Reviewerly/exHarmony huggingface dataset:
Efficient Data Placement and Replication for QoS-Aware Approximate Query Evaluation of Big Data Analytics

Enterprise users at different geographic locations generate large-volume data that is stored at different geographic datacenters. These users may also perform big data analytics on the stored data to identify valuable information in order to make strategic decisions. However, it is well known that performing big data analytics on data in geographical-located datacenters usually is time-consuming and costly. In some delay-sensitive applications, the query result may become useless if answering a query takes too long time. Instead, sometimes users may only be interested in timely approximate rather than exact query results. When such approximate query evaluation is the case, applications must sacrifice timeliness to get more accurate evaluation results or tolerate evaluation result with a guaranteed error bound obtained from analyzing the samples of the data to meet their stringent timeline. In this paper, we study quality-of-service (QoS)-aware data replication and placement for approximate query evaluation of big data analytics in a distributed cloud, where the original (source) data of a query is distributed at different geo-distributed datacenters. We focus on the problems of placing data samples of the source data at some strategic datacenters to meet stringent query delay requirements of users, by exploring a non-trivial trade-off between the cost of query evaluation and the error bound of the evaluation result. We first propose an approximation algorithm with a provable approximation ratio for a single approximate query. We then develop an efficient heuristic algorithm for evaluating a set of approximate queries with the aim to minimize the evaluation cost while meeting the delay requirements of these queries. We finally demonstrate the effectiveness and efficiency of the proposed algorithms through both experimental simulations and implementations in a real test-bed, real datasets are employed. Experimental results show that the proposed algorithms are promising.
Towards blockchain-based urban planning

The potential of urban planning to solve environmental problems in general and manage waste, in particular, is important because the illegal dumping of solid waste is one of the events related to illegal waste treatment activities. The waste management process is very poor, especially when it comes to confirming the correct destination for the delivery of waste. In this work, we are proposing a chain-based approach to waste tracking to enable waste data reporting in a single system. The blockchain is a technology that has already proven itself in the financial sector. It securely records transactions in a large, time-stamped ledger using the proof of work algorithm (PoW), and allows us to write smart contracts on the ethereum network. The application of this technology to the waste sector will enable reliable, transparent and secure recording of all waste movements, thus enabling waste to be traced from source to treatment and disposal. Users will be able to import and export data through a user interface offering different levels of functionality and access. It will also identify and act on illegal wasteful crimes.
Multi-client Cloud-based Symmetric Searchable Encryption

We propose a multi-client Symmetric Searchable Encryption (SSE) scheme based on the single-user protocol [3] . The scheme allows any user to generate a search query by interacting with any θ is a threshold parameter) `helping' users. It preserves the privacy of a database content against the server assuming a leakage of up to θ-1 users' keys to the server while hiding the query from the θ-1 `helping users'. To achieve the query privacy, we design a new distributed key-homomorphic pseudorandom function (PRF) that hides the PRF input (search keyword) from the `helping' users. We present a concrete construction of our randomizable distributed PRF. By distributing the utilized keys among the users, the need for a constant online presence of the data owner to provide services to the users is eliminated, while providing resilience against a user key exposure. We extended our scheme to support user revocation in two different scenarios. In addition, we give a solution for fast update of the encryption key with the overhead significantly smaller than the re-encryption and re-uploading the database. Moreover, our scheme is secure against passive and active collusion between the server and a subset of users.
Utilizing Knowledge Graphs for Neural Machine Translation Augmentation

While neural networks have led to substantial progress in machine translation, their success depends heavily on large amounts of training data. However, parallel training corpora are not always readily available. Moreover, out-of-vocabulary words---mostly entities and terminological expressions---pose a difficult challenge to Neural Machine Translation systems. Recent efforts have tried to alleviate the data sparsity problem by augmenting the training data using different strategies, such as external knowledge injection. In this paper, we hypothesize that knowledge graphs enhance the semantic feature extraction of neural models, thus optimizing the translation of entities and terminological expressions in texts and consequently leading to better translation quality. We investigate two different strategies for incorporating knowledge graphs into neural models without modifying the neural network architectures. Additionally, we examine the effectiveness of our augmented models on domain-specific texts and ontologies. Our knowledge-graph-augmented neural translation model, dubbed KG-NMT, achieves significant and consistent improvements of +3 BLEU, METEOR and chrF3 on average on the newstest datasets between 2015 and 2018 for the WMT English-German translation task.
Confusion Prediction from Eye-Tracking Data

Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user's confusion is correlated with primarily fixation-level features. We find that random forest achieves an accuracy of more than 70% when prediction user confusion using only fixation features. In addition, adding user-level features (age and gender) improves the accuracy to more than 90%. We also find that balancing the classes before training improves performance. We test two balancing algorithms, Synthetic Minority Over Sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) finding that SMOTE provides a higher performance increase. Overall, this research contains implications for researchers interested in inferring users' cognitive states from eye-tracking data.
Expanding Our Reflexive Toolbox

Addressing harm and ethical dilemmas that arise from the design of socio-technical systems requires methodologies that foster critical investigations of normative values, perspectives, and experiences. In this paper, we propose duoethnography as a feminist methodological tool for collaboratively researching interactions between users, devices, and data. Drawing on an interdisciplinary body of work and reflexive methodological traditions, we propose and describe four facets of the methodology: relationality, difference, dialogic process, and critical subjectivity. Next, we provide examples of the methodology "in practice" by detailing how the facets were implemented in a six-month diary study of digital health tracking. We use the study findings to illustrate how the application of these four facets facilitated a collaborative and dialogic process that enhanced self-knowledge and resulted in a multifaceted understanding of individual and shared health experiences. Finally, we conclude by discussing the methodological contributions of duoethnography for socio-technical research critically examining normative values and ethics in design. We contend that the collaborative intentionality of duoethnography offers a unique contribution and unifying methodology that views the personal as a valuable site of knowledge production, positions knowledge formation as a dialogic process, and promotes alternative ways of knowing and meaning making.
Unsupervised Neural Quantization for Compressed-Domain Similarity Search

We tackle the problem of unsupervised visual descriptors compression, which is a key ingredient of large-scale image retrieval systems. While the deep learning machinery has benefited literally all computer vision pipelines, the existing state-of-the-art compression methods employ shallow architectures, and we aim to close this gap by our paper. In more detail, we introduce a DNN architecture for the unsupervised compressed-domain retrieval, based on multi-codebook quantization. The proposed architecture is designed to incorporate both fast data encoding and efficient distances computation via lookup tables. We demonstrate the exceptional advantage of our scheme over existing quantization approaches on several datasets of visual descriptors via outperforming the previous state-of-the-art by a large margin.
Replication of Studies in Empirical Software Engineering: A Systematic Mapping Study, From 2013 to 2018

Context: In any discipline, replications of empirical studies are necessary to consolidate the acquired knowledge. In Software Engineering, replications have been reported since the 1990s, although their number is still small. The difficulty in publishing, the lack of guidelines, and the unavailability of replication packages are pointed out by the community as some of the main causes. Objective: Understanding the current state of replications in Software Engineering studies by evaluating current trends and evolution during the last 6 years. Method: A Systematic Mapping Study including articles published in the 2013-2018 period that report at least one replication of an empirical study in Software Engineering. Results: 137 studies were selected and analysed, identifying: i) forums; ii) authors, co-authorships and institutions; iii) most cited studies; iv) research topics addressed; v) empirical methods used; vi) temporal distribution of publications; and vii) distribution of studies according to research topics and empirical methods. Conclusions: According to our results, the most relevant forums are the Empirical Software Engineering and Information and Software Technology journals, and the Empirical Software Engineering and Measurement conference. We observed that, as in previous reviews by other researchers, most of the studies were carried out by European institutions, especially Italian, Spanish, and German researchers and institutions. The studies attracting more citations were published mainly in journals and in the International Conference on Software Engineering. Testing, requirements, and software construction were the most frequent topics of replication studies, whereas the usual empirical method was the controlled experiment. On the other hand, we identified research gaps in areas such as software engineering process, software configuration management, and software engineering economics. When analysed together with previous reviews, there is a clear increasing trend in the number of published replications in the 2013-2018 period.
Design and the Politics of Collaboration

This workshop aims to advance our knowledge of how CSCW technologies can be better aligned with grassroots politics of collaboration. What politics are inherent in CSCW tools and techniques? How can we examine whether so- ciotechnical systems support collaboration in ways that lead to equitable solutions for all and not just a select few? What can we learn about collaborative systems and practices from other communities of people with lived experiences of politics of collaboration? Our workshop will incorporate communal practices of grassroots movement building to explore what it means to examine designs of CSCW arti- facts and practices for the politics they embody and pro- mote. The workshop simultaneously is about grassroots approaches, and also leverages lessons we have learned from grassroots movements in the workshop structure.
How Teachers in India Reconfigure their Work Practices around a Teacher-Oriented Technology Intervention

The proliferation of mobile devices around the world, combined with falling costs of hardware and Internet connectivity, have resulted in an increasing number of organizations that work to introduce educational technology interventions into low-income schools in the Global South. However, to date, most prior HCI research examining such interventions has focused on interventions that target students. In this paper, we expand prior literature by examining an intervention, called Meghshala, that targets teachers in low-income schools as its primary users. Through interviews and observations with 39 participants from 12 government schools in India, we show how the introduction of a teacher-focused technology intervention causes teachers to reconfigure their work practices, including lesson preparation, in-classroom teaching practices, bureaucratic work processes, and post-teaching feedback mechanisms. We use the concept of material agency to analyze our findings with respect to teacher agency and reconfiguration, and use theories of teacher knowledge to highlight the kinds of knowledge production that teachers in our research context tend to focus on (e.g., content knowledge). Finally, we offer design opportunities for future teacher-focused technology interventions.
DSIC: Deep Stereo Image Compression

In this paper we tackle the problem of stereo image compression, and leverage the fact that the two images have overlapping fields of view to further compress the representations. Our approach leverages state-of-the-art single-image compression autoencoders and enhances the compression with novel parametric skip functions to feed fully differentiable, disparity-warped features at all levels to the encoder/decoder of the second image. Moreover, we model the probabilistic dependence between the image codes using a conditional entropy model. Our experiments show an impressive 30 - 50% reduction in the second image bitrate at low bitrates compared to deep single-image compression, and a 10 - 20% reduction at higher bitrates.
Search-based procedural content generation for GVG-LG

Search-based Procedural Content Generation has been proven an efficient technique for the generation of different and diverse types of content. In this article, we generate general levels for 2D games using the FI2POP genetic algorithm. Generating entertaining levels is subjective. Therefore in this work, we focus on the aesthetics and difficulty of a level. For experimentation purposes, we generated levels for five different games in the General Video Game Level Generation track. Our results indicate that the generated levels are symmetrical, balanced, dense and reachable.
Memory-Reduced Network Stacking for Edge-Level CNN Architecture With Structured Weight Pruning

This paper presents a novel stacking and multi-level indexing scheme for convolutional neural networks (CNNs) used in energy-limited edge-level systems. Basically, the proposed scheme offers multiple accuracy modes by adopting a structured weight pruning method that enables a CNN to be trained once with multiple pruning ratios and thereby allows for adaptive energy-accuracy trade-offs. The memory overhead required to store several different networks is kept to a minimum by adopting a novel method for including smaller lower-accuracy networks as subnetworks of larger higher-accuracy networks and by using a unique multi-level indexing scheme that can effectively store compressed weight data for the proposed stacked-CNN architecture. Experimental results show that the proposed method successfully reduces the memory footprint by up to 33% when compared to a baseline CNN architecture. An FPGA-based multimode CNN accelerator that implements the proposed scheme has been designed. Energy usage analysis with a case study shows that the inference energy required for on-device CNN processing can be reduced by up to 1.94 times over the baseline design.
Scaling Object Detection by Transferring Classification Weights

Large scale object detection datasets are constantly increasing their size in terms of the number of classes and annotations count. Yet, the number of object-level categories annotated in detection datasets is an order of magnitude smaller than image-level classification labels. State-of-the art object detection models are trained in a supervised fashion and this limits the number of object classes they can detect. In this paper, we propose a novel weight transfer network (WTN) to effectively and efficiently transfer knowledge from classification network's weights to detection network's weights to allow detection of novel classes without box supervision. We first introduce input and feature normalization schemes to curb the under-fitting during training of a vanilla WTN. We then propose autoencoder-WTN (AE-WTN) which uses reconstruction loss to preserve classification network's information over all classes in the target latent space to ensure generalization to novel classes. Compared to vanilla WTN, AE-WTN obtains absolute performance gains of 6% on two Open Images evaluation sets with 500 seen and 57 novel classes respectively, and 25% on a Visual Genome evaluation set with 200 novel classes.
OAuthGuard

Millions of users routinely use Google to log in to websites supporting the standardised protocols OAuth 2.0 or OpenID Connect; the security of OAuth 2.0 and OpenID Connect is therefore of critical importance. As revealed in previous studies, in practice RPs often implement OAuth 2.0 incorrectly, and so many real-world OAuth 2.0 and OpenID Connect systems are vulnerable to attack. However, users of such flawed systems are typically unaware of these issues, and so are at risk of attacks which could result in unauthorised access to the victim user's account at an RP. In order to address this threat, we have developed OAuthGuard, an OAuth 2.0 and OpenID Connect vulnerability scanner and protector, that works with RPs using Google OAuth 2.0 and OpenID Connect services. It protects user security and privacy even when RPs do not implement OAuth 2.0 or OpenID Connect correctly. We used OAuthGuard to survey the 1000 top-ranked websites supporting Google sign-in for the possible presence of five OAuth 2.0 or OpenID Connect security and privacy vulnerabilities, of which one has not previously been described in the literature. Of the 137 sites in our study that employ Google Sign-in, 69 were found to suffer from at least one serious vulnerability. OAuthGuard was able to protect user security and privacy for 56 of these 69 RPs, and for the other 13 was able to warn users that they were using an insecure implementation.
The VirtualSign Channel for the Communication Between Deaf and Hearing Users

Deaf students, who use sign language as their mother language, continuously experience difficulties to communicate with non-deaf in their daily lives. This is a severe handicap in education settings seriously jeopardizing deaf people chances to progress in their professional career. Deaf people's comprehension of texts is limited due to grammar differences between sign and oral languages. There is a need to improve the communication between deaf and non-deaf and to support deaf students in environments where they are unable to be accompanied by sign interpreters. This article details the improvements and current structure of the VirtualSign platform, a bidirectional sign language to text translation tool in development since 2015. The platform has two main components, sign to text and text to sign, that are both described. Translation from text to sign relies on a 3D avatar. Translation from sign to text relies on a set of data gloves and Kinect. In this paper we discuss the relevance of different types of data gloves. VirtualSign is being developed in cooperation with the deaf communities from six different European countries and Brazil. This solution to support deaf students in educational settings has received positive feedback on several tests and pilot experiments. Some planned improvements and future functionalities for the tool are also mentioned and detailed.
Nonparametric Regression via Variance-Adjusted Gradient Boosting Gaussian Process Regression

Regression models have broad applications in data analytics. Gaussian process regression is a nonparametric regression model that learns nonlinear maps from input features to real-valued output using a kernel function that constructs the covariance matrix among all pairs of data. Gaussian process regression often performs well in various applications. However, the time complexity of Gaussian process regression is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) O(n3) for a training dataset of size n n. The cubic time complexity hinders Gaussian process regression from scaling up to large datasets. Guided by the properties of Gaussian distributions, we developed a variance-adjusted gradient boosting algorithm for approximating a Gaussian process regression (VAGR). VAGR sequentially approximates the full Gaussian process regression model using the residuals computed from variance-adjusted predictions based on randomly sampled training subsets. VAGR has a time complexity of O(nm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) O(nm3) for a training dataset of size n n and the chosen batch size m m. The reduced time complexity allows us to apply VAGR to much larger datasets compared with the full Gaussian process regression. Our experiments suggest that VAGR has a prediction performance comparable to or better than models that include random forest, gradient boosting machines, support vector regressions, and stochastic variational inference for Gaussian process regression.
Systematic literature review and empirical investigation of motivators for requirements change management process in global software development

Abstract Most software development firms have adopted the concepts of global software development (GSD) to develop high‐quality and low‐cost products. However, GSD is not a straightforward process. It is associated with many challenges that are mostly related to requirements change management (RCM). In this study, we explore the motivators that contribute to managing RCM activities in the GSD environment. We extracted a total of 25 motivators using the systematic literature review (SLR) approach and conducted a survey study to empirically evaluate the findings of the SLR. The results of the applied Spearman's statistical test show that the findings of the SLR and survey study had a positive moderate correlation ( r s (25) = 0.566). We further analyzed the reported motivators according to organization size. Finally, we developed taxonomies of the identified motivators based on the framework proposed by Ramasubbu and the project management body of knowledge. We believe that the taxonomies provide a robust framework for tackling the challenges of RCM, which is significant for the success and evolution of GSD firms.