Miracle-dz/UniFAR-FTUs huggingface dataset:
Radio Propagation in Terrestrial Broadcasting Television Systems: A Comprehensive Survey

During the last two decades, terrestrial TV broadcasting has evolved from analog to digital technology, permitting a better spectrum efficiency, being more resistant to noise and interference as well as improving signal quality. High Definition TeleVison (HDTV) has been introduced to enhance the viewer’s experience. HDTV and Ultra High Definition TeleVision bring the challenge of having a quasi-error free performance at a bit error rate as low as 10−12; i.e. less than one uncorrectable error during one hour’s continuous transmission of 5 Mbps data stream. Such low error rates require robust standards, careful network planning and optimized service operation. Propagation phenomena have a direct (and possibly critical) impact on those three pillars. This paper fills the current gap of a thorough survey on propagation methods and models for broadcasting. We contribute to this field with a description and analysis of propagation phenomena concerning different aspects of the broadcast network planning, broadcast standard evaluation, and broadcast service operation. The paper provides also a technical perspective of state-of-the-art TV broadcast standards and discusses the relevancy of propagation studies with future and development and regulation challenges.
Annotated dataset for the fake news classification in Slovak language

Fake news detection currently presents an active field of research. Detection methods based on natural language processing and machine learning are being developed to automatically identify the possible misinformation contained within the news articles. To successfully train these models, annotated data are needed. In English language, multiple human-annotated datasets already are available and are being widely used in the research. The main objective of the work presented in this paper, was to create similar dataset consisting of articles in Slovak language. We collected the data from the various local news portals including reputable publishers as well as suspicious conspiratory portals. To obtain the annotations, we used crowdsourcing approach. Annotated dataset was used in preliminary experiments, in which neural network classifier was trained and evaluated.
Exploring Knowledge Domain Bias on a Prediction Task for Food and Nutrition Data

Human understanding and knowledge about food and nutrition is constantly evolving, and has significantly improved recently, one of the main contributor to this is data. The possibilities of gaining knowledge from food and nutrition-related data are yet to be explored. One of the most important information about food is nutrient content, which is very relevant for patients suffering from various diseases, professional athletes, and slowly part of everyday life of many for health or fitness goals. In this paper, we explore the effect of domain bias in a predictive study in the food and nutrition domain. Having a machine learning pipeline for predicting nutrient values with learned vector representations from short text description of recipes, we introduce domain knowledge before the prediction algorithms are applied. On a large corpus of recipe data containing short description and nutrient values we introduce word and paragraph embeddings, learn concept representations for the textual descriptions, introduce domain knowledge for clustering the data, and apply machine learning algorithms for predicting the nutrient content of the recipes. We explore the impact of the domain knowledge by introducing two different criteria of clustering the dataset - using graph embedding of the FoodEx2 codes, and using the traffic light labelling system from the Food Standards Agency; at the end we compare the two different criteria. The results from this study imply that inferring domain knowledge beforehand is crucial for the task of predicting nutrient content.
Off‐line monitoring of butyl acrylate and vinyl acetate homopolymerization and copolymerization in toluene

A study of butyl acrylate (BA) and vinyl acetate (VAc) solution homopolymerization and copolymerization in toluene was carried out. The conversion and copolymer composition were monitored using traditional techniques (gravimetry and 1H-NMR spectroscopy) and attenuated total reflectance-Fourier transform IR (ATR-FTIR) spectroscopy with a diamond-composite probe and light conduit technology. The peak height of the characteristic absorbances of the monomer(s) during the course of the reaction was used to calculate the conversion and copolymer composition for the ATR-FTIR monitoring. The data obtained using a ReactIR™ 1000 reaction analysis system in the off-line mode showed very good agreement with data obtained using traditional techniques. The solvent effects on BA and VAc solution homopolymerizations and copolymerizations in toluene were also investigated. Improvement to model predictions was obtained by allowing the lumped constant (kp/k) to vary with the solvent concentration. Experimental data and model predictions of the number- and weight-average molecular weights for the investigated systems are also presented. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 82: 2958–2977, 2001
Low-Complexity Hybrid Precoder Design for GenSM-Aided mmWave MIMO

MmWave MIMO communications has been recognized as one revolutionary new technology in wireless communication area, which can support wide ban width and high data rate transmission compared with conventional communications. Recently, hybrid precoding is widely utilized to reduce the number of RF chains and power consumption, which has perfect performance on spectral efficiency (SE) and energy efficiency (EE). In this paper a new GenSM-aided low-complexity hybrid precoding algorithm is proposed, which enjoys higher spectral efficiency and lower complexity compared with the existed low-complexity methods.
Requirements Engineering Practices and Challenges in the Context of Big Data Software Development Projects: Early Insights from a Case Study

This paper reports on the results of an exploratory case study on a large-scale Big Data systems development project in the Oil&Gas domain within a non-profit organisation. The aim of this study was to investigate the RE practices and challenges in such projects, currently bereft in the scientific literature. This investigation was focused on: (a) RE practices; (b) sources and distribution of requirements; (c) the role of Big Data characteristics and technologies in RE and systems design; and (d) RE challenges in engineering Big Data Systems. The main results show that (a) there is a lack of specific project tailored RE practices, tools, and frameworks for elicitation, specification and modelling, analysis, and prioritisation of requirements; (b) 40% of the system’s requirements are considered Big Data-related from which 75% are identified from internal sources; (c) Big Data characteristics and technologies play an important role in defining quality requirements and system’s architecture; (d) five challenges in eliciting, documenting, and analysing Big Data related requirements were identified and discussed. The findings suggest academics and practitioners opportunities to engage in further research in this area.
On the Large-scale Graph Data Processing for User Interface Testing in Big Data Science Projects

In functional User Interface testing, test scenarios are written with respect to the requirements that are specified by test analysts. Usually, a test analyst focuses on base URLs and HTML components while collecting requirements of User Interface test scenarios. A base URL is essentially a unit segment of large scale graph data. It has mostly dynamic shape and is used to navigate pages amongst application’s pages. We argue that even though dynamic URLs have additional important information about the content of the page, they are not being utilized in generating User Interface test scenarios. In this study, we address this lack of capability and focus on the development of a methodology that can support the usage of large-scale dynamic URL datasets in UI test script generation. Our proposed methodology is designed as an add-on tool that can be used on the top of the existing UI test automation tools to improve testing quality. We introduce a higher quality testing methodology to make the results more accurate, and we discuss the proposed methodology and give an overview of the implementation details followed by the evaluation results. We perform various performance evaluations to investigate how well the proposed algorithms scale under increasing data sizes. The results are promising and show the usability of the proposed methodology.
Explainable Student Performance Prediction Models: A Systematic Review

Successful prediction of student performance has significant impact to many stakeholders, including students, teachers and educational institutes. In this domain, it is equally important to have accurate and explainable predictions, where accuracy refers to the correctness of the predicted value, and explainability refers to the understandability of the prediction made. In this systematic review, we investigate explainable models of student performance prediction from 2015 to 2020. We analyze and synthesize primary studies, and group them based on nine dimensions. Our analysis revealed the need for more studies on explainable student performance prediction models, where both accuracy and explainability are properly quantified and evaluated.
Understanding and Mitigating Threats from Android Hybrid Apps Using Machine Learning

The Android platform has emerged as the most popular computing platform that has more than 2.5 billion devices [1] working across the globe. These devices include not only mobiles and tablets, but even Android Auto modules in cars, various Android versions running on Televisions, watches and host of other smart devices. What makes things more challenging and interesting for the Android Developers and security experts is the fact that various versions of Android Operating System, from Android 2.3. 3 (Ginger Bread) to Android 11.0 coexist in this ecosystem. This paper discusses threats that emanate from Hybrid Android Apps. These Hybrid Apps use WebView Component for handling web content within Android Apps. WebView allows HTML and JavaScript to run and render webpages inside Apps, thereby allowing them to download content from Web Servers on the Internet. It is used by several popular Apps, like Facebook, Twitter, Instagram, etc. WebView even allows JavaScript code to call Android code for completing various tasks. While this feature gives tremendous capability to create interactive Hybrid Apps, however, it also opens a route for malicious content to infect the Android Platform using targeted JavaScript based malwares. Any malicious JavaScript, from untrusted or even from trusted source, can thus find its way to exploit this unique linkage with Android Platform. In this paper we analyze Android Web View’s security vulnerabilities, access authorization, kind of attacks that it can encounter, and mechanisms to prevent these attacks. To do so, we have developed two Android Apps, viz., "WebView Tool" and "WebView Monitor". Our analysis and detection mechanisms are based on Machine Learning techniques.
Learning Minimum Bounding Rectangles for Efficient Trajectory Similarity Search

Early pruning of dissimilar trajectories is important in similar trajectory search on a big mobility data. R-trees can perform the pruning effectively, but the search and index size become inefficient due to numerous overlapping of minimum bounding regions in a dense and big dataset. Thus, we introduce the extended usage of learned index to learn the minimum bounding rectangles for trajectory similarity search. Our approach is designed to provide an effective pruning for trajectory similarity search with less storage size.
AI-Driven Collaborative Resource Allocation for Task Execution in 6G-Enabled Massive IoT

In the foreseeable future, the rapid growth of devices in the Internet of Things (IoT) will make it difficult for 5G networks to ensure sufficient network resources. 6G technology has attracted increasing attention, bringing new design concepts to the dynamic real-time resource allocation. The resource requirements of devices are usually variable, so a dynamic resource allocation method is needed to ensure the smooth execution of tasks. Therefore, this article first designs a 6G-enabled massive IoT architecture that supports dynamic resource allocation. Then, a dynamic nested neural network is constructed, which adjusts the nested learning model structure online to meet training requirements of dynamic resource allocation. An AI-driven collaborative dynamic resource allocation (ACDRA) algorithm is proposed based on the nested neural network combined with Markov decision process training for 6G-enabled massive IoT. Extensive simulations have been carried out to evaluate ACDRA in terms of several performance criteria, including resource hit rate and decision delay time. The results validated that ACDRA improves the average resource hit rate by about 8% and reduces the average decision delay time by about 7% compared with three reference existing algorithms.
EvoBench – A Framework for Benchmarking Schema Evolution in NoSQL

Schema evolution is a timeless topic in database research. However, there is only a limited amount of research regarding benchmarking of NoSQL schema evolution systems available. To compare schema evolution systems regarding their overall capabilities and their efficiency to evolve schemas and migrate data, a standardized benchmark is required. In this article, we propose a conceptual framework for a NoSQL schema evolution benchmark with all required components. Beside the benchmark schema and its data sets, we provide the evolution workloads and the metrics as the benchmark’s centerpiece in detail.
Detection of Previously Unseen Malware using Memory Access Patterns Recorded Before the Entry Point

Recently it has been shown, that it is possible to detect malware based on the memory access patterns produced before executions reaches its Entry Point. In this paper, we investigate the usefulness of memory access patterns over time, i.e to what extent can machine learning algorithm trained on “old” data, detect new malware samples, that was not part of the training set and how does this performance change over time. During our experiments, we found that machine learning models trained on memory access patterns of older samples can provide both high accuracy and a high true positive rate for the period from several months to almost a year from the update of the model. We also perform a substantial analysis of our findings that may aid researchers who work with malware and Big Data.
SVM based Pedestrian Detection System for Sidewalk Snow Removing Machines

In this paper, we present a novel pedestrian detection system for sidewalk snow removing vehicles particularly for night driving scenarios. The information in front of the snowplow is obtained by clustering and classifying objects using LiDAR point clouds. A robust pedestrian detection and classification algorithm using the support vector machine(SVM) is proposed. We tested the system on an actual machine and the accuracy our method is verified by experiments.
A 60-GHz Variable Gain Amplifier with Phase-compensated Variable Attenuator

A 60-GHz variable-gain amplifier with a phase-compensated variable attenuator is proposed herein. The proposed circuit comprises a four-stage common-source amplifier and a phase-compensated attenuator. The proposed circuit is implemented using a 65-nm CMOS process and occupies 360 μm × 765 μm including pads. The measured peak gain is 15.2 dB, and the 3-dB bandwidth exceeds 17.5 GHz from 49.5 to over 67 GHz. In addition, the proposed circuit achieves a gain control range exceeding 14.7 dB within a 3-dB bandwidth. Owing to the proposed phase compensation technique, the phase-compensated attenuator exhibits a maximum phase error of 2.9° and a RMS phase error of 1.7° at 56 GHz while consuming only 11.8 mW of DC power.
Data Over Cable Services – Improving the BICM Capacity

The Data Over Cable Service Interface Specification (DOCSIS) provides Internet access via Hybrid Fiber Coax networks. The current version DOCSIS 3.1 and the planned next iteration DOCSIS 4.0 use Quadrature Amplitude Modulation (QAM) and Low-Density Parity Check (LDPC) codes for Bit-Interleaved Coded Modulation (BICM). This paper analyzes the BICM performance characteristics of the DOCSIS downstream. We find that there is a gap of approximately 3.5 dB between DOCSIS and the theoretical Shannon limit. To reduce this gap, we suggest the use of alternative LDPC codes and Non-Uniform Constellations (NUCs) as specified by the ATSC 3.0 standard. To adjust the ATSC 3.0 specification for cable networks, we introduce novel NUCs that target very high signal-to-noise ratios. The proposed BICM chain reduces the distance to the Shannon limit by up to 1 dB.
Meta Learning With Paired Forward and Inverse Models for Efficient Receding Horizon Control

This paper presents a model-learning method for Stochastic Model Predictive Control (SMPC) that is both accurate and computationally efficient. We assume that the control input affects the robot dynamics through an unknown (but invertable) nonlinear function. By learning this unknown function and its inverse, we can use the value of the function as a new control input (which we call the input feature) that is optimised by SMPC in place of the original control input. This removes the need to evaluate a function approximator for the unknown function during optimisation in SMPC (where it would be evaluated many times), reducing the computational cost. The learned inverse is evaluated only once at each sampling time to convert the optimal input feature from SMPC to a control input to apply to the system. We assume that the remaining unknown dynamics can be accurately represented as a model that is linear in a set of coefficients, which enables fast adaptation to new conditions. We demonstrate our approach in experiments on a large ground robot using a stereo camera for localisation.
Motion Analysis of Transfer Operation from Bed to Wheelchair for Care-giver and -receiver with Wearable Motion Capture

Low back pain (LBP) is a common major health problem in the caregiving works. There is strong epidemiological evidence that physical demands of works can be associated with increased reports of back pain. High-quality education for caregivers must play an important role to avoid such workrelated injuries. However, evidence-based education for caregivers has not been sufficiently authorized. To establish the measurements and analysis of the caregiving operation which composes many body contacts, we developed a motion analysis system and software as the educational tool for caregivers. This system composed of two sets of wearable motion capture sensor with 31 inertial measurement units (IMU). In addition, we measured motions of transfer operations from a bed to a wheelchair for professional and amateur caregivers and compared their features.
Power Management of a Grid Interactive PV - Fuel Cell Hybrid System with Power Dispatch to the Grid Using Modified LMS Control

Hybrid systems comprising renewable energy sources based distributed energy generation connected to the grid with energy storage systems increase the cost and control complexity. But, to provide reliable power to the grid, storage systems are essential. Here, a grid-connected Photovoltaic-Fuel cell hybrid system with a fixed power dispatch to the grid using a modified least mean square control has been proposed. This has been realized to control the power flow from the sources and the load. Any variation in the photovoltaic power is balanced by power from the fuel cell by introducing a power management scheme. The change in power flow to the load arising at the transient stage due to the slow response of the fuel cell is met by power support from the grid. Moreover, the hybrid system mitigates power quality issues at the point of interconnection in the grid. The performances of the system during steady-state and transient conditions are verified by extensive simulation analysis with MATLAB with its simpower system block set.
Autonomous Mobile Robot for Apple Plant Disease Detection based on CNN and Multi-Spectral Vision System

This paper presents an autonomous system for apple orchard inspection and early stage disease detection. Various sensors including hyperspectral, multispectral and visible range scanners are used for disease detection. For localization and obstacle detection 2D LiDARs and RTK GNSS receivers are used. The proposed system allows to minimize the use of pesticides and increase harvests. The detection approach is based on the use of neural networks for both plant segmentation and disease detection.
Analytical Surface Potential-Based Compact Model for Independent Dual Gate a-IGZO TFT

A surface potential-based compact model for independent dual gate (IDG) amorphous In-Ga-Zn-O thin-film transistors (IDG a-IGZO TFTs) is proposed here. The transport theories of percolation conduction, trap-limited conduction (TLC), and variable range hopping (VRH) in extended and localized states are first considered simultaneously via Schroder method, obtaining a physical description of the transport mechanism under different conditions of temperature and gate voltage. Moreover, a single formulation of front and back surface potentials which is valid and extremely accurate in all operation regimes is developed. Based on the transport theories and surface potentials, the complete compact model is developed and verified using both numerical simulation and experiment with an excellent agreement, and the threshold compensation effect is also included. Finally, the compact model is coded in Verilog-A, and implemented in a vendor CAD environment, which suggested that the proposed model can be successfully applied to circuit design.
Efficient Record Linkage in Data Streams

Nowadays, a vast amount of information is collected in real-time on a daily basis via users’ handheld devices, web-based applications, and customer service interactions (among many others). The sheer volume of this data and the unprecedented rate at which it becomes available for processing, potentially combined with other attributes that are commonly met in traditional data sets, calls for novel online record linkage1 techniques that can handle streams of data to discover records that refer to the same real-world entity.This paper introduces UniBlock, an online record linkage approach, supported by a novel data structure, that can adapt to any blocking algorithm to separate the most frequently accessed blocks from the rest, and maintain these blocks in main memory. In UniBlock, this separation is performed in a randomized way, where the probability of eviction of a block is inversely proportional to its frequency of access, empowering our approach with simplicity and effectiveness. Additionally, UniBlock provides accurate estimations of the proportion of matching record pairs in the underlying data sets in sublinear running time. Through experimental evaluation, we show that our approach outperforms the state-of-the-art methods in both accuracy and efficiency, being able to scale well to data streams.