Reviewerly/exHarmony huggingface dataset:
The Kullback-Leibler divergence as the logarithm of the quantum bit error rate and the lost of information at the BB84 protocol

We demonstrate that the KullbackLeibler Divergence emerges in a natural manner from a theory of entropy inside of BB84 protocol under the assumption that the sending and receiving of data are governed by polarized photons without any explicit law that defines the coincidence of bits in both parties. More than an analysis that explore the concepts of encryption, in this paper we focus on the possible apparition of events of order and disorder. In this manner we built a formalism entirely based on the entropy from random events that are generated as part of the strategy of security. For a photon linear polarization, the entropy might be high enough that would be a source of errors as reflected on the lost of information by a 10% due to polarization superposition of states.
Impact of Distortions on the Performance of Feature Extraction and Matching Techniques

An image feature, such as edges and interest points, provides rich information on the image content and plays an important role in the area of image processing. These correspond to local regions in the image and are fundamental in many applications in image analysis. Raw data are complex and difficult to process without extracting or selecting appropriate features in advance. Feature extraction, a data reduction technique, is the transformation of large input data into a low-dimensional feature vector. It lowers the computational cost and also helps in controlling the issue of dimensionality. There are different methods of exacting features from an image and these techniques have different domains of applications. In this paper, four widely used feature detection algorithms, Harris, SURF, FAST, and BRISK feature detection algorithms are compared in terms of accuracy and time complexity for extraction and matching of feature points correctly. For this purpose, different types of transformations are added to the original images for computing the evaluating parameters like the number of features detected, matched features, and execution time required by each algorithm. Experimental results show that SURF performs better than other feature extractions and matching algorithms in terms of accuracy and run time.
An Adoption of a Contradiction Detection Task to Assist the Summarization of Online Debates

In online debates, there are two opposing sides in which proponents and opponents sentimentally make arguments on various controversial topics. Currently, most debate summarization systems have focused on the generation of generic summaries. However, we view that these summaries may not entirely fulfill the needs of readers. On some occasions, readers may need to access the actual arguments that the proponents and opponents are debating on. For these reasons, we aim to generate contradiction summaries from online debates. In this paper, we prepare new datasets grounded on the online debate summaries generated by [7] and investigate whether a contradiction detection task could be effectively used to assist the generation of contradictory summaries for online debates. We observe which combination of features provides success in classifying contradiction. Our observation into the features and the qualitative analysis highlight that the employed can detect contradiction in online debates. To improve the classification results, more insight on coreference techniques and world knowledge that is hidden in the text should be extensively focused.
Image Encryption Based on DNA Substitution and Chaotic Theory

In the present era, the progression of the Internet has given a huge impetus to the sharing of multimedia over various communication channels. But the data once distributed over the communication network is prone to several security risks. To handle these security risks, various encryption algorithms have been proposed to date. In this chapter, a new color image encryption algorithm is presented using DNA sequence substitution and chaos. The work suggested in this chapter is aimed at strengthening the encryption to improve the security of the system. A DNA sequence obtained from an RGB image is encrypted using a random DNA sequence. This encryption is performed using DNA substitution. Further, this encrypted sequence is doubly secured by performing the XOR operation of this sequence with a sequence generated using chaos. The result of this conversion is an encrypted RGB image. Thus, the encrypted image is doubly secured using the proposed algorithm. The evaluation of the algorithm presented in this chapter shows strong key sensitivity along with large key space and can be seen from the experimental results. The results obtained for the proposed method stand testimony that the proposed technique shows high resistance to statistical attacks and thus can prove to be a good candidate for image encryption.
Pitfalls in Networked and Versioned Ontologies

The listing and automatic detection of ontology pitfalls are crucial in ontology engineering. Existing work mainly focused on detecting pitfalls in stand-alone ontologies. Here, we introduce a new categorization of ontology pitfalls: stand-alone ontology pitfalls, pitfalls in versioned ontologies and, pitfalls in ontology networks. We investigate pitfalls in a situation of ontology co-evolution and we provide a systematic categorization of the different cases that could occur during the co-evolution process over two ontology portals: the Linked Open Vocabulary and BioPortal. We also identify 9 candidate pitfalls that may affect versioned ontologies or ontology networks. We evaluate the importance and potential impact of the candidate pitfalls by means of a web-based survey we conducted in the semantic web community. Participants agreed that listing and investigating ontology pitfalls can effectively enhance the quality of ontologies and affect positively the use of ontologies. Moreover, the participants substantially agreed with the new categorization we proposed. We conclude by providing a set of recommendations to avoid or solve the different pitfalls we identified.
Research on Fabric Image Mosaics Based on SURF

The SURF algorithm is widely used because of its high registration accuracy. However, because the algorithm detects the feature points of the entire image, it cannot meet the characteristics of single texture, similarity and density of fabric images. At the same time, because of the long search time and low computational efficiency of image registration using KNN method for feature detection and matching, this paper studies the image registration algorithm based on KD tree search and SURF features. The input of the algorithm is to register the set of feature points in the image, and generate a feature description vector, and then establish the KD tree index of the feature description vector. When performing image registration, the ratio method is used as the criterion for matching feature points. Each feature point in the registered image is regarded as a query point, and the first two feature points closest to the query point are found in the KD tree. Finally, the MASC algorithm combining geometric motion parameter estimation and interpolation is used to purify the matching points. The experimental results show that the KD tree-based SURF algorithm, which combines the MASC algorithm to eliminate mismatched point pairs, is better than other algorithms in terms of speed and accuracy in feature detection.
Sentiment classification with syntactic relationship and attention for teaching evaluation texts

In this paper, teaching evaluation refers to the students' evaluation of teaching. To help complete the teaching evaluation work better, we construct the corpus of teaching evaluation texts and complete the sentiment classification on it. The corpus is collected from a university and processed, which includes 10,299 Chinese sentences. The annotators manually label texts according to the rules designed by educational experts. These texts are divided into three categories, which are positive, negative, and neutral. This paper proposes a sentiment classification method for teaching evaluation texts based on Attention and BiLSTM (Bi-directional Long Short-Term Memory) combined with Syntactic Relationships (BLASR). In this model, the syntactic relationships of sentences are fused into the BiLSTM for feature learning. The weights of different words in the sentences are calculated through an attention layer. The sentiment classification of the teaching evaluation texts is completed by a dense layer. The experimental results show that the classification accuracy of BLASR proposed in this paper on the dataset of teaching evaluation texts is 89.04%, which outperforms baselines. It can satisfy the needs of teaching evaluation in colleges.
PENERAPAN APLIKASI ANDROID “OJEK GT” SEBAGAI STARTUP INDUSTRI KREATIF MENUJU SOCIETY 5.0

Implementation of technology that supports Society 5.0 needs to be done because the principal itself that focused towards technology implementation and not inclined emphasize to the technology development. Furthermore, 5.0 era also priotize technology implementation to all element of the society that could be used by diverse aspect in the community, such as: economics, education. Infrastructure, and community expertises. One of the support technology utilization is Costless Transportation Application Ojek GT base don Android Client and PHP: Hypertext Preporcessor (PHP) Web as server. This application designed using both of the platform above to fulfill the user’s need. Both of the platform are integrated using Application Programming Interface (API) as a data exchange medium to the database. The implementation of the software itself follows the Community Service Freedom Technology (CSFT) that designed as software development pattern which aims to push support technology utilization towards Society 5.0 era. In the future, this software is expected to be a simple of support technology utilization in Society 5.0 era that could be implemented to all the aspect in the community cope. Beside as a solution of costless technology utilization, Ojek GT could also be expected as Business Startup in the creative industry scope.
Scene Change Based Video Watermarking Algorithm

In this work, a fully digital video watermarking algorithm is proposed. In other words, a novel hybrid DWT-based digital and blind video watermarking algorithm, associated with error correcting code (HDWT-DB-VW-ECC), is introduced. Interestingly, our proposed algorithm partitions the watermark depending on the number of cover video scenes and accordingly embeds each part of the watermark considering different video's scene frames and wavelet domain. To make sure that the algorithm's robustness is maintained, the error correcting code is incorporated into the watermark and particularly embedded in the audio channel. Actually, our HDWT-DB-VW-ECC algorithm mostly takes into consideration all types of attacks including intentional, unintentional, geometric, statistical and other attacks to which the video frames could be prone. Furthermore, it is a blind scheme, that is to say, it retrieves the embedded watermark without the need of the original video. In addition, the watermark embedded is perceptually invisible. The proposed algorithm design, experimental results, and comparisons with many other video watermarking schemes are fully described.
An Adaptive Seam-Tracking System with Posture Estimation for Welding

In this paper, a robotic seam tracking system with welding posture estimation is proposed that can adapt to different conditions encountered in the field of welding, such as uncertain working positions and complex workpiece shapes. In the proposed system, the target coordinate system of the welding torch is established in real-time at each welding position, and the rotation angles are obtained to change the welding posture of the robot. Gaussian kernel correlation filter is used to track the weld feature in real-time that improves the accuracy and robustness of welding seam tracking. Compared with morphological methods, this method can quickly and accurately find the position of the weld from the noisy image. Finally, experimental results show that the method can be used to calculate the welding posture, which meet the welding requirements of a complex environment.
Evolutionary Strategy for Graph Embedding

Graph embedding is an important method for learning low-dimensional representations of vertices in graph data. The problem of graph embedding requires that a better embedding method be used to optimize the corresponding objective function. There are two challenges associated with graph embedding. First, the optimization algorithm is based on gradient descent and falls easily into the local optimum. Second, whether the objective function design is reasonable has a huge impact on the embedding results. To tackle this two challenges, evolutionary strategies are used as the optimization algorithm for graph embedding. Evolutionary strategies do not need to know the specific analytical form of the objective function, and can effectively overcome the challenge of the problem of optimum. In addition, to tackle the challenge of the objective function, this paper improves on the design of the objective function based on the previous research. To verify the effectiveness of the algorithm, experiments on multi-label classification tasks were carried out on four real network data sets. Experiments show the effectiveness and potential of evolutionary strategy for graph embedding.
Consensus International Solar Resource Standards and Best Practices Development

Standardization and best practices of data sets and models enable the industry to develop widely accepted protocols adapted to various stages of solar project development and operations. In collaboration with the International Energy Agency Photovoltaic Power Systems Programme Task 16, the National Renewable Energy Laboratory (NREL) developed and regularly updates the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications. In collaboration with ASTM's radiometry subcommittee, NREL leads the development of various radiometric standards. These contribute to increased accuracy in the measurement of the solar resource available to conversion systems and boost the development and improvement of radiative models. These models can then provide the long-term geographic or climatic distribution of solar radiation for areas where measured solar data are not available. Further, these standards play a preeminent role in all aspects of solar energy projects, including standard conditions, methods and instrumentation, accelerated testing, and service lifetime of materials.
Adversarial learning with collaborative attention for facial makeup removal

In this paper, we propose an adversarial learning method with collaborative attention to remove facial makeup in profile photos using generative adversarial networks (GANs). One challenge of makeup removal is how to preserve key characteristics, such as eye color, while thoroughly removing makeup. A collaborative attention mechanism is proposed to selectively manipulate face regions. In the mechanism, intra-attention takes the self-correlation of faces with makeup into consideration, which restricts inter-attention to pay more attention to regions with heavier makeup while leaving other regions untouched (e.g., eyes and hair). Another challenge is that the similarity between light makeup and non-makeup images results in a small domain gap. In such cases, it is hard to distinguish and thoroughly remove light makeup. To strengthen discriminative ability, we propose using input makeup images as negative samples to train a new-style classifier for the discriminator. The classifier enhances the discriminator to extract more discriminative style features and helps the generator to thoroughly remove light makeup. To validate the proposed algorithm, a large-scale makeup/non-makeup dataset was built. Extensive experiments demonstrated the effectiveness of the proposed method, achieving new state-of-the-art performance.
Identification of Underground Faults using Internet of Things (IoT)

Abstract This paper aims at the detection of the fault occurring in an underground cable system, pinpoint the location of the fault using Extreme Learning Machine and convey the parameters of the fault occurred using Internet of Things (IoT). The underground cables have seen a steep increase in usage due to its many inherent advantages compared to the overhead cables. However, various methods to pinpoint the presence of fault in the underground cables of large lengths has proved futile and the communication of the fault information has been proved to be expensive. Our proposed project uses Extreme Learning Machine to detect the type of fault and to pinpoint the location of the fault in the system. Extreme Learning Machine Classification and Regression Algorithms are utilized to predict the type of fault in the system and the distance of the fault from the sending end. The regression algorithm is compared to other ML algorithms relevant to the subject matter at hand. The fault location conveyed by the use of GSM is limited by the fact that only text messages in the form of Short Messaging Services can be sent to the user concerned increasing the cost incurred by the user. Our project uses the internet to communicate through a big arsenal of methods, like e-mail and other social media services possible, thus widening the scope of communication while decreasing the size of the product.
Information Technology in the Search for Employment

Employability is one of the main concerns of the citizens of developed countries. Over the last 10 years, it has become popular to use technology to find employment and better career opportunities. Currently, there are many technology-powered tools available which offer their users (candidates and companies) the possibility of finding the best job opportunities/employees. However, technology is becoming increasingly advanced and current employment-oriented websites must keep up with those standards. Thanks to the computing and information processing capabilities provided by artificial intelligence, today's websites are not mere directories of jobs and candidates; instead, they make it possible to automatically filter search results according to the characteristics of candidates and jobs. This chapter presents a review of state-of-the-art technologies aimed at improving employability and analyzes the technological advances in this sector.
COLLEGIAL METAGRAMMATICS FOR MODELING DYNAMICALLY CHANGING PROGRAMS FOR CREATING INFORMATION SECURITY SYSTEMS

The article suggests using a new class of metagrammatics – collegial metagrammatics – to solve the problem of forming programs for creating information security systems in the conditions of their dynamic adjustment. A variant of the classification collegial metagrammatics is presented, and the features of their main classes are considered in relation to solving the problem of creating information security systems. For the practical use of collegial metagrammatics in solving the problem under consideration and conducting experimental research, the development of a specialized software comlex KMG_NET was carried out. The study of the dependence the number production rules and the complexity of the used Chomsky grammars in the implementation of models promising programs for creating systems and tools for ensuring information security based on collegial metagrammatics and conventional grammars was carried out. For the practical use of collegial metagrammatics in solving the problem under consideration and conducting experimental research, the development of a specialized software complex KMG_NET was carried out. KMP_NET is an interactive environment whose main data element is graphs, formal grammars, and KMG. This allows you to solve a representative set of scientific and applied problems, such as building formal models of generated programs based on KMG, setting production rules for their dynamic change, developing convenient interfaces for entering and analyzing source data and parsing procedures for the considered application area. The study of the dependence the number production rules and the complexity of the Used Chomsky grammars in the implementation of models promising programs for creating systems and tools for ensuring information security based on collegial metagrammatics and conventional grammars.
El aprendizaje colaborativo y su incidencia en la aprehensión de los contenidos

Collaborative learning is a study technique that allows students to set the goals and objectives of the activity, so that they feel motivated and committed when working to achieve them. It allows students to be organized in teams and to create small working groups of four people who will be responsible for carrying out different functions. It is essentially based on the participation of all students in the apprehension of knowledge, but for such participation to be truly effective, students must have a similar social status, which will lead to difficulties in achieving satisfactory group cohesion in the classroom. This article will be based on the literature review about the topic under investigation.
PENINGKATAN DUKUNGAN KELUARGA DALAM MENENTUKAN STUDI LANJUT SISWA

This study aims to describe an increase in family support in determining further studies of class IX students of SMP Negeri 6 Sukoharo. This type of research is qualitative research using literature study methods from library sources that support and are related to research as a solution methodology. So that increasing family support in determining further studies can be built through pre-existing theories. The results of the study show that family support is a form of interpersonal relations that includes attitudes, actions and acceptance of family members, so that family members feel someone is paying attention. Social support is an important strategy that must be in a time of stress for the family. There are four dimensions of family support, namely: First, Emotional support serves as a port of rest and recovery and helps emotional mastery and increases family morale. Second, information support occurs and is given by the family in the form of advice, advice and discussion on how to overcome or solve existing problems. Third, instrumental support is directly provided by family support which includes material assistance. Fourth, award support takes place through positive appreciation expressions that involve agreeing statements and positive judgments on other people's ideas, feelings and performance that are positively proportional to individuals and others. So that in further studies a student must get family support from various aspects or dimensions, especially for class IX students of SMP Negeri 6 Sukoharo. They can continue to high school, vocational, or MA according to their talents and interests, the family must fully support their choices.