Reviewerly/exHarmony huggingface dataset:
Analysis On The Grammatical Errors In The Student’s Writing Of Simple Present Tense

This study is about gammatical errors in student’s writing of the simple present tense. This research was conducted at the University of PGRI Palangka Raya, especially for the first semester students of non-English study programs. The research process begins by making observations among students in applying the basic concepts of the simple present tense in daily activities. Before analyzing the research, the researcher conducted observations and written test on students. In observation, the researcher found that more than 50% of students could not understand the basic concepts of the simple present tense well. They often make some grammatical mistakes. The topic of this research is chosen based on the researcher's experience as an English teacher. Researcher often finds students' grammatical errors, especially in the simple present tense. The research objectives are formulated as follows: (1). To find out the types of grammatical errors made by the students in making simple present tense sentences (2).To find out the causes of grammatical errors made by the students. This study is qualitative research. The data collection technique is by doing written test. To collect the data, students are asked to write down their daily activities in English. After that, the data is analyzed. In this written test, there are 30 out of 40 students (75%) who can write simple present tense sentences correctly, somo other student still make errors gramatically. Errors that are mostly made by students are Omission errors, Addition Errors, Formation Errors, and ordering Errors. The causes of grammatical errors in student’s Writing are the first is Overgeneralization defined as a use of a grammar pattern strategy, available for use in new situations, and the second is Ignorance of rules restrictions include intralingual errors related to analogies.&#x0D;
Experience of Breastfeeding Failure by Primiparas within 3 Months after Childbirth

Purpose: The study aimed to understand and describe the experiences of breastfeeding failure of primiparas within three months after childbirth. Methods: Data were collected through individual in-depth interviews of 6 primiparas from May 2022 to June 2022, and the transcribed data were analyzed using Colaizzi''s phenomenological six-step method. Results: 6 theme clusters and 13 themes were extracted from meaningful statements about the primiparas'' experience of breastfeeding failure within three months after childbirth. The six thematic clusters are "Preparation and Expectation of Breastfeeding during Pregnancy", "Barriers to Breastfeeding after Childbirth", "Efforts to Continue Breastfeeding", "Conflicts on Stopping Breastfeeding", "Decision to Stop Breastfeeding'', and Regret Coming from Breastfeeding Failure.” Conclusion: By profoundly understanding the experiences of primiparas who fail to breastfeed early after childbirth, this study will be able to provide evidence for effective ways to continue breastfeeding and to develop educational programs.
Joint Optimization of Multiple Loss Function for Fine-Grained Image Recognition

Fine-grained image recognition is a challenging task to distinguish objects with subtle differences between subcategories, whose difficulties lie in the small inter-class difference and large intra-class difference. So the extraction of more discriminative feature is the core of the task. In this paper, a joint optimization of multi-loss function for deep neural network is designed for this task with an aim to improve the recognition accuracy. Taking AlexNet and ResNet-18 as the backbones, we build a multi-loss function by the combination of cross entropy loss function and triplet loss function, which can compensate the defects of using a single loss function. The proposed algorithm achieves recognition rate of 75.819%, 82.905% and 78.543% on three public fine-grained image data sets CUB-200-2011, Orchid2608 and Fashion-60 respectively, which is an obvious improvement against using a single loss.
Dynamic data augmentation parameter learning-based unbalanced facial emotion recognition

Training a robust artificial facial expression recognition system requires diverse samples. In-the-lab datasets are collected under class-balanced distribution. By contrast, samples in the real world are more complex and imbalanced. Traditional data augmentation (DA) methods apply random operation amplitudes and unidirectional expansions in the feature space, which leads to bias in classes with more samples. Our paper proposes a DA model and combines it with a feature enhancement mechanism for unbalanced facial emotion recognition. We introduce a learning-based dynamic sample adjustment mechanism that selects the most valuable samples to guide the training process of each sample’s augmentation parameters. It explores each sample’s maximum augmentation potential and increases the accuracy in a class-balanced way. Compared with state-of-the-art methods on the facial emotion recognition task, it achieves the best average accuracy of 86.08% on the real-world affective faces database, outperforming the state-of-the-art imbalanced learning methods. Primarily, it shows significant accuracy improvement for extreme minority classes, which improve by over 15% at best compared with previous works.
Numerical simulations of noisy quantum circuits for computational chemistry

Abstract The opportunities afforded by near-term quantum computers to calculate the ground-state properties of small molecules depend on the structure of the computational ansatz as well as the errors induced by device noise. Here we investigate the behavior of these noisy quantum circuits using numerical simulations to estimate the accuracy and fidelity of the prepared quantum states relative to the ground truth obtained by conventional means. We implement several different types of ansatz circuits derived from unitary coupled cluster theory for the purposes of estimating the ground-state energy of sodium hydride using the variational quantum eigensolver algorithm. We show how relative error in the energy and the fidelity scale with the levels of gate-based noise, the internuclear configuration, the ansatz circuit depth, and the parameter optimization methods.
Identification of Shortwave Radio Communication Behavior Based on Autocorrelation Spectrogram Features

Cognitive communication behavior is becoming a research hotspot in the field of communication confrontation. In theory, the behavioral intention of noncooperating parties can be obtained by analyzing communication signals. Considering the complexity of the actual electromagnetic environment, even when the signal-to-noise ratio (SNR) is low, a certain accuracy still needs to be guaranteed. In this paper, according to five types of physical burst waveforms defined by the shortwave radio interoperability standard, a signal feature extraction method based on autocorrelation spectrogram features is proposed, and a two-input convolutional neural network (CNN) for classification is designed to improve the identification ability of shortwave communication behavior. The experimental results illustrate that the five kinds of shortwave radio communication behaviors can be accurately identified even when the noise is large. The research in this paper can directly analyze the communication behavior through physical layer signal without demodulation, which has the ability to grasp the communication behavior of the shortwave radio station in real time.
SpellInk: Interactive Correction of Spelling Mistakes in Handwritten Text

Despite the current dominance of typed text, writing by hand remains the most natural mean of written communication and information keeping. Still, digital pen input provides limited user experience and lacks flexibility, as most of the manipulations are performed on a digitalized version of the text. In this paper, we present our prototype that enables spellchecking for handwritten text: it allows users to interactively correct misspellings directly in a handwritten script. We plan to study the usability of the proposed user interface and its acceptance by users. Also, we aim to investigate how user feedback can be used to incrementally improve the underlying recognition models.
Classification of Imbalanced Data Set in Financial Field Based on Combined Algorithm

In view of the imbalance of data categories in financial data mining, a two-stage classification algorithm was proposed based on SVM and KNN to classify imbalanced data. In the first stage, two one-class SVM classifiers are constructed, and the samples are divided into four types: majority class (MC), minority class (mC), boundary, and outlier while the KNN algorithm is introduced to classify boundary and outlier samples in the second stage. In addition, the effectiveness of the algorithm is verified by several imbalanced data sets in the financial field. The results show that the proposed algorithm has predominant execution in financial precision marketing analysis, and compared with other algorithms, the proposed algorithm achieves better performance in G-mean and AUC and F1 indexes. This research provides an effective way for the classification of imbalanced data in the financial field.
Assessment of Information Security on Regional Financial Management Information System using KAMI Index 4.2

The security aspect concerns confidentiality, integrity, and availability.Problems with the implementation of security in SIMDA Finance have never held an information security assessment, there is no official policy regarding access rights to use SIMDA so data changes often occur.BPKAD requires an information security assessment to get an overview of the application of information security.The assessment is carried out using a questionnaire from the Information Security (KAMI) Index which refers to the ISO/IEC 27001:2013 standard, where respondents fill out questionnaires in 7 assessment areas.The results of the assessment show that the final score in the Electronic System area is in the High category with a score of 27, which means that SIMDA Finance is a part that is tied to the ongoing work process.The maturity level with a total score obtained from the results of the 5 areas of the KAMI Index is 218 which is at the Infeasible level.Meanwhile, for the maturity level, it gets groups I to II so it does not meet the feasibility of ISO 27001:2013 standardization.The results of the recommendations are in accordance with the results of the analysis, so SIMDA Finance needs to document all ongoing processes.Agencies need to pay attention to the implementation of information security internally and externally.
Facial Expression Recognition Using Hyper-Complex Wavelet Scattering and Machine Learning Techniques

Human emotion recognition is an active research topic in analysing the emotional state of humans over the past few decades. It is still a challenging task in artificial intelligence and human–computer interaction due to its high intra-class variation. Facial emotion analysis achieved more appreciation in academic and commercial potential challenges mainly in the field of behaviour prediction and recommendation systems. This paper proposes a novel scattering approach for recognizing facial dynamics using image sequences. Initially, we extract the temporal information from the facial frame by applying a saliency map and hyper-complex Fourier transform (HFT). Later the extracted high-level features are fed to the scattering transform method and machine learning algorithms to classify the seven emotions from the MUG dataset. The performance of proposed wavelet scattering network was evaluated on four different machine learning algorithms and achieved a high rate of recognition accuracy in all classes. In the experimental results, K-NN exhibits the proposed architecture’s effectiveness with an accuracy rate of 97% for the MUG dataset, 95.7% for SVM, 93.7% for decision tree and 91.2% naive Bayes, respectively.
Background Subtraction and Singular Value Decomposition Based Movement Epenthesis Detection in Isolated Gestures of Sign Language

This paper addresses the issue of movement epenthesis detection in isolated gestures. A novel method based on background subtraction and singular value decomposition to detect movement epenthesis in isolated gestures are proposed in this paper. The singular values of the absolute difference matrix obtained after the background subtraction provide a set of discriminative features to segment the movement epenthesis frames and sign frames. An adaptive threshold value is determined using the statistical properties of singular values for movement epenthesis detection. We tested our method on the ChaLearn LAP IsoGD dataset and the videos containing Indian Sign Language words. Experimental results show that our approach detected movement epenthesis frames with an accuracy of 91.14% and 93.73% on the ChaLearn LAP IsoGD dataset and the Indian Sign Language dataset, respectively.
Dense SIFT-Based Facial Expression Recognition Using Machine Learning Techniques

Facial analysis is an active research topic in examining the emotional state of humans over the past few decades. It is still a challenging task in computer vision due to its high intra-class variation, head pose, suitable environment conditions like lighting and illumination factors in behaviour prediction and recommendation systems. This paper proposes a novel facial emotion representation approach based on dense descriptors for recognizing facial dynamics on image sequences. Initially, the face is detected using the Haar cascade classifer to extract the temporal information from the facial frame by applying a scale invariant feature transform by combining a bag of visual words. Later, the extracted high-level features are fed to machine learning algorithms to classify the seven emotions from the MUG dataset. The proposed dense SIFT clustering performance was evaluated on four different machine learning algorithms and achieved a high rate of recognition accuracy in all classes. In the experimental results, K-NN exhibits the proposed architecture’s effectiveness with an accuracy rate of 91.8% for the MUG dataset, 89% for SVM, 87.6% for Naive Bayes, and 85.7% decision tree, respectively.
Adaptive Assessment and Guessing Detection Implementation

Computerized adaptive testing (CAT) is a context-based adaptive assessment. However, the assessment result may not be valid because the examinee might cheat or guess the answers. Although there are many guessing detection methods, there are not many discussions about their implementation into CAT. Therefore, this paper presents an example of a modification of an existing software so the newly modified software can detect guessed answers and be able to select questions adaptively. The system can detect assuming behavior by recording the examinee’s answer time. Also, the designed system can like questions adaptively by connecting Fuzzy logic, which calculates what level the question should select for the next iteration. The system is responded well by elementary and college students. A total of 56.6% felt the system was straightforward to use. The detection methods can detect guessing behavior of about 73%. However, the system’s sensitivity is low if the method is forced to classify answers which answered in a long response time / general guessing. Nevertheless, when we limit the data classified within 10s response time (rapid-guessing), the method’s sensitivity rises to 68.78%.
On Anomaly Detection in Graphs as Node Classification

Graphs have been exercised as appealing candidates for modeling relational datasets in different domains such as cryptocurrency transaction networks, social networks, rating platforms, and many more. Recently, different powerful methods have emerged to analyze datasets where the complex underlying data connectivity can be modeled by graphs. These methods have demonstrated promising performance on graph common tasks including node classification. In this paper, we explore the impact of graph-based techniques for detecting anomalous entities on real-world networks. We focus on modeling the problem of detecting anomalous entities on a network as a node classification task, and inspect the role of different approaches together with the evaluation setup and metrics to provide several useful recommendations for practical applications. We investigate different ways of handling the imbalance issue of the datasets which is a common problem when dealing with datasets containing anomalies, and demonstrate how a method that is agnostic to the dataset imbalance may show misleading performance. Through extensive experiments on six real-world datasets in balanced and unbalanced setting for a node classification task, we provide several recommendations that can shed more lights on challenges of selecting the appropriate methods, settings, and performance metrics that better align with the intrinsic attributes of a specific dataset and task.
Efficient computation of spaced seeds

Background The most frequently used tools in bioinformatics are those searching for similarities, or local alignments, between biological sequences. Since the exact dynamic programming algorithm is quadratic, linear-time heuristics such as BLAST are used. Spaced seeds are much more sensitive than the consecutive seed of BLAST and using several seeds represents the current state of the art in approximate search for biological sequences. The most important aspect is computing highly sensitive seeds. Since the problem seems hard, heuristic algorithms are used. The leading software in the common Bernoulli model is the SpEED program. Findings SpEED uses a hill climbing method based on the overlap complexity heuristic. We propose a new algorithm for this heuristic that improves its speed by over one order of magnitude. We use the new implementation to compute improved seeds for several software programs. We compute as well multiple seeds of the same weight as MegaBLAST, that greatly improve its sensitivity. Conclusion Multiple spaced seeds are being successfully used in bioinformatics software programs. Enabling researchers to compute very fast high quality seeds will help expanding the range of their applications.
Neighboring Algorithm for Visual Semantic Analysis toward GAN-Generated Pictures

Generative adversarial network (GAN)-guided visual quality evaluation means scoring GAN-propagated portraits to quantify the degree of visual distortions. In general, there are very few image- and character-evaluation algorithms generated by GAN, and the algorithm's athletic ability is not capable. In this article, we proposed a novel image ranking algorithm based on the nearest neighbor algorithm. It can obtain automatic and extrinsic evaluation of GAN procreate images using an efficient evaluation technique. First, with the support of the artificial neural network, the boundaries of the variety images are extracted to form a homogeneous portrait candidate pool, based on which the comparison of product copies is restricted. Subsequently, with the support of the K-nearest neighbors algorithm, from the unified similarity candidate pool, we extract the most similar concept of K-Emperor to the generated portrait and calculate the portrait quality score accordingly. Finally, the property of generative similarity that produced by the GAN models are trained on a variety of classical datasets. Comprehensive experimental results have shown that our algorithm substantially improves the efficiency and accuracy of the natural evaluation of pictures generated by GAN. The calculated metric is only 1/9-1/28 compared to the other methods. Meanwhile, the objective evaluation of the GAN and human consistency has increased by more than 80% in line with human visual perception.
Prospective Case-control Study of Contact Tracing Speed for Emergency Department-based Contact Tracers

Introduction: In Snohomish County, WA, the time from obtaining a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test and initiating contact tracing is 4-6 days. We tested whether emergency department (ED)-based contact tracing reduces time to initiation and completion of contact tracing investigations. Methods: All eligible coronavirus disease 2019 (COVID-19)-positive patients were offered enrollment in this prospective case-control study. Contact tracers were present in the ED from 7 AM to 2 AM for 60 consecutive days. Tracers conducted interviews using the Washington State Department of Health’s extended COVID-19 reporting form, which is also used by the Snohomish Health District (SHD). Results: Eighty-one eligible SARS-CoV-2 positive patients were identified and 71 (88%) consented for the study. The mean time between positive COVID-19 test result and initiation of contact tracing investigation was 111 minutes with a median of 32 minutes (range: 1-1,203 minutes). The mean time from positive test result and completion of ED-based contact tracing investigation was 244 minutes with a median of 132 minutes (range: 23-1,233 minutes). In 100% of the enrolled cases, contact tracing was completed within 24 hours of a positive COVID-19 test result. For comparison, during this same period, SHD was able to complete contact tracing in 64% of positive cases within 24 hours of notification of a positive test result (P &lt; 0.001). In the ED, each case identified a mean of 2.8 contacts as compared to 1.4 contacts identified by SHD-interviewed cases. There was no statistically significant difference between the percentage of contacts reached through ED contact tracing (82%) when compared to the usual practice (78%) (P = 0.16). Conclusion: When contact tracing investigations occur at the point of diagnoses, the time to initiation and completion are reduced, there is higher enrollment, and more contacts are identified.
The Education of Gifted in Special Education Domain and the Role of ICTs

Gifted and talented students do not constitute one homogenious group. This fact evokes the need for the definition of their characteristics and identification of this population. Undoubtedly metacognitive skills can lead one to recognize his/her strengths and weaknesses and lead him/herself to the upper levels of the pyramid of knowledge. Moreover, identification of giftedness at school requires more than IQ tests, as it is multidimensional. Research argues that early identification and intervention are crucial due to fulfilling their needs for education. Furthermore, early detection of giftedness for students with special needs provides equal opportunities to dual strength students.
Analysis of Text Mining Keyword Issues for Defense Drone on Social Network

Research on weapon systems has been conducted, and defense drones have been developed. However, few analyses that identify the needs of the general public have been conducted. The purpose of this study is to analyze the issues of interest in social networks regarding defense drones and extract keywords for activation. In this study, big data analysis tools such as Textom, Ucinet6, and NetDraw were used. The results of the keyword frequency analysis, centrality analysis, and CONCOR analysis showed that public information regarding social networks expressed keywords in defense drones, such as Defense Daily, News, Enterprise, Defense Science Research Institute, University, Hydrogen Battery, Army, Air Force, Urban Transportation, Fire, Regional, Enterprise, and Research. It is necessary to revitalize media publicity in developing cooperative technologies by companies, universities, the National Defense Research Institute, urban transportation, and firefighting, using army and air force drones, and developing future hydrogen batteries. In addition, issues of interest were observed for contents, such as applications in the United States, Israel, North Korea, Iran, and Afghanistan, and fostering university education. There is a need to present a case for the actual military use of defense drones and train the department of defense drones at universities.