Reviewerly/exHarmony huggingface dataset:
Heuristic Evaluation In Mobile Augmented Reality Applications In Designing Houses

The Augmented Reality Mobile application is software designed to make it easy for users to design a virtual home. The application created will display an image of a 3D object building a house from outside and inside. Evaluation is a test of the level of use and functionality of the system that is carried out in the laboratory, in the field, or in collaboration with users. Heuristic evaluation is an evaluation system for user-based computer software. The purpose of heuristic evaluation is to improve the design effectively. Testing in this application uses a heuristic evaluation by applying the ten principles of Nielsen, including visibility of system status, match with the real world, user control and freedom, consistency and standards, error prevention, recognition than recall, flexibility and efficiency of use, aesthetic and minimalist design, help users recognize, diagnose, and recover from errors, and Help and documentation. The evaluation results obtained by the augmented reality mobile application have shortcomings in its interface, this can be seen from the measurement results that show the principles of “Consistency and standards” and “Error Prevention” each of these principles has a lower value than the other 8 principles. For that, it is necessary to make improvements. The heuristic evaluation obtained on the results of the evaluation by prospective customers has an average value of 80% and an architect of 81%.
A Distributed Motion Planning Method based on Routing and Local Dynamic Programming

In this paper, a distributed motion planner based on routing and local dynamic programming is proposed. First, the routing planner generates a high-level reference based on the road segments and zones defined in the Road Network Definition File (RNDF). Then the physical and logical structure of the environment is used to construct a uniform sampling space along the reference, in which a feasible trajectory is grown by numerous incremental sampling points. Finally, a smooth trajectory that meets kinematic constraints in the dynamic and uncertain environment is efficiently generated by use of forward simulation of the vehicle-controller system. In this paper, both the safety and comfort are considered to meet the driving experience, and a recursive function is used to make the trajectory search easier and faster. The proposed motion planner is implemented and tested on a real unmanned vehicle in three different scenarios, and the results show that the framework can output high-quality trajectories to guide the vehicle to its destination safely.
Object Detection Based on Center Point Proposals

Anchor-based detectors are widely adopted in object detection. To improve the accuracy of object detection, multiple anchor boxes are intensively placed on the input image, yet most of them are invalid. Although anchor-free methods can reduce the number of useless anchor boxes, the invalid ones still occupy a high proportion. On this basis, this paper proposes an object-detection method based on center point proposals to reduce the number of useless anchor boxes while improving the quality of anchor boxes, balancing the proportion of positive and negative samples. By introducing the differentiation module in the shallow layer, the new method can alleviate the problem of missing detection caused by overlapping of center points. When trained and tested on COCO (Common Objects in Context) dataset, this algorithm records an increase of about 2% in APS (Average Precision of Small Object), reaching 27.8%. The detector designed in this study outperforms most of the state-of-the-art real-time detectors in speed and accuracy trade-off, achieving the AP of 43.2 in 137 ms.
Student’s educational goal and formalization of its representation in E-learning

Abstract The transition to e-learning implies the student’s independent setting of the individual educational goal. In this case, the educational platform of the university is used as a pedagogical tool for calculating the optimal educational trajectory and ensuring the student’s guaranteed achievement of the educational goal by organizing the educational process on the specified platform. The main problem here is the formalization of the individual educational goal of the student with programming languages of various levels. The authors propose an algorithm for formalizing the individual educational goal of a student and building on its basis a logical-semantic model of the educational process, suitable for implementation by means of computer technology. It is shown that in the logical-semantic model of the educational process, the individual educational goal of the student can be adequately described by the pedagogical category "Educational situation", which is defined as a set, the elements of which are quantitative values of the level of formation and development of basic qualities of competencies. It is concluded that the educational platform of the university in this case is a set of educational situations of various levels, and the student is the current educational situation. It is shown that the current educational situation and educational situations of the educational platform of the university are equal sets on a single scale, which makes it possible to carry out mathematical operations for comparing sets. Based on the results obtained during the comparison operations, a conclusion is made about the achievement of the educational goal by the student, or the individual trajectory of its achievement is calculated. The description of the pedagogical experiment is given. Based on the data obtained, a conclusion is made about the adequacy of the model to the process under consideration.
Parsing Expression Grammars and Their Induction Algorithm

Grammatical inference (GI), i.e., the task of finding a rule that lies behind given words, can be used in the analyses of amyloidogenic sequence fragments, which are essential in studies of neurodegenerative diseases. In this paper, we developed a new method that generates non-circular parsing expression grammars (PEGs) and compares it with other GI algorithms on the sequences from a real dataset. The main contribution of this paper is a genetic programming-based algorithm for the induction of parsing expression grammars from a finite sample. The induction method has been tested on a real bioinformatics dataset and its classification performance has been compared to the achievements of existing grammatical inference methods. The evaluation of the generated PEG on an amyloidogenic dataset revealed its accuracy when predicting amyloid segments. We show that the new grammatical inference algorithm achieves the best ACC (Accuracy), AUC (Area under ROC curve), and MCC (Mathew’s correlation coefficient) scores in comparison to five other automata or grammar learning methods.
Object-Oriented Database and O/R Mapping Technology

The O/R mapping technology and object-oriented database technology are compared and analyzed to expound the advantages and disadvantages of these two technologies and the development trend of these two technologies.
Advances in geochemical footprint, iron speciation, and Pb isotopes in the tropical regolith of north-northwestern Amazonia, Brazil

A dataset with information on samples of gabbro, charnockite, gneiss, granite, basalt, and sandstone-derived iron lateritic duricrusts, concretions, nodules, and soils was analyzed to address the multi-element chemical composition, background values, scavenger trace elements in Fe oxyhydroxides, parent rock inheritance, and weathering effect on Pb isotopes. Tropical weathering causes high mass loss of SiO2, Ba, Rb, and Sr assigned to the mineral rock framework (quartz, feldspar, and mica). The aqua regia leaching indicates that part of these elements was probably accumulated in authigenic Al-phosphates minerals affecting their mobility in the lateritic terrains. There is a generally Sc and V high accumulation in the lateritic duricrusts with Fe oxyhydroxide mineral framework (goethite, hematite, and amorphous). Th, U, and Pb are also accumulated on the gabbro and granite-derived lateritic duricrusts, Cr on the basalt-derived ones, REE on the gabbro-derived ones, as well as Th and U low to high loss on gneiss-derived ones, thus allowing to track inheritance of parent rocks. The more refined geochemical information obtained from the Pb isotopes related to the Fe oxyhydroxides of the lateritic duricrusts, concretions and soils indicated changes according to the parent rocks since the gabbro-derived lateritic duricrust samples are more radiogenic while the gneiss and granite-derived ones are less radiogenic. The basalt and sandstone-derived lateritic duricrust samples maintain the radiogenic signature of the respective parent rocks. Although homogenization was expected, the mineral, the main and trace elements, and Pb isotope compositions produced by leaching and accumulation in the tropical regolith highlight the parent rock signature and a complex weathering process.
Relationship Between the Structure Function of Random Time Series and the Discrete Chebyshev Spectrum

The technique of Chebyshev polynomials of discrete variable is used to analyze a random time series. The main result of the work is a new theoretical relationship between the structure function and the discrete Chebyshev spectrum. The relationship is used for the trend-resistant structural analysis of electrochemical noise of corrosion process and for the trend-resistant structural analysis of electronic noise of measuring instrument. Not only the structure function of the analyzed random series, but also the structure function of trend is estimated. The theoretical relationship between the structure function and the discrete Chebyshev spectrum can be used for the trend-resistant structural analysis of random time series of any nature.
Social Media Application in Agriculture Extension Programming for Small Scale Rural Farmers: Is Knowledge Impeding the Lack of Adoption?

The purpose of this study was to assess the knowledge level of farmers on basic computer literacy, social media use, and to explore which social and demographic factors affected their knowledge capacity. The study had a final sample of 176 participants from the northern, southern and central regions of Trinidad and Tobago. A survey instrument comprising of 14 multiple-choice questions with one accurate response was developed to decrease bias of farmers randomly selecting the accurate response. The questions addressed knowledge on basic computer and social media literacy. Analysis was conducted using one-way ANOVA with posthoc testing. Results indicated that there were significant differences in farmers’ performance in the knowledge test based on characteristics such as age, education, and household use of social media and the internet. Based on the findings, minimal training in computer and social media literacy did not impede the farmers’ use of the computer or social media. These discoveries highlight the potential of extension programs using the internet and social media applications to improve communication efficiency among agricultural stakeholders within farming communities. Keywords: agriculture, computer literacy, Extension, knowledge, social media
GAMIFICATION BASED ON LOCAL STORIES’ EFFECT ON STUDENTS’ LEARNING MOTIVATION

In the 21st century, the use of innovative and digital-based teaching media are required. This research aimed at examining the effect of gamification based on local stories as teaching media on fifth grade elementary school students’ motivation. Quasi experimental with one group pre-test post-test design was implemented. The study was conducted at SD N 1 Penarukan where 25 of fifth grade students in academic year 2019/2020 were involved as the sample of study. Three instruments were administered to collect the data; pre-test post-test, treatment instrument and interview. The result showed that post-test mean score has significantly improved compared to pre-test score (42.29 &gt; 23.35). Hypothesis testing result (paired sample t-test) showed the tobs value was higher than tcv (20.811&gt;1.677) with α = 0.05, which indicated that gamification positively affected students’ motivation. The interview result revealed that the students are highly motivated to learn by using gamification.
Improve Spark-based Application Performance Using Minimizer

SpaRC(Spark Reads Clustering) is a generic sequence clustering algorithm based on Spark, which provides a scalable solution for billions of reads. However, SpaRC measures the correlation between reads by employing k-mer. This method can effectively complete computing tasks when the the amount of data is small. However, as the amount of data increases, the shortcomings of long running time and large memory resources are increasingly prominent. Here we explored a sequence similarity measurement method to alleviate these problems by using minimizer to measure sequence similarity between reads, without long running time and large memory resources. This method combines the minimizer measurement strategy and extracts the overlap rate information of reads to measure the sequence similarity between different reads, instead of the traditional method using k-mer. Results indicate that the method offers great improvement in clustering performance. Compared with the traditional k-mer method, this method can effectively improve the use of memory resources by SpaRC.
Adaptive Voting Online Sequential Extreme Learning Machine based on Glowworm Swarm Optimization Selective Ensemble Algorithm

In this paper, view of the unstable output of a single online sequential learning machine, we propose a selective ensemble algorithm based on glowworm swarm optimization. On the basis of this algorithm, we design an adaptive learning framework of multiple learning machines, which can judge whether to use multiple learning machines for selective ensemble according to the preset threshold. The experimental results show that the proposed approach has higher classification accuracy and generalization performance compared with the basic online sequential extreme learning machine as well as the voting online sequential extreme learning machine.
Linear codes and incidence structures of bent functions and their generalizations

In this paper, we consider further applications of (n,m)-functions for the construction of 2-designs. For instance, we provide a new application of the extended Assmus-Mattson theorem, by showing that linear codes of certain APN functions with the classical Walsh spectrum support 2-designs. With this result, we give several sufficient conditions for an APN function with the classical Walsh spectrum to be CCZ-inequivalent to a quadratic one. On the other hand, we use linear codes and combinatorial designs in order to study important properties of (n,m)-functions. In particular, we provide a characterization of a quadratic Boolean bent function by means of the 2-transitivity of its automorphism group. Finally, we give a new design-theoretic characterization of (n,m)-plateaued and (n,m)-bent functions and provide a coding-theoretic as well as a design-theoretic interpretation of the extendability problem for (n,m)-bent functions.
To what extent does content selection affect surface realization in the context of headline generation?

Headline generation is a task where the most important information of a news article is condensed and embodied into a single short sentence. This task is normally addressed by summarization techniques, ideally combining extractive and abstractive methods together with sentence compression or fusion techniques. Although Natural Language Generation (NLG) techniques have not been directly exploited for headline generation, they may provide better mechanisms than summarization techniques to paraphrase the information of a text. Therefore, this paper analyzes and evaluates the effectiveness of NLG techniques for generating headlines. In NLG, both content selection and surface realization are equally important—there is no point in generating text without knowing the topic. Considering this premise, we therefore take HanaNLG—a hybrid surface realization approach—as a basis, and we analyze the effect in the generated text when different content selection strategies are integrated at macroplanning stage. The experiments conducted show that, despite not using any sophisticated summarization method, the proposed approach provided the following benefits: i) it generated a coherent, linguistically structured headline; ii) it obtained results on standard datasets (i.e., DUC 2003 and DUC 2004) that were comparable to several competitive systems, in terms of the content of the generated headline; and, iii) the headlines generated by the whole approach (PLM-HanaNLG) were preferred by human assessors compared to those generated by the best performing system in DUC 2003.
A Pre-joined Service Composition Approach with Dynamic Services in a Graph Database

Database-based composition approaches are evolving and coming into researchers' notice. In this paper, we present a pre-joined service composition approach with dynamic services in a Graph database. Firstly, services' information is stored in a graph database. Secondly, a composition network is constructed. Last but not least, a solution by converting the composition problem into graph database queries is fetched. Considering that services are dynamic and lead to frequent changes of service compositions, we discuss how to update the graph database, e.g., service addition, disappearance and update. Preliminary experiment indicates that the proposed approach can support service composition with services' dynamic changes.
Evolutionary Generative Contribution Mappings

Although convolutional neural networks (CNNs) have significantly evolved and demonstrated outstanding performance, their uninterpretable nature is still considered to be a major problem. In this study, we take a closer look at CNN interpretability and propose a new method called Evolutionary Generative Contribution Mappings (EGCM). In EGCM, CNN models incorporate both a classification mechanism and an interpreting mechanism in an end-to-end training process. Specifically, the network generates the class contribution maps, which indicate the discriminative regions for the model to identify a specific class. Additionally, these maps can be directly used for classification tasks; all that is needed is a global average pooling and a softmax function. The network is represented by a directed acyclic graph and optimized using a genetic algorithm. Architecture search enables EGCM to deliver reasonable classification performance while maintaining high interpretability. We apply the EGCM framework on several datasets and empirically demonstrate that the EGCM not only achieves excellent classification performance but also maintains high interpretability.
Efficient Bokeh Effect Rendering using Generative Adversarial Network

The bokeh effect in photography is an artistic effect that makes the subject stand out by intentionally distorting the focus of the background while focusing on the subject. In this paper, we propose an end-to-end deep learning model that generates bokeh images from wild HD images. The proposed generative adversarial network model consists of a generator and a double-scale discriminator. It is so lightweight that it can carry out bokeh effect rendering in 0.03 seconds per image on a PC. In addition, we show that in a mobile environment, HD images can be processed in 1.3 seconds per image.
On Open-Set, High-Fidelity and Identity-Specific Face Transformation

In this paper, a Generative Adversarial Networks-based framework has been proposed for identity-specific face transformation with high fidelity in open domains. Specifically, for any face, the pro-posed framework can transform its identity to the target identity, while preserving attributes and details (e.g., pose, gender, age, facial expression, skin tone, illumination and background). To this end, an auto-encoder network is adopted to learn the transformation mapping, which encodes the source image into the latent representation, and reconstruct it with the target identity. In addition, the face parsing pyramid is introduced to help the decoder restore the attributes. Moreover, a novel perceptual constraint is applied to the transformed images to guarantee the correct change of the desired identity and to help retrieve the lost details during face identity transformation. Extensive experiments and comparisons to several open-source approaches demonstrate the efficacy of the proposed framework: it can achieve more realistic identity transformation while better preserving attributes and details.
Application and Challenge of Blockchain in Supply Chain Finance

Along With the progress of the times and the rapid development of science and technology, the era of the Internet of Everything has arrived, and the rise of blockchain has opened a new chapter in technology. It has gradually been applied to the fields of finance, the Internet of Things, logistics, and public services, and has also developed rapidly in financial supply chain. With the support of blockchain technology, the digitization of accounts receivable, bills, warehouse receipts and other assets can be realized, and the data is retained to reduce the risk of ticket fraud and repeated pledge, alleviate the problem of information asymmetry, and reflect the blockchain. The smart contract properties of the company ensure the smooth development of supply chain financial services. However, blockchain technology has been widely used in the field of financial technology, but it still faces challenges in business, technology, and risk management. In order to better apply blockchain to the field of supply chain finance (SCF), this article starts from the status quo of SCF. Research and analyze industry pain points in traditional supply chain financing, and propose the application superiority of blockchain in SCF. At the same time, government agencies should actively adjust regulatory strategies to find a regulatory method suitable for the country’s development.
Toward a Knowledge-Based Model to Fight Against Cybercrime Within Big Data Environments: A Set of Key Questions to Introduce the Topic

It has become universally recognized, by all specialists in the digital world, that cybercrime is a constant threat with serious consequences and includes all forms of digital crime that mostly target data. Big data is a special type of data that has attracted the attention of academics and practitioners over the past two decades. Technically, in the big data field, analysis is a major concern while security is a responsibility which requires qualified skills and high level knowledge. Today, several disciplines (Computer Science, law, etc.) are interested in the inevitable interference between big data and cybercrime what mobilizes various research activities. In addition, the vocation of the mutual relationship between knowledge and data is important because data allows the creation of knowledge while knowledge ensures the protection of data. In this perspective, this chapter aims to propose a knowledge-based approach to support the fight against cybercrime in the big data context. But, we will answer, at the beginning, a large number of comprehension questions to facilitate as best as possible, to those interested in the subject of “big data and cybercrime”, the understanding of its different axes.