Reviewerly/exHarmony huggingface dataset:
Toward Enhancing the Energy Efficiency and Minimizing the SLA Violations in Cloud Data Centers

Recently, the problem of Virtual Machine Placement (VMP) has received enormous attention from the research community due to its direct effect on the energy efficiency, resource utilization, and performance of the cloud data center. VMP is considered as a multidimensional bin packing problem, which is a type of NP-hard problem. The challenge in VMP is how to optimally place multiple independent virtual machines into a few physical servers to maximize a cloud provider’s revenue while meeting the Service Level Agreements (SLAs). In this paper, an effective multiobjective algorithm based on Particle Swarm Optimization (PSO) technique for the VMP problem, referred to as VMPMOPSO, is proposed. The proposed VMPMOPSO utilizes the crowding entropy method to optimize the VMP and to improve the diversity among the obtained solutions as well as accelerate the convergence speed toward the optimal solution. VMPMOPSO was compared with a simple single-objective algorithm, called First-Fit-Decreasing (FFD), and two multiobjective ant colony and genetic algorithms. Two simulation experiments were conducted to verify the effectiveness and efficiency of the proposed VMPMOPSO. The first experiment shows that the proposed algorithm has better performance than the algorithms we compared it to in terms of power consumption, SLA violation, and resource wastage. The second indicates that the Pareto optimal solutions obtained by applying VMPMOPSO have a good distribution and a better convergence than the comparative algorithms.
Interpreting Multivariate Shapley Interactions in DNNs

This paper aims to explain deep neural networks (DNNs) from the perspective of multivariate interactions. In this paper, we define and quantify the significance of interactions among multiple input variables of the DNN. Input variables with strong interactions usually form a coalition and reflect prototype features, which are memorized and used by the DNN for inference. We define the significance of interactions based on the Shapley value, which is designed to assign the attribution value of each input variable to the inference. We have conducted experiments with various DNNs. Experimental results have demonstrated the effectiveness of the proposed method.
Zero Watermarking Algorithm for Vector Geographic Data Based on the Number of Neighboring Features

Zero watermarking is an important part of copyright protection of vector geographic data. However, how to improve the robustness of zero watermarking is still a critical challenge, especially in resisting attacks with significant distortion. We proposed a zero watermarking method for vector geographic data based on the number of neighboring features. The method makes full use of spatial characteristics of vector geographic data, including topological characteristics and statistical characteristics. First, the number of first-order neighboring features (NFNF) and the number of second-order neighboring features (NSNF) of every feature in vector geographic data are counted. Then, the watermark bit is determined by the NFNF value, and the watermark index is determined by the NSNF value. Finally, combine the watermark bits and the watermark indices to construct a watermark. Experiments verify the theoretical achievements and good robustness of this method. Simulation results also demonstrate that the normalized coefficient of the method is always kept at 1.00 under the attacks that distort data significantly, which has the superior performance in comparison to other methods.
An Analysis of Students’ Motivation in Studying English During Covid-19 Pandemic

This study focuses to investigate students’ motivation in studying English during Covid-19 Pandemic, whether they had intrinsic or extrinsic motivation. This research was a survey method and the technique sampling used in this study was random sampling. Furthermore, the instrument used questionnaire to collect the data. The questionnaire consists of 20 questions in which question 1-10 are questions for intrinsic motivation. The question 11-20 are extrinsic motivation. The result of this study shows that the total mean score of intrinsic motivation is higher than the total mean score of extrinsic motivation 4.20 &gt; 3.39. It means that first semester of English students at Faculty of Foreign Language, Mahasaraswati Denpasar University had the higher intrinsic motivation than extrinsic motivation in learning English during Covid-19 Pandemic. In other words, the motivation to learn English that comes from inside students was higher than the motivation comes from outside students.&#x0D; Keywords: Students’ Motivation, Studying English, Covid-19 Pandemic
Secure and Lightweight Encryption Model for IoT Surveillance Camera

We are witnessing the era of Internet of Things (IoT), where its applications such as smart cities and smart homes catch sensitive data gathered mostly by IoT surveillance cameras among other sensors or devices.Therefore, security and privacy protection is a key concern during transmitting such sensitive data across the IoT network to be processed and stored on Cloud.In this paper, we proposed a lightweight encryption model that complies with the limited resources of IoT devices in terms of process and memory.Also, the encryption model also provides a high level of security for the transmitted data through a constant change of the key used for encrypting of transmitted IoT data.In addition, the key size used to encrypt transmitted data in the proposed model is large enough which makes it hard to break by the attackers.The experimental results show outstanding results with an average of 170.7 ms of encryption time for a key size 80 bits where the key size is relatively large and with an average PSNR of 7.7 compared to other algorithms.
Google dataset search and DOI for data in the ESA space science archives

Reproducibility is at the core of science. For this reason, a common practice for authors is to provide links to the data used in their manuscripts upon submission to peer-reviewed journals. In space science, reference to archives’ websites of major data providers like space agencies are still accepted by most journals. However, even these websites are not persistent links and will eventually not be accepted once journals require full compliance with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. On the contrary, Digital Object Identifiers (DOIs) associated to data could provide persistent links. But so far, the publishing industry is systematically minting DOIs related to their articles or books without requiring DOIs for the data used in their publications. Furthermore, DOIs related to data would not only link refereed publications to the data location. It could also enable a more precise attribution to the data producers and publishers while providing the capability to eventually trace their use. These latter points require that journals eventually request or even enforce the use of these DOIs. For data producers, proper attribution and usage are important in the context of securing research grants or extension of projects. Additionally, structured metadata included in the DOI landing pages offer the possibility for datasets to be discoverable by the new Google Dataset Search (GDS) engine, available since January 2020. This paper describes the on-going activities at the European Space Agency (ESA) to mint DOIs for datasets served across the space science archives, and how a subset of these DOIs are already discoverable by GDS.
DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data

Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer’s disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi’s vessel enhancement with subsequent thresholding, are capable of segmenting medium to large vessels but often fail to segment small vessels. The sensitivity of these methods to small vessels can be increased by extensive parameter tuning or by manual corrections, albeit making them time-consuming, laborious, and not feasible for larger datasets. This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a small imperfect semi-automatically segmented dataset of only 11 subjects; using six for training, two for validation, and three for testing. The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance. The proposed technique was evaluated quantitatively and qualitatively against the test set and achieved a Dice score of 80.44 ± 0.83. Furthermore, the result of the proposed method was compared against a selected manually segmented region (62.07 resultant Dice) and has shown a considerable improvement (18.98%) with deformation-aware learning.
Introducing 360-Degree Video in Higher Education: An Overview of the Literature

According to international research and institutions, the Higher Education sector needs to deeply innovate his didactic methodologies. In this sense, the integration and use of ICTs have been seen as a possible driver for the improvement of education’ quality, and showed good affordances for teaching and learning in terms of engagement, communication and collaboration. New emerging technologies are now under experimentation, especially as for immersive environments from augmented to virtual reality. In this paper we will focus on 360-degree video, offering a first overview of its potential in educational contexts, as they emerge from a systematic literature review.
Breast cancer diagnosis system using hybrid support vector machine-artificial neural network

Breast cancer is the second most common cancer occurring in women. Early detection through mammogram screening can save more women’s lives. However, even senior radiologists may over-diagnose the clinical condition. Machine learning (ML) is the most used technique in the diagnosis of cancer to help reduce human errors. This study is aimed to develop a computer-aided detection (CAD) system using ML for classification purposes. In this work, 80 digital mammograms of normal breasts, 40 of benign and 40 of malignant cases were chosen from the mini MIAS dataset. These images were denoised using median filter after they were segmented to obtain a region of interest (ROI) and enhanced using histogram equalization. This work compared the performance of artificial neural network (ANN), support vector machine (SVM), reduced features of SVM and the hybrid SVM-ANN for classification process using the statistical and gray level co-occurrence matrix (GLCM) features extracted from the enhanced images. It is found that the hybrid SVM-ANN gives the best accuracy of 99.4% and 100% in differentiating normal from abnormal, and benign from malignant cases, respectively. This hybrid SVM-ANN model was deployed in developing the CAD system which showed relatively good accuracy of 98%.
A Novel IoT Based Positioning and Shadowing System for Dementia Training

A rapid increase in the number of patients with dementia, particularly memory decline or impairment, has led to the loss of self-care ability in more individuals and increases in medical and social costs. Numerous studies, and clinical service experience, have revealed that the intervention of nonpharmacological management for people with dementia is effective in delaying the degeneration caused by dementia. Due to recent rapid developments in information and communications technology, many innovative research and development and cross-domain applications have been effectively used in the dementia care environment. This study proposed a new short-term memory support and cognitive training application technology, a “positioning and shadowing system,” to delay short-term memory degeneration in dementia. Training courses that integrate physical and digital technologies for the indoor location of patients with dementia were constructed using technologies such as Bluetooth Low Energy, fingerprint location algorithm, and short-range wireless communication. The Internet of Things was effectively applied to a clinical training environment for short-term memory. A pilot test verified that the results demonstrated learning effects in cognitive training and that the system can assist medical personnel in training and nursing work. Participants responded with favorable feedback regarding course satisfaction and system usability. This study can be used as a reference for future digital smart cognitive training that allows observation of the performance of patients with dementia in activities of daily living.
Fundamental bounds on qubit reset

The authors show that the shortest possible qubit reset time depends on the choice of qubit-ancilla interaction and qubit control whereas the maximally achievable fidelity is determined by the Hilbert space dimension of the ancillary system.
A Deep-Learning-Based Bug Priority Prediction Using RNN-LSTM Neural

Context: Predicting the priority of bug reports is an important activity in software maintenance.Bug priority refers to the order in which a bug or defect should be resolved.A huge number of bug reports are submitted every day.Manual filtering of bug reports and assigning priority to each report is a heavy process, which requires time, resources, and expertise.In many cases mistakes happen when priority is assigned manually, which prevents the developers from finishing their tasks, fixing bugs, and improve the quality.Objective: Bugs are widespread and there is a noticeable increase in the number of bug reports that are submitted by the users and teams' members with the presence of limited resources, which raises the fact that there is a need for a model that focuses on detecting the priority of bug reports, and allows developers to find the highest priority bug reports.This paper presents a model that focuses on predicting and assigning a priority level (high or low) for each bug report.Method: This model considers a set of factors (indicators) such as component name, summary, assignee, and reporter that possibly affect the priority level of a bug report.The factors are extracted as features from a dataset built using bug reports that are taken from closed-source projects stored in the JIRA bug tracking system, which are used then to train and test the framework.Also, this work presents a tool that helps developers to assign a priority level for the bug report automatically and based on the LSTM's model prediction.Results: Our experiments consisted of applying a 5-layer deep learning RNN-LSTM neural network and comparing the results with Support Vector Machine (SVM) and K-nearest neighbors (KNN) to predict the priority of bug reports.The performance of the proposed RNN-LSTM model has been analyzed over the JIRA dataset with more than 2000 bug reports.The proposed model has been found 90% accurate in comparison with KNN (74%) and SVM (87%).On average, RNN-LSTM improves the F -measure by 3% compared to SVM and 15.2% compared to KNN.Conclusion: It concluded that LSTM predicts and assigns the priority of the bug more accurately and effectively than the other ML algorithms (KNN and SVM).LSTM significantly improves the average F -measure in comparison to the other classifiers.The study showed that LSTM reported the best performance results based on all performance measures (Accuracy = 0.908, AUC = 0.95, F -measure = 0.892).
Improved Identity Based Encryption System (IIBES): A Mechanism for Eliminating the Key-Escrow Problem

A revolutionary change to public-key cryptography can be considered as an Identity Based Cryptography (IBC) in which identity of the receiver is being used as a public key for encrypting a message and Key Generation Centre (KGC). IBC will generate and distribute the private key to each user to decrypt a message. The thought behind presenting the scheme was to improve and reduce the complexity of certificate and key management, but it also gives rise to key escrow problem, access to encrypted information to unauthorized users. The paper represents Improved Identity-Based Encryption Scheme (IIBES) for Domain Name System (DNS) security which provides confidentiality and authentication through modified identity based encryption and identity based digital signatures. The IIBES comprises key revocation mechanism for non-revoked users and also eliminates key escrow problem. Thus, the IIBES aids to implement the identity-based cryptography more safely in reality and protects DNS against cache poisoning, spoofing attack and masquerade attack. Doi: 10.28991/esj-2021-01259 Full Text: PDF
3D Convolutional Neural Networks Initialized from Pretrained 2D Convolutional Neural Networks for Classification of Industrial Parts

Deep learning methods have been successfully applied to image processing, mainly using 2D vision sensors. Recently, the rise of depth cameras and other similar 3D sensors has opened the field for new perception techniques. Nevertheless, 3D convolutional neural networks perform slightly worse than other 3D deep learning methods, and even worse than their 2D version. In this paper, we propose to improve 3D deep learning results by transferring the pretrained weights learned in 2D networks to their corresponding 3D version. Using an industrial object recognition context, we have analyzed different combinations of 3D convolutional networks (VGG16, ResNet, Inception ResNet, and EfficientNet), comparing the recognition accuracy. The highest accuracy is obtained with EfficientNetB0 using extrusion with an accuracy of 0.9217, which gives comparable results to state-of-the art methods. We also observed that the transfer approach enabled to improve the accuracy of the Inception ResNet 3D version up to 18% with respect to the score of the 3D approach alone.
Optical Flow Estimation by Matching Time Surface with Event-Based Cameras

In this work, we propose a novel method of estimating optical flow from event-based cameras by matching the time surface of events. The proposed loss function measures the timestamp consistency between the time surface formed by the latest timestamp of each pixel and the one that is slightly shifted in time. This makes it possible to estimate dense optical flows with high accuracy without restoring luminance or additional sensor information. In the experiment, we show that the gradient was more correct and the loss landscape was more stable than the variance loss in the motion compensation approach. In addition, we show that the optical flow can be estimated with high accuracy by optimization with L1 smoothness regularization using publicly available datasets.
State Management for Cloud-Native Applications

The stateless cloud-native design improves the elasticity and reliability of applications running in the cloud. The design decouples the life-cycle of application states from that of application instances; states are written to and read from cloud databases, and deployed close to the application code to ensure low latency bounds on state access. However, the scalability of applications brings the well-known limitations of distributed databases, in which the states are stored. In this paper, we propose a full-fledged state layer that supports the stateless cloud application design. In order to minimize the inter-host communication due to state externalization, we propose, on the one hand, a system design jointly with a data placement algorithm that places functions’ states across the hosts of a data center. On the other hand, we design a dynamic replication module that decides the proper number of copies for each state to ensure a sweet spot in short state-access time and low network traffic. We evaluate the proposed methods across realistic scenarios. We show that our solution yields state-access delays close to the optimal, and ensures fast replica placement decisions in large-scale settings.
Text Coherence Analysis based on Misspelling Oblivious Word Embeddings and Deep Neural Network

Text coherence analysis is the most challenging task in Natural Language Processing (NLP) than other subfields of NLP, such as text generation, translation, or text summarization.There are many text coherence methods in NLP, most of them are graph-based or entity-based text coherence methods for short text documents.However, for long text documents, the existing methods perform low accuracy results which is the biggest challenge in text coherence analysis in both English and Bengali.This is because existing methods do not consider misspelled words in a sentence and cannot accurately assess text coherence.In this paper, a text coherence analysis method has been proposed based on the Misspelling Oblivious Word Embedding Model (MOEM) and deep neural network.The MOEM model replaces all misspelled words with the correct words and captures the interaction between different sentences by calculating their matches using word embedding.Then, the deep neural network architecture is used to train and test the model.This study examines two different types of datasets, one in Bengali and the other in English, to analyze text consistency based on sentence sequence activities and to evaluate the effectiveness of this model.In the Bengali language dataset, 7121 Bengali text documents have been used where 5696 (80%) documents have been used for training and 1425 (20%) documents for testing.And in the English language dataset, 6000 (80%) documents have been used for training and 1500 (20%) documents for model evaluation out of 7500 text documents.The efficiency of the proposed model is compared with existing text coherence analysis techniques.Experimental results show that the proposed model significantly improves automatic text coherence detection with 98.1% accuracy in English and 89.67% accuracy in Bengali.Finally, comparisons with other existing text coherence models of the proposed model are shown for both English and Bengali datasets.