Reviewerly/exHarmony huggingface dataset:
A novel Bayesian image despeckling method using 2D CGARCH-M model in 2D dost framework

Image denoising is a crucial task in the field of image processing, and Bayesian estimation has emerged as a prominent approach. To effectively employ Bayesian estimation, it is essential to assume a priori distribution corresponding to the transform coefficients of the noise-free image. In this paper, we propose a novel Bayesian image despeckling method that leverages the 2D Complex Generalized Autoregressive Conditional Heteroscedasticity Mixture (CGARCH-M) model within the framework of the 2D Discrete Orthonormal Stockwell Transform (DOST). Prior to introducing this method, we present a novel statistical analysis of the 2D DOST coefficients of log-transformed images. Speckle noise, commonly found in synthetic aperture radar (SAR) images, is modeled as multiplicative noise. To effectively suppress speckle noise, our proposed method utilizes a novel adaptive Bayes risk estimator known as compound maximum a posteriori (CMAP). By employing CMAP, we estimate the noise-free 2D DOST coefficients from the noisy ones, which are modeled using the 2D CGARCH-M. Notably, this paper introduces the 2D CGARCH-M model for 2D complex stochastic processes for the first time, extending the capabilities of the GARCH model and its subsequent extensions that were limited to real-valued processes. The proposed model incorporates location-dependent conditional variances to capture the non-Gaussian statistics of 2D DOST coefficients of log-transformed images and the dependencies between them. Through our statistical analysis, we establish the compatibility between the 2D CGARCH-M model and these coefficients. Our proposed method takes into account both magnitude and phase by utilizing the real and imaginary components of the DOST coefficients concurrently. It provides an optimal and closed-form solution, significantly reducing memory and computational requirements. Moreover, unlike state-of-the-art approaches in this domain, our method exhibits robustness to initial parameter settings. To evaluate the effectiveness of our approach, we conduct comparisons with other denoising methods on artificially speckled aerial images and actual SAR images. The results demonstrate the superior performance of our method.
On the possibility of merging the subjective and the objective

Abstract In The Journal of Nervous and Mental Disease, vol. 70, there is a paper by Dr. P. Schilder entitled “The somatic basis of the neurosis”, in which the author recognizes that in what we, I and my co-workers, called neurosis even in our experimental animals (dogs) studied by the conditional reflexes method, “all the phenomena of the neuroses are found”. This recognition by a specialist, of course, is very much appreciated by us. But I must emphatically disagree with what the author says further about the contrastive studies of these neuroses in humans and in animals. The author makes the following statement: “These important experiments of Pavlov and his school can be understood only if we look upon them in the light of our experiences in the neuroses. We cannot interpret the neurosis by means of the conditioned reflex, but by means of the psychic mechanisms we have studied in the neurosis we can certainly very well interpret what takes place in a conditioned reflex.”
Comparison study of unsupervised paraphrase detection: Deep learning—The key for semantic similarity detection

Abstract Automatic detection of concealed plagiarism in the form of paraphrases is a difficult task, and finding a successful unsupervised approach for paraphrase detection is necessary as a precondition to change that. This comparative study identified the most efficient methods for unsupervised paraphrased document detection using similarity measures alone or combined with Deep Learning (DL) models. It proved the hypothesis that some DL models are more successful than the best statistically‐based methods in that task. Many experiments were carried out, and their results were compared. The text similarities between documents are obtained from 60 different methods using five paraphrase corpora, including the new one made by authors, as an important original contribution. Some DL models achieved significantly better results than those obtained by the best statistical methods, especially pre‐trained transformer‐based language models with average values of Accuracy and F1 of 85.8% and 88.3%, respectively, with top values of 99.9% and 98.4% for Accuracy and F1 on some corpora. These results are even better than those of supervised and combined approaches. Therefore, here presented results prove that detecting concealed plagiarism becomes an attainable goal. This study highlighted those language models with the best overall results for paraphrase detection as best suited for further research. The study also discussed the choice of similarity/distance measure paired with embeddings produced by DL models and some advantages of using cosine similarity as the fastest measure. For 60 different methods, complexity has been defined in O notation. Times needed for their implementation have also been presented. The article's results and conclusions are a firm base for future semantic similarity, paraphrasing, and plagiarism detection studies, clearly marking state‐of‐the‐art tools and methods.
Ice hockey puck tracking through broadcast video

Ice-hockey puck tracking is a non-trivial task in hockey video analysis as it highlights the puck in the video for broadcasting, tactical play analysis, or referee assisting. However, difficulties, such as high speed, low texture features in images and constantly changing shape, make well-developed object tracker fail to track the puck. This paper introduces a real-time online-learning ice hockey puck detection and tracking system solely depending on video input to tackle this problem. The proposed approach categorizes pucks into free-moving and control-moving states, using a combination of contour fitting, correlation filter, and motion estimation techniques to detect and track them. A thorough analysis is performed focusing on the tracking scenario using broadcast video. To our knowledge, this is the first approach addressing detection and tracking nearly invisible high-speed pucks when shooting actions take place. Experiments with a comparison between a previous work targeting puck tracking show promising results in detection and tracking the ice hockey puck through broadcast video.
Eye-Controlled Cursor Stabilization Technique Based on Optimal Estimation

Abstract In this study, we propose a stabilization algorithm based on optimal estimation to improve the stability of the eye-controlled cursor. The main principle is that the optimal result of the cursor state at the previous moment is used to calculate the predicted value of the current cursor state (a priori estimate). Then, the actual state of the cursor given by the eye tracker is used to correct the predicted value so as to move the cursor smoothly and stably. To verify the performance of the proposed eye-controlled cursor stabilization technique, we designed two sets of control experiments to compare our technique with two classical eye-controlled cursor stabilization techniques: manual and gaze input cascaded and warping to target centre. The experimental results show our stabilization algorithm significantly improves the performance speed of the eye-controlled cursor and reduces the error rate. It is additionally more accessible for users and has good application potential.
Constructing condensed memories in functorial time

If episodic memory is constructive, experienced time is also a construct. We develop an event-based formalism that replaces the traditional objective, agent-independent notion of time with a constructive, agent-dependent notion of time. We show how to make this agent-dependent time entropic and hence well-defined. We use sheaf-theoretic techniques to render agent-dependent time functorial and to construct episodic memories as sequences of observed and constructed events with well-defined limits that maximise the consistency of categorisations assigned to objects appearing in memories. We then develop a condensed formalism that represents episodic memories as pure constructs from single events. We formulate an empirical hypothesis that human episodic memory implements a particular time-symmetric constructive functor, and discuss possible experimental tests.
Talk or Text? The Role of Communication Modalities in the Adoption of a Non-directive, Goal-Attainment Coaching Chatbot

Abstract Despite the proliferation of chatbots (conversational agents) in increasingly varied contexts, user satisfaction with chatbot interactions remains a challenge. We do not yet fully understand chatbot usability and adoption factors or how to customize chatbots based on users' personality traits. One important and under researched aspect of chatbot design is users' perceptions of different communication modalities such as voice and text. In this between-group study (n = 393 participants), we asked participants to rate an equivalent text-based (n = 189) and voice-based (n = 204) non-directive, goal-attainment coaching chatbot in terms of usability, performance expectancy and risk perception. We also considered participants' personality in terms of extraversion. For usability across all participants, there was no difference between the chatbots for all participants; however, a higher rating of the voicebot was observed in the group classified as introverts and no difference was found for participants classified as extroverts. For performance expectancy all participants, extroverts and introverts rated the textbot higher. Risk ratings showed no difference between bots for all participants, extroverts and introverts. The results suggest that the voicebot was considered slightly easier to use for some participants while the textbot was considered to perform better by all participants. Creators of chatbots should consider using voice as a modality to attract users and text as a mode to accomplish complex tasks. Extraversion did not play a significant part in chatbot communication modality choice. These results may assist in designing context and audience-specific chatbots for increased efficacy and user satisfaction.
The Post-Truth Condition in Capitalist Society: A Critical Enquiry

It is being increasingly recognized that a post-truth phenomenon has arrived where lies and deception inform politics and everyday life as a normal state of affairs in (advanced) capitalist societies. In the post-truth world, feelings are more accurate than facts, and lies are constantly told for the purpose of the political subordination of reality. The post-truth condition is deeply skeptical of objective truth and experts/scientists. This article discusses the various traits of the post-truth phenomenon, explains its connection to post-modernist idealist philosophy, and its association with Far Right politics. It also explains the post-truth phenomenon in terms of the crisis-ridden bourgeois political economy and bourgeois politics, including the contradictions of imperialism. It argues that the post-truth phenomenon is only partly because of Far Right politics and that its ultimate cause lies in the capitalist system as a whole. This article concludes by pointing out what is to be done to counter the post-truth condition.
Enhancing Dynamism of IoT Service Composition

As IoT systems become more complex and interconnected, their ability to adapt to changes becomes essential. However, this dynamism needs to be addressed from early on and at different levels. Failure to recognize this will hinder the resulting system's flexibility. This article presents an analysis of composition approaches and technologies based on several criteria, in an attempt to identify common patterns or constructs that enhance specific dynamic traits which should be considered during development. Then, the identified elements within those criteria's methods and tools are mapped against the desired dynamic traits. By using cross classification, it is possible to recognize the most adequate alignment of dynamic traits among approaches. A comparative analysis is produced containing our findings. These outcomes are expected to contribute to the research community in developing more flexible distributed dynamic systems.
Signal Processing in Music Production: The Death of High Fidelity and the Art of Spoilage

There is a curious paradox which permeates the world of commercial music production. There is a clash between the basic orientation of electronic engineers, and the aesthetic desires of studios. The overall tendency of electronic engineers to pursue fidelity is countered by the aesthetic and commercial desires of music consumers and producers. In response to market pressures, music producers overwhelmingly reject the concept of “Hi-Fi” as an aesthetic for their final product. According to one rejection, the messiness of historic analogue technologies is nurtured. According to another approach, the computer is used to explore ever more exotic ways to manipulate sound, most of which have no basis in the real-world. To this end a variety of approaches are used. They may vary from the utilisation of traditional technologies (e.g., vacuum tubes, transformers), through computer emulation of classic analogue hardware, right up to cutting edge software which modifies audio signals in ways which could not have been imagined half a century ago.
Path planning and search effectiveness of USV based on underwater target scattering model

Abstract A multi-USV optimal path planning algorithm based on underwater target scattering model is proposed for the difficult problem of combining path planning and underwater target sonar search for Unmanned Surface Vehicle (USV). The algorithm is based on the characteristics of multi-USV collaborative search problem, and then constructs the partition search model with improved k-means algorithm and the sub-region path planning algorithm model with improved particle swarm algorithm (PSO). And in order to be closer to the needs of the actual scenario, the target submarine dispersion model and the dipped sonar search model are constructed. Combined with the area partitioning algorithm module and the path planning algorithm module, it is applied to the effectiveness calculation of searching for submarine targets. Through simulation and comparison experiments, we verified the effectiveness and superiority of the proposed path planning algorithm in this paper.
Siamese Network with multi-scale fusion attention for Visual Tracking

Abstract The existing trackers based on the Siamese network have poor tracking robustness in the face of complex situations such as target occlusion, rapid target movement and scale changes. To address this problem, we propose a new tracking framework based on the Siamese network with multi-scale fusion attention. The algorithm proposed in this paper increases the receptive field of the last two layers of the backbone network, strengthens the ability to capture target information, and outputs the last three layers of the backbone for feature fusion. We add an improved visual attention model, and the multi-scale channel attention model is used for the first time in this work, so as to strengthen the long-range dependence of feature information and the learning of channel attention, so that the network can select better salient features of the target. In this paper, to avoid to generate complex hyper-parameters for the target candidate frame and speeds up the training speed of the network, we introduce an anchor-free classification and regression network model. The experimental evaluation conducted on the OTB100 and VOT2016 datasets shows that the algorithm in this paper has good robustness in the face of challenges such as target occlusion, rapid target movement and scale changes, and effectively improves the accuracy of our algorithm.
3D observation instrument for pathology tissues

Abstract Pathology telemedicine uses digital data of pathological samples to acquire technical support from sophisticated remote experts, aiding cancer diagnosis in less developed regions. In this paper, we propose a color 3D observation system composed of three cameras and a digital projector, achieving complete range 3D imaging of pathology tissues. A new phase mapping method for the projector calibration is proposed by locating the geometric centroid of the checkboard’s white squares, avoiding the center positioning error of traditional circular markers introduced by perspective distortion. A denoising method successively using amplitude modulation judge, phase monotonicity check, and phase abruptness constraint is proposed, which facilitates obtaining clean 3D point clouds.
A Heterogeneous Ensemble Approach for Terrain Classification with Tracked Mobile Robots

Abstract Terrain classification plays a vital role in the autonomous driving of tracked mobile robots. This article proposes a heterogeneous ensemble method to enhance the classification performance with weak classifiers. Combined with principal component analysis (PCA) and ReiefF, a feature selection approach aims to find an effective feature set. Based on the proposed method, the framework has been developed to simplify the signal process in feature extraction. Extreme Learning Machine (ELM) is utilized as the individual classifier for three kinds of signal sources. With the learning process experience, ensemble weights have been generated to support the whole ensemble flow, which is closely correlated with terrain categories, classifiers and signal sources. Due to the structure of tracked mobile robots, the application maneuvers are extended to the front and rear drive modes, while the steady-state turning and straight-line maneuvers. The results demonstrate that the proposed method can significantly enhance the performance of terrain classification.
Application of two variable hermite splines in digital image processing

During the research, the use of piece-polynomial methods in digital processing of images was considered. The Hermite spline function is chosen from piece-polynomials as a mathematical model in digital processing of signals, and the construction of a two-variable third-order Hermite spline function is presented. An image restoration algorithm was developed based on the constructed mathematical model.
The influence of educational information visualization trends in higher education for students in the Russian Federation

The establishment of scientific foundations for the integration of information technologies and innovative educational processes in student learning activities entails the necessity of creating a basis for transitioning from information processes to information technologies. The research aims to study the effectiveness of innovative information technologies in higher education and assess student involvement in using innovative information technologies. The study relies on the experimental survey method to assess respondents’ involvement in the use of innovative information technologies. In addition, the modeling method was used to increase students’ motivation to study through visualization and other modern technologies. A training program based on visualization and innovative information technologies aimed to study their impact on the teaching process in higher education was developed. The study results show that visualization technologies improve students’ intrinsic motivation to learn. The practical significance and further research prospects are due to the possibility of applying the training program developed in the study in higher education. The program can also become the basis for developing new training programs to study other subjects as it is not tied to studying a certain discipline and requires only computer skills.
A far-field super-resolution imaging method based on phase constructive

Abstract Super-resolution imaging under passive illumination is always a crucial issue to be addressed. Because the wavefront of an incoherent light field is not easily detected, it is hard to achieve super-resolution imaging by synthetic aperture under passive illumination. In this work, we proposed a super-resolution method based on phase constructive. We use a quadrature phase-shifting algorithm to retrieve the wavefront from a series of correlation patterns and reconstruct the super-resolution image with a wavefront synthesis algorithm. A simulation result verifies the effectiveness of our proposed method with a USAF-1951 target as the object.
Securely Importing of Back-Up File into Cryptowallet

Blockchain is one of the rapidly growing technologies in the current world, based on which various applications are being implemented. Similarly, various cryptowallets are being developed which are used for storing cryptocurrencies and other assets. These cryptowallets will play a major role in every cryptosystem, so a safe and secure wallet creation is the primary objective. Current cryptowallets are made to perform operations like transactions, backing-up cryptocurrencies, storing NFTs, etc. When it comes to backing up and importing the wallet, there are not many sufficient security measures that make sure that the stolen wallet's back-up file is not being imported by the attacker into the attacker's wallet. This paper proposes a safety measure that can be implemented for importing the back-up file of the wallets in a secure way through the secret sharing algorithm.
An image restoration method for motion-blurred objects

Abstract In military applications, the motion of high-speed objects usually causes motion blur in images, which makes it difficult to detect objects. We propose an image restoration method based on the line features and the degradation process of motion-blurred images. By analysing these features, we can estimate the motion state of the target and restore the images. The images processed by this method can get better performance in tasks like object detection and object tracking.
Wavelet Monte Carlo: a principle for sampling from complex distributions

Abstract We present Wavelet Monte Carlo (WMC), a new method for generating independent samples from complex target distributions. The methodology is based on wavelet decomposition of the difference between the target density and a user-specified initial density, and exploits both wavelet theory and survival analysis. In practice, WMC can process only a finite range of wavelet scales. We prove that the resulting $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> approximation error converges to zero geometrically as the scale range tends to $$(-\infty ,+\infty )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mo>-</mml:mo> <mml:mi>∞</mml:mi> <mml:mo>,</mml:mo> <mml:mo>+</mml:mo> <mml:mi>∞</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . This provides a principled approach to trading off accuracy against computational efficiency. We offer practical suggestions for addressing some issues of implementation, but further development is needed for a computationally efficient methodology. We illustrate the methodology in one- and two-dimensional examples, and discuss challenges and opportunities for application in higher dimensions.