Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Attentive Transaction Embedding With Intra- and Inter-Transaction Dependencies for Next-Item Recommendation

A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependencies considered are intra-transaction dependence and inter-transaction dependence. Most existing TBRSs recommend next item by only modeling the intra-transaction dependence within the current transaction while ignoring inter-transaction dependence with recent transactions that may also affect the next item. However, as not all recent transactions are relevant to the current and next items, the relevant ones should be identified and prioritized. In this article, we propose a novel hierarchical attentive transaction embedding (HATE) model to tackle these issues. Specifically, a two-level attention mechanism integrates both item embedding and transaction embedding to build an attentive context representation that incorporates both intra- and inter-transaction dependencies. With the learned context representation, HATE then recommends the next item. Experimental evaluations on two real-world transaction datasets show that HATE significantly outperforms the state-of-the-art methods in terms of recommendation accuracy.
Local 5G mmWave Signal Measurement and Analysis for Spectrum Database

The concept of the smart spectrum has been proposed to deal with the problem of shortage of spectrum, which is a limited resource. In smart spectrum, a database is constructed based on the measured data of the radio environment to manage the spectrum, thus realizing highly efficient spectrum utilization, and its usefulness has been confirmed in several systems. On the other hand, for 5G NR signals, the database based on the smart spectrum has not been studied and its usefulness has not been confirmed. In particular, since 5G millimeter wave (mmWave) signals adopt beamforming technology, it is necessary to estimate the directivity and propagation characteristics of multiple beams. Therefore, in the database construction, we need to measure and manage not only the conventional management for each base station and frequency but also each beam. In this paper, we conduct a measurement campaign of mmWave signals from a local 5G base station in Imabari City, Ehime Prefecture, Japan, and construct a spectrum database. We also discuss the analysis results of mmWave signals based on the constructed database, including the radio propagation of each beam and the comparison with anchor band signals. and confirm the usefulness.
The Application of Information Technology in the Teaching and Training of Taekwondo in College Physical Education

The rapid development of modern information technology has brought earth-shaking changes to our lives. The traditional education field cannot stand alone, and the application of information technology is gradually introduced, which we call "education informatization". Among the ranks of higher education in the domestic, the weakest is physical education. There are still many practical training problems in the teaching of Taekwondo in traditional physical education, but these problems can be solved well after adapting to information technology. Therefore, this article proposes information technology. Research on the application of taekwondo teaching and training in college physical education. This article uses literature method, questionnaire survey method, mathematical statistics method to analyze the application of information technology in the teaching process, the application in the physical training process and the application in the field of physical training fatigue recovery, and analyzes the Taekwondo teachers and students Attitudes towards the application of information technology in Taekwondo teaching and training, and the influencing factors. Only 35% of teachers support the application of information technology, but 56% of students support it. This article hopes to promote the development of the application of Taekwondo teaching in college physical education by summarizing the specific problems in college sports Taekwondo training, and then combining with information technology, to put forward some feasible suggestions.
A Novel Signal Processing Scheme for Static Person Localization Using M-Sequence UWB Radars

Developments in sensing technology have shown that UWB radars are becoming increasingly valuable sensing devices that can be used for monitoring of humans in military/police and civilian areas. It is known that the applicability of particular methods of human localization depends on the character of persons’ motion. With respect to this finding, the researchers’ attention has been aimed at two fundamental directions. While the former is focused on the localization of moving persons (MP), the latter approach is intended to localize static persons (SP). Then, a proper fusion of the methods developed for MP and SP localization allows monitoring of persons moving with an unknown time-variable character of motion (MP-SP). The analyses of the currently known methods of MP and SP localization in terms of their use for MP-SP localization have shown that while MP localization methods are in principle well developed, SP localization methods are not sufficiently adapted for their use in MP-SP monitoring. Motivated by these findings, we would like to introduce a new radar signal processing procedure for SP localization (SPL) that could be an efficient component of algorithms to be applied for MP-SP monitoring. The novel features of the proposed SPL consist especially in a new approach to SP detection and inclusion of SP tracking in SPL. Moreover, SPL is characterized by relatively low computational complexity and is, therefore, suitable for real-time implementation. Experimental results have shown that SPL introduced in this paper provides very good performance for multiple person localization for line-of-sight and through-the-wall scenarios.
Smart City Services in Various Cities: A Systematic Literature Review

In the course of recent many years, a developing concern has been created over the heightened advancement of urbanization, where more than 70% of the total populace by 2050 is being projected to be living in metropolitan zones is being projected to be living in metropolitan zones. Governments and organizations are going to 'smart city' ideas with the plan to upgrade the productivity of key framework, utilities and administrations to establish a more maintainable metropolitan climate that improves the personal satisfaction for its residents and improves financial turn of events. This paper analyze smart city services in various cities. This research provides benefits for the government in knowing smart city services in various cities and their service areas.
Glyphosate: ZnO Nanocrystal Interaction Controlled by pH Changes

The study of the interaction of the herbicide glyphosate with zinc oxide (ZnO) nanocrystals (NCs) was investigated under controlled pH conditions. These changes alter the various chemical groups’ vibrational frequencies of the glyphosate molecule according to the infrared spectra. Scanning electron microscopy (SEM), and atomic force microscopy (AFM) images suggest that the pH changes promote specific conformational changes due to the protonation of glyphosate. Then, the glyphosate is organized as a mycelium around ZnO NCs at neutral pH. Also, as the pH varies from acidic to alkaline, new crystal structures occur, which goes from star-like to volcano-like shapes. For an efficient quantification of glyphosate using ZnO NCs, it is essential to correlate the pH to its method. The acidic medium is recommended for optical methods, while the basic medium is more suitable for gravimetric methods, and neutral pH is ideal for spectroscopic analysis in FTIR and Raman. Therefore, in this study, we demonstrated that tuning pH could change the identification method to improve accuracy.
The Impact of Translating Resource-Rich Datasets to Low-Resource Languages Through Multi-Lingual Text Processing

Urdu is still considered a low-resource language despite being ranked as world’s $10^{th}$ most spoken language with nearly 230 million speakers. The scarcity of benchmark datasets in low-resource languages has led researchers to utilize more ingenious techniques to curb the issue. One such option widely adopted is to use language translation services to replicate existing datasets from resource-rich languages such as English to low-resource languages, such as Urdu. For most natural language processing tasks, including polarity assessment, words translated via Google translator from one language to another often change the meaning. It results in a polarity shift causing the system’s performance degradation, particularly for sentiment classification and emotion detection tasks. This study evaluates the effect of translation on the sentiment classification task from a resource-rich language to a low-resource language. It identifies and enlists words causing polarity shift into five distinct categories. It further finds the correlation between the language with similar roots. Our study shows 2-3 percentage points performance degradation in sentiment classification due to polarity shift as a result of translation from resource-rich languages to low-resource languages.
Efficient Design Procedure for Circular Filter Banks

In this work an efficient analytic design method for 2D zero-phase circular filter banks is described. They are obtained from 1D prototype filters with specified bandwidth, to which a particular frequency transformation is applied. The filters resulted through this procedure have a precise circular shape even close to frequency plane margins and they have a very steep transition for relatively a low order, thus being very efficient. The frequency response results directly factored, thus the large filter matrices are decomposed as a convolution of smaller sixe matrices, which simplifies the implementation and allows to realize the filtering sequentially, in several steps. A design example is provided for a circular filter bank with eight components. The proposed method is based entirely on accurate approximations and frequency mappings, without resorting to any global numerical optimization algorithms. Simulation results of filtering on test images are also provided.
Metasurface-Based Holographic Display With All-Dielectric Meta-Axilens

In a classical metasurface-based holographic display system, a clear holographic reconstruction image can be obtained only in the back-focal-plane of the lens. However, when the receiving plane deviates from the focal plane, the reconstructed holographic image suffers from degradations that limit the quality of the images. Hence, we propose a novel metasurface-based holographic display to realize clearly continuous imaging within the specified position range by superimposing the meta-axilens phase. It can effectively relieve the alignment requirements of the imaging system. Firstly, the parameters and properties of all-dielectric meta-atom is analyzed. Then we compare and demonstrate two sets of metasurface-based optical elements (meta-axicon, meta-lens, and meta-axilens), made by silicon meta-atoms working at 610 nm. Furthermore, we show that the metasurface hologram with the axilens phase can form a series of the relatively clear holographic images away from the focal plane. Our proposal suggests a method to reconstruct holographic wavefront in different planes simultaneously, and one can find their application domain, such as 3D biological imaging, spectroscopy, optical information storage, and encryption.
Surface Defect Detection Using U-net and transfer learning

In the process of strip production, it is easy to produce various defects, which affect the quality of steel. Therefore, there are many researchers studying how to use machine vision to solve this problem. However, the accuracy of traditional machine learning based methods is not high. In this paper, a method based on U-net and transfer learning is proposed. First, extend the dataset using data augmentation. Then use EfficientNet as a backbone to extract features. Use U-net to fuse features at multiple scales. Finally, a four-class segmented map with a size of 512*512*4 is obtained by one layer of convolution. Experiments show that this algorithm is more accurate and robust than other algorithms. Mean Dice coefficients reached 0.905.
Algebraic Construction of Structurally Shaped Polar Codes

Shaped polar codes were recently proposed to improve the spectral efficiency of polar coded modulation, based on probabilistic shaping. The key principle for them is to adjust the probability of occurrence of 1’s in each codeword by introducing the shaping bits, which makes the distribution of modulated symbols close to the Gaussian distribution. But, they have a serious problem from a practical viewpoint, since additional polar decoding is required to determine the shaping bits. In this paper, we investigate an algebraic approach to the design of structurally shaped polar codes. We first select a shaping set in an algebraic and systematic way and determine the shaping bits by using simple binary and integer operations without any additional polar decoding. We then propose a method to construct structurally shaped polar codes, and analyze their shaped probability in an approximate way. Finally, we present the encoding and decoding procedures for the proposed shaped polar codes. Numerical results show that the proposed shaping scheme has similar performance than the conventional shaping scheme regardless of whether cyclic redundancy check is employed, while the former can be implemented in an extremely simpler way than the latter.
QIH: An Efficient Q-Learning Inspired Hole-Bypassing Routing Protocol for WSNs

This paper addresses the local minimum phenomenon, routing path enlargement, and load imbalance problems of geographic routing in wireless sensor networks (WSNs) with holes. These issues may degrade the network lifetime of WSNs since they cause a long detour path and a traffic concentration around the hole boundary. Aiming to solve these problems, in this work, we propose a novel geographic routing protocol for WSNs, namely, Q-learning Inspired Hole bypassing (QIH), which is lightweight and efficient. QIH’s conceptual idea is to leverage Q-learning to estimate the distance from a node to the holes. QIH makes routing decisions following the nodes’ residual energy, their estimated distance to the holes, and their distance to the destination. We first confirm the effectiveness of QIH by theoretical analysis. Then, we conduct extensive simulations of QIH in comparison to state-of-the-art protocols. The simulation results show that QIH outperforms the other protocols in terms of network lifetime, packet latency, and energy consumption.
The IoT and Cloud Based Smart Home Automation for a Better Energy Efficiency

This paper proposes a prototype of an improved smart home automation system, with the integration of IoT, cloud technologies, and intelligence embedding. The automation and the ability for users to control their home appliances remotely are enabled with the use of IoT. Since a smart home also requires to consider the importance of energy efficiency while offering a certain level of comfort, the energy consumption is also monitored by placing several current and volatage sensors. The Raspberry Pi microcontroller board is used to gather and process information from sensors and allows the uploading of the data to the cloud database. A fuzzy-based algorithm is employed for helping the smart home controller to provide the right decisions based on the current home condition when the application runs in automatic mode. Meanwhile KMeans Algorithms is implemented to cluster the smart home user’s energy consumption.
Intellectual Analysis of Major Crops Area due to Climate Changes in Ukraine

As a result of climate change in agriculture, certain changes are taking place that affect the economic component of Ukraine in the domestic and foreign markets. The statistical analysis of the time series of the majority crops areas for 22 years (from 1998 to 2020) is carried out, and also the influence of agroclimatic zones on the area of cultivation of the main crops is analyzed.
A 98.1-dB SNDR 188-dB FoMS Noise-Shaping SAR ADC Using Series Connection Capacitors

This paper proposes a noise-shaping (NS) successive approximation register (SAR) analog-to-digital converter (ADC), which can be well applied in very high-precision and low-power-applications for Internet of Things (IoTs). An error feedback (EF) through the series connection of capacitors is implemented in the topology, which ensures that the input signal and feedback signal are not attenuated. Therefore, a small gain dynamic amplifier can be used, which has advantages of low power consumption and process-friendly characteristics. Designed in 55-nm CMOS process, the prototype of proposed NS-SAR ADC consumes very low power consumption of 623.6 μW when operating at 40 MS/s, which achieves a peak Schreier FoM of 188 dB with 98-dB signal to noise and distortion ratio (SNDR) at an oversampling ratio (OSR) of 16.
Measurement and Ray-Tracing Simulation for Millimeter-Wave Automotive Radar

The underlying requirement of autonomous driving test orienting the future Internet of Intelligent Vehicles (IoIV) needs knowledge of authentic millimeter-wave (mmWave) radar simulation system. In this paper, a mmWave radar measurement system, which is validated by two corner reflectors with a constant radar cross-sectional (RCS), is set up to carry out the parallel parking measurement campaign on road. The key characteristics of the measurement, including Range-Power profile, Range-Doppler spectrum, and Range-Angle spectrum are obtained and analyzed for the subsequent ray tracing (RT) simulation. The RCSs of key targets are estimated based on the measurement results and provide a visible cue for calibrating EM parameters, which are then matched with the measured ones. The calibrated simulation results are then compared with the measurement results, which realizes the closed-loop verification and analysis.
Target Classification by mmWave FMCW Radars Using Machine Learning on Range-Angle Images

In this paper, we present a novel multiclass-target classification method for mmWave frequency modulated continuous wave (FMCW) radar operating in the frequency range of 77 - 81 GHz, based on custom range-angle heatmaps and machine learning tools. The elevation field of view (FoV) is increased by orienting the Radar antennas in elevation. In this orientation, the radar focuses the beam in elevation to improve the elevation FoV. The azimuth FoV is improved by mechanically rotating the Radar horizontally, which has antenna elements oriented in the elevation direction. The data from the Radar measurements obtained by mechanical rotation of the Radar in Azimuth are used to generate a range-angle heatmap. The measurements are taken in a variety of real-world scenarios with various objects such as humans, a car, and an unmanned aerial vehicle (UAV), also known as a drone. The proposed technique achieves accuracy of 97.6 % and 99.6 % for classifying the UAV and humans, respectively, and accuracy of 98.1 % for classifying the car from the range-angle FoV heatmap. Such a Radar classification technique will be extremely useful for a wide range of applications in cost-effective and dependable autonomous systems, including ground station traffic monitoring and surveillance, as well as control systems for both on-ground and aerial vehicles.
Differential Scale based Multi-objective Task Scheduling and Computational Offloading in Fog Networks

Cloud computing suffers from various challenging issues in Internet of Things (IoT) networks like real-time response, energy-efficient execution, and cost of computation. Fog is an emerging distributed computing paradigm which is useful for delay-sensitive tasks in IoT network. An offloading strategy decides where to offload the task and a task scheduling strategy chooses an appropriate fog node based on the requirements of the task while meeting the quality of services (QoS) criteria. Although the computational offloading and task scheduling problem has been widely studied, there is very limited research on delay-energy tradeoff. We propose a fog network that follows an M/M/c queue for computational offloading and a differential scale-based Best Worst Method (BWM) for computation of optimal weights in multi-objective task scheduling. The optimization problem minimizes the execution delay while meeting QoS criteria. The numerical experiments show the efficacy for the different QoS criteria.
Design of W-band Substrate Integrated Waveguide Horn Antenna for Active Phased Array

A novel SIW horn antenna in W-band has been proposed and demonstrated in this paper. The proposed antenna is fabricated on a single layer of silicon substrate. In order to improve the gain of the antenna, a dielectric slab using the same silicon substrate is installed at the front-end of the antenna. In order to raise the front-to-back ratio, two slots are symmetrically arranged in the top and bottom metal surface which are close to the radiating aperture. Moreover, since the width of the antenna is less than 75% wavelength and thus it is suitable for active phased array. The proposed SIW horn antenna and its 1×9 array have been investigated. A wide beam scanning range of ± 20°in H-plane covering frequency form 91.5 to 96 GHz with respect to VSWR less than 2 is achieved and a high front-to-back radio of 21.3 dB is obtained. Since our proposed W-band SIW horn antenna can be easily fabricated by existing mature CMOS processing technology, it provides a low-cost solution in millimeter-wave (mmW) active phased array application for wireless communication and radar detection.
An H-Plane multi-Horn Antenna Using Substrate Integrated Waveguide Technique

In this paper, a new H-plane multi-horn antenna is designed for small nonmetallic unmanned aerial vehicle (UAV) application. Four H-plane horn antennas are integrated into a single square substrate using the substrate integrated waveguide (SIW) concept. In fact, they are placed concentrically and directed at the four edges of the substrate. In order to control the resonant frequency at 5.8 GHz and to obtain proper matching, the co-axial connector is placed the center of the structure. The antenna provides the quasi-omnidirectional radiation instead of the directional radiation pattern at the H-plane. The laboratory prototype of the structure is measured to validate the theoretical results. This antenna is suitable for UAV applications.
Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Object identification and localization in indoor and outdoor environments are paramount issues in object–human interaction. Recent advancements in the data fusion capabilities of multi-sensor systems have paved the way for research on emerging object identification and positioning techniques. This review describes techniques and methods used in positioning technologies. State-of-the-art localization technologies are classified into range-based, range-free and AI-based categories. An in-depth analysis of localization approaches based on laser range finder, radio-frequency identification, ultra-wideband, inertial measurement unit, etc., are presented by providing a detailed comparison based on range, accuracy, measurement method, advantages, disadvantages, and their applications. Furthermore, we investigate state-of-the-art multimodal data fusion techniques that utilize probabilistic methods for the precise estimation of object identification in motion and its localization.