Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Hand-Guidance during Human Robot Collaborative Task Execution: a Case Study

We present and discuss a human-robot collaboration system suitable for supervising the execution of structured manipulation tasks in industrial assembly scenarios. As a case study, we consider the application domain proposed in the context of the project (PON R& I 2014-2020) ICOSAF (Integrated collaborative systems for Smart Factory) in which a human operator physically interacts with a collaborative robot (Cobot) to perform multiple item insertion tasks in a shared workspace. The proposed system combines hierarchical task orchestration and human intention recognition during human-robot interaction through hand-guidance. We provide an overview of the system discussing an initial experimental evaluation.
IdeaNet: Potential Opportunity Discovery for Business Innovation

This paper introduces an ongoing project for mining potential business opportunities from the existing business innovation cases. Crevate (a consulting company) has collected thousands of cases from news articles and columns that are in textual narratives. We aim to transform the cases into knowledge graphs that cover what kinds of ideas (e.g., untact) are applied to which industrial areas (e.g., cafe). By using link prediction methods, we will be able to evaluate the prominence of combinations (e.g., untact cafe) between ideas and domains. This study focuses on explaining the collected cases and knowledge graphs with running examples.
The Utility of Neural Network Test Coverage Measures

In this position paper, we are interested in what test coverage measures can, and cannot, tell us about neural networks. We begin with a review of the role of test coverage measures in traditional development approaches for safety-related software. We show how those coverage measures, in the neural network sense, cannot achieve the same aims as their equivalents in the traditional sense. We provide indications of approaches that can partially meet those aims. We also indicate the utility of current neural network coverage measures.
A Framework for Intelligent Collaborative Enterprise Systems. Concepts, opportunities and challenges

This paper presents a framework for Intelligent Collaborative Enterprise Sys - tems (ICES) and discusses its evolution in the world of Big Data and Artificial Intelligence (AI) technologies for Decision Making (DM). The premise of this paper is to demonstrate the need for enterprise systems to evolve from being data and information oriented to intelligence sharing systems capitalizing on modern-day technologies of AI and machine learning. A collaborative enterprise system results from sophistication in communications technologies combined with vastly dispersed big data and its accompanying analytics. Col - laborations within an enterprise system framework comprise multiple Cloud-based big data storages, sourcing of data from end-user IoT devices and back-end servers, storage and security of data, execution of business processes across multiple organizations and offering Analytics-as-a-Service to the users and consumers. Furthermore, the intelligence within these collaborations utilizes machine learning algorithms to provide continuous optimiza - tion and efficient decision-making processes for the entire enterprise eco-system. The out - come of our study is the delineation of a conceptual framework examining key elements that comprise ICES. We analysed the evolution of ICES, and how big data and its analyt - ics based on techniques of artificial Intelligence improves decision making and prediction. We have outlined concepts and challenges of the conceptual framework implementation.
A Semidynamic Bidirectional Clustering Algorithm for Downlink Cell-Free Massive Distributed Antenna System

Cell-free massive distributed antenna system (CF-MDAS) can further reduce the access distance between mobile stations (MSs) and remote access points (RAPs), which brings a lower propagation loss and higher multiplexing gain. However, the interference caused by the overlapping coverage areas of distributed RAPs will severely degrade the system performance in terms of the sum-rate. Since that clustering RAPs can mitigate the interference, in this paper, we investigate a novel clustering algorithm for a downlink CF-MDAS with the limited-capacity backhaul. To reduce the backhaul burden and mitigate interference effectively, a semidynamic bidirectional clustering algorithm based on the long-term channel state information (CSI) is proposed, which has a low computational complexity. Simulation results show that the proposed algorithm can efficiently achieve a higher sum-rate than that of the static clustering one, which is close to the curve obtained by dynamic clustering algorithm using the short-term CSI. Furthermore, the proposed algorithm always reveals a significant performance gain regardless of the size of the networks.
Physicochemical and Functional Properties of Freeze-Dried and Oven Dried Corn Gluten Meals

ABSTRACT The physicochemical and functional properties of convection oven- and freeze-dried gluten meals of two corn varieties were evaluated. The physicochemical properties (water solubility index, water absorption index, Hunter color parameters, and bulk density) and functional properties (water absorption, oil absorption, least gelation concentration, protein solubility, and emulsification properties) of convection-oven and freeze-dried corn gluten meals were compared with each other and soy flour. Freeze-dried corn gluten meals was observed to have lower bulk density (0.244–0.263 kg/m3) and was lighter in color (high L and ΔE) compared to their counterpart convection oven–dried gluten meals. Freeze-dried gluten meals from both corn varieties showed significantly higher oil absorption, water absorption, pH, emulsification, and protein solubility compared to oven-dried corn gluten meals. The gluten meals from both corn varieties had lower water absorption and bulk density but higher oil absorption than soy flour, suggesting the hydrophobic nature of corn proteins. Corn gluten meals formed thin (pourable) emulsions compared to soy flour emulsions, which were thick salad dressing type. Freeze- and convection oven–dried corn gluten meals showed significantly lower protein solubility measured at different pH than soy flour.
Development of a Wizard-of-Oz based Framework for QTrobot to Facilitate the Learning of Handwriting in Children with Dysgraphia

This article presents a framework for a companion robot aimed at facilitating the learning of handwriting in children with dysgraphia. The framework is composed by a Wizard of Oz (WOZ) interface, a library of social behaviors and a perception system. The WOZ interface has been realized as a web service via Flask for cross-platform use, controlling the robot through ROS. A robot behaviors library has been designed by integrating robot gestures, facial expressions, and speech. The visual perception of the robot exploited a RGB-Depth camera to recognize humans in terms of their skeleton. The presented framework is a prototype that will be evaluated in the field during long-term child robot interactions. This framework is represents a basic building block in the development of a semi-autonomous system for the training of handwriting and fine motor skills.
Classification Confidence Scores with Point-wise Guarantees

Quantifying the probability of a machine learning prediction being correct on a given test point enables users to better decide how to use those predictions. Conﬁ-dence scores are pointwise estimates of such probabilities. Ideally, these would be the label probabilities (a.k.a. the labeling rule ) of the data generating distribution. However, in learning scenarios the learner does not have access to the labeling rule. The learner aims to ﬁgure out a best approximation of that rule based on training data and some prior knowledge about the task at hand, both for predicting a label and for possibly providing a conﬁdence score. We formulate two goals for conﬁdence scores, motivated by the use of these scores for safety-critical applications. First, they must not under-estimate the probability of error for any test point. Second, they must not be trivially low for most points. We consider a few common types of learner’s prior knowledge and provide tools for obtaining point-wise conﬁdence scores based on such prior knowledge and the relation between the test point and the training data. We prove that under the corresponding prior knowledge assumptions, our proposed tools meet these desired goals.
Using WhatsApp for focus group discussions: ecological validity, inclusion and deliberation

WhatsApp’s ubiquity in many people’s everyday lives points at new possibilities for conducting online and mobile focus groups. Yet, research on the benefits and potential pitfalls of this is negligible. This paper offers new empirical insights from using the method as part of a digital ethnography with young activists in Western Kenya. The presence of WhatsApp in participants’ everyday lives offers a context with high ecological validity. The paper suggests that this opens up new options for designing online focus groups, transcending the traditional categorisation between synchronous and asynchronous interactions and some limitations of both approaches. WhatsApp also offers opportunities for creating more inclusive group discussions. Using discourse analysis of the WhatsApp focus group, the paper also finds that this familiarity and inclusivity affords the potential for group deliberation, which can be particularly valuable in participatory research.
Evaluating the Impact of Drone Signaling in Crosswalk Scenario

The characteristic pillars of a city are its economy, its mobility, its environment, its inhabitants, its way of life, and its organization. Since 1980, the concept of smart city generally consists of optimizing costs, organization, and the well-being of inhabitants. The idea is to develop means and solutions capable of meeting the needs of the population, while preserving resources and the environment. Owing to their little size, their flexibility, and their low cost, Unmanned Aerial Vehicles (UAV) are today used in a huge number of daily life applications. UAV use cases can be classified into three categories: data covering (like surveillance and event covering), data relaying (like delivery and emergency services), and data dissemination (like cartography and precise agriculture). In addition, the interest to Cooperative Intelligent Transportation Systems (C-ITS) has risen in these recent years, especially in the context of smart cities. In such systems, both drivers and traffic managers share the information and cooperate to coordinate their actions to ensure safety, traffic efficiency, and environment preservation. In this work, we aimed at introducing a UAV in a use case that is likely to happen in C-ITS. A conflict is considered involving a car and a pedestrian. A UAV observes from the top of the scene and will play the role of the situation controller, the information collector, and the assignment of the instructions to the car driver in case of a harmful situation to avoid car-pedestrian collision. To this end, we highlight interactions between the UAV and the car vehicle (U2V communication), as well as between the UAV and infrastructure (U2I communication). Hence, the benefit of using UAV is emphasized to reduce accident gravity rate, braking distance, energy consumption, and occasional visibility reduction.
POLITECNICO DI TORINO Corso di Laurea Magistrale in Ingegneria Civile FINITE ELEMENT ANALYSIS OF THE STRUCTURAL BEHAVIOUR OF THE PORTREATH HARBOUR WALL UNDER WAVE LOADING

The Portreath Harbour wall is today what remains of the vital Portreath Harbour, one of the busiest harbours of Cornwall in the past. The structure was damaged during a storm in 2014, which washed away the iconic Monkey Hut at the top of the wall, and again in 2018 when storm Eleanor completely destroyed the toe of the structure. Repair works were undertaken during the years, which rebuilt the Monkey Hut, the destroyed part of the wall and increased the stiffness of the structure. In this work, a study of the Portreath harbour wall structural behaviour under wave loading is presented. After the review of an appropriate literature, a preliminary investigation of the structure’s material and of the geometry has been conducted by an in situ survey. Particularly, a Schmidt hammer test was performed. Analysis has been carried out by creating a FEM model of both the damaged parts of the wall and results were analysed and discussed. For the model of the front of the wall, which regards the part damaged in 2014, a 3D model of the structure was created and both blocks and mortar were modelled. It has been found that the collapse was due to a progressive damage of the masonry. Indeed, the highest storm wave generated stresses below the material strength properties. For the part damaged in 2018, a parametric study was conducted in order to define the failure load. Since no wave theories were applicable in that part, a CFD analysis was run and the actual wave load was calculated. Again, it was below the failure load. The failure load was also used for testing the rebuilt wall. The study is a preliminary work and further investigation with appropriate theories are required for a better understanding.
346-351 Milani M.pmd

Metronidazole resistance is an important factor related to failure in the treatment of Helicobacter pylori. The mutation in the rdxA and frxA genes is the most important cause of resistance to metronidazole. Since the resistance rate of metronidazole is high in our region, we decided to assess the frequency of these mutations among H. pylori clinical isolates. Antral gastric biopsy specimens were cultured and minimal inhibitory concentrations (MICs) of metronidazole were determined by the E-test method. The rdxA and frxA genes were amplified in all isolates through the use of PCR with the specific primers. PCR products were purified for sequencing. The resultant sequences were compared with the wild type reference sequences to find any possible mutations. According to our findings, the rate of metronidazole resistance was 77%, with the MICs ranging from 0.25-1 μg/ml for metronidazolesensitive group and from 16-256 μg/ml for resistance group. H. pylori isolates containing a single mutation in rdxA or frxA genes demonstrated a low MIC (8-16 μg/ml), while those containing mutations in both genes showed a higher MIC (32-256 μg/ml). In this study, all resistant H. pylori isolates contained single or multiple nucleotide substitutions in the mentioned genes. Nevertheless, no nucleotide substitutions were found in the sensitive clinical isolates. The results of our study showed that the mutations in rdxA are mostly related to metronidazole resistance, and mutations in frxA are able to enhance H. pylori resistance.
Classification of Chaotic Signals of the Recurrence Matrix Using a Convolutional Neural Network and Verification through the Lyapunov Exponent

This study classified chaotic time series data, including smooth and nonsmooth problems in a dynamic system, using a convolutional neural network (CNN) and verified it through the Lyapunov exponent. For this, the classical nonlinear differential equation by the Lorenz model was used to analyze a smooth dynamic system. The vibro-impact model was used for the nonsmooth dynamic system. Recurrence is a fundamental property of a dynamic system, and a recurrence plot is a representative method to visualize the recurrence characteristics of reconstructed phase space. Therefore, this study calculated the Lyapunov exponent by parametric analysis and visualized the corresponding recurrence matrix to show the dynamic characteristics as an image. In addition, the dynamic characteristics were classified using the proposed CNN model. The proposed CNN model determined chaos with an accuracy of more than 92%.
14-26 Basir R.pmd

Background Emergence of chloroquine (CQ) resistance among different strains of Plasmodium falciparum is the worst incident that has ever faced the dedicated efforts to eradicate malaria. The main cause of CQ resistance is over-activity of the pumping mechanism that ousts CQ outside the DV. This urged the scientists to look for other alternatives or adjuvants that augment its action. CQ The study aimed to test the potential of five coumarin derivatives, namely; umbeliferon, esculetin, scopoletine, herniarin and 3-aminocoumarine to inhibit plasmodium growth and reverse CQ resistance in Plasmodium falciparum K1 and 3D7. They are highly ubiquitous in nature and are famous by their diverse pharmacological effects. SYBRE green-1 based drug sensitivity assay was used to screen the effect of CQ and each coumarin on the parasite growth and isobologram technique was to assess the interaction of the coumarins with CQ. Effect of each coumarin on both RBCs and Vero cells stability as well as on RBCs fragility were screened to exclude any toxic impact on normal cells. On the other hand, their effect on hemozoin formation was screened to investigate about their molecular mechanism. For molecular characterization, Their antioxidant properties were determined using the conventional in vitro tests and their characters were obtained from Molinspiration Simulation Software. Results showed that all of them were safe to human cells, have weak to moderate plasmodial growth inhibitory effect and only umbeliferon, 3aminocoumarin and esculetin has interacted effectively with CQ. These actions are neither correlated with hemozoin formation inhibition nor to the antioxidant mechanisms. Further studies recommended to investigate the mechanism of their action. Overall, all the tested coumarins are not ideal to be used in the conventional malaria therapy and only umbeliferon, 3-aminocoumarin and esculetin can be suggested to potentiate CQ action.
Game-Theoretic Planning for Self-Driving Cars in Multivehicle Competitive Scenarios

In this article, we propose a nonlinear receding horizon game-theoretic planner for autonomous cars in competitive scenarios with other cars. The online planner is specifically formulated for a multiple-car autonomous racing game, in which each car tries to advance along a given track as far as possible with respect to the other cars. The algorithm extends previous work on game-theoretic planning for single-integrator agents to be suitable for autonomous cars in the following ways: 1) by representing the trajectory as a piecewise polynomial; 2) incorporating bicycle kinematics into the trajectory; and 3) enforcing constraints on path curvature and acceleration. The game-theoretic planner iteratively plans a trajectory for the ego vehicle and then the other vehicles in sequence until convergence. Crucially, the trajectory optimization includes a sensitivity term that allows the ego vehicle to reason about how much the other vehicles will yield to the ego vehicle to avoid collisions. The resulting trajectories for the ego vehicle exhibit rich game strategies such as blocking, faking, and opportunistic overtaking. The game-theoretic planner is shown to significantly outperform a baseline planner using model-predictive control, which does not take interaction into account. The performance is validated in high-fidelity numerical simulations with three cars, in experiments with two small-scale autonomous cars, and in experiments with a full-scale autonomous car racing against a simulated vehicle (video is available at https://youtube.com/playlist? list=PLmIcLEh8KMje4rYBqRANDuKvqFvj7LCRp).
GEOSTATISTICAL MODELLING OF RESERVOIR QUALITY OVER'BRIGHT' FIELD,NIGER DELTA.

Article history Received: 14 January 2021 Accepted: 26 January 2021 Published Online: 31 January 2021 The quality of any hydrocarbon-bearing reservoir is vital for a successful exploitation work. The reservoir quality is a function of its petrophysical parameters. Hence the need to model these properties geostatistically in order to determine the quality away from well locations.Composite logs for four wells and 3-D seismic data were used for the analysis. A reservoir named Sand X was mapped and correlated across wells 1 through 4. The four reservoir quality indicators Effective porosity, permeability, volume of shale and net-to-grosswere estimated and modelled across the field. Sequential Gaussian simulation algorithm was employed to distribute these properties stochastically away from well locations and five realizations were generated. The volume of shale varied from 0.025 (Well 1, second realization) to 0.18(Well 2, first realization). The net-to-gross varied from 0.81 to 0.96 in wells 3 and 4 respectively, for the third realization, while the effective porosity varied from 0.125 to 0.295 for the fifth realization in Wells 3 and 4 respectively. The permeability is above 5000mD at all the existing well locations.These realizations were ranked using Lp norm statistical tool to pick the best for further evaluation. The reservoir quality deduced from the analyzed indicators was favourably high across the reservoir.The application of geostatistics has laterally enhanced the log data resolution away from established well locations.
In Silico Metabolic Modeling Reveals Potential Muscle Metabolite Markers of Insulin Resistance in Newly Diagnosed Diabetic Patients

Background Type 2 diabetes mellitus (T2DM) is a challenging and globally ubiquitous metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a great role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose hemostasis and the development of insulin resistance and T2DM. Here, we attempted to find metabolic dysregulations that are associated with the onset of T2DM. Besides, metabolite markers of T2DM were explored. Methods We reconstructed a human muscle-specific metabolic model and applied it to perform metabolic analysis in newly diagnosed T2DM patients. We investigated the metabolism reprogramming by using two topology-based and constraint-based approach. Moreover, we applied a machine learning method to predict potential metabolite markers of insulin resistance in muscle.Results Our results showed that metabolic alterations have occurred in carbohydrate, fatty acids, lipids, amino acids, and inositol phosphate metabolisms as well as pathways implicated in building extracellular matrix (ECM). Also, dysregulation of coenzyme Q10 metabolism was observed. Moreover, 13 exchange metabolites were predicted as the potential metabolite markers of insulin resistance in skeletal muscle. The efficiency of these markers in detecting insulin-resistant muscle was validated using a separate muscle gene expression data from another diabetes-related study. Conclusion In this study, the most updated muscle-specific metabolic model was generated and successfully was validated. This model was used for the investigation of metabolic disturbances at the onset of T2DM. Our results indicated the significance of ECM metabolites in insulin resistance, and reinforce the role of coenzyme Q10 as a candidate for further research in insulin resistance and T2DM treatment. The model is freely available and can be used for other muscle metabolic studies. We also predicted metabolite markers of insulin resistance in the skeletal muscle, which can be considered for further empirical investigations.