Reviewerly/exHarmony huggingface dataset:
An Empirical Client Cloud Environment to Secure Data Communication with Alert Protocol

Cloud computing is a technology that provides resources and utility services based on user demand. Due to this demand, efficient cloud security protocols are highly required, especially at the time of data communication for user authentication and data aggregation. The data communication scenarios are majorly affected by the security threats in the cloud computing environment. This article provides a practical approach to developing an efficient and empirical cloud framework in terms of cloud protocol. The framework uses fuzzy c-means (FCM) algorithm to group data, and calculation is done individually or associatively to rank the text data. Uploaded data are passed to a simple additive weighting (SAW) algorithm for ranking and making decision selection. The framework executes in three phases, namely data preprocessing, clustering, and automatic data security with an alert mechanism. The process is completely automated so there is no need of considering the individual files for the processing and the data held will be appropriately correlated with the sharing inter-cloud environment. To inspect security issues, the proposed framework is secured by three different security algorithms. The encryption process is completed by Rivest Cipher 6 (RC6); the substitution process is done by Advanced Encryption Standard (AES); and key generation is done by RC6, AES, and Rivest-Shamir-Adleman (RSA) approaches collectively. Based on the given situations, these standard approaches were automatically applied separately or collectively. Unauthorized access trapping and data deletion mechanism are also provided in the proposed framework. The experimental results with a comparative study depicted the effectiveness of the proposed work.
School literacy movement program in elementary school, Indonesia: Literature review

The Ministry of Education and Culture developed the school literacy movement (SLM) program. This program aims to foster student interest in reading and improve reading skills so that knowledge can be mastered better. The author uses a literature review design by collecting data from various journal sources relevant to the SLM program in elementary schools, Indonesia. This research is an analytical descriptive study, which is a regular breakdown of the data that has been obtained, then understanding and explanation are given so that it can be understood well by the reader. The selected journal criteria are journals published in 2015-2021. SLM activities in elementary schools are influenced by supporting and inhibiting factors. The supporting factor is that the principal has a good commitment to carry out SLM activities, teachers and students and other components of the school also contribute to the success of SLM activities in elementary schools. Availability of sufficient funds to provide the necessary books is also a factor supporting SLM. While the inhibiting factor is that there are still very few books available so students cannot choose reading books that match their interests. Reading books is the main factor that must exist to make this SLM activity a success. Therefore, the procurement of reading books is very necessary. Students' reading habits are still low, they are still waiting for the teacher's orders to carry out SLM activities. The SLM schedule is not fully for 15-minute reading activities as specified in the manual. Lack of parental involvement in SLM activities.
Yolov5, Yolo-x, Yolo-r, Yolov7 Performance Comparison: A Survey

YOLOv7 algorithm have taken the object detection domain by the storm as its real-time object detection capabilities out ran all other previous algorithms both in accuracy and speed [1]. YOLOv7 advances the state of the art results in object detection by inferring more quickly and accurately than its contemporaries. In this paper, we are going to present our work of implementing this SOTA deep learning model on a soccer game play video to detect the players and football. As the result, it detected the players, football and their movement in real time. We also analyzed and compared the YOLOv7 results against its previous versions including YOLOv4, YOLOv5 and YOLO-R. The code is available at: https://github.com/RizwanMunawar/YOLO-RX57-FPS-Comparision
Deriving task specific performance from the information processing capacity of a reservoir computer

Abstract In the reservoir computing literature, the information processing capacity is frequently used to characterize the computing capabilities of a reservoir. However, it remains unclear how the information processing capacity connects to the performance on specific tasks. We demonstrate on a set of standard benchmark tasks that the total information processing capacity correlates poorly with task specific performance. Further, we derive an expression for the normalized mean square error of a task as a weighted function of the individual information processing capacities. Mathematically, the derivation requires the task to have the same input distribution as used to calculate the information processing capacities. We test our method on a range of tasks that violate this requirement and find good qualitative agreement between the predicted and the actual errors as long as the task input sequences do not have long autocorrelation times. Our method offers deeper insight into the principles governing reservoir computing performance. It also increases the utility of the evaluation of information processing capacities, which are typically defined on i.i.d. input, even if specific tasks deliver inputs stemming from different distributions. Moreover, it offers the possibility of reducing the experimental cost of optimizing physical reservoirs, such as those implemented in photonic systems.
Adaptive multiple imputations of missing values using the class center

Abstract Big data has become a core technology to provide innovative solutions in many fields. However, the collected dataset for data analysis in various domains will contain missing values. Missing value imputation is the primary method for resolving problems involving incomplete datasets. Missing attribute values are replaced with values from a selected set of observed data using statistical or machine learning methods. Although machine learning techniques can generate reasonably accurate imputation results, they typically require longer imputation durations than statistical techniques. This study proposes the adaptive multiple imputations of missing values using the class center (AMICC) approach to produce effective imputation results efficiently. AMICC is based on the class center and defines a threshold from the weighted distances between the center and other observed data for the imputation step. Additionally, the distance can be an adaptive nearest neighborhood or the center to estimate the missing values. The experimental results are based on numerical, categorical, and mixed datasets from the University of California Irvine (UCI) Machine Learning Repository with introduced missing values rate from 10 to 50% in 27 datasets. The proposed AMICC approach outperforms the other missing value imputation methods with higher average accuracy at 81.48% which is higher than those of other methods about 9 – 14%. Furthermore, execution time is different from the Mean/Mode method, about seven seconds; moreover, it requires significantly less time for imputation than some machine learning approaches about 10 – 14 s.
A Deep Learning-Based Approach for the Diagnosis of Acute Lymphoblastic Leukemia

Leukemia is a deadly disease caused by the overproduction of immature white blood cells (WBS) in the bone marrow. If leukemia is detected at the initial stages, the chances of recovery are better. Typically, morphological analysis for the identification of acute lymphoblastic leukemia (ALL) is performed manually on blood cells by skilled medical personnel, which has several disadvantages, including a lack of medical personnel, sluggish analysis, and prediction that is dependent on the medical personnel’s expertise. Therefore, we proposed the Multi-Attention EfficientNetV2S and EfficientNetB3 state-of-the-art deep learning architectures using transfer learning-based fine-tuning approach to distinguish the normal and blast cells from microscopic blood smear images that both are pretrained on large-scale ImageNet database. We simply modified the last block of both models and added additional layers to both models. After including this Multi-Attention Mechanism, it not only reduces the model’s complexities but also generalizes its network quite well. By using the proposed technique, the accuracy has improved and the overall loss is also minimized. Our Multi-Attention EfficientNetV2S and EfficientNetB3 models achieved 99.73% and 99.25% accuracy, respectively. We have further compared the proposed model’s performance to other individual and ensemble models. Upon comparison, the proposed model outclassed the existing literature and other benchmark models, thus proving its efficiency.
A novel discrete Rat swarm optimization (DRSO) algorithm for solving the traveling salesman problem

Metaheuristics are often used to find solutions to real and complex problems. These algorithms can solve optimization problems and provide solutions close to the global optimum in an acceptable and reasonable time. In this paper, we will present a new bio-inspired metaheuristic based on the natural chasing and attacking behaviors of rats in nature, called Rat swarm optimizer. Which has given good results in solving several continuous optimization problems, and adapted it to solve a discrete, NP-hard, and classical optimization problem that is the traveling salesman problem (TSP) while respecting the natural behavior of rats. To test the efficiency of the adaptation of our proposal, we applied the adapted rat swarm optimization (RSO) algorithm on some reference instances of TSPLIB. The obtained results show the performance of the proposed method in solving the traveling salesman problem (TSP).
Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer

With the development of deep learning, the performance of image semantic segmentation in remote sensing has been constantly improved. However, the performance usually degrades while testing on different datasets because of the domain gap. To achieve feasible performance, extensive pixel-wise annotations are acquired in a new environment, which is time-consuming and labor-intensive. Therefore, unsupervised domain adaptation (UDA) has been proposed to alleviate the effort of labeling. However, most previous approaches are based on outdated network architectures that hinder the improvement of performance in UDA. Since the effects of recent architectures for UDA have been barely studied, we reveal the potential of Transformer in UDA for remote sensing with a self-training framework. Additionally, two training strategies have been proposed to enhance the performance of UDA: (1) Gradual Class Weights (GCW) to stabilize the model on the source domain by addressing the class-imbalance problem; (2) Local Dynamic Quality (LDQ) to improve the quality of the pseudo-labels via distinguishing the discrete and clustered pseudo-labels on the target domain. Overall, our proposed method improves the state-of-the-art performance by 8.23% mIoU on Potsdam→Vaihingen and 9.2% mIoU on Vaihingen→Potsdam and facilitates learning even for difficult classes such as clutter/background.
A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose

Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks to achieve higher accuracy. Computational complexity and model size are often neglected, despite being key characteristics for practical use of human mesh reconstruction models (e.g. virtual try-on systems). In this paper, we present GTRS, a lightweight pose-based method that can reconstruct human mesh from 2D human pose. We propose a pose analysis module that uses graph transformers to exploit structured and implicit joint correlations, and a mesh regression module that combines the extracted pose feature with the mesh template to reconstruct the final human mesh. We demonstrate the efficiency and generalization of GTRS by extensive evaluations on the Human3.6M and 3DPW datasets. In particular, GTRS achieves better accuracy than the SOTA pose-based method Pose2Mesh while only using 10.2% of the parameters (Params) and 2.5% of the FLOPs on the challenging in-the-wild 3DPW dataset. Code is available at https://github.com/zczcwh/GTRS
MARIO

We address the multimedia recommendation problem, which utilizes items' multimodal features, such as visual and textual modalities, in addition to interaction information. While a number of existing multimedia recommender systems have been developed for this problem, we point out that none of these methods individually capture the influence of each modality at the interaction level. More importantly, we experimentally observe that the learning procedures of existing works fail to preserve the intrinsic modality-specific properties of items. To address above limitations, we propose an accurate multimedia recommendation framework, named MARIO, based on modality-aware attention and modality-preserving decoders. MARIO predicts users' preferences by considering the individual influence of each modality on each interaction while obtaining item embeddings that preserve the intrinsic modality-specific properties. The experiments on four real-life datasets demonstrate that MARIO consistently and significantly outperforms seven competitors in terms of the recommendation accuracy: MARIO yields up to 14.61% higher accuracy, compared to the best competitor.
Visual Object Tracking in First Person Vision

Abstract The understanding of human-object interactions is fundamental in First Person Vision (FPV). Visual tracking algorithms which follow the objects manipulated by the camera wearer can provide useful information to effectively model such interactions. In the last years, the computer vision community has significantly improved the performance of tracking algorithms for a large variety of target objects and scenarios. Despite a few previous attempts to exploit trackers in the FPV domain, a methodical analysis of the performance of state-of-the-art trackers is still missing. This research gap raises the question of whether current solutions can be used “off-the-shelf” or more domain-specific investigations should be carried out. This paper aims to provide answers to such questions. We present the first systematic investigation of single object tracking in FPV. Our study extensively analyses the performance of 42 algorithms including generic object trackers and baseline FPV-specific trackers. The analysis is carried out by focusing on different aspects of the FPV setting, introducing new performance measures, and in relation to FPV-specific tasks. The study is made possible through the introduction of TREK-150, a novel benchmark dataset composed of 150 densely annotated video sequences. Our results show that object tracking in FPV poses new challenges to current visual trackers. We highlight the factors causing such behavior and point out possible research directions. Despite their difficulties, we prove that trackers bring benefits to FPV downstream tasks requiring short-term object tracking. We expect that generic object tracking will gain popularity in FPV as new and FPV-specific methodologies are investigated.
Adaptation of higher education students’ digital skills survey to Russian universities

Digital skills are essential for a technologized society. For younger generations, it has become almost necessary to have such skills. This study focuses on a valid and reliable measurement tool to determine the digital skills of university students. The research was carried out with the participation of a total of 463 university students. Exploratory factor analysis and confirmatory factor analysis were conducted to investigate the validity and reliability of the digital skills survey. Consequently, research on the validity and reliability of the digital skills survey in the Russian environment was conducted. 25 items and six factors (access to and management of digital content, digital empathy, use of digital means, digital safety, communication of digital content, creation of digital content) were identified after the analysis. Future research should employ the adapted survey to assess the level of students’ digital skills. In addition, the survey’s validity may be analyzed in the context of other cultures.
DNS privacy with speed?

Over the last decade, Web traffic has significantly shifted towards HTTPS due to an increased awareness for privacy. However, DNS traffic is still largely unencrypted, which allows user profiles to be derived from plaintext DNS queries. While DNS over TLS (DoT) and DNS over HTTPS (DoH) address this problem by leveraging transport encryption for DNS, both protocols are constrained by the underlying transport (TCP) and encryption (TLS) protocols, requiring multiple round-trips to establish a secure connection. In contrast, QUIC combines the transport and cryptographic handshake into a single round-trip, which allows the recently standardized DNS over QUIC (DoQ) to provide DNS privacy with minimal latency. In the first study of its kind, we perform distributed DoQ measurements across multiple vantage points to evaluate the impact of DoQ on Web performance. We find that DoQ excels over DoH, leading to significant improvements with up to 10% faster loads for simple webpages. With increasing complexity of webpages, DoQ even catches up to DNS over UDP (DoUDP) as the cost of encryption amortizes: With DoQ being only ~2% slower than DoUDP, encrypted DNS becomes much more appealing for the Web.
Journalists' Perceptions towards Employing Artificial Intelligence Techniques in Jordan TV's Newsrooms

The study aimed to reveal the perceptions of journalists towards employing artificial intelligence techniques in Jordan TV's Newsrooms. The study sample consisted of journalists working in Jordan TV's Newsrooms. The field study was applied to a simple random sample of (106) through the questionnaire tool. The study used the descriptive exploratory approach. The study concluded that there is a clear effect of employing artificial intelligence techniques according to the perceptions of journalists in the newsrooms of Jordan Television, and the presence of differences between the responses of the respondents of the journalists in the reasons why the newsrooms of the Jordanian television are not ready to employ artificial intelligence techniques. In addition to the above, the study indicated that there were no statistically significant differences in the other reasons for the lack of readiness, where the values of X2 (Chi-squared) were not a significant. However, journalists in the newsrooms of Jordan Television possessed various skills, including: using social networks for research, publishing news stories, and automated content production programs. Finally, there were no statistically significant differences between journalists in digital media skills, and the X2 values were not significant at the significance level (0.05), with the exception of info graphic production skills.
Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet

Traffic light detection and recognition technology are of great importance for the development of driverless systems and vehicle-assisted driving systems. Since the target detection algorithm has the problems of lower detection accuracy and fewer detection types, this paper adopts the idea of first detection and then classification and proposes a method based on YOLOv5s target detection and AlexNet image classification to detect and identify traffic lights. The method first detects the traffic light area using YOLOv5s, then extracts the area and performs image processing operations, and finally feeds the processed image to AlexNet for recognition judgment. With this method, the shortcomings of the single-target detection algorithm in terms of low recognition rate for small-target detection can be avoided. Since the homemade dataset contains more low-light images, the dataset is optimized using the ZeroDCE low-light enhancement algorithm, and the performance of the network model trained after optimization of the dataset can reach 99.46% AP (average precision), which is 0.07% higher than that before optimization, and the average accuracy on the traffic light recognition dataset can reach 87.75%. The experimental results show that the method has a high accuracy rate and can realize the recognition of many types of traffic lights, which can meet the requirements of traffic light detection on actual roads.
Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph-structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.
Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

a b s t r a c t Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs).HE has played a role in many accidents and outage incidents in NPPs.Despite the increased automation in NPPs, HE remains unavoidable.Hence, the need for HE detection is as important as HE prevention efforts.In NPPs, HE is rather rare.Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE.In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs.More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE.We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest.Our results show that the proposed GAN provides improved anomaly detection performance.Our study is promising for the future development of artificial intelligence based HE detection systems.