Miracle-dz/UniFAR-FTUs huggingface dataset:
A Flow Experience Analysis on the Virtual Reality Artwork: La Camera Insabbiata

The virtual world is a manner in which people can perceive an alternate form of beauty. In it, users can interact through their senses of sight, hearing, and touch; their body perception briefly escapes from the real world, as if they were completely immersed in the illusion. The present study focused on the artwork La Camera Insabbiata, an award-winning virtual reality (VR) artwork. Based on the flow theory proposed by Csikszentmihalyi, this study analyzed the flow experience of the artwork. The findings revealed that a significant difference exists in the degrees of "skills," "control," and "feedback" among various age groups; a significant difference exists in the degrees of "Concentration" and "feedback" among various previous VR experience groups. The findings also indicated that no significant difference exists in the degree of flow experience degree between genders. Therefore, La Camera Insabbiata can be accepted by both men and women, making it easy to promote the artwork to the public. Experiencing this artwork through "flying" provides viewers with a sense of presence. During a VR experience, viewers are immersed in the creators' consciousness, because the viewer becomes a co-creator of the artwork. In this respect, the mind of the creator and that of the viewers can be said to merge into one.
Sociodemographic determinants and health outcome variation in individuals with type 1 diabetes mellitus: A register-based study

Background Socioeconomic status, origin or demographic attributes shall not determine the quality of healthcare delivery, according to e.g. United Nations and European Union rules. Health equity has been defined as the absence of systematic disparities and unwarranted differences between groups defined by differences in social advantages. A study was performed to investigate whether this was applicable to type 1 diabetes mellitus (T1D) care in a setting with universal, tax-funded healthcare. Methods This retrospective registry-study was based on patient-level data from individuals diagnosed with T1D during 2010–2011 (n = 16,367) in any of seven Swedish county councils (covering ~65% of the Swedish population). Health equity in T1D care was analysed through multivariate regression analyses on absolute HbA1c level at one-year follow-up, one-year change in estimated glomerular filtration rate (eGFR) and one-year change in cardiovascular risk score, using selected sociodemographic dimensions as case-mix factors. Results Higher educational level was consistently associated with lower levels of HbA1c, and so was being married. Never married was associated with worse eGFR development, and lower educational level was associated with higher cardiovascular risk. Women had higher HbA1c levels than men, and glucose control was significantly worse in patients below the age of 25. Conclusion Patients’ sociodemographic profile was strongly associated with absolute levels of risk factor control in T1D, but also with an increased annual deterioration in eGFR. Whether these systematic differences stem from patient-related problems or healthcare organisational shortcomings is a matter for further research. The results, though, highlight the need for intensified diabetes management education and secondary prevention directed towards T1D patients, taking sociodemographic characteristics into account.
Endovascular Thrombectomy >24-hr From Stroke Symptom Onset

Background: Trials have demonstrated efficacy for endovascular thrombectomy (EVT) for anterior circulation acute ischaemic stroke (AIS) up to 24-h from symptom onset. The magnitude of effect suggests benefit may exist beyond 24-h. Objectives: To perform a retrospective review of all patients undergoing EVT for anterior circulation LVO stroke beyond 24-h from symptom onset and assess safety and efficacy. Methods:A prospectively maintained database of EVT patients treated at two comprehensive stroke centers between January 2016 and December 2017 was retrospectively screened. Patients undergoing EVT for anterior circulation AIS >24-h from symptom onset were selected. Results: A total of 429 AIS patient underwent EVT in the study period. Five patients treated >24-h from symptom onset were identified. The median age was 72 (range 42–84); median ASPECTS 8 (range 6–8); median baseline-NIHSS 9 (range 4–17); and median time from symptom onset to groin puncture 44 h and 55 min (range 25:07-90:10). One patient underwent CT perfusion imaging. The remaining four patients were selected based on non-contrast CT brain and CT-angiography. Two patients had tandem cervical carotid lesions and underwent acute stenting. Modified thrombolysis in cerebral ischaemia (mTICI) 3 reperfusion was achieved in four patients. No hemorrhagic transformation occurred. All patients were alive at 90-day follow-up. Four patients achieved functional independence at 90-days (mRS 0-2). Conclusion: Endovascular thrombectomy for AIS patients beyond 24-h from symptom onset appears to be safe and effective in this limited study. There is a need for further evidence-based trials of benefit vs. risk in very prolonged time windows.
Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference

This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.
An Effective Approach for Regression Test Case Selection Using Pareto Based Multi-Objective Harmony Search

Regression testing is a way of catching bugs in new builds and releases to avoid the product risks. Corrective, progressive, retest all and selective regression testing are strategies to perform regression testing. Retesting all existing test cases is one of the most reliable approaches but it is costly in terms of time and effort. This limitation opened a scope to optimize regression testing cost by selecting only a subset of test cases that can detect faults in optimal time and effort. This paper proposes Pareto based Multi-Objective Harmony Search approach for regression test case selection from an existing test suite to achieve some test adequacy criteria. Fault coverage, unique faults covered and algorithm execution time are utilised as performance measures to achieve optimization criteria. The performance evaluation of proposed approach is performed against Bat Search and Cuckoo Search optimization. The results of statistical tests indicate significant improvement over existing approaches.
SDN for QUIC: an enhanced architecture with improved connection establishment

Quick User Datagram Protocol Internet Connection (QUIC) 1 boosts web traffic performance by solving a number of transport-layer and application-layer problems. QUIC provides key advantages to HTTP/2 such as reduced connection establishment latency, improved congestion control, forward error correction, and connection migration. However, QUIC suffers from three limitations: (1) it performs poorly under high bandwidth and heavy traffic conditions, (2) it suffers from some security issues, (3) it implements static Forwarding Error Correction (FEC) XOR "group" mechanism, which performs poorly under variable network conditions, influencing the utilized bandwidth. Software Defined Networking (SDN), being centralized by nature, provides a global view of the network. When integrated with QUIC, SDN improves resource utilization and network security as we show in this paper. We propose an SDN-enhanced QUIC that achieves improved security at higher bandwidth utilization when transferring large amounts of data.
A Metamorphic Testing Approach to NASA GMSEC's Flexible Publish and Subscribe Functionality

Today's software systems are increasingly required to be flexible which is achieved by providing various forms of loose coupling and configuration options. While loose coupling and configuration options facilitate quick adaptation to changing requirements, such flexibility increases the difficulty of system testing. It is often relatively straight-forward to create different configuration options as test cases, but it is typically much more difficult to formulate the expected system behavior, which is known as the oracle problem. NASA's GMSEC software bus is such a flexible system that serves as a central communication channel for software components based on a publish and subscribe architecture where several software components can be dynamically connected to the system. To cope with the difficulties in testing such a flexible software system, we present a metamorphic testing approach that explicitly addresses the test oracle problem. In this paper, we focus on testing the publish and subscribe functionality of GMSEC motivated by the fact that its middleware-based system architecture is the foundation of many of NASA's missions.
Observations of Computing Students on the Homogeneity of Their Classrooms

This paper aims to provide some insight into the experiences and challenges faced by a cohort of homogeneously male nal year students in a third level computing degree programme. It looks at their perceptions of how this homogeneity impacts them. Despite a large volume of research into the gender imbalance in STEM, studies of the male perspective have largely been absent from the literature. The study originally intended to examine their perceptions on how the gender imbalance impacted their education. However, the resulting research gave voice to a number of concerns. This work focuses on the concerns surrounding the industry they are entering, as well as potential outcomes of the imbalanced learning environment. This work in particular seeks to look at how the normative masculinity experienced by the students in third level that could be seen to disadvantage or hurt women also constrains the men experiencing them.
Visual fingerprint-based indoor localization

This paper proposes a new method of the visual fingerprint-based indoor localization to locate mobile robot. Specifically, the purpose is to estimate the robot location by finding the matching images in a database of geotagged images. To this end, a new framework is proposed for using five monocular cameras to collect images to improve accuracy and use phase correlation method to achieve precise localization. First, employ Discrete Cosine Transform (DCT) to compress images and reduce the dimension by Singular Value Decomposition (SVD) to form global feature vectors. Then use whitening to reduce its redundancy. Next, the image sequence with the highest similarity is founded according to the similarity of the global feature vectors. Last, employ a ceiling-facing camera to estimate the rotation angle of the robot. Experimental results show that the accuracy of the proposed method can reach 0.5cm, it achieves better localization accuracy than other approaches.
RDF shape induction using knowledge base profiling

Knowledge Graphs (KGs) are becoming the core of most artificial intelligent and cognitive applications. Popular KGs such as DBpedia and Wikidata have chosen the RDF data model to represent their data. Despite the advantages, there are challenges in using RDF data, for example, data validation. Ontologies for specifying domain conceptualizations in RDF data are designed for entailments rather than validation. Most ontologies lack the granular information needed for validating constraints. Recent work on RDF Shapes and standardization of languages such as SHACL and ShEX provide better mechanisms for representing integrity constraints for RDF data. However, manually creating constraints for large KGs is still a tedious task. In this paper, we present a data driven approach for inducing integrity constraints for RDF data using data profiling. Those constraints can be combined into RDF Shapes and can be used to validate RDF graphs. Our method is based on machine learning techniques to automatically generate RDF shapes using profiled RDF data as features. In the experiments, the proposed approach achieved 97% precision in deriving RDF Shapes with cardinality constraints for a subset of DBpedia data.
Experience of Industry Case Studies: A Comparison of Multi-Case and Embedded Case Study Methods

Context: Case studies are a useful approach for conducting empirical studies of software engineering, in part because they allow a phenomenon to be studied in its real-world context. However, given that there are several kinds of case studies, each with its own strengths and weaknesses, researchers need to know how to choose which kind to employ for a specific research study. Aim: The objective of this research is to compare two case study approaches: embedded, longitudinal case studies, and multi-case studies. Approach: We compared two actual software engineering case studies: a multi-case study involving interviews with 46 practitioners at 9 international companies engaged in offshoring and outsourcing, and a single case, participant observation embedded case study lasting 13 months in a mid-sized Irish software company. Both case studies were exploring similar problems of understanding the activities performed by members of scrum development teams. Results: We found that both multi-case and embedded case studies are suitable for exploratory research (hypothesis development) but that embedded research may also be more suitable for explanatory research (hypothesis testing). We also found that longitudinal case studies offer better confirmability, while multi-case studies offer better transferability. Conclusion: We propose a set of illustrative research questions to assist with the selection of the appropriate case study method.
Human-level performance in 3D multiplayer games with population-based reinforcement learning

Artificial teamwork Artificially intelligent agents are getting better and better at two-player games, but most real-world endeavors require teamwork. Jaderberg et al. designed a computer program that excels at playing the video game Quake III Arena in Capture the Flag mode, where two multiplayer teams compete in capturing the flags of the opposing team. The agents were trained by playing thousands of games, gradually learning successful strategies not unlike those favored by their human counterparts. Computer agents competed successfully against humans even when their reaction times were slowed to match those of humans. Science, this issue p. 859 Teams of artificial agents compete successfully against humans in the video game Quake III Arena in Capture the Flag mode. Reinforcement learning (RL) has shown great success in increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete with other agents. We used a tournament-style evaluation to demonstrate that an agent can achieve human-level performance in a three-dimensional multiplayer first-person video game, Quake III Arena in Capture the Flag mode, using only pixels and game points scored as input. We used a two-tier optimization process in which a population of independent RL agents are trained concurrently from thousands of parallel matches on randomly generated environments. Each agent learns its own internal reward signal and rich representation of the world. These results indicate the great potential of multiagent reinforcement learning for artificial intelligence research.
Leishmanicidal and Immunomodulatory Activities of the Palladacycle Complex DPPE 1.1, a Potential Candidate for Treatment of Cutaneous Leishmaniasis

The present study focused on the activity of the palladacycle complex DPPE 1.1 on Leishmania (Leishmania) amazonensis. Promastigotes of L. (L.) amazonensis were destroyed in vitro by nanomolar concentrations of DPPE 1.1, whereas intracellular amastigotes were killed at drug concentrations fivefold less toxic than those harmful to macrophages. L. (L.) amazonensis-infected BALB/c mice were treated by intralesional injection of DPPE 1.1. Animals treated with 3.5 and 7.0 mg/kg of DPPE 1.1 showed a significant decrease of foot lesion sizes and a parasite load reduction of 93 and 99%, respectively, when compared to untreated controls. Furthermore, DPPE 1.1 was non-toxic to treated animals. The cathepsin B activity of L. (L.) amazonensis amastigotes was inhibited by DPPE 1.1 as demonstrated spectrofluorometrically by use of a specific fluorogenic substrate. Analysis of T-cells populations in mice treated with DPPE 1.1 and untreated controls was performed by fluorescence-activated cell sorter (FACS). IFN-γ was measured in supernatants of lymphocytes from popliteal and inguinal lymph nodes isolated from treated and untreated mice and stimulated with L. (L.) amazonensis amastigotes extract and active TGF-β was evaluated in supernatants of foot lesions; both dosages were carried out by means of a double-sandwich ELISA assay. A significant increase of TCD4+ and TCD8+ lymphocytes and IFN-γ secretion was displayed in mice treated with DPPE 1.1 compared to untreated animals, whereas a significant reduction of active TGF-β was observed in treated mice. These findings open perspectives for further investment in DPPE 1.1 as an alternative option for the chemotherapy of cutaneous leishmaniasis.
Safe Execution of Mobile and Untrusted Code : The Model-Carrying Code Project

Starting from the Melissa email virus of 1999, threats posed by software from untrusted sources have grown enormously. Untrusted code can install spyware or steal confidential information, including identities and financial information. Even worse, it can turn an unsuspecting user’s computer into a so-called “zombie” that can be commandeered by an attacker to carry out criminal activities, including the launching of attacks on other systems on the Internet. These threats can all be eliminated by simply preventing users from accessing any untrusted code or data. However, such an approach isn’t practical: users have become accustomed to a wealth of information as well as software on the Internet that have significantly simplified their day-today activities and enhanced their productivity. Thus, the goal of our model-carrying code (MCC) project was to develop an infrastructure and software tools that enable users to access benign mobile code, while bounding their risks due to malicious mobile code. Our approach enables code producers and consumers to collaborate in order to achieve security, yet it doesn’t impose a significant burden on either one of them. This paper provides an overview of the MCC approach, surveys the scientific contributions of the project, and summarizes its practical outcomes.
Generating Domain Ontology from Chinese Customer Reviews to Analysis Fine-gained Product Quality Risk

With the rapid development of E-commerce in China, quality of the products on online shopping platforms has caused wide concern. Customer reviews, which commented by people who bought the very product, now have been one of the most important resources for analyzing product's quality risk. We can get fine-gained, aspect-oriented risk information of a product by mining its reviews. Unfortunately, people tend to write reviews with casual grammar or just omit parts of components of a sentence. Both these features will cause negative impacts when parsing the raw customer reviews directly. Thus a knowledge base which is built totally beyond the reviews could be used to analyze it despite the drawbacks above. In this paper, we generate a domain ontology from raw text in the online encyclopedia. It can be viewed as a graph whose nodes represent domain concepts and edges represent the relations between these concepts. In our work, we integrate syntactic tree structure in linear-chain CRFs for recognizing domain concepts and train SVMs and MaxEnt models on elaborate features for clarifying three types of relationship, namely "Attribute-of", "Part-of" and "Instance-of". Once the ontology has been built, product properties with potential risk will be extracted by our matching method. Experiment show that our approach achieves 64.4% precision and 82.4% recall on risky property extraction task.
Towards the targeted environment-specific evolution of robot components

This research considers the task of evolving the physical structure of a robot to enhance its performance in various environments, which is a significant problem in the field of Evolutionary Robotics. Inspired by the fields of evolutionary art and sculpture, we evolve only targeted parts of a robot, which simplifies the optimisation problem compared to traditional approaches that must simultaneously evolve both (actuated) body and brain. Exploration fidelity is emphasised in areas of the robot most likely to benefit from shape optimisation, whilst exploiting existing robot structure and control. Our approach uses a Genetic Algorithm to optimise collections of Bezier splines that together define the shape of a legged robot's tibia, and leg performance is evaluated in parallel in a high-fidelity simulator. The leg is represented in the simulator as 3D-printable file, and as such can be readily instantiated in reality. Provisional experiments in three distinct environments show the evolution of environment-specific leg structures that are both high-performing and notably different to those evolved in the other environments. This proof-of-concept represents an important step towards the environment-dependent optimisation of performance-critical components for a range of ubiquitous, standard, and already-capable robots that can carry out a wide variety of tasks.
Predicting the possibilistic score of OWL axioms through modified support vector clustering

We address the problem of predicting a score for candidate axioms within the context of ontology learning. The prediction is based on a learning procedure based on support vector clustering originally developed for inferring the membership functions of fuzzy sets, and on a similarity measure for subsumption axioms based on semantic considerations and reminiscent of the Jaccard index. We show that the proposed method successfully learns the possibilistic score in a knowledge base consisting of pairs of candidate OWL axioms, meanwhile highlighting that a small subset of the considered axioms turns out harder to learn than the remainder.
Multimodel correspondence through inter-model constraints

The synchronisation of n≥ 2 heterogenously typed models requires a thorough understanding of global consistency rules. After having related these models by determining identical entities in them, we express the global rules in terms of diagrammatic predicates imposed on a comprehensive metamodel, which integrates the structural properties of all involved model spaces. If the global rules are violated, a possible subsequent consistency restoration can make use of formal descriptions for the verification of these rules. The comprehensive metamodel is constructed in the category of directed graphs. If there is an arbitrary number of related models, comprehensive artefacts can formally be represented by colimits, i.e. by universal categorical constructions. The goal of the paper is to establish a practical algorithm for this construction. The main example, a web service integration scenario from the health care domain, also shows that relating graph morphisms may be non-injective, which is incorporated into the algorithm.