Miracle-dz/UniFAR-FTUs huggingface dataset:
Win, Draw, or Lose? Global Positioning System-Based Variables’ Effect on the Match Outcome: A Full-Season Study on an Iranian Professional Soccer Team

The aim of the study was to determine the between-match and between-halves match variability of various Global Positioning System (GPS) variables and metabolic power average (MPA) in competitions, based on the match results obtained by professional soccer players over a full season. Observations on individual match performance measures were undertaken on thirteen outfield players competing in the Iranian Premier League. The measures selected for analysis included total duration, accelerations in zones (AccZ1, 2, and 3), decelerations in zones (DecZ1, 2, and 3), and MPA collected by the Wearable Inertial Measurement Unit (WIMU). The GPS manufacturer set the thresholds for the variables analyzed as follows: AccZ1 (<2 m·s−2); AccZ2 (2 to 4 m·s−2); AccZ3 (>4 m·s−2); DecZ1 (<−2 m·s−2); DecZ2 (−2 to −4 m·s−2); DecZ3 (>−4 m·s−2). The results revealed significant differences between wins and draws for the duration of the match and draws compared to wins for the first- half duration (p ≤ 0.05; ES = 0.36 [−0.43, 1.12]), (p ≤ 0.05; ES = −7.0 [−8.78, −4.78], respectively. There were significant differences on AccZ1 during the first-half between draws and defeats (p ≤ 0.05; ES = −0.43 [−1.32, 0.46]), for AccZ3 in the second-half between draws and defeats (p ≤ 0.05; ES = 1.37 [0.48, 2.25]). In addition, there were significant differences between wins and draws (p ≤ 0.05; ES = 0.22 [−0.62, 1.10]), and wins and defeats for MPA in the first- half (p ≤ 0.05; ES = 0.34 [−0.65, 1.22]). MPA showed further differences between draws and defeats in the second- half (p ≤ 0.05; ES = 0.57 [−0.22, 1.35]). Descriptive analysis revealed differences between the first and second half for wins in AccZ2 (p = 0.005), DecZ2 (p = 0.029), and MPA (p = 0.048). In addition, draws showed significant differences between the first and second half in duration, AccZ1, AccZ2, and DecZ2 (p = 0.008), (p = 0.017), (p = 0.040), and (p = 0.037) respectively. Defeats showed differences between the first and second half in AccZ1, AccZ3, and MPA (p = 0.001), (p = 0.018), and (p = 0.003) respectively. In summary, the study reveals large variations between the match duration, accelerometer variables, and MPA both within and between matches. Regardless of the match outcome, the first half seems to produce greater outputs. The results should be considered when performing a half-time re-warm-up, as this may be an additional factor influencing the drop in the intensity markers in the second half in conjunction with factors such as fatigue, pacing strategies, and other contextual variables that may influence the results.
Research on Energy Efficiency of NOMA–SWIPT Cooperative Relay Network Using GS-DinkelBach Algorithm

In order to improve the energy efficiency (EE) performance of cooperative networks, this study combines non-orthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT) technologies to construct a cooperative relay network composed of one base station (BS), multiple near users, and one far user. Based on the network characteristics, a time-division resource allocation rule is proposed, and EE formulas regarding direct-link mode and cooperative mode are derived. Considering user selection and decoding performance, to obtain the optimal EE, this study utilizes a DinkelBach iterative algorithm based on the golden section (GS-DinkelBach) to solve the EE optimization problem, which is affected by power transmitted from the BS, achievable rates under three communication links, and quality of service (QoS) constraints of users. The simulation results show that the GS-DinkelBach algorithm can obtain precise EE gains with low computational complexity. Compared with the traditional NOMA–SWIPT direct-link network model and the relay network model, the optimal EE of the established network model could be increased by 0.54 dB and 1.66 dB, respectively.
Energy Trading among Power Grid and Renewable Energy Sources: A Dynamic Pricing and Demand Scheme for Profit Maximization

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid.
Robot Transparency and Anthropomorphic Attribute Effects on Human–Robot Interactions

Anthropomorphic robots need to maintain effective and emotive communication with humans as automotive agents to establish and maintain effective human–robot performances and positive human experiences. Previous research has shown that the characteristics of robot communication positively affect human–robot interaction outcomes such as usability, trust, workload, and performance. In this study, we investigated the characteristics of transparency and anthropomorphism in robotic dual-channel communication, encompassing the voice channel (low or high, increasing the amount of information provided by textual information) and the visual channel (low or high, increasing the amount of information provided by expressive information). The results showed the benefits and limitations of increasing the transparency and anthropomorphism, demonstrating the significance of the careful implementation of transparency methods. The limitations and future directions are discussed.
All-Electronic Emitter-Detector Pairs for 250 GHz in Silicon

The spread of practical terahertz (THz) systems dedicated to the telecommunication, pharmacy, civil security, or medical markets requires the use of mainstream semiconductor technologies, such as complementary metal-oxide-semiconductor (CMOS) lines. In this paper, we discuss the operation of a CMOS-based free space all-electronic system operating near 250 GHz, exhibiting signal-to-noise ratio (SNR) with 62 dB in the direct detection regime for one Hz equivalent noise bandwidth. It combines the state-of-the-art detector based on CMOS field-effect-transistors (FET) and a harmonic voltage-controlled oscillator (VCO). Three generations of the oscillator circuit are presented, and the performance characterization techniques and their improvement are explained in detail. The manuscript presents different emitter–detector pair operation modalities, including spectroscopy and imaging.
The Simulation of In-Situ Groundwater Detector Response as a Means of Identifying Beta Emitting Radionuclides by Linear Regression Analysis

The in-situ characterisation of strontium-90 contamination of groundwater at nuclear decommissioning sites would represent a novel and cost-saving technology for the nuclear industry. However, beta particles are emitted over a continuous spectrum and it is difficult identify radionuclides due to the overlap of their spectra and the lack of characteristic features. This can be resolved by using predictive modelling to perform a maximum-likelihood estimation of the radionuclides present in a beta spectrum obtained with a semiconductor detector. This is achieved using a linear least squares linear regression and relating experimental data with simulated detector response data. In this case, by simulating a groundwater borehole scenario and the deployment of a cadmium telluride detector within it, it is demonstrated that it is possible to identify the presence of 90Sr, 90Y, 137Cs and 235U decay. It is determined that the optimal thickness of the CdTe detector for this technique is in the range of 0.1 to 1 mm. The influence of suspended solids in the groundwater is also investigated. The average and maximum concentrations of suspended particles found at Sellafield do not significantly deteriorate the results. It is found that applying the linear regression over two energy windows improves the estimate of 90Sr activity in a mixed groundwater source. These results provide validation for the ability of in-situ detectors to determine the activity of 90Sr in groundwater in a timely and cost-effective manner.
The Relative Contributions of Climate and Grazing on the Dynamics of Grassland NPP and PUE on the Qinghai-Tibet Plateau

Net primary productivity (NPP) and precipitation-use efficiency (PUE) are crucial indicators in understanding the responses of vegetation to global change. However, the relative contributions of climate change and human interference to the dynamics of NPP and PUE remain unclear. During the past few decades, the impacts of climate change and human activities on alpine grasslands on the Qinghai-Tibet Plateau (QTP) have been intensifying. The aims of the study were to investigate the spatiotemporal patterns of grassland NPP and PUE on the QTP during 2000–2017 and quantify how much of the variance in NPP and PUE can be attributed to the climatic factors (precipitation and temperature) and grazing intensity. The results showed that: (1) grassland NPP significantly increased with a rate of 0.6 g C m−2 year−1 over the past 18 years, mainly induced by the increased temperature and the enhanced precipitation. The temperature was the dominant factor for NPP interannual variation in mid-eastern QTP, and precipitation restrained vegetation growth most in the southwest and northeast. (2) The PUE was higher on the eastern and western parts of the plateau, but lower at the center. Regarding grassland types, the PUE of alpine steppe (0.19 g C m−2 mm−1) was significantly lower than those of alpine meadow (0.31 g C m−2 mm−1) and desert steppe (0.32 g C m−2 mm−1). (3) Precipitation was significantly and negatively correlated with PUE and contributed the most to the temporal variation of grassland PUE on the QTP (52.7%). (4) Furthermore, we found that the grazing activities had the lowest contributions to both NPP and PUE interannual variation, compared to temperature and precipitation. Thus, it is suggested that climate variability rather than grazing activities dominated vegetation changes on the QTP.
Artificial intelligence in educational assessment: 'Breakthrough? Or buncombe and ballyhoo?'

Artificial Intelligence is at the heart of modern society with computers now capable of making process decisions in many spheres of human activity. In education, there has been intensive growth in systems that make formal and informal learning an anytime, anywhere activity for billions of people through online open educational resources and massive online open courses. Moreover, new developments in Artificial Intelligence-related educational assessment are attracting increasing interest as means of improving assessment efficacy and validity, with much attention focusing on the analysis of the large volumes of process data being captured from digital assessment contexts. In evaluating the state of play of Artificial Intelligence in formative and summative educational assessment, this paper offers a critical perspective on the two core applications: automated essay scoring systems and computerized adaptive tests, along with the Big Data analysis approaches to machine learning that underpin them.
Schizophrenia Detection Using Machine Learning Approach from Social Media Content

Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.
Advanced Driver Assistance Systems (ADAS) Based on Machine Learning Techniques for the Detection and Transcription of Variable Message Signs on Roads

Among the reasons for traffic accidents, distractions are the most common. Although there are many traffic signs on the road that contribute to safety, variable message signs (VMSs) require special attention, which is transformed into distraction. ADAS (advanced driver assistance system) devices are advanced systems that perceive the environment and provide assistance to the driver for his comfort or safety. This project aims to develop a prototype of a VMS (variable message sign) reading system using machine learning techniques, which are still not used, especially in this aspect. The assistant consists of two parts: a first one that recognizes the signal on the street and another one that extracts its text and transforms it into speech. For the first one, a set of images were labeled in PASCAL VOC format by manual annotations, scraping and data augmentation. With this dataset, the VMS recognition model was trained, a RetinaNet based off of ResNet50 pretrained on the dataset COCO. Firstly, in the reading process, the images were preprocessed and binarized to achieve the best possible quality. Finally, the extraction was done by the Tesseract OCR model in its 4.0 version, and the speech was done by the cloud service of IBM Watson Text to Speech.
UDC: 616

The components of the dorsal vagal complex: the nucleus of the solitary tract (NST) and the dorsal motor nucleus (DMN) are key structures in BP regulation. The maintenance of BP normal level depends on functional state of these structures. it is possible to estimate the neuron morpho-functional state not only with direct electrophysiological methods but also analyzing the nucleus size and nucleic acid (NA) concentration in it. Therefore, the aim of our work was to determine the features of morpho-densitometric indices in the nuclei of the NST and DMN neurons in AH of various origins in experimental animals and to give a comparative analysis of these data. Materials and methods: the study was carried out on 30 mature male rats: 10 control Wistar rats, 10 Wistar rats with modeled endocrine-salt AH (ESAH) and 10 spontaneously hypertensive rats (SHR) which are well-established model of human essential arterial hypertension (EAH). The histochemical method of staining with gallocyanine-chrome alum by Einarson was used for the evaluation of morpho-densitometric characteristics of the DMN and NTS neurons` nuclei: the nucleus area, the content and concentration of nucleic acids in the nucleus.
A Very-Low Pilot Scheme for mmWave Hybrid Massive MIMO-OFDM Systems

To reduce the large training overhead required by a millimeter-wave (mmWave) massive MIMO system, this letter proposes a pilot placement scheme and the corresponding receiver technique for multi-user uplink channel estimation and data detection over frequency-selective fading channels when OFDM is employed. Specifically, a small group of subcarriers transmit a small segment of pilots followed by data payloads; and all other subcarriers transmit data payloads only. To recover all channels and payload data, the basic idea is that for pilot subcarriers, a semi-blind method is employed for channel estimation and data detection based on low-rank matrix completion; while for the non-pilot subcarriers, the sparsity of mmWave channels in the delay-domain is exploited, based on which the channel estimation can be solved using the compressed-sensing (CS) technique. Simulation results show that significant performance gains can be achieved by our proposed scheme with very low pilot overhead.
Capturing Attacker Identity with Biteback Honeypot

Cyber attacks are increasing at a rapid pace targeting financial institutions and the corporate sector, especially during pandemics such as COVID-19. Honeypots are implemented in data centers and servers, to capture these types of attacks and malicious activities. In this work, an experimental prototype is created simulating the attacker and victim environments and the results are consolidated. Attacker information is extracted using the Meterpreter framework and uses reverse TCP for capturing the data. Normal honeypots does not capture an attacker and his identity. Information such as user ID, Internet Protocol(IP) address, proxy servers, incoming and outgoing traffic, webcam snapshot, Media Access Control(MAC) address, operating system architecture, and router information of the attacker such as ARP cache can be extracted by this honeypot with "biteback" feature.
Performance Investigation of Different Modulation Schemes for an 11-level NPC Inverter Fed Induction Motor Drive

The neutral point clamped (NPC) inverter is the most prevalent topology among all the multilevel converter topologies because of its good power quality and high efficiency. For past few decades, NPC inverter is popularly being used in industrial motor drive applications. The modulation strategies have a significant impact on the power efficiency of inverter fed motor drive systems. This paper presents a comparative study on the performances of a three phase 11-level NPC fed induction motor for several common pulse width modulation (PWM) based multilevel converter modulation schemes using MATLAB/ Simulink. This study includes the results of line voltage and current profiles of an inverter, total harmonic distortion characteristics, power loss performance of NPC as well as induction motor’s torque and speed profiles. The main aim of this paper is to explore different recently developed modulation strategies to determine a suitable one to ensure high efficiency of an NPC converter fed induction motor drive system.
Hadamard’s Defense Against Adversarial Examples

Adversarial images have become an increasing concern in real-world image recognition applications with deep neural networks (DNN). We observed that all the architectures in DNN use one-hot encoding after a softmax layer. The attacker can make minute modifications in the adversarial example rendering those changes imperceptible to a human observer. The Hamming distance between pairs of one-hot codes is two, independent of the number of classes, and we gather this is at the heart of a successful attack. This paper proposes increasing the Hamming distance between class codes as a generic defense against adversarial attacks from the above observation. It is only natural to use error-correcting codes for this task. We present an experimental comparison of our proposal against SOTA adversarial defenses. We conducted tests for six types of white-box attacks and one black-box scenario described in the literature. In all the experiments, our proposal’s precision surpasses the state-of-art defenses by a wide margin.
The potential of real-time crowding information in reducing bus bunching under different network saturation levels

Bus bunching is a well-known problem in public transport networks. It is characterized by a self-amplifying relationship between uneven distribution of rising passenger loads and deteriorating service regularity. The focus of this study is to analyse whether this negative feedback loop can be addressed by providing real-time crowding information (RTCI) on next vehicle departures at stops. We integrate a departure choice model based on stated-preference analysis of passengers’ willingness to wait with RTCI. A proof-of-concept application to a toy-network model shows that this prevents further progression of bunching effects in certain demand conditions. The RTCI usage reveals substantial benefits - in terms of relative reductions in on-board (over)crowding, headway deviations, as well as mitigated denial-of-boarding risk - in moderately saturated network. These gains may diminish though as high overcrowding eventually emerges in PT network. Nevertheless, our findings indicate that RTCI has the potential to improve travel experience and service utilisation efficiency, even without resorting to supply-side control strategies.
A Comparative Survey of LiDAR-SLAM and LiDAR based Sensor Technologies

Simultaneous Localization and Mapping (SLAM) accomplishes the goal of concurrent localization and map creation based on self-recognition. LiDAR-based SLAM technology has advanced exponentially as a result of the widespread use of LiDAR sensors in a variety of technological sectors. This paper begins with a brief comparison of the different sensor technologies such as Radar, Ultrawideband positioning, Wi-Fi with LiDAR, and its functional importance in automation, robotics, and other fields. Classification of LiDAR sensors is also briefly discussed in tabular form. Then, a LiDAR-based SLAM is introduced by discussing its general graphical and mathematical modeling. After it three main features of LiDAR SLAM i.e., mapping, localization, and navigation are discussed. Finally, the comparison of LiDAR SLAM is discussed with other SLAM technologies and the challenges faced during its implementation.