Reviewerly/exHarmony huggingface dataset:
Learning High-Fidelity Face Texture Completion without Complete Face Texture

For face texture completion, previous methods typically use some complete textures captured by multiview imaging systems or 3D scanners for supervised learning. This paper deals with a new challenging problem - learning to complete invisible texture in a single face image without using any complete texture. We simply leverage a large corpus of face images of different subjects (e. g., FFHQ) to train a texture completion model in an unsupervised manner. To achieve this, we propose DSD-GAN, a novel deep neural network based method that applies two discriminators in UV map space and image space. These two discriminators work in a complementary manner to learn both facial structures and texture details. We show that their combination is essential to obtain high-fidelity results. Despite the network never sees any complete facial appearance, it is able to generate compelling full textures from single images.
Fairness in ERC Token Markets: A Case Study of CryptoKitties

Fairness is an important trait of open, free markets. Ethereum is a platform meant to enable digital, decentralized markets. Though many researchers debate the market’s fairness, there are few discussions around the fairness of automated markets, such as those hosted on Ethereum. In this paper, using pilot studies, we consider unfair factors caused by adding the program. Because CryptoKitties is one of the major blockchain-based games and has been in operation for an extended period of time, we focus on its market to examine fairness. As a result, we concluded that a gene determination algorithm in this game has little randomness, and a significant advantage to gain profit is given to players who know its bias over those who do not. We state incompleteness and impact of the algorithm and other factors. Besides, we suppose countermeasures to reduce CryptoKitties’ unfairness as a market.
Mapping the photovoltaic potential of the roads including the effect of traffic

After developing the methodology, we applied it to the case of the Netherlands highways. We show that the average irradiation on the Dutch highway network is around 880 kWh/m2/y, 35% less than the potential of an optimally tilted conventional PV system in the south of the Netherlands. Covering the entire 1600 km of the Dutch highways network with solar road modules of poly c-Si, mono c-Si and CIGS would respectively generate 5.2 TWh/y, 6.6 TWh/y, and 3.4 TWh/y of DC electricity. This could be used to fully power the Dutch national public lighting demand. Moreover, to include the effect of traffic on these values, a model was developed to account for the energy potential reduction due to vehicles shading. Using real traffic data from two of the top-four busiest roads in the Netherlands, the A12 and A16, it was found that traffic accounts for an average of 3% reduction of solar road irradiation and DC yield potential. The maximum reduction of 9% was observed in particular locations, such as bridges and nearby ramp roads. The result of such mapping methodology could serve as a useful tool for research advisory, private industry, and governmental projects.
Progressive kernel pruning with saliency mapping of input-output channels

As the smallest structural unit of feature mapping, the convolution kernel in a deep convolution neural networks (DCNN) convolutional layer is responsible for the input channel features to output channel features. A specific convolution kernel belongs to a specific group from the perspective of the input channel, and it belongs to a specific filter from the perspective of the output channel. If the input and output channels are simultaneously considered in the pruning process, the performance of the pruning model can be further improved. This paper proposes progressive kernel pruning with salient mapping of input-output channels, introduces the concept of input-output channel saliency and defines single-port salient mapping channels and dual-port salient mapping channels. This study demonstrates that single-port salient mapping channels can ensure that each input channel signal has a relatively strong convolution kernel mapped to the output channel, and vice versa. The dual-port salient mapping channel is a channel with high feature mapping abilities from both the input and output directions. Additionally, the average mapping ability measure index is defined, which is used to control the kernel pruning process of the single-port salient mapping channel to switch to the kernel pruning process of the dual-port salient mapping channel. The experimental results and analysis show that the method proposed in this paper can be used to effectively prune a heavyweight model and a lightweight model and can obtain a better accuracy under the conditions of higher compression ratio and acceleration ratios. For example, when VGG-16 is pruned on CIFAR-10, the compression ratio and acceleration ration are 91.00× and 15.24×, respectively, and the classification accuracy of the model decreased slightly by 0.22%. When ResNet-101 is pruned on ImageNet, the compression ratio and acceleration ratio are 3.90× and 3.38×, respectively, and the classification accuracy of the model decreased slightly by 0.48%. The proposed method is significantly better than state-of-the-art methods.
Improving Low-Precision Network Quantization via Bin Regularization

Model quantization is an important mechanism for energy-efficient deployment of deep neural networks on resource-constrained devices by reducing the bit precision of weights and activations. However, it remains challenging to maintain high accuracy as bit precision decreases, especially for low-precision networks (e.g., 2-bit MobileNetV2). Existing methods have been explored to address this problem by minimizing the quantization error or mimicking the data distribution of full-precision networks. In this work, we propose a novel weight regularization algorithm for improving low-precision network quantization. Instead of constraining the overall data distribution, we separably optimize all elements in each quantization bin to be as close to the target quantized value as possible. Such bin regularization (BR) mechanism encourages the weight distribution of each quantization bin to be sharp and approximate to a Dirac delta distribution ideally. Experiments demonstrate that our method achieves consistent improvements over the state-of-the-art quantization-aware training methods for different low-precision networks. Particularly, our bin regularization improves LSQ for 2-bit MobileNetV2 and MobileNetV3-Small by 3.9% and 4.9% top-1 accuracy on ImageNet, respectively.
Development of Cybersecurity Technology and Algorithm Based on Quantum Computing

Many hacking incidents are linked to work files because most companies work with them. However, a variety of file encryption and decryption methods have been proposed. Existing file encryption/decryption technologies are under threat as hacking technologies advance, necessitating the development of stronger encryption algorithms. Therefore, in this study, we propose a modified advanced encryption standard (AES) algorithm and use quantum computing to encrypt/decrypt AES image files. Because the shift is regular during the AES Shift Row procedure, the change technique led the shift to become irregular when using quantum random walk. Computing resources and speeds were simulated using IBM Qiskit quantum simulators for performance evaluation, whereas encryption performance was assessed using number of pixels change rate (NPCR) and unified average changing intensity (UACI).
Faster Support Vector Machines

The time complexity of support vector machines (SVMs) prohibits training on huge datasets with millions of data points. Recently, multilevel approaches to train SVMs have been developed to allow for time-efficient training on huge datasets. While regular SVMs perform the entire training in one—time-consuming—optimization step, multilevel SVMs first build a hierarchy of problems decreasing in size that resemble the original problem and then train an SVM model for each hierarchy level, benefiting from the solved models of previous levels. We present a faster multilevel support vector machine that uses a label propagation algorithm to construct the problem hierarchy. Extensive experiments indicate that our approach is up to orders of magnitude faster than the previous fastest algorithm while having comparable classification quality. For example, already one of our sequential solvers is on average a factor 15 faster than the parallel ThunderSVM algorithm, while having similar classification quality. 1
Deep learning models

In this chapter, we discuss state-of-the-art deep learning models. We start with different types of deep learning models, where different learning objectives, CNN architectures, and models that are based on learning strategies are taken into account. Then we provide several different elements involved in deep learning models, such as data preprocessing, model selection, model validation, hyperparameter tuning, and performance metrics. Besides, the chapter covers different deep learning models for a variety of applications, such as classification, localization, and segmentation. The chapter aims to make readers ready for implementing projects (Chapters 4 and 5).
Homomorphic Encryption for Multiple Users With Less Communications

Keeping privacy for every entity in outsourced computation is always a crucial issue.For efficient secure computation, homomorphic encryption (HE) can be one of nice solutions.Especially, multikey homomorphic encryption (MKHE) which allows homomorphic evaluation on encrypted data under different keys can be one of the simplest solutions for a secure computation which handles multiple users' data.However, the current main problem of MKHE is that the dimension of its evaluated ciphertext relies on the number of users.To solve this problem, there are several variants of multikey homomorphic encryption schemes to keep the size of ciphertext constant for a fixed number of users.However, users interact one another before computation to provide their inputs, which increases setup complexity.Moreover, all the existing MKHE schemes and their variants have unique benefits which cannot be easily achieved at the same time in one scheme.In other words, each type of scheme has a suitable computational scenario to put its best performance.In this paper, we suggest more efficient evaluation key generation algorithm for the existing variants of MKHE schemes which have no ciphertext expansion for a fixed number of users.Our method only requires a very simple and minor pre-processing; distributing public keys, which is not counted as a round at all in many other applications.As a result, participants have less communication, computation, and memory cost in online phase.Moreover, we provide a practical conversion algorithm between the two types of schemes in order to efficiently utilize both schemes' advantages together in more various applications.We also provide detailed comparison among similar results so that users can choose a suitable scheme for their homomorphic encryption based application scenarios.
Character-Level Street View Text Spotting Based on Deep Multisegmentation Network for Smarter Autonomous Driving

Urban scenes are full of street entities with sign boards. Therefore, in autonomous driving, street view text spotting techniques will play a significant role in the precise understanding of surrounding scenes during driving, because texts contained in the images usually provide important clues for accurate image understanding, while it is often ambiguous for existing computer vision algorithms to understand scene images without texts. In this work, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> egmentation network for character-level scene <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ext <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> etection (MSTD). The MSTD introduces a densely connected atrous spatial pyramid pooling module to enlarge the receptive field of the feature extraction layer, so as to localize long as well as large-sized text instances. Moreover, it devises a double segmentation subnetwork to utilize two independent but inherently complementary losses to co-optimize the network and increase the reliability of the confidence scores in predicting the text/nontext areas. With the character instances detected by the MSTD, one can easily perform scene text spotting with classic object recognition networks such as ResNet and DenseNet. We carried out extensive experiments on nine scene text datasets to demonstrate the outstanding performance of the MSTD on character-level and line-level text instance localization and scene text recognition, where the MSTD significantly outperforms the state-of-the-art scene text detection methods and the sequence-to-sequence-learning-based scene text recognizers.
Blockchain-Enabled Trust in Intercompany Networks Applying the Agency Theory

Globalization, digitalization and disruptions recently driven by the Covid19 pandemic are affecting today's supply chains and thus are challenging companies in maintaining their businesses. Resulting uncertainties lead to potential over- and undercapacities and therefore to economic inefficiencies in companies. Engaging in intercompany networks can be a way to circumvent inefficiencies by sharing resources via electronic markets. Here, negotiation mechanisms can be used to allocate the exchanged goods tailored to the needs and payment conditions of the network participants. Ensuring trust and enabling cooperation between the participants in such a virtual ecosystem is a major challenge and essential for raising its potentials. Lacks of trust within the single transaction phases impede the negotiation process and in worst case the maintaining of the network. For this reason, Distributed Ledger Technologies (DLT) and their inherent consensus-building functionalities as well as abilities to utilize smart contracts deserve closer investigation. The aim of this paper is to provide a literature review of DLT functionalities coping with behavioral uncertainties, with a closer view on the context of supply chain. The paper examines, to what extend the integration of DLT provides a beneficial contribution to solving trust problems occurring in intercompany negotiations.
Light Source Guided Single-Image Flare Removal from Unpaired Data

Causally-taken images often suffer from flare artifacts, due to the unintended reflections and scattering of light inside the camera. However, as flares may appear in a variety of shapes, positions, and colors, detecting and removing them entirely from an image is very challenging. Existing methods rely on predefined intensity and geometry priors of flares, and may fail to distinguish the difference between light sources and flare artifacts. We observe that the conditions of the light source in the image play an important role in the resulting flares. In this paper, we present a deep framework with light source aware guidance for single-image flare removal (SIFR). In particular, we first detect the light source regions and the flare regions separately, and then remove the flare artifacts based on the light source aware guidance. By learning the underlying relationships between the two types of regions, our approach can remove different kinds of flares from the image. In addition, instead of using paired training data which are difficult to collect, we propose the first unpaired flare removal dataset and new cycle-consistency constraints to obtain more diverse examples and avoid manual annotations. Extensive experiments demonstrate that our method outperforms the baselines qualitatively and quantitatively. We also show that our model can be applied to flare effect manipulation (e.g., adding or changing image flares).
Wikibase as an Infrastructure for Knowledge Graphs: The EU Knowledge Graph

Knowledge graphs are being deployed in many enterprises and institutions. An easy-to-use, well-designed infrastructure for such knowledge graphs is not obvious. After the success of Wikidata, many institutions are looking at the software infrastructure behind it, namely Wikibase.In this paper we introduce Wikibase, describe its different software components and the tools that have emerged around it. In particular, we detail how Wikibase is used as the infrastructure behind the “EU Knowledge Graph”, which is deployed at the European Commission. This graph mainly integrates projects funded by the European Union, and is used to make these projects visible to and easily accessible by citizens with no technical background.Moreover, we explain how this deployment compares to a more classical approach to building RDF knowledge graphs, and point to other projects that are using Wikibase as an underlying infrastructure.
Violent Video Detection by Pre-trained Model and CNN-LSTM Approach

Violence is typically seen as a public health and social problem. Therefore, it has become more and more crucial to develop a technology-based application to tackle with this problem. Then, a question coming up is how to detect violent behaviors in video surveillance. In this study, we propose a deep neural network by using the pre-trained model—Darknet19 combined with convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models. The spatial features are extracted by pre-trained model Darknet19. Training our CNN model based on spatial features will get the temporal features. Then, these features are fed to the LSTM to classify the video-based activities into specific categories (violence or non-violence behaviors). The experiments are conducted on Hockey Fight dataset, and the accuracy score is used to evaluate our model. By combination of the superior features of our own CNN and LSTM models, our proposed model has obtained encouraging experimental results much more than the others.
Outlier Detection in Data Streams — A Comparative Study of Selected Methods

Outlier detection is an increasingly important and intensively developing area of research. This paper focuses on the problem of outlier detection in data streams. It presents a performance comparison of selected statistical algorithms: AutoRegressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space Model (ETS), Seasonal Hybrid Extreme Studentized Deviation (SHESD), Non-parametric methodology (NMV), and Chen-Liu method (CHL). Based on four data streams from the Kaggle Repository and DataHub Repository, the study provides results concerning the number of outliers detected by each algorithm and the algorithms’ operation times. The experiments were performed on data streams of different lengths (from a few hundred to 1200 records), characterized by the presence of different types of outliers.
Enabling Energy Trading in Cooperative Microgrids: A Scalable Blockchain-Based Approach With Redundant Data Exchange

Blockchain has recently been regarded as an important enabler for building secure energy trading in microgrid systems due to its inherent features of distributively providing immutable data record, storage and sharing across networks in a Peer-to-Peer (P2P) manner.However, designing highly-efficient and scalable blockchain-enabled energy trading mechanisms is extremely challenging because of the unique features of microgrid systems, e.g., bandwidth constrained and high-latency communications and large-scale Renewable Energy Source (RES) integration.To address this challenge, we propose a novel scalable blockchain-based energy trading (SBET) framework for cooperative micro-grid systems, which includes four planes, i.e., data plane, consensus plane, smart plane, and application plane.Different from the existing solutions without consideration of network transmission, these four planes are designed with the capability of perceiving the status of block generation and transmission over interrupted P2P networks, and thus proactively improving the consensus process to guarantee the reliability of energy trading in cooperative microgrids.Meanwhile, built on this framework, a novel redundant data exchange strategy is proposed to improve the scalability of block creation with the presence of large-scale RES penetration and interrupted and dynamic communication links.Simulation results show that the proposed system framework outperforms the benchmark blockchain solutions.Furthermore, we investigate the potential applications of the proposed solutions in the practical microgrid systems to facilitate a clear understanding of the mechanisms of the proposed solutions.
Blockchain Technologies: Probability of Double-Spend Attack on a Proof-of-Stake Consensus

Two double-spend attack strategies on a proof-of-stake consensus are considered. For each strategy, the probability of its success is obtained, which depends on the network parameters and the number of confirmation blocks. These results can be used to define how many confirmation blocks a vendor should wait after a correspondent transaction before sending goods or services.
CKGG: A Chinese Knowledge Graph for High-School Geography Education and Beyond

As part of a long-term research effort to provide students with better computer-aided education, we create CKGG, a Chinese knowledge graph for the geography domain at the high school level. Using GeoNames and Wikidata as a basis, we transform and integrate various kinds of geographical data in different formats from diverse sources, including gridded temperature data in NetCDF, precipitation data in HDF5, solar radiation data in AAIGrid, polygon data in GPKG, climate and ocean current data in images, and government data in tables. The current version of CKGG contains 1.5 billion triples and is accessible as Linked Data. We also publish a reified version for provenance tracking. We illustrate the potential application of CKGG with a prototype.
Automating the Comparison of Quantum Compilers for Quantum Circuits

For very specific problems, quantum advantage has recently been demonstrated. However, current NISQ computers are error-prone and only support small numbers of qubits. This limits the executable circuit size of an implemented quantum algorithm. Due to this limitation, it is important that compiled quantum circuits for a specific quantum computer are as resource-efficient as possible. A variety of different quantum compilers exists supporting different programming languages, gate sets, and vendors of quantum computers. However, comparing the results of several quantum compilers requires (i) deep technical knowledge and (ii) large manual effort for translating a given circuit into different languages. To tackle these challenges, we present a framework to automate the translation, compilation, and comparison of a given quantum circuit with multiple quantum compilers to support the selection of the most suitable compiled quantum circuit. For demonstrating the practical feasibility of the framework, we present a prototypical implementation.