Reviewerly/exHarmony huggingface dataset:
Efficient VM Selection Strategies in Cloud Datacenter Using Fuzzy Soft Set

A cloud data center is established to meet the storage demand due to the rate of growth of data. The inefficient use of resources causes an enormous amount of power consumption in data centers. In this paper, a fuzzy soft set-based virtual machine (FSS_VM) consolidation algorithm is proposed to address this problem. The algorithm uses four thresholds to detect overloaded hosts and applies fuzzy soft set approach to select appropriate VM for migration. It considers all factors: CPU utilization, memory usage, RAM usage, and correlation values. The algorithm is experimentally tested for 11 different combinations of choice parameters where each combination is considered as fuzzy soft set and compared with existing algorithms for various metrics. The experimental results show that proposed FSS_VM algorithm achieves significant improvement in optimizing the objectives such as power consumption, service level agreement violation rate, and VM migrations compared to all existing algorithms. Moreover, performance comparison among the fuzzy soft set-based VM selection methods are made, and Pareto-optimal fuzzy soft sets are identified. The results show that the Pareto-based VM selection improves the QoS. The time complexity of the proposed algorithm increases when it finds best VM for migration. The future work will reduce the time complexity and will concentrate on developing an efficient VM placement strategy for VM migration since it has the greater impact on improving QoS in VM placement.
Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia

Earthquakes have been universally recognised as seismological disasters that pose a threat to civilization and need to be monitored through prediction models. The development and usage of traditional statistical predicting models, which require the understanding of underlying physical scientific processes in a system and large amounts of data preparation, can be challenging and costly. Artificial intelligence-based models, specifically machine learning models, are able to easily review mass data volumes and identify complex data trends to make predictions, making them beneficial to be utilized as prediction models. Terengganu, located on the east coast of Peninsular Malaysia, has experienced three earthquakes in the last four decades and has the potential to be hit or affected by earthquakes due to its location within the vicinity of the South China Sea where the seismologically active Manila Trench is situated. This makes the development of machine learning models for the prediction of earthquakes in Terengganu important for future disaster analysis and management. Therefore, this study suggests artificial neural network (ANN) models as a tool to predict ground motion parameters, namely earthquake acceleration, depth, and velocity, in Terengganu. However, this study presents the comparison of the results of ANN with the results of Random Forest (RF). The data used to develop the models were collected by six seismological stations for two channels in Terengganu and provided by the Malaysian Meteorological Department. The data was partitioned into six sets for each ground motion parameter in each channel, with each set utilizing data from a different grouping of five stations for training and one station for testing. Earthquake depth was able to be modelled with accuracy univariately, that is using only the respective output parameter, which is earthquake depth, as the input parameter. Earthquake acceleration and velocity could not be modelled with accuracy univariately, and were improved by adding earthquake depth as an input parameter. Based on the analysis and evaluation of the results using four selected performance criteria, the ANN models show good performance in predicting earthquake acceleration, depth, and velocity.
Biomedical Image Segmentation: A Survey

Medical Image Segmentation is the process of segmenting and detecting boundaries of anatomical structures in various types of 2D and 3D-medical images. The latter come from different modalities, such as Magnetic Resonance Imaging (MRI), X-Rays, Positron Emission Tomography (PET)/Single-Photon Emission Computed Tomography, Computed Tomography (CT), and Ultrasound (US). It is a key supporting technology for medical applications including diagnostics, planning, monitoring, and guidance. Hence, a large number of segmentation methods have been published in past decades. This paper presents a comprehensive review of the current medical segmentation techniques. In particular, we reviewed the most important medical segmentation methods that have been utilized for almost all types of medical images. We grouped these methods into categories and then compared, contrasted, and highlighted their main advantages and limitations.
Class-Incremental Learning with Generative Classifiers

Incrementally training deep neural networks to recognize new classes is a challenging problem. Most existing class-incremental learning methods store data or use generative replay, both of which have drawbacks, while ‘rehearsal-free’ alternatives such as parameter regularization or bias-correction methods do not consistently achieve high performance. Here, we put forward a new strategy for class-incremental learning: generative classification. Rather than directly learning the conditional distribution p(y|x), our proposal is to learn the joint distribution p(x, y), factorized as p(x|y)p(y), and to perform classification using Bayes’ rule. As a proof-of-principle, here we implement this strategy by training a variational autoencoder for each class to be learned and by using importance sampling to estimate the likelihoods p(x|y). This simple approach performs very well on a diverse set of continual learning benchmarks, outperforming generative replay and other existing baselines that do not store data.
Multi-Goal Path Planning Using Multiple Random Trees

In this letter, we propose a novel sampling-based planner for multi-goal path planning among obstacles, where the objective is to visit predefined target locations while minimizing the travel costs. The order of visiting the targets is often achieved by solving the Traveling Salesman Problem (TSP) or its variants. TSP requires to define costs between the individual targets, which — in a map with obstacles — requires to compute mutual paths between the targets. These paths, found by path planning, are used both to define the costs (e.g., based on their length or time-to-traverse) and also they define paths that are later used in the final solution. To enable TSP finding a good-quality solution, it is necessary to find these target-to-target paths as short as possible. We propose a sampling-based planner called Space-Filling Forest (SFF*) that solves the part of finding collision-free paths. SFF* uses multiple trees (forest) constructed gradually and simultaneously from the targets and attempts to find connections with other trees to form the paths. Unlike Rapidly-exploring Random Tree (RRT), which uses the nearest-neighbor rule for selecting nodes for expansion, SFF* maintains an explicit list of nodes for expansion. Individual trees are grown in a RRT* manner, i.e., with rewiring the nodes to minimize their cost. Computational results show that SFF* provides shorter target-to-target paths than existing approaches, and consequently, the final TSP solutions also have a lower cost.
Multi-Candidate Voting Model Based on Blockchain

Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting. However, the difficulties it introduces into the supervision of the vote counting, as well as its need for a concurrent guaranteed trusted third party, should not be overlooked. With the advent of blockchain technology in recent years, its features such as decentralization, anonymity, and non-tampering have made it a good candidate in solving the problems that electronic voting faces. In this study, we propose a multi-candidate voting model based on the blockchain technology. With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm, votes can be counted without relying on a third party, and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements. Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.
An Advanced Morphological Component Analysis, Steganography, and Deep Learning-Based System to Transmit Secure Textual Data

A potential to extract detailed textual image texture features is a key characteristic of the suggested approach, instead of using a single spatial texture feature. For the generation of MCs, four textured characteristics (including horizontal and vertical) are assumed in this paper that are content, coarseness, contrast, and directionality. The morphological parts of a clandestine text-based image were further segmented and then usually inserted into the least significant bit in cover pixels utilising spatial steganography. This same reverse process for steganography and MCA is conducted on the recipient side after transmission. The results demonstrate that the proposed method based on fusion of MCA and steganography provides a higher performance measure, for instance peak signal-to-noise ratio, SSIM, than the previous method.
Efficient image tampering localization using semi-fragile watermarking and error control codes

In this paper, we propose an image tampering localization algorithm using semi-fragile watermarking and Error-Locating codes in the DWT domain. By introducing different families of codes, we show the benefit in terms of image tampering localization and complexity of using control code error localization as an authentication function. Indeed, we first experimentally show that error localization block codes is as precise as using classical error correcting codes (Reed-Solomon and BCH codes) to locate image tampering. However, their corresponding decoding algorithms complexity is at least quadratic which make them impractical for some real time applications. To solve this problem, we introduce error-control codes called Error-Locating codes where error localization is reduced to a single syndrome computation performed with low number of binary operations (detailed later in the paper). We provide comparisons of image quality and tampering localization performances using error-detection, error-localization and error-correction approaches with different error control codes.
Estimates of Cases and Hospitalizations Averted by COVID-19 Case Investigation and Contact Tracing in 14 Health Jurisdictions in the United States

Context: The implementation of case investigation and contact tracing (CICT) for controlling COVID-19 (caused by SARS-CoV-2 virus) has proven challenging due to varying levels of public acceptance and initially constrained resources, especially enough trained staff. Evaluating the impacts of CICT will aid efforts to improve such programs. Objectives: Estimate the number of COVID-19 cases and hospitalizations averted by CICT and identify CICT processes that could improve overall effectiveness. Design: We used data on the proportion of cases interviewed, contacts notified or monitored, and days from testing to case and contact notification from 14 jurisdictions to model the impact of CICT on cumulative case counts and hospitalizations over a 60-day period. Using the Centers for Disease Control and Prevention's COVIDTracer Advanced tool, we estimated a range of impacts by assuming either contacts would quarantine only if monitored or would do so upon notification of potential exposure. We also varied the observed program metrics to assess their relative influence. Results: Performance by jurisdictions varied widely. Jurisdictions isolated between 12% and 86% of cases (including contacts that became cases) within 6 to 10 days after infection. We estimated that CICT-related reductions in transmission ranged from 0.4% to 32%. For every 100 remaining cases after other nonpharmaceutical interventions were implemented, CICT averted between 4 and 97 additional cases. Reducing time to case isolation by 1 day increased averted case estimates by up to 15 percentage points. Increasing the proportion of cases interviewed or contacts notified by 20 percentage points each resulted in at most 3 or 6 percentage point improvements in averted cases. Conclusions: We estimated that CICT reduced the number of COVID-19 cases and hospitalizations among all jurisdictions studied. Reducing time to isolation produced the greatest improvements in impact of CICT.
VizLinter: A Linter and Fixer Framework for Data Visualization

Despite the rising popularity of automated visualization tools, existing systems tend to provide direct results which do not always fit the input data or meet visualization requirements. Therefore, additional specification adjustments are still required in real-world use cases. However, manual adjustments are difficult since most users do not necessarily possess adequate skills or visualization knowledge. Even experienced users might create imperfect visualizations that involve chart construction errors. We present a framework, VizLinter, to help users detect flaws and rectify already-built but defective visualizations. The framework consists of two components, (1) a visualization linter, which applies well-recognized principles to inspect the legitimacy of rendered visualizations, and (2) a visualization fixer, which automatically corrects the detected violations according to the linter. We implement the framework into an online editor prototype based on Vega-Lite specifications. To further evaluate the system, we conduct an in-lab user study. The results prove its effectiveness and efficiency in identifying and fixing errors for data visualizations.
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation

In this paper, we develop an orthogonal frequency-division multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL). In particular, the local gradients in massive Internet of things (IoT) devices are modulated by an analog waveform and are then transmitted using the same wireless resources. To this end, achieving perfect waveform superposition is the key challenge, which is difficult due to the existence of frame timing offset (TO) and carrier frequency offset (CFO). In order to address these issues, we propose a two-stage waveform pre-equalization technique with a customized multiple access protocol that can estimate and then mitigate the TO and CFO for the OTA aggregation. Based on the proposed solution, we develop a hardware transceiver and application software to train a real-world FL task, which learns a deep neural network to predict the received signal strength with the global positioning system information. Experiments verify that the proposed OTA aggregation solution can achieve comparable performance to offline learning procedures with high prediction accuracy.
MURAL: Multimodal, Multitask Representations Across Languages

Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages.We use both types of pairs in MURAL (MUltimodal, MUltitask Representations Across Languages), a dual encoder that solves two tasks: 1) image-text matching and 2) translation pair matching.By incorporating billions of translation pairs, MURAL extends ALIGN (Jia et al., 2021)-a state-of-the-art dual encoder learned from 1.8 billion noisy image-text pairs.When using the same encoders, MURAL's performance matches or exceeds ALIGN's cross-modal retrieval performance on wellresourced languages across several datasets.More importantly, it considerably improves performance on under-resourced languages, showing that text-text learning can overcome a paucity of image-caption examples for these languages.On the Wikipedia Image-Text dataset, for example, MURAL-BASE improves zero-shot mean recall by 8.1% on average for eight under-resourced languages and by 6.8% on average when fine-tuning.We additionally show that MURAL's text representations cluster not only with respect to genealogical connections but also based on areal linguistics, such as the Balkan Sprachbund.
Attention-Guided deep atrous-residual U-Net architecture for automated gland segmentation in colon histopathology images

In digital pathology, gland segmentation plays a dominant part in the diagnosis and quantification of colon cancer. Thus, this paper presents a clinically relevant deep learning-based automated gland segmentation technique called Attention-Guided deep Atrous-Residual U-Net that aims to seize small and intricate variations in medical images whilst preserving spatial information. It is a modified U-Net architecture that comprises enhanced learning capability as its Atrous-Residual units extract more discriminative and multi-level feature representations while alleviating the diminishing-gradient issue. Its Attention units extract more gland-specific detailed features, perform target refinement, and help in comparable semantic features concatenation. Its Transitional Atrous units incorporate dense multi-scale features and handle resolution degradation problems. Also, various data augmentation and stain normalization techniques are utilized to improve its generalization capability. Extensive experiments are performed using two publicly available datasets (GlaS challenge and CRAG) and a private hospital dataset HosC that helps to build invariance of the proposed architecture towards digital variability present in clinical applications and verifying its robustness. The proposed model achieves competitive results over existing state-of-art techniques having a significant improvement in F1-score (at least 2% for GlaS and 3.7% for CRAG), Object-Dice Index (at least 2.3% for GlaS and 3.5% for CRAG), and Object-Hausdorff Distance (at least 2.89% for GlaS and 3.11% for CRAG). Also, for the private HosC dataset, it achieves an F1-score of 0.947, Object-Dice Index of 0.912, and Object-Hausdorff Distance of 89.78. In addition, the final output results are validated by multiple pathologists in terms of their score i.e., for GlaS (0.9184 for test A and 0.91 for test B), 0.9032 for CRAG, and 0.904 for HosC that verify its clinical relevancy and suitability in the facilitation of the proposed model for gland detection and segmentation applications in clinical practice. It can further aid the pathologists to create a precise diagnosis and plan of treatment.
A Unified Efficient Pyramid Transformer for Semantic Segmentation

Semantic segmentation is a challenging problem due to difficulties in modeling context in complex scenes and class confusions along boundaries. Most literature either focuses on context modeling or boundary refinement, which is less generalizable in open-world scenarios. In this work, we advocate a unified framework (UN-EPT) to segment objects by considering both context information and boundary artifacts. We first adapt a sparse sampling strategy to incorporate the transformer-based attention mechanism for efficient context modeling. In addition, a separate spatial branch is introduced to capture image details for boundary refinement. The whole model can be trained in an end-to-end manner. We demonstrate promising performance on three popular benchmarks for semantic segmentation with low memory footprint.
Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of many of these applications thanks to its ability to identify areas and/or objects of interest without knowing them a priori. In this paper, a One-Class Support Vector Machine (OC-SVM) anomaly detector based on customized Haralick textural features for aerial video surveillance at low-altitude is presented. The use of a One-Class SVM, which is notoriously a lightweight and fast classifier, enables the implementation of real-time systems even when these are embedded in low-computational small-scale UAVs. At the same time, the use of textural features allows a vision-based system to detect micro and macro structures of an analyzed surface, thus allowing the identification of small and large anomalies, respectively. The latter aspect plays a key role in aerial video surveillance at low-altitude, i.e., 6 to 15 m, where the detection of common items, e.g., cars, is as important as the detection of little and undefined objects, e.g., Improvised Explosive Devices (IEDs). Experiments obtained on the UAV Mosaicking and Change Detection (UMCD) dataset show the effectiveness of the proposed system in terms of accuracy, precision, recall, and F1-score, where the model achieves a 100% precision, i.e., never misses an anomaly, but at the expense of a reasonable trade-off in its recall, which still manages to reach up to a 71.23% score. Moreover, when compared to classical Haralick textural features, the model obtains significantly higher performances, i.e., ≈20% on all metrics, further demonstrating the approach effectiveness.
Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier

Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy.
Technology Usage and Academic Performance in the Pisa 2018 Report

In the last two decades, the great technological advances sweeping society have made inroads into the educational sphere. The use of information and communication technology and social networks has opened up new possibilities for student learning, which require appropriate treatment by family and teachers. This quantitative study takes a new approach to investigating the relationship between Spanish teenage students’ academic success and their use of technology and social networks. It analyses data published in the 2018 PISA report to assess whether the use of these resources is appropriate, and to determine their impact on students’ learning and performance in reading, mathematics and science. The study takes a new approach in terms of the variables selected and the analysis of the data through two statistical measures. The results suggest that excessive use of technology and social networks, both during the week and at weekends, impairs performance. This finding is more acute in the case of male students, as the data indicates that they start at an earlier age and are more likely to use social media for the detrimental activity of online gaming.