Reviewerly/exHarmony huggingface dataset:
Does Data Augmentation Lead to Positive Margin

Data augmentation (DA) is commonly used during model training, as it significantly improves test error and model robustness. DA artificially expands the training set by applying random noise, rotations, crops, or even adversarial perturbations to the input data. Although DA is widely used, its capacity to provably improve robustness is not fully understood. In this work, we analyze the robustness that DA begets by quantifying the margin that DA enforces on empirical risk minimizers. We first focus on linear separators, and then a class of nonlinear models whose labeling is constant within small convex hulls of data points. We present lower bounds on the number of augmented data points required for non-zero margin, and show that commonly used DA techniques may only introduce significant margin after adding exponentially many points to the data set.
Towards the Modelling of Adaptation Rules and Histories for Multi-Cloud Applications

Currently, there is a move towards adopting multi-clouds due to their main benefits, including vendor lock-in avoidance and optimal application realisation via different cloud services. However, such multi-cloud applications face a new challenge related to the dynamicity and uncertainty that even a single cloud environment exhibits. As such, they cannot deliver a suitable service level to their customers, resulting in SLA penalty costs and application provider reputation reduction. To this end, we have previously proposed a cross-level and multi-cloud application adaptation architecture. Towards realising this architecture, this paper proposes two extensions of the CAMEL language allowing to specify advanced adaptation rules and histories. Such extensions not only enable to cover cross-level application adaptation by executing adaptation workflows but also to progress such an adaptation to address both the application and exploited cloud services evolution.
Asymptotic Distributions and Rates of Convergence for Random Forests and other Resampled Ensemble Learners.

Random forests remain among the most popular off-the-shelf supervised learning algorithms. Despite their well-documented empirical success, however, until recently, few theoretical results were available to describe their performance and behavior. In this work we push beyond recent work on consistency and asymptotic normality by establishing rates of convergence for random forests and other supervised learning ensembles. We develop the notion of generalized U-statistics and show that within this framework, random forest predictions can potentially remain asymptotically normal for larger subsample sizes than previously established. We also provide Berry-Esseen bounds in order to quantify the rate at which this convergence occurs, making explicit the roles of the subsample size and the number of trees in determining the distribution of random forest predictions.
Prediction of crime tendency of high-risk personnel using C5.0 decision tree empowered by particle swarm optimization

The research on the big data in the security and protection industry has been increasingly recognized as the hotspot in case of the rapid development of the big data. This paper mainly focuses on addressing the problem that predicts the criminal tendency of the high-risk personnel based on the recorded behavior data of the high-risk personnel. Therefore, we propose a novel predictive model that is the crime tendency of high-risk personnel using C5.0 based on particle swarm optimization. In this model, the C5.0 decision tree algorithm is first used as a classifier, in which repeated tenfold cross-validation is used and then continuously tuned according to the custom fitness function based on particle swarm optimization. In addition, the classification accuracy, the reduced number of feature subset, specificity and sensitivity under different algorithms are compared. Finally, the proposed model has higher accuracy through the optimal value of the particle position, the error rate of the cost under different iterations and the trend and the concavity and convexity of ROC curve. The experimental results show that the proposed model has a good effect on the predictive classification, which may provide guidance for predicting crime tendency of high-risk personnel.
ESRF Data Policy, Storage, and Services

The ESRF was the first synchrotron to adopt a data policy implementing principles that today are known as the FAIR principles (Findable, Accessible, Interoperable, Reusable) [1]. The ESRF Data Poli...
Ramp Distribution-Based Contrast Enhancement Techniques and Over-Contrast Measure

Our approach to contrast enhancement (CE) of images is based on natural scene statistics (NSS). We show, in this paper, that the average intensity distribution of natural images can be linearly approximated to the ramp distribution in an ordered histogram domain as the contrast increases. Based on this finding, we propose ramp distribution-based global and local CE algorithms. The ramp distribution-based slant thresholding (RDST) algorithm is proposed as a global CE method which uses slant thresholding in an ordered histogram domain to yield a contrast-enhanced image. Also, the ramp distribution-based adaptive slant thresholding (RDAST) algorithm is proposed as a local CE method. It adaptively adjusts a slant angle of the ramp distribution in each block to suppress noise amplification in uniform regions and maximizes contrast in non-uniform regions. The RDAST also employs a scaled global modified histogram to minimize sensitivity to block size changes. Moreover, we propose a metric to measure the amount of over-contrast in an image to evaluate all CE algorithms more correctly. The experimental results show that the proposed algorithms have better or competitive performance as well as computational efficiency.
Data Distribution Centre Support for the IPCC Sixth Assessment

The information provided in the Intergovernmental Panel on Climate Change (IPCC; <a href="http://ipcc.ch" target="_blank">http://ipcc.ch</a>) Assessment Reports (ARs) inform climate change policy development. Within the IPCC the scientific coordination of the ARs is conducted by three Working Groups (WGs) comprising of the Bureaus supported by their Technical Support Units (TSUs). Data management support is provided by the IPCC Data Distribution Centre (DDC; <a href="http://ipcc-data.org" target="_blank">http://ipcc-data.org</a>), which is overseen by the Task Group on Data Support for Climate Change Assessments (TG-Data; formerly TGICA). The DDC is a federated structure that is currently managed by the Centre for Environmental Data Analysis (CEDA; <a href="http://www.ceda.ac.uk/" target="_blank">http://www.ceda.ac.uk/</a>), United Kingdom; the World Data Center for Climate (WDCC; <a href="http://www.wdc-climate.de" target="_blank">http://www.wdc-climate.de</a>), Germany; and the Center for International Earth Science Information Network (CIESIN; <a href="http://www.ciesin.columbia.edu/" target="_blank">http://www.ciesin.columbia.edu/</a>) at Columbia University, U.S. For the IPCC Sixth Assessment cycle (AR6), analyses of climate simulations and observations published in scientific literature will be assessed. The reports will include figures and tables prepared from the underlying digital information. The DDC plays an increasingly important role in facilitating the exchange of data, as well as curating the assessed datasets, scripts and provenance records to facilitate the assessment process and to support the traceability of AR6 results through long-term continuity of data management and curation. These issues, among others, are addressed by the DDC support group (<a href="https://cedadev.github.io/ipcc_ddc" target="_blank">https://cedadev.github.io/ipcc_ddc</a>) currently consisting of members from the three TSUs and the three DDC managers.
A regression approach for prediction of Youtube views

YouTube has grown to be the number one video streaming platform on Internet and home to millions of content creator around the globe. Predicting the potential amount of YouTube views has proven to be extremely important for helping content creator to understand what type of videos the audience prefers to watch. In this paper, we will be introducing two types of regression models for predicting the total number of views a YouTube video can get based on the statistic that are available to our disposal. The dataset we will be using are released by YouTube to the public. The accuracy of both models are then compared by evaluating the mean absolute error and relative absolute error taken from the result of our experiment. The results showed that Ordinary Least Square method is more capable as compared to the Online Gradient Descent Method in providing a more accurate output because the algorithm allows us to find a gradient that is close as possible to the dependent variables despite having an only above average prediction.
Automatic Quality-of-Service Evaluation in Service-Oriented Computing

Formally describing and analysing quantitative requirements of software components might be important in software engineering; in the paradigm of API-based software systems might be vital. Quantitative requirements can be thought as characterising the Quality of Service – QoS provided by a service thus, useful as a way of classifying and ranking them according to specific needs. An efficient and automatic analysis of this type of requirements could provide the means for enabling dynamic establishing of Service Level Agreements – SLA, allowing for the automatisation of the Service Broker. In this paper we propose the use of a language for describing QoS contracts based on convex specification, and a two-phase analysis procedure for evaluating contract satisfaction based on the state of the art techniques used for hybrid system verification. The first phase of the procedure responds to the observation that when services are registered in repositories, their contracts are stored for subsequent use in negotiating SLAs. In such a context, a process phase of contract minimisation might lead to great efficiency gain when the second, and recurrent, phase of determining QoS compliance is run.
A trust assurance technique for Internet of things based on human behavior compliance

Summary The advent of the Internet of things (IoT) has radically changed the way in which computations and communications are carried out. People are just becoming another component of IoT environments, and in turn, IoT environments are becoming a mixture of platforms, software, services, things, and people. The price we pay for such dynamic and powerful environment is an intrinsic uncertainty and low trustworthiness due to its opaque perimeter, the multitude of different data sources with unknown providers, and uncertain responsibilities. Trustworthiness of observables collected by smart devices (from minuscle sensors to bigger machines) is fundamental to build a chain of trust on a decision process taken according to these observables. Some assurance solutions evaluate the quality of collected data, although they are difficult to apply in IoT environments for performance and cost reasons. In this paper, we take a different approach and put forward the idea that, in many cases, the behavior of people owning smart devices can contribute to the evaluation of the trustworthiness of collected data and, in turn, of the whole decision process. We therefore define an assurance methodology based on data analytics evaluating the compliance of people to behavioral policies. The more people behavior is compliant, the higher the trustworthiness of data collected through their smart devices.
<i>Retracted on January 26, 2021</i> : 3D-based video recognition acceleration by leveraging temporal locality

This Work has been Retracted by ACM because one or more of the authors of this Work were proven to have known or believed that the Work contained incorrect and/or falsified results prior to publication of the Work and one or more of the authors of this Work violated the anonymity and independence of the review process for their paper "3D-based video recognition acceleration by leveraging temporal locality" to the Proceedings of the 46th International Symposium on Computer Architecture (ISCA '19). Association for Computing Machinery, New York, NY, USA, 79-90.
Robust Pan/Tilt Compensation for Foreground–Background Segmentation

In this paper, we describe a robust method for compensating the panning and tilting motion of a camera, applied to foreground–background segmentation. First, the necessary internal camera parameters are determined through feature-point extraction and tracking. From these parameters, two motion models for points in the image plane are established. The first model assumes a fixed tilt angle, whereas the second model allows simultaneous pan and tilt. At runtime, these models are used to compensate for the motion of the camera in the background model. We will show that these methods provide a robust compensation mechanism and improve the foreground masks of an otherwise state-of-the-art unsupervised foreground–background segmentation method. The resulting algorithm is always able to obtain F 1 scores above 80 % on every daytime video in our test set when a minimal number of only eight feature matches are used to determine the background compensation, whereas the standard approaches need significantly more feature matches to produce similar results.
Visual Quality Assessment Considering Ultra HD, Full HD Resolution and Viewing Distance

Interest in multimedia content is rapidly increasing. In recent years there was extremely progress in improving Quality of Experience (QoE) in multimedia systems. As the next generation of TV, Ultra High Definition Television (Ultra HDTV) is attracting more and more people's attention. Ultra HDTV can provide a new viewer experience. In the case of HDTV, the observation distance plays an important role. If the observer sits further away from the television, that is the optimal viewing distance, and he may lose the benefit of HDTV. The results showed that for the case of non-optimal viewing distance, viewer's preference of Ultra HD resolution is not significantly higher. The result for the optimal viewing distance is very dependent on the video content and the bitrate. In our case, base on a questionnaire, it was found that 82 % of respondents owning Ultra HDTV do not sit at optimal distance.
Kernel-Based Efficient Lifelong Learning Algorithm

Multitask learning leverages shared structure across multiple tasks to obtain classifiers with generalization capability surpassing that of independent single task learning. Lifelong learning further tackles the challenge of performing multitask learning in an online fashion for a continual stream of tasks. In this work, kernel-based lifelong learning algorithm is proposed to capture significant nonlinear structure in the data. It is postulated that the classifiers accommodate a union-of-subspace model in the feature space. A shared library of atoms are then learned based on online kernel dictionary learning in a reproducing kernel Hilbert space. To alleviate the inherent complexity of nonparametric learning which grows with the data set size, an approximate classifiers are also obtained, which are representable using a parsimonious pool of selected examples. Preliminary tests verify the efficacy of the proposed methods.
Optimal path planning of UAV using grey wolf optimiser

This paper aims at proposing a novel approach for path planning of unmanned aerial vehicle (UAV) along with obstacle avoidance. The path planning is achieved through swarm intelligence algorithm inspired by the behaviour of grey wolves known as grey wolf optimiser (GWO). The optimal path planning of UAV using GWO is obtained by proper choice of objective function for targets and obstacle avoidance condition. The algorithm has three search agents namely alpha, beta and gamma which help in proper convergence of solution to the target while avoiding obstacle. The proposed approach is tested with different test cases of target and obstacles conditions and the simulated results have been reported. Simulation is carried out in MATLAB environment.
Deep Neuro-Fuzzy Architectures

Fuzzy logic inspires from the non-deterministic behaviour of human brain computations. The fusion of neural networks and fuzzy logic such as neuro-fuzzy architectures is natural, as both represent elementary inspiration from brain computations involving learning, adaptation and ability to tolerate noise. This chapter focuses on neuro-fuzzy and alike solutions for machine learning from perspective of functionality, architectures and applications.
Nonlocality distillation and quantum voids

Via nonlocality distillation, a number of copies of a given nonlocal correlation can be turned into a new correlation displaying a higher degree of nonlocality. Apart from its clear relevance in situations where nonlocality is a resource, distillation protocols also play an important role in the understanding of information-theoretical principles for quantum theory. Here, we derive a necessary condition for nonlocality distillation from two copies and apply it, among other results, to show that $1$D and $2$D quantum voids --faces of the nonlocal simplex set with no quantum realization-- can be distilled up to PR-boxes. With that, we generalize previous results in the literature. For instance, showing a broad class of post-quantum correlations that make communication complexity trivial and violate the information causality principle.
Multi-lingual Scene Text Detection by Local Histogram Analysis and Selection of Optimal Area for MSER

The problem of scene text detection has been quite intriguing for the research fraternity due to wide scope of applications. In recent times, some robust technique like Maximally Stable Extremal Region (MSER) has been developed and have gained immense popularity in detection and localization of text components in the wild. However, the problem of an optimal threshold and range of area in MSER has been addressed by very few researchers. In this work, we address this problem by dynamic thresholding through local histogram analysis and formulating a generalized range of the area for the MSER function. The detected MSERs are then analyzed by observing their region properties to distinguish text components from non-text ones and finally bounding the text components. The proposed technique is evaluated on a set of 200 images with multi-lingual text comprising English, Bangla, Hindi and Oriya languages.
This would work perfectly if it weren’t for all the humans: Two factor authentication in late modern societies

Late modern societies are now dependent on innumerable digitally networked technologies, yet there are intractable incongruencies between the technologies that we develop, and the corresponding technological literacies of users. This disjuncture has greatly increased the scope and scale of the risks to which globalized publics are exposed. With public cybersecurity literacies necessarily in decline as a result of the techno-social dynamism of “liquid modernity”, we now face an immense and exponentially growing matrix of cyberthreats and vulnerabilities, of which many carry potentially catastrophic consequences. Our interrogation of two-factor authentication systems, popularly implemented through short messaging services (SMSs), is demonstrative of vulnerabilities that continue to emerge as a result of widespread and entrenched disjunctures between the design of contemporary ICT systems, and the various flawed assumptions that undergird their implementation. We examined 400 authentication messages that were automatically posted to a public forum by Web sites commonly used to receive SMS authentication tokens on behalf of users. We found that 76.5 percent of those messages included the name of the application for which the message was intended: in so doing, over three quarters of our sample risked compromising their accounts. Occasionally, we even observed usernames and passwords posted together. The socio-technical implications of our findings for ICT system design in today’s globalized late modern societies are discussed.
An Improved Method for Model-Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Occlusion Scenes

Detection and pose estimation of texture-less objects still faces several challenges such as foreground occlusions, background clutter, multi-instance objects, large scale and pose changes to name but a few. In this paper, we present an improved method for template-based detection and pose estimation of texture-less objects, LINEMOD, in order to improve the robustness of pose estimation with partial foreground occlusions. For template creation, we divided template divided into four equal parts. After this process, four patches of the template are matched independently of each other. And we use image pyramid searching method for fast template matching. We improve the accuracy of fine pose estimation. Experimental results show that our approach is more robust to estimate the pose of texture-less objects with partial foreground occlusions.
An Architecture for the Real-Time Data Stream Monitoring in IoT

The IoT allows a new kind of monitoring strategy based on the heterogeneity of the devices and its lower cost. It implies a challenge in terms of the data interoperability and the associated semantic when they must support the real-time decision making. In this chapter, an integrated and interdisciplinary view of the data processing in the heterogeneous contexts is presented at the light of the Processing Architecture based on Measurement Metadata (PAbMM). The processing architecture gathers the data stream processing with the batch processing related to the Big Data repositories under the umbrella of the measurement projects. Thus, the integration between the measurement and evaluation (M&E) framework and the real-time processing is detailed. Followed, the interoperability is addressed from the M&E project definitions and the data interchanging related to PAbMM. The decision-making support is strengthened by a similarity mechanism which allows looking for similar experiences when a given situation lack of a specific knowledge. Finally, an application of the processing architecture based on Arduino technology for the “Bajo Giuliani” (La Pampa, Argentina) lagoon monitoring is shown.
AliveInLean: A Verified LLVM Peephole Optimization Verifier

Ensuring that compiler optimizations are correct is important for the reliability of the entire software ecosystem, since all software is compiled. Alive [12] is a tool for verifying LLVM’s peephole optimizations. Since Alive was released, it has helped compiler developers proactively find dozens of bugs in LLVM, avoiding potentially hazardous miscompilations. Despite having verified many LLVM optimizations so far, Alive is itself not verified, which has led to at least once declaring an optimization correct when it was not. We introduce AliveInLean, a formally verified peephole optimization verifier for LLVM. As the name suggests, AliveInLean is a reengineered version of Alive developed in the Lean theorem prover [14]. Assuming that the proof obligations are correctly discharged by an SMT solver, AliveInLean gives the same level of correctness guarantees as state-of-the-art formal frameworks such as CompCert [11], Peek [15], and Vellvm [26], while inheriting the advantages of Alive (significantly more automation and easy adoption by compiler developers).
Reimagining Early Childhood Educator Professional Development: An online community shares literacy learning strategies

Communities serve as a vital resource for educators seeking ideas to advance their practice. Online professional development capitalizes on technological communication to facilitate such sharing.