Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis about Turkish TV Series with Web Scraping

A person's preferences for a product or service are influenced by other people's opinions about that product or service. One of the sectors where this situation is most common is digital media platforms that broadcast TV series. Therefore, the automatic extraction of other people's feelings and opinions from comments about TV series on digital media platforms by sentiment analysis allows users to have information and judgment about series easily. This study aims to conduct sentiment analysis on the Turkish Tv Series with machine learning algorithms. First, we collect comments about digital media platform TV series from a user-generated website, namely Ekşi Sözlük, with web scraping techniques and apply preprocessing to this data. Then, we conduct sentiment analysis with Support Vector Machine (SVM), Logistic Regression (LR), K-nearest Neighbors machine learning techniques with Bag of Words (BoW), Term Frequency Inverse Document Frequency (TF-IDF), Word2vec vector models. Our results show that the SVM classifier trained on the TF -IDF vector model gives the highest prediction macro averaged F -score with 0.631.
Collect Security Process Data

Organizations must collect the appropriate security process data once security controls are tested. NIST SP 800-137 provides guidelines for developing an information security continuous monitoring (ISCM) program. Security process data should include account management, management review, key performance and risk indicators, backup verification data, training and awareness, and disaster recovery and business continuity. It’s the job of the security professionals to ensure that the security process data have those included.
XFeatur: Hardware Feature Extraction for DNN Auto-tuning

In this work, we extend the auto-tuning process of the state-of-the-art TVM framework with XFeatur; a tool that extracts new meaningful hardware-related features that improve the quality of the representation of the search space and consequently improve the accuracy of its prediction algorithm. These new features provide information about the amount of thread-level parallelism, shared memory usage, register usage, dynamic instruction count and memory access dependencies. Optimizing ResNet-18 with the proposed features improves the quality of the search space representation by 63% on average and a maximum of 2× for certain tasks, while it reduces the tuning time by 9% (approximately 1.1 hours) and produces configurations that have equal or better performance (up to 92.7%) than the baseline.
Research and Application of Image Style Transfer Method

Image style transfer is a technology that uses specific algorithms in deep learning to separate image content and style. It combines the style of one image with the content of another. This technology can be applied to film and television special effects, artistic creation and other fields. It is a research hotspot in the field of image processing in recent years. This paper summarizes the scholars' research on image style transfer technology. Convolution neural network is used to realize image style transfer. The style image and content image are input into the convolution neural network. The loss functions of content image and style image are defined. Vgg-19 model is established, and the loss function is weighted into a complete loss function. In each iteration, the pixel value of the image is adjusted according to the total loss function. After many iterations, the fusion effect image of content and style is obtained.
Deep Learning-Based Approach to Detect Leukemia, Lymphoma, and Multiple Myeloma in Bone Marrow

Bone marrow cancer is one of the life-threatening diseases which may cause death to many individuals. Leukemia, lymphoma, multiple myeloma, and other cancers that form in the blood-forming stem cells of the bone marrow constitute bone marrow cancer. Early detection can increase the chance for remission. Accurate and rapid segmentation techniques can assist physicians to identify diseases and provide better treatment at the right time. CAD systems can be useful for the early discovery of bone marrow cancer. It features the latest updated algorithm that combines deep learning with MATLAB for health assessment. This can assist in the early detection of leukemia, lymphoma, and multiple myeloma. For denoising histopathological images, new K-SVD and fast non-local mean filter algorithms are employed. For pre-processing, algorithms like multilayer perceptron and novel hybrid histogram-based soft covering rough k-means clustering techniques are employed. Three classifiers, namely R-CNN, ResNet 50, and LSTM, are used to classify, and the performance is compared based on the accuracy.
The NLP Powered BI Toolkit: The Case of MESOC

In this paper, we present a business intelligence (BI) toolkit based on natural language processing (NLP) methods for the arts and culture domain, collected in the Measuring the Social Dimension of Culture (MESOC) project. The main NLP methods in the underlying pipeline are keyword extraction, multi-label classification of texts, and detection of potential social impacts of cultural policies and practices, all trained on texts from open-access academic publications. The MESOC Toolkit is a georeferenced visualization tool for analyzing impact on social value creation in the areas of health and well-being, urban regeneration, and social cohesion, and enables semantic search for content in the MESOC domain. Therefore, the presented research can serve as a prototype for measuring societal value by identifying recurrent pathways of transformational processes in society that reach beyond the selected field of art and culture.
P‐66: Application of Ergonomics in VR HMD Exit Pupil Positioning Design

To achieve the actual exit pupil distance and ensure display effect of the VR HMD, the positioning structure of exit pupil is studied from three aspects:design data extraction, experimental verification and data compensation. The surface model of sponge which is used to ensure the best fit between the sponge and face is extracted according to the data of ergonomic dimensions. The sponge designed with this model should be made to measure the actual exit pupil distance of the VR HMD by the method studied in this paper. The data of sponge compression from the test should be added to the size before. According to this method, the actual exit pupil distance can be closest to the design to achieve the best display effect.
Prediction of Tumor in Mammogram Images Using Data Mining Models

Data Mining (DM) is a process of extracting patterns from large datasets to represent knowledge, and focuses on issues related to its feasibility, usefulness, effectiveness and scalability. There are different stages for data preprocessing, namely, (i) Data cleaning (iii) Data integration (iv) Data selection (v) Data transformation, after which they are made ready for the mining task. DM techniques have contributed significantly towards several important researches and in the field of traditional sciences such as Astronomy, High Energy Physics, Biology, World Wide Web, Big Data Analytics, High Performance Computing, Cloud Computing, Medicine and some other related domains. This research work evaluates the performances of clustering algorithms by means of classifying the produced results based on the number of white color pixels, using classification algorithms. Totally, 10 attributes are taken for classification. From the results of clustering methods, the intensity of images (number of pixels) is considered as one of the significant attribute and it is included along with the existing nine other attributes. Classification techniques such as CART, SVM, Naive Bayes, JRip and J48 are used with various accuracy measures like FP rate, TP rate, Recall, Precision, ROC Area and F-measure.
Quantum Control Mechanisms of Superdense Coding

Control frames in quantum schemes for superdense coding are considered. A generalized solution is proposed for quantum control schemes that allow long (n-bit) sequences to be transmitted using a single qubit. The results of experiments on the implementation of quantum circuits controlled by both qubits and classical bits in hybrid communication networks are presented.
One-Shot Learning for Face Recognition, Method Comparison

Deep learning uses multiple layers to represent data at different levels to extract the required information. Where this technology flourished and became an important area of research direction in 2014 after its use to prevent the penetrations of Deep-ID and Deep-face. This technology is characterized by a hierarchical shape that works on merging the pixels together to form the image of the face. This has led to a significant improvement in advanced performance and an effective contribution to the success of applications in the world. As for facial recognition in one case, it is a unique case that characterizes the human race, and researchers have tried to create one-shot deep learning algorithms for the purpose of simulating this ability. Despite the excellent performance of deep learning and its methods for classifying different image problems, its performance often depends on the presence of a large number of training samples. In this research, we compare a face-recognition methodology utilizing neural networks CNN but using different databases through published research in this field, and the focus was also on one-shot learning because it is one of the great challenges at the present time in order to be a simplified reference for researchers in the field of one-shot face recognition and the key to launching their research.
An Improved Lattice-Based Ring Signature With Unclaimable Anonymity in the Standard Model

Abstract Ring signatures enable a user to sign messages on behalf of an arbitrary set of users, called the ring, without revealing exactly which member of that ring actually generated the signature. The signer-anonymity property makes ring signatures have been an active research topic. Recently, Park and Sealfon (PS; CRYPTO’19) presented an important anonymity notion named signer-unclaimability and constructed a lattice-based ring signature scheme with unclaimable anonymity in the standard model; however, it did not consider the unforgeable w.r.t. adversarially chosen-key attack (the public key ring of a signature may contain keys created by an adversary) and the signature size grows quadratically in the size of ring and message. In this work, we propose a new lattice-based ring signature scheme with unclaimable anonymity in the standard model. In particular, our work improves the security and efficiency of PS work, which is unforgeable w.r.t. adversarially chosen-key attack, and the ring signature size grows linearly in the ring size.
Effective Cybersecurity

Cyber safety is information systems security and their hardware networks due to theft or damage.
Strategies for applying a competency-based approach in STEM training

The article presents strategies for skills development and for measuring and evaluating students' competencies and some approaches for evaluating the effectiveness of pedagogical practices in STEM training.In an innovative high school with a language profile and opportunities for additional profiling in mathematics, IT, entrepreneurship, various STEM initiatives have been implemented, in which students are researchers of real problems, creators of authentic content and creative products.The article shares the experience of teachers in their activities in creating: the structure of Project Based Learning /PBL/; the formulation of learning objectives; planning individual lessons; the involvement of students in the creative implementation of the planned tasks; monitoring their motivation and commitment; the assessment of the competencies acquired by the students in different fields; the organization of teacher work teams.Details of the place of study, models of interaction between university professors and professors are presented. The mechanisms for planning learning scenarios are studied, the results of PBL are analyzed, and practices for development of various competencies are indicated. The applied methodological approaches are commented on, the tools for evaluation of the results are analyzed, the challenges in the work are described and the proposed possibilities for overcoming them are described.
Classification of fired cartridge cases using <scp>3D</scp> image capture and a comparison of database correlation method performance

When firearms are used, they leave unique marks on the fired bullets and shells. Examining these marks gives clues about the crime scene and the weapon of the crime. However, due to the increase in the number of incidents in which firearms are used, the increase in the amount of evidence makes ballistic investigations very difficult and prolongs the analysis time of the evidence. With the automatic image analysis and identification system, ballistic evidence can be examined very quickly and used to classify possible matching evidences. In this study, we present an approach based on Image Similarity Measurement for forensic examination of cartridges from automatic pistols. For this purpose, we used 500 images of 9 × 19 mm and 500 images of 7.65 × 17 mm cartridge cases obtained from 20 different brands and models of pistols. We divided the images of bullet cartridges into four different categories as breech face, firing pin, ejector mark, and combined evaluation, and we obtained these images from the BALİSTİKA 2010 system. We investigated the classification and matching performance of the obtained four category images using 4 different methods: Structural Similarity (SSIM), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Universal Image Quality Index (UQI). The results show that our Structural Similarity (SSIM) approach is effective in classifying and matching ballistic evidence.
Developing “friend or foe” protocol in residue number system code for satellite authentication

Abstract Low-earth orbit (LEO) satellite communication systems are successfully used in control and monitoring systems for the production and transportation of hydrocarbons from fields located beyond the Arctic Circle. As the number of countries engaged in the development of such fields expands, the number of groupings of LEO satellite communication systems grows as well. To prevent the possibility of intercepting and imposing a delayed command to control the intruder satellite, it is necessary to use the “friend or foe” identification system for the LEO satellite communication systems. At the same time, it is proposed to use authentication protocols with zero knowledge to ensure high imitation resistance in such systems. To increase their efficiency, the article proposes to use the codes of the residual number system (RNS). The novelty of this idea lies in the fact that the use of parallel RNS codes will reduce the time spent on performing arithmetic operations implemented in authentication protocols. This will reduce the likelihood of an intruder satellite picking up the correct response signal. Therefore, the development of an authentication protocol for LEO satellite communication systems based on RNS codes, the use of which will reduce the time for satellite identification, is an urgent task.
An Intelligent System for Assessing the Emotional Connotation of Textual Statements

The objective was to use classical machine learning algorithms (without neural networks) to train a model capable of recognizing emotions in a text with an accuracy as close as possible to transformers. The following methods were considered: support vector method, linear models with stochastic gradient descent, K-nearest neighbor method, Random Forest with boosting (CatBoost). To reduce outliers, the GoEmotions dataset was pre-processed by removing annotations with consistency less than two, selecting the most popular annotations, cleaning the text from punctuation and other outliers, and stemming. The paper specified suboptimal parameters for four methods of model building based on machine learning. The study concluded that GoEmotions texts in the feature space have a complex connected structure within classes and therefore are difficult to partition using classical methods.
Software Robotics Driven Goods and Services Tax Reconciliation and Matching

Goods and services tax (GST) is one direct tax for the country. It has proven to be a revolutionary step in simplifying the process of tax in India. Every vendor is made to undergo the process of maintaining their balance sheets of the goods and services bought and sold. Tax payment in such a case becomes redundant. Every entity registered under GST has the flexibility to claim input tax credit (ITC). ITC, by definition, is the payment of the balance amount from the difference of taxes on output and input transactions to the government. Handling extensive records of invoices by the organization becomes a tedious process to ensure the matching of transactions between records. The manual workforce is susceptible to error in this cumbersome and time-consuming process. The establishment of a digital workforce using robotic process automation or software robotics creates a stable work process, able to achieve highly accurate results, and mitigates risks. Of the many vendors at the forefront of robotic process automations, Automation Anywhere has been the leader. Leveraging their robotic process automation tool, the development of a digital workforce to perform matching and reconciliation between documents of interest to yield accurate results helps to accelerate the process of tax claims.
The Study of Efficacy of Gaussian Mixture Model In Image Tracking System Using the Canny Optical Flow Technique

Detecting and tracking moving objects in a sequence of video images is an important operation in the field of computer vision. However, problems of image noise, complex object forms and motions, and real-time processing are few of the challenging facing existing flow calculation methods as they are computationally complicated and highly vulnerable to noise. Hence, this paper presents a technique less susceptible to noise and computationally efficient. Canny Optical Flow (Canny-O-Flow) was used in this study to develop a framework for recognizing and tracking moving objects in video. Video datasets in avi and mp4 format were acquired from MathWorks and YouTube respectively. The obtained datasets were preprocessed by splitting the videos into numerous frames. The video dataset's rapid shift in intensity and Object's borders were discovered using Gaussian Mixture Model and Canny Edge technique, respectively. The performance of Canny-O -Flow and O-Flow techniques were investigated by compared using Accuracy, Precision, False Acceptance Rate, False Rejection Rate and Processing time as the performance metrices. Additionally, the techniques were validated by t-test to compare the differences between the two techniques at 5% significance level. It showed that p=0.008 for test of significance between the Canny-O-Flow and O-Flow techniques. The developed Canny-O-Flow framework was able to recognize and track moving objects more efficiently and with less processing time than the standard technique.
A Bi-Factor Biometric Authentication System for Secure Electronic Voting System

Because voting is the most important way for people to express their thoughts and choose their preferred candidates or administrations, it is an essential aspect of the democratic process. The Nigerian Independent National Electoral Commission (INEC) employed a partial e-voting device with the use of a card reader to identify and authenticate the electorate in general elections in 2015 and 2019. As a result of the deployment of the card reader, the country's voting processes have improved considerably in terms of legitimacy and trustworthiness. This great concept, however, was not without problems, since a lack of fingerprint recognition and identification prevented a substantial number of people from voting. This is due to the continual shifting of the skin of the finger as a result of the vast majority of voters in this category's farming and tiresome work. If this remains unchecked, it will not only disenfranchise a large number of voters, but it will also call into question the process' legitimacy. This work proposed a voting system with a bi-factor biometric authentication mechanism to address these concerns (fingerprint and iris). This will speed up the voter identification and authentication procedure, as well as prevent voting fraud and voter disenfranchisement as a result of the card reader failing to identify them. An accuracy of 94 percent was obtained as a result of the analyses. The Iris response time for voter enrolment and verification is 15s and 20s, respectively, while the fingerprint response time is 3s and 9s, respectively.
Techno-Tradition: A Foray into Technology-Integrated Traditional Japanese Theatre

Traditional Japanese theatre is a broad category that usually refers to noh, kyōgen, kabuki and bunraku. These genres formed from the fourteenth through seventeenth centuries, and are still regularly performed. Despite the many significant roles of technologies in these genres today, their increasing presence in the genres and myriad manifestations are seldom discussed. This article is an initial examination of the presence and significance of new technologies and new media in the traditional Japanese theatre ecology, focusing on developments in the past two decades including the staging of a ‘fusion noh’ with holograms as performers, to the later kabuki and bunraku plays performed with vocaloid, as well as Virtual Reality noh. By examining select case studies and focusing on the technological aspects of traditional theatre, we hope to bring focus to a critical development in traditional Japanese theatre and open further discussions about this phenomenon. We also wish to widen discussions regarding the parameters delineating what constitutes ‘traditional’ when speaking of Japanese theatre genres.
Observations on the regulatory effectiveness of Article 25 GDPR

Article 25 of GDPR is relatively novel to Europe in terms of being a legal stipulation. This article refers to the well-known data protection principles. Yet, its role should be more than just a reiteration of the sacred chalices provided by Article 5 of GDPR. This applied research paper is meant to discover the role that EU data protection authorities are giving to the concept of data protection by design and by default. The analysis applies machine learning on the forty-nine cases in which this article has been referred to as a reason for a fine. Machine learning is used to find the potential correlation between the severity of infringements and the number of fines within the presence of Article 25. The argument is that while there is not yet a single case in which the monetary fine was issued because of a sole infringement of Article 25, as time progresses, the authorities are developing a more detail-oriented approach in the investigation and fining practices. Hence, the hypothesis is constructed around the statement that data protection by design and by default is for now only a complementary article, where the controller infringed other ones. As we know, "all models are wrong, but some of them are useful." Therefore, the result might be subject to criticism due to the relative data-poor environment in which the model is generated. However, future work is potentially promising with growing case-count that fuels such modelling efforts. This will support researchers to become not only data-rich but also information-smart.