Reviewerly/exHarmony huggingface dataset:
Abnormal Student Detection Model Based on Student Feature Extraction

Moral education is an important mission of colleges and universities, and the identification and tracking of abnormal students is an important part of moral education.However, problems such as large amount of data related to college students and the majority of text information are prominent.In this paper, the semantic library is used to quantify the text information, and the emotional feature vector of students is obtained.Combined with the digital data, the feature vector of students is effectively obtained.The K-Means clustering and neural network ensemble learning are used to realize the identification of characteristic students, and the accuracy rate can reach 82.02%, which has certain reference significance.
Online Education: The Transformation in the Post-epidemic Era

The outbreak of COVID-19 in 2020 has brought about a blowout outbreak of online education, which has greatly promoted the development of online education and accumulated rich experience in online education.With the stabilization of the epidemic and the reflection of a series of pending problems faced by online education in the epidemic, OMO (online-merge-offline) mode, namely the integration of online and offline modes, has become a new trend in the development of online education in the post-epidemic era.Therefore, this paper provides new ideas for OMO mode construction in the epidemic era from three aspects: teaching arrangement, assessment method, and environmental construction, and helps the transformation and development of online education.
Investment Decision of Cryptocurrency in Millennials and Gen Z

Cryptocurrencies have experienced exponential growth in the 10 years after their discovery in 2009.Despite the popularity of cryptocurrencies as an investment, there is still much to be understood about the factors that influence cryptocurrency investment.This study seeks to find the influence of behavioral finance factors, specifically herding, heuristics, and prospects on investment decision for millennials and gen Z in Indonesia.The research method used is multiple regression with several stages including reliability and validity tests, descriptive analysis, and hypothesis testing analysis.With a sample of 140 respondents, it is concluded that herding and heuristic factors have a significant influence on investment decisions in the cryptocurrency market, while the prospect factor does not have a significant influence on investment decisions in the cryptocurrency market.The results of this study indicate that non-fundamental factors are still high as reflected in herding and heuristics on millennials and gen Z investors in Indonesia.Another implication is that many investment decisions are "not yet" sound in a very dynamic situation in the cryptocurrency market.
Quantum Feasibility Labeling for NP-complete Vertex Coloring Problem

Many important science and engineering problems can be converted into NP-complete problems which are of significant importance in computer science and mathematics. Currently, neither existing classical nor quantum algorithms can solve these problems in polynomial time. To address this difficulty, this paper proposes a quantum feasibility labeling (QFL) algorithm to label all possible solutions to the vertex coloring problem, which is a well-known NP-complete problem. The QFL algorithm converts the vertex coloring problem into the problem of searching an unstructured database where good and bad elements are labeled. The recently proposed variational quantum search (VQS) algorithm was demonstrated to achieve an exponential speedup, in circuit depth, up to 26 qubits in finding good element(s) from an unstructured database. Using the labels and the associated possible solutions as input, the VQS can find all feasible solutions to the vertex coloring problem. The number of qubits and the circuit depth required by the QFL each is a polynomial function of the number of vertices, the number of edges, and the number of colors of a vertex coloring problem. We have implemented the QFL on an IBM Qiskit simulator to solve a 4-colorable 4-vertex 3-edge coloring problem.
Towards the Development a Cost-effective Earthquake Monitoring System and Vibration Detector with SMS Notification Using IOT

As one of the countries situated in the Pacific Ring of Fire, the Philippines suffers from an inexhaustible number of natural disasters every year.One of the most destructible ones is the occurrence of earthquakes.Because of the high damage that earthquakes incur, along with their inevitability and unpredictability, developing effective methods of earthquake damage mitigation as well as disaster preparedness is imperative to lessen the negative impacts it is capable of producing in communities.One efficient way of doing this is by implementing an earthquake early warning (EEW) system that is capable of sending message alerts to receivers to warn them in the event of a hazardous earthquake.With this objective, this study centers on creating an earthquake detector with SMS messaging to function as an EEW system with an added advantage of being low-cost to make it more accessible to the public.Using electronic components based on an Arduino Mega 2560 and a Global System for Mobile Communications (GSM) module, the earthquake detector and its alert message system were created.A series of tests in different locations across Butuan City was then performed to assess the device's accuracy in measuring different Intensity levels when subjected to surface vibrations.Comparative analysis showed that its recorded values.Corresponded with the values obtained from accelerometer-based mobile applications.In conclusion, the study was deemed functional in its ability to detect low and high surface vibrations, which proves that it is successful in detecting earthquake tremors and vibrations in the event of an earthquake.
Automated Transcription of Historical Encrypted Manuscripts

Abstract This paper deals with historical encrypted manuscripts and introduces an automated method for the detection and transcription of ciphertext symbols for subsequent cryptanalysis. Our database contains documents used in the past by aristocratic families living in the territory of Slovakia. They are encrypted using a nomenclator which is a specific type of substitution cipher. In our case, the nomenclator uses digits as ciphertext symbols. We have proposed a method for the detection, classification, and transcription of handwritten digits from the original documents. Our method is based on Mask R-CNN which is a deep convolutional neural network for instance segmentation. Mask R-CNN was trained on a manually collected database of digit annotations. We employ a specific strategy where the input image is first divided into small blocks. The image blocks are then passed to Mask R-CNN to obtain detections. This way we avoid problems related to the detection of a large number of small dense objects in a high-resolution image. Experiments have shown promising detection performance for all digit types with minimum false detections.
A Primary Study on User Perception of Phishing in Banking Sector

Phishing is the malicious act of retrieving sensitive information from users disguising oneself as trust worthy entities. In banking, phishing is the retrieval of privileged information such as username, passwords, ATM pin and other account related intelligence from users by enacting as their banking representative. The objectives of the study: To determine the level of awareness of the term phishing among bank users, to analyze the satisfaction level of the users towards the awareness methods implemented by the banks and to discover the most commonly used media for phishing attack from the point of view of the users. The study made use of both primary data and secondary data. Primary data was collected using well-framed questionnaires on Google Forms. Nondiscriminative snowball sampling was used for sample selection and included 385 samples. The study found out that most users were aware of the term phishing, were dissatisfied by the way banks provided awareness, and the most commonly used mediu for phishing attack was SMSs.
Mitigation the Challenges of Cloud Storage Security Using Hybrid Algorithms

Security of the cloud computing is one of the most challenge which limit the idea of adopting the technology of clouding if the level of the security is not significant to protect the stored data, many researches had implemented several algorithms for this purpose. This paper implemented a hybrid algorithm based on AES and ECC to give high level of security. A theoretical study is presented in the first part of this search to explain the basic concepts of clouding as well as the security of this technique. After that the performance of the proposed scheme had been evaluated. The result showed that the AES combined with ECC offer extra feature which ensure high level of protection for the data.
Data Center Participates in the Design of Typical Scenarios for Power Demand Response

Data centers have high energy consumption and large volume, and have great energy-saving potential. Through the transfer of data load among multiple data centers, the transfer of electric power between regional power grids can be realized. The latest progress of three typical application modes of current data center leasing and value-added services, edge content distribution and computing node joint deployment services, and edge-cloud computing collaborative services are analyzed. The feasibility and necessity of data center participating in demand-side resource scheduling are analyzed, and the preliminary work that needs to be completed in this stage to deeply excavate the adjustment potential of data center to participate in demand response is proposed.
The impact of new technologies on the connotation of audit profession

Blockchain is increasingly conquering the finance sector. Blockchain technology enables a decentralized, distributed registry with durable and traceable data access in real time. Due to this, business processes are transforming. Currently the work of an auditor is predominantly retrospective and focuses on the accuracy and consistency of a company´s financial statements. Real-time data accessibility would alter this. The purpose of this paper is to understand how digital revolution affects audit profession. In this regard, the focus is emphasized fresh possibilities and associated difficulties. Research published in financial journals provide the foundation of this paper and shows, that many companies are consulting firms such as the Big Four KPMG or Deloitte on the potential impact of technological advances. There are preparations and accompaniments for the implementation of new technologies. Besides the literature review, a quantitative content analysis is elaborated. The analysis indicates, that well-known auditing firms and companies are actively investigating blockchain and are already launching the first initiatives to implement the new technologies. The relevance of Blockchain is noticeable present. However, there are uncertainties about the current legal framework and the rapid pace of change due to digital revolution. Thus, this research can add new dimensions on audit profession and, particularly, express the benefits and opportunities of Blockchain as part of the auditing process.
dycdtools: an R Package for Assisting Calibration and Visualising Outputs of an Aquatic Ecosystem Model

The high complexity of aquatic ecosystem models (AEMs) necessitates a large number of parameters that need calibration, and visualisation of their multifaceted and multi-layered simulation results is necessary for effective communication. Here we present an R package "dycdtools" that contains calibration and post-processing tools for a widely applied aquatic ecosystem model (DYRESM-CAEDYM). The calibration assistant function within the package automatically tests a large number of combinations of parameter values and returns corresponding values for goodness-of-fit, allowing users to narrow parameter ranges or optimise parameter values. The post-processing functions enable users to visualise modelling outputs in four ways: as contours, profiles, time series, and scatterplots. The "dycdtools" package is the first open-source calibration and post-processing tool for DYRESM-CAEDYM, and can also be adjusted for other AEMs with a similar structure. This package is useful to reduce the calibration burden for users and to effectively communicate model results with a broader community.
Creating a DevOps Course

Software engineering is regularly listed as a top career, but typical curricula rarely equip college graduates to join latest industry practices. Providing a course on software deployment and operations (DevOps) gives students a place to learn how the practices of agile software engineering align with organizational culture and business objectives. Students can get hands-on experience with current industry practices, tools, and technologies along with broader context of their utility and purposes. This work invites discussion on the structure of a recently developed course on DevOps, including course modules, assignments, discussions, and projects. Reflections from a pilot offering are given, along with lessons learned and planned changes for the next offering.
Alice or Bob?: Process Polymorphism in Choreographies

We present PolyChor$\lambda$, a language for higher-order functional \emph{choreographic programming} -- an emerging paradigm by which programmers write the desired cooperative behaviour of a system of communicating processes and then compile it into distributed implementations for each process, a translation called \emph{endpoint projection}. Unlike its predecessor, Chor$\lambda$, PolyChor$\lambda$ has both type and \emph{process} polymorphism inspired by System F$_\omega$. That is, PolyChor$\lambda$ is the first (higher-order) functional choreographic language which gives programmers the ability to write generic choreographies and determine the participants at runtime. This novel combination of features also allows PolyChor$\lambda$ processes to communicate \emph{distributed values}, leading to a new and intuitive way to write delegation. While some of the functional features of PolyChor$\lambda$ give it a weaker correspondence between the semantics of choreographies and their endpoint-projected concurrent systems than some other choreographic languages, we still get the hallmark end result of choreographic programming: projected programs are deadlock-free by design.
Development of Identification Instruments for Children with Specific Learning Disability in Elementary School

This study aims to facilitate the identification of students in elementary school who have specific learning dissability. This study employs a qualitative approach to the literature review process. The study collected data from books and articles that discuss identifying specific learning dissability. The data collection technique is based on a literature review of scientific books and journals and interviews with professional practitioners and academics, subsequently validated. The findings of this study are presented in the form of a draft identification guide for elementary school-aged children with specific learning dissability. Teachers and parents can easily use the draft guidelines for identifying children with specific learning dissability because they are presented concisely and clearly that refers to the theory and validated by four validators, including psychologists, special education lecturers, and inclusive school and special education teachers. The identification guideline draft for children with specific learning dissability in elementary school-age includes procedures for identifying children with specific learning dissability in general and instruments for identifying children with dyslexia, dyscalculia, and dysgraphia. This procedure was proposed to assist teachers and parents in identifying children with specific learning dissability in elementary school.
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks

Deep neural networks (DNNs) are widely used in various application domains such as image processing, speech recognition, and natural language processing. However, testing DNN models may be challenging due to the complexity and size of their input domain. Particularly, testing DNN models often requires generating or exploring large unlabeled datasets. In practice, DNN test oracles, which identify the correct outputs for inputs, often require expensive manual effort to label test data, possibly involving multiple experts to ensure labeling correctness. In this paper, we propose DeepGD, a black-box multi-objective test selection approach for DNN models. It reduces the cost of labeling by prioritizing the selection of test inputs with high fault revealing power from large unlabeled datasets. DeepGD not only selects test inputs with high uncertainty scores to trigger as many mispredicted inputs as possible but also maximizes the probability of revealing distinct faults in the DNN model by selecting diverse mispredicted inputs. The experimental results conducted on four widely used datasets and five DNN models show that in terms of fault-revealing ability: (1) White-box, coverage-based approaches fare poorly, (2) DeepGD outperforms existing black-box test selection approaches in terms of fault detection, and (3) DeepGD also leads to better guidance for DNN model retraining when using selected inputs to augment the training set.
Path Planning Technology of Mars Rover Based on Griding of Visibility-graph Map Direction Search Method

Based on the Mars surface exploration, this paper studies the autonomous path planning technology of the rover on the Mars surface under the circumstance of the limited computing resources of the Mars Rover. In this paper, the visibility-graph method and the grid method are combined to optimize the grid division method and make the map model more suitable for search processing. On this basis, a direction search method with low computational resource occupancy is proposed to search the established map. Finally, the results of griding of visibility-graph map direction search method are compared with the Dijkstra algorithm. It shows that the griding of visibility-graph map direction search method has less computational time complexity, and can conduct synchronous search during the progress process to obtain a better path. It is a path search algorithm suitable for the process of traveling on the surface of Mars.
DWT and Quantization Based Digital Watermarking Scheme Using Kernel OS-ELM

In recent years, an extensive amount of digital content is being shared over the Internet. This has increased the copyright, authenticity, and security concerns. Digital watermarking is a well-known mechanism which ensures the digital rights. This paper proposes a discrete wavelet transform (DWT) and quantization-based semi-blind watermarking scheme. The proposed scheme comprises of three phases, namely model generation, watermark embedding, and watermark extraction. The host image is subjected to four-level DWT, and the approximation coefficient vector (LL4) thus obtained is quantized using a quantization parameter (Q). The approximation coefficients and their quantized values are used as the target output and as an input for training the kernel online sequential extreme learning machine (KOS-ELM). The generated model is then utilized in watermark extraction to recover the binary watermark from the signed image in a semi-blind manner. Experimental results exhibit that the proposed scheme has high degree of robustness and high level of imperceptibility with an average peak signal-to-noise ratio (PSNR) of 42.54 dB. We have also performed statistical analysis using Wilcoxon signed rank test to compare our work with the recently published works. The statistical comparison as well as experimental results demonstrate that this work outperforms other recently published works in terms of imperceptibility as well as robustness.
An Analysis of The Small Sample Datasets Based on Machine Learning

In machine learning, building the optimal model for small sample data has become a widespread issue in the data science community. Some methods have been proven to achieve high accuracy in training small sample datasets. However, the solution to more extreme minor sample problems still lacks further exploration. Therefore, this paper will explore the prediction accuracy of machine learning methods for small sample datasets. Collecting the forest fire dataset and pulsar dataset from Kaggle as examples, the prediction of various machine learning models (SVM, random forest, neural networks, regression) was carried out, respectively. It was found that the machine learning model failed to achieve high prediction accuracy in the imbalanced samples represented by the forest fire dataset. Because of the small number and the imbalanced distribution, the model cannot obtain an apparent discrimination degree for each feature.
Affective Design Analysis of Explainable Artificial Intelligence (XAI): A User-Centric Perspective

Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to understand its inner workings even with limited knowledge of the machine learning algorithms it uses. As a result, the field grew, and development flourished. However, concerns have been expressed that the techniques are limited in terms of to whom they are applicable and how their effect can be leveraged. Currently, most XAI techniques have been designed by developers. Though needed and valuable, XAI is more critical for an end-user, considering transparency cleaves on trust and adoption. This study aims to understand and conceptualize an end-user-centric XAI to fill in the lack of end-user understanding. Considering recent findings of related studies, this study focuses on design conceptualization and affective analysis. Data from 202 participants were collected from an online survey to identify the vital XAI design components and testbed experimentation to explore the affective and trust change per design configuration. The results show that affective is a viable trust calibration route for XAI. In terms of design, explanation form, communication style, and presence of supplementary information are the components users look for in an effective XAI. Lastly, anxiety about AI, incidental emotion, perceived AI reliability, and experience using the system are significant moderators of the trust calibration process for an end-user.
Data monitoring platform based on Modbus protocol

The Modbus protocol has a wide range of applications in industry, enabling communication on different operating systems and physical devices. Under the Visual Studio 2019 platform, based on the Modbus TCP communication protocol to design the host computer software, through TCP/IP and ModSIM32 for point simulation communication receiving register data, ID station number-1 data are water meter data, ID station number-2 data is meter data and added MQTT protocol can publish, subscribe to topics, water_data is the topic of water meter data carrier, ele_ Data is the topic of the meter data carrier, and users can view energy data on demand by subscribing to the topic. At the same time, it is connected to the bank's management system, and the bank automatically deducts fees from the user's deposit to realize online charges