Reviewerly/exHarmony huggingface dataset:
THE MODULAR EXPONENTIATION WITH PRECOMPUTATION OF REDUSED SET OF RESIDUES FOR FIXED-BASE

Context. Modular exponentiation is an important operation in many applications that requires a large number of calculations Fast computations of the modular exponentiation are extremely necessary for efficient computations in theoretical-numerical transforms, for provide high crypto capability of information data and in many other applications. Objective – the runtime analysis of software functions for computation of modular exponentiation of the developed program that uses the precomputation of redused set of residuals for fixed-base. Method. Modular exponentiation is implemented using of the development of the right-to-left binary exponentiation method for a fixed basis with precomputation of redused set of residuals. To efficient compute the modular exponentiation over big numbers, the property of a periodicity for the sequence of residuals of a fixed base with exponents equal to an integer power of two is used. Results. Comparison of the runtimes of five variants of functions for computing the modular exponentiation is performed. In the algorithm with precomputation of redused set of residuals for fixed-base provide faster computation of modular exponentiation for values larger than 1K binary digits compared to the functions of modular exponentiation of the MPIR and Crypto++ libraries. The MPIR library with an integer data type with the number of binary digits from 256 to 2048 bits is used to develop an algorithm for computing the modular exponentiation. Conclusions. In the work has been considered and analysed the developed software implementation of the computation of modular exponentiation on universal computer systems. One of the ways to implement the speedup of computing modular exponentiation is developing algorithms that can use the precomputation of redused set of residuals for fixed-base. The software implementation of modular exponentiation with increasing from 1K the number of binary digit of exponent shows an improvement of computation time with comparison with the functions of modular exponentiation of the MPIR and Crypto++ libraries.
Mutual authentication for cardless ATM withdrawal using location factor

Many banks offer cardless ATMs, using a mobile banking application on the smartphone to overcome the high risk of attack with traditional PIN-based cards. Several pieces of research into cardless ATMs have focused on security strengthening, while others have focused on improving usability. Extra hardware devices may be required to increase both security and usability. This paper presents a location-based mutual authentication scheme. This technique combines both security and usability to achieve usable security without requiring additional hardware at the ATM machine. In addition, this paper analyses and discusses the security and usability issues of the proposed scheme, comparing with other systems using a simulation study. The results show that the proposed system has higher security levels with an equivalent standard of usability.
Gesture Elicitation as a Computational Optimization Problem

Gesture elicitation studies are commonly used for designing novel gesture-based interfaces. There is a rich methodology literature on metrics and analysis methods that helps researchers understand and characterize data arising from such studies. However, deriving concrete gesture vocabularies from this data, which is often the ultimate goal, remains largely based on heuristics and ad hoc methods. In this paper, we treat the problem of deriving a gesture vocabulary from gesture elicitation data as a computational optimization problem. We show how to formalize it as an optimal assignment problem and discuss how to express objective functions and custom design constraints through integer programs. In addition, we introduce a set of tools for assessing the uncertainty of optimization outcomes due to random sampling, and for supporting researchers' decisions on when to stop collecting data from a gesture elicitation study. We evaluate our methods on a large number of simulated studies.
Sentiment Analysis of Beauty Product E-Commerce Using Support Vector Machine Method

Customers who buy goods will provide an assessment in the form of a review. If negative reviews dominate an item, other customers will be reluctant to buy at that store, so customers look for other stores, affecting the store's revenue. Therefore, this study aims to classify e-commerce beauty product reviews using the Support Vector Machine to create a model to categorize beauty product reviews and analyze accuracy. The research phase begins by collecting 50,000 datasets consisting of 35,000 training data and 15,000 test data. After the data is collected, the data labeling stage is carried out, labeled positive and negative. Then the preprocessing step is carried out so that the data is ready to be processed in the feature extraction step. The feature extraction step aims to explore potential information that represents words. Furthermore, the resulting data is evaluated to obtain an accuracy value and determine whether the model made is feasible to use. The results showed that the Support Vector Machine could classify beauty product reviews well with an accuracy of 80.06%.&#x0D;
Multi-view Unsupervised Graph Representation Learning

Both data augmentation and contrastive loss are the key components of contrastive learning. In this paper, we design a new multi-view unsupervised graph representation learning method including adaptive data augmentation and multi-view contrastive learning, to address some issues of contrastive learning ignoring the information from feature space. Specifically, the adaptive data augmentation first builds a feature graph from the feature space, and then designs a deep graph learning model on the original representation and the topology graph to update the feature graph and the new representation. As a result, the adaptive data augmentation outputs multi-view information, which is fed into two GCNs to generate multi-view embedding features. Two kinds of contrastive losses are further designed on multi-view embedding features to explore the complementary information among the topology and feature graphs. Additionally, adaptive data augmentation and contrastive learning are embedded in a unified framework to form an end-to-end model. Experimental results verify the effectiveness of our proposed method, compared to state-of-the-art methods.
A Comparative Analysis of Healthcare Monitoring Systems Using WSN

Wearable sensors and forms of social media are very helpful in providing a new way to collect medical data for effective healthcare monitoring. Technological solutions have benefited people's daily lives practically in every industry. Sensing devices that detect and gather signals from the sources such as human, environment, and machines, design the response accordingly. Innovative sensor techniques are used in a variety of applications in healthcare, production, lifestyle, fitness, and everyday life. These sensors can be used as wrist watch, implanted under the body or skin, and can be attached with the treads of clothes. Wearable sensors, which have transmission, computing, storage, and sensing capabilities, are known as a wireless body sensor network (WBSN). The Internet of Things (IoT) is an emerging information technology that enables performance enhancement and improved solutions in the health sector. This review gives a brief outline of the various types of sensors that are used for healthcare monitoring, WBSN components, and its applications.
Multi-Granularity Feature Fusion for Person Re-Identification

Due to the limited ability of feature expression learned by one branch, designing a Multi-granularity network for feature extraction has become one of the important directions in the field of person re-identification. This paper designs a Multi-granularity feature fusion network (MFN) to enhance person feature extraction. The network is composed of global branches and local branches, and the former use the convolution pyramid to extract multiple scales features, through the channel attention module (Split Attention, SA) fusion of global branches and local branches, so that the global branch to obtain a strong ability to express persons features; the local branches is a feature map extracted from the backbone network based on the idea of Part-based convolutional baseline. Split the feature map horizontally into 4 branches, and the ID loss is calculated separately for each local branch. The classification loss in the total loss is consist of the ID loss of the 4 local branches. The two interact in parallel to improve the recognition ability. The experimental results on the Market1501 and DukeMTMC-ReID datasets, Rank-1/mAP reached 94.9%/86.4% and 87.5%/76.8% respectively.
Augmented Reality Smart Glasses in Focus: A User Group Report

Augmented Reality Smart Glasses (ARSG) are a recent development in consumer-level personal computing technology. Research on ARSGs has largely focused on new forms of etiquette for these personal computing devices, but little else has been examined due in part to consumer availability. The most well-known example of ARSGs is Google Glass, which are no longer available for consumer purchase due to privacy concerns. Google has more recently transitioned to industry-focused applications with the Glass Enterprise Edition [1]. Recent consumer-facing iterations on the technology include Snapchat Spectacles and Ray-Ban Stories, which reignite some of the anxieties surrounding wearable cameras. Focals by North, the ARSG product studied in this project, do not have the capacity to record video or audio, thus mitigating the risk of privacy breaches. This study examines how users of Focals employ the device, successfully or not, to facilitate daily activities such as scheduling, communication, wayfinding, and how non-users perceive the interactions of Focals users. Participants wrote blog responses and participated in a focus group on their daily experiences with the glasses; they also speculated on potential uses and features of future iterations relating to accessibility and entertainment purposes. Focals by North, a relatively low-cost ARSG, aims to make this tech mass market to “seamlessly [blend] technology into our world” [2]. However, this study found participants preferred choice when receiving notifications, and greatly questioned the need for notifications to appear in their field of vision. We anticipate that these results will inform frameworks for assessing consumer facing ARSG products in future work.
Comparative Study of Multimedia Question Answering System Models

Question Answering Systems is a mechanized way to deal with finding right solution to the inquiries posed by human in their own language. The enormous measure of data returned by web crawlers like Google, Yahoo, and so on, clients are over-burden to track down the right data from the web search tools. This QA approach takes care of these issues. Rather than returning a rundown of reports and connections from the momentum web crawler, the QA framework gives data from a bunch of replied, alongside reasonable media information. Question answering procedures plans to exploit top to bottom etymological and media content investigation just as area information to return accurate responses to regular language questions. QA framework has rundown of boundary like: question size, m-dimension, technique count, answer cardinality, and answer range. This paper investigates the advancement of mixed media question replying research, their plan boundary, and subtleties its future perspectives. Multiple Line Questions and Video Question Answering methods have been discussed at length and their performance measured on QA framework parameters.
Hyperparameter Optimization of Long-Short Term Memory using Symbiotic Organism Search for Stock Prediction

Producing the best possible predictive result from long-short term memory (LSTM) requires hyperparameters to be tuned by a data scientist or researcher. A metaheuristic algorithm was used to optimize hyperparameter tuning and reduce the computational complexity to improve the manual process. Symbiotic organism search (SOS), which was introduced in 2014, is an algorithm that simulates the symbiotic interactions that organisms use to survive in an ecosystem. SOS offers an advantage over other metaheuristic algorithms in that it has fewer parameters, allowing it to avoid parameter determination errors and produce suboptimal solutions. SOS was used to optimize hyperparameter tuning in LSTM for stock prediction. The stock prices were time-series data, and LSTM has proven to be a popular method for time-series forecasting. This research employed the Indonesia composite index dataset and assessed it using root mean square error (RMSE) as a key indicator and the fitness function for the metaheuristic approach. Genetic algorithm (GA) and particle swarm optimization (PSO) were used as benchmarking algorithms in this research. The hybrid SOS-LSTM model outperformed GA-LSTM and PSO-LSTM with an RMSE of 78.799, compared to the GA-LSTM model with an RMSE of 142.663 and the PSO-LSTM model with an RMSE of 529.170.
Spell Checker for the Indonesian Language: Extensive Review

Abstract— Typographical errors are common in written languages, including Indonesian. It will, however, lead to a misunderstanding of the meaning of the words. Nevertheless, an Indonesian spell checker is still uncommon. Furthermore, no extensive literature review of spell checkers for the Indonesian language has been conducted. This study aimed to present extensive literature on spelling correction in the Indonesian language. The methods used were discovering any literature related to the study topic for the period 2017-2022, applying some keywords, and enforcing inclusion and exclusion criteria. According to the findings of this study, in the previous five years, research on spell checkers has increased, and many researchers from various provinces in Indonesia have used different methods or algorithms to evaluate word errors. Keywords— Indonesian language, Methods, spell checker, extensive literature review, typographical error
Gold Price Prediction System

Abstract: This paper aims to promote the use of the LSTM, random forest Regression &amp; Linear Regression algorithm to predict stock prices of India and to compare their accuracy. These are machine learning algorithms used for historical real-time gold prices. The Historical Gold data we made with reference of www.goldpriceindia.com. The full source code of the project was written via Python. It is thought that the LSTM model are more compatible than the Linear Regression and Random Forest model for the Gold price Prediction forecasting model. Gold is a valuable metal and has been historically owned and traded as an asset /commodity. The price of Gold is often a derivative of the investor’s sentiment and perception of other asset classes (real estate, equities, commodities, futures and cash equivalents) as Gold has very little fundamentals of its own. Our project is aimed at studying the relationship between gold price, selected economies and various market variables to try and accurately predict the future price of gold using Machine Learning algorithms. Keywords: Machine Learning LSTM, Random Forest, Linear Regression, Gold , Prediction, Time Series, Historical Data, Python , TensorFlow.
Real-Time Credit Card Fraud Detection Using Machine Learning

Credit card fraud occurs often and results in significant financial losses [1]. The number of online transactions has increased dramatically, and online shopping has become increasingly popular. Credit card transactions account for a significant portion of these transactions. As a result, banks and financial institutions provide services. credit card fraud detection software has a lot of utility. demand. Fraudulent transactions can take many forms. and can be classified into several types. The subject of this paper is four main fraud occasions in real-world transactions. Each a series of machine learning models are used to combat fraud. An evaluation is used to choose the optimal method. This assessment gives you a step-by-step approach to picking the right company. With regard to the type of frauds and the appropriate method with a suitable performance, we demonstrate the evaluation. Real-time credit card fraud detection is another important aspect of our project. As a result, we Consider the implementation of predictive analytics to determine if a machine learning model and an API module are appropriate. Is a specific transaction authentic or fraudulent? We also evaluate an innovative technique for dealing with the skewed data distribution. The information we used in our research came from a variety of sources according to an open source and community- maintained website. Keywords: Credit Card, Fraud Detection Software, Open Source, Realtime credit card fraud, Skewed Data Distribution
A Right Transfer Access Control Model of Internet of Things Based on Smart Contract

Sensor nodes play a crucial role in the promotion of development of Internet of Things (IoT). Through this transaction, RO defines access control policies in script form based on ABAC's access control model to grant access right. The identity of all users in the model is identified by address. This paper builds a more flexible right transfer access control model by means of combining the Attribute-Based Access Control model (ABAC) and blockchain technology. Owing to the characteristics of ABAC’s attributes and right association, the massive problems of some sensor nodes can be solved. At the same time, for the sake of addressing the dynamic problems such as node access and right transfer, right transfer contract (TS) and access control contract (CS) are employed on the chain to ensure efficient and safe transmission of rights. To solve on-chain storage problems and ensure transparency of the operation, the idea of Rollup in Ethereum expansion is used to upload the final state of protocol policy and right exchange to the chain. Any user can know the policy and current right transfer status at any time. Finally, comparative and security analysis show that the model presented here can solve IoT devices’ massive and dynamic problems more effectively and it is more secure than the traditional models.
Learning Correlation for Online Multiple Object Tracking

Existing multiple object tracking methods usually strengthen data association by discriminative identity embeddings. However, many works treat object detection and association as two individual tasks, thus gaining limited benefits. In this paper, we follow the joint detection and tracking paradigm to learn correlation for online multiple object tracking. The proposed method, named LCTrack, links the two tasks by an attention mechanism. Specifically, for robust feature representations, we introduce an identity-aware attention module to extract reliable identity embeddings and model their correlation between two consecutive frames. Furthermore, for effective correlation learning, we design a target-aware loss to train the identity embedding extraction, which is well compatible with the detection task. Therefore, LCTrack can boost the position prediction and data association by the enhanced feature representation. Experimental results on the MOTChallenge benchmarks demonstrate the effectiveness and favorable performance of the proposed LCTrack in comparison with state-of-the-art methods.
Library and Information Science as the Factor of Social Transformations

The Review on the Monograph «Scientific Researches in Libraries:Topics, Organization, Presentation of Results»
Real-time passenger social distance monitoring with video analytics using deep learning in railway station

&lt;span lang="EN-US"&gt;Recently, at the end of December, the world faced a severe problem which is a pandemic that is caused by coronavirus disease. It also must be considered by the railway station's authorities that it must have the capability of reducing the covid transmission risk in the pandemic condition. Like a railway station, public transport plays a vital role in managing the COVID-19 spread because it is a center of public mass transportation that can be associated with the acquisition of infectious diseases. This paper implements social distance monitoring with a YOLOv4 object detection model for crowd monitoring using standard CCTV cameras to track visitors using the DeepSORT algorithm. This paper used CCTV surveillance with the actual implementation in Bandung Railway Station with the accuracy at 96.5 % result on people tracking with tested in real-time processing by using minicomputer Intel(R) Xeon(R) CPU E3-1231 v3 3.40GHz RAM 6 GB around at 18 FPS.&lt;/span&gt;
An Efficient Compression Method of Underwater Acoustic Sensor Signals for Underwater Surveillance

In this paper, we propose a new compression method using underwater acoustic sensor signals for underwater surveillance. Generally, sonar applications that are used for surveillance or ocean monitoring are composed of many underwater acoustic sensors to detect significant sources of sound. It is necessary to apply compression methods to the acquired sensor signals due to data processing and storage resource limitations. In addition, depending on the purposes of the operation and the characteristics of the operating environment, it may also be necessary to apply compression methods of low complexity. Accordingly, in this research, a low-complexity and nearly lossless compression method for underwater acoustic sensor signals is proposed. In the design of the proposed method, we adopt the concepts of quadrature mirror filter (QMF)-based sub-band splitting and linear predictive coding, and we attempt to analyze an entropy coding technique suitable for underwater sensor signals. The experiments show that the proposed method achieves better performance in terms of compression ratio and processing time than popular or standardized lossless compression techniques. It is also shown that the compression ratio of the proposed method is almost the same as that of SHORTEN with a 10-bit maximum mode, and both methods achieve a similar peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index on average.
The Case for a Kidney Emoji

Kidney disease affects approximately 10% of the population globally, and as of 2019 kidney diseases are the 10th most common cause of mortality worldwide, with 1.3 million deaths annually.1,2 Acute kidney injury and kidney failure affect populations with lower socioeconomic status at increased rates.3 These populations are already at risk for limited health care literacy.4 The COVID-19 pandemic has exacerbated risks for vulnerable patients with kidney disease and amplified the need for clear public health messaging.
Research Hotspot and Evolution Trend of REITs Since the New Century—from the Perspective of Bibliometrics

To understand the research status of REITs since the new century, this paper uses VOSviewer and CiteSpace software to conduct quantitative research and visual analysis on 515 relevant documents in the WOS core database from 2000 to 2022. It has found that: 1) the core author group of the research topic has been formed, the cluster structure of the cooperation network among the core authors is obvious, and the distribution of the cooperation groups is relatively clear; 2) Core journals and related journals in the research field have already been formed; 3) The research on this topic has close international cooperation. Generally speaking, as the country that first gave birth to REITs, the United States has the highest scientific productivity in this field; 4) The research presents an overview of this field through the co-occurrence analysis and evolution analysis of keywords. Combined with the analysis of mutant words, it can be considered that this research field has formed a relatively stable research trend. Through the combination of econometric analysis and visualization, the research grasps the development context of this field and provides a comprehensive and clear research framework and structure for REITs research topics.