Reviewerly/exHarmony huggingface dataset:
A Deep Learning Assisted Intelligent Monitoring System for Smart Grid

The smart grid provides effective power supply by integrating the traditional grid system with the latest information technologies. Video surveillance is widely used in smart grid to monitor the system behavior as well as detect illegal intrusions. Currently, this video data is normally processed and analyzed manually in real-world applications, which is not only cost inefficient but also error-prone. To enhance the utilization of this video data, we investigate how to design a deep learning assisted intelligent monitoring system for smart grid. First, we classify the characteristics of various target detection algorithms and discuss their potential applications in smart grids. Second, we analyze the challenges of employing target detection algorithms in smart grids. Finally, we propose a framework for constructing intelligent processing systems for smart grids. Our work contributes novel research ideas for the construction of a strong smart grid.
An evaluation method of innovation and entrepreneurship education in agricultural and forestry colleges and universities based on elm neural network

Rural Rehabilitation has been adopted in China. The agricultural and forestry management major needs an entirely different educational approach, which incorporates broad technical competence, great accountability, and related ethical guidelines. Many agricultural and forestry students have lacked self-awareness for a lengthy moment, resulting in a lack of study effort and entrepreneurial spirit. As a result, it is critical to establish a sense of entrepreneurialism and innovation in pupils and continuously improve educational practices. An ELM neural network (ELM-NN) approach for predicting students' success in innovation and entrepreneurial education is proposed in this research. The sample of participants is initially acquired for this inquiry. On the other hand, the evaluation index method encourages students to think creatively and entrepreneurially. Finally, the proposed approach is used to predict students' performance using this data. The proposed ELM-NN system in this study is tested and compared to other current methods to see how well it performs
Multilingual Sentiment Analysis Using the Social Eagle-Based Bidirectional Long Short-Term Memory

Rapid evolvement in the field of social media constitutes an increasing demand in sentiment prediction for effective communication.Sentimental analyses are employed to extract opinions from individuals in order to invigorate the quality of material or a product for the effective growth of an organization.Researchers are aspired to predict the sentiment through comments of different languages, but there is a lack in the dictionaries accessible for different languages.In this research, hybridized Social Eagle Algorithm-based deep bidirectional long short-term memory (SoEo Algorithm-based deep BiLSTM) is proposed to predict the sentiment effectively.The languages are identified and converted into a standard format by the process of Transliteration and the features are extracted from these standardized data.The hunting strategy of the bald eagle and the adaptation behavior of coyotes are hybridized and executed in both forward and backward directions utilizing the BiLSTM classifier.The simulation outcome shows that the proposed model obtained an accuracy of 91.572%, precision of 89.196 %, recall of 91.551 % and F1 measure of 89.019%, which will be more efficient compared to the state-of-art methods.
The Structured Cyber Attack Scenario Expression Model based on Diamond Model and Adversarial States

Threat modeling is a process to determine effective cybersecurity measures to secure industrial control systems (ICSs). However, deep cybersecurity knowledge is required to perform threat modeling processes and those results including prioritization of cyber attack scenarios are depended on security analysts. To reduce the dependency of analysts, it is required to express cyber attack scenarios with a structured model. In this study, a new structured model that expresses cyber attack scenarios for ICSs is proposed. The proposed model is based on the Diamond model and capable of expression behaviors of adversary in a targeted system by a structured form. The model makes it possible to express structured cyber attack scenarios and reduce dependency of analysts. This paper provides a detail of the proposed model and evaluation results of the model in terms of the usefulness of threat modeling.
3D Computer Graphics and Virtual Reality

This chapter is dedicated to the description of 3D computer graphics used for the needs of virtual reality. Virtual reality (VR) is the use of computer technology to create a 3D virtual environment. The chapter presents some graphical features used in an environment as well as an explanation of good design practice. The chapter contains also the description of lighting settings, 3D objects/models and virtualization sequence, camera, and scenes where the wheelchair simulator is used as an example of the implementation environment.
Aurora-Trinity: A Super-Light Client for Distributed Ledger Networks Extending the Ethereum Trinity Client

Light clients for distributed ledger networks can verify blockchain integrity by downloading and analyzing blockchain headers. They are designed to circumvent the high resource requirements, i.e., the large bandwidth and memory requirements that full nodes must meet, which are unsuitable for consumer-grade hardware and resource-constrained devices. Light clients rely on full nodes and trust them implicitly. This leaves them vulnerable to various types of attacks, ranging from accepting maliciously forged data to Eclipse attacks. We introduce Aurora-Trinity, a novel version of light clients that addresses the above-mentioned vulnerability by relying on our original Aurora module, which extends the Ethereum Trinity client. The Aurora module efficiently discovers the presence of malicious or Byzantine nodes in distributed ledger networks with a predefined and acceptable error rate and identifies at least one honest node for persistent or ephemeral communication. The identified honest node is used to detect the latest canonical chain head or to infer the state of an entry in the ledger without downloading the header chain, making the Aurora-Trinity client extremely efficient. It can run on consumer-grade hardware and resource-constrained devices, as the Aurora module consumes about 0.31 MB of RAM and 1 MB of storage at runtime.
Decision tree–based improved software fault prediction: a computational intelligence approach

Software plays a significant role in our daily lives. The use of smart real-time devices has increased dramatically in the recent decade, necessitating the creation of fault-tolerant, high-reliability software. The basic goal of dependable and robust software is to reduce the quantity of failures that occur when a program is executed. Software fault prediction is a key activity for increasing quality assurance efficiency, economy, and precision. Fault prediction is critical for identifying software components that are prone to flaws. The majority of previous software fault prediction research has concentrated on categorizing software modules whether they are faulty or not. The most important criterion for developing an effective fault prediction model is to identify a dependable fault prediction technique. Due to some inherent constraints, manual techniques of forecasting and finding defects in complex systems may not guarantee a fault-free system, and they are generally time intensive. Computational intelligence (CI) techniques provide promising approaches for solving such problems. In this chapter, we investigate the applications of CI in optimizing various phases of software development. Further, the application of decision tree regression (DTR) for improving fault percentage prediction in different scenarios is the main contribution of this chapter. Two datasets of different sizes from PROMISE repository are extracted and used for performance analysis of the proposed model. The results reveal that DTR generated significant prediction accuracy in intra-release projects.
Software protection and software piracy

Abstract This paper explores the dynamic long‐term relationship between software protection and software piracy rates across 108 countries over the period of 20 years (1994–2015), with the help of the ARDL model. The analysis employs a new way of utilising the membership duration of Trade related Intellectual Property Rights agreement and World Intellectual Property Organisation Copyright Treaty agreements of different countries in a panel framework, apart from constructing an index of copyright software protection by focusing on enforcement provisions. The paper overcomes various shortcomings of earlier literatures by thoughtful inclusion of independent variables and methodological techniques. Results show that both copyright software protection and income share a negative long‐term relationship with software piracy. Even the enforcement provisions are important in mitigating software piracy. In addition, the model finds a negative relationship between demand for intellectual property protection and software piracy rates.
Program Synthesis—A Survey

The construction of programs from the user intends is called program synthesis. Program synthesis is being used in our daily life in various ways. For instance, Microsoft Excel has a feature called FlashFill that is capable of string manipulation on the data as per the users’ interest. Depending on the specification of users intend, program synthesizers are classified into a deductive, inductive, and natural language-based synthesizer. Deductive synthesizers use specific descriptions or algorithms in generating programs. Inductive program synthesis uses inductive reasoning on generating programs out of this specification. Programming by examples (PBE) method comes under this type. The specification can be a set of exemplary data used ad input and output, use case diagrams, and so on. In natural language-based, normal English sentences are given as specifications. The synthesizers use artificial intelligence to synthesize programs. This survey provides an overview of program synthesis from its evolution to real-world applications. This survey also includes a comparison of theory and development of different approaches to program synthesis. ...
Face and Hand Gesture Recognition Based Person Identification System using Convolutional Neural Network

Person identification system is now become the most hyped system for security purpose.It's also gaining a lot of attention in the field of computer vision.For verification of human, facial recognition and hand gesture recognition are the most common topics of research.In the current days, various researchers focused on facial and hand gesture recognition using various shallow techniques and Deep Convolutional Neural Network (DCNN).However, using one feature of human for person identification is the most researched topic till now.In this paper, we proposed a Convolutional Neural Network (CNN) based system which will identify a person using two traits i.e., face and hand gesture of number sign of that person.For feature extraction and recognition Neural Network have shown immense good result.This proposed system works on two models, one is a VGG16 architecture model for face recognition and another model is for hand gesture which is based on simple CNN with two convolutional layers.With two customized dataset our face model gained 98.00% accuracy and hand gesture (number sign) model gained an accuracy of 98.33%.
Is the GDPR a ticking time bomb for companies?

Computer Fraud & SecurityVol. 2022, No. 3 The SandboxIs the GDPR a ticking time bomb for companies?Mundeep NayyarMundeep NayyarDeepeoSearch for more papers by this authorMundeep NayyarPublished Online:31 Mar 2022https://doi.org/10.12968/S1361-3723(22)70562-3AboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References ‘Cyber Security Breaches Survey 2021’. UK Department for Digital, Culture, Media & Sport (DCMS), 24 Mar 2021. Accessed Mar 2022. www.gov.uk/government/statistics/cyber-security-breaches-survey-2021. Google Scholar FiguresReferencesRelatedDetails 1 March 2022Volume 2022Issue 3ISSN (print): 1361-3723ISSN (online): 1873-7056 Metrics History Published online 31 March 2022 Published in print 1 March 2022 Information© MA Business Ltd.
UNDERSTANDING THE STYLE TRANSFER DEEP-LEARNING TECHNIQUE THROUGH A WEB DASHBOARD

J.M. Jaén-Lorites1, M. Pérez-Pelegrí1, V. Laparra2, D. Moratal 1 1Universitat Politècnica de València (SPAIN) 2Universitat de València (SPAIN)
Deep Learning for Computer Vision: A Brief Review

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.
The modern CISO: where marketing meets security

Chief information security officers (CISOs) have evolved from technical experts to become business leaders in their own right. They are building relationships and developing strong marketing and communication skills to influence others, and their storytelling is also bringing them closer to the board at a time when cyber security needs to be clearly and simply understood. As a result, CISOs are working differently with their teams, inviting them to look at the bigger picture and consider the real-world impact of projects, and acting as stewards, advisers, mentors and coaches.
Transductive Instance Transfer Learning for Cross-Language Defect Prediction

Predicting defects (bugs) is critical to increasing software quality. Many software defect prediction algorithms have been presented, and many of them have shown to be effective in practice. However, because existing works are largely limited to a single project, their effectiveness in predicting cross-project defects is usually poor. This is primarily due to the issue of class imbalance and discrepancies in feature distribution between the source and destination projects. However, because of the disparities in distribution amongst datasets from different studies, developing high-quality Cross Project Defect Prediction (CPDP) models remains a difficulty. In our study, instead of collecting data from a single project, we have collected source code from multiple code submissions on a programming contest website and employed Natural Language Processing (NLP) models to detect software defects in them.
IP Rights to AI-Generated Works: Barriers Presented by Existing Law and Reformations Needed

Previously we have seen computers as only the aiding tools for different human activities. Modern technological development created computers with Artificial Intelligence (AI) capable to perform works autonomously or with the least intervention of human beings. These Ai-generated works resemble human thought-created. The use of AI-capable computers is increasing in different fields of health, commerce, industries, arts, literature, music composition, scientific operations, etc. Consequent to this technological development, the claims for Intellectual Property rights ((IP) like copyrights and patents are being raised by several persons or institutions for the AI-generated works as they are produced with their investment and plan. Such claims raised the legal battle which is now at the peak in the 2020s between the modern necessity and the traditional legal base that conceived only the creative works of human intellect (or mind) are eligible for IP legal protection, and not the work machine-generated. This article attempted to analyze some important legal issues centering the IP to AI-generated works, indeterminacy, industrial and commercial necessity, and finding the legal way out to the emerging issues for granting IP to AI-generated works.
Spatio-Temporal Solar–Wind Complementarity Assessment in the Province of Kalinga-Apayao, Philippines Using Canonical Correlation Analysis

Increased utilization of renewable energy (RE) resources is critical in achieving key climate goals by 2050. The intermittent nature of RE, especially solar and wind, however, poses reliability concerns to the utility grid. One way to address this problem is to harmonize the RE resources using spatio-temporal complementarity analysis. Two RE resources are said to be complementary if the lack of one is balanced by the abundance of the other, and vice versa. In this work, solar–wind complementarity was analyzed across the provinces of Kalinga and Apayao, Philippines, which are potential locations for harvesting RE as suggested by the Philippine Department of Energy. Global horizontal irradiance (GHI) and wind speed data sets were obtained from the NASA POWER database and then studied using canonical correlation analysis (CCA), a multivariate statistical technique that finds maximum correlations between time series data. We modified the standard CCA to identify pairs of locations within the region of study with the highest solar–wind complementarity. Results show that the two RE resources exhibit balancing in the resulting locations. By identifying these locations, solar and wind resources in the Philippine islands can be integrated optimally and sustainably, leading to a more stable power and increased utility grid reliability.
Best Practices in Establishing Business Case for Implementing Blockchain Solution in Aerospace

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The aircraft asset life cycle processes are rapidly being digitalized. Many novel technologies enabled processes of recording these electronic transactions are being emerged. One such technology for recording electronic transactions securely is Blockchain, defined as distributed ledger technologies which includes enterprise blockchain. Blockchain is not widely used in the aerospace industry due to lack of technical understanding and questions about its benefits. Assessment and establishment of business case for implementing blockchain based solution is needed. The aerospace industry is very conservative when it comes to technology adoption and hence it is difficult to change legacy processes. Additionally, the industry is very fragmented. The technology is advancing at a faster rate and applies across geographies under various regulatory oversight which makes blockchain based solution implementation challenging.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;G-31 electronic transactions for aerospace standards committee of SAE International has conducted a study on determination of cost benefits from implementing a blockchain solution. This study resulted in development of Aerospace Recommended Practice (ARP) 6984 and was published recently. This recommended practice lays out a methodology for qualifying and quantifying the benefits of replacing or augmenting a legacy process with a blockchain solution. This paper presents summary and overview of this study and provides a teaser for ARP 6984.&lt;/div&gt;&lt;/div&gt;
A block-chain excellency in the mining of crypto currency

Bitcoin is the most prevalent out of several crypto currencies in operation which was launched in 2009. Its value has been increased to such an extent that it has reached 19.7 Billion USD by January 2018 (Statista 2018). As the pricing of Bitcoin rose day by day various terrorist group and illegal organization are using the crypto currency to finance their terror activities around the world through by passing the legal mechanism of banking system. This study is an effort to understand the mechanism of crypto currencies mining. This study will give a clear perspective about the risks involved in the mining of crypto currency. It also presented the methodology of mining the crypto currency. This study can be an eye-opener for some new entrants in area of crypto currency, on the other hand it can attracts many researchers to invade this field.
PUBA: Privacy-Preserving User-Data Bookkeeping and Analytics

Abstract In this paper we propose Privacy-preserving User-data Bookkeeping &amp; Analytics (PUBA), a building block destined to enable the implementation of business models (e.g., targeted advertising) and regulations (e.g., fraud detection) requiring user-data analysis in a privacy-preserving way. In PUBA, users keep an unlinkable but authenticated cryptographic logbook containing their historic data on their device. This logbook can only be updated by the operator while its content is not revealed. Users can take part in a privacy-preserving analytics computation, where it is ensured that their logbook is up-to-date and authentic while the potentially secret analytics function is verified to be privacy-friendly. Taking constrained devices into account, users may also outsource analytic computations (to a potentially malicious proxy not colluding with the operator).We model our novel building block in the Universal Composability framework and provide a practical protocol instantiation. To demonstrate the flexibility of PUBA, we sketch instantiations of privacy-preserving fraud detection and targeted advertising, although it could be used in many more scenarios, e.g. data analytics for multi-modal transportation systems. We implemented our bookkeeping protocols and an exemplary outsourced analytics computation based on logistic regression using the MP-SPDZ MPC framework. Performance evaluations using a smartphone as user device and more powerful hardware for operator and proxy suggest that PUBA for smaller logbooks can indeed be practical.
Detection and Tracking of a Moving Object Using Canny Edge and Optical Flow Techniques

Aims: In the discipline of computer vision, detecting and tracking moving objects in a succession of video frames is a critical process. Image noise, complicated object motion and forms, and video real-time processing are some of the challenges faced by existing methods. Hence, they are computationally complex and susceptible to noise. This work utilized Canny Edge and Optical Flow (CE-OF) techniques for identifying and tracking moving objects in video files.&#x0D; Methodology: Video sequence datasets in Avi and Mp4 format from MathWorks and YouTube were used to evaluate the developed CE-OF technique. The video clip's frames were sampled several times and the frame rate display was calculated. The original images were converted to grayscale, preprocessed, and CE-OP was applied to identify and track the moving object. The results of the CE-OF and optical flow techniques in terms of accuracy, precision, false acceptance rate, false rejection rate, and processing time were obtained and compared. The performance of the developed technique was evaluated using accuracy, precision, false acceptance rate (FAR), false rejection rate (FRR) and processing time. The results obtained were 94.12%, 92.86%, 25.00%, 25.00%, and 19.51s for Mp4; and 93.33%, 90.91%, 20.00%, 20.00%, and 44.11s for Avi video 1 format, respectively.&#x0D; Conclusion: The developed CE-OF is a better competition in terms of accuracy and time compared with well-known techniques in the literature. The CE-OF technique performed better compared with the conventional methods in detecting and tracking a moving object. Therefore, it can be adopted in the designing of intelligent surveillance systems.