Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Text Retrieval Models based on DNN, CNN, RNN and Transformer: A review

The development of deep learning technology provides a new development direction for text retrieval. Researchers have applied deep learning techniques to different information retrieval objects and carried out rich studies on them, such as web pages, scientific literature, and scientific data. This paper selects 40 research papers on related topics in the past 10 years through a step-by-step selection and conducts a review on the dimensions of model input, model structure, and its performance. Firstly, according to the differences in methods, we divided the deep learning text retrieval model into four categories: DNN-based, CNN-based, RNN-based, and Transformer-based, and analyzed the classical model structure and retrieval effect of each category. Secondly, we analyzed and compared the application scenarios of different types of models, and summarized some classic retrieval datasets. Finally, we discussed the main challenges and future research trends of deep text retrieval. This review is expected to provide basic knowledge and effective research entry points for scholars engaged in deep learning text retrieval.
Test of the second postulate of special relativity using positron annihilation

An experiment to directly test the second postulate of special relativity is described. The speed of photons, resulting from the annihilation of either thermal positrons or in-flight positrons (moving with relativistic velocity), is measured using two complementary variations of the same basic experiment. For both at rest and moving positrons the constancy of the speed of light was confirmed to an accuracy of approximately 1%. This apparatus can be used in an advanced undergraduate laboratory and also used to place limits on alternative theories to special relativity that have transformations other than the Lorentz transformation.
Design & Development of Deep Learning Algorithm with Convolutional Neural Networks for Breast Cancer Classification

This paper presents a method for classification of normal and abnormal tissues in mammograms using a deep learning approach. Breast biopsies based on the results of mammnography and ultrasound have been diagnosed as benign at a rate of approximately 40 to 60 percent. Breast cancer is one of the primary causes of cancer death among the women world population. For early detection of breast cancer in women Mammography and ultrasound methods are used. This is a holistic methodology which can help classify a whole mammography exam, containing the MLO views along with CC and the segmentation maps, as compared to the classification of individual lesions, which is the dominant approach in the field. We are also demonstrating that the system discussed in this paper is capable of using the segmentation maps that are generated by micro-calcification detection systems and automated mass detection systems, and still produces highly accurate results.
?-? circuit-breaker arc modelling around current zero: II. Arc-circuit-interaction simulation using Boltzmann analysis

This paper reports the initial results of a complete physico-chemical modelling of a circuit-breaker arc. The time-dependent results of a collisional-radiative model combined with Boltzmann analysis to determine the electron transport properties in a uniform zero-dimensional space are given. Microscopic processes leading to extinction of the arc under the influence of the electrical circuit are evaluated and discussed. It is shown that, under some conditions the electron temperature is elevated considerable over the gas temperature driven by the electrical field strength inherently built up by arc-circuit interaction after current zero. The related electron-impact, recombination and association processes, which are decisive for the failure or success of the current interruption by the circuit breaker, are studied in detail. Whereas exhibits entirely favourable physico-chemical properties for current interruption, nitrogen requires additional cooling by a mechanical blast because of a combination of highly exothermic association reactions, inefficient electron removal and efficient ionization of atomic nitrogen.
Analysis Performance of Support Vector Regression (SVR) for Congestion Prediction

The increasing population in urban areas has led to an increase in the number of vehicles. The growth of vehicles is not comparable with the growth of roads that caused traffic congestion. To solve the congestion problem, traffic conditions are predicted. The method used to predict traffic congestion is Support Vector Regression (SVR). The map-Matching algorithm is used to adjust vehicle position data that is not accurate, resulting in an accurate position. In this research, the performance of SVR was tested for congestion prediction. The test was conducted to determine the performance of the SVR model by calculating the Means Absolute Error (MAE) and Root Mean Squared Error (RMSE). K-fold cross-validation is used to show the accuracy of the SVR model on the dataset after being divided into k sections and FCE to determine the level of congestion. We conclude that the SVR has good performance for congestion prediction, this is indicated by a small error value and high accuracy value. The prediction results using SVR are the same as the actual condition.
On the Impact of Deep Learning and Feature Extraction for Arabic Audio Classification and Speaker Identification

In recent times, machine learning and deep learning algorithms have contributed to the advances in audio and speech recognition. Despite the progress, there is limited emphasis on the classification of cantillation audio using deep learning. This paper introduces a dataset containing two labeled styles of cantillation from six reciters. Deep learning architectures including convolutional neural networks (CNN) and deep artificial neural networks (ANN) were used to classify the recitation styles using various spectrogram features. Moreover, the classification of the six reciters was also performed using deep learning. The best performance was achieved using a CNN model and Mel spectrograms resulting in an F1-score of 0.99 on the test set for classifying recitation style and an F1-score of 1.00 on the test set for classifying reciters. The results obtained in this work outperform the existing works in the literature. The paper also discusses the impact of various audio features and deep learning algorithms that apply to audio genre and speaker identification tasks.
Depth Patch Selection for Decoder-Side Depth Estimation in MPEG Immersive Video

The MPEG immersive video (MIV) standard has been developed to efficiently compress volumetric video content and enable an immersive user experience. MIV deals with an enormous amount of data that comes in the form of multi-view plus depth videos, which is efficiently reduced in the process of pruning, by tackling the redundancies among the views. This paper presents a novel approach for improving the existing immersive video coding scheme. The proposed approach reduces the amount of transmitted depth data, leveraging the fact that the depth information is partially contained in texture videos. The study proposes a method that ensures a reliable recovery of depths at the decoder-side. This method provides BD-rate improvements on both high and low bitrate ranges, with up to 22.57% Y-PSNR, 25.76%VMAF, 24.07% MS-SSIM, and 22.94% IV-PSNR metric gain, given a low bitrate setting.
Design and Implementation of a Temperature, Humidity and Air Quality IoT Monitoring Telemetry System

In this paper the Design and implementation of temperature, humidity and air quality IoT monitoring telemetry systems are described. The idea is to collect and monitor the telemetry data from sensors for temperature, humidity and air quality. For this purpose embedded system Raspberry Pi and sensors used the to create an IoT device. The general architecture, hardware and software realization are shown.
Optimal Beacon Placement for Indoor Positioning Using Constraint Programming

Indoor location is a growing topic for hospitals, retirement homes and in case of emergency. For the resource efficient (indoor) positioning of mobile individuals an optimized distribution of the used sensors is necessary. The placement of beacons (sensors) in a building (indoor positioning) can be a difficult and laborious task, especially if done by hand. Multiple researchers already tried to tackle this problem using different algorithms and under the consideration of distinct use cases. However, none of the currently known methods incorporate constraint programming by using only Boolean variables. In this paper we tried to develop a new method for an efficient placement of Bluetooth Low Energy (BLE) beacons in an indoor scenario. More specifically, we try to optimize the beacons for a trilateration algorithm used for indoor positioning. This algorithm requires that three beacons should be in range for every possible position in the building. In a next step the initially calculated beacon positions are further optimized. This is done by trying to reduce the number of beacons used. Afterwards we evaluate the quality of the beacon placements by comparing it against a manually optimized beacon placement and evaluating it in an existing building by checking the quality of multiple sample positions.
Property Graph Access Control Using View-Based and Query-Rewriting Approaches

Managing and storing big data is non-trivial for traditional relational databases (RDBMS). Therefore, the NoSQL (Not Only SQL) database management system emerged. It is ca-pable of handling the vast amount and the heterogeneity of data. In this research, we are interested in one of its trending types, the graph database, namely, the Directed Property Graph (DPG). This type of database is powerful in dealing with complex relationships ($\mathrm{e}.\mathrm{g}$., social networks). However, its sen-sitive and private data must be protected against unauthorized access. This research proposes a security model that aims at exploiting and combining the benefits of Access Control, View-Based, and Query-Rewriting approaches. This is a novel combination for securing DPG.
Subjective test methodology optimization and prediction framework for Just Noticeable Difference and Satisfied User Ratio for compressed HD video

Just Noticeable Difference (JND) and Satisfied User Ratio (SUR) has been widely investigated for compressed image and video to use the least resources (e.g., storage and bandwidth) without damaging the Quality of Experience (QoE) for end users. However, the current JND subjective test methodologies are extremely time consuming due to the large range of encoding parameters. Besides, the state-of-the-arts SUR/JND prediction models get non-negligible prediction error due to the limited masking effect features. To this end, we first proposed a preprocessing method to reduce the JND subjective test time by using dynamic range of encoding parameters and collected a new Video-Wise JND (VW-JND) datasets for HD videos: HDVJND. Afterwards, based on the collected datasets, we proposed a SUR prediction framework by extracting 3 types of features 1) masking effect features; 2) bitstreams features; 3) content features. Feature selection is applied to extracted features before regression. Besides, we also compared the direct and indirect SUR value predictions methods. Experiment results shows that our proposed optimization can reduce 7.14% of the subjective experiment time compared to the widely used Robust Binary Search (RBS). Furthermore, the proposed SUR and JND prediction frameworks outperform the SOTA model in HD-VJND datasets.
The transition metal dichalcogenides discussion and interpretation of the observed optical, electrical and structural properties

Abstract The transition metal dichalcogenides are about 60 in number. Two-thirds of these assume layer structures. Crystals of such materials can be cleaved down to less than 1000 A and are then transparent in the region of direct band-to-band transitions. The transmission spectra of the family have been correlated group by group with the wide range of electrical and structural data available to yield useful working band models that are in accord with a molecular orbital approach. Several special topics have arisen; these include exciton screening, d-band formation, and the metal/insulator transition; also magnetism and superconductivity in such compounds. High pressure work seems to offer the possibility for testing the recent theory of excitonic insulators.
Algorithms for the Development of Adaptive Web Interfaces: A Systematic Literature Review

The User Interface (UI) is a fundamental part of web interfaces, being considered a fundamental aspect of user satisfaction when using a web application. The UI that offers a good User Experience (UX) is the one that makes the interaction as simple and efficient as possible for the user to achieve the previously desired goals. In this way, web interfaces that have adaptive interfaces tend to offer a better UX, as they seek to adapt to the needs and desires of users. Therefore, this paper carries out a Systematic Literature Review (SLR) to identify the most used algorithms for the development of Adaptive Web Interfaces (AWI). The results indicate that the use of Machine Learning (ML) techniques, both to improve the performance of some task and to induce results, are the most popular ways to develop AWI. During the execution of SLR, 294 articles were analyzed using automated and manual searches, and inclusion, exclusion and quality criteria were applied to them. The research findings allow us to state that the topic is quite relevant to the academic community, with an average publication of 20 papers each year.1
Research on Situation Element Extraction Model of Internet of Vehicles Based on Optimized Random Forest

Situational awareness technology can provide security situation assessment and prediction analysis for the Internet of vehicles, enabling security managers to have macro and comprehensive control over the security situation in the network environment. Element extraction is the core basis of situational awareness system, which directly affects the accuracy of assessment and prediction work. Aiming at the problem that it is difficult to obtain situational elements in the Internet of Vehicles environment with complex heterogeneous networks, this paper proposes a random forest (RF) element extraction method based on Principal Component Analysis (PCA) and Bayesian Optimization (BO). This method firstly performs attribute reduction and denoising and dimensionality reduction for traffic data through principal component analysis, and then the classification training of the processed dataset is carried out by using the Random forest model optimized by Bayes. In order to verify the effectiveness of the proposed method, the experiment uses the controller area network (CAN) traffic data set of the vehicle, and the results verified the effectiveness of the proposed method, which has better accuracy and generalization ability compared with the traditional extraction method.
A deep learning method using SPECT images to diagnose remaining thyroid tissue post-thyroidectomy

After total thyroidectomy, Single-photon emission computed tomography (SPECT) is used to diagnose whether thyroid tissue remains in patients' bodies. Physicians visually diagnose the residual thyroid tissue in patient base on their expertise, so it is difficult for making a quick and accurate diagnostic. In present, Computer-aided Diagnosis systems (CAD) are becoming more widely in medical treatment, thyroid cancer is a potential field where CAD can improve diagnostic accuracy. This paper proposes a novel approach for diagnosing whether residual thyroid tissues remain in patient using thyroid SPECT scintigraphy by fine-tuning pre-trained Deep neural networks. Our proposed method achieved sensitivity of 84.85% and specificity is 89.11%, these results demonstrate that CAD is promising in diagnosing remaining thyroid tissue after total thyroidectomy.
Efficiency Evaluation of Digital Transformation on Performance Improvement in Chinese Listed Companies

From the perspective of improving enterprise efficiency through digital transformation, the DEA-VRS (variable returns to scale of data envelopment analysis) method was applied to discuss the improvement of enterprise efficiency by inputting digital elements of Chinese listed companies during 2018-2021. The findings were as follows: (1) Chinese listed companies had presented low efficiency with digital factor input, which was specifically manifested as the improvement of pure technical efficiency and the decline of scale efficiency. (2) The profitability of Northeast China was the strongest, and its input of digital elements also resulted in the highest pure technical efficiency and the lowest scale efficiency. Enterprises in the eastern region had shown the weakest utilization ability of digital elements. Companies in the western region had been the most stable operation in the past four years, and the comprehensive efficiency and pure technical efficiency had increased the most from 2018 to 2021, with a high scale decreasing effect. (3) The profitability and innovation ability of the manufacturing industry had been significantly higher than those of non-manufacturing enterprises, but with higher diminishing returns to scale. Digital elements could be used more effectively in non-manufacturing businesses. (4) The comprehensive efficiency brought by digitalization in non-state-owned enterprises had been higher than that of state-owned enterprises, mainly due to the improvement of pure technical efficiency and stability of scale efficiency.
A State-of-the-Art Reverse Engineering Approach for Combating Hardware Security Vulnerabilities at the System and PCB Level in IoT Devices

In this paper, we have presented a systematic hardware reverse engineering (HRE) approach for IoT -based electronic devices. The objective of the developed HRE approach is to understand and test various hardware security vulnerabilities associated with IoT devices while working back through interconnecting the components and creating the main schematics. The paper has brought up state-of-art knowledge, tools, and techniques used for testing hardware security vulnerabilities at the system-level and printed circuit board (PCB)-level reverse engineering. Besides the known hardware security testing techniques e.g., side-channel attacks and faults injections, this paper has introduced a unique approach that includes documentation, initial screening, functional test, battery management system, tamper resistance (PCB and system level), and PCB reverse engineering with best practices. Even though this paper mainly covers non-invasive attacks at the system and PCB level, the HRE approach presented will be very helpful to IoT device manufacturers, testing labs, and researchers for setting up testing and research labs on hardware reverse engineering and developing competency through education, training, and capacity-building programs.
Analysis of Interpretable Handwriting Features to Evaluate Motoric Patterns in Different Neurodegenerative Diseases

Clinicians currently use handwriting as one of the methods to establish the presence and monitor the progression of neurodegenerative diseases (NDs). While common handwriting evaluation methods are valuable means to detect fine motor and cognitive impairments associated with NDs, these are observer-dependent and subjective. In the present study, we analyzed a broad array of interpretable features, some proposed for the first time in this study, obtained from online handwriting data of participants with NDs and control subjects (CTRL). ND participants have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). Hand-writing data from three different neuropsychological tasks was used: Copy Text task, Copy Cube task, and Copy Image task. Then, we arranged three complementary sets of features and conducted a statistical analysis to test their significance between groups. Overall results suggested that subjects with AD reported a significantly higher $(p < 0.05)$ amount of data points and total duration with respect to the CTRL group in almost all the tasks under assessment. On the other hand, subjects with PD showed a significantly lower $(p < 0.05)$ horizontal width (both on tablet and in the air). Even though the AD and PDM groups showed a significantly lower velocity and acceleration $(p < 0.05)$, their number of inversions in velocity and acceleration was significantly greater $(p < 0.05)$, which indicates disfluency in writing. The features that we have used were found to provide good results in differentiating the studied groups and could be considered as part of diagnostic tools for the assessment and monitoring of NDs in clinical trials.
Speech Recognition Model with Spatio-Temporal Encoding and Brain-Like Neural Networks

Deep learning has achieved great success in various applications, such as image processing and audio signal processing. However, deep learning methods usually require large and powerful computing resource, which greatly limit the practical application of deep learning methods. The spiking neural network is a new paradigm of neural networks and has the potential for low-power consumption. In this paper, we proposed a new computational model with the aim to provide an efficient and low-power computational model for speech recognition tasks. In the proposed model, the threshold coding method is employed to transform the speech signals to spike train patterns. The encoded spike patterns are then presented to the designed multi-layer spiking neural networks and trained by the surrogate-learning algorithm. After learning, the classification is decided by the firing rate of the neurons in the last layer. Experimental results demonstrate that the proposed method gets good accuracy in the speech recognition task. In addition, the proposed computational model works in an event-driven manner, hence, it holds great potential for low-latency and low-power consumption in neuromorphic hardware.
Video Transmission Trial by Wireless Multi-hop Network based on Wi-SUN FAN

The wireless smart ubiquitous network field area network (Wi-SUN FAN), a wireless communication standard for Internet of Things (IoT) applications, such as smart metering and monitoring, has been attracting attention. Conventionally, the information transmitted by the Wi-SUN FAN is numerical values and/or character strings, such as metering information. It has been thought that the Wi-SUN FAN cannot transmit video images because the data rate is at most in hundreds of kbps. However, owing to improvements in video compression and transmission technologies in recent years, video images can be transmitted at a transmission rate of hundreds of kbps. In this study, the feasibility of video transmission over the Wi-SUN FAN is evaluated using low-rate video compression and transmission technology. First, several network topologies for video transmission by the current Wi-SUN FAN with multi-hop are evaluated by computer simulations. Consequently, adequate numbers of nodes and multi-hops in the Wi-SUN FAN are recommended. Subsequently, using a recommended value, an experimental system is configured and the video transmission performance over the Wi-SUN FAN is evaluated experimentally.
Rap-Style Commentary Generation in an Audience Participation Fighting Game

This paper presents a rap-style commentary system for enhancing audience participation games. In this work, an audience participation fighting game played by two AI players is presented. This game has two unique features. The first is that commentaries are generated in a rap-style fashion. The second is AI-player strength adjustment based on chat messages from audiences-these chat messages also affect the abovementioned commentaries. Experimental results show that the proposed system significantly promotes audience experience.