Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Docker and LXD with ApacheBench

Container is a popular approach in computer virtualization. If virtual machine separated operating system resources, container will receive same core resources with main operating system. Because of that, container size is smaller than virtual machine. Docker and LXD are part of a popular container vendor. Even though it has a different way to working, docker and LXD both have advantages. Docker only allows computer application to run in single thread mode, so it canprotect hardware resources from process overload. LXD allows application to run in multi thread mode, so the application can run normally like installed in normally operating system. This paper tried to compare the performance of docker and LXD. Web server application installed on the docker and LXD container, then we do a stress test using ApacheBench. ApacheBench send a large number of request package to test the container performance. Better container condition can handle more number of request package.
Invited Paper: Blockchain Design - A PFMI Viewpoint

Blockchains (BCs) received significant attentions from FinTeh in recent years. Many proof-of-concepts (POCs) projects and experiments have been carried out to explore the strengths and weaknesses of constructing financial market infrastructures upon BCs. The experiments showed that considerable improvements are still necessary to meet financial requirements such as safety, efficiency, reliability, scalability, security and transparency. The Principles for Financial Market Infrastructure (PFMI), issued by the Committee on Payment and Settlement System (CPSS) and International Organization of Securities Commissions (IOSCO), is a key standard for designing Financial Market Infrastructures (FMIs) so that global financial markets could be more robust and stable, especially, could withstand financial shocks. It is thus necessary to design BC-based FMIs according to PFMI. This position paper analyzes financial system requirements and the challenges, and discusses the design issues of BCs in meeting PFMI.
Instance-Based Cost-Sensitive Boosting

Many classification algorithms aim to minimize just their training error count; however, it is often desirable to minimize a more general cost metric, where distinct instances have different costs. In this paper, an instance-based cost-sensitive Bayesian consistent version of exponential loss function is proposed. Using the modified loss function, the derivation of instance-based cost-sensitive extensions of AdaBoost, RealBoost and GentleBoost are developed which are termed as ICSAdaBoost, ICSRealBoost and ICSGentleBoost, respectively. In this research, a new instance-based cost generation method is proposed instead of doing this expensive process by experts. Thus, each sample takes two cost values; a class cost and a sample cost. The first cost is equally assigned to all samples of each class while the second cost is generated according to the probability of each sample within its class probability density function. Experimental results of the proposed schemes imply 12% enhancement in terms of [Formula: see text]-measure and 13% on cost-per-sample over a variety of UCI datasets, compared to the state-of-the-art methods. The significant priority of the proposed method is supported by applying the pair of [Formula: see text]-tests to the results.
Scheduling dependent coflows to minimize the total weighted job completion time in datacenters

Datacenter networks are critical to cloud computing. The coflow abstraction is a major leap forward of application-aware network scheduling. In the context of multi-stage jobs, there are dependencies among coflows. As a result, there is a large divergence between coflow-completion-time (CCT) and job-completion-time (JCT). To our best knowledge, this is the first work that systematically studies: how to schedule dependent coflows of multi-stage jobs, so that the total weighted job completion time can be minimized. We present a formal mathematical formulation. Inspired by the optimal solution of the relaxed linear programming, we design an algorithm that runs in polynomial time to solve this problem with an approximation ratio of (2M+1) in general case, and 3 in special case, where M is the number of hosts. Evaluation results demonstrate that, the largest gap between our algorithm and the lower bound is only 9.14%. In testbeds, we reduce the JCT by up to 81.65% comparing with pure DCTCP. In simulations, we reduce the average JCT by up to 33.48% comparing with Aalo, a heuristic multi-stage coflow scheduler; we reduce the total weighted JCT by up to 83.58% comparing with LP-OV-LS, the state-of-the-art approximation algorithm of coflow scheduling.
X-GACMN: An X-Shaped Generative Adversarial Cross-Modal Network with Hypersphere Embedding

How to bridge heterogeneous gap between different modalities is one of the main challenges in cross-modal retrieval task. Most existing methods try to tackle this problem by projecting data from different modalities into a common space. In this paper, we introduce a novel X-Shaped Generative Adversarial Cross-Modal Network (X-GACMN) to learn a better common space between different modalities. Specifically, the proposed architecture combines the process of synthetic data generation and distribution adapting into a unified framework to make sure the heterogeneous modality distributions similar to each other in the learned common subspace. To promote the discriminative ability, a new loss function that combines intra-modality angular softmax loss and cross-modality pair-wise consistent loss is further imposed on the common space, hence the learned features can well preserve both inter-modality structure and intra-modality structure on a hypersphere manifold. Extensive experiments on three benchmark datasets show the effectiveness of the proposed approach.
Modeling Effect of Dust Particles on Performance Parameters of the Solar PV Module

The deposition of dust over the photovoltaic module is a function of factors such as dust properties (size, shape, and weight), wind parameters (speed and direction), panel characteristics (surface smoothness and inclination angle), and environmental factors (dust storms, rainfall, humidity, dew, and the wavelength of light). The dust accumulated over the PV modules leads to a decrease in the values of their output power. The study presents the effect of dust properties on the power output of the PV panels. A synthetic experiment has been conducted to simulate and show the dust effect on the performance parameters of the photovoltaic module concerning its I-V and P-V characteristics.
Rebooting Our Computing Models

Innovative and new computing paradigms must be considered as we reach the limits of von Neumann computing caused by the growth in necessary data processing. This paper provides an introduction to three emerging computing models that have established themselves as likely post-CMOS and post-von Neumann solutions. The first of these ideas is quantum computing, for which we discuss the challenges and potential of quantum computer architectures. Next, a computational system using intrinsic oscillators is introduced and an example is provided which shows its superiority in comparison to a typical von Neumann computational system. Finally, digital memcomputing using self-organizing logic gates is explained and then discussed as a method for optimization problems and machine learning.
Statistically Rigorous Testing of Clustering Implementations

Clustering is a widely-used and well-studied AI branch, but defining clustering correctness, as well as verifying and validating clustering implementations, remains a challenge. To address this, we propose a statistically rigorous approach that couples differential clustering with statistical hypothesis testing, namely we conduct statistical hypothesis testing on the outcome (distribution) of differential clustering to reveal problematic outcomes. We employed this approach on widely-used clustering algorithms implemented in popular ML toolkits; the toolkits were tasked with clustering datasets from the Penn Machine Learning Benchmark. The results indicate that there are statistically significant differences in clustering outcomes in a variety of scenarios where users might not expect clustering outcome variation.
Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed by governments and organisations. However, as of today, most of this data has been mostly unknown, or of difficult access, to the general public. In this paper, we present research that aims to leverage this data to promote tourism. Our first field of application focuses on the French Pyrenees. In order to achieve our goal, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms; and (ii) the proposition of an open-source algorithm and framework capable of recommending a sequence of cultural heritage points of interests (POIs) to be visited by tourists. This itinerary recommendation approach is original in many aspects: it not only considers the user preferences and popularity of POIs, but it also integrates different contextual information about the user as well as the relevance of specific sequences of POIs (strong links between POIs). The ability to export the cultural heritage data as open data and to recommend sequences of POIs are being integrated in a first prototype.
Morphology and Field Association of Calcretes from Quaternary Sediments of Purna Alluvial Basin, Maharashtra

The Quaternary deposits of Purna alluvial basin are mainly represented by areno-argillaceous sediments with good development of calcretes in both lateral and vertical profiles. Calcretes from thirteen river cut sections, lying along the main channel of Purna River, have been studied for their morphological characteristics. Total six morphotypes, i.e., nodular calcrete, laminar calcrete, hardpan calcrete, root calcrete, rhizolith balls and vein calcrete, have been identified, of which the first was dominant. The lithological association of various calcretes reveals their development mostly in fine- to medium-grained sediments, whereas root calcretes and rhizolith balls are mostly restricted to clayey–silty units. The predominance of calcretes in entire basin indicates the presence of the pedogenic activities under arid to semiarid climatic conditions during the deposition of sediments.
RiskiM: Toward Complete Kernel Protection with Hardware Support

The OS kernel is typically the assumed trusted computing base in a system. Consequently, when they try to protect the kernel, developers often build their solutions in a separate secure execution environment externally located and protected by special hardware. Due to limited visibility into the host system, the external solutions basically all entail the semantic gap problem which can be easily exploited by an adversary to circumvent them. Thus, for complete kernel protection against such adversarial exploits, previous solutions resorted to aggressive techniques that usually come with various adverse side effects, such as high performance overhead, kernel code modifications and/or excessively complicated hardware designs. In this paper, we introduce RiskiM, our new hardware-based monitoring platform to ensure kernel integrity from outside the host system. To overcome the semantic gap problem, we have devised a hardware interface architecture, called PEMI, by which RiskiM is supplied with all internal states of the host system essential for fulfilling its monitoring task to protect the kernel even in the presence of attacks exploiting the semantic gap between the host and RiskiM. To empirically validate the security strength and performance of our monitoring platform in existing systems, we have fully implemented RiskiM in a RISC-V system. Our experiments show that RiskiM succeeds in the host kernel protection by detecting even the advanced attacks which could circumvent previous solutions, yet suffering from virtually no aforementioned side effects.
An Improved Robust Method for Pose Estimation of Cylindrical Parts with Interference Features

Horizontal docking assembly is a fundamental process in the aerospace assembly, where intelligent measurement and adjustable support systems are urgently needed to achieve higher automation and precision. Thus, a laser scanning approach is employed to obtain the point cloud from a laser scanning sensor. And a method of section profile fitting is put forward to solve the pose parameters from the data cloud acquired by the laser scanning sensor. Firstly, the data is segmented into planar profiles by a series of parallel planes, and ellipse fitting is employed to estimate each center of the section profiles. Secondly, the pose of the part can be obtained through a spatial straight line fitting with these profile centers. However, there may be some interference features on the surface of the parts in the practical assembly process, which will cause negative effects to the measurement. Aiming at the interferences, a robust method improved from M-estimation and RANSAC is proposed to enhance the measurement robustness. The proportion of the inner points in a whole profile point set is set as a judgment criterion to validate each planar profile. Finally, a prototype is fabricated, a series of experiments have been conducted to verify the proposed method.
A Formal Access Control Model for SE-Floodlight Controller

Software defined networking (SDN) offers a promising approach for the next generation of networking technology. However, at present there is no widely accepted model for network applications authorization. One reason for lack of access control system is the absence of clear definition of an authorization model in SDN. Porras et al \citeporras2015securing recently developed SE-Floodlight for this purpose. They partly employed the notion of the well-known role-based access control (RBAC) model. They informally presented a role-based authorization system to manage applications access rights to network operations, submitted during the interaction between the application layer and the switch-side infrastructure. In this paper we develop a formal role-based authorization model in SDN using SE-Floodlight as a reference controller. Based on the formal model we discuss security aspects and propose some extensions. We also provide an administrative model for the authorization system. We show a configuration of the formal model for a use case scenario and discuss the security aspects of the authorization model and describe some security issues related to over-privileged apps, limitations of role hierarchy, app upgrading, and app downgrading problem. Finally, we propose a refined role hierarchy to address these problems.
Phase measurement of nonuniform phase-shifted interferograms using the frequency transfer function.

In this paper, we propose a phase measurement method for interferograms with nonuniform phase shifts. First, we measure the phase shifts between consecutive interferograms. Second, we use these values to modify the spectrum of the interferogram data. Then, by analyzing this spectrum, we design a suitable phase-shifting algorithm (PSA) using the frequency transfer function formalism. Finally, we test our PSA with experimental data to estimate the surface of an aluminum thin film. Our result is better than those obtained using the Fourier transform method, the principal component analysis method, and the least-squares PSA.
Machine-vision-guided rotation axis alignment for an optomechanical derotator

An image derotator is a device that can compensate the image blur caused by the rotation of an object. However, a key problem when using a derotator is to ensure the coincident alignment between the rotation axes of the derotator and measured object. An approach of machine-vision-guided rotation axis alignment is proposed in this paper. Circular markers and a laser dot are deployed to indicate the rotation axes of the derotator and object, respectively. A binocular stereo sensor is established to estimate the poses of both axes. With the misalignment information between the two axes updated in real-time, axis alignment can be achieved through manual adjustment. The validity and precision of the proposed approach are demonstrated through experiments.
AutoViDev: A Computer-Vision Framework to Enhance and Accelerate Research in Human Development

Interdisciplinary exchange of ideas and tools can accelerate scientific progress. For example, findings from developmental and vision science have spurred recent advances in artificial intelligence and computer vision. However, relatively little attention has been paid to how artificial intelligence and computer vision can facilitate research in developmental science. The current study presents AutoViDev—an automatic video-analysis tool that uses machine learning and computer vision to support video-based developmental research. AutoViDev identifies full body position estimations in real-time video streams using convolutional pose machine-learning algorithms. AutoViDev provides valuable information about a variety of behaviors, including gaze direction, facial expressions, posture, locomotion, manual actions, and interactions with objects. We present a high-level architecture of the framework and describe two projects that demonstrate its usability. We discuss the benefits of applying AutoViDev to large-scale, shared video datasets and highlight how machine learning and computer vision can enhance and accelerate research in developmental science.
Variability management in safety‐critical systems design and dependability analysis

Abstract Safety‐critical systems are of paramount importance for many application domains, where safety properties are a key driver to engineer critical aspects and avoid system failures. For the benefits of large‐scale reuse, software product lines (SPL) have been adopted in critical systems industry. However, the integration of safety analysis in the SPL development process is nontrivial. Also, the different usage contexts of safety‐critical systems complicates component fault modeling tasks and the identification of potential hazards. In this light, better methods become necessary to estimate the impact of dependability properties during Hazard Analysis and Risk Assessment. Existing methods incorporating the analysis of safety properties in SPL are limited as they do not include hazard analysis and component fault modeling. In this paper, we present the novel DEPendable Software Product Line Engineering (DEPendable‐SPLE) approach, which extends traditional SPL processes to support the reuse of safety assets. We also present a detailed analysis of the impact of product and context features on the SPL design, safety analysis, and safety requirements. We applied DEPendable‐SPLE to a realistic case study from the aerospace domain to illustrate how to model and reuse safety properties. DEPendable‐SPLE reduced the effort of safety analysis for certifying system variants.
Real-time facial expression recognition using local appearance-based descriptors

In human-computer interaction the automatic face sensing and recognition of facial expressions is still a challenging task of affective computing, psychology and biomedical applications. The main goal of this paper is to increment a recognition rate of approaches for unobtrusive face sensing and au tomatic interpretation of emotions. The proposed approach explores local scale invariant feature transform descriptors for extraction of face key points used for face detection, recognition and then for encoding facial deformations in terms of Ekman’s Facial Action Coding System (FACS). Real-time face tracking and recognition is provided by quadratic discriminant analysis and Bayesian approaches as classification tools. Based on detected fiducial points, the accurate automatic recognizing six prototypical human facial expressions as well as detecting affective states in real-time scenes is provided by fuzzy inference system based on the proposed reasoning model. Carried out experiments demonstrate that Ekman’s FACS traditionally used in affective computing may be extended to interpretation of non-prototypical compound emotions using Plutchik psychological model of emotional responses. Conducted tests with faces from standard databases confirm that the proposed approaches for analysis of local image features provide robust, quite accurate, fast and low computational cost face sensing and facial expression interpretation.
Post-Quantum Cryptography

The goal of post-quantum cryptography is to develop cryptographic systems that are secure against both quantum and classical computers, and compatible with existing communication protocols and networks. Quantum computing is a winsome field that deals with theoretical computational systems combining visionary ideas of computer science, physics, and mathematics. It concerns the behavior and nature of energy at the quantum level to improve the efficiency of computation. The process of code-based cryptography is a commutation between efficiency and security. McEliece cryptosystem is the most successful cryptosystem, based on extended Golay code. The concept of knapsack-type cryptosystem is proposed by based on Reed–Solomon codes. NTRUEncrypt is a public-key cryptosystem developed by three mathematicians: Hoffstein, Pipher, and Silverman in 1998. Goldreich–Goldwasser–Halevi is the most instinctive encryption scheme based on lattices.
An Overview of Clustering Models with an Application to Document Clustering

This paper presents an overview of selected clustering models and shows an application of K-Means algorithm to document clustering. In the introductory part, the definitions of basic concepts and common characteristics of clustering models are described. Then an overview of clustering models is given. The methods of clustering, basic characteristics, visualization and possible input data for each algorithm are presented. The authors also explain the assessment of each algorithm taking into consideration measures such as Rand index, homogeneity completeness, V-measure and Silhouette coefficient. Furthermore, the paper describes the application of the K-Means algorithm to document clustering showing the final result and elaborating the procedures applied when clustering the documents.
ToCo: An Ontology for Representing Hybrid Telecommunication Networks

The TOUCAN project proposed an ontology for telecommunication networks with hybrid technologies – the TOUCAN Ontology (ToCo), available at http://purl.org/toco/ , as well as a knowledge design pattern Device-Interface-Link (DIL) pattern. The core classes and relationships forming the ontology are discussed in detail. The ToCo ontology can describe the physical infrastructure, quality of channel, services and users in heterogeneous telecommunication networks which span multiple technology domains. The DIL pattern is observed and summarised when modelling networks with various technology domains. Examples and use cases of ToCo are presented for demonstration.