Reviewerly/exHarmony huggingface dataset:
A CPU Contention Predictor for Business-Critical Workloads in Cloud Datacenters

Resource contention is one of the major problems in cloud datacenters. Many types of resource contention occur, with important impact on the performance and sometimes even the reliability of applications running in cloud datacenters. Cloud applications run together on the same physical machines with different workloads resulting in non-synchronized accesses to the shared resources. This leads to cases where co-hosted applications are contending for the common resources and not receiving the demanded resource amounts. In this work, we investigate the contention in CPU resources, as CPU is allowed to be over-committed by typical SLAs. We propose a CPU-contention predictor for the demanding business-critical workloads, which require low resource contention to deliver the required performance to customers. Our predictor is based on a set of regression models and metrics which we evaluate extensively. We tune the predictor with data collected from a real-world cloud operation spanning multiple datacenters and servicing business-critical workloads.
A Generic Improvement to Deep Residual Networks Based on Gradient Flow

Preactivation ResNets consistently outperforms the original postactivation ResNets on the CIFAR10/100 classification benchmark. However, these results surprisingly do not carry over to the standard ImageNet benchmark. First, we theoretically analyze this incongruity in terms of how the two variants differ in handling the propagation of gradients. Although identity shortcuts are critical in both variants for improving optimization and performance, we show that postactivation variants enable early layers to receive a diverse dynamic composition of gradients from effectively deeper paths in comparison to preactivation variants, enabling the network to make maximal use of its representational capacity. Second, we show that downsampling projections (while only a few in number) have a significantly detrimental effect on performance. We show that by simply replacing downsampling projections with identitylike dense-reshape shortcuts, the classification results of standard residual architectures such as ResNets, ResNeXts, and SE-Nets improve by up to 1.2% on ImageNet, without any increase in computational complexity (FLOPs).
Digital Forensics Design of IOS Operating System

With the rapid popularization of mobile devices, mobile devices such as smart phones have become an indispensable tool in people's daily life. Mobile devices not only bring convenience to human beings, but also bring criminal activities based on mobile devices such as SMS fraud, dissemination of harmful information, virus software and so on. Therefore, digital forensics for mobile devices under IOS operating system is of great significance for combating crime, information security and other issues.
A Generic Optimization Based Cartesian Controller for Robotic Mobile Manipulation

Typically, the problem of robotic manipulation is divided among two sequential phases: a planning one and an execution one. However, since the second one is executed in open loop, the robot is unable to react in real time to changes in the task (e.g. moving object). This paper addresses the mobile manipulation problem from a real-time, closed loop perspective. In particular, we propose a generic optimization-based Cartesian controller, that given a continuous monitoring of the goal, determines the best motion commands. We target our controller to a robotic system comprising an arm and a mobile platform. However, the approach can in principle be extended to more complex mechanisms. The approach is based on shifting the problem to velocity space, where end effector velocity is a linear function of joint and base platform velocities. Our approach was quantitatively evaluated both on simulation and on a real service robot. It was also integrated into a mobile service robot architecture targeting domestic tasks and evaluated on the RoboCup@Home scientific competition. Our results show that the controller is able to reach random arm configurations with a high probability of success.
A Novel Real-time Driver Monitoring System Based on Deep Convolutional Neural Network

In this paper, we propose a novel real-time driver monitoring system based on deep convolutional neural network. Our system can efficiently detect head and facial features. It also allows to accurately estimate the distance between driver's head and camera, and vertical and horizontal rotation angles of head. Our work is inspired by the third version of YOLO (YOLOv3), a well-known objects detection algorithm. We have successfully introduced important improvements on YOLOv3 to further fasten the detection speed for excellent accuracy. Indeed, we reduced the depth and width of the backbone network of YOLO. We called this refined network HeadNet. Then, we implemented K-means algorithm to find appropriate anchors for head and facial features. Furthermore, we designed a new four-layer network knows as OrganNet to detect facial features. Our experiments show that our system has a high detection accuracy of 96.2% to detect head, and 91.5% to detect facial features, with a fast detection speed of 18 frames per second.
DNS Negative Caching in the Wild

short-paper Share on DNS Negative Caching in the Wild Authors: Lior Shafir Tel Aviv University Tel Aviv UniversityView Profile , Yehuda Afek Tel Aviv University Tel Aviv UniversityView Profile , Anat Bremler-Barr The Interdisciplinary Center The Interdisciplinary CenterView Profile , Neta Peleg The Interdisciplinary Center The Interdisciplinary CenterView Profile , Matan Sabag The Interdisciplinary Center The Interdisciplinary CenterView Profile Authors Info & Claims SIGCOMM Posters and Demos '19: Proceedings of the ACM SIGCOMM 2019 Conference Posters and DemosAugust 2019 Pages 143–145https://doi.org/10.1145/3342280.3342338Published:19 August 2019Publication History 1citation535DownloadsMetricsTotal Citations1Total Downloads535Last 12 Months34Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Calibration method of light-field camera for photogrammetry application

As a novel imaging method, the light-field camera can extract different ray traces in the space due to the micro-lens array fixed ahead of the imaging sensor. However, when it is used in photogrammetry such as light pen orientation, attitude estimation and indoor navigation, the classical collinear constraint cannot be applied directly since the refraction effect from the main lens. Therefore, this paper has proposed a mathematical model to calibrate the positions of sub-apertures on the main lens by using bundle adjustment algorithm. Firstly, the reference position for the sub-apertures will be calibrated by using center sub-aperture images. Secondly, the virtual imaging points of the target bar through different sub-apertures can be obtained by the focal length of the main lens. Then the sub-apertures on the main lens can be calculated by using bundle adjustment algorithm. Finally, the experiments results verified the accuracy and effectiveness of the proposed method.
A multi-threshold approach and a realistic error measure for vanishing point detection in natural landscapes

Vanishing Point (VP) detection is a computer vision task that can be useful in many different fields of application. In this work, we present a VP detection algorithm for natural landscape images based on an multi-threshold edge extraction process that combines several representations of an image, and on novel clustering and cluster refinement procedures. Our algorithm identifies a VP candidate in images with single-point perspective and improves detection results on two datasets that have already been tested for this task. Furthermore, we study how VP detection results have been reported in literature, pointing out the main drawbacks of previous approaches. To overcome these drawbacks, we present a novel error measure that is based on a probabilistic consistency measure between edges and VP hypothesis, and that can be tuned to vary the strictness on the results. Our reasoning on how our measure is more correct is supported by an intuitive analysis, simulations and an experimental validation.
Modelling socio-political competition

This paper continues the investigation of the logic of competing theories, be they scientific, social, political etc. We introduce a many-valued, multi-type modal language which we endow with relational semantics based on enriched reflexive graphs, inspired by Plo\v{s}\v{c}ica's representation of general lattices. We axiomatize the resulting many-valued, non-distributive modal logic of these structures and prove a completeness theorem. We illustrate the application of this logic through a case study in which we model competition among interacting political promises and social demands within an arena of political parties social groups.
Learning Discriminatory Deep Clustering Models

Deep convolutional auto-encoder (DCAE) allows to obtain useful features via its internal layer and provide an abstracted latent representation, which has been exploited for clustering analysis. DCAE allows a deep clustering method to extract similar patterns in lower-dimensional representation and find idealistic representative centers for distributed data. In this paper, we present a deep clustering model carried out in presence of varying degrees of supervision. We propose a new version of DCAE to include a supervision component. It introduces a mechanism to inject various levels of supervision into the learning process. This mechanism helps to effectively reconcile extracted latent representations and provided supervising knowledge in order to produce the best discriminative attributes. The key idea of our approach is distinguishing the discriminatory power of numerous structures, through varying degrees of supervision, when searching for a compact structure to form robust clusters. We evaluate our model on MNIST, USPS, MNIST fashion, SVHN datasets and show clustering accuracy on different supervisory levels.
Flow-guided feature propagation with occlusion aware detail enhancement for hand segmentation in egocentric videos

Recently, deep convolutional networks have achieved great success in image hand segmentation. However, such CNN-based methods mainly focus on per-frame inference, which is inefficient for hand segmentation in videos as continuity and redundancy exist in contiguous video frames. We present an approach to extending CNN-based hand segmentation methods from still image to video. It consists of two main branches: flow-guided feature propagation and light-weight occlusion aware detail enhancement. The flow-guided feature propagation branch runs an image segmentation network only on sparse frames and warps intermediate features to other frames according to the cross-frame flow field. Compared with the per-frame inference, it achieves significant speedup while causing large accuracy degradation due to distortion and occlusion issues in the warping. By introducing the light-weight occlusion aware detail enhancement branch, which extracts low-level detail features from each frame with spatial attention on occlusion regions, our approach becomes more robust against the distortion and occlusion issues and achieves a better accuracy–latency tradeoff. Experiments on three public egocentric video datasets, namely Egohands, GTEA and EDSH, have demonstrated the effectiveness and efficiency of our approach.
An Efficient Tool for Learning Bengali Sign Language for Vocally Impaired People

The sign language is the only mean of communication for the vocally impaired people throughout the world. This is possible for a vocally impaired person who has undergone special training. In Bangladesh, learning sign language is a hard job because not enough institutes are available for teaching sign language, and not enough supporting materials or tools are available online. In this paper, an efficient learning tool was developed for vocally impaired people of Bangladesh to learn Bengali alphabet without any assistance or supervision of another person. The system consists of a computer software and a special sensor-fitted glove. The software shows various letters with associated signs to the user. The user can imitate the sign using the glove, while the system can detect the bending of the fingers and tilt of the hand and check whether the sign is correct or not. A between-subject experiment with 18 vocally impaired people were conducted to assess the performance and user experience of the proposed learning tool. The study results showed that the tool is effective for the vocally impaired people to learn Bengali alphabet comparing to the traditional learning approach. The results also showed the developed system is efficient, useful and acceptable to users.
Proof of Shared Ownerships and Construct A Collaborative Cloud Application

Distributed storage stages guarantee advantageous applications to clients for sharing records and cooperate with coordinated efforts. The main challenge in a shared cloud-based application is the security and privacy of sensitive information stored by a particular user. For instance, Cloud users has the ability to erase documents and disavow them without counselling the alternate cloud associates. To handle this drawback, Proof of Shared belonging (PoSW) is proposed, a particular PoSW is subjected to execute information protection mechanism, confirmation and information reduplication. Furthermore, PoSW tends to utilize the diagonal cryptography equation to ensure the security of the mutual data records in order to provide a common information sharing approval, document ownership, and develop an absolutely particular relation between the document holders. Hence, integrating PoSW into the cloud server assists it to check the common belonging and overcome the data reduplication challenges in the common cloud-based data records.
A Novel Surrogate-assisted Differential Evolution for Expensive Optimization Problems with both Equality and Inequality Constraints

Surrogates have recently shown excellent abilities in assisting evolutionary algorithms for solving computationally expensive constrained optimization problems (ECOPs). However, the effectiveness of such surrogate-assisted evolutionary algorithms has only been verified on ECOPs with inequality constraints. In this paper, a Novel Surrogate-Assisted Differential Evolution (NSADE) algorithm is proposed for solving ECOPs with equality and inequality constraints, in which a trial vector generation mechanism and two surrogate-assisted local search phases are carried out iteratively. The trial vector generation mechanism based on information exchange between the historical elite solution set and current population is utilized to balance exploiting potential areas and exploring unknown areas. Then the expectation improvement-based local search is used to not only guide the current population to move towards feasible region but also alleviate the inaccuracy of the surrogate on the constraint boundary. Finally, a solution identification-based local search is utilized to further optimize two different types of historical elite solutions. Empirical studies on fifteen widely used benchmark problems demonstrate that the proposed NSADE can effectively obtain high-quality feasible solutions on ECOPs with equality constraints under a limited computational budget.
An Ontological Model for Map Data in Automotive Systems

Digital map data is an important source of information for the perception of the environment around cars for advanced driver assistance functions. These functions use map data to acquire information about the road infrastructure beyond the visual horizon of the driver. Embedded software components in today's cars typically use code-based processing of the map data to offer this support to advanced driver assistance functions, but the complexity of automotive systems continues to grow towards the realization of autonomous driving. To facilitate the representation and extraction of knowledge, we explore the feasibility of using ontologies for modelling and processing the map data in cars. We describe the challenges of adequately modelling the knowledge and present a proof of concept implementation that is used in a PC-based simulation to evaluate the knowledge extraction capabilities of this approach considering the requirements of representative advanced driver assistance functions.
Axiomatic and dual systems for constructive necessity, a formally verified equivalence

We present a proof of the equivalence between two deductive systems for constructive necessity, namely an axiomatic characterisation inspired by Hakli and Negri's system of derivations from assumptions for modal logic , a Hilbert-style formalism designed to ensure the validity of the deduction theorem, and the judgmental reconstruction given by Pfenning and Davies by means of a natural deduction approach that makes a distinction between valid and true formulae, constructively. Both systems and the proof of their equivalence are formally verified using the state-of-the-art proof assistant Coq. The proof approach taken throughout the paper unveils the use of some alternative proof methods that allow for a smooth transition from the high-level mathematical proofs to their mechanised counterparts.
Modeling hierarchical usage context for software exceptions based on interaction data

Traces of user interactions with a software system, captured in production, are commonly used as an input source for user experience testing. In this paper, we present an alternative use, introducing a novel approach of modeling user interaction traces enriched with another type of data gathered in production—software fault reports consisting of software exceptions and stack traces. The model described in this paper aims to improve developers’ comprehension of the circumstances surrounding a specific software exception and can highlight specific user behaviors that lead to a high frequency of software faults. Modeling the combination of interaction traces and software crash reports to form an interpretable and useful model is challenging due to the complexity and variance in the combined data source. Therefore, we propose a probabilistic unsupervised learning approach, adapting the nested hierarchical Dirichlet process, which is a Bayesian non-parametric hierarchical topic model originally applied to natural language data. This model infers a tree of topics, each of whom describes a set of commonly co-occurring commands and exceptions. The topic tree can be interpreted hierarchically to aid in categorizing the numerous types of exceptions and interactions. We apply the proposed approach to large scale datasets collected from the ABB RobotStudio software application, and evaluate it both numerically and with a small survey of the RobotStudio developers.
Improving unsupervised saliency detection by migrating from RGB to multispectral images

Abstract Saliency detection has been an important topic during the last decade. The main goal of saliency detection models is to detect the most relevant objects in a given scene. Most of these models use RGB (Red, Green, Blue) images as an input because they mainly focus on applications where features (eg, faces, textures, colors, or human silhouettes) are extracted from color images, and there are many labeled databases available for RGB‐based saliency data. Nevertheless, the use of RGB inputs clearly limits the amount of information from where to extract the salient regions as spectral information is lost during the color image recording. On the contrary, multispectral systems are able to capture more than three bands in a single capture and can retrieve information from the full spectrum at a pixel. The main aim of this study is to investigate the advantages of using multispectral images instead of RGB images for saliency detection within the framework of unsupervised models. We compare the performance of several unsupervised saliency models with both RGB and multispectral images using a specific dataset of multispectral images with ground‐truth data extracted from observers' fixation patterns. Our results show a general improvement when multispectral information is taken into account. The saliency maps estimated by using the multispectral features are closer to the ground‐truth data, with the simplest Graph‐based visual saliency and Boolean Map‐based models showing good relative gain compared with other approaches.
Analyzing Sequence Pattern Variants in Sequential Pattern Mining and Its Application to Electronic Medical Record Systems

Sequential pattern mining (SPM) is widely used for data mining and knowledge discovery in various application domains, such as medicine, e-commerce, and the World Wide Web. There has been much work on improving the execution time of SPM or enriching it via considering the time interval between items in sequences. However, no study has evaluated the sequence pattern variant (SPV) that is the original sequence containing frequent patterns including variants, and studied the factors that lead to the variants. Such a study is meaningful for medical tasks such as improving the quality of a disease's treatment method. This paper proposes methods for evaluating SPVs and understanding variant factors based on a statistical approach while considering the safety and efficiency of sequences and the relating static and dynamic information of the variants. Our proposal is confirmed to be effective by experimentally evaluating the electronic medical record system's real dataset and feedback from medical workers.
Ethical Concerns of the Use of Virtual Avatars in Consumer Entertainment

Many questions still remain of the uses both positive and negative of virtual avatars. With virtual avatars primed for consumer entertainment, the effects they can elicit must be further investigated before mainstream adoption. In this paper we present an overview of the potential risks posed by virtual avatars. Followed by a study designed to investigate these risks, and finally ethical research considerations for researchers interested in conducting research with virtual avatars.
From "Normal" to "Abnormal": A Concept for Determining Expected Self-Adaptation Behaviour

Self-adaptation and self-organisation found their way into technical systems to be able to master challenges resulting from the growing complexity of interconnected systems. In general, these adaptations are driven by dynamics of the environment and internal or external disturbances. However, a system that continuously changes its behaviour will result in acceptance problems by users. This paper addressed the question "How much adaptation is really necessary?" by (i) presenting a first concept for determining "normal"' adaptation and (ii) raising challenges for research on such a measurement framework.