Reviewerly/exHarmony huggingface dataset:
Two-Component Unit Weibull Mixture Model to Analyze Vote Proportions

In this paper, we present a two-component Weibull mixture model. An important property is that this new model accommodates bimodality, which can appear in data representing phenomena in some heterogeneous populations. We provide statistical properties, such as the quantile function and moments. Additionally, the expectation-maximization (EM) algorithm is used to find maximum-likelihood estimates of the model parameters. Further, a Monte Carlo study is carried out to evaluate the performance of the estimators on finite samples. The new model’s relevance is shown with an application referring to the vote proportion for the Brazilian presidential elections runoff in 2018. The proportion of votes is an important measure in analyzing electoral data. Since it is a variable limited to the unitary interval, unit distributions should be considered to analyze its probabilistic behavior. Thus, the introduced model is suitable for describing the characteristics detected in these data, such as the asymmetric behavior, bimodality, and the unit interval as support. In the application, the superiority of the proposed model is verified when comparing the fit with the two-component beta mixture models.
Cold-start item recommendation for representation learning based on heterogeneous information networks with fusion side information

In recommendation systems, the cold-start problem refers to the situation that users or items have too few interaction events with the system to be recommended, such as clicks or ratings. The cold-start problem leads to the loss of new users and difficulty in recommending new items and weakens the performance of recommendations. In this paper, we focus on alleviating the cold-start problem of items. Previous work has used the structure of item homogeneous graphs to establish connections between items and generate item embeddings. However, the homogeneous graph of a single node cannot fully explore the potential structural features between users and goods. Due to the flexibility of heterogeneous data, recommendation systems can also employ the Heterogeneous Information Network(HIN) to characterize complex, heterogeneous data. In this paper, a novel Heterogeneous Graph Embedding Learning based Skip-Gram (HSG) model is proposed to mitigate the cold-start problem. First, we introduce three different types of subnetworks and combine them to construct heterogeneous graphs. Then meaningful sequences of nodes are generated via meta-path-based random walk for network embedding. Finally, a Skip-Gram model incorporating side information is used to learn item representations. The learned node embeddings are used as pre-training for the Matrix Factorization (MF) model. The HSG model is proven to be effective by extensive experiments on four publicly available datasets.
Improving solar radiation source efficiency using adaptive dynamic squirrel search optimization algorithm and long short-term memory

Artificial intelligence and machine learning are used to optimize the design parameters of renewable energy sources, which are now regarded as vital components in current clean energy sources. As a result, system requirements can be reduced, and a well-designed system can improve performance. Artificial intelligence approaches in renewable energy sources and system design would significantly cut optimization time while maintaining high modeling accuracy and optimum performance. This study examines machine learning in depth, emphasizing how it can be used in developing renewable energy sources because of the vast range of technologies it can use. This paper approximates the hourly tilted solar irradiation using climate factors. The irradiance is estimated using a hybrid ensemble-learning approach. This approach combines a proposed adaptive dynamic squirrel search optimization algorithm (ADSSOA) with long short-term memory (LSTM) methods. To the best of our knowledge, this combination has not been used for solar radiation. The results are analyzed and contrasted with the outcomes of several recent swarm intelligence algorithms, such as the genetic algorithm, particle swarm optimization, and gray wolf optimizer. The binary ADSSOA approach performed as expected, with an average error of 0.1801 and a standard deviation of 0.0656. The ADSSOA–LSTM model had the lowest root mean square error (0.000388) compared to LSTM’s (0.001221). In addition, the statistical analysis uses 10 iterations of each presented and evaluated method to provide accurate comparisons and reliable results.
Transferemble: a classification method for the detection of fake satellite images created with deep convolutional generative adversarial network

As the number of government and commercial satellites increases, there is a large increase in Earth observation (EO) imagery. Using different locations and tools, images can be taken from more than one satellite. Manipulations are carried out on these images using a variety of different methods. The number of studies that have been done on the manipulation of EO images is very small. In recent years, generative adversarial networks (GANs), a major breakthrough in deep learning, have made it very easy to obtain fake images. In this study, scene-by-scene fake images were obtained with the deep convolutional GAN on the EuroSAT dataset, which is one of the EO image sets, and fake scene images were obtained from the original scenes. In this study, a dataset called RF-EuroSAT was created. It consists of 14 classes and 36,000 images. Five transfer learning models (VGG-16, DenseNet201, MobileNetV2, RegNetY320, and ResNet152V2) were used to classify this dataset. Using these models as feature extraction and ensemble models (XGBoost, CatBoost, and LightGBM) as classifiers, the classification process was performed using our proprietary transferemble model. The best result was obtained with an accuracy of 91.55% using our transferemble model, which is developed in a modular structure.
Entanglement Dynamics Induced by a Three-Level Laser Coupled to the Squeezed Vacuum Reservoir

We have studied the squeezing and entanglement properties of the light induced by a three-level laser coupled to the squeezed vacuum reservoir. Applying the pertinent master equation, we have obtained the evolution equations for the cavity mode variables. Using the solutions of the resulting equations, the squeezing properties, entanglement amplification, and the normalized second-order correlation function of the cavity radiation are described. We have seen that the light generated by a three-level laser is in a squeezed state, and the squeezing occurs in the plus quadrature. In addition, we have also established that the effect of the squeezed parameter increases the mean and variance of the photon number. It is found that the squeezing and entanglement in the two-mode light are directly related. As a result, an increase in the degree of squeezing directly leads to an increase in the degree of entanglement and vice versa. This shows that, whenever there is squeezing in the two-mode light, there exists an entanglement in the system.
Музыка в романе Евгения Водолазкина Брисбен

The author of the article examines the role of music in Evgeny Vodolazkin’s novel Brisbane, analyzing individual manifestations of the widely understood intermediacy. The musical code of the text is expressed in audibility of the sounds the world of the novel is filled with, and in musical ecphrasis (the profession of the protagonist, professional vocabulary and slang, references to musical compositions; descriptions of the world of music through the prism of the characters’ perception). These techniques perform plot and semantic functions, combine two equal plot plans, and constitute the image of the protagonist and other important characters. Fragments with musical ecphrasis have a vivid narrative, emotionally saturated (“moments of explosion”). The main emotional codes (or affects) find expression in the selection of musical compositions that assert the dramatic direction of the narrative. Music in the novel expresses the hero’s inner state, his relationship to the world, and symbolizes the metaphysical breakthrough experienced by the hero: from love and sound, to silence and eternity.
Automatic Differentiation in Prolog

Abstract Automatic differentiation (AD) is a range of algorithms to compute the numeric value of a function’s (partial) derivative, where the function is typically given as a computer program or abstract syntax tree. AD has become immensely popular as part of many learning algorithms, notably for neural networks. This paper uses Prolog to systematically derive gradient-based forward- and reverse-mode AD variants from a simple executable specification: evaluation of the symbolic derivative. Along the way we demonstrate that several Prolog features (DCGs, co-routines) contribute to the succinct formulation of the algorithm. We also discuss two applications in probabilistic programming that are enabled by our Prolog algorithms. The first is parameter learning for the Sum-Product Loop Language and the second consists of both parameter learning and variational inference for probabilistic logic programming.
Fairness First Clustering: A Multi-Stage Approach for Mitigating Bias

Fair clustering aims to partition a dataset while mitigating bias in the original dataset. Developing fair clustering algorithms has gained increasing attention from the machine learning community. In this paper, we propose a fair k-means algorithm, fair first clustering (FFC), which consists of an initialization stage, a relaxation stage, and an improvement stage. In the initialization stage, k-means is employed to cluster each group. Then a combination step and a refinement step are applied to ensure clustering quality and guarantee almost fairness. In the relaxation stage, a commonly used fairness metric, balance, is utilized to assess fairness, and a threshold is set to allow for fairness relaxation while improving the clustering quality. In the improvement stage, a local search method is used to improve the clustering quality without changing the fairness. Comparisons of fairness and clustering quality are carried out between our method and other state-of-the-art fair clustering methods on 10 datasets, which include both synthetic and real-world datasets. The results show that compared to the method with the second highest balance value, FFC shares the same SSE value on one dataset and achieves lower SSE values on six datasets.
The role of conscientiousness and cue utilisation in the detection of phishing emails in controlled and naturalistic settings

Email phishing is a serious and potentially catastrophic threat to organisations and individuals. Understanding what factors may influence individual susceptibility to phishing attacks is essential to protecting against cybercrime. We investigated the potential interplay between conscientiousness and cue utilisation in individuals’ ability to accurately differentiate between phishing and legitimate emails. University students (N = 255) completed a phishing detection task, the Mini International Personality Item Pool, and the phishing edition of the Expert Intensive Skill Evaluation (2.0) battery. After, they were sent simulated phishing emails to their student email address. A Signal Detection Theory approach revealed that higher cue utilisation was associated with a greater ability to tell whether an e-mail was phishing or not in the detection task. For the simulated phishing emails, participants with lower conscientiousness were more likely to click an embedded link in an unsophisticated phishing email, however cue utilisation had no association with email engagement in a naturalistic setting. The findings provide insight into why some people are more susceptible to phishing scams and reveal important differences in phishing sensitivity as a function of context, which has implications to interventions.
Alleviating Long-Tailed Image Classification via Dynamical Classwise Splitting

With the rapid increase in data scale, real-world datasets tend to exhibit long-tailed class distributions (i.e., a few classes account for most of the data, while most classes contain only a few data points). General solutions typically exploit class rebalancing strategies involving resampling and reweighting based on the sample number for each class. In this work, we explore an orthogonal direction, category splitting, which is motivated by the empirical observation that naive splitting of majority samples could alleviate the heavy imbalance between majority and minority classes. To this end, we propose a novel classwise splitting (CWS) method built upon a dynamic cluster, where classwise prototypes are updated using a moving average technique. CWS generates intra-class pseudo labels for splitting intra-class samples based on the point-to-point distance. Moreover, a group mapping module was developed to recover the ground truth of the training samples. CWS can be plugged into any existing method as a complement. Comprehensive experiments were conducted on artificially induced long-tailed image classification datasets, such as CIFAR-10-LT, CIFAR-100-LT, and OCTMNIST. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve significant performance gains on long-tailed datasets.
The new ethical thinking in CGI immersive journalism

Virtual reality (VR) journalism has become one of the popular formats of journalism, where people experience presence, empathy, and immersion through VR technology in digital environments. Although the term ‘virtual’ may appear to be the opposite of ‘real,’ the boundary between virtuality and reality has become blurred due to the virtual embodiment being part of reality for participants. As VR and real virtuality converge, little is known about the ethical issues in producing computer-generated imagery (CGI) immersive journalism and the viewers’ responses to CGI VR news reporting. Our study applied a concurrent mixed-method approach, including in-depth interviews with eight CGI professionals from the U.K. and a quantitative survey in Taiwan, to understand the production issues, ethical concerns, and audience perceptions toward CGI immersive journalism. Results from the interviews suggested that digital journalists need to rethink their ethical codes, particularly the representation of truth while producing news stories using CGI images and VR. Findings from the post-test survey showed that both immersive presence and interactivity in VR news resulted in a greater formation of believing and motivations to take action regarding the topic presented in the VR news.
Development and Validation of a Core Competency Assessment Tool Customized for B University

This study aims to develop an institutional assessment tool of undergraduates’ core competencies that epitomize the image of talent of University B. To this end, a comprehensive literature review was conducted to explore the theoretical basis of core competencies, and the importance and relevance of those competencies derived from the prior procedure were reviewed via a survey of its faculty members and undergraduate students. Core competencies and sub-competencies were then identified. A preliminary survey and main survey were performed on the assessment tool, and its validation was carried out by using confirmatory factor analysis and item response theory. This study showed that University B's core competency assessment tool consisted of six core competencies, 12 sub competencies, and a total of 58 questions. An array of rigorous statistical tests, such as reliability analysis, exploratory factor analysis, confirmatory factor analysis, and item response analysis, confirmed that the appropriate level of both reliability and validity was achieved. This study contributes to developing a core competency assessment tool for undergraduate students at a comprehensive university in general, and a customized assessment tool aligned with institution-specific goals of education in particular.
Attention-Guided Huber Loss for Head Pose Estimation Based on Improved Capsule Network

Head pose estimation is an important technology for analyzing human behavior and has been widely researched and applied in areas such as human-computer interaction and fatigue detection. However, traditional head pose estimation networks suffer from the problem of easily losing spatial structure information, particularly in complex scenarios where occlusions and multiple object detections are common, resulting in low accuracy. To address the above issues, we propose a head pose estimation model based on the residual network and capsule network. Firstly, a deep residual network is used to extract features from three stages, capturing spatial structure information at different levels, and a global attention block is employed to enhance the spatial weight of feature extraction. To effectively avoid the loss of spatial structure information, the features are encoded and transmitted to the output using an improved capsule network, which is enhanced in its generalization ability through self-attention routing mechanisms. To enhance the robustness of the model, we optimize Huber loss, which is first used in head pose estimation. Finally, experiments are conducted on three popular public datasets, 300W-LP, AFLW2000, and BIWI. The results demonstrate that the proposed method achieves state-of-the-art results, particularly in scenarios with occlusions.
Giving life to dead: role of WayBack Machine in recovery of dead URLs

Purpose The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs. Design/methodology/approach The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The URLs were then executed directly in the internet browser (Chrome), search engine (Google) and Internet Archive (WayBack Machine). The collected data were recorded in an excel file and presented in tables/diagrams for further analysis. Findings From the total of 1,083 web references, a maximum number was retrieved by the WayBack Machine (786; 72.6 per cent) followed by Google (501; 46.3 per cent) and the lowest by Chrome (402; 37.1 per cent). The study concludes that the WayBack Machine is more efficient, retrieves a maximum number of missing web citations and fulfills the mission of preservation of web sources to a larger extent. Originality/value A good number of studies have been conducted to analyze the persistence and decay of web-references; however, the present study is unique as it compared the dead URL retrieval effectiveness of internet explorer (Chrome), search engine giant (Google) and WayBack Machine of the Internet Archive. Research limitations/implications The web references of a single journal, namely, Library Hi Tech , were analyzed for 5 years only. A major study across disciplines and sources may yield better results. Practical implications URL decay is becoming a major problem in the preservation and citation of web resources. The study has some healthy recommendations for authors, editors, publishers, librarians and web designers to improve the persistence of web references.
An Axiomatic System Based on Ladd-Franklin's Antilogism

This paper sketches the antilogism of Christine Ladd-Franklin and historical advancement about antilogism, mainly constructs an axiomatic system Atl based on first-order logic with equality and the wholly-exclusion and not-wholly-exclusion relations abstracted from the algebra of Ladd-Franklin, with soundness and completeness of Atl proved, providing a simple and convenient tool on syllogistic reasoning. Atl depicts the empty class and the whole class differently from normal set theories, e.g. ZFC, revealing another perspective on sets and set theories. Two series of Dotterer and the theorem proved by Russinoff are re-characterized in antilogistic formulas corresponding to the square of opposition and twenty-four moods of syllogism. Then the restricted equivalence between two forms of antilogism and minimal inconsistency is built up, implying the internal connection between antilogisms and minimal inconsistency and leaving two related conjectures not proved. Besides, this paper provides a new explanation for the reason why contrariety, subcontrariety, and subalternation and the nine special moods require the non-empty assumption.
A context-aware multi-agent reasoning based intelligent assistive formalism

In recent years, the rapid proliferation of intelligent smart resource-bounded devices and autonomous software technologies has significantly influenced human life and made human life much smarter, more efficient, flexible, comfortable, and secure but device dependent. These intelligent devices are equipped with the feature of context awareness that often run in a highly decentralized environment with minimal and/or without human intervention. As these systems are often distributed in nature, they exhibit complex adaptive behavior by exchanging contextualized information among different resource-bounded devices and/or systems. However, due to the limitation of resources (computational memory), there are still many challenges for a real-time deployment. In this paper, we present a resource-bounded multi-agent reasoning-based context-aware model that assists users intelligently to take the right action autonomously whenever and wherever needed. The core emphasis is given to the lightweight, efficient reasoning mechanism to manipulate rapidly triggering contextual information with the least utilization of computational resources. We develop algorithms along with the complexity factors to evaluate the system’s efficacy in computational time and memory utilization. To suitably formalize the specification and model the proposed formalism using UPPAAL model checker, we develop the case study of a context-aware intelligent assistant along with its rule-based reasoning strategies and priorities for the execution of prioritized tasks according to the customized needs of the user and verify the correctness properties.
Cloud security game theory scoring from predation models in simulation

The economics of cloud computing result from amassing computation resources while being able to distribute workload in space and time. The backbone of this ability is virtualization, which abstracts the host hardware, sharing it through virtual machines. This means of interface is also a primary vehicle and target for attackers. The counter-measures to this threat consider the costs and benefits to the cloud’s essential functions. Where the future development of the cloud is also considered, this competition between attackers and victims can be modeled in extended game theory. Yet, the attacker and victim costs and benefits, expressed as measures of expense and utility, necessary for game-theory methods are elusive. This paper establishes such a game as a predator–prey contest played out on a data-center environment. A set of contestant parameters are applied at the threshold of a viable model to the characteristic boundaries. Measurement of system health is extracted in relief with individual cost and benefit then contrasted to risk. An examination of metrics capable of validating extended interaction is found to demonstrate variation on three orders of magnitude.