Reviewerly/exHarmony huggingface dataset:
A Dynamic Multi–Objective Approach for Dynamic Load Balancing in Heterogeneous Systems

Modern standards in High Performance Computing (HPC) have started to consider energy consumption and power draw as a limiting factor. New and more complex architectures have been introduced in HPC systems to afford these new restrictions, and include coprocessors such as GPGPUs for intensive computational tasks. As systems increase in heterogeneity, workload distribution becomes a more core problem to achieve the maximum efficiency in every computational component. We present a Multi-Objective Dynamic Load Balancing (DLB) approach where several objectives can be applied to tune an application. These objectives can be dynamically exchanged during the execution of an algorithm to better adapt to the resources available in a system. We have implemented the Multi-Objective DLB together with a generic heuristic engine, designed to perform multiple strategies for DLB in iterative problems. We also present Ull Multiobjective Framework (UllMF), an open-source tool that implements the Multi-Objective generic approach. UllMF separates metric gathering, objective functions to be optimized and load balancing algorithms, and improves code portability using a simple interface to reduce the costs of new implementations. We illustrate how performance and energy consumption are improved for the implemented techniques, and analyze their quality using different DLB techniques from the literature.
Retrieval performance of Google, Yahoo and Bing for navigational queries in the field of “life science and biomedicine”

Purpose The purpose of this study is to assess the retrieval performance of three search engines, i.e. Google, Yahoo and Bing for navigational queries using two important retrieval measures, i.e. precision and relative recall in the field of life science and biomedicine. Design/methodology/approach Top three search engines namely Google, Yahoo and Bing were selected on the basis of their ranking as per Alexa, an analytical tool that provides ranking of global websites. Furthermore, the scope of study was confined to those search engines having interface in English. Clarivate Analytics' Web of Science was used for the extraction of navigational queries in the field of life science and biomedicine. Navigational queries (classified as one-word, two-word and three-word queries) were extracted from the keywords of the papers representing the top 100 contributing authors in the select field. Keywords were also checked for the duplication. Two important evaluation parameters, i.e. precision and relative recall were used to calculate the performance of search engines on the navigational queries. Findings The mean precision for Google scores high (2.30) followed by Yahoo (2.29) and Bing (1.68), while mean relative recall also scores high for Google (0.36) followed by Yahoo (0.33) and Bing (0.31) respectively. Research limitations/implications The study is of great help to the researchers and academia in determining the retrieval efficiency of Google, Yahoo and Bing in terms of navigational query execution in the field of life science and biomedicine. The study can help users to focus on various search processes and the query structuring and its execution across the select search engines for achieving desired result list in a professional search environment. The study can also act as a ready reference source for exploring navigational queries and how these queries can be managed in the context of information retrieval process. It will also help to showcase the retrieval efficiency of various search engines on the basis of subject diversity (life science and biomedicine) highlighting the same in terms of query intention. Originality/value Though many studies have been conducted highlighting the retrieval efficiency of search engines the current work is the first of its kind to study the retrieval effectiveness of Google, Yahoo and Bing on navigational queries in the field of life science and biomedicine. The study will help in understanding various methods and approaches to be adopted by the users for the navigational query execution across a professional search environment, i.e. “life science and biomedicine”
Relevance feedback based online learning model for resource bottleneck prediction in cloud servers

Cloud servers are highly prone to resource bottleneck failures. In this work, we propose an ensemble learning model to build LSTM-based multiclass classifier for resource bottleneck identification. The workload at cloud servers is highly dynamic in nature. To support continuous online learning of resource bottleneck identification models, we propose relevance feedback based online learning of proposed ensemble model. Here we propose to analyse, catastrophe forgetting and incremental architectural evolution as two fundamental challenges associated with online adaptation of LSTM-based multiclass classifier models. To avoid catastrophic forgetting, we propose a combination of distillation loss and the standard crossentropy loss. For architectural evolution, we propose and analyse three different alternatives to update the architecture of the bottleneck identification model on the fly. We evaluate the proposed approaches on a real world dataset collected in an industrial case study and on a dataset collected in a virtual environment setup using Docker containers. The experimental results show that the proposed approaches outperform existing state-of-the-art methods for bottleneck identification.
HMNT: Hash Function Based on New Mersenne Number Transform

In the field of information security, hash functions are considered important as they are used to ensure message integrity and authentication.Despite various available methods to design hash functions, the methods have been proven to time inefficient and have security flaws (such as a lack of collision resistance or susceptibility to birthday attacks).In the current study, we propose a novel hash function scheme based on a new Mersenne number transform.The suggested hash function called Hash Mersenne Number Transform (HMNT) takes an arbitrary length as input to generate a hash value with variable lengths (128, 256 and 512-bits or longer).The proposed scheme is evaluated in terms of the sensitivity of the hash value to the message, secret key and image, distribution of hashes, confusion and diffusion, robustness against collision and birthday attacks, alongside flexibility.Based on the simulation outcomes, the suggested scheme possess high sensitivity to the original message, the secret key and images, along with strong collision resistance.In conclusion, the proposed hash scheme is simple and efficient compared with the existing hash functions, making it viable for practical implementation.
Model-Based Collaborative Filtering for Recommender Systems: An Empirical Survey

Saving time is one of the most important things. A Recommender system certainly saves our time by generating a prediction of things for us from an extensive database that we might like. Due to the widespread use of the internet, it became a popular area of research in the machine learning community. It is being used in almost all web sites these days to keep track, what users want or like so that when they visit next time, it can predict something for them. A lot of research has been done on this field, but still, the performance of these systems is not that good to be accepted by the end-users. Among these, model-based collaborative filtering is widely used in commercial recommender systems due to its scalability and capability of handling sparse datasets. In this paper, we will provide a detailed insight into these techniques along with their advantages as well as disadvantages.
The Balance of Attention

There is a long history of research exploring how augmented and mixed reality systems can be used to support visitors to cultural heritage locations, but the technological or application specific focus of much of this research means that our understanding of how these experiences work is more of a collection of insights, rather than a coherent theory about how the elements of the experience come together. There is a danger that without developing this knowledge further, our systems will be technologically complex, but experientially simplistic. In this article, we explore how one form of mixed reality experience, digital locative storytelling, can impact the experience of place, and in turn how place impacts the experience of story. We have analyzed 33 interviews, and 25 participant observations from 12 story deployments at 2 different sites. Our findings confirm that locative storytelling experiences not only impart information to readers, but also help them to rediscover familiar places and see hidden relationships—especially through time. But our findings also show how the success of the experience is reliant on the balance of attention between the virtual and real (the story and the place), and that issues with navigation, social interactions, and technology are problematic because they can disrupt this balance. Digital locative experiences therefore need to be designed carefully in order to create a balance of attention (e.g., by aligning the elements of the story with the topology and character of place). We call this a state of Loco-Narrative Harmony, in which place and story are working together and reader attention is balanced, creating an effect that is greater than the sum of its parts.
Uranium and Its Correlation with other Geogenic Contaminants in Ground Water of Ganga Yamuna Doab, Fatehpur District, Uttar Pradesh, India

Uranium is a naturally occurring radioactive element present throughout the environment, atmosphere, food, soil, rocks, and natural waters along with Pb, Cr, As, Zn & Fe. The exposure to the elements may rise in the cases wherever potable water is obtained directly from geogenically affected ground water sources. The uranium impacts are primarily from its chemical activities, instead of long-run tomographical toxic effects, which may lead to urinary organ damage and its non functional. Uranium in 17% ground water samples has been found beyond limit along with other health effecting toxic metals (Pb, Cr, Mn, Fe, As, Zn) in Fatehpur district, Uttar Pradesh bordered by Ganga and Yamuna rivers in the north and south respectively. The affirmative values of uranium on liquid scintillation counter was observed in positive correlation with chromium and lead near the interface of the saturated-unsaturated zone, while zinc, arsenic and iron were very poorly correlated.
An improved and secured digital image watermarking technique using DCT, fuzzy entropy and image scrambling in hybrid domain

The basic concept of watermarking is to hide the digital content like metadata within some cover data to maintain the ownership of the cover data. Watermark embedding should be done in such a way that it should not damage the original image and its security should also be preserved. In this paper, a secured and robust scheme is proposed which uses the concept of fuzzy entropy and DCT and image scrambling in hybrid domain. The proposed algorithm gives very convincing results by providing a good PSNR and MSE values and various types of attacks are also performed on watermarked image which gives very good NC value this shows that proposed scheme provides the improved imperceptibility and robustness.
Laplacian-optimized diffusion for semi-supervised learning

Semi-supervised learning (SSL) is fundamentally a geometric task: in order to classify high-dimensional point sets when only a small fraction of data points are labeled, the geometry of the unlabeled data points is exploited to gain better classifying accuracy. A number of state-of-the-art SSL techniques rely on label propagation through graph-based diffusion, with edge weights that are evaluated either analytically from the data or through compute-intensive training based on nonlinear and nonconvex optimization. In this paper, we bring discrete differential geometry to bear on this problem by introducing a graph-based SSL approach where label diffusion uses a Laplacian operator learned from the geometry of the input data. From a data-dependent graph of the input, we formulate a biconvex loss function in terms of graph edge weights and inferred labels. Its minimization is achieved through alternating rounds of optimization of the Laplacian and diffusion-based inference of labels. The resulting optimized Laplacian diffusion directionally adapts to the intrinsic geometric structure of the data which often concentrates in clusters or around low-dimensional manifolds within the high-dimensional representation space. We show on a range of classical datasets that our variational classification is more accurate than current graph-based SSL techniques. The algorithmic simplicity and efficiency of our discrete differential geometric approach (limited to basic linear algebra operations) also make it attractive, despite the seemingly complex task of optimizing all the edge weights of a graph.
Cooperative Parameter Estimation on the Unit Sphere Using a Network of Diffusion Particle Filters

We introduce in this paper novel Bayesian distributed estimation algorithms for tracking the hidden state of a system that evolves on a spherical manifold. In the proposed method, different nodes on a partially-connected network run particle filters (PFs) that assimilate local data and cooperate with their neighbors via Random Exchange (RndEx) and Adapt-then-Combine (ATC) diffusion techniques. To implement the diffusion filters, we introduce parametric approximations that abide by the geometric restrictions imposed on the state variables. Numerical simulations show that the proposed methodology outperforms equivalent non-cooperative PF algorithms and competing extended Kalman Filter (EKF) approaches.
Handwritten Digit Recognition with Feed-Forward Multi-Layer Perceptron and Convolutional Neural Network Architectures

Nowadays, Artificial Intelligence (AI) is playing a vital role in data classification. In this work, a Python library called as Keras, is used for classification of MNIS T dataset, a database consisting of 60000 training images and 10000 test images of handwritten digits. Two of the popular network architectures, namely, feed-forward network with Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN) are used for feature extraction and training of model. Both the network architectures have been optimized using Categorical Cross Entropy Cost Function and their performance have been evaluated in terms of Accuracy, Training Time and Error.
A Smart Sensor-based Gesture Recognition System for Media Player Control

With the rapid advancement of technology in the current era, mobile and wearable devices have attracted the attention of end users. Smartwatches available in the market have embedded movement sensors whose potential applications have not been explored to a great extent. This study proposes a gesture recognition module using a smart sensor Metawear which can potentially be used to control various functions of a media player. Inertial sensors data from 9 subjects for 12 different gestures (3 trials for each gesture) are acquired. Fifteen different features are extracted from the acquired data. Wrapper method for feature selection is applied on the extracted features which select features from six features groups among the fifteen extracted feature groups. Classification of gestures is performed using three different classifiers i.e., random forest (RF), Naive Bayes (NB) and k-nearest neighbors (kNN) algorithm. The best classification accuracy of 85.80% is achieved by using a fusion of features extracted from accelerometer and gyroscope data by kNN classifier. Experimental results show that the proposed scheme outperforms the existing schemes in literature in terms of classification accuracy for the larger number of gestures.
Smart Autonomous Moving Platforms

In recent years, several advances in artificial intelligence such as deep learning, computer vision, automatic driving, cognitive computing, and robotics, have laid the technological foundation for the large-scale application of Smart-AMPs in complex environments. Potential solutions have appeared for smart transportation, aerospace exploration, smart logistics and smart cities. This article summarizes all kinds of autonomous moving platforms, proposes a definition of Smart- AMPs, and investigates taxonomy, technologies, applications, open issues and key challenges for Smart-AMPs. This article is suitable as a pedagogical overview of Smart-AMPs.
Application of machine learning techniques to the flexible assessment and improvement of requirements quality

It is already common to compute quantitative metrics of requirements to assess their quality. However, the risk is to build assessment methods and tools that are both arbitrary and rigid in the parameterization and combination of metrics. Specifically, we show that a linear combination of metrics is insufficient to adequately compute a global measure of quality. In this work, we propose to develop a flexible method to assess and improve the quality of requirements that can be adapted to different contexts, projects, organizations, and quality standards, with a high degree of automation. The domain experts contribute with an initial set of requirements that they have classified according to their quality, and we extract their quality metrics. We then use machine learning techniques to emulate the implicit expert’s quality function. We provide also a procedure to suggest improvements in bad requirements. We compare the obtained rule-based classifiers with different machine learning algorithms, obtaining measurements of effectiveness around 85%. We show as well the appearance of the generated rules and how to interpret them. The method is tailorable to different contexts, different styles to write requirements, and different demands in quality. The whole process of inferring and applying the quality rules adapted to each organization is highly automated.
Unveiling the Hardware and Software Implications of Microservices in Cloud and Edge Systems

Cloud services progressively shift from monolithic applications to complex graphs of loosely-coupled microservices. This article aims at understanding the implications microservices have across the system stack, from hardware acceleration and server design, to operating systems and networking, cluster management, and programming frameworks. Toward this effort, we have designed an open-sourced DeathstarBench, a benchmark suite for interactive microservices that is both representative and extensible.
The enslaved ontology: Peoples of the historic slave trade

We present the Enslaved Ontology (V1.0) which was developed for integrating data about the historic slave trade from diverse sources in a use case driven by historians. Ontology development followed modular ontology design principles as derived from ontology design pattern application best practices and the eXtreme Design Methodology. Ontology content focuses on data about historic persons and the event records from which this data can be taken. It also incorporates provenance modeling and some temporal and spatial aspects. The ontology is available as serialized in the Web Ontology Language OWL, and carries modularization annotations using the Ontology Pattern Language (OPLa). It is available under the Creative Commons CC BY 4.0 license.
State Estimation With Implicit Constraints of Circular Trajectory Using Pseudomeasurements

In some target tracking scenarios, tracking performance can be improved by the incorporation of a constraint imposed by a preset trajectory. When the complete information about the trajectory is known, the constraint can be formulated in an explicit way. However, in practical applications, only partial information about the trajectory may be available. This article deals with the modeling of implicit constraints, imposed by a circular trajectory, along with the constrained state estimation. First, constraint models for two kinds of representative implicit constraints, the destination constraint and the trajectory shape constraint, are proposed for various cases with different conditions of prior information. In the cases with destination constraints, the destination information and the trajectory shape information are assumed to be known a priori. For each case, two forms of constraint models are derived. One is a direct form model where the constraint relationships are used to eliminate the unknowns in the final model, whereas the other is a state augmentation form model, where the unknowns are augmented into the state vector being estimated along with the target state. In the cases with trajectory shape constraints, only the trajectory shape information is available. For each case, the state augmentation form models are formulated. Second, constrained state estimation methods are proposed by utilizing pseudomeasurement constructed based on the constraint models. Furthermore, an extension to the maneuvering target tracking by integrating the proposed constraint filters into an interacting multiple model estimator is also presented. Numerical simulations in two scenarios are provided to show the effectiveness of the proposed constraint models and state estimation methods.
State of Art of the Effectiveness in Adopting Blockchain Technology - UAE Survey Study

The Government of the United Arab Emirates (UAE) is moving towards the Smart City and integrating Blockchain (BC) technology among its services to the public. In April 2018, the UAE government launched a Blockchain Strategy of 2021. Under this initiative, 50 percent of government transactions will be processed over a BC platform. Eleven billion dirhams are expected to be saved in transactions and documents prepared in the government sector. The UAE 2021 strategy includes four pillars, focusing on residence happiness, government efficiency, advanced legislation, and global entrepreneurship. In October 2016, the emirate of Dubai, launched a local BC strategy to become the first energy-powered city that leads the future economy by 2020. This motivation placed the Government of UAE as a unique and pioneered country in innovation and enabling the ecosystem to be addressed in both the public and private sectors. This strategy is based on three domains: government effectiveness, industry innovation, and international, domestic leadership. BC projects will be experimenting throughout the country in many sectors such as energy, transportation, logistics, tourism, health, education and employment, economic development, safety and justice, social services, municipal and land works. This paper study the state of art of the UAE strategy towards adopting BC technology among all its entities. A survey has been conducted to evaluate the strategic effectiveness of this new technology. The study involves a combination of 71 public and private companies that operate in the UAE. The outcome shows that there is a need to integrate this technology. At the same time, the survey concluded that 92.0% of the companies are aware of the technology. It also highlighted that 55% of companies suffer from the lack of experienced consultants and outsources their services. The survey confirmed that BC technology had been used in different industrial use cases such as supply chain (35%), Internet of things (27 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ), and financial transactions (25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ).