Reviewerly/exHarmony huggingface dataset:
Arabic Corpus Linguistics and Related Tools

Mark Van Mol provides a critical review of the issues involved in the construction of usable Arabic corpora and the solutions that programmers have attempted in resolving them. One such issue is whether a corpus is made freely available or is placed behind a paywall. This distinction often translates into corpus size, as well, with freely available corpora generally being larger and untagged for parts of speech (POS) and those hidden behind paywalls being smaller and POS-tagged. The reason for this is clear: POS tagging requires large amounts of painstaking labour; on the other hand, scouring large amounts of text from the Internet with web scrubber applications can be done in seconds. As for corpus size, different qualifications make it difficult to compare. Size may be expressed in the number of articles, hours, tokens, kilobytes, megabytes, sentences, words, and sometimes paragraphs that the corpus encompasses. One of the reasons for this is that defining the searchable units of Arabic texts presents complications. Such considerations pertain directly to questions of corpus representativeness. With that arises the question of the nature of the phenomenon under scrutiny, whether the corpora are intended to represent Classical Arabic, modern written Arabic, or Arabic dialects.
Design and Research of Japanese Computer Continuous Speech Recognition System Based on HTK

Abstract The main meaning of speech recognition is reflecting the human voice in the form of text through computer technology,it is to convert speech into text.HTK is used in speech recognition, speech synthesis, character recognition and many other fields, and its application is more and more extensive and mature.
Simulation Based Projects on IoT Systems

Systems and networks for the Internet of Things (IoT) are getting extremely widespread, complex, diverse, and pervasive. They incorporate a wide range of physical devices (IoT devices and sensors) that communicate via various networking connections (cellular, WiFi) and are distributed across several design levels (cloud, fog, edge). IoT systems, in other words, span both virtual and physical realms. Designing and testing IoT services is difficult due to the vast scale and variety of IoT devices and networks. During the early design phase, prototyping with a large number of hardware nodes may not be feasible.
IoT-Based Autonomous Pay-As-You-Go Payment System with the Contract Wallet

In recent years, increasingly many companies have entered the pay-as-you-go business because it has become easier to monitor services constantly due to the development and increase in the number of Internet of Things (IoT) devices. Research is in progress to introduce cryptographic assets into the payment, but if a private key is stolen, the cryptographic assets associated with it can be stolen. To address this issue, this paper proposes a secure automated payment system using contract wallets. This method ensures the security of cryptographic assets even if the private key is stolen. Secure automated payments are enabled by issuing transactions from IoT devices and using internal transactions to link contract wallets and smart contracts that handle the payment of cryptographic assets. Furthermore, the effectiveness of the proposed system is demonstrated on the Ethereum blockchain as a proof of concept, and the cost of gas is measured.
Mapping a Navigation System for Confined Space using CNN

In today's world, a GPS based navigation system is inevitable. Though GPS provides a better navigation system, it compromises one's location privacy. Unintentional broadcast of one's location may open the gateway for malicious 3rd party applications that may not operate on a legal basis. To overcome privacy related issues, this paper proposes a method of navigation without the use of GPS. This can be achieved by combining Convolutional Neural Network(CNN) along with Dijkstra's Algorithm. In this work, initially the starting point of the user is identified by capturing the image of the nearest building, thereafter using the CNN model to process the image and identify the location. The dataset of more than 6,000 images was built considering various circumstances for training and testing of models. This dataset was fed to different deep learning models and CNN performed the best. The shortest path between the locations is calculated using a modified Dijkstra's algorithm. It uses priority queue to reduce the time complexity of traditional Dijkstra's algorithm. This paper will help to navigate through different college campuses/universities and other big premises without using GPS.
Keyword Extraction Based on Semantic Similarity Metric and Multi-Feature Computing

Keyword extraction is an effective method to extract keywords from a text which can express the main content of the text. However, it is difficult to extract useful information quickly from a large number of documents. In this paper, a semantic similarity metric and multi-feature computing based keyword extraction algorithm is presented. First, a semantic similarity algorithm based on WordNet semantic dictionary toward distance, information and property (SSDIPA) is proposed, which quantifies the degree of association between words. Then, a multi-feature tuple (word frequency, word length, word span, word position, semantic similarity) based computing method is presentssd to score the candidate keywords. Finally, the feature decision tree is used to adjust the proportional relationship among the features so that the keyword extraction results can meet the use’s subjective preferences. The result shows that the proposed algorithm outperforms other comparison algorithms, and the multi-feature computing based keywords extraction algorithm can improve the accuracy.
Clinical Misunderstandings of Enterprise Precision Marketing Under the Background of Wireless Network

Since the birth of the Internet, advertising has been developing rapidly through the combination of Internet. Since the first Internet advertisement was released in China, China's Internet advertising has been on the way of development. The rapid development of Internet advertising has brought many opportunities to social economy, but at the same time, it also brings many problems that have not been encountered before. The purpose of this paper is to design a marketing strategy based on big data, collect, analyze and process customer information, so as to realize marketing in a more accurate way. This paper makes statistical research on the advertisement of websites in the precision marketing of big data. Through statistics, 69 web advertisements are found on 5 websites. 25 emails of the author are rubbish advertisements. Therefore, we can see that the current network marketing ethics is seriously out of standard, and the relevant countermeasures are put forward. According to the experiment, the banner advertisement accounts for 67% of the five websites’ advertisements, 4% of the advertisements are plug-in advertisements, 26% are text link advertisements, and 3% of the advertisements are floating picture advertisements. There are the above percentage can be seen, in these several websites, banner advertising is more used, followed by text link advertising.
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain

In recent years, there has been an increasing research interest in image de-noising due to an emphasis on sparse representation. When sparse representation theory is compared to transform domain-based image de-noising, the former indicates that the images have more information. It contains structural characteristics that are quite similar to the structure of dictionary-based atoms. This structure and the dictionary-based method is highly unsuccessful. However, image representation assumes that the noise lack such a feature. The dual-tree complex wavelet transform incorporates an increase in transform data density to reduce the effects of sparse data. This technique has been developed to decrease the image noise by selecting the best-predicted threshold value derived from wavelet coefficients. For our experiment, Discrete Cosine Transform (DCT) and Complex Wavelet Transform (CWT) are used to examine how the suggested technique compares the conventional DCT and CWT on sets of realistic images. As for image quality measures, DT-CWT has leveraged superior results. In terms of processing time, DT-CWT gave better results with a wider PSNR range. Further, the proposed model is tested with a standard digital image named Lena and multimedia sensor images for the denoising algorithm. The suggested denoising technique has delivered minimal effect on the MSE value.
The Future Through QR Code

The choice of this work arises from the interest in the topics of innovation and technology transfer in the context of launching new services to target people. The authors sought to integrate a technology that could bring more interest to a product/service, namely with the modern growth of mobile activities and apps. Among some options considered, they chose the QR code technology. With the help of a strategic plan, they evaluate whether there is a market and propensity for its widespread use in Portugal. Then, an interactive prototype of the supporting platform is presented for its validation with users. Most participants know the QR code and consider the idea interesting and promising. They even suggested add-ons such as more video functions and a greater variety of events and resources for combining. Given the challenges of modern society, the authors recognize its eventual application to real-time requests for sharing specialized information or resources in critical activities of areas such as health and tourism. Features such as augmented reality, security, and privacy are future issues to address.
Generative Difference Image for Blind Image Quality Assessment

Image quality usually refers to the degree of error of the distorted image relative to the reference image in the human visual perception system. Image quality assessment is to score the image quality objectively. No-reference image quality assessment is limited to distorted image information, which is more challenging in the field of computer vision. In this paper, we proposed an approach based on difference image generation to address this problem. First, by removing the up-sampling layer and batch normalization layer in the Super-Resolution Generative Adversarial Network (SRGAN) to build a difference image generation model, and applying the content loss function to optimize the model. Then, the regression network is constructed based on the convolutional neural network (CNN). The regression network contains 4 convolutional layers and 2 fully connected layers and learns the correlation between the generated difference image and the image quality score to predict the distorted image quality. Finally, comparative experiments were evaluated on three public datasets. Compared with the previous state-of-the-art methods, our method obtains similar results on the LIVE dataset and achieves significant improvement on the TID2013 and CSIQ datasets. The results demonstrate that our proposed approach achieves state-of-the-art image quality prediction.
Assessment of Open Source Tools and Techniques for Network Security

Providing network security with an open source has been a huge concern today. Data transmitted over the network shall not be assumed to be stable. There are numerous attacks such as phishing, spoofing, and sniffing. This paper provides a summary of the tools available to fight against these network attacks and threats. There are several resources in the open source that are built to use to handle these threats. Resources including OpenSSH, OpenSSL, NMap, digital certificate, and IP tables were discussed in this article.
Simulation Tests of Tokyo Quantum Network

Abstract In this modern era where secure communication is imperative, scientists have always been in search of a new solutions for this problem. One of the best solutions discovered in this century, uses the superiorities of the quantum world. This theorem, called no cloning, mainly uses the collapse of quantum state when observing attribute of qubits. In this study, we will examine the Tokyo Quantum Network [1], which is based on the protocols derived from the BB84 [2] and B92 protocols by implementing in three quantum network simulators.
Quantum Simulation of Quantum Berezinskii–Kosterlitz–Thouless Transition for Interacting Light-Matter System: A Few Exact Results

An attempt is made to understand the quantum simulation of quantum Berezinskii–Kosterlitz–Thouless (QBKT) transition for interacting light-matter system. We derive the QBKT equation for our system along with the exact results. We present emergence of different quantum phases in the different regimes of parameter space for this quantum simulated system. We interpret the collective excitations of the system in terms of bosonic excitations (Bose metal phase) as well as the photon blocked state (photonic insulator state) in our study. We present the behavior of renormalization group (RG) flow lines from the numerical studies of RG flow equations and also from the exact solution. We study the quantum critical surface of the simulated model Hamiltonian and also present the exact results for Majumdar–Ghosh point as well as for refermionization point from the perspective of quantum simulation.
Interactive Multi-Channel Network for Fast Clothing Style Transfer

As an application of image style transfer, clothing style transfer has received considerable attention from academia and fashion industry due to its visual solutions for the generation and transfer of clothing styles. In this paper, we present a solution to interactive multi-style transfer for clothing that better preserves both structure and visual richness of clothing with fast generation. The core idea of our network model is to use multiple style channels to transfer the style of the labelled area and use the image fusion method to generate personalized clothing styles. Users can control the spatial distribution of clothing stylized textures and colors through the semantic annotation. We demonstrate the effectiveness and superiority of our method through extensive comparisons with state-of-the-art algorithms.
Research on Algorithm of fighter landing gear in bad video image

Abstract The retracted and retracted state of landing gear is a problem that needs to be paid close attention to in the landing process of fighter. The traditional visual observation is easy to be affected by the climate environment. Aiming at the problems of small target, low definition and difficult detection of fighter landing gear in bad video images, a YOLOv4 target detection algorithm based on image enhancement is proposed to detect fighter landing gear. In order to improve the accuracy of landing gear detection, ACE algorithm is first applied to the video image to enhance the video image, then the main feature extraction network of YOLOv4 is used to extract the feature information, and K-means clustering algorithm is used to cluster and analyze the number and aspect ratio dimension of target candidate frames. Experiments on the dataset show that the detection accuracy after image enhancement reaches 88.12%, which is 1.19% higher than the original image; The recall rate reached 73.44%, an increase of 4.17%; The value of mAP reached 82.65%, an increase of 1.08%.
Ship detection based on Improved YOLO Algorithm

With the continuous development of marine economy, marine ship detection has a very wide application prospect. To solve the problems of low ship detection accuracy and easy missing detection of small targets in complex marine environment, an improved YOLOv3 target detection algorithm is proposed. Firstly, before data training, the methods of image blending and small target enhancement are adopted to overcome the influence of complex environment background on target detection and the imbalance of small target data, and improve the network's ability to extract image features and detect small targets during the training process. Then, the convolution in the original network is replaced by Octave convolution, which reduces the amount of calculation during network training and improves the detection speed of the network through image frequency division. At the same time, GIOU loss function is introduced to alleviate the problems that the gradient is zero when the detection frames do not overlap, and IOU cannot distinguish the alignment of prediction frames and annotation data in different ways, thus improving the accuracy of detecting the target position. The experimental results show that the mAP of the improved YOLOv3 algorithm on the self-made ship data set can reach 84.07%, and the improved algorithm is improved by 3.52% compared with the original algorithm, and the detection speed can reach 31.8 frames/s, which effectively improves the accuracy and speed of detecting ships at sea.
SpaAbr: Size Prediction Assisted Adaptive Bitrate Algorithm for Scalable Video Coding Contents

Dynamic Adaptive Streaming over HTTP(DASH) is a video transmission ptotocol to adapt to different network conditions and heterogeneous client devices. The client first requests and parses the Media Presentation Description(MPD) file from the DASH server to obtain basic information including the average bitrate list and segment URLs. After that, the adaptive bitrate(ABR) algorithm decides the quality of the next requested segment based on the network and buffer conditions. However, there is a big discrepancy between the segment size calculated with this coarse-grained average bitrate and the real size due to the constantly changing video scenes, which is especially obvious for variable bitrate(VBR) encoded videos. And this error is transparent to the ABR algorithm. This paper first analyzes and confirms the inter-layer correlation of scalable video coding(SVC) encoded contents, then designs a segment size prediction module to cooperate with the ABR algorithm. Experimental results show that predicting a certain enhancement layer(EL) segment size by all its previous layers can increase the probability that the predicted value falls within the accurate interval by 20% −58% compared to predicting the EL only by base layer(BL). Besides, the ABR algorithm assisted with size prediction module can increase the average bitrate by 29.2%, reduce the average bitrate switches by 13.4% and the average rebuffering events by 78.7% compared with the independent ABR algorithm.
Consideration of interlocking control and standard messages to secure joint and interoperability

In modern warfare, since operations are carried out in a situation in which a variety of complex weapon systems are mixed, it is a conflict conducted in the 21st century that the implementation of integrated operations under a network-centric operation that can integrate and operate various weapon systems as one is the most important issue. This could be confirmed through the form. Therefore, a weapon system acquired on the Korean Peninsula, where there is always the possibility of war, must necessarily secure interoperability for such an integrated operation. In this regard, we intend to conduct a study to secure interoperability of weapon systems and battlefield management systems, focusing on standard conformity to secure coherence and interoperability, interoperability and information exchange for interlocking control
Exploring Human Emotions for Depression Detection from Twitter Data by Reducing Misclassification Rate

The growth of social networking sites has been tremendous in the last few decades. It has emerged as a platform to share one’s thoughts and opinions and also interact with new people every day. It is possible to predict a person’s emotions by analysing their tweets and posts over a period of time. The main objective is to detect depression among twitter users by performing a sentiment analysis of their tweets. Real time tweets are extracted from Twitter and detected for negative emotions using a lexicon-based ensemble method and a novel Neutral Negative Scoring algorithm. The user history of potentially “depressed” people is obtained and examined by training a neural network to confirm the onset of depression. The depressed people will be given recommendations for initiating positive actions using a recommendation system. It is found that a Bidirectional Long-Short Term Memory network has the highest accuracy of 90% in detecting users with depression.
Review of Stack-Based Binary Exploitation Techniques

Binary exploitation focuses on making use of flaws and oversights in the program design to accomplish a task that the program wasn’t intended toward. Some flaws are fairly common programming errors, while some are not so obvious and give rise to complex exploitation techniques. This paper addresses some of the techniques extensively used to test the vulnerabilities and security holes in the system, and their mitigation techniques. The goal of these techniques is to hijack the target binary’s execution flow.
Comparative analysis of laser and simulated speckle pattern for single shot 3D reconstruction

Stereo photogrammetry is a fundamental technique for 3D measurements in different applications. As passive stereo methods struggle in texture-less regions, different light-coded patterns are projected to solve the correspondence problem. When the reconstruction method requires a series of sequentially coded light patterns, the system is prone to movement-related errors. A single pattern is utilized where the potential subjects are dynamic or change the position rapidly. A random speckle pattern is a good candidate for such systems. Primarily, there are two approaches to generate the speckle pattern in stereoscopic systems. The speckles can be generated by the coherent illumination of a laser diode, and the laser speckle-like pattern can also be simulated and projected by a professional projector. The purpose of both is to solve the stereo correspondence problem; however, the performance of both can differ, subjective to employed 3D sensing algorithm. In this Letter, we compare the performance of both for single-shot 3D reconstruction. We have discussed the pros and cons of both methods and analyze their ability to resolve long range 3D reconstruction.
Behavioral Anomaly Model for Detecting Compromised Accounts on a Social Network

The previous two decades have given birth to a new popular phenomenon - online social networking. Its range, diversity and constantly growing impact on our lives provides space for the creation of new and profitable, highly professionalized industry of cybercrime. It is based on phishing, social engineering, brute force password guessing or malware collecting passwords. The aim is to penetrate another’s account and monetize it by blackmailing, searching for cryptocurrency wallets, obtaining passwords, influencing public opinion in favor of an idea, or at least sending spam. Today’s hackers are well organized into massive, internally specialized criminal networks recruited from the best IT specialists, and as the latest results of the British investigative initiative Bellingcat show, they are also often the cornerstone of the ongoing hybrid war. On top of this, in February 2021, the “mother of all breaches” appeared on the darknet - the COMB database - consisting of 3.2 billion of unique pairs of emails and cleartext passwords. This is a clear and present danger for every user of any social network and their providers have to act immediately to protect accounts of their users. This paper describes how a social network operator with 250,000 daily active users deals with the problem of account compromise by deploying anomaly detection responsive to sudden changes in the behavior of a user trying to log in to the account.