Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Hadoop YARN Job Schedulers in a Multi-Tenant Environment on HiBench Benchmark Suite

Big data processing technology marks a prominent place in today's market. Hadoop is an efficient open-source distributed framework used to process big data with fewer expenses utilizing a cluster of commodity machines (nodes). In Hadoop, YARN got introduced for effective resource utilization among the jobs. Still, YARN over-allocates the resources for some tasks of a job and keeps the cluster resources underutilized. This paper has investigated the CAPACITY and FAIR schedulers' practical utilization of resources in a multi-tenancy shared environment using the HiBench benchmark suite. It compares the above MapReduce job schedulers' performance in two scenarios and proposes some open research questions (ORQ) with potential solutions to help the upcoming researchers. On average, the authors found that CAPACITY and FAIR schedulers utilize 77% of RAM and 82% of CPU cores. Finally, the experimental evaluation proves that these schedulers over-allocate the resources for some of the tasks and keep the cluster resources underutilized in different scenarios.
An improved DFA based kernel ensemble learning machine using local feature representations for face recognition

In recent years, the Face recognition task has been an active research area in computer vision and biometrics. Many feature extraction and classification algorithms are proposed to perform face recognition. However, the former usually suffer from the wide variations in face images, while the latter usually discard the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the Key points Local Binary/Tetra Pattern (KP-LTrP) and Improved Hough Transform (IHT) with the Improved DragonFly Algorithm-Kernel Ensemble Learning Machine (IDFA-KELM) is proposed to address the face recognition problem in unconstrained conditions. Initially, the face images are collected from the publicly available dataset. Then noises in the input image are removed by performing preprocessing using Adaptive Kuwahara filter (AKF). After preprocessing, the face from the preprocessed image is detected using the Tree-Structured Part Model (TSPM) structure. Then, features, such as KP-LTrP, and IHT are extracted from the detected face and the extracted feature is reduced using the Information gain based Kernel Principal Component Analysis (IG-KPCA) algorithm. Then, finally, these reduced features are inputted to IDFA-KELM for performing FR. The outcomes of the proposed method are examined and contrasted with the other existing techniques to confirm that the proposed IDFA-KELM detects human faces efficiently from the input images.
A Secure Cloud Infrastructure towards Smart Healthcare: IoT Based Health Monitoring

The magnitude of big data is estimated to grow in the field of healthcare. Big Data when focused on the sector of health care can be best described as structured, unstructured and semi structured way of representing a patient’s personal data such as his/her medical conditions, prescriptions, etc. Handling and processing such huge chunks of data can be a daunting task. To avoid scenarios of this type, many of the healthcare sectors have adapted cloud computing as the solution. Amidst the pandemic such as the novel covid-19, remote working has become the new normal. Healthcare sectors are gradually adapting to the idea of remote treatment also known as telemedicine. A patient’s medical condition has to be coordinated with a team of specialists, nutritionists, nurses, etc. By using cloud computing one can completely avoid a physical interaction, it’s very useful in current situations like covid-19. Hence cloud computing is chosen by many health sectors to receive and store huge chunks of patients data and manage their electronic records. This huge chunk of electronic records helps in the process of mining the data, identifying the patterns and developments in the big data healthcare sector. Main things that are to be taken care of : Privacy related issues and Security related concerns, Dynamically acceptable storage, Regulatory issues , Electronic documents containing patients data, handwritten medical editions ,various images such as X-ray, MRI scan, radiology images, etc. can be retrieved at any time and from any place. This can be an advantage and a disadvantage at the same time. The main challenges involved are failures of the network, security related concerns, and privacy related issues of patient information that is being ill-treated by users/customers, hackers, network downtime, malware, and etc.
Adversarial learning in quantum artificial intelligence

Quantum artificial intelligence exploits the interplay between artificial intelligence and quantum physics: on the one hand, a plethora of tools and ideas from artificial intelligence can be adopted to tackle intricate quantum problems; on the other hand, quantum computing could also bring unprecedented opportunities to enhance, speed up, or innovate artificial intelligence. Yet, quantum learning systems, similar to classical ones, may also suffer adversarial attacks: adding a tiny carefully-crafted perturbation to the legitimate input data would cause the systems to make incorrect predictions at a notably high confidence level. In this paper, we introduce the basic concepts and ideas of classical and quantum adversarial learning, as well as some recent advances along this line. First, we introduce the basics of both classical and quantum adversarial learning. Through concrete examples, involving classifications of phases of two-dimensional Ising model and three-dimensional chiral topological insulators, we reveal the vulnerability of classical machine learning phases of matter. In addition, we demonstrate the vulnerability of quantum classifiers with the example of classifying hand-written digit images. We theoretically elucidate the celebrated no free lunch theorem from the classical and quantum perspectives, and discuss the universality properties of adversarial attacks in quantum classifiers. Finally, we discuss the possible defense strategies. The study of adversarial learning in quantum artificial intelligence uncovers notable potential risks for quantum intelligence systems, which would have far-reaching consequences for the future interactions between the two areas.
Wavelets and adapted waveform analysis

Our goal is to describe tools for adapting methods of analysis to various tasks occurring in harmonic and numerical analysis and signal processing. The main point of this presentation is that by choosing an orthonormal basis, in which space and frequency are suitably localized, one can achieve both understanding of structure and efficiency in computation. We describe a fingerprint image segmentation algorithm, an alternative factorization for the FFT, and a wavelet-based denoising algorithm.
Anomaly Detection in Location and Trajectory Datasets

Detection of location and trajectory anomalies of moving objects such as people and vehicles are crucial to identify abnormal behaviour during their daily activities. In this study, it is aimed to determine these anomalies using the tracking device under development. For that purpose, performance of different state-of-the-art online data mining and machine learning algorithms are compared using GPS traces including short-term (two weeks) data.
AMFNet: an attention-based multi-level feature fusion network for ground objects extraction from mining area’s UAV-based RGB images and digital surface model

Accurate extraction of ground objects in mining areas using high spatial resolution imagery can effectively assist in the arrangement and deployment of the production work in mining areas. Therefore, it is essential to extract ground objects of mining areas quickly and accurately. However, the task is very difficult due to the variety of complex scenes, the high intra-class variation, low inter-class variation, similar appearance, and large size difference among the ground objects in mining areas. Traditional extraction methods based on RGB images can no longer meet the requirement of high precision. To solve these problems, we propose an attention-based multi-level feature fusion network (AMFNet), which extracts objects more accurately from unmanned aerial vehicle-based RGB images and digital surface model (DSM). The DSM of the mining area provides the height information of the ground objects, which is another supplementary key feature while the objects cannot be distinguished only by appearance in the RGB images. AMFNet is a dual-branch network of encoder–decoder structure using RGB images and DSM as the double-branch input separately. Specifically, the channel attention module, MF, and atrous convolution are introduced in the encoder network to make better use of dual-branch features. The decoder network upsamples the deep features and uses skip connections to fuse the multi-level shallow features obtained by the encoder network with the deep features. Finally, we set up multiple comparisons and ablation experiments in the test dataset, which demonstrate the performance advantages of AMFNet from different dimensions.
Global linear convergence of evolution strategies with recombination on scaling-invariant functions

Evolution Strategies (ESs) are stochastic derivative-free optimization algorithms whose most prominent representative, the CMA-ES algorithm, is widely used to solve difficult numerical optimization problems. We provide the first rigorous investigation of the linear convergence of step-size adaptive ESs involving a population and recombination, two ingredients crucially important in practice to be robust to local irregularities or multimodality. We investigate the convergence of step-size adaptive ESs with weighted recombination on composites of strictly increasing functions with continuously differentiable scaling-invariant functions with a global optimum. This function class includes functions with non-convex sublevel sets and discontinuous functions. We prove the existence of a constant r such that the logarithm of the distance to the optimum divided by the number of iterations converges to r. The constant is given as an expectation with respect to the stationary distribution of a Markov chain—its sign allows to infer linear convergence or divergence of the ES and is found numerically. Our main condition for convergence is the increase of the expected log step-size on linear functions. In contrast to previous results, our condition is equivalent to the almost sure geometric divergence of the step-size on linear functions.
Blockchain-based Preferential E-Voting System DApp using Smart Contract

For long, different e-voting systems have been provided with the goal of increasing security and minimizing cost. Blockchain is a major breakthrough in the technological industry that provide immense secured platform. With the launch of Ethereum, a decentralized platform which runs decentralized applications (DApps) on it, a secured voting system now seems possible. Many organizations have now shifted their focus on voting through blockchain platforms. There’s a very high chance that a normal voting method won’t lead to a clear majority. There can be many ways to deal with this issue which includes another voting process to take place which can be quite expensive in terms of time and resources. In our paper, we introduce the vote-trading concept where the votes can be redistributed to other candidates in case if there is no clear majority and also this ‘ majority’ factor can be set by the organization according to their requirement. We discuss the design for the blockchain based preferential e-voting system using the Solidity programming language where instead of one vote per candidate, we provide the concept of giving preference to the candidates.
Adversarial Attacks on Graphs: How to Hide Your Structural Information

Deep learning has enjoyed the status of crown jewels in artificial intelligence, showing an impressive performance in various fields, especially in computer vision. However, most deep learning models are vulnerable and easy to be fooled by some slight disturbances in the input, which are called adversarial attacks. As the deep learning models are extended to graphs, adversarial attacks also threaten various graph data mining tasks, e.g., node classification, link prediction, community detection, and graph classification. One can modify the topology or features of graphs, such as manipulating a few edges or nodes, to downgrade the performance of graph algorithms. The vulnerability of these algorithms may largely hinder their applications and thus receives tremendous attention. In the current chapter, we overview the existing researches on graph adversarial attacks. In particular, we briefly summarize and classify the existing graph adversarial attack methods, e.g., heuristic, gradient and reinforcement learning, and then choose several classic adversarial attack methods on different graph tasks for detailed introduction. And finally, we also summarize the challenges in this area.
SwiftS: A Dependency-Aware and Resource Efficient Scheduling for High Throughput in Clouds

An increasing number of large-scale data analytics frameworks moves towards larger degrees of parallelism aiming at high throughput guarantees. It is challenging to design a scheduler with high throughput and high resource utilization due to task dependency and job heterogeneity. The state-of-the-art schedulers in cloud/datacenters cannot well handle the scheduling of heterogeneous jobs with dependency constraints (e.g., dependency among tasks of a job) for simultaneously achieving high throughput and high resource utilization. We propose SwiftS: a dependency-aware and resource efficient scheduling for high throughput in clouds.
TRAINING OF MEDICAL STAFF FOR USING DATA – USAGE OF THE FULL CAPABILITY OF SMART DEVICES

The development of computer technology shows the impact on all aspects of human activity. One of the most intensively developing areas using modern information and computer technologies is medical- and healthcare.The main part of the medical equipment is computer-based, and the activity in the direction of computerization of medical activities is a main priority.The use of smart technology in healthcare has been advancing steadily over the past several years, putting devices like smart insulin pens, connected inhalers, asthma monitors, and more in the hands of everyday consumers and allowing them to better manage and address their own health needs – as well as to quickly access help in case of a medical emergency. Wearable devices like biosensors can also allow healthcare professionals to remotely monitor ongoing conditions and gather data, allowing observation and treatment that was previously only possible in an institutional setting to take place anywhere.With digital health on a smart device, one can expect health indications to become more real-time. The whole of the healthcare and medical care field is somewhat research, but it cannot be complete without users in the loop. Nowadays, many people wear smart devices, so the medical staff can collect data to identify the possible reason for their impaired health.Major advances in miniaturization and computing power are driving innovation leading to the development of an increasing number of connected medical devices that are able to generate, collect, analyze and transmit data.The research is focused on presenting a methodology of medical staff' the training referred to systems that collect information from smart devices. The key points are ways to identify the most important information and how this quantity of information can save human life; an assessment of knowledge of medical staff about new technologies such as IoT(The Internet of Things is a network of physical devices and other items, embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data) The purpose of the research is to identify IoT devices that can provide additional information about the patient's condition. This includes data on the environment - temperature, humidity; data on the human body - heart rate, temperature, blood sugar, etc.This article may be a start point for developing a course for the needs of training the medical staff using innovative technologies to "read" patients' physical indicators.
E-Learning Proposal System in Public Secondary School Learning

Abstract&#x0D; The background of this research is to know how the e-learning system in the teaching and learning system at the public high school level, because the Covid 19 pandemic still allows us to learn online at home, with the e-learning system, the learning system can be very effective. remotely, because this research this time will discuss how a good system is applied to the learning process with an e-learning system. The method used in this research is to use the literature review method by referring to previously published books and journals, with the existence of books and journals that discuss this research before, it is certain that this research will produce the latest research and can become the basis for future research. . There are many remote learning method systems during the Covid 19 pandemic, with this remote learning system, the spread of the Corona virus will be greatly reduced, because students do not meet in person and cannot spread directly through physical contact, with the distance learning method. the teaching and learning system continues even though the Covid 19 pandemic spreads. In this research, we will produce a system proposal that will be the basis for the development of distance teaching and learning methods, using e-learning media at an educational level, namely public high schools with a proposed system, so it can be the best suggestion in the future. thiscovid 19 pandemic.&#x0D; Keyword: E-learning, Learning, High School, Online.
Sensitivity Context Awareness based Privacy Preserving Recommender System

The Recommender Systems plays a vital role in today’s Business World. To deliver beneficial recommendations on various products to the consumers is a core objective of the custom-made recommendation system. The recommendations service requires several types of consumer data like earlier product purchased details, consumer information, demographics, and realistic details. The consumers also called Data Providers are prone to the revelation of individual information. This information will be distorted/misused by attackers or malicious third party Data Analyzer. To defend against personal information privacy breaches, it is necessary to modify the consumer information using a privacy preservation technique and generate recommendations by building correct information. The malicious third party and/or Data Analyst of organization not able to access/alter data which will be used for recommendation. Our proposed Sensitivity Context Awareness based Privacy Preserving Recommender System mainly focuses on consumer consent to publish data for the recommendation and at same time conserves privacy of consumers and utility of data which will be used to generate recommendation.
LIORI at SemEval-2021 Task 2: Span Prediction and Binary Classification approaches to Word-in-Context Disambiguation

This paper presents our approaches to SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation task.The first approach attempted to reformulate the task as a question answering problem, while the second one framed it as a binary classification problem.Our best system, which is an ensemble of XLM-R based binary classifiers trained with data augmentation, is among the 3 best-performing systems for Russian, French and Arabic in the multilingual subtask.In the post-evaluation period, we experimented with batch normalization, subword pooling and target word occurrence aggregation methods, resulting in further performance improvements.
Hierarchical Transformer Encoders for Vietnamese Spelling Correction

In this paper, we propose a Hierarchical Transformer model for Vietnamese spelling correction problem. The model consists of multiple Transformer encoders and utilizes both character-level and word-level to detect errors and make corrections. In addition, to facilitate future work in Vietnamese spelling correction tasks, we propose a realistic dataset collected from real-life texts for the problem. We compare our method with other methods and publicly available systems. The proposed method outperforms all of the contemporary methods in terms of recall, precision, and f1-score. A demo version ( https://nlp.laban.vn/wiki/spelling_checker/ ) is publicly available.
Experiments on Properties of Hidden Structures of Sparse Neural Networks

Sparsity in the structure of Neural Networks can lead to less energy consumption, less memory usage, faster computation times on convenient hardware, and automated machine learning. If sparsity gives rise to certain kinds of structure, it can explain automatically obtained features during learning. We provide insights into experiments in which we show how sparsity can be achieved through prior initialization, pruning, and during learning, and answer questions on the relationship between the structure of Neural Networks and their performance. This includes the first work of inducing priors from network theory into Recurrent Neural Networks and an architectural performance prediction during a Neural Architecture Search. Within our experiments, we show how magnitude class blinded pruning achieves 97.5% on MNIST with 80% compression and re-training, which is 0.5 points more than without compression, that magnitude class uniform pruning is significantly inferior to it and how a genetic search enhanced with performance prediction achieves 82.4% on CIFAR10. Further, performance prediction for Recurrent Networks learning the Reber grammar shows an $$R^2$$ of up to 0.81 given only structural information.
TEXT VECTORIZATION USING DATA MINING METHODS

In the text mining tasks, textual representation should be not only efficient but also interpretable,as this enables an understanding of the operational logic underlying the data miningmodels. Traditional text vectorization methods such as TF-IDF and bag-of-words are effective andcharacterized by intuitive interpretability, but suffer from the «curse of dimensionality», and theyare unable to capture the meanings of words. On the other hand, modern distributed methods effectivelycapture the hidden semantics, but they are computationally intensive, time-consuming,and uninterpretable. This article proposes a new text vectorization method called Bag of weightedConcepts BoWC that presents a document according to the concepts’ information it contains. Theproposed method creates concepts by clustering word vectors (i.e. word embedding) then uses thefrequencies of these concept clusters to represent document vectors. To enrich the resulted documentrepresentation, a new modified weighting function is proposed for weighting concepts basedon statistics extracted from word embedding information. The generated vectors are characterizedby interpretability, low dimensionality, high accuracy, and low computational costs when used indata mining tasks. The proposed method has been tested on five different benchmark datasets intwo data mining tasks; document clustering and classification, and compared with several baselines,including Bag-of-words, TF-IDF, Averaged GloVe, Bag-of-Concepts, and VLAC. The resultsindicate that BoWC outperforms most baselines and gives 7 % better accuracy on average