Reviewerly/exHarmony huggingface dataset:
A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition

We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-of-speech (POS) tags, and morphological information. While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information. Thus, we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity. Our evaluation results over a standard dataset show sizable improvements over the state-of-the-art for Bulgarian NER.
A Statistical Defense Approach for Detecting Adversarial Examples

Adversarial examples are maliciously modified inputs created to fool deep neural networks (DNN). The discovery of such inputs presents a major issue to the expansion of DNN-based solutions. Many researchers have already contributed to the topic, providing both cutting edge-attack techniques and various defensive strategies. In this work, we focus on the development of a system capable of detecting adversarial samples by exploiting statistical information from the training-set. Our detector computes several distorted replicas of the test input, then collects the classifier's prediction vectors to build a meaningful signature for the detection task. Then, the signature is projected onto the class-specific statistic vector to infer the input's nature. The classification output of the original input is used to select the class-statistic vector. We show that our method reliably detects malicious inputs, outperforming state-of-the-art approaches in various settings, while being complementary to other defensive solutions.
One Hidden Semantic Model Based on Intergroup Effects for E-Commerce

E-commerce systems often collect data that clearly express user preferences without considering the remaining negative cases, which gives rise to the hidden semantic problem. In this paper, we improve the original hidden semantic model and propose an intergroup effect model that incorporates users’ historical browsing behavior, user type, and browsing content; by adopting the weighting and add weighting factors, we can predict users’ preferences for different products more accurately and match the candidate products with users’ current behaviors, so as to give more reasonable and effective product recommendation results; by adding the group effect model of user group and product group, we can achieve more accurate prediction of user preferences and make the recommendation more reasonable and effective. The research shows that the hidden semantic method based on intergroup effects information is better than other basic methods at a certain identified evaluation stage. In practice, users’ purchasing preferences change with time, and using a hidden semantic method based on intergroup effects recommendation can effectively improve the recommendation quality of e-commerce recommendation systems.
PENINGKATAN SIKAP PERCAYA DIRI SISWA MELALUI MODEL TEAMS GAMES TOURNAMENT (TGT) BERBANTUAN MEDIA KARTU KELAS IV SD 2 BAKALAN KRAPYAK

This research aims to measure the improvement of students’ confidence and improvement of teacher’ teaching skills the fourth grade students at SD 2 Bakalan Krapyak.This classroom action research was carried out in two cycles.The result of this research shown there are significant improvement of Students'confidence in learning realmof attitude at the pre-cycle level was 46% as a not good category, after doing the research in the cycle I it changed into 69% as a good enough category and increased more in thecycle IIto be 84% as a good category. The management of Teams Games Tournament model learning also increased in cycle I 77% as a good category to 87% as a good category in cycle II. It proves that used of Teams Games Tournament model use cards media can improve attitude self confidence in fourth grade students theme 4 at SD 2 Bakalan Krapyak.
The Existence of Pragmatic Markers in Americasâ€™ Got Talent Judgesâ€™ Commentaries

This study attempts to explain the use of pragmatic markers in Americasâ€™ Got Talent judgesâ€™ commentaries. The aims of the study are to analyze the existence of verbal and visual pragmatic markers and explain their relationship. The verbal pragmatic markers are categorized into four types based on the typology of pragmatic markers proposed by Fraser (1996). They are basic markers, commentary markers, parallel markers, and discourse markers. While the visual pragmatic markers divided into thinking face, pointing with gaze and hand movement and smile following the pragmatic function facial gestures from Bavelas &amp; Chovil (2013). This research employed descriptive method with qualitative approach. The object of the study is judges of Americasâ€™ Got Talent season 13 which consist of Simon Cowell, Heidi Klum, Mell B, and Howie Mendel. The study revealed that the judges used the basic markers to express the main message of the comment, commentary markers to express the message contains in the comment toward the main message, parallel markers to express the complement message toward the main message and the discourse markers to express the relation between the main message and the other utterance. The visual pragmatic markers performed also signals certain message related to verbal markers. The judges performed the thinking face to signal the word search, pointing with gaze and hand movement to emphasize the messages convey in utterance and smile to signal pleasure. This research could provide an understanding of EFL learners in using pragmatic markers as a way to improve communication strategy in communication.
Sistem Pengelolaan Peserta Didik yang Baik Demi Pendidikan Berkualitas

To achieve the goal of quality education requires that education management be able to collaborate with all the elements in the education system. That educational management is linked to the management of learners in which it is both management and the accomplishment. Home-management systems are still using conventional means and place more emphasis on developing intelligence in the narrow sense and giving less attention to the development of the creative talent of learners. Whereas creativity aside from good self-development the self-educated is also a need for self-realization as one of mankind's highest needs. The current education arrangement should be worked to provide special services to learners who have creativity as well as different incentives for education to be better directed.
Artikel Administrasi Sarana dan Prasarana

Educational facilities and infrastructure is one of the important and main resources in supporting the learning process in school. For this reason, it is necessary to increase the utilization and management so that the expected goals can be achieved. In srana and educational infrastructure management includes planning, procuring, maintaining, inventorying and eliminating school infrastructure. Administrative activities in the field of educational facilities and infrastructure include; planning, procurement, inventory, distribution, utilization and maintenance, deletion, and supervision of educational facilities and infrastructure. The role of the teacher in the administration of school infrastructure: involved in planning the procurement of teaching aids, involved in the use and maintenance of teaching aids used by teachers, supervision in the use of practical tools by students.
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data

Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for subsequent planning. In this paper, we propose semi-supervised anomaly detection considering the imbalance of normal situations. In particular, driving data consists of multiple positive/normal situations (e.g., right turn, going straight), some of which (e.g., U-turn) could be as rare as anomalous situations. Existing machine learning based anomaly detection approaches do not fare sufficiently well when applied to such imbalanced data. In this paper, we present a novel multi-task learning based approach that leverages domain-knowledge (maneuver labels) for anomaly detection in driving data. We evaluate the proposed approach both quantitatively and qualitatively on 150 hours of real-world driving data and show improved performance over baseline approaches.
Dual embedding with input embedding and output embedding for better word representation

Recent &lt;span lang="EN-US"&gt;studies in distributed vector representations for words have variety of ways to represent words. We propose a various ways using input embedding and output embedding to better represent words than single model. We compared the performance in terms of word analogy and word similarity with each input and output embeddings and various dual embeddings which are the combination of those two embeddings. Performance evaluation results show that the proposed dual embeddings outperform each single embedding, especially with the way of simply adding input and output embeddings. We figured out two things in this paper, i) not only input embedding but also output embedding has such meaning to represent the words and ii) combining input embedding and output embedding as dual embedding outperforms the single embedding when we use input embedding and output embedding individually.&lt;/span&gt;
Gender and race classification using geodesic distance measurement

&lt;span lang="EN-US"&gt;Gender and ethnicity classifications are a long-standing challenge in the face recognition’s field. They are key-demographic traits of individuals and applied in real-world applications such as biometric and demographic research, human-computer interaction (HCI), law enforcement and online advertisements. Thus, many methods have been proposed to address gender or/and race classifications and achieved various accuracies. This research improves race and gender classification by employing a geodesic path algorithm to extract discriminative features of both gender and ethnicity. PCA is also utilized for dimensionality reduction of Gender-feature and race-feature matrices. KNN and SVM are used to classify the extracted feature. This research was tested on the face recognition technology (FERET) dataset, with classification results demonstrating high-level performance (100%) in distinguishing gender and ethnicity.&lt;/span&gt;
Breast cancer recognition based on performance evaluation of machine learning algorithms

Breast cancer is the one common cause of death in both developed worlds and the most death-causing disease diagnosed among women. &lt;span lang="EN-US"&gt;Early recognition of this condition can help to minimize death rates. The breast problem statement, in brief, is not reliable for accuracy recognition. They have a high degree of classification accuracy as well as diagnostic capabilities. The most common classifications are normal, benign cancer, and malignant cancer. Machine learning (ML) techniques are now widely used in the classification of breast cancer. In this paper, some machine learning technics have been investigated to diagnose breast cancer (BC) on magnetic resonance imaging (MRI) images using multi-step processes. The first step has been to take the MRI image as an input image and have been pre-processing an image, then use feature extraction by using (scale-invariant feature transform (SIFT), histogram of oriented gradient (HOG), local binary patterns (LBP), bag of words (BoW), and edge-oriented histogram (EOH)). Next step we implement the classifying algorithms (KNN, decision tree (DT), naïve Bayes, ANN, SVM, RF, AdaBoost), have been used to detect and classify the normal or breast cancer region for this purpose datasets like ACRIN-Contralateral-Breast-MRI, In and breast cancer MRI dataset) has been collected our breast cancer MRI images from Erbil and Sulaymaniyah hospital the results was 91.9%, the result of ACRIN was 97% and the results Breast Cancer was 92.3%.&lt;/span&gt;
Machine learning model to classify modulation techniques using robust convolution neural network

&lt;p&gt;In wireless c&lt;span&gt;communications receiver plays a main role to recognize modulation techniques which were used at the transmitter. While transferring information from transmitter to receiver, the receiver must retrieve original information. In order to achieve this goal we introduced a neural network architecture that recognizes the types of modulation techniques. The applications of deep learning can be categorized into classification and detection. The CNN architecture is used to perform feature extraction based on the layers to build a model which classifies the input data. A model that classifies the radio communication signals using deep learning method. The robust c (RCNN) is used to train the modulated signals; the transformations are used to help the neural network which estimate the signal to noise ratio of each signal ranges from -20dB to 18dB with loss and accuracy of 89.57% at SNR 0dB.&lt;/span&gt;&lt;/p&gt;
Low feature dimension in image steganographic recognition

Steganalysis &lt;span&gt;aids in the detection of steganographic data without the need to know the embedding algorithm or the "cover" image. The researcher's major goal was to develop a Steganalysis technique that might improve recognition accuracy while utilizing a minimal feature vector dimension. A number of Steganalysis techniques have been developed to detect steganography in images. However, the steganalysis technique's performance is still limited due to their large feature vector dimension, which takes a long time to compute. The variations of texture and properties of an embedded image are clearly seen. Therefore, in this paper, we proposed Steganalysis recognition based on one of the texture features, such as gray level co-occurrence matrix (GLCM). As a classifier, Ada-Boost and Gaussian discriminant analysis (GDA) are used. In order to evaluate the performance of the proposed method, we use a public database in our proposed and applied it using IStego100K datasets. The results of the experiment show that the proposed can improve accuracy greatly. It also indicates that in terms of accuracy, the Ada-Boost classifier surpassed the GDA. The comparative findings show that the proposed method outperforms other current techniques especially in terms of feature size and recognition accuracy&lt;/span&gt;.
Detecting face mask using eigenfaces and vanilla neural networks

Coronavirus has &lt;span&gt;become one of the most deadly pandemics in 2021. Starting in 2019, this virus is now a significant medical issue all over the world. It is spreading extensively because of its modes of transmission. The virus spreads directly, indirectly, or through close contact with infected people. It is proclaimed that people should wear a mask in public areas as a counteraction measure, as it helps in suppressing transmission. A portion of the spaces, where the virus has broadly fanned out, is because of inappropriate wearing of facial cover. In crowded areas, keeping a check on facial masks manually is difficult. To automate this process, an effective and robust face mask detector is required. This paper discusses a hybrid approach using a machine learning technique called eigenfaces, along with vanilla neural networks. The accuracy was compared for three different values of principal components. The test accuracy achieved was 0.87 for 64 components, 0.987 for 512 components, and 0.989 for 1,000 components. Hence, this approach proved to be more promising and efficient than its counters.&lt;/span&gt;
Hindi to English transliteration using multilayer gated recurrent units

Transliteration is &lt;span lang="EN-US"&gt;the task of translating text from source script to target script provided that the language of the text remains the same. In this work, we perform transliteration on less explored Devanagari to Roman Hindi transliteration and its back transliteration. The neural transliteration model in this work is based on a sequence-to-sequence neural network that is composed of two major components, an encoder that transforms source language words into a meaningful representation and the decoder that is responsible for decoding the target language words. We utilize gated recurrent units (GRU) to design the multilayer encoder and decoder network. Among the several models, the multilayer model shows the best performance in terms of coupon equivalent rate (CER) and word error rate (WER). The method generates quite satisfactory predictions in Hindi-English bilingual machine transliteration with WER of 64.8% and CER of 20.1% which is a significant improvement over existing methods.&lt;/span&gt;
A Novel Machine Learning Technique for Fraud Detection on Credit Card Financial Data

The credit card exchanges has expanded decisively in the new year’s, as has how much fakes and card robberies. Ordinary robbery, skimming fakes, fake cheats and erroneous card application extortion are ways that Visa misrepresentation can be committed. Albeit the fraudster in every one of these cases uses a material card, an actual belonging isn't expected to commit Visa extortion. "Cardholder-not-present" misrepresentation, in which simply the card's information are introduced, is one of the most well-known kinds of extortion (e.g., via telephone). The most direct kind of credit card robbery is the utilization of a taken card. In this present circumstance, the fraudster generally attempts to burn through however much cash as could reasonably be expected in as brief period as doable. A progression of AI models are utilized to battle misrepresentation, with the ideal arrangement being picked after an assessment. The AI models are utilized to identify fakes and recognizes the certifiable and deceitful exchanges.
Adiabatic based Algorithm for SAT: A comprehensive algorithmic description

This paper concerns quantum heuristics that are able to extend the domain of quantum computing defining a promising way in the large number of well-known classical algorithms. Quantum approximate heuristics take advantage of alternation between one Hamiltonian defining the problem to solve and one mixing Hamiltonian. The adiabatic theorem initially defines in quantum physic allow to compute a solution for the Schrödinger equation, but the foundation of this methods requires strong skill in physics and mathematics. Our main objectives in this paper are at first to provide an algorithm-based presentation (as close as possible of the classical computer science community in operational research practice) of the adiabatic optimization and secondly to give a comprehensive resolution of the well-known SAT problem. This presentation gives opportunities to provide a concise but explicit analysis of the adiabatic capability to define a new efficient operational research trend. Our experiments encompass numerical evaluations on both simulator and on real quantum computer provided by IBM. Numerical evaluations using the QLM library meet the Qiskit evaluations. This contribution is at the crossroad of physic and computer science in the sense it proves the capabilities of quantum concepts to define a new and promising research trends in optimization.
Analysis of the Subtitle Translation Strategy of the Film Confucius

Subtitle translation is an important part of cross-cultural communication in films. This paper attempts to explore the subtitle translation of the film Confucius from a teleological perspective. The translator uses a combination of translation strategies and methods such as dissimilation, naturalization, reduction and addition to achieve the function of cultural communication and dissemination according to the characteristics and purposes of film subtitle translation.
A Novel Discrimination Structure for Assessing Text Semantic Similarity

&lt;p&gt;Discrimination of semantic textual similarity refers to comparing the similarity between two or more entities (including words, short texts and documents) through certain strategies to obtain a specific quantitative similarity value. Traditional research put more experience into the similarity calculation of the original text content, using the matching degree or distance of characters or words as the yardstick to judge whether the text pairs are similar. However, there are still some problems to be solved in the following aspects: the key points of sentence meaning and word semantics, which play important role in the semantic expression of natural language, are not well integrated into the similarity discrimination, and the interactive features between texts are not fully utilized. To solve the above problems, this paper proposes a novel discrimination structure based on the Siamese Network model and the idea of text matching. In this structure, we introduce sentence meaning key information and word semantic information to realize the extraction of word interaction feature information, and then we realize the text vector representation by using Siamese BiLSTM. The experimental results showed that the accuracy of the proposed model is higher than that of the basic models.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A Specific Kind of Representation: How Systematics May Ease Cognitive Overload

Multiple representations are beneficial for meaningful understanding. However, three or more representations may add to the cognitive overload of students, if not in interactive diagrams and dynamic geometry. How a well-known representation consisting of more than 3 or more representational registers may overcome the problem of cognitive overload without being too complicated. In this study, an old but well-structured representation that was used even over 40 years was analyzed. The critical points of a function, asymptotes, x / y-intercepts, inflection points, and graphing can be identified easily. It is prepared in the form of a table and the factors of the first derivative of the function and the second derivative and their roots indicate the function’s increasing and decreasing intervals and its graph. This representation is very systematic and it acts like a method to draw the function’s graph with no-fault possible. Yet, besides being used for many years, is still used for courses like Calculus, etc. We argue that cognitive overload theory cannot alter this representation due to its systematic nature. In content analysis, some examples of this representation are shared via the reader, and some qualitative aspects about it are analyzed. Finally, its systematicity, well-structured nature, and nature in reducing extraneous cognitive load are emphasized. The important thing here is that it is very strategic not to lose some representations for the sake of new ones if their value is already known but not discussed too much.
Visualisasi Scattering Cross Section Versus Scattering Angle Pada Hamburan Rutherford Atom Timbal dengan Matrix Laboratory

Atom is the smallest component of a chemical element that cannot be changed chemically. The concept of the atom was put forward by several scientists, one of which was Rutherford. From the results of Rutherford's experiment, it is known that most of the alpha particles fired at a thin gold plate are transmitted or scattered with a small scattering angle. In this study, the effect of incident energy (E) on the scattering cross section (σ) and scattering angle (Θ) will be simulated using Matrix Laboratory (MATLAB). Based on the results of the research that has been done, it can be concluded that in Rutherford scattering, the greater the scattering angle, the smaller the scattering cross section. This is shown in the graph generated in the Matrix Laboratory (MATLAB) simulation in the form of a decreasing exponential graph.