Reviewerly/exHarmony huggingface dataset:
A Case Study on the Development and Operation of Competency－Based Liberal Arts Courses in a Modularized Non-Curriculum Program

The purpose of this study is to conduct a case study of the development and operation of the ‘G-MAP blueprint’, a competency-based liberal arts subject that has modularized the non-curricular program of W University. In order to achieve this goal, we conducted an analysis of the operation status of liberal arts and non-curricular programs, focus interviews with professors, and a satisfaction survey of education consumers. The results of this study are as follows. First, this course is absolutely necessary for new students to improve their understanding and adaptability to college life in general. Appropriate guidance is available, and opportunities are provided to prevent dropout of freshmen, Second, a feasible case of developing liberal arts subjects for nurturing dedicated human resources and expanding the number of credits required for graduation from liberal arts is presented even at universities that do not have an organization dedicated to liberal arts education within the university. Third, in order to raise awareness of the importance of liberal arts education at the university level, the scalability of liberal arts education is suggested by sharing practical case studies that can be operated together by professors in charge of liberal arts subjects and professors in charge of majors. Fourth, by introducing the competency-based liberal arts development process and procedure through modularization of the non-curricular program, it presented important basic data for diversifying the operation of university liberal arts education and seeking developmental directions in the future. Fifth, it has important educational significance in that it presents a case in which it is possible to expand the educational performance and effectiveness of liberal arts education through a three-year longitudinal study. Therefore, although this study may have limitations as a case study at a junior college, it is expected that it will be of value to be used as a continuous research data in the future.
Social Networks Fake Detection

Nowadays, social platforms have become part of our day by days life, we are more associated with these platforms, we use OSNs to stay connected to friends, share news, and we also use social media platforms to show our talent, run our e-business. This all-social media platform generates a large amount of personal data this all-personal data attracts attackers and imposters to steal personal data, share fake news, and perform malware attacks. By creating Human-generated or bot-generated fake profiles on social media, Therefore, in this project, we create a detection model, which detects fake profiles and genuine profiles on Twitter, based on visible features like followers count, friends count, status counts, and more by using various machine learning methods.
A Primer on Path Integrals, Aharonov–Bohm Effect and the Geometric Phase

This work reports an effort to introduce a few advanced concepts in quantum physics to undergraduate students who have only a minimal familiarity with wave mechanics. The geometric phase is not introduced in most introductory courses on quantum mechanics. It is of pivotal significance for the understanding of topological phase transitions which are becoming increasingly important in a large number of physical phenomena, including those that involve platforms for robust quantum computing. This article would provide a gentle introduction to the geometric phase. Toward this goal, Feynman’s path integral approach to quantum mechanics and its application in the interpretation of the wondrous Aharonov–Bohm effect is introduced in a gentle manner. A computer simulation of this interesting phenomenology is presented to help gain an early insight into some advanced concepts.
Inferring Search User Language Proficiency from Eye Gaze Data

User language proficiency has been shown to significantly affect search result language preferences, search behaviors, and even interface preferences. Consequently, search systems may consider adapting to a user's language proficiency when retrieving, composing, and presenting search results. In order to perform such adaptation, it is necessary to first get an estimate of a user's proficiency, ideally through simply observing their behaviors while searching. To this end, this paper investigates the extent to which a user's language proficiency can be inferred from their eye movements while they are evaluating search results. Classification results involving data from English-, Spanish-, and Chinese-speaking study participants show that such an inference is indeed possible, and with relatively high accuracies for all languages. It is also shown that feature sets involving statistics from an entire search result page, combined with gaze data from individual results, have the highest classification accuracies. Moreover, a user's Average Fixation Durations, Refixations, and Pupil Dilations are found to be the most significant features.
Effect of photo album service on the construction of network image resources in the wireless network environment

With the rapid development of information technology and the further popularization of the Internet, various industries in the whole society rely more and more on information. The network album system is one of the products in the information age. This paper analyzes and studies the role of album service in image resource construction based on wireless network environment, and aims to reveal the optimization of image resource construction in this process through the research on image storage, acquisition, browsing and sharing of “small direction” network album. This research mainly focuses on two aspects: the analysis and acquisition of data, the service carrier of network album - photo and image resource structure. Firstly, define the construction standard of the network album system, and analyze the specific performance requirements and non functional requirements of the network album system. In terms of system architecture, multi server architecture is adopted to realize basic load balancing and improve system performance. The comparison before and after system application is carried out in combination with image spatial resolution, image spectrum and image characteristics, so as to highlight the role of album service in wireless network environment. The experimental results show that the highest value of the detection spectrum can reach 0.912. In the process of image browsing, the image spatial resolution has been greatly improved under the construction of wireless network environment. Among the 20 images in the experiment, the highest is 7.89.
Cheating in (halftone-secret) visual cryptography: Analysis of blind authentication schemes

In visual cryptography (VC), cheating is an important security concern where dishonest participants will fool honest ones and make them accept a fake secret by providing fake shares. Share and blind authentications are two categories of cheating prevention, and the last one relies on the inherent robust of shares against cheating attacks. In the previous studies, cheating in VC only focuses on operating a ‘pixel block’ instead of a region of adjacent pixels. However, the well-known advantage of VC is to decode the secret image by using the human vision system (HVS), so it leads to a natural issue to reconsider cheating a region. In this paper, we formally address the binocular cheating attack (BCA) for a region to augment effectiveness of original cheating for a block. Finally, we demonstrate how to realize BCA by presenting non-trivial techniques against some blind authentication schemes, and further obtain implausible results. The BCA can also be applied to halftone secret.
Enabling Continuous Object Recognition in Mobile Augmented Reality

Mobile Augmented Reality (MAR) applications enable users to interact with physical environments through overlaying digital information on top of camera views. Detecting and classifying complex objects in the real world presents a critical challenge to enable immersive user experiences in MAR applications. Aiming to provide continuous MAR experiences, we address a key challenge of continuous object recognition, which requires accommodating an increasing number of recognition requests on different types of images in MAR systems and possible new types of images in emerging applications. Inspired by the latest advance in continual learning approaches in computer vision, this paper presents a novel MAR system to enhance its scalability with continual learning in realistic scenarios. Our experiments demonstrate that 1) the system enables efficiently recognising objects without requiring retraining from scratch; and 2) edge computing further reduces latency for continual object recognition.
Would You Give Me Your Password?

ABSTRACT We examine the effect of information security policy on the likelihood of password sharing in a simulated work environment where ethical values are also expected to guide individuals to do the “right thing.” Graduate and undergraduate students who participated in our study received a “reminder” of the organizational information security policy or a “reminder” of the code of ethics while they were completing the experimental tasks. All participants were asked to share their password with their trusted assistant; the password was either for their desktop or for a spreadsheet containing sensitive customer information. Our results suggest that presenting the organizational information security policy was associated with a lower likelihood of password sharing than presenting the code of ethics. In addition, the participants did not differentiate between the type of password requested for desktop access and for spreadsheet access. Our study provides timely and practical insights to improve security of systems. Data Availability: Data available from the first author.
Malaria Disease Prediction with Ensemble Learning Technique

The use of machine learning will continue to emerge, especially in the field of automated diagnostics and forecasting, because malaria is a major public health problem worldwide, and disease control requires rapid and accurate diagnosis. According to the World Health Organization by 2021, an estimated 235 million people worldwide have been diagnosed with malaria in Southeast Asia, including India, Bangladesh, and Belarus. Malaria is a major problem in tropical and subtropical countries. Therefore, it is necessary to develop a mechanism for early detection of the plasmodium parasite, which is infections. In the conventional method, which is used to diagnose malaria in the laboratory by capturing cells with a microscope, a series of tests are performed and the results are analyzed. In this paper discussed the random forest classifier for malaria detection. First, select the random samples from a given database. Next, algorithm will create a decision tree for each sample and prediction effect on all decision trees. The final result will be generated from the prediction result which is based on voting. This paper strongly recommend that you complete the development of a model to improve the diagnosis of malaria using machine learning techniques.
Construction of Intellectual Property Legal Protection System for Overseas Investment of Chinese Enterprises Under the Background of Data Mining

As a new force with great potential in China’s economic development, Chinese enterprises’ overseas investment is playing a more and more important role. Through the investigation of the current situation of intellectual property protection of Chinese overseas investment enterprises, this paper finds that they lack the awareness of interest protection, have a small number of intellectual property rights, and their legal protection system is not perfect, which leads to many problems, such as the intellectual property rights of Chinese overseas investment enterprises are very vulnerable to infringement. Therefore, it is an inevitable choice for Chinese overseas investment enterprises to protect the intellectual property rights of Chinese overseas investment enterprises and formulate a legal guarantee system suitable for their own development.
Estimation for finite mixture of mode regression models using skew-normal distribution

The mode of a distribution provides a significant summary. A regression model with skew-normal errors provides a useful extension for traditional normal regression models when the data involve asymmetric outcomes. Finite mixture of regression (FMR) models are frequently used to analyze data that arise from a heterogeneous population. This work develops a new data analysis tool called finite mixture of mode regression (FMMoR) in order to explore skewed data from several subpopulations. The main virtue of considering the FMMoR models for skewed data is that this class of models has a nice hierarchical representation which allows easy implementation of inferences. A productive clustering method via mode identification is applied to select the number of components. A modified expectation-maximization algorithm facilitated by the two-point step size gradient descent method (GDEM), simultaneously, is developed for the inference. The proposed methods are evaluated through some simulation studies and illustrated by a real dataset.
A Comparative Study on Evolution of Natural Language Processing and Natural Language Understanding

Nowadays, human language is processed and analyzed in a large manner named NLP or natural language processing. Basically, based on some computational techniques, modern computers evolve from a word to billions of page searches performed by NLP. In addition, NLP could not become so successful without NLU or natural language understanding. There are several NLP and NLU techniques for text mining including assessment of individual points of attraction by their writing which refers to text processing. Also, in this time, our investigation gathered information about the distinct approaches for NLP and NLU which were introduced last decade. Study on natural language also helps us to provide information that how natural language research is changing its track for lexical semantics to compositional semantics and also introduced a deep and brief insight on narrative base natural language techniques for the upcoming generations. This review paper intended to present recent developments in NLP and NLU with a clear look at the past and what could happen in this field in the near-coming days.
Individuation by agreement and disagreement

It is common to explain agreement and disagreement in terms of relations among mental states. The main purpose of the present discussion is to present an alternative way of thinking about the relationship between mental states and agreement and disagreement. The idea is to connect agreement and disagreement with the individuation of mental states. More specifically, for at least some mental states, standing in the same relations of agreement and disagreement is both necessary and sufficient for identity. This provides us with a framework for discussing issues related to agreement and disagreement. It is for instance well-known that de se beliefs raise questions about agreement and disagreement. The present framework allows us to discuss such beliefs while relying on assumptions that are interestingly different from the standard assumptions in the literature. While most of the discussion focuses on beliefs, there are other mental states, such as desires, that can seemingly also stand in relations of agreement and disagreement. This raises questions about how far the framework can be extended.
ML based Implementation for Documents Forensic and Prediction of Forgery using Computer Vision Framework

Facial Recognition, Biometric Verification, Internet of Things (IoT), Criminal Investigation, Signature Identification in banking are just a few of the fields benefiting from breakthroughs in Computer Vision and Optical Image Processing right now. These applications prepare for dealing with numerous applications for breaking down and determining through the use of images and live video. There are several workouts that make use of computer vision, including checking, face recognition, movement recognition, and object placement. Due to advances in long-distance informal communication stages such as Facebook and Instagram, the amount of picture data being generated has increased. Image and video manipulation software usage poses a huge risk to Facebook because of the manipulated images that users submit to their social network. These sorts of pictures are habitually referred to as phony and utilized in noxious ways like rousing viciousness and passing. Need to confirm the sketchy pictures before make a move. It is extremely difficult to guarantee photograph validness because of the force of photograph controls. Picture arrangement can be dictated by picture measurable strategies. The strategy of picture duplication is utilized to hide missing regions.
Application of Hybrid Teaching Mode in College Students’ English Reading Using Intelligent Wireless Communication Multimedia

The development of multimedia technology and the rise of mixed teaching modes have introduced new forms of teaching organization to the field of education, the traditional teaching methods have been improved, and the advantages of the traditional teaching methods have been brought into play. At the same time, the development of online courses has also provided a new basis for teaching and improved the current teaching environment. The current rapid development of intelligent technology and wireless communication networks provides opportunities for the innovation of multimedia technology and also provides technical support for offline and online teaching in a hybrid teaching mode. What this article needs to discuss is the application of hybrid teaching mode in the English reading teaching of college students under the background of intelligent wireless communication network multimedia technology and explore whether the combination of hybrid teaching mode and intelligent technology can improve the teaching level of college students’ English reading, etc. This paper shows through experimental research that under the teaching organization form of the mixed teaching mode under the background of intelligent wireless communication multimedia, college students’ interest in the course of English reading has increased significantly, and their English reading scores will increase by at least 93%, which can improve students’ autonomous learning ability for college English reading courses.
Mobile Manipulation-Based Path Planner for Object Clean-up Operations

Mobile manipulation systems have been used in several industrial operations, one of these being object cleanup. The objective of this paper is to devise a mobile manipulation-based path planner which uses a weighted cost approach to generate priorities and utilizes the A* algorithm to generate the optimal path for multi-object clean-up operations. The paper also focuses on combining vision-based navigation approaches with the notion of modeling of obstacles and classifying them as per their type, pose and spatial measurements. The mobile manipulation-based path planner has been tested under different weights and scenarios to simulate the object clean-up task. The performance of proposed algorithm is evaluated using criteria like the total number of nodes explored and total path cost for all the cycles.
Evaluating the Design and Teaching Effect of College Mobile English Learning Using Deep Learning Technique

Mobile learning has emerged as a popular model for the teaching and learning process, which can also be described as a modernised version of the traditional classroom learning process. As modern education has been implemented, the educational level of both students and teachers has improved. This advancement is made possible by increased knowledge of how to use mobile technology with the Internet. In this study, a multiprocessor learning-based CNN algorithm is proposed to improve students’ English language speaking fluency. Additionally, the students and the teachers are considered available in the remote area, and the communication between them is done through an interactive system. The model is designed to focus on a teacher-centric perspective, in which the teacher has to make use of multiple resources or applications to perform an easier understanding of the course for the students. Hence, an intelligent system is designed to make the learning processes successful. The results show that the proposed algorithm works well in improving the teaching efficiency of mobile English learning. It also revealed that the accuracy level of the learning system used by college and university students has increased by 10%.
On the Numbers of Particles in Cells in an Allocation Scheme Having an Even Number of Particles in Each Cell

We consider the usual random allocation model of distinguishable particles into distinct cells in the case when there are an even number of particles in each cell. For inhomogeneous allocations, we study the numbers of particles in the first K cells. We prove that, under some conditions, this K-dimensional random vector with centralised and normalised coordinates converges in distribution to the K-dimensional standard Gaussian law. We obtain both local and integral versions of this limit theorem. The above limit theorem implies a χ2 limit theorem which leads to a χ2-test. The parity bit method does not detect even numbers of errors in binary files; therefore, our model can be applied to describe the distribution of errors in those files. For the homogeneous allocation model, we obtain a limit theorem when both the number of particles and the number of cells tend to infinity. In that case, we prove convergence to the finite dimensional distributions of the Brownian bridge. This result also implies a χ2-test. To handle the mathematical problem, we insert our model into the framework of Kolchin’s generalized allocation scheme.
End-to-End Speech to Braille Translation in Japanese

This study addresses an end-to-end braille translation approach from Japanese speech for the deaf-blind. In Japan, automatic Braille translation from spoken language is expected to improve information accessibility for deaf-blind people. Japanese Braille has a high affinity for automatic speech recognition (ASR) because it primarily comprises hiragana characters (kana) reflecting Japanese phonetic features. Therefore, we attempted to use neural networks to translate Japanese Braille directly from speech in an end-to-end (E2E) manner. We also compared our proposed E2E approach with an existing method that combines the ASR and automatic Braille translation.
Display Week 2022 Technical Symposium Highlights

Abstract This premier gathering of researchers, technologists, and innovators will offer noteworthy presentations in displays and related technology.