Miracle-dz/UniFAR-FTUs huggingface dataset:
Report of the Spanish Computing Scientific Society on Computing Education in Pre-University Stages

In recent years, many developed countries have addressed computing education in primary or secondary education. Potential benefits for students in these educational stages and for the society are great, existing a wide range of approaches to such a computing education. The Spanish Computing Scientific Society (SCIE), with the support of the Spanish Board of Deans of Computing Schools (CODDII) created in September 2017 a working group formed by experts in either computing or computing education with the goal of elaborating a report with specific recommendations about this issue for the Spanish government. The report was issued in July 2018 and recommends, in conformance to Spanish educational laws, to establish a matter called "Informatics". This matter would preferably be implemented as a mandatory course offered from primary to secondary education. The course contents comprise six areas: programming, computers and operating systems, networks and the Internet, data, digital content, and security. The course would include issues of both digital literacy and computing as a discipline, with digital literacy contents based on the European DIGCOMP framework.
HeuRL: A Heuristically Initialized Reinforcement Learning Method for Autonomous Driving Control Task

Although reinforcement learning (RL) shows great intelligence in many simulation tasks, it hasn’t been widely applied in real-world vehicle control tasks due to the simulation-to-real-world (Sim2Real) transferring difficulties. Vehicle models and road conditions in real world can be very different from those in simulators. As a result, the RL models trained by simulators usually fail and need to be trained again under a new environment, which is dangerous and time-consuming. Some hand-craft heuristic methods, by contrast, are independent of environmental characters and perform more reliably in an unfamiliar situation. In this paper, we introduce a heuristically initialized RL model (HeuRL), which sped up the learning convergence by 4 times and decreased the collisions by 90% during the training process under a new environment. The experiments were conducted in The Open Racing Car Simulator (TORCS), an open-source platform for real-time car racing simulation.
EEG analysis of the Brain Language Processing oriented to Intelligent Teaching Robot

Here we present a robotic language teaching assistant that also monitor the EEG band relative power to check the concentration and interest level of its students during recall phase. If the concentration level of student is lower, the word can be repeated and presented in other ways. In that way, lessons will be repeated and altered depending upon students' focus. It's an assisted learning paradigm where individuals first learn the Chinese characters with and without pinyin and then trained themselves. And for the judgment of training, there is recall phase. Neuronal oscillations with its relative frequency relate in important ways to memory processes and aid as a model for learning oscillatory activity in cognition. More imprecisely theta relativity is increased markedly during training phase while memorizing of character without pinyin at the parietal lobe. While at same instant gamma relativity dropped at the same region. It is also observed that the gamma relativity is increased with pinyin associated with the character during training and recall phase at temporal lobe. The results showed a strong association between theta and gamma relativity during learning, training and recall phase. These findings add to our understanding of how theta and gamma oscillations interact within frontal, central, parietal temporal and occipital lobes for the service of memory and how can we use these results to increase our efficiency during learning the Chinese language.
Diarrhoea, enteric pathogen detection and nutritional indicators among controls in the Global Enteric Multicenter Study, Kenya site: an opportunity to understand reference populations in case–control studies of diarrhoea

Abstract Given the challenges in accurately identifying unexposed controls in case–control studies of diarrhoea, we examined diarrhoea incidence, subclinical enteric infections and growth stunting within a reference population in the Global Enteric Multicenter Study, Kenya site. Within ‘control’ children (0–59 months old without diarrhoea in the 7 days before enrolment, n = 2384), we examined surveys at enrolment and 60-day follow-up, stool at enrolment and a 14-day post-enrolment memory aid for diarrhoea incidence. At enrolment, 19% of controls had ⩾1 enteric pathogen associated with moderate-to-severe diarrhoea (‘MSD pathogens’) in stool; following enrolment, many reported diarrhoea (27% in 7 days, 39% in 14 days). Controls with and without reported diarrhoea had similar carriage of MSD pathogens at enrolment; however, controls reporting diarrhoea were more likely to report visiting a health facility for diarrhoea (27% vs. 7%) or fever (23% vs. 16%) at follow-up than controls without diarrhoea. Odds of stunting differed by both MSD and ‘any’ (including non-MSD pathogens) enteric pathogen carriage, but not diarrhoea, suggesting control classification may warrant modification when assessing long-term outcomes. High diarrhoea incidence following enrolment and prevalent carriage of enteric pathogens have implications for sequelae associated with subclinical enteric infections and for design and interpretation of case–control studies examining diarrhoea.
Towards Providing Debugging in the Domain-Specific Modeling Languages for Software Agents

Domain-specific modeling languages (DSMLs) for Multi-agent Systems (MAS) mostly provide checks and validations on modeled systems according to the related syntax and semantics descriptions. However, they do not have a built-in support for debugging MAS models which makes the control of model correctness difficult. Hence, in this paper, we present our ongoing work which aims at providing debugging inside MAS DSMLs. We describe two possible ways of deriving debuggers for MAS DSMLs. The first alternative is based on the construction of a mapping between MAS model entities and the generated code while the second one considers the metamodel-based description of the operational semantics of executing agents. Pros and cons of each approach are also discussed.
Engineered SH2 domains with tailored specificities and enhanced affinities for phosphoproteome analysis

Protein phosphorylation is the most abundant post‐translational modification in cells. Src homology 2 (SH2) domains specifically recognize phosphorylated tyrosine (pTyr) residues to mediate signaling cascades. A conserved pocket in the SH2 domain binds the pTyr side chain and the EF and BG loops determine binding specificity. By using large phage‐displayed libraries, we engineered the EF and BG loops of the Fyn SH2 domain to alter specificity. Engineered SH2 variants exhibited distinct specificity profiles and were able to bind pTyr sites on the epidermal growth factor receptor, which were not recognized by the wild‐type Fyn SH2 domain. Furthermore, mass spectrometry showed that SH2 variants with additional mutations in the pTyr‐binding pocket that enhanced affinity were highly effective for enrichment of diverse pTyr peptides within the human proteome. These results showed that engineering of the EF and BG loops could be used to tailor SH2 domain specificity, and SH2 variants with diverse specificities and high affinities for pTyr residues enabled more comprehensive analysis of the human phosphoproteome.
Social Networks and Railway Passenger Capacity: An Empirical Study Based on Text Mining and Deep Learning

Railway passenger transport is essential to modern transportation in China. The prediction of railway passenger capacity is of vital importance for ensuring the safety of railway transportation. This paper introduces social network text data into the prediction of railway passenger capacity. In the process of analyzing social network text data, text mining methods are used to analyze the text data, and the information related to railway passenger flow is extracted from the text and added to the prediction model. Meanwhile, in order to obtain better prediction results, this paper applies deep learning method on the data. The combination of text mining and deep learning method has greatly improved the accuracy of our prediction model. Experimental results show that a good accuracy rate has been achieved.
Catch and Prolong: recurrent neural network for seeking track-candidates

One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hits, produced when flying particles fire detector coordinate planes. Unfortunately, the tracking is seriously impeded by the famous shortcoming of multiwired, strip and GEM detectors due to appearance in them a lot of fake hits caused by extra spurious crossings of fired strips. Since the number of those fakes is several orders of magnitude greater than for true hits, one faces with the quite serious difficulty to unravel possible track-candidates via true hits ignoring fakes. We introduce a renewed method that is a significant improvement of our previous two-stage approach based on hit preprocessing using directed K-d tree search followed a deep neural classifier. We combine these two stages in one by applying recurrent neural network that simultaneously determines whether a set of points belongs to a true track or not and predicts where to look for the next point of track on the next coordinate plane of the detector. We show that proposed deep network is more accurate, faster and does not require any special preprocessing stage. Preliminary results of our approach for simulated events of the BM@N GEM detector are presented.
Effects of Lombard Reflex on the Performance of Deep-learning-based Audio-visual Speech Enhancement Systems

Humans tend to change their way of speaking when they are immersed in a noisy environment, a reflex known as Lombard effect. Current speech enhancement systems based on deep learning do not usually take into account this change in the speaking style, because they are trained with neutral (non-Lombard) speech utterances recorded under quiet conditions to which noise is artificially added. In this paper, we investigate the effects that the Lombard reflex has on the performance of audio-visual speech enhancement systems based on deep learning. The results show that a gap in the performance of as much as approximately 5 dB between the systems trained on neutral speech and the ones trained on Lombard speech exists. This indicates the benefit of taking into account the mismatch between neutral and Lombard speech in the design of audio-visual speech enhancement systems.
GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition

As a unique biometric feature that can be recognized at a distance, gait has broad applications in crime prevention, forensic identification and social security. To portray a gait, existing gait recognition methods utilize either a gait template, where temporal information is hard to preserve, or a gait sequence, which must keep unnecessary sequential constraints and thus loses the flexibility of gait recognition. In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames. We propose a new network named GaitSet to learn identity information from the set. Based on the set perspective, our method is immune to permutation of frames, and can naturally integrate frames from different videos which have been filmed under different scenarios, such as diverse viewing angles, different clothes/carrying conditions. Experiments show that under normal walking conditions, our single-model method achieves an average rank-1 accuracy of 95.0% on the CASIA-B gait dataset and an 87.1% accuracy on the OU-MVLP gait dataset. These results represent new state-of-the-art recognition accuracy. On various complex scenarios, our model exhibits a significant level of robustness. It achieves accuracies of 87.2% and 70.4% on CASIA-B under bag-carrying and coat-wearing walking conditions, respectively. These outperform the existing best methods by a large margin. The method presented can also achieve a satisfactory accuracy with a small number of frames in a test sample, e.g., 82.5% on CASIA-B with only 7 frames. The source code has been released at https://github.com/AbnerHqC/GaitSet.
The Application of Relay to Massive Non-Orthogonal Multiple Access

This paper considers the application of relay to enhance the performance of massive non-orthogonal multiple access (NOMA) systems and solve the challenge of channel state information acquisition in the case of a massive number of users. First, we design a general framework for a multiple-relay-aided massive NOMA system. Then, we analyze the performance of the multiple-relay-aided massive NOMA system, and derive a closed-form expression for a lower bound on the spectral efficiency. In particular, we reveal the impact of system parameters on the spectral efficiency via asymptotic analysis in three important scenarios, e.g., a large number of antennas at the base station (BS), a high transmit power at the BS or the relays, and a large number of relays. To further improve the spectral efficiency in the context of massive access, we propose two effective schemes to optimize the transmit power at the BS and relays, respectively. Finally, extensive simulation results validate the effectiveness of the proposed multiple-relay-aided massive NOMA scheme.
Comparative Analysis of Two Loop Integer and Fractional Order PID Controller for Inverted Pendulum

Inverted pendulum is highly non-linear and unstable system, hence it's a challenging plant to control. So, the aim is to effectively control the inverted pendulum. This paper focuses on control of inverted pendulum by using proportional integral derivative (PID) and fractional order PID (FOPID) controllers. The system is controlled by using two loop PID controller. Further improvement in control is obtained by using two loop FOPID controller. The comparison of both the techniques is shown. Controllers are analyzed based on peak overshoot, control efforts, and performance indices. Simulation results demonstrate that the two loop FOPID controller gives improved response as compared to the two loop PID controller.
The Dual‐Domain Porosity Apparatus: Characterizing Dual Porosity at the Sediment/Water Interface

The characterization of pore‐space connectivity in porous media at the sediment/water interface is critical in understanding contaminant transport and reactive biogeochemical processes in zones of groundwater and surface‐water exchange. Previous in situ studies of dual‐domain (i.e., mobile/less‐mobile porosity) systems have been limited to solute tracer injections at scales of meters to hundreds of meters and subsequent numerical model parameterization using fluid concentration histories. Pairing fine‐scale (e.g., sub‐meter) geoelectrical measurements with fluid tracer data over time alleviates dependence on flowpath‐scale experiments, enabling spatially targeted characterization of shallow sediment/water interface media where biogeochemical reactivity is often high. The Dual‐Domain Porosity Apparatus is a field‐tested device capable of variable rate‐controlled downward flow experiments. The Dual‐Domain Porosity Apparatus facilitates inference of dual‐domain parameters, i.e., mobile/less‐mobile exchange rate coefficient and the ratio of less mobile to mobile porosity. The Dual‐Domain Porosity Apparatus experimental procedure uses water electrical conductivity as a conservative tracer of differential loading and flushing of pore spaces within the region of measurement. Variable injection rates permit the direct quantification of the flow‐dependence of dual‐domain parameters, which has been theorized for decades but remains challenging to assess using existing experimental methodologies.
From Innovative Niches to a Cooperative IoT Ecosystem Creating Business Value for Federated and Interoperable IoT Platforms

This paper presents a novel approach for maximising IoT resources’ value built on top of IoT platform federation considerations. The work presented is based on the premise of unifying the fragmented IoT environment that directly impacts on the market and matching heterogeneous user groups while supporting existing businesses. Moreover, it proposes, business value creation in a cooperative environment based on the principles of the sharing economy and the experience and lessons learnt from the symbIoTe project. symbIoTe is part of the IoT European Platform Initiative (IoT-EPI) and aims to enable IoT platform federation towards more cooperative IoT ecosystems.
Lexical Meaning Formal Representations Enhancing Lexicons and Associated Ontologies

The paper represents an outline of a new technique aimed at improvement of lexicon-to-ontology mapping technology. The technique is integrated within the Ontolex-Lemon lexicon model by supplying a lexical concept of an ontology class with a description logic based formal definition. A natural language definition used to describe the lexical concept is transformed into a DL-definition; the resulting DL-definition is associated with a graph-like join of domain ontology properties. As a result, a related lexical unit is mapped to several bound ontology units rather than to a single ontology class. DL-based and graph-based lexical meaning formal representations are applied for lexical sense disambiguation within a lexicon and for extension of class and property taxonomies of an associated ontology.
Depolarizing Chipless RFID Sensor Tag for Characterization of Metal Cracks Based on Dual Resonance Features

In structural health monitoring (SHM) applications, chipless RFID sensor systems are potential for acquiring multiple information of metal defects, such as defect orientation and size, due to its broadband operation. However, the issue of limited readability of chipless RFID tags in the real environment and particularly on a large metallic platform raises the need for applying the cross-polarization reading, and thus depolarizing sensor tags are required. This paper introduces a depolarizing chipless RFID sensor tag design for characterization of metal cracks based on dual resonance features. The proposed chipless RFID sensor tag integrates a modified circular patch antenna with notched edges as the crack sensor and diagonal bent dipoles as the tag ID. The notches on the diagonal edges make the circular patch able to depolarize the incident waves and generates two resonances for sensing purpose. To obtain the frequency signature of the sensor tag, a chipless RFID sensor reader is developed from an ultra-wideband (UWB) radar module applying the short-time Fourier transform (STFT). The simulation and experimental studies show that the dual resonance features generated by the depolarizing circular patch can be used to indicate the crack orientation as well as to characterize the crack width in millimeter. With the sensor’s readability over a large metallic platform and the usability of the dual resonance features in crack characterization, the developed chipless RFID system is potential for real implementations of the internet of things (IoT)-based SHM.
Evaluation Criteria for Inside-Out Indoor Positioning Systems Based on Machine Learning

Real-time tracking allows to trace goods and enables the optimization of logistics processes in many application areas. Camera-based inside-out tracking that uses an infrastructure of fixed and known markers is costly as the markers need to be installed and maintained in the environment. Instead, systems that use natural markers suffer from changes in the physical environment. Recently a number of approaches based on machine learning (ML) aim to address such issues. This paper proposes evaluation criteria that consider algorithmic properties of ML-based positioning schemes and introduces a dataset from an indoor warehouse scenario to evaluate for them. Our dataset consists of images labeled with millimeter precise positions that allows for a better development and performance evaluation of learning algorithms. This allows an evaluation of machine learning algorithms for monocular optical positioning in a realistic indoor position application for the first time. We also show the feasibility of ML-based positioning schemes for an industrial deployment.
Registration and Merging Maps with Uncertainties

In this paper we address the problem of registering and merging two maps in two dimensions, given covariance estimates of the two maps. We show that if two maps are given in the same coordinate system, then the problem of merging them in a statistically optimal way can be formulated as a linear least squares problem, but if they are given in different coordinate systems as well the problem becomes highly non-linear and nonconvex. We show how we can relax the problem slightly in order to optimize over the registration (i.e. putting the two maps in the same coordinate system) and at the same time optimize over the merged map. The approach is based on finding all stationary points of the optimization problem and evaluating these to choose the global optimum. We show on synthetic data that in many cases the proposed approach gives better results than naively registering and merging the maps. We also show results on real data, where we merge maps given by time-of-arrival measurements, and in these cases simpler linear methods perform just a good as the proposed method.
Does Predictability Play a Role in Task Management? An Experimental Study With a Financial Trading Simulation

In many complex time-critical tasks such as financial trading, cyber security monitoring, and patient monitoring in critical care, external interruptions and multiple-task situations disrupt the flow of tasks performed by operators leading to errors and accidents. There is an abundance of work reported on interruptions, which informs system designers and researchers on the potential cost of interruptions at different points within a task. However, a gap exists in our understanding of the relationship between interruption disruptiveness and the predictability of events that require an operator's response. To understand this better, we conducted an experiment involving 22 participants and a financial trading task. The experiment involved two levels of predictability (low and high) and two levels of task load (low and high). The experiment showed that task load had an overall negative effect on events. The results also showed that interruptions negatively affected responses to predictable events. However, we found that interruptions did not affect responses to unpredictable events. Overall, our research suggests that to leverage the role of predictability, the goal-activation model should be used to determine the impact of various design options about visual cues and predictable-trend durations. The research also reveals that unpredictable events may be cognitively different from predictable events when understanding the influence of interruptions on work, suggesting that interruption management tools may need to treat the situational context (predictable or unpredictable) differently, in providing a supportive workflow for the management of interruptions.