Reviewerly/exHarmony huggingface dataset:
Cognitive biases in software development

Cognitive biases are hardwired behaviors that influence developer actions and can set them on an incorrect course of action, necessitating backtracking. Although researchers have found that cognitive biases occur in development tasks in controlled lab studies, we still do not know how these biases affect developers' everyday behavior. Without such an understanding, development tools and practices remain inadequate. To close this gap, we conducted a two-part field study to examine the extent to which cognitive biases occur, the consequences of these biases on developer behavior, and the practices and tools that developers use to deal with these biases. We found about 70% of observed actions were associated with at least one cognitive bias. Even though developers recognized that biases frequently occur, they are forced to deal with such issues with ad hoc processes and suboptimal tool support. As one participant (IP12) lamented: There is no salvation!
Improvement of facial attributes' estimation using Transfer Learning

Nowadays, we are experiencing the emergence of intelligent applications, capable of recognizing a human face by using shape, gender, face attributes or even emotions. Those application are deployed in numerous real-world sites, like facial recognition systems, Law enforcement applications or security purposes. For these reasons, we propose to improve available facial attributes' estimation in the main model, by adding other attributes, such as (medical-mask or face cover, head scarf, tattoo) and using transfer learning (TL). To do this end, using TL one stage, we suggest retraining the main model on new attributes all at once, and using TL multistage training, where we employ a TL network for each attribute. The main model is trained on the CelebA dataset with 40 attributes using a CNN model, while for the aforementioned three attributes, we use our constructed dataset. The obtained results show that the second method outruns the first in terms of metrics, but the first one is better in prediction rates, especially for the attributes of the main model, this is a problem caused by many TL networks losing data.
Boosting Vision-and-Language Navigation with Direction Guiding and Backtracing

Vision-and-Language Navigation (VLN) has been an emerging and fast-developing research topic, where an embodied agent is required to navigate in a real-world environment based on natural language instructions. In this article, we present a Direction-guided Navigator Agent (DNA) that novelly integrates direction clues derived from instructions into the essential encoder-decoder navigation framework. Particularly, DNA couples the standard instruction encoder with an additional direction branch which sequentially encodes the direction clues in the instructions to boost navigation. Furthermore, an Instruction Flipping mechanism is uniquely devised to enable fast data augmentation as well as a follow-up backtracing for navigating the agent in a backward direction. Such a way naturally amplifies the grounding of instruction in the local visual scenes along both forward and backward directions, and thus strengthens the alignment between instruction and action sequence. Extensive experiments conducted on Room to Room (R2R) dataset validate our proposal and demonstrate quantitatively compelling results.
Investigating Cross-Modal Approaches for Evaluating Error Acceptability of a Recognition-Based Input Technique

Emerging input techniques that rely on sensing and recognition can misinterpret a user's intention, resulting in errors and, potentially, a negative user experience. To enhance the development of such input techniques, it is valuable to understand implications of these errors, but they can very costly to simulate. Through two controlled experiments, this work explores various low-cost methods for evaluating error acceptability of freehand mid-air gestural input in virtual reality. Using a gesture-driven game and a drawing application, the first experiment elicited error characteristics through text descriptions, video demonstrations, and a touchscreen-based interactive simulation. The results revealed that video effectively conveyed the dynamics of errors, whereas the interactive modalities effectively reproduced the user experience of effort and frustration. The second experiment contrasts the interactive touchscreen simulation with the target modality - a full VR simulation - and highlights the relative costs and benefits for assessment in an alternative, but still interactive, modality. These findings introduce a spectrum of low-cost methods for evaluating recognition-based errors in VR and a series of characteristics that can be understood in each.
Teaching Algorithms and Programming First Year University Students on Base of Distance Learning System DL.GSU.BY

This article describes the technology of teaching text programming to first-year students based on the DL.GSU.BY website. The main advantages of the technology include “zero entry threshold”, training adapted to the student, many years of experience in practical application, effectiveness, and scalability. The following issues are consistently considered in the article: idealized goal setting, students' use of a programming language of their choice from a variety of modern programming languages when perform-ing practical tasks in the subject, clear verification of goal achievement, blended learning, effective per-sonalization of the educational process, non-standard organizational and technical solutions, learning effectiveness. The idealized goal setting includes the need to teach students to algorithmic reformulation of the problem condition; possession of a set of basic language constructs, as well as basic built-in pro-cedures and functions; ability to use basic algorithms on one-dimensional and two-dimensional arrays, sets of plane points, lines, queue; ability to develop and debug new algorithms. Effective personaliza-tion of the educational process is ensured by using the following methods: each lesson the student is offered a choice of activities that correspond to his current level of training; automatic verification of solutions is provided with the service "assignment of tests"; the system of automatic differentiated learning is used.
A Study on Blockchain Architecture Design Decisions and Their Security Attacks and Threats

Blockchain is a disruptive technology intended to implement secure decentralised distributed systems, in which transactional data can be shared, stored, and verified by participants of the system without needing a central authentication/verification authority. Blockchain-based systems have several architectural components and variants, which architects can leverage to build secure software systems. However, there is a lack of studies to assist architects in making architecture design and configuration decisions for blockchain-based systems. This knowledge gap may increase the chance of making unsuitable design decisions and producing configurations prone to potential security risks. To address this limitation, we report our comprehensive systematic literature review to derive a taxonomy of commonly used architecture design decisions in blockchain-based systems. We map each of these decisions to potential security attacks and their posed threats. MITRE’s attack tactic categories and Microsoft STRIDE threat modeling are used to systematically classify threats and their associated attacks to identify potential attacks and threats in blockchain-based systems. Our mapping approach aims to guide architects to make justifiable design decisions that will result in more secure implementations.
Research on Cardiorespiratory Endurance Test and Training Method Based on Structural Equation Model

In order to study the cardiorespiratory endurance test and training method under the structural equation model, a structural equation model built in conjunction with ANOS7.0 is proposed. This method uses confirmatory factor analysis and analyzes the influencing factors of motor skills. Studies indicate that df = 173 > 0 of the structural equation model, to over-identify the model, the absolute value of the standardized coefficient value is 0.257 to 0.758, less than 0.95, and there is no value with a large estimated standard deviation. The model did not violate the estimation phenomenon. Among the factors affecting motor skills, cardiopulmonary function, and muscle endurance, the weight of the back muscles is less than 0.4, the power of coordination and explosive power of lower limbs is greater than 0.6, and limb muscle strength, body mass, and the stretch and flexibility of the muscles have a greater weight on basketball skills than 0.5. The structural equation model is an over-recognition model. The absolute value of the standardized coefficient value does not have a value with an excessively large standard deviation estimate, and the model does not violate the estimate.
Recurrent Convolution based Graph Neural Network for Node Classification in Graph structure data

Many datasets in various machine learning applications are structural and naturally represented as graphs. They comprise data from the analyses of social and communication networks, predictions of traffic, and fraud detection. Graphbased Deep Learning (DL) aims to construct and train graph datasets attuned models for various graph-structured based tasks. In this work, we presented a model of Graph Neural Network (GNN) for the node classification task. We have compared our proposed model with a baseline model on three citation network datasets: CORA, PUBMED, and CITESEER. We examined the baseline and proposed models predictions on new data instances by randomly generating binary work vectors concerning the work presence probabilities for all three datasets. The proposed model is significantly better than the baseline model on the CORA and CITESEER datasets.
Shrink and Eliminate: A Study of Post-Training Quantization and Repeated Operations Elimination in RNN Models

Recurrent neural networks (RNNs) are neural networks (NN) designed for time-series applications. There is a growing interest in running RNNs to support these applications on edge devices. However, RNNs have large memory and computational demands that make them challenging to implement on edge devices. Quantization is used to shrink the size and the computational needs of such models by decreasing weights and activation precision. Further, the delta networks method increases the sparsity in activation vectors by relying on the temporal relationship between successive input sequences to eliminate repeated computations and memory accesses. In this paper, we study the effect of quantization on LSTM-, GRU-, LiGRU-, and SRU-based RNN models for speech recognition on the TIMIT dataset. We show how to apply post-training quantization on these models with a minimal increase in the error by skipping quantization of selected paths. In addition, we show that the quantization of activation vectors in RNNs to integer precision leads to considerable sparsity if the delta networks method is applied. Then, we propose a method for increasing the sparsity in the activation vectors while minimizing the error and maximizing the percentage of eliminated computations. The proposed quantization method managed to compress the four models more than 85%, with an error increase of 0.6, 0, 2.1, and 0.2 percentage points, respectively. By applying the delta networks method to the quantized models, more than 50% of the operations can be eliminated, in most cases with only a minor increase in the error. Comparing the four models to each other under the quantization and delta networks method, we found that compressed LSTM-based models are the most-optimum solutions at low-error-rates constraints. The compressed SRU-based models are the smallest in size, suitable when higher error rates are acceptable, and the compressed LiGRU-based models have the highest number of eliminated operations.
Characterization of tephra deposits using VNIR and MIR spectroscopy: A comprehensive terrestrial tephra spectral library

Laboratory spectral libraries of well-characterized natural samples are necessary for accurate interpretation of remote sensing spectral data. Tephra deposits, the result of explosive volcanic eruptions, are potentially found on all differentiated terrestrial bodies and are important chronologic and compositional marker beds on Earth. Here we present a visible/near-infrared (VNIR, 0.35–2.5 μm) and mid-infrared (MIR, 3.5–25 μm) spectral library composed of nineteen natural tephra samples from ten volcanic sources that span a range of compositions and components. The bulk, glass, and mineral phase compositions of each sample are measured and spectra from multiple size fractions of each sample were collected. The library can be found via the Terrestrial Analog portal (DOI: https://doi.org/10.5066/P9O54M4Q).
Utilizing subjectivity level to mitigate identity term bias in toxic comments classification

Toxic comment classification models are often found biased towards identity terms, i.e., terms characterizing a specific group of people such as “Muslim” and “black”. Such bias is commonly reflected in false positive predictions, i.e., non-toxic comments with identity terms. In this work, we propose a novel approach to debias the model in toxic comment classification, leveraging the notion of subjectivity level of a comment and the presence of identity terms. We hypothesize that toxic comments containing identity terms are more likely to be expressions of subjective feelings or opinions. Therefore, the subjectivity level of a comment containing identity terms can be helpful for classifying toxic comments and mitigating the identity term bias. To implement this idea, we propose a model based on BERT and study two different methods of measuring the subjectivity level. The first method uses a lexicon-based tool. The second method is based on the idea of calculating the embedding similarity between a comment and a relevant Wikipedia text of the identity term in the comment. We thoroughly evaluate our method on an extensive collection of four datasets collected from different social media platforms. Our results show that: (1) our models that incorporate both features of subjectivity and identity terms consistently outperform strong SOTA baselines, with our best performing model achieving an improvement in F1 of 4.75% over a Twitter dataset; (2) our idea of measuring subjectivity based on the similarity to the relevant Wikipedia text is very effective on toxic comment classification as our model using this has achieved the best performance on 3 out of 4 datasets while obtaining comparative performance on the remaining dataset. We further test our method on RoBERTa to evaluate the generality of our method and the results show the biggest improvement in F1 of up to 1.29% (on a dataset from a white supremacist online forum).
Adequacy of Effectual Ensemble Classification Approach to Detect Drift in Data Streams

Data stream mining has become an essential task in the current scenario as most of the applications are producing a wide range of information in the form of streams. It could be in education, IoT, social media, entertainment, banking, and a variety of other fields. This stream of data has several unique properties, such as volume, creation speed, and the inventiveness of concepts at any given time. This vividness of data is the most difficult aspect of data processing, and thus data mining and analysis. In this study, we examine the major three strategies for constructing ensemble-based classifiers with datasets with varying drifts in depth. This research has yielded data that will provide further insight into the building of ensembles for various environments.
An Attention-based U-Net Network for Anomaly Detection in Crowded Scenes

Anomaly detection in surveillance video is of great significance for public safety. Deep autoencoder has been widely used in anomaly detection. Because of its good generalization ability, sometimes abnormal samples can still be reconstructed very well. Some scholars use memory module constructed by using the normal samples to reconstruct the test samples. The memory items need to be retrieved and updated during the training and testing process, hence more memory space is required to store memory module, which greatly increases the training and test costs. We tackle the problem of the excessive generalization ability of autoencoder from a new perspective. We introduce an attention mechanism to propose an attention-based U-Net network to detect anomalies. The network adds an attention module before the skip connection of U -Net network, so that the model pays more attention to the foreground targets. During the training process, the normal foreground targets are learned more fully. Therefore, in the test, the proposed method can achieve more accurate prediction of normal targets that appear frequently, so that the rare abnormal targets can be highlighted because of the large prediction errors. We conduct experiments on real surveillance videos of UCSD Ped l and ShanghaiTech datasets, and the experimental results demonstrate that the proposed method is an efficient model.
Survey of Open Data Concepts Within Fundamental Physics: An Initiative of the PUNCH4NFDI Consortium

Abstract PUNCH4NFDI (Particles, Universe, NuClei and Hadrons for the NFDI) aims at developing concepts and tools for the efficient management of digital research products in fundamental physics research. At the heart of the research products are scientific data sets that should be made interoperable and available to a broad scientific community and the public for a sustainable usage (“open data”). The first PUNCH4NFDI “Open Data Workshop” gave the opportunity for an initial survey of existing and planned open data initiatives within the PUNCH science field. The paper addresses the conceptual differences and commonalities of the participating communities presented in the workshop. Existing open data collections were presented and discussed. This is an inquiry into the community’s requirements for a better use of open data and in this context also of “Open Science”.
Design of optical imaging system for full-ocean-depth low-light colors

In this paper, prism beam-splitting structure is proposed in the design of optical system to solve the problem of full-color imaging under low-light environment of the deep-sea. Optimization of optical aberrations is performed considering the specific aberration properties of the sea water and the window materials in the optical system. The designed optical system (field of view 60° and F/4) shows a clear imaging for all fields of view where MTFs is over 0.5 (@77lp/mm). The optical performance demonstrates that the system is very promising for large depth of field and full-color imaging under deep-sea environment.
Investigating diachronic change in dependency distance of Modern English: A genre-specific perspective

Dependency distance is regarded as an index of working memory load and an indicator of syntactic difficulty. Previous studies have shown that natural languages tend to minimize the dependency distance due to the limited cognitive resources of language users, a phenomenon termed as Dependency Distance Minimization (DDM). More recent research approached DDM from a diachronic perspective, and found a temporal trend towards decreased dependency distance in an English corpus of political speeches. However, it remains unanswered whether the diachronic minimization of dependency distance could be generalized to other genres and how genre differences affect the diachronic development of dependency distance. To address these issues, the present study aims to explore the diachronic change of dependency distance in Modern English with a genre-specific perspective. Based on the large-scale Corpus of Historical American English (COHA), the dependency distance of the four genres (Fiction, News, Magazine, and Non-Fiction) in the corpus were calculated and compared across a time span of roughly 100 years (1900–2009). Results of the study showed that the changing patterns of dependency distance vary among the examined genres. Such differences suggest that multiple and complex factors may be at work behind the diverging temporal trends of dependency distance in different genres of Modern English.
Artificial Intelligence in the Auxiliary Guidance Function of Athletes’ Movement Standard Training in Physical Education

Artificial Intelligence (AI) is a new technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. This paper is based on the research of AI in the auxiliary guidance function of athletes’ standard training in physical education. It aims to conduct data mining from different aspects such as different joint angular speeds, motion injury screening and different parts of sports injuries and then integrate these aspects. Create a way to reduce athletes’ injuries and scientific training. In order to improve the recognition efficiency of athletes’ movement patterns, nonlinear auto regressive neural networks are used to recognize the movement patterns of athletes’ limb surface signals. Through this research work, it can provide a certain reference basis and practice platform for the research on the auxiliary guidance role of AI in the sports standard training of athletes in physical education. Performance design and implementation include four modules: Image acquisition, preprocessing, motion detection and human motion recognition. Between them, the image acquisition module uses a memory mapping path to determine the format of the camera frame image, and the image format conversion is completed through channel conversion. Experimental data shows that athletes’ strength quality, speed of action response, technical continuity, psychological stability and physical control ability have all been greatly improved. Among them, the most obvious is that with the assistance of AI technology, the psychological stability has reached 9.2; the strength quality has reached 9.1.
Some Methods for Substantiating Diagnostic Decisions Made Using Machine Learning Algorithms

Various classification algorithms used in the diagnosis of breast cancer based on microwave radiometry data are considered. In particular, their principles of operation and the possibility of substantiating diagnoses using numerical data are discussed. A substantiation algorithm based on decision trees and a naive Bayesian classifier is presented. Examples of substantiation are given for breast cancer.
A Codebook Modification Method of Vector Quantization to Enhance Compression Ratio

The performance of an image compression algorithm is based on the amount of compression ratio achieved keeping the visual quality of the decompress image up to the mark. In an image compression algorithm, two performance measurement parameters, compression ratio, and visual quality of the decompress image are inversely proportional. So, improving the compression ratio of the compression algorithm, keeping visual quality of the decompress image as close to the original is a major challenging task. Vector quantization is one of the widely used lossy image compression techniques found in literature. The compression ratio of this algorithm basically depends on the size of the index matrix and codebook generated during the process. In this present work, a new technique is proposed which represents each and every value of the codebook by 5 bits instead of 8 bits that means it reduces the amount of memory required to store the codebook by 37.50% and which increases the compression ratio of the algorithm significantly. The proposed method is applied on many standard color images found in literature and images from UCIDv.2 database. Experimental results show that the proposed method increases the compression ratio significantly, keeping the visual quality of the decompressed image almost same or slightly lower.