Reviewerly/exHarmony huggingface dataset:
Autonomous schema markups based on intelligent computing for search engine optimization

With advances in artificial intelligence and semantic technology, search engines are integrating semantics to address complex search queries to improve the results. This requires identification of well-known concepts or entities and their relationship from web page contents. But the increase in complex unstructured data on web pages has made the task of concept identification overly complex. Existing research focuses on entity recognition from the perspective of linguistic structures such as complete sentences and paragraphs, whereas a huge part of the data on web pages exists as unstructured text fragments enclosed in HTML tags. Ontologies provide schemas to structure the data on the web. However, including them in the web pages requires additional resources and expertise from organizations or webmasters and thus becoming a major hindrance in their large-scale adoption. We propose an approach for autonomous identification of entities from short text present in web pages to populate semantic models based on a specific ontology model. The proposed approach has been applied to a public dataset containing academic web pages. We employ a long short-term memory (LSTM) deep learning network and the random forest machine learning algorithm to predict entities. The proposed methodology gives an overall accuracy of 0.94 on the test dataset, indicating a potential for automated prediction even in the case of a limited number of training samples for various entities, thus, significantly reducing the required manual workload in practical applications.
Matrix method of reflecting activity in the digital twin of the social system

The paper considers the approach to solving the problem of exact reflection of processes taking place in the company in the digital twin of the social system – not only technological and production ones but also the processes of interaction between subjects. The approach presented is the development aimed at the creation of the digital twin of the comprehensive mathematical model of the social system functioning in the active environment. Due to the presentation of agents’ actions as the transformation act of the resource base controlled by them, there appeared an opportunity to use multidimensional matrixes reflecting the phase transition of the social system resource base for solving the problem of process fixation. Combined with the calculation, the probabilities of a human to perform certain conditioned matrix actions reflecting resource transformations allow the digital twin to forecast the activity results, calculate deviations from the target trajectory of the system motion and calculate the required control actions. The novelty lies in the activity representation as a multidimensional matrix. As an example, the paper considers the use of three-dimensional matrix but the possible need in using matrixes of larger dimensionality is pointed out.
A Reconfigurable Hardware Library for Robot Scene Perception

Perceiving the position and orientation of objects (i.e., pose estimation) is a crucial prerequisite for robots acting within their natural environment. We present a hardware acceleration approach to enable real-time and energy efficient articulated pose estimation for robots operating in unstructured environments. Our hardware accelerator implements Nonparametric Belief Propagation (NBP) to infer the belief distribution of articulated object poses. Our approach is on average, 26× more energy efficient than a high-end GPU and 11× faster than an embedded low-power GPU implementation. Moreover, we present a Monte-Carlo Perception Library generated from high-level synthesis to enable reconfigurable hardware designs on FPGA fabrics that are better tuned to user-specified scene, resource, and performance constraints.
Fuse and Mix

Analog computing has been recognized as a promising low-power alternative to digital counterparts for neural network acceleration. However, conventional analog computing is mainly in a mixed-signal manner. Tedious analog/digital (A/D) conversion cost significantly limits the overall system's energy efficiency. In this work, we devise an efficient analog activation unit with magnetic tunnel junction (MTJ)-based analog content-addressable memory (MACAM), simultaneously realizing nonlinear activation and A/D conversion in a fused fashion. To compensate for the nascent and therefore currently limited representation capability of MACAM, we propose to mix our analog activation unit with digital activation dataflow. A fully differential framework, SuperMixer, is developed to search for an optimized activation workload assignment, adaptive to various activation energy constraints. The effectiveness of our proposed methods is evaluated on a silicon photonic accelerator. Compared to standard activation implementation, our mixed activation system with the searched assignment can achieve competitive accuracy with >60% energy saving on A/D conversion and activation.
Why Post-Truth Cannot Be Our Epistemological Compass

This paper tackles some of the arguments Steve Fuller – arguably the best advocate of post-truth currently on the scene – put forward to show that, correctly understood, post-truth is the best conceptual tool to get a clear picture not only of what is happening in our societies today, but also of what has happened throughout the secular history of Western culture. The implicit assumption is that post-truth represents a reliable ‘epistemological compass’ – that is, a notion (or a set of notions) for proper orientation in both cultural and physical environments. The aim of the paper is to show that Fuller’s arguments do not work, because an epistemological compass can only be centered on a plausible notion of objectivity, and – it will be contended – this is exactly what Fuller lacks. Accordingly, it will be stressed how the upshot of his theses is the opposite of what he presumes it to be and, moreover, that his theses prove lethal to his own position.
Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.
Teaching Viscosity to Students with Special Needs in Vocational High Schools from Daily Products: From Literature Review on Concept, Misconception, to Teaching and Learning Process

Students with special needs are students who have barriers in their developmental and academic aspects. They need a learning media that meets their needs. This study aimed to demonstrate the strategies in teaching using the experimental demonstration to students with special needs and to identify misconceptions during the teaching and learning process. To support our teaching strategies, an experimental demonstration (recorded by video as the learning media) was used. This study used an experimental method (single subject research) with A-B (Pre-test and Post-test) research design for students with special needs (i.e., intellectual disabilities and hearing impairment), and the results of teaching students with special needs were compared to that of teaching general vocational high school students. The experimental activity was done to get the viscosity level of eight substances (i.e., syrup, cooking oil, hand soap, liquid detergent, floor cleaner liquid, condensed milk, and mineral water). Condensed milk has the highest viscosity, and mineral water is the lowest. The result of this study showed that a combination of experimental demonstration and video is effective for improving the understanding of students with special needs, confirmed by the increasing pre and post-test results and supported by interviews. In addition, the identification data of the misconception test showed a decrease in misconceptions after carrying out the learning process using video. The use of interesting media with simple explanations made it easier for students with special needs to understand the material being taught.
How to help teachers deal with students’ cheating in Online Examinations: Design and Implementation of International Chinese Online Teaching Test Anti-Cheating Monitoring System (OICIE-ACS)

The authenticity and effectiveness of teaching testing and evaluation is an important evaluation index for the development of online teaching, and how to combat cheating behavior has become a significant impediment in this process. This paper introduces a hybrid technology based on a fuzzy evaluation method that is used to judge and prompt (suspicious) cheating behavior in international Chinese online teaching tests or evaluations. The goal of this hybrid technology applied to international Chinese online teaching is to assist teachers in monitoring and judging international students’ cheating behavior in online testing or evaluation, and making sound judgments on it, to ensure the authenticity and effectiveness of the test or evaluation results. Based on the technical root of cheating behavior in the online testing process, as well as the technical flaws in various existing online testing systems, this system proposes the idea and scheme of online examination cheating detection using fuzzy cluster analysis, as well as the specific implementation steps. We applied the system to the small-scale practical teaching process to test its security and stability, and we got the expected positive results.
Reducing Data Dependencies in the Feedback Loop of the CCSDS 123.0-B-2 Predictor

On-board multi- and hyperspectral instruments acquire large volumes of data that need to be processed with the limited computational and storage resources. In this context, the CCSDS 123.0-B-2 standard emerges as an interesting option to compress multi- and hyperspectral images on-board satellites, supporting both lossless and near-lossless compression with low complexity and reduced power consumption. Nonetheless, the inclusion of a feedback loop in the CCSDS 123.0-B-2 predictor to support near-lossless compression introduces significant data dependencies that hinder real-time processing, particularly due to the presence of a quantization stage within this loop. This work provides an analysis of the aforementioned data dependencies and proposes two strategies aiming at maximizing throughput in hardware implementations and thus enabling real-time processing. In particular, through an elaborate mathematical derivation, the quantization stage is removed completely from the feedback loop. This reduces the critical path, which allows for shorter initiation intervals in a pipelined hardware implementation and higher throughput. This is achieved without any impact in the compression performance, which is identical to the one obtained by the original data flow of the predictor.
Context-Driven Visual Object Recognition Based on Knowledge Graphs

Current deep learning methods for object recognition are purely data-driven and require a large number of training samples to achieve good results. Due to their sole dependence on image data, these methods tend to fail when confronted with new environments where even small deviations occur. Human perception, however, has proven to be significantly more robust to such distribution shifts. It is assumed that their ability to deal with unknown scenarios is based on extensive incorporation of contextual knowledge. Context can be based either on object co-occurrences in a scene or on memory of experience. In accordance with the human visual cortex which uses context to form different object representations for a seen image, we propose an approach that enhances deep learning methods by using external contextual knowledge encoded in a knowledge graph. Therefore, we extract different contextual views from a generic knowledge graph, transform the views into vector space and infuse it into a DNN. We conduct a series of experiments to investigate the impact of different contextual views on the learned object representations for the same image dataset. The experimental results provide evidence that the contextual views influence the image representations in the DNN differently and therefore lead to different predictions for the same images. We also show that context helps to strengthen the robustness of object recognition models for out-of-distribution images, usually occurring in transfer learning tasks or real-world scenarios.
Unsupervised Domain Adaptation based Modulation Classification for Overlapped Signals

As the electromagnetic environment complexity increases, the importance of automatic modulation classification for overlapped signals (OS-AMC) becomes more evident. Existing deep learning methods utilizing supervised learning are heavily challenged because the actual application environment is more complicated than the ideal training environment, and the raw samples may not be labeled due to time constraints when it comes to an emergency. Hence, in this letter, we develop an unsupervised domain adaptation-based OS-AMC method to transfer the model trained under an additive white Gaussian noise (AWGN) channel to a multi-path channel. To the best of our knowledge, solving the OS-AMC problem in a complex environment has not been addressed yet.
Dynamic spanning trees for connectivity queries on fully-dynamic undirected graphs

Answering connectivity queries is fundamental to fully dynamic graphs where edges and vertices are inserted and deleted frequently. Existing work proposes data structures and algorithms with worst case guarantees. We propose a new data structure, the dynamic tree (D-tree), together with algorithms to construct and maintain it. The D-tree is the first data structure that scales to fully dynamic graphs with millions of vertices and edges and, on average, answers connectivity queries much faster than data structures with worst case guarantees.
Color Correction and Local Contrast Enhancement for Underwater Image Enhancement

Underwater images suffer from various quality degradation problems such as color cast, low contrast, and blurred details. To solve these issues, a novel underwater image enhancement method that can implement color correction, detail sharpening, and contrast enhancement in stages. In particular, the proposed method combines multi-channel color compensation with color correction. It solves detail blurring and low contrast by the Gaussian differential pyramid and the local contrast enhancement of contrast limited adaptive histogram equalization, respectively. The proposed method mainly includes color compensation, color correction, detail sharpening, and contrast enhancement. Qualitative and quantitative comparisons demonstrate that the proposed method can effectively remove the blur of the image, realize the color correction, and significantly improve the clarity of the image.
Time-Efficient Qudit Gates through Incremental Pulse Re-seeding

Current efforts to build quantum computers focus mainly on the two-state qubit, which often involves suppressing readily-available higher states. In this work, we break this abstraction and synthesize short-duration control pulses for gates on generalized d-state qudits. We present Incremental Pulse Reseeding, a practical scheme to guide optimal control software to the lowest-duration pulse by iteratively seeding the optimizer with previous results. We find a near-linear relationship between Hilbert space dimension and gate duration through explicit pulse optimization for one- and two-qudit gates on transmons. Our results suggest that qudit operations are much more efficient than previously expected in the practical regime of interest and have the potential to significantly increase the computational power of current hardware.
Critical Radical Analysis Network for Chinese Character Recognition

Zero-shot learning is a challenging problem in many tasks due to the lack of training samples of the unseen classes. The radical-based zero-shot Chinese character recognition methods treat Chinese characters as a combination of radicals and structures, and recognize Chinese characters by identifying the radicals and structures contained in them. Current approaches generally treat the contribution of all radicals to Chinese character recognition as the same, and the recognition results rely on the network’s ability to recognize radicals and their corresponding position information, ignoring the potential value of radicals themselves in eliminating the uncertainty of Chinese characters. In this paper, we model the problem of radical-based Chinese character recognition as an uncertainty elimination problem and propose a Critical Radical Analysis Network (CRAN) to explore the Ideographic Description Sequence (IDS) information for zero-shot Chinese character recognition. Specifically, we propose a novel method to compute the critical values of radicals based on information theory using the predefined Chinese character IDS dictionary. In recognition, we use an iterative approach to translate the predicted radical sequence to target Chinese characters. That is, the radicals of the predicted sequence are sorted in descending order of the critical value, and then the radicals are continuously selected in this order as the information obtained to eliminate the uncertainty of the Chinese character until the character is recognized. We conduct experiments on the CTW, CASIA-AHCDB, and CASIA-HWDB datasets. The experimental results show that the proposed method improves the ability of recognizing unseen Chinese characters, demonstrating the effectiveness of the proposed method.
Deep Reinforcement Learning with Reward Design for Quantum Control

Deep reinforcement learning (DRL) has been recognized as a powerful tool in quantum physics, where DRL's reward design is non-trivial but crucial for quantum control tasks. To address the problem of over-reliance on human empirical knowledge to design DRL's rewards, we propose a deep reinforcement learning approach with a novel reward paradigm designed by the learning process information (DRL-LPI), where the learning process information (LPI) comprises the state information and the experiences. In DRL-LPI, the state information after being classified by a fidelity threshold, and the experiences are first stored simultaneously in the respective sequences, and this process is repeated until a similar-segment ends. Then, the stored state information is converted to the real value and used to design the reward value by applying a self-amplitude function. Next, the designed reward values are integrated with the stored experiences to compose transitions for DRL's training. Through comparisons to five representative reward schemes, the proposed DRL-LPI is evaluated on two typical quantum control tasks, i.e., the spin-(1/2) quantum state control and many-coupled qubits state control, and the experimental results show the superior learning efficiency and control performance of the proposed approach. More results show that DRL-LPI exhibits the ability to learn the control strategy with few control actions compared to stochastic gradient descent (SGD) and genetic algorithm (GA).
Towards Automatic Inventory Checking Using an Autonomous Unmanned Aerial Vehicle

Autonomous unmanned aerial vehicles (UAVs) are prime candidates for use in applications like inventory checking of large warehouses. They facilitate access to the tall racks while being agile to maneuver through the confined space of the corridors of the warehouse. They are capable of carrying the sensors needed to perform the application of warehouse inspection and are capable of sustaining flight during extended periods of time. While the environment of an industrial warehouse is well structured, it is also a challenging surrounding for visual odometry due to reflections, moving parts and light sources which might be occurring at the same time. At low altitudes, the movement of the drone is less stable because of turbulence caused by a backflow of air from the drone. The goods in the warehouse are man-placed, which might cause damage to inspection targets or create obstacles in the space. In spite of all the challenges present in this environment, the drone has to perform effortlessly its application of inventory checking. In this paper, we will focus on extending the automated process of inventory checking, presented in previous work, to the real environment of an existing warehouse and provide approaches to problems that arise from practice. We will present our approach to mission planning considering the presented challenges. The system of capturing and processing application data is introduced. The developed solution uses a UAV called AV Discovery built by Airvolute s.r.o. deployed into a real environment of an active warehouse.
Rapid Test Stock Prediction of Priority Health Facilities Using Naïve Bayes Algorithm

Handling the rapid spread of the Coronavirus that is currently engulfing the world is critical to suppress the development of the Coronavirus. one of them is rapid test distribution management. The rapid Test is an initial screening method for detecting antibodies, namely IgM and IgG, which are produced by the body to fight the Coronavirus / Antibodies that will be formed by the body when there is exposure to the Coronavirus. So, a rapid test is a screening tool, not an examination, to diagnose Coronavirus infection or COVID-19. For this reason, in current conditions, it is necessary to have a good arrangement in terms of distribution and regulation of the rapid test stock. Distributing the Corona (Covid 19) rapid Test prioritizes health facilities in areas that are the epicenter of the Corona spread. For this reason, this study provides a Rapid Test Using the Naïve Bayes Data Mining Algorithm. In this study, data obtained from data collection were then classified based on the framework used to conduct further analysis using the Simple Additive Weighting and Naive Bayes algorithm. This research results to determine the distribution of the rapid test tool based on stock data to be used for forecasting the distribution model.