Reviewerly/exHarmony huggingface dataset:
Mapping Riset Auditing dengan Analisis Bibliometric

Studying accounting or auditing issues from various previousstudies will be able to open new opportunities to find more variedresearch and to get updates from a study, it is necessary to analyzeprevious research by mapping related research in the field ofauditing. This study aims to conduct research related to the field ofauditing published in various international journals from 2016-2021.This study uses a bibliometric analysis method which consists ofdefining search keywords, namely 'auditing', (2.) initial searchresults, (3.) narrowing of search results, (4.) preparation of initialdata statistics, and (5.) data analysis. The findings of the study were488 articles from 17 publishers, the most published articles relatedto auditing were published by the publishing agency 'Springer'. Themost published articles occurred in 2020 as many as 95 articles.While the most cited article citations in 2019 were 824 with 85articles and the most citations per author occurred in 2017 as manyas 1102 with 78 articles. Based on the results of the research that hasbeen done, it can be concluded that research on auditing hasincreased every year and has become a big concern, this can be seenfrom the results of the publications that are most widely publishedin the journals of Springer and Elsevier.
Estimation With Range Depended Sensor Model

This paper focuses on the improvement of target tracking accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. We present two approaches to tracking a target with constant velocity model. The first approach uses Extended Kalman Filter (EKF) framework to estimate target’s states and the unknown proportionality coefficient with linearization on the observation model on the predicted states at each time step. The second one uses a measurement conversion method to estimate the target’s scaled by the unknown constant term states, which is shown to be unbiased. This conversion results in linear state and observation models, hence the standard Kalman filter can be applied. The actual target state is computed by application of back scaling with the estimate of unknown scale factor. We evaluate the approaches in desktop simulations.
Use of Electronic Evidence in Criminal Proceedings in Ukraine

The article deals with the issues of documenting and using electronic evidence in criminal proceedings in Ukraine.The task of the study led to the use of dialectical, historical-legal, comparative-legal, system-structural, statistical and formallogical methods.It is emphasized that the features of electronic (digital) evidence are that it is hidden (like fingerprints or DNA); easily crosses borders of jurisdiction; may be altered, damaged or destroyed; can be vulnerable, time sensitive.Signs and types of electronic evidence and basic principles of working with them are defined.The peculiarities of conducting operational and technical measures and unspoken investigative (investigative) actions with electronic evidence are analyzed: removal of information from transport telecommunication networks and removal of information from electronic information systems.Fixing of electronic evidence obtained as a result of investigative (investigative) actions or unspoken investigative (investigative) actions envisaged by the Criminal Procedure Code of Ukraine is carried out in compliance with certain requirements to the form and content.It is noted that forensic research is the main focus of documenting electronic evidence.It is emphasized that law enforcement officials should be properly trained in order to be able to effectively use electronic evidence in criminal proceedings, as well as to document the illegal activities of both individual offenders and organized criminal groups.
Balancing Accuracy and Efficiency: Fast Motion Planning Based on Nonlinear Model Predictive Control

Nonlinear Model Predictive Control (NMPC) is an effective method for the motion planning of automated vehicles. With the working conditions become more nonlinear and complicated, it is necessary to design an efficient NMPC method while guarantee the control accuracy simultaneously. This work presents a fast NMPC method aiming for balancing accuracy and efficiency in multiple obstacle vehicles situations. Given the high nonlinearity of vehicle dynamics, Lagrange interpolation is adopted to discretize vehicle dynamics function and objective function to ensure accuracy with less discretization points. The interpolation order is chosen adaptively based on the numerical analysis of the distribution characteristics of discretization error. Moreover, a hybrid strategy is presented to construct the constraints for obstacle avoidance by combing the elliptic and linear time-varying ones together to make a good balance between the control accuracy and solving efficiency. The acceleration effect and performance of the motion planning algorithm designed by NMPC are validated by comparative numerical analysis in multiple obstacle vehicles scenarios.
Finding Good Binary Linear Block Codes based on Hadamard Matrix and Existing Popular Codes

Because of their algebraic structure and simple hardware implementation, linear codes as class of errorcorrecting codes, are used in a multitude of situations such as Compact disk, backland bar code, satellite and wireless communication, storage systems, ISBN numbers and so more.Nevertheless, the design of linear codes with high minimum Hamming distance to a given dimension and length of the code, remains an open challenge in coding theory.In this work, we propose a code construction method for constructing good binary linear codes from popular ones, while using the Hadamard matrix.The proposed method takes advantage of the MacWilliams identity for computing the weight distribution, to overcome the problem of computing the minimum Hamming distance for larger dimensions.
Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning

Adversarial examples causing evasive predictions are widely used to evaluate and improve the robustness of machine learning models. However, current studies focus on supervised learning tasks, relying on the ground truth data label, a targeted objective, or supervision from a trained classifier. In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation. Our framework exploits a mutual information neural estimator as an information theoretic similarity measure to generate adversarial examples without supervision. We propose a new MinMax algorithm with provable convergence guarantees for the efficient generation of unsupervised adversarial examples. Our framework can also be extended to supervised adversarial examples. When using unsupervised adversarial examples as a simple plugin data augmentation tool for model retraining, significant improvements are consistently observed across different unsupervised tasks and datasets, including data reconstruction, representation learning, and contrastive learning. Our results show novel methods and considerable advantages in studying and improving unsupervised machine learning via adversarial examples.
Measurement for the Area of Red Blood Cells From Microscopic Images Based on Image Processing Technology and Its Applications in Aplastic Anemia, Megaloblastic Anemia, and Myelodysplastic Syndrome

Background Aplastic anemia (AA), megaloblastic anemia (MA), and myelodysplastic syndrome (MDS) were common anemic diseases. Sometimes it was difficult to distinguish patients with these diseases. Methods In this article, we proposed one measurement method for the area of red blood cells (RBCs) from microscopic images based on image processing technology and analyzed the differences of the area in 25 patients with AA, 64 patients with MA, and 68 patients with MDS. Results The area of RBCs was 44.19 ± 3.88, 42.09 ± 5.35, 52.87 ± 7.68, and 45.75 ± 8.07 μm 2 in normal subjects, patients with AA, MA, and MDS, respectively. The coefficients of variation were 8.78%, 10.05%, 14.53%, and 14.00%, respectively, in these groups. The area of RBCs in patients with MA was significantly higher than normal subjects ( p &amp;lt; 0.001). Compared with patients with AA and MDS, the area of RBCs in patients with MA was also significantly higher ( p &amp;lt; 0.001). The results of correlation analysis between the area of RBCs and mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), MCH concentration (MCHC), and red cell distribution width showed no significant correlations ( p &amp;gt; 0.05). The area under the curve (AUC) results of the Receiver Operating Characteristic (ROC) curves of RBCs area were 0.421, 0.580, and 0.850, respectively, in patients with AA ( p = 0.337), MDS ( p = 0.237), and MA ( p &amp;lt; 0.001). Conclusion Identifying the area of RBCs in peripheral blood smears based on the image processing technology could achieve rapid and efficient diagnostic support for patients with MDS and MA, especially for patients with MA and in combination with MCV. However, a larger sample study is needed to find the cutoff area values.
What motivates Jordanians to adopt mobile commerce? An empirical study of the most relevant factors

This study aims to empirically investigate the most influential factors that affect the adoption of Mobile commerce (m-commerce). in Jordan. The factors explored in this study are: Perceived Risk (PR), Perceived enjoyment (PE), perceived ease of use (PEOU), relative advantage (RA), and social Influence (SI). A conceptual framework was proposed to show relationships between variables. Data was collected using surveys collected from 358 participants. Hypotheses were tested using a Structural Equation Modeling [SEM] and the results showed that all independent variables being investigated had significant effects on users' intention to adopt mobile commerce in Jordan. Based on the research findings, several recommendations and implications were reported for academics and companies. Suggestions for future research were also provided.
A Hierarchical Network with User Memory Matrix for Long Sequence Recommendation

In many recommendation scenarios, the interactions between users and items are divided into a series of sessions according to the time interval. The traditional Recurrent Neural Network has some shortcomings, such as limited memory ability, inflexible access to memory data, and obvious deficiency in feature capture for long sequences. To deal with the mentioned issues, we propose a hierarchical network with user memory matrix, named HNUM2, which utilizes the memory network to store users' long-term and short-term interests. The memory network is more flexible to access memory data, which can solve the problem of insufficient capture of long sequence features. The proposed model is a hierarchical recommendation algorithm, which consists of two layers. The first layer is the session-level GRU model, which obtains the sequence characteristics of the current session to predict the next item. The second layer is the user-level memory network model which exploits the attention mechanism and incorporates the write module and read module. The experimental results on two public available datasets show that HNUM2 has achieved significant performance improvement comparing to the state-of-the-art methods.
Optimization of University Department Management: The Increase of Foreign Language Teachers’ Self-Efficacy by Preventing Job Burnout

Th e paper aims to present theoretical and empirical research on investigating the optimization of university department management by preventing job burnout in university teaching staff which resulted in increasing their self-efficacy.Th e research sample was selected with the use of a non-random sampling technique.All foreign languages teachers invited agreed to take part in the study.To collect the empirical data from 205 foreign languages teachers a mixed methods approach was applied.Th e quantitative data were collected using the questionnaire on general self-effi cacy created by R. Schwarzer and M. Jerusalem (1995) and the Maslach Burnout Inventory (1996).Th e qualitative methods included observations and interviews.Th e obtained data were then analysed by means of Statistical Package for the Social Sciences (SPSS soft ware).Th e authors theoretically substantiated and practically demonstrated the effi ciency of the proposed programme aimed at optimizing university department management by preventing job burnout in foreign language teachers.
Geopolitical Forecasting and Actionable Intelligence

In geopolitics, even rough probabilistic forecasts are difficult to make. Scenario design, table-top simulations and structured discussions are heuristic activities that focus the attention of analysts and increase policymaker satisfaction with intelligence products, but their usefulness is not based on confidence that forecasts are accurate. Traumatised by 9/11 and encouraged by advances in social science, computerised data collection and artificial intelligence, the US government has greatly increased investments in reliable geopolitical forecasting. Despite progress by using computerised data processing, sophisticated statistical methods and machine learning, the results have so far been of limited practical assistance. Forecasters must not only provide credible probability judgements but also show the causal pathways underlying them to enable policymakers to mitigate harms and exploit opportunities. Building that capacity means enhancing the role of social scientists in the design and testing of computer simulation and other forecasting techniques.
Pengembangan Model Pembelajaran Berbasis Web Powtoon Untuk Merangsang Minat Belajar Siswa Pada Mata Pelajaran Al-Qur’an Hadits Kelas 3 MI

This study aims to develop a web-based learning model so that it can provide innovation in the learning process of Al-Qur'an Hadith subjects in the Surah An-Nas material which students must memorize and can remember the meaning, so researchers want to develop web-based learning with powtoon media to provide an interesting learning experience and can provide convenience to students. The research method using research and development and the development model carried out in this study is the ADDIE model which consists of five steps, namely: analysis (analyze), design (design), development (development), implementation (implementation), evaluation (evaluation). This research was conducted at MIN Hulu Sungai Selatan II. This research provides a product in the form of animated video powtoon to be tested for feasibility by media experts, material experts, and two groups of 18 students. The results of the trial were media experts with an average score of 3.6 which was categorized as very valid, material experts with an average value of 4 and categorized as very valid, and student respondents with an average score of 3.62 in the very good category.
Development of E-Commerce for Selling Honey Bees in the COVID-19 Era

During the Covid-19 pandemic, which has not yet ended, the business of selling honey is a type of business that is able to survive and even increase its market. The trend of selling honey in the era of the Covid-19 pandemic is quite stable and has the potential to be a good source of income because honey can be used as medicine and can increase the body's immunity. Fitorajo Bee Farm is a UMKM engaged in bee cultivation which is located in Pinang City, South Labuhanbatu Regency, North Sumatra Province with original honey bee products packaged in plastic bottle containers. The marketing system carried out by Fitorajo Bee Farm is carried out through word of mouth, Facebook social media, or through the WhatsApp application. Seeing the promising potential of the honey business in the Covid-19 pandemic era, and in order to reach a wider market, Fitorajo Bee Farm should improve and innovate by adopting e-commerce technology. Conventional marketing, which so far only reaches consumers on a limited scale, must be changed with a marketing system that can reach consumers from any corner. The purpose of this research is to build a marketing media for Fitorajo Bee Farm honey bees by implementing web-based e-commerce. The system development method used is the waterfall model, while the programming language used is PHP framework codeigniter with MySQL DBMS. The results showed that the codeigniter framework and MySQL DBMS can be applied to build e-commerce web-based honey bee marketing media.
Pedagogy of uncertainty

Educators around the world are facing the challenges and opportunities of 21st Century education, such as the COVID-19 pandemic, STEAM education, and the rise of digital immersive technologies presenting a promising field for the development of new ways to maximize the learning experience (Bakker, Cai &amp; Zenger, 2021) The integration of science, technology, engineering, art and mathematics (STEAM) offers an approach to educational design based on curricular integration and learning by doing with analog and virtual technologies (Quigley et al., 2020). In turn, STEAM promotes important pedagogical changes that encourage the development of new skills focused on collaborative work, inquiry and creativity in the face of a challenge or problem to be solved, as well as optimal sensorimotor deployment through haptic and visual perception when using emerging digital immersion technologies such as virtual and augmented reality (Videla-Reyes, Aguayo &amp; Veloz, 2021). All these changes lead to a new framework of pedagogical action based on uncertainty, since they are unfamiliar or unknown in the field of traditional education.&#x0D; &#x0D; Based on the above, we propose here the idea of a ​​’pedagogy of uncertainty’, which can be read in light of the latest and unpredictable changes that 21st Century education is experiencing due to the COVID-19 pandemic, the emergence of technological tools and unfamiliar virtual and online platforms that teachers and students had to learn and use during the march of the virus. The approach that we suggest here is based on the potential of STEAM educational environment design that focuses on providing signs or patterns of an emerging world, unlike traditional teaching methods in which the path to which students should arrive is already laid down in advance. From a STEAM educational design approach, the teacher and her/his students lay down a path in walking together, a motto used by the enactive approach to cognition that considers "cognition as embodied action that is always oriented towards something absent: on the one hand, there is always a next step for the system in its perceptually guided action; for the rest, the acts of the system are always directed towards situations that are not yet in act” (Varela, Thompson &amp; Rosch, 1991, p.238). In this presentation, we explore the notion of pedagogy of uncertainty in the light of enactivism, based on theoretical and empirical evidence about how teachers and students deal with an uncertain world by actively participating in integrated educational environments based on learning by doing approaches (Abrahamson, Dutton &amp; Bakker, 2021). In particular, we make special reference to how teachers can make their students learn from clues, impoverished traces, or traces of information available within their learning environments to solve a challenge or problem, to the extent that they investigate, create, manufacture and/or actively participate in technology inside and beyond the classroom.
The Analysis of Differential High-Quality Development of Economy by Deep Neural Network and Internet of Things

This study solves the problems of insufficient objectivity and a large amount of calculation in traditional regional economic development level evaluation methods. The conventional Back Propagation (BP) neural network can easily fall into local extremes and slow convergence speed. Firstly, an improved BP algorithm is proposed. The LM (Levenberg-Marquardt) algorithm optimizes the BP neural network. Secondly, the evaluation model of Henan County’s economic development level is constructed based on the improved BP algorithm. Finally, Matlab software is used to design simulation experiments. The BP model is used to evaluate the comprehensive economic development level of 107 counties in Henan Province. The results show that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Square Error (MSE) of the output of the proposed BP model are 0.9, 1.72, and 3, respectively. This is far lower than other popular algorithms and is more suitable for estimating the comprehensive development level of the region. The overall economic development level of counties in Henan Province is relatively balanced but partially uneven. The polarization of development is more serious. The middle-level and middle- and low-level development counties account for a relatively large area, and the overall distribution characteristics are “convex”. This study aims to provide necessary technical support for a more accurate analysis of Henan counties’ comprehensive economic development level distribution and then to promote the coordinated development of counties in the province.
Attention-Augmented Electromagnetic Representation of Sign Language for Human-Computer Interaction in Deaf-and-Mute Community

In order to provide a new interface between computers and deaf-and-dumb users, this paper proposed a method of translating sign language into a sequence of time-frequency spectrograms based on a 24 GHz 1T-2R Doppler radar sensor. By processing two pairs of the immediate frequency I/Q signals based on time-frequency analysis, a complete sign sentence can be captured and segmented according to the electromagnetic wave-based patterns. Rather than the traditional classifier, a convolutional neural network was utilized to classify the basic signs and make the complete sentence lucid to the computer. For greater accuracy, an attention module was augmented to the network. The proposed methods could reach the accuracy of 96% in translating short sentences such as &#x201C;Yes&#x201D;, &#x201C;No&#x201D;, &#x201C;Thanks&#x201D;, and &#x201C;Hello&#x201D;, which are with the highest usage rate in sign language. The work done by this paper can be considered as a supplement to current human-computer interactions, especially for the deaf-and-dumb community.
Sentence Subjectivity and Sentiment Classification

As discussed in Chapter 3, document-level sentiment classification is too coarse for practical applications. We now move to the sentence level and look at methods that classify sentiment expressed in each sentence. The goal is to classify each sentence in an opinion document (e.g., a product review) as expressing a positive, negative, or neutral opinion. This gets us closer to real-life sentiment analysis applications, which require opinions about sentiment targets. Sentence-level classification is about the same as document-level classification because sentences can be regarded as short documents. Sentence-level classification, however, is often harder because the information contained in a typical sentence is much less than that contained in a typical document owing to their length difference. Most document-level sentiment classification research papers ignore the neutral class mainly because it is more difficult to perform three-class classification (positive, neutral, and negative) accurately. However, for sentence-level classification, the neutral class cannot be ignored because an opinion document can contain many sentences that express no opinion or sentiment. Note that neutral opinion often means no opinion or sentiment expressed.