Reviewerly/exHarmony huggingface dataset:
A Virtual Multi-Channel GPU Fair Scheduling Method for Virtual Machines

In modern virtual computing environment, the 2D/3D rendering performance and parallel computing potential of GPU (graphics processing unit) must be fully exploited for multiple virtual machines (VMs). Existing GPU virtualization techniques are unable to take full advantage of a GPU's powerful 2D/3D hardware-accelerated graphics rendering performance or parallel computing potential, or it has not been considered that the internal resources of a GPU domain are fairly allocated between VMs with different performance requirements. Therefore, we propose a multi-channel GPU virtualization architecture (VMCG), model the corresponding credit allocating and transferring mechanisms, and redesign the virtual multi-channel GPU fair-scheduling algorithm. VMCG provides a separate V-Channel for each guest VM (DomU) that competes with other VMs for the same physical GPU resources, and each DomU submits command request blocks to its respective V-Channel according to the corresponding DomU ID. Through the virtual multi-channel GPU fair-scheduling algorithm, not only do multiple DomUs make full use of native GPU hardware acceleration, but the fairness of GPU resource allocation is significantly improved during GPU-intensive workloads from multiple DomUs running on the same host. Experimental results show that, for 2D/3D graphics applications, performance is close to 96 percent of that of the native GPU, performance is improved by approximately 500 percent for parallel computing applications, and GPU resource-allocation fairness is improved by approximately 60-80 percent.
Time Estimation and Resource Minimization Scheme for Apache Spark and Hadoop Big Data Systems With Failures

Apache Spark and Hadoop are open source frameworks for big data processing, which have been adopted by many companies.In order to implement a reliable big data system that can satisfy processing target completion times, accurate resource provisioning and job execution time estimations are needed.In this paper, time estimation and resource minimization schemes for Spark and Hadoop systems are presented.The proposed models use the probability of failure in the estimations to more accurately formulate the characteristics of real big data operations.The experimental results show that the proposed Spark adaptive failure-compensation and Hadoop adaptive failure-compensation schemes improve the accuracy of resource provisions by considering failure events, which improves the scheduling success rate of big data processing tasks.
A parallel content-based image retrieval system using spark and tachyon frameworks

With the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps. (i) image indexation step, in which we use MapReduce distributed model on Spark in order to speed up the indexation process. We also use a memory-centric distributed storage system, called Tachyon, to enhance the write operation (ii) image retrieving step which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on MapReduce model implemented under Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.
Joint Workload Scheduling and Energy Management for Green Data Centers Powered by Fuel Cells

The fuel cell is a promising power source for green data centers due to its high energy efficiency, low carbon emissions, and high reliability. However, because of the mechanical limitations related to fuel delivery, fuel cells are slow in adjusting power output when the energy demand quickly changes, which is called limited load following. Many recent work have studied to mitigate the limited load following by using energy storage to adjust energy supply, but achieves limited successes because of the constraint of energy storage size. In this paper, we address this challenge by changing both energy supply and demand, via joint workload scheduling and energy management. Specifically, we consider multiple geo-distributed data centers powered by both fuel cells and energy storage. An online algorithm has been proposed to minimize the gap between energy supply and demand by jointly managing the fuel cells output and migrating workloads among data centers. Simulations results based on real-world traces show that the proposed algorithms can achieve satisfactory performance.
A top-down saliency model with goal relevance

Most visual saliency models that integrate top-down factors process task and context information using machine learning techniques. Although these methods have been successful in improving prediction accuracy for human attention, they require significant training data and are unable to provide an understanding of what makes information relevant to a task such that it will attract gaze. This means that we still lack a general theory for the interaction between task and attention or eye movements. Recently, Tanner and Itti (2017) proposed the theory of goal relevance to explain what makes information relevant to goals. In this work, we record eye movements of 80 participants who each played one of four variants of a Mario video game and construct a combined saliency model using features from three sources: bottom-up, learned top-down, and goal relevance. We use this model to predict the eye behavior and find that the addition of goal relevance significantly improves the Normalized Scanpath Saliency score of the model from 4.35 to 5.82 (p < 1 × 10-100).
Surveying Human Habit Modeling and Mining Techniques in Smart Spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field.
The Use of Incorrect Informative Priors in the Estimation of MIMIC Model Parameters with Small Sample Sizes

Recently, advancements in Bayesian structural equation modeling (SEM), particularly software developments, have allowed researchers to more easily employ it in data analysis. With the potential for greater use, come opportunities to apply Bayesian SEM in a wider array of situations, including for small sample size problems. Effective use of Bayseian estimation hinges on selection of appropriate prior distributions for model parameters. Researchers have suggested that informative priors may be useful with small samples, presuming that the mean of the prior is accurate with respect to the population mean. The purpose of this simulation study was to examine model parameter estimation for the Multiple Indicator Multiple Cause model when an informative prior distribution had an incorrect mean. Results demonstrated that the use of incorrect informative priors with somewhat larger variance than is typical, yields more accurate parameter estimates than do naïve priors, or maximum likelihood estimation. Implications for practice are discussed.
Semantical analysis of weak Kleene logics

This paper presents a semantical analysis of the Weak Kleene Logics K3w and PWK from the tradition of Bochvar and Halldén. These are three-valued logics in which a formula takes the third value if at least one of its components does. The paper establishes two main results: a characterisation result for the relation of logical consequence in PWK – that is, we individuate necessary and sufficient conditions for a set Δ of formulas to follow from a set Γ in PWK – and a characterisation result for logical consequence in K3w. The paper also investigates two subsystems of K3w and PWK and discusses the relevance of the results against existing background. Finally, the paper discusses some issues related to Weak Kleene Logics – in particular, their philosophical interpretation and the reading of conjunction and disjunction – and points at some open issues.
Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice, computational frameworks are required, which can handle and visualize many objectives simultaneously, provide optimal solutions quickly and representatively, all while remaining simple and intuitive to use and understand by practitioners. Addressing these issues, this work introduces SAGESSE (Systematic Analysis, Generation, Exploration, Steering and Synthesis Experience), a decision support methodology, which relies on interactive multiobjective optimization. Its innovative aspects reside in the combination of (i) parallel coordinates as a means to simultaneously explore and steer the underlying alternative generation process, (ii) a Sobol sequence to efficiently sample the points to explore in the objective space, and (iii) on-the-fly application of multiattribute decision analysis, cluster analysis and other data visualization techniques linked to the parallel coordinates. An illustrative example demonstrates the applicability of the methodology to a large, complex urban planning problem.
Emerging NUI-Based Methods for User Authentication: A New Taxonomy and Survey

As the convenience and cost benefits of natural user interface (NUI) technologies are hastening their wide adoption, computing devices equipped with such interfaces are becoming ubiquitous. Used for a broad range of applications, from accessing email and bank accounts to home automation and interacting with a healthcare provider, such devices require, more than ever before, a secure yet convenient user authentication mechanism. This paper introduces a new taxonomy and presents a survey of “point-of-entry” user-device authentication mechanisms that employ a natural user interaction. The taxonomy allows a grouping of the surveyed techniques based on the sensor type used to capture user input, the actuator a user applies during interaction, and the credential type used for authentication. A set of security and usability evaluation criteria are then proposed based on the Bonneau, Herley, Van Oorschot, and Stajano framework. An analysis of a selection of techniques and, more importantly, the broader taxonomy elements they belong to, based on these evaluation criteria, are provided. This analysis and taxonomy provide a framework for the comparison of different authentication alternatives given an application and a targeted threat model. Similarly, the taxonomy and analysis also offer insights into possibly unexplored, yet potentially rewarding, research avenues for NUI-based user authentication that could be explored.
A comparison of t-SNE, SOM and SPADE for identifying material type domains in geological data

The standard mine modelling practice often involves investing significant effort into the interpretation of the deposit and identification of the geological domains using the chemical assays and geophysics. These domains and their accuracy play a key role in grade estimation using spatial modelling approaches, such as Gaussian processes and kriging. However, the domains developed for grade estimation do not always produce regions with well-defined correlations between the material types that are present in the deposit. The material type is based on the ore characteristics, such as mineralogy, texture and other visible petrological properties. Therefore a new and potentially more flexible methodology for domaining of material types is needed. This study applies t-SNE, SOM and SPADE to the material type data to embed high-dimensional data into low dimensions and thus facilitate clustering. These methods were tested on a banded iron formation hosted iron ore deposit located in the Hammersley region of Western Australia. All three methods produced clusters that were purer mixtures of the material types than the original domains. Due to the geologist input SPADE produced clusters closest to the original domains. However, this may not be the best clustering for material type modelling. Additionally, SPADE produced the best results for the ore, and highlighted how the user input can focus this method on the region of greatest interest. t-SNE and SOM are more automatic, but had results that were further from the original clusters. t-SNE identified clusters that were better spatially grouped than SOM, which was generally the most affected by the high variation in material within the detritals.
Natural Language Processing: Speaker, Language, and Gender Identification with LSTM

Long short-term memory (LSTM) is a state-of-the-art network used for different tasks related to natural language processing (NLP), pattern recognition, and classification. It has been successfully used for speech recognition and speaker identification as well. The amount of training data and the ratio of training to test data are still the key factors for achieving good results, but have their implications on the real usage. The main contribution of this paper is to achieve a high rate of speaker recognition for text-independent continuous speech using small ratio of training to test data, by applying long short-term memory recursive neural network. A comparison with the probabilistic feed-forward neural network has been made for speaker recognition as well as gender and language identification.
Design of finger gestures for locomotion in virtual reality

Background Within a virtual environment (VE) the control of locomotion (e.g., self-travel) is critical for creating a realistic and functional experience. Usually the direction of locomotion, while using a head-mounted display (HMD), is determined by the direction the head is pointing and the forward or backward motion is controlled with a hand held controllers. However, hand held devices can be difficult to use while the eyes are covered with a HMD. Free hand gestures, that are tracked with a camera or a hand data glove, have an advantage of eliminating the need to look at the hand controller but the design of hand or finger gestures for this purpose has not been well developed. Methods This study used a depth-sensing camera to track fingertip location (curling and straightening the fingers), which was converted to forward or backward self-travel in the VE. Fingertip position was converted to self-travel velocity using a mapping function with three parameters: a region of zero velocity (dead zone) around the relaxed hand position, a linear relationship of fingertip position to velocity (slope or/J) beginning at the edge of the dead zone, and an exponential relationship rather than a linear one mapping fingertip position to velocity (exponent). Using a HMD, participants moved forward along a virtual road and stopped at a target on the road by controlling self-travel velocity with finger flexion and extension. Each of the 3 mapping function parameters was tested at 3 levels. Outcomes measured included usability ratings, fatigue, nausea, and time to complete the tasks. Results Twenty subjects participated but five did not complete the study due to nausea. The size of the dead zone had little effect on performance or usability. Subjects preferred lower β values which were associated with better subjective ratings of control and reduced time to complete the task, especially for large targets. Exponent values of 1.0 or greater were preferred and reduced the time to complete the task, especially for small targets. Conclusions Small finger movements can be used to control velocity of self-travel in VE. The functions used for converting fingertip position to movement velocity influence usability and performance.
How Transferable Are Features in Convolutional Neural Network Acoustic Models across Languages?

Characterization of the representations learned in intermediate layers of deep networks can provide valuable insight into the nature of a task and can guide the development of well-tailored learning strategies. Here we study convolutional neural network (CNN)-based acoustic models in the context of automatic speech recognition. Adapting a method proposed by [1], we measure the transferability of each layer between English, Dutch and German to assess their language-specificity. We observed three distinct regions of transferability: (1) the first two layers were entirely transferable between languages, (2) layers 2-8 were also highly transferable but we found some evidence of language specificity, (3) the subsequent fully connected layers were more language specific but could be successfully finetuned to the target language. To further probe the effect of weight freezing, we performed follow-up experiments using freeze-training [2]. Our results are consistent with the observation that CNNs converge `bottom up' during training and demonstrate the benefit of freeze training, especially for transfer learning.
Performance-Aware Energy Saving for Data Center Networks

Today's data center networks (DCNs) tend to have tens to hundreds of thousands of servers that provide massive and sophisticated services. The architectural design of DCNs are usually over-provisioned for peak workloads and fault tolerance. Statistically, DCNs remain highly under-utilized, with typical utilization of around 30%. Network over-provisioning and under-utilization can be exploited for energy-saving. Most research efforts on DCN energy saving focus on how to save maximum energy but have little or no consideration to the performance of the residual network. Thus, the DCN performance can become degraded and the network left vulnerable to sudden traffic surges. In this paper, we have studied the energy-saving problem in DCNs while preserving network performance. The problem was formulated as mixed integer linear problem (MILP) solvable by CPLEX in order to minimize the energy consumed by DCN; meanwhile, safety threshold constraints for links utilization are met. To overcome CPLEX high computational time, a heuristic algorithm to provide practical and efficient solution for the MILP is introduced. The heuristic algorithm uses switches grouping and links consolidation to switch the traffic to a small number of network devices and turn off unused switches and links. Valiant load balancing is used to distribute the loads over active links. Simulation experiments using synthetic and real packet traces were conducted to validate the heuristic in terms of energy consumption and network performance. The results show that the heuristic can save up to 45% of the network energy and improve the average imbalance scores for links and switches by more than 50% with minimal effect on network performance.
Fintech and Shariah Principles in Smart Contracts

FinTech, or financial technology, is an emerging technology in financial transaction. It is disruptive technology which is changing the banking behavior for stakeholders. The thriving emergence of FinTech not only affects the conventional finance industry but also Islamic finance industry, as Islamic finance sector is also embracing FinTech as part of financial revolution. Thus, the Islamic FinTech emerges as faith-based FinTech. This is because Islamic FinTech claimed to comply with Shariah principle in their mechanism and smart contract. There is limited discussion on smart contract and Islamic FinTech and its Shariah principles. Hence, the Shariah principle in smart contract of Islamic FinTech need to be addressed. This chapter tries to delve into the smart contract concept in Islamic FinTech and Shariah principles in the mechanism. The review found that smart contract is currently in the early stage and so is Islamic FinTech. The scholars agreed that FinTech is a Maslahah (interest) to mankind's benefit. However, the smart contract is still in discussion and review.
Minimum Viable Products for Internet of Things Applications: Common Pitfalls and Practices

Internet of Things applications are not only the new opportunity for digital businesses but also a major driving force for the modification and creation of software systems in all industries and businesses. Compared to other types of software-intensive products, the development of Internet of Things applications lacks a systematic approach and guidelines. This paper aims at understanding the common practices and challenges among start-up companies who are developing Internet of Things products. A qualitative research is conducted with data from twelve semi-structured interviews. A thematic analysis reveals common types of Minimum Viable Products, prototyping techniques and production concerns among early stage hardware start-ups. We found that hardware start-ups go through an incremental prototyping process toward production. The progress associates with the transition from speed-focus to quality-focus. Hardware start-ups heavily rely on third-party vendors in term of development speed and final product quality. We identified 24 challenges related to management, requirement, design, implementation and testing. Internet of Things entrepreneurs should be aware of relevant pitfalls and managing both internal and external risks.
Automatic classification of human facial features based on their appearance

Classification or typology systems used to categorize different human body parts have existed for many years. Nevertheless, there are very few taxonomies of facial features. Ergonomics, forensic anthropology, crime prevention or new human-machine interaction systems and online activities, like e-commerce, e-learning, games, dating or social networks, are fields in which classifications of facial features are useful, for example, to create digital interlocutors that optimize the interactions between human and machines. However, classifying isolated facial features is difficult for human observers. Previous works reported low inter-observer and intra-observer agreement in the evaluation of facial features. This work presents a computer-based procedure to automatically classify facial features based on their global appearance. This procedure deals with the difficulties associated with classifying features using judgements from human observers, and facilitates the development of taxonomies of facial features. Taxonomies obtained through this procedure are presented for eyes, mouths and noses.