Reviewerly/exHarmony huggingface dataset:
Radiator threat evaluation with missing data based on improved technique for order preference by similarity to ideal solution

To solve the problem of the missing data of radiator during the aerial war, and to address the problem that traditional algorithms rely on prior knowledge and specialized systems too much, an algorithm for radiator threat evaluation with missing data based on improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been proposed. The null estimation algorithm based on Induced Ordered Weighted Averaging (IOWA) is adopted to calculate the aggregate value for predicting missing data. The attribute reduction is realized by using the Rough Sets (RS) theory, and the attribute weights are reasonably allocated with the theory of Shapley. Threat degrees can be achieved through quantization and ranking of radiators by constructing a TOPSIS decision space. Experiment results show that this algorithm can solve the incompleteness of radiator threat evaluation, and the ranking result is in line with the actual situation. Moreover, the proposed algorithm is highly automated and does not rely on prior knowledge and expert systems.
RESTful Web Service for Madurese and Indonesian Language Translator Applications on Android Devices

There is no doubt that an Indonesian and Madurese bilingual translator system that can run on all platforms is highly needed. On the bright side, a Web-based Indonesian and Madurese bilingual translator is available at Madura.web.id. However, there is no translator application on Android devices that can be widely used by smartphone users. This study aims to create a mobile application for Indonesian-Madurese translators using RESTful API with JSON data format. In order to build a translator system that can be used by all platforms, including Android, a web service must be created. Web service is a standard and a programming method for sharing data between several applications. The architecture used in this study is RESTful Web Service because it is lighter and faster. As for text format used for data exchange, JSON format is easier to encode or decode by mobile devices. Based on the test results of response time for Madurese and Indonesian bilingual translators on Android devices, it was faster than the translator system on the existing website. The measurement result of testing on 2G networks showed the average web service response time was 31% faster than the website, 77% faster for 3G networks, and 73% faster for 4G networks.
Hidden Markov models as recurrent neural networks: An application to Alzheimer's disease

Hidden Markov models (HMMs) are commonly used for disease progression modeling when the true patient health state is not fully known. Since HMMs typically have multiple local optima, incorporating additional patient covariates can improve parameter estimation and predictive performance. To allow for this, we develop hidden Markov recurrent neural networks (HMRNNs), a special case of recurrent neural networks that combine neural networks' flexibility with HMMs' interpretability. The HMRNN can be reduced to a standard HMM, with an identical likelihood function and parameter interpretations, but it can also combine an HMM with other predictive neural networks that take patient information as input. The HMRNN estimates all parameters simultaneously via gradient descent. Using a dataset of Alzheimer's disease patients, we demonstrate how the HMRNN can combine an HMM with other predictive neural networks to improve disease forecasting and to offer a novel clinical interpretation compared with a standard HMM trained via expectation-maximization.
Alphanumeric Database Security through Digital Watermarking

As the demand of online data availability increases for sharing data, business analytics, security of available data becomes important issue, data needs to be protected from unauthorized access as well as it needs to provide authority that the data is received from a trusted owner. To provide owners identity digital watermarking technique is used since long time for multimedia data. This paper proposed a technique which supports watermarking on database as most of the data available today is in database format. The characters to be entered as watermark are converted into binary values; these binary values are hidden in the database using space character. Each bit is hidden in each tuple randomly. Ant colony optimization algorithm is proposed to select tuples where watermark bits are inserted. The proposed system is enhanced in terms of security due to use of ant colony optimization and resilient because even if some bits are modified the hidden text remains almost same.
Research and Application of Lightning Monitoring and Early Warning Network for Transmission Lines

The important transmission lines have the characteristics of large transmission capacity, long line, high tower, complex corridor terrain, which make the risk of lightning trip high. According to the demand of lightning early warning for transmission lines, the construction scheme of lightning early warning network for transmission lines is proposed. Firstly, the lightning risk level of transmission lines is evaluated, and the topological relationship between lightning movement trend and transmission line trend is considered. Combined with the operation and site selection characteristics of the early warning device, the distribution method of lightning early warning devices is proposed. A construction scheme of lightning monitoring and early warning network based on lightning strike risk assessment level of transmission lines is proposed, which provides technical support for the application of lightning early warning in power grid.
Scientometric Knowledge and Tools in Modern Information Web Space

In recent years, modern problems of scientometrics have become one of the most actual topics of discussion, both among the scientific community and among government representatives in the field of science and education. The reason is the introduction into scientific reality of a system for assessment the effectiveness of Russian researchers. Accordingly, competent coverage in the information space of the development and application of scientometry requires fundamentally new communication approaches. In this scientific article, the authors analyze methods in promoting scientometric knowledge in the Russian-language information web space and identify the most effective ones.
Role of Artificial Intelligence in Bank’s Asset Management

Banking operations have been supported by latest technology-based systems along with the traditional processes which prevailed in earlier banking era. With the large number of smart phone users, there has been a paradigm shift in increasing demand for digital products like mobile banking, Internet banking, E-wallets, etc. A common trend in banking technology is using an application programming interface (API) that enables a third-party application to use an interface through which the customer can access a variety of services. Besides APIs, technologies like blockchain and artificial intelligence (AI) have great impact in changing the face of banking industry. However, this exposes the banks to a variety of cybersecurity threats which may cause service disruptions to the customers. Therefore, this study discusses the pros and cons of these AI techniques along with their scope of application for banks’ asset management.
Ethical Issues with Using Internet of Things Devices in Citizen Science Research: A Scoping Review.

Our chapter presents a scoping review of published scientific studies or case studies of scientific studies that utilise both citizen scientists and Internet of Things devices. Specifically, we selected studies where the authors had included at least a short discussion of the ethical issues encountered during the research process. Having conducted a search of five databases (IEEE Xplore, Scopus, Web of Science, ProQuest, and PubMed), we identified 631 potential results. Following abstract and title screening, and then full text eligibility assessment, we identified 34 published articles that matched our criteria. We then analysed the full text for these articles inductively and deductively, coding ethical issues into three main categories. These categories were autonomy and data privacy, data quality, and intellectual property. We also analysed the full text of these articles to see what strategies researchers took to resolve these ethical issues, as well as any legal implications raised. Following this analysis, our discussion provides recommendations for researchers who wish to integrate citizen scientists and Internet of Things devices into their research. First, all citizen science projects should integrate a data privacy protocol to protect the confidentiality of participants. Secondly, scientific researchers should consider any potential issues of data quality, including whether compromises might be required, before establishing a project. Finally, all intellectual property issues should be clarified both at the start of the project and during its life-cycle. Researchers should also consider any ethical issues that might flow from the use of commercially available Internet of Things devices for research.
An Evaluation of Tools for Identifying Vulnerabilities in Open Source Dependencies for NodeJS Applications

Software developers commonly use open source libraries, known as “software dependencies”, to build their applications to faster development of larger software applications. One way to track software dependencies is to use tools designed for just that purpose. Dependence vulnerability analyzers evaluate software dependency versions and compare them against lists of known vulnerabilities. This research will evaluate these tools by comparing the ease of installation and configuration, simplicity of keeping them up to date, readability of their reports, and ability to have them run automatically against a codebase. Finally, we will also compare the scan results of them.
A Convolution Operation Method Based on Domain Transformation

Abstract The convolution operation takes up a large proportion in the neural network, which is a kind of computation resource consuming operation. In this paper, domain transformation is introduced to participate in convolution, which reduces the computation of convolution operation. The acceleration is obvious in the inference operation of convolutional neural network.
Smart bookcase based on image recognition

With the improvement of people’s living standards, the demand for books and the efficiency and accuracy of accessing books are also increasing. Because the information displayed to readers on the library management system is the position of the book when it is neatly arranged, sometimes the position of the book is inconsistent with the actual position of the book, which increases the difficulty for readers to find the target book. The traditional bookcase has a simple structure, and it takes a certain amount of time to search for books on weekdays. In view of the time-consuming situation of borrowing books, and in order to make it convenient for borrowers to find books, it is extremely important to develop a new type of networkable smart bookcase for libraries.
x86 System Management Mode (SMM) Evaluation for Mixed Critical Systems

As autonomous driving, industry 4.0, smart cities, etc. become very popular, safety relevant computing is demanding high performance processors to manage an increasing number of sensors, actuators and control units. In this context, safety critical environments (typically run by real time operating systems) have to co-exist with one or multiple functional rich environments, e.g., Linux. Existing virtualization technologies today are considered not secure enough to isolate these two execution environment types. For this reason this paper evaluates x86 System Management Mode (SMM) as a technology for building mixed critical virtualization solutions. Considering them as key performance indicators, interrupt context switch and the minimal round trip time overheads have been measured. The obtained results on an Intel platform of respectively 1.39 \(\upmu \)s and 12.73 \(\upmu \)s, confirm a high potential for SMM. At the best of our knowledge, this is the first work considering SMM as possible solution for mixed critical environments.
Case Studies: Child Sexual Exploitation

The methods of producing, disseminating and communalising child sexual abuse material (CSAM) using the dark web are not a new phenomenon. What does differ however is the introduction of modern and innovative detection, prevention and protection methods used by law enforcement agencies (LEAs). By delving into three case studies concerning CSAM, this chapter discussed how LEAs in the USA, the UK and wider Europe proactively cooperate deploy methods and unconventional techniques to tackle cases particularly on the dark web. The case studies into PlayPen and the investigation into Matthew Falder present two cases where LEAs have stretched collaboration across traditional sovereign borders to successfully remove CSAM content and safeguard young victims of blackmail. In contrast Operation Sweetie reflects the growing number of unorthodox methods used by law enforcement to detect and prevent the rise in webcam sex tourism on a global scale. In conclusion, these methods display how LEAs are overcoming the challenges which the transition of CSAM offences to dark web surfaces has facilitated and set an optimistic trajectory into new methods of policing.
Cascaded Multi-Channel Feature Fusion for Object Detection

In this paper, we propose and evaluate a novel object detection architecture called Cascaded Multi-Channel Feature Pyramid Network, or CM-FPN. The proposed network, which is based on Feature Pyramid Network by Lin et al., employs multi-stage cascaded top-down feature pyramid networks to extract more semantic multiresolution feature maps for object region proposal and object classification. Depths of the feature maps are adjusted so that the feature maps in the later stages of the cascades where they are more semantic have higher channel depths. Experimental evaluation of the proposed approach has shown that the proposed method produces higher object detection accuracy.
Implementing Distance Technologies in the Educational Process of the Master's Program

The article presents the results of analysing the problems that arise in the process of training in a Master's program in one group of students, both with and without basic education in the major of the Master's program. Teaching options are offered for groups of undergraduates with different levels of basic knowledge in major subjects, based on the experience of teaching undergraduates at the Technological University. As the main method, we offer additional distance learning for undergraduates which aims at forming the basic knowledge necessary for the quality masters training.
Small Target Detection Algorithm in Remote Sensing Image Based on Improved Yolo

Aiming at the problem of low detection accuracy of small remote sensing image targets with small size and unobvious features in existing target detection algorithms, a small target detection algorithm based on improved yolo_v4 is proposed. The algorithm expands the scale to four during target detection, and removes the feature scale map of the minimum perception field when output; replaces the convolution layer of the feature fusion network with a hole convolution to maintain a higher resolution and a larger receptive field; The deconvolution operation performs up-sampling of high-level features, so that low-level features can learn richer semantic information. The experiment analyzed the remote sensing image with the high-voltage electric tower as the small target, and the average accuracy was increased from 90.06% to 91.47%, indicating that the algorithm can effectively improve the detection accuracy of the small target in the remote sensing image.
Implicit Quantification Made Explicit: How to Interpret Blank Nodes and Universal Variables in Notation3 Logic

Since the invention of Notation3 Logic, several years have passed in which the theory has been refined and applied in different reasoning engines like Cwm, EYE, and FuXi. But despite these developments, a clear formal definition of Notation3’s semantics is still missing. This does not only form an obstacle for the formal investigation of that logic and its relations to other formalisms, it has also practical consequences: in many cases the interpretations of the same formula di er between reasoning engines. In this paper we tackle one of the main sources of that problem, namely the uncertainty about implicit quantification. This refers to Notation3’s ability to use bound variables for which the universal or existential quantifiers are not explicitly stated, but implicitly assumed. We provide a tool for clarification through the definition of a core logic for Notation3 that only supports explicit quantification. We specify an attribute grammar which maps Notation3 formulas to that logic according to the different interpretations and thereby define the semantics of Notation3. This grammar is then implemented and used to test the impact of the differences between interpretations on practical cases. Our dataset includes Notation3 implementations from former research projects and test cases developed for the reasoner EYE. We find that 31% of these files are understood differently by different reasoners. We further analyse these cases and categorize them in different classes of which we consider one most harmful: if a file is manually written by a user and no specific built-in predicates are used (13% of our critical files), it is unlikely that this user is aware of possible differences. We therefore argue the need to come to an agreement on implicit quantification, and discuss the different possibilities.
Knowledge-based Defect Prediction and Test Case Generation for Radar Software Fault Prevention

Software is playing an increasingly important role in modern radar software. Software faults often lead to serious system failure. A fault prevention approach is proposed for radar software. The proposed approach follows the quality assurance link of “knowledge-prediction-control” and combines knowledge collection, defect prediction, test case recommendation, and automatic test case generation. Case studies show the proposed approach can effectively help fault detection on radar software.
Deep Learning and its Applications: A Real-World Perspective

Deep Learning (DL), a division of Machine Learning (ML) is a highly focused field of data science. DL is the most active approach for ML. DL algorithms excerpt the complex high-level data features via a hierarchical learning process. These complex abstractions at a given hierarchical level are learned based on the abstractions framed in the previous level of the hierarchy. The capability of DL to analyze and learn huge quantities of unsupervised data makes it a powerful tool for Big Data Analytics where data are mostly unorganized and unlabeled. With the significant advancements and tremendous performance of DL, it is broadly used across numerous domains such as business, health, government, and so on. This chapter focuses on the overview and applications of DL from a real-world perspective, which covers a variety of areas such as Speech Recognition, Text Classification, Document Summarization, Fraud Detection, Visual Recognition, Personalization’s, and so on.
The Knowledge Acquisition Analytical Game Framework for Cognitive System Design

Cognitive technologies aim at making sense of information, with the goal of supporting human cognitive abilities of inferring, predicting and taking decisions. While pursuing this objective, cognitive technologies try to mimic human reasoning abilities and schemes. Knowledge acquisition techniques to elicit such schemes from humans suffer from the drawbacks of time inefficiency, biases and prevented access to tacit knowledge. The knowledge engineering domain is exploring the potentiality of using gaming approaches to overcome some of these limitations. Knowledge Acquisition Analytical Games (K2AGs) are analytical games designed to support the knowledge acquisition of cognitive systems, capitalising on aspects such as engagement, ease of use and ability to access tacit knowledge. In this paper, we present the Knowledge Acquisition Analytical Game framework (K2AG-F), which provides an initial high-level guideline on how to design K2AGs and we summarise two instances of such a framework.
Adaptive Image Compression Projection Based on Deep Neural Network

Lossless image compression is particularly unique areas like healthcare imaging, remote realizing, along with systematic imaging. An interesting predictor for lossless compression based upon plane fitting. The main contribution of ours is a Long Short Term Memory (LSTM) algorithm which from the local market selects the greatest predictor for every pixel based upon context. The preceding newspaper used a brand new lossless hue picture compression algorithm according to the hierarchical prediction as well as context-adaptive arithmetic coding. The deep neural network (DNN) will bring this compression from top to bottom using multiple layers for compressing. Protecting the sharpness of the first impression is a visible element in picture compression. Innovative systems are needed within protecting the sharpness and also decrease in bitrates to come down with additional advancement. The coding might be approximated by picture actions. Additionally, the quantization parameter might be also worn for forecasting the compression quality the suggested system is called Modified Hierarchical Prediction that eliminates the overwhelming prediction blunder speed approach tips as well as preserves the sharpness of pictures. This particular paper proposed the vertical, diagonal, and horizontal (remaining upwards, remaining bad as well as correctly upwards, correctly down) predictors to foresee pixels. We have achieved the accuracy level on comparing with the existing model.
The Airborne Information Processing System Development Research

The airborne information processing system, which is subordinate to the avionics system, is the basic sub-system performing information data processing in the aircraft platform. It provides computing, storage, information transmission and other basic resources for the whole aircraft, and can provide various functions and services such as computing, management, configuration, scheduling and other. The future development of airborne information processing system should be driven by the basic computer technology development and military requirements of aircraft platform. From these two perspectives, on the basis of full analysis of the development status, this paper summarizes the future airborne information processing system architecture and some key technical characteristics of the processing platform, laying a foundation for future research work.