the CPS paradigm from the perspective of self-adaptive systems. Indeed, this will allow CPSs to modify their behavior, at runtime, in response to environment changes. We propose a multi-layer architecture for CPSs. The latter contains the main software modules that convert CPS to be self-aware, self-learning and self-reconfiguring. A case study about fault prediction in industrial CPS helps to evaluate the proposed architecture.
DZchatbot: A Medical Assistant Chatbot in the Algerian Arabic Dialect using Seq2Seq Model

In light of the global crisis like COVID-19, many people are afraid to leave the house and visit the doctor for fear of these epidemics. On the other side, the amazing development of artificial intelligence has led to chatbots' emergence and use in several fields. Therefore, in this paper, we propose to build an automated chatbot system that interacts with people in the Arabic Algerian dialect and helps patients ask general medical questions. To achieve this purpose, we propose three sequence-to-sequence models based on three Recurrent Neural Networks encoder-decoders: Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Gated Recurrent Unit, to understand the user's request and provide the right useful answer. Experimentally, we have collected medical data of 2150 pairs. The results were very promising, and the proposed chatbot performed excellently in handling user questions.