Reviewerly/exHarmony huggingface dataset:
Deep learning based MIMO systems using open-loop autoencoder

This article introduces two novel multiple input multiple output spatial division multiplexing (MIMO-SDM) systems based on deep learning techniques using bit-wise (BW) and symbol-wise (SW) open-loop autoencoders (AE), abbreviated as SWAE-MIMO and BWAE-MIMO. Based on the detection methods, we introduce three detectors at the receiver: Radio Transformation Network (RTN), Minimum Mean Square Error (MMSE) technique, and the combination of MMSE technique and neural network (MMSEnet), which can suppress co-channel interference (CCI) among received signal streams resulting in low bit error rates (BER). Furthermore, the considered systems are trained in a single phase to optimally synchronize the transmitter’s and receiver’s learning parameters, thus successfully exploiting spatial diversities to improve the error performance of conventional MIMO systems using the MMSE detector with different numbers of transmit-receive antennas. In a specific case, the BWAE-MIMO system using the RTN detector (BWAE-MIMO-RTN) achieves a BER comparable to that of the conventional MIMO system with a maximum likelihood detector (MIMO-ML) when both systems are equipped with two transmitting and receiving antennas.
On Periodic Generalized Poisson INAR(1) Model

AbstractAbstractIn this paper, we introduce a first-order Periodic Generalized Poisson Integer-Valued Autoregressive model PGPINAR(1) which has been shown to be useful to describe overdispersion, equidispersion and underdispersion feature encountered in periodically correlated Integer-Valued time series. Some probabilistic and statistical properties are established, such as the periodically correlated stationarity conditions, in the first and the second moments are provided and the closed-forms of these moments are, under these conditions, derived. Moreover, the structure of the periodic autocovariance is obtained. The estimation problem is addressed through the Yule-Walker (YW), the Two-Stage Conditional Least Squares (CLS) and the Conditional Maximum Likelihood (CML) methods. The performance of these methods is done through an intensive simulation study and an application on real data set is accomplished. Keywords and phrases: Periodic Generalized Poisson, Integer-Valued Autoregressive, Periodically correlated process, periodically stationary condition.Keywords: Periodic Generalized PoissonInteger-valued autoregressivePeriodically correlated processPeriodically stationary conditionAMS subject classification: 62F1262M10 AcknowledgementsThe authors would like to present their most sincere thanks and their deep gratitude to Professor N. Balakrishnan, Editor-in-Chief, for his helps and continued encouragements. Also, they present to the anonymous referee their most sincere thanks and profound acknowledgements for his pleasant assistance, important corrections, constructive remarks and suggestions which have allowed us to improve the quality and the readability of the paper.Appendix
Distance metric learning based on the class center and nearest neighbor relationship

Distance metric learning has been a promising technology to improve the performance of algorithms related to distance metrics. The existing distance metric learning methods are either based on the class center or the nearest neighbor relationship. In this work, we propose a new distance metric learning method based on the class center and nearest neighbor relationship (DMLCN). Specifically, when centers of different classes overlap, DMLCN first splits each class into several clusters and uses one center to represent one cluster. Then, a distance metric is learned such that each example is close to the corresponding cluster center and the nearest neighbor relationship is kept for each receptive field. Therefore, while characterizing the local structure of data, the proposed method leads to intra-class compactness and inter-class dispersion simultaneously. Further, to better process complex data, we introduce multiple metrics into DMLCN (MMLCN) by learning a local metric for each center. Following that, a new classification decision rule is designed based on the proposed methods. Moreover, we develop an iterative algorithm to optimize the proposed methods. The convergence and complexity are analyzed theoretically. Experiments on different types of data sets including artificial data sets, benchmark data sets and noise data sets show the feasibility and effectiveness of the proposed methods.
Underwater image enhancement combining dual color space and contrast learning

With the continuous increase of human society’s demand for marine resources, underwater vision plays an increasingly important role in marine information acquisition. However, due to the absorption and scattering of light, underwater images have characteristics such as blurring, low contrast, and color distortion, which are not conducive to the acquisition of underwater image information. The rapid development of deep learning and powerful feature learning capabilities have been widely used in underwater image enhancement tasks, but current research still has problems such as loss of local details and oversaturation. Aiming at the above problems, this paper proposes an underwater image enhancement method combining dual color space and contrast learning, including RGB color space and Lab color space. The RGB color space is used for color correction and artifact removal, and the Lab color space is used to enhance saturation and detail texture. The contrastive learning of positive and negative samples makes the generated image closer to the clear underwater image and deviates from the real underwater image. Experimental results on real underwater datasets show that the proposed method achieves the best results on PSNR, SSIM and UIQM compared to other underwater image enhancement methods.
AGURF: An adaptive general unified representation frame for imbalanced interval-valued data

Interval-valued data (IVD) is a kind of data in which each feature is an interval, and embeds some uncertainty and variability information. Due to the inherent structural particularity of IVD, in addition to the skewed distribution classes, another imbalance for IVD is the biased range distribution on each interval-valued data. This internal imbalance depicts the internal distribution of IVD in detail, but is usually ignored in representation. This work proposes an adaptive general unified representation frame (AGURF), which may expand the representation frame of IVD. Based on the unified representation frame (URF) proposed in previous work, an adaptive general unified representation frame is constructed firstly. Then the offset-center is defined to re-measure the location of each interval-valued data more accurate. Meanwhile, a rule to set the adaptive factors for each class automatically, which serves as base factors to balance the relationship between offset-center and radius, is proposed. Finally, several general classifiers are also used to verify AGURF. The experiment results on synthetic and real-world datasets demonstrate that the proposed method can better represent imbalanced IVD, obtain good classification performance and reduce time cost simultaneously.
Experience in the Development of Archaeological Multimedia Content for Students of Pedagogical Universities

Purpose . The article is devoted to the generalization of the integrating the Local History Museum and the equipment of the Altai State Pedagogical University Universal Pedagogical Competences Technopark funds with the resources of the historical park of the Novosibirsk region “Russia is My History” experience in order to create multimedia educational content applicable when teaching the “Archaeological tourism” course. Results . The importance of multi-channel perception of archaeological heritage objects (description, photo, drawing, 3D modeling) in the pedagogical process is shown. The development features of a program with elements of the game based on the materials of the archaeological sites of Altai (“Antiquities of Altai”) associated with their attributive and informative properties are discussed. Much attention is paid to the requirements for the description and analysis of the selection criteria for objects exhibited in multimedia. The features of the multimedia content integration into the educational process are considered. Conclusion . It is shown that situational gamification contributes to the figurative perception of specific archaeological eras and the material complex associated with them. It is concluded that the continued introduction of multimedia technologies in the teaching of historical disciplines will increase interest in science among students of the Pedagogical University. The combination of traditional and innovative teaching methods applied in practice both in higher and secondary schools will lead to an increase in the quality of historical and general education.
STA-TCN: Spatial-temporal Attention over Temporal Convolutional Network for Next Point-of-interest Recommendation

Recent years have witnessed a vastly increasing popularity of location-based social networks (LBSNs), which facilitates studies on the next Point-of-Interest (POI) recommendation problem. A user’s POI visiting behavior shows the sequential transition correlation with previous successive check-ins and the global spatial-temporal correlation with those check-ins that happened a long time ago at a similar time of day and in geographically close areas. Although previous POI recommendation methods attempted to capture these two correlations, several limitations remain to be solved: (1) RNNs are widely adopted to capture the sequential transition correlation, whereas training an RNN is rather time-consuming given the long input check-in sequence. (2) The pairwise proximities on time of day and geographical area of check-ins are crucial for global spatial-temporal correlation learning, but have not been comprehensively considered by previous methods. To tackle these issues, we propose a novel next POI recommendation framework named STA-TCN. Specifically, instead of RNNs, STA-TCN augments the Temporal Convolutional Network with gated input injection to learn sequential transition correlation. Furthermore, STA-TCN fuses two novel grid-difference and time-sensitivity learning mechanisms with attention network to learn the pairwise spatial-temporal proximities among a user’s check-ins. Extensive experiments are conducted on two large-scale real-world LBSN datasets, and the results show that STA-TCN outperforms the best state-of-the-art baseline with an average improvement of 9.71% and 7.88% on hit rate and normalized discounted cumulative gain, respectively.
Emojis and stock returns

Purpose The authors explore how the sentiment expressed by emojis in comments on stocks is associated with the stocks' subsequent returns. Design/methodology/approach By applying our own analyzer, the authors find a sentiment effect of emojis on stocks returns separately to the plain text-expressed sentiment in Reddit posts about meme stocks such as Gamestop during the Covid-19 pandemic. Findings The authors document that a one-standard deviation change in emoji sentiment magnitude measured as the quantity of positive emoji sentiment posts over the previous hour is associated with an 0.06% (annualized: 109.2%) one-hour abnormal stock return compared to a mean of 0.03% (annualized: 54.6%). If the stock exhibits a higher intra-hour volatility, a proxy for uninformed noise trading, this relation is more pronounced and even stronger compared to stock return's relation to plain text sentiment. Research limitations/implications The authors are not able to show causation that is open to future research. It also remains an open question how emojis impact market price efficiency. Practical implications Emojis are positively related to stock returns in addition to plain text-expressed content if they are discussed heavily by retail investors in Internet boards such as Reddit. Social implications Shared emotions expressed by emojis might have an influence on how disconnected individuals make homogeneous decisions. This argument might explain our found relation of emojis and stock returns. Originality/value So, the study findings provide empirical evidence that emojis in Reddit posts convey information on future short-term stocks returns distinct from information expressed in plain text, in the case of volatile stocks, with a higher magnitude.
Predicting Bitcoin Prices Using Machine Learning

In this paper we predict Bitcoin movements by utilizing a machine-learning framework. We compile a dataset of 24 potential explanatory variables that are often employed in the finance literature. Using daily data from 2nd of December 2014 to July 8th 2019, we build forecasting models that utilize past Bitcoin values, other cryptocurrencies, exchange rates and other macroeconomic variables. Our empirical results suggest that the traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching an accuracy of 66%. Moreover, based on the results, we provide evidence that points to the rejection of weak form efficiency in the Bitcoin market.
Balancing Password Security and User Convenience: Exploring the Potential of Prompt Models for Password Generation

With the increasing prevalence of cyber attacks and data breaches, the importance of strong passwords cannot be overstated. Password generating software has been widely used to generate complex passwords that are difficult to crack, but it has its limitations. One of the main problems with this kind of software is that it often generates passwords that are difficult to remember, leading to users write them down or reuse them across multiple accounts. In recent years, prompt models such as ChatGPT have emerged as a promising solution for generating strong and memorable passwords. By leveraging machine learning algorithms, these models can generate unique and complex passwords tailored to individual users’ preferences, making them easier to remember and more secure. However, the use of prompt models to generate passwords also raises concerns about exposing vulnerable passwords. Hackers can potentially use these models to predict passwords by analyzing a user’s online activity and personal data. Additionally, the constant need to change passwords to stay secure poses a challenge for both password generating software and prompt models. As technology continues to evolve, finding a balance between password security and user convenience remains a complex issue. While prompt models such as ChatGPT can offer a promising solution, it is essential to consider the potential risks and challenges associated with their use, including the constant need for password changes and the potential vulnerability of the generated passwords.
Cost effective Tanh activation function circuits based on fast piecewise linear logic

In this paper, an approximate 16-bit non-linear Tanh function circuit design is proposed for activation function in neural networks and digital signal processing, which is based on piecewise linear calculation. By sacrificing acceptable accuracy in computation, the proposed logic circuit structure can furtherly improve the overall performance of the design, with marked reduction in delay, area and power consumption. Linear calculation segments with simple shift-and-add logic architectures are adopted for easy hardware implementation. Additionally, tailored bias tuning and approximate accumulator design make a fine balance between complexity and error distribution. The mean absolute error (MAE) of the entire design is 0.0049. Compared with other state-of-art works in 90 nm CMOS process, the area of the circuit is 582μm2, and the energy is 0.0996 mW/GHz at 0.71 GHz. Moreover, the area-delay product is reduced by over 79.7% and the energy-delay product is reduced by over 42.4%. In the evaluation of its suitability in large applications, the accurate Tanh circuit and the proposed Tanh circuit are adopted in two neural networks to classify the Fashion-MNIST data and emotional EEG data. When the model converges, the accuracies of the test set only differs with a maximum of 0.2% and 0.3%.
HUDNet: A dynamic calibration method for automotive augmented reality head-up-displays

The automotive augmented reality head-up-display (AR-HUD) system relies on multiple free-form surfaces to project the virtual image into human eyes. A wide range of views and the extensive display area result in extraordinarily complex distortions. Providing a way to correct such distortions is a major leap forward. Methods widely used for calibration, such as lookup tables and interpolation, require considerable memory capacity, complicated calibration procedures, etc. Additionally, when attempting to meet the high-accuracy requirements for such systems, computational memory grow rapidly. In this context, we propose a fully connected neural network (HUDNet) for an automotive AR-HUD to correct dynamic distortion. Here, we propose a parallel prediction framework for the center and the edge of the image, and separate the distortion information from the disparity information in order to balance the unstable central field of view error. Ultimately, we introduce the transfer learning method to improve accuracy and its effect on accuracy is assessed. The present study provides a reference for us to apply deep learning methods to predict dynamic image distortion of various complex multi-freeform surface reflection systems in the future.
Alabib-65: A Realistic Dataset for Algerian Sign Language Recognition

Sign language recognition (SLR) is a promising research field that aims to blur boundaries between Deaf and hearing people by creating a system that can transcribe signs into a written or vocal language. There is a growing body of literature that investigates the recognition of different sign languages, especially American sign language. So far, to the best of our knowledge, no study has considered the Algerian SLR. This is mainly due to the lack of datasets. To address this issue, we created the Alabib-65, the first Algerian Sign Language dataset. It consists of up to 6,238 Videos recorded from 41 native signers under realistic settings. This dataset is challenging due to a variety of reasons. First, there is a little inter-class variability. The 65 sign classes are similar in terms of hands’ configuration, placement, or movement and can share the same sub-parts. Second, there is a large intra-class variability. Furthermore, compared to other SL datasets that were collected from an indoor environment with a static and simple background, our videos were recorded from both indoor and outdoor environments with 22 backgrounds varying from simple to cluttered, and from static to dynamic. To underpin future research, we provided baseline results on this new dataset using state-of-the-art machine learning methods, namely: IDTFs with Fisher vector and SVM-classifier, VGG16-GRU, I3D, I3D-GRU, and I3D-GRU-Attention. The results show the validity and the challenges of our dataset.
A-contrario detection and tracking from optical telescope data

The precipitous growth of the Anthropogenic Space Object population demands new approaches to high-volume detection and measurement for orbital analyses and conjunction assessment. Optical sensors, which can be smaller and more cost-effective than other mechanisms such as radar sensors, have potential for rapid network expansion, community-driven data acquisition, and space-based sensing. However, traditional detection methods of source extraction and track filtering require high-fidelity a-priori assumptions of data content and are limited by extensive calibration requirements costing time, resources, and expert knowledge of particular detection pipelines. Machine learning methods mitigate a-priori assumptions but require extensive training data which is costly to manually label and restricts applicable use cases. We instead consider a new approach to optical Space Surveillance and Tracking based on a-contrario analysis, detecting features which cannot be attributed to noise from the hardware, atmosphere, or background celestial environment. In such an approach, we reduce epistemic uncertainty by recursive conditioning and inference of data structures. This approach is implemented and assessed on data from the ASTRIANet telescope network of The University of Texas at Austin, and we demonstrate the capacity for orbit determination by quantifying detection rates for actively tracked objects, discussing applicability to sidereal tracking and object discovery, and evaluating the resulting orbit estimates.
Learning textual features for Twitter spam detection: A systematic literature review

Background—Nowadays, with the rise of Internet access and mobile devices around the globe, more people are using social networks for collaboration and receiving real-time information. Twitter, the microblogging site that is becoming a critical source of communication, has also grabbed the attention of spammers to distract users. So far, researchers have introduced various defense techniques to detect spams and combat spammers' activities. To overcome this problem, many novel techniques have been offered by researchers, which have greatly enhanced spam detection performance. Objective—The purpose of this paper is to identify, taxonomically classify, and compare current Twitter spam detection approaches in a systematic way. Method—This study presents a comprehensive Systematic Literature Review (SLR) method for spam detection on Twitter regarding 70 most relevant papers published between 2010 and October 2022. Literature review analysis reveals that most of the existing Twitter spam detection techniques are based on textual content and messages (tweets) that rely on Machine Learning (ML)-based algorithms. The major differences in these ML algorithms which use various classification and clustering algorithms are related to various feature selection methods. Hence, we propose a classification based on different feature selection analyses, namely content analysis, user analysis, tweet analysis, network analysis, and hybrid analysis. Results—Various parameters are identified to investigate the Twitter spam detection approaches, and each of the papers was examined to find the research methodology and present comparative studies on current approaches. Conclusion—This paper demonstrates that the existing Twitter spam detection approaches have encountered several open issues, including scalability, streaming data analysis, and processing. The most obvious unresolved issues are spam drift and non-English tweets.
Neural additive time-series models: Explainable deep learning for multivariate time-series prediction

Deep neural networks are one of the most important methods in machine learning. The advantages of neural networks are their excellent prediction performance and versatility using deep architecture and generalized input–output forms. However, as neural networks are black-box models, they lack explanatory power for their predictions. In this study, we propose a new neural network architecture that includes the interpretability of predictions for multivariate time-series (MTS) data by employing a generalized additive method. In addition, we examine parameter sharing networks to decrease the model's complexity, along with hard-shared networks. We conducted experiments to demonstrate that the interpretable neural architecture can quantify the contributions of each input value to the prediction of each MTS by every time step and variable. Experimental results involving a toy example and four real-world datasets demonstrate that the performance of the proposed method in predicting MTS data is comparable to that of state-of-the-art neural networks, while providing reasonable importance for each input value.