Reviewerly/exHarmony huggingface dataset:
Intelligent Attendance System With Attention Analysis

Uniqueness of an individual is by his face. A face can be analyzed using its relative position, size and/or shape of eyes, nose cheekbones and jaws. As the human face is having a high degree of variability in its appearance through various angles, this makes face detection a difficult problem in the field of computer vision and image processing. In face recognition system precision and recognition speed complexity plays a vital role. This paper aims to provide an analysis of the various face detection and recognition methods. Also to provide an outline of a complete image-based facial recognition attendance maintenance system with the advantages of better accuracy, speed and response rate. The analysis provided is constructed by performing various tests on a large database of student images in terms of poses, orientation and emotions.
Extended Classification of Signature-only Signature Models

In this paper, we extend the existing classification of signature models by Cao. To do so, we present a new signature classification framework and migrate the original classification to build an easily extendable faceted signature classification. We propose 20 new properties, 7 property families, and 1 signature classification type. With our classification, theoretically, up to 11 541 420 signature classes can be built, which should cover almost all existing signature schemes.
Vertical evacuation planning to reduce a risk of a tsunami disaster in teluk segara district, bengkulu city

Abstract Bengkulu Province is classified as a province that is very prone to earthquakes and tsunamis. In order to minimize the impact of the tsunami hazard, preparedness to mitigate the tsunami hazard is needed. One of the ways is by providing information about the optimal evacuation route, assembly points and evacuation shelter buildings (ESB) and ESB capacity. The purpose of this study is to obtain an optimal evacuation route model to minimize the impact of the tsunami hazard in Teluk Segara District, Bengkulu City. The determination of the minimum evacuation route using the Floyd Warshall algorithm based on the minimum time of evacuation. In determining the minimum evacuation time, several factors are considered i.e. road width, road density, road capacity, walking speed of evacuees and group walking speed. This research produces the horizontal and vertical tsunami evacuation routes equipped with ESB capacities.
Research on the Application of Blockchain Technology in the Construction of Power Internet of Things

In order to implement the work plan of “research on new energy business model based on blockchain,” State Grid Jibei Electric Power Company Limited Existing blockchain research and application results, innovative application scenarios, and exploration of business operation models are studied. This paper first briefly describes the implementation background, basic concepts, and core technologies of blockchain technology, and then analyzes Blockchain and the Internet of Things of electricity. The strategic significance of integration and development, and finally, the application of blockchain technology to the construction of power IoT, detailing the blockchain technology in the power IoT. Application analysis in multiple business scenarios of networking.
Learning Log-Determinant Divergences for Positive Definite Matrices

Representations in the form of Symmetric Positive Definite (SPD) matrices have been popularized in a variety of visual learning applications due to their demonstrated ability to capture rich second-order statistics of visual data. There exist several similarity measures for comparing SPD matrices with documented benefits. However, selecting an appropriate measure for a given problem remains a challenge and in most cases, is the result of a trial-and-error process. In this paper, we propose to learn similarity measures in a data-driven manner. To this end, we capitalize on the αβ-log-det divergence, which is a meta-divergence parametrized by scalars α and β, subsuming a wide family of popular information divergences on SPD matrices for distinct and discrete values of these parameters. Our key idea is to cast these parameters in a continuum and learn them from data. We systematically extend this idea to learn vector-valued parameters, thereby increasing the expressiveness of the underlying non-linear measure. We conjoin the divergence learning problem with several standard tasks in machine learning, including supervised discriminative dictionary learning and unsupervised SPD matrix clustering. We present Riemannian gradient descent schemes for optimizing our formulations efficiently, and show the usefulness of our method on eight standard computer vision tasks.
Measuring Semantic Similarity of Bengali Texts with Parts-of-Speech Tags and Word-Level Semantics

The semantic textual similarity is essential for many applications related to natural language processing. But measuring the semantic similarity is not an easy task. Because there are different types of sentences and the diversities of sentences' structure make assessing the semantic similarity a formidable task. When two texts are lexicographically dissimilar but semantically similar, the traditional lexical matching cannot return the actual degree of semantic similarity. Besides these, the lack of well-recognized language processing resources for Bengali texts makes the semantic textual similarity calculation a difficult task. In this paper, we tried to measure the semantic similarity of Bengali texts using word-level similarity and parts-of-speech tags. To assess the semantic similarity, we exploit the Bengali parts-of-speech tagger and pre-trained word-embedding model. Then, the maximum word-to-word similarity of the words is employed if the words belong to identical parts-of-speech tag. We also introduced a grammatical role level similarity in our proposed method to measure sentences' similarity. To validate the performance of our method, we conducted experiments on a publicly available benchmark Bengali dataset. The results of the experiments demonstrated that our proposed method is effective to measure the degree of similarity of Bengali texts and achieved state-of-the-art performance.
A Dual-Watermarking Scheme Robust to Affine Transformation

Abstract This paper proposes a robust watermarking algorithm based on dual watermarking and robust affine transformation. First, the color image is divided into three channels, feature point extraction, watermark embedding, and location watermark extraction will be performed respectively. Feature points are used for image reconstruction when extracting watermarks. The watermark embedding needs to select a square area not at the edge of the image as the watermark embedding position, and then embed the watermark in the frequency domain of the corresponding area. According to the watermark embedding position, extract the zero watermark in another channel as the location watermark. Because the zero watermark does not modify the original image information, it has good robustness and can assist the attacked image to locate the watermark embedding position.
Standalone Mobile Device for Pattern Recognition

This article is a proof that it is now possible to create modern technological solutions to human problems using open-source software. As a function for testing, this work implements a simple face recognition function, namely, it uses the Haar algorithm for which the testing was carried out. During the work of the system, the problem of the recognition method is investigated.
Voice Password-Based Secured Communication Using RSA and ElGamal Algorithm

Secured voice authentication-based communication is the main aim of this study. Here, eight speech keywords have been recorded and stored in computer memory. One speaker recognition model was used for voice password authentication. Then, the speech keywords were encrypted using private key. The message was encrypted using RSA or ElGamal algorithm. The message was modulated using FSK digital modulation technique and sent through the communication channel. The speech samples were demodulated and decrypted at the receiver. The received speech samples matched with the original transmitted voice samples. The equality ratio for this study is 0.6 and above. In this study, secured authentication technique has been adopted. After voice authentication, secured communication has been done successfully.
A New Lateral Cephalogram Image Stitching Technique Using Gaussian Mixture Model and Normalized Cross-Correlation

In dental X-ray lateral cephalogram imaging, linear scanning with a small plate detector is used to obtain sequence images in order to control the radiation dose of the imaging, which requires the image stitching algorithm to stitch it into a complete image. Therefore, this paper proposes a new algorithm based on Gaussian mixture model and normalized cross-correlation to solve the continuous stitching problem of small size X-ray sequence images. The gaussian mixture model is firstly used to segment the high-brightness background area according to the gray information distribution of X-ray image. Then, the target region is used to register the adjacent images, and the genetic algorithm is selected as the optimization algorithm to save the registration time. After the registration parameters between adjacent images are obtained, image fusion is used to make the overlapping area more natural. Finally, after image fusion, the feedback module of gap detection is added to ensure that the registration parameters of each part reach the optimal level and avoid the impact of registration errors. The experimental results show that the algorithm has no stitching errors and gaps, and can effectively solve the stitching problem of small-size X-ray sequence images, it can provide clinical basis for orthodontics treatment.
Zero sums in restricted sequences

Suppose A⊂Zn∖{0}. A sequence x=(x1,…,xm) of elements of Zn is called an A-weighted Davenport Z-sequence if there exists a≔(a1,…,am)∈(A∪{0})m∖0m such that ∑iaixi=0, where 0m=(0,…,0)∈Znm. Similarly, the sequence x is called an A-weighted Erdős Z-sequence if there exists a=(a1,…,am)∈(A∪{0})m with |Supp(a)|=n, such that ∑iaixi=0, where Supp(a)≔{i:ai≠0}. A Zn-sequence x is called k-restricted if no element of Zn appears more than k times in x. In this paper, we study the problem of determining the least value of m for which a k-restricted Zn-sequence of length m is an A-weighted Davenport Z-sequence (resp. an A-weighted Erdős Z-sequence). We also consider the same problem for random Zn-sequences and some very natural choices of the set A.
An Exploration of One-Shot Learning Based on Cognitive Psychology

The Gestalt cognition challenges interpretability for deep neural networks and still be underestimated. One-shot or transfer learning may be used to investigate the Gestalt. This paper gathers the few research in one-shot learning, where the Gestalt is partly represented as “concept activation vector (CAV)”. This study claims that the method CAV do not capture this classification uncertainty of Gestalt cognition, and thus shows how far transfer learning still be from the depths reached by human vision.
Research on Key Techniques for Super-resolution Reconstruction of Satellite Remote Sensing Images of Transmission Lines

Abstract The data point target on the satellite remote sensing image of the transmission line is restricted by satellite remote sensing imaging equipment and transmission conditions, which makes it difficult to guarantee the clarity of the transmission line. Image super-resolution technology aims to recover high-resolution images from low-resolution images to improve the detailed information of the transmission line itself, which is of great significance for the intelligent inspection of transmission line satellite remote sensing and hidden danger monitoring. Aiming at the problems of traditional methods relying on multi-frame image sequences and the reconstruction results are too smooth, this paper proposes a single-frame remote sensing image super-resolution method based on the boundary balance generation confrontation network. Experimental results show that this method can provide more high-frequency information, and the reconstruction result is closest to the real image. Compared with neighbor interpolation, bicubic interpolation and other methods based on deep convolutional neural networks, the PSNR of the results in this paper is significantly improved, and effectively enhances the detailed information of the transmission line and the surrounding environment.
Research on Power Equipment System of Knowledge Graph under Electric Energy in Smart Grid

Abstract In order to realize technological innovation in the electric power field, the article uses artificial intelligence, big data analysis and mining, knowledge graphs, natural language processing and other technologies to construct Chinese professional dictionaries and knowledge graphs in the electric power field. And on this basis, the thesis puts forward the technical architecture of a comprehensive energy service support system based on the concept of “a picture of the power grid”, a spatiotemporal data management platform and a graph database query application implementation mechanism. Finally, research and design the visualization of the hierarchical organization of the data, use the association relationship between the data to organize the data into multi-topic classification and hierarchical organization, and provide a friendly interactive man-machine interface.
Exploring Web Scraping with Python

People who gradually own electronic devices and resource-rich companies are becoming milestones for data factories that inject incredible amounts of information into the World Wide Web (WWW) every day. This myriad volume of data is allocated across huge numbers of Web sites and their applications. The web scrapers’ overlooking principles and approaches are juxtaposed, and it describes with respect to the operation of how the scraper is planned. This artwork is allotting three portions—the web scraper pile-up the preferred web links, afterward the data is gathered to procure the desired data from those sources and eventually depositing that data to a CSV file for further referencing. Here, the python language is applied for the accomplishment of this task. Resultantly, consolidation of these with the conscientious of libraries and pushing on their proficiency, and acquire the desired contents using this suitable scraper. Because of a vast group and library assets of python and the magnificence of codes in python programming language, it is best suited for retrieving the intended information from the anticipated Web site. Being so, the demand and connotation of extracting the data from the stupendous connectivity all over the world are successively fitting user-friendly.
Structure adjustment of early warning information system based on timeliness

Aimed at the high requirement of timeliness in the process of information assurance, this paper describes the average time delay of information transmission in the system, and designs a timeliness index that can quantitatively describe the ability of early warning information assurance. In response to the problem that system capability cannot meet operational requirements due to enemy attacks, this paper analyzes the structure of the early warning information system, Early warning information complex network model is established, based on the timeliness index, a genetic algorithm based on simulated annealing with special chromosome coding is proposed.the algorithm is used to adjust the network model structure, the ability of early warning information assurance has been improved. Finally, the simulation results show the effectiveness of the proposed method.
Integration of Supply Management System in Auto Parts Company Using Web Services

Abstract The web industry at this point is getting more and more aggressive to implement the concept of web services that provide message interoperability and data exchange interfaces. this is very useful in managing data from various web sites where data integration might occur. The case study of this research was carried out on managing auto parts supply, using research methods collecting and analyzing data structures, architectural design data, building web service, building APIs, testing, and measurement. Background and research objectives can be formulated how to combine data from various information system applications, into a supply management system that is designed using web service technology by managing API data on the REST server so that it can be used by REST clients. This paper results in the creation of a web service through a testing phase with 4 trials with a total data received 18.31KB and a total response time of under 2000ms.
Automatic Terminology Extraction Using a Dependency-Graph in NLP

Automatic Terminology Extraction (ATE) is a technique for extracting phrases representing a dataset. This technique is required for translating specialistic books and documents. An existing method focused on the fact that terminologies tend to be composed of two or more single nouns. However, it does not deal with modification relations but only co-occurrence relations among single nouns. Moreover, we have to consider the fact that phrases defined as terminology tend to be explained in another sentence when we propose a novel approach. In this study, we propose a method for extracting terminologies from a dataset considering the modification relations obtained by dependency analysis. In particular, we propose how to extract features enabling us to distinguish whether or not the phrase is terminology from a dependency structure of a sentence.
Multiple Objects Association System for the Smart City

The increase of population in large cities require safety systems improvement for urban environment. The traditional Smart City security systems are highly centralized to process a large number of video streams in the massive-parallel data centers. As a result, this requires an expensive network infrastructure and makes it available only for large metropolitan areas. In this paper, we propose a distributed system for analyzing the luggage and people relations in crowded places. The system processes video on the camera's nodes and detects dependent tracks for objects moving. Then we form a combined scene in the central node to accurately represent the association of people and luggage. In this research stage, we present the basics concepts of multiple object re-identification and association measurement, as well as knowledge graph constructing. We describe the methodology of data obtaining and show experimental results of visual object recognition on camera nodes.
An Intelligent Disease Query System Based on Rasa NLU

Intelligent Customer Service is a very popular application at present. Using the previous user's Q & A records and the existing data in the database, combined with semantic analysis, users can quickly acquire what they want to know. The system takes the Taiwan Disease Control center data as an example, which is proved by experiments that the system can replace the traditional customer service work and can provide users with instant information about diseases.
A Talmudic norms approach to many-valued logic

Abstract In three-valued logic, the third truth-value is often interpreted as undefined. However, the value of a logical term may be well defined if its ‘history’ is taken into account. Following this approach, the analogy to the Sorites paradox meets the eye, which in turn has recently been addressed by means of a ‘Talmudic Norms’ approach. This paper thus lays bare a religious contribution to the field of many-valued logic and implements a simplified dynamic model for the theory of Talmudic mixtures—and, thus, for the outlined Sorites logic—using standard AI search and planning techniques.
Extraction of Specific Arguments from Chinese Financial News with out-of-domain Samples

The object and evidence are two important parts of financial fraud detection (FFD). In traditional FFD tasks, the detected object generally refers to the enterprise involved in typical financial fraud events, and the evidence comes from corporate statements such as auditing report, etc. Thus, previous FFD methods based on financial statements are limited by the scope of detected objects and the availability of evidence. In this study, we design a financial event library to enlarge the detection scope and expand the source of evidence to financial news, and thus financial entities involved in high-risk events are extracted. The financial event library contains common negative events, each of which corresponds to a negative behaviour that may increase financial risk. Moreover, we propose a novel method to convert the event detection task into the Q&A mode task, which also contributes to the enlargement of the original hand-built dataset. Compared with existing methods on the enlargement of financial dataset, our approach does not require additional annotation. We apply our method into Chinese financial news corpus, and achieves good performance in the extraction task.
THE USE OF SCHOOLOGY AS AN ALTERNATIVE OF LEARNING APPLICATIONS IN THE COVID-19 PANDEMIC PERIOD TO IMPROVE STUDENTS 'WRITING ABILITY

The research objective entitled "Schoology : an alternative of learning applications to improve students’ writing skills in the Covid 19” to is to determine the effect of Schoology online learning media on students' writing skills and to identify students' perceptions about the use of Schoology. The research method used a descriptive qualitative research. Technique in collecting the data were test, interview,questionnaire with research subject is 44 PGSD Students from Class of 2019. The content analysis method was used to analyze the data that was started from 1)data reduction, 2)data presentation, 3)drawing conclusion and verification. The results showed that 85.59% of students agreed that the use of Schoology improved their English language skills in general and 85.90% of students agreed it improved their writing skills. Perceptions of students using Schoology : 13.42% that Schoology is easy to use, 20.13% of students have an ordinary impression, 65.77% of students have the impression that is difficult to use. From their submitted task, there was improving of 40% of score and lessen writing error found.It is to be concluded that Schoology did improve writing skill because the language of instruction in Schoology was English with user friendly outlook hence force the students to study harder using and writing in English.
Using Metadata-indexing to Improve the Efficiency of Complex Operations

The article discusses a way to improve the efficiency of complex query execution in modern DBMS. The method is based on the use of tree structures, key hash codes, and the possibility of optimization based on partitioning. As an additional aspect of optimization, the method of parallel operation of the proposed method is described.
Genesis of the Notion “Educational and Cognitive Activity”

The paper aims to reveal essence of the notion “educational and cognitive activity”, to identify specificity of educational and cognitive activity in secondary educational institutions. The article analyses the cognate terms “educational activity”, “cognitive activity”, “teaching”, “learning”, compares approaches to understanding educational and cognitive activity. Educational and cognitive activity is considered as an autonomous phenomenon. Scientific originality of the study lies in the fact that relying on the conducted analysis, the author clarifies the notion “educational and cognitive activity”, which is defined as a learner’s activity to acquire educational-cognitive competences. This activity facilitates learning outcomes and promotes personal changes. The research findings broaden understanding of educational and cognitive activity, which is considered as an independent activity, and can serve as a theoretical basis for modelling a learner’s educational and cognitive activity.