Miracle-dz/UniFAR-FTUs huggingface dataset:
Robot swarms as an educational tool: The Thymio’s way

Robotics provides useful tools for educational purposes, allowing to engage students in learning within disparate domains, from computer science and artificial intelligence – traditionally the main domains for educational robotics – to general education, human and social sciences and arts. Robots can be used with different purposes, from being simple tools to be programmed with some specific behaviour, to being peers with whom to engage in a fruitful interaction for a collaborative learning purpose. In this sense, they can also foster learning of transversal skills such as communication and cooperation. In this article, we propose robot swarms as a novel educational tool to target exactly those transversal skills that are difficult to account otherwise. The usage of multiple robots interacting to solve a common problem can support the learning of concepts related to cooperation and collective actions and can make accessible notions about complex systems that are common in physical, biological, economic and social sciences. Additionally, the possibility to interact and participate in the collective behaviour displayed by the robot swarm can strongly increase the comprehension and engagement with the proposed concept. Motivated by this picture, we propose a roadmap for the utilization of swarm robotics for educational purposes, which is hinged on the Thymio robot, a simple but powerful educational robot that presents all the features required for swarm robotics experimentation. We propose two case studies and we substantiate the proposal with preliminary results from a demonstration of robot swarms performed during a recent robotics festival.
Novel Anti-CD70 Antibody Drug Conjugate for the Treatment of Adult T-Cell Leukemia (ATL)

Background/Aim: Adult T-cell leukemia (ATL) is a hematological malignancy caused by infection with human T-cell leukemia virus type 1 (HTLV-1). Chemotherapy, antibody therapy, and bone marrow transplantation are used to treat this disease, however, median survival time has not been significantly improved. Our aim was to develop and evaluate a novel antibody-drug conjugate (ADC) with regards to cell cytotoxicity and target specificity. Materials and Methods: In this study, we have constructed a novel ADC, which is composed of an anti-CD70 single chain Fv-Fc antibody conjugated with the anticancer agent emtansine using a novel antibody modification method. Cell cytotoxicity and target specificity were assessed using a cell proliferation assay. Results: The anti-CD70 ADC selectively killed HTLV-1-infected cells and ATL cells without affecting other cells. Conclusion: The anti-CD70 ADC offers some chemotherapeutic potential for the treatment of ATL.
Performance Comparison of Saliency Detection Methods for Food Region Extraction

Several methods for extracting food regions from food images use visual saliency to improve accuracy. The effectiveness of saliency detection methods for food extraction, however, has not been discussed sufficiently. Thus, the effectiveness of well-known saliency detection methods is compared thoroughly for the future development of highly accurate food-extraction methods. Ten saliency detection methods, which consisted of seven handcrafted feature-based approaches and three deep learning-based approaches, were tested by applying them to 240 food images. The results suggest that MSI, which uses only neural networks without the assumption that food regions tend to be found at the center of images, predicted food regions most accurately in terms of areas under a receiver operating characteristic curve (AUC). Additionally, GMR, which assumes that food regions tend not to be found around the four sides of an image, was also effective on the food extraction task. The AUCs of these methods were more than 4% larger than that of a center model that is frequently used as a baseline for saliency detection. Furthermore, this paper supports these results by comparing other methods and determining the properties of food images.
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction

A key challenge for an agent learning to interact with the world is to reason about physical properties of objects and to foresee their dynamics under the effect of applied forces. In order to scale learning through interaction to many objects and scenes, robots should be able to improve their own performance from real-world experience without requiring human supervision. To this end, we propose a novel approach for modeling the dynamics of a robot’s interactions directly from unlabeled 3D point clouds and images. Unlike previous approaches, our method does not require ground-truth data associations provided by a tracker or any pre-trained perception network. To learn from unlabeled real-world interaction data, we enforce consistency of estimated 3D clouds, actions and 2D images with observed ones. Our joint forward and inverse network learns to segment a scene into salient object parts and predicts their 3D motion under the effect of applied actions. Moreover, our object-centric model outputs action-conditioned 3D scene flow, object masks and 2D optical flow as emergent properties. Our extensive evaluation both in simulation and with real-world data demonstrates that our formulation leads to effective, interpretable models that can be used for visuomotor control and planning. Videos, code and dataset are available at http://hind4sight.cs. uni-freiburg.de
Empirical study of Data Completeness in Electronic Health Records in China

Abstract Background: As a dimension of data quality in electronic health records (EHR), data completeness plays an important role in improving quality of care. Although many studies of data management focus on constructing the factors that influence data quality for the purpose of quality improvement, the constructs that are developed for interpreting factors influencing data completeness in the EHR context have received limited attention. Methods: Based on related studies, we constructed the factors influencing EHR data completeness in a conceptual model. We then examined the proposed model by surveying clinical practitioners in China. Results: Our results show that the data quality management literature can serve as a starting point to derive a conceptual model of factors influencing data completeness in the EHR context. This study also demonstrates that “resources” should be added as a factor that influences data completeness in EHR. Conclusion: Our resulting conceptual model shows a substantial explanation of data completeness in EHR assessed in this study. Although the proposed relationships between the included factors were previously supported in the literature, our work provides the beginning empirical evidence that some relationships may not be always significantly supported. The possible explanation of these differences has been discussed in the present research. This study thus benefits decision makers and EHR program managers in implementing EHR as well as EHR vendors in the EHR integration by addressing data completeness issues. Available at: https://aisel.aisnet. org/pajais/vol12/iss2/4/ Recommended Citation Liu, Caihua; Zowghi, Didar; Talaei-Khoei, Amir; and Jin, Zhi (2020) "Empirical study of Data Completeness in Electronic Health Records in China," Pacific Asia Journal of the Association for Information Systems: Vol. 12: Iss. 2, Article 4. DOI: 10.17705/1pais.12204 Available at: https://aisel.aisnet. org/pajais/vol12/iss2/4
Deep Learning Residual-like Convolutional Neural Networks for Optic Disc Segmentation in Medical Retinal Images

Eye diseases such as glaucoma, if undiagnosed in time, can have irreversible detrimental effects, which can lead to blindness. Early detection of this disease by screening programs and subsequent treatment can prevent blindness. Deep learning architectures have many applications in medicine, especially in medical image processing, that provides intelligent tools for the prevention and treatment of diseases. Optic disk segmentation is one of the ways to diagnose eye disease. This paper presents a new approach based on deep learning, which is accurate and fast in optic disc segmentation. By Comparison proposed method with the best-known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, the proposed algorithm is much faster, which can segment the optic disc in 0.008 second with outstanding performance concerning IOU and DICE scores. Therefore, this method can be used in ophthalmology clinics to segment the optic disc in retina images and videos as online medical assistive tool.
Ordinary Differential Equation and Complex Matrix Exponential for Multi-resolution Image Registration

Autograd-based software packages have recently renewed interest in image registration using homography and other geometric models by gradient descent and optimization, e.g., AirLab and DRMIME. In this work, we emphasize on using complex matrix exponential (CME) over real matrix exponential to compute transformation matrices. CME is theoretically more suitable and practically provides faster convergence as our experiments show. Further, we demonstrate that the use of an ordinary differential equation (ODE) as an optimizable dynamical system can adapt the transformation matrix more accurately to the multi-resolution Gaussian pyramid for image registration. Our experiments include four publicly available benchmark datasets, two of them 2D and the other two being 3D. Experiments demonstrate that our proposed method yields significantly better registration compared to a number of off-the-shelf, popular, state-of-the-art image registration toolboxes.
Fine-scale geographic variations of rates of renal replacement therapy in northeastern France: Association with the socioeconomic context and accessibility to care

Background The strong geographic variations in the incidence rates of renal replacement therapy (RRT) for end-stage renal disease are not solely related to variations in the population's needs, such as the prevalence of diabetes or the deprivation level. Inequitable geographic access to health services has been involved in different countries but never in France, a country with a generous supply of health services and where the effect of the variability of medical practices was highlighted in an analysis conducted at the geographic scale of districts. Our ecological study, performed at the finer scale of townships in a French area of 8,370,616 inhabitants, investigated the association between RRT incidence rates, socioeconomic environment and geographic accessibility to healthcare while adjusting for morbidity level and medical practice patterns. Methods Using data from the Renal Epidemiology and Information Network registry, we estimated age-adjusted RRT incidence rates during 2010–2014 for the 282 townships of the area. A hierarchical Bayesian Poisson model was used to examine the association between incidence rates and 18 contextual variables describing population health status, socioeconomic level and health services characteristics. Relative risks (RRs) and 95% credible intervals (95% CrIs) for each variable were estimated for a 1-SD increase in incidence rate. Results During 2010–2014, 6,835 new patients ≥18 years old (4231 men, 2604 women) living in the study area started RRT; the RRT incidence rates by townships ranged from 21 to 499 per million inhabitants. In multivariate analysis, rates were related to the prevalence of diabetes [RR (95% CrI): 1.05 (1.04–1.11)], the median estimated glomerular filtration rate at dialysis initiation [1.14 (1.08–1.20)], and the proportion of incident patients ≥ 85 years old [1.08 (1.03–1.14)]. After adjusting for these factors, rates in townships increased with increasing French deprivation index [1.05 (1.01–1.08)] and decreased with increasing mean travel time to reach the closest nephrologist [0.92 (0.89–0.95]). Conclusion These data confirm the influence of deprivation level, the prevalence of diabetes and medical practices on RRT incidence rates across a large French area. For the first time, an association was found with the distance to nephrology services. These data suggest possible inequitable geographic access to RRT within the French health system.
Population Dynamics of Razor Clams in Samut Songkram, Thailand

We have measured the population density of razor clams, Solen regularis, as well as a related but economically less valuable species, Solen vitreus, at the same locations in the Don Hoi Lord mud flats in Samut Songkram, Thailand at monthly intervals from November, 1995 to March, 1997. The density of clams in each size class vs. time was fit by a simple population evolution model in terms of birth, growth, and death (e.g., by harvesting). The results indicate a growth rate of approximately 1 cm per month, the appearance of new clams during March-April and July-August, and intensive harvesting of larger clams during April-November. In addition to seasonal variations, a comparison with data from previous studies indicates a twelve-fold decline in the population of razor clams (S. regularis) since 1994-1995, while the population of S. vitreus declined only four-fold. We interpret this to indicate that environmental factors are responsible for a four-fold decrease in both clam populations, while intensive, selective harvesting of S. regularis has driven down its population by an additional factor of three.
Assessment of Long-Term Effects of Sports-Related Concussions: Biological Mechanisms and Exosomal Biomarkers

Concussion or mild traumatic brain injury (mTBI) in athletes can cause persistent symptoms, known as post-concussion syndrome (PCS), and repeated injuries may increase the long-term risk for an athlete to develop neurodegenerative diseases such as chronic traumatic encephalopathy (CTE), and Alzheimer’s disease (AD). The Center for Disease Control estimates that up to 3.8 million sport-related mTBI are reported each year in the United States. Despite the magnitude of the phenomenon, there is a current lack of comprehensive prognostic indicators and research has shown that available monitoring tools are moderately sensitive to short-term concussion effects but less sensitive to long-term consequences. The overall aim of this review is to discuss novel, quantitative, and objective measurements that can predict long-term outcomes following repeated sports-related mTBIs. The specific objectives were (1) to provide an overview of the current clinical and biomechanical tools available to health practitioners to ensure recovery after mTBIs, (2) to synthesize potential biological mechanisms in animal models underlying the long-term adverse consequences of mTBIs, (3) to discuss the possible link between repeated mTBI and neurodegenerative diseases, and (4) to discuss the current knowledge about fluid biomarkers for mTBIs with a focus on novel exosomal biomarkers. The conclusions from this review are that current post-concussion clinical tests are not sufficiently sensitive to injury and do not accurately quantify post-concussion alterations associated with repeated mTBIs. In the current review, it is proposed that current practices should be amended to include a repeated symptom inventory, a cognitive assessment of executive function and impulse control, an instrumented assessment of balance, vestibulo-ocular assessments, and an improved panel of blood or exosome biomarkers.
Picturing Organisms and Their Environments: Interaction, Transaction, and Constitution Loops

Changing conceptions of the relation between organisms and their environments make up a crucial chapter in the history of psychology. This may be approached by a comparative study of how schematic diagrams portray this relation. Diagrams drive the communication and the teaching of ideas, the sedimentation of epistemic norms and methods of analysis, and in some cases the articulation of novel concepts through pictographic variants. Through a sampling of schematic representations, I offer a concise comparison of how different authors, with different interests and motivations, have portrayed important aspects of the organism–environment relation. I compare example diagrams according to the features they underscore (or omit) and group them into classes that emphasize interaction, transaction, and constitution loops.
LLVM Cookbook

Over 80 engaging recipes that will help you build a compiler frontend, optimizer, and code generator using LLVM About This BookWrite a frontend for any language to generate LLVM IRCreate optimization passes to optimize the IR code using LLVM Pass Infrastructure and Pass ManagerDesign and implement structures for highly-optimized compilers using LLVM, through detailed step-by-step recipesWho This Book Is ForThe book is for compiler programmers who are familiar with concepts of compilers and want to indulge in understanding, exploring, and using LLVM infrastructure in a meaningful way in their work.This book is also for programmers who are not directly involved in compiler projects but are often involved in development phases where they write thousands of lines of code. With knowledge of how compilers work, they will be able to code in an optimal way and improve performance with clean code.What You Will Learn Introduction to LLVM modular design and LLVM tools Write a frontend for a language Add JIT support and use frontends for different languages Learn about the LLVM Pass infrastructure and the LLVM Pass Manager Create analyses and transform optimization passes Build a LLVM TOY backend from scratch Optimize the code at SelectionDAG level and allocate registers to variables In DetailLLVM is a compiler framework with libraries that provides a modern source-and target-independent optimizer, along with a code generator.This book not only explains the effective use of the compiler infrastructure that LLVM provides, but also helps you implement it in one of your projects. You start with a simple task to get you up-and-running with LLVM, followed by learning the process of writing a frontend for a language, which includes writing a lexer, a parser, and generating IR code. You will then see how to implement optimizations at different levels, generate target-independent code, and then map this generated code to a backend. Finally, you will look into the functionalities that the LLVM infrastructure provides, such as exception handling, LLVM Utility Passes, using sanitizers, the garbage collector, and how we can use these in our projects.
Susceptibility for antibiotics among faecal indicators and pathogenic bacteria in sewage treated effluents

The objective of this work was to study the prevalence of antibiotic resistance phenotypes among total coliforms (TC), E. coli, E. faecalis and Salmonella spp. in the sewage treated effluents generated from three sewage treatment plants in Penang Malaysia. Among the isolates tested, TC and E. coli occurred high resistance for cephalexin (100 and 90.47%), ampicillin (80.93 and 95.23%) and ciprofloxacin (19.06 and 14.3%) compared to E. faecalis (42.86, 71.4 and 4.7%) and Salmonella spp. (59.8, 47.46 and 14.3%) respectively. All E. coli strains, 76.18% of TC, 66.66% of E. faecalis and 35% of Salmonella spp. were multi-resistant.
Intelligent Conversational Agents in Mental Healthcare Services: A Thematic Analysis of User Perceptions

Abstract Background: The emerging Artificial Intelligence (AI) based Conversational Agents (CA) capable of delivering evidence-based psychotherapy presents a unique opportunity to solve longstanding issues such as social stigma and demand-supply imbalance associated with traditional mental health care services. However, the emerging literature points to several socio-ethical challenges which may act as inhibitors to the adoption in the minds of the consumers. We also observe a paucity of research focusing on determinants of adoption and use of AI-based CAs in mental healthcare. In this setting, this study aims to understand the factors influencing the adoption and use of Intelligent CAs in mental healthcare by examining the perceptions of actual users. Method: The study followed a qualitative approach based on netnography and used a rigorous iterative thematic analysis of publicly available user reviews of popular mental health chatbots to develop a comprehensive framework of factors influencing the user’s decision to adopt mental healthcare CA. Results: We developed a comprehensive thematic map comprising of four main themes, namely, perceived risk, perceived benefits, trust, and perceived anthropomorphism, along with its 12 constituent subthemes that provides a visualization of the factors that govern the user’s adoption and use of mental healthcare CA. Conclusions: Insights from our research could guide future research on mental healthcare CA use behavior. Additionally, it could also aid designers in framing better design decisions that meet consumer expectations. Our research could also guide healthcare policymakers and regulators in integrating this technology into formal healthcare delivery systems. Available at: https://aisel.aisnet. org/pajais/vol12/iss2/1/ Recommended Citation Prakash, Ashish Viswanath and Das, Saini (2020) "Intelligent Conversational Agents in Mental Healthcare Services: A Thematic Analysis of User Perceptions," Pacific Asia Journal of the Association for Information Systems: Vol. 12: Iss. 2, Article 1. DOI: 10.17705/1pais.12201
DCBC: A Distributed High-performance Block-Cipher Mode of Operation

: Since the rise of Big Data, working with large ﬁles became the rule and no longer the exception. Despite this fact, some data at-rest encryption modes of operation, namely CBC, are being used even though they do not take into account the heavy cost of running sequential encryption operations over a big volume of data. This led to some attempts that aim to parallelizing such operations either by only chaining isolated subsets of the plaintext, or by using hash functions to reﬂect any changes made to the plaintext before running parallel encryption operations. However, we noticed that such solutions present some security issues of different levels of severity. In this paper, we propose a Distributed version of CBC, which we refer to as DCBC, that uses an IV generation layer to ensure some level of chaining between multiple CBC encryption operations that run in parallel, while keeping CPA security intact and even adding new operations such as appending data without compromising the encryption mode’s security. We will, also, make a theoretical performance comparison between DCBC and CBC under different circumstances to study optimal conditions for running our proposed mode. We show in this comparison that our solution largely outperforms CBC, when it comes to large ﬁles.
iTLM: A Privacy Friendly Crowdsourcing Architecture for Intelligent Traffic Light Management

Vehicle-to-everything (V2X) interconnects participants in vehicular environments to exchange information. This enables a broad range of new opportunities. We propose a self learning traffic light system which uses crowdsoured information from vehicles in a privacy friendly manner to optimize the overall traffic flow. Our simulation, based on real world data, shows that the information gain vastly decreases waiting time at traffic lights eventually reducing CO2 emissions. A privacy analysis shows that our approach provides a significant level of k-anonymity even in low traffic scenarios.