Reviewerly/exHarmony huggingface dataset:
Multi-modal deep convolutional dictionary learning for image denoising

Leveraging the capabilities of traditional dictionary learning (DicL) and drawing upon the success of deep neural networks (DNNs), the recently proposed framework of deep convolutional dictionary learning (DCDicL) has exhibited remarkable behaviours in image denoising. Note that, the application of the DCDicL method is confined to single modality scenarios, whereas the images in practice often originate from diverse modalities. In this paper, to broaden the application scope of the DCDicL method, we design a multi-modal version of it, dubbed MMDCDicL. Specifically, within the mathematical model of MMDCDicL, we adopt an analytical approach to tackle the sub-problem linked to the guidance modality, harnessing its inherent reliability. Meanwhile, like in DCDicL, we utilize a network-based learning approach for the noisy modality to extract trustworthy information from the data. Based on the solution, we establish an interpretable network structure for MMDCDicL. Additionally, wherein, we design a multi-kernel channel attention block (MKCAB) in the structure to efficiently integrate the information from diverse modalities. Experimental results suggest that MMDCDicL can reconstruct higher-quality outcomes both quantitatively and perceptually. Code is available at http://www.diplab.net/lunwen/mmdcdicl.htm.
Sabiá: Portuguese Large Language Models

As the capabilities of language models continue to advance, it is conceivable that "one-size-fits-all" model will remain as the main paradigm. For instance, given the vast number of languages worldwide, many of which are low-resource, the prevalent practice is to pretrain a single model on multiple languages. In this paper, we add to the growing body of evidence that challenges this practice, demonstrating that monolingual pretraining on the target language significantly improves models already extensively trained on diverse corpora. More specifically, we further pretrain GPT-J and LLaMA models on Portuguese texts using 3% or less of their original pretraining budget. Few-shot evaluations on Poeta, a suite of 14 Portuguese datasets, reveal that our models outperform English-centric and multilingual counterparts by a significant margin. Our best model, Sabiá-65B, performs on par with GPT-3.5-turbo. By evaluating on datasets originally conceived in the target language as well as translated ones, we study the impact of language-specific pretraining in terms of 1) capturing linguistic nuances and structures inherent to the target language, and 2) enriching the model's knowledge about a domain or culture. Our results indicate that most benefits stem from the domain-specific knowledge acquired through monolingual pretraining. Finally, we show that our optimized model for Portuguese demonstrates a reduced performance in English tasks, thereby substantiating the inherent compromise in refining models for specific linguistic domains.
Virtual Threats and Asymmetric Military Challenges

Security challenges are faced by every single country on this planet. These threats can occur through violent non-state habitats, any organized group of criminals, economic threats, disasters, or native citizens. In ancient wars, nations used physical forces and weapons as a tool to acquire regions, resources. Whereas, in the future, a wide range of technologies like automated weapons, drones, and cyber warfare may come into action. A single person can control an army of computers, and the person behind the actions can go unidentified. The surging need for the use of computers has greatly imposed an effect on the military. Unlike traditional warfare, where large mobilizations of military forces were involved, in modern warfare, integration with latest innovations in military forces like guided munition systems, explosives, and nuclear weapons are brought into use. As a result, there rises an inevitable need to acquire insight into the field of cyber military technology. This chapter mainly aims to analyse the nation's virtual threats and asymmetric military challenges.
AI: A knowledge sharing tool for improving employees’ performance

The utilisation of artificial intelligence (AI) is progressively emerging as a significant mechanism for innovation in human resource management (HRM). The capacity to facilitate the transformation of employee performance across numerous responsibilities. AI development, there remains a dearth of comprehensive exploration into the potential opportunities it presents for enhancing workplace performance among employees. To bridge this gap in knowledge, the present work carried out a survey with 300 participants, utilises a fuzzy set-theoretic method that is grounded on the conceptualisation of AI, KS, and HRM. The findings of our study indicate that the exclusive adoption of AI technologies does not adequately enhance HRM engagements. In contrast, the integration of AI and KS offers a more viable HRM approach for achieving optimal performance in a dynamic digital society. This approach has the potential to enhance employees’ proficiency in executing their responsibilities and cultivate a culture of creativity inside the firm.
A Methodological Approach for the Smartification of a University Campus: The Smart ESI Use Case

As the term smart begins to move away from inflationary usage, mostly related to communication capabilities, and towards a more effective dimension, there is room for research into the systematic process of transforming the things that were not originally designed to be smart. Such a process is referred to as smartification, which evokes the digital refinement of an existing product by embedding digital technologies and smart services. The state of the art shows only a few articles that investigate such methodological approaches to provide smartness on several application domains (e.g., home appliances, buildings, cities), while the smart university domain has not been deeply faced up yet. Based on the hypothesis that a methodology synthesizes a set of suitable procedures oriented to simplify the process of measuring the degree of smartification, we propose in this work a methodology that uses technological and academic criteria for designing a tool to measure the smartification of universities. The main conclusions of this work are the following: (1) The review of the current state of the art related to smart universities and smartification reveals the need for a new vision for a more adequate and accurate assessment of the degree of smartness of universities; (2) A formal method based on Multiple-Attribute Decision Making and case study research aimed at assessing the degree of smartification within the scope of smart universities; (3) A demonstration of the feasibility and application of our method to quantify the smartification degree of the School of Computer Science (ESI) at the University of Castilla-La Mancha under the project Smart ESI; and (4) A valuable method that allows equal comparison between smart universities through a set of standardized indicators, which offers the opportunity to analyze in depth the strength and weakness, and potential achievements of any smart initiatives. To the authors’ knowledge, this is the first approach aimed at assessing the extent of the smartification process in smart universities through a formal method. The outcome of this research can benefit different stakeholders: university community, researchers, and, ultimately, the digital transformation of society.
Efficient approach for mining high-utility patterns on incremental databases with dynamic profits

High-utility itemset mining (HUIM) is one of the heavily studied fields of data mining, which is due to its high compatibility with real-world applications. HUIM is a process of extracting a complete set of interesting patterns by considering the importance and the quantity of each item. Incremental high-utility itemset mining (IHUIM) further increases the compatibility of HUIM by mining from a data stream instead of a static database. It does this by mining interesting patterns in a single database scan and storing prior knowledge in a compact data structure. However, there is a critical limitation, because the importance of each item has to be guaranteed to remain unchanged throughout the data stream. This limitation hinders the compatibility of IHUIM to the real-world applications, because the importance of an item changes from time to time in the real world. Conventional IHUIM approaches consequently have to use outdated information when mining interesting patterns. This paper proposes a novel problem of mining high-utility itemsets in an incremental and dynamic profit environment in order to account for the fluctuation of the item's importance. Furthermore, a novel approach to mining patterns in this type of environment is introduced using an efficient list structure and tight upper bounds. Experiments on real and synthetic datasets show that the proposed approach performs well compared to the state-of-the-art approaches in terms of the runtime and memory usage, and it scales better than the other approaches.
Exploration and quantification of magnetocaloric effect in EuTiO3 perovskite using extreme learning machine intelligent computational method

Perovskite EuTiO3 is a multi-ferroic titanate oxide material that exhibits huge magnetocaloric effect at low magnetic field without associated hysteresis loss due to the magnetic field and thermal energy. These unique features coupled with the presence of strong coupling existing between spin and lattice as well as the possibility to switch the magnetic ground state between ferromagnetism and anti-ferromagnetism, foster the potentials of this material for magnetocaloric effect based cooling technology application. Maximum magnetic entropy change of perovskite EuTiO3 determines the strength of magnetocaloric effect in the material and ultimately influences the usability of this material for cooling technology. However, experimental measurement of maximum magnetic entropy change is challenging and consumes appreciable resources and time. Maximum magnetic entropy change of intrinsic and doped EuTiO3 perovskite is modeled in this work using extreme learning machine intelligent algorithm (ELMIA) with the ionic radii, elemental concentration and applied magnetic field descriptors. The developed ELMIA-SINE with sine activation function performs better than ELMIA-SIG model with sigmoid activation function with performance superiority of 42.46%, 44.58% and 1.76% using root mean square error, mean absolute error and correlation coefficient performance yardstick, respectively. The developed ELMIA-SINE model further outperforms the existing particle swarm based support vector regression model with polynomial (PSVR-P) and Gaussian (PSVR-G) function with performance improvement of 37.03% and 14.11%, respectively. The dependence of applied magnetic field and dopant concentration on hugeness of magnetocaloric effect was established using the developed model. The precision and the ease of implementation of the developed ELMIA model as compared with the existing models would definitely facilitate exploration of magnetocaloric effect based system of refrigeration for addressing the present energy crisis.
General Class of Continuous Variable Entanglement Criteria

We present a general class of entanglement criteria for continuous variable systems. Our criteria are based on the Husimi Q distribution and allow for optimization over the set of all concave functions rendering them extremely general and versatile. We show that several entropic criteria and second moment criteria are obtained as special cases. Our criteria reveal entanglement of families of states undetected by any commonly used criteria and provide clear advantages under typical experimental constraints such as finite detector resolution and measurement statistics.
Qualitative Research Methods for Large Language Models: Conducting Semi-Structured Interviews with ChatGPT and BARD on Computer Science Education

In the current era of artificial intelligence, large language models such as ChatGPT and BARD are being increasingly used for various applications, such as language translation, text generation, and human-like conversation. The fact that these models consist of large amounts of data, including many different opinions and perspectives, could introduce the possibility of a new qualitative research approach: Due to the probabilistic character of their answers, “interviewing” these large language models could give insights into public opinions in a way that otherwise only interviews with large groups of subjects could deliver. However, it is not yet clear if qualitative content analysis research methods can be applied to interviews with these models. Evaluating the applicability of qualitative research methods to interviews with large language models could foster our understanding of their abilities and limitations. In this paper, we examine the applicability of qualitative content analysis research methods to interviews with ChatGPT in English, ChatGPT in German, and BARD in English on the relevance of computer science in K-12 education, which was used as an exemplary topic. We found that the answers produced by these models strongly depended on the provided context, and the same model could produce heavily differing results for the same questions. From these results and the insights throughout the process, we formulated guidelines for conducting and analyzing interviews with large language models. Our findings suggest that qualitative content analysis research methods can indeed be applied to interviews with large language models, but with careful consideration of contextual factors that may affect the responses produced by these models. The guidelines we provide can aid researchers and practitioners in conducting more nuanced and insightful interviews with large language models. From an overall view of our results, we generally do not recommend using interviews with large language models for research purposes, due to their highly unpredictable results. However, we suggest using these models as exploration tools for gaining different perspectives on research topics and for testing interview guidelines before conducting real-world interviews.
The role of institutional quality in the relationship between financial development and economic growth: Emerging markets and middle-income economies

In this study, the relationship between economic growth and financial development was analyzed for emerging markets and middle-income economies. The effect of financial development on growth, whether there is institutional quality or not, has also been investigated. In addition, which financial development indicator is more effective for growth has been examined. Six institutional quality indicators and seven financial development indicators were used. According to the Dumitrescu–Hurlin causality test results, there is a causality relationship between all financial development indicators and growth. According to the estimation results, financial development indicators have a positive effect on growth in the presence of institutional quality. However, if institutionalization is not included in the model, the effect of financial development indicators on economic growth is statistically insignificant.
Adaptive neuro‐fuzzy inference systems controller design on Buck converter

Abstract Adaptive neuro‐fuzzy inference system (ANFIS) approach is designed for a Buck converter. Because DC–DC converters are under the negative impact of different disturbances, a need for a well‐behaved technique is felt to provide higher robustness in various scenarios, including parametric variations, load uncertainty, supply voltage variation, and noise. Therefore, the fuzzy logic‐based controller is adopted for this structure that provides better error detection and correction, more comprehensive range of operating conditions, and is more readily customizable. However, the fuzzy technique suffers from slow dynamics, lack of reliability against broader range of disturbances, and has a huge computational burden. To overcome the weaknesses addressed before, this technique combined with an artificial neural network (ANN) system that can tune the fuzzy part resulting in an adaptive and robust structure. ANFIS method is a promising approach that has two soft‐computing control structures, including a fuzzy logic‐based part consisting of ANN. This combination has provided many significant benefits over fuzzy logic, such as low computational burden with faster dynamics, higher flexibility with adaptable rules, and a simple structure providing ease of practical implantation; also, it does not need a mathematical moulding of the system since the whole system has been considered as a Black‐box system. To better show the superiority of this method, two other control schemes are designed as fuzzy‐based PID technique and PID controller optimized by PSO algorithm. Finally, the ANFIS control strategy is tested in various working cases through simulation and experiment results as a beneficial alternative for practical applications.
Controlling the target pattern of projected LED arrays for smart lighting

High-resolution, pixelated LED arrays allow flexible illumination. By addressing certain areas of the LED matrix and projecting the emitted light, selective illumination can be achieved. When combined with computer vision, smart, autonomous lighting systems are within reach. However, limitations of the used projection optics, in combination with the fact that the LED array and camera can be at a different position, severely complicates the problem of calculating which LED pixels to address in order to achieve a desired target pattern. This work proposes a least-squares deconvolution-based calculation method to solve this problem. The method relies on an initial calibration step that characterizes the complete point-spread-function of the LED array for the considered illumination configuration. This allows using the system for various settings. The method is experimentally validated for an off-axis illumination configuration that demonstrates the accuracy and flexibility of the approach. Because the proposed algorithm is fast and guarantees a global optimum, it opens new avenues towards accurate, smart and adaptive illumination.
An investigation of the imputation techniques for missing values in ordinal data enhancing clustering and classification analysis validity

Missing data can significantly impact dataset integrity and suitability, leading to unreliable statistical results, distortions, and poor decisions. The presence of missing values in data introduces inaccuracies in clustering and classification and compromises the reliability and validity of such analyses. This study investigates multiple imputation techniques specifically designed for handling missing values in ordinal data commonly encountered in surveys and questionnaires. Quantitative approaches are used to evaluate different imputation methods on various datasets with varying missing value percentages. By comparing the performance of imputation techniques using clustering metrics and algorithms (e.g., k-means, Partitioning Around Medoids), the study provides valuable insights for selecting appropriate imputation methods for accurate data analysis. Furthermore, the study examines the impact of imputed values on classification algorithms, including k-Nearest Neighbors (kNN), Naive Bayes (NB), and Multilayer Perceptron (MLP). Results demonstrate that the decision tree method is the most effective approach, closely aligning with the original data and achieving high accuracy. In contrast, random number imputation performs poorly, indicating limited reliability. This study advances the understanding of handling missing values and emphasizes the need to address this issue to enhance data analysis integrity and validity.
Two-branch crisscross network for realistic and accurate image super-resolution

Convolutional Neural Networks have made remarkable progress in single-image super-resolution. However, existing methods struggle to balance reconstruction accuracy and perceptual quality, resulting in unsatisfactory outcomes. To address this challenge, we propose the Two-Branch Crisscross Generative Adversarial Network (TBCGAN) for achieving accurate and realistic super-resolution results. TBCGAN employs two asymmetric branches that separately reconstruct high-frequency (HF) and low-frequency (LF) images, leveraging their distinct information and reconstruction requirements. To ensure coherent results, we apply different supervision to the reconstructed HF, LF, and super-resolution (SR) images while facilitating information interaction through the interleaving and fusion of HF and LF features. Extensive experimental evaluations demonstrate that TBCGAN achieves an excellent balance between reconstruction accuracy and perceptual quality, outperforming GAN-based methods in reconstruction accuracy and MSE-based methods in perceptual quality.
DiPS: Discriminative pseudo-label sampling with self-supervised transformers for weakly supervised object localization

Self-supervised vision transformers (SSTs) have shown great potential to yield rich localization maps that highlight different objects in an image. However, these maps remain class-agnostic since the model is unsupervised. They often tend to decompose the image into multiple maps containing different objects while being unable to distinguish the object of interest from background noise objects. In this paper, Discriminative Pseudo-label Sampling (DiPS) is introduced to leverage these class-agnostic maps for weakly-supervised object localization (WSOL), where only image-class labels are available. Given multiple attention maps, DiPS relies on a pre-trained classifier to identify the most discriminative regions of each attention map. This ensures that the selected ROIs cover the correct image object while discarding the background ones, and, as such, provides a rich pool of diverse and discriminative proposals to cover different parts of the object. Subsequently, these proposals are used as pseudo-labels to train our new transformer-based WSOL model designed to perform classification and localization tasks. Unlike standard WSOL methods, DiPS optimizes performance in both tasks by using a transformer encoder and a dedicated output head for each task, each trained using dedicated loss functions. To avoid overfitting a single proposal and promote better object coverage, a single proposal is randomly selected among the top ones for a training image at each training step. Experimental results1 on the challenging CUB, ILSVRC, OpenImages, and TelDrone datasets indicate that our architecture, in combination with our transformer-based proposals, can yield better localization performance than state-of-the-art methods.