Reviewerly/exHarmony huggingface dataset:
Assessing transformation methods for group comparisons under violated assumptions: type I error rate and test power

ABSTRACT In this study, some transformation methods that are applied when the assumptions of analysis of variance are not met are evaluated in terms of type I error rate and the test power, under circumstances with different distributions, number of groups, number of observations, variance ratios, and different standard deviation differences. The data set used in the study consisted of random numbers generated from N (0,1), and χ2(3) distributions using the random function of the Numpy library in the Python programming language. The logarithmic, square root and root transformations were evaluated on ANOVA based on simulation combinations. It was observed that the transformation techniques of taking the square root after adding 0.5 and 0.375 to the data were relatively more reliable compared to other transformations in terms of type I error rate. However, in every case, type I error rate determined at the beginning of the experiment increased both before and after the transformation was applied. In particular, interestingly, the third and fourth degree root transformations gave better results of test power in the right skewed distribution. In addition, we compared the transformation techniques in question to determine the normality of the data and the homogeneity of variances by a real data.
Guarding isometric subgraphs and cops and robber in planar graphs

In the game of Cops and Robbers, one of the most useful results is that an isometric path in a graph can be guarded by one cop. In this paper, we introduce the concept of wide shadow in a subgraph, and use it to characterize all 1-guardable graphs. As an application, we show that 3 cops can capture a robber in any planar graph with the added restriction that at most two cops can move simultaneously, proving a conjecture of Yang and strengthening a classical result of Aigner and Fromme.
Entity generation algorithm based on reference expansion

— The extraction and understanding of text knowledge become increasingly crucial in the age of big data. One of the current research areas in the field of natural language processing (NLP) is how to accurately understand the text and collect accurate linguistic information because Chinese vocabulary is diverse and ambiguous. This paper mainly studies the candidate entity generation module of the entity link system. The candidate entity generation module constructs an entity reference expansion algorithm to improve the recall rate of candidate entities. In order to improve the efficiency of the connection algorithm of the entire system while ensuring the recall rate of candidate entities, we design a graph model filtering algorithm that fuses shallow semantic information to filter the list of candidate entities, and verify and analyze the efficiency of the algorithm through experiments. By analyzing the related technology of the entity linking algorithm, we study the related technology of candidate entity generation and entity disambiguation, improve the traditional entity linking algorithm, and give an innovative and practical entity linking model. The recall rate exceeds 82%, and the link accuracy rate exceeds 73%. Efficient and accurate entity linking can help machines to better understand text semantics, further promoting the development of NLP and improving the users’ knowledge acquisition experience on the text.
The AMR-PT corpus and the semantic annotation of challenging sentences from journalistic and opinion texts

ABSTRACT One of the most popular semantic representation languages in Natural Language Processing (NLP) is Abstract Meaning Representation (AMR). This formalism encodes the meaning of single sentences in directed rooted graphs. For English, there is a large annotated corpus that provides qualitative and reusable data for building or improving existing NLP methods and applications. For building AMR corpora for non-English languages, including Brazilian Portuguese, automatic and manual strategies have been conducted. The automatic annotation methods are essentially based on the cross-linguistic alignment of parallel corpora and the inheritance of the AMR annotation. The manual strategies focus on adapting the AMR English guidelines to a target language. Both annotation strategies have to deal with some phenomena that are challenging. This paper explores in detail some characteristics of Portuguese for which the AMR model had to be adapted and introduces two annotated corpora: AMRNews, a corpus of 870 annotated sentences from journalistic texts, and OpiSums-PT-AMR, comprising 404 opinionated sentences in AMR.
Progressive-Augmented-Based DeepFill for High-Resolution Image Inpainting

Image inpainting aims to synthesize missing regions in images that are coherent with the existing visual content. Generative adversarial networks have made significant strides in the development of image inpainting. However, existing approaches heavily rely on the surrounding pixels while ignoring that the boundaries might be uninformative or noisy, leading to blurred images. As complementary, global visual features from the remote image contexts depict the overall structure and texture of the vanilla images, contributing to generating pixels that blend seamlessly with the existing visual elements. In this paper, we propose a novel model, PA-DeepFill, to repair high-resolution images. The generator network follows a novel progressive learning paradigm, starting with low-resolution images and gradually improving the resolutions by stacking more layers. A novel attention-based module, the gathered attention block, is further integrated into the generator to learn the importance of different distant visual components adaptively. In addition, we have designed a local discriminator that is more suitable for image inpainting tasks, multi-task guided mask-level local discriminator based PatchGAN, which can guide the model to distinguish between regions from the original image and regions completed by the model at a finer granularity. This local discriminator can capture more detailed local information, thereby enhancing the model’s discriminative ability and resulting in more realistic and natural inpainted images. Our proposal is extensively evaluated over popular datasets, and the experimental results demonstrate the superiority of our proposal.
Adaptive Reversible 3D Model Hiding Method Based on Convolutional Neural Network Prediction Error Expansion

Although reversible data hiding technology is widely used, it still faces several challenges and issues. These include ensuring the security and reliability of embedded secret data, improving the embedding capacity, and maintaining the quality of media data. Additionally, irregular data types, such as three-dimensional point clouds and triangle mesh-represented 3D models, lack an ordered structure in their representation. As a result, embedding these irregular data into digital media does not provide sufficient information for the complete recovery of the original data during extraction. To address this issue, this paper proposes a method based on convolutional neural network prediction error expansion to enhance the embedding capacity of carrier images while maintaining acceptable visual quality. The triangle mesh representation of the 3D model is regularized in a two-dimensional parameterization domain, and the regularized 3D model is reversibly embedded into the image. The process of embedding and extracting confidential information in carrier images is symmetrical, and the regularization and restoration of 3D models are also symmetrical. Experiments show that the proposed method increases the reversible embedding capacity, and the triangle mesh can be conveniently subjected to reversible hiding.
Mix MSTAR: A Synthetic Benchmark Dataset for Multi-Class Rotation Vehicle Detection in Large-Scale SAR Images

Because of the counterintuitive imaging and confusing interpretation dilemma in Synthetic Aperture Radar (SAR) images, the application of deep learning in the detection of SAR targets has been primarily limited to large objects in simple backgrounds, such as ships and airplanes, with much less popularity in detecting SAR vehicles. The complexities of SAR imaging make it difficult to distinguish small vehicles from the background clutter, creating a barrier to data interpretation and the development of Automatic Target Recognition (ATR) in SAR vehicles. The scarcity of datasets has inhibited progress in SAR vehicle detection in the data-driven era. To address this, we introduce a new synthetic dataset called Mix MSTAR, which mixes target chips and clutter backgrounds with original radar data at the pixel level. Mix MSTAR contains 5392 objects of 20 fine-grained categories in 100 high-resolution images, predominantly 1478 × 1784 pixels. The dataset includes various landscapes such as woods, grasslands, urban buildings, lakes, and tightly arranged vehicles, each labeled with an Oriented Bounding Box (OBB). Notably, Mix MSTAR presents fine-grained object detection challenges by using the Extended Operating Condition (EOC) as a basis for dividing the dataset. Furthermore, we evaluate nine benchmark rotated detectors on Mix MSTAR and demonstrate the fidelity and effectiveness of the synthetic dataset. To the best of our knowledge, Mix MSTAR represents the first public multi-class SAR vehicle dataset designed for rotated object detection in large-scale scenes with complex backgrounds.
Meta In-Context Learning: Harnessing Large Language Models for Electrical Data Classification

The evolution of communication technology has driven the demand for intelligent power grids and data analysis in power systems. However, obtaining and annotating electrical data from intelligent terminals is time-consuming and challenging. We propose Meta In-Context Learning (M-ICL), a new approach that harnesses large language models to classify time series electrical data, which largely alleviates the need for annotated data when adapting to new tasks. The proposed M-ICL consists of two stages: meta-training and meta-testing. In meta-training, the model is trained on various tasks that have an adequate amount of training data. The meta-training stage aims to learn the mapping between electrical data and the embedding space of large language models. In the meta-testing stage, the trained model makes predictions on new tasks. By utilizing the in-context learning ability of large language models, M-ICL adapts models to new tasks effectively with only a few annotated instances (e.g., 1–5 training instances per class). Our contributions lie in the new application of large language models to electrical data classification and the introduction of M-ICL to improve the classification performance with the strong in-context learning ability of large language models. Furthermore, we conduct extensive experiments on 13 real-world datasets, and the experimental results show that the proposed M-ICL improves the average accuracy over all datasets by 19.06%, 12.06%, and 6.63% when only one, two, and five training instances for each class are available, respectively. In summary, M-ICL offers a promising solution to the challenges of electrical data classification.
Power Requirements Evaluation of Embedded Devices for Real-Time Video Line Detection

In this paper, the comparison of the power requirements during real-time processing of video sequences in embedded systems was investigated. During the experimental tests, four modules were tested: Raspberry Pi 4B, NVIDIA Jetson Nano, NVIDIA Jetson Xavier AGX, and NVIDIA Jetson Orin AGX. The processing speed and energy consumption have been checked, depending on input frame size resolution and the particular power mode. Two vision algorithms for detecting lines located in airport areas were tested. The results show that the power modes of the NVIDIA Jetson modules have sufficient computing resources to effectively detect lines based on the camera image, such as Jetson Xavier in mode MAXN or Jetson Orin in mode MAXN, with a resolution of 1920 × 1080 pixels and a power consumption of about 19 W for 24 FPS for both algorithms tested.
A Comparative Assessment of Annual Solar Irradiance Trends between Mpumalanga and Northern Cape Province in South Africa Using PVGIS

South Africa has committed to reducing its greenhouse emissions by sixty-five percent by 2030 in their National Integrated Energy Plan (NEIP). The lack of investment and development for renewable energy sources put the country on an uncertain trajectory in fulfilling its 2030 energy commitments. At the same time, the country has been labeled as a region with one of the highest solar energy potentials. Provinces such as Mpumalanga and Northern Cape are on opposite ends of the matter, with Northern Cape is one of the leading provinces for renewal energy, while the Mpumalanga province remains the host to eighty-five per cent of the country’s coal plants. Solar energy is an abundant renewable energy source and can be assessed using Geographic Information Systems (GIS) techniques. In this paper, the geostatistical technique, Kriging, is employed to predict, estimate, and compare the regional distribution, potential, and variability of annual optimum solar energy (irradiance) between the Mpumalanga Province and Northern Cape Province. Spot-based radiation data are available for solar energy analyses from the GIS Web-based tool Photovoltaic Geographical Information Systems (PVGIS). Kriging was used to estimate the spatial variability of solar energy at an average error of 1.98505% for the Northern Cape Province and 2.32625% for the Mpumalanga Province. It was identified that the Northern Cape receives the highest annual optimum irradiation and has a low overall spatial variation in irradiation over its provincial area. Mpumalanga receives lesser amounts of irradiation but has high overall spatial variation over its provincial area. Most of Northern Cape’s central to northwestern regions have the highest annual optimum irradiation ranging from 2583 kWh/m2 to 2638 kWh/m2, while Mpumalanga’s highest regions of annual irradiation occur primarily on its western and northwestern parts and ranges in highs of 2345 kWh/m2 to 2583 kWh/m2.
Non-adaptive measurement-based quantum computation on IBM Q

Abstract We test the quantumness of IBM’s quantum computer IBM Quantum System One in Ehningen, Germany. We generate generalised n -qubit GHZ states and measure Bell inequalities to investigate the n -party entanglement of the GHZ states. The implemented Bell inequalities are derived from non-adaptive measurement-based quantum computation (NMQC), a type of quantum computing that links the successful computation of a non-linear function to the violation of a multipartite Bell-inequality. The goal is to compute a multivariate Boolean function that clearly differentiates non-local correlations from local hidden variables (LHVs). Since it has been shown that LHVs can only compute linear functions, whereas quantum correlations are capable of outputting every possible Boolean function it thus serves as an indicator of multipartite entanglement. Here, we compute various non-linear functions with NMQC on IBM’s quantum computer IBM Quantum System One and thereby demonstrate that the presented method can be used to characterize quantum devices. We find a violation for a maximum of seven qubits and compare our results to an existing implementation of NMQC using photons.
On the validity of retrospective predictive performance evaluation procedures in just-in-time software defect prediction

Abstract Just-In-Time Software Defect Prediction (JIT-SDP) is concerned with predicting whether software changes are defect-inducing or clean. It operates in scenarios where labels of software changes arrive over time with delay, which in part corresponds to the time we wait to label software changes as clean (waiting time). However, clean labels decided based on waiting time may be different from the true labels of software changes, i.e., there may be label noise. This typically overlooked issue has recently been shown to affect the validity of continuous performance evaluation procedures used to monitor the predictive performance of JIT-SDP models during the software development process. It is still unknown whether this issue could potentially also affect evaluation procedures that rely on retrospective collection of software changes such as those adopted in JIT-SDP research studies, affecting the validity of the conclusions of a large body of existing work. We conduct the first investigation of the extent with which the choice of waiting time and its corresponding label noise would affect the validity of retrospective performance evaluation procedures. Based on 13 GitHub projects, we found that the choice of waiting time did not have a significant impact on the validity and that even small waiting times resulted in high validity. Therefore, (1) the estimated predictive performances in JIT-SDP studies are likely reliable in view of different waiting times, and (2) future studies can make use of not only larger (5k+ software changes), but also smaller (1k software changes) projects for evaluating performance of JIT-SDP models.
Social technologies in the era of “supervisory capitalism”: digitalization and the power of algorithms

“Surveillance capitalism” is not yet a sustainable term used in the social sciences, although there has long been scientific debate about the basic technologies of this economic order- digital information and communication technologies, algorithms, data, artificial intelligence, neural networks, the Internet of things, etc. At the time, Google had revolutionized the field of predictive analysis and promoted “surveillance capitalism”. The company began to pay special attention to extracting and analyzing data in translation operations, speech recognition, image processing, ranking, etc. Google began to turn data (raw materials) into intelligent products – algorithms designed to predict user behavior. These predictive products have been used for sale to other organizations that are increasing their profits by improving their predictive marketing capabilities. Smart machines are no longer just learning to know a man his behavior, they are trying to push him into making certain decisions, into programmed behavioral responses to some stimulus, which leads to an increase in the earnings of “supervising capitalists” (and, above all, the income of owners of digital platforms). That is, by automating various processes, machines set the human behavior that leads to the emergence of a new type of power – the “instrumental” power (automation of life of individuals by means of universal implementation of “smart” network devices, formation of “smart” space, “smart” houses). Algorithms penetrate into a variety of spheres, on the basis of algorithms management decisions are made and modern cities function. However, questions inevitably arise about the social consequences of widespread algorithmization and digitalization, the security of data storage, the limits of digitizing the social world.
Working conditions and cybersecurity: Time pressure, autonomy and threat appraisal shaping employees’ security behavior

With the increasing importance of cybersecurity in organizations, it becomes crucial that employees behave securely. In the present article, we investigate the interplay of antecedents on this behavior. We conceptualize cybersecurity behavior through the components of compliance and participation and investigate the relationship between security knowledge, threat appraisal (i.e., severity and susceptibility), and working conditions (i.e., time pressure, decision-making autonomy). We conducted an online survey in four public organizations, collecting quantitative cross-sectional data from 214 employees. The survey captured subjective perceptions of the concepts. Findings showed a positive effect of security knowledge on security compliance and security participation. The perception of severity and susceptibility strengthened both effects. Additionally, the presence of time pressure reduced the effect of security knowledge on security compliance, while having decision-making autonomy increased the effect of security knowledge on security participation. Our study demonstrates the interplay between antecedents and highlights the role of working conditions in employees’ cybersecurity behavior. Implications for practice in terms of training approaches considering work design are discussed.
Leveraging smart contracts for enhanced efficiency in the agro-industrial complex and agriculture

This article explores the potential application and impact of smart contracts on the agro-industrial complex and agriculture. Recognizing the significance of these sectors to global economies, the study investigates how the implementation of blockchain-based smart contracts can address existing challenges such as transaction inefficiencies, transparency issues, and disputes in contractual obligations. Utilizing a mixed-methods research design, data is collected from both primary and secondary sources and subsequently analyzed. Preliminary findings suggest that smart contracts can substantially enhance operational efficiency, transparency, and trust among stakeholders in the agro-industry. However, the adoption process poses its own challenges, including a steep learning curve and resistance to change. The study concludes by providing recommendations for effective smart contract implementation in the agro-industrial sector and suggesting areas for future research. The article contributes to the understanding of technology-driven solutions in the agriculture industry and can inform policy and decision-making processes.
Transformation of approaches in teaching information technologies to engineering students

The main difference of the Industry 4.0 era is the global digitalization and the ever-increasing complexity of production systems. The successful digital transformation of the entire economy and individual enterprises requires the training of a new generation of engineers. The well-known models of information technology based training, which are widely used today, are distance learning and blended learning. And if these approaches have already been methodically worked out, IT training in engineering specialties raises a lot of questions. The article provides a selective review of the literature on IT training for engineers. In particular, attention is focused on various existing approaches in the field of teaching programming, but it is emphasized that much remains to be implemented. It is obvious that modern digital technologies can radically change the methods of teaching and learning in IT. This means, first of all, that the development of a new theory of education is required, taking into account the prospects for total digitalization of all processes in the economy. The aim of the article is to study the transformation of approaches in IT training of engineering students and the vector of scientific developments in this direction.