Reviewerly/exHarmony huggingface dataset:
Remembering with my chatbot

Did you know that humans can develop friendly or even romantic feelings towards social chatbots that can turn into close human-chatbot relationships? The phenomenon of human-chatbot relationship is starting to gain substantial media attention, and research on this topic is now emerging. Social chatbots –what are they? you may ask. Well, you have probably seen them on the App store or on Google play under names such as Replika or Kuki. To put it simple, a social chatbot is a form of conversational AI developed with the purpose of having normal day-to-day conversations with its users. New developments in AI and NLP created the conditions for the design and development of sophisticated social chatbots capable of becoming your best friend or even romantic partner. Social chatbots are generally good at showing empathy, providing emotional support and companionship. They have essentially grown into conversational affective machines which makes it possible for the users to form close relationships with them. This makes you wonder; how does such relationship develop?
Weber Global Statistics Tri- Directional Pattern (WGSTriDP): A Texture Feature Descriptor for Image Retrieval

The texture is a high-flying feature in an image and has been extracted to represent the image for image retrieval applications. Many texture features are being offered for image retrieval. This paper proposes a local binary pattern-based texture feature called Weber Global Statistics Tri-Directional Pattern (WGSTriDP) to retrieve the images. This pattern combines the advantages of differential excitation components in the Weber Local Binary Pattern (WLBP), sign and magnitude components in the Local Tri-Directional Pattern (LTriDP), and global statistics. Differential Excitation (DE) and Global Statistics TriDirectional Pattern (GSTriDP) are two components of WGSTriDP. The WGSTriDP gains the benefit of discrimination concerning human perception from differential excitation as well as incorporates global statistics into sign and magnitude components in the pattern derived from the local neighborhoods. The effectiveness of the pattern in image retrieval is experimented with in two benchmark databases, such as ORL (face database) and UIUC (texture database). According to the results of the experiments, WGSTriDP outperforms other local patterns in retrieving similar images from the database.
Browser Selection for Android Smartphones Using Novel Fuzzy Hybrid Multi Criteria Decision Making Technique

IT and Telecommunication sector has grown massively over the past few decades. Mobile phones that were initially developed for making calls and now become an essential item and are just not restricted to calling. They have dominated most of the gadgets like computers, cameras etc. Regularly people come across an extensive number of enhanced and better-quality features being inbuilt with them. A variety of mobile phones with different shapes and sizes are manufactured within a wide range of budgets. This is the key motivation behind an exponential growth in the number of users and the arrival of new manufacturers in the field. Along with this growth, there is a fast growth of mobile application software providers also. Apart from calling, many consumers use smartphones for browsing the internet. This puts users into a dilemma to select a better browser for their smartphone to fulfill their requirements. With this aim, an attempt is made in this paper for the evaluation and selection of a better browser. To achieve this, a hybrid Multi Criteria Decision Making (MCDM) approach is proposed by combining COPRAS (Complex Proportional Assessment of alternatives) technique and Fuzzy Analytical Hierarchy Process (FAHP).
The Capacities of Learning and Knowledge Management in the Performance of Organizational Mediations in the Telecommunications Sector

The telecommunication industry is rapidly developing along with the development of technology infrastructure. These conditions force telecommunications companies to improve the company's performance to be able to grow a competitive advantage over other competitors. This study aims to identify important factors in improving company performance. The data in this study were collected from 290 questionnaires filled out by workers from one of the government-owned telecommunications companies, which were divided into several units within one Directorate of Finance and Risk Management. The data obtained in the process using the PLS-SEM data analysis technique with an exploratory approach. This study found the relationship between organizational trust and organizational performance was found to have a positive and significant influence, the relationship between leadership and organizational performance was found to have no influence. The relationship between organizational trust and organizational learning capability was found to have a positive and significant influence. organizational trust was found to have a positive and significant influence on knowledge management. Leadership was found to have a positive and significant influence on Knowledge Management. knowledge management had a positive and significant effect on organizational learning capability. organizational learning capability was found to have a positive and significant effect on organizational performance. knowledge management has a positive and significant effect on organizational performance. In indirect relationship found organizational learning capability and knowledge management are essential in mediating the relationship between organizational trust and leadership. The results in this study can be used as references and suggestions for both the company and further researchers.
Preliminary Analysis of the Utilization of PET Plastic Waste in Tourism Areas for Energy Recovery

Waste management at tourist sites is an unavoidable problem due to the massive number of visitors. One of the types of waste is polyethylene terephthalate (PET) plastic bottles. This study aims to analyze the potential use of PET for waste to energy in tourist areas. This research was conducted in a tourist area in Klungkung Regency, Bali Province. This study only uses grab sampling in measuring waste generation and composition. This research was conducted by proximate analysis and thermal gravimetry analysis (TGA). The measurement of waste generation shows that the PET produced is 1.24 kg/day, or about 43.5% of the total waste generation. Proximate analysis measurements show the value of moisture, ash, volatile, and fixed carbon content, which are 1.53, 0.14, 95.92, and 2.42, respectively. Meanwhile, the caloric value of PET waste is 22.71 MJ/kg. The residue from the TGA results showed 2.575%. The thermal degradation process starts at a temperature of 280ᵒC, and the initial temperature of degradation starts at 406 – and ends at 468ᵒC. Utilization of PET waste at this tourist location can also help the purpose of waste management in Klungkung Regency, namely by implementing the Waste to Energy concept at the Local Waste Processing Facility (TOSS).
Calculation of Determinant of a two-variable Polynomial Matrix in Complex Basis

In this paper, an innovation in evaluation part of Evaluation Interpolation technique (E-I) is presented. The variables of this technique have been defined in Complex domain. The use of complex numbers instead of real ones in evaluation part, provides us with the opportunity of using conjugate properties. With these properties, the number of evaluations in evaluation part is being decreased. The interpolation part is common for both methods.
Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

Abstract Similarity measures are crucial for electing neighbors for the users of recommender systems. However, the massive amount of the processed data in such applications may hide the inner nature of the utilized similarity measures. This paper devotes lots of studies to three standard similarity measures based on many synthetic and real datasets. The aim is to uncover the hidden nature of such measures and conclude their suitability for the recommender systems under different scenarios. Moreover, we propose a novel similarity measure called the normalized sum of multiplications (NSM) and two different variants of it. For experimentation, we examine all measures at three levels; a toy example, synthetic datasets, and real-world datasets. The results show that sometimes Pearson correlation coefficient and cosine similarity exclude similar neighbors in favor of less valuable ones. The former measures the correlation direction, while the latter measures the angle. However, both direction and angle are not similarity but an indication to it and can have the same values for two far vectors. On the other hand, the proposed similarity measure constantly reveals the exact similarity and tracks the closest neighbors. The results prove its robustness and its very good predictive accuracy compared to the traditional ones.
A method for automatic Arabic text summarization combining Noun Extraction and Fuzzy Logic

Abstract Text summarization has been a field of intensive research over the last 50 years, especially for commonly used and relatively simple-grammar languages such as English. Moreover, the unprecedented growth in the amount of online information available to users and businesses, including news articles and social media, has made it difficult and time consuming for users to identify and consume sought after content. Hence, an automatic text summarization system to generate accurate and relevant summaries from the huge amount of information available is essential nowadays. The Arabic language is no exception in terms of the upsurge in available information, especially online, however, techniques and methodologies for automatic Arabic text summarization are still immature due to the inherent complexity of the Arabic language in terms of both structure and morphology. The work presented in this paper attempts to improve the performance of Arabic text summarization. We propose a novel Arabic text summarization approach based on a noun extraction method and Fuzzy Logic (FL). The proposed system is one of the first Arabic text summarizers to use FLFL to improve the summarization accuracy. The proposed summarization system is evaluated on EASC corpus and compared against popular state of the art Arabic text summarization systems. The results indicate that our proposed FLFL based approach with noun extraction outperforms existing prior art systems.
You Are Revoked and Out: Towards Directly Revocable Ciphertext-Policy Attribute-Based Encryption

Ciphertext-policy attributed-based encryption (CP-ABE) is considered as a promising cryptographic primitive to enable fine-grained access control over encrypted data. Throughout the life circle of the data encrypted with CP-ABE, every single data user might opt out or be identified to be malicious and hence should be revoked to keep continuous access control over sensitive data. In this study, we propose a directly revocable CP-ABE with backward and forward secrecy, which means that a revoked user cannot successfully decrypt ciphertexts after sufficient revocation while valid users will not be disturbed. Combined with an effective proxy mechanism, the proposed scheme delegates complete workloads of re-encryption and ciphertext updating to a semi-trusted third party (e.g., a cloud service provider). In addition, even collusion among users is useless in our construction, which is not well addressed in previous works. The security analysis indicates that the presented CP-ABE is selectively secure against chosen-plaintext attacks in the standard model, and the performance analysis demonstrates that our proposal is practical compared with existing schemes.
Exploration of Cross-Border Language Planning Using the Graph Neural Network for Internet of Things-Native Data

This work aims to study applying the graph neural network (GNN) in cross-border language planning (CBLP). Consequently, following a review of the connotation of GNN, it puts forward the research method for CBLP based on the Internet of Things (IoT)-native data and studies the classification of language texts utilizing different types of GNNs. Firstly, the isomorphic label-embedded graph convolution network (GCN) is proposed. Then, it proposes a scalability-enhanced heterogeneous GCN. Subsequently, the two GCN models are fused, and the research model-heterogeneous InducGCN is proposed. Finally, the model performances are comparatively analyzed. The experimental findings suggest that the classification accuracy of label-embedded GNN is higher than that of other methods, with the highest recognition accuracy of 97.37% on dataset R8. The classification accuracy of the proposed heterogeneous InducGCN fusion model has been improved by 0.09% more than the label-embedded GNN, reaching 97.46%.
On the Effect of Clock Frequency on Voltage and Electromagnetic Fault Injection

We investigate the influence of clock frequency on the success rate of a fault injection attack. In particular, we examine the success rate of voltage and electromagnetic fault attacks for varying clock frequencies. Using three different tests that cover different components of a System-on-Chip, we perform fault injection while its CPU operates at different clock frequencies. Our results show that the attack's success rate increases with an increase in clock frequency for both voltage and EM fault injection attacks. As the technology advances push the clock frequency further, these results can help assess the impact of fault injection attacks more accurately and develop appropriate countermeasures to address them.
Timestamp Boson Sampling

Quantum advantage, benchmarking the computational power of quantum machines outperforming all classical computers in a specific task, represents a crucial milestone in developing quantum computers and has been driving different physical implementations since the concept was proposed. Boson sampling machine, an analog quantum computer that only requires multiphoton interference and single-photon detection, is considered to be a promising candidate to reach this goal. However, the probabilistic nature of photon sources and inevitable loss in evolution network make the execution time exponentially increasing with the problem size. Here, we propose and experimentally demonstrate a timestamp boson sampling that can reduce the execution time by 2 orders of magnitude for any problem size. We theoretically show that the registration time of sampling events can be retrieved to reconstruct the probability distribution at an extremely low-flux rate. By developing a time-of-flight storage technique with a precision up to picosecond level, we are able to detect and record the complete time information of 30 individual modes out of a large-scale 3D photonic chip. We successfully validate boson sampling with only one registered event. We show that it is promptly applicable to fill the remained gap of realizing quantum advantage by timestamp boson sampling. The approach associated with newly exploited resource from time information can boost all the count-rate-limited experiments, suggesting an emerging field of timestamp quantum optics.
Application of a Hybrid Camel Traveling Behavior Algorithm for Traveling Salesman Problem

Camel Traveling Behavior Algorithm (CA) is a nature-inspired meta-heuristic proposed in 2016 by Mohammed Khalid Ibrahim and Ramzy Salim Ali. There exist few publications that measure the performance of the CA on scientific literature. CA was implemented to global optimization and some engineering problems in the literature. It was shown that CA demonstrates better performance than Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in global optimization. However, it gives poor solutions at combinatorial optimization as well as in traveling salesman problems (TSP). Besides, a modified camel algorithm (MCA) was applied in the field of engineering and was proved that it is better than Cuckoo Search (CS), PSO, and CA. Therefore, it is a need for improvement in CA by hybridizing with a constructive heuristic (Nearest Neighbor Algorithm-NN). A set of thirteen small and medium-scale datasets that have cities scales ranging from 29 to 195 was used in the comparative study. The results show that the hybrid algorithm (HA) outperforms Tabu Search (TS), GA, CA, and Ant system (AS) for 70% of all datasets, excluding wi29, eil76, pr76, and rat99. Also, it was given that a detailed analysis presents the number of best, worst, average solutions, standard deviation, and average CPU time. The metrics also stress that the hybrid meta-heuristic demonstrates 64% performance in finding acceptable solutions. Finally, the hybrid algorithm solves the discrete problem in short computational times when compared to other test algorithms for small and medium-scale datasets.
Understanding Occupational Safety and Health in Technology Teacher Training in the Context of Economic Growth of Society

The article examines the role of future vocational teachers in the social and labor dynamics of the development of society. Emphasised on the self-realization of a young person in work, which requires a new approach to considering the concept of "work", which stems from the 17 Sustainable Development Goals adopted by the UNO in 2015. The author presents the functions of work. accentuated on understanding of Occupational Safety and Health, Occupational Hygiene in the training vocational teachers. Underlined, that in the pre-pandemic era, new technologies were gradually introduced taking into account artificial intelligence (artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks), currently the COVID-19 pandemic and accompanying social distancing measures have accelerated the process of innovation and technological changes. Such rapid transformations in technologies and processes require new approaches to consideration of Life Safety, behavior in youth readiness for Health-Preserving Activities and Occupational Hygiene, formation of Health-Preserving Competence. The author comes to the conclusion that the professional training future vocational teachers must be constantly improved due to the challenges of rapid technological progress, which requires at each period of the development of society the production of new knowledge and training new people capable of meeting modern production technologies.
Special education in science teaching

During the past years, Information and Communication Technologies (ICTs) have been studies in a large number of fields. The term ICTs refers to all kinds of technologies that let users to have access, use and manipulate information (Drigas &amp; Ioannidou, 2013a). ICT can bridge the gap between groups of people with special (educational) needs. The growing number of researches that connects ICTs and assistive technologies (Drigas &amp; Ioannidou, 2013), shows us that the use of them in the life of children and adults with special education needs is huge. In this part of the research we will report some of the most indicative uses of ICTs in the education of people with different learning needs (autism, visually impaired, hearing impaired/loss, sensory and physical impaired and dyslexic learners). Using ICTs and assistive technologies, involves more than overcoming environmental barriers, it is also a means of communication and expression of the personal and social characteristics of the learner. ICTs in special education (and education generally) can be used in every domain to improve it.
The Relationship between Language Learning Strategies used by Vocational Students and Level of Proficiency

The vocational college students instructed by the teacher to used self-directed learning in learning English outside classroom or during the internship. Due to the requirement of graduation which they should pass the TOEFL test after the internship. The self-directed learning is exercised by managing students’ Language Learning Strategies (LLS) which known could improve language proficiency. Hereby, this study identifies: (1) the language learning strategy used by vocational college learners based on high and low proficient, (2) the relationship between language learning strategy and English proficiency of high proficient, (3) the relationship between language learning strategy and English proficiency of high proficient. The participant of this study is 52 students of Politeknik Kota Malang which willing to complete the Strategy Inventory of Language Learning (SILL) and done the TOEFL test. This study employed correlational research design to identify the relationship between variables which is multiple regression analysis employed in this study. There are six strategies of language learning strategies by Oxford (1990) as the independent variables (predictors) and English proficiency (criterion) as the dependent variable. The result showed that Although, language learning strategy cannot simultaneously predict the English proficiency of high proficient and low proficient, compensation strategy reported as high frequently used by high proficient learners. While metacognitive strategies reported used by low proficient learners.
Indonesian Interference in Students' Japanese Essays of SMK Penerbangan Cakra Nusantara School

This research discusses Indonesian language interference that occurs in Japanese essays written by SMK Penerbangan Cakra Nusantara students. This research aims to describe the types of interference found in the form of sentences in students' essays. This research used qualitative descriptive. The subject of this research are students of XII APP 1 class, meanwhile the object of this research are interference sentences in Japanese essays. The data was collected through note taking technique. The data was analysed using a theory proposed by Miles and Hubberman by performing data reduction, analysing data, and making conclusions. The results of this research are 1) the types of interference that occured based on linguistic aspects are morphological and syntactic interference; and 2) syntactic interference occured in three types of interference such as permutation of core constituents (I) and modifier (M), permutation of predicate (P) and object (O), and ommision of particles (joshi).
Compound Words Found in Seventy-Seven Thousand Service-Trees (Sri Chinmoy)

This research deals with compound words used in Seventy-Seven Thousand Service-Trees by Sri Chinmoy. The case is many people did not recognize and aware of using it. This research is trying to describe the types and the structure of compound words and supported by two theories, those are Booij (2007) and McCarthy (2002). In achieving the objectives, the researcher uses descriptive qualitative as the type of the research. The data are in the form of compound words. In collecting the data the writer applies documentation technique and the steps are reading attentively, selecting, collecting, and classifying the data systematically by following the types and the structure of compound words. The results of this research show that there are three kinds of compound words. From 390 the data of compound words, there are 348 data are classified as a compound noun, 20 data are classified as a compound adjective, and 22 data are classified as a compound verb. In this research, it is found that only two ways in the spelling of compound words are solid and hyphenated. There are three sub-classes of compound words found in this study those are endocentric compound, exocentric compound, and copulative compound.