Reviewerly/exHarmony huggingface dataset:
Self-Supervised Continuous Meta-Learning for Few-shot Image Classification

Few-shot classification aims to adapt the knowledge learned from base classes with sufficient data to new classes with limited data, where meta-learning methods are usually leveraged for this challenging task. However, most existing algorithms suffer from insufficient representation and testing bias issues, accordingly failing to exploit useful semantic information while being prone to cause the gap of classification accuracy between training classes and testing classes. To this end, we propose the Self-Supervised Continuous Meta-Learning (SS-CML) framework to simultaneously handle the mentioned problems, which consists of two key modules. i.e., Self-Supervised Embedding network and Self-Supervised GNN. Specifically, Self-Supervised Embedding network can extract informative semantic information from training images so that the learned prototype are more representative for the classification task. Moreover, Self-Supervised GNN learn reactions between nodes without true labels, which can improve the reliability of knowledge prior to classify images of new classes, thereby reducing the excessive dependence of training classes and alleviating the testing bias issue. Furthermore, these two modules are jointly leveraged in our SS-CML to generalize the prior knowledge to novel classes. Extensive experimental results on MiniImageNet and TieredImageNet show up the effectiveness of both self-supervised branches which boost classification performance.
Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language +

Abstract We extend probabilistic action language $p{\cal BC}$ + with the notion of utility in decision theory. The semantics of the extended $p{\cal BC}$ + can be defined as a shorthand notation for a decision-theoretic extension of the probabilistic answer set programming language LP MLN . Alternatively, the semantics of $p{\cal BC}$ + can also be defined in terms of Markov decision process (MDP), which in turn allows for representing MDP in a succinct and elaboration tolerant way as well as leveraging an MDP solver to compute a $p{\cal BC}$ + action description. The idea led to the design of the system pbcplus2mdp , which can find an optimal policy of a $p{\cal BC}$ + action description using an MDP solver.
New Advances in E-Commerce

Advances in computers design and communication technology have led to the introduction of global, dynamic, and intelligent e-business models and new tools for e-commerce. These models use artificial intelligence techniques and Web 2.0 technologies to perform online transactions between businesses and customers. As the number of clients over the internet grows rapidly, advanced intelligent e-business models are needed to efficiently respond to clients with an optimum selection for their requests. In B2C models there are a large number of buyers and sellers where buyers specify their preferences and sellers specify their products and selling prices. The major role of AI techniques in B2C e-commerce is to come up with the best match between a buyer and seller. In B2B the major role of AI techniques is supply chain management to help clients make the right decisions. Modern tools used by intelligent e-commerce models include algorithmic trading, decentralized autonomous corporations (DAC), crypto-currency systems, smart contracts, internet of things, cloud computing, and big data analytics.
Anomaly detection of ladle driving force based on Temporal Convolutional Network

The driving force of ladle is an important index to reflect the normal operation of ladle sliding nozzle. At present, a steel plant uses sliding nozzle to regulate the flow of molten steel, so that the molten steel in the ladle can be safely, stably and reliably injected into the tundish or vibration exciter. In order to diagnose the normal operation of the ladle sliding nozzle, the driving force of the ladle is collected to analyze whether the driving force is abnormal to diagnose the operation state of the ladle sliding nozzle, so as to avoid the occurrence of leakage and other accidents. Considering that the driving force of ladle has strong time-series data characteristics, this paper uses temporal convolution network to extract data features and detect anomalies. The detection effect has been tested in the actual production, with good detection effect.
Enhancing the Binary Watermark-Based Data Hiding Scheme Using an Interpolation-Based Approach for Optical Remote Sensing Images

Computer networking and internet developments create new challenges in information security and copyright protection. In order to protect the multimedia data and also in some cases, for information management, the authors can use watermarking schemes to achieve more security. In this article, the authors firstly review a watermarking scheme for remote sensing applications, represented by Zhu et al.; They also explain two problems in Zhu et al.'s scheme and in addition, introduce two solutions for these problems. Generally, Zhu et al.'s scheme is a non-blind scheme that also does not have any attention to watermarked image quality, thus they try to represent ways in order to achieve the blind and quality-aware watermarking. Experimental results confirm that both of their modifications have suitable effectiveness than the original scheme whereas in practice, their modifications create an output with embedding capacity like the original scheme but it is high quality and also blind.
Management of SME's Semi Structured Data Using Semantic Technique

Management of data for an organization is crucial task but when data goes to its complex form then it becomes multifaceted as well as vital. In today era most of the organizations generating semi structured or unstructured data that requires special techniques to handle and manage. With the needs to handle unstructured data, semantic web technology provides a way to come up with the effective solution. In this chapter Synthetic Semantic Data Management (SSDM) is explained that is based semantic web technique and will helps to manage data of small and Midsized Enterprise (SME). SSDM provide the procedure to handle, store, manages and retrieval of semi structured data.
Application of Natural-Inspired Paradigms on System Identification

In this chapter, the application of nature-inspired paradigms on system identification is discussed. A review of the recent applications of techniques such as genetic algorithms, genetic programming, immuno-inspired algorithms, and particle swarm optimization to the system identification is presented, discussing the application to linear, nonlinear, time invariant, time variant, monovariable, and multivariable cases. Then the application of an immuno-inspired algorithm to solve the linear time variant multivariable system identification problem is detailed with examples and comparisons to other methods. Finally, the future directions of the application of nature-inspired paradigms to the system identification problem are discussed, followed by the chapter conclusions.
Quantum effects

At the nanolevel, the principles of quantum mechanics are governed, therefore, this chapter presents the basic concept related to wave–particle duality and how Louis de Broglie gave a clue as to the wave-like nature of electrons. The experimental evidence of de Broglie’s hypothesis and uncertainty principle are also discussed in detail. A brief introduction to quantum dots and their applications is given, which is of great interest for researchers and scientists. The development of nanotechnology is based on Moore’s law, which predicts the rate of miniaturization and the costs of producing nanostructure components. Lastly, the important phenomenon of tunneling, its physical importance, and its applications are presented.
BAL-Net: Bi-Attention Learning for Single Image De-raining

Within the image-to-image translation framework, single image de-raining can be addressed by training a network with pairs of rainy and clean images, and training the network can be interpreted as learning to distill knowledge from the clean contextual regions to predict the corrupted pixels. To overcome the problem of over or under de-raining with existing models, a bi-attention mechanism is formulated for better rainy image restoration including both raindrop and rain streak removal. Specifically, the contextual attention map (directing where to focus on the background scenes) and rainy attention map (indicating corrupted regions to process) are firstly learned, and are then concatenated with the rainy image as input to the de-raining network for more effective representation learning and rainy image restoration. To better model the non-uniformly distributed raindrops or rain streaks and generate detail-preserving de-rained images, a multi-branch multi-scale module is proposed and incorporated into the dense residual blocks, which are stacked for constructing the bi-attention guided single image de-raining network. Comprehensive evaluation on both synthetic and real rainy datasets demonstrates the superiority of the proposed models over other methods.
Motivational and Ethical Issues in Seamless Learning

This chapter aims to explain seamless learning with regard to motivational issues and ethical usage of technology in the context of a mobile and ubiquitous Technology-Enhanced Learning (TEL). The examined TEL environment consisted of a popular technology integration project in Turkey called FATIH (in English, The Movement to Enhance Opportunities and Improve Technology). The FATIH project aims to provide students with opportunities for self-regulated, mobile, and ubiquitous learning via tablet computers, technology-rich classroom environments, and online digital sources. This chapter investigates and presents findings on the effects of technology-enhanced learning environments in terms of students' motivation and ethical issues during their interaction with the learning environment and community.
Introduction

Data mining is a new domain that has elevated the confluence of numerous disciplines with massive databases. The inspiring stimulus behind data mining is that these massive databases consist of information that is of high value to the dataset owners, but this information is concealed and remains uncovered. The motivating fact behind the data mining is to extract valuable information from the massive database which is closely related to exploratory data analysis. The exploration and analysis of massive data are extremely difficult and require huge computational time for analyzing the data. The visualization of data mining assists to deal with complex data wherein the user is directly related to the data mining technique. There are more data visualization methods that are designed to support the exploration of huge datasets. This chapter describes the introductory part of data mining techniques and the methodologies adapted for extracting the interesting data.
Policies

Local governments provide public services including police, fire departments, emergency services, and others. Operations are accomplished by following policies, laws, and regulations. Communities will need to integrate cybersecurity concepts into established community policies. Existing policies need to be reviewed for cybersecurity evaluating them for cyber integration and identifying critical services that could be disrupted or impacted by a cyber incident. Communities need to identify an authorization hierarchy that will makes decisions in regard to critical services being impacted by a cyber-attack. Roles need to be established and integrated into policies to identify existing capabilities to address cyber incidents and ultimately who will respond if needed. Public-private partnerships need to be reviewed and legal agreements crafted and signed before an incident occurs. These considerations are initial steps that can be taken as the community strives to improve its cybersecurity posture where community policies are concerned.
Research on the Application of Big Data Information Processing Technology in the Improvement of English Language Fossilization

Many students generally face a problem In the acquisition stage of language and culture. Some language errors are still unavoidable after repeated corrections, and a solidified error mode is gradually formed, that is, language "fossilization". This article attempts to adopt a teaching mode combining big data to cultivate students' cognitive flexibility and construct contextual knowledge with foreign language features, so as to alleviate fossilization and enable learners to master the real target language.
Study on the Introduction of High-Quality Educational Resources for Sino-Foreign Cooperative Education

In Sino-foreign cooperative education, high-quality introduced educational resources must benefit the growth and development of students, facilitate the school’s capacity building and the improvement of overall educational standards, and promote national socioeconomic development. It is necessary to establish and perfect the various working mechanisms for Sino-foreign cooperative education, restructure the strategic distribution of high-quality introduced educational resources, formulate relevant policy measures, explore new paths for cooperative education, strengthen initiative in cooperative education, and establish central planning and coordinating mechanisms for high-quality introduced educational resources; at the same time, it is necessary to strengthen capital investment, build a good social environment and atmosphere, establish a high-quality corps of talent for Sino-foreign cooperative education, and reinforce theoretical research on Sino-foreign cooperative education.
An Event-Based Data Warehouse to Support Decisions in Multi-Channel, Multi-Service Contact Centers

Multi-channel contact centers are an increasingly important component of today's business world. They serve as a primary customer-facing channel for firms in many different industries, and employ millions of operators across the globe. During their operation, they generate vast amounts of data, ranging from automatically registered logs to handwritten notes and voice recordings. Unfortunately, in most firms, data of interest is unstructured, and stored in several databases, making their exploitation very hard. This article presents a decision support system for a multi-channel, multi-service contact center for front office business process outsourcing, along with its prospective extension to a decision management system. Its core is an enterprise-wide data warehouse, based on the general concept of an event. The proposed system supports a broad new set of advanced analysis tasks, ranging from operator performance assessment to call-flow simulation and data mining, providing operational and management staff the basis for taking effective operative and strategic decisions.
Optimal Robot Path Planning With Cellular Neural Network

This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.
Multi-target tracking method based on FCM clustering

In order to accurately estimate the target number of the probability hypothesis density (PHD) filter, this paper proposes a cardinality compensation method based on fuzzy C-means (FCM) clustering. In the actual application of PHD filter tracking, when the noise and clutter in the measurement are high, the tracking loss will occur. For this reason, the measurement cardinality is obtained through data clustering. In order to verify the performance of the proposed method, simulations are carried out, and it is proved that the tracking performance of multiple targets has been improved, and the base estimation is more accurate.
Prostatic Adenocarcinoma, Diagnosis “Gleason Score”

Prostate Cancer is the one of the leading causes of death in the world among men, and one of the cancers that management has the most evolved in recent years. Indeed, the incidence of prostate cancer is increasing with diagnoses made more in younger patients. Prostate cancer is before all a pathological diagnosis. It is always based on a histological examination and or cytology. The variant, score and grade also the differentiation have to be correct and sure for an effective therapy. The only criterion that Gleason’s score depends is the architecture that is based on this last.
Cloud Computing in the 21st Century

The constant changes in technology has posed serious challenges to top management teams, employees, and customers on how to collect, store, and process data for competitive advantage and to make better decisions. In this chapter, to address this issue, we present the managerial perspective of cloud computing that provides the infrastructure and/or tools for decision making in the 21st century. Since the year 2000, the interest in cloud computing has had a steady increase. (Mason, 2002) Not only has cloud computing substantially lowered computing costs for corporations, it continues to increase their abilities for market offerings and to access customers' information with ease. Cloud computing has allowed managers to focus more on their business plans and bottom line to enhance competitive advantage.
Incorporating the Negotiation Process in Urban Planning DSS

Cooperation in multi-agent systems is necessary in order to perform complex tasks and lead Multi-agent System (MAS) towards its objective. Contract-Net Protocol (CNP) is one of the communication and coordination mechanisms used by multi-agent systems which prefer cooperation through interaction protocols. This paper proposes a new cooperation and negotiation protocol based on the principals of the Contact Net Protocol (CNP). The auhtors' suggested negotiation protocol is used to solve one of the problems in the context of the city planning which is the problem of election of urban projects. Their proposed protocol is intended to the decision makers in order to help them resolve the problem of the evaluation and the selection of the best urban project without the need to be together in a decision urban room.
From Citizens to Decision-Makers

Citizens' participation is a form of democracy in which citizens are part of the decision-making process with regard to the development of their society. In today's emergence of Information and Communication Technologies, citizens can participate in these processes by submitting inputs through digital media such as social media platforms or dedicated websites. From these different means, a high quantity of data, of different forms (text, image, video), can be generated. This data needs to be processed in order to extract valuable data that can be used by a city's decision-makers. This paper presents natural language processing techniques to extract valuable information from comments posted by citizens. It applies the Latent Semantic Analysis on a corpus of citizens' comments to automatically identify the subjects that were raised by citizens.
Character model identification and interactive AI simulation application

AI is the theory and development of computer systems that can operate and perform functions which would normally require human input, such as visual perception, speech recognition, and translating one language into another. Dr Pei-Chun Lin is a researcher based in the Department of Information Engineering and Computer Science at Feng Chia University in Taiwan. She is collaborating with other academics around the world to develop a robot that incorporates AI and will act in a way that suggests it is a thinking and feeling robot which humans can relate to. The character model identification is especially important for creating an AI robot. The team applied data analysis tools to confirm the model and incorporate fuzzy logic, which holds significant promise for the field of AI application. Lin believes that there is a great deal of potential for AI robots to improve the lives of people around the world.
A Classroom Students Convergent Behavior Analysis System Based on Image Recognition

Classroom behavior analysis is an effective way to evaluate the teaching effectiveness in the field of learning analytics. However, traditional classroom behavior analysis mainly focuses on the teacher's observation or manual analysis of the classroom videos, which are time-consuming, laborious and subjective. In this paper, we design and implement a classroom students convergent behavior analysis system which based on image recognition. To adapt to the teaching scene, a student face detection method based on MTCNN (Multi-task Cascaded Convolutional Networks) and a student head pose estimation method based on SSR-Net are proposed respectively. The face detection method is improved through NMS (Non-Maximum Suppression), pooling and convolution to alleviate the problems of partial occlusion, variable posture, small scale and large number of students in the classroom environment. For head pose estimation, we embed the ECA (Efficient Channel Attention) mechanism to improve detection accuracy and speed. We use the face detection and head pose estimation methods to identify the behavior of the student's head-up and then analyze the convergence of students. In the experiments, we first demonstrate the head-up detection approach which is the basic of the convergent behavior analysis is feasible and strong timeliness. Then, the equal interval sampling experiments of different classrooms prove that the convergence behavior analysis of the head-up can accurately feedback students' classroom learning and is practicality for teaching evaluation.
A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification

Artificial Neural Network (ANN) has played a significant role in many areas because of its ability to solve many complex problems that mathematical methods failed to solve. However, it has some shortcomings that lead it to stop working in some cases or decrease the result accuracy. In this research the authors propose a new approach combining particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to increase the classification accuracy of ANN. The proposed approach utilizes the advantages of both PSO and GA to overcome the local minima problem of ANN, which prevents ANN from improving the classification accuracy. The algorithms start with using backpropagation algorithm, then it keeps repeating applying GA followed by PSO until the optimum classification is reached. The proposed approach is domain independent and has been evaluated by applying it using nine datasets with various domains and characteristics. A comparative study has been performed between the authors' proposed approach and other previous approaches, the results show the superiority of our approach.