Reviewerly/exHarmony huggingface dataset:
Slack squeeze coded computing for adaptive straggler mitigation

While performing distributed computations in today's cloud-based platforms, execution speed variations among compute nodes can significantly reduce the performance and create bottlenecks like stragglers. Coded computation techniques leverage coding theory to inject computational redundancy and mitigate stragglers in distributed computations. In this paper, we propose a dynamic workload distribution strategy for coded computation called Slack Squeeze Coded Computation (S2C2). S2C2 squeezes the compute slack (i.e., overhead) that is built into the coded computing frameworks by efficiently assigning work for all fast and slow nodes according to their speeds and without needing to re-distribute data. We implement an LSTM-based speed prediction algorithm to predict speeds of compute nodes. We evaluate S2C2 on linear algebraic algorithms, gradient descent, graph ranking, and graph filtering algorithms. We demonstrate 19% to 39% reduction in total computation latency using S2C2 compared to job replication and coded computation. We further show how S2C2 can be applied beyond matrix-vector multiplication.
Hackerspace Network: Prefiguring Technopolitical Futures?

ABSTRACT Under the practical symbol of “hacking,” computer expertise has been translated into renewed forms of political action in the past two decades. In this article, I examine this phenomenon through the study of self‐organized collectives for technoscientific experts who identify as “hackers.” Based on multisited field research in global cities of the Pacific Rim, I describe the formation of a network of community spaces called “hackerspaces” with a focus on their exchange practices and transnational ties. The alternative globalization of hacking through these convivial spaces for socialization and self‐training has created new political locations and horizons, shifting the moral valence of hacking toward more open and collaborative practices. The hackerspace network is one of the key sites for the observation of this transformation for prefiguring new technical and political practices, hence its relevance for anthropological studies of technology and politics in the contemporary moment. [ computing, expertise, hacking, technopolitics, hackerspaces ]
A Safe, Secure, and Predictable Software Architecture for Deep Learning in Safety-Critical Systems

In the last decade, deep learning techniques reached human-level performance in several specific tasks as image recognition, object detection, and adaptive control. For this reason, deep learning is being seriously considered by the industry to address difficult perceptual and control problems in several safety-critical applications (e.g., autonomous driving, robotics, and space missions). However, at the moment, deep learning software poses a number of issues related to safety, security, and predictability, which prevent its usage in safety-critical systems. This letter proposes a visionary software architecture that allows embracing deep learning while guaranteeing safety, security, and predictability by design. To achieve this goal, the architecture integrates multiple and diverse technologies, as hypervisors, run time monitoring, redundancy with diversity, predictive fault detection, fault recovery, and predictable resource management. Open challenges that stems from the proposed architecture are finally discussed.
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks

Group convolution, which divides the channels of ConvNets into groups, has achieved impressive improvement over the regular convolution operation. However, existing models, \eg ResNext, still suffers from the sub-optimal performance due to manually defining the number of groups as a constant over all of the layers. Toward addressing this issue, we present Groupable ConvNet (GroupNet) built by using a novel dynamic grouping convolution (DGConv) operation, which is able to learn the number of groups in an end-to-end manner. The proposed approach has several appealing benefits. (1) DGConv provides a unified convolution representation and covers many existing convolution operations such as regular dense convolution, group convolution, and depthwise convolution. (2) DGConv is a differentiable and flexible operation which learns to perform various convolutions from training data. (3) GroupNet trained with DGConv learns different number of groups for different convolution layers. Extensive experiments demonstrate that GroupNet outperforms its counterparts such as ResNet and ResNeXt in terms of accuracy and computational complexity. We also present introspection and reproducibility study, for the first time, showing the learning dynamics of training group numbers.
Task-Oriented Deep Reinforcement Learning for Robotic Skill Acquisition and Control

Reinforcement learning (RL) and imitation learning (IL), especially equipped with deep neural networks, have been widely studied for autonomous robotic skill acquisition and control tasks. However, these methods and their extensions require extensive environmental interactions during training, which greatly prevents them from being applied to real-world robots. To alleviate this problem, we present an efficient model-free off-policy actor-critic algorithm for robotic skill acquisition and continuous control, by fusing the task reward with a task-oriented guiding reward, which is formulated by leveraging few and imperfect expert demonstrations. In this framework, the agent can explore the environment more intentionally, thus sampling efficiency can be achieved; moreover, the agent can also exploit the experience more effectively, thereby substantially improved performance can be realized simultaneously. The empirical results on robotic locomotion tasks show that the proposed scheme can lower sample complexity by 2-10 times in contrast with the state-of-the-art baseline deep RL (DRL) algorithms, while achieving performance better than that of the expert. Furthermore, the proposed algorithm achieves significant improvement in both sampling efficiency and asymptotic performance on tasks with sparse and delayed reward, wherein those baseline DRL algorithms struggle to make progress. This takes a substantial step forward to implement these methods to acquire skills autonomously for real robots.
Several two-party protocols for quantum private comparison using entanglement and dense coding

Quantum private comparison (QPC) allows at least two participants who do not trust each other to compare whether their secret data are the same while maintaining data privacy, in which the security is based on the principles of quantum mechanics. In this paper, we propose several QPC protocols using Bell states, genuinely entangled five-qubit states, four-qubit cluster states and χ-type states. In each of the proposed protocols, we use dense coding technology to make the qubit efficiency of the protocol reach 100%, which is much higher than that of most existing protocols. The quantum states acting as information carriers are transmitted through a circular path, which can effectively reduce resource consumption. Decoy photon technology and one-time-pad encryption are used to ensure the security of the proposed protocols, making both external and internal attacks invalid.
AutoGRD

The widespread use of machine learning algorithms and the high level of expertise required to utilize them have fuelled the demand for solutions that can be used by non-experts. One of the main challenges non-experts face in applying machine learning to new problems is algorithm selection - the identification of the algorithm(s) that will deliver top performance for a given dataset, task, and evaluation measure. We present AutoGRD, a novel meta-learning approach for algorithm recommendation. AutoGRD first represents datasets as graphs and then extracts their latent representation that is used to train a ranking meta-model capable of accurately recommending top-performing algorithms for previously unseen datasets. We evaluate our approach on 250 datasets and demonstrate its effectiveness both for classification and regression tasks. AutoGRD outperforms state-of-the-art meta-learning and Bayesian methods.
"My cousin bought the phone for me. I never go to mobile shops."

The intersection of Islam and gender affect technological and social interactions for Muslim women in significant ways and remains an understudied domain for CSCW and related fields. Building on 73 qualitative interviews with low-income women in Punjab, Pakistan, we analyze the complexity of family relationships and the subsequent dynamics of authority around technology uptake and usage by women within non-Western contexts, and, specifically, within the Islamic world. We argue that a Pakistani woman's experience with technology depends on many factors, including gendered roles, generational differences in a family, and wider socio-cultural and religious influences against the backdrop of a culturally conservative and patriarchal society. Our paper highlights the rich family dynamics, including key life events, that transform the roles of both Muslim women and their relatives. Our work is intended to inform scholars, practitioners within development agencies and industry, and other individuals studying technology and development about household dynamics that influence Muslim women's use of technology to encourage them to consider these dynamics during design and implementation processes for technological inclusion.
Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece

This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series.
Distributed nonlinear Kalman filter with communication protocol

This paper proposes an optimal design of the general distributed nonlinear Kalman-based filtering algorithm to tackle the discrete-time estimation problem with noisy communication networks. The algorithm extends the Kalman filter by enabling it to predict the noisy communication data and fuse it with the received neighboring information to produce a posterior estimate value. In the prediction step, the unscented transformations of the estimate values and covariances originated in the Unscented Kalman Filter (UKF) are exploited. In the update step, a communication protocol is appended to the posterior estimator, which consequently leads to a modified posterior error covariance containing the covariance of the communication term with its communication gain. Both Kalman and communication gains are then optimised to collectively minimise the mean-squared estimation error. Afterwards, stochastic stability analysis is performed to guarantee its exponential boundedness. To exemplify the performance, this algorithm is applied to a group of robots in a sensor network assigned to estimate an unknown information distribution over an area in the optimal coverage control problem. Comparative numerical experiments finally verify the effectiveness of our design.
Developing Current Research Information Systems (CRIS) as Data Sources for Studies of Research

Current research information systems ( current research information system (CRIS) ) are increasingly being used to standardize and ease documentation, communication, and administration of research. With broad coverage and sufficient completeness, data quality, and standardization, CRIS systems can also be used as data sources for studies of research. Making CRIS interoperable and comparable across institutions and countries is necessary for the further development of CRIS for research purposes. Integration of CRIS for administrative purposes is already on the European agenda. This chapter focuses on challenges and solutions to the development of internationally integrated CRIS. Most of the remaining challenges are not related to technical solutions, but to an efficient sharing and use of contents. The chapter starts with the situation at the international level before it moves on to an example of CRIS at the national level to describe challenges and possible solutions even more concretely. The last section of the chapter provides examples of the type of studies that can be performed if progress is made for internationally integrated CRIS.
Sifter

Distributed tracing is a core component of cloud and datacenter systems, and provides visibility into their end-to-end runtime behavior. To reduce computational and storage overheads, most tracing frameworks do not keep all traces, but sample them uniformly at random. While effective at reducing overheads, uniform random sampling inevitably captures redundant, common-case execution traces, which are less useful for analysis and troubleshooting tasks. In this work we present Sifter, a general-purpose framework for biased trace sampling. Sifter captures qualitatively more diverse traces, by weighting sampling decisions towards edge-case code paths, infrequent request types, and anomalous events. Sifter does so by using the incoming stream of traces to build an unbiased low-dimensional model that approximates the system's common-case behavior. Sifter then biases sampling decisions towards traces that are poorly captured by this model. We have implemented Sifter, integrated it with several open-source tracing systems, and evaluate with traces from a range of open-source and production distributed systems. Our evaluation shows that Sifter effectively biases towards anomalous and outlier executions, is robust to noisy and heterogeneous traces, is efficient and scalable, and adapts to changes in workloads over time.
A Study on Metaheuristic-Based Neural Networks for Image Segmentation Purposes

Image segmentation is one of the most important tasks of visual machine and image processing that helps to recognize the pattern. Grouping of the pixels of an image helps to understand it. One of the successful methods for segmentation of the complicated images is artificial neural networks. Artificial neural networks are a subset of data science that can be used to extract significant data from the image. There are different methods that are proposed for the development of neural networks. The purpose of this chapter is to study different evolutionary algorithms for optimizing neural networks in different ways for image segmentation purposes.
Sigmoidal approximations of Heaviside functions in neural lattice models

A sigmoidal neural field lattice system is developed based on an Amari-Hopfield neural field lattice model, in which the Heaviside function is replaced by a simplifying sigmoidal function characterized by a small parameter ε. First the existence and uniqueness of solutions of the resulting sigmoidal lattice system is studied. Then the solutions of the sigmoidal lattice system are shown to converge to the solution of the Heaviside lattice system as ε→0, through an inflated lattice system which contains both of them. Moreover the attractors for the sigmoidal and inflated lattice systems are studied and compared to the attractor for the Heaviside lattice system.
Excellence in Exploratory Testing: Success Factors in Large-Scale Industry Projects

Based on interviews with 20 interviewees from four case study companies, this paper presents a list of key factors that enable efficient and effective exploratory testing of large-scale software systems. The nine factors are grouped into four themes: “The testers’ knowledge, experience and personality”, “Purpose and scope”, “Ways of working” and “Recording and reporting”. According to the interviewees, exploratory testing is a more creative way to work for the testers, and was therefore considered to make better use of the testers. Exploratory testing was also described as a good way to test system-wide and to test large-scale systems, especially exploratory testing with an end-user perspective. The identified key factors were confirmed by a series of follow-up interviews with the 20 interviewees and a cross-company workshop with 14 participants. This strengthens the generalizability of the findings, supporting that the list of key factors can be applied to projects in a large segment of the software industry. This paper also presents the results from a systematic literature review including 129 publications related to exploratory testing. No publication were found that summarizes the key factors that enable efficient and effective exploratory testing, which supports the novelty of the findings.
A performance analysis of Software Defined Network based prevention on phishing attack in cyberspace using a deep machine learning with CANTINA approach (DMLCA)

This paper discusses a novel frame work approach of Software Defined Network based prevention on phishing attack with the help of the deep machine learning with CANTINA approach (DMLCA) in the cyberspace. Cyber security is a significant concern in the operations of coalition, and is a complex challenge because of some needs in operational effectiveness and also the trust relationship limit which exists over the coalition partners. In networking, new promising paradigms like Software Defined Networks (SDN), offer a method to deal more efficiently with their security constraints. This machine learning approach is to deal with the phishing attack problem based on the SVM (support vector machine) and this machine learning technique with SVM helps to effectively to solve classification problems. The CANTINA approach helps to support the robust hyperlinks with the help of evaluating the term frequency (TF) and inverse document frequency and (IDF). This information retrieval algorithm helps to compare, classify and retrieve various documents. The objective is to improve the detection accuracy with the help of the DMLCA method with the various parameters such as detection accuracy based on the true positive ratio and false positive ratio, precision and recall.
From Clinical Trials to Highly Trustable Clinical Trials: Blockchain in Clinical Trials, a Game Changer for Improving Transparency?

Quality of clinical research is undermined by severe misconducts, errors, frauds, which are detrimental to the trust it should arouse. In this perspective article, we show how Blockchain may trace and control processes of Clinical Trials preventing from the above issues or at least discourage them since they would become traceable and opposable. Then, we propose a short and doable program where, amidst the complex stream of events that a Clinical Trials consist of, we select sensitive and misconduct-prone steps that could dramatically benefit from Blockchain, through either simple core features as traceability and incorruptibility of its data registration, or through more refined automation tools called Smart Contracts.
Geometry Normalization Networks for Accurate Scene Text Detection

Large geometry (e.g., orientation) variances are the key challenges in the scene text detection. In this work, we first conduct experiments to investigate the capacity of networks for learning geometry variances on detecting scene texts, and find that networks can handle only limited text geometry variances. Then, we put forward a novel Geometry Normalization Module (GNM) with multiple branches, each of which is composed of one Scale Normalization Unit and one Orientation Normalization Unit, to normalize each text instance to one desired canonical geometry range through at least one branch. The GNM is general and readily plugged into existing convolutional neural network based text detectors to construct end-to-end Geometry Normalization Networks (GNNets). Moreover, we propose a geometry-aware training scheme to effectively train the GNNets by sampling and augmenting text instances from a uniform geometry variance distribution. Finally, experiments on popular benchmarks of ICDAR 2015 and ICDAR 2017 MLT validate that our method outperforms all the state-of-the-art approaches remarkably by obtaining one-forward test F-scores of 88.52 and 74.54 respectively.
Visiting a virtual graveyard

Virtual reality offers a potential solution to enable visiting inaccessible cultural heritage sites. We present the design, prototyping and evaluation (n = 6) of a virtual visit to a historic graveyard. The Salla World War II graveyard is located in an inaccessible border zone between Finland and Russia. Our virtual graveyard, accessed through a head mounted display, aimed to create an as accurate as possible simulation of the Salla graveyard, including its atmosphere. Users are enabled to navigate the virtual graveyard and place a candle on a grave. Although the simulation was considered immersive, participants wished for more authenticity and details, e.g. being able to light the virtual candle. The work opens discussion on the need for dignity in the design of virtual experiences for sensitive cultural heritage sites.