Reviewerly/exHarmony huggingface dataset:
Undersampling with Support Vectors for Multi-Class Imbalanced Data Classification

Learning from imbalanced data poses significant challenges for the classifier. This becomes even more difficult, when dealing with multi-class problems. Here relationships among classes are no longer well-defined and it is easy to loose performance on one of the classes while gaining on other. In last years this topic has gained increased interest from the machine learning community - however, still there is a need for developing new and efficient algorithms to handle this challenge. In this paper we propose a new approach for balancing multi-class imbalanced problems. It is based on a two-step undersampling methodology. In the first step, a one-class classifier is being trained on each of the classes, achieving skew-insensitive data description. Support vectors for each class are extracted and used as new class representatives, thus achieving significant reduction in the terms of used instances. In the second step, an evolutionary undersampling approach is being used on these support vectors in order to further balance the training set. By applying this technique on a set of support vectors and not on a full dataset, we achieve a significant reduction of the computational time and increased accuracy. Finally, a standard multi-class classifier is being trained on the balanced data set. A thorough experimental study proves the usefulness of the proposed approach in comparison with state-of-the-art approaches for handling multi-class imbalanced data.
An effective deep network using target vector update modules for image restoration

Image restoration (IR) has been widely used in many computer vision applications. The model-based IR methods have clear theoretical bases. However, numerous hyper-parameters need to be set empirically, which is often challenging and time-consuming. Because of the powerful nonlinear fitting ability, deep convolutional neural networks (CNNs) have been widely used in IR tasks in recent years. However, it is challenging to design new network architecture to further significantly improve the IR performance. Inspired by the plug and play (P&P) methods, we first decouple the original IR problem into two subproblems with the variable splitting technique. Then, derived from the model-based methods, a novel deep CNN framework in the transformation domain is proposed to mimic the optimization process of the two subproblems. The proposed framework is driven effectively by the target vector update (TVU) module. Extensive experiments demonstrate the effectiveness of our proposed method over other state-of-the-art IR methods.
F³A-GAN: Facial Flow for Face Animation With Generative Adversarial Networks

Formulated as a conditional generation problem, face animation aims at synthesizing continuous face images from a single source image driven by a set of conditional face motion. Previous works mainly model the face motion as conditions with 1D or 2D representation (e.g., action units, emotion codes, landmark), which often leads to low-quality results in some complicated scenarios such as continuous generation and large-pose transformation. To tackle this problem, the conditions are supposed to meet two requirements, i.e., motion information preserving and geometric continuity. To this end, we propose a novel representation based on a 3D geometric flow, termed facial flow, to represent the natural motion of the human face at any pose. Compared with other previous conditions, the proposed facial flow well controls the continuous changes to the face. After that, in order to utilize the facial flow for face editing, we build a synthesis framework generating continuous images with conditional facial flows. To fully take advantage of the motion information of facial flows, a hierarchical conditional framework is designed to combine the extracted multi-scale appearance features from images and motion features from flows in a hierarchical manner. The framework then decodes multiple fused features back to images progressively. Experimental results demonstrate the effectiveness of our method compared to other state-of-the-art methods.
An Immersive Virtual Reality Platform for Training CBRN Operators

In the domain of CBRN (Chemical, Biological, Radiological, Nuclear) hazards, first responders need a high-quality training to avoid fatal errors that can compromise the success of operations. Nevertheless, CBRN exercises are often expensive and require a complex management. Furthermore, for preserving trainees’ safety and for logistic constraints, trials may reproduce just an approximation of the real hazard scenario. In order to cope with these issues, a prototype of an Immersive Virtual Reality (VR) training platform was developed by Politecnico di Torino and LINKS Foundation in cooperation with CBRN experts from the Italian Air Force, particularly from Terzo Stormo – Aeronautica Militare di Villafranca di Verona. The platform aims at allowing CBRN operators to train, alone or in a team, testing their ability to carry out required operational procedures in a digital environment that exhibits the complexity of a real-life situation, but does not expose them to life-threatening dangers.
Blind Image Quality Index for Authentic Distortions With Local and Global Deep Feature Aggregation

Blind image quality assessment (BIQA) for authentic distortions is still a great challenge, even in today’s deep learning era. It has been widely acknowledged that local and global features are both indispensable for IQA, which play complementary roles. While combining local and global features is straightforward in traditional handcrafted feature-based IQA metrics, it is not an easy task in the deep learning framework. This is mainly due to the fact that deep neural networks typically require input images with a fixed size. Current metrics either resize the image or use local patches as input, which are problematic in that they cannot integrate local and global aspects as well as their interactions to achieve comprehensive quality evaluation. Motivated by the above facts, this paper presents a new BIQA metric for authentic distortions by aggregating local and global deep features in a Vision-Transformer framework. In the proposed metric, selective local regions and global content are simultaneously input for complementary feature extraction, and the Vision-Transformer is employed to build the relationship between different local patches and image quality. Self-attention mechanism is further adopted to explore the interaction between local and global deep features, producing the final image quality score. Extensive experiments on five authentically distorted IQA databases demonstrate that the proposed metric outperforms the state-of-the-arts in terms of both prediction performance and generalization ability.
A Comprehensive Survey on Training Acceleration for Large Machine Learning Models in IoT

The ever-growing artificial intelligence (AI) applications have greatly reshaped our world in many areas, e.g., smart home, computer vision, natural language processing, etc. Behind these applications are usually machine learning (ML) models with extremely large size, which require huge data sets for accurate training to mine the value contained in the big data. Large ML models, however, can consume tremendous computing resources to achieve decent performance and thus, it is difficult to train them in resource-constrained Internet of Things (IoT) environments, which would prevent further development and application of AI techniques in the future. To deal with such challenges, there are many efforts on accelerating the training process for large ML models in IoT. In this article, we provide a comprehensive review on the recent advances toward reducing the computing cost during the training stage while maintaining comparable model accuracy. Specifically, the optimization algorithms that aim to improve the convergence rate are emphasized over various distributed learning architectures that exploit ubiquitous computing resources. Then, the article elaborates the computation hardware acceleration and communication optimization for collaborative training among multiple learning entities. Finally, the remaining challenges, future opportunities, and possible directions are discussed.
Education, automation and AI: a genealogy of alternative futures

The relationship between technical development and education is a reciprocal one, where education always stands in relation to those skills, competencies, and techniques that are anticipated as necessary in a technological future. At the same time, skills and competencies are also necessary to drive innovation and technical development for the progressive creation of desirable futures. Jumping back to the 1950s, this article illustrates how automation and AI have been anticipated as both problems and solutions in society, and how education has been used to solve these problems or realize these solutions. That is, computerization debates have concentrated on both the growing opportunities and the increasing risks, but almost always also on the need for corresponding education. The article uses a genealogical approach to show how, from the 1950s and up until today, education has been mobilized as an important tool for governance in computer policies.
Exploring attention mechanisms based on summary information for end-to-end automatic speech recognition

Recent studies have confirmed that attention mechanisms with location constraint strategy are helpful to reduce the misrecognition caused by incorrect alignments in attention-based end-to-end automatic speech recognition (E2E ASR) systems. The significant advantage of these mechanisms is that they consider the monotonicity of the alignment by employing a location constraint vector. This vector is directly obtained from historical attention scores for most such attention mechanisms. However, an unreasonable vector may become an additional interference when an inaccurate historical attention score occurs. Moreover, the subsequent process of attention scoring will be affected by the interference continuously. To address the problem, we obtain a reasonable location constraint vector from the matching relationship between the historical output information and the summary information, where the summary information includes content and temporal information about speech sequence. We further propose an enhanced location constrained attention mechanism, i.e., summary constrained (SC) attention mechanism, to generate the vector by a matching relationship-based neural network. We use a summary subspace embedding learned by a linear subspace projection to represent the summary information. Furthermore, considering the complementarity of the SC and typical location constrained attention mechanisms, a fused attention mechanism is used to generate a more reasonable vector by combining the two mechanisms. The SC and fused attention mechanisms-based E2E ASR systems were evaluated on a Switchboard conversational telephone speech recognition. The experimental results show that our mechanisms obtained the relative reductions of 10.6% and 16.7% in the word error rate compared with the baseline system.
A Machine Learning Approach for Gas Price Prediction in Ethereum Blockchain

Ethereum is a blockchain-based platform that pro-vides a global computational infrastructure to run smart contracts. In order to assign a cost to smart contract and transaction execution, the Ethereum Blockchain adopts a gas-based metering approach which is designed to motivate miners to operate the network and protect it against attacks. More precisely, miners receive fees from all transactions included in the mined block in addition to the mining reward. Hence, the higher the gas price in the transactions, the higher the fee paid to the miner will be, resulting in faster selection and execution of higher priced gas transactions. Therefore, an Ethereum transaction sender is exposed to the non-trivial task of having to choose an optimal gas price, as underpaying likely results in a transaction not being picked by miners, whereas overpaying leads to superfluous costs. This paper provides recommendation approach that proposes an appropriate gas price to users. More precisely, it investigates different approaches of forecasting algorithms applied for gas price predictions for the next block in Ethereum Blockchain. The gas price is predicted using the Prophet model and the deep learning models, Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). It also aims to compare these approaches with the most used gas price oracles. An evaluation of the obtained results show that the LSTM and GRU proposed models outperform Prophet model as well as the gas price oracle Geth. In this case, LSTM and GRU provide a low mean squared error (MSE) of 0,008 whereas Geth gives an MSE of 0.016 and Prophet gives an MSE of 0.014.
An intelligent model of green urban distribution in the blockchain environment

Recently, with the deterioration of the environment, increasing companies choose horizontal cooperation to achieve the goal of reducing environmental pollution and cost in the urban distribution industry. However, companies worry that the business information is leaked in the process of horizontal cooperation. This kind of mistrust often leads to the failure of horizontal cooperation. The emergence of blockchain technology has become a great means to resolve trust problem between partners, which ensures data sharing and trust through peer-to-peer, consensus mechanism and encryption technology. In response, this study proposes architecture of blockchain-based urban distribution system for horizontal cooperation that analyzes the components and layers of the urban distribution. Meanwhile, a smart contract, the innovative applications of blockchain, is designed to match the resource of supply and demand to design the distribution routes in the urban distribution system. To achieve the above goal, an open vehicle routing model of urban distribution taking into account environmental pollution factors is developed as the mathematical logic of smart contract, which aims at the lowest total cost including fixed, fuel, penalty, carbon emission and pollutant emission costs. Furthermore, the genetic algorithm is developed to support the implementation of smart contract, and the effectiveness of the smart contract is verified through a real case. This study narrows the knowledge gap in applying blockchain technology to urban distribution, and has brought contributions to the fields of blockchain and urban distribution. Finally, the limitation and future research direction are discussed.
Central bank digital currencies: motives, economic implications and the research frontier

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Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing

According to prior work, the type of relationship between the person consuming alcohol and others in the surrounding (friends, family, spouse, etc.), and the number of those people (alone, with one person, with a group, etc.) are related to many aspects of alcohol consumption, such as the drinking amount, location, motives, and mood. Even though the social context is recognized as an important aspect that influences the drinking behavior of young adults in alcohol research, relatively little work has been conducted in smartphone sensing research on this topic. In this study, we analyze the weekend nightlife drinking behavior of 241 young adults in Switzerland, using a dataset consisting of self-reports and passive smartphone sensing data over a period of three months. Using multiple statistical analyses, we show that features from modalities such as accelerometer, location, application usage, bluetooth, and proximity could be informative about different social contexts of drinking. We define and evaluate seven social context inference tasks using smartphone sensing data, obtaining accuracies of the range 75%-86% in four two-class and three three-class inferences. Further, we discuss the possibility of identifying the sex composition of a group of friends using smartphone sensor data with accuracies over 70%. The results are encouraging towards (a) supporting future interventions on alcohol consumption that incorporate users' social context more meaningfully, and (b) reducing the need for user self-reports when creating drink logs.
Ontology-based modelling of lifecycle underground utility information to support operation and maintenance

Operation and maintenance (O&M) of underground utilities is crucial to ensure normal functionality of utilities by performing different inspection and maintenance activities. A variety of data are required by maintenance staff to evaluate utility condition and make decisions in planning maintenance works. Nevertheless, data required for utility O&M are usually originated from different sources, with various formats or representations. Interpreting and integrating such heterogenous data for utility O&M remains a significant challenge. Therefore, this paper proposes an ontology-based framework for modelling lifecycle utility information such as to facilitate activities and support decision making during utility O&M. The framework consists of (1) a data layer to collect various types of utility lifecycle data, (2) a knowledge layer to extract common mechanisms and methods for utility O&M, (3) an ontology layer where a domain ontology, Utility Operation and Maintenance Ontology (UOMO), is developed with entities and semantic rules to represent different categories of information related to utility projects, (4) an application layer to automate manual tasks and support decision making by leveraging the query and reasoning capability of ontology. To validate the framework, a case study is performed using real-world inspection data where a user interface is developed to allow users to implement different functions without technical knowledge. The case study demonstrates that the framework can, efficiently and automatically, extract and integrate various information to assist different activities, e.g. defect rating, condition assessment and validation, as well as to support decision making in prioritizing maintenance works, and selecting appropriate maintenance methods. This study contributes by proposing the first ontology-based framework for improving efficiency in utility O&M, where the query and semantic reasoning capabilities of the ontology are fully utilized. The developed ontology with lifecycle utility information is expected to facilitate data interoperability in the utility domain and can be extended with other knowledge for large-scale infrastructure management.
A Constrained Optimization Approach for Calibrated Recommendations

In recommender systems (RS) it is important to ensure that the various (past) areas of interest of a user are reflected with their corresponding proportions in the recommendation lists. In other words, when a user has watched, say, 60 romance movies and 40 Comedy movies, then it is reasonable to expect the personalized list of recommended movies to contain about 60% romance and 40% comedy movies as well. This property is known as calibration, and it has recently received much attention in the RS community. Greedy heuristic approaches have been proposed to calibrate recommendations, and although they provide great improvements, they can result in inefficient solutions in that a better one can be missed because of the myopic nature of these algorithms. This paper addresses the calibration problem from a constrained optimization perspective and provides a model to combine both accuracy and calibration. Experimental results show that our approach outperforms the state-of-the-art heuristics for calibration in most cases on both accuracy of the recommendations and the level of calibrations the recommendation lists achieve. We give a small example to illustrate why the heuristic fails to find the optimal solution.
Atomic cross-chain settlement model for central banks digital currency

Current securities settlement systems employ various strategies to manage risks in settlement effectively. However, exchanging and verifying information between the market participants and intermediaries is time consuming.In addition, the back office and coordination costs are very high. To address such challenges, blockchain technology has attracted the attention of researchers due to its potential as a new settlement system. In this study, we propose a blockchain-based settlement system using cross-chain atomic swaps that can be implemented for the central banks digital currency (CBDC). The proposed model adds an administrator ledger to the system to remove settlement failure and improve efficiency in market management. Along with our settlement system, we also propose a new lattice-based sequential aggregate signature scheme generally known for its advantage on resisting future quantum attacks. Proof-of-concept experiments with the proposed system were conducted to analyze the effect of numerous variables in the blockchain ecosystem on the settlement system.
Learning Frequency Domain Priors for Image Demoireing

Image demoireing is a multi-faceted image restoration task involving both moire pattern removal and color restoration. In this paper, we raise a general degradation model to describe an image contaminated by moire patterns, and propose a novel multi-scale bandpass convolutional neural network (MBCNN) for single image demoireing. For moire pattern removal, we propose a multi-block-size learnable bandpass filters (M-LBFs), based on a block-wise frequency domain transform, to learn the frequency domain priors of moire patterns. We also introduce a new loss function named Dilated Advanced Sobel loss (D-ASL) to better sense the frequency information. For color restoration, we propose a two-step tone mapping strategy, which first applies a global tone mapping to correct for a global color shift, and then performs local fine tuning of the color per pixel. To determine the most appropriate frequency domain transform, we investigate several transforms including DCT, DFT, DWT, learnable non-linear transform and learnable orthogonal transform. We finally adopt the DCT. Our basic model won the AIM2019 demoireing challenge. Experimental results on three public datasets show that our method outperforms state-of-the-art methods by a large margin.