Miracle-dz/UniFAR-FTUs huggingface dataset:
BCI-Controlled Assistive Manipulator: Developed Architecture and Experimental Results

In this article, we present a control architecture for a robotic manipulator finally aimed at helping people with severe motion disabilities in performing daily life operations, such as manipulating objects or drinking. The proposed solution allows the user to focus the attention only on the operational tasks, while all the safety-related issues are automatically handled by the developed control architecture. The user commands the manipulator sending high-level commands via a P300-based brain–computer interface. A perception module, relying on an RGB-D sensor, continuously detects and localizes the objects in the scene, tracking the position of the user and monitoring the environment for identifying static and dynamic obstacles, e.g., a person entering in the scene. A lightweight manipulator is controlled relying on a task-priority inverse kinematics algorithm that handles task hierarchies composed of equality-based and set-based tasks, including obstacle avoidance and joint mechanical limits. This article describes the overall architecture and the integration of the implemented software modules, that are based on common frameworks and software libraries, such as the robotic operating system (ROS), BCI2000, OpenCV, and PCL. The experimental results on a use case scenario using a Kinova 7DOFs Jaco2 robot helping a user to perform drinking and manipulation tasks show the effectiveness of the developed control architecture.
Multistage Control of Weak Microgrid with Power Quality Improvement Based on APF

The power quality of weak microgrid is sensitive due to the nonlinear loads and power electronic devices are applied, and the adjusting automatically compensation function of equipment self is inadequate and ineffective. For this, the control characteristic of active power filter(APF)is studied, and the droop control is analyzed in weak microgrid especially under the isolated island, and the principles of parameter adjustment are proposed. Then the multistage control algorithm of weak microgrid is proposed that improve the power quality and reliability based on APF, the algorithm is that the APF execute multistage filtering process according to the characteristics of harmonic and the other source are designed under droop control scheme, and the constraint condition which considering the THD of load current makes it contributing for the stability and reliability between APF and sources. Further, the mutual influence between multistage control algorithm and transition loads shedding considering different degrees of importance are discussed so that the scheme achieve a higher reliability. Finally, the effectiveness of the control algorithm proposed that taking oil field microgrid as an example in this paper is demonstrated through simulation and analyses.
Smart Forgetting for Safe Online Learning with Gaussian Processes

The identification of unknown dynamical systems using supervised learning enables model-based control of systems that cannot be modeled based on first principles. While most control literature focuses on the analysis of a static dataset, online learning control, where data points are added while the controller is running, has rarely been studied in depth. In this paper, we present a novel approach for online learning control based on Gaussian process models. To avoid computational difficulties with growing datasets, we propose a safe forgetting mechanism. Using an entropy criterion, data points are evaluated with respect to the future trajectory of the closed loop system and are ``forgotten'' if the stability of the system can further be guaranteed. The approach is evaluated in a simulation and in a robotic experiment to show its real-time capability.
A Multimodal Educational Corpus of Oral Courses: Annotation, Analysis and Case Study

This corpus is part of the PASTEL (Performing Automated Speech Transcription for Enhancing Learning) project aiming to explore the potential of synchronous speech transcription and application in specific teaching situations. It includes 10 hours of different lectures, manually transcribed and segmented. The main interest of this corpus lies in its multimodal aspect: in addition to speech, the courses were filmed and the written presentation supports (slides) are made available. The dataset may then serve researches in multiple fields, from speech and language to image and video processing. The dataset will be freely available to the research community. In this paper, we first describe in details the annotation protocol, including a detailed analysis of the manually labeled data. Then, we propose some possible use cases of the corpus with baseline results. The use cases concern scientific fields from both speech and text processing, with language model adaptation, thematic segmentation and transcription to slide alignment.
MU-MIMO 유저 스케줄링 기법 ( A MU-MIMO User Scheduling Mechanism based on Active CSI Exchange )

User scheduling boosts the Multi-User Multi-Input Multi-Output (MU-MIMO) gain by selecting an optimal set of users to increase the 802.11 Wi-Fi system capacities. Many kinds of user scheduling algorithms, however, fail to realize the advantages of MU-MIMO due to formidable Channel State Information (CSI) overhead. In this paper, we propose a user scheduling method considering such CSI exchange overhead and its MAC protocol, called ACE (Active CSI Exchange based User Scheduling for MU-MIMO Transmission). Unlike most proposals, where user scheduling is performed after an Access Point (AP) receives CSI from all users, ACE determines the best user set during the CSI exchange phase. In particular, the AP broadcasts a channel hint about previously scheduled users, and the remaining users actively send CSI reports according to their Effective Channel Gains (ECGs) calculated from the hint. Through trace-driven MATLAB simulations, we prove that the proposed scheme improves the throughput gain significantly.
Effects of cue congruence and perceived cue authenticity in online group buying

The study investigates how the congruence of online deal popularity and star rating influences service quality expectation in online group buying (OGB) websites. It also investigates the role of authenticity perceptions of online cues. ,Two experiments are used to assess the effects of congruence between deal popularity and star rating on service quality expectation for service deals in an OGB website. ,The findings suggest that a combination of congruently high deal popularity and high star rating has a stronger effect on expected service quality than a combination of congruently low cues. The findings further suggest that expected service quality is greater under the combination of high deal popularity and low star rating than the combination of low deal popularity and high star rating, showing the differences between incongruent cue combinations. The findings also show the moderating effect of consumer authenticity perceptions of cues on the expected service quality. ,The novel contribution of the study is to extend cue congruence theory to explain how congruent online information cues and the consumers' authenticity perceptions of the cues influence consumers' judgment of online deals. The contribution is validated empirically in the context of OGB. The findings advance current knowledge concerning how consumers use online information cues.
Machine Learning for Smartphone-based Monitoring and Treatment of Unipolar and Bipolar Disorders

Background: Bipolar disorder is a prevalent mental disease imposing a high societal burden. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention and eventually help prevent costly hospitalizations. While several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood one or more days ahead of time. Objective: The objective of this work is to examine the feasibility of forecasting daily subjective mood based on daily self-assessments collected from bipolar disorder patients via a smartphone-based system in a randomized clinical trial. Methods: We apply hierarchical Bayesian regression models, a multi-task learning method, to account for individual differences and forecast mood up to seven days ahead based on 15,975 smartphone self-assessments from 84 bipolar disorder patients participating in a randomized clinical trial. We report the results of two time-series cross-validation one day ahead prediction experiments corresponding to two different real-world scenarios and compare the outcomes to commonly used baselines methods. We then apply the best model to evaluate a seven-day forecast. Results: The best performing model used a history of 4 days of self-assessments to predict future mood scores with historical mood being the most important predictor variable. The proposed hierarchical Bayesian regression model outperformed pooled and separate models in a one-day forecast time series cross-validation experiment and achieved predicted R" = 0.51 and RMSE = 0.32 for mood scores on a scale of -3 to 3. When increasing the forecast horizon, forecast errors also increase and the forecast regresses towards the mean of the data distribution. Conclusions: We found that our proposed method can forecast mood several days ahead with low error compared to common baseline methods. The applicability of a mood forecast in clinical treatment of bipolar disorder is also discussed.
A defeasible logic programming approach to the integration of rules and ontologies

The Semantic Web is a vision of the current Web where resources have exact meaning assigned in terms of ontologies, thus enabling agents to reason about them. As inconsistencies cannot be treated by standard reasoning approaches, we use Defeasible Logic Programming (DeLP) to reason with possibly inconsistent ontologies. In this article we show how to integrate rules and ontologies in the Semantic Web. We present an approach that can be used to suitably extend the SWRL standard by incorporating classical and default negated literals in SemanticWeb rules in the presence of incomplete and possibly inconsistent information. The rules and ontologies will be interpreted as a DeLP program allowing the rules to reason on top of a set of (possibly inconsistent) ontologies.
Graphview: a Kahn Process Network Visualization Tool

Compaan is a compiler that automatically generates Kahn Process Networks (KPNs) from small sequential programs written in Matlab. A designer can maps these KPNs onto different compute platforms like FPGAs, the Intel IXP network processors, or the IBM Cell processor. Neverthless, a KPN generated by Compaan contains lots of complex data and the designer may get overwhelmed by this amount of data. Therefore, to help the designer in the mapping process, we want to use visualization to organize the complex data present in KPNs. In this presentation, we present the Graphview editor that is a visualization tools for Kahn Process Networks (KPN). We build Graphview as an Eclipse plugin on top of the Graphical Editing Framework (GEF) as it allows developers to take an existing application model and quickly create a rich graphical editor. We will explain how we realized the Graphview editor and show results. [1]
A Modeling Method for Automatic Extraction of Offshore Aquaculture Zones Based on Semantic Segmentation

Monitoring of offshore aquaculture zones is important to marine ecological environment protection and maritime safety and security. Remote sensing technology has the advantages of large-area simultaneous observation and strong timeliness, which provide normalized monitoring of marine aquaculture zones. Aiming at the problems of weak generalization ability and low recognition rate in weak signal environments of traditional target recognition algorithm, this paper proposes a method for automatic extraction of offshore fish cage and floating raft aquaculture zones based on semantic segmentation. This method uses Generative Adversarial Networks to expand the data to compensate for the lack of training samples, and uses ratio of green band to red band (G/R) instead of red band to enhance the characteristics of aquaculture spectral information, combined with atrous convolution and atrous space pyramid pooling to enhance the context semantic information, to extract and identify two types of offshore fish cage zones and floating raft aquaculture zones. The experiment is carried out in the eastern coastal waters of Shandong Province, China, and the overall identification accuracy of the two types of aquaculture zones can reach 94.8%. The results show that the method proposed in this paper can realize high-precision extraction both of offshore fish cage and floating raft aquaculture zones.
Risk-Adapted, Combined-Modality Therapy for Unfavorable Pediatric Hodgkin Lymphom

Background and Objectives: Risk-adapted therapy for children with HL is directed toward high survival, minimal toxicity and optimal quality of life, with long term follow up. We assess the impact of prognostic factors associated with local treatment failure of pediatric HL patients with unfavorable criteria treated with combined modality: Alternating ABVD (Doxorubicin, Bleomycin, Vinblastine and Decarbazine) and COEP (Cyclophosphamide, Oncovin, Etoposide and Prednisone) chemotherapy and response-based, involved-field radiation for newly diagnosed unfavorable pediatric HL patients, also will detect toxicities and long-term complications observed in the patients. Methods: This prospective study was carried out from January 2010 to January 2018, with a median follow up of 74 months (range 8 - 103 months). 54 patients were eligible for this study stratified into two groups: intermediate risk (IR) and high-risk group (HR). Patients were treated with (4 - 6 cycles) and (6 - 8 cycles) respectively of alternating ABVD/COEP chemotherapy followed by involved-field radiation therapy (IFRT): 15 Gy for patients achieved complete response, and 25.5 Gy for those achieved a partial response. Results: 27 patients were IR and 27 patients were HR. There were 16 treatment failures; 5 patients had progressive disease; and 11 patients had a relapse. 9 patients died from their disease progression. The 5-year overall survival (OS) and event-free survival (EFS) rates (±SE) were 81.8% ± 5.7% and 71.8% ± 6.2% respectively. Multivariate analysis revealed that the only independent factor for inferior OS was radiotherapy. Conclusion: Treatment results of unfavourable HL patients in our study are satisfactory for with IR group but not for HR group who needs intensification of therapy. Radiotherapy is considered as a cornerstone in the treatment of the patients with unfavourable criteria with better assessment of early responders needed by PET-CT to identify patients at risk for relapse.
Toxoplasmosis congénita en el valle de México. Resultados de una serie de casos

ANTECEDENTES: En México prevalecen cepas “virulentas” de Toxoplasma gondii, por lo que el cuadro clínico puede ser diferente a los reportados para regiones donde circulan cepas no virulentas, como Europa, Estados Unidos y Canadá. MÉTODOS: Se reporta una serie de casos con toxoplasmosis congénita reclutados a partir de 2005 por tamiz prenatal o postnatal, o porque llegaron como casos clínicos al INP, y que conforman parte de una serie de casos.RESULTADOS: Todos los pacientes presentaron manifestaciones clínicas, pero el grupo de tamiz prenatal presentó una infección más localizada y menos grave, principalmente con espasticidad y retraso psicomotor leve. El grupo de tamiz postnatal presentó características clínicas intermedias, con retraso psicomotor leve, moderado o grave, bajo peso al nacimiento, micro o hidrocefalia, ictericia, hepatomegalia, esplenomegalia, calcificaciones intracraneales y espasticidad. Los pacientes más afectados fueron aquellos que se presentaron como casos clínicos, con una amplia variedad de manifestaciones clínicas y el peor pronóstico, incluida la muerte. Seis pacientes presentaron comorbilidades. Veinte casos fueron positivos para dos o más pruebas de laboratorio; el resto sólo para una.CONCLUSIONES. Nuestros resultados apoyan que la toxoplasmosis congénita en México es más grave que en otros países, y que urge implementar tamiz prenatal efectivo, para prevención y profilaxis.PALABRAS CLAVE: complejidad diagnóstica; evaluación prenatal; heterogeneidad clínica; tamiz postnatal; tamiz prenatal; toxoplasmosis congénita; Toxoplasma gondii
The Euler-Equation Approach in Average-Oriented Opinion Dynamics

We consider the models of average-oriented opinion dynamics. An opinion about an event is distributed among the agents of a social network. There are an optimization problem and two game-theoretical models when players as centers of influence aim to make the opinions of the agents closer to the target ones in a finite time horizon minimizing their costs. The optimization problem and the games of competition for the agents’ opinion are linear-quadratic and solved using the Euler-equation approach. The optimal strategies for optimization problem and the Nash equilibria in the open-loop strategies for the games are found. Numerical simulations demonstrate theoretical results.
Transcriptomics of resistance response in Coffea arabica L.

Coffee is one of the most important agricultural export commodities in the world. It represents the he main export resource for some developing countries. Despite the tremendous economic importance of coffee, our understanding of its biology is still very poor and the genetics of Coffea is almost non existing. The reasons for such a low research effort are several and they range from the low economic resources allocated to fundamental genetic research to the complexity of its genome.
Dance movement strategies training to help rebuild social capital in Colombia

Building social capital within a post-conflict scenario is key to achieving sustainable peace. The authors implemented an intervention consisting of a 120-h training programme in dance movement strategies in five violence-affected municipalities in Colombia (n = 150). The aim of the intervention was to assess any possible changes in the participants’ states of mindfulness, bodily connection, emotional intelligence, somatic complaints, aggressive reaction, empathy, agency, and subjective emotional experience. We selected the tested variables as conditions necessary to rebuild social capital within a community affected by violence. Post-test measures revealed statistically significant changes in mindfulness, bodily connection, emotional intelligence and regulation, somatic complaints, aggressive reaction, agency, perspective taking, sleep and appetite. Key implications for practice Dance movement strategies help to create the necessary individual conditions for rebuilding social capital in regions heavily affected by an armed conflict Training the community leaders in dance movement strategies positively impacts some aspects that are associated with better trust and reciprocity within communities Community leaders trained in dance movement strategies should be encouraged to replicate these strategies within their own communities.
Robust Controller Design for an Autonomous Underwater Vehicle

Worldwide there has been a surge of interest in Autonomous Underwater Vehicles (AUV). The ability to operate without human intervention is what makes this technology so appealing. On the other hand, the absence of the human narrows the AUV operation to its control system, computing, and sensing capabilities. Therefore, devising a robust control is mandatory to allow the feasibility of the AUV. Motivated by this fact, this thesis aims to present, discuss and evaluate two linear control solutions being proposed for an AUV developed by a consortium led by CEiiA. To allow the controller design, the dynamic model of this vehicle and respective considerations are firstly addressed. Since the purpose is to enable the vehicle’s operation, devising suitable guidance laws becomes essential. A simple waypoint following and station keeping algorithm, and a path following algorithms are presented. To devise the controllers, a linear version of the dynamic model is derived considering a single operational point. Then, through the decoupling of the linear system into three lightly interactive subsystems, four Proportional Integral Derivative controllers (PIDs) are devised for each Degree Of Freedom (DOF) of the vehicle. A Linear Quadratic Regulator (LQR) design, based on the decoupling of the linear model into longitudinal and lateral subsystems is also devised. To allocate the controller output throughout the actuators, a control allocation law is devised, which improves maneuverability of the vehicle. The results present a solid performance for both control methods, however, in this work, LQR proved to be slightly faster than PID.
Personal Productivity Management in the Digital Age: Measures from Research and Use of Conventional Tools

In view of the ongoing alarming numbers of incapacity to work due to mental illness, it is important to pay attention to the factors that maintain long-term productivity of the individual. Recent research is concerned with examining relevant parameters that are measurable through technology and play a role for recognizing productivity factors such as cognitive performance or stress. However, in practice there are constraints regarding the available data sources and motives of people to use tools for self-tracking and management. In this article, we first present results from a literature review on productivity measures from research and then, complement it with initial results from an online questionnaire, which asked for the use of conventional tools by individuals. Besides frequencies of usage, we highlight major drivers for people to use applications for collecting data and managing oneself.
HEPATOPROTECTIVE ACTIVITY OF PANCHAGAVYA GHRITA AGAINST CARBONTETRACHLORIDE INDUCED HEPATOTOXICITY IN RATS

Objective: To investigate the hepatoprotective activity of Panchagavya Ghrita (PG) against CCl 4 induced hepatotoxicity. Methods: The hepatoprotective activity of PG was tested against carbontetrachloride induced hepatotoxicity in albino rats. The degree of protection was determined by measuring levels of serum marker enzymes like serum glutamate oxaloacetate transaminase (SGOT) serum glutamate pyruvate transaminase (SGPT), alkaline phosphatase (ALP) and acid phosphatase (ACP). The histological studies were also carried out. Silymarin was used as the standard drug for comparison. Results: Administration of Panchagavya Ghrita (150-300 mg/kg, p.o.) markedly prevented CCl 4 induced elevation of levels of serum GPT, GOT, ACP and ALP. The results are comparable to that of silymarin. A comparative histopathological study of liver exhibited almost normal architecture, as compared to control group. Conclusion: Treatment with Panchagavya Ghrita significantly reduced the CCl 4 induced hepatotoxicity. A comparative histological study of liver from different groups further confirmed the hepatoprotective activity of Panchagavya Ghrita.