Miracle-dz/UniFAR-FTUs huggingface dataset:
Bringing Order to Chaos: Conceptualizing a Personal Research Knowledge Graph for Scientists

Research and work-related information is often manifold for a scientist, and the absence of an organizing system may impede their research. Information that holds immense personal interest and importance to a scientist may not be relevant to other users, yet it must be easily accessible to the scientist to enhance their productivity. We aim to address the need for such a system with our proposal of a knowledge graph for scientists, termed the Personal Research Knowledge Graph (PRKG). To identify the components of a PRKG, we interview scientists at an academic institution for higher education and research regarding the issues that the presence of a PRKG could solve. We translate the scientists’ requirements into separate KGs, collectively known as the ‘Vitamins of PRKG’, and discuss methods for data acquisition to construct and maintain the PRKG. A smart virtual agent is proposed as a medium of interaction between the user and the PRKG. We also outline future research tracks, including those focused on maintaining the PRKG and protecting personal data and privacy.
Property-Based Testing in Practice

Property-based testing (PBT) is a testing methodology where users write executable formal specifications of software components and an automated harness checks these specifications against many automatically generated inputs. From its roots in the QuickCheck library in Haskell, PBT has made significant inroads in mainstream languages and industrial practice at companies such as Amazon, Volvo, and Stripe. As PBT extends its reach, it is important to understand how developers are using it in practice, where they see its strengths and weaknesses, and what innovations are needed to make it more effective. We address these questions using data from 30 in-depth inter-views with experienced users of PBT at Jane Street, a financial technology company making heavy and sophisticated use of PBT. These interviews provide empirical evidence that PBT’s main strengths lie in testing complex code and in increasing confidence beyond what is available through conventional testing methodologies, and, moreover, that most uses fall into a relatively small number of high-leverage idioms. Its main weaknesses, on the other hand, lie in the relative complexity of writing properties and random data generators and in the difficulty of evaluating their effectiveness. From these observations, we identify a number of potentially high-impact areas for future exploration, including performance improvements, differential testing, additional high-leverage testing scenarios, better techniques for generating random input data, test-case reduction, and methods for evaluating the effectiveness of tests.
East African Journal of Education Studies

This study investigated the influence of school environment on physics teacher effectiveness in secondary schools in Kigezi Sub-region, Uganda. The focus of the study was to establish the influence of administrative support on physics teacher effectiveness; to establish the influence of collegiality of teachers on physics teacher effectiveness and to find out the influence of professional development like workshops, seminars on physics teacher effectiveness. This study employed both quantitative and qualitative techniques to collect and sequentially analyse the data. The study adopted a mixed research design on a sample of 234 physics teachers, fourteen (14) head teachers and six (06) education officials. Data were collected using a self-administered questionnaire, interview guide, focus group discussions and classroom environment checklist. Quantitative data was analysed through the statistical software programs SPSS and SPSS AMOS while qualitative data was analysed using content
A Modified Innovation Resistance Theory Approach to E-Tourism Resistance Intention in Bangladesh

Technology adoption in business is essential for advancement, even if it frequently encounters user resistance. The current study model examined the resistance to adopting e-tourism by online tourists in Bangladesh using the innovation resistance theory (IRT) components such as usage barriers, value barriers, risk barriers, tradition barriers, and image barriers. The influence of technophobia and facilitating conditions were considered to extend the IRT model. The purposive sampling technique was utilized in this study. Data were collected using both Google Forms and printed questionnaires from users of online travel agencies in Bangladesh. Structural Equation Modeling (SEM) was used to analyze data from 292 online travelers. SPSS version 25 and AMOS version 26 were used for the analysis. Results showed that technophobia is the leading cause of resistance to e-tourism, followed by facilitating conditions, risk barriers, tradition barriers, and usage barriers. However, the current study has significant theoretical and practical ramifications for executives of the e-tourism industry with regard to developing strategies to deal with the factors responsible for the resistance to e-tourism.
Targeted Muscle Reinnervation: An Innovative Solution for Nerve Pain

Nerve injuries often result in long-term disability due to pain and immobility. Interventions that improve pain and facilitate the use of prosthetics can lead to better outcomes and quality of life. Conventional treatment options have been limited in their long-term efficacy because they have primarily focused on symptoms rather than underlying etiology. Targeted muscle reinnervation, on the other hand, promotes nerve regeneration and functional recovery. Through its several applications, targeted muscle reinnervation reduces pain and improves the ability to properly use modern prosthetics. Those who are properly trained in targeted muscle reinnervation can significantly relieve pain and suffering in nerve injury patients, and continuing research is helping to optimize targeted muscle reinnervation techniques to both improve and expand the procedure
Anomaly Detection Using LSTM Autoencoder Network In Ensuring Reliable Operation At Offshore Utilities System

Continuous monitoring of offshore utilities system performance is essential in ensuring smooth operation and avoiding unplanned deferments (UPD). A proper prediction tool to identify anomalous patterns could help flag potential problems that may lead to a UPD event. With very few recorded and validated UPD events, formulating a suitable methodology that gives high accuracy score and low false positive signals is a challenge. This paper presents our unsupervised machine learning approach using long short-term memory (LSTM) autoencoder network, in identifying anomalous pattern in offshore utilities system. This network learns the normal behavior of multiple sensors of interest. It first performs a compression process to represent data in lower dimension and then decompress them back to its original dimension. This compression-decompression method produces a distribution of expected reconstruction error. When the trained model is presented with data exceeding its normal behavior, anomaly is identified when the residual between actual and reconstructed data is beyond the maximum value of reconstruction error; defined as T hard . T soft is an additional threshold that we provide for the system to give earlier warning before it reaches T hard . This network has been trained on two years of a fuel gas system data with frequency of two minutes. When tested with more than two years of data, our results have shown that this model successfully captures all five UPD events during the tested period, with some showing much earlier deviation pattern than the real UPD event.
Achieving Private Veriﬁcation in Multi-stakeholder Environment and Application to Stable Matching

: Services to support decision-making, such as resource allocation and recommendations, are becoming popular and essential. This paper focuses on two-sided matching as a form of decision-making support. The stable marriage problem has been thoroughly studied as an exciting research topic related to two-sided matching. Stability is a property in which there is no man and woman who would agree to leave their assigned partner, and this property is recognized as an ideal condition for participants. This paper assumes a system where participants provide their preference orders to an assignee, and the assignee provides them with a stable matching. When considering a multi-stakeholder environment, not only the participants’ requirements but also the assignee’s intention should be respected. That is, the assignee should be given the discretion to select the matching which is the best for the assignee among all the stable matchings. However, there is a possibility that if the assignee is malicious, he/she falsiﬁes and provides an unstable matching in order to maximize his/her beneﬁt with ignoring the participants’ requirements. It is difﬁcult for the participants to detect it if they want to keep their preference orders secret from others. This paper proposes a solution of protocol including a private veriﬁcation algorithm to judge whether the received matching is stable while keeping their preference orders private. The proposed protocol is based on fully homomorphic encryption (FHE) and assumes the use of a semi-honest third-party server. This paper also proposes a general solution that does not limit to speciﬁc requirements from participants.
A Model of Successful Implementation of Cloud Enterprise Resource Planning in Sudan Civil Aviation Authority

A cloud-based enterprise resource planning (ERP) system offers organizations integrated software to manage the organization and operations and financial information. It also provides access to business information from any connected device in real time. However, the lack of strategy in implementing cloud-based ERP in government organizations can cause a failure in the efficiency and performance of the organization. The Sudan Civil Aviation Authority (SCAA), a unique large government organization that regulates the aviation sector in Sudan, has failed to fully implement cloud ERP. Therefore, this study aims to investigate the implementation of an ERP System in the aviation industry and develop a model for successfully implementing cloud ERP in the civil aviation authority of Sudan. This study uses De Lone & McLean’s updated model of successful IS implementation. This research uses a quantitative approach by conducting a survey using a questionnaire as an instrument to collect data from the target population in SCAA, which has many subordinates. The results of this research are expected to show a theoretical and practical approach toward the successful implementation of Cloud ERP in SCAA and other aviation industries.
SHORT COMMUNICATION Reduced Expression of Hydrogen Sul ﬁ de e Generating Enzymes Down-Regulates 15-Hydroxyprostaglandin Dehydrogenase in Chorion during Term and Preterm Labor

Chorionic NAD-dependent 15-hydroxyprostaglandin dehydrogenase (PGDH) plays a pivotal role in controlling the amount of prostaglandins in the uterus and has been implicated in the process of labor. Prior studies identi ﬁ ed hydrogen sul ﬁ de e generating enzymes cystathionine-b -synthetase (CBS) and cystathionine-g -lyase (CSE) in fetal membranes. We investigated whether hydrogen sul ﬁ de is involved in the regulation of PGDH expression in the chorion during labor. The chorionic tissues were obtained from pregnant women at preterm in labor and at term in labor or not in labor at term. Levels of CSE and CBS and hydrogen sul ﬁ de production rate were down-regulated in term in labor and preterm in labor groups compared with not in labor at term group. The CBS level correlated to PGDH expression in the chorion. Hydrogen sul ﬁ de donor NaHS and precursor L -cysteine dose-dependently stimulated PGDH expression and activity in cultured chorionic trophoblasts. The effect of L -cysteine was blocked by CBS
Seasonal predictive skill of intraseasonal synoptic type variability over the Cape south coast of South Africa by making use of the Met Office Global Seasonal Forecast System 5

The skill in predicting intraseasonal characteristics of synoptic type occurrences at the seasonal time scale over the all‐year rainfall region of South Africa (35°–33°S and 21°–27°E) is assessed by utilizing an ensemble of simulations performed using the GloSea5 coupled ocean–atmosphere model. Hindcasts of daily sea‐level pressure fields of 14 austral spring [September–October–November (SON)] and summer [December–January–February (DJF)] seasons, initialized in August and November, respectively, are analysed. The skill assessment is achieved through the use of self‐organizing maps. Deterministic and probabilistic assessment of synoptic type frequency forecasts indicate that intraseasonal circulation variability over the Cape south coast region is marginally predictable at seasonal time scales, more so during SON than DJF. In particular, the results obtained demonstrate that there is potential for the skillful seasonal prediction of the anomalous frequency of occurrence of high‐impact rainfall events associated with cut‐off lows within SON seasons.
Influence of Online Reviews on Purchase Intentions for Culinary Tourism: The RED Platform Study

– This study investigates the expanding culinary tourism sector in China, driven by a leisure-oriented consumer market. It focuses on the impact of electronic word-of-mouth (e-WOM) through reviews on RED, a widely-used Chinese social media platform rich in user-generated content. The research explores how RED's reviews shape food choices in culinary tourism, guided by the Stimulus-Organism-Response (S-O-R) framework and the Elaboration Likelihood Model (ELM). A proposed model examines the relationship between these reviews and consumer visit intentions, highlighting the strategic application of e-WOM on platforms like RED to influence culinary tourism behavior. The findings emphasize the importance of effectively utilizing social media feedback in this evolving market.
Impact of Dementia Severity on the Volume of the Limbic System-Related Brain Components in Women with Alzheimer’s Disease

Alzheimer’s Disease (AD) is a progressive, neurodegenerative disorder where two-thirds of the affected population are women. Along with cognitive impairments, AD is associated with behavioral changes such as aggression towards caretakers. The limbic system consists of various brain structures that play a role in emotions and behavioral reactions. Some of the limbic system-related areas are the amygdala, hippocampus, thalamus, corpus collosum (CC), and white matter (WM). Cognitive changes with AD can be measured using the clinical dementia rating (CDR) scale. Physical changes in living patients require brain imaging tools, such as Magnetic Resonance Imaging (MRI). As there is currently sparse research present for these areas relating to the female brain, we used clinical data and FreeSurfer-processed imaging data from an open-access database, OASIS-3, to explore the associations between dementia severity and the volume of the limbic system-related brain components in women. A control group consisted of participants with no dementia and multiple brain scans while comparison groups consisted of participants with a single brain scan and 1) no dementia 2) mild dementia or 3) moderate/severe dementia. Hemisphere differences with increasing CDR were found for the thalamus as well as simple hemisphere differences for the hippocampus, thalamus, and WM. When using age-matched controls and normalized volume data, the amygdala, hippocampus, thalamus, and CC volumes for subjects with AD were different than those in the control group, with the amygdala and hippocampus also showing statistically significant volume loss with increasing dementia severity. As the areas included in this study are related to the limbic system, this provides insight into the physical changes occurring in the brain of women with increasing AD, who often show changes in emotions. This can be an area to longitudinally explore whether there are associated behavioral changes as physical changes at the individual level occur over time. Abbreviations
Biomarkers , Genomics , Proteomics , and Gene Regulation Identification of Potential Therapeutic Targets in Malignant Mesothelioma Using Cell-Cycle Gene Expression Analysis

From the Department of Medicine, Surgery, and Dentistry,* Division of Pathology, the University of Milan Medical School, AOS Paolo and Fondazione Ospedale Maggiore Policlinico, Mangiagalli and Regina Elena, Milan; the Center for Preclinical Investigation, † Fondazione Ospedale Maggiore Policlinico Mangiagalli e Regina Elena, Milan; the Department of Pathology, University of Milan, Istituto di Ricovero e Cura a Carattere Scientifico, Istituto Clinico Humanitas, Milan; and the Clinical Research Center, Center of Excellence on Aging, University Foundation, Chieti; the Department of Surgery, Thoracic Unit, the University of Milan Medical School, Fondazione Ospedale Maggiore Policlinico, Mangiagalli and Regina Elena, Milan, Italy
Mutual Cross-Attention in Dyadic Fusion Networks for Audio-Video Emotion Recognition

Multimodal emotion recognition is a challenging problem in the research fields of human-computer interaction and pattern recognition. How to efficiently find a common subspace among the heterogeneous multimodal data is still an open problem for audio-video emotion recognition. In this work, we propose an attentive audio-video fusion network in an emotional dialogue system to learn attentive contextual dependency, speaker information, and the interaction of audio-video modalities. We employ pre-trained models, wav2vec, and Distract your Attention Network, to extract high-level audio and video representations, respectively. By using weighted fusion based on a cross-attention module, the cross-modality encoder focuses on the inter-modality relations and selectively captures effective information among the audio-video modality. Specifically, bidirectional gated recurrent unit models capture long-term contextual information, explore speaker influence, and learn intra-and inter-modal interactions of the audio and video modalities in a dynamic manner. We evaluate the approach on the MELD dataset, and the experimental results show that the proposed approach achieves state-of-the-art performance on the dataset.
A Dynamic Dual-Processing Object Detection Framework Inspired by the Brain’s Recognition Mechanism

There are two main approaches to object detection: CNN-based and Transformer-based. The former views object detection as a dense local matching problem, while the latter sees it as a sparse global retrieval problem. Research in neuroscience has shown that the recognition decision in the brain is based on two processes, namely familiarity and recollection. Based on this biological support, we propose an efficient and effective dual-processing object detection framework. It integrates CNN- and Transformer-based detectors into a comprehensive object detection system consisting of a shared backbone, an efficient dual-stream encoder, and a dynamic dual-decoder. To better integrate local and global features, we design a search space for the CNN-Transformer dual-stream encoder to find the optimal fusion solution. To enable better coordination between the CNN- and Transformer-based decoders, we provide the dual-decoder with a selective mask. This mask dynamically chooses the more advantageous decoder for each position in the image based on high-level representation. As demonstrated by extensive experiments, our approach shows flexibility and effectiveness in prompting the mAP of the various source detectors by 3.0~3.7 without increasing FLOPs.
Spatio-Temporal Feature Integration for Quality Assessment of Stitched Omnidirectional Images

Stitched omnidirectional images are created by capturing overlapping images and seamlessly merging them together. When observing stitched omnidirectional images, users wear head-mounted display that encompass their entire visual field and are positioned near the eyes. The presence of significant distortion in the presented scene can lead to pronounced physical and mental discomfort. Therefore, it becomes crucial to explore methods for effectively evaluating the quality of stitched omnidirectional images. Given that the process of observing stitched omnidirectional images deviates from that of traditional images, resembling more closely the experience of watching videos, we posit the existence of correlation between adjacent viewport images during the viewing process. Accordingly, we have proposed a Spatio-Temporal Feature Integration Network (STFINet) that incorporates this correlation from both global and local perspectives, simulating it in the form of pseudo-videos. The STFINet utilizes 3D convolutional neural networks to extract feature vectors and integrates optical flow to capture pixel mobility among adjacent viewport images. Furthermore, we introduce a temporal hysteresis model to handle abrupt transitions. Our experimental results demonstrate the superior performance and effectiveness of STFINet in evaluating the quality of Stitched Omnidirectional images.
Sources of Facial Expression Synchrony

Dyadic synchrony—the temporal coordination of behavior and emotions between individuals—is a key aspect of social interactions. Although synchrony in social interactions has been extensively studied, researchers continue to investigate the underlying factors and processes that contribute to this phenomenon of dyadic synchrony. We argue that synchrony in social interactions could potentially emerge from two sources: (1) people are responding to each other’s expressions or (2) people are responding to a shared task. It is also possible that synchrony results from a combination of both these factors. The present study investigated the sources of dyadic synchrony during an iterated prisoner’s dilemma (IPD) task. We examined two conditions: a still condition, where dyads only saw a still image of their partner, and a video condition, where dyads had real-time visual access to their partner’s facial reactions. We assessed synchrony using Dynamic Time Warping in real dyads and randomly paired dyads. Our findings revealed that synchrony was present in real interactions even without visual cues, suggesting that shared experiences of reacting to a joint outcome contribute to synchrony. The ability to see the partner’s facial reaction in the video condition further enhanced synchrony, particularly when dyad members’ decisions were not aligned. These results underscore the interplay between shared experiences and visual cues in driving dyadic synchrony during interactive social situations, providing valuable insights into the mechanisms underlying social coordination and alignment in cooperative and competitive contexts.
Multi-objective Optimisation of RISC-V CV32A6 for ML application

RISC-V architectures are rapidly gaining in popularity in embedded systems, in which each mW counts. Since AI-related applications such as image recognition or neural networks tend to be highly energy consuming, low-power techniques are required to optimise the autonomy of systems using SoCs to run such applications.However, the trade-off between energy consumption, application performance and resource use requires a multi-objective optimisation with a potentially very important number of optimisation parameters to be performed on the SoC. As they rely on heuristics that are likely to only return locally optimal solutions, empirical methods must be excluded.To address this issue, a technology-agnostic mathematical model is introduced to represent how optimisations are applied to a SoC, and a workflow designed to perform an intelligent exhaustive exploration of the optimisation space has been developed to highlight a subset of optimal processor configurations.Optimisation of the ARIANE/CV32A6 RISC-V processor, running a CNN propagation on a Xilinx Zynq 7020 FPGA, has shown very encouraging results using low degree configurations, and is likely to perform even better with higher degree configurations.