Reviewerly/exHarmony huggingface dataset:
Visual Semantic Context Encoding for Aerial Data Introspection and Domain Prediction

Visual semantic context describes the relationship between objects and their environment in images. Analyzing this context yields important cues for more holistic scene understanding. While visual semantic context is often learned implicitly, this work proposes a simple algorithm to obtain explicit priors and utilizes them in two ways: Firstly, irrelevant images are filtered during data aggregation, a key step to improving domain coverage especially for public datasets. Secondly, context is used to predict the domains of objects of interest. The framework is applied to the context around airplanes from ADE20K-SceneParsing, COCO-Stuff and PASCAL-Context. As intermediate results, the context statistics were obtained to guide design and mapping choices for the merged dataset SemanticAircraft and image patches were manually annotated in a one-hot manner across four aerial domains. Three different methods predict domains of airplanes: An original threshold-algorithm and unsupervised clustering models use context priors, a supervised CNN works on input images with domain labels. All three models were able to achieve acceptable prediction results, with the CNN obtaining accuracies of $$69\%$$ to $$85\%$$ . Additionally, context statistics and applied clustering models provide data introspection enabling a deeper understanding of the visual content.
Research on Door-Opening Strategy Design of Mobile Manipulators Based on Visual Information and Azimuth

At present, the manipulator has become an important auxiliary tool in the production and processing operations, so new requirements are put forward for the control strategy of the manipulator. In production, because traditional manipulators can only perform tasks according to the set procedures and cannot fully realize the intelligence and collaboration of control, how to improve the operating efficiency of manipulators has become an urgent problem to be solved. The autonomous door-opening control of robots is a challenging research topic in the field of automation. In the robot movement, target recognition and positioning and the coordinated control of manipulators and mobile platforms are difficult points in the research of autonomous door-opening control. Through the research on autonomous door-opening control, the robot has a broader working space, which greatly improves the robot’s ability to serve humans and has important theoretical value and practical significance.
The Challenge of Copyright Protection of Artificial Intelligence Products to the Field of Intellectual Property Legislation Based on Information Technology

The rise of artificial intelligence plays an important role in social progress and economic development, which is a hot topic in the Internet industry. In the past few years, the Chinese government has vigorously increased policy support to promote the golden age of artificial intelligence. However, with the rapid development of artificial intelligence, the copyright protection and intellectual property legislation of artificial intelligence products have brought some challenges.
Towards Better Evidence Extraction Methods for Fact-Checking Systems

Given current levels of misinformation spread, never before have fact-checking frameworks been so critical. Unfortunately, the performance of Automated Fact-checking systems is still poor due to the complexity of the task. In this paper, we present an ablation study of a framework submitted to the FEVER 1.0 challenge. Based on our findings, we explore how triple-based information retrieval, coreference resolution, and recent language model representations can impact the performance of each subtask. We show the importance of recall and precision in the retrieval of documents and sentences that can be provided to justify the veracity of a given claim. We reach state-of-the-art results in the Document Retrieval task and we show promising results when using coreference resolution to improve the Sentence Retrieval task.
Transparent Management of Adjacencies in the Cubic Grid

We propose an integrated data structure which represents, at the same time, an image in the cubic grid and three well-composed images, homotopy equivalent to it with face-, edge- and vertex-adjacency. After providing an algorithm to build the structure, we present examples showing how, thanks to such data structure, image processing algorithms can be written in a transparent way w.r.t. the adjacency type. Applications include rapid prototyping and teaching.
Sentiment Analysis in Low-Resource Bangla Text Using Active Learning

Sentiment analysis has become a very crucial and extensive dispute in the field of natural language processing and data mining. In supervised learning methods, it needs a huge number of annotated training examples for training. But it is time-consuming, costly, and takes a huge effort to build an annotated dataset. Bangla as a low resource language, it is more adamant to get an annotated dataset for sentiment analysis. In this paper, we proposed an active learning method for sentiment analysis in the Bangla language to reduce the cost of data annotation, preserving classification accuracy. We conducted the feasibility study of active learning with feature extraction methods like TF-IDF and Word Embedding with classifiers like Support Vector Machine(SVM), Logistic Regression(LR), Artificial Neural Network(ANN) along with multiple sample selection strategies on our large dataset. Our result shows active learning can reduce data annotation costs and increase accuracy significantly.
Bug Characteristics in Probabilistic Programming Systems: A Comprehensive Study

Probabilistic programming systems allow developers to model random phenomena and perform reasoning about the model efficiently. As the number and adoption of probabilistic programming systems are growing significantly, the reliability of such systems is also becoming increasingly important. It is thus crucial to analyze existing bugs in similar systems to ensure the reliability of these systems. This paper conducts an empirical study to investigate bugs and their features on real probabilistic programming systems. We select three popular probabilistic programming projects on GitHub, namely Edward, Pyro, and PyMC3, and then manually analyze a sample set of bugs to label them in different categories. The results show that 29% of the bugs belong to the algorithm/method category, 22.33 % belong to the internal interface category, and 13.67 % of the bugs belong to the external interface category. We also report the relationship between bug categories and bug-fixing duration, the effort needed to fix the bugs, and bug impacts on files.
Textural Feature Analysis Technique for Copy-Move Forgery Detection

The technique of image processing is used to process information available in the pixels of an image. A forgery detection algorithm can detect any region of an image that has been copied earlier. This research work makes use of decision wavelet transform (DWT), and singular wavelet transform (SWT) approaches for copy-move forgery detection (CMFD). The presented SWT-DWT performs the detection tasks in different steps. These steps are known as preprocessing, feature extraction, feature matching, and forgery detection. The efficiency of the presented algorithm has been measured in terms of certain metrics.
Stop Oversampling for Class Imbalance Learning: A Critical Approach Review

For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is, models trained on fictitious data may fail spectacularly when put to real-world problems. The fundamental difficulty with oversampling approaches is that, given a real-life population, the synthesized samples may not truly belong to the minority class. As a result, training a classifier on these samples while pretending they represent minority may result in incorrect predictions when the model is used in the real world. We analyzed a large number of oversampling methods in this paper and devised a new oversampling evaluation system based on hiding a number of majority examples and comparing them to those generated by the oversampling process. Based on our evaluation system, we ranked all these methods based on their incorrectly generated examples for comparison. Our experiments using more than 70 oversampling methods and three imbalanced real-world datasets reveal that all oversampling methods studied generate minority samples that are most likely to be majority. Given data and methods in hand, we argue that oversampling in its current forms and methodologies is unreliable for learning from class imbalanced data and should be avoided in real-world applications.
Building Web-Based Job Management System Using the Laravel 8.0 Framework (Case Study: Muhammadiyah Lamongan Hospital)

The Job Management System is a system that can make easier for Directors to monitor their completing tasks/projects employees. The application of Job Management at the SIRS Unit of the Muhammadiyah Lamongan Hospital is still done manually. Actually this is not a big thing, but it can affect the efficiency and effectiveness of employees in terms of collecting tasks/projects assigned by the project leader. In addition, the director as the highest leader at Muhammadiyah Lamongan Hospital also has to wait for a report from the project chairman to be able to get the final results of the project that is being worked on. For this reason, a web-based work management system was created using the waterfall method, Laravel 8.0 Framework , and MySQL database . The purpose of this system is to help the Director to be able to monitor the progress of a project carried out by employees in each unit. In addition, this system can assist the project leader in monitoring the progress of the project given by the Director. And the benefits that employees get are that they don't have to wait for offline meetings or regular meetings to collect tasks that have been done. This system also supports project team members to improve effectiveness, time efficiency, and quality of work.
Optimizing the Human: A Posthuman Taxonomy in the Works of Theresa Hannig

This chapter analyzes Theresa Hannig’s novels, Die Optimierer (2017) and Die Unvollkommenen (2019), as a creative literary intervention into discourses of posthumanism. Hannig’s work exposes the different arguments regarding the development of human enhancement, consciousness transfer, sentient robots, and artificial intelligences and negotiates posthuman positions of subjectivity and the rights of personhood for artificial beings. This chapter presents the evolving concepts of an artificially created posthumanity in the novels and reveals Hannig’s perspective on how to deal with a becoming-machine.
Video Steganography Based on Edge Detector and Using Lifting Wavelet Transform

A method which is a mixture of edge detector along with a key generator to scramble the secret data is projected in this paper. Initially, in this process, carrier frames are ascertained from the input video. After assessing the key frames, a hybrid of Canny and Prewitt edge detectors are applied to get the pixels of the edge. On the edge pixels, lifting wavelet transform (LWT) is applied and HH and LL subbands are selected to entrench the secret data. Prior to embedding, the secret data is shuffled using a key to increase the security of the proposed system. The proposed method attained an embedding capacity of 62% and PSNR of 59 dB. The experimental results reveal a better security, good quality of video and robustness.
Research on the Construction Method of Zhuang Word Embedding Evaluation Set

Word embedding is the first process to study the natural language processing. However, most of the researches on word embedding of minority languages, such as Zhuang language, lacks a word embedding evaluation method since the cost of manually constructing the test set of word embedding is high, and there are few minority language applications. In addition, the test set of word embedding of minority languages constructed by the traditional method usually lacks diversity. This paper proposes an improved sampling algorithm SPXY_Word to automatically create the comprehensive minority word embedding test set. Taking Zhuang as an example, we realize the automatic construction of the Zhuang word embedding test set Zhuang-Benchmark900. The experimental results show that the test set has balanced data, good discrimination between different word embedding models, highly similar evaluation ability with the Chinese test set: taking Pearson coefficient as the evaluation index, the similar evaluation ability of the two test sets is 97% in similarity evaluation and is 60% in relevance evaluation. This study provides a new solution for constructing a word embedding evaluation set for minority languages, which is helpful to slow the decline of minority languages and cultures.
Hybrid Feature Selection Model for Credit Card Data Classification

This initiative is primarily concerned with identifying credit card fraud detection in the real world. The exponential growth in credit card spending has resulted in a recent surge in fraudulent conduct. Its aim is to obtain products without payment or to remove funds from the account without authorization. To minimize their costs, it has become critical for all credit card issuers to introduce an efficient fraud identification scheme. Not the card or the cardholder at the time of payment is one of the most significant challenges in growing a company. The dealer has no way of knowing if the person making the order is an approved cardholder. Then, by processing any of the given functionality, the feature selection of the dataset will be done by PCA and SelectK-Best algorithms for getting better accuracy and the fraud accuracy will be detected using different classification machine learning algorithms like Random Forest, SVM, Logistic Regression, KNN, Decision Tree, XGBoost and a graphical model simulation will be provided. The method’s efficiency is measured in terms of their precision, sensitivity, specificity, and accuracy.
Development of a Multi-Object Detection and Human Tracking System from Cooperative Dual Cameras in an Unmanned Aerial Vehicle

Object detection and human tracking are vital for pairing computer vision to a quadrotor. One application for this is monitoring traffic by either classifying vehicles or tracking people through the crowd. This was proven true by the developed system, garnering a detection rate of 77% for various vehicles in the Philippines, and showing the ability to track people on a dual camera system albeit as a proof-of-concept only. Though this can be a potentially huge field for digital image processing, there is still a challenge of creating a fast and responsive application through wireless communication. This study presented a quantitative data when approaching a quadcopter equipped with dual camera system streamed through WiFi connection.
Calculate optimum solar cell angle based on maximum solar intensity and minimum solar cell temperature

Solar energy is one of the renewable energy resources. The main usage of solar energy in generating electricity with photovoltaic systems (PV systems). The PV efficiency in maximum radiation is peak value; but the temperature of cell is high. With decreasing cell’s temperature, the efficiency will increase. The goal of this research is finding the appropriate solar cell angle which the radiation be peak value and the rise of temperature be minimum on it. The research has been done using analytical method and simulation in EnergyPlus Software. This research was performed in Tehran, Tabriz and Yazd monthly and annually. Two solar cell angles have been obtained (yearly and monthly) based on maximum radiation and minimum cell’s temperature. The result of analytical method shows that at the optimal angle based on the power (minimum cell’s temperature) can generate more energy than the optimal angle based on maximum radiation. So, the optimal solar cell angle should be selected using power generated by solar cell. The simulation in EnergyPlus Software has same result compared to analytical method.
Insulation Based Models and Financial Analysis of Photovoltaic Installations in Three Localities in Burkina—Faso

This article focused on the economic and financial calculations concerning the production of electrical energy from photovoltaic (PV) installations connected to the grid. In this study, the models were proposed for localities of Dori in the North, Ouagadougou in the center, and Bobo-Dioulasso in the South of Burkina Faso. Financial analysis was performed for Dori and Bobo-Dioulasso. The cost of energy produced by PV installations during their operational life span was calculated and compared with other economic parameters. Regression models for estimating global solar radiation on a horizontal surface in these localities were proposed. The results obtained are satisfactory with the coefficient of determination between “0.9745 and 0.9977” and a statistical test t between “0 and 0.09.” The Leveled Cost of Energy (LCOE) of 60 Fcfa/kWh for a discount rate of 4% is almost equal to half the average price of electricity in Burkina Faso, which is equal to 119 Fcfa/kWh.
Formalization and Verification of Cyclic Group

At present, the formal method is an important system design verification method, which effectively compensates the “incomplete” problem of the traditional methods such as simulation and testing in the system design verification. Since the logical method as a typical formal method is our research direction, we naturally choose the first-order logic language in the logical method to formalize Group theory in the field of mathematics. Based on some formalized conclusions of Group theory in TPTP, this paper completes the formal description of missing definitions about the Group in TPTP, namely the order of element in group, nth-order cyclic group and Klein four-group. Some propositions and theorems related to these definitions are further formal described, and the correctness of these descriptions is verified by the theorem tool Prover9.
Application of Intelligent Translation Software for English Translation Based on Data Management Technology

With the development of new technologies related to computers, data in all walks of life has grown rapidly, and people's requirements for the use and storage time of data have become higher and higher, making efficient management of data and accurate and fast querying of data have become a social hot spot. Especially the application of data management technology in English-to-English intelligent translation software has attracted the attention of many educators. This article mainly summarizes the characteristics of data management technology and English-to-English intelligent translation software, as well as the advantages and main applications of data management technology in English-to-English intelligent translation software. Based on this, this article issued 300 questionnaires based on the current situation of college students using English-to-English intelligent translation software. The analysis of the survey results showed that: 81.15% of the people who have used Youdao's English-to-English intelligent translation software, it is the most used English-to-English intelligent translation software, which shows that the choices of different individuals are affected by personal preferences; the number of language majors using Sogou's English translation intelligent translation software is 34.57%, and the number of non-language majors using the software is only 10.26%, indicating that language majors have a higher demand for translation software; Although computer translation is stable, only 38 people use the English-to-English intelligent translation software through the computer, and 143 people use the English-to-English intelligent translation software through the mobile phone, indicating that students tend to choose more convenient equipment for English translation learning.
Dynamic wavefront measurement with a point diffraction interferometer based on birefringence polarization beam splitting

In this study, we proposed a point diffraction interferometer based on birefringence polarization beam splitting (BPBS-PDI) for transmission wavefront measurements. Using the polarization beam splitting property of birefringent crystals and a specially designed calcite crystal as a polarization beam splitter, two beams of linearly polarized light with orthogonal polarization directions and a small angular separation can be obtained to produce the reference and test beams with perpendicular polarization directions through a pinhole point diffraction plate. By introducing spatial synchronization phase-shifting technology, influencing factors such as environmental vibrations on the measurement results, are reduced. Subsequently, the birefringent crystal and system error calibration methods were studied. Finally, a BPBS-PDI experimental device was set up to obtain the wavefront distribution of the lens to be tested. The experimental results are consistent with those of the ZYGO interferometer, indicating that the BPBS-PDI wavefront measurement method can be used to measure a lens transmission wavefront with high accuracy.
Research on Multi-UAV Task Allocation Method with Complex Timing Coupled Constraints

Faced with complex application scenarios, Multi-UAV system can break down their own capability barriers and form diverse styles. Task allocation has an extraordinary significance for the multi-UAV system, with the goal of obtaining reasonable plan. The timing coupled constraints place special requirements on task plan, increasing the complexity of task allocation calculations. The timing coupled constraints contain task coupling relationships, execution sequences and time-window requirement. In this paper, a modified method is proposed for the constraints, containing constraint description module and dynamic calculation module. The constraint description module describes the diverse coupling relationships in a uniform way. The dynamic calculation module allows for adaptive selection of tasks to participate in the calculation. This method can be effectively combined with other types of constraint handling methods or different algorithms. The method is verified by simulation experiments in the heterogeneous scenarios.