Reviewerly/exHarmony huggingface dataset:
How to make embeddings suitable for PLDA

Probabilistic linear discriminant analysis (PLDA) is widely implemented in speaker verification tasks. However, PLDA has limitations owing to its assumptions. In this study, we explore how to make deep speaker embeddings suitable for PLDA in complex situations. We analyze PLDA in detail and summarize its three important properties, Gaussianity, simplicity, and domain sensitivity. For the Gaussianity, by comparing the discrimination and Gaussianity of embeddings extracted from different layers of speaker extractors with different numbers of segment-level fully connected (Fc) layers, we demonstrate that embeddings extracted from the first Fc layer of models with two segment-level Fc layers are more suitable for PLDA. Secondly, several common speaker datasets comprise multiple short-duration speech segments extracted from long speech. We find that embeddings of short speech segments extracted from the long speech are less reliable and have complex within-class distributions. By determining the weighted average of embeddings extracted from short-duration speech segments, we simplify the embeddings distribution and make the embeddings suitable for PLDA. Thirdly, PLDA is sensitive to domain mismatches. We propose data adaptation methods that work directly on raw speech to eliminate explicit mismatches, such as the codecs and the environment noise mismatches. We prove that the data adaptation methods achieve performance improvements of PLDA and show strong complementarity with backend adaptation methods. We conduct extensive experiments, using the NIST SRE CTS superset, VoxCeleb, and SRE16 as the training set, and the SRE21 set as the evaluation set mainly. The experimental results show that our methods effectively improve the overall performance of PLDA.
Features of Approaches to Information Educational Components Design for Energy Universities

Despite the wide variety of automated information educational resources, they all have one common problem – low didactic efficiency. The reason for this is the complexity of the mathematical description of the semi-structured parts of the educational process and the lack of a unified mathematical approach to the formalization of educational components. Another problem is a narrow technical approach to design, in which learning systems are seen only as technical devices. At the same time, complex problems associated with the design in the field of philosophy of education, pedagogy, psychology, control theory, and circuitry engineering lack due attention. The purpose of the research is to identify problematic features that reduce the efficiency of information educational systems design. The article presents the results of the analysis of the efficiency of training systems in various forms of testing and methodology for the use of information educational resources. The features of the formalization of synergetic ergatic models of the educational process components are reviewed, considering the psychodiagnostics of the personality traits of the educational process participants. The practical significance of the research is that it provides the information system creators with alternative psychological and pedagogical methods allowing to improve the efficiency of teaching system design.
Research on Photovoltaic Power Prediction Based on BP Neural Network Algorithm

Abstract The output power of photovoltaic (PV) power plant is influenced by the fluctuation of meteorological conditions, which, coupled with the intermittent nature of the power output, has raised concerns about the stability of power grid. According to the variations in meteorological conditions, PV power prediction technology can predict the PV output power in advance to decrease the influence of PV power generation on the stable operation of the power grid. In this paper, the PV power prediction model is built on Matlab simulation software with a BP neural network algorithm. After that, the power prediction calculation of a PV power station is carried out under the operation conditions of sunny and cloudy days. The results show that, in the periods of prediction, the power curve of the predicted value is close to the power curve of the real value, which demonstrates the great accuracy of the BP prediction model established in this paper.
Imagery evoking visual and verbal information presentations in mobile commerce: the roles of augmented reality and product review

Purpose The purpose of this study is to investigate the effects of visual and verbal information presentations on mental imagery, perceived informativeness and purchase intention. The study assesses two types of product-related information: (1) visual information: static product images and augmented reality (AR) and (2) verbal information: abstract and concrete product reviews. Design/methodology/approach A total of 320 mobile consumers participated in the experiment. To increase external validity, this study was conducted in an existing digital shopping environment. Findings The results suggest that AR has a greater effect on consumers' shopping outcomes than static images. The findings further reveal that concrete product reviews are important in increasing mental imagery, perceived informativeness and purchase intention when visual information does not provide an AR function. Originality/value This study contributes to the current literature by providing empirical support for AR effects and concrete reviews on consumer responses. The results further provide an important perspective for retailers seeking ways to develop effective information presentations in digital retailing.
A systematic review of literature on credit card cyber fraud detection using machine and deep learning

The increasing spread of cyberattacks and crimes makes cyber security a top priority in the banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional anomaly detection and rule-based techniques are two of the most common utilized approaches for detecting cyber fraud, however, they are the most time-consuming, resource-intensive, and inaccurate. Machine learning is one of the techniques gaining popularity and playing a significant role in this field. This study examines and synthesizes previous studies on the credit card cyber fraud detection. This review focuses specifically on exploring machine learning/deep learning approaches. In our review, we identified 181 research articles, published from 2019 to 2021. For the benefit of researchers, review of machine learning/deep learning techniques and their relevance in credit card cyber fraud detection is presented. Our review provides direction for choosing the most suitable techniques. This review also discusses the major problems, gaps, and limits in detecting cyber fraud in credit card and recommend research directions for the future. This comprehensive review enables researchers and banking industry to conduct innovation projects for cyber fraud detection.
Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has an imbalanced nature. Moreover, we designed a deep learning model that combines a convolution-based feature extractor and deep neural network blocks, the LSTM block, and the transformer encoder block, followed by a position-wise feedforward layer. We found that combining the input sequence with the convolution-learned features of different kernels tends to increase the performance of the fall-detection model. Last, we analyzed that the sensor signals collected by both accelerometer and gyroscope sensors can be leveraged to develop an effective classifier that can accurately detect falls, especially differentiating falls from near-falls. Furthermore, we also used data from sixteen different body parts and compared them to determine the better sensor position for fall-detection methods. We found that the shank is the optimal position for placing our sensors, with an F1 score of 0.97, and this could help other researchers collect high-quality fall datasets.
A Novel Integrated Heuristic Optimizer Using a Water Cycle Algorithm and Gravitational Search Algorithm for Optimization Problems

This paper presents a novel composite heuristic algorithm for global optimization by organically integrating the merits of a water cycle algorithm (WCA) and gravitational search algorithm (GSA). To effectively reinforce the exploration and exploitation of algorithms and reasonably achieve their balance, a modified WCA is first put forward to strengthen its search performance by introducing the concept of the basin, where the position of the solution is also considered into the assignment of the sea or river and its streams, and the number of the guider solutions is adaptively reduced during the search process. Furthermore, the enhanced WCA is adaptively cooperated with the gravitational search to search for new solutions based on their historical performance within a certain stage. Moreover, the binomial crossover operation is also incorporated after the water cycle search or the gravitational search to further improve the search capability of the algorithm. Finally, the performance of the proposed algorithm is evaluated by comparing with six excellent meta-heuristic algorithms on the IEEE CEC2014 test suite, and the numerical results indicate that the proposed algorithm is very competitive.
An AdaBoost Method with K′K-Means Bayes Classifier for Imbalanced Data

This article proposes a new AdaBoost method with k′k-means Bayes classifier for imbalanced data. It reduces the imbalance degree of training data through the k′k-means Bayes method and then deals with the imbalanced classification problem using multiple iterations with weight control, achieving a good effect without losing any raw data information or needing to generate more relevant data manually. The effectiveness of the proposed method is verified by comparing it with other traditional methods based on numerical experiments. In the NSL-KDD data experiment, the F-score values of each minority class are also greater than the other methods.
Multi-source statistics on employment status in Italy, a machine learning approach

Abstract In recent decades, National Statistical Institutes have started to produce official statistics by exploiting multiple sources of information (multi-source statistics) rather than a single source, usually a statistical survey. In this context, one of the research projects addressed by the Italian National Statistical Institute (Istat) concerned methods for producing estimates on employment in Italy using survey data and administrative sources. The former are drawn from the Labour Force survey conducted by Istat, the latter from several administrative sources that Istat regularly acquires from external bodies. We use machine learning methods to predict the individual employment status. This approach is based on the application of decision tree and random forest techniques, that are frequently used to classify large amounts of data. We show how to construct a “new” response variable denoting agreement of the data sources: this approach is shown to maximise the information we may derive by machine learning approach in some problematic cases. The methods have been applied using the R software.
Further validation of the estimates of the downwelling solar radiation at ground level in cloud-free conditions provided by the McClear service: the case of Sub-Saharan Africa and the Maldives Archipelago

Abstract. Being part of the Copernicus Atmosphere Monitoring Service (CAMS), the McClear service provides estimates of the downwelling shortwave irradiance and its direct and diffuse components received at ground level in cloud-free conditions, with inputs on ozone, water vapor and aerosol properties from CAMS. McClear estimates have been validated over several parts of the world by various authors. This article makes a step forward by comparing McClear estimates to measurements performed at 44 ground-based stations located in Sub-Saharan Africa and the Maldives Archipelago in the Indian Ocean. The global irradiance received on a horizontal surface (G) and its direct component received at normal incidence (BN) provided by the McClear-v3 service were compared to 1 min measurements made in cloud-free conditions at the stations. The correlation coefficient is greater than 0.96 for G, whereas it is greater than 0.70 at all stations but five for BN. The mean of G is accurately estimated at stations located in arid climates (BSh, BWh, BSk, BWk) and temperate climates without a dry season and a hot or warm summer (Cfa, Cfb) or with a dry and hot summer (Csa) with a relative bias in the range [−1.5, 1.5] % with respect to the means of the measurements at each station. It is underestimated in tropical climates of monsoon type (Am) and overestimated in tropical climates of savannah type (Aw) and temperate climates with a dry winter and hot (Cwa) or warm (Cwb) summer. The McClear service tends to overestimate the mean of BN. The standard deviation of errors for G ranges between 13 W m−2 (1.3 %) and 31 W m−2 (3.7 %) and that for BN ranges between 31 W m−2 (3.0 %), and 70 W m−2 (7.9 %). Both offer small variations in time and space. A review of previous works reveals no significant difference between their results and ours. This work establishes a general overview of the performances of the McClear service.
Solutions to the Knower Paradox in the Light of Haack’s Criteria

Abstract The knower paradox states that the statement ‘We know that this statement is false’ leads to inconsistency. This article presents a fresh look at this paradox and some well-known solutions from the literature. Paul Égré discusses three possible solutions that modal provability logic provides for the paradox by surveying and comparing three different provability interpretations of modality, originally described by Skyrms, Anderson, and Solovay. In this article, some background is explained to clarify Égré’s solutions, all three of which hinge on intricacies of provability logic and its arithmetical interpretations. To check whether Égré’s solutions are satisfactory, we use the criteria for solutions to paradoxes defined by Susan Haack and we propose some refinements of them. This article aims to describe to what extent the knower paradox can be solved using provability logic and to what extent the solutions proposed in the literature satisfy Haack’s criteria. Finally, the article offers some reflections on the relation between knowledge, proof, and provability, as inspired by the knower paradox and its solutions.
Image Style Transfer Based on Dynamic Convolutional Manifold Alignment of Halo Attention

The objective of image style transfer is to render an image with artistic features of a style reference while preserving the details of the content image. With the development of deep learning, many arbitrary style transfer methods have emerged. From the recent arbitrary style transfer algorithms, it has been found that the images generated suffer from the problem of poorly stylized quality. To solve this problem, we propose an arbitrary style transfer algorithm based on halo attention dynamic convolutional manifold alignment. First, the features of the content image and style image are extracted by a pre-trained VGG encoder. Then, the features are extracted by halo attention and dynamic convolution, and then the content feature space and style feature space are aligned by attention operations and spatial perception interpolation. The output is achieved through dynamic convolution and halo attention. During this process, multi-level loss functions are used, and total variation loss is introduced to eliminate noise. The manifold alignment process is then repeated three times. Finally, the pre-trained VGG decoder is used to output the stylized image. The experimental results show that our proposed method can generate high-quality stylized images, achieving values of 33.861, 2.516, and 3.602 for ArtFID, style loss, and content loss, respectively. A qualitative comparison with existing algorithms showed that it achieved good results. In future work, we will aim to make the model lightweight.
Application of Human-Computer Interaction Technology in Remote Language Learning Platform

In order to realize the deep integration of Internet and education, and realize the efficient allocation and comprehensive coverage of educational resources, the remote language learning platform based on human-computer interaction technology is proposed. The overall architecture of the system is designed with the help of PaaS and IaaS, and the hardware and software of the system are designed according to the overall architecture. In the hardware design, Hibernate framework and SpringJDBC are used to realize the interaction between business logic layer and data layer, and the interaction of language learning function and practice function. In addition, the remote wireless communication module is designed to complete the rapid communication of the system. The learning analysis model is constructed in the software design to determine the actual online learning needs of college students, and the collaborative filtering algorithm is used to recommend online interactive learning information data, so as to realize the function of long-distance language online interactive learning system in colleges and universities. The experimental results show that with the increase of the number of users, the page jump time of the three functions such as sending information, submitting homework and asking for leave are all below 0.04 s, especially the page jump time of the function of sending information always keeps the lowest. The login or registration function takes a long time to go back to the page. The maximum value is about 0.07 s. The results show that the system can still show excellent operation ability for large-scale users, and the sending information rate is fast, and the remote online interaction effect has obvious advantages.
Inference-Optimized High-Performance Photoelectric Target Detection Based on GPU Framework

Deep learning has better detection efficiency than typical methods in photoelectric target detection. However, classical CNNs on GPU frameworks consume too much computing power and memory resources. We propose a multi-stream inference-optimized TensorRT (MSIOT) method to solve this problem effectively. MSIOT uses knowledge distillation to effectively reduce the number of model parameters by layer guidance between CNNs and lightweight networks. Moreover, we use the TensorRT and multi-stream mode to reduce the number of model computations. MSIOT again increases inference speed by 9.3% based on the 4.3–7.2× acceleration of TensorRT. The experimental results show that the model’s mean average accuracy, precision, recall, and F1 score after distillation can reach up to 94.20%, 93.16%, 95.4%, and 94.27%, respectively. It is of great significance for designing a real-time photoelectric target detection system.
Artificial Intelligence-Empowered Art Education: A Cycle-Consistency Network-Based Model for Creating the Fusion Works of Tibetan Painting Styles

The integration of Tibetan Thangka and other ethnic painting styles is an important topic of Chinese ethnic art. Its purpose is to explore, supplement, and continue Chinese traditional culture. Restricted by Buddhism and the economy, the traditional Thangka presents the problem of a single style, and drawing a Thangka is time-consuming and labor-intensive. In response to these problems, we propose a Tibetan painting style fusion (TPSF) model based on neural networks that can automatically and quickly integrate the painting styles of the two ethnicities. First, we set up Thangka and Chinese painting datasets as experimental data. Second, we use the training data to train the generator and the discriminator. Then, the TPSF model maps the style of the input image to the target image to fuse the two ethnicities painting styles of Tibetan and Chinese. Finally, to demonstrate the advancement of the proposed method, we add four comparison models to our experiments. At the same time, the Frechet Inception Distance (FID) metric and the questionnaire method were used to evaluate the quality and visual appeal of the generated images, respectively. The experimental results show that the fusion images have excellent quality and great visual appeal.
IMF: Interpretable Multi-Hop Forecasting on Temporal Knowledge Graphs

Temporal knowledge graphs (KGs) have recently attracted increasing attention. The temporal KG forecasting task, which plays a crucial role in such applications as event prediction, predicts future links based on historical facts. However, current studies pay scant attention to the following two aspects. First, the interpretability of current models is manifested in providing reasoning paths, which is an essential property of path-based models. However, the comparison of reasoning paths in these models is operated in a black-box fashion. Moreover, contemporary models utilize separate networks to evaluate paths at different hops. Although the network for each hop has the same architecture, each network achieves different parameters for better performance. Different parameters cause identical semantics to have different scores, so models cannot measure identical semantics at different hops equally. Inspired by the observation that reasoning based on multi-hop paths is akin to answering questions step by step, this paper designs an Interpretable Multi-Hop Reasoning (IMR) framework based on consistent basic models for temporal KG forecasting. IMR transforms reasoning based on path searching into stepwise question answering. In addition, IMR develops three indicators according to the characteristics of temporal KGs and reasoning paths: the question matching degree, answer completion level, and path confidence. IMR can uniformly integrate paths of different hops according to the same criteria; IMR can provide the reasoning paths similarly to other interpretable models and further explain the basis for path comparison. We instantiate the framework based on common embedding models such as TransE, RotatE, and ComplEx. While being more explainable, these instantiated models achieve state-of-the-art performance against previous models on four baseline datasets.