Reviewerly/exHarmony huggingface dataset:
Enhanced semantic feature pyramid network for small object detection

Feature-pyramid network-based models, which progressively fuse multi-scale features, have been proven highly effective in object detection. However, these models often learn multi-scale features with ambiguous boundaries, due to small objects with only a few pixels that easily lose information during top-down propagation, which makes multi-scale feature representation less effective. In this work, we propose an efficient Enhanced Semantic Feature Pyramid Network(ES-FPN), which combines semantic information at high-level with contextual information at low-level to improve multi-scale feature learning in small object detection. Specifically, the proposed network first exploits the rich semantic information in lateral connections that enables the features to be more semantic. Then, it excavates the lost information in high-level/low-res feature maps with rich contextual information in low-level/high-res. In this way, the high-level layers suffer the reduced loss of important contextual information during the progressive feature fusion that avoids object disappearance, which is useful to utilize rich semantic information in high-level. Finally, ES-FPN fuses the distributed features of each layer stage-by-stage and the final features are more semantically and better for localizing the object. Extensive experimental results over three widely used object detection benchmarks(MS COCO, VOC and Cityscapes) demonstrate that our network can accurately locate fairly complete objects with clear boundaries and outperforms previous feature pyramid-based methods.
Development of Language Models for Continuous Uzbek Speech Recognition System

Automatic speech recognition systems with a large vocabulary and other natural language processing applications cannot operate without a language model. Most studies on pre-trained language models have focused on more popular languages such as English, Chinese, and various European languages, but there is no publicly available Uzbek speech dataset. Therefore, language models of low-resource languages need to be studied and created. The objective of this study is to address this limitation by developing a low-resource language model for the Uzbek language and understanding linguistic occurrences. We proposed the Uzbek language model named UzLM by examining the performance of statistical and neural-network-based language models that account for the unique features of the Uzbek language. Our Uzbek-specific linguistic representation allows us to construct more robust UzLM, utilizing 80 million words from various sources while using the same or fewer training words, as applied in previous studies. Roughly sixty-eight thousand different words and 15 million sentences were collected for the creation of this corpus. The experimental results of our tests on the continuous recognition of Uzbek speech show that, compared with manual encoding, the use of neural-network-based language models reduced the character error rate to 5.26%.
Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images

Cancer has a disproportionately large influence on the death rate of adults. A patient needs to get a diagnosis of their condition as quickly as is humanly feasible in order to have the greatest chance of surviving their sickness. Skilled medical professionals use medical imaging and other traditional diagnostic methods to search for clues that may indicate the presence of malignant tendencies inside the body. Nevertheless, manual diagnosis may be time-consuming and subjective owing to the wide range of interobserver variability induced by the enormous number of medical imaging data. This variability is caused by the fact that medical imaging data are collected. Because of this, the process of accurately diagnosing a patient could become more difficult. To execute jobs that included machine learning and the interpretation of complicated imagery, cutting-edge computer technology was necessary. Since the 1980s, researchers have been working on developing a computer-aided diagnostic system that would help medical professionals in the early diagnosis of various malignancies. According to the most recent projections, prostate cancer will be discovered in the body of one out of every seven men at some time throughout the course of their life. It is unacceptable how many men are being told that they have prostate cancer, and the condition is responsible for the deaths of a rising number of men every year. Because of the high quality and multidimensionality of the MRI pictures, you will also need a powerful diagnosis system in addition to the CAD tools. Since it has been shown that CAD technology is beneficial, researchers are looking at methods to improve the accuracy, precision, and speed of the systems that use it. The effectiveness of CAD technology has been shown. This research proposes a strategy that is both effective and efficient for the processing of images and the extraction of features as well as for machine learning. This work makes use of MRI scans and machine learning in an effort to detect prostate cancer at an early stage. Histogram equalization is used while doing the preliminary processing on photographs. The image’s overall quality is elevated as a result. The fuzzy C means approach is used in order to segment the images. Using a Gray Level Cooccurrence Matrix (GLCM), it is feasible to extract features from a dataset. The KNN, random forest, and AdaBoost classification algorithms are used in the classification process.
CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies

Clinical Cohort Studies (CCS), such as randomized clinical trials, are a great source of documented clinical research. Ideally, a clinical expert inspects these articles for exploratory analysis ranging from drug discovery for evaluating the efficacy of existing drugs in tackling emerging diseases to the first test of newly developed drugs. However, more than 100 articles are published daily on a single prevalent disease like COVID-19 in PubMed. As a result, it can take days for a physician to find articles and extract relevant information. Can we develop a system to sift through these articles faster and document the crucial takeaways from each of these articles? In this work, we propose CCS Explorer, an end-to-end system for relevance prediction of sentences, extractive summarization, and patient, outcome, and intervention entity detection from CCS. CCS Explorer is packaged in a web-based graphical user interface where the user can provide any disease name. CCS Explorer then extracts and aggregates all relevant information from articles on PubMed based on the results of an automatically generated query produced on the back-end. For each task, CCS Explorer fine-tunes pre-trained language representation models based on transformers with additional layers. The models are evaluated using two publicly available datasets. CCS Explorer obtains a recall of 80.2%, AUC-ROC of 0.843, and an accuracy of 88.3% on sentence relevance prediction using BioBERT and achieves an average Micro F1-Score of 77.8% on Patient, Intervention, Outcome detection (PIO) using PubMedBERT. Thus, CCS Explorer can reliably extract relevant information to summarize articles, saving time by ~660×.
5G-Enabled Cyber-Physical Systems for Smart Transportation Using Blockchain Technology

The physical world can be controlled directly over the Internet once a Cyber-Physical 1 System (CPS) infrastructure is established. The Intelligent Transportation System (ITS) encompasses Wireless Sensor Network (WSN), Vehicular ad hoc network (VANET), and 5G-enabled Internet of Things (IoT) solutions to transform traditional transportation into an ITS. This research investigates the option of running a blockchain-driven security assurance model to safeguard intelligent roads and smart vehicles as part of ITS. The proposed model considers a semi-distributed model in blockchain deployment to ensure satisfactory Internet of Vehicles (IoV) service while mining acceptable security assurance. The experimental outcomes on intelligent roads and smart parking management indicate that the proposed model achieves comparably good data delivery and reduced latency, paving the way to an innovative deployment of blockchain technologies in IoV for a dependable and trustworthy ITS.
A Privacy-Preserving Federated-MobileNet for Facial Expression Detection from Images

Facial expression recognition is an intriguing research area that has been explored and utilized in a wide range of applications such as health, security, and human-computer interactions. The ability to recognize facial expressions accurately is crucial for human-computer interactions. However, most of the facial expression analysis techniques have so far paid little or no concern to users' data privacy. To overcome this concern, in this paper, we incorporated Federated Learning (FL) as a privacy-preserving machine learning approach in the field of facial expression recognition to develop a shared model without exposing personal information. The individual models are trained on the different client devices where the data is stored. In this work, a lightweight Convolutional Neural Network (CNN) model called the MobileNet architecture is utilised to detect expressions from facial images. To evaluate the model, two publicly available datasets are used and several experiments are conducted. The result shows that the proposed privacy-preserving Federated-MobileNet approach could recognize facial expressions with considerable accuracy compared to the general approaches.
Forecasting the Value of Oil and Gas Exports in Indonesia using ARIMA Box-Jenkins

The objective of the study was to forecast the value of oil and gas exports in Indonesia using the ARIMA Box-Jenkins. With this prediction, it is hoped that it can be a study for future policy making. This oil and gas export data is obtained from the Indonesian Central Bureau of Statistics (BPS) website, in raw data from January 2010 to March 2022. This data is predicted using the ARIMA method with the help of R software. The stages of data analysis with ARIMA include: data stationary test, build the model indication, parameter estimation and significance test, and residual diagnostic test of the model. The results of data analysis conducted in this study show that there are 3 indications of models that were generated, namely ARIMA(1,1,0); ARIMA(0,1,1); and ARIMA(1,1,0). From these 3 model indications, the best model was ARIMA(0,1,1) with AIC value of 2047.65.
Metaverse applied to musculoskeletal pathology: Orthoverse and Rehabverse

The Metaverse is 'an integrated network of 3D virtual worlds.' It incorporates digitally created realities into the real world, involves virtual copies of existing places and changes the physical reality by superimposing digital aspects, allowing its users to interact with these elements in an immersive, real-time experience. The applications of the Metaverse are numerous, with an increasing number of experiences in the field of musculoskeletal disease management. In the field of medical training, the Metaverse can help facilitate the learning experience and help develop complex clinical skills. In clinical care, the Metaverse can help clinicians perform orthopedic surgery more accurately and safely and can improve pain management, the performance of rehabilitation techniques and the promotion of healthy lifestyles. Virtualization can also optimize aspects of healthcare information and management, increasing the effectiveness of procedures and the functioning of organizations. This optimization can be especially relevant in departments that are under significant care provider pressure. However, we must not lose sight of the fundamental challenges that still need to be solved, such as ensuring patient privacy and fairness. Several studies are underway to assess the feasibility and safety of the Metaverse.
Label noise analysis meets adversarial training: A defense against label poisoning in federated learning

Data decentralization and privacy constraints in federated learning systems withhold user data from the server. As a result, intruders can take advantage of this privacy feature by corrupting the federated network using forged updates obtained on malicious data. This paper proposes a defense mechanism based on adversarial training and label noise analysis to address this problem. To do so, we design a generative adversarial scheme for vaccinating local models by injecting them with artificially-made label noise that resembles backdoor and label flipping attacks. From the perspective of label noise analysis, all poisoned labels can be generated through three different mechanisms. We demonstrate how backdoor and label flipping attacks resemble each of these noise mechanisms and consider them all in the introduced design. In addition, we propose devising noisy-label classifiers for the client models. The combination of these two mechanisms enables the model to learn possible noise distributions, which eliminates the effect of corrupted updates generated due to malicious activities. Moreover, this work conducts a comparative study on state-of-the-art deep noisy label classifiers. The designed framework and selected methods are evaluated for intrusion detection on two internet of things networks. The results indicate the effectiveness of the proposed approach.
MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022 .
CCTseg: A cascade composite transformer semantic segmentation network for UAV visual perception

Semantic segmentation could obtain the pixel level classification of surrounding environments which is an essential task for autonomous vehicles and mobile robots visual perception. Most existing semantic segmentation networks were focused on the visual perception of autonomous vehicles. Little attention is paid to the semantic segmentation for UAV (Unmanned Aerial Vehicle) visual perception, which is crucial to UAV autonomous flight and landing spot searching. Compared with views from autonomous vehicles, the UAV-based views were more challenging for the semantic segmentation task due to images captured by UAV containing large-scale variation of objects size caused by different altitude and angle. The existing semantic segmentation networks for the visual perception of autonomous vehicles are generally inadequate to effectively extract the representative features of UAV images which required contain context information and local information simultaneously. A cascade composite transformer-based semantic segmentation network is proposed in this study for UAV visual perception. A cascade composite encoder is designed which consists of three transformer-based feature extraction backbones and cascade fused low-level features, middle-level features and high-level features to achieve better feature representation capacity. The spatial enhanced transformer block is implemented as the basic feature extraction block of each backbone to make the extracted features contain context information of environments and local information of objects. A symmetric rhombus decoder is proposed to integrate multi-stage features and make fully utilise of middle stage features which contained abundance of useful information, thus accurately pixel level prediction could be obtained in this way. Ablation studies and comparison experiments for the proposed CCTseg have been conducted on two public UAV imagery datasets suitable for UAV autonomous flight and landing spot observing. Experimental results have demonstrated the effectiveness of the proposed network structure and the superiority of proposed network over other state-of-the-art methods for the semantic segmentation of UAV visual perception.
Applications Analyses, Challenges and Development of Augmented Reality in Education, Industry, Marketing, Medicine, and Entertainment

This study aims to develop systematic research about augmented reality (AR) problems, challenges, and benefits in the current applications of five fields of interest. Articles were selected from scientific, technical, academic, and medical databases of digital journals and open access papers about AR. Therefore, the method used to develop the investigation was PRISMA, which allowed us to observe interesting facts and coincidences about complexities and successful cases of AR implementation in the disciplines of education, marketing, medicine, entertainment, and industry. The summary provided in this study was the result of the exploration of 60 recent articles found and selected by relevance using the PRISMA method. The main objective of this paper is to orient and update researchers regarding current applications, benefits, challenges, and problems in AR implementation for future studies and developments.
Estimation of optimal tilt angles for photovoltaic panels in Egypt with experimental verifications

Abstract The principal target of this work is to compute the optimal tilt angle (OTA) for Photovoltaic (PV) panels. To perform this task, comprehensive simulations are done starting from altering the tilt angle (TA) daily, to use one fixed TA for all the year. The mathematical models for extra-terrestrial radiation (ETR) of both horizontal and inclined surfaces are presented firstly. At a later stage, the optimization formulation for the maximizing the solar radiation (SR) is adapted, and then the daily, monthly, seasonally, half-yearly and optimal fixed TAs are obtained. Although, the daily OTA produces the maximum SR, it is costly and impractical. It is found that altering the TA twice a year at optimal values that are computed as 5° and 50° for Suez city, gives the best results that are very near to the daily altering of the OTA. The difference between the two methods is 1.56% which is very small. Also, the two OTAs has SR better than that of the fixed OTA which is 28° by 7.77%. Also, it is found that the yearly fixed OTA (28°) is nearly equal to the latitude angle of Suez city which is 30°. The two OTAs method of this paper is different from the commonly used method that suggests two TAs. The first TA is used for winter months which is obtained by adding 15° to the latitude angle while the second TA is obtained by subtracting 15° from the latitude angle for the summer months. This commonly used method produces lesser SR than the two OTAs method of this paper. The theoretical work has been proved by an experimental work on two PV systems constructed at 25° and 30° TAs. The results of the experimental work agree with the theoretical results.
Word Equations in Synergy with Regular Constraints

We argue that in string solving, word equations and regular constraints are better mixed together than approached separately as in most current string solvers. We propose a fast algorithm, complete for the fragment of chain-free constraints, in which word equations and regular constraints are tightly integrated and exchange information, efficiently pruning the cases generated by each other and limiting possible combinatorial explosion. The algorithm is based on a novel language-based characterisation of satisfiability of word equations with regular constraints. We experimentally show that our prototype implementation is competitive with the best string solvers and even superior in that it is the fastest on difficult examples and has the least number of timeouts.
Chatbots shouldn’t use emojis

Artificial intelligence that can manipulate our emotions is a scandal waiting to happen. Artificial intelligence that can manipulate our emotions is a scandal waiting to happen.
Suppressing Quantum Circuit Errors Due to System Variability

We present a quantum circuit optimization technique that takes into account the variability in error rates that is inherent across present-day noisy quantum computing platforms. This method can be run after qubit routing or postcompilation and consists of computing isomorphic subgraphs to input circuits and scoring each using heuristic cost functions derived from system calibration data. Using an independent standard algorithmic test suite, we show that it is possible to recover on average nearly 40% of missing fidelity using better qubit selection via efficient to compute cost functions. We demonstrate additional performance gains by considering qubit placement over multiple quantum processors. The overhead from these tools is minimal with respect to other compilation steps, such as qubit routing, as the number of qubits increases. As such, our method can be used to find qubit mappings for problems at the scale of quantum advantage and beyond.Received 13 October 2022Accepted 22 February 2023DOI:https://doi.org/10.1103/PRXQuantum.4.010327Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum information architectures & platformsQuantum softwareQuantum Information
Artificial intelligence and edge computing for teaching quality evaluation based on 5G-enabled wireless communication technology

Abstract Cloud computing and artificial intelligence are now widely used for classroom teaching in higher learning institutes. The digital teaching supported to ICT technologies in colleges serves as a central point for the advancement of modern education; and has become as a mode of instruction and an approach to teaching. Digital teaching has emerged as a major driving force in the advancement of digital economy and digitization of education in colleges. In this paper, we investigate the movable information management system utilized in the digital teaching using edge computing and 5G wireless communication technology. Furthermore, we explain the idea of a mobile data scheme and presents a teaching platform based on the edge computing and 5G-enabled wireless communication technology. The main objective of this work is to develop a digital teaching framework for college students that, in fact, enables digital teaching, the collection, and incorporation of teaching information, the provision of modern education, and sharing of resources. Cutting-edge technology advancements in the educational platform have the potential to improve 5G communication. To implement the cutting-edge technology, all types of technological devices, smart devices, and gadgets from the Internet of Things (IoT) platform are used. We evaluated the proposed system through reasonable assumptions and numerical simulations. The experimental results reveal that the suggested system has significantly improved the teaching efficiency with which digital teaching management is managed in colleges. Moreover, the edge and 5G technology can significantly improve the system performance, in terms of response time, that can be as high as 11.45% when compared to non-cloud based approaches.