Reviewerly/exHarmony huggingface dataset:
Efficient Orchestration of Host and Remote Shared Memory for Memory Intensive Workloads

Since very few contributions to the development of an unified memory orchestration framework for efficient management of both host and remote idle memory have been made, we present Valet, an efficient approach to orchestration of host and remote shared memory for improving performance of memory intensive workloads. The paper makes three original contributions. First, we redesign the data flow in the critical path by introducing a host-coordinated memory pool that works as a local cache to reduce the latency in the critical path of the host and remote memory orchestration. Second, Valet utilizes unused local memory across containers by managing local memory via Valet host-coordinated memory pool, which allows containers to dynamically expand and shrink their memory allocations according to the workload demands. Third, Valet provides an efficient remote memory reclaiming technique on remote peers, based on two optimizations: (1) an activity-based victim selection scheme to allow the least-active-block of data to be selected for serving the eviction requests and (2) a migration protocol to move the least-active-block of data to less-memory-pressured remote node. As a result, Valet can effectively reduce the performance impact and migration overhead on local nodes. Our extensive experiments on both NoSQL systems and Machine Learning (ML) workloads show that Valet outperforms existing representative remote paging systems by up to 282 × throughput improvement and up to 98% latency decrease over conventional OS swap facility for big data and ML workloads, and by up to 5.3 × throughput improvement and up to 80% latency decrease over the state-of-the-art remote paging systems. Valet is open sourced at https://github.com/git-disl/Valet.
Hyperspectral Parallel Image Compression on Edge GPUs

Edge applications evolved into a variety of scenarios that include the acquisition and compression of immense amounts of images acquired in space remote environments such as satellites and drones, where characteristics such as power have to be properly balanced with constrained memory and parallel computational resources. The CCSDS-123 is a standard for lossless compression of multispectral and hyperspectral images used in on-board satellites and military drones. This work explores the performance and power of 3 families of low-power heterogeneous Nvidia GPU Jetson architectures, namely the 128-core Nano, the 256-core TX2 and the 512-core Xavier AGX by proposing a parallel solution to the CCSDS-123 compressor on embedded systems, reducing development effort, compared to the production of dedicated circuits, while maintaining low power. This solution parallelizes the predictor on the low-power GPU while the entropy encoders exploit the heterogeneous multiple CPU cores and the GPU concurrently. We report more than 4.4 GSamples/s for the predictor and up to 6.7 Gb/s for the complete system, requiring less than 11 W and providing an efficiency of 611 Mb/s/W.
The Next Generation of Virtual Reality: Recommendations for Accessible and Ergonomic Design

Virtual reality (VR) device prices drastically declined in the past decade, leading to a shift in the VR market. As VR enters the mainstream, the design of both VR hardware and software must evolve to meet the needs of diverse users. Current systems appeal to niche consumers who are willing and able to use VR despite bulky hardware and accessibility barriers in software. The current review outlines the limitations of current VR systems and proposes ways in which designs can be iterated to produce more ergonomic and accessible systems for a broader range of users.
Biaxial Equatorial Solar Tracker with High Precision and Low Consumption: Modelling and Realization

The solar tracker is a mechanism that helps the photovoltaic panel to maximize its performance, while keeping it oriented towards direct solar radiation. In order to specify tracking, most solar trackers use two axes, one horizontal and the other vertical, which implies an increase of the consumed energy and a decrease in precision, since we have to make both motors operate simultaneously. This paper is a modelling of a biaxial solar tracker, with the principle of an equatorial mount, allowing it to precisely follow the sun via a single axis (equatorial axis), while the second axis (tilt axis) makes a small daily correction of few seconds at sunrise. In this way, our model keeps precision to the maximum, with minimum energy consumption. A detailed simulation clearly shows that the proposed model receives the maximum solar irradiation that a normal surface to solar radiation can receive and may in a certain period of the year receive a gain in the amount of solar irradiation; we have up to 63.47% compared to a fixed installation. The study details the different tracking methods, in order to adapt the concept model to the type of solar panel used. We closed finish the study with the realization of the prototype with a detailed explanation of the concept movement. To validate the simulation, we have made an experience that gives us the same results as given by simulation.
Application Potential of Blockchain Technologies in the Travel and Tourism Industry

Blockchain technology creates a decentralized chain and gives access to digital assets in a way that their history remains unalterable and transparent by employing cryptographic hashing. Blockchain is a state-of-the-art technology that promises to reduce risk, minimize chances of fraud, and bring transparency in a scalable way for multiple uses across different sectors and industries, including travel and tourism. This paper attempts to do a rigorous systematic literature review of research articles that discuss the concept of blockchain among the leading travel and tourism journals. This review identifies 11 thematic applications of blockchain as discussed in these journal papers including, for example, smart contracts, digital currency, disintermediation, identity management, service customization, bringing authenticity in customer reviews, and building innovative loyalty programs. Finally, going beyond these thematic applications, this review also discusses present blockchain usage in the travel and tourism sector.
Neural computing in four spatial dimensions

Relationships among near set theory, shape maps and recent accounts of the Quantum Hall effect pave the way to neural networks computations performed in higher dimensions. We illustrate the operational procedure to build a real or artificial neural network able to detect, assess and quantify a fourth spatial dimension. We show how, starting from two-dimensional shapes embedded in a 2D topological charge pump, it is feasible to achieve the corresponding four-dimensional shapes, which encompass a larger amount of information. Synthesis of surface shape components, viewed topologically as shape descriptions in the form of feature vectors that vary over time, leads to a 4D view of cerebral activity. This novel, relatively straightforward architecture permits to increase the amount of available qbits in a fixed volume.
Explaining Deep Neural Network using Layer-wise Relevance Propagation and Integrated Gradients

Machine learning has become an integral part of technology in today's world. The field of artificial intelligence is the subject of research by a wide scientific community. In particular, through improved methodology, the availability of big data, and increased computing power, today's machine learning algorithms can achieve excellent performance that sometimes even exceeds the human level. However, due to their nested nonlinear structure, these models are generally considered to be “Black boxes” that do not provide any information about what exactly leads them to provide a specific output. This raised the need to interpret these algorithms and understand how they work as they are applied even in areas where they can cause critical damage. This article describes Integrated Gradients [1] and Layer-wise Relevance Propagation [2] methods and presents individual experiments with. In experiments we have used well-known datasets like MNIST[3], MNIST-Fashion dataset[4], Imagenette and Imagewoof which are subsets of ImageNet [5].
A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques

This study presents a novel hybrid intelligent approach using Extreme Learning Machine (ELM) and Equilibrium Optimiser (EO) (ELM-EO) for predicting resilient modulus, Mr of Unbound Granular Materials (UGMs). Fourteen various blends of Recycled Concrete Aggregate (RCA) with Recycled Clay Masonry (RCM), and Electric Arc Furnace Steel (EAFS) slag with limestone aggregates were tested in the laboratory using routine and advanced tests. The laboratory Mr testing produced 224 measurements based on the average of triplicate samples for each blend. The performance of the ELM-EO approach was evaluated and compared with conventional regression, ELM-biogeography-based optimisation (BBO) (ELM-BBO) and ELM-genetic algorithm (ELM-GA) approaches using the same input properties. The inputs used for the Mr prediction are the bulk stress, percent of RCM, and/or percent of EAFS. The results demonstrate that the performance of ELM-EO and ELM-BBO approaches is better than ELM-GA and regression approaches for predicting Mr. The overall statistical measures of the proposed approaches show that the ELM-EO approach ranks first as it outperforms the other approaches with coefficient of determination (R2) of 0.924 and Root Mean Square Error (RMSE) of 37.08 MPa.
An integrated cuckoo search optimizer for single and multi-objective optimization problems

Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test problems and comparisons with other recent methods illustrate the effectiveness of the proposed integrated mechanism of different search strategies and demonstrate the performance superiority of the proposed algorithm.
AutoML for Stream k-Nearest Neighbors Classification

The last few decades have witnessed a significant evolution of technology in different domains, changing the way the world operates, which leads to an overwhelming amount of data generated in an open-ended way as streams. Over the past years, we observed the development of several machine learning algorithms to process big data streams. However, the accuracy of these algorithms is very sensitive to their hyper-parameters, which requires expertise and extensive trials to tune. Another relevant aspect is the high-dimensionality of data, which can causes degradation to computational performance. To cope with these issues, this paper proposes a stream k-nearest neighbors (kNN) algorithm that applies an internal dimension reduction to the stream in order to reduce the resource usage and uses an automatic monitoring system that tunes dynamically the configuration of the kNN algorithm and the output dimension size with big data streams. Experiments over a wide range of datasets show that the predictive and computational performances of the kNN algorithm are improved.
Protecting operations on qudits from noise by continuous dynamical decoupling

We develop a procedure of generalized continuous dynamical decoupling (GCDD) for an ensemble of d-level systems (qudits), allowing one to protect the action of an arbitrary multiqudit gate from general noise. We first present our GCDD procedure for the case of an arbitrary qudit and apply it to the case of a Hadamard gate acting on a qutrit. This is done using a model that, in principle, could be implemented using the three magnetic hyperfine states of the ground energy level of Rb87 and laser beams whose intensities and phases are modulated according to our prescription. We show that this model allows one to generate continuously all the possible SU(3) group operations which are, in general, needed to apply the GCDD procedure. We finally show that our method can be extended to the case of an ensemble of qudits, identical or not.Received 13 December 2019Revised 22 October 2020Accepted 15 February 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013235Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum computationQuantum controlQuantum fluctuations & noiseQuantum gatesQuantum information with atoms & lightPhysical SystemsAlkali metalsAtomic ensembleQuantum Information
Predicting Consequences of Cyber-Attacks

Cyber-physical systems posit a complex number of security challenges due to interconnection of heterogeneous devices having limited processing, communication, and power capabilities. Additionally, the conglomeration of both physical and cyber-space further makes it difficult to devise a single security plan spanning both these spaces. Cyber-security researchers are often overloaded with a variety of cyber-alerts on a daily basis many of which turn out to be false positives. In this paper, we use machine learning and natural language processing techniques to predict the consequences of cyberattacks. The idea is to enable security researchers to have tools at their disposal that makes it easier to communicate the attack consequences with various stakeholders who may have little to no cybersecurity expertise. Additionally, with the proposed approach researchers' cognitive load can be reduced by automatically predicting the consequences of attacks in case new attacks are discovered. We compare the performance through various machine learning models employing word vectors obtained using both tf-idf and Doc2Vec models. In our experiments, an accuracy of 60% was obtained using tf-idf features and 57% using Doc2Vec method for models based on LinearSVC model.
CryptoRevocate: A Cryptographic Accumulator based Distributed Certificate Revocation List

Verification of the certificate revocation status is a crucial process for Public Key Infrastructure (PKI) system reliability. Failing to detect a revoked certificate may lead to catastrophic system compromises. Existing verification systems use slow and centralized approaches like Certificate Revocation Lists (CRLs) and Online Certificate Status Protocol (OCSP). These systems are known to cause verification failures (soft fails) because of system and network delays. Additionally, the availability of these systems are a major concern. Recent developments in distributed ledger (blockchain) technologies enable this information to be reliably published on the Internet, in a distributed manner. However in a distributed system, synchronizing large amounts of data among the nodes is an expensive task. One way to combat this issue is to use cryptographic accumulators, a tool that can be used to reduce data size; when only membership test statuses are necessary from a set of data. In this study, we focus on the reliable and effective distribution of certificate revocation information. We present a design of an asymmetric cryptographic accumulator based effective certificate revocation system. To the best of our knowledge, this is the first study using asymmetric cryptographic accumulators to distribute certificate revocation data via blockchain.
An Explainable Bayesian Decision Tree Algorithm

Bayesian Decision Trees provide a probabilistic framework that reduces the instability of Decision Trees while maintaining their explainability. While Markov Chain Monte Carlo methods are typically used to construct Bayesian Decision Trees, here we provide a deterministic Bayesian Decision Tree algorithm that eliminates the sampling and does not require a pruning step. This algorithm generates the greedy-modal tree (GMT) which is applicable to both regression and classification problems. We tested the algorithm on various benchmark classification data sets and obtained similar accuracies to other known techniques. Furthermore, we show that we can statistically analyze how was the GMT derived from the data and demonstrate this analysis with a financial example. Notably, the GMT allows for a technique that provides explainable simpler models which is often a prerequisite for applications in finance or the medical industry.
A Novel Design of Blowfish Algorithm for Image Security

Abstract In order to improve the security and efficiency of image encryption systems comprehensively an image encryption based on enhanced design of Blowfish scheme is proposed. The proposed system is employed block based image encryption technique combined with chaotic map properties. Firstly the digital image is scrambled and decomposed into several key based blocks randomly to decorrelated the relationship between original and processed image then each block is passed through an enhanced blowfish algorithm. The enhancement in design is to gain advantage of the strong facility, which is maintained by blowfish algorithm by overcoming its flaws, which leads to a significant improvement in security/performance. As a result the proposed system offers good performance for image encryption. The proposed algorithm is 320-bit Blowfish-like block cipher, where cascaded looking composition of F-functions is used instead of rounds. The key is accepted a variable length up to 400 bytes. The pragmatic aim of the proposed system is to decrease memory requirements and execution time while keeping the cipher simple and highly adaptable to future demands. To ensure improved encryption algorithm, the implementation of both techniques has been carried out for experimental purposes which is showed that the original image has a flat histogram after encrypted, a decreasing correlation between adjacent pixels in all color components and increasing entropy for the cases studied. The proposed algorithm has a sufficiently large key space and a very high sensitivity to the key. A comparative study with previous Blowfish algorithm shows the superiority of the modified algorithm.
Exploiting inter-frame regional correlation for efficient action recognition

• We proposed a novel temporal feature extraction method for action recognition. • The novel method explores inter-frame correlation on the regional level. • Our method achieves state-of-the-art performance on benchmark datasets. Temporal feature extraction is an important issue in video-based action recognition. Optical flow is a popular method to extract temporal feature, which produces excellent performance thanks to its capacity of capturing pixel-level correlation information between consecutive frames. However, such a pixel-level correlation is extracted at the cost of high computational complexity and large storage resource. In this paper, we propose a novel temporal feature extraction method, Attentive Correlated Temporal Feature (ACTF), by exploring inter-frame correlation within a certain region. The proposed ACTF exploits both bilinear and linear correlations between successive frames on the regional level. Our method has the advantage of achieving performance comparable to or better than optical flow-based methods while avoiding the introduction of optical flow. Experimental results demonstrate our proposed method achieves the competitive performances of 96.3 % on UCF101 and 76.3 % on HMDB51 benchmark datasets.
Classification and Prioritisation of Software Requirements using Machine Learning – A Systematic Review

Requirement Engineering (RE) plays an integral role throughout the process of software development. Requirement identification and prioritisation are the foremost phases of the RE process. Latest RE research work uses Machine Learning (ML) algorithms to tackle RE problems such as identifying requirements and assigning priorities to requirements, which have given better results than that of traditional natural language processing methods. An adequate understanding of these ML methods, however, is still lacking. The aim of this study is to understand which of the ML algorithms is likely to classify and prioritise the requirements efficiently and how they can be evaluated. It is observed that the current approaches are having constraints of scalability and complexity. Different methods used for the text preprocessing of requirements from SRS and user reviews are also proposed. 6 different ML algorithms and 6 different prioritisation algorithms, which are most common methods, are found. The most popular performance parameters used are accuracy, precision and recall. The limitations of these ML approaches are irrespective of dependency of requirements, priorities are assigned to requirements, the results with respect to scalability and speed is inferior.
Dynamic network embedding via structural attention

Network embedding aims to learn low-dimensional vector representations for each node in a network, which facilitates various learning tasks such as node classification, link prediction and so on. The majority of existing embedding methods mainly focus on static networks. However, many real-world networks are dynamic and change over time. Although a small number of very recent literatures have been developed for dynamic network embedding, they either need to be retrained without closed-form expression, or suffer high-time complexity. Additionally, a large number of real-world networks may be both large and noisy, presenting great challenges to effective network representation learning. In this paper, we propose a novel method named Dynamic Network Embedding via Structural Attention (DNESA). Specifically, we incorporate the attention mechanism into network embedding, which facilitates our method mainly concentrating on task-related parts of the given graph while avoiding or ignoring noisy parts of the network. Furthermore, we can capture the evolving characteristic of dynamic networks and learn embedding vectors of each node at different time steps by modeling the process of developing an open triad into a closed triad under the attention mechanism. Meanwhile, we carefully design an optimization function for preserving both the first-order and second-order proximities. Empirical experiments conducted on six real-world networks illustrate the efficiency of the proposed method, which outperforms state-of-the-art network embedding methods in applications including link prediction and node classification.
Interpreting deep learning models with marginal attribution by conditioning on quantiles

Abstract A vast and growing literature on explaining deep learning models has emerged. This paper contributes to that literature by introducing a global gradient-based model-agnostic method, which we call Marginal Attribution by Conditioning on Quantiles (MACQ). Our approach is based on analyzing the marginal attribution of predictions (outputs) to individual features (inputs). Specifically, we consider variable importance by fixing (global) output levels, and explaining how features marginally contribute to these fixed global output levels. MACQ can be seen as a marginal attribution counterpart to approaches such as accumulated local effects, which study the sensitivities of outputs by perturbing inputs. Furthermore, MACQ allows us to separate marginal attribution of individual features from interaction effects and to visualize the 3-way relationship between marginal attribution, output level, and feature value.