Reviewerly/exHarmony huggingface dataset:
A Dual Entropy-Based Digital Random Number Generator

This paper introduces the dual-entropy method for oscillator-based digital random number generators (RNG). The standard model for elaborating jitter-based RNG is expanded to account for the sampling time uncertainty. It is explained that combining the jitter of the sampling clock with the uncertainty of the sampled signal proves to be the most efficient method for harvesting entropy from jitter. Dual-entropy configuration also improves the robustness of the RNG against correlation, interference, and injection-based attacks in hardware. Numerical analysis and experimental results show that the negative effect of external interference on the entropy of the output bitstream is significantly reduced in dual-entropy-based RNG. The proposed method is demonstrated on a ring-oscillator-based design and implemented on FPGA. It is tested using NIST 800-22, Dieharder, and TestU01 batteries and observed to produce successful random bitstreams at a maximum center frequency of 45 MHz on Zedboard Zynq and 114 MHz on ZCU102 Zynq Ultrascale+ evaluation kits without post-processing.
Multimedia Classifiers: Behind the Scenes

This tutorial provides an in-depth understanding of the art and science behind the decision-making in a multimedia classifier. A multimedia classifier typically takes image, text, waveform, ordinal number or categorical data or their combination as the input and produces a single output indicating the class of the input pattern. Such a piece of AI ML system is extensively used as a decision-making element in several autonomous systems. The yardstick used by the human experts for decision making for the same input pattern often differs from the system, still producing the same output. In some cases, the outputs differ for the same input data and throws open a question on the reliability of the model. If such models are used in critical applications, which is often the case in an autonomous system, adequate mitigations for minimizing impact of the misjudgment has to be taken. It calls for ripping open the decision making process in the black box classifiers. Unwinding the black box is the need of the hour for the regulatory bodies as well. EU region has already made it mandatory to provide the details of the decision-making mechanism if it involves some form of AI ML components. This tutorial throws light on the decision making process in a classifier that may be used for a variety of applications. More than one technique to get a glimpse of the classifier in action would be discussed. The explanation can come in the form of ta heatmap indicating the relevant features influencing the decision making process, patterns learnt by the neurons or Textual description of the attributes of the input. The bottom line is these explanations are to be consistent. The mechanism to achieve the coherent explanation would be detailed in this tutorial.
Physics and Nature

This chapter is devoted to the tentative definition of the subject of your studies: physics. We already know that trying to define precisely what physics is about is, to say the least, very difficult, if not impossible. However, it has clearly emerged that physics needs measurements: this is the subject of this book.
TreeMerge: Efficient Generation of Minimal Hitting-Sets for Conflict Sets in Tree Structure for Model-Based Fault Diagnosis

For many high-tech fields such as space exploration, nuclear technology, and smart automobiles, it is vital to timely find faulty components of man-made devices to ensure safety. However, there is nearly <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">no</i> enough diagnostic experience accumulated in these new devices, and thus, it is hardly suitable to only apply the traditional expert/experience-based fault diagnosis approach. Thus, model-based diagnosis was proposed for efficient detection of faulty components; this approach explores the behavioral and structural information of the device to be diagnosed, and no experience is required. In model-based diagnosis, for a device to be diagnosed, minimal conflict sets of components are first generated, and all minimal hitting-sets for them will be derived as candidate diagnoses. Therefore, it is vital to efficiently generate all minimal hitting-sets to find the final diagnosis. Unfortunately, it is proven to be NP-hard when deriving all minimal hitting-sets for given minimal conflict sets. To improve the computing efficiency, in this article, we propose a novel approach called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> TreeMerge <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> , which considers a special type of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tree</i> structure of minimal conflict sets of large sizes since structural information usually plays an important role in solving complex problems. Theoretically, compared with other algorithms, the time complexity of the new algorithm is greatly reduced, as the time complexity of the new algorithm becomes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">linear</i> rather than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quadratic</i> . Furthermore, experimental results on multiple synthetic and benchmark examples show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TreeMerge</i> algorithm is more efficient than many other state-of-the-art methods, with a reduction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">several orders of magnitude</i> runtime (seconds).
Self-learning Visual Servoing for Robot Manipulation in Unstructured Environments

Current visual servoing methods used in robot manipulation require system modeling and parameters, only working in structured environments. This paper presents a self-learning visual servoing for a robot manipulator operated in unstructured environments. A Gaussian-mapping likelihood process is used in Bayesian stochastic state estimation (SSE) for Robotic coordination control, in which the Monte Carlo sequential importance sampling (MCSIS) algorithm is created for robotic visual-motor mapping estimation. The Bayesian learning strategy described takes advantage of restraining the particles deterioration to maintain the robot robust performance. Additionally, the servoing controller is deduced for robotic coordination directly by visual observation. The proposed visual servoing framework is applied to a manipulator with eye-in-hand configuration no system parameters. Finally, the simulation and experimental results demonstrate consistently that the proposed algorithm outperforms traditional visual servoing approaches.
Employing Multi-granularity Features to Extract Entity Relation in Dialogue

Extracting relational triples from unstructured text is essential for the construction of large-scale knowledge graphs, QA and other downstream tasks. The purpose of dialogue relation extraction is to extract the relations between entities from the multi-person dialogue texts. The existing dialogue relation extraction models only focused on coarse-grained global information and ignored fine-grained local information. In this paper, we propose a dialogue relation extraction model BERT-MG to capture the features on different granularity at different BERT layers to take advantage of the fine-grained dialogue features. Moreover, we design a type-confidence mechanism to use the entity type information to assist relation inference. Experimental results on the DialogRE dataset prove that our proposed model BERT-MG outperforms the SOTA baselines.
Performance Study of Ant Colony Optimization for Feature Selection in EEG Classification

Feature Selection (FS) is a contemporary challenge for the scientific community since new methods are being discovered and new forms of algorithmic design are required. In this sense, classic bio-inspired swarm intelligence algorithms can be explored to get a new suitable feature selection application where simple agents interact locally with each other while searching a global solution. Therefore, this work proposes an innovative utilization of the ant colony optimization algorithm for FS (ACO-FS) with the objective of reducing the high number of features present in Electroencephalogram (EEG) signals. Specifically, a base ant colony optimization algorithm and two variants have been implemented and evaluated in terms of execution time, energy consumption, and classification rates to point out the strength and weakness of each variant. The preliminary results demonstrate that the proposed method provides a classification rate close to 86% when the whole dataset is reduced from 3,600 features to only 22.
A Virtual Reality System for Automation Assisted Interior Design

With the economic development and people's growing demand for a better life, the proportion of home decoration market in the economic market is also gradually rising. However, under the impact of the downturn of the general environment and the new mode of Internet, many offline home decoration companies cannot bear the heavy burden and are struggling in the market operation. For a variety of marketing methods of businesses, consumers have immunity, little effect, so home decoration technology needs to produce a new model in the Internet environment. Though combining virtual reality technology with interior design, a new intelligent automation interior design system based on VR technology is introduced. This paper introduces the design and implementation of virtual system for interior design, and analyzes the key technologies of VR application. The main design idea of this system is to automatically design a perfect indoor layout with lighting effect, reflection effect and visual effect as the technical indicators according to different room types, including the functions of importing CAD drawings to dynamically create houses, automatically calculating scene layout, and accepting user interaction to change the design. In the specific implementation process, through the improved LOD technology, that is, according to the characteristics of VR scene, focus on rendering objects in the first person perspective, for all kinds of actors out of the perspective of high LOD optimization, PBR Technology (Physics Based Rendering Technology), combined pre-rendering and real-time Rendering can not only avoid the influence of the first person perspective effect caused by the general global rendering of the scene, but also avoid the high cost of computer performance caused by the rendering process.
Pathless Scala: a calculus for the rest of Scala

Recent work on the DOT calculus successfully put core aspects of Scala on a sound foundation, but subtyping in DOT is structural and therefore not easily amenable to studying the parts of Scala that are deeply tied to its nominal subtyping system. On the other hand, the Featherweight Java calculus has proven to be a great basis for studying many aspects of Java and Java-like languages. Continuing this tradition, we present Pathless Scala: an extension of Featherweight Generic Java that closely models multiple inheritance and intersection types as they exist in the Scala language today. We define the semantics of Pathless Scala by erasing it to a simpler calculus in a way that closely models how Scala is compiled to Java bytecode in practice. More than a one-off, we believe that this calculus could be extended to describe many more features of Scala, although reconciling it with DOT remains an open problem.
Construction of Logistics Public Information Platform of Port Logistics Network based on Artificial Intelligence

Through the comprehensive application of NIT, CT and communication technology, an open PO information management platform is constructed to improve the quality and level of PO information management. Therefore, this paper discusses the feasibility, design and implementation of logistics public information platform based on PO network. The results show that the biggest change of SC service profit model is 44%, and the smallest change of after-sales service profit model is 1.2 billion yuan.
MANAGING THE PROCESS OF PROFESSIONAL DEVELOPMENT OF YOUNG TEACHERS

The article discusses the importance and relevance of managing the process of professional development of young teachers in the country. The process of adapting a teacher to the profession and becoming a specialist is characterized as one of the most important stages. The purpose of the study was to create motivation to stay in school and improve their professional skills by managing an important moment in the life of a teacher. In accordance with the research topic, the analysis of the scientific literature was carried out. In order to identify the difficulties that a graduate of a pedagogical institution faces when employmed at school, a survey was conducted and it was found out what motives can serve as a reason for further development in an educational organization as a teacher. Based on the collected data, methods and techniques for managing the process of professional development of young teachers on the basis of personnel management were proposed. The specific ways of replenishment of school personnel by young specialists, specific features and ways of training qualified, educated specialists-teachers are shown. Specific methods and ways of strengthening their professional skills and experience have been identified. The methods of educating young specialists as professionally seasoned, real teachers capable of management are proposed.
Application Barrier-Free Deep Reinforcement Learning-Based Navigation in Mapless Environment

This paper presents a case study of a novel model-free training framework for indoor mapless navigation without any prior expert demonstrations. A model-free Deep Reinforcement Learning (DRL) approach is implemented, which is capable of good sample efficiency and convergence rate. The navigation policy learns directly end-to-end from raw lidar information in a virtual environment and can be deployed directly on a real mobile robot platform. Without any map or self-location information, the robot is only provided with raw lidar information and the relative position to the target calculated by the inertial measurement unit and encoder which can achieve a large number of success of collision-free navigation tasks during the real-world test. We present a thorough evaluation of different input information, different reward functions, different DRL algorithm structures and different training environments both in simulation and real world. The results show that with appropriate reward function, DRL algorithm structures and environments, the trained agent is able to reach the target without collision. In addition, the policy deployed from virtual to the real robot performs better after processing the input information. ...
MACNet: A Lightweight Face Detector

In order to meet the requirements of face detection between the accuracy and efficiency, a lightweight face detector named MAC-Net, based on fusing context information and extracting feature channel attention is proposed. This network has designed a new block to construct a network. In this new block, we add the context information module and feature channel attention module to enrich features. The image feature pyramid is used to achieve multi-scale features learning. By adding a facial landmark module, the quality of face detection is inproved. Two models of different sizes are trained and tested on datasets such as AFW, FDDB, and PASCAL face. Experiments show that our lightweight algorithms can reach state-of-the-art with an accuracy of 99.28%, 97.0%, and 97.91% on these three datasets respectively. Our lightweight MAC-Net can run at 18 FPS on a CPU device for VGA-resolution images.
Double-factor Fading Memory Kalman Filter for Compensating Model Uncertainty

In order to solve the divergence problem of Kalman filter caused by the model uncertainty in the dynamic system, this paper proposes an adaptive filter called double-factor fading memory Kalman filter. In fact, the recursive formulas of state estimations in Kalman filter can be derived by minimizing a quadratic cost function. To compensate for the different degrees of model uncertainty in the motion equation and the measurement equation, this paper introduces two fading factors into the weight matrices of the lost function. The values of fading factors can be calculated by iteratively solving the equations that assume the theoretic covariance matrices of innovations and residuals equals to the sample covariance matrices of innovations and residuals respectively, and then state estimations expressed by fading factors can be obtained.
RecycleNet

Text recognition is the key pillar for many real-world multimedia applications. Existing text recognition approaches focus on recognizing isolated instances, whose text fields are visually separated and have no interference with each other. Moreover, these approaches cannot handle overlapped instances that often appear in sheets like invoices, receipts and math exercises, where printed templates are generated beforehand and extra contents are added afterward on existing texts. In this paper, we aim to tackle this problem by proposing RecycleNet, which automatically extracts and reconstructs overlapped instances by fully recycling the intersecting pixels that used to be obstacles for recognition. RecycleNet parallels to existing recognition systems, and serves as a plug-and-play module to boost recognition performance with zero-effort. We also released an OverlapText-500 dataset, which helps to boost the design of better overlapped text recovery and recognition solutions.
Error Compensation Analysis of Three-Axis Magnetic Sensor Based on M-estimation

In the error compensation algorithm of the three-axis magnetic sensor, an M-estimation supplementary strategy is introduced to solve the problem that the abnormal sampling point will affect the results of geomagnetic data correction. M-estimation calculates the weight of each sampling point through the selected cost function, thereby reducing the influence of outliers. The Huber, Hampel and Tukey cost functions are selected to complete the error compensation algorithm of three-axis magnetic sensor based on M-estimation, and these three methods are compared in terms of mean square error, average error and running time through simulation. The simulation results show that the three methods are similar in the case of small deviation and small number of outliers. And the compensation effect based on the Tukey cost function is the best when the outliers are offset large and the number is large.
A Unified Platform for Information Extraction with Two-Stage Process

The multi-format Information Extraction (IE) task in Language and Intelligence Challenge 2021 (LIC2021) consists of three subtasks: Relation Extraction (RE), Sentence-level Event Extraction (SentEE) and Document-level Event Extraction (DocEE). Deep learning methods have made great progress in each subtask these years. However, most of them cannot solve these subtasks by a unified platform. In this paper, we develop a unified neural model with two-stage process, which adopt the Enhanced NER module in stage one to obtained the ELEMENTs and corresponding LABELs. In stage two, we designed the customized manoeuvres to solve challenges in different subtasks. The submission shows that our model achieves competitive performance, which ranks 3rd on the final leaderboard.
Ordered conditional approximation of Potts models

Potts models, which can be used to analyze dependent observations on a lattice, have seen widespread application in a variety of areas, including statistical mechanics, neuroscience, and quantum computing. To address the intractability of Potts likelihoods for large spatial fields, we propose fast ordered conditional approximations that enable rapid inference for observed and hidden Potts models. Our methods can be used to directly obtain samples from the approximate joint distribution of an entire Potts field. The computational complexity of our approximation methods is linear in the number of spatial locations; in addition, some of the necessary computations are naturally parallel. We illustrate the advantages of our approach using simulated data and a satellite image.
Control of Panoramic Video Flow for UAV

With the development of Unmanned Aerial Vehicle (UAV) applications, UA V communication has become one of hot issues. Aiming at decreasing bandwidth consumption for UAV, the control of UAV panoramic video is proposed in this paper. The method is based on the dynamic frame rate techniques, which adaptively remove the redundant B frames from video stream according to the attention tracking. Preliminary experiments show that our method can obtain 15%-25% of bandwidth saving, compared with the traditional panoramic video stream.
Automated Student Attendance Management System Using Face Recognition

Automated face reputation (AFR) technologies have made many improvements inside the converting international. Clever Attendance using actual-Time Face popularity is an actual-international solution that comes with the day after day activities of handling pupil attendance machine. Face recognition-based attendance machine is a process of spotting the student's face for taking attendance through using face bio-metrics primarily based on high-definition display video and different facts generation. In my face recognition assignment, a computer system can be able to find and apprehend human faces speedy and precisely in pictures or movies which might be being captured via a surveillance digital camera. Several algorithms and techniques were advanced for enhancing the performance of face reputation but the idea to be carried out here is deep getting to know. It allows in the conversion of the frames of the video into photos so that the face of the student can be effortlessly recognized for his or her attendance so that the attendance database can be effortlessly pondered automatically. Inside the ever-changing world, automatic face reputation (AFR) systems have finished numerous advancements. Smart Attendance with real-Time Face popularity is a sensible alternative for handling student attendance systems on a day-by-day foundation. Face reputation-primarily based attendance device is a way of recognizing a student's face for the motive of taking attendance, primarily based on high-resolution facial biometrics.
Digital Water Marking for ROI and RONI with Break Through Visibility Parameter

Due to fast growth of computer network, multimedia technology, digital media things can easily duplicate and distribute. Internet is used for the applications like telemedicine, online banking etc..Telemedicine is the communication transferring medical images from one place to other. This is helpful for the patience those who are staying in remote areas and easy to communicate with specialist from faraway places. But while transmitting if any changes in medical images whether intentionally or accidentally then it is effects to health or life of patient. The Main intention of secure medical images by applying digital watermarking on ROI and RONI by zero watermarking and reversible water marking and finding invisibility by applying parameters.