Reviewerly/exHarmony huggingface dataset:
STUDENTS’ WRITING ERROR WRITTEN BY EIGHTH GRADE STUDENTS OF SMP JOSEPH KHATULISTIWA SINTANG

The background of this research is students’ errors in writing descriptive texts. The students do not understand the correct structure of writing descriptive text. The objective of this research is to find out the types of errors and to identify the dominant types of errors that found in students’ writing products. The subject of this research is the students’ writing descriptive text products written by eighth grade students of SMP Joseph Khatulistiwa Sungai Tebelian. This research used qualitative method, by classifying the errors using Surface Strategy Taxonomy. It consisted of four types of errors in the students’ writing descriptive text product. These were omission, addition, misformation and misordering. Qualitative data was collected from documentation of students’ writing descriptive text products. Qualitative data were analyzed through collection of a sample of learner language, identification of errors, description of errors and explanation of errors. The data analysis in this research was taken from 10 students of SMP Joseph Khatulistiwa Sungai Tebelian. The researcher analyzed 10 worksheets of students writing products. The researcher found the total of students’ errors were 46 errors of students errors. The finding showed there were four types of errors such as 26 errors of omission on percentage 57%, 12 errors of misformation on percentage 26%, 6 errors of addition on percentage 13%, and 2 errors of misordering on percentage 4%. The dominant type of error was omission with 27 errors.
Comparison of two causal inference methods: linear regression and matching score and introduction of causal forest

Under the context of the rapid development of artificial intelligence, the introduction of causal inference will improve the accuracy of machine analysis of data. This work aims to introduce three methods of calculating causal effect, linear regression, propensity score matching and causal forest, illuminate some about the combination of causal inference and matching-learning. Here, we substitute the dataset National Supported Work experiment by Lalonde (1986) into three methods and compare the results. We show experimentally that causal forest Causal forests can minimize data bias and obtain more accurate estimates of causal effects.
PSO-based procedure to find number of clusters and better initial centroids for K-means algorithm: Image segmentation as case study

In this paper, we propose a combination of K-means algorithm and Particle Swarm Optimization (PSO) method. The K-means algorithm is utilized for data clustering. On one hand, the number of clusters (K) should be determined by expert or found by try-and-error procedure in the K-means algorithm. On the other hand, initial centroids and number of clusters (K) are influenced on the quality of resulted grouping. Therefore, the aim of the proposed procedure is using PSO and the Structural Similarity Index (SSIM) criterion as a fitness function in order to find the best value for K parameter and better initial clusters' center. Due to different value of K parameter, the number of initial centroids which should be produced is variant. Thus, length of particles in PSO method may be different in each iteration. Experimental results show the superiority of this approach in comparison with standard K-means algorithm and both of them are evaluated on image segmentation problem.
Afterword: Describing Languages Systemic-Functionally

In this invited Afterword Matthiessen positions this volume as the third step in a series of books introducing students and colleagues to Systemic Functional Linguistics (SFL) – following on from the general introduction in Matthiessen and Halliday (1997/2009) and the introduction to formulating system networks in Martin, Wang and Zhu (2013). It also positions the work on English, Spanish and Chinese in this volume in relation to work on other languages, much of which has been curated and/or mentored by Matthiessen. In addition this afterword reviews a number of key issues arising in relation to language description based on SFL. These include the paradigmatic orientation of system descriptions, cryptogrammatical reasoning, trinocular vision (from about, from roundabout and from below), metafunction (ideational, interpersonal and textual), rank and functional language typology.
Problems and Solutions of Blackboard Writing Design in Teaching Skills Training of Primary Mathematics

Blackboard design is called "micro-lesson plan", which is also an indispensable teaching means.Blackboard writing design is an important teaching skill for normal students majoring in primary school education.By observing and recording the teaching skills training process of normal school students, this paper summarizes the problems existing in the design of primary school mathematics blackboard writing of normal school students from the perspective of science, standardization and planning, and analyzes the causes of the problems.At last, the author puts forward some strategies to solve the problems in the design of blackboard writing in primary school mathematics.These strategies include systematic learning of blackboard design knowledge, strengthening and improving the basic skills training, refining the content of blackboard writing, highlighting the important and difficult points of teaching, paying attention to cooperate with the explanation, and presenting blackboard writing in a timely manner.
Developing a comprehensive, adaptive system for large-scale x-ray images

Inspection of commerce entering the country is currently a burdensome process, assisted greatly by non-intrusive inspection (NII) technologies such as X-Ray imaging. There are a variety of singular ML techniques that assist in the adjudication process, many are well known, common and widely available with proper data. However, the challenge is to develop a comprehensive system exhibiting certain properties that leverages many techniques simultaneously and combines results to improve overall performance. Here we describe the concepts, components, data and implementation of a system we use for NII adjudication that is well suited to adapting to future needs. We demonstrate several components of this system the benefits of it. Specifically, we show the combination of results from common methods achieves superior performance in concert. First, ensembeling has proven successful in combining results from a variety of object detection models. We show the impact of using our novel graph clique-based method to increase performance over independent models. Additionally, because of performant and reliable object detection results, we demonstrate how constructing an image level contextualization of a scene allows analysis at the metadata level. Lastly, we leverage variational autoencoder (VAE) based similarity models to describe anomalies. We deploy these algorithms in a hierarchical fashion, developing optical signatures for whole NII scans as well as their components (e.g, entire vehicle, trunk, wheels). Here, we quantify the impacts of the inclusion of these methods in image anomaly detection task and demonstrate them in the extensible framework we have developed for NII applications. This paper will serve as an overview of our approach and show results from both combinatorial techniques.
The Decision Problem for Effective Procedures

The “somewhat vague, intuitive” notion from computability theory of an effective procedure (method) or algorithm can be fairly precisely defined even if it is not sufficiently formal and precise to belong to mathematics proper (in a narrow sense)—and even if (as many have asserted) for that reason the Church–Turing thesis is unprovable. It is proved logically that the class of effective procedures is not decidable, i.e., that there is no effective procedure for ascertaining whether a given procedure is effective. This result is proved directly from the notion itself of an effective procedure, without reliance on any (partly) mathematical lemma, conjecture, or thesis invoking recursiveness or Turing-computability. In fact, there is no reliance on anything very mathematical. The proof does not even appeal to a precise definition of ‘effective procedure’. Instead, it relies solely and entirely on a basic grasp of the intuitive notion of an effective procedure. Though the result that effectiveness is undecidable is not surprising, it is also not without significance. It has the consequence, for example, that the solution to a decision problem, if it is to be complete, must be accompanied by a separate argument that the proposed ascertainment procedure invariably terminates with the correct verdict.
Empirical Research on the Factors Affecting Residents’ Intention of Deposit

There are many factors that can affect economy, such as production, technology, consumption, imports, exports, investment, etc.Among them, consumption has a huge driving effect on the economy.Poor consumption can easily cause economic contraction.On the other hand, the intention of residents to deposit directly affect their consumption ability.This paper focus on the impact of deposit and takes an empirical perspective, trying to find out what factors could affect people's intention of deposit, and how to deal with the problems resulting from that.
Technological Advances Affect Journalists' Reporting Styles

This study focuses on exploring how journalists' reporting style is affected by the continuous development of emerging technologies.This paper analyzes how journalists' reporting styles are affected by technological advances by analyzing four reporting styles in the digital domain.On the other hand, it also puts forward new requirements for current journalists to constantly learn and have digital skills.From a macro point of view, the change in reporting methods also positively impacts current journalism and journalists.For example, in data journalism, journalists introduce different data to support the validity of the news.It enables readers to understand important information about the content of the news, based on the fact that data is added to support the news.At the level of narrative journalism style, journalists are increasingly using news stories to attract readers and expose them to real news sources.In the case of interactive journalism, this style of journalism (such as news games) has been shown to help journalists run news stories smoothly and meet the needs of readers.In inverted pyramid journalism, some journalists stick to this style, for the obvious reason that it allows readers to get to key news content in a concise and clear way.This is undoubtedly positive.
The Influence of Modern Science and Technology on the Taste of the Year

The Spring Festival is the most solemn traditional festival of the Chinese nation, and the Spring Festival culture is also an important representative of Chinese excellent traditions.In recent years, with the development of national economy and the arrival of the era of science and technology, people's life style and values have changed obviously.People have lost their freshness and interest in the traditional Chinese New Year culture and customs, and many scientific and technological elements have continuously penetrated into people's lives, and the taste of China's new year is fading.Modern science and technology pose many new challenges to China's annual culture, and a series of social hot issues caused by the change of annual flavor caused by science and technology also need to be solved urgently.The purpose of this research is to solve the problems caused by the change of annual flavor under the influence of modern science and technology, and to provide feasible suggestions for better inheriting Chinese excellent traditional culture in the era of rapid development of science and technology.
ROBUST FEATURE-BASED AUTOMATED MULTI-VIEW HUMAN ACTION RECOGNITION SYSTEM

Automated human action recognition has the potential to play an important role in public security, for example, in relation to the Multiview surveillance videos taken in public places, such as train stations or airports.This paper compares three practical, reliable, and generic systems for Multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier (GMM), and the nearest mean classifier.To describe the different actions performed in different views, view-invariant features are proposed to address Multiview action recognition.These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution.Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets.The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes.The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets.
Inter-script Stylistics Comparison among Familiar and Non-Familiar Writers

Learning to write by hand begins with copybook models, which gradually evolve into individual features as writers acquire their own traits over time.In this paper, two separate scripts were used, and commonalities between the two scripts were found based on the way the two scripts formed letters that looked similar to one another.Nine distinct lookalike letters were noted and investigated.200 handwriting samples from 100 people were gathered (50 each from familiar and non-familiar writers).These observations have been compared using a graph.The t-test, a statistical tool, was used to examine the significance of the overall hypothesis.4.5547 was the t-test value at 5% significance, which is significant at df 16.
Overview of Attack Surfaces in Blockchain

In blockchain technology, cybercriminals exploit new attack surfaces for non-legitimate persons who wish a dissemination of fake information, encourage cyber attacks, unethical behavior, etc. Attacks are believed to be attributed to the blockchain cryptographic architecture, the bottom-line architecture, and the substance in which they are applied. The main threat is focused on the progressive defense research activity. It is currently believed that other attacks on the blockchain can be launched without being able to withstand traditional defenses, a few of which may be used to deliver any other attack. By delineating these attacks and examining their countermeasures, it highlights the direction of new research that should be persisted for safer and more competent use of blockchain. In spite of vulnerabilities and attacks, this technology offers key features like hashing, immutability and digital signature, which enrich the blockchain. Researchers also contributed to developing a security shield for blockchain by using an intelligent combination of these features. Basically, these features are required to achieve CIA (Confidentiality, Integrity and Authentication). The contribution of this chapter would be systematically investigating the attack surface of Blockchain technology. The attack surface is the sum of all conceivable security risk exposures. It is also the total of all known and undiscovered threats, possible vulnerabilities, and controls, including all resources. To do this, we set attack vitality on the attack surface to a variety of attacks.
DESAIN ARSITEKTUR APLIKASI QR CODE SEBAGAI ANTI PHISHING SERANGAN QR CODE

QR Codes are very vulnerable to falsification because it is difficult to distinguish the original QR Code from a fake QR Code. Because of this vulnerability, the scanning process on fake QR Codes can direct users to dangerous sites with important information or data from the user. To assess QR Code security vulnerabilities and actions using a secure Application-based QR Code Architecture as Anti Phishing against QR Code attacks using hash functions and digital signatures. In experiments simulated attack types to malicious QR codes that redirect users to phishing sites. The real URL is disguised into the QR Code, where the user does not suspect, the URL is redirected to the fake site. As a result, intruders can easily use QR codes as vectors for phishing attacks targeted at smartphone users, even if they are using a browser that has security features.
[Solution] Mason: Scalable, Contiguous Sequencing for Building Consistent Services

This paper introduces a contiguous multi-sequence abstraction for building consistent, scalable services.The multi-sequence abstraction is useful when dealing with sharded datastores; the contiguity property strengthens the multi-sequence abstraction to guarantee no gaps in the sequence can be left even in the presence of failures.The outcome is an increased simplicity in developing consistent services, which is crucial for practical systems.The paper also presents MASON, a system that implements the multi-sequence abstraction and solves the scalability problem of a single monolithic sequencer.For the sake of evaluating its performance, MASON has been integrated into a distributed shared log based on CORFU, and a distributed prototype of the coordination service ZooKeeper.Overall, reviewers found the problem very relevant and timely.The presented solution is well presented, and well evaluated.
Face Recognition Using Convolutional Neural Network (CNN)

Facial Recognition possesses great importance in today’s tech-savvy world and is considered one of the prominent biometric replacements to the traditional techniques of PINs and Passwords. A stored database of images is exploited using the image processing techniques available and feature extraction, identification, and classification are done using various algorithms. The techniques used for this whole process are based on machine learning because of its higher accuracy and better efficiency than other techniques available. Face recognition is accomplished using the sub-field of Deep learning, i.e., Convolutional Neural Networks. It is typically a multi-layer neural network of neurons, trained to perform discrete tasks using extraction and classification.
Identification of Spoofing URLs Using Hybrid Algorithms

Spoofing detection has been a significant concern since the first twenty-first century due to tremendous electronic transfer accesses like financial transitions and fraud via online services. The spoofing websites look like the same sort as legitimate websites and can ask the user to enter their direction. Among several detection tactics, including information-based approaches, uniform resource locator (URL)-based detection is frequently utilized not only for its similar accuracy but also for its flexibility to a wide range of data types (for example, embedding URLs in spam messages or emails) Several machine learning algorithms may be used to increase forecast accuracy. To improve forecast accuracy, we can employ several machine learning methods. In our technique, we use just the information about the URL of a web webpage to see regardless of whether the site could be a spoofing site. Along these lines, there's no need to genuinely visit a web webpage to work out regardless of whether it's spoofing. This additionally permits the client to not visit the spoofing sites and open themselves to vindictive codes that it will convey.
Computer Vision Explainability for Object Detection in Safety Surveillance

The need for explainability is especially critical as deep learning solutions become more prevalent. Creating interpretations and visualizations of features captured by convolutional neural networks (CNNs) for various computer vision applications is becoming an increasingly difficult challenge due to the growing complexity of CNNs. Nowadays, understanding the behavior of complex machine learning models is highly essential, as important responsibilities in different fields are being taken over by artificial intelligence systems. Consequently, many explainability techniques have come into existence to make machine learning models more interpretable, such as occlusion methods, local surrogate models, and pixel attribution. This paper aims to convey the significance of explainable computer vision and its application in different fields, with a focus on safety surveillance. This objective is achieved through the agency of an explainability technique called Eigen CAM (Muhammad and Yeasin (2020) In: 2020 international joint conference on neural networks (IJCNN). IEEE), and YOLOv5 (Jocher, Ultralytics/yolov5: v6.2—YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci. ai integrations), which is the object detection framework whose predictions are explained.
DESIGN AND EVALUATION OF AN IOTA-BASED MEDICAL INFORMATION SYSTEM

The traditional medical information systems are plagued by issues such as data breaches, lack of privacy, and data integrity concerns. This paper presents the design and evaluation of an IOTA-based medical information system aimed at addressing these challenges. In recent years, blockchain technology has emerged as a powerful tool for securing and managing data in a decentralized manner. One area where this technology has the potential to revolutionize the way we do things is in e-medicine. E-medicine, or electronic medicine, refers to the use of technology to deliver healthcare services remotely. This includes telemedicine, online consultations, and remote monitoring of patients' health status. IOTA blockchain technology, in particular, has a lot of potential in e-medicine. IOTA is a distributed ledger technology that uses a directed acyclic graph (DAG) instead of a traditional blockchain. The main ad-vantage of this approach is that it eliminates the need for miners and makes the system more scalable, fast, and energy-efficient. IOTA is also designed to be feeless, making it an ideal choice for microtransactions. In e-medicine, IOTA can be used in several ways. One potential use case is for secure and decentralized storage of patients' medical records. Medical records are highly sensitive and contain confidential information that needs to be protected from unauthorized access. By using IOTA's tamper-proof and immutable ledger, patients can have more control over their medical records and choose who has access to them. This can be especially useful in situations where patients need to share their medical records with multiple healthcare providers or research institutions. By leveraging the unique features of IOTA, such as its feeless microtransactions, scalability, and distributed ledger technology, the proposed system enhances security, privacy, and interoperability in healthcare information management. The evaluation of the system involves performance tests, and a comparison with existing solutions.
Attendance Management System Using Face Recognition

Today’s educational institutions are concerned with students’ ongoing performance. One problem causing the reduction in student performance is the insufficient attendance. The most well-known methods of recording attendance include calling or signaling the students. It got harder and took longer. It is now necessary to have a computer-based student attendance monitoring system that enables the teacher to keep attendance records. In this project, we employed a clever face recognition-based attendance system. We have recommended setting up a “multi-use attendance management system using face recognition” in the area. Face permission allows for facial recognition in modern implementations, which reduces time and eliminates the possibility of proxy attendance. Additionally, this establishes when a particular predefined order was inserted, information on their presence. This project is implemented using Python, and development is done using PyCharm. The face landmark estimation set of guidelines is used in our work. Finding their faces and recording their attendance is the difficult part. This method eliminates the possibility of fake attendance because it uses the face as a biometric for authentication.