Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating Graph Computations on 3D NoC-Enabled PIM Architectures

Graph application workloads are dominated by random memory accesses with the poor locality. To tackle the irregular and sparse nature of computation, ReRAM-based Processing-in-Memory (PIM) architectures have been proposed recently. Most of these ReRAM architecture designs have focused on mapping graph computations into a set of multiply-and-accumulate (MAC) operations. ReRAMs also offer a key advantage in reducing memory latency between cores and memory by allowing for PIM. However, when implemented on a ReRAM-based manycore architecture, graph applications still pose two key challenges—significant storage requirements (particularly due to wasted zero cell storage), and significant amount of on-chip traffic. To tackle these two challenges, in this article, we propose the design of a 3D NoC-enabled ReRAM-based manycore architecture. Our proposed architecture incorporates a novel crossbar-aware node reordering to reduce ReRAM storage requirements. Secondly, its 3D NoC-enabled design reduces on-chip communication latency. Our architecture outperforms the state-of-the-art in ReRAM-based graph acceleration by up to 5× in performance while consuming up to 10.3× less energy for a range of graph inputs and workloads.
Skin Cancer Detection using Ensemble Learning and Grouping of Deep Models

Melanoma remains the most dangerous form of skin cancer which has a high mortality rate. When detect early, melanoma can be easily cured and millions of lives might be saved. The use of automatic detection models in clinical decision support can increase the ability to address this issue and improve survival rates. In this work, we proposed an automated pipeline for melanoma detection, which combines the predictions of deep convolutional neural network models through ensemble learning techniques. Furthermore, our automated pipeline includes various strategies such as image augmentation, upsampling, image cropping, digital hair removal and class weighting. Our pipeline was trained and tested using the image data acquired from the Society for Imaging Informatics in Medicine and the International Skin Imaging Collaboration SIIM-ISIC 2020. Our proposed pipeline has demonstrated a high performance compared to the other state-of-the-art pipelines for melanoma disease prediction with an accuracy of 97.77% and an AUC of 98.47%.
Segmenting partially annotated medical images

Segmentation of medical images using learning based systems remains a challenge in medical computer vision: training a segmentation model requires medical images exhaustively annotated by experts that are difficult and expensive to obtain. We propose to explore the usage of partially annotated images, i.e., all images are annotated but not all regions of a given class are annotated. In this paper, we propose several approaches and we experiment them on the segmentation of intra-oral images. First, we propose to modify the loss function to consider only the annotated areas, and second to integrate annotation from non-expert, as well as the combination of these methods. The experiments we conducted showed an improvement up to 33% on the segmentation performance. This approach allows to obtain better quality annotation masks than the initial human annotation using only partially annotated areas or non-expert annotations. In the future, these approaches can be extended by combination with active learning methods.
A large-scale TV video and metadata database for French political content analysis and fact-checking

In this paper, we introduce a large-scale multimodal publicly available dataset1 for the French political content analysis and fact-checking. This dataset consists of more than 1,200 fact-checked claims that have been scraped from a fact-checking service with associated metadata. For the video counterpart, the dataset contains nearly 6,730 TV programs, having a total duration of 6,540 hours, with metadata. These programs have been collected during the 2022 French presidential election with a dedicated workstation.
A Toolchain for Extracting and Visualising Road Traffic Data

We demonstrate a toolchain for visualising detailed road traffic data from multimodal sensors consisting of (i) the automatic, real-time extraction of movement paths of road users and noteworthy events from traffic monitoring cameras or LIDAR sensors, (ii) extraction of audio events from individual microphones and microphone arrays, (iii) a spatial data managment system storing the extracted information together with a geographic information system (GIS), and (iv) a web based viewer allowing to interactively visualise all these data in the context of a high-definition digital twin of the traffic environment. This system enables the collection of a considerable amount of objective data on road use and can be used for planning changes to traffic facilities as well as for assessing changes in the traffic environment.
Availability-aware Provision of Service Function Chains in Mobile Edge Computing

With the advent of Network Function Virtualization (NFV) and Mobile Edge Computing (MEC), outsourcing network functions (NFs) to the MEC is becoming popular among network service providers (NSPs), since it brings the scalability and flexibility for NF deployment and maintenance. Each user’s request will go through a service function chain (SFC), which consists of several virtual network functions (VNFs, software substitutions for traditional hardware-based middleboxes) in a specific order, and then get a response. Unlike conventional hardware-based middleboxes, VNFs are not very reliable due to potential software faults and host malfunctions. Thus, a sensible way is to add redundancy for the primary VNFs of an SFC to enhance its availability. Nevertheless, which MEC node to place each VNF, and how many backup instances are enough to ensure the availability requirement of each SFC? These issues have not yet been resolved. In this article, we present the availability-aware provision of SFC (APoS) in the MEC environment with the primary goal of maximizing the number of served requests while meeting the requirements and reliability expectations of SFCs. For the APoS, we have primarily addressed the following two fundamental challenges: (i) First, how to efficiently map these primary and backup VNFs to meet the availability requirements of SFCs? At this point, we formulate it as an integer nonlinear programming (INLP) under the limitation of each MEC node’s resources. This issue is NP-hard, and a novel binary N-back search method is proposed to derive the optimal solution for the primary and backup VNFs mapping; (ii) Second, how can we reduce the latency for users to access their desired SFCs? Then, we investigate how to minimize the average delay for all requests in each time slot. To solve this problem, we design an online service switching (OSS) method, which jointly considers the queuing delay, communication delay, and switching delay. It achieves the optimal solution with a theoretical guarantee. Finally, we evaluate the proposed methods with real-world datasets. The results demonstrate that, compared with the benchmarks, our practices can achieve approximately 20% request acceptance gain and up to 30% delay reduction, on average.
Self-Supervised Spiking Neural Networks applied to Digit Classification

The self-supervised learning (SSL) paradigm is a rapidly growing research area in recent years with promising results, especially in the field of image processing. In order for these models to converge towards the creation of discriminative representations, a data augmentation is applied to the input data that feeds two-branch networks. On the other hand, Spiking Neural Networks (SNNs) are attracting a growing community due to their ability to process temporal information, their low-energy consumption and their high biological plausibility. Thanks to the use of Poisson process stochasticity to encode the same data into different temporal representations, and the success of using surrogate gradient on learning, we propose a self-supervised learning method applied to an SNN network, and we make a preliminary study on the generated representations. We have shown its feasibility by training our architecture on a dataset of images of digits (MNIST), then we have evaluated the representations with two classification methods.
A Data-Driven Analysis of Behaviors in Data Curation Processes

Understanding how data workers interact with data, and various pieces of information related to data preparation, is key to designing systems that can better support them in exploring datasets. To date, however, there is a paucity of research studying the strategies adopted by data workers as they carry out data preparation activities. In this work, we investigate a specific data preparation activity, namely data quality discovery, and aim to (i) understand the behaviors of data workers in discovering data quality issues, (ii) explore what factors (e.g., prior experience) can affect their behaviors, as well as (iii) understand how these behavioral observations relate to their performance. To this end, we collect a multi-modal dataset through a data-driven experiment that relies on the use of eye-tracking technology with a purpose-designed platform built on top of iPython Notebook. The experiment results reveal that: (i) ‘copy–paste–modify’ is a typical strategy for writing code to complete tasks; (ii) proficiency in writing code has a significant impact on the quality of task performance, while perceived difficulty and efficacy can influence task completion patterns; and (iii) searching in external resources is a prevalent action that can be leveraged to achieve better performance. Furthermore, our experiment indicates that providing sample code within the system can help data workers get started with their task, and surfacing underlying data is an effective way to support exploration. By investigating data worker behaviors prior to each search action, we also find that the most common reasons that trigger external search actions are the need to seek assistance in writing or debugging code and to search for relevant code to reuse. Based on our experiment results, we showcase a systematic approach to select from the top best code snippets created by data workers and assemble them to achieve better performance than the best individual performer in the dataset. By doing so, our findings not only provide insights into patterns of interactions with various system components and information resources when performing data curation tasks, but also build effective and efficient data curation processes through data workers’ collective intelligence.
Ecological Impact Assessment Framework for areas affected by Natural Disasters

The forest's biodiversity consists of relations between trees, animals, the environment, and surrounding communities. Their existence required a certain balance both in number and composition. The diversity of the element itself creates a chain that connects each of the living things. Consistently, those mutual relationships are sometimes disturbed by pressures, whether man-made pressures or natural pressures. As a consequence of that event, the biodiversity loses its balance and becomes vulnerable to disaster. The fact that forest fire cases damage every living thing in the forest is becoming a massive issue in forest management. In some instances, the balance of forest biodiversity assembles an ecological resilience essential to the forest condition in combating disturbance. This paper reviews the biodiversity elements and their relationship to the extent to which elements will support ecological resilience. This is a review of 58 studies related to biodiversity balance and ecological resilience. The review discovered evidence that biodiversity components are connected and support each other. However, not every relation contributes to ecological resilience. As a result, we assess several biodiversity elements that might be useful in supporting ecological resilience, which are tree, environment, animal, and community. We also provide 2 case examples case to get the value of some biodiversity elements using a deep learning method.
Implicit and electrostatic particle-in-cell/Monte Carlo model in two-dimensional and axisymmetric geometry: II. Self-bias voltage effects in capacitively coupled plasmas

With an implicit particle-in-cell/Monte Carlo model, capacitively coupled plasmas are studied in two-dimensional and axisymmetric geometry. Self-bias dc voltage effects are self-consistently considered. Due to finite length effects, the self-bias dc voltages show complex relations to the electrode areas. Two-dimensional kinetic effects are also illuminated. Compared with the fluid model, PIC/MC model is numerical-diffusion-free and more detailed properties of the plasmas can be shown.
Separation of equilibration time scales in the gradient expansion

We study thermalization by applying gradient expansion to the Kadanoff-Baym equations of the two-particle-irreducible effective action to two-loop in a theory with Dirac fermions coupled to scalars. In addition to those chemical potentials that equilibrate in the on-shell limit, we identify modes that are conserved in this approximation, but which relax when off-shell effects are taken into account. This implies that chemical equilibration does not require higher loop contributions to the effective action and is compatible with the gradient expansion. We explicitly calculate the damping time scales of both, on- and off-shell, chemical equilibration rates. It is shown that off-shell equilibration is suppressed by the thermal width of the particles in the plasma, which explains the separation of on- and off-shell chemical equilibration time scales.
Virtual Support and Intimate Partner Violence Services: A Scoping Review

Purpose This article reports on a scoping review that maps the empirical research on virtual intimate partner violence (IPV) interventions. Method Following the Joanna Briggs Institute’s nine-step scoping review methodology, 25 studies were selected. Results The majority of the studies (72%) were published in the United States, and included quantitative (56%), mixed methods (24%), and qualitative study designs (20%). The most frequent focus of the virtual interventions to support survivors of IPV included safety (52%), with other interventions focusing mainly on treatment (36%), and education and prevention (12%). Forms of interventions included interactive, internet-based (72%), smartphone apps (16%), live videoconferencing (8%), and an asynchronous chatroom (4%). Barriers and strengths of virtual IPV interventions are identified. Discussion From this analysis, emerging models in virtual IPV intervention are identified, as well as next steps necessary in virtual IPV practice and research. The article concludes with implications for social work practice and research.
Cocreating with Young Fathers: Producing Community-Informed Training Videos to Foster more Inclusive Support Environments

Our contribution draws upon a collaborative project called ‘Diverse Dads’, which ran between October 2020 and April 2021, during the 2020–2021 COVID-19 pandemic. The team comprised members of the North East Young Dads and Lads (NEYDL) Project, advisors from support organisations that champion inclusivity, and the Following Young Fathers Further (FYFF) research team. ‘Diverse Dads’ sought to identify and address gaps in service provision for young minoritised dads in the North East, and to promote cross-sector conversations concerning inclusive support for young dads from diverse communities. With support provided remotely by the FYFF team, peer research was undertaken by three young men from NEYDL. Our work highlights how methods of coproduction and cocreation were achieved during the pandemic. NEYDL is also embarking on an ambitious new digital service journey with partners DigiDAD, producing digital outputs that are made by and for young fathers. Working collaboratively, the peer research team and DigiDAD produced a set of videos designed to support training for professionals in mainstream, family, and statutory services. Showcasing one of these creative outputs, our contribution will discuss the continued value of coproduction and cocreation with young people and using creative, digital methods to support productive discussions between young fathers, professionals, and researchers.
Preoperative and postoperative high angular resolution diffusion imaging tractography of cerebellar pathways in posterior fossa tumors

This study aimed to utilize high angular resolution diffusion magnetic resonance imaging (HARDI) tractography in the mapping of the pathways of the cerebellum associated with posterior fossa tumors (infratentorial neoplasms) and to determine whether it is useful for preoperative and postoperative evaluation. Retrospective data from 30 patients (age 2–16 yr) with posterior fossa tumor (17 low grade, 13 high grade) and 30 age‐sex‐matched healthy controls were used. Structural and diffusion‐weighted images were collected at a 3‐tesla scanner. Tractography was performed using Diffusion Toolkit software, Q‐ball model, FACT algorithm, and angle threshold of 45 degrees. Manually assessed regions of interest were placed to identify reconstructed fiber pathways passing through the superior, medial, and inferior cerebellar peduncles for the preoperative, postoperative, and healthy control groups. Fractional anisotropy (FA), apparent diffusion coefficient (ADC), and track volume measures were obtained and analyzed. Statistically significant differences were found between the preop/postop, preop/control, and postop/control comparisons for the volume of the tracts in both groups. Displacement and disruption of the pathways seemed to differ in relation to the severity of the tumor. The loss of pathways after the operation was associated with selective resection during surgery due to tumor infiltration. There were no FA differences but significantly higher ADC in low‐grade tumors, and no difference in both FA and ADC in high‐grade tumors. The effects of posterior fossa tumors on cerebellar peduncles and reconstructed pathways were successfully evaluated by HARDI tractography. The technique appears to be useful not only for preoperative but also for postoperative evaluation.
Population genetic structure of Wikstroemia monnula highlights the necessity and feasibility of hierarchical analysis for a highly differentiated species

Population genetic structure can provide valuable insights for conserving genetic resources and understanding population evolution, but it is often underestimated when using the most popular method and software, STRUCTURE and delta K, to assess. Although the hierarchical STRUCTURE analysis has been proposed early to overcome the above potential problems, this method was just utilized in a few studies and its reliability needs to be further tested. In this study, the genetic structure of populations of Wikstroemia monnula was evaluated by sequencing 12 nuclear microsatellite loci of 905 individuals from 38 populations. The STRUCTURE analysis suggested the most likely number of clusters was two, but using multi-hierarchical structure analysis, almost every population was determined with an endemic genetic component. The latter result is consistent with the extremely low gene flow among populations and a large number of unique cpDNA haplotypes in this species, indicating one level of structure analysis would extremely underestimate its genetic component. The simulation analysis shows the number of populations and the genetic dispersion among populations are two key factors to affect the estimation of K value using the above tools. When the number of populations is more than a certain amount, K always is equal to 2, and when a simulation only includes few populations, the underestimation of K value also may occur only if these populations consist of two main types of significantly differentiated genetic components. Our results strongly support that the hierarchical STRUCTURE analysis is necessary and practicable for the species with lots of subdivisions.
The impact of parenting styles on undergraduate students’ emotion regulation: The mediating role of academic-social student-faculty interaction

Based on the survey data of 4,462 undergraduate students in Zhejiang Province, mainland China, this study investigated the influence of parenting styles on emotion regulation and the mediating role of student-faculty interaction. The study found that: (1) Male students scored significantly higher than female students on emotion regulation, overprotective parenting style and student-faculty interaction. (2) Parenting style has a direct positive effect on emotion regulation, and warm parenting style has a much greater effect on emotion regulation than overprotective parenting style. (3) The mediating effect of student-faculty interaction in the relationship between parenting style and emotion regulation holds true, with the mediating effect of academic student-faculty interaction being much higher than that of social student-faculty interaction. (4) The influence of warm parenting style on emotion regulation relies more on the direct effect, while the influence of overprotective parenting style on emotion regulation relies more on the mediating effect of student-faculty interaction.
International perspective on guidelines and policies for child custody and child maltreatment risk evaluations: A preliminary comparative analysis across selected countries in Europe and North America

Little knowledge exists on how evaluators in child custody and child maltreatment cases are informed by guidelines, the kinds of qualifications required and the types of training provided in different countries. The purpose of this paper is to provide an international preliminary comparison on how child custody and child maltreatment risk assessments are conducted in selected Western countries, and how the assessments are informed by best practice guidelines. Another aim is to increase knowledge on how the guidelines and best-practice standards could be developed further to reflect recent research findings. A total number of 18 guidelines were included in the analyses: four from Canada, five from the United States, three from the United Kingdom, three from the Netherlands, two from Finland, and one from Germany. We conducted a content analysis of the included guidelines in the database, focusing on how the guidelines address the best interest of the child criteria, guidelines for conducting the assessments, considerations for evaluative criteria, and specific guidance for conducting specific assessment procedures (e.g., interviews and observations). Findings show that the qualifications of and training provided to evaluators in child custody and child maltreatment risk evaluations are largely heterogeneous across the countries represented. Guidelines differ in whether and how they highlight the importance of evidence-based practices and scientifically validated assessment measures. Implications are drawn from the review and contextualized by international expert authors in the fields of forensic psychology, and family law. After the content analysis, discussion sessions within the expert group were held. The authors provide both commentaries and suggestions to improve the development of standard methods for conducting both child custody and child maltreatment risk evaluations and to consider a more transparent and judicious use of social science research to guide methods and the recommendations offered within these assessments.