Reviewerly/exHarmony huggingface dataset:
Deep Learning Network for Object Detection Under the Poor Lighting Condition

Object detection is a part of image processing that holds great importance in this modern world. To locate things in images or videos, a computer vision approach called object detection is utilized. To identify items in both still photos and moving films, numerous algorithms and models have been created. One of the main issues with such object detection algorithms is that they struggle to accurately identify things in images with poor lighting. This study tries to develop the best deep learning algorithm that can be used to predict items from photos with very little or no lighting to address this issue. For this,from GitHub, a dataset of images of various objects, such as tables, cats, and dogs, has been compiled. After categorization, the collected dataset is examined using two factors. The images are then preprocessed utilizing picture format conversion and histogram equalization. Two different deep learning models were produced by two different algorithms. The YOLO algorithm and the speedier RCNN algorithm are the two algorithms. The models are then trained and evaluated using the preprocessed dataset. The performance of the models during training and validation is assessed using a metric called the AP score. The YOLO algorithm has the better AP score overall, according to the analysis of the AP score. The YOLO is once again determined to be superior when the findings are displayed in a bar graph for easier understanding. The YOLO algorithm's prediction results are also examined, and it is discovered that even under conditions of extremely poor lighting, the algorithm can correctly predict the presence of one or more objects.
Research on Gesture Recognition Algorithm Based on Lightweight YOLOv4

Aiming at the problems of a large number of parameters and low detection speed of the gesture recognition model based on the YOLOv4 algorithm, a lightweight object detection algorithm n-YOLOv4 was proposed in this study. Firstly, GhostNet was used to replace the YOLOv4 backbone feature extraction network, and deep separable convolution was used to replace the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\times 3$</tex> conventional convolution of the non-backbone feature extraction network, which reduced the number of parameters in the model. Secondly, the CSC structure was used to replace part of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 1$</tex> conventional convolution in the network to expand the receptive field of the network. Finally, residual edges and quintic convolution modules were added to enhance the feature extraction capability of the PANet structure. In addition, the idea of transfer learning was introduced in the training process to save training time, and the data enhancement method was used to improve the generalization of the model. The experimental results showed that when the detection accuracy was only slightly affected, the weight file size decreased by 76.72 %, and the FPS increased by 10.85 %. This method provides a new idea for the research of gesture recognition algorithms.
Storage Optimized Secured Data Transaction of IoT Sensors

Owing to the strategies and development towards smart things dimensions, the Internet of Things (IoT) has acquired significant interest. This paper introduces layered architecture for compression techniques that lowers the volume of IoT data until it is uploaded to the cloud. With a growing number of Internet-connected devices and sensors, enormous quantities of data are produced at every second, requiring amounts of extra storage to be processed. Compression algorithms technologies will, therefore, minimize the complexity of the input and the need for capacity by more effectively compacting the information. We implemented a secured compression system for IoT connected devices in this paper that limits the quantity of data by preserving the minimal error margin and preventing network bandwidth wastage. In our suggested compression algorithms system, we obtained 50% compression ratio with secured data that can be stored onto the cloud after using secured hash algorithms. We further demonstrated that even after decompression, the deviation ranged from the source information by less than 1%. This paper also illustrates the blockchain-based approaches may be better than the existing IoT paradigm, that relies primarily on centralized cloud storage, in several ways. By using Secured hash algorithm after compression, the data is further compressed as well as secured by using AES (SHA-256 Instance) to get the 256 bytes of data after encryption, hardly matter the input size, it will always give 256 bytes of data. Also, this work highlights the various IoT security threats at each architectural level and its possible blockchain solution is also presented.
Percentile Clipping based Low Bit-Precision Quantization for Depth Estimation Network

Low bit-precision quantization is an efficient method of reducing the computation complexity of neural networks. However, low bit-precision quantization of complex tasks such as depth estimation is a challenging issue due to the multiple skip connections of U-Net like networks. To maintain accuracy while lowering bit width, activation statistics should be considered. In this paper, we propose a percentile clipping technique for low bit-precision quantization for depth estimation network. By selecting the clipping point as the percentile value considering the distribution of the skip connection parts, our proposed technique can efficiently reduce the relative squared quantization error by 34.78% when using uniform 4-bit weights and activations.
Investigation of the Effect of Adding Random Noise to Noisy Biological Signals on the Classification of Neural Network

Time series classification is an important and challenging problem in data analysis. Recently, time series analysis using neural networks (NN) has attracted much attention. However, the analysis of noisy time series data with complex oscillations is difficult. Therefore, it is important to search for effective features of the data. In this study, we add the data to the noise and search for features suitable for NN classification.
Convolutional Neural Networks Based on Improved SSD Algorithm for Recognition of Financial Bills

VAT invoice information is very important in the financial field. Automatic identification of invoice information can save labor cost and time cost. Therefore, this paper proposes to design an automatic invoice recognition method based on improved SSD convolutional neural network. After the improved SSD convolutional neural network algorithm is applied to invoice recognition, The verification shows that the invoice information recognition rate is improved by applying the improved SSD convolutional neural network algorithm.
Online Learning Process Tracking System of Distance Education Based on Network Environment

In today’s world, science and technology are changing with each passing day, and everything is changing rapidly. If you want to be based on the present and adapt to the informationization and gradually enter the era of high-speed exchange, the transmission of information is very necessary. Education is an important activity of information transmission. The face-to-face knowledge learning mode of traditional education has long been unable to meet people’s needs. The distance education online learning based on the network environment in this paper enables learners to learn at anytime and anywhere with a complete self-centered approach. However, in the current online learning method, there is a lack of a general means to manage the learning process of learners, which has become an urgent problem to be solved. Therefore, this paper discusses the design, development and application of the online learning process tracking management system, summarizes the backwardness and shortcomings of traditional technical means, and realizes an online learning platform with complete functions that can be easily used by users. The online learning process tracking system is a monitoring system. It provides methods and means to provide feedback on students’ online learning situation. It is helpful for students to keep abreast of their own learning situation and their position in the class. It is conducive to students’ self-motivation and reasonable arrangement of online learning. The final results of the research show that the distance education online learning process tracking system was tested by four testers, among which the test time of tester one was 114 minutes, the test score was 88 points, and the prediction accuracy was 87.44%. Distance education online learning During the process, the students’ prediction rate of grades was higher than 90%.
Application Research of Similarity Algorithm in the Design of English Intelligent Question Answering System

Question answering systems can answer user questions accurately and instantly, and are gradually becoming the focus and hotspot of scholars' research. In various question answering systems, the key to determining the accuracy of question answering is how to find similar sentences, so the study of sentence similarity algorithm has become a very basic and important task. The purpose of this article is to study the application of similarity algorithm in the design of intelligent English question answering system. It first introduces the related technologies of intelligent question answering system, and then mainly introduces the calculation method of sentence similarity based on WordNet. Among them, the WordNet semantic dictionary is used to calculate the semantic information of the sentence, find the longest word matching method in the sentence, and then apply the WordNet sentence similarity algorithm to the automatic question answering system. This new method is used for the design of the intelligent English question answering system with realization. Finally, the system performance test is carried out, 500 repeated sentences are divided into 5 groups, and the correct rate of each test set group is finally calculated. The results show that the average accuracy of the entire answering system is accurate, and the percentage is above 75%, which has shown an excellent level.
Applying Two-rounds Collaborative Peer Review to Assess Students' Programming Assignments

As peer code review gradually becomes a necessary skill for software developers, to adapt the mechanism of peer code review, the concept of peer review will be introduced into the course of education for students to learn the process of code review. However, most of the peer code reviews currently implemented in education are to allow students to review each other's programming work, and most of them only implement a single-round review, not a two-rounds review that is more in line with the actual software development company's use. The single-round review causes students to lack the habit and opportunity to correct their code errors, resulting in limited progress for students in peer code reviews. To improve the problems mentioned above, we propose a code-based assessment system that integrates the mechanism of the two-rounds review and modifies the original system architecture for both students and teachers. On the student side, students can revise their code and upload it, so that students can learn programming skills and code quality through code revision. On the teacher side, the new review feedback score function allows the teacher to know whether student review comments are helpful or not, and to know how well students understand the review metrics and programming concepts through the feedback scores. The teacher can also help the students to review the program so that each student can receive review comments after each round of review. Lastly, through the performance of the experimental group and the control group to analyze the impact of the two-rounds peer review on students' learning and use the UTAUT model to analyze the students' acceptance of the two-rounds review evaluation system.
RTGAN: An Novel GAN-Based Rule Transfer Learning Method for Scalable Industrial Inference Engine

Nowadays deterministic inference based on rules is playing an increasingly important role in industrial applications. Compared with non-deterministic inference, it is fast, accurate and interpretable. However, the current rule-based inference engine costs a large amount of manpower and lacks of ability to adapt to production line changes and version changes. Therefore, combined with the idea of analogical reasoning of human being, we propose RTGAN, a novel GAN-based rule transfer learning method for scalable industrial inference engine. RTGAN constructs generators using transfer domain knowledge graphs and explicit representation of analogous relationships using an attention-based mechanism of the embedding graph matching algorithm. The RTGAN based inference engine will be implemented and adequate experimentation will be carried out in large-scale rule sets.
Dynamic information and analytical system for assessing and managing acquired knowledge

The paper presents the concept of a system for the intellectual digital e-university of the future (IDEUF) based on an integrated database that ensures the correct function of the whole university system. Particular attention is paid to the process of checking students’ required and current level of acquired knowledge, which is considered one of the main parts of the educational process. The article provides a detailed comparative analysis of actual testing systems and presents a proper testing system model within the IDEUF framework. The proposed system considers the testing principles, scales, and technologies for test building and preparing educational materials.
Learning-based Protocol for Routing in Quantum Networks

With the imminent advent of quantum communications head-on and its landing in what is going to be called the Quantum Internet, it is essential to research different ways of how it could be done in its most efficient way. In this work, we propose a game theory based self-adaptive system that, by accumulating reward, manages to learn how to minimize the latency of packets that are sent in a congested quantum network. To begin with, we study under which circumstances the quantum protocol outperforms its classical equivalent. First, analyzing the minimum number of packets moving through the network as a function of the size of the network necessary for guaranteeing an improvement in the quantum protocol. Then, studying the minimum size of the network based on the number of nodes needed to ensure the aforementioned advantage. Furthermore, taking advantage of the concepts of non-cooperative games and reinforcement learning, a protocol is designed and the behavior of a network with quantum nodes is evaluated. In order to analyze this protocol, different algorithms and scenarios are tested by measuring their efficiency and adaptability.
Non-parametric identification of upper bound covariance matrices for min-sup Robust Kalman Filter: Application to the AR case

The min-sup type Robust Kalman Filter (RKF) introduced in Azhmyakov [2002] guarantees a robust estimate in uncertain linear dynamic systems under relatively weak assumptions related to the state and observation noises. In particular, it is supposed that the system and observation noises have some unknown probability distribution functions from some classes of centered distributions with bounded covariances with the known upper bound matrices. In our paper, we address the identification problem for upper-bound matrices in RKF, in the case of scalar observations. We use a novel Penalized Uncoverage (PU) function and an advanced optimization technique for this purpose. The novel PU-RKF methodology we develop in this paper is applied to robust state estimation in the stationary autoregressive model. We finally compare computationally our new PU-RKF algorithm with a classical approach involving a combination of the maximum-likelihood estimation and Kalman Filter (ML-KF) for Gaussian and some non-Gaussian noises.
The effect of individualized education on stylistic changesamong M.I. patients

Life is a precious, Lord -given gift to mankind. Pulse rate represents life &amp; lack of it pronounces death. The heart pumps blood throughout the body from conception till the death. A heart is essential to sustain life. A true experimental, posttest research design was adopted. A sample of 60 post M I patients were selected by using non-probability convenient sampling technique, 30 in each experimental and control group. The mean knowledge level score and standard deviation of the experimental group was 21.86 (SD=2.20) higher than mean (10.57) and standard deviation (SD=3.49) of the control group and „t‟ value 14.72.Two mean „t‟ for independent samples showed a significant difference in the post test knowledge score on stylistic changesamong experimental and control group at 0.05 level. This finding indicated that the individualized education was highly effective. : Individualized education on the stylistic changeswas highly effective, eminent and cost-effective intervention for improving the knowledge and creating awareness among post myocardial infarction patients about the stylistic changesand helped them to adopt a healthy lifestyle post myocardial infarction.
A Quick Review of Data Security, Privacy in Cloud Computing

Data security has been the major issue in information technology. People from all over the world put all kinds of information like public, private and confidential information in the cloud. Also data in the cloud are from various storage devices like servers, PC, mobile devices, smart phones and wireless sensor networks. So data security becomes a serious issue in the cloud environment. Protecting the data from unauthorized access is tedious. There are a lot of issues related to data security and privacy in cloud computing technology. This paper focuses on understanding the security and privacy issues in the cloud environment and comprehending compliance management in cloud computing
Absence of Belemnites in the Post-Cenomanian Marine Successions of Cauvery Basin, India: Role of Sea Level, Paleogeography, and Belemnite Paleoecology

Belemnites were important Mesozoic marine organisms, and Cretaceous belemnite rostra are found worldwide, including the Cauvery Basin, south-eastern India. However, in the Cauvery Basin, belemnites of the families Belemnopseidae and Dimitobelidae are found only from the Albian-Cenomanian rocks but are absent in the overlying Turonian-Maastrichtian rocks. Although Belemnopseidae became globally extinct in the Cenomanian, Dimitobelidae was still thriving in the nearby Austral regions, so it is not clear why belemnites suddenly suffered local extinction. Moreover, in the post-Cenomanian interval, the newly evolved family Belemnitellidae was thriving globally but is absent in India. To understand the causes of this local extinction as well as the causes of the complete absence in the younger rocks, an analysis of different paleoecological-paleoenvironmental conditions was carried out of all Albian to Maastrichtian formations of the Cauvery Basin against the background of the global belemnite paleobiogeography and evolutionary patterns. The study reveals, during the end Cenomanian, shallowing of the basin caused the local extinction of the shelf-living Belemnopseidae and Dimitobelidae in this basin. In the younger intervals, since India was migrating towards the equator, the stenothermal Dimitobelidae did not survive the warmer water surrounding India and only thieved in the cooler Austral water. Perhaps for the same reason, Dimitobelidae never invaded India, even during the Late Cretaceous transgression. Moreover, during its northern voyage, India was surrounded by deep ocean, acting as a physical barrier for the shelf-dwelling Belemnitellidae of the Boreal Realm to invade India.
Using Spatial Autocorrelation for the Hypsometric Integral to Differentiate Geology and Neotectonics: A Case Study in Bailongjiang Drainage Basin (Eastern Tibet Plateau)

Hypsometric integral (HI) has been widely used in Geomorphology study as an index to depict the recent and active structures for its sensitivity to the influence of tectonic and different lithology. The traditional methods to study HI often use drainage basins, this may be dependent on basin geometry and drainage area. To avoid this influence, HIs using two digital elevation models of 30 and 90 m of pixels resolution were calculated in Bailongjiang basin. The relations between relief amplitude, mean elevation and HI are also analysed in different analytical window. Meanwhile, spatial pattern analysis using Global Moran's index and Getis-Ord Gi* index was applied to measure the spatial autocorrelation of HI and the distribution of hot spots. It can be concluded that spatial distributions of HI do not show clear spatial patterns and correlation with mean elevation and relief amplitude. HI are independent of the resolution of DEM and lithology but are scale influenced. The hot spots define the upliftment of the region and active faults in the upstream of Bailongjiang basin, the cold spots define tectonic inactivity or subsidence in the downstream of Bailongjiang and some river valley regions.
Logics for Strategic Reasoning of Socially Interacting Rational Agents: An Overview and Perspectives

This paper is an overview of some recent and ongoing developments of formal logical systems designed for reasoning about systems of rational agents who act in pursuit of their individual and collective goals, explicitly specified in the language as arguments of the strategic operators, in a socially interactive context of collective objectives and attitudes which guide and constrain the agents’ behavior.
Grammatical collocations in English exam texts written by Czech secondary-school students

This paper focuses on the frequency and accuracy of five types of grammatical collocations G8E-G8I (Benson et al. 1986) in CZEMATELC, an English language learner corpus (8,338 types; 211,503 tokens) consisting of 1,841 English exam texts from the written part of the national school-leaving exams between 2015 and 2019. The findings reveal the prevalence of A1-A2 CEFR level colligations relying on a limited number of verb lemmas, a wide incorrect pattern variation and preference for patterns which are also the most frequent patterns of their Czech equivalents.
Promoting transparency in forensic science by integrating categorical and evaluative reporting through decision theory

Forensic science standards often require the analyst to report in categorical terms. Categorical reporting without reference to the strength of the evidence, or the strength threshold that must be met to sustain or justify the decision, obscures the decision-making process, and allows for inconsistency and bias. Standards that promote reporting in probabilistic terms require the analyst to report the strength of the evidence without offering a conclusive interpretation of the evidence. Probabilistic reporting is often based on a likelihood ratio which depends on calibrated probabilities. While probabilistic reporting may be more objective and less open to bias than categorical reporting, the report can be difficult for a lay jury to interpret. These reporting methods may appear disparate, but the relationship between the two is easily understood and visualized by a simple decision theory construct known as the receiver operating characteristic (ROC) curve. Implementing ROC-facilitated reporting through an expanded proficiency testing regime may provide transparency in categorical reporting and potentially obviate some of the lay jury interpretation issues associated with probabilistic reporting.