Reviewerly/exHarmony huggingface dataset:
Analysis on the construction of long-term mechanism of school enterprise cooperation in the integration of industry and education

The foundation of the current undergraduate colleges is to establish a complete system of integration of production and education, which is of great significance to the cultivation of talents. In reality, there are many serious shortcomings in the current production-education integration mechanism and school-enterprise cooperation of undergraduate colleges. Solving these problems requires the correct guidance of some institutions. From the government’s standpoint, some relevant laws and regulations are adopted to promote school-enterprise cooperation, so as to build a new mechanism of integration of production and education. This article expounds the necessity of constructing the integration of production and education, school-enterprise cooperation and the obstacles encountered in the construction process, and discusses the path of constructing a new mechanism.
Correct Assessment of Students’ Achievements in Elementary Classes and Content Lines

The article discusses the acquisition of knowledge, skills and habits by students in general education schools, the adoption of high levels of standards, the correct assessment of student achievement, the results of the correct assessment carried out by the teacher, interaction in the process of assessment and learning, the correct construction of teacher–student–parent relationships to improve the quality of teaching in the educational process. The article also talks about the importance of content lines in primary grades, about the five content lines of the mathematics course in grades I–IV, about inter-subject and intra-subject connections in long-term school practice, about modern information and communication technologies, elements of statistics and probability. The article discusses in detail the actions of addition and subtraction, written multiplication and division, the concept of numbers, fractions, parts, 4 arithmetic operations within a million, used in classes’ I–IV. It also mentions 5 content lines of the secondary school mathematics course: cognitive (cognitive) activity, emotional (affective) activity, motor (psycho-motor) activity, as well as the need for these actions.
Discriminating the Original Region from the Duplicated in Copy-Move Forgery

Since images are used as evidence in many cases, validation of digital images is essential. Copy-move forgery is a special kind of manipulation in which some parts of an image is copied and pasted into other parts of the same image. Various methods, proposed to detect copy-move forgery, have achieved promising results. In previous methods, a binary mask determining the original and forged region is presented as the final result. However, it is not usually specified which part of the mask is the forged region. It should be noted that discriminating the original region from the duplicated one is not usually feasible by human visual system (HVS). On the other hand, exact localization of the forged region can be helpful for automatic forgery detection especially in combined forgeries. In real-world forgeries, some manipulations are performed in order to provide a visibly realistic scene. These modifications are usually applied on the boundary of the duplicated snippets. In this research, the texture information of the border regions of both the original and copied patches have been statistically investigated. Based on this analysis, we propose a method to discriminate the copied snippets from the original ones. In order to validate our method, several benchmark datasets are employed.
Catalog Integration of Heterogeneous and Volatile Product Data

The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
A Case Study of Augmented Physical Interface by Foot Access with 3D Printed Attachment

We propose an attachment creation framework that allows foot access to existing physical interfaces designed to use hands such as doorknobs. The levers, knobs, and switches of furniture and electronic devices are designed for the human hand. These interfaces may not be accessible for hygienic and physical reasons. Due to the high cost of parts and initial installation, sensing or automation is not preferable. Therefore, there is a need for a low-cost way to access physical interfaces without hands. We have enabled foot access by extending the hand-accessible interface with 3D-printed attachments. Finally, we proposed a mechanism (component set) that transmits movement from a foot-accessed pedal to an interface with attachments. And we attached it to the doorknob, water faucet, and lighting switch interface. A case study was conducted to verify the system’s effectiveness, which consisted of 3D-printed attachments and pedals.
Secure perception-driven control of mobile robots using chaotic encryption

This paper considers perception-driven control of a mobile robot for reference tracking where perception is performed by a machine learning system. The robot is subject to passive attacks and evasion attacks on image transmission. A robust output feedback controller together with a chaotic encryption system ensures input-to-state stability of the closed-loop system, and the chaotic encryption approach keeps image transmission secure. Simulations are conducted in the CARLA simulator to demonstrate robust reference tracking and secure image transmission.
Virtual Reality Experience in Tourism: A Factor Analysis Assessment

The objective of this paper is to ascertain the dimensions of virtual reality (VR) experience in tourism. Past studies on VR experience mainly focus on VR system and VR content with 29 indicators all together. They have not identified yet the underlying dimensions forming VR experience. Driven by this research gap, this study attempts to explore the dimensions of experience in VR tourism. A self-administered questionnaire was distributed online and 396 valid responses were generated from tourists who visited Australian tourism destinations through VR. An exploratory factor analysis was executed to produce new underlying dimensions of VR experience. Then, confirmatory factor analysis was performed to ensure the validity of the new dimensions. The result of the factor analysis shows that five new dimensions are formed, namely; enjoyment and learning, escape, involvement, application, and peace of mind. The result of the confirmatory factor analysis confirms the dimensionality of the VR experience construct. This study contributes to the literature on VR experience and its dimensions.
A Novel Current Mode Multifunction Inverse Filter using Single CFA

A novel current-mode multifunction inverse filter configuration using single current feedback amplifier (CFA) is presented. The proposed filter employs only one CFA and few passive components. The proposed circuit realizes inverse lowpass, inverse bandpass and inverse highpass filter functions with proper admittances. The characteristics of proposed multifunction inverse filter configuration are- current-mode realization; use of only one CFA; use of grounded passive components except one virtually grounded and realizing all three basic inverse filters. The proposed current-mode inverse filter circuit has been tested by TINA PRO simulation program and results justified the theoretical analysis.
Text Steganography in Image depending on Radon _ Barker code Transforms

Abstract - With the widespread usage of the internet around the world, both public and private data are now widely disseminated on the internet. The security of these online records is a pressing issue that must be addressed immediately. Text watermarking is a technique for adding watermarks to text documents to help preserve their authenticity and credibility. For many years, text watermarking has been a hot topic of research. This paper describes a new method for embedding text in a picture that uses a radon transform-based Barker code to make the device more reliable and ensure that the text is returned without error. The main advantages of this research are the high security and the big capacity of data to be hiding since the use of radon transform is very helpful in extracting the data. All the image noise does not effect on the stego-image except the paper and salt noise effect on Root Mean Square (RMS) since its value is 0.274 and contract enhancement effect on RMS and Mean Square Error (MSE) by the values 0.586 and 0.343 respectively. Also the SSIM value is very acceptable since there is an obvious similarity between the original image and the stego-image (after stegonagraphy process).
Unsupervised Segmentation of Non-Intersecting Manifolds

Manifold learning has been an important research area as from literature it is evident that patterns in most real-life data sets can be embedded in low-dimensional space while maintaining the original structure of high-dimensional space. This work concentrates on one of the major research areas of manifold learning, which is the segregation of manifolds where more than one non-intersecting manifolds are present. The proposed method presents a solution to the problem by detecting the number of manifolds in a dataset using the Laplacian graph matrix and segregate the manifolds using agglomerative clustering. Eventually, locally linear embedding has been used for dimensionality reduction of every individual manifold in such a way that manifolds remain segregated and also holds the original global structure. The proposed method achieves finer results when applied on benchmark synthetic data sets SCurve, SwissRoll, Helix and real-life datasets COIL-20, optical digit recognition, att_faces, extended Yale Face Database B. While the state of the art methods fails to detect the number of manifolds in a dataset, the proposed method not only eclipses the performance of them but also carry the separable structure in the lower dimensional space.
Image Colored-Pencil-Style Transformation Based on Generative Adversarial Network

Generative Adversarial Network (GAN) has an excellent performance in “ Image-to-Image Translation” field, this paper proposes an effective algorithm to solve the problem of image colored-pencil-style transformation based on Pix2Pix of GAN. The improved generator and discriminator are designed to be suitable for high resolution image generation. In the loss function, TV Loss is using for improving the smoothness of images and removing noise and overlapping shadows. In order to adapt to the practical application requirement of colored-pencil-style transformation, our data set comes from Flickr, the world's largest photo-sharing site, and includes high-resolution images of different types. The experimental results show that our proposed algorithm not only improves the resolution of images, but also makes the details clearer. Moreover, in all terms of objective indicators, our proposed algorithm is superior to Pix2Pix in different categories of data set. Therefore, through the comparison in visual subjectively and in indicators objectively, it is proved that the proposed algorithm has better performance than Pix2Pix.
Location Privacy Protection in Mobile Wireless Networks Through Hidden Forest and Fog Computing

As we know that the key to personal freedom is mobility and due to which many providers begin to offer location-based services and these services enrich our mobility experiences and with this the purpose of security increases as the location-based services can easily track the location of the user. Many of the current limitations would inherit several upcoming features as security and privacy issues have not fully been addressed by standardization bodies. Existing methodologies propose some method of protecting location privacy in mobile wireless networks but it has some drawbacks. So in this paper, drawbacks have been resolved as shown in comparative study, and in proposed methodology, two technologies have been used, i.e. hidden forest and fog computing where hidden forest method is used for protecting location privacy in mobile wireless networks and to increase more security, i.e. for 2-layer security, fog computing technology has been used to create illusion and mislead the attacker. To find hidden forest, Chinese remainder theorem (CRT) method has been used and able to protect the location privacy of the mobile wireless networks. And fog computing technology has been used to create false data and illusion so that our actual data will be safe. Fog computing based on its networking edge computes feature where the location-based service and applications are the most popular. For simulation tools, cloud analyst and CloudSim are used.
Relentless villains or fervent netizens?: The alt-right community in Korea, <i>Ilbe</i>

Previous studies on the alt-right, a far-right movement based online, focus on its rise in Western countries. Alt-right communities have emerged more publicly in recent years and have contributed to strengthening overt expressions of hate. Such studies, however, have rarely focused on the alt-right beyond Western countries. It is critical to delve into alt-right communities in other parts of the world in order to understand the institutionalized hate of contemporary digital mediascapes. From an online textual analysis, the current study traces the developmental process of Ilbe, a representative alt-right group of Korea: its transition from a digital storehouse for provocative jokes to an influential alt-right group, and finally, to a virtual hideout where people express their hatred toward others, putting aside its far-right ideology. In doing so, the current study investigates how this alt-right group shares significant characteristics of digital media culture and develop them in its own fashion. This research contributes to understanding Asian Internet histories, including an exploration of the alt-right community and its online culture of hate. Moreover, this study demonstrates how such a trend in digital media culture influences the larger society.
Towards Explainable Practical Agency

Practical reasoning is such an essential cornerstone of artificial intelligence that it is impossible to see how autonomous agents can be realized without it. As a first step of practical reasoning, an autonomous agent is required to form its intentions by choosing amongst its motivations in light of its beliefs. An autonomous agent is also expected to seamlessly revise its intentions whenever its beliefs or motivations change. In the modern world, it becomes an impelling priority to endow agents with explainable practical reasoning capabilities in order to foster the trustworthiness of artificial agents. An adequate framework of practical reasoning must be able to (i) capture the process of intention formation, (ii) model the joint revision of beliefs and intentions, and (iii) provide explanations for the chosen beliefs and intentions. Despite the abundance of approaches in the literature for modelling practical reasoning, such approaches fail to possess at least one of the previously mentioned capabilities. In this paper, we present formal algebraic semantics for a logical language that can be used for practical reasoning. We demonstrate how our language possesses all of the aforementioned capabilities providing an adequate framework for explainable practical reasoning.
Simon’s Algorithm and Autocorrelation Spectrum

Simon’s algorithm is another important evidence that more efficient evaluations of certain properties are possible in quantum domain over the classical paradigm. Given a vectorial Boolean function on n variables that has certain periodicity, this algorithm is capable of obtaining the period in time complexity polynomial in n. In this chapter, we explain this algorithm in detail. Then we concentrate on the autocorrelation spectrum of a single output Boolean function and study how the quantum strategies fare in this domain. We present quantum algorithms to study the Walsh spectrum of higher order derivatives of a Boolean function. Examples and Qiskit codes are presented to explain the methods.
Delegated Authorization Framework for EHR Services Using QR Code-Based Encryption

The point of this examination is to investigate the intentions belated the appropriation or dismissal of electronic health records (EHR) frameworks in the USA by narcotic workplaces. The goal is to lessen excess and increment quality by sharing medical data among various wellbeing associations like emergency clinics, doctor workplaces, laboratories, and medical organizations. The achievement of such change requires the cooperation and coordinated effort of every one of these substances and their resigned. Earlier testing conveys that the selection of electronic medical records frameworks by clinics and doctor workplaces has been clear, however, at a degree that is slower than in different nations. Besides money-related hindrances, specialized, mental, social/legitimate and hierarchical boundaries exist. So as to understand these effects, we make our task. The primary objective of our undertaking is to plan a versatile application for patients through online application the board that gains secure access to understand records through distributed storage administration. Administrator can remotely store persistent data into the cloud with sheltered and make sure about distributed storage through a patient can without much of a stretch access their information which are put away in cloud dependent on EHR service the board. Remote information respectability of wellbeing record is proposed to ensure the uprightness of the made sure about information put away in the cloud. In some regular distributed storage frameworks, cloud document may contain some delicate data. Encoding the entire shared record can understand the sensitive data concealing, however, will make this mutual record unfit to be used by others. Right now, propose a remote report reference id consequently convert into the QR code then simply filter client module at that point download the specific record respectability that acknowledges information offering to delicate data covering up. Marks are utilized to check the document in the period of honesty wellbeing records.
Fast, Safe, and Proactive Runtime Planning and Control of Autonomous Ground Vehicles in Changing Environments

Autonomous ground vehicles (UGVs) traversing paths in complex environments may have to adapt to changing terrain characteristics, including different friction, inclines, and obstacle configurations. In order to maintain safety, vehicles must make adjustments guided by runtime predictions of future velocities. To this end, we present a neural network-based framework for the proactive planning and control of an autonomous mobile robot navigating through different terrains. Using our approach, the mobile robot continually monitors the environment and the planned path ahead to accurately adjust its speed for successful navigation toward a desired goal. The target speed is selected by optimizing two criteria: (1) minimizing the rate of change between predicted and current vehicle speed and (2) maximizing the speed while staying within a safe distance from the desired path. Additionally, we introduce random noise into the network to model sensor uncertainty and reduce the risk of predicting unsafe speeds. We extensively tested and validated our framework on realistic simulations in Gazebo/ROS with a UGV navigating cluttered environments with different terrain frictions and slopes.
Configurations of tunnel deceleration area

Abstract The deceleration area of the tunnel has always been the place where accidents occur frequently. Recently published statistics on tunnel accidents showed that 42 percent of which are rear-end collisions. The main reason lies on the luminance difference between the inside and the outside parts of the tunnel. The inside part of the tunnel is much darker than that of the outside part, which results in a “sight shock” and shortage of clear sight for drivers at the tunnel portal. If the tunnel deceleration area is not long enough and the vehicle is at a high speed, rear-end collisions will most likely occur at this area. Therefore, it is important to design the deceleration area of the tunnel in order to avoid rear-end collisions. This paper explains the cause of rear-end collision at deceleration area from the principles of physics and physiology. The mathematical relations among the speed, time and distance of trucks and cars entering the tunnel are given from the mathematical point of view. Finally, this paper puts forward the design idea and reasonable suggestions for the configuration of tunnel deceleration area.
Deep Learning-Based Approach for Human Activity Recognition

Deep learning for the use of human activity detection in smart homes is predicted by the use of this work. Feature extraction and predicting further movement are a challenge, and one-time prediction at a particular time is possible. To accomplish, this RNN with HAR feature extraction is accomplished within the deep learning mechanism. The critical features are extracted at first place, and the rest of the features are extracted only if required. This process identifies the activities at a much faster rate as compared to plain deep learning mechanisms. Classification accuracy of prediction is also better as error rate decays using the HAR feature in deep learning.
Attentive Intra-modality Fusion for Multimodal Sentiment Analysis

The growing trend of sharing opinion videos on social media platforms leads to more and more attention to multimodal sentiment analysis research. A number of approaches in multimodal sentiment analysis have been proposed and continual improved results were produced. Most of the existing studies have paid more attention to cross-modalities fusion, ignoring valuable information fusion within each uni-modality. To this end, we propose an AGTL (Attentive Global and Temporal Local information) component to capture the global semantic information and the local semantic representation containing temporal relationships and get the different contributions of global or local information in each uni-modality. After later fusion of representations of different modalities learned by AGTL, we get the final representation of multi modalities. Different comparative and ablation experiments are conducted on a public multimodal sentiment analysis dataset, and the experimental results demonstrate the effectiveness of our model in multimodal sentiment analysis.