Miracle-dz/UniFAR-FTUs huggingface dataset:
Talk2Car: Taking Control of Your Self-Driving Car

A long-term goal of artificial intelligence is to have an agent execute commands communicated through natural language. In many cases the commands are grounded in a visual environment shared by the human who gives the command and the agent. Execution of the command then requires mapping the command into the physical visual space, after which the appropriate action can be taken. In this paper we consider the former. Or more specifically, we consider the problem in an autonomous driving setting, where a passenger requests an action that can be associated with an object found in a street scene. Our work presents the Talk2Car dataset, which is the first object referral dataset that contains commands written in natural language for self-driving cars. We provide a detailed comparison with related datasets such as ReferIt, RefCOCO, RefCOCO+, RefCOCOg, Cityscape-Ref and CLEVR-Ref. Additionally, we include a performance analysis using strong state-of-the-art models. The results show that the proposed object referral task is a challenging one for which the models show promising results but still require additional research in natural language processing, computer vision and the intersection of these fields. The dataset can be found on our website: http://macchina-ai.eu/
Learning in situ: a randomized experiment in video streaming

We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a "simple" scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the variability and heavy-tailed nature of network and algorithm behavior create hurdles for robust learned algorithms in this area. We developed an ABR algorithm that robustly outperforms other schemes in practice, by combining classical control with a learned network predictor, trained with supervised learning in situ on data from the real deployment environment. To support further investigation, we are publishing an archive of traces and results each day, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control.
Efficient Path Finding Method Based Evaluation Function in Large Scene Online Games and Its Application

In large scene online games, eﬃcient path ﬁnding is an important technology. In this paper, we provide an eﬃcient path ﬁnding method based evaluation function in large scene online games. Firstly, we divide the big scene map into many map blocks, build map areas, organize them in index table. After that, we load the scene map dynamic. Then, we set the evaluation function, and we use the heuristic search to complete the path ﬁnding in one map area or cross map areas. We smooth the path and roam in large scene online game. Finally, we implement our system and provide the results. The experiment results show that the eﬀect of dividing the scene map into map blocks of 256*256 is the best, when dynamic load scene map. And our methods is better than the A* path ﬁnding method, especially in ﬁnding smooth and natural path when virtual character ﬁnd path cross map areas. Our system has been used in practical projects, and our system is running smoothly and stably.
SeqScout: Using a Bandit Model to Discover Interesting Subgroups in Labeled Sequences

It is extremely useful to exploit labeled datasets not only to learn models but also to improve our understanding of a domain and its available targeted classes. The so-called subgroup discovery task has been considered for a long time. It concerns the discovery of patterns or descriptions, the set of supporting objects of which have interesting properties, e.g., they characterize or discriminate a given target class. Though many subgroup discovery algorithms have been proposed for transactional data, discovering subgroups within labeled sequential data and thus searching for descriptions as sequential patterns has been much less studied. In that context, exhaustive exploration strategies can not be used for real-life applications and we have to look for heuristic approaches. We propose the algorithm SeqScout to discover interesting subgroups (w.r.t. a chosen quality measure) from labeled sequences of itemsets. This is a new sampling algorithm that mines discriminant sequential patterns using a multi-armed bandit model. It is an anytime algorithm that, for a given budget, finds a collection of local optima in the search space of descriptions and thus subgroups. It requires a light configuration and it is independent from the quality measure used for pattern scoring. Furthermore, it is fairly simple to implement. We provide qualitative and quantitative experiments on several datasets to illustrate its added-value.
Streaming Cost Minimization-based Optimal Peer Selection Method

In this paper, we first proposed a modified incentive mechanism to encourage the cooperative behavior among peers. Then, we defined a simple streaming cost function by considering the heterogeneity of the Internet, the limitation of end-to-end delay, the unpredictable behavior of peers and the peers’ social properties, and proposed an optimal peer selection algorithm by minimizing the total streaming cost subject to the download rate constraint. Finally, we showed that the proposed scheme can significantly improve the performance of a P2P streaming system.
Multi-touch-based assessment of hand mobility, dexterity and function. Preliminary study of validity, reliability and sensitivity to upper limb impairment severity in individuals with stroke

Impairments of the upper limb function are among the most common and disabling sequelae after stroke, and their rehabilitation poses a major challenge for current physical and occupational therapy interventions. While restoration of certain degree of proximal and global movements and function is expected, the recovery of premorbid hand mobility and dexterity is less likely to occur. Although great efforts are usually made to rehabilitate these skills, with customized particular and analytic exercises, their assessment is rarely made with the same level of specificity. In general, hand and finger mobility and functionality are indirectly assessed in the clinic with standardized clinical scales and tests that require the use of these skills to perform some tasks. Although these scales are easy to administer, they can present poor accuracy, be biased, and do not allow for isolated assessment of joints and movements. Multi-touch technology enables interaction with electronic devices, such as smartphones and tablets, with on-screen finger touches. Although there is a very limited number of studies that investigate the potential of this technology to rehabilitate hand mobility and dexterity, the use of this technology for assessment of these skills remains unexplored. This study evaluates the feasibility of a multi-touch-based assessment tool (app) of hand mobility and dexterity in a sample of post-stroke survivors. Our results provide preliminary evidence that this technology can be used to design valid and reliable instruments for assessing hand mobility and dexterity that, in addition, are sensitive to the severity of the motor impairment of the upper limb after stroke, which could complement conventional clinical assessment.
Parametrised Data Sampling for Fairness Optimisation

Improving machine learning models’ fairness is an active research topic. Most approaches to it focus on a particular fairness definition. We propose a parametrised training-set-resampling method, which allows optimising in both a fairness-definition and classificationmodel agnostic manner. Given a binary protected attribute and a binary label, we correct the positive rate for both the favoured and unfavoured groups through four different resampling methods. Three fairness definitions are adjusted to ratio forms allowing us to measure a classifier’s level of fairness. Results of experiments over three public benchmark datasets (Census Income, COMPAS and German Credit) are presented, with the ultimate goal of optimising the correction level for a particular dataset and fairness definition. ACM Reference Format: Vladimiro G. Zelaya, Paolo Missier, and Dennis Prangle. 2019. Parametrised Data Sampling for Fairness Optimisation. In Proceedings of Explainable AI for Fairness, Accountability & Transparency Workshop (KDD XAI). ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn
Vectorized Beam Search for CTC-Attention-Based Speech Recognition

This paper investigates efﬁcient beam search techniques for end-to-end automatic speech recognition (ASR) with attention-based encoder-decoder architecture. We accelerate the decoding process by vectorizing multiple hypotheses during the beam search, where we replace the score accumulation steps for each hypothesis with vector-matrix operations for the vectorized hypotheses. This modiﬁcation allows us to take advantage of the parallel computing capabilities of multi-core CPUs and GPUs, resulting in signiﬁcant speedups and also enabling us to process multiple utterances in a batch simultaneously. Moreover, we extend the decoding method to incorporate a recurrent neural network language model (RNNLM) and connectionist temporal classiﬁcation (CTC) scores, which typically improve ASR accuracy but have not been investigated for the use of such parallelized decoding algorithms. Experiments with LibriSpeech and Corpus of Spontaneous Japanese datasets have demonstrated that the vectorized beam search achieves 1.8 × speedup on a CPU and 33 × speedup on a GPU compared with the original CPU implementation. When using joint CTC/attention decoding with an RNNLM, we also achieved 11 × speedup on a GPU while maintaining the beneﬁts of CTC and RNNLM. With these beneﬁts, we achieved almost real-time processing with a small latency of 0.1 × real-time without streaming search process.
The differential effects of Jesus and God on distributive behaviour

This study shows that different belief concepts within the same religion can have different effects on distributive behaviour. A dictator game experiment measures the causal effects of the concepts of God and Jesus on both the pro-sociality of Christians and their propensity to discriminate against LGBTQ people. The concept of Jesus significantly raises the amounts Christians donate, but the concept of God does not. Christians are found, at borderline significance, to discriminate against LGBTQ people, but this discrimination is not significantly increased by the concepts of Jesus or God. Neither concept significantly affects the behaviour of a non-Christian sample.
Study of Serum Interleukin-17 and Interleukin-4 Levels in South Indian Leprosy Patients across Clinico-Histopathological Spectrum

Mycobacterium leprae. Tuberculoid leprosy (TT) has predominance of CD4+T cells and lepromatous leprosy (LL) of CD8+ and type 2 cytokines. In this study, clinico-histopathological profile of leprosy has been analyzed and correlated with serum Interleukin-17 and interleukin-4 levels. Thirty newly diagnosed leprosy cases fulfilling the inclusion criteria and thirty healthy controls constituted the study carried out over a period of one and half years. Clinical assessment, slit skin smear (SSS) and skin biopsy were done and classified according to Ridley Jopling. Serum IL-17 and IL-4 were estimated by ELISA. Among 30 cases, most were of 41-50 years (43.3%). Twelve (40%) were borderline leprosy who constituted majority. Four cases were in Type 1 reaction (T1R) and three in Type 2 reaction (T2R). Clinico-histopathological correlation was 100% TT/LL poles of spectrum, it varied in borderline leprosy. Serum IL-17 was significantly lower in leprosy cases (Mean = 3.8969 pg/mL), compared to controls (P = 0.001). Serum IL-4 was elevated in cases (Mean = 37.8346 = pg/mL) than controls (Mean = 6.1693 pg/mL) (P = 0.001), highest level being LL patients (Mean = 48.3403 pg/mL). Serum IL-4 had the highest level in type 2 reactions (T2R) (Mean = 48.5563 pg/ml) (P = 0.269). Defective secretion of IL-17 appears to correlate with disease acquisition and progression towards lepromatous pole. Overproduction of IL-4 in patients with lepromatous leprosy may predisposeto development of ENL. Further studies are required on a larger representative numbers to for better and definitive role of these cytokines in leprosy.
miR-320 and Inflammation Regulation in Experimental Autoimmune Encephalomyelitis Through Interference With Tumor Growth Factor-β Signaling Pathway

Background: MicroRNAs are small non-coding RNAs that regulate gene expression and involve in many cellular and physiological mechanisms. Recent studies have revealed that dysregulation of microRNAs might contribute to autoimmune disorders such as Multiple Sclerosis (MS). Based on these findings, we examined the potential role of miR-320 isoforms; miR-320-3p and miR-320-5p, in the context of autoimmune neuroinflammation and pathogenesis of EAE, which is an animal model of MS.Materials and Methods: The expression levels of miR-320-3p and miR-320-5p, and their predicted target genes, TGFBR2 and Smad2, were quantified in the CNS tissue in mice with Experimental Autoimmune Encephalomyelitis (EAE) using RT-PCR method. The expression was also examined in splenocytes macrophages and astrocytes. To examine the interaction of miR-320-3p and miR-320-5p with the 3′-UTR of potential target transcripts, the mimic sequences of both isoforms were transfected into splenocytes and then examined by RT-PCR. Results: The expression of both isoforms of miR-320 significantly increased in different phases of EAE and activated lymphocytes, whereas the levels of their predicted target genes, Smad2 and TGFBR2 decreased in these cells. Obtained data revealed that miR-320-5p level significantly increased in activated macrophages and astrocytes; however, the miR­320-3p level did not show significant changes in these cells after Lipopolysaccharide (LPS) stimulation. The levels of TGFBR2 and Smad2 decreased in transfected splenocytes.Conclusion: Our findings suggest that upregulation of miR-320 isoforms might be involved in the neuroinflammation and pathogenesis of MS through targeting and suppression of TGFBR2 and Smad2, i.e. protective genes in MS.
Antimicrobial Efficacy of Low Concentration PVP-Silver Nanoparticles Deposited on DBD Plasma-Treated Polyamide 6,6 Fabric

In this study, a low concentration (10 μg·mL−1) of poly(N-vinylpyrrolidone) (PVP)-coated silver nanoparticles (AgNPs) were deposited by spray and exhaustion (30, 70 and 100 °C) methods onto untreated and dielectric barrier discharge (DBD) plasma-treated polyamide 6,6 (PA66) fabric. DBD plasma-treated samples showed higher AgNP deposition than untreated ones for all methods. After five washing cycles, only DBD plasma-treated samples displayed AgNPs on the fabric surface. The best-performing method was exhaustion at 30 °C, which exhibited less agglomeration and the best antibacterial efficacy against S. aureus (4 log reduction). For E. coli, the antimicrobial effect showed good results in all the exhaustion samples (5 log reduction). Considering the spray method, only the DBD plasma-treated samples showed some bacteriostatic activity for both strains, but the AgNP concentration was not enough to have a bactericidal effect. Our results suggest DBD plasma may be a low cost and chemical-free method for the preparation of antibacterial textiles, allowing for the immobilization of a very low—but effective—concentration of AgNPs.
EDGE AND TEXTURE PRESERVING HYBRID ALGORITHM FOR DENOISING INFIELD ULTRASOUND MEDICAL IMAGES

Medical Ultrasound Imaging is a rapidly growing allied field of Imaging Technology which is widely used around the world for diagnosis by clinicians. Non ionizing radiation is what makes ultrasound imaging safe for non-invasive imaging of human tissues. However, visual quality of ultrasound images poses a challenge for the medical practitioner due to multiple reflections of ultrasound signals. Numerous attempts have been made previously to improve the visual quality of the ultrasound images. The paper presents a novel, structured visual quality improvement mechanism based on daubechies (db) wavelet transform. In the proposed methodology, the segmentation of the ultrasound medical image is carried out with the help of active contour technique. The segmented image and the original image are transformed into wavelet domain. Selected wavelet coefficients are combined to improve the visual quality in terms of contrast and edges enhancements. Visual quality enhancement is emphasized with experimentation on medical ultrasound images obtained from AMMA Hospital radiology scanning center in India. Usefulness of the proposed algorithm is judged against denoising algorithms such as empirical mode decomposition (EMD), linear filtering (LF), median filtering (MF), wiener filtering (WF), wavelet based hard and soft thresholding and wavelet block based soft and hard thresholding. Visual quality metrics computed are peak signal to noise ratio (PSNR), normalized cross correlation (NCC), edge strength (ES), image quality index (IQI) and structured similarity index (SSI). Simulations demonstrate that the proposed enhancement algorithm outperformed the existing de-noising algorithms, instigating for actual medical application.
Handling class label noise in medical pattern classification systems

Pattern classification systems play an important role in medical decision support. They allow to automatize and speed-up the data analysis process, while being able to handle complex and massive amounts of information and discover new knowledge. However, their quality is based on the classification models built, which require a training set. In supervised classification we must supply class labels to each training sample, which is usually done by domain experts or some automatic systems. As both of these approaches cannot be deemed as flawless, there is a chance that the dataset is corrupted by class noise. In such a situation, class labels are wrongly assigned to objects, which may negatively affect the classifier training process and impair the classification performance. In this contribution, we analyze the usefulness of existing tools to deal with class noise, known as noise filtering methods, in the context of medical pattern classification. The experiments carried out on several real-world medical datasets prove the importance of noise filtering as a pre-processing step and its beneficial influence on the obtained classification accuracy.
Compiler Optimization of FPGA-Based Acceleration of Programs

Spatial Computing (SC) (most often referred to as reconfigurable computing) is a computing paradigm where the computation is formed as a circuit where data is streamed rather than a collection of instructions (as in temporal computing). The increased popularity of this model in the past few years is mostly due to the increases in both size and speed of commercially available FPGAs. In its most common form one or more FPGA is used as a code accelerator: frequently executed code segments (loops) are formed into a circuit that is mapped onto the FPGA(s) while the host CPU manages the streaming of data through that circuit. Reported speed-ups have ranged from one over three orders of magnitude [1][2][3]. These speedups are due to two main factors (1) The very large scale of parallelism that can be mapped on a typical FPGA and (2) The efficiency of the SC model as compared to the classical von Neumann model [4]. While initially this model seemed to be confined to the high-performance embedded computing market, in the past year we have seen a rapid growth in interest in the traditional supercomputing segment: companies such as Cray, SGI and SRC currently offer machines that support
Recent progresses in data and knowledge integration for decision support in agri-food chains

: Research in Agri-food and related fields dealing with sustainability has undergone important changes in the past years and tends to be more integrative, collaborative, and interdisciplinary. In this article, we present an excerpt of recent and ongoing projects in the French community proposing data integration guided by an ontology for collective decision support in the Agri-food domain. The use of ontologies for primary production and secondary production, i
Extracellular-vesicles derived from human Wharton-Jelly mesenchymal stromal cells ameliorated cyclosporin A-induced renal fibrosis in rats

Objective: To observe the therapeutic effects of human Wharton-Jelly mesenchymal stromal cells derived extracellular vesicles (MSCs-EVs) for cyclosporin-A-induced renal injury in rats and further to investigate the mechanism. Methods: EVs from MSCs were made using the ultra-centrifugation method. The cyclosporin A-induced renal injury model in rats was set up, and MSCs-EVs were administrated at d7 and d21 intravenously. The animals were sacrificed at d28, and the serum and kidneys were obtained. Renal fibrosis was assessed using Masson’s staining and α-SMA IHC staining. Renal function was determined using serum creatinine. The SOD and malondialdehyde (MDA) in the renal tissues were also assayed. In vitro, HK2 cells were injured by CsA for 24 h as well as incubated with MSCs-EVs administration, and ROS and α-SMA expression were assessed. Results: In this animal study, renal fibrosis was alleviated and the epithelial-mesenchymal transition (EMT) was suppressed after the MSCs-EVs treatment compared with the control group. Renal function was also improved. The oxidative stress which was reflexed by the MDA and SOD change was declined after the EVs therapy. In vitro, NRK-52E cells were injured by CsA. The EVs therapy reduced α-SMA expression and ROS generation. Conclusion: MSCs-EVs alleviated CsA-induced renal injury and improved renal function, which could be attributed to the anti-oxidative property of MSCs-EVs.