Reviewerly/exHarmony huggingface dataset:
Optimization in Software Engineering: A Pragmatic Approach

Empirical software engineering is concerned with the design and analysis of empirical studies that include software products, processes, and resources. Optimization is a form of data analytics in support of human decision-making. Optimization methods are aimed to find best decision alternatives. Empirical studies serve both as a model and as data input for optimization. In addition, the complexity of the models used for optimization triggers further studies on explaining and validating the results in real-world scenarios. The goal of this chapter is to give an overview of the as-is and of the to-be usage of optimization in software engineering. The emphasis is on a pragmatic use of optimization, and not so much on describing the most recent algorithmic innovations and tool developments. The usage of optimization covers a wide range of questions from different types of software engineering problems along the whole life cycle. To facilitate its more comprehensive and more effective usage, a checklist for a guided process is described. The chapter uses a running example Asymmetric Release Planning to illustrate the whole process. A Return-on-Investment analysis is proposed as part of the problem scoping. This helps to decide on the depth and breadth of analysis in relation to the effort needed to run the analysis and the projected value of the solution.
Gesture Recognition in Sign Language Videos by Tracking the Position and Medial Representation of the Hand Shapes

This paper proposes a gesture recognition approach in which morphological and trajectories are analysed. A video input containing the gesture demonstration is given to the algorithm as an input and the analysis are carried out frame by frame in 3 stages. During the Primary processing stage, the location and shape of the face and hands are identified. Secondary processing is concerned with extracting information on the change in these parameters throughout the video sample. At the decision-making stage, a two-step algorithm is used based on comparison of the motion trajectories and key object shapes between the test gesture and the reference gestures. The experiments were performed on a set of selected classes from UOM-SL2020 sign language data set and a high recognition $$F_1$$ -score of 0.8 was achieved.
A Novel Method to Restrain Ambient Noise Interference in Transformer Noise Test

Abstract A preprocessing method based on wavelet packet decomposition and spectral subtraction algorithm is proposed in this paper. The tested original noise signal is decomposed with the wavelet packet analysis. It is divided into several bands with different frequency range. In these bands, the noisy signals are further processed by spectral subtraction method. The theoretical analysis result shows that the proposed method is effective in restraining the influence of surrounding noises on the result of transformer noise measurement. The proposed method can be referenced for the noise measurement of power transmission and transformation projects.
Unsupervised Domain Adaptation with Unified Joint Distribution Alignment

Unsupervised domain adaptation aims at transferring knowledge from a labeled source domain to an unlabeled target domain. Recently, domain-adversarial learning has become an increasingly popular method to tackle this task, which bridges the source domain and target domain by adversarially learning domain-invariant representations. Despite the great success in domain-adversarial learning, these methods fail to achieve the invariance of representations at a class level, which may lead to incorrect distribution alignment. To address this problem, in this paper, we propose a method called domain adaptation with Unified Joint Distribution Alignment (UJDA) to perform both domain-level and class-level alignments simultaneously in a unified learning process. Instead of adopting the classical domain discriminator, two novel components named joint classifiers, which are provided with both domain information and label information in both domains, are adopted in UJDA. Single joint classifier plays the min-max game with the feature extractor by the joint adversarial loss to align the class-level alignment. Besides, two joint classifiers as a whole also play the min-max game with the feature extractor by the disagreement loss to achieve the domain-level alignment. Comprehensive experiments on two real-world datasets verify that our method outperforms several state-of-the-art domain adaptation methods.
Medición del potencial de explotación de energía fotovoltaica y eólica en Tamaulipas, región Altamira

Now a day, Mexico is looking the way to generate electricity by using renewable, clean and ecofriendly sources according the actual legal framework. This project is a meteorological portable station which will record either direction and speed of wind, light irradiation and environment air temperature in a data logger unit conformed by a microprocessor and a memory. The data recorded in the device will be mathematically analyzed and processed in a delimited period of time and plotted on different type of graphs or documents for to support feasibilities studies which could identify the power potential of photovoltaic energy and wind energy in the city of Altamira Tamaulipas. The main goal of this project is contribute with actual studies which will determine the feasibility of energy generation by using clean energy sources in the south part of our state promoting the development of energy power industry, mainly in the city of Altamira. Objectives: To build a portable meteorological station and to carry out a study of the measurement of the photovoltaic and wind energy exploitation potential in Tamaulipas, Altamira region, based on the data of the environmental variables measured and collected during a certain time, to determine the viability of generation electricity through wind and solar resource in this region. Methodology: In order to carry out the evaluation of the photovoltaic and wind energy potential measurement in Tamaulipas, Altamira region, a customized portable weather station module will be manufactured, to perform measurements of wind speed, wind direction measurements, temperature measurements, and irradiation measurements, at the site of interest. In addition, a system will be installed to register all the variables described in a micro SD memory and a communication system through a bluetooth module. This portable weather station will be installed, at the top of the library building of the Polytechnic University of Altamira. The exact geographical location is shown by means of Google Maps with a Latitude 22.462239 and longitude: -97.970186 (Google, 2018). Contribution: Provide information on the meteorological variables of our environment, adding a solar panel that demonstrates its viability. Implement an interface that allows constant monitoring and the different measurements made at the meteorological station, using sensors and microntrollers.
Language and Free Speech

Maitra and McGowan, drawing inspiration from J. S. Mill, argue that whether speech ought to be regulated depends on whether it constitutes legally recognized harms in the right sort of way. Maitra and McGowan suggest that this distinction between causing and constituting a harm may helpfully underlie a more fruitful reframing of the debate over free speech protections.
Image Security Using Hyperchaos and Multidimensional Playfair Cipher

Image is utilized in diverse fields due to its vividness, intuitiveness, and copiousness. Existing methods for image encryption have limitations such as only support for 24-bit pixel images, greater cipher image sizes, low resistance against differential attacks, high correlation between cipher images obtained by using a 1-bit modification of the plain images, restriction on the sizes of the secret images, and overhead of sharing secret key among participants for every secret image. The proposed encryption method uses five-dimensional hyperchaos-based triple permutation, XORing, and multidimensional Playfair encryption that overcomes the limitations of the existing methods. The statistical analysis is performed in respect of the parameters: correlation coefficient, encryption quality, diffusion characteristics, histogram analysis, key and search space analysis, and computational complexity. The empirical results indicate that the proposed method has the potential to be an effective method for image encryption.
An E-Commerce Dataset in French for Multi-modal Product Categorization and Cross-Modal Retrieval

A multi-modal dataset of ninety nine thousand product listings are made available from the production catalog of Rakuten France, a major e-commerce platform. Each product in the catalog data contains a textual title, a (possibly empty) textual description and an associated image. The dataset has been released as part of a data challenge hosted by the SIGIR ECom'20 Workshop. Two tasks are proposed, namely a principal large-scale multi-modal classification task and a subsidiary cross-modal retrieval task. This real world dataset contains around 85K products and their corresponding product type categories that are released as training data and around 9.5K and 4.5K products are released as held-out test sets for the multi-modal classification and cross-modal retrieval tasks respectively. The evaluation is run in two phases to measure system performance, first on 10% of the test data, and then on the rest 90% of the test data. The different systems are evaluated using macro-F1 score for the multi-modal classification task and recall@1 for the cross-modal retrieval task. Additionally, a robust baseline system for the multi-modal classification task is proposed. The top performance obtained at the end of the second phase is $$91.44\%$$ macro-F1 and $$34.28\%$$ recall@1 for the two tasks respectively.
Wittgenstein's Uniqueness Rule as an Elimination Rule of Inductive Types:

We discuss the equational representations of the elimination rule of inductive types, with a focus on the type “natural number”, in the context of the series of approaches to separating an equational calculus from logic. We go back to a source of the purely equational representation of the elimination rule, Wittgenstein's uniqueness rule. We analyze Wittgenstein's argument, in comparison with others', which gives supplementary remarks to Marion-Okada (2018).
국내 대학 부설 한국어 교육 기관 현황 분석

In this paper, the current status of Korean language institutions affiliated with universities in Korea was analyzed. Starting with Yonsei University’s Korean Language Institute, the Korean language institutions affiliated with universities in Korea has spread to universities in the Seoul metropolitan area and regions since the 2000s. Most institutions operate for four semesters a year and 10 weeks and 200 hours per semester, reflecting exams, attendance, attitudes, assignments, occasional evaluations and participation in their grades. In addition, it was found that there were many institutions that set the standard of completion with an attendance rate of 80% and a score of 70, and there were 28 institutions with self~developed textbooks.Based on this, I would like to propose the following tasks. First, contents related to regular courses should be disclosed and managed on the homepage of educational institutions. Second, it is necessary to share and discuss various ways and forms of operation of different Korean language courses for each institution. Finally, it is necessary to design a regular curriculum or teaching materials with reference to the International Standard Curriculum for Korean Language. (Silla University)
Development of prototype e-learning mathematics learning tools using Moodle

Abstract E-learning is very supporting the implementation of learning, because it uses teaching materials that are independent, so it can be accessed at any time. E-learning can improve students’ absorption of subject matter, active student participation, and the ability and quality of information that when using ordinary devices will be difficult. If carefully designed, e-learning can increase levels of learning interactions. Unlike the case with conventional learning, not all students can ask questions or express their opinions in the discussion, because the opportunities are very limited. Based on these assumptions, it is necessary to conduct a study of the e-learning prototype development strategy. Specifically, the purpose of this study was to develop a prototype of an electronic learning device using Moodle. This research is Research and Development with the Four-D development model. The results of the study were in the form of a prototype e-learning CASE-based Mathematics learning device. In conclusion, the prototype can be developed into a complete mathematics learning media.
Chicken Swarm Optimization Algorithm Based on Hybrid Improvement Strategy

To solve the problem of high dimensional complex optimization, a global Hybrid Improved Chicken Swarm Optimization (HICSO) algorithm is proposed. Firstly, the initial population is constructed by a hybrid chaotic mapping to increase the diversity of the population. Then, according to the searching algorithms characteristics, the nonlinear dynamic inertial strategy equilibrium algorithm is introduced to improve the exploration capability and mining capability. Finally, the reverse learning search strategy of “failed” rooster is proposed to improve the search performance of the algorithm. Through the simulation results on six test functions, this paper verifies the comprehensive optimization performance of HICSO algorithm.
Facial bones segmentation from skull point clouds based on deviation angle

Abstract Facial bones segmentation is an important step to understanding a skull. In this paper, a method for segmenting facial bones from skull point clouds is proposed. The segmentation is based on the deviation angle features. The method consists of three phases: surface normal estimation, feature extraction, and point clouds classification. The method is applied to skull point clouds derived from computed tomography images. For evaluation, the method is compared with manual segmentation. The method has succeeded in segmenting facial bones with Precision = 0.836, Recall = 0.951, and F = 0.890.
Inference for partially observed Riemannian Ornstein–Uhlenbeck diffusions of covariance matrices

We construct a generalization of the Ornstein–Uhlenbeck processes on the cone of covariance matrices endowed with the Log-Euclidean and the Affine-Invariant metrics. Our development exploits the Riemannian geometric structure of symmetric positive definite matrices viewed as a differential manifold. We then provide Bayesian inference for discretely observed diffusion processes of covariance matrices based on an MCMC algorithm built with the help of a novel diffusion bridge sampler accounting for the geometric structure. Our proposed algorithm is illustrated with a real data financial application.
A Filtering Algorithm of Main Word Frequency for Online Commodity Subject Classification in E-Commerce

—Based on the traditional classification of plain text in E-Commerce, this article has put forward a processing method in accordance with semi-structured data and main information in web pages, which enhances the accuracy of the product distribution. On the basis of the traditional textmining, combined with the structure and links of web page, this article has proposed an improved web page text representation model in E-Commerce based on supporting vector machines and web text classification algorithm, but there are still a lot of shortcomings waiting for further improvement. According to the data contrast in precision ratio, recall ratio and F-measure, the effect of the improved experiment with LDF-IDF is comprehensively better than that of tf-idf. The precision rate in certain classification can reach 100%, but there is low precision rate caused by items with fewer samples or samples fuzziness. Therefore, the classification of the correct category will directly affect the effect of classification.
New upper bounds on the maximal size of an arc in a projective Hjelmslev plane

In this paper we present new upper bounds on the size of an arc in the projective Hjelslev planes PHG(2, R), where R is a chain ring of mutilicity index 2.
Summarization and Analysis of Text Using Network Theory

The paper focuses on bringing improvization in existing summarization algorithms to get deeper insights into properties of text that gives an edge to explore new relations of sentences in text and helps attain high accuracy. The goal is to amalgamate the existing network graph theory and its properties with the existing algorithms to evolve and resolve the unanswered questions and discover new insights to improve the precision and exactness. The thought is to develop a novel approach that discovers new ways to summarize using network summarization using significant similarity measures that indeed plays a significant role. The paper presents a network embedding approach that helps to find nearest neighbors in the embedding space and make recommendations or clusters depending on similarity. The network graph theory provides an edge for visualization of concepts and discovers the relations between different categories.
Modern Software Lifecycle Management leveraging the power of Blockchain

Many distributed ledger technologies (DLT) offer smart contract platforms, that are already used in various industries such as healthcare, transportation, government, banking, and insurance. Software Lifecycle Management (SLM) is another area where DLT and smart contract platforms could play an important role in the future. SLM processes are covering the initial setup, technical and business configuration and ongoing maintenance of software systems that often consist of many cloud services, on-premise servers, and/or edge devices, spread on many geographical locations. In SLM there are many participants that should communicate each other to achieve common goal, for example the software provisioning. In case something wrong happens in their communication, it is very hard to prove what is the reason and which party failed to deliver, since the environment is complex and the protocols of responsibilities are not bound to the software dependencies. The purpose of this paper is to demonstrate how DLT-based smart contract platforms supports SLM processes for complex business systems where various participants are involved.
Public-key Projective Arithmetic Functional Encryption

Ananth and Sahai proposed the projective arithmetic functional encryption (PAFE) and showed that PAFE derives a single-key selective secure functional encryption with the help of the randomizing polynomials scheme (RP), namely PAFE with RP achieves the indistinguishability obfuscation (iO). Their PAFE considers a secret-key type functional encryption only and a public-key counterpart is not known.We propose the public-key version: pkPAFE, and show that pkPAFE with RP derives a public-key functional encryption which is single-key selective secure. This means that our pkPAFE achieves iO as well as the original PAFE by Ananth and Sahai.
Potential of Incorporating Motion Estimation for Image Captioning

Automatic image captioning has various important applications such as indexing images on the Web or the depiction of visual contents for the visually impaired. Recently, deep learning based probabilistic frameworks have been greatly researched for image captioning. However the existing deep learning methods are only established on visual features, which have problems generating captions related to motions, because visual features from images do not include motion features. In this paper, we propose a novel, end-to-end trainable, deep learning image captioning model that estimates motion features from a image to help generate captions. Our proposed model was evaluated on two datasets, MSR-VTT2016-Image, and several copyright free images. We demonstrate that our proposed method using motion features improves performance on caption generation and that the quality of motion features is important to generate captions.
Image Compression Using Deep Convolutional Adversarial Networks

Image compression is a kind of compression of data, which is used to images for minimizing its cost in terms of storage and transmission. Neural networks are supposed to be good at this task. One of the major problem in image compression is long-range dependencies between image patches. There are mainly two approaches to solve this problem; one is to develop a better residual patch-based encoder, and the second one is to create an entropy coder capable of collecting long-term dependencies inside the picture between patches. We address both the problems in this paper and fuse the two possible solutions to improve compression levels for a given material. Results of the simulation reveal that the new algorithm works much better in all parameters than in the current model.
Research on Compliance Supervision Data Analysis Model Based on Mass Chat Records in the Inter-Bank Market

This paper studies the data analysis model of compliance regulation based on massive chat records in the Inter- Bank Market. Considering the country's urgent regulatory requirements to regulate bond market participants' use of compliance chat tools to conduct financial transaction business and promote the stable and healthy development of the bond market, we use natural language processing (NLP) technology to analyze the Inter-Bank Market chat records. Based on this, it is proposed to use Event Detection and Style-aware NER technology to identify and extract the bond description information in the exchange group of traders. Using latest development in representation learning, this paper introduces BERT embedding as character based pretrained model and incorporates it with BiLSTM-CRF to transform the natural language text describing bond information into structured text to improve the work efficiency and system entry accuracy in bond information collection scenarios. Based on iDeal's thousands of chat records, as well as QQ, enterprise QQ, RM, and IB platform conversation chat records collected through technical means, build the data foundation that the algorithm relies on and conduct experiments. The evaluation results based on Precision, Recall and F1-score show that the proposed method is more suitable for the task of bond information extraction than the traditional rule-based method, and has stable performance and strong generalization under different conditions.
Universalization of the intelligence definition problem

The main problem today in the research and development of AI is the lack of a scientific definition of Intelligence, since it is impossible to do something incomprehensible. This fundamentally delegitimizes all developments in this area and science as a whole as a product of exclusively intellectual activity, and any scientific use of the term "Intellect" in its strict sense is unreasonable. In this paper, this problem is solved by transition to a more general universal paradigm of cognition, which allowed us to deduce the desired definition and universal formalism of Intelligence in its strong sense. Unlike previous publications, the ontology and properties of Intelligence are specified here as necessary components of Intelligence, which are subject to subsequent concretization and materialization in different niches of existence. The results of the work are of both fundamental and applied general scientific importance for all technical and humanitarian applications of Intelligence.