Reviewerly/exHarmony huggingface dataset:
On combining deep features and machine learning for automatic edge pedestrian detection task

Due to the restriction of cloud computing for video processing and distribution in networks, this paper designs an machine learning‐based automatic edge detection framework for video processing task in the Internet of Things (IoT) networks. Since the video processing algorithm is complex and consumes massive computing resources, it can reduce the network transmission to put the video processing model on the edge servers, uploading the processing results from the edge servers to the cloud computing center. Obviously, the processing result is much smaller than the original video, which can implement real‐time transmission. On the edge servers, the video is processed by utilizing a faster region convolutional neural network (Faster R‐CNN) which integrates feature extraction with classification. The experiments, performed on several pedestrian flow detection datasets, demonstrate the effectiveness of the designed automatic edge detection framework.
Improving Text Generation via Neural Discourse Planning

Recent Transformer-based approaches to NLG like GPT-2 can generate syntactically coherent original texts. However, these generated texts have serious flaws. One of them is a global discourse incoherence. We present an approach to estimate the quality of discourse structure. Empirical results confirm that the discourse structure of currently generated texts is inaccurate. We propose the research directions to plan it and fill in the text in its leaves using the pipeline consisting of two GPT-based generation models. The suggested approach is universal and can be applied to different languages.
Neural Machine Transliteration Of Indian Languages

Transliteration is a task of converting one language written in a foreign script to its written form in native script. It&#x0027;s not only important to understand the written form of language for transliteration but also the sound associated with the written words of the language. Hindi and Punjabi are two of the most widely spoken languages in the world with a combined base of around 500 million speakers. While English is widely understood now, regional languages remain the mainstay for spoken and written conversation. Most of the modern devices still come with English keyboards which makes it very difficult to express in regional languages. This research is aimed at developing a scalable and universal architecture that gives state of the art results for the transliteration of Hindi and Punjabi languages. It explores different heuristics in sequence to sequence modelling, attention and transformer networks to determine the best suited architecture for transliteration of Indian languages. Out of these variants, character/grapheme level bi-directional encoder and auto-regressive decoder model proved to be best-performing architecture and gave the state of the art results for both transliteration and back transliteration tasks with SOTA BLEU score of 0.88 on Punjabi and 0.97 on Hindi.
An Integrated Authentication Scheme for Supervisory Control and Data Acquisition System Based on Quantum Key Distribution

The supervisory control and data acquisition (SCADA) is a combined system of elements of hardware and software that is used for controlling industrial processes either remotely or locally. It also useful for monitoring, gathering and processing real time data, and directly interacting with different electronic as well as integrated devices such as valves, pumps, motors, and sensors. Over the years, it has become increasingly interconnected with the internet, making it vulnerable to cyber threats. In order to contain the system&#x0027;s internet attacks, various methods have been used, especially the encryption techniques such as symmetric and asymmetric approaches as well as the decoy SCADA method. However, with the arrival more advanced security threats such as those based on quantum computing, there is a need to introduce security measures that can neutralize such threats. One promising method is quantum cryptography, which is an encryption technique based on quantum mechanics theories. In this study, a quantum-cryptography-based algorithm is proposed. It is based on the QKD and hash-based signature scheme, which act as key generators and authenticators respectively. In order to evaluate the algorithm&#x0027;s performance, it is subjected to a random substitute and intercept resend attack. Using the probabilistic model checker tool, the probability of succumbing to the two attacks is checked. The results show that the algorithm would significantly improve SCADA&#x0027;s resistance against quantum and classical cyber threats.
Process of designing a set tools for training students in skill of applying knowledge into practice in teaching biology at high school

Applying knowledge into practice is the target of the teaching process. Biology is an experimental science with many contents associated with real life, health, food safety, environmental protection, biotechnology. In order to develop students' skills to apply knowledge into practice, the best ways are teaching going as a pair with practice, teaching through practice and teaching by practice. In this article, on the basis of definition, role determination, and the process of training students' skills to apply knowledge into practice, we propose a process to determine practical problems, a process to design a set of tools which helps practice the skills to apply knowledge into practice for students in teaching and some examples of applying processes in teaching Biology at high school.
Virtual classroom for the continuity of educational services in indigenous communities, during social displacement restrictions (Covid-19), based on medium range Wi-fi antennas

This work includes an experience of implementation and testing of a virtual classroom developed from the use of a Wi- Fi antenna. It was conceived to be used in indigenous communities where there is no internet or any other telecommunications service, and arises as a necessity for the continuity of educational services during social displacement restrictions decreed by the Peruvian government since March 2020. The set of biosecurity measures decreed by the Peruvian government includes the continuity of educational services through distance mechanisms, which includes the use of traditional means of communication or digital media. Most of the indigenous peoples of the Amazon have neither traditional nor digital media coverage, which also generates costs that are largely unaffordable for families. This has generated the stagnation and intermittency of educational services in most indigenous peoples of the Peruvian Amazon. We propose the use of a medium range Wi-FI antenna to be used in indigenous communities to connect as many students as possible from their homes without physical interaction. Functional tests have been carried out on the types of services (electronic blackboards, chats, videoconferencing applications, file exchange, use of software, etc.), with the participation of 14 students. Among the most relevant results, it is possible to conduct virtual classes with the school population; however, it is still necessary to find or experiment with different configurations regarding antenna coverage (in the future we will experiment with more powerful Wi- Fi antennas), energy consumption and even aspects of the connection and computational performance of the personal computer used as the server.
Performance Analysis of Flower Pollination Algorithms Using Statistical Methods: An Overview

Flower pollination algorithm and its variants are bio-inspired metaheuristics. The performance analysis of flower pollination algorithm and variants of the same, has been carried out with the help of statistical analysis to a certain degree. Their comparison with other metaheuristic algorithms has also been done; sometimes, with the help of statistical methods and mostly with the help of benchmarking functions. More exploration can be done in this regard. This paper is an attempt to take a bird’s eye view of some of the work done in the context of flower pollination algorithm and a few of its variants and the insights gained in the context of each one of them, along with an overview of the statistical methods that have been used so far in carrying out the performance analysis of the same. The insights listed herein also point toward further research that can be possibly conducted in this context.
Machine Learning in Insurance Underwriting Context

This paper discusses different machine learning models that can be used for insurance underwriting. The underwriting process for insurance is conventionally done by well-trained individuals, but the whole process is time-consuming and tedious, especially as the applications are getting increasingly more complex nowadays. The advancement in technology has enabled us to seek an alternative solution to this tedious and labor-intensive process of underwriting. Many believe that tools from Artificial Intelligence and more specifically, methods in Machine Learning, can provide time-efficient and accurate evaluations of risk. Using the publicly available dataset from Prudential Insurance, several feature engineering methods are implemented and tested for their usefulness. Specifically, kth nearest neighbor, multinomial logistic regression, random forest, and gradient boosting trees are fitted, and gradient boosting is shown to have the best performance and robustness.
A Image Deblurring Approach Using U-Shaped Invertible Network Driven by Sparse Latent Variables

Deep learning architectures are utilized to generate desired images typically using separated encoder and decoder, which is inefficient for the feature analyze and synthesis. Recently, invertible networks have been introduced to generate spatial details from sampling and controlling latent variables. In this paper, we focus on a codec-unified invertible network to precisely guide image deblurring achieved. Inspired by U-Net, a U-shaped architecture is utilized to establish the multi-resolution correlations between the blur and sharp features. In addition, a L1 regularization loss for the sparse latent variables is applied to alleviate the information dispersion, which is caused by the high-dimension inference of invertible networks. Finally, we fine-tune the weights of invertible modules by calculating a similarity loss between blur-sharp variable pairs. Experimental results in a variety of real scenarios show that the proposed architecture significantly outperforms state-of-the-art models on both qualitative and quantitative evaluations.
Edge Terminal Data Sharing Architecture Under Smart Grid Panoramic Information Perception

In order to achieve higher requirements for collaborative optimization capabilities of edge terminals at the field level, and to meet the demand of data sharing between perception layer terminals and local decision-making under the authorization of the smart grid, a smart grid panoramic information perception of edge terminal data sharing architecture is designed. Based on the analysis of terminal data sharing and coordination demand, and research on the weak communication between the existing on-site terminals of the distribution network, the article expands the local communication mode, realizes the data sharing architecture design of edge terminal, which can sense, share and make local decisions in the perception layer, and breaks through the limitations of single terminal in terms of function, performance and access resources. Finally, through the examples of edge terminal sharing and its application scenario analysis, the specific data sharing route and the rationality of the architecture design are explained.
Wearable Fall-Detection Using Deep Embedded Clustering Algorithm

Falls in elderly people are common. Detecting near-fall situations can prevent fall-related injuries. Wearable technology has made a significant impact in this direction and fall-detection from wearable sensors has become an important research problem in ambient assisted living. Although a number of machine learning algorithms exist for wearable fall-detection, most of them are based on supervised learning. These algorithms require a huge amount of training data and generating such data is very time-consuming process. This paper employs deep embedded clustering, an unsupervised learning approach, for wearable fall-detection. For experimental purpose, Kaggle fall-detection dataset is considered. Results indicate that deep embedded clustering achieves higher accuracy in attaining fall-detection.
Advanced Smart Educational Systems Using Blockchain Technology

Blockchain is one of the new modern technologies of the tech world. Bitcoin was first introduced with this technology and is being extremely tested for several cryptocurrencies continuously. The research scholars and businesses have been concentrating on its pertinence in furthering many zones such as education, business, healthcare, logistics, and some more areas. This chapter shows the benefits of blockchain technology in the educational area. The advantage of blockchain technology in the educational sector is support in improving the convenience and value of educational areas. There are some zones in education that are essential: continuous observations of students and student correlated data and transcripts, decrease in physical and administration, and more. Further, the monitoring and tracing capability of the blockchain and some solutions to the problems have been proposed.
Towards Accurate File Tracking Based on AST Differences

In the field of software development, version control systems such as Git are imperative tools that help software teams manage source code. Git can detect a change history of each file individually. Even if a file was renamed in the past, Git can identify and track the before renamed file based on content similarities, which are calculated as the ratio of lines that match pre- and post-change files to the total number of lines. However, line-based comparison techniques do not consider source code structures and have coarse granularity, which can result in misidentifying pre-change files and tracking interruptions. To resolve these problems, this paper proposes a technique that calculates file content similarities using source code differences based on an abstract syntax tree. In experiments conducted on 197 open source Java-based projects, we found that the number of rename detections increased 3.3 &#x0025;, and that, on average, our technique tracked commits 1.37 times more frequently than previous technique. We also measured accuracy levels and found that the maximum F - measure was 0.943, which is higher than the 0.926 maximum value of the line-based technique.
Convolutional neural network microseismic event detection based on variance fractal dimension

Abstract Microseismic event detection helps to predict outbreak catastrophic problems and has essential applications in resource exploration. Low SNR microseismic signal detection is a challenging task in microseismic detection. In this paper, we propose a (convolutional neural network microseismic detection method based on variance fractal dimension) VFD-CNN method based on the variance fractal dimension (VFD). In this method, signals and background noise are first measured by variance fractal dimension, which can effectively extract seismic nonlinear features. These fractal features are then fed into VFD-CNN to distinguish signal and noise. Finally, the variance fractal dimension of the test data is fed into the optimal model to detect microseismic events. The VFD-CNN method can significantly improve the detection capability of low SNR microseismic signals. To verify the performance of the VFD-CNN method, We use the VFD-CNN method to synthesize microseismic data. Furthermore, the comparison experiments were conducted using VFD-CNN and short-term averaging to long-term averaging (STA/LTA) algorithms. The results show that the VFD-CNN method can significantly improve the detection of low SNR microseismic signals, and its precision is substantially higher than the STA/LTA algorithm.
Exception Analysis for Errors Detection in the SharpChecker Static Analyzer for C#

Exception handling is a widely used runtime error management method in object-oriented languages, especially C#, where it is deeply embedded in the standard library (CoreFX). Accurate and efficient exception analysis is an important requirement for an industrial-level static analyzer. We propose a novel approach to manage exceptions while performing static symbolic execution of C# programs, which is able to process the newest features of the language, such as catch filters. In contrast to the approaches implemented in other static analysis tools, it is much more accurate, because it is path-sensitive thanks to consideration of exception conditions, field- and object-sensitive thanks to the representation of separate exceptions and its fields with distinct symbolic values. We evaluate our approach on a large set of open source projects having 4.2 mln LOC and nearly 2000 reviewed warnings of different types.
Short Text Classification Combining Keywords and Knowledge

With the continuous development of the Internet, short text data are increasing. However, short text has poor classification performance due to sparse features. Nowadays, integrating external knowledge into the classification model has become one of the best solutions to the sparse problem of short text data, but this method is still insufficient. When the mapping result between knowledge and text is not enough, we choose keywords as content feature extensions to compensate for the lack of external knowledge. For the problem of noise generated by external knowledge. We design a context-based attention mechanism, which combines semantic information to make the model more focused on knowledge that has a more positive impact on classification. We conducted experiments on common data sets to verify the effectiveness of the model.
Automatic Modulation Recognition Models Based on Transfer Learning and Simulated Radio Signals in AWGN Channels

Automatic Modulation Recognition (AMR) has been applied in several areas in wireless communication. However, out of all the developed AMR models, none has successfully catered for large number of blind detection of digital modulation schemes. In this current study, we simulated and labeled datasets for fifteen digital modulation schemes. The Signal-to-Noise Ratio (SNR) of the data ranges from −20 dB to +20 dB, over different channel impairment scenarios such as Line-of Sight (LOS) with Additive White Gaussian Noise (AWGN) and Non-Line of Sight (NLOS) with AWGN. Transfer learning approach was employed to develop the AMR models using pre-trained Convolutional Neural Network (CNN) architectures. The results obtained, which ranged from training accuracy values of 91.67% (+5 dB) to 100% (−15 dB) for LOS, and 92.50% (−20 dB) to 100% (15 dB) for NLOS, proved significant. The lowest testing results for LOS and NLOS were 70% (+5 dB) and 81.67% (+10 dB) respectively, and 100% testing accuracies were attained in all cases for +20 dB SNR. The proposed AMR model is thus efficient and reliable for modulation recognition in different software-defined and cognitive radio use cases.
Garbage Sorting Front-end System Based on Deep Learning and Embedded Technology

General garbage sorting systems is mainly based on high-performance servers, which require a large amount of computing resources and complex network connections. It is not suitable for the characteristics of distributed layout and resource shortage of garbage sorting front-end devices. At the same time, the lack of daily garbage sample data also affects the universality and accuracy of the garbage classification. In this paper, a garbage sorting front-end system for residents&#x0027; daily life is proposed. The system could realize daily garbage intelligent classification based on MobileNetV3 and transfer learning. A garbage classification lightweight model for embedded systems is obtained by fine-tuning based on ImageNet pre-training model. Taking RaspberryPi as the core, the garbage sorting front-end system was established and tested, and the average accuracy was 92.10&#x0025;. The result of experiment proves that the model is easy to deploy on the embedded system, which could effectively classify daily garbage with less data.
Impact of Aerosols on Photovoltaic Energy Production Using a Spectrally Resolved Model Chain: Case Study of Southern West Africa

Based on an analysis of the aerosol optical properties for Cotonou, Benin from the DACCIWA measurement campaign, we investigate the impact of aerosols on PV power for polycrystalline silicon and amorphous silicon technology using a spectrally resolved model chain. The model considers both spectral effects on global irradiance due to different aerosol properties as well as the spectral response of different PV technologies. The results show that aerosol emissions due to the biomass outbreak lead to solar flux losses of up to 55%, which correspond to a reduction in photovoltaic power of up to 81% for the polycrystalline cell and 78% for the amorphous cell. Comparing the effects of aerosols on the photovoltaic power between the two technologies, we find that during the morning and evening hours, when there is more diffuse irradiance, the amorphous cell suffers a greater reduction in power (36%) than the polycrystalline cell (27%). Conversely, in the middle of the day, we observe a greater PV power reduction of 12% for the polycrystalline cell compared to that for the amorphous cell (8%).
Tautologies, inferential processes and constraints on evoked knowledge

In the literature on nominal tautologies, it is assumed that common knowledge is a crucial ingredient for their interpretation. This paper explores a different approach: we argue that invoking shared knowledge is at the same time too strict and too vague as a condition for the understanding of tautologies in context. More specifically, we claim that, on the one hand, the hearer's previous knowledge about some specific set of properties of the entity referred to in the tautology is not always necessary: lack of previous knowledge can be repaired by accommodating new assumptions or compensated by providing additional explicit content in discourse. On the other hand, the hearer's previous knowledge about some specific set of properties of the entity referred to in the tautology is not always sufficient: only permanent, classificatory properties can be evoked by a tautology; transitory states, by contrast, are systematically rejected, even if they constitute shared knowledge and are supported by the context. We provide evidence for our claims both from the corpus study, analysing examples of tautologies with proper names from COCA and web-based sources, and experimental study designed as a verification task, additionally measuring reaction times for replying to a given question.
Forming a team of cost-effective and well-collaborated experts in social networks based on hierarchical skill model

Social network-based team formation problem has been widely studied from different aspects. However, the skills in earlier works were treated equally, and cannot be substituted by other related or similar skills. In addition, assigning experts who possess alternative skills for a required skill is not allowed. To better fit real world scenarios, we propose a novel hierarchical skill model to let skills interchangeable. By considering the communication cost and the personnel cost, we develop an optimization framework under the hierarchical skill model to deal with the trade-off between communication and personnel cost. The experiments show that our proposed framework and the hierarchical skill model is reasonable and has better performance than earlier works.