Reviewerly/exHarmony huggingface dataset:
Smart Classroom System Based on Internet of Things Wireless Communication Technology

With the continuous development of the Internet of Things technology, the "Internet of Everything" has gradually emerged from papers and laboratories, and has been widely used in education, medical and other fields. In order to overcome the difficulty of efficient centralized management and control of traditional classrooms, the paper mainly proposes a method of combining NB-IoT and Wi-Fi technology, relying on Alibaba Cloud and other IoT platforms to collect, process and analyze data, showing the trend of data changes. We use edge computing technology and face recognition technology to extract effective information in video data while ensuring customer privacy. Compared with traditional IoT solutions, this solution has lower cost and higher flexibility. Experimental results show that using Wi-Fi modules instead of NB-IoT modules can significantly reduce costs and reduce battery life pressure through sleep when necessary. Using DTU equipment as the gateway of the entire system can achieve flexible deployment and reduce the amount of construction.
R-Opitools – An Opinion Analytical Tool for Big Digital Text Document (DTD)

Since the year 2000, various computational intelligence techniques have been developed for analyzing sentiments of users in the field of natural language processing (NLP). To date, the majority of the techniques as deployed across various fields, including social sciences (Ansari et al., 2020; Nikolovska & Ekblom, 2020; Somasundaran & Wiebe, 2010) and market research (Al-Otaibi et al., 2018; Feldman et al., 2011), have focused largely on detecting subjectivity, and/or extracting and classifying sentiments and opinions in a text document. Building on this existing work, the current paper advances an opinion impact analytical tool, named Opitools, that not only extracts inherent themes from within a digital text document (DTD), but also evaluates the extent to which a specified theme may have contributed to the overall opinions expressed by the document. Based on this advancement, Opitools has wider applications in the aforementioned application fields. For example, in law enforcement, the package can be deployed to understand factors (themes) that drive public perception of police services (Adepeju & Jimoh, 2021); and in product marketing, to identify factors that underlie customers satisfaction in a product.
Image Encryption for Wireless Sensor Networks with Modified Logistic Map and New Hash Algorithm

Aiming at the limitations of nodes in wireless sensor networks in terms of energy consumption and communication, this paper proposes a symmetric image encryption algorithm with an improved Logistic map. First, the image is scrambled at the bit level to destroy the interference between adjacent pixels, then use non-linear diffusion operations to complete the image encryption; This paper applies the public key cryptosystem and completes the security identity authentication by constructing a new Hash function. Experiments have proved that the proposed image algorithm can effectively resist typical attacks and increase the reliability of sending information between nodes.
Deep Convolutional Neural Network Transfer Learning Optimization Based on Visual Interpretation

In image classification tasks, the training of deep convolutional neural networks generally requires a large amount of data, and due to the constraints of environment, resources and time, it is of great practical importance to use fewer training samples to obtain a higher recognition rate in the shortest possible time. A deep convolutional neural network transfer learning optimization method based on visual interpretation is proposed for a specific image classification task. Firstly, we use class activation mapping visualization as a visual interpretation, output the class activation heat map of the validation set images, and analyze the reasons for misrecognition of the images. Secondly, we introduce “feedback” by pre-recognizing and visualizing the optimized dataset with the model trained on the original dataset, selecting the images that have a greater impact on improving the recognition rate, and maximizing the impact of the optimized images on the original model. Finally, the model is retrained on the optimized training set. The experimental results show that this method can effectively improve the recognition rate of the transfer learning model for image classification.
A Deep Learning Model Based on Neural Bag-of-Words Attention for Sentiment Analysis

In the field of Natural Language Processing, sentiment analysis is one of core research directions. The hot issue of sentiment analysis is how to avoid the shortcoming of using fixed vector to calculate attention distribution. In this paper, we proposed a novel sentiment analysis model based on neural bag-of-words attention, which utilizes Bidirectional Long Short-Term Memory (BiLSTM) to capture the deep semantic features of text, and fusion these features by attention distribution based on neural bag-of-words. The experimental results show that the proposed method has improved 2.53%–6.46% accuracy compared with the benchmark.
Blockchain Validity Register for Healthcare Environments

Currently, health information systems are decentralised and hyperconnected to third-party data sources and systems. For their proper use, they require adequate security measures to verify the validity of information, thus avoiding potential harm to patients using erroneous, corrupt or altered data. These systems must be able to avoid vulnerabilities to decentralisation, prevent record modification and data integrity problems, and facilitate fast and efficient verification operations, since it is very important that security mechanisms must not hinder the operation of these systems on which human lives depend. In this paper, we propose a decentralised registry system for healthcare environments based on Blockchain technology. This register can provide the same security as transaction registers in distributed databases, but allows for load balancing as it is a decentralised system, thus facilitating fast and efficient operations, and provides Blockchain mechanisms to maintain the integrity of the validation register.
Sentiment Analysis of Product Reviews on Social Media

Social media is a platform where people share their interests and opinions. Users posting their opinions about a product or a service they used is very common nowadays. This information can be important to marketers. Knowledge about how a product is perceived in a society is crucial to improve the sales and formulate marketing business strategies. The data flow in social media is of extreme high volume and this data is generated in a scale of hundreds of gigabytes each day. There are many applications which have been built to make the most use of this idle data one of the most important one being sentiment analysis.Sentiment analysis is the process of determining the classification of opinions expressed in a text as positive, negative or neutral.In this project, we would like to develop an application that mimics the functions of a sentiment analysis system by using different third-party APIs/libraries to collect data and analyze it. A user can use this application to get the sentiment of users for a product on social media.
Bayesian filter based on grid filtration and its application to Multi-UAV tracking

A filtering method called Grid Filtration Filter (GFF) is proposed based on Bayesian inference. First, we select the high-probability region of the current state according to the confidence parameter α, and obtain samples uniformly in this region. Second, these samples are regarded as discretized “potential states” and their posterior weights are calculated based on the Bayesian inference. Third, the grid filtration method is used to choose these “potential states” with high weights, and these selected “potential states” and their normalized weights are used to represent the posterior distribution, and thus estimate the state. we finally verify the feasibility of the GFF algorithm in a typical two-dimensional linear non-Gaussian filtering scenario. Results show that the GFF has a slightly better accuracy than the particle filter (PF) algorithm, and the calculation speed is better by a factor of approximately 45 compared with the PF with 10,000 particles. We finally validate the GFF algorithm in a collaborative target tracking scenario. Results show that the accuracy of the GFF estimation is slightly better than that of the extended Kalman filtering and that of the unscented Kalman filtering, while the advantage of the estimation accuracy of velocity and acceleration is more obvious.
Defensive Walls for Detecting and Preventing SQL Injection and XSS attacks in Dynamic Content Delivery Network Web Applications

There is need for Designing and developing Defensive security walls for dynamic web applications using Content Delivery Networks features etc.WAF is used to secure web server Web Application Firewall (web app). A benefit of getting WAF is, even though there is no validation on the web app, that it is able to detect potential attacks.WAF secure the web application even if WAF itself may vulnerable to attackslike XSS, SQL injection. Four methods of testing are generally used for WAF testing, for example floating, loading, bypassing and foot printing. There are several WAF test tools available, but it seems that only one or two test methods are available. This means that a tester must have many tools and learn how each tool will measure WAF using all the test methods. The objective of this research is to solve this problem by creating a WAF Project X testing tool that offers all methods of testing. Project X was tested and the findings show that its criteria were fulfilled. In addition, Github provides ProjectX to any developer who wishes to upgrade or add additional features.
Segmentation and Selective Feature Extraction for Human Detection to the Direction of Action Recognition

Detection as well as classification of different object for machine vision application is a challenging task. Similar to the other object detection and classification task, human detection concept provides a major role for the ad- vancement in the design of an automatic visual surveillance system (AVSS). For the future automation system if it is possible to include human detection and tracking, human action recognition, usual as well as unusual event recognition etc. concept for future AVSS, it will be a greater success in the transformable world. In this paper we have proposed a proper human detection and tracking technique for human action recognition toward the design of AVSS. Here we use median filter for noise removal, graph cut for segment the human images, mathematical morphology to refine the segmentation mask, extract selective feature points by sing HOG, classify human objects by using SVM with polynomial ker- nel and finally particle filter for tracking those of detected human. Due to the above mentioned combinations our system can independent to the variations of lightening conditions, color, shape, size, clothing etc. and can handle the occlusion. Our system can easily detect and track human in different indoor as well as outdoor environ- ment with a automatic multiple human detection rate of 97:61% and total multiple human detection and tracking accuracy is about 92% for AVSS. Due to the use of HOG to extract features af- ter graph cut segmentation operation, our system requires less memory for store the trained data therefore processing speed as well as accuracy of detection and tracking will be better than other techniques which can be suitable for action classification task.
Utilizing Policy Machine for Attribute-Based Access Control in Permissioned Blockchain

In recent times, as the number of blockchain use cases continues to grow, methods and technologies utilized by fraudsters continue to become sophisticated. The most notable form of cyber-attack utilizes a security breach in the internal security of blockchain systems, leading to illegal access to application services. A complex system like the blockchain network requires a dynamic, flexible, and scalable access control mechanism. There are numerous research efforts to leverage smart contracts in implementing access control based on blockchain. However, most of these contributions are either focused on blockchain-based access control for an off-chain resource. Other effects implement blockchain-based access control for a specific domain. We present a generic system architecture with a deployment on Hyperledger Fabric using the NIST Next Generation Access Control (NGAC) for deciding access to the protected resources on the blockchain.
Sampling Designs Dependent on a Continuous Auxiliary Variable

In contrast to the previous chapters, this chapter concerns the estimation of the continuous variable expected value of the variable under study, supported by a continuous auxiliary variable whose density function is known. Continuous versions of the definition of a sampling design and scheme are presented. In this context, the properties of the well-known Horvitz–Thompson statistic are considered. The two-dimensional gamma distribution of the variable under study and the auxiliary variable are taken into account. The mean of the variable under study is estimated by means of a ratio-type estimator. A continuous sampling design dependent on the order statistic of the uniformly distributed auxiliary variable is proposed and its inclusion functions are derived. Finally, this sampling design is used to estimate the mean. The accuracy of this estimation is studied in the case of the continuous joint distribution of the variable under study and the auxiliary variable.
Dilated Convolutional Neural Network with Joint Training for Chinese Word Segmentation

The task of Chinese word segmentation (CWS) is to segment a continuous Chinese text sequence into individual word sequences according to certain rules. As the most basic step of Chinese natural language processing tasks, the study of CWS is of great significance. In this paper, based on the method of deep learning, the radical features were added when pretraining the character vectors, which has led to the improvement of the outcome. Dilated Convolutional Neural Network (DCNN) was trained and compared with the baseline Bi-LSTM to verify the segmentation ability, and Conditional Random Field (CRF) was applied with two neural network models above-mentioned respectively to confirm the best model. The experiment results show that the DCNN model is better than the widely used Bi-LSTM model in the performance. The joint training method was also applied to further improve the training outcome, and it comes out the improvement on F1 value of joint training with several corpora.
TEBC-Net: An Effective Relation Extraction Approach for Simple Question Answering over Knowledge Graphs

Knowledge graph simple question answering (KGSQA) aims on answering natural language questions by the lookup of a single fact over a knowledge graph. As one of the core tasks in the scenarios, relation extraction is critical for the quality of final answers. To improve the accuracy of relation extraction in KGSQA, in this paper, we propose a new deep neural network model called TEBC-Net, which is constructed based on the combination of Transformer Encoder, BiLSTM and CNN Net in a seamless way. We give the detailed design of our approach and have conducted an experimental evaluation with a benchmark test. Our results demonstrate that TEBC-Net can achieve higher accuracy on relation extraction and question answering tasks in KGSQA, compared to some current methods including the state-of-the-art.
Energy-Efficient Resource Allocation Approaches for Cloud Computing Systems: A Survey and Taxonomy

As cloud computing is growing increasingly and clients are demanding more resources and better performance, load balancing for the cloud has become a very interesting and relevant research field. Several algorithms have been suggested to provide successful frameworks and algorithms to allocate the requests of the client to available cloud nodes. These techniques are aimed at enhancing the overall efficiency of the cloud and delivering more enjoyable and effective services for the customer. One of the most significant research challenges in cloud computing is the use of energy-aware technologies along with the management of service level agreements. In this article, we discuss the numerous algorithms suggested in cloud computing to solve the problem of energy-effective techniques.
A Comparative Study of the Implementation of SJF and SRT Algorithms on the GPU Processor Using CUDA

GPUs (Graphical Processing Units) have become in a few years very powerful tools for the calculation of parallel tasks. They are currently used in several fields such as image processing applications and in bioinformatics and medical applications, video and audio coding and decoding, digital modeling. Their advantages are low cost, faster processing and lower power consumption compared to the equivalent power of the processor (central processing unit). It’s simple to program a GPU graphics processor using a programming language such as CUDA C to perform tasks that are typically calculated in parallel. But then you need to understand the different architectural aspects of GPU GPU. In this article, we will define and implement the Shortest Job First (SJR) and Shortest Remaining Time (SRT) algorithms in a single-threaded CPU environment using the C language, then the same algorithms are implemented on GPU using the CUDA language. C, in order to compare the different performances of the implementation of the two algorithms on the GPU and CPU processors and to ascertain the effectiveness of this study.
Noise Removal Algorithm for Out-of-focus Blurred Images Based on Bilateral Filtering

The feature resolution of traditional methods for fuzzy image denoising is low, for the sake of improve the strepitus removal and investigation ability of defocused blurred night images, a strepitus removal algorithm based on bilateral filtering is suggested. The method include the following steps of: Building an out-of-focus blurred night scene image acquisition model with grid block feature matching of the out-of-focus blurred night scene image; Carrying out information enhancement processing of the out-of-focus blurred night scene image by adopting a high-resolution image detail feature enhancement technology; Collecting edge contour feature quantity of the out-of-focus blurred night scene image; Carrying out grid block feature matching design of the out-of-focus blurred night scene image by adopting a bilateral filtering information reconstruction technology; And building the gray-level histogram information location model of the out-of-focus blurred night scene image. Fuzzy pixel information fusion investigation method is used to collect gray features of defocused blurred night images. According to the feature collection results, bilateral filtering algorithm is used to automatically optimize the strepitus removal of defocused blurred night images. The simulation results show that the out-of-focus blurred night scene image using this method for machine learning has better strepitus removal performance, shorter time cost and higher export peak signal-to-strepitus proportion.
The Evaluation of College English Extracurricular Reading Independent Learning Model Based on Big Data

In order to better cultivate contemporary college students’ extracurricular English reading ability, improve their ability of independent learning after class, and adapt to the requirements of college English curriculum and teaching reform in the new era, this paper first analyzes the theories and technical support related to big data and the constructed independent learning model of extracurricular English reading based on big data, then implements an experiment by selecting some college students and dividing them into experimental group and control group. The final experimental results show that the independent learning model of English extracurricular reading based on big data helps to cultivate the improvement of students’ comprehensive English application ability, helps to improve students’ English performance, and also provides a new idea for the reform of English course teaching.
Product Review Translation using Phrase Replacement and Attention Guided Noise Augmentation.

Product reviews provide valuable feedback of the customers and however and they are available today only in English on most of the e-commerce platforms. The nature of reviews provided by customers in any multilingual country poses unique challenges for machine translation such as code-mixing and ungrammatical sentences and presence of colloquial terms and lack of e-commerce parallel corpus etc. Given that 44% of Indian population speaks and operates in Hindi language and we address the above challenges by presenting an English–to–Hindi neural machine translation (NMT) system to translate the product reviews available on e-commerce websites by creating an in-domain parallel corpora and handling various types of noise in reviews via two data augmentation techniques and viz. (i). a novel phrase augmentation technique (PhrRep) where the syntactic noun phrases in sentences are replaced by the other noun phrases carrying different meanings but in similar context; and (ii). a novel attention guided noise augmentation (AttnNoise) technique to make our NMT model robust towards various noise. Evaluation shows that using the proposed augmentation techniques we achieve a 6.67 BLEU score improvement over the baseline model. In order to show that our proposed approach is not language-specific and we also perform experiments for two other language pairs and viz. En-Fr (MTNT18 corpus) and En-De (IWSLT17) that yield the improvements of 2.55 and 0.91 BLEU points and respectively and over the baselines.
Problematic Issues of a Signature Examination as a Small Volume Handwriting Object

The paper highlights the current state of the theory and practice of forensic handwriting analysis of signatures, main provisions and definitions. Topical issues of the study of signatures as a kind of small volume handwriting objects are considered. The problems of the study of a signature produced by using high-tech devices, i.e. plotters, or draft machines, are touched upon. The authors carry out an analysis of such objects taking into account the pressure characteristics of the signature, the differentiation of the original signature and its imitation. The problems of the study of signatures made with the help of copiers and the ways of their solution are considered. Some topical problems of the theory and practice of forensic handwriting examination are presented, the need to adapt the theoretical provisions of certain private methods to modern handwriting objects is indicated. In addition, the paper deals with the use of special knowledge of experts in the field of forensic handwriting studies and technical and forensic examination of documents.
FERMAT: Feature Engineering with Grammatical Evolution

Feature engineering is a key step in a machine learning study. We propose FERMAT, a grammatical evolution framework for the automatic discovery of an optimal set of engineered features, with enhanced ability to characterize data. The framework contains a grammar specifying the original features and possible operations that can be applied to data. The optimization process searches for a transformation strategy to apply to the original dataset, aiming at creating a novel characterization composed by a combination of original and engineered attributes. FERMAT was applied to two real-world drug development datasets and results reveal that the framework is able to craft novel representations for data that foster the predictive ability of tree-based regression models.