Reviewerly/exHarmony huggingface dataset:
Fused pyramid attention network for single image super‐resolution

Abstract In image super‐resolution, deep neural networks with various attention mechanisms have achieved noticeable performance in recent years, for example, channel attention and layer attention. Although many researchers have achieved good super‐resolution results with only a certain style of attention, the divergence and the complementarity focused by multiple attention mechanisms are ignored. In addition, most of these methods fail to utilize the diverse information from multi‐scale features. To efficiently manipulate the above rich information, this paper strives to combine multi‐scale structure and multi‐attention schemes in architecture and module levels for super‐resolution. Especially, in the architecture level, a fused pyramid attention network is developed to extract deep features with the multi‐scale context information from multiple different sizes of receptive field recurrently with skip connections. For the module level, a fused pyramid attention module is designed to fuse the two attention mechanisms to further refine the deep features with fine‐grained information. Compared with the common fusion strategy, the adopted feature fusion structure can maintain better structural information while establishing long‐range dependency. Extensive experimental results demonstrate that the proposed network achieves favorable performance quantitatively and visually.
Error and optimism bias regularization

In Machine Learning, prediction quality is usually measured using different techniques and evaluation methods. In the regression models, the goal is to minimize the distance between the actual and predicted value. This error evaluation technique lacks a detailed evaluation of the type of errors that occur on specific data. This paper will introduce a simple regularization term to manage the number of over-predicted/under-predicted instances in a regression model.
On a family of linear MRD codes with parameters $$[8\times 8,16,7]_q$$

In this paper we consider a family $$\mathcal {F}$$ of 2n-dimensional $${\mathbb {F}}_q$$ -linear rank metric codes in $${\mathbb {F}}_q^{n\times n}$$ arising from polynomials of the form $$x^{q^s}+\delta x^{q^{\frac{n}{2}+s}}\in {\mathbb {F}}_{q^n}[x]$$ . The family $$\mathcal {F}$$ was introduced by Csajbók et al. (JAMA 548:203–220) as a potential source for maximum rank distance (MRD) codes. Indeed, they showed that $$\mathcal {F}$$ contains MRD codes for $$n=8$$ , and other subsequent partial results have been provided in the literature towards the classification of MRD codes in $$\mathcal {F}$$ for any n. In particular, the classification has been reached when n is smaller than 8, and also for n greater than 8 provided that s is small enough with respect to n. In this paper we deal with the open case $$n=8$$ , providing a classification for any large enough odd prime power q. The techniques are from algebraic geometry over finite fields, since our strategy requires the analysis of certain 3-dimensional $${\mathbb {F}}_q$$ -rational algebraic varieties in a 7-dimensional projective space. We also show that the MRD codes in $$\mathcal {F}$$ are not equivalent to any other MRD codes known so far.
Smart Personality Development Model in Software Developer

In this Industry 4.0 era, digital facilities are used by companies from all fields. In this digitalization development era, there are more software development companies established to offer software development services. It makes software developers an increasingly popular job today. In order to get qualified software developers, companies need to identify the smart personality of software developers. Smart personality is very useful for knowing the company’s needs in determining the criteria for prospective software developers. This research aims to analyze the development of smart personality in software developers. Data in this research were obtained using the descriptive qualitative research method and through in-depth interviews. The data collected were analyzed by thematic analysis in accordance with the results of interviews. Based on the results, it can be concluded that a software developer with smart personality has the following characteristics: they do not have to be good looking, they have to be well-skilled in their field, have strong/good logic, have good analysis skill, and can do coding neatly and carefully. The main smart personality types that can make a software developer good are they have to be well-skilled in their field, have strong/good logic and have good analysis skill.
Feature difference and feature correlation learning mechanism for skeleton-based action recognition

In recent years, skeleton-based action recognition has become increasingly popular in the field of human action recognition, and graph convolutional networks (GCNs) have shown better advantages in this task. Many GCN-based methods are insufficient in the latent relationship between features, which affects the discriminability of features being not rich enough. These potential feature relationships can manifest as feature differences that change due to actions and feature correlations that interact with each other. Therefore, we propose a feature difference and feature correlation learning mechanism to learn discriminative augmentation features, including feature differences in actions and feature correlations between joints. First, we propose a temporal feature difference and correlation learning module (FDCL) (TFDCL). In adjacent temporal frames, we extract feature correlations between related parts. Feature differences are captured through changes in joints over the overall long-term timeline. Second, we propose a channel FDCL module. Different channels contain different types of features for actions. We use convolution operations to interact between channels, continuously extracting the strongest features to obtain feature maps. Third, we propose a temporal channel context topology (TCCT) module to dynamically learn global contextual features of all joints during motion. Finally, experiments are conducted on the NTU-RGBD 60 dataset and the kinetics-skeleton 400 dataset to verify the effectiveness of the network.
Low-area and high-speed hardware architectures of KLEIN lightweight block cipher for image encryption

Internet of Things (IoT) connects a wide range of small devices over a large network, allowing for a wide range of applications. With the advancement of open-source public networks (such as IoT), image transmission for resource-constrained devices is becoming more insecure. In this paper, two hardware implementations for KLEIN block cipher are proposed that can provide security to encrypt different sets of images under resource-constrained applications (such as wireless sensor network, radio frequency identification, IoT etc.). An improvement in maximum operating frequency of 32.72% and 75.66% in the proposed serial and pipelined architectures is observed compared with the state-of-the-art design, respectively. The implemented pipelined architecture yields high throughput of 1840.15 MHz, and the serialized architecture implies 108 numbers of slices implemented on field-programmable gate array (FPGA) xc5vlx50t-3ff1136 device. On application-specific integrated circuit (ASIC) platform, the proposed serialized KLEIN implementation consumed less area in terms of gate equivalent. The existing solution was also subjected to changing correlation coefficients, number of pixels changing rate, unified average changing intensity, and entropy values. The simulation results validate the effectiveness of the performance of the proposed encryption scheme.
Code smell prioritization in object‐oriented software systems: A systematic literature review

Abstract Code smells indicate poor design and imperfection in coding style. They increase maintenance costs and degrade software quality. Detecting and mitigating these code smells reduce the effects of flaws present in the software design. However, to save time and effort, it is a good practice to prioritize critical code smells and resolve them first. There have been various empirical studies on code smell prioritization in the last decades. Although the research community has been continuously working on the prioritization of code smells, yet a systematic mapping of all these studies would provide a panorama of research work. With this motivation, we selected 39 primary studies published between May 2011 and May 2022 and classified them based on seven factors: (i) code smells, (ii) factors, (iii) evaluation techniques, (iv) tools, (v) ranking formulas, (vi) data sets used, and (vii) measurements used for validating the research work. This paper aims to outline and discuss the prioritization procedure for code smells in object‐oriented software systems. The issues and recommendations for every question act as input for the researchers and industrialists for further research. Some issues for further investigation are less coverage of architecture smells, limited ranking formulas, lack of automated evaluation techniques, and so forth.
The role of ethics in technology acceptance: analysing resistance to new health technologies on the example of a COVID-19 contact-tracing app

New health technologies promise enormous benefits for human wellbeing, yet their diffusion is often inhibited by potential users’ uncertainty about their consequences. Since ethics help individuals to cope with uncertainty, this study investigates the potential of ethics to promote technology acceptance. To this end, we test the influence of typical acceptance predictors (perceived health threat, privacy attitudes, and technology readiness) and two new ethics-related variables (‘ethical optimism’ and ‘perceived governmental responsibility’) on resistance to a government-issued COVID-19 contact-tracing app. Using structural equation modelling, we find significant effects of the typical acceptance predictors, and that ethical optimism and perceived governmental responsibility interact in determining the resistance to the tracing app. The paper highlights the relevance of ethics for the acceptance of new (health) technologies and suggests that the role of ethics is not confined to being a ‘moral watchdog’ but can serve as a powerful lever to promote their acceptance.
Sensor-Based Activity Recognition Using Frequency Band Enhancement Filters and Model Ensembles

Deep learning methods are widely used in sensor-based activity recognition, contributing to improved recognition accuracy. Accelerometer and gyroscope data are mainly used as input to the models. Accelerometer data are sometimes converted to a frequency spectrum. However, data augmentation based on frequency characteristics has not been thoroughly investigated. This study proposes an activity recognition method that uses ensemble learning and filters that emphasize the frequency that is important for recognizing a certain activity. To realize the proposed method, we experimentally identified the important frequency of various activities by masking some frequency bands in the accelerometer data and comparing the accuracy using the masked data. To demonstrate the effectiveness of the proposed method, we compared its accuracy with and without enhancement filters during training and testing and with and without ensemble learning. The results showed that applying a frequency band enhancement filter during training and testing and ensemble learning achieved the highest recognition accuracy. In order to demonstrate the robustness of the proposed method, we used four different datasets and compared the recognition accuracy between a single model and a model using ensemble learning. As a result, in three of the four datasets, the proposed method showed the highest recognition accuracy, indicating the robustness of the proposed method.
Towards Explainable Occupational Fraud Detection

Occupational fraud within companies currently causes losses of around 5% of company revenue each year. While enterprise resource planning systems can enable automated detection of occupational fraud through recording large amounts of company data, the use of state-of-the-art machine learning approaches in this domain is limited by their untraceable decision process. In this study, we evaluate whether machine learning combined with explainable artificial intelligence can provide both strong performance and decision traceability in occupational fraud detection. We construct an evaluation setting that assesses the comprehensibility of machine learning-based occupational fraud detection approaches, and evaluate both performance and comprehensibility of multiple approaches with explainable artificial intelligence. Our study finds that high detection performance does not necessarily indicate good explanation quality, but specific approaches provide both satisfactory performance and decision traceability, highlighting the suitability of machine learning for practical application in occupational fraud detection and the importance of research evaluating both performance and comprehensibility together.
Limits of XAI Application-Grounded Evaluation: An E-Sport Prediction Example

EXplainable AI (XAI) was created to address the issue of Machine Learning’s lack of transparency. Its methods are expanding, as are the ways of evaluating them, including human performance-based evaluations of explanations. These evaluations allow us to quantify the contribution of XAI algorithms to human decision-making. This work performs accuracy and response time measurements to evaluate SHAP explanations on an e-sports prediction task. The results of this pilot experiment contradict our intuitions about the beneficial potential of these explanations and allow us to discuss the difficulties of this evaluation methodology.
Measuring the Burden of (Un)fairness Using Counterfactuals

In this paper, we use counterfactual explanations to offer a new perspective on fairness, that, besides accuracy, accounts also for the difficulty or burden to achieve fairness. We first gather a set of fairness-related datasets and implement a classifier to extract the set of false negative test instances to generate different counterfactual explanations on them. We subsequently calculate two measures: the false negative ratio of the set of test instances, and the distance (also called burden) from these instances to their corresponding counterfactuals, aggregated by sensitive feature groups. The first measure is an accuracy-based estimation of the classifier biases against sensitive groups, whilst the second is a counterfactual-based assessment of the difficulty each of these groups has of reaching their corresponding desired ground truth label. We promote the idea that a counterfactual and an accuracy-based fairness measure may assess fairness in a more holistic manner, whilst also providing interpretability. We then propose and evaluate, on these datasets, a measure called Normalized Accuracy Weighted Burden, which is more consistent than only its accuracy or its counterfactual components alone, considering both false negative ratios and counterfactual distance per sensitive feature. We believe this measure would be more adequate to assess classifier fairness and promote the design of better performing algorithms.
Is Attention Interpretation? A Quantitative Assessment on Sets

The debate around the interpretability of attention mechanisms is centered on whether attention scores can be used as a proxy for the relative amounts of signal carried by sub-components of data. We propose to study the interpretability of attention in the context of set machine learning, where each data point is composed of an unordered collection of instances with a global label. For classical multiple-instance-learning problems and simple extensions, there is a well-defined "importance" ground truth that can be leveraged to cast interpretation as a binary classification problem, which we can quantitatively evaluate. By building synthetic datasets over several data modalities, we perform a systematic assessment of attention-based interpretations. We find that attention distributions are indeed often reflective of the relative importance of individual instances, but that silent failures happen where a model will have high classification performance but attention patterns that do not align with expectations. Based on these observations, we propose to use ensembling to minimize the risk of misleading attention-based explanations.
Gender Stereotyping Impact in Facial Expression Recognition

Facial Expression Recognition (FER) uses images of faces to identify the emotional state of users, allowing for a closer interaction between humans and autonomous systems. Unfortunately, as the images naturally integrate some demographic information, such as apparent age, gender, and race of the subject, these systems are prone to demographic bias issues. In recent years, machine learning-based models have become the most popular approach to FER. These models require training on large datasets of facial expression images, and their generalization capabilities are strongly related to the characteristics of the dataset. In publicly available FER datasets, apparent gender representation is usually mostly balanced, but their representation in the individual label is not, embedding social stereotypes into the datasets and generating a potential for harm. Although this type of bias has been overlooked so far, it is important to understand the impact it may have in the context of FER. To do so, we use a popular FER dataset, FER+, to generate derivative datasets with different amounts of stereotypical bias by altering the gender proportions of certain labels. We then proceed to measure the discrepancy between the performance of the models trained on these datasets for the apparent gender groups. We observe a discrepancy in the recognition of certain emotions between genders of up to $$29 \%$$ under the worst bias conditions. Our results also suggest a safety range for stereotypical bias in a dataset that does not appear to produce stereotypical bias in the resulting model. Our findings support the need for a thorough bias analysis of public datasets in problems like FER, where a global balance of demographic representation can still hide other types of bias that harm certain demographic groups.
Online Anomaly Explanation: A Case Study on Predictive Maintenance

Predictive Maintenance applications are increasingly complex, with interactions between many components. Black-box models are popular approaches due to their predictive accuracy and are based on deep-learning techniques. This paper presents an architecture that uses an online rule learning algorithm to explain when the black-box model predicts rare events. The system can present global explanations that model the black-box model and local explanations that describe why the black-box model predicts a failure. We evaluate the proposed system using four real-world public transport data sets, presenting illustrative examples of explanations.
An improved quantum algorithm for data fitting

As researchers focus on the field of quantum machine learning, there are more and more quantum algorithms for data fitting. However, the majority of quantum algorithms for data fitting are only applicable to sparse matrices, which bring a large number of limitations to their application. In this paper, to solve this problem, we propose a quantum algorithm for data fitting for general matrices, not only sparse and dense matrices. The quantum algorithm is mainly composed of three subroutines which are generating target state, quantum state tomography and estimating fit quality. The time complexity of the quantum algorithm is Oκ4‖A‖Fpolylog(MN)(M′2log(M′)2/ɛ3+1/(ɛϵ2)), where A∈RM×N, κ is the conditional number of matrix A†A∈RN×N, ||A||F is the Frobenius norm of A, M′ is the maxima number of fit functions in allowing fit, ɛ is the fitting error, and ϵ is the error of fit quality estimation. Moreover, our quantum algorithm is independent of the sparsity of the matrix, so our algorithm has wide application in dense matrices and poses a new state of the art for solving dense data fitting.
Image Steganography Based on Chaos Function and Randomize Function

The exchange of data is not limited to personal text information or information about institutions and governments, but includes digital media transferred via the Internet including everything, whether texts, images or videos and audio, or animation. These media need high-security protection and high speed during its transmission from one site to another. In this study, a new method is suggested for hiding a gray-level image within a larger color image based on the proposed steganography map that merged chaotic function and randomize function. The size of the chaos and randomize functions is 16 bytes. Experimental results obtained a successful method based on mean squared error, signal-to-noise ratio, peak signal noise rate, embedding capacity, entropy, and histogram. This method can rapidly hide and extract ciphertext in and from the gray image. The original image and the stego image are difficult to distinguish because the correlation between them is very close to 1, indicating that attackers cannot easily differentiate these images with the naked eye. This condition can successfully hide information on the Internet.
TF-IDF Analysis on the Changes of National Balanced Development Policy by Regime and Its Implications

본 연구는 대통령 취임사 분석을 통해 각 정권별 균형발전정책을 비교 분석하여 시사점을 제시하는데 목적이 있다. 분석결과, 국가균형발전정책은 대통령의 이념에 영향을 받는 것으로 나타났다. 진보성향의 노무현대통령과 문재인 대통령의 경우 국가균형발전을 국정 최우선과제로 추진하고 있는 반면, 보수주의 이념을 가진 이명박 대통령과 박근혜 대통령은 균형발전보다는 지역발전에 초점을 두고 지역발전을 추진한 것으로 나타났다. 윤석열 대통령의 경우, 보수주의 이념을 가지고 있으나 국정 과제 발표 시 지역균형발전을 핵심정책으로 비전을 제시하였다. 취임사 분석 결과 ‘자유’가 취임사의 핵심 단어로 대두된 만큼 향후 지역균형발전정책의 추진 방향에 대한 우려가 있다. 균형발전정책이 지속적으로 추진되기 위해서 무엇보다 중요한 것은 균형발전에 대한 대통령의 의지라고 볼 수 있다. This paper aims to explore the national balanced development policies by regime through big data analysis based on the president’s inauguration speech. As a result of the analysis, it would be found that the national balanced development policy was mainly influenced by the president’s ideology. In the case of liberal President Roh Moo-hyun and Moon Jae-in, balanced development is a top priority, while conservative Lee Myung-bak and Park Geun-hye have focused on regional development rather than national balanced development. In Yoon Suk-yeol Government, the President has a conservative ideology, but when announcing state affairs, he presented a vision as a core policy for balanced regional development. As a result of big data analysis of the presidential inaugural address, “freedom” has emerged as a key word in the inaugural address, there are concerns about eh direction of the balanced regional development policy in the future. In order for the balanced development policy to be continuously promoted, it is the most importantly, it can be seen that the president’s will for balanced development.