Reviewerly/exHarmony huggingface dataset:
Neural mechanism of visual paralanguage based on computer network

Various communication-based computer network technologies have penetrated the lives of people because of the widespread use of smartphones and the rapid development of computer network technology. In social communication, people can not only transmit information through various language messages (such as speech, vocabulary, and sentences), but also transmit emotions and attitudes through Paralanguage Cues (such as tones, body posture and facial expression). However, the lack of expression, tone, gestures, and other non-verbal symbols in online communication will affect the transmission of information. To solve this problem, various expressions of Internet emoticons have emerged, such as emojis and stickers. In order to explore the differences between paralanguage and language in Internet communication, we reviewed the literature findings based on ERP technology （i.e., event related potentials technology can provide a more accurate time-window for the brain to respond to events in the last 20 years and found that the early neural mechanisms (activation of attention and configuration recognition around 100-200milliseconds activated in cerebral cortex) of paralanguage based on Internet network are generally similar to those induced by language. In addition, the Neural mechanism of visual paralanguage is like language, also can configuration cognition (around 200 milliseconds), and semantic activation in the middle stage (around 400 milliseconds) as well as reanalysis of syntactic processing in the later stage (around 600 milliseconds).
Abnormal trajectory recognition algorithm of networked vehicles based on improved particle swarm optimization

To improve the recognition hit rate of the abnormal trajectory of networked vehicles, reduce the actual recognition difference, and expand the corresponding trajectory coverage, this paper studies the abnormal trajectory recognition algorithm of networked vehicles based on improved particle swarm optimization. In this paper, the abnormal trajectory conversion is preprocessed, and the calculation matrix of unit recognition is designed, and then the abnormal trajectory recognition algorithm model under the improved particle swarm optimization is constructed. Finally, the design of recognition algorithm is realized by using the dimension reduction processing of abnormal data. The test results show that compared with the traditional tracking anomaly recognition algorithm test group and the traditional multi-dimensional feature anomaly recognition test group, the improved particle swarm recognition algorithm test group designed in this paper has a relatively high recognition hit rate, which indicates that the algorithm has a small error and high accuracy in the process of practical application and has practical application value.
Mimic adjudication scheme based on homomorphic encryption

In the information times, with the rapid development of network technology, emerging technologies such as big data and artificial intelligence have gradually entered daily life, but various network security risks have also followed. In recent years, as a new security strategy, mimic defense technology has been a new force suddenly rises, providing a new idea and direction for protecting network security. In the actual application scenario, the resource consumption of the mimic defense service is large, and it is generally deployed and provided by the cloud service provider. Although the mimic defense can play a good role in defending against external attacks, the data transmission in the current system is based on plaintext, which cannot guarantee the privacy of user data. Homomorphic encryption technology, as an encryption supporting data ciphertext operation, can protect data privacy on the premise of completing data operation requirements. As an important field of homomorphic technology, homomorphic hash technology has been widely used in cloud storage scenarios. Aiming at the problem that the cloud service mimic defense system cannot protect data privacy, this paper introduces homomorphic encryption technology into the mimic defense system to protect the privacy of user data. The scheme proposed in this paper is based on the mimic defense architecture, which optimizes the adjudication process of the adjudication module through the homomorphic encryption technology, make full use of the characteristics of homomorphic operation, protects the privacy of user data, and improves the system security. Moreover, this paper builds a mimic routing platform for experimental analysis. The experimental results show that the malicious attacker can see the user’s private information under the traditional defense mechanism. However, after homomorphic encryption optimization, each user authentication is still less than 1 millisecond, and the user information data become invisible to malicious attackers.
Face tracking using Kanade-Lucas-Tomasi algorithm

Face recognition and tracking is a new topic of image analysis and computer vision systems study. We propose a face tracking method based on feature point tracking in this research. Face detection and face tracking are the two aspects of the proposed face tracking system. Before utilizing the KLT technique to track faces, use the Viola-Jones face detection algorithm to detect all faces in an image or video series. After detecting face objects in consecutive frames, the KLT algorithm tracks them and maintains long-term monitoring as faces come in and out. According to the experiments, the Viola-Jones algorithm is effective at detecting frontal faces. The KLT algorithm was put to the test by tracking human faces in a variety of films shot by a laptop camera. The KLT application is successful, and even if the identified face turns left or right, the face may be tracked.
Integration and optimization of higher mathematics teaching resources based on collaborative filtering algorithm

As a basic subject in cuniversities, advanced mathematics education has become more and more important, and with the rapid improvement of message technique, the application of message technique in advanced mathematics education has become a general trend. The traditional higher mathematics education methods are outdated, the education efficiency is low, and the graphics display effect is not ideal. With the improvement of science and technique and the advent of the modern electrical age, education methods need to advance with the times. It is particularly important to explore a education method that can organically combine the traditional mathematics education mode with modern message technique, unify the practicability and feasibility of education and education, which can not only facilitate teachers' education but also help students to improve their learning effects. This paper is based on the collaborative filtering algorithm. In the personalized recommendation system, more core is the recommendation algorithm. Collaborative filtering is the most familiar and widely used recommendation algorithm.
Phase unwrapping method for 3D measurement based on multi-frequency asynchronous phase shift

Aiming at the problems that 3D optical measurement is easy to be affected by peripheral noise, difficult to calculate relative height and low measurement accuracy, a multi-frequency asynchronous phase shift algorithm based on time series changes was proposed. In order to improve the measurement accuracy and anti-interference ability of the system, five kinds of frequency grating projections were adopted. In order to improve the efficiency of phase undoing, the asynchronous phase shift method was used to extract the phase principal value. In view of the multi-frequency heterodyne phase solution method is prone to produce phase jump error, a phase jump error correction method based on threshold discrimination is proposed. The experimental results show that the improved algorithm has better measurement accuracy than the original algorithm, and compared with the traditional multi-frequency heterodyne method, it has strong anti-interference ability and higher measurement accuracy.
Construction of interference and noise model for high-frequency channels based on alpha-stable distribution

Noise and interference are the main factors affecting high-frequency channels, and it is important to generate noise and interference that match the actual high-frequency channel characteristics when modeling high-frequency channels. To address the noise characteristics in wide-band high-frequency channels, we construct an Alpha-Stable distribution-based noise and interference model for wide-band high-frequency channels based on the analysis of Alpha-Stable distribution theory. Simulation results show that our model not only generates noise that is more consistent with the actual wide-band high-frequency channel but also effectively reduces the complexity of the current state-of-the-art wide-band high-frequency channel noise model. In order to better simulate the noise characteristics of the wide-band high-frequency channel, swept-frequency interference is added to our model, and the simulation results show that the wide-band high-frequency channel model with swept-frequency interference can better simulate the noise characteristics of the real wide-band high-frequency channel.
I politici e l’immigrazione su Facebook: come si (de)legittima il discorso migratorio durante l’emergenza sanitaria

In this article we analyse how the immigration issue is narrated during the Covid-19 outbreak by several Italian political actors. We select Facebook as the main digital arena of political communication in the Italian public sphere. Quantitative analysis and Critical Discourse Analysis have been applied to politicians’ posts aiming at identifying the linguistic strategies that contribute to instrumentalizing the emergency and aim to reinforce the politicization of the issue. Findings suggest that the main discursive strategies used by politicians do not only include migrants as a danger for the spread of the virus, but the migratory narration is systematically organized on negative campaigning blaming political opponents. The contribution helps to reveal how the anti-migration discourse is reproduced during the Covid-19 outbreak and how the politicization of the migration serves as a context for the normalization of migrant’s exclusion.
A knowledge graph-based Q and A system for agricultural planting technology

In today's agricultural information age, knowledge services for farmers cannot be provided without the Internet, and with the expansion of information data on the Internet, the field of agricultural planting technology has also generated a huge amount of digital information resources, among which professional knowledge presents redundancy, sparseness and diversity, requiring new means and methods to collect and store these big data and knowledge as well as to conduct knowledge Q&A in a timely manner. The aim of this paper is to study a question and answer system for agricultural cultivation technology based on knowledge graphs. Firstly, the knowledge graph model of agricultural cultivation technology is designed, and the existing deep text matching algorithm DSSM algorithm is selected. Based on the DSSM algorithm, a Bert+DSSM algorithm is proposed to improve the performance of the algorithm. The text matching algorithm, question and answer corpus and crop planting knowledge map are applied to the system to build an agricultural planting technology question and answer system, and the experimental results show that the relevant knowledge question and answer function of the system has a high accuracy rate.
Managing cyber-risk in offender populations

Technology dramatically increases the effects of criminal behavior and therefore poses a unique risk to the community. An offender with a computer and Internet access can easily victimize countless individuals and even whole communities, without regard to geographic or jurisdictional limitations. A computer, even in the hands of a juvenile, can cause significant harm. Unfortunately, the risks posed by cyber-offenders does not end with their arrest and even conviction. At the same time computer and Internet access is needed for offenders to successfully navigate in today's society. Managing cyber-risk is increasingly becoming a reality for prisons as well as community corrections officers supervising defendants/offenders on pretrial supervision, probation, parole, or supervised release. This chapter will explore the history and the challenges in managing cyber-risk in offender populations.
The Role of ‘I Don’t Know’ Option in the Vocabulary Size Test

The current study aims at examining whether an “I don’t know” option affects EFL learners’ guessing behavior in the Vocabulary Size Test (VST) and whether the effects of the option differ depending upon their vocabulary size levels. For this study, 138 college students were randomly assigned to two groups of the VST with or without an “I don’t know” option, in which they were again divided into two groups depending upon their vocabulary size levels (high and low). They were asked to take the VST test and then the meaning recall test a weak later. The findings were, in general, that the test-takers’ guessing behaviors in the VST were influenced by vocabulary size level, but not by their partial knowledge. Specifically, the “I don’t know” option deterred the participants, high-level learners in particular, from guessing the correct answers in the test, whereas it failed to wield influence on the use of partial knowledge in the test, irrespective their vocabulary size level. Discussion and implications are followed. (Chungnam National University)
The Analysis of Income Gap in Lower Middle-Income Countries

Sustainable economic growth is the goal of every country in the world. However, the economic growth that occurs is not in line with the welfare of the population in the lower middle-income countries. The high level of income inequality between the upper and lower classes indicates that there are problems that need to be resolved. This study aims to determine the direction and magnitude of the influence of trade openness, tax revenue, digital economy, corruption perception index, and government spending on the level of income inequality in six countries classified as lower Middle-Income countries in 2017-2021, namely (Bolivia, El Salvador, Honduras, Indonesia, Kyrgyzstan, and Ukraine) using panel data regression. The results with the Fixed Effects approach show that trade openness and the digital economy have a positive effect on the income gap, while government spending has a negative effect on the income gap. Meanwhile, tax expenditures and the corruption perception index have no effect on the income gap. Based on the results of this study, the government in each country should be able to pay attention to the development and support the economic facilities of the people in the lower middle class with various assistances that can directly increase their income.
THE IMPLEMENTATION OF E-MODULE IN SEMANTIC CLASS AT ENGLISH STUDY PROGRAM: STUDENTS’ PERCEPTION

E-module as a learning tool is a new experience for English students. Based on the interviewing results of monitoring and information to students, it is found that the e-module has not been yet implemented or created other than in class 1 Semantic. Therefore, it is important to know how they perceive the implementation of an electronic module in the Semantic class in the English Study Program. The research in this paper aims to investigate English students’ perception of implementing the E-module in Semantic classes at the English Study Program. The research design is mixed methods (quantitative and qualitative). The data were gathered through a questionnaire and interview. Cluster random sampling was used to select the research sample, which involved 40 students. Ten students were interviewed for the qualitative data. The findings indicate that the students had a high perception of the implementation of e-module in the Semantic class. They also expect that e-module can be further developed and applied for other courses because most of them perceive e-module as a fun, complete, and total innovation in learning media that can facilitate their learning anytime and anywhere.
Reflections on Huang Yanpei’s Education View in the New Era

One hundred years ago, Huang Yanpei put forward important educational ideas such as “the use of hands and brains,” “the unity of doing and learning,” and “the power of both hands,” which have guided the development of vocational education in China. At present, China is in a critical period of economic transformation and technological upgrading, which results in higher requirements for vocational education. It is the mission of vocational education to cultivate excellent and skilled talents. This paper examines Huang Yanpei’s views on vocational education and puts forward six targeted recommendations. The research results of this paper can provide references for the formulation of education policies.
INNOVATIVE METHODS OF EDUCATION IN SOCIAL ENTREPRENEURSHIP

This paper examines the value of dynamic innovative pedagogies in the contextof social entrepreneurship education.The author explores the literature on innovative pedagogy and social entrepreneurship education, as well as the educational program of the Social Entrepreneur School IBS of Fil.Dr. Jan-U.Sandal Institute (Norway), which focuses on training social entrepreneurs through an experiential, problem-based learning approach.
An Empirical Study on Machine Learning for Enterprise Cloud Computing Service Management

A machine learning method is proposed to aim at the problems of large data processing, complex data, and limited system resources in cloud computing service management. First, multi-department analysis is carried out on cloud service management data, and relevant management data is standardized to form a standardized management data collection; Machine learning is used to classify, mine, and extract service management data, and corresponding management measures are taken promptly to improve the management level of cloud computing services. MATLAB simulation shows that machine learning can improve the level of cloud computing service management, simplify the process of cloud computing service management, shorten the management time of cloud computing services, and meet the actual needs of service management.
Recent Research Trends of CAVE-based Virtual Reality

The CAVE, which was first developed in 1992 at the University of Illinois Electronic Visualization Laboratory in Chicago, is named by using a recursive abbreviation for Cave Automatic Virtual Environment, and refers to an immersive virtual reality environment created by projecting virtual scenes onto the walls of its cube-shaped room.Initially, the walls of CAVE were composed of projection screens, but with the development of display technology, flat panel displays are employed recently.In this environment, a computer-generated virtual three-dimensional space is displayed to the user through the walls.Due to wearing special glasses equipped with a tracking device, the virtual environment shown on the wall displays the three-dimensional scene interactively according to the movement of the user.Moreover, the speakers placed at various angles in the CAVE provide a three-dimensional sound, assisting the three-dimensional presence effectively.In this study, we review the history and recent research trends of CAVE, and explore its future.
About This Issue

March 24 2023 About This Issue Douglas Keislar Douglas Keislar Search for other works by this author on: This Site Google Scholar Author and Article Information Douglas Keislar Online ISSN: 1531-5169 Print ISSN: 0148-9267 © 2023 Massachusetts Institute of Technology2023Massachusetts Institute of Technology Computer Music Journal 1. https://doi.org/10.1162/comj_e_00640 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Douglas Keislar; About This Issue. Computer Music Journal 2023; doi: https://doi.org/10.1162/comj_e_00640 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsComputer Music Journal Search Advanced Search This content is only available as a PDF. © 2023 Massachusetts Institute of Technology2023Massachusetts Institute of Technology Article PDF first page preview Close Modal You do not currently have access to this content.
New Integral Distinguishers On Permutation Of Whirlpool

Abstract Whirlpool is a hash function that has been standardized by ISO/IEC. In this paper, we develop a new type of distinguishing property for its underlying permutation $ W $. Division property proposed by Todo at EUROCRYPT 2015 was initially used in the integral cryptanalysis of symmetric-key algorithms. This work for the first time utilizes the MILP method to search for the integral distinguishers of $ W $ in both the forward and backward directions while concentrating on word-based division property. Under the known-key model, the fact that the permutation used in the hash function does not depend on any secret parameters allows the previous properties to be exploited from the middle, i.e. from an intermediate internal state. Therefore, we apply the inside-out strategy which is the essential step in the zero-sum property to connect the trails in opposite directions. Consequently, we obtain new distinguishers up to full rounds for the $ W $. To further reduce the complexity of the integral distinguishers, we add one round in the middle with the help of subspace trails. Finally, we succeed in extending the length and improving the complexity of the integral distinguishers. To the best of our knowledge, all the results in this paper are competitive with the previous work in both computational cost and memory complexity. It is worth mentioning that the methods presented in this paper are applicable to a broad class of hash functions.
Triple Color Image Encryption Using Hybrid Digital/Optical Scheme Supported by High-Order Chaos

Image security has attracted increasing interest, wherein image encryption is an effective and direct method that can be implemented using digital, optical, or hybrid techniques. The challenge is how to design a high-speed, and high-security level multiple color image encryption scheme which makes use of advanced techniques such as chaotic system and deep learning. This issue is addressed in this paper where a nine-dimensional (9D) chaotic-based Hybrid Digital/Optical Encryption (HDOE) scheme is proposed for triple color images. The scheme consists of cascading digital and optical encryption parts controlled separately by the chaotic sequences. The nine chaotic sequences are grouped into three sets, and each set is responsible for the encryption of one of the RGB channels independently. The digital part uses fusion, XOR operation, and scrambling. The optical part uses two independent chaotic phase masks in the optical Fourier transforms domain. A Denoising Convolution Neural Network (DnCNN) is designed to assist the robustness of the decrypted images against Gaussian noise. The simulation results reveal that the proposed triple-image HDOE scheme offers entropy of 7.9991, 7.9987, and 7.9991 bits for R, G, and B channels, respectively,&nbsp; and infinite Peak Signal-to-Noise Ratio (PSNR) for the decrypted images.