Reviewerly/exHarmony huggingface dataset:
Designing a Hybrid Approach for Web Recommendation Using Annotation

Web recommendation is the process of providing personalization recommendations to the requirements of specific users. A lot of research is conducted on recommender systems by a broad range of communities including computer scientists and interdisciplinary researchers. In response, a lot of recommender systems have been developed so far. However, the complexity of these systems can lead to information overload and decreased utility for the users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. In this chapter, the authors cover recent improvements in recommender systems and explore the major challenges. This chapter finally presents a prototype for adaptation to end-users. The model emphasizes an annotation-based recommendation system for generating recommendations.
Deontic Logic and the Propositional Nature of Norms

In this paper, we examine a solution to Jørgensen’s dilemma advanced by Hilpinen in several papers. According to Hilpinen, the distinction between two ways of using normative sentences is a distinction between two kinds of utterances of normative propositions. An authoritative utterance of a deontic sentence ‘creates’ a norm and, at the same time, it makes such a norm true in a certain community. In other words, the contents of norms are true propositions. However, the same proposition can also be descriptively uttered in order to provide information about the normative status of certain actions or states of affairs. Therefore, Hilpinen claims that there would be no difference between the logic of norms (directives) and the logic of normative statements to the extent that normative and assertoric utterances of a given deontic sentence have the same content: a normative proposition. Contrary to Hilpinen, we claim that it is necessary to make a clear distinction between the content of norms and a description of norms. Following Alchourrón and Bulygin, we show that a logic of norms cannot be isomorphic with a logic of propositions about norms. However, contrary to Alchourrón and Bulygin, we emphasize that a logic of propositions about norms should not assume that normative sets include all logical consequences.
Artificial Intelligence and Intelligent Information Systems Based Multi-Dimensional Classroom Computer Evaluation Model

The conventional evaluation methods pay much attention to test scores and artificial participation, and lack of objective indicators in teaching process and classroom. In this paper we study the evaluation mechanism of classroom participation rate, and propose a new multi-dimensional classroom participation evaluation mechanism based on artificial intelligence. This mechanism evaluates classroom participation of students from the dimensions of attendance rate,head up rate, expression and eye recognition, and statistics of students who answers questions, which saves time and energy of both teachers and students, expands the objectivity dimension of teaching evaluation, and makes the overall evaluation more stable and reliable.
Application of Computer Aided Instruction in Taekwondo Teaching

With the rapid development of science and technology, the tide of information has also brought huge impact on the field of education. The computer-aided teaching method is infiltrating into the classroom of colleges and universities at an unpredictable speed, bringing about changes for modern higher education. This paper mainly studies the application of computer aided instruction in taekwondo teaching. This paper first introduces the concept of computer-aided instruction, and makes clear the influence and function of computer-aided instruction in higher education. Then the computer aided technology is used to score taekwondo movements to improve the learning effect of students. It can be seen from the difference between the experimental class and the control class that the computer-aided teaching technology can make the teaching method more free, the learning atmosphere more relaxed and interesting, effectively improve students’ academic performance, stimulate students’ learning enthusiasm.
Research on the Application of Natural Language Processing in the Virtual Comment’s Classification

Natural language processing (NPL) is a booming technical field in artificial intelligence, and it is closely related to our lives. NPL studies how to enable computers to understand and use human language to achieve information interaction between humans and computers, and then extract effective text information from a large amount of text. This includes not only allowing computers to understand natural language texts, but also extending the ability to enable computers to express deep intentions based on natural language texts. This research briefly outlines some processes of natural language processing in practical applications. Network virtual comment classification problem is considered with a two-layer LSTM model to train the original data set, and the results show the effectiveness of the proposed design.
Machine Vision-based Recognition and Positioning System for Domestic Garbage

Domestic garbage classification is a global issue, and the robotic arm grasping system for garbage sorting can greatly improve sorting efficiency. Such robotic systems recognize and locate garbage by machine vision. The problems of these existing systems are as follows: the recognition rate for destroyed and part-missing garbage is low; garbage positioning is inaccurate and time-consuming when the environment is dim or light-reflection. To this end, the paper collects 3116 images of domestic garbage, and divides them into 4 major categories and 12 minor categories; and then proposes an improved CenterNet target detection algorithm which is combined with the convolutional attention and feature fusing; normalized cross correlation matching algorithm has been reformed to enhance the positioning accuracy in the light-reflection environment. In addition, pre-cutting and pre-matching are executed to improve object positioning. The experiments shown in this paper prove that the system can recognize domestic garbage which is destroyed and part-missing and locate it in the dim and light-reflection environment.
Fast-QMIX: Accelerating Deep Multi-Agent Reinforcement Learning with Virtual Weighted Q-values

Cooperation between agents in a multi-agent system (MAS) is prevalent in real or virtual environments. In a fully cooperative environment, each agent learns its own policy by using the overall reward. QMIX adds an additional parametric network to replace the linear summation of values in Value-Decomposition Networks (VDNs), aiming to learn more complex relationships between agents. However, QMIX can not determine the best value for each agent during the process, resulting in slower convergence, instability of training, and poor performance. In this paper, we further dynamically assign virtual weighted Q-values based on QMIX with an additional network to further utilize the global information as an auxiliary guide and expand the ability to explore while also expand the robustness of the algorithm. Experiments show that our method not only outperforms original QMIX in different scenarios of the StarCraft Multi-Agent Challenge (SMAC) but also converges faster and more stable with the addition of calibration.
Conditional Context-Aware Feature Alignment for Domain Adaptive Detection Transformer

Detection transformers have recently gained increasing attention, due to its competitive performance and end-to-end pipeline. However, they suffer significant performance drop when the test and training data are drawn from different distributions. Existing domain adaptive detection transformer methods adopt feature distribution alignment to alleviate the domain gaps. While effective, they ignore the class semantics and rich context preserved in attention mechanism during adaptation, which leads to inferior performance. To tackle these problems, we propose Conditional Context-aware Feature Alignment (CCFA) for domain adaptive detection transformer. Specifically, a context-aware feature alignment module is proposed to map the high-dimensional context into low-dimensional space, so that the rich context can be utilized for distribution alignment without optimization difficulty. Moreover, a conditional distribution alignment module is adopted to align features of the same object class from different domains, which better preserves discriminability during adaptation. Experiments on three common benchmarks demonstrate CCFA's superiority over state-of-the-arts.
Evolution of Big Data in Medical Imaging Modalities to Extract Features Using Region Growing Segmentation, GLCM, and Discrete Wavelet Transform

Big data refers to the massive amount of data from sundry sources (gregarious media, healthcare, different sensor, etc.) with very high velocity. Due to expeditious growth, the multimedia or image data has rapidly incremented due to the expansion of convivial networking, surveillance cameras, satellite images, and medical images. Healthcare is the most promising area where big data can be applied to make a vicissitude in human life. The process for analyzing the intricate data is mundanely concerned with the disclosing of hidden patterns. In healthcare fields capturing the visual context of any medical images, extraction is a well introduced word in digital image processing. The motive of this research is to present a detailed overview of big data in healthcare and processing of non-invasive medical images with the avail of feature extraction techniques such as region growing segmentation, GLCM, and discrete wavelet transform.
Application of Hybrid Intelligence for Security Purposes

In this paper we’ll present the main idea and areas of application of hybrid human-artificial intelligence methods in security technologies. Such paradigm support creation of user-centered cryptographic procedures, which can be oriented on specific persons, or areas of application thanks to the involvement of AI approaches. Such protocols will use personal preferences and some user’s features analyzed by AI.
Geometry of the Minimum Volume Confidence Sets

Computation of confidence sets is central to data science and machine learning, serving as the workhorse of A/B testing and underpinning the operation and analysis of reinforcement learning algorithms [1]. This paper studies the geometry of the minimum-volume confidence sets for the multinomial parameter. When used in place of more standard confidence sets and intervals based on bounds and asymptotic approximation, learning algorithms can exhibit improved sample complexity. Prior work [2] showed the minimum-volume confidence sets are the level-sets of a discontinuous function defined by an exact p-value. While the confidence sets are optimal in that they have minimum average volume, computation of membership of a single point in the set is challenging for problems of modest size. Since the confidence sets are level-sets of discontinuous functions, little is apparent about their geometry. This paper studies the geometry of the minimum volume confidence sets by enumerating and covering the continuous regions of the exact p-value function. This addresses a fundamental question in A/B testing: given two multinomial outcomes, how can one determine if their corresponding minimum volume confidence sets are disjoint? We answer this question in a restricted setting.
Energy-Aware Load Balancing in Dynamic Cloud Environment Using Nature-Inspired Technique

Cloud computing has shown its eminence in almost all fields of the technologies spread over the globe. It has enormous benefits in provisioning the cloud resources to the users, being anywhere, at any moment, on a hiring basis as a utility. A few studies regarding dynamic cloud load balancing with energy efficiency have been proclaimed so far. Hence, adequate models in this area are still rare. This paper has proposed a nature-inspired hybridized cuckoo search algorithm (HCS-LB), combining the searching technique implemented by the cuckoo to lay eggs in the nest of another bird and the multi-rumen anti-grazing behavior of the herbivores to balance the load of tasks in multiple cloud datacenters, as a result minimizing the energy consumption in the cloud environment as a whole. This hybrid algorithm uses cuckoo search to allocate appropriate virtual machines to the migrated tasks and the multi-rumen anti-grazing approach to assign tasks to other datacenters as per the requirement. Here, CloudSim is used to simulate the result, and the proposed model is found outperforming over the other existing techniques. This will contribute a bit to the huge requirement to minimize the expenses of the datacenters incurred by the energy consumption and thereby reduce the cost of usage of the cloud resources by the general public.
Artificial Intelligence with Enhanced Prospects by Blockchain in the Cyber Domain

The various modern state-of-the-art applications with increasing utility of Artificial Intelligence (AI) have enabled human lives to thrive in a prevalent and convenient digital world. It has also led to the usage of huge amount of sensors data emanated out of different sources. A precise and scalable data analysis performed by AI to examine the colossal amount of data in a cyber space has potential to open yawing inroads for many future applications. At the same time, Blockchain being another promising technology can support a secure decentralized data communication network comprised of diverse independent nodes in a cyber domain. It has potential to overcome prevalent associated challenges of AI through resourceful data sharing. This paper brings out the specific facets of AI in a cyber domain facilitating IoT after giving brief overview on AI and blockchain. A marked progress in cryptographic techniques with strong support of AI in a cyber domain has opened the new avenues in digital applications. This paper aims in exploring viable data communication architecture with support of blockchain and AI by proposing it to support an extensive range of existing and upcoming smart applications. Both qualitative and quantitative examinations have been carried out to deal with computational complications and to evaluate the system performance to support the proposal. Lastly, the paper discusses about the future prospects and the possible challenges to be confronted in the cyber domain over an AI and blockchain integrated data communication network architecture.
Citizen Feedback Management Using Sentimental Analysis

Government authorities usually collect feedback from the citizens on the main aspects of living such as water supply, electricity, housing, sanitation and other entities required for a decent and hassle-free living experience. Current technique for feedback mining is to use manual methods which focus mainly on quantitative comments and hence, a deeper analysis cannot be evaluated. Through the course of this project, we develop an online mechanism for feedback management, by applying a sentiment analysis algorithm to provide authorities a deeper insight into the complaint categories and hence identify the true sentiments of citizens. Our platform, after collecting feedback from people, will perform sentimental analysis to produce a word cloud that depicts the major problem being faced by people in real time. This proposed system is a well-organized, systematic and structured approach for providing qualitative feedback for the government authorities that in turn enriches the citizens’ quality of living.
Architecture-Based Semantic Description Framework for Model Transformation

In order to solve the problems of description and verification of semantic properties in model driven development, process algebra is introduced on the basis of extending typed category theory. A unified semantic description framework is established for the description and transformation of component-based software models, as well as the maintenance and verification of semantic properties in the process of model transformation. Category diagram is used to describe the semantics of architecture model, and typed morphism implies the dependency relationship between component objects, and typed functor is used to describe the mapping mechanism before and after model transformation. Application research shows that the framework well follows the essence and process requirements of model-driven development, and provides a new guidance framework for understanding, cognitive learning and promotion of software development research on the basis of model transformation.
An Intrinsic Structured Graph Alignment Module With Modality-Invariant Representations for NIR-VIS Face Recognition

Most existing near-infrared to visible (NIR-VIS) face recognition (FR) methods rely on global feature representations to reduce cross-modality discrepancies, but ignore the structural relationships between local features, e.g., the relative positions of eyes, nose, and mouth. Precise alignment of these local features can enhance the learning of modality-invariant face representations, thereby improving the performance of NIR-VIS FR. Therefore, in this letter, we propose an intrinsic structured graph alignment (ISGA) module that aims to obtain a graph-level local feature alignment across modalities. To this end, we first construct an intrinsic structure graph to model the inherent structural relationships of local features, and then enhance the discriminative feature representation by aligning the graphs between modalities. To jointly encourage cross-modality class consistency between semantics and structural relationships, a cross-modality class distribution (CMCD) loss is proposed to add an identity-preserving constraint to each class distribution in the embedding space between two modalities. To solve the resulting problem of suppressed class divisibility, we maximize the mutual information between inputs and class predictions. Extensive experiments on challenging NIR-VIS datasets indicate that our approach outperforms the state-of-the-arts. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JianYu777/ISGA-CMCD</uri> .
Data anomaly intelligent detection based on machine learning

In order to distinguish whether it is abnormality with risk or without risk when environmental data collected by the device sensor is abnormal, this paper adpots k-means algorithm and hierarchical clustering algorithm in machine learning respectively to divide it, and compares the two algorithms on this research. The clustering results show that the risk-free abnormal data has the character of regularity, independence and contingency. The abnormal data with risk has the character of continuity and linkage.
GSMI: A Gradient Sign Optimization Based Model Inversion Method

The vulnerabilities of deep learning models on security and privacy have attracted a lot of attentions. Researchers have revealed the possibility of reconstructing training data of a target model. However, the performances of current works are highly rely on auxiliary datasets. In this paper, we investigate the model inversion problem under a strict restriction, where the adversary aims to reconstruct plausible samples of the target class without help of auxiliary information. To solve this challenge, we propose a Gradient Sign Model Inversion (GSMI) method based on the idea of adversarial examples generation. Specifically, we make three modifications on a popular adversarial examples generation method i-FGSM to generate plausible samples. 1) increasing the number of attack iterations and 2) superposing noises to reveal more obvious features learned by target model. 3) removing subtle noises to make reconstructed samples more plausible. However, we find samples generated by GSMI still contain noisy components. Furthermore, we adopt the idea of image adjacent regions to design a two-pass components selection algorithm to generate more reasonable sample of the target class. Through experiments, we find that the inversion samples of GSMI are close to real target class samples with some fluctuations on different classes. In addition, we also provide detail analysis for reasons of limitations on the optimization-based model inversion methods.
Hide and Mine in Strings: Hardness, Algorithms, and Experiments

Data sanitization and frequent pattern mining are two well-studied topics in data mining. Our work initiates a study on the fundamental relation between data sanitization and frequent pattern mining in the context of sequential (string) data. Current methods for string sanitization hide confidential patterns. This, however, may lead to spurious patterns that harm the utility of frequent pattern mining. The main computational problem is to minimize this harm. Our contribution here is as follows. First, we present several hardness results, for different variants of this problem, essentially showing that these variants cannot be solved or even be approximated in polynomial time. Second, we propose integer linear programming formulations for these variants and algorithms to solve them, which work in polynomial time under realistic assumptions on the input parameters. We complement the integer linear programming algorithms with a greedy heuristic. Third, we present an extensive experimental study, using both synthetic and real-world datasets, that demonstrates the effectiveness and efficiency of our methods. Beyond sanitization, the process of missing value replacement may also lead to spurious patterns. Interestingly, our results apply in this context as well.
Teaching Chinese on YouTube Based on Information Platform

The 21st century is the era of big data. With the rapid development of this trend, big data is not only used to collect information, but also more or less involved in various disciplines. In recent years, network teaching has become popular, especially in the past year. Under the influence of the COVID-19, network teaching websites and some APPs have begun to develop, which is a natural impact on the traditional teaching, but also a major reform of teaching methods. From the perspective of the trend of network teaching, this paper focuses on the study of YouTube, a popular video platform around the world, to find out the changes and advantages of Chinese teaching in the information age, and at the same time to analyze its disadvantages.
Research and Application of Artificial Neural Network Technology in the Recognition Model of Employee Satisfaction

In a highly competitive industry, the phenomenon of brain drain is even more prominent. Many industries recognize the role of talents in the development of the industry. Attracting and retaining outstanding talents should improve employee job satisfaction and cultivate a sense of belonging to the "people-oriented" corporate culture. As a leader, you must constantly communicate with employees to understand their real needs. In many industries, the problem is how to complete the measurement of each employee's job satisfaction and improve the management of employee job satisfaction. This paper studies the application of artificial neural network technology in the employee satisfaction recognition model. After understanding employee satisfaction and artificial neural network based on relevant literature data, it proposes an employee satisfaction evaluation system based on artificial neural network technology, and design it, finally test the designed system. Through the test results, it is concluded that the neural network algorithm used by the system in this paper has a faster convergence speed. For the system usage evaluation, the overall usage evaluation of the system in this paper is better, 3 points the number of people accounted for more than 35%.