Reviewerly/exHarmony huggingface dataset:
Bitcoin daily close price prediction using optimized grid search method

Abstract Cryptocurrencies are digital assets that can be stored and transferred electronically. Bitcoin (BTC) is one of the most popular cryptocurrencies that has attracted many attentions. The BTC price is considered as a high volatility time series with non-stationary and non-linear behavior. Therefore, the BTC price forecasting is a new, challenging, and open problem. In this research, we aim the predicting price using machine learning and statistical techniques. We deploy several robust approaches such as the Box-Jenkins, Autoregression (AR), Moving Average (MA), ARIMA, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and Grid Search algorithms to predict BTC price. To evaluate the performance of the proposed model, Forecast Error (FE), Mean Forecast Error (MFE), Mean Absolute Error (MAE), Mean Squared Error (MSE), as well as Root Mean Squared Error (RMSE), are considered in our study.
Pengembangan Media Pembelajaran Video Menggunakan Sparkol Videoscribe Pada Materi Statistika Kelas IV SD

The purpose of this research is to develop learning media in the form of videos using the sparkol videoscribe application which is appropriate to help the mathematics learning process in statistics material. This research method uses development research with the ADDIE development model (Analysis, Design, Development, Implementation, Evaluation) but the researchers only use three stages, namely Analysis, Design, and Development. The instruments used in this study were interview guidelines and validation sheets consisting of material expert validation sheets, media expert validation and linguist validation. Data analysis using descriptive statistics in the form of calculating the average score. Based on the results of the study, an assessment of the videoscribe media from the material expert assessment obtained a valid category with a percentage of 91.87%. The media expert's assessment got a valid category with a percentage of 93.35%. The assessment of linguists is in the valid category with a percentage of 93.74%. From these results, the developed videoscribe media is in the valid category so that the videoscribe learning media can be used in the classroom.
A Blockchain-based Database System for Decentralized Information Management

Blockchain has attracted wide attention in both industry and academia due to its decentralized and anti-tamper characteristics. To query the blockchain data, all the transactions in the blockchain must be iterated one by one, which is quite inefficient. Existing solutions improve the query efficiency by exporting transactions to external databases, and yet they incur the incompleteness and incorrectness of query results. To avoid above weaknesses, we propose an efficient query method based on the internal blockchain database and apply it to the construction engineering management to solve the problems of information management. In our design, we construct a dual-index based on the B+ tree and the key-value pair through smart contracts, and it supports multiple query operations, such as range query and file-type query. We implement the designed method by simulating the blockchain testbed and the experimental results demonstrate the efficiency of our design.
Consistent Discrepancy Learning for Intra-Camera Supervised Person Re-Identification

Since annotating pedestrians across different views is extremely costly, intra-camera supervised person re-identification (ReID) aims to learn a ReID model from the intra-view labeled data. Under this setting, the most challenge lies in learning a view-invariant feature embedding in the absence of the cross-view annotations. Previous works focus on assigning a pseudo identity label for each image based on the feature similarity and learn view-invariant features by classification loss. However, because of the cross-view variations in lighting, background, etc., the pseudo labels are often noisy, and therefore not reliable for classification. In this paper, we explore learning a consistent discrepancy for pairwise images. Our main idea is that the discrepancy between pedestrian images should be consistent across different views regardless of view change so that it mainly depicts the identity difference. Due to the lack of cross-view annotations, we project images into different views and obtain likelihood prototypes for cross-view learning. These likelihood prototypes are used to measure the discrepancies between pairwise images under different views. And then, we propose an intra-view discrepancy preservation module to enforce the discrepancy to be view-consistent so as to encourage the model to distinguish the images based on the identities regardless of view change. Extensive experiments on multiple datasets show that our method outperforms existing related methods by clear margins and our method is comparable to supervised counterparts. Code will be made publicly available.
A Bounded Measure for Estimating the Benefit of Visualization (Part I): Theoretical Discourse and Conceptual Evaluation

Information theory can be used to analyze the cost-benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to estimate nor intuitive to interpret. In this work, we propose to revise the existing cost-benefit measure by replacing the unbounded term with a bounded one. We examine a number of bounded measures that include the Jenson-Shannon divergence, its square root, and a new divergence measure formulated as part of this work. We describe the rationale for proposing a new divergence measure. In the first part of this paper, we focus on the conceptual analysis of the mathematical properties of these candidate measures. We use visualization to support the multi-criteria comparison, narrowing the search down to several options with better mathematical properties. The theoretical discourse and conceptual evaluation in this part provides the basis for further data-driven evaluation based on synthetic and experimental case studies that are reported in the second part of this paper.
Machine Learning Language Models: Achilles Heel for Social Media Platforms and a Possible Solution

Any uptick in new misinformation that casts doubt on COVID-19 mitigation strategies, such as vaccine boosters and masks, could reverse society's recovery from the pandemic both nationally and globally.This study demonstrates how machine learning language models can automatically generate new COVID-19 and vaccine misinformation that appears fresh and realistic (i.e.human-generated) even to subject matter experts.The study uses the latest version of the GPT model that is public and freely available, GPT-2, and inputs publicly available text collected from social media communities that are known for their high levels of health misinformation.The same team of subject matter experts that classified the original social media data used as input, are then asked to categorize the GPT-2 output without knowing about its automated origin.None of them successfully identified all the synthetic text strings as being a product of the machine model.This presents a clear warning for social media platforms: an unlimited volume of fresh and seemingly human-produced misinformation can be created perpetually on social media using current, off-the-shelf machine learning algorithms that run continually.We then offer a solution: a statistical approach that detects differences in the dynamics of this output as compared to typical human behavior.
Efficient pattern matching in elastic-degenerate strings

Motivated by applications in bioinformatics and image searching, in what follows, we study the classic pattern matching problem in the context of elastic-degenerate strings: the generalised notion of gapped strings. An elastic-degenerate string can be seen as an ordered collection of k strings interleaved by k−1 elastic-degenerate symbols, where each such elastic-degenerate symbol corresponds to a set of two or more variable-length strings. We present efficient algorithms for two variants of the pattern matching problem on elastic-degenerate strings: first, for a solid pattern and an elastic-degenerate text; second, for an elastic-degenerate pattern and a solid text. A proof-of-concept implementation of the former is provided.
Graph convolutional networks

Deep learning approaches have been very successful in many machine learning tasks including compute vision, natural language processing, audio processing, and speech recognition. However, deep neural networks typically work with grid-structured data represented in the Euclidean space and despite their recent successes, they poorly generalize to applications where the data is represented in non-Euclidean space. Recently, due to the increasing amount of graph structured data produced in different areas such as social networks, stock markets, and knowledge bases, there is an increasing need to develop learning methods capable of capturing the relational structures in graph data. Recently, graph neural networks have attracted great research attention as they have demonstrated the ability to provide high performance in many of the learning tasks with non-Euclidean data structures. In this chapter, we provide a detailed overview of graph convolutional networks, which extend the convolution operation to graph structured data. We group the existing graph convolutional networks in four different categories, and we provide a discussion on their efficiency and scalability in large graph structures. The application of these networks in different learning tasks is also discussed in this chapter, along with a summary of the existing benchmark data sets and open-source libraries.
Text-rating review discrepancy (TRRD): an integrative review and implications for research

Abstract The large number of online product and service review websites has created a substantial information resource for both individuals and businesses. Researching the abundance of text reviews can be a daunting task for both customers and business owners; however, rating scores are a concise form of evaluation. Traditionally, it is assumed that user sentiments, which are expressed in the text reviews, should correlate highly with their score ratings. To better understand this relationship, this study aims to determine whether text reviews are always consistent with the combined numeric evaluations. This paper reviews the relevant literature and discusses the methodologies used to analyse reviews, with suggestions of possible future research directions. From surveying the literature, it is concluded that the quality of the rating scores used for sentiment analysis models is questionable as it might not reflect the sentiment of the associated reviews texts. Therefore, it is suggested considering both types of sources, reviews’ texts and scores in developing Online Consumer Reviews (OCRs) solution models. In addition, quantifying the relationship degree between the text reviews and the scores might be used as an instrument to understand the quality of rating scores, hence its usefulness as labels for building OCRs solution models.
Analysis and Development of Education and Training Management Information Systems at the Jambi Province Human Resources Development Agency

The Jambi Province Human Resources Development Agency has created a training information system in an effort to manage human resource management in regencies, cities and throughout the province of Jambi. However, along with the implementation of the use of this training system, several problems were still found in the use of the system, both from the apparatus in the BPSDM agency and the apparatus as training participants. There are some obstacles to the implementation of the system, such as the lack of consistency of the organizing committee in using the education information system on the grounds that it is still difficult to understand the interface pattern in the application system, so the committee is reluctant to input data and use the application system as a registration facility for training participants. So in the implementation of activities, there are often errors in data and information of training participants which result in errors in the training implementation report. To solve this problem, the researchers conducted an analysis and development of the education management information system at the Jambi Province Human Resources Development Agency with the aim of making users it easier forto easily understand the use of these web-based applications.
LEVEL OF USE OF BLOCKCHAIN TECHNOLOGY BY UKRAINIAN ENTERPRISES IN THE FIELD OF ACCOUNTING AND AUDITING

The use of various electronic gadgets and Internet communication has accelerated the development of the company in various areas of its activities, including in terms of accounting. The article presents data on the state and expected results of blockchain development in the world and in Ukraine, illustrates that the introduction of blockchain technology is currently taking place in various fields, one of the most promising of which is accounting and auditing. Transfer of all possible registers, investment projects to blockchain, connection of electronic services to block technology – one of the key tasks set by Ukraine at the opening of the fifth conference "Blockchain &amp; Bitcoin Conference Kyiv", which discussed the introduction of distributed register technology in government processes and business, as well as the creation of a regulatory framework for the cryptocurrency industry and Initial coin offering (ICO). The purpose of the article is to investigate the level of use of blockchain technology by Ukrainian companies today in the field of accounting and auditing. The advantage of using blockchain technology is that it allows a company to record both sides of a transaction simultaneously in a shared book in real time, rather than simply storing verified records of financial transactions in separate privately created databases or ledgers. Among the most promising areas of implementation of the blockchain in Ukraine and the world, practitioners and experts called the accounting of receivables and payables and its repayment, accrual and payment of taxes, operational accounting and reporting, as well as work with documents and their storage. In the article, it is substantiated that the blockchain as an environment for economic processes and operations will lead to changes in the methodological tools of accounting and virtualization of accounting processes.
Screening and Ranking Resumes using Stacked Model

Talent acquisition is essential for all companies irrespective of the size of their business. As it is next to impossible to look through numerous resumes manually, we have created an automated resume screening application. This system makes use of Machine Learning algorithms such as KNN, Linear SVC, and XGBoost. A two-level stacked model containing all these algorithms is constructed which helps in predicting specific job profiles from a text description accurately. This framework can be valuable for organizations to waitlist competitors and furthermore for the applicants who can check if their resume is very much shaped for the system to recognize right work profiles from it. A ranking system is also implemented, for the companies, featuring the most relevant profiles on the top.
DAAMSIM: A simulation framework for establishing detect and avoid SYSTEM requirements

Performance requirements for detect, alert, and avoid (DAA) systems for remotely piloted aircraft systems (RPAS) are under development by many regulatory agencies and standards bodies. A DAA system can be decomposed into three functions, “detect” — situational awareness; “alert” — determination of traffic that may be in conflict, evaluation of the de-conflicting flight path, and informing the pilot-in-command; and “avoid” — avoidance maneuver execution, and determination of “clear of conflict”. The “Detect” function of a DAA system depends on the sensor, target, and environment characteristics (e.g., signal-to-noise ratio of the target vs. background). The “alert” function depends on conflict prediction algorithms and human factors requirements. The “avoid” function depends on the RPAS maneuvering performance, airspace “rules”, and the size of the protection volume. The aforementioned factors impact the time required to calculate, and execute, an avoidance maneuver that will guarantee a prescribed miss distance, and dominate the “detect” requirements of a sensor. This paper describes DAAMSim: a publicly available modeling and simulation framework, developed by the National Research Council of Canada, to support the determination of DAA system requirements, and evaluation of DAA system performance. The framework described herein incorporates the functional components including various sensor, tracker, and avoid models; data replay; visualization tools; and offline metrics. Further, this paper presents sample results of the framework’s ability to determine DAA system requirements for various degrees of RPAS and intruder performance, and concludes with a description of future work activities.
An efficient binary harris hawks optimization based on quantum SVM for cancer classification tasks

Cancer classification based on gene expression increases early diagnosis and recovery, but high-dimensional genes with a small number of samples are a major challenge. This work introduces a new hybrid quantum kernel support vector machine (QKSVM) combined with a Binary Harris hawk optimization (BHHO) based gene selection for cancer classification on a quantum simulator. This study aims to improve the microarray cancer prediction performance with the quantum kernel estimation based on the informative genes by BHHO. The feature selection is a critical step in large-dimensional features, and BHHO is used to select important features. The BHHO mimics the behavior of the cooperative action of Harris hawks in nature. The principal component analysis (PCA) is applied to reduce the selected genes to match the qubit numbers. After which, the quantum computer is used to estimate the kernel with the training data of the reduced genes and generate the quantum kernel matrix. Moreover, the classical computer is used to draw the support vectors based on the quantum kernel matrix. Also, the prediction stage is performed with the classical device. Finally, the proposed approach is applied to colon and breast microarray datasets and evaluated with all genes and the selected genes by BHHO. The proposed approach is found to enhance the overall performance with two datasets. Also, the proposed approach is evaluated with different quantum feature maps (kernels) and classical kernel (RBF).
HEVC Video Encryption With High Capacity Message Embedding by Altering Picture Reference Indices and Motion Vectors

A high capacity message embedding in encrypted HEVC video is proposed in this paper. The challenges addressed in this paper include keeping the encrypted video compliant with standardized decoders, correctly decrypting the video and finally, correctly extracting the message bits. The message embedding is achieved by altering the values of reference picture indices and motion vectors which results in scrambled video. Sixteen picture references are used in this work and therefore, combined with alteration of motion vectors, a maximum of six message bits can be embedded per coding unit. Motion vectors are altered by swapping their <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> components and/or changing their signs. This is achieved with full compliance with the HEVC video syntax. To extract message bits, an authorized decoder builds a classification model per video sequence and uses it for predicting the true values of the reference indices and motion vectors. As such, message bits are extracted and the video is correctly reconstructed to its unscrambled state. Coding units that result in misclassification are identified at the encoder and excluded from message embedding. This results in slightly lower embedding rates but ensures accurate video reconstruction. Using nine video sequences of various resolutions that are compressed using four different quantization parameters, the experimental results revealed that the true average message embedding rate is 2.7 bits per coding unit or 173 kbit/s. This is achieved with accurate video reconstruction at the expense of increasing the bitrate of the encoder by 3%. Comparison with existing work shows that the proposed solution is superior in terms of embedding capacity whilst reducing the excessive bitrate of the encoder.
Common Vulnerability Scoring System for SDN Environment

The identification of component vulnerability is an important criterion for securing the networks and it is an open issue for Software Defined Network (SDN). The First.org provides an open tool namely Common Vulnerability Scoring system for identification of component vulnerability, but there is no distinctive correspondence between CVSS and SDN. With this paper, we try to identify such correspondence so that it will help network researchers to determine the network vulnerability and improve the SDN security soon. This paper also includes a comparison between the last three CVSS versions. For this comparison, one can understand what are the new features CVSS incorporating for identifying new vulnerabilities in the emerging environment.
User-Guided Deep Human Image Matting Using Arbitrary Trimaps

Image matting is widely studied for accurate foreground extraction. Most algorithms, including deep-learning based solutions, require a carefully edited trimap. Recent works attempt to combine the segmentation stage and matting stage in one CNN model, but errors occurring at the segmentation stage lead to unsatisfactory matte. We propose a user-guided approach for practical human matting. More precisely, we provide a good automatic initial matting and a natural way of interaction that reduces the workload of drawing trimaps and allows users to guide the matting in ambiguous situation. We also combine the segmentation and matting stage in an end-to-end CNN architecture and introduce a residual-learning module to support convenient stroke-based interaction. The proposed model learns to propagate the input trimap and modify the deep image features, which can efficiently correct the segmentation errors. Our model supports arbitrary forms of trimaps from carefully edited to totally unknown maps. Our model also allows users to choose from different foreground estimations according to their preference. We collected a large human matting dataset consisting of 12K real-world human images with complex background and human-object relations. The proposed model is trained on the new dataset with a novel trimap generation strategy that enables the model to tackle different test situations and highly improves the interaction efficiency. Our method outperforms other state-of-the-art automatic methods and achieve competitive accuracy when high-quality trimaps are provided. Experiments indicate that our interactive matting strategy is superior to separately estimating the trimap and alpha matte using two models.