Reviewerly/exHarmony huggingface dataset:
CFIWSE: A Hybrid Preprocessing Approach for Defect Prediction on Imbalance Real-World Datasets

Software Defect Prediction (SDP) predicts new defects through machine learning trained with historical defect data. The distribution of software defects is highly unbalanced, which hinders the construction of defect prediction models. In addition, previous studies were usually validated by public datasets based on code metrics instead of real-world data. In this work, SNA metrics and code metrics are computed on 9 representative real-world projects. A hybrid preprocessing approach for defect prediction named CFIWSE is proposed to improve SDP performance through feature selection, minority sample synthesis and noise reduction, consisting of CFS and IWSE. CFS uses correlation analysis and nearest neighbor theory for feature selection. IWSE utilizes information weights and edited nearest neighbor rule to alleviate overfitting and noise introduced from minority sample synthesis. The proposed method is verified by experiments on real-world data, and the contribution of the method components and parameter sensitivity are explored.
Automated Modeling of Web Service Composition and Testing: A UML Sequence Diagram-Based Approach

The application of Service-Oriented Architecture (SOA) system software in safety-critical areas such as aerospace is gradually increasing, and the demand of high-quality SOA system software is increasing too. Due to the feature of loose coupling, SOA system software depends strongly on the correctness of the interfaces among services. Web service composition (WSC) testing in the integration phase of service composition thus becomes exceptionally critical. In a model-based testing framework., the test model lays a foundation for the subsequent test activities to proceed smoothly. In this paper, we propose an approach to automate modeling for WSC testing. The idea is to automate the construction of a highly visualized model by fetching adequate test information from two documents in the design phase. Compared to the existing modeling methods, the proposed method is more sufficient in WSC test information extraction. In addition, we also promote the degree of visualization and automation for test modeling. In the end, a case study is conducted to verify the effectiveness of the proposed modeling approach.
Technical Usability Assessment of Security Analysis Tools for Ethereum Based Smart Contracts

Over the past few years, Ethereum has surfaced as a widely adopted standard Blockchain platform that is increasingly being utilized to develop Decentralized Applications (DApps). By introducing Smart Contracts to software developers and programmers, Ethereum has triggered the development of countless Blockchain solutions. Among its main applications, many involve the exchange of valuable financial assets. Simply put, we cannot afford to base our Blockchain solutions or applications on potentially vulnerable smart contracts. This is where the Security Analysis Tools come into picture, for the timely detection of vulnerabilities in the Smart Contracts. Since this is a recent phenomenon, it offers a lot of research opportunities for us to contribute towards improving the existing state of security analysis tools and resolving their shortcomings. Although most of these tools have been evaluated in terms of effectiveness, installation and reliability; the literature largely lacks the technical usability perspective i.e. execution and evaluation. Therefore, based on a selection criteria, we committed our time to 4 such tools for an extensive usability assessment. We designed our usability study in a manner that combined the advantages of multiple evaluation methods. The results were useful not only in terms of comparative analysis, but also as a validation of the need of identified usability improvements.
Space-Time Video Super-Resolution 3D Transformer

Space-time video super-resolution, which aims to generate a high resolution (HR) and high frame rate (HRF) video from a low frame rate (LFR), low resolution (LR) video. Simply combining video frame interpolation (VFI) and video super-resolution (VSR) network to solve this problem cannot bring satisfying performance, which also requires a heavy computational burden. In this paper, we investigate a one-stage network to jointly up-sample video both in time and space. In our framework, a 3D pyramid structure with channel attention is proposed to fuse input frames and generate intermediate features. The features are fed into the 3D Transformer network to model global relationships between features. Our proposed network, 3DTFSR, can efficiently process videos without explicit motion compensation. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves better quantitative and qualitative performance compared to a two-stage network.
Effect of Trust and Institutional Quality on Cloud Federation Formation Using Agent-Based Modeling

Cloud providers make decisions on whom to interact with based on trust between cloud service providers. However, achieving trust has been a major obstacle to a wide cloud federation adoption. It has been observed that trust is largely determined by the provider, but trust can also be influenced by the external environment (such as by institutional trust, cultural trust, or legal framework) as well as by government policies or strategies. As a result of the cloud federation model, participants from different countries can collaborate with each other, sharing resources, transferring data, and exchanging knowledge, which is directly influenced by an organizational trust as well as institutional trust. Transferring data from one jurisdiction to another requires a legal framework and is a sensitive issue. Data protection concerns are not adequately addressed in a similar way in all countries, which negatively impacts trust. In this paper, we discuss trust from the perspective of institutional trust and propose an evaluation mechanism for trust during cloud federation formation. The proposed trust evaluation model utilizes neighbors’ feedback and institutional quality, to evaluate the cloud service provider trust. The evaluation of the model is conducted by utilizing an agent-based approach and the Netlogo simulation tool. The results show that the proposed model generates profit in the three scenarios while the comparative model, which doesn’t consider institutional trust, generates profits in only the two scenarios.
HOCC: An Ontology for Holistic Description of Cluster Settings

Ontologies have become the de-facto information representation method in the semantic web domain, but recently gained popularity in other domains such as cloud computing. In this context, ontologies enable service discovery, effective comparison and selection of IaaS, PaaS and SaaS offerings and ease the application deployment process by tackling what is known as the vendor lock-in problem. In this paper we propose a novel ontology named HOCC: holistic ontology for effective cluster comparison. The ontology design process is based on four different information categories, namely Performance, SLA, cost and environmental impact. In addition we present our approach for populating, managing and taking advantage of the proposed ontology as developed in a real world Kubernetes cluster setting, as well as instantiating the ontology with example services and data (namely performance aspects of a serverless function).
Towards the conceptual design of ML-enhanced products: the UX value framework and the CoMLUX design process

Abstract With the increasing utilization of machine learning (ML) to enhance products’ capabilities, the design research community has begun to explore how to support the conceptual design of ML-enhanced products. However, UX value creation of ML-enhanced products is still challenging because of ML's unique characteristics and numerous complex factors in conceptual design. To help designers create UX value for ML-enhanced products, we developed the UX value framework and the CoMLUX design process. The proposed framework describes how ML, stakeholders, and context co-create the UX value of ML-enhanced products, and identifies the growability and opacity of ML, helping designers systematically understand the co-creators while avoiding cognitive overload. The CoMLUX design process provides practical guidance for designing ML-enhanced products with growability and transparency. At last, we demonstrate the usage methods of the framework and process in an actual project and summarize the inspirations and limitations of our work.
Machine learning and deep learning techniques for detecting fraud involving credit cards

A credit card is still a highly common form of installment that allows for cashless purchases and is acceptable both offline and online to conduct payments and other trades. The prevalence of cloned credit cards is rising along with innovation. Budgetary fraud always seems to get worse when international communication gets better. These infringements have generated billions of dollars. These actions are executed with such skill that they vaguely align actual commercial transactions. Simple plan requirements and other less composite techniques won't function as a consequence. In order to lessen confusion and re-establish order, all banks currently need a well-organized approach for identifying credit card fraud. It has taken into consideration a number of common machine learning and deep learning methods for the classification of default accounts, which basically entails analyzing credit card fraud. It consist of synthetic neural networks, decision trees, random forests, and logistic regression (ANN). For every approach, the model has been trained and its accuracy has been assessed. The classification computations are merged to create a sample, and then it is compared to different deep learning and machine learning algorithms.
Decision-Based DCNs

This chapter first provides a brief overview of memory-based Deep Cognitive Networks (DCNs). Then, representative models from two aspects in terms of normative decision and descriptive decision are introduced and analyzed, as well as their relation to important theories, computational models and experimental evidences in cognitive psychology. At last, this chapter is briefly summarized.
The Curation, Interfacing and Analysis Lifecycle of Blockchain Application Data

This paper discusses the lifecycle of data for next generation Web3 decentralized applications, describing how data can be specified, curated, and analyzed. The focus of this work is to study the different types of approaches for indexing blockchain data in a way that application data and relationships can be designed, retained and exposed. Blockchain transactions conform to smart contracts definitions, which can have inherent structures through standards such as NFTs, but can also be designed in a custom way for bespoke use cases. Existing Web3 approaches either focus on service-based approaches that build custom pipelines that extract data in a specific way, or by creating indexes after data has been written without prior influence on how that data should be consumed. SIMBA Chain is a platform that simplifies the management of a Web3 application’s lifecycle and takes an alternative approach by enabling design-time specification of transaction interrelationships. SIMBA enables smart contracts to annotate data relationships between smart contract methods to effectively enable schemas to be defined a priori within the source code. By using these relationships, it then autogenerates a GraphQL schema for convenient consumption. We discuss the pros and cons of such an approach and present three application use cases in coffee tracking, NFT car titles and a supply chain scenario, where the approach was extended to also connect and search data across multiple blockchains.
Attention-Based DCNs

This chapter first provides a brief overview of attention-based Deep Cognitive Networks (DCNs). Then, representative models from two aspects in terms of hard attention and soft attention are introduced and analyzed, as well as their relation to important theories, computational models and experimental evidences in cognitive psychology. At last, this chapter is briefly summarized.
A Black-Box Graph Partitioner for Generalized Deep Neural Network Parallelization

In the quest for higher accuracy, large deep neural networks (DNNs) have grown significantly over the past few years. Training and executing large networks with trillions of parameters requires high-end hardware that is expensive to own or rent. A more economical alternative is to distribute the workload to several less powerful but cheaper machines. To devise an efficient workload division, existing parallelization strategies require users to posses intricate knowledge of the model, available hardware, and algorithms. In this paper, we present BBGraP, a device- and model-agnostic black-box partitioner that computes efficient parallelization plans for deep learning inference. For a given network and hardware configuration, BBGraP generates a data-parallel execution plan for each machine. The initial workload partition is optimized by eliminating redundant operations, data transfers, and synchronization points. As a proof-of-concept, BBGraP is applied to a set comprising three distributed nodes and achieves a 30% reduced latency compared to a single node.
Serverless Computing: Introduction and Research Challenges

Serverless computing is a technology that offers the ability to create modular, highly-scalable, fault-tolerant applications, leveraging container-based virtualisation to deploy applications and services. It is revolutionising the way we think about application development, and serverless platforms are already ubiquitous in the public and private sectors. Commercial solutions dominate the market with widespread adoption from industry giants such as Amazon, Google and Microsoft, though open-source solutions do exist such as Apache OpenWhisk, Fission and OpenFaaS. This tutorial will present the state-of-the-art in serverless computing research, and provide useful insights into the main challenges that motivate researchers to work on this topic. It will also identify research gaps for future research.
Introduction

This chapter provides an overview of the book. First, we introduce the background of Deep Cognitive Networks (DCNs), including their importance and brief history. Then, we analyze the motivation of DCNs and define their scope of modeling key cognitive mechanisms such as attention, memory, reasoning and decision. Finally, we outline the content organization of this book.
A survey report of different technique or component for dimensionality reduction in data science

To train a machine learning model using a data record which has multiple properties, is typically a difficult task. The development of over fitting of the susceptible model and the growth of model characteristics are always inversely correlated. Since not all of the traits are always significant, this observation was made, and for instance, several attributes might merely make the data noisier. Techniques for dimensionality reduction are employed to address this issue. In this paper we have also discussed the different approaches and techniques of dimensionality reduction techniques.
Review on various face recognition databases

Face recognition is one of the multimedia items that has seen a remarkable increase in popularity in recent years. Face continues to be the most difficult study topic for experts in the field of computer vision and image processing since it is an item with different properties for detection. We have attempted to handle the most challenging facial aspects in this survey work, including posture invariance, aging, illuminations, and partial occlusion. When applied to facial photographs, they are regarded as essential components of face recognition systems. The most recent face detection methods and techniques are also examined in this paper, including Eigenface, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor Wavelets, Elastic Bunch Graph Matching, 3D Morphable Models, and Hidden Markov Models. Many testing face databases, such as AT &amp; T (ORL), AR, FERET, LFW, YTF, and Yale, also reviewed. However, the purpose of this study is to present a thorough literature assessment on face recognition and its applications.
An Architecture to Suit You

In this chapter, you will look through some possible architectural patterns that can be used to build .NET MAUI applications. The objective is to provide you with enough detail to help you find the architecture that best fits you. I want to point out that there are no right answers concerning which architecture to choose. The best option is to go with one that you feel will benefit you and your team.
Low-Resource Learning

This chapter discusses another important topic that is closely related to transfer learning: low-resource learning. Low-resource learning refers to the situation where there are not sufficient hardware resources or labeled data to train a model or even perform fine-tuning. Low-resource learning has wide applications in real world. Particularly, we will first introduce how to compress transfer learning models. Then, we introduce three low-resource learning paradigms: semi-supervised learning, meta-learning, and self-supervised learning. While transfer learning has made consistent success in multiple fields, its combination with other learning paradigms can often generate bigger impacts beyond single transfer learning. We will introduce their basic problem definitions, algorithms, and possible applications.
Generalization in Transfer Learning

While fine-tuning and domain adaptation focus on the performance on the target domain, we discuss the generalization of transfer learning in this chapter. Specifically, we introduce the problem definition, main algorithms, and theory of domain generalization.
Transfer Learning in Complex Environments

The application environment is dynamically changing, so does the algorithm. In order to cope with the changing environment, there are several new transfer learning algorithms being developed. We define the complex environment mainly based on the traits of the training data since data is the key to modern transfer learning. In this chapter, we briefly introduce some of these complex environments and show how transfer learning algorithms can be adapted to deal with such new challenges. Note that there are multiple new settings in recent literature and we cannot cover them all.
Introduction

In this chapter, we introduce the background of transfer learning to give an overview of this area. This chapter can be thought of as a broad introduction to readers who have never experienced transfer learning. Thus, this chapter is self-contained. Experienced readers can skip it with no harm.
Pengaruh Media Video Animasi terhadap Kemampuan Keaksaraan dan Berpikir Simbolik Anak TK

This study aims to determine the effect of animated video media on literacy and symbolic thinking abilities of group A children in Aisyiyah Kindergarten, Ngunut Tulungagung District. This research was conducted using a quasi-experimental design through a quantitative approach. The experimental design used is a nonequivalent control group design. The research subjects were group A children of TK Aisyiyah Bustanul Athfal 3 Ngunut and TK Aisyiyah Pulosari, with a total of 30 children in the experimental group and 30 children in the control group. The data analysis technique in this study used non-parametric statistics, the Mann Whitney test. The results of the study, there are differences in the average value of literacy skills. The average value of literacy ability of the experimental group at the time of the pretest was 1.58 and after being given treatment, the posttest average value of 3.30. While the average value of the ability to recognize patterns in the control group during the pretest was 1.54 and after being treated, the posttest was 2.55. In conclusion, there is an effect of animated video media on children's literacy skills and there is an influence of animated video media on the symbolic thinking ability of group A children in Aisyiyah Kindergarten, Ngunut Tulungagung District.&#x0D; &#x0D; Keywords: Literacy Ability, Symbolic Thinking Ability, Animated Video Media
Capturing Cross-View Dynamics Using Recurrent Neural Networks for Multi-modal Sentiment Analysis

Sentiment analysis through multi-modal approaches has shown the potential to outperform uni-modal approaches. One of the challenges in this domain is to effectively model cross-view dynamics from view-specific dynamics. This paper proposes a model that captures both dynamics, and applies attention over the contributing features from each modality, to predict utterance-level sentiments. In the model, the paper introduces a deep learning pipeline called the Cross-view Recurrent Neural Network Pair to compute cross-view dynamics and integrate them with view-specific dynamics, to obtain contextually rich utterance representations. The proposed model is evaluated on CMU Multi-modal Opinion-level Sentiment Intensity (CMU-MOSI) and CMU Multi-modal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) datasets. The model achieves an accuracy of 81.78% on CMU-MOSI and 80.45% on CMU-MOSEI.
Labeling Algorithms for Sensitive Areas of Visual Images in Multimedia Environment

With the development of computers, multimedia information has become an indispensable part of people’s lives. In the field of visual image recognition, how to process and detect images is very important and difficult. Therefore, studying image features and their annotation algorithms is the basis of image recognition and processing. This article mainly uses experimental method and comparison method to detect the standard rate, over-standard rate and miss-standard rate of the selected images. Experimental results show that for the calibration of birds and human bodies, the missing and over-standard rates are significantly higher than those of other static objects, and the standard rates are only 26% and 35%. The EM algorithm can be used to identify and calibrate relatively static objects, but the calibration of dynamic objects needs further improvement.
Recommendation Algorithm Based on Product Category Path

In recent years, due to the rise of deep learning, most researchers are trying to combine the traditional recommendation system with deep learning, however the traditional recommendation model is still suitable for most scenes or be widely used as an auxiliary model of deep learning recommendation model because of its strong interpretability, fast training and deployment. When the recommendation algorithm calculates the recommended goods for users, this paper establishes the category path of goods, then find the goods according to the category path of goods and recommend them. At the same time, according to the characteristics of the data obtained in this paper, the similarity of goods is calculated by the combination of TF-IDF and Jaccard similarity coefficient, so as to infer the list of goods recommended to users.