Reviewerly/exHarmony huggingface dataset:
Quantum Logical Depth and Shallowness of Streaming Data by One-Way Quantum Finite-State Transducers (Preliminary Report)

The logical depth of a piece of data has served as a complexity measure to describe the amount of “usefulness” and “non-randomness” of information stored in the data itself, which is originated from Kolmogorov complexity. This notion of logical depth has been further expanded to various models of computation in the past literature to accommodate the needs for handling different computational circumstances. We focus on streaming data and streaming algorithms. With the use of one-way (or real-time) quantum finite-state automata equipped with write-once output tapes (called transducers) for recovering the desired information from the incoming compressed data sets, we introduce the notions of quantum finite-state depth and shallowness to capture the usefulness of the streaming data sets. We first layout a general setting of decompression of streaming data and, using its fundamental properties, we then argue the existence of deep and shallow data sets.
An Improved Magnetic Spot Navigation for Replacing the Barcode Navigation in Automated Guided Vehicles

The barcode navigation based on QR (quick response) codes is widely employed in industrial logistics due to its accurate localization and flexible movement paths. However, the regular repair of damaged barcodes and robot speed control when approaching the barcodes are required. In this study, we presented an improved magnetic spot navigation approach to replace the barcode navigation for automated guided vehicles (AGVs). The fusion of the high-precision magnetic tracking method and odometer based on AGV encoders can overcome the disadvantages of barcode navigation. The magnetic tracking approach provides the AGV pose relative to the nearest magnet spot, instead of the low-precision longitudinal and lateral measurement via a magnetic ruler. Besides, with the benefit of the adaptive weighted fusion algorithm, the distance between the adjacent barcode can be set from 500 to 1000 mm via magnetic spots. Experimental results show that the mean path accuracy and mean magnet spot localization accuracy of the improved magnetic spot navigation were 110 ± 30 mm and 14.5 ± 0.87 mm, respectively. The proposed approach provides a novel possibility for large-area and high-precision navigation in AGVs-based industrial logistics, especially for large outdoor scenarios.
Get the GIST: An Interactive Toolkit to Support Generative Design through Metaheuristic Optimization

Generative design tools afford designers new ways of creating complex objects from 3D models to machine knittable textures. However, implementing the optimization algorithms that make generative design possible requires the rare combination of programming and domain expertise. I present the Generative Interactive Synthesis Toolkit (GIST) to simplify the implementation of generative design tools in novel domains. GIST factors common optimization algorithms into elements of an extensible library and structures optimization tasks around objectives and tactics specified by domain experts in a simple GUI. This moves the burden of domain expertise from programmers to domain experts. I demonstrate GIST in three unique domains: machine knitting, cookie recipes, and tactile maps for blind users. These show the versatility of GIST’s structure.
Research on distortion correction of particleboard surface defect image

In order to improve the barrel distortion of the acquired image in the surface defect detection of particleboard. In this paper, a method based on Zhang Zhengyou calibration is used to solve the camera distortion problem, so as to improve the accuracy of surface defect image processing of particleboard. Firstly, the camera was calibrated, and then the improvement of the camera correction information accuracy was judged by the correction of external parameters and reprojection error of image visualization. The internal parameter matrix and distortion coefficient of the camera were calculated accurately, and the barrel distortion of the image was corrected finally. The position of the inner corner points detected by the camera is accurate, and the reprojected points were included in the inner corner points, which improves the correction accuracy of the image to be measured. It can be clearly seen from the external parameters of visualization that the placement of the 16 sample patterns is within the range of vision. The oblique Angle deviation between the images is within 150mm. The average value of the re projection error was 0.1570 pixels calculated by the point of the camera re projection, which meets the need of correction. In conclusion, the image quality can be improved by accurately correcting the distortion of particleboard image. It lays a foundation for the surface defect extraction of particleboard.
Cascading and Residual Connected Network for Single Image Superresolution

Convolution neural networks facilitate the significant process of single image super-resolution (SISR). However, most of the existing CNN-based models suffer from numerous parameters and excessively deeper structures. Moreover, these models relying on in-depth features commonly ignore the hints of low-level features, resulting in poor performance. This paper demonstrates an intriguing network for SISR with cascading and residual connections (CASR), which alleviates these problems by extracting features in a small net called head module via the strategies based on the depthwise separable convolution and deformable convolution. Moreover, we also include a cascading residual block (CAS-Block) for the upsampling process, which benefits the gradient propagation and feature learning while easing the model training. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed method is superior to the latest SISR methods in terms of quantitative indicators and realistic visual effects.
Segmentation of Finger Nails Image based on Image Processing methods

This research has aim to segmentation of finger nails image using image processing methods and k-means region growing. Finger nails image could be used as early detection of diabetes mellitus. The steps in research are preprocessing step and segmentation step. Preprocessing step consist of image grayscale conversion, median filter, edge detection sobel operator and dilation. The aim of preprocessing step is to enhancement image before segmentation process is applied in image. Therefore, segmentation step using k-means region growing approach. The result show that accuracy rate of methods is 54.67%. In the next research, segmentation of finger nails image could use other methods that show better result.
Multi-Scale Feedback Convolutional Sparse Coding Network for Saliency Detection in Remote Sensing Images

Due to the huge difference in the shooting conditions of remote sensing images (RSI), the RSI itself has variable scales, multiple scenes and cluttered backgrounds, which lead to the poor detection effect of the SOD method for natural scene images (NSI). By exploring the multi-scale characteristics of RSI, combining convolutional sparse coding (CSC) and convolutional neural network (CNN), this paper proposes a multiscale feedback CSC (MFC) network for SOD of optical RSI. Specifically, the soft threshold shrinkage (SST) function and the CNN components are first used to construct the CSC block (CSCB). Then, the multi-scale image representations are fed into the stacked CSCB to extract the features thoroughly. Finally, the side-out features are integrated through the cross-feature fusion module (CFF) with a top-down feedback strategy. The comprehensive evaluation results with the state-of-the-art (SOTA) competitors on the ORSSD and EORSSD datasets demonstrate the proposed model's priority.
Automated Grading of Exam Responses: An Extensive Classification Benchmark

Automated grading of free-text exam responses is a very challenging task due to the complex nature of the problem, such as lack of training data and biased ground-truth of the graders. In this paper, we focus on the automated grading of free-text responses. We formulate the problem as a binary classification problem of two class labels: low- and high-grade. We present a benchmark on four machine learning methods using three experiment protocols on two real-world datasets, one from Cyber-crime exams in Arabic and one from Data Mining exams in English that is presented first time in this work. By providing various metrics for binary classification and answer ranking, we illustrate the benefits and drawbacks of the benchmarked methods. Our results suggest that standard models with individual word representations can in some cases achieve competitive predictive performance against deep neural language models using context-based representations on both binary classification and answer ranking for free-text response grading tasks. Lastly, we discuss the pedagogical implications of our findings by identifying potential pitfalls and challenges when building predictive models for such tasks.
Practical differentially private online advertising

Powered by machine learning technology, online advertising achieves accurate advertisement delivery to potential customers according to online user profiles. However, it raises serious privacy concerns since the learning process may reveal sensitive information in the profiles. It is highly desirable to provide high-quality advertisement recommendations while respecting an individual’s privacy. To address the privacy issues, we propose a practical privacy-preserving system that predicts advertisement Click Through Rate (CTR) accurately without revealing any sensitive information of individuals. Our system combines both offline and online training to achieve the goal. In the offline training phase, we develop two differentially private algorithms built upon Gradient Boosting Decision Tree (GBDT) and Field-aware Factorization Machine (FFM) algorithms, respectively, to transform features and train a privacy-preserving offline model. In the online training phase, we propose a privacy-preserving online learning algorithm to compensate for the decline of offline performance when online feature changes occur. We perform extensive experiments to evaluate the performance of our system based on three real-world datasets. The results demonstrate that our system can protect user privacy without compromising the accuracy of CTR prediction, with reasonable computation overhead.
Behavior Recognition Algorithm Based on the Fusion of SE-R3D and LSTM Network

In view of the fact that the existing behavior recognition algorithms can not fully extract abstract behavior features, this paper proposes a SE-R3D-LSTM behavior recognition algorithm based on 3D residual convolutional neural network (R3D), which integrates Squeeze-and-excitation network (SENet) and long short-term memory (LSTM). First of all, a residual module is added to the 3D Convolutional Neural Network (3D-CNN) to avoid problems such as gradient dispersion caused by the deepening of the network layer; Secondly, not only the global average pooling layer but also the global maximum pooling layer is used in the SENet network, which can fully extract global information and achieve feature calibration. In the meantime, expand the SENet network to three-dimensional, which can make the connection of the spatiotemporal feature channels closer. Afterwards, the 3D-SE module is introduced into the R3D network, which can enhance the effective spatiotemporal features and suppress the invalid spatiotemporal features; Since, because LSTM can perform timing modeling on high-level features and learn more effective feature information, the LSTM network is introduced into the SE-R3D network. Finally, Softmax is used for classification. Experimental results show that the recognition rate of the SE-R3D-LSTM network on the UCF101 data set reaches 96.5%.
Blockchain-Based Supply Chain Finance Design Pattern

With a series of policies issued by the state in recent years to encourage the development of supply chain finance (SCF) and to serve micro, small and medium-sized enterprises (MSMEs), domestic supply chain finance has developed rapidly. However, affected by the COVID-19 epidemic, many MSMEs experienced a cash flow crisis and were on the brink of bankruptcy. Therefore, we propose a series of blockchain-based supply chain finance design patterns, aiming to solve the trust problem through blockchain, so as to better solve the cash flow, financing difficulties and financing costs of MSMEs.
Towards a General Theory of Decomposability in Abstract Argumentation

The paper introduces a general model for the study of decomposability in abstract argumentation, i.e. the possibility of determining the semantics outcome based on local evaluations in subframeworks. As such, the paper extends a previous work by generalizing over the kind of information locally exploited. While not concerned with specific semantics, the paper shows the range of decomposable semantics with varying degrees of local information. It also introduces the notion of a canonical local function, which can enforce decomposability whenever this is possible.
FoV Privacy-aware VR Streaming

Proactive tile-based virtual reality (VR) video streaming can use the trace of FoV and eye movement to predict future requested tiles, then render and deliver the predicted tiles before playback. The quality of experience (QoE) depends on the combined effect of tile prediction and consumed resources. Recently, it has been found that with the field of view (FoV) and eye movement data collected for a user, one can infer the identity and preference of the user. Existing works investigate the privacy protection for eye movement, but never address how to protect the privacy in terms of FoV and how the privacy protection affects the QoE. In this paper, we strive to characterize and satisfy the FoV privacy requirement. We consider "trading resources for privacy". We first add camouflaged tile requests around the actual FoV and define spatial degree of privacy (SDoP) as a normalized number of camouflaged tile requests. By consuming more resources to ensure SDoP, the actual FoVs can be hidden. Then, we proceed to analyze the impacts of SDoP on the QoE by jointly optimizing the durations for prediction, computing, and transmission that maximizes the QoE given arbitrary predictor, configured resources, and SDoP. We find that a larger SDoP requires more resources but degrades the performance of tile prediction. Simulation with state-of-the-art predictors on a real dataset verifies the analysis and shows that a user requiring a larger SDoP can be served with better QoE.
About building virtual worlds. Experience in teaching an interfacultural course at Lomonosov Moscow State University

Subject of the article is actual as never given the speed and volatility of data transfer and it’s processing. Authors have attended the topic of “digital transformation” multiple times, researching both negative and positive outcomes of this process. To define some of the potentially negative - alienation from other elements of life due to division of labor, personal and professional diviations. Some of the standing out positives - RnD and creative potential provided by virtual space, ability to effectively model technical and social processes, gamification as an additional tool of education development. This article is focused around the methodology of the elective education course “Constructing virtual worlds: game, science fiction and futurology” that was held in 2020/2021. Authors provide detailed description of how this course has been formed and method of its delivery to the audience. Being done as the dialogue between speakers and students, through the means of constant discussion, the course allowed not just to constantly receive and apply feedback to real-time, but also to form a thought process, reasonable for subjects in discussion. The course has provoked a lot of interest both in academic and industrial circles and has been discussed at multiple professional media platforms. Major results are ability to develop disciplines for new masters programs and programs of additional specialization. Authors are also confident that the course can be the beginning of new research and practical transdisciplinary fields aimed to explore vision and ideas of virtual space, cyber space and metaverse.
Deep Neural Network Retrieval

With the rapid development of deep learning-based techniques, the general public can use a lot of "machine learning as a service" (MLaaS), which provides end-to-end machine learning solutions. Taking the image classification task as an example, users only need to update their dataset and labels to MLaaS without requiring the specific knowledge of machine learning or a concrete structure of the classifier. Afterward, MLaaS returns a well-trained classifier to them. In this paper, we explore a potential novel task named "deep neural network retrieval" and its application which helps MLaaS to save computation resources. MLaaS usually owns a huge amount of well-trained models for various tasks and datasets. If a user requires a task that is similar to the one having been finished previously, MLaaS can quickly retrieve a model rather than training from scratch. We propose a pragmatic solution and two different approaches to extract the semantic feature of DNN representing the function of DNN, which is analogous to the usage of word2vec in natural language processing. The semantic feature of DNN can be expressed as a vector by feeding some well-designed litmus images into the DNN or as a matrix by reversely constructing the most desired input of DNN. Both methods can consider the topological information and parameters of the DNN simultaneously. Extensive experiments, including multiple datasets and networks, also demonstrate the efficiency of our method and show the high accuracy of deep neural network retrieval.
Detection of Transients in Vibration Signals Using Reverse Dispersion Entropy

Abstract In this work, the reverse dispersion entropy (RDE) is used to process the squared envelope (SE) signal in order to detect nonstationarites. Based on the idea of spectral kurtosis (SK) and kurtogram, the squared envelope signal is first extracted by applying the short time Fourier transform to vibration signal. Then, as an alternative to negative Shannon entropy, the RDE is used to process the squared envelope to detect the range of frequencies at which the transients occur. The RDEgram color-coded map is used to represent the RDE values as a function of frequency and frequency resolution from which the ideal filter parameters can be inferred. Once the best frequency and frequency bandwidth pair are found, an optimum finite impulse response filter can be designed to filter the original vibration signal. The proposed method is tested against simulated and actual vibration signals and proved to be superior to existing methods.
Directional mean curvature for textured image demixing

Approximation theory plays an important role in image processing, especially image deconvolution and decomposition. For piecewise smooth images, there are many methods that have been developed over the past thirty years. The goal of this study is to devise similar and practical methodology for handling textured images. This problem is motivated by forensic imaging, since fingerprints, shoeprints and bullet ballistic evidence are textured images. In particular, it is known that texture information is almost destroyed by a blur operator, such as a blurred ballistic image captured from a low-cost microscope. The contribution of this work is twofold: first, we propose a mathematical model for textured image deconvolution and decomposition into four meaningful components, using a high-order partial differential equation approach based on the directional mean curvature. Second, we uncover a link between functional analysis and multiscale sampling theory, e.g., harmonic analysis and filter banks. Both theoretical results and examples with natural images are provided to illustrate the performance of the proposed model.
FaceTrack

In this paper, a novel tool for facial and gesture analysis aiming to quantify different subjective measures employed in the Speech Language Pathologist area is proposed. Through an input video (from a simple monocular camera) showing a person's face, the developed tool can track the movements and expressions from it in order to extract useful morphological and gestural parameters that are of interest in different fields of study, such as, Speech Language Pathologist, Neurology, etc. A modified version of a 3D face model, named Candide-3, is employed in the tracking stage. Since the original 3D model cannot handle asymmetrical facial movements, a new set of animation units was implemented in order to effectively track asymmetrical gestures. To enhance the tracking accuracy, a fusion scheme is proposed in the facial gesture tracking stage by means of the combination of the 3D face model previously described and facial landmarks detected using deep learning models. This tool will be made open source, both the software application (oriented to health professionals, no need to have any programming knowledge), and the source code for the computer vision community. Several perceptual experiments were carried out, achieving promising results.
Hybrid multi-objective optimization approach intended for mobile robot path planning model

In the field of mobile robot decision making and control, path planning is an essential element as it defines the performance of the design. It is one of the hot topics in artificial intelligence and researchers pay more attention to develop an efficient model. The key requirements that must be considered while designing a navigational system for mobile robots are origin point, obstacles, destination point, path planning, and realistic decision mechanism. However, conventional systems have limitations as slow response, long planning, large turns, and unsafe factors. Aiming at the problems, this research work presents a hybrid optimized path planning model for a mobile robot. Improved particle swarm optimization and Modified Whale optimization models are incorporated as a hybrid multi-objective approach to obtain the shortest, smoothest, and safest path for a mobile robot. Experimental results demonstrate that the proposed hybrid optimization model is suitable for mobile robot navigation for dynamic environments by obtaining a shorter, smoother, and safer path than existing algorithms.
The Choice-Preferred Semantics for Relevance-Oriented Acceptance of Admissible Sets of Arguments

In abstract argumentation, multiple argumentation semantics for choosing sets of jointly acceptable arguments have been defined. In the principle-based approach, multiple principles have been proposed and formalized in order to guide the choice for a semantics and the search for new semantics. Admissibility is a central principle satisfied by many semantics, including complete, stable, grounded and preferred. A more recently introduced principle is the INRA principle, motivated by considerations about the relevance of arguments and supported by a cognitive study. This paper additionally introduces and motivates the SAFWOC principle in order to positively distinguish less abstention-friendly semantics like preferred and stable from more abstention-friendly semantics like grounded and complete. After observing that no existing semantics satisfies these three principles, we define the novel choice-preferred semantics that satisfies the three principles. Additionally we show that choice-preferred satisfies further desirable principles like existence, directionality, SCC-recursiveness and completeness.