Reviewerly/exHarmony huggingface dataset:
Are You Flirting, Objectifying or What? a Conversation Analysis of “you’re very sexy” Conversational Turn

Intent identification is one of the most critical components in conversational agent design. Conversational agent “is any dialogue system that not only conducts natural language processing but also responds automatically using human language.” (Conversational Agent, 2019). The crux of designing human-like conversational agent is to mimic how human understands another human and then responds “naturally”. The current study attempts to answer the fundamental question: how to model human processes of understanding another human? In order to answer that question, it starts from exploring some basic concepts relevant to intent identification from Conversation Analysis (CA). CA is a mature field that studies authentic human interaction. The basic concepts from CA are then synthesised into a model that potentially fit to existing framework and paradigm in conversational agent design, i.e. Natural Conversation Framework (NCF) and Intent-Entity-Context-Response (IECR) paradigm. Instead of using a made-up sentence, the model is then tested to an authentic conversational turn seksi sekali dirimu ‘you’re very sexy’. The test shows that the model is able to detect several possible intents contain in this authentic conversational turn. The model is also able to handle Conversational Indonesian and multi-modality. Considering the versatility of Conversation Analysis, in all likelihood the model will be able to handle any language and all kinds of modalities. Future study can be done to analyse more Conversational Indonesian data (to develop library of intent for Conversational Indonesian Language), as well as conversational data from different languages and conversational data containing diverse modalities.
FoFA: Diet Information for Children with Autism with Semantic Technology in Android Based Application

The number of people with autism in Indonesia increases by 0.15% or 6,900 children per year. One of the actions that can be done to overcome developmental disorders of children with autism is to do Feingold and Failsafe Diet, Specific Carbohydrate Diet (SCD diet), and Casein-Free Gluten Free diet (CFGF diet) on foodstuffs given to children with autism. There is a need for socialization and presentation of information regarding the regulation of food items given to children with autism. Currently, there is no presentation of information in the form of mobile-based applications as a forum for parents to exchange information, especially those that utilize semantic technology. By utilizing semantic technology, the Food For Autism (FoFA) application was created to share knowledge for users related to food and beverage diet menus for children with autism. The test results show that the application of FoFA can apply semantic technology related to diet and food diets for children with autism.
METHOD OF IMPLEMENTING HOMOMORPHIC DIVISION

The article deals with the problems of homomorphic cryptography. Homomorphic cryptographyis one of the young directions of cryptography. Its peculiarity lies in the fact that it is possibleto process encrypted data without preliminary decryption in such a way that the result of operationson encrypted data is equivalent, after decryption, to the result of operations on open data.The article provides a brief overview of the areas of application of homomorphic encryption. Tosolve various applied problems, support for all mathematical operations is required, including thedivision operation, and the ability to perform this operation homomorphically will expand thepossibilities of using homomorphic encryption. The paper proposes a method of homomorphicdivision based on an abstract representation of the ciphertext in the form of an ordinary fraction.The paper describes in detail the proposed method. In addition, the article contains an example ofthe practical implementation of the proposed method. It is proposed to divide the levels of dataprocessing into 2 levels – cryptographic and mathematical. At the cryptographic level, a completely homomorphic encryption algorithm is used and the basic homomorphic mathematical operationsare performed – addition, multiplication and difference. The mathematical level is a superstructureon top of the cryptographic level and expands its capabilities. At the mathematical level,the ciphertext is represented as a simple fraction and it becomes possible to perform thehomomorphic division operation. The paper also provides a practical example of applying thehomomorphic division method based on the Gentry algorithm for integers. Conclusions and possibleways of further development are given.
STUDY ON USING ENGLISH SONG IN TEACHING BIOLOGY 10 AND ONE EXAMPLE IN PRACTICE

Although the application of English songs in teaching Biology is one of the new teaching methods in Vietnam, it can become an effective method to help the students change both their looks and their ways to approach the natural science curriculum. The combination of smooth melodies and easy-to-remember content of the songs will help students easily remember general knowledge of the lesson. Moreover, the content of English songs will help them understand, memorize special vocabulary effectively, and improve their foreign language skills. A statistical investigation on the status of teaching Biology in English was carried out at some high schools in Hanoi. Several English songs had been used in teaching Biology grade 10. Pedagogical experiments have shown that Biology songs in English will benefit to create interest in learning and especially help them memorize biological terms in English easily.
The Implementation of Computer Assisted Language Learning (CALL) to Improve Speaking Ability of Students in Bali State Polytechnic

Preliminary observations conducted in Bali State Polytechnic found that the students' learning outcomes in speaking were still shallow. Modern facilities equipped with advanced technology such as computer devices known as CALL (Computer Assisted Language Learning) are vital to helping lecture in language teaching and learning process, including speaking. This study aims to improve student speaking learning outcomes in English courses by conducting CALL and how CALL can make a contribution in increasing speaking ability in Vocational Higher Education. The research method used in this research was mix method approach. The subjects were the students of Electrical Engineering, Bali State Polytechnic. The data were analyzed quantitatively and descriptively. The results showed that the video-assisted CALL method's application could improve student learning outcomes in English courses.int shows the differences in the pre-test and post-test results, where there is an increase of 14.34%. Besides, the CALL method also allows students to have more expressive activities in presenting conversation among their friends to feel happy and interested. Similarly, CALL can also create fun learning since the students learn using the internet and computer, which are commonly used
Interpolation Data Hiding Using Dual Folding Methodology

Interpolation is an important technique in digital image processing. This technique allows of reconstructing the intermediate pixels that are distorted in the process of stretching image appropriately. Therefore, this is a technical branch in the image hiding technique and it is considered to apply in steganography. Neighbor Mean Interpolation (NMI) technique was proposed by Jung and Yoo and later was improved on the Interpolation of Neighbouring Pixels (INP) technique by Lee and Huang to be more effective in securing the confidential information to third parties. Lee and Huang have improved the payload of 56% up to 108% from Jung and Yoo's method. However, both two methods have relatively low image quality. The strategy of Lu et al. has high effect in improving PSNR and payload because the basis of this technique is to minimize the value of the secret data before hiding them into the cover image. The proposed scheme applies Lu et al.’s central folding strategy twice to effectively enhance the PSNR and payload parameters compared with (NMI) and the Interpolation of Neighbouring Pixels technique (INP).
Sentiment Classification of Tweets in Twitter using CNN and Dropouts in RNN

Sentimental analysis is the computational study of people's opinion, attitudes and emotions toward entities, individuals, issues, events or topic.A lot of research has been done to improve the accuracy of sentiment analysis, varying from simple linear models to more complex deep neural network models.Recently, Deep learning has shown great success in the field of sentiment analysis and is considered as the stateof-the-art model.The twitter data imposes many challenges, due to its complex structure, various dialects, in addition to the lack of its resources.Although, the recent Deep learning models have improved the accuracy of the twitter sentiment analysis, there is still more chance for improvement.This encouraged to explore different deep learning hybrid models that have not been applied to twitter data, in order to improve the twitter sentiment analysis accuracy.The objective of this paper is to improve the accuracy of sentiment analysis of twitter data by implementing a hybrid model of CNN-RNN techniques and by introducing dropout in the hybrid model and also compare the performance of the proposed method with existingmodels.
A value-based role-playing game as a method of prospective teacher’s moral culture development

This paper substantiates the importance of prospective teachers moral culture development. The author argues the values realized in the value-orientational activity of the teacher as moral guidelines that indicate the path of spiritual renewal of society. The author shows the relationship between the value-orientational activity of the teacher and his or her moral culture. It is stated that a value-based role-playing game is an effective method of prospective teachers moral culture development. The author finds similarities and differences between a value-based role-playing game and an ethical dialogue. The paper also contains the differences between spontaneous classroom pedagogical situations with value content and specially prepared and played out value-based role-playing games according to a specially prepared scenario. There are some examples of spontaneous and specially prepared value-based role-playing games in this paper. The author substantiates the importance of value-based role-playing games for prospective teacher understanding of his or her purpose in life in general and in prospective professional activities in particular, as well as in the search for moral life and pedagogical guidelines. The author provides evidence that in the process of participation in value-based role-playing games prospective teachers, mastering the knowledge about the moral guidelines of the value-orientational activity of the teacher, extract new ideas, which are subsequently transformed into principles that become the foundations of ethical and pedagogical activity. The purpose of a value-based role-playing game as a method of prospective teachers moral culture development is revealed: a value-based role-playing game allows to organize the interaction of students with values that clarify the meaning of pedagogical activity, life and the teachers purpose in life.
The history of holographic vibration analysis

This paper describes the evolution of holographic vibration analysis from its discovery in 1964 to its present form as a technique of digital holography. The speckle interferometer is discussed as a replacement for real-time holographic interferograms and electronic speckle pattern interferometry (ESPI) as the precursor to digital holography. The development of methods for converting vibratory (Jo) fringe patterns into numerical data is discussed along with applications to Modal Assurance Criteria (MAC) calculations. Finally, an application is presented for vibratory strain measurement in one dimension.
PRACTICAL PRODUCTIVE ACTIVITIES FOR THE DEVELOPMENT OF MATHEMATICAL COMPETENCES

Development of knowledge, skills and competences for the mathematical quantity of “length” and its measurement units represent important part of the overall education in mathematics for students from Grades 1. – 4. &#x0D; This research work systematizes some theoretical concepts related to the competences. Based on the analysis of the relevant legislation in force in the Republic of Bulgaria researchers presented the relations between the specific mathematical competences and terminology from the competency Cluster “Measurement”.&#x0D; The research work is aimed at the development of such a methodology system of work which gives priority to practical productive activities thus facilitating development in primary school students of a higher level of knowledge, skills and competences related to the mathematical quantity of “length” and its measurement units.&#x0D; The new system of work was applied during the compulsory classes in mathematics in Grades 1. – 4. The research work presents some of the developed and applied options of practical activity.&#x0D; The researchers studied the objectiveness, the validity and the reliability of the set of tools and means used for doing diagnostics as well as the difficulty and the dividing (separating) strength of the mathematical tasks included in it. The results from the experimental work were statistically processed.
Dynamic resistance training device for evaluation of tongue motor function

In this study, we propose a device for isotonic training of tongue-pushing movements. This device realizes dynamic constant external force resistance training by applying a constant load by a DC geared motor to the tongue during the tongue up-and-down movement. This has a back-drive capability by using a series elastic actuator (SEA) with elasticity in the transmission shaft, and the force can be controlled without using a torque sensor. In addition, vibration is damped by impedance control to ensure a safe response for the tongue movement. The tongue muscle ability of five healthy subjects was evaluated using the proposed device. Subjects with higher maximal isometric contraction rates also had higher rates of upward and downward movement at light loads (1.5 N), but subjects who were able to quickly vocalize /ta/ syllables at oral diadochokinesis (OD) rates tended to have less tongue movement when using this device. From the experimental results, it was found that this device can measure motor agility under constant loading, and the index obtained under light loading correlates with the maximum tongue pressure during isometric contraction and negatively correlates with the OD rate results.
Task execution combined with in-contact obstacle navigation by exploiting torque feedback of sensitive robots

Collaborative redundant manipulators are becoming more popular in industry. Lately, sensitive variants of those robots are introduced to the market. Their sensitivity is owed to the unique technology of integrating torque sensors into their joints. This technology has been used extensively for collision detection. Nevertheless, it can be used in other collaborative applications. In this study, we present a novel control method that uses the torque feedback at the joints to perform automatic adjustment of the self-motion manifold during a contact with surrounding obstacles, while allowing the user to control the robot at the end-effector (EEF) level. This makes the interaction with sensitive redundant manipulators more intuitive to users. Experimental tests on KUKA iiwa robot proved the effectiveness of the proposed method for navigating obstacles during a contact with robot’s structure while keeping the precision in the task under execution.
Automatic diagnosis of stomach adenocarcinoma using Riesz wavelet transform

Adenocarcinoma originates from the glands. It causes changes in the gland architecture. The detection of adenocarcinoma requires histopathological examination of tissue specimens. At present, diagnosis and grading of the cancer depends on the visual interpretation of the biopsy samples by pathologist and thus, it may lead to a considerable amount of inter and intra-observer variability. To overcome this drawback and to reduce the reliance on the human interpretation and thereby reducing the workload of pathologists, many methods have been proposed. In this paper, a novel method to quantify a tissue for the purpose of automated stomach cancer diagnosis and grading is introduced. The stomach tissue images are preprocessed to compensate for colour variations. The Riesz wavelet transform is applied to the preprocessed stomach tissue images. From Riesz wavelet coefficients, 14 different statistical features were extracted. The wrapper-based feature selection is used. The reliability check on the final dataset is performed using ANOVA. In diagnosis, the tissue is classified into normal (non-malignant), well differentiated, moderately differentiated, poorly differentiated and tissue. The proposed system yielded a classification accuracy of 93.2% in diagnosing and 98.33% in grading.
Enhanced Security over Accounting Data: A Fuzzy-Based Evaluation Model to Aid Organizations in Safeguarding their Accounting Systems

Attacks on information are an ever-increasing threat to every industry.To protect financial information from accounting applications, organizations require general information technology controls (GITC) to operate effectively and comply with laws and regulations.GITC related to change management or system change controls (SCC) are critical in ensuring the accuracy and completeness of the aforementioned information.Alarmingly, the literature evidences traditional change management assessment methodologies that do not promote effective evaluation of SCC, prompting for the development of additional methods to assist organizations in protecting their financial information.This research proposes the development of a decision-support methodology, using fuzzy set theory, that can better safeguard accounting applications by allowing for a more robust implementation of SCC.It is argued that evaluating SCC using fuzzy set theory leads to a more precise assessment, resulting in a more secure financial environment.
The Impact of Human Resources Management Practices on Job Satisfaction among Telecommunication Firms in Jordan

Job satisfaction has become the target of many organizations which sought to achieve it through various practices to motivate the employee and give the best in their jobs. The major purpose of this research is to examine the impact of human resource management HRM practices on job satisfaction in Jordanian telecommunication firms. The data used in this study were collected through a survey questionnaire was designed and adapted to fit the scope of the study and measuring the variables involving in this work to provide new valued insights from 223 samples from three different telecommunication firms operating in Jordan named Orange, Umniah and Zain. This sector has an essential contribution to this sector in the national economy and foreign investment. The results showed a positive impact of HRM practices on job satisfaction which consists of three constructs in this research name workplace environment; incentives and rewards; and job security. The findings were consistent with some studies and found a significant effect of the different activities of HRM on job satisfaction. The research also provides several helpful implications for practitioners and academicians to make new studies and filling the existed research gap by examining new variables handles contemporary issues and providing innovative business solutions. Keywords : human resource management; job satisfaction; telecommunication firms; Jordan. DOI: 10.7176/EJBM/12-17-02 Publication date: June 30th 2020
IDDF2020-ABS-0078 Immunoscore Classification from Hepatocellular Carcinoma Histopathology Images Using Deep Learning: A Preliminary Study

<h3>Background</h3> Immunotherapy is a recent advance for the treatment of hepatocellular carcinoma (HCC). Immunoscore assessment plays a critical role in precision immunotherapy and can predict prognosis in patients with HCC. This study aims to develop a deep-learning model to automated analyze histopathology images for classification of immunoscore (CD3 or CD8, 0–2 vs. 3–4) in HCC. <h3>Methods</h3> We trained a patch-based deep convolutional neural network (Resnet-18) on whole-slide images to automatically classify immunoscore into 0–2 or 3–4. The data were randomly split into a training and testing dataset. The performance was first estimated on the training dataset with nine-folded cross-validation and then further validated on the testing dataset. Cross-entropy was used as a model-optimized loss function and the accuracy as well as the area under the receiver operating characteristic curve (AUC) were calculated for the identification values. Heatmaps were also generated by our model to visualize the regions the most associated with the classification. <h3>Results</h3> We included 28 images from a study cohort of 28 HCC patients for training (18 images) and testing (10 images) the model. After iterative training, an optimized architecture achieved an AUC of 0.71 was used as our final model. For validation on the testing dataset, the model yielded an accuracy of 90% and AUC of 0.93 (95% CI: 0.76 to 1.00) while the percentage of patches positively classified, and outperforms average of the probabilities of the corresponding patches (accuracy 70%; AUC 0.79, 95% CI: 0.50 to 1.00) using the same optimal threshold of 0.33. The heatmaps show that almost all of patches are highly identified to show the regions of immunoscore ((figure 1) A. Immunoscore of 3–4 [positive]. B. Immunoscore of 0–2 [negative]). <h3>Conclusions</h3> The automated deep-learning model achieved good performance and could potentially assist clinicians in the identification of HCC patients who are more likely to respond to immunotherapy, or at least, providing second opinions on therapeutic decision-making.
A Thesaurus Based Semantic Relation Extraction for Agricultural Corpora

Semantic relations exist two concepts present in the text. Semantic relation extraction becomes an essential part of building an efficient Natural Language Processing (NLP) applications such as Question Answering (QA) and Information Retrieval (IR) system. Automatic semantic relation extraction from text increases the efficiency of these systems by aiding in retrieving more accurate information to the user query. In this research work, we have proposed a framework that extracts agricultural entities and finds the semantic relation exist between entities. Entity extraction is done using a Parts Of Speech (POS) tagger, Word Suffixes and Thesaurus without using any of the external domain-specific knowledge bases, such as Ontology and WordNet. Semantic relation exists between entities are done by using Multinomial Naïve Bayes (MNB) classifier. This paper extracts two entities, namely disease and treatment and focuses on two semantic relations namely "Cure" and "Prevent". The "Cure" semantic relation expresses the remedial measure for the diseases that prevail in the crops, and the "Prevent" semantic relation shows the precautionary measures that could prevent the crop from being affected. The proposed approach has been trained with 2281 sentences and tested against 553 sentences and then evaluated using standard metrics.