Reviewerly/exHarmony huggingface dataset:
Survey on Blockchain Technology

The digital world has brought efficiencies, innovative new products and strong relationships with customers worldwide through the effective use of mobile devices, IoT (Internet of Things), social media, analytics, and cloud technologies to generate models for better decisions. Blockchain was recently introduced and revolutionized the digital world by bringing a new perspective on the security, resilience, and efficiency of systems. Although originally popularized by bitcoin, blockchain is much more than oneBase for cryptocurrency. It provides a secure way to share any kind of good, service or transaction. In addition, blockchain offers lower business costs with a trusted contract that is monitored without third-party intervention, which may not add direct value. It enables smart contracts, commitments and agreements with strong, inherent cyber security features. This paper contains a complete description of blockchain technology..
Synergistically Employing User Stories and Use Cases in the Practice and Teaching of Systems Analysis and Design

Over the past three decades, user stories and use cases have become increasingly dominant systems analysis and design (SA&amp;D) requirements techniques. Both support articulating functional requirements for software projects, although they evolved within different software development approaches—user stories from agile development and use cases from traditional software engineering—and they differ significantly in terms of their grammar, syntax, and the level of detail they can capture. As such, user stories and use cases are neither synonyms nor mutually exclusive alternatives. Rather, they can and should be complementary in the systems requirements process. Unfortunately, this mix of similarities and differences—coupled with a lack of formal standards for either—make understanding and synergistically employing user stories with use cases confusing and challenging for practitioners and students alike. To address this, this paper first provides a descriptive overview of the evolution of user stories, use cases, and their interrelationship. Second, it fills a gap in the literature by providing a prescriptive, detailed approach to employing user stories and use cases together. This prescriptive approach is illustrated via a comprehensive tutorial example, providing practitioners with actionable skills and SA&amp;D teachers and students with a new pedagogical tool.
Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa

Abstract The field of health data management poses unique challenges in relation to data ownership, the privacy of data subjects, and the reusability of data. The FAIR Guidelines have been developed to address these challenges. The Virus Outbreak Data Network (VODAN) architecture builds on these principles, using the European Union's General Data Protection Regulation (GDPR) framework to ensure compliance with local data regulations, while using information knowledge management concepts to further improve data provenance and interoperability. In this article we provide an overview of the terminology used in the field of FAIR data management, with a specific focus on FAIR compliant health information management, as implemented in the VODAN architecture.
Cryptanalysis of RSA with smooth prime sum

Let N = pq be an RSA modulus with balanced prime factors, that is q &lt; p &lt; 2q. There exist infinitely many integers x, y and z such that ex - ϕ (N ) y = ( p + q -1)z. We show that if the prime sum p + q -1 has only small prime factors and e satisfies an equation of the form ex - ϕ (N ) y = ( p + q -1)z with suitably small integers x, y and Z, then one can factor the RSA modulus in polynomial time. In addition we show that the number of such RSA moduli, as well as the number of exponents that are vulnerable to our attack is non-negligible.
Sesli Asistanlarda Parametre Etiketleme Slot Filling for Voice Assistants

Voice Assistant applications are increasing in popularity and getting deployment in industrial and daily life tasks. In Voice Assistant and Dialog Systems applications, the task of Slot Filling constitutes a major design problem. The voice assistant, tasked with executing a designated job, needs to extract the necessary parameters from the spoken query input by the user. In this study, we use Turkish and English datasets to attack Slot Filling problem with Machine Learning and Deep Learning algorithms. We perform Slot Filling with Conditional Random Fields and compare its performance with recently popularized BERT-like Transformer-based pre-trained language models. We make use of Precision, Recall and F1 Score metrics in model evaluation. Experimental results show that Slot Filling performance of Transformer-based pre-trained language models exceeds the performance of conventional Conditional Random Fields. Furthermore, we observe that multilingual and cross-lingual pretrained language models outperform the models that are pretrained only on the target language. It is expected that the deployed methods and obtained results would contribute to the Dialog Systems and Voice Assistant technologies.
Aktif Öğrenme Yöntemi Kullanarak Nesne Tespiti Object Detection Using Active Learning

In the last decade, deep learning-based object detection models have achieved high performance. However, to train these object detection models, a large amount of labeled images is required. Active learning is a machine learning procedure that is useful in reducing the amount of labeled data required to achieve the targeted performance. With active learning, it is possible to obtain high performing models on real-world data where annotation is time-consuming, while decreasing the labeling cost. It helps reduce the cost of data labeling by efficiently selecting a subset of informative samples from a large repository of unlabeled data. In this study, we developed an object detection model combined with active learning. The results of the experiments show that almost the same level of success was achieved by labeling a smaller amount of data with the active learning framework, compared to labeling and using all the data, leading to lower labeling costs.
Segmentation of Pectoral Muscle Region in MLO Mammography Images by Backboned U-Net

The pectoral muscle region on MLO mammography images appears prominently similar to suspicious areas. For this reason, Computer-Aided Detection (CAD) systems remove this region to reduce false-positive rates in the mass detection process. In some cases, the pectoral muscle region is exposed to distortions due to the superposition effects caused by the mammography technique. As a result, segmentation error rates of the pectoral muscle region, whose characteristic features are deteriorated, appear. In this study, a method to identify impaired pectoral muscle regions with MobileNetV2 backboned U-Net Deep Learning method is proposed. The proposed method was tested on 84 and 201 mammography images taken from both MIAS and InBreast databases and segmented with 1.81% and 1.92% false-negative (FN) and 0.25% and 0.37% false positive (FP) rates, respectively. Particularly for distorted pectoral muscle regions, the proposed method has been shown to outperform some pioneering studies in this area.
Responding to the user interactions

As a program responds swiftly to the user&apos;s actions, it feels comfortable and works naturally with it. In this sense, traditional techniques such as polling can lose user actions. For this reason, in this chapter, the use of event structures is explained, which is an asynchronous communication structure that allows responding to all user actions, the chapter starts by describing what it is and how to use the event structure, it will show the use of data event nodes and event filter nodes, after that, an event state machine will be implemented, and finally, an example of Event-Driven State Machine will be implemented.
Organizing front panel and block diagram

During the code implantation in graphical programming languages, it is desirable to take care of the organization and cleanliness of the code, this is in order to increase its readability. In this sense, when there are multiple cables whose use is closely related, clusters are recommended [15]. This chapter explains how to create clusters to group data, it also explains how to create *.ctl files (control type files), this for the implementation of custom controls, type definitions, and strict type definitions, which allow the programmer to determine the appearance and functionality characteristics that want to be kept between custom controls.
Friend Recommendation System using Image Encryption and Deep learning

Abstract The recommendation system functions similarly to a content filtering system, providing information depending on the preferences and behavior of the user. People use image sharing platforms such as Facebook, LinkedIn, and Instagram to share photographs of various elements to convey their sentiments with linked peers. As image sharing has been increasingly popular among individuals in recent years, it plays an important part in our daily lives. However, because of photo leaks and misuse, people are becoming increasingly concerned about their privacy. Encrypting photographs before publishing to social media is a better approach but decrypting the images and then searching for comparable images is computationally costly. The prior model advocated encrypting pictures using AES encryption and classifying them with deep autoencoders which is only a 2-step compression technique and takes quite more time for classifying. We provide a feasible privacy-protected friend recommendation system in proposed model by encrypting images using the Gaussian blur technique and a deep learning strategy for feature extraction and image classification for user recommendation. For training the dataset, the deep learning system utilizes the Keras model with CNN method, and a new model is generated from the features extracted from the blurred images. After that, the test dataset is compared to the model developed for similar image prediction. The accuracy measurements used to plot the graph are val acc and val loss. The accuracy result for the original image is 91.8%, while the accuracy result for the blurred image is 86.2% percent, demonstrating that our model is highly efficient and results in lower data loss.
Exploring and evaluating personalized models for code generation

Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling source code. Transformers are usually pre-trained on large unsupervised corpora, learning token representations and transformations relevant to modeling generally available text, and are then fine-tuned on a particular downstream task of interest. While fine-tuning is a tried-and-true method for adapting a model to a new domain -- for example, question-answering on a given topic -- generalization remains an on-going challenge. In this paper, we explore and evaluate transformer model fine-tuning for personalization. In the context of generating unit tests for Java methods, we evaluate learning to personalize to a specific software project using several personalization techniques. We consider three key approaches: (i) custom fine-tuning, which allows all the model parameters to be tuned; (ii) lightweight fine-tuning, which freezes most of the model's parameters, allowing tuning of the token embeddings and softmax layer only or the final layer alone; (iii) prefix tuning, which keeps model parameters frozen, but optimizes a small project-specific prefix vector. Each of these techniques offers a trade-off in total compute cost and predictive performance, which we evaluate by code and task-specific metrics, training time, and total computational operations. We compare these fine-tuning strategies for code generation and discuss the potential generalization and cost benefits of each in various deployment scenarios.
Seismic Soundoff: Staying safe during earthquakes

In this episode, Sara McBride discusses her recent paper in Geophysics, “Evidence-based guidelines for protective actions and earthquake early-warning systems.” McBride highlights the various protective actions people can take during an earthquake, the latest research in warning people of danger, and a helpful path forward to continue to save lives. Hear the full episode at https://seg.org/podcast/post/14798 .
AtteSTNet -- An attention and subword tokenization based approach for code-switched text hate speech detection

Recent advancements in technology have led to a boost in social media usage which has ultimately led to large amounts of user-generated data which also includes hateful and offensive speech. The language used in social media is often a combination of English and the native language in the region. In India, Hindi is used predominantly and is often code-switched with English, giving rise to the Hinglish (Hindi+English) language. Various approaches have been made in the past to classify the code-mixed Hinglish hate speech using different machine learning and deep learning-based techniques. However, these techniques make use of recurrence on convolution mechanisms which are computationally expensive and have high memory requirements. Past techniques also make use of complex data processing making the existing techniques very complex and non-sustainable to change in data. Proposed work gives a much simpler approach which is not only at par with these complex networks but also exceeds performance with the use of subword tokenization algorithms like BPE and Unigram, along with multi-head attention-based techniques, giving an accuracy of 87.41% and an F1 score of 0.851 on standard datasets. Efficient use of BPE and Unigram algorithms help handle the nonconventional Hinglish vocabulary making the proposed technique simple, efficient and sustainable to use in the real world.
CORRIGENDUM: [Guest editorial: Music perception and cognition in music technology]

Cognitive Computation and SystemsVolume 4, Issue 4 p. 400-400 CORRIGENDUMOpen Access CORRIGENDUM: [Guest editorial: Music perception and cognition in music technology] This article corrects the following: Guest editorial: Music perception and cognition in music technology Zijin Li, Stephen McAdams, Volume 4Issue 2Cognitive Computation and Systems pages: 97-99 First Published online: June 30, 2022 First published: 01 September 2022 https://doi.org/10.1049/ccs2.12071AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat The authors wish to bring to the readers' attention the following error in the article by Zijin Li and Stephen McAdams, “Guest editorial: Music perception and cognition in music technology” [1]. The co-author Stephen McAdams' name should be removed from the article. REFERENCE 1Zijin Li, Stephen McAdams.: Guest editorial: Music perception and cognition in music technology. Cogn. Comput. Syst. 4(2), 97– 99 (2022). https://doi.org/10.1049/ccs2.12066 Volume4, Issue4December 2022Pages 400-400 ReferencesRelatedInformation
Performance impact of the MVMM algorithm for virtual machine migration in data centres

Virtual machine (VM) migration mechanisms and the design of data centres for the cloud have a significant impact on energy cost and SLA constraints. The recent work focuses on how to use VM migration to achieve stable physical machine utilisation with the objective of reducing energy consumption, understated SLA constraints. This paper presents and evaluates a new scheduling algorithm called MVMM (Minimisation of Virtual Machine Migration) for VM. It makes use of a DBN (Dynamic Bayesian Network) to decide where and when a particular VM migrates. Indeed, the DBN takes as input the data centre parameters then computes a score for each VM candidate for migration in order to reduce the energy consumption by decreasing the number of future migrations according to the probabilistic dependencies between the data centre parameters. The performance results show that the use of the MVMM algorithm can reduce energy consumption by up to 35%.
Securing utility computing using enhanced elliptic curve cryptography and attribute-based access control policy

This paper proposes a data security model for utility computing that integrates securely mapped plain text using Elliptic Curve Cryptography (ECC) without certified keys, attribute-based access control using Reduced Ordered Binary Decision Diagram (ROBDD), cryptographically secure 256 bit pseudorandom numbers and fingerprint security. ECC uses standard elliptic curves of large prime and ROBDD is built with positive and negative attributes of data users and 256 bit pseudorandom secrets along its path. Secret key that is generated based on the attributes along the valid path of the policy enhances the security. The proposed model allows secure key exchange by making use of the property of elliptic curve to convert numbers to points and secure data storage by associating authenticated secrets of end users with the original secret key is beneficial in decentralised architecture. The result reveals that proposed model achieved high security and less space and time complexity for securing cloud data.
A Fully Distributed Secure Approach for Database Security in Cloud Computing

Database-as-a-Service is one of the prime services provided by cloud computing. It is used to provide data storage and management services to the individuals, enterprises, and organizations on pay and use basis, in which any enterprise or organizations can outsource their database to the cloud service provider (CSP) and query the data whenever and wherever required through any devices connected to the Internet. The advantage of this service is that enterprises or organizations can reduce the cost of establishing and maintaining infrastructure locally. However, as cloud service providers are not trusted third parties, there exist some major challenges such as database security, privacy, and query performances to access data, and to overcome these issues, many authors contributed their work and developed various database security models. In this paper, we proposed a new model for cloud database security and we proved that our proposed model enhanced database security level and improved the query execution performance.
A Preliminary Study on the Augmented Reality (AR) game-based application with the second-generation HoloLens

The implementation of Augmented Reality (AR) in the field of education is growing fast. Recently, AR increasing in popularity, and AR also gives users access to additional digital information that is integrated into their existing physical environments. In this preliminary study, we purpose to use a two-condition Pre-test and Post-test with participants comparing the Levene's Test and T-test analysis to develop how to use Augmented Reality (AR) game-based application with the second-generation HoloLens. The strong potential of AR can help the student to improve their knowledge and skill which affects on media structure for improving the education system in Thailand. Experimental results indicate that Levene's test can be performed with. 000(8.174) and T-test analysis with. 000(2.120) is statistically significant.
A Study on Optical Character Recognition for Tilted Images

Optical Character Recognition (OCR) technology is used for several applications. The accuracy of recognition of OCR decreases with tilted images, so we propose the technology to improve the recognition accuracy in the character image with the inclination. In this method, Hough transformation is applied to improve the accuracy. From experiments, it was confirmed that the proposed method improves the accuracy.
Down-Sampling Dark Channel Prior of Airlight Estimation for Low Complexity Image Dehazing Chip Design

Image dehazing is an image processing technique to restore a hazy image back to hazy-free one. Airlight estimation plays an important role in image dehazing algorithm. Dark Channel Prior (DCP) is an efficient algorithm to predict airlight. However, the sorting process of DCP causes tremendous computation requirement, limiting its potential in image dehazing chip design. To overcome this limitation, Down-sampling DCP (DS-DCP) is proposed to provide a low complexity algorithm for airlight estimation. Experiment results demonstrate that the computation saving ratio (CSR) of DS-DCP is as high as 98%, while keeping error as minor as 0.22%.
Human Factors Design for Space Station

Manned spacecraft provides the necessary environment for astronauts’ survival, life and work. In order to meet the requirements of astronauts’ long-time work and life in the space station, it is necessary to study the human factor design method for the space station. Combined with the human factor design characteristics of China space station, this paper puts forward the human factor design principles of manned spacecraft, and puts forward the human factor design methods for configuration layout, safety, maintainability and livability. These methods are applied in the human factor design of China Space Station. Astronauts have lived and worked in the core module of the China space station for about seven months, which shows that the proposed human factor design method ensures the efficient work and life of astronauts on orbit, and improves the livability and reliability of manned spacecraft. It provides a practical engineering method for human factors.
Development of Wheelchair Basketball Player Tracker Using LED and Omni-camera

This paper describes the development of a tracker for basketball wheelchair players using special flashing LEDs and an omnidirectional camera. Our previous trackers required some image processing to find multiple LEDs in the video captured by the omnidirectional camera. The problem was that these image processes were time consuming. This study uses convolutional neural networks to reduce the time it takes to find multiple LEDs in that video.
Action Segmentation for RGB Video Frames Using Skeleton 3D Data of NTURGB+D

Action segmentation or video segmentation which used to extract action from video frames. It plays role in various applications, i.e., visual effect assistance in the movies, scene understanding in detail, virtual background creation, and a design CAD system that can identify automatically human action from videos without any object interference. This paper presents a system that automatically segments actions from videos. The window size is variable and depends on input video. The dataset used to show experimental data is NTURGB+D. The action segmentation has been shown using 3D skeleton information on RGB videos of NTURGB+D. The experimental results have shown the performance and it test results on 5 random action videos.