Reviewerly/exHarmony huggingface dataset:
A Multistage Hybrid Deep Learning Model for Enhanced Solar Tracking

Solar tracking helps maximize the efficiency of solar applications, such as photovoltaic (PV) solar panels. In the recent past, machine learning (ML) techniques have been extensively used to implement automatic solar tracking. However, applying predictive models in solar trackers is a non-trivial task due to the randomness and non-linearity of meteorological data, limiting their ability to clearly represent the underlying data patterns. Most existing predictive models take a monolithic approach to addressing limitations related to meteorological data, thereby limiting their performance. Therefore, this paper proposes a deep hybrid learning (DHL) model to enhance solar tracking performance. Furthermore, the proposed model improves feature representation in the data by using combined normalization methods and conversion of numerical data to images. In a nutshell, the model integrates sine and cosine transformations (SCT) to reveal cyclical patterns in the data, sigmoid and minimum-maximum data transformations to scale the data to a Gaussian distribution, and Gramian Angular Fields (GAF) to convert tabular data into 2D image representation to take advantage of the feature extraction capability of a convolutional neural network (CNN). The model also utilizes long-term short-term memory (LSTM) and gated recurrent units (GRU) for both spatial and temporal feature extraction. The results show that the aggregation of the above-mentioned methods significantly enhances solar tracking. The proposed hybrid model outperforms existing methods on a publicly available dataset, achieving outstanding performance with MAE, MAPE, and RMSE scores of 0.0073, 1.4635, and 0.0097, respectively.
University information policy as a tool for supporting civic engagement of student youth

The article presents the author's approach to the study of the information field of a modern university as a unique virtual and real space of interaction, communication and consolidation of social forces, initiation of activity of student audiences. Information policy is a set of strategies of conscious management of the information field. The authors have made a research in 2020-2023 at Kazan Federal University by support of other universities of the Republic of Tatarstan. Different methods (case analysis and forecasting of effects, surveys and focus groups) allowed identifying the key elements of effective information policy: synchronization, integration of information policy of the university and the state, interactivity, creation of own platforms, updated communication channels, involvement of students as newsmakers and authors in the creation of newsmaking content.
Review of ways to apply machine learning methods in software engineering

This article reviews the integration of machine learning (ML) techniques into Software Engineering (SE) across various phases of the software development life cycle (SDLC). The purpose is to investigate the applications of ML in SE, analyze its methodologies, present findings, and draw conclusions regarding its impact. The study categorized ML applications in SE and assessed the performance of various ML algorithms. Authors identified ML applications in SDLC phases, including requirements analysis, design, implementation, testing, and maintenance. ML algorithms, such as supervised and unsupervised learning, are employed for tasks like software requirement identification, design pattern recognition, code generation, and automated testing. In summary, we find that ML-based techniques are experiencing a substantial surge in adoption within the field of software engineering. Nevertheless, it is evident that substantial endeavors are needed to establish thorough comparisons and synergies among these approaches, perform meaningful evaluations grounded in detailed real-world implementations that are applicable to industrial software development. Therefore, our key takeaway is the necessity for a shift in focus towards reproducible research, prioritizing this over isolated novel concepts. Failure to do so may result in the limited practical implementation of these promising applications.
Quantum Information Universe and Paradigms of Cosmological Origins and Evolutionary Epoch

The paper discusses divergent aspects of quantum information and quantum computation universe with different paradigms of universal origin and cosmological theories including pre-bang vacuum models, cyclucal and bubble universe and multiverse, and finally dimensionally compacted quantum vacuum primordial hypermedium.
Er en dom et bedre præjudikat, hvis den har flere citationer? – Om netværksanalyser baseret på citationstal [Is a Judgment a Better Precedent if It Has More Citations? - On Network Analyses Based on Citation Counts]

English Abstract: Judgments cite each other, and some judgments are cited more than others. For many years, social scientists have used this fact to analyze the legal system, especially through the use of so-called network analyses. This method of analysis has now also found its way into legal research, where a handful have begun to use judgment citations to quantify the decisions precedential value. The method has also found its way to Danish courtrooms, where the Supreme Court in at least two cases has faced arguments based on a network analysis.While network analyses come with an aura of objectivity, one must always investigate whether the analysis in question actually measures what it portents to measure and whether the researchers have followed a process that supports objectivity. To show what these validity and process considerations mean in practice, I apply them to a network analysis of 637 ECtHR judgments, which attempts to measure the precedential value of the judgments based on their citation counts. I show that the number of citations is a poor measure of precedential value and that the authors' process has not contributed to objectivity.Danish Abstract: Domme citerer hinanden, og nogle domme citeres mere end andre. Samfundsvidenskabelige forskere har i mange år brugt dette faktum til at analysere retssystemet, især ved brug af såkaldte netværksanalyser. Analysemetoderne har nu også fundet vej til den juridiske forskning, hvor en håndfuld er begyndt at bruge dommes citationer til at kvantificere deres præjudikatværdi. Metoderne har også fundet vej til de danske retssale, hvor Højesteret mindst to gange har mødt argumenter baseret på netværksanalyser af gældende ret.Mens sådanne analyser kommer med en aura af objektivitet, er det nødvendigt, at man altid spørger sig selv, om analyserne faktisk måler det, de prøver at måle, og om de har fulgt en proces, der understøtter objektivitet. For at vise, hvad disse validitets- og procesovervejelser konkret betyder, anvender jeg dem på en netværksanalyse af 637 domme fra EMD, der forsøger at måle dommenes præjudikatværdi på baggrund af deres citationstal. Jeg viser, at antallet af citationer er et dårligt mål for præjudikatværdi, og at forfatternes proces ikke har bidraget til objektivitet.
CCD-BSMG: Composite-Curve-Dilation-Based Brush Stroke Model Generator for Robotic Chinese Calligraphy

Brush stroke training models for Chinese brush play an important role in robotic Chinese calligraphy as the basis of stroke generation. How to combine the end-to-end method and physical model needs to be further clarified. As a large number of brush strokes collected by robotic arm for learning and training lacks operability. The simulated brush stroke model can be used as a source of training datasets for deep learning and training instead of real samples collected by robot. With different combinations of physical parameters of the robotic arm, a sufficient amount of sample data can be generated by the simulated brush stroke model and then to train a generator based on the datasets. In this way, we propose a composite-curve-dilation brush stroke model generator (CCD-BSMG) based on our composite-curve-dilation brush stroke model (CCD-BSM), which was formed by composite curve and morphological dilation directly according to the physical parameters of robotic arm and writing posture of the brush without a large number of samples for parameter estimation. The CCD-BSMG can generate the graphics with the dataset simulated by CCD-BSM for deep learning and training. Furthermore, with the output parameters of CCD-BSMG, the images reconstructed by robotic arm can have a better performance. Compared with other stroke generative models based on disordered pixels, our generator is based on parameterized brush strokes and provides a better foundation for robotic writing in deep learning or other fields. Compared with existing model and real stroke graphics written by robots, the results of several experiments prove that the proposed CCD-BSMG can generate stroke graphics well and show that it outperformed state-of-the-art stroke models. The results demonstrate the advantages of our proposed model in terms of high similarity and especially show the robustness and efficacy.
Joint Appearance and Motion Model With Temporal Transformer for Multiple Object Tracking

The problem of multi-object tracking (MOT) in the real world poses several challenging tasks, such as similar appearance, occlusion, and extreme articulation motion. In this paper, we propose a novel joint appearance and motion model, which is robust to diverse motion and objects with similar uniform appearance. The proposed MOT method includes a temporal transformer, a motion estimation module and a ReID embedding module. The temporal transformer is designed to convey object-aware features to the ReID embedding and motion estimation modules. The ReID embedding module extracts ReID features of the detected objects, while motion estimation module predicts expected locations of the previously tracked objects in the current frame. Also, we present a motion-guided association to fuse outputs of the appearance and motion modules effectively. Experimental results demonstrate that the proposed MOT method outperforms the state-of-the arts on the TAO and DanceTrack datasets that have objects with diverse motions and similar appearance, while provides stable performance on MOT17 and MOT20 that contain objects with simple and regular motion patterns.
Demystifying the Regional Phishing Landscape in South Korea

The ever-increasing phishing campaigns around the globe have been one of the main threats in cyber security. In response, the global anti-phishing entity ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., APWG) collectively maintains the up-to-date blacklist database ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., eCrimeX) against phishing campaigns, and so do modern browsers ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., Google Safe Browsing). However, our finding reveals that such a mutual assistance system has been remaining a blind spot when detecting geolocation-based phishing campaigns. In this paper, we focus on phishing campaigns against the web portal service with the largest number of users (42 million) in South Korea. We harvest 1,558 phishing URLs from varying resources in the span of a full year, of which only a small fraction (3.8%) have been detected by eCrimeX despite a wide spectrum of active fraudulence cases. We demystify three pervasive types of phishing campaigns in South Korea: i) sophisticated phishing campaigns with varying adversarial tactics such as a proxy configuration, ii) phishing campaigns against a second-hand online market, and iii) phishing campaigns against a non-specific target. Aligned with previous findings, a phishing kit that supports automating the whole phishing campaign is prevalent. Besides, we frequently observe a hit-and-run scam where a phishing campaign is immediately inaccessible right after victimization is complete, each of which is tailored to a single potential victim over a new channel like a messenger. As part of mitigation efforts, we promptly provide regional phishing information to APWG, and immediately lock down a victim’s account to prevent further damages.
HazARdSnap: Gazed-based Augmentation Delivery for Safe Information Access while Cycling

During cycling activities, cyclists often monitor a variety of information such as heart rate, distance, and navigation using a bike-mounted phone or cyclocomputer. In many cases, cyclists also ride on sidewalks or paths that contain pedestrians and other obstructions such as potholes, so monitoring information on a bike-mounted interface can slow the cyclist down or cause accidents and injury. In this paper, we present HazARdSnap, an augmented reality-based information delivery approach that improves the ease of access to cycling information and at the same time preserves the user's awareness of hazards. To do so, we implemented real-time outdoor hazard detection using a combination of computer vision and motion and position data from a head mounted display (HMD). We then developed an algorithm that snaps information to detected hazards when they are also viewed so that users can simultaneously view both rendered virtual cycling information and the real-world cues such as depth, position, time to hazard, and speed that are needed to assess and avoid hazards. Results from a study with 24 participants that made use of real-world cycling and virtual hazards showed that both HazARdSnap and forward-fixed augmented reality (AR) user interfaces (UIs) can effectively help cyclists access virtual information without having to look down, which resulted in fewer collisions (51% and 43% reduction compared to baseline, respectively) with virtual hazards.
Development of Recommended Online Courses Based on Machine Learning

This article investigates the development of recommended online courses based on machine learning, addressing the complexities of course selection in the online education landscape. The results indicate that the machine learning-driven recommendation system enhances the online learning experience, offering a potential solution to the challenges faced by learners in navigating the world of online education.
New Coarse-to-Fine Approaches for Age Estimation Based on Separable Convolutions

In this paper, we study lightweight age estimation methods based on a coarse-to-fine approach in which the network performs age prediction with multiple stages. In each stage, the network only focuses on refining the coarse age prediction generated from the previous stage. The final age prediction is the combination of all staged prediction values. We observe that these stages have a causal relationship, that is, the output of each stage is highly correlated with outputs of its former stages. Thus, each stage should share the information of its previous stage before making a refined prediction. Based on this observation, we construct a new compact CNN model called Homologous Stagewise Regression Network (HSR-Net). In HSR-Net, each stage shares the information of the last convolutional layer and then generates its own refined value. In addition, HSR-Net also addresses the age group ambiguity problem by utilizing an easy dynamic range construction. In order to enhance the prediction performance of HSR-Nets, it is naive to increase the number of kernels in each convolutional layer of HSR-Nets. However, the constructed HSR-Net has extremely large parameter size. To address this problem, we propose the separable HSR-Nets (SepHSR-Nets) where standard convolutions are replaced by depth-wise separable convolutions in the convolutional layers of HSR-Nets. In general, the parameter size of SepHSR-Nets ranges from 10K to 75K without sacrificing prediction performance. Experimental results show that SepHSR-Nets achieve competitive performance compared with the state-of-the-art compact models. Our code, data, and models are available at https://github.com/yanjenhuang/hsr-net.
Taxonomy of Quality Assessment for Intelligent Software Systems: A Systematic Literature Review

The increasing integration of AI software into various aspects of our daily lives has amplified the importance of evaluating the quality of these intelligent systems. The rapid proliferation of AI-based software projects and the growing reliance on these systems underscore the urgency of examining their quality for practical applications in both industry and academia. This systematic literature review delves into the study of quality assessment metrics and methods for AI-based systems, pinpointing key attributes and properties of intelligent software projects that are crucial for determining their quality. Furthermore, a comprehensive analysis of this domain will enable researchers to devise novel methods and metrics for effectively and efficiently evaluating the quality of such systems. Despite its importance, this area of development is still relatively nascent and evolving. This paper presents a systematic review of the current state of the taxonomy of quality assessment for AI-based software. We analyzed 271 articles from six different sources that focused on the quality assessment of intelligent software systems. The primary objective of this work is to provide an overview of the field and consolidate knowledge, which will aid researchers in identifying additional areas for future research. Moreover, our findings reveal the necessity to establish remedial strategies and develop tools to automate the process of identifying appropriate actions in response to abnormal metric values.
Exploring Uni-Modal Feature Learning on Entities and Relations for Remote Sensing Cross-Modal Text-Image Retrieval

Remote sensing cross-modal text-image retrieval (RSCTIR) has recently received unprecedented attention due to its advantages of flexible input and efficient query on enormous remote sensing (RS) images. However, most RSCTIR methods focus obsessively on the cross-modal semantic alignment between text and image modalities and are easily stuck in the dilemma of information redundancy, leading to the degradation of retrieval accuracy. To address the issues, we construct a novel RSCTIR framework based on mask-guided relation modeling with entity loss (MGRM-EL), to fully explore uni-modal feature learning on entities and relations in the cross-modal model learning process. Specifically, we take advantage of the Transformer encoder architecture for its ability of capturing long-distance dependencies from the global view, and build two uni-modal (visual and textual) Transformer encoders combined with convolutional neural network (CNN) to extract the spatial inter-region relations of images as well as long-term inter-word relations of texts for prominent feature embedding of visual and semantic representations. A mask-guided attention strategy is further introduced to learn the salient regions and words, with the aim of enhancing the RSCTIR model’s uni-modal learning ability and eliminating unnecessary and redundant information of each modality. Unlike existing methods that simply compute the semantic similarity between images and texts on the loss functions, we present a novel uni-modal entity loss, which treats each image as an image entity and merges similar texts into a text entity, to learn the independent distribution of entities in each modality. We conduct extensive experiments on public RSCTIR benchmarks including RSICD and RSITMD datasets, which demonstrate the state-of-the-art performance of the proposed method on the RSCTIR task.
Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map

Counting objects in crowded scenes remains a challenge to computer vision. The current deep learning based approach often formulate it as a Gaussian density regression problem. Such a brute-force regression, though effective, may not consider the annotation displacement properly which arises from the human annotation process and may lead to different distributions. We conjecture that it would be beneficial to consider the annotation displacement in the dense object counting task. To obtain strong robustness against annotation displacement, generalized Gaussian distribution (GGD) function with a tunable bandwidth and shape parameter is exploited to form the learning target point annotation probability map, PAPM. Specifically, we first present a hand-designed PAPM method (HD-PAPM), in which we design a function based on GGD to tolerate the annotation displacement. For end-to-end training, the hand-designed PAPM may not be optimal for the particular network and dataset. An adaptively learned PAPM method (AL-PAPM) is proposed. To improve the robustness to annotation displacement, we design an effective transport cost function based on GGD. The proposed PAPM is capable of integration with other methods. We also combine PAPM with P2PNet through modifying the matching cost matrix, forming P2P-PAPM. This could also improve the robustness to annotation displacement of P2PNet. Extensive experiments show the superiority of our proposed methods.
Why Does the Inefficient and Energy-intensive Bitcoin Remain Highly Valued?

Despite energy-efficient alternatives, why does energy-intensive Bitcoin still lead the crypto market? This paper introduces a feedback loop between Bitcoin production and its valuation as a plausible explanation. Higher investor emphasis on network security increases Bitcoin's price elasticity with respect to network security. This increased elasticity motivates Bitcoin miners to enhance network security, given that a higher Bitcoin value amplifies mining profitability. Consequently, more computational power joins the mining competition which fuels a feedback loop that further bolsters network security and Bitcoin's price. However, external factors like high energy prices or regulatory restrictions on Bitcoin mining can disrupt this momentum.