Reviewerly/exHarmony huggingface dataset:
Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

In cloud computing (CC), resources are allocated and offered to the clients transparently in an on-demand way.Failures can happen in CC environment and the cloud resources are adaptable to fluctuations in the performance delivery.Task execution failure becomes common in the CC environment.Therefore, fault-tolerant scheduling techniques in CC environment are essential for handling performance differences, resource fluxes, and failures.Recently, several intelligent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics.With this motivation, this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme (GTO-FTASS) in CC environment.The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account.The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops.Besides, the GTO-FTASS model derives a fitness function involving two parameters such as expected time of completion (ETC) and failure probability of executing a task.In addition, the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model.The experimental validation of the GTO-FTASS model has been performed and the results are inspected under several aspects.Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.
Ensemble Based Learning with Accurate Motion Contrast Detection

Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization (SPSOM) and Incremental Deep Convolution Neural Networks (IDCNN) for detecting multiple objects.However, the study considered optical flows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence, the overall object detection performance is reduced significantly.Thus, consideration of object motions in videos efficiently is a critical issue to be solved.To overcome the above mentioned problems, this research work proposes a method involving ensemble approaches to and detect objects efficiently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model (SGSOM) for detecting multiple significant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8% in Mean Absolute Error (MAE) while achieving 86% in accuracy, 81.5% in precision, 85% in recall and 81.6% in F-measure and thus proving its utility in detecting multiple objects.
Deep learning approach for full-field displacement estimation in depth-resolved wavelength-scanning interferometry

To minimize the effect of optical crosstalk-generated noise (crosstalk), we present a deep learning approach to precisely estimate the full-field displacements for depth-resolved wavelength-scanning interferometry (DRWSI). A deep convolution neural network, where the transformer block is introduced to effectively capture higher-order features of the wrapped phase difference map in a strong noise environment, is applied for phase unwrapping. Furthermore, a binary phase noise map is used to update the loss function in an improved training model, enhancing the network generalization. Finally, the full-field displacements are estimated from phase unwrapping maps with a high signal-to-noise ratio. The simulation and the loading experiment verified that a higher accuracy reconstruction of displacement is implemented using our approach. The contribution of this work can make the DRWSI more practical in quantifying the mechanical property inside the sample.
A motional but temporally consistent physical video examples

Adversarial examples (AEs) attract extensive attention due to their inherent security-related properties of attacking Deep Neural Networks (DNNs) through carefully constructed modifications. Recently, they have been extended to video tasks. Human action recognition based on DNNs is a crucial task in video tasks. AEs of human action recognition models attract much focus and previous works demonstrated the vulnerability of AEs of human action recognition in the digital world. However, the adversarial videos for attacking human action recognition models in the physical world are still in the open stage. The design of physical adversarial videos is crucial for helping to evaluate the robustness of some critical applications based on human action recognition, e.g., surveillance and pedestrian detection. Unlike the digital attacks on action recognition models, the perturbations of physical adversarial videos should be motional but temporally consistent across each whole video. The attacks need to destroy the spatial interactions and temporal interactions in videos. Previously developed attacks for video models in the digital world are difficult to transfer to the physical world. In this paper, we close this gap, and we are the first to attack human action recognition models in the physical world. We first generate a dynamic mask via an improved object tracking method and then use the center location to construct a motion location map. Finally, gradient sharing method is used to generate temporally consistent perturbations and optimize the perturbations into robust patches. Experiments show that these patches can successfully attack a real-time human action recognition system, and the proposed approach has a 77.5% success rate in this setting.
Sistem Informasi Administrasi Kepegawaian pada Dinas Tenaga Kerja di Kabupaten Mandailing Natal Berbasis Web

The development of technology and information is currently accelerating, this development has made many conveniences ranging from the use of computer technology to automated management handling. In this study, we will try to create a Web-Based Information System for Personnel Administration at the Office of the Manpower Office in Mandailing Natal Regency which currently uses computers for operation, but the data processing process is still manual. So we need a system that can accelerate performance and be developed more modern in order to operate using a computerized system. The method used in designing and making this web application is the Native Web Method, the programming language used is PHP, and MySQL as data storage. For display design using the Bootstrap AdminLTE Framework which provides basic HTML and CSS templates for the application of typography, forms, buttons, navigation, and many other interface components. This research will produce a Web-Based Personnel Administration Information System at the Office in Mandailing Natal.&#x0D; Kata kunci : sistem,administration,web
<i>The Zone</i>: A Study of Sound Art as Hyperreality

Abstract This article concerns The Zone, an interactive sound installation by the collaborative group Volumetric Units. It focuses on the installation’s philosophical and creative aspects through a reflective analysis. The Zone expresses the phenomenological experience of hyperreal cyberspace: a condition where the shift in consciousness causes an inability to distinguish reality from the simulation of reality. The installation is an investigation of the sociopolitical effects of hidden online processes. The work intends to provoke a feeling of condensed confusion like the hyperreality that we experience online, a sense of dissolution of information that is followed by a dissolution of meaning.
Investigating the Effect of <i>Jo-Ha-Kyū</i> on Music Tempos and Kinematics across Cultures: Animation Design for 3D Characters Using Japanese Bunraku Theater

Abstract Bunraku theater is a traditional Japanese performing art. Bunraku puppeteering can invoke deep unconscious affective reactions from the audience, overcoming what is known as the uncanny valley effect. The authors analyze Bunraku plays, showing that the music tempo and puppet movements follow the Jo-Ha-Kyū principle, which refers to recursive and fractal artistic modulations such as changes of tempo and rhythm breaks. The authors then illustrate the difference between Bunraku and European dance and finally propose the application of Jo-Ha-Kyū in character animation design.
<i>Signal to Noise Loops</i>: A Cybernetic Approach to Musical Performance with Smart City Data and Generative Music Techniques

Abstract This article introduces the Signal to Noise Loops project, which consisted of a series of performances and installations that took place worldwide between 2017 and 2022. The project utilized open data from a network of Internet of Things sensors placed around Dublin, Ireland, in the context of experimental music performance and composition. This network was underpinned by a theoretical framework from the field of cybernetics that united and integrated methods and approaches from the wide-ranging fields of data-driven music, generative music, rhythm analysis, and smart cities research.
Exploring Research Trends in Healthcare 4.0

In recent years, healthcare is being challenged to deal with global aging, an increasing number of chronic patients, and growing demand for more personalized services. From this standpoint, it is comprehensible that health is longing for solutions to enhance its quality while reducing costs. In this light, the technological development brought by Industry 4.0 arises surrounded by high expectations of solving, or at least minimizing, some of the aforementioned questions. Therefore, this paper aims to investigate the current state of the health revolution, named Healthcare 4.0, and follows a systematic methodology to select the documents retrieved from Web of Science and Scopus. Firstly, keywords were chosen to execute the search in the scientific databases. Then, inclusion and exclusion criteria were used to define the final sample of 130 articles. Lastly, a bibliometric analysis was performed for a better understanding of the ongoing health revolution. With the assistance of the bibliometrix R-tool, graphics and statistics were designed in order to recognize publication patterns. It was concluded that, although Healthcare 4.0 is still a recent research topic, increasing attention is being given to it. The most influential authors and countries were also detected, enabling the recognition of India as the main reference in the subject. Moreover, the relevance of IEEE journals to Healthcare 4.0 was identified, and the importance of artificial intelligence was also revealed.
Novel Approach for Improving Security and Confidentiality of PHR in Cloud Using Public Key Encryption

Personal health records (PHR) are a new type of health information exchange that allows PHR possessor to share their personal health details with a wide range of people, inclusive of healthcare professionals, close relative and best friends. PHR details are typically externalization and kept in third-party cloud platforms, reducing PHR possessor of the load of managing their PHR details while improving health data accessibility. To ensure the secrecy of personal health records maintained in public clouds, encrypting the health details prior to uploading on the cloud is a typical practice. The secrecy of the information from the cloud is guaranteed since the cloud does not know the keys used to encrypt the information. Elliptic curve cryptography encryption is used to attain secure and systematic way to interact PHRs with re-encryption server.
Text Summarization Using Lexical Chaining and Concept Generalization

Paul, Amala Mary Salim, A.Text summarization is the process of generating a condensed text form from single or multiple text documents. Extractive summaries are generated by stringing together selected sentences taken from the input text document without any modification, whereas abstractive summaries convey the salient ideas in the input, may reuse phrases or clauses from the input text, and may add new phrases/sentences which are not part of the input text. Extractive summarization guarantees that the generated sentences will be grammatically correct, whereas abstractive methods can generate summaries that are coherent, at the expense of grammatical errors. To this end, we propose to integrate an extractive method based on lexical chains and an abstractive method that uses concept generalization and fusion. The former method tries to identify the most important concepts in the input document with the help of lexical chains and then extract the sentences that contain those concepts. The latter method identifies generalizable concepts in the input and fuses them to generate a shorter version of the input. We evaluated our method using ROUGE and the results show that the integrated approach was successful in generating summaries that are more close to human generated versions.
A Survey on Blockchain Security Issues Using Two-Factor Authentication Approach

In recent times, blockchain technologies have emerged to the maximum to provide a successful and promising era. Blockchain technology has proven promising application potentialities. It has modified people’s issues in a few areas due to its fantastic influence on many businesses and industries. Blockchain is the generation that the cryptocurrency is built on, and bitcoin is the first digital crypto-forex. It is a ledger that is publicly dispensed and facts every bitcoin transaction. Though there are research done on the security and privacy issues of blockchain, yet they lack a scientific examination on the safety of the blockchain system. This paper provides a scientific look at the security threats and discusses how two-factor authentication has been developed to cope with the lack of ability of single authentication structures. The foremost point of blockchain generation is that, it improves privacy, but then features additional two-component authentication to the block for a further layer of safety. Two-element authentication is also referred to as two-FA. Moreover, this paper includes, evaluation on the safety enhancement answers for blockchain, which may be used in the development of diverse blockchain structures and recommends few destiny directions to stir the research efforts in this field.
eHealth: A Focus on Software Architectures for Brazilian Public Health

There is constant pressure to increase the quality of healthcare services and reduce costs, which drives the use of digital technologies and an optimized management process for a large volume of data. The existence of different archetypes, which have several legacy systems and which, in many cases, are difficult to be used, makes the environment even more challenging for healthcare organizations to create innovative solutions. This article aimed to map the software architectures related to Brazil’s Unified Health System (in Portuguese, Sistema Único de Saúde - SUS), using a systematic literature review as a procedure. To this end, an exploratory and descriptive study with a qualitative approach was developed based on the collection and analysis of secondary data. Among the results, seven architectures stood out in the context of Brazilian public health, as well as efforts to develop a flexible and comprehensive software architecture were observed, with less bureaucracy, correction of flaws in real-time, and prioritizing a responsive and resolutive system. This study contributed both to professionals and researchers by providing a document that brings together the existing architectures, highlighting the main points of each one and identifying the existing gaps.
Spam Message Filtering Based on Machine Learning Algorithms and BERT

The constant traffic of messages keeps increasing whether be it email or SMS. This leads to a direct increase of attacks from spammers. Mobile spams are particularly threatening as spam messages are often disguised as messages from banks, which cause immense harm. Due to this, having a method to identify if the messages are spam or not becomes pivotal. Several techniques exist where deep learning tasks show higher accuracy compared to classic machine learning tasks. Hence, this paper compares the BERT with traditional machine learning techniques which are used in this paper are logistic regression, multinomial Naive Bayes, SVM, and random forest. This paper uses an open-source dataset from Kaggle of labelled spam and not spam messages. It has a total of 5585 messages of which 4825 are labelled as spam and 760 as spam. This paper uses various machine learning algorithms and an algorithm with BERT. In comparison with the other machine learning techniques, it was discovered that the BERT algorithm delivered the highest testing accuracy of 98%. BERT has an encoding layer which generates encodings such that they are not biased and are then fine-tuned for spam detection. With this attribute, the algorithm obtains a higher accuracy.
Transprecision Gaussian Average Background Modelling Technique for Multi-vehicle Tracking Applications

Background subtraction is a classical approach for real-time segmentation of moving objects in a video sequence. Background subtraction involves thresholding the error between an estimate of the reference frame without moving objects and the current frame with moving objects. There are numerous approaches to this problem, and each differs in the type of estimation technique used for generating the reference background frame. This paper implements a transprecision Gaussian average background subtraction methodology to count as well as track the moving vehicles present in complex road scenes. The background information at pixel level is collected periodically for frames in order to track the moving vehicles in foreground, hence the name ‘transprecision’ Gaussian average background subtraction. It is observed that this technique improves the accuracy of vehicle tracking and vehicle counting when compared to the conventional techniques. We have illustrated the robust performance of the proposed method in various operating conditions including repetitive motion from clutter, different view angles and long-term scene changes, words.
A Survey of Deep Q-Networks used for Reinforcement Learning: State of the Art

Hafiz, A. M.Reinforcement learning (RL) is being intensely researched. The rewards lie with the goal of transitioning from human-supervised to machine-based automated decision making for real-world tasks. Many RL-based schemes are available. One such promising RL technique is deep reinforcement learning. This technique combines deep learning with RL. The deep networks having RL-based optimization goals are known as Deep Q-Networks after the well-known Q-learning algorithm. Many such variants of Deep Q-Networks are available, and more are being researched. In this paper, an attempt is made to give a gentle introduction to Deep Q-networks used for solving RL tasks as found in existing literature. The recent trends, major issues and future scope of DQNs are touched upon for benefit of the readers.
User Credibility-Based Trust Model for 5G Wireless Networks

The 5G wireless networks are expected to provide fastest mobile Internet connectivity, efficient network access, capable of handling large amount of data traffic, connecting large number of mobile devices with high-throughput and very-low latency. The new technologies such as cloud computing, network function virtualization (NFV), and software-defined networking (SDN) are being used in 5G wireless networks. The number of small cells in 5G networks creating a heterogeneous network environment (HetNet), where users will join and leave the network frequently causing repeated authenticated vertical handoff across the different cells leading to delay in the network. There are new security requirements and challenges in 5G mobile wireless network due to its advanced features. Therefore, in this paper to deal with secured vertical handoff, a trusted authenticating mechanism is proposed to secularly authenticate the user based on the credibility in 5G wireless networks. It is generating a trust relationship between user, base station, and home networks based on the user credibility and performs quick and secured handoff. The user credibility is comprises the direct credibility and indirect credibility calculation. Based on the user credibility, the trustworthiness of user equipment (UE) is identified and vertical handoff performed without re-authentication across different heterogeneous small cells in 5G wireless networks.
Learning Human Viewpoint Preferences from Sparsely Annotated Models

Abstract View quality measures compute scores for given views and are used to determine an optimal view in viewpoint selection tasks. Unfortunately, despite the wide adoption of these measures, they are rather based on computational quantities, such as entropy, than human preferences. To instead tailor viewpoint measures towards humans, view quality measures need to be able to capture human viewpoint preferences. Therefore, we introduce a large‐scale crowdsourced data set, which contains 58 k annotated viewpoints for 3220 ModelNet40 models. Based on this data, we derive a neural view quality measure abiding to human preferences. We further demonstrate that this view quality measure not only generalizes to models unseen during training, but also to unseen model categories. We are thus able to predict view qualities for single images, and directly predict human preferred viewpoints for 3D models by exploiting point‐based learning technology, without requiring to generate intermediate images or sampling the view sphere. We will detail our data collection procedure, describe the data analysis and model training and will evaluate the predictive quality of our trained viewpoint measure on unseen models and categories. To our knowledge, this is the first deep learning approach to predict a view quality measure solely based on human preferences.
Learning State-Variable Relationships in POMCP: A Framework for Mobile Robots

We address the problem of learning relationships on state variables in Partially Observable Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we focus on Partially Observable Monte Carlo Planning (POMCP) and represent the acquired knowledge with a Markov Random Field (MRF). We propose, in particular, a method for learning these relationships on a robot as POMCP is used to plan future actions. Then, we present an algorithm that deals with cases in which the MRF is used on episodes having unlikely states with respect to the equality relationships represented by the MRF. Our approach acquires information from the agent's action outcomes to adapt online the MRF if a mismatch is detected between the MRF and the true state. We test this technique on two domains, rocksample, a standard rover exploration task, and a problem of velocity regulation in industrial mobile robotic platforms, showing that the MRF adaptation algorithm improves the planning performance with respect to the standard approach, which does not adapt the MRF online. Finally, a ROS-based architecture is proposed, which allows running the MRF learning, the MRF adaptation, and MRF usage in POMCP on real robotic platforms. In this case, we successfully tested the architecture on a Gazebo simulator of rocksample. A video of the experiments is available in the Supplementary Material, and the code of the ROS-based architecture is available online.