Reviewerly/exHarmony huggingface dataset:
Light field compression using translation-assisted view estimation

Light field technology has recently been gaining traction in the research community. Several acquisition technologies have been demonstrated to properly capture light field information, and portable devices have been commercialized to the general public. However, new and efficient compression algorithms are needed to sensibly reduce the amount of data that needs to be stored and transmitted, while maintaining an adequate level of perceptual quality. In this paper, we propose a novel light field compression scheme that uses view estimation to recover the entire light field from a small subset of encoded views. Experimental results on a widely used light field dataset show that our method achieves good coding efficiency with average rate savings of 54.83% with respect to HEVC.
Stealth Projection: Visually Removing Projectors from Dynamic Projection Mapping

In the present study, we propose a stealth projection method that visually removes the ProCam system in dynamic projection mapping (DPM). In recent years, DPM has been actively studied to change the appearance of moving and deforming objects by image projection. Various objects, such as an object held by the user, clothes, a human body, and a face, are projection targets, and the possibility of expressing these objects has continuously evolved. However, in order to realize this, high-speed and multiplexed special projection systems are needed, and objects are being closely enclosed by the systems. In DPM that seamlessly connects the real world and the virtual world, a complex device is an unnecessarily visually disturbing factor and should be removed in order to further exploit the potential effects of DPM. Therefore, in the present research, we propose a stealth projection method using a ProCam system that cannot be seen by combining a method that is capable of high-speed tracking with a single IR camera and all-around projection technology applying aerial image display technology.
Plenty is Plague: Fine-Grained Learning for Visual Question Answering

Visual Question Answering (VQA) has attracted extensive research focus recently. Along with the ever-increasing data scale and model complexity, the enormous training cost has become an emerging challenge for VQA. In this article, we show such a massive training cost is indeed plague. In contrast, a fine-grained design of the learning paradigm can be extremely beneficial in terms of both training efficiency and model accuracy. In particular, we argue that there exist two essential and unexplored issues in the existing VQA training paradigm that randomly samples data in each epoch, namely, the "difficulty diversity" and the "label redundancy". Concretely, "difficulty diversity" refers to the varying difficulty levels of different question types, while "label redundancy" refers to the redundant and noisy labels contained in individual question type. To tackle these two issues, in this article we propose a fine-grained VQA learning paradigm with an actor-critic based learning agent, termed FG-A1C. Instead of using all training data from scratch, FG-A1C includes a learning agent that adaptively and intelligently schedules the most difficult question types in each training epoch. Subsequently, two curriculum learning based schemes are further designed to identify the most useful data to be learned within each inidividual question type. We conduct extensive experiments on the VQA2.0 and VQA-CP v2 datasets, which demonstrate the significant benefits of our approach. For instance, on VQA-CP v2, with less than 75 percent of the training data, our learning paradigms can help the model achieves better performance than using the whole dataset. Meanwhile, we also shows the effectivenesss of our method in guiding data labeling. Finally, the proposed paradigm can be seamlessly integrated with any cutting-edge VQA models, without modifying their structures.
Final Report of the NTCIR-14 QA Lab-PoliInfo Task

The NTCIR-14 QA Lab-PoliInfo aims to achieve real-world complex question-answering (QA) technologies using Japanese political information, such as local assembly minutes and newsletters. QA Lab-PoliInfo has three tasks, namely, segmentation, summarization and classification. We describe the dataset used, formal run results, and comparison between human marks and automatic evaluation scores.
Cyclist Detection Using Tiny YOLO v2

This paper seeks to evaluate the performance of the state of the art object classification algorithms for the purpose of cyclist detection using the Tsinghua–Daimler Cyclist Benchmark. This model focuses on detecting cyclists on the road for its use in development of autonomous road vehicles and advanced driver-assistance systems for hybrid vehicles. The Tiny YOLO v2 algorithm is used here and requires less computational resources and higher real-time performance than the YOLO method, which is extremely desirable for the convenience of such autonomous vehicles. The model has been trained using the training images in the mentioned benchmark and has been tested for the test images available for the same. The average IoU for all the truth objects is calculated and the precision-recall graph for different thresholds was plotted.
Problems Caused by Semantic Drift in WordNet Synset Construction

In this study, we summarize the semantic drift problem that occur in specific synsets of KeNet, a Turkish WordNet, which is caused by mis-merging of semantically-related lexical items, morphological markings and false part of speech (POS) matchings. We present our approach to these problems in order to eliminate the semantic drift. We have re-analyzed the dictionary definitions of the items, placed those that possess different verbal markings into separate synsets, and divided synsets based on the POS of the items in them.
Blockchain in or as governance? Evolutions in experimentation, social impacts, and prefigurative practice in the blockchain and DAO space

Blockchain has become much more than simply a fintech technology, and is enabling and inspiring new conversations around politics, governance, organization, institutions and power structures. I published an attempt to start a rigorous academic treatment of ideology in the blockchain space through my master’s thesis titled Toward a Political Sociology of Blockchain at Queen’s University. Since then, there have been continued rich discussions around politics and experimentation in line with the ideals of the blockchain movement. Examples include discussion of blockchain governance as a social contract, discussion of social scalability and how this enables us to organize, forums such as etherean.org, and movements such as RadicalxChange.org and the concept of Liberal Radicalism. Some of these movements have been started by similar actors in the blockchain space: Vitalik Buterin of the Ethereum project co-authored the paper on Liberal Radicalism which is deeply intertwined with RadicalxChange and the ethos of decentralization, and Lane Rettig is an Ethereum core developer and launched Etherean.org to be a non-maximalist community-based discussion of social aspects and implications around the emerging technology. While the cryptocurrency markets trended downward throughout 2018, there have been strong social indicators of a more mature system though increased interdisciplinary participation and development around the technology as well as increased interest by the public sector for use cases such as records keeping and identity solutions. I expand upon some of the concepts discussed in my thesis, and provide further evidence for the particular observations based on additional events I attended and took part in through late 2018 and early 2019. Primarily, additional evidence is taken from my attendance at both the ETHDenver and RadicalxChange conferences. I further develop some of the ideas and connections between the community that has emerged around blockchain technology, and the more recent adaptations in the political sphere, as well as implications of new forms of social organization allowed through the use of such technology.
Modelling and Prediction of Resource Utilization of Hadoop Clusters

Hadoop is a distributed computing framework that has a large number of configurable parameters. These parameters have impact on system resources and execution time. Optimizing the performance of a Hadoop cluster by tuning such a large number of parameters is a tedious task. Most current big data modeling approaches does not include complex interaction between configuration parameters and the cluster environment changes such as different datasets or query. This makes it difficult to predict the performance or resource utilization of a cluster when we use real-world datasets because of their size and content. This paper presents the modeling of resource utilization of Hadoop cluster on the basis of Hadoop configuration parameters and dataset structure. Our approach builds a machine learning based-model using Hive-based Hadoop query and then predict the outcome for a particular parameter setting and query type. We used decision trees to build models for each of our performance metric measures. Decision rules were extracted from these tree-based models and evaluated for their ability to generalize to unseen data. Our experiments predicted that the percentage of columns selected, mappers and replica has a statistically significant impact over the utilization of different resources in Hadoop cluster.
The Likeability-Success Tradeoff: Results of the 2<sup>nd</sup> Annual Human-Agent Automated Negotiating Agents Competition

We present the results of the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> Annual Human-Agent League of the Automated Negotiating Agent Competition. Building on the success of the previous year's results, a new challenge was issued that focused exploring the likeability-success tradeoff in negotiations. By examining a series of repeated negotiations, actions may affect the relationship between automated negotiating agents and their human competitors over time. The results presented herein support a more complex view of human-agent negotiation and capture of integrative potential (win-win solutions). We show that, although likeability is generally seen as a tradeoff to winning, agents are able to remain well-liked while winning if integrative potential is not discovered in a given negotiation. The results indicate that the top-performing agent in this competition took advantage of this loophole by engaging in favor exchange across negotiations (cross-game logrolling). These exploratory results provide information about the effects of different submitted “black-box” agents in human-agent negotiation and provide a state-of-the-art benchmark for human-agent design.
J-OPT: A Joint Host and Network Optimization Algorithm for Energy-Efficient Workflow Scheduling in Cloud Data Centers

Workflows are a popular application model used for representing scientific as well as commercial applications, and cloud data centers are increasingly used in the execution of workflow applications. Existing approaches to energy-efficient workflow scheduling in cloud computing environments have primarily focused on the optimization of server utilization. The majority of works have ignored the impact of scheduling decisions on the data center network (DCN). However, studies have revealed that the DCN consumes 10-20% of the total data center power, and this percentage could rise much higher depending on the utilization level of the data center. This paper proposes an energy-efficient workflow scheduling approach (J-OPT) that jointly optimizes the power consumption of servers and networking elements in cloud data centers. J-OPT considers precedence constraints and data dependencies among workflow tasks as well as communication requirements among task instances in the formulation of topology-aware scheduling decisions. The proposed approach is evaluated using synthetic and real world workflow traces in a simulated environment. Results of the experiments demonstrate that J-OPT outperforms state-of-the-art algorithms in terms of total power savings by 8% and 30% under high and low data center utilization levels, respectively.
Stochastic topic models for large scale and nonstationary data

Many traditional database’s processing schemes are batch-based with their abilities to utilize the entire information available at a time. Though, their limitations include storage (memory issues) and computational speed (often slow) for large scale applications. Another major disadvantage of the batch processing is that any small change or update in the database often requires a reevaluation using all the data at a time. This is not efficient as it is time consuming and exhausting. So, the approach seems to be a little obsolete in this new generation of fast computation. Furthermore and recently, the decrease in the cost of performing computations online promoted the increase in streaming and online-based models. In other words, new systems are taking advantage of the online setting to build models that are able to perform in real time and handle fast computations with real time updates. Traditional models could no longer scale to very large applications. So, much support has been given to online framework as these massive and nonstationary data could not keep up with the available storage. In the case of generative models, usually, the lack of flexible priors and sometimes the high complexities in the methods often hindered their performances. In addition and most importantly, many online-based models still use traditional inference approaches such as variational Bayes (VB) and Markov chain Monte Carlo (MCMC) which individually are not flexible enough as they suffer from either accuracy or efficiency. As a result, we propose in this paper, a new model that operates in online fashion with BL (Beta-Liouville) prior due to its flexibilities in topic correlation analysis. Carrying only very few parameters (compared to the generalized Dirichlet distribution, for instance), the BL is now coupled with a robust and stochastic generative process within a new hybrid inference that combines only the advantages of the VB and Gibbs sampling in the collapsed space. This insures an efficient, fast, and accurate processing. Experimental results with nonstationary datasets for face detection, image classification, and text documents processing show the merits of the new stochastic approach.
Facilitating spin squeezing generated by collective dynamics with single-particle decoherence

We study the generation of spin-squeezing in arrays of long-lived dipoles subject to collective emission, coherent drive, elastic interactions, and spontaneous emission. Counter-intuitively, it is found that the introduction of spontaneous emission leads to an enhancement of the achievable spin-squeezing, relative to that which emerges in the steady-state of the purely collective dynamics for the same model parameters. This behavior is connected to the dynamical self-tuning of the system through a dissipative phase transition that is present in the collective system alone. Our findings will be applicable to next-generation quantum sensors harnessing correlated quantum matter, including cavity-QED and trapped ion systems.
Unsupervised software repositories mining and its application to code search

Summary Software repositories are crucial resources for many software tasks, including code retrieval and annotation. Programming forums provide questions and answers (Q&amp;A) from software developers, containing abundant code‐description posts for exchanging knowledge about programming issues. However, most posts provide personal opinions of users that are often not adequately confirmed or outdated. Mining software repositories in such open and unrestricted forums is challenging. Since the posts can be arbitrary and noisy, it is difficult to get unified labels for supervised noise elimination. Different from existing mining approaches, this paper proposes Code‐Description Mining Framework (CodeMF), an unsupervised framework to eliminate noisy posts and extract high quality software repositories from programming forums. CodeMF treats all social features of the posts as discrete‐time signals for kernel principal component analysis and further performs wavelet transform feature fusion to find the delicate changes (noises in temporal signals). We conduct comprehensive experiments on StackOverflow. Experimental results demonstrate that CodeMF can effectively reduce running time and improve precision via mining high‐quality software repositories for various programming languages, especially for the large‐scale codebases. To further illustrate the effect of CodeMF applied in software tasks, we introduce it to improve the performance of query‐expansion code search. Meanwhile, for SQL and C# programs, compared to the state‐of‐the‐art query‐expansion method QECK, the improvement of QECK CodeMF is 2% and 6% on Recall@10, and 4% and 14% on mean reciprocal rank, respectively.
Comparisons of Pap Smear Classification with Deep Learning Models

We presented a comparative work of deep learning models for Pap smear classification. The benchmark parameters used to compare are accuracy, specificity, computation time, and sensitivity. Five convolution neural network models were employed to compare performance in detecting the presence of cervical precancerous or cancerous cells from a Pap smear database. The best deep learning model for multiclass classification is the densenet161 with an efficiency of 68.0% which will use to implement in our custom-made whole slide imager.
User-centred participatory design of visual cues for isolation precautions

Isolation precautions are intended to prevent transmission of infectious agents, yet healthcare provider (HCP) adherence remains suboptimal. This may be due to ambiguity regarding the required precautions or to cognitive overload of HCPs. In response to the challenge of changing HCP behaviour, increasing attention should be paid to the role of engineering controls and facility design that incorporate human factors elements. In the current study, we aimed to develop an isolation precaution signage system that provides visual cues, serves as a cognitive aid at the point of care, and removes ambiguity regarding which precautions are necessary (e.g. masks, gowns, gloves, single rooms) when caring for isolated patients.We employed a user-centred, participatory design approach in which HCPs were actively involved in generating an isolation precaution signage system based on human factors design principles. HCPs were purposefully sampled for each design phase to include a representative sample of potential system users. We conducted front-end analysis through interviews and observations to identify challenges related to the existing signage and to establish design requirements for new signage. This was followed by the creation of user personas, design thinking workshops, and prototyping, which then underwent iterative cycles of evaluation. Graphical symbols were developed and tested for comprehensibility.Front-end analysis revealed several barriers to use of the current signage system such as unclear target audience, low signal-to-noise ratio, and ambiguity regarding the applicable precautions. A comprehensive list of design requirements was generated. The project ultimately resulted in a collection of validated, comprehensible symbols and signs for contact, droplet, and airborne isolation, as well as the identification of several systems-level solutions for work re-organisation to improve compliance with isolation precautions.The introduction of visual cues in the form of signage offers a promising opportunity to make guidelines available directly at the frontline. Anecdotal evidence based on observations and interviews with HCP have shown that the current solution is superior to previous isolation signage. User-centred participatory design was a useful approach that holds potential for further improving design in healthcare settings.
On the Design of a Youth-Led, Issue-Based, Crowdsourced Global Monitoring Framework for the SDGs

In this paper, we propose a novel methodology and design to contribute towards the achievement of the 17 Sustainable Development Goals (SDGs) adopted by member states of the United Nations for a better and more sustainable future for all. We particularly focus on achieving SDG 4.7—using education to ensure all learners acquire the knowledge and skills needed to promote sustainable development. We describe the design of a crowdsourced approach to monitor issues at a local level, and then use the insights gained to indicate how learning can be achieved by the entire community. We begin by encouraging local communities to identify issues that they are concerned about, with an assumption that any issue identified will fall within the purview of the 17 SDGs. Each issue is then tagged with a plurality of actions taken to address it. Finally, we tag the positive or negative changes in the issue as perceived by members of the local community. This data is used to broadly indicate quantitative measures of community learning when solving a societal problem, in turn telling us how SDG 4.7 is being achieved. The paper describes the design of a unique, youth-led, technology-based, bottom-up approach, applicable to communities across the globe, which can potentially ensure transgressive learning through participation of and monitoring by the local community leading to sustainable development.
BlockML

The computation required by permissionless blockchains to securely moderate the block proposal rate is wasteful both in terms of energy and computation. We present BlockML, an alternative system that replaces Bitcoin's crypto puzzle with the training of Machine Learning (ML) models, providing a useful side product to the consensus protocol of permissionless blockchains.
Quantum speedup of Bayes’ classifiers

Data classification is a fundamental problem in machine learning. We study quantum speedup of the supervised data classification algorithms (quadratic, linear and naïve Bayes classifiers) based on Bayes' theory. The main technique we use to achieve quantum speedup is block-encoding. However, to apply this technique effectively, we propose a general method to construct the block-encoding. As an application, we show that all the three classifiers achieve exponential speedup at the number of samples over their classical counterparts. As for the dimension of the space, quantum quadratic and linear classifiers achieve varying degrees of polynomial speedup, while quantum naïve Bayes' classifier achieves an exponential speedup. The only assumption we make is the qRAM to prepare quantum states of the input data.
Not So Cute but Fuzzy: Estimating Risk of Sexual Predation in Online Conversations

The sexual exploitation of minors is a known and persistent problem for law enforcement. Assistance in prioritizing cases of sexual exploitation of potentially risky conversations is crucial. While attempts to automatically triage conversations for the risk of sexual exploitation of minors have been attempted in the past, most computational models use features which are not representative of the grooming process that is used by investigators. Accurately annotating an offender corpus for use with machine learning algorithms is difficult because the stages of the grooming process feed into one another and are non-linear. In this paper we propose a method for labeling risk, tied to stages and themes of the grooming process, using fuzzy sets. We develop a neural network model that uses these fuzzy membership functions of each line in a chat as input and predict the risk of interaction.