Reviewerly/exHarmony huggingface dataset:
Deep Mixture of Adversarial Autoencoders Clustering Network

As one of the most fundamental technologies in machine learning, unsupervised clustering methods have been vastly promoted due to the rapid development of deep learning. A widely utilized assumption is that the data is a set of low-dimensional manifolds, thus the key challenge for clustering methods is to ensure the potential features be completely separated. In this paper, we propose a mixture of adversarial autoencoders clustering (MAAE) network to solve the above problem. The data of each cluster is represented by one adversarial autoencoder. By introducing the adversarial information, the aggregated posterior of the hidden code vector of the autoencoder can better match with the prior distribution which is constructed based on the generalized Gaussian mixture model. So the latent features embedded in data can be well separated. The clustering network jointly learns the nonlinear data representation and the collection of adversarial autoencoders. The clustering results are obtained by minimizing the reconstruction loss of the mixture of adversarial autoencoders network. We perform experiments on popular standard datasets, and the results show that the proposed algorithm is effective in both clustering and feature separation.
Multi-criteria Confidence Evaluation for Robust Visual Tracking

To solve the challenge of visual tracking in complex environment, a multi-criteria confidence evaluation strategy is proposed in this paper. Three kinds of criteria are introduced to comprehensively evaluate and analyze the confidence of those tracking results obtained by adaptive spatially-regularized correlation filters (ASRCF). The evaluation result is further utilized to establish the sample management and template updating mechanism, which aims to obtain the best template in the tracking process. The obtained template is used to update both scale filters and position filters in ASRCF. Experimental results on OTB100 and TC128 verify that the proposed method is more robustness compared with other similar algorithms.
Multi-scale Convolutional Neural Networks-Based Severity Prediction

The software defect report is an important part of the software repair process, and its severity attribute is very important. According to the description of the severity attribute of the software defect report, it can assist the developer to solve the defects described in the important defect report within a limited time. But manually assessing the severity is a tedious task. In this paper, we use a multi-scale convolutional neural network algorithm to automatically predict the severity attribute of defect reports. First, we use natural language processing technology to preprocess the defect report. Then create a vector for the defect report. Finally, the constructed vector input classifier based on multi-scale convolutional neural network is used to predict the severity. By comparing with other severity prediction algorithms, it can be found that our method improves the accuracy of the severity prediction of defect reports.
TMD-FS: Improving Few-Shot Object Detection with Transformer Multi-modal Directing

Few-shot object detection (FSOD) is a vital and challenging task in which the aim is to detect unseen object classes with a few annotated samples. However, the discriminative semantic information existing in the new category is not well represented in most existing approaches. To address this issue, we propose a new few-shot object detection model named TMD-FS with Transformer multi-model directing, where the lost discriminative information is mined by adapting multi-modal semantic alignment. Specifically, we transfer the multi-model information into a mixed sequence and map the visual and semantic information into the embedding space. Moreover, we propose a Semantic Visual Transformer (SVT) module to incorporate and align the visual and semantic embedding. Finally, the distance in terms of the visual and semantic embedding is minimized on the basis of the attention loss. Experimental results demonstrate that the performance of the model significantly with few samples. In addition, it achieves state-of-the-art performance when the amount of samples increases.
Low-Level Developer Tools and Productivity

Developer productivity is a highly valued research topic for academics and industry professionals alike. Reducing the amount of time spent on tasks like foraging for information, navigating code, and debugging can provide substantial benefits to developers of all skill levels. There are many works that explore this topic in-depth (e.g., [1]–[3]). One of these studies, for example, showed that 50% of programmers' time was spent searching for information [2]. Additionally, there are numerous tools that have been built to address these pain points and boost software developer productivity. These tools often provide improved code editor interfaces (e.g., Patchworks [4]) or debugging tools for high-level languages (e.g., Gzoltar [5]).
Adaptive Robotic Path Planning Using ML

Currently, the trail arranging issue is one among the preeminent investigated points in self-governing mechanical technology. That is the reason tracking down a safe way during a jumbled climate for a portable robot is a pivotal necessity for the achievement of any such versatile robot project. In this work, a created calculation upheld free sections and a defining moment technique for tackling the issue of robot way arranging during a static climate is introduced. The point of the defining moment approach is to scan a protected way for the portable robot, to make the robot move from a beginning situation to an objective situation without hitting snags. This proposed calculation handles two unique goals which are the path wellbeing and accordingly the way length. Also, a solid control law which is named sliding mode control is proposed to direct the adjustment of a self-governing portable robot to follow an ideal direction. At last, reproduction results show that the created approach might be a decent choice to get the satisfactory way and exhibit the productivity of the proposed control law for vigorous following of the portable robot.
Processing Algorithm of Irregular Table Image in Tobacco Package Based on Dual-coding Difference of Gaussians Method

Objectives: Currently, in a large number of print-out report documents from tobacco package, there exist irregular phenomena such as discontinuous vertical lines, misplaced frame lines and multi-page tables. Thus, the existing table recognition algorithm cannot be adopted to perform digital identification. In order to solve this problem, this paper proposes a table image processing algorithm based on the dual-coding difference of Gaussians iterative clustering. Firstly, the method of local regional sub-block is used to the skew correction threshold to conduct image correction. Secondly, the corrected images are coded by rows and columns, and 2D image features are transformed into 1D image features. Thirdly, the Gaussian differenced operation is adopted to obtain effective characteristic matrices that are stable and easily distinguishable. Then the iterative clustering analysis is performed to obtain the feature values of effective frame lines. Fourthly, after finishing the tasks, such as the table positioning, inner structure reconstruction, and text information identification, the dichotomy judgmentsof the integrity of multi-page tablesare realized according to the local pixel features. Finally, the text information inside the local regions and the reconstructed regions are merged, and the digital reproduction of the multi-page tables is realized. To validate the effectiveness of the proposed algorithm, an experiment in the sample set containing 12,840 table images with different resolutionsis carried out. The average detection accuracies of table positioning, table cell reconstructionand multi-page incompleteness are 98.95%, 99.80%, and 95.85%, respectively. The experimental results show that the proposed algorithm is simple and effective, and can accomplish the digital reproduction of irregular tables.
FP Growth Application for the Prediction of Terrorist Attacks

The present work partakes in the contemporary efforts to investigate AI applications to the study of terrorism. First, we introduce the contemporary debate on AI and security and review the literature presenting machine learning applications to counter-terrorisms. Second, we illustrate the potential of the Global Terrorism Database (GTD) and FP Growth algorithm. Third, using GTD data, we develop an in-depth predictive model for of terrorist attacks applying the FP Growth algorithm. Our study focuses on 4 core rules: ATTACK TYPE, COUNTRY, GROUP NAME and TARGET TYPE, and their sub-groups. The algorithm reveals hidden association rules and patterns prevision, predicting new terrorist attacks at an almost certain probability level (80–100%).
Flight Test Intelligent Mission Planning System Based on Big Data Platform

In order to improve the capability of aircraft flight test data, this paper proposes an intelligent flight test mission planning system which is based on a big data platform, combined with artificial intelligence algorithms, modeling and simulation technologies. This system can meet the flight test requirements of the new aircraft, and complete the test flight syllabus, decompose and integrate the test flight task, make the test flight plan for the specific sortie and verify the simulation. Finally, it can formulate a scientific and efficient test flight plan. The intelligence of the system runs through the entire workflow, including the design, validation and finalization of the flight test plan. This article will introduce the main principles and methods of this system.
Evaluating the validity of music as an authentication key

In this paper, we investigate the validity of music as an authentication key. Recently, it is often necessary to use a personal authentication system when we would like to use web sites and personalized systems. We often need to memorize a password to use them. Although a long password is preferable from system side, it is not preferable from user side because it is hard to remember it. Although a short password is preferable from user side, it is not preferable from system side because it is easy to crack it. We aim to solve this problem by using music as a key for a personal authentication system. We verified the usefulness of music-based personal authentication through some experiments.
PoI: Proof of Identity and PoDI: Proof of Data Integrity for Secure Data Deduplication in the Cloud

Cloud storage is the decisive service for the organization, hospitals etc., for storing huge amount of data within their budget. But, Cloud Service Providers (CSPs) are facing the major problem of duplicate and redundant data being accumulated by Data Owners (DO) in the cloud storages. Deduplication is one of the major technique to solve the above problem but faces the major drawback in the form of security flaws. Secure deduplication is a very promising solution proposed by the researchers for addressing this issue. In this paper, problems like revocation of DOs, brute force attack and duplicate faking attack are addressed as part of secure deduplication. The proposed approach is having two protocols, namely, Proof of Identity (PoI) and Proof of Data Integrity (PoDI) which solves the above-mentioned problems. The proposed approach improves the efficiency of secure deduplication, enhances data privacy and addresses Data Owner (DO) revocation problem.
Design and analysis of a wheel-track composite stair climbing wheelchaire

research-article Design and analysis of a wheel-track composite stair climbing wheelchaire Share on Authors: Tian-Syung Lan Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Zhi-Wei Zhang Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Xuan-Jun Dai Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Wei-Yuan Shi Jinling Institute of Technology, China Jinling Institute of Technology, ChinaView Profile Authors Info & Claims ICIIP 2021: 2021 6th International Conference on Intelligent Information ProcessingJuly 2021 Pages 162–166https://doi.org/10.1145/3480571.3480600Online:29 October 2021Publication History 0citation17DownloadsMetricsTotal Citations0Total Downloads17Last 12 Months17Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research Status and Development of Feeding Device of Rubber Extruder

research-article Research Status and Development of Feeding Device of Rubber Extruder Share on Authors: Faguo Huang Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Haifeng Zhou Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Jiafang Pan Guilin University of Technology, China Guilin University of Technology, ChinaView Profile Authors Info & Claims ICIIP 2021: 2021 6th International Conference on Intelligent Information ProcessingJuly 2021 Pages 197–201https://doi.org/10.1145/3480571.3480607Online:29 October 2021Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Improving Unsupervised Learning of Monocular Depth and Ego-Motion via Stereo Network

Unsupervised learning of monocular depth and ego-motion is a challenging task, which uses the photometric loss as the supervision to train the networks. Although existing unsupervised methods can get rid of expensive annotations, they are still limited in estimation accuracy. In this paper, we explore the use of stereo depth network for improving the performance of monocular depth estimation and ego-motion estimation. To this end, we propose a novel two-stage unsupervised learning framework. Specifically, in the first stage, we jointly train the stereo depth network and ego-motion network in an unsupervised manner, in order to get a more accurate ego-motion estimator. Then we transfer and freeze the ego-motion network to the second stage, and only train the monocular depth network in this stage. Moreover, we propose a dense feature fusion module to further enhance the expressive ability of monocular depth network without increasing the number of network parameters. Extensive experiments on the KITTI and Make3D datasets demonstrate that our proposed method achieves superior performance on both monocular depth estimation and ego-motion estimation to existing unsupervised methods.
Designing for Creative Making in the Everyday Environment

The development of communication and information technology has complicated the composition of space and place in the modern era, impacting human cognition and information interaction. To capture the changing nature of the information environment, I investigate information practice of arts and crafts hobbyists and their interaction with space and place. My dissertation project is grounded upon the literature on space and place in the broad information science field, regarding space and place both as meaningful social products that serve more than passive containers. Using diary studies and individual interviews, I study the adult hobbyist community without limiting the scope of discussion on where making may take place. In particular, I focus on the character of everyday space—a space that is not dedicated to support making—and how people approach and adapt to it during the actual making process. My goal is to gain a holistic understanding of spatiality by probing its influence on creative making. The findings are expected to shed new light on the design of computing technology to afford the making practice in everyday life.
Importance of Cyberlaw

Cybercrime is ever increasing. There is a need for a legal system that can address various cybercrimes taking place. Without any legal framework in place, it is difficult to gain people’s trust on a particular system like the usage of the Internet or so-called cybersociety. In this chapter, various issues related to cybercrime are highlighted. Efforts have been made to present the legal landscape across the world concerning cybercrime in a concise manner. The emerging cybercrimes are highlighted, and the need of an appropriate legal system to handle emerging cybercrimes has also been emphasized.
WITHDRAWN: Modified MD5 for enhancing performance of hashing algorithm in Block-Chain

This article has been withdrawn at the request of the editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
Instruction Pictograms for Interactive Entertainment

With advancements in detection devices and cognitive technology, experience-based displays (exhibits) reacting to user behaviors are gaining popularity. These systems use sensors to recognize users’ upper arm and forearm movements to switch images or the environment in images; however, communicating exactly what user action is required for the display to respond is difficult. To have viewers (users) behave in the manner intended by experience-based displays incorporated in media art and museum exhibits, this study proposes methods for instructing behaviors through pictograms. This study produced still (frozen) and moving pictograms after elaborating on forms and compositions that specify desired behaviors and compared the correct answer rates and average time spent by users in their twenties to seventies to achieve the desired behaviors. For the assessment experiment targeting users in their twenties, the correct answer rates varied among different still images, whereas all moving images successfully navigated >88.5% of users to correctly behave. Pictograms were successful in making middle-aged and older users understand the desired behaviors; however, they were frequently required more time to absorb the instructions. This study introduces use cases incorporating these results into a real-world experience-based system.
Using Agent Based Modeling to Frame Autonomous Vehicle Navigation as Complex Systems

Highly mobile populations can quickly overwhelm an existing urban infrastructure as large numbers of people move into the city. In this paper, we frame autonomous vehicle (AV) simulation by implementing an agent-based model. Our work includes ideas of incorporating cultural differences which emerge during driving; for example, simulating the case when the AVs move through different countries. Our models are decentralized model based on our thinking that this will provide more robust and resilience AVs simulation studies and, in our case leads us to a new concept based on tokens emerges as a novel approach for future AVs. Procedural content generation (PCG) and agent-based modelling concepts are proposed and implemented, to frame AVs simulation as a complex system.
Design of Energy Efficient True Random Number Generator using MUX-Metastable Approach

The design of a meta-stable ring oscillator-based true random number generator (TRNG) is presented in this paper. The proposed circuit is designed in 40nm CMOS technology and simulated in cadence virtuoso simulation environment using a combination of current starved-based ring oscillators (ROs), multiplexer, linear feedback shift register (LFSR), UP/DOWN counter followed by a meta-stable circuit. The current starved inverter-based RO is used in place of the power-hungry comparator to save a significant amount of power dissipation observed in the existing design. The metastable circuit is used for providing randomness in the design. The proposed design had resulted in the generation of approximately 40Mbps speed, the power dissipation of 730μW, and energy efficiency of 18.25pJ/bit when designed at 1.1V. The design is fully validated by NIST 800.22 statistical test suite. The proposed design is thus highly suitable for random number generation.
The Ten Commandments for Responsible Augmented Reality

Augmented reality (AR) brings us into a new phase in the information society, whereby the virtual domain spills over into the physical domain. Precisely because of the direct link between AR and the physical world, the technology raises new ethical and societal issues. In this paper, we highlight the main developments in AR that put pressure on seven public values from our analysis of the scientific literature: privacy, safety, autonomy, balance of power, human dignity, justice, and rights over augmented space. Furthermore, in order to safeguard these public values, we formulate ten rules to shape AR in a socially and ethically responsible way. These rules should form the point of departure for the drafting of governmental policy for a livable hybrid world.
A Study on Classroom Teaching Practice of Art Education Based on Learning Feedback System

With the development of mobile learning theory, the Internet not only enters people’s daily lives, but also provides abundant resources for school teaching activities. In the classroom teaching of art education, there is an important link for feedback mitigation, which is obviously not possible with traditional classroom teaching. Based on this, this article combines the computer network to study the art education classroom practice based on the learning feedback system. This article first conducts research on the current situation of student learning effect feedback system, combined with the current situation of art education classroom teaching time. Analyze the existing problems and shortcomings, and finally combine the learning feedback system to conduct practical research on its application. This article systematically expounds the application of the learning feedback system in the art education classroom. And adopted comparative analysis method, field investigation method and other research forms to carry out experimental research on the theme of this article. Research shows that compared with traditional teaching methods, art education classroom teaching based on the learning feedback system is superior in many aspects, especially in terms of learning efficiency exceeding 15%, which fully embodies the feasibility of the art education course teaching based on the learning feedback system studied in this article.
Sound and Thing

Abstract ‘Sound and Thing’ examines the tensions between the idea of a thing as stable, durable, locatable, and weighty versus the notion of sound as ephemeral, eventual, diffuse, and dematerialized. Considering recordings that focus on capturing the sounds of things (Resonance FM, ‘One Hour As ….’), composers and sound theorists who attempt to pin down the thingness of a sound (David Dunn, Pierre Schaeffer, Salomé Voegelin), and philosophers who articulate the surprisingly complex dynamics of things (Martin Heidegger, Dr. Seuss), this essay offers an uneasy rapprochement between sound and thing. But if sounds, finally, are things, they exhibit an unusual and dynamic thingness, a sonic vitalism, that resists the positivism of our age and of our leading technologies.