Reviewerly/exHarmony huggingface dataset:
E <sup>2</sup> -VOR: An End-to-End En/Decoder Architecture for Efficient Video Object Recognition

High-resolution video object recognition (VOR) evolves so fast but is very compute-intensive. This is because VOR leverages compute-intensive deep neural network (DNN) for better accuracy. Although many works have been proposed for speedup, they mostly focus on DNN algorithm and hardware acceleration on the edge side. We observe that most video streams need to be losslessly compressed before going online and an encoder should have all the video information. Moreover, as the cloud should have abundant computing power to handle sophisticated VOR algorithms, we propose to take a one-shot effort for a modified VOR algorithm at the encoding stage in cloud and integrate the full VOR regeneration into a slightly extended decoder on the device. The scheme can enable lightweight VOR with server-class accuracy by simply leveraging the classic and economic video decoder universal to any mobile device. Meanwhile, the scheme can save massive computing power for not repetitively processing the same video on different user devices that makes it extremely sustainable for green computing across the whole network. We propose E 2 -VOR, an end-to-end encoder and decoder architecture for efficient VOR. We carefully design the scheme to have minimum impact on the video bitstream transmitted. In the cloud, the VOR extended video encoder tracks on a macro-block basis and packs intelligent information into the video stream for increased VOR accuracy and fast regenerating process. On the edge device, we extend the traditional video decoder with a small piece of dedicated hardware to enable the efficient VOR regeneration. Our experiment shows that E 2 -VOR can achieve 5.0× performance improvement with less than 0.4% VOR accuracy loss compared to the state-of-the-art FAVOS scheme. On average, E 2 -VOR can run over 54 frames-per-second (FPS) for 480P videos on an edge device.
English Lexical Analysis System of Machine Translation Based on Simple Recurrent Neural Network

With the increasing communication between countries, machine translation has become an important way of communication between ethnic groups in different language systems. In order to further improve the coverage mechanism and vocabulary translation quality of the machine translation model of neural network, this paper will study the machine translation English lexical analysis system based on simple recurrent neural network. In order to improve the effect of word alignment, marking and attention mechanism are introduced into the English lexical analysis model. The experimental results show that the tagging of named entity words in the system can reduce the problem of unregistered words. Combined with the attention mechanism, it can significantly improve the effect of word alignment and label recall. It not only improves the controllability of translation but also has a positive impact on the quality of translation and its application effect in specific scenes. The evaluation of translation quality indicators shows that the system can effectively improve the accuracy and quality of Chinese-English translation.
Deploying Virtual Air Drum Tool to Detect Sound Frequency in Computer Vision

Buying a drum set is an expense that most drummers must eventually bear to pursue their profession. This examination is hoped to expedite the acquaintance of drummers with the drumming experience without the expenses, as well as to consider drummers to have some degree of nonchalance regarding practicing without a full drum set. The experience of drumming thus becomes more accessible to a broader audience. A model virtual drum set has been proposed that would just require clients to have a computer with a camera, along with readily accessible markers addressing the tips of drum sticks like shading papers. This was accomplished through OpenCV in tandem with Python, and the idea of color-based mass identification was utilized to distinguish the markers. This model has additionally displayed to have potential for additional advancement as a delivered application, alongside the chance of significantly more improvement later on.
Between adjective and noun

Abstract We look at the presuffixal vowels occurring after adjectival and nominal stems in Hungarian. We show that their “low” or “nonlow” status depends not only on the morphological category and arbitrary lexical properties of the stem, but also on its semantic properties and syntactic position, as well as the identity of the suffix and the typical environments in which the suffix occurs. Syntactic positions can be arranged in a scale ranging from more adjectival (less nominal) to less adjectival (more nominal). The same scale may be applied to suffixes typical of these syntactic positions. The lowness of the presuffixal vowels neatly follows these scales, with no variation at the two edges and a zone of variation in the middle of the scale.
A Comprehensive Analysis on Outlier Prediction using Learning Approaches

The explanation of the identified outliers is frequently provided to the users when other techniques are used to detect the outliers that are suggested in the study. This situation is quite challenging for the users to take the relevant actions that are regarded with the identified outliers as the outcome. If the outliers are detected, then the explanations need to be provided with outliers to reduce the challenge. Some surveys are available for the outlier interpretation, and some surveys are available to detect an outlier. The survey presents an outlier interpretation in the proposed system where useful knowledge is obtained from anomalous data to fill the gap and describe the data. The types of outlier explanations are defined, and the difficulties are discussed while creating every type. The existing outlier interpretation approaches are reviewed, and the processes of mentioning the difficulties are discussed. The applications of outlier interpretation are discussed, and the existing techniques are utilised for the determination of outlier explanations are reviewed. Further, the possible future research ways are discussed.
English Language Features in Linguistics by High-Performance Computing

High-performance computing clusters are mainly used to deal with complex computing problems and are widely used in the fields of meteorology, ocean, environment, life science, and computer-aided engineering. Language is the way humans communicate and communicate. Linguistic features are the stylistic features that distinguish all languages from other languages. This paper aims to study how to analyze English language features based on high-performance computing. This paper addresses the problem of linguistic feature analysis, which is built on high-performance computing. Therefore, this paper expounds the related concepts and algorithms, and designs and analyzes the characteristics of English language. The experimental results show that among the 160 English sentences in two different journals, complex sentences are the most used, with a total of 55 sentences, accounting for 34.38%. The second is mixed sentence types, 47 of which are mixed sentence structures, accounting for 29.38%. Among them, the combination of simple sentences + coordinating complex sentences + complex sentences constitutes the most mixed sentences, which appear 12 times and 8 times in ELT Journal and SSCI, respectively, accounting for 15.00% and 10.00% of their respective corpora.
Bank Application: One-Time Password Generation

One-time password systems are being installed in banks, government projects, and other stability industries in order to prevent the harm caused by phishing and spyware assaults. However, it can be used to spoof the stoner formerly, minimising the damage. A single sentence is all it takes. As useful as one-time password (OTP) procedures are, they are nevertheless vulnerable to the most sophisticated phishing assaults. Phishing and spyware are two of the most common online threats, but they're not the only ones. There are few businesses and government agencies using one-time password schemes because of this (OTP). These systems require users to keep a list of their passwords with them at all times, either on paper or electronically, and to use a separate account each time they enter in. Bushwhackers should be unable to fake the user unless they have this set of passwords in their possession.
Developing a collaboration system for pancreatic cancer research: a clinical design science study

Great potential exists for the use of information systems (IS) in medical research, as they already form the backbone of several long-term medical research projects. IS can potentially support practitioner-researchers (practitioners who conduct research in their areas of practice) by enabling collaboration, particularly when the number of cases is low and the treatment strategy is centralised. However, practitioner-researchers and academic researchers differ in their research approaches. In this clinical IS research effort, we designed an IS, together with a local hospital, for supporting research on pancreatic cancer by applying a design science research (DSR) approach. We also observed how DSR is accepted and used by practitioner-researchers in this context. Consequently, we not only developed an IS but also gained insights into the benefits of DSR in projects with heterogeneous participants, namely those from various fields with different experiences and requirements. The results show that DSR contributes to mutual understanding, transparency in the design process and long-term benefits for all parties involved, which was further confirmed by post-hoc interviews.
Construction of a Low-Cost Layered Interactive Dashboard with Capacitive Sensing

In the present work, a methodology for the low-cost crafting of an interactive layered dashboard is proposed. Our aim is that the tangible surface be constructed using domestic materials that are easily available in every household. Several tests were performed on different capacitive materials before the selection of the most suitable one for use as a capacitive touch sensor. Various calibration methods were evaluated so that the behavior of the constructed capacitive touch sensors is smooth and reliable. The layered approach is achieved by a menu of few touch buttons on the left side of the dashboard. Thus, various different layers of content can be projected over the same construction, offering extendibility and ease of use to the users. For demonstration purposes, we developed an entertaining plus an educational application of projection mapping for the pervasive and interactive projection of multimedia content to the users of the presented tangible interface. The whole design and implementation approach are thoroughly analyzed in the paper and are presented through the illustration and application of various multimedia layers over the dashboard. An evaluation of the final construction proves the feasibility of the proposed work.
License Plate Image Super Resolution Using Generative Adversarial Network(GAN)

Super resolution of images in the field of Computer Vision is a widely used for the conversion of images into high resolution without the loss of pixel data into the images. Due to fast movement of vehicles and low quality of camera the image cannot be verified easily so, the techniques of Generative Adversarial network have been applied for the Super resolution of license plate Images which works to recover the loss data of license plate images without loss of pixel data. Earlier, mean square error (MS E) and peak signal to noise ratio (PSNR) was used as content loss to minimize the error but at optimal minimization the images get over smoothen and pixel data were lost. This paper has proposed and applied VGG-19 as pretrained neural network along with MSE and PSNR to minimize the content loss which overall optimizes the perpetual loss, and over smoothness of the images gets controlled which saves pixel data. Later, the pre-trained neural network is integrated with Generative Adversarial Network [GAN] of discriminator and generator to produce high resolution images. Taking the PSNR as an evaluation metrices for the images, it increases from 26.184 to 28.696 and accuracy from 58% to 84%.
Attribute based Access Control Model for Electronic Health Record

The transfer of digital medical images for E-health services has recently gained a significant research attention. However, existing implementations of this service emphasize on the patient's confidentiality and data security. The need for rapid and secure communication is increasing in the medical industry. Image transfer/Image based communication has become more common nowadays. This situation necessitates the need to develop an effective technique, which can be accessed through the internet. This research study describes a new approach for encrypting an image for achieving a secure and denoised transmission. This work discusses about the image cryptography, data hiding, and steganography. Furthermore, a hybrid paradigm based on key there in public realm Steganography and Elli Curve Cryptography (ECC) has been proposed. This research work evaluates the methods that are far more secure by employing a unique ECC/Steganography.
Parallel and private generalized suffix tree construction and query on genomic data

Several technological advancements and digitization of healthcare data have provided the scientific community with a large quantity of genomic data. Such datasets facilitated a deeper understanding of several diseases and our health in general. Strikingly, these genome datasets require a large storage volume and present technical challenges in retrieving meaningful information. Furthermore, the privacy aspects of genomic data limit access and often hinder timely scientific discovery.In this paper, we utilize the Generalized Suffix Tree (GST); their construction and applications have been fairly studied in related areas. The main contribution of this article is the proposal of a privacy-preserving string query execution framework using GSTs and an additional tree-based hashing mechanism. Initially, we start by introducing an efficient GST construction in parallel that is scalable for a large genomic dataset. The secure indexing scheme allows the genomic data in a GST to be outsourced to an untrusted cloud server under encryption. Additionally, the proposed methods can perform several string search operations (i.e., exact, set-maximal matches) securely and efficiently using the outlined framework.The experimental results on different datasets and parameters in a real cloud environment exhibit the scalability of these methods as they also outperform the state-of-the-art method based on Burrows-Wheeler Transformation (BWT). The proposed method only takes around 36.7s to execute a set-maximal match whereas the BWT-based method takes around 160.85s, providing a 4× speedup.
Mining of Frequent and High Utility Fuzzy Sequential Patterns

Sequential pattern mining is one of the very popular data mining techniques to find patterns from databases. The variation in technique is based on the threshold used for mining which can be frequency, utility, or both. The use of fuzzy theory helps to provide natural representation by expressing parameters in linguistic terms. This paper proposes a hybrid fuzzy sequential pattern mining algorithm that expresses quantity in linguistic terms. The proposed method generates less number of patterns than the existing algorithms, according to the results of the experiments.
Application of Reinforcement Learning for Control of Autonomous Vehicles

Learning based on interaction with the environment is a natural phenomenon, for example, a baby learns through direct sensory interaction with the environment, without the supervision of any other person. This sensory connection with the environment is the source of all of his/her information about the causes and effects of the consequences of his/her behavior, and what must be done to realize some immediate objective. This kind of interaction with the environment is the main source of knowledge, for all humans, about our environment and also about ourselves. While we are learning to drive or having a conversation, we are very conscious of how our environment responds to our actions and we try to influence what happens by responding through our actions. Learning from interaction is the fundamental idea of almost all learning and intelligence theory. In this article, we explore a computational method using an agent that learns from interaction. It does not directly depend on the learning techniques of humans or animals, but mainly analyzes idealizes from them and then evaluates the effectiveness. We try to implement this approach by applying the techniques of what is called Deep Reinforcement Learning. Compared with other machine learning methods, it focuses more on targeted learning from interactions with the environment.
Problems of Information Security in the System of Distance Education

In this article, the properties of distance education systems, the principle of modern education were studied. The problems and types of distance learning technologies used in the learning process have also been studied. The pros and cons of distance education are considered. The threats to the normal functioning of the e-learning system and the risks associated with students and the risks associated with the teaching staff have been studied.
Exploiting European GNSS and Ethereum in location proof systems

Location-Based Services (LBSs) are essential in many application contexts like ride-sharing or navigation apps. There are cases where users could gain an advantage by submitting fake locations. The problem faced in this paper concerns the possibility that the geographic location declared by a user is not the actual location in which the user is placed. Some solutions are based on centralized or distributed verification in the literature, and other solutions are based on witnesses or infrastructure. In this paper, we highlight the limitations of such approaches and propose a new scheme that exploits signals coming from satellites to provide trustworthy location proofs, also respecting users' privacy. The proposed approach is decentralized because location proofs are stored by users in a suitably-encrypted way, and a blockchain is adopted to guarantee data integrity and authenticity. We show that the proposed approach overcomes the state of the art through a detailed analysis.
Design and implementation of an augmented reality mobile application for navigating ATM counters (AR-ATM)

Purpose This paper aims to bring in augmented reality (AR) into navigation systems to rectify the issues mentioned. This paper proposes an AR enhanced navigation system for location automated teller machine (ATM) counters (AR-ATM) and branches of banks based on user’s choice. Upon selecting the ATM, the navigational path to the destination is drawn from the current location, thereby the user can reach the ATM through the optimal path. Design/methodology/approach Traditional navigation systems require users to map with the real world environment as and when required and also may lead to incorrect path due to minor difference in distance. The traditional navigation systems’ also does not take into consideration the ergonomics and safety of the user. Findings In this system, a camera lens is used, which is directed down the street at eye level and the application displays the location of ATMs and bank branches and also provides information about the locations like distance and time through the AR superimposed object. Originality/value The application also provides indoor navigation, especially in a multi-storeyed building. Experiments are performed on smartphones that support AR, and the results are promising with no lag in time frame of the real object and virtual object. To determine the factors that regulate the suggested AR tracking mechanism, a quantitative evaluation of the experimental data is also performed. The testing of implemented AR-ATM from the standpoint of end-users is undertaken to evaluate real-time usage comfortability, and the results have been determined to be extremely satisfactory.
Browser Forensic Investigations of Instagram Utilizing IndexedDB Persistent Storage

Social media usage is increasing at a rapid rate. Everyday users are leaving a substantial amount of data as artifacts in these applications. As the size and velocity of data increase, innovative technologies such as Web Storage and IndexedDB are emerging. Consequently, forensic investigators are facing challenges to adapt to the emerging technologies to establish reliable techniques for extracting and analyzing suspect information. This paper investigates the convenience and efficacy of performing forensic investigations with a time frame and social network connection analysis on IndexedDB technology. It focuses on artifacts from prevalently used social networking site Instagram on the Mozilla Firefox browser. A single case pretest–posttest quasi-experiment is designed and executed over Instagram web application to produce artifacts that are later extracted, processed, characterized, and presented in forms of information suited to forensic investigation. The artifacts obtained from Mozilla Firefox are crossed-checked with artifacts of Google Chrome for verification. In the end, the efficacy of using these artifacts in forensic investigations is shown with a demonstration through a proof-of-concept tool. The results indicate that Instagram artifacts stored in IndexedDB technology can be utilized efficiently for forensic investigations, with a large variety of information ranging from fully constructed user data to time and location indicators.
Comparison between Regressive and Classifying Neural Networks for PID Controlled Path-Following

In this paper, a regressive CNN-based system is developed with the objective of overcoming the turning angle limitations of a previously developed path-following classifying neural network. Images from a forward facing camera are put through a CNN and the result is supplied to a PID controller to guide the robot down a path. Trials were run for both current and previous methods and their results are compared and contrasted, with the new method completing all courses. Our regressive CNN was determined to have a RMSE of 3.584 and a R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.987.