Reviewerly/exHarmony huggingface dataset:
Learning From Imbalanced Data

A very challenging issue in real-world data is that in many domains like medicine, finance, marketing, web, telecommunication, management, etc. the distribution of data among classes is inherently imbalanced. A widely accepted researched issue is that the traditional classifier algorithms assume a balanced distribution among the classes. Data imbalance is evident when the number of instances representing the class of concern is much lesser than other classes. Hence, the classifiers tend to bias towards the well-represented class. This leads to a higher misclassification rate among the lesser represented class. Hence, there is a need of efficient learners to classify imbalanced data. This chapter aims to address the need, challenges, existing methods, and evaluation metrics identified when learning from imbalanced data sets. Future research challenges and directions are highlighted.
UEIN: A User Evolving Interests Network for Click-Through Rate Prediction

The Click-Through Rate (CTR) prediction via modeling the change of user interest is of great significance task to industrial applications. Most of the existing methods based on collaborative filtering (CF) and recurrent neural network (RNN) that focus on capturing user's long-term and short-term interests separately, or combine these interests in a tough way. However, the effect of long- and short-term interests accounts different ratios to the next-item decision of user, rather than equally. To address this issue, we propose UEIN, a novel User Evolving Interest Network for CTR prediction task, that can capture the change of user preference via adaptively fusion the user's long-term and short-term interests over time. To explicitly learn the user preference on item category, we add the item category information into user behavior sequence, by clustering the items into different categories. Then we divide user historical behaviors into two sequence parts, long-term and short-term behaviors sequence. Based on this, we leverage the attention network to learn the user's current interest from the short-term behaviors sequence, while a dense network is adopted to extract the user's general interest from the long-term sequence. After that, an adaptive fusion mechanism is proposed to combine the current and general interest adaptively, and the prediction result is generated based on the user's combined interest. Extensive experimental results on industrial datasets demonstrated that the superiority of our proposed model, compared to the state-of-the-art models.
A Framework for Modeling Non-Functional Requirements for Business-Critical Systems

Proper definition and implementation of NFRs is critical. In case they are Over-specify, then the solution may be too costly to be viable; in case they are underspecified or underachieve them, the system will be inadequate for its intended use. An adaptive and incremental approach to exploring, defining, and implementing NFRs is vital for the successful delivering of NFRs. NFRs are not product backlog items. The constraints on development that limit degree of design freedom while building system. These constraints are articulated in the acceptance criteria for multiple product backlog items. For e.g., SAML-based Single Sign-on - SSO is a requirement for the product. SSO is a functional requirement, while SAML is the constraint. In that sense, any backlog item building sign-on functionality would reference the SAML constraint in its acceptance criteria. The sections that follow describe the phases of NFR journey from discovery to deployment for a large complex business critical systems including the NFR modelling framework.
A Three-Vector Approach to Blind Spots in Cybersecurity

Cyberattacks are often successful due to “blind spots”: biases and preconceived information that affect human decision making. Blind spots that obstruct a person's view of malicious activity may result in massive economic losses. This chapter examines eight cases of successful cyberattacks from economic, technological, and psychological perspectives to blind spots, termed the “core vectors.” While previous research has focused on these vectors in isolation, this chapter combines the vectors for an integrated view. As a result, the chapter provides a novel list of blind spots that enable cybercrime.
Intelligent Algorithm-based Online Aided Learning System for Ideological and Political Education in Computer Major Courses

In order to improve the online auxiliary learning ability of ideological and political courses for computer majors, and to improve the network management and informatization level of online auxiliary information for ideological and political courses for computer majors, this paper puts forward the design method of online auxiliary learning system for ideological and political courses for computer majors based on intelligent algorithm. The system is divided into general structure design, algorithm design and software development design. The semantic extraction method is used to retrieve the information of online auxiliary learning of ideological and political courses for computer majors, and the semantic correlation feature quantity of online auxiliary information of ideological and political courses for computer majors is extracted. The similarity feature analysis model of online auxiliary learning of ideological and political courses for computer majors is constructed. The XML model and Zigbee technology are adopted. The network modular design and the networking control design of Internet of Things are realized. RFID radio frequency tags are used to identify the two-dimensional code tags of online auxiliary information of computer professional courses. Through the software development and network modular design under XML model and Zigbee technology, the online auxiliary information of computer professional courses is realized. The test shows that the designed online auxiliary learning system of ideological and political courses for computer majors has good output stability, and the local sharing level of online auxiliary information of ideological and political courses for computer majors is high.
RAYGO: Reserve As You GO

The capability to predict the precise resource requirements of a microservice-based application is a very important problem for cloud services. In fact, the allocation of abundant resources guarantees an excellent quality of experience (QoE) for the hosted services, but it can translate into unnecessary costs for the cloud customer due to the reserved (but unused) resources. On the other side, poor resource provisioning may turn out in scarce performance when experiencing an unexpected peak of demand. This paper proposes RAYGO, a novel approach for dynamic resource provisioning to microservices in Kubernetes that (i) reliefs the customers from the definition of appropriate execution boundaries, (ii) ensures the right amount of resources at any time, according to the past and the predicted usage, and (iii) operates at the application level, acknowledging the dependency between multiple correlated microservices.
TD-GAT: Graph Neural Network for Fault Diagnosis Knowledge Graph

As an important resource for artificial intelligence applications, the ultimate goal of the knowledge graph (KG) is to make artificial intelligence smarter. In recent years, graph representation learning and graph neural networks are essential to the improvement and application of knowledge graph. In this paper, in order to solve the fault diagnosis problem of the system at runtime and realize the intelligent auxiliary decision-making of fault diagnosis, we introduce the knowledge graph technology. However, according to existing research, although some papers have used knowledge graphs in the field of fault diagnosis, they are only limited to use existing fault knowledge data to build knowledge graphs. therefore, this paper proposes a new method of knowledge graph embedding and knowledge reasoning for fault diagnosis knowledge graph, called TransD Graph Attention Network (TD-GAT). This model (TD-GAT) explicitly learns the entity representation of the knowledge graph in an end-to-end learning mode. Finally, this paper uses the fault entity classification task as an example to demonstrate the superiority of our model (TD-GAT) in the fault diagnosis knowledge graph we have established.
HyperBench

Virtualization is becoming increasingly common in data centers due to its various advantages. However, how to choose among different platforms, including both software and hardware, is a considerable challenge. In this context, evaluating the virtualization capabilities of different platforms is critically important. Regrettably, the existing benchmarks are not qualified for meeting this requirement. Different hardware mechanisms and hypervisor designs introduce many different hypervisor-level events, such as transitions between VMs and the hypervisor, two-dimensional page walk, and binary translation. These events are key factors affecting virtualization performance. Existing benchmarks either overlook these changes or are tightly coupled to a particular hypervisor. In this paper, we present HyperBench, a benchmark suite that focuses on the capabilities of different virtualization platforms. Currently, we design 15 hypervisor benchmarks covering CPU, memory, and I/O. The virtualization-sensitive operation in each benchmark triggers hypervisor-level events, which examines the platform's ability in the target area. HyperBench is designed as a custom kernel which can adapt to different hypervisors and architectures. What's more, adding a new benchmark is pretty easy. Finally, we perform a series of experiments on the host machine and several popular hypervisors, such as QEMU, KVM, and Xen, demonstrating that HyperBench is capable of revealing the performance implications of the hardware mechanism and hypervisor design.
STEM PROJECT — TOOL FOR THE FORMATION OF COMPETENCIES OF APPLICANTS FOR EDUCATION

The article attempts to define the concepts: “STEM-project” and “project with elements of STEM”. It is proposed to interpret the concept of “project with elements of STEM” as a set of practical forms and methods that effectively form the key competencies needed by each person for successful life, personality development, motivation to learn, creative thinking, research skills, ability to self-study through implementation competence approach during the implementation of STEM-projects. The necessity of implementation of project-research activity with STEM elements is substantiated not only at mastering of subjects of a natural-mathematical cycle, but also at mastering of all school courses that is quite real considering high level of integration of knowledge and skills of pupils in modern conditions. The introduction of STEM-projects in educational institutions determines the self-determination of young people, the effective solution of problems and tasks of career guidance work. The project activity is one of the most promising components of the educational process, which creates conditions for creative self-development and self-realization of students, forms all the necessary life competencies: multicultural, speech, information, political and social. The independent search for knowledge, their systematization, the ability to navigate in the information space, see the problem and make decisions is through the method of projects — effective learning technology. The relevance of the article is due to the need to expand the scientific and conceptual apparatus of STEM-education, providing practical advice on the implementation of forms, methods and techniques of STEM training in the general educational process.
A Chinese Medical Question Answering System Based on Knowledge Graph

Recently, medicine and health have been more and more concerned with people. This paper focuses on building a Chinese medical question answering system based on a knowledge graph. Firstly, a medical knowledge graph is built by structured data and is stored in the Neo4j, which contains 36825 entities, 210018 relationships, and collects 14366 kinds of diseases. When getting a question input, overlap-degree, edit-distance, cosine-similarity, are combined to match the target disease, with up to 75.49% accuracy. Tf-corpus algorithm is designed to extract features of the question, and the Naive Bayes Classifier is used to judge intention, with up to 76.84% accuracy. The answer can be found by disease and intention, and the Q & A task has been completed.
Improved Explainability through Uncertainty Estimation in Automatic Target Recognition of SAR Images

There have been numerous advancements in machine learning technologies in recent years, which has led to the application of machine learning algorithms to automatic target recognition. Two key challenges for these methods are the lack of sufficient training datasets and non-transparent deep models. In this paper, experiments are conducted that investigate the application of target detection using a model trained on the MSTAR to detect targets in another dataset, as well as the investigation of uncertainty estimates in Bayesian convolutional neural networks and how these outputs can improve confidence in the model’s predictions. The model can correctly detect targets in the test scene’s, as well as targets not seen from the MSTAR dataset. The output of the Bayesian convolutional neural network is used to create uncertainty heat maps. The epistemic uncertainty is the uncertainty created by the model and aleatoric is created by the data. These heat maps are overlaid on SAR images, thereby aiding in explainability by highlighting regions in the SAR images that exhibit high uncertainty from a classification point of view. Hence, uncertainty estimates from the Bayesian model give insight into the confidence of its predictions and show promise to improve trust between users and the model.
Interpretable Neural Networks Based on Continuous-Valued Logic and Multi-criteria Decision Operators

In this last chapter, we demonstrate how combining neural networks with continuous logic and multi-criteria decision-making tools can reduce the black-box nature of neural models. We show that nilpotent logical systems studied in the previous chapters offer an appropriate mathematical framework for the hybridization of continuous nilpotent logic and neural models, and help improve the interpretability and safety of machine learning. In this approach, perceptrons model soft inequalities; namely membership functions and continuous logical operators. We design the network architecture before training, using continuous logical operators and multi-criteria decision tools with given weights working in the hidden layers. Designing the structure appropriately leads to a drastic reduction in the number of parameters to be learned. Our theoretical basis offers a straightforward choice of activation functions (the cutting function or its differentiable approximation, the squashing function), and it also offers an explanation for the great success of the rectified linear unit (ReLU). In this study, we focus on the architecture of a hybrid model and illustrate the results using practical neural applications.
Clustering algorithms

This chapter covers the clustering process. Clustering has been, and will continue to be, a core capability for data analytics in biomedical applications. The reason it is so important is the volume and diversity of data encountered. This chapter reviews the proximity measures and then explores the most popular clustering algorithms. Through the sections of this chapter, the reader will be introduced to many clustering algorithms, exploring its origin, math, and their applications. The last section of this chapter covers the adaptive resonance theory and its different algorithms. This chapter suppose to provide more than intermediate level of knowledge about clustering algorithms to the reader. The last part of chapter three discusses the Adaptive resonance theory, its derivatives, enhancements, and their applications.
Visualizing Network Data

UpDrawing, graph|( until this point, we have spoken only loosely of displaying network graphs, although we have shown several examples already. Here in this chapter we consider the problem of display in its own right. Techniques for displaying network graphs are the focus of the field of graph drawing or graph visualization. Such techniques typically seek to incorporate a combination of elements from mathematics, human aesthetics, and algorithms.
Nominalizations in Requirements Engineering Natural Language Models

It is a usual practice to use natural language in any document intended for clients and users in the requirements engineering process of a software development. This facilitates the comprehension of the requirements engineer's proposals to clients and users. However, natural language introduces some drawbacks, such as ambiguity and incompleteness, which attempt a good comprehension of those documents. Glossaries help by reducing ambiguity, though they introduce their own linguistic weaknesses. The nominalization of verbs is one of them. There are sometimes appreciable differences between using a verb form or its nominal form, while in other cases they may be synonyms. Therefore, the requirements engineer must be aware of the precise meaning of each term used in the application domain in order to correctly define them and properly use them in every document. In this chapter, guidelines about treatment of verb nominalization are given when constructing a specific glossary called Language Extended Lexicon.
Special issue on informatics/data analytics in smart tourism

With the rapid growth of multimedia data, cross-media hashing has gained more and more attention. However, most existing cross-modal hashing methods ignore the multi-label correlation and only apply binary similarity to measure the correlation between two instances. Most existing methods perform poorly in capturing the relevance between retrieval results and queries since binary similarity measurement has limited abilities to discriminate minor differences among different instances. In order to overcome the mentioned shortcoming, we introduce a novel notion of instance similarity method, which is used to evaluate the semantic correlation between two specific instances in training data. Base on the instance similarity, we also propose a novel deep instance hashing network, which utilizes instance similarity and binary similarity simultaneously for multi-label cross-model retrieval. The experiment results on two real datasets show the superiority of our proposed method, compared with a series of state-of-the-art cross-modal hashing methods in terms of several metric evaluations.
Private Estimation of Symptom Distribution for Infectious Disease Analysis in Edge Computing

Distribution estimation of physical symptom is a powerful statistical technique to monitor and predict the situation of infectious diseases. With the widespread popularization of smart devices, edge computing is an efficient computing paradigm, which takes advantage of these devices to implement the safe and fast collection and distribution analysis of physical symptoms. However, the sharing of individuals' data, especially of sensitive information (e.g., age, symptom), inevitably raises the great privacy concerns of people due to the possible leakage and the illegal utilization. It is necessary and important to not only implement the highly intelligent epidemic analysis, but also guarantee the privacy of data. In this paper, we focus on locally private distribution estimation of physical symptom with attribute constraints in edge computing. At first, we present an edge-computing-based framework for distribution estimation of physical symptoms, in which each component undertakes the respective task. Then, we propose a naive privacy algorithm, i.e., NAIVEPRIVDISTEST, to directly perturb the values of quasi-identifying attributes and physical symptoms. Finally, extensive experiments demonstrate that NAIVEPRIVDISTEST has the accurate distribution estimation under the privacy constraints.
Wide-Area Multistatic Sonar Tracking

Sensors with poor bearing resolution pose a significant challenge for multi-target tracking, as cross-range error becomes very large at long ranges. While multi-sensor fusion provides benefit towards higher-precision tracking, there are two key difficulties to confront. The first is to address measurement association ambiguities, which we address via advanced multiple-hypothesis tracking. The second is to perform robust track initialization and filtering, which we achieve via a two-point filter initialization approach followed by (sequential) extended Kalman filtering. In the specific context of active sonar tracking, the impact of finite sound speed poses an additional challenge. Addressing this requires a generalized MHT solution that accounts for measurement-specific time stamps and allows for out-of-sequence measurement processing. The enhancements discussed in this paper yield a robust capability for wide-area multistatic sonar tracking.
Smart economics

This chapter provides an overview of the Smart Economics approach, embedding it historically in international gender policies, outlining the contours of its propositions together with critiques, and highlighting the way it has been embraced by businesses. It argues that the approach is more than a World Bank gender mainstreaming strategy: instead, it is the ideological component of a hegemonic project to give neoliberalism a feminist face. The approach incorporates feminism into neoliberalism, coopting feminist ideas; but these ideas come to inflect neoliberalism, creating potential openings for feminist agendas. The demand for gender equality has seemed to stand in tension with the Bank’s single-minded focus on economic growth, a tension that the Bank could safely ignore as long as its gender equality policies mostly focused on improving women’s “human capital,” that is, education and health. Smart Economics and womenomics provide the ideological underpinnings of politico-economic project, which is driven by transnational social forces, including corporations and international intergovernmental and nongovernmental organizations.
Human Machine Interface Wheel Chair by using Wi-Fi Communication for Disabled Person

The truly tested individuals are facing the challenges in strolling due to the disease, injury, or inability. Wheelchair offers them portability with the ability to enhance their quality of lives. The individual gives their voice through the android versatile and further the voice will get transformed into content. Then, the yield will be given to the small scale controller. This framework development is controlled by utilizing Bluetooth module with the help of servo engines. This proposed framework has a battery controlled wheelchair with servo engines. Moreover, the existing joystick-based framework should also get appended to the current framework. This framework remains straight forward and effective to disentangle the matter of truly tested individuals and furthermore, it remains as the least difficult usefulness with increased straight forwardness and minimal effort.
CONSUMER REQUIREMENTS FOR ELECTRONIC INFORMATION SERVICES AS A BASIS FOR ASSESSING THEIR QUALITY

Issues of assessing the quality of electronic information services of libraries are considered. The importance of taking into account consumer requirements for information services for managing the quality of customer service is noted. The concept of the quality of library information service is characterized as an applied concept related to the requests and expectations of the user to whom the service is provided. Consumer requirements for electronic information services, highlighted by library and information specialists, as well as specialists in the field of qualimetry, are described.