Reviewerly/exHarmony huggingface dataset:
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding

Scoring functions (SFs), which measure the plausibility of triplets in knowledge graph (KG), have become the crux of KG embedding. Lots of SFs, which target at capturing different kinds of relations in KGs, have been designed by humans in recent years. However, as relations can exhibit complex patterns that are hard to infer before training, none of them can consistently perform better than others on existing benchmark data sets. In this paper, inspired by the recent success of automated machine learning (AutoML), we propose to automatically design SFs (AutoSF) for distinct KGs by the AutoML techniques. However, it is non-trivial to explore domain-specific information here to make AutoSF efficient and effective. We firstly identify a unified representation over popularly used SFs, which helps to set up a search space for AutoSF. Then, we propose a greedy algorithm to search in such a space efficiently. The algorithm is further sped up by a filter and a predictor, which can avoid repeatedly training SFs with same expressive ability and help removing bad candidates during the search before model training. Finally, we perform extensive experiments on benchmark data sets. Results on link prediction and triplets classification show that the searched SFs by AutoSF, are KG dependent, new to the literature, and outperform the state-of- the-art SFs designed by humans.
Working from home during COVID-19 crisis: a cyber security culture assessment survey

This paper aims to evaluate the cyber security culture readiness of organizations from different countries and business domains when teleworking became a necessity due to the COVID-19 crisis. We have designed a targeted questionnaire and conducted a web-based survey addressing employees while working from home during the COVID-19 spread over the globe. The questionnaire contained no more than 23 questions and was available for almost a month, from 7th April 2020 until 3rd May 2020. During that period, 264 participants from 13 European countries spent approximately 8 minutes to answer it. Gathered data were analyzed from different perspectives leading to evolutionary findings regarding information security readiness and resilience of both individuals and organizations. In this paper, results are being presented and discussed in detail while focusing on future scientific routes and research paths that need to be explored. It concludes on a number of cyber security recommendations addressing both the emerged vulnerabilities and the need for security culture evolution.
Trident Dehazing Network

Most existing dehazing methods are not robust to nonhomogeneous haze. Meanwhile, the information of dense haze region is usually unknown and hard to estimate, leading to blurry in dehaze result for those regions. Focusing on these two issues, we propose a novel coarse-to-fine model, namely Trident Dehazing Network (TDN), to learn the hazy to hazy- free image mapping with automatic haze density recognition. In detail, TDN is composed of three sub-nets: the EncoderDecoder Net (EDN) is the main net of TDN to reconstruct the coarse hazy-free feature; the Detail Refinement sub-Net (DRN) helps to refine the high frequency details that was easily lost in the pooling layers in the encoder; and the Haze Density Map Generation sub-Net (HDMGN) can automatically distinguish the thick haze region with thin one, to prevent over-dehazing or under-dehazing in regions of different haze density. Moreover, we propose a frequency domain loss function to make supervision of different frequency band more uniform. Extensive experimental results on synthetic and real datasets demonstrate that our proposed TDN outperforms the state-of-the-arts with better fidelity and perceptual, generalizing well on both dense haze and nonhomogeneous haze scene. Our method won the first place in NTIRE2020 nonhomogeneous dehazing challenge.
A Multigrid Method for Efficiently Training Video Models

Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Following standard practice for training image models, video model training has used a fixed mini-batch shape: a specific number of clips, frames, and spatial size. However, what is the optimal shape? High resolution models perform well, but train slowly. Low resolution models train faster, but are less accurate. Inspired by multigrid methods in numerical optimization, we propose to use variable mini-batch shapes with different spatial-temporal resolutions that are varied according to a schedule. The different shapes arise from resampling the training data on multiple sampling grids. Training is accelerated by scaling up the mini-batch size and learning rate when shrinking the other dimensions. We empirically demonstrate a general and robust grid schedule that yields a significant out-of-the-box training speedup without a loss in accuracy for different models (I3D, non-local, SlowFast), datasets (Kinetics, Something-Something, Charades), and training settings (with and without pre-training, 128 GPUs or 1 GPU). As an illustrative example, the proposed multigrid method trains a ResNet-50 SlowFast network 4.5x faster (wall-clock time, same hardware) while also improving accuracy (+0.8% absolute) on Kinetics-400 compared to baseline training. Code is available online.
When Person Re-identification Meets Changing Clothes

Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset, by collecting a small yet representative real dataset for testing whilst building a large realistic synthetic dataset for training and deeper studies. Facilitated by our new datasets, we are able to conduct various interesting new experiments for studying the influence of clothes inconsistency. We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes. Representative existing ReID models are adopted to show informative results on such a challenging setting, and we also provide some preliminary efforts on improving the robustness of existing models on handling the clothes inconsistency issue in the data. We believe that this study can be inspiring and helpful for encouraging more researches in this direction. The dataset is available on the project website: https://wanfb.github.io/dataset.html.
Weakly-Supervised Domain Adaptation via GAN and Mesh Model for Estimating 3D Hand Poses Interacting Objects

Despite recent successes in hand pose estimation, there yet remain challenges on RGB-based 3D hand pose estimation (HPE) under hand-object interaction (HOI) scenarios where severe occlusions and cluttered backgrounds exhibit. Recent RGB HOI benchmarks have been collected either in real or synthetic domain, however, the size of datasets is far from enough to deal with diverse objects combined with hand poses, and 3D pose annotations of real samples are lacking, especially for occluded cases. In this work, we propose a novel end-to-end trainable pipeline that adapts the hand-object domain to the single hand-only domain, while learning for HPE. The domain adaption occurs in image space via 2D pixel-level guidance by Generative Adversarial Network (GAN) and 3D mesh guidance by mesh renderer (MR). Via the domain adaption in image space, not only 3D HPE accuracy is improved, but also HOI input images are translated to segmented and de-occluded hand-only images. The proposed method takes advantages of both the guidances: GAN accurately aligns hands, while MR effectively fills in occluded pixels. The experiments using Dexter-Object, Ego-Dexter and HO3D datasets show that our method significantly outperforms state-of-the-arts trained by hand-only data and is comparable to those supervised by HOI data. Note our method is trained primarily by hand-only images with pose labels, and HOI images without pose labels.
Joint Demosaicing and Denoising With Self Guidance

Usually located at the very early stages of the computational photography pipeline, demosaicing and denoising play important parts in the modern camera image processing. Recently, some neural networks have shown the effectiveness in joint demosaicing and denoising (JDD). Most of them first decompose a Bayer raw image into a four-channel RGGB image and then feed it into a neural network. This practice ignores the fact that the green channels are sampled at a double rate compared to the red and the blue channels. In this paper, we propose a self-guidance network (SGNet), where the green channels are initially estimated and then works as a guidance to recover all missing values in the input image. In addition, as regions of different frequencies suffer different levels of degradation in image restoration. We propose a density-map guidance to help the model deal with a wide range of frequencies. Our model outperforms state-of-the-art joint demosaicing and denoising methods on four public datasets, including two real and two synthetic data sets. Finally, we also verify that our method obtains best results in joint demosaicing , denoising and super-resolution.
Blockchain reputation-based consensus: A scalable and resilient mechanism for distributed mistrusting applications

Consensus mechanisms in blockchain applications allow mistrusting peers to agree on the global state of the chain. Most of the existing consensus mechanisms, however, are constrained by low efficiency and high energy consumption. In this paper, we propose the Blockchain Reputation-Based Consensus (BRBC) mechanism in which a node must have the reputation score higher than a given network trust threshold before being allowed to insert a new block in the chain. A randomly-selected set of judges monitors the behaviour of each node involved in the consensus and updates the node reputation score. Every cooperative behaviour results in a reward, and a non-cooperative or malicious behaviour results in a punishment. BRBC also uses the reputation score to revoke access to nodes with a reputation score below a given threshold. We present a security analysis, and we demonstrate that BRBC resists against a set of known attacks in the blockchain network. Finally, we simulate a blockchain network to assert the mechanism scalability and resilience to malicious actions in various network scenarios and different rates of malicious actions. The results show BRBC to be efficient to expel all nodes that acted with more than 50% of malicious actions.
Yoga-82: A New Dataset for Fine-grained Classification of Human Poses

Human pose estimation is a well-known problem in computer vision to locate joint positions. Existing datasets for learning of poses are observed to be not challenging enough in terms of pose diversity, object occlusion and view points. This makes the pose annotation process relatively simple and restricts the application of the models that have been trained on them. To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82, for large-scale yoga pose recognition with 82 classes. Yoga-82 consists of complex poses where fine annotations may not be possible. To resolve this, we provide hierarchical labels for yoga poses based on the body configuration of the pose. The dataset contains a three-level hierarchy including body positions, variations in body positions, and the actual pose names. We present the classification accuracy of the state-of-the-art convolutional neural network architectures on Yoga-82. We also present several hierarchical variants of DenseNet in order to utilize the hierarchical labels.
Recent Advances in Smart Contracts: A Technical Overview and State of the Art

Smart contracts, as an added functionality to blockchain, have received increased attention recently. They are executable programs whose instance and state are stored in blockchain. Hence, smart contracts and blockchain enable a trustable, trackable, and irreversible protocol without the need for trusted third parties which generally constitute a single point of failure. If a user creates and distributes a smart contract, others will be able to interact with it while the underlying blockchain ensures a trustable execution. In this paper, we aim to introduce state-of-the-art technologies of the smart contract protocol. We firstly introduce the history of blockchain and smart contracts followed by their step-by-step operations. Then, we introduce the survey results which are classified into four categories based on their purposes: cryptography, access management, social application, and smart contract structure. By presenting the most recent knowledge, this paper will contribute to the advances and proliferation of smart contracts.
Conversational transfer learning for emotion recognition

Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences. Recent approaches have focused on modeling these dependencies primarily via supervised learning. However, purely supervised strategies demand large amounts of annotated data, which is lacking in most of the available corpora in this task. To tackle this challenge, we look at transfer learning approaches as a viable alternative. Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for detecting emotions in context. We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target). In addition to the popular practice of using pre-trained sentence encoders, our approach also incorporates recurrent parameters that model inter-sentential context across the whole conversation. Based on this idea, we perform several experiments across multiple datasets and find improvement in performance and robustness against limited training data. TL-ERC also achieves better validation performances in significantly fewer epochs. Overall, we infer that knowledge acquired from dialogue generators can indeed help recognize emotions in conversations.
Diversified service recommendation with high accuracy and efficiency

Collaborative filtering-based recommender systems are regarded as an important tool to predict the items that users will appreciate based on the historical usage of users. However, traditional recommendation solutions often pay more attentions to the accuracy of the recommended items while neglect the diversity of the final recommended list, which may produce partial redundant items in the recommended list and as a result, decrease the satisfaction degree of users. Moreover, historical usage data for recommendation decision-makingoften update frequently, which may lead to low recommendation efficiency as well as scalability especially in the big data environment. Considering these drawbacks, a novel method called DivRec_LSH is proposed in this paper to achieve diversified and efficient recommendations, which is based on the historical usage records and the Locality-Sensitive Hashing (LSH) technique. Finally, we compare our method with existing methods on the MovieLens dataset. Experiment results indicate that our proposal is feasible in addressing the triple dilemmas of recommender systems simultaneously, i.e., high efficiency, accuracy and diversity.
One month-ahead forecasting of mean daily global solar radiation using time series models

Accurate forecasting’s of solar radiation are crucial to quantifying future solar power production or integration. The integration of solar energy into the electricity grid has become predominant among researchers and scientists due to the current demand for energy as well as the depletion of fossil fuel reserves and environmental impacts. This paper aims to introduce a method for multi month-ahead forecasting (one, two and three month ahead) of monthly mean daily global solar radiation time series and forecasting of a large scale solar radiation data driven. ARMA and ARIMA models are used to predict the coming value of the global solar radiation time series. Both models are applied to stationary and non-stationary time series of the solar irradiation data. Taken into account the advantage of ARMA and ARIMA models, the two models are compared in terms of goodness-of-fit value determining by the AIC and BIC criterion. As a result, the ARMA (2, 1) and ARIMA (0, 2, 1) are selected as optimal models due to the minimize values of AIC and BIC criterion. Based on the statistical error value, ARIMA (0, 2, 1) models give good accuracy in three period ahead forecasting consecutive in comparison with ARMA (2, 1). The lower value of the optimum model indicates that ARIMA (0, 2,1) is more suitable to forecast monthly mean daily global solar radiation for Tetouan city and may be for other locations of similar weather conditions.
Density peak clustering based on relative density relationship

The density peak clustering algorithm treats local density peaks as cluster centers, and groups non-center data points by assuming that one data point and its nearest higher-density neighbor are in the same cluster. While this algorithm is shown to be promising in some applications, its clustering results are found to be sensitive to density kernels, and large density differences across clusters tend to result in wrong cluster centers. In this paper we attribute these problems to the inconsistency between the assumption and implementation adopted in this algorithm. While the assumption is based totally on relative density relationship, this algorithm adopts absolute density as one criterion to identify cluster centers. This observation prompts us to present a cluster center identification criterion based only on relative density relationship. Specifically, we define the concept of subordinate to describe the relative density relationship, and use the number of subordinates as a criterion to identify cluster centers. Our approach makes use of only relative density relationship and is less influenced by density kernels and density differences across clusters. In addition, we discuss the problems of two existing density kernels, and present an average-distance based kernel. In data clustering experiments we validate the new criterion and density kernel respectively, and then test the whole algorithm and compare with some other clustering algorithms.
Generative Adversarial Networks and Markov Random Fields for oversampling very small training sets

In this work, we propose a new method for oversampling the training set of a classifier, in a scenario of extreme scarcity of training data. It is based on two concepts: Generative Adversarial Networks (GAN) and vector Markov Random Field (vMRF). Thus, the generative block of GAN uses the vMRF model to synthesize surrogates by the Graph Fourier Transform. Then, the discriminative block implements a linear discriminant on features measuring clique similarities between the synthesized and the original instances. Both blocks iterate until the linear discriminant cannot discriminate the synthetic from the original instances. We have assessed the new method, called Generative Adversarial Network Synthesis for Oversampling (GANSO), with both simulated and real data in experiments where the classifier is to be trained with just 3 or 5 instances. The applications consisted of classification of stages of neuropsychological tests using electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data and classification of sleep stages using electrocardiographic (ECG) data. We have verified that GANSO can effectively improve the classifier performance, while the benchmark method SMOTE is not appropriate to deal with such a small size of the training set.
The Decentralized Financial Crisis

The Global Financial Crisis of 2008, caused by the accumulation of excessive financial risk, inspired Satoshi Nakamoto to create Bitcoin. Now, more than ten years later, Decentralized Finance (DeFi), a peer-to-peer financial paradigm which leverages blockchain-based smart contracts to ensure its integrity and security, contains over 702m USD of capital as of April 15th, 2020. As this ecosystem develops, it is at risk of the very sort of financial meltdown it is supposed to be preventing. In this paper we explore how design weaknesses and price fluctuations in DeFi protocols could lead to a DeFi crisis. We focus on DeFi lending protocols as they currently constitute most of the DeFi ecosystem with a 76% market share by capital as of April 15th, 2020. First, we demonstrate the feasibility of attacking Maker's governance design to take full control of the protocol, the largest DeFi protocol by market share, which would have allowed the theft of 0.5bn USD of collateral and the minting of an unlimited supply of DAI tokens. In doing so, we present a novel strategy utilizing so-called flash loans that would have in principle allowed the execution of the governance attack in just two transactions and without the need to lock any assets. Approximately two weeks after we disclosed the attack details, Maker modified the governance parameters mitigating the attack vectors. Second, we turn to a central component of financial risk in DeFi lending protocols. Inspired by stress-testing as performed by central banks, we develop a stress-testing framework for a stylized DeFi lending protocol, focusing our attention on the impact of a drying-up of liquidity on protocol solvency. Based on our parameters, we find that with sufficiently illiquidity a lending protocol with a total debt of 400m USD could become undercollateralized within 19 days.
Blockchain at the Edge: Performance of Resource-Constrained IoT Networks

The proliferation of IoT in various technological realms has resulted in the massive spurt of unsecured data. The use of complex security mechanisms for securing these data is highly restricted owing to the low-power and low-resource nature of most of the IoT devices, especially at the Edge. In this article, we propose to use blockchains for extending security to such IoT implementations. We deploy a Ethereum blockchain consisting of both regular and constrained devices connecting to the blockchain through wired and wireless heterogeneous networks. We additionally implement a secure and encrypted networked clock mechanism to synchronize the non-real-time IoT Edge nodes within the blockchain. Further, we experimentally study the feasibility of such a deployment and the bottlenecks associated with it by running necessary cryptographic operations for blockchains in IoT devices. We study the effects of network latency, increase in constrained blockchain nodes, data size, Ether, and blockchain node mobility during transaction and mining of data within our deployed blockchain. This study serves as a guideline for designing secured solutions for IoT implementations under various operating conditions such as those encountered for static IoT nodes and mobile IoT devices.