Reviewerly/exHarmony huggingface dataset:
Dcgan for the Synthesis of Multivariate Multifractal Textures: How do We Know it Works?

Deep Learning is nowadays widely used for several tasks in image processing. Notably, it has been massively used for image synthesis, mostly however with strong geometrical contents. Focused on a race for better performance via more complex architectures, research on Deep Learning, however, left behind the critical issue of assessing quantitatively and in a reproducible manner the quality of the synthesized images, notably for the case of pure textures. The present work aims to study the ability of Deep Convolutional Generative Adversarial Networks to synthesize multivariate textures characterized by rich multiscale multivariate statistics (multifractals). The focus is thus on quantifying the quality of the synthesized textures, on assessing the reproducibility of the learning procedure and on studying the impact of loss functions and of training dataset sizes, rather than on proposing yet another architecture.
Generative Adversarial Networks for Fast Simulation: distributed training and generalisation

In High Energy Physics, the Monte Carlo simulation of the detector response to the particles traversing it is one of the most computing intensive tasks.For some applications, a certain level of approximation is acceptable and it is therefore possible to implement fast simulation models that have the advantage of being less computationally intensive, in comparison to standard detailed Monte Carlo techniques.As a consequence, research on fast simulation solutions, including deep Generative Models, is very active.3DGAN is a Generative Adversarial Network prototype, based on 3 dimensional convolutions, developed to simulate the response of highgranularity calorimeters.We have previously reported on its physics performance and the results we obtained by introducing a data parallel approach to accelerate training, using the Horovod library in conjunction with the Keras and Tensorflow packages.One of the major bottleneck in the 3DGAN application is the training time.In this contribution we present improved results on 3DGAN distributed training: we report on the reduction of training time and high scaling efficiency up to 256 Intel Xeon TM 8260 (codenamed "Cascade Lake") nodes.We demonstrate how HPC centers could be utilized to globally optimize deep learning models, thanks to their large computation power and excellent connectivity.In addition we document our preliminary studies on the use of genetic algorithms as an alternative method to train and optimise neural networks.
Neural Game Engine: Accurate learning of generalizable forward models from pixels

Access to a fast and easily copied forward model of a game is essential for model-based reinforcement learning and for algorithms such as Monte Carlo tree search, and is also beneficial as a source of unlimited experience data for model-free algorithms. Learning forward models is an interesting and important challenge in order to address problems where a model is not available. Building upon previous work on the Neural GPU, this paper introduces the Neural Game Engine, as a way to learn models directly from pixels. The learned models are able to generalize to different size game levels to the ones they were trained on without loss of accuracy. Results on 10 deterministic General Video Game AI games demonstrate competitive performance, with many of the games' models being learned perfectly both in terms of pixel predictions and reward predictions. The pre-trained models are available through the OpenAI Gym interface and are available publicly for future research here: https://github.com/Bam4d/Neural-Game-Engine.
Towards Game-Playing AI Benchmarks via Performance Reporting Standards

While games have been used extensively as mile-stones to evaluate game-playing AI, there exists no standardised framework for reporting the obtained observations. As a result, it remains difficult to draw general conclusions about the strengths and weaknesses of different game-playing AI algorithms. In this paper, we propose reporting guidelines for AI game-playing performance that, if followed, provide information suitable for unbiased comparisons between different AI approaches. The vision we describe is to build benchmarks and competitions based on such guidelines in order to be able to draw more general conclusions about the behaviour of different AI algorithms, as well as the types of challenges different games pose.
You Are How You Use

Gas theft of restaurants is a major concern in the gas industry, which causes revenue losses for gas companies and endangers the public safety seriously. Traditional methods of gas theft detection highly rely on active human efforts that are extremely ineffective. Thanks to the gas consumption data collected by smart meters, we can devise a data-driven method to tackle this issue. In this paper, we propose a gas-theft detection method msRank to discover suspicious restaurant users when only scarce labels are available. Our method contains three main components: 1)data pre-processing, which filters reading noises and excludes data-missing or zero-use users; 2)normal user modeling, which quantifies the self-stable seasonality of normal users and distinguishes them from unstable ones; and 3)gas-theft suspect detection, which discovers gas-theft suspects among unstable users by RankNet-based suspicion scoring on extracted deviation features. By using detected normal users as negative samples to train RankNet, the component of normal user modeling and that of gas-theft suspect detection are seamlessly connected, overcoming the problem of label scarcity. We conduct extensive experiments on three real-world datasets, and the results demonstrate advantages of our approach. We have deployed a system GasShield which provides a gas-theft suspect list weekly for a gas group in northern China.
A Multi-view Deep Convolutional Neural Network for Reduction of False Positive Findings in Breast Cancer Screening

Screening mammography is commonly the only imaging exam allowing early-stage detection of breast cancer. The early detection is, in fact, associated with a decreased breast cancer mortality rate amongst women. However, false positive recall is one of the main limitations of screening practices and it is often associated with unnecessary workups and biopsies. To tackle this issue and improve the medical image classification performance in order to carry out a screening/diagnosis task, we propose to use a multi-view deep convolutional neural network - the proposed network can extract discriminative features from Cranial Caudal (CC) and Medio-Lateral Oblique (MLO) views for each breast of a patient (a set of four images). We experiment it on an augmented-data based subset selected from the open Digital Database for Screening Mammography (DDSM) using 5400 images. We show how the proposed method can lead to a better performance than the state-of-the-art ones, especially in terms of prediction accuracy and false positive rate reduction. In fact, The results show statistically significant reduction in false findings without increasing false negative cases. Our method achieves a specificity rate of 98% and an accuracy rate of 98.88%.
Who is Delivering My Food?

The recent paramount success of the gig economy has introduced new business opportunities in different areas such as food delivery service. However, there are food delivery ride abusers who break the company rule by driving unauthorized vehicles that are not stated in the contract. These abusers are particularly problematic because they break the transportation regulations and unfairly take more orders. However, detecting these abusers are challenging because of lack of labeled datasets and these anomalous abusers do not frequently occur compared to normal riders. Furthermore, sequential patterns of abusing behaviors are not easy to model.
Cohort Studies in Software Engineering

Background. Most Mining Software Repositories (MSR) studies cannot obtain causal relations because they are not controlled experiments. The use of cohort studies as defined in epidemiology could help to overcome this shortcoming.
Evaluating User Experience in 3D Interaction: a Systematic Review

Modern Information Systems often produce or utilize large sets of complex, multivariate data and, while understanding and visualizing this data can be of critical importance, particularly to support decision-making, it is frequently a challenging task. Information Visualization techniques exist to aid in this task and often take advantage of tridimensional visualizations and animations to explore complex data. But users may also find that difficult or intimidating and, according to the Technology Acceptance Model, they must judge such 3D information visualization favorably regarding not only perceived usefulness but also perceived ease-of-use before adopting it. Therefore, User Experience is critical for the acceptance and use of these tools. And a more natural way to interact in this domain are 3D Interfaces, which are also increasingly coming to market in several areas such as education, entertainment, health, large-display or automotive interaction, etc, even though there is still a gap in the literature regarding the evaluation of User Experience in 3D Interaction. This paper presents a Systematic Literature Review to identify, compare and offer a critical analysis of the main methods, tools, and metrics used to evaluate User Experience in 3D Interaction in Information Systems. Our search string returned 158 unique papers from IEEE and ACM repositories, out of which 44 were selected based on the first filtering with inclusion and exclusion criteria and 34 remained after the second filtering. Questionnaires were the most frequently used tool, not all standardized, and several different criteria related to emotions or performance were evaluated.
Grading System Recommendations for Students using Fuzzy Mamdani Logic

The use of decision making is highly needed to ease teacher in providing a grade. Grade given by the teacher to a student can be optimized by using fuzzy logic. Mamdani fuzzy method is one part of the Fuzzy Inference System that was to create a control system by synthesizing rules obtained from experienced human operators. This fuzzy system uses 2 input variables, there are student test scores and student behavior scores. Output of this system is a recommendation for the teacher to provides grade to a student. The implementation of the fuzzy Mamdani logic in grading system recommendation has no different result in manual calculating and application, indicates that the application is worth to use.
Cross-sentence N-ary Relation Extraction using Entity Link and Discourse Relation

This paper presents an efficient method of extracting n-ary relations from multiple sentences which is called Entity-path and Discourse relation-centric Relation Extractor (EDCRE). Unlike previous approaches, the proposed method focuses on an entity link, which consists of dependency edges between entities, and discourse relations between sentences. Specifically, the proposed model consists of two main sub-models. The first one encodes sentences with a higher weight on the entity link while considering the other edges with an attention mechanism. To consider various latent discourse relations between sentences, the second sub-model encodes discourse relations between adjacent sentences considering the contents of each sentence. Experiment results on the cross-sentence relation extraction dataset, PubMed, and the document-level relation extraction dataset, DocRED, show that the proposed model outperforms state-of-the-art methods of extracting relations across sentences. Furthermore, ablation study proves that both the two main sub-models have noticeable effect on the relation extraction task.
Ontological Approach for Chinese Language Interface Design

The natural language user interface is a subclass of user interfaces that allows user and system to communicate using natural language. It is the development direction of the user interface of the intelligent system. The key technology for implementation of natural language user interface is the computer processing of natural language text. Due to the diversity and complexity of natural language, its understanding hasn’t completely achieved yet. By comparing Chinese language with other European languages, this article describes the characteristics of Chinese language and the difficulties in Chinese language processing. After an analysis of current mainstream natural language processing methods, it was shown that the knowledge base plays an important role in the natural language processing model. The knowledge base is the basis for natural language processing. This article proposes a method of computer processing of Chinese language text based on Chinese linguistic ontology and domain ontologies. The ontologies are used to build a unified semantic model of Chinese linguistic knowledge and domain knowledge for the processing of Chinese language text. In this way the Chinese linguistic knowledge is integrated in the Chinese language processing model, the application of Chinese linguistic knowledge makes the Chinese language processing model more interpretative.
A study on object recognition using deep learning for optimizing categorization of radioactive waste

The smart management of radioactive waste by deep learning technology is becoming of great concern, as it could decrease the workload and errors of workers in categorizing radioactive waste, thereby reducing the waste volume. In this paper, we try to maximize the efficiency of categorization for new or temporary workers instead of skilled workers by training the categorization using deep learning technology. The waste recognition system based on the deep learning technology was trained with a total of 86,084 images for 50 epochs with a subdivision of 8 and a batch of 128, which were extracted from video data that were taken in a waste sorting site. The image recognition was applied for four typical radioactive wastes (vinyl, rubber, cotton, and paper) with no object with hands (no object) and without hands (empty). The waste recognition was tested with a total of 21,521 images to evaluate the accuracy. It was determined that the accuracy of the image recognition with a deep neural network was 99.67%.
LifeCount: A Device-free CSI-based Human Counting Solution for Emergency Building Evacuations

During large scale building evacuations, it is difficult to ascertain how many people have left the premises safely. To assist in the rescue effort, indoor counting solutions can provide emergency personnel with the number of people who have evacuated the building, and from which floors. LifeCount implements a novel two stage neural network-based algorithm to accurately count the number of people passing through a hallway. Experimental results show that LifeCount can attain a zero counting error accuracy of 96.9%.
FastText and XGBoost Content-Based Classification for Employment Web Scraping

The purpose of this paper is to present the design and results of experiments that focus on universal, autonomous data extraction (web scraping) system fed by publicly available online job listings. In particular, methods of automated crawling, preprocessing and classifying data from job offers will be presented together with the aggregation of the acquired data stored in large-scale, structured databases. We tested two models to classify the content of job portals: fastText and XGBoost. We obtained promising results in the experimental phase, with 88% accuracy by both methods.
Controlling Garbage Collection and Request Admission to Improve Performance of FaaS Applications

Runtime environments like Java's JRE, .NET's CLR, and Ruby's MRI, are popular choices for cloud-based applications and particularly in the Function as a Service (FaaS) serverless computing context. A critical component of runtime environments of these languages is the garbage collector (GC). The GC frees developers from manual memory management, which could potentially ease development and avoid bugs. The benefits of using the GC come with a negative impact on performance; that impact happens because either the GC needs to pause the runtime execution or competes with the running program for computational resources. In this work, we evaluated the usage of a technique - Garbage Collector Control Interceptor (GCI) - that eliminates the negative impact of GC on performance by controlling GC executions and transparently shedding requests while the collections are happening. We executed experiments simulating AWS Lambda's behavior and found that GCI is a viable solution. It benefited the user by improving the response time up to 10.86% at 99.9th percentile and reducing cost by 7.22%, but it also helped the platform provider by improving resource utilization by 14.52%.
SimNet: Simplified Deep Neural Networks for OFDM Channel Estimation

In this paper, a simplified deep neural network is proposed, which can be used for channel estimation and signal detection in OFDM system and reduce complexity. To be specific, the method of deep learning is introduced to optimize the channel estimation module of OFDM system. By building deep neural networks and training parameters at the signal-to-noise ratio of 10dB and 25dB, respectively, the channel estimation results can be optimized at a wider range of signal-to-noise ratio. In addition, the influence of training model size for channel estimation and signal detection is also researched. Compared with some other artificial intelligence aided OFDM receivers, proposed deep neural networks has shorter training time and simpler architecture. The simulation results show that by using proposed deep neural networks and training method in OFDM channel estimation, smaller mean square error and lower bit error rate can be obtained, especially in the case of clipping distortion and wide range of signal-to-noise ratio.
Improving Quality of a Post’s Set of Answers in Stack Overflow

Community Question Answering platforms such as Stack Overflow help a wide range of users solve their challenges on-line. As the popularity of these communities has grown over the years, both the number of members and posts have escalated. Also, due to the diverse backgrounds, skills, expertise, and viewpoints of users, each question may obtain more than one answer. Therefore, the focus has changed toward producing posts that have a set of answers more valuable for the community as a whole, not just one accepted-answer aimed at satisfying only the question-asker. Same as every universal community, a large number of low-quality posts on Stack Overflow require improvement. We call these posts "deficient", and define them as posts with questions that either have no answer yet or can be improved by other ones. In this paper, we propose an approach to automate the identification process of such posts and boost their set of answers, utilizing the help of related experts. With the help of 60 participants, we trained a classification model to identify deficient posts by investigating the relationship between characteristics of 3075 questions posted on Stack Overflow and their need for better answers set. Then, we developed an Eclipse plugin named SOPI and integrated the prediction model in the plugin to link these deficient posts to related developers (in terms of their development context and expertise area) and help them improve the answer set. We evaluated both the functionality of our plugin and the impact of answers submitted to Stack Overflow with the help of 10 and 15 expert industrial developers, respectively. Our results indicate that decision trees, specifically the J48 algorithm, predicts a deficient question better than the other methods with 94.5% precision and 90.3% recall. We conclude that not only our plugin helps programmers contribute more easily to Stack Overflow, but also it improves the quality of existing answers.
Emotion-preserving Blendshape Update with Real-time Face Tracking

Blendshape representations are widely used in facial animation. Consistent semantics must be maintained for all the blendshapes to build the blendshapes of one character. However, this is difficult for real characters because the face shape of the same semantics varies significantly across identities. Previous studies have handled this issue by asking users to perform a set of predefined expressions with specified semantics. We observe that facial emotions can be used to define semantics. Herein, we propose a real-time technique that directly updates blendshapes without predefined expressions. Its aim is to preserve semantics based on the emotion information extracted from an arbitrary facial motion sequence. In addition, we have designed corresponding algorithms to efficiently update blendshapes with large- and middle-scale face shapes and fine-scale facial details, such as wrinkles, in a real-time face tracking system. The experimental results indicate that using a commodity RGBD sensor, we can achieve real-time online blendshape updates with well-preserved semantics and user-specific facial features and details.
Towards natural 3D interaction for laparoscopic augmented reality registration

Augmented reality (AR) is a widely researched route for navigation support in laparoscopic surgery. Accurate registration is a crucial component for such AR systems. We introduce two methods for interactive registration that aim to be minimally invasive to the workflow and to mimic natural manipulation of 3D objects. The methods utilise spatially tracked laparoscopic tools to manipulate the virtual 3D content. We comparatively evaluated the methods against a reference, landmark-based registration method in a user study with 12 participants. We tested the methods for registration accuracy, time, and subjective usability perception. Our methods did not outperform the reference method on these parameters but showed promising results. The results indicate that our methods present no finalised solutions but that one of them is a promising approach for which we identified concrete improvement measures to be implemented in future research.
Sovereign Digital Currencies: The Future of Money and Payments?

Technology is reshaping money and payment systems in unprecedented ways. Catalysts include the launch of Bitcoin in 2009, the evolution of both decentralised and centralised technologies, the announcement of Libra in 2019, live trials of China’s Digital Yuan, and COVID-19, both in 2020.This paper focusses on how technology might reshape money and payments going forward. It considers the policy issues and choices associated with cryptocurrencies, stablecoins and sovereign digital currencies and emphasises there is no single model for sovereign digital currency design. The catalysts reshaping monetary and payment systems challenge regulators. While Bitcoin and its thousands of progenies could be ignored safely by regulators, Facebook’s proposal for Libra, a global stablecoin, brought an immediate and potent response from regulators globally. This proposal by the private sector to move into the traditional preserve of sovereigns – the creation of currency – was always likely to trigger such a regulatory response and also the launch of sovereign digital currencies by central banks. China has moved first with its Digital Yuan – an initiative that may well in time provoke a chain of central bank digital currency issuances around the globe. COVID-19 is driving digitalisation to new heights, particularly in electronic payments. In this context, we argue that central banks should focus not on rolling out novel new forms of sovereign digital currencies, but rather on transforming their payment systems. Further into the future, we expect domestic money and payment systems to involve public central banks cooperating with (new and old) private entities to launch digital currencies which underpin better monetary and payment systems at the domestic and international levels.