Reviewerly/exHarmony huggingface dataset:
Multi-Stage Hybrid Federated Learning Over Large-Scale D2D-Enabled Fog Networks

Federated learning has generated significant interest, with nearly all works focused on a "star" topology where nodes/devices are each connected to a central server. We migrate away from this architecture and extend it through the network dimension to the case where there are multiple layers of nodes between the end devices and the server. Specifically, we develop multi-stage hybrid federated learning (MH-FL), a hybrid of intra- and inter-layer model learning that considers the network as a multi-layer cluster-based structure. MH-FL considers the topology structures among the nodes in the clusters, including local networks formed via device-to-device (D2D) communications, and presumes a semi-decentralized architecture for federated learning. It orchestrates the devices at different network layers in a collaborative/cooperative manner (i.e., using D2D interactions) to form local consensus on the model parameters and combines it with multi-stage parameter relaying between layers of the tree-shaped hierarchy. We derive the upper bound of convergence for MH-FL with respect to parameters of the network topology (e.g., the spectral radius) and the learning algorithm (e.g., the number of D2D rounds in different clusters). We obtain a set of policies for the D2D rounds at different clusters to guarantee either a finite optimality gap or convergence to the global optimum. We then develop a distributed control algorithm for MH-FL to tune the D2D rounds in each cluster over time to meet specific convergence criteria. Our experiments on real-world datasets verify our analytical results and demonstrate the advantages of MH-FL in terms of resource utilization metrics.
Modelling Data Pipelines

The following topics are dealt with: software development management; software quality; software engineering; software maintenance; project management; formal specification; public domain software; learning (artificial intelligence); software prototyping; software architecture.
Augmenting Automated Game Testing with Deep Reinforcement Learning

General game testing relies on the use of human play testers, play test scripting, and prior knowledge of areas of interest to produce relevant test data. Using deep reinforcement learning (DRL), we introduce a self-learning mechanism to the game testing framework. With DRL, the framework is capable of exploring and/or exploiting the game mechanics based on a user-defined, reinforcing reward signal. As a result, test coverage is increased and unintended game play mechanics, exploits and bugs are discovered in a multitude of game types. In this paper, we show that DRL can be used to increase test coverage, find exploits, test map difficulty, and to detect common problems that arise in the testing of first-person shooter (FPS) games.
Collective dynamics of dark web marketplaces

Dark web marketplaces are websites that facilitate trade in illicit goods, mainly using Bitcoin. Since dark web marketplaces are unregulated, they do not offer any user protection, so police raids and scams regularly cause large losses to marketplace participants. However, the uncertainty has not prevented the proliferation of dark web marketplaces. Here, we investigate how the dark web marketplace ecosystem reorganises itself following marketplace closures. We analyse 24 separate episodes of unexpected marketplace closure by inspecting 133 million Bitcoin transactions among 38 million users. We focus on "migrating users" who move their trading activity to a different marketplace after a closure. We find that most migrating users continue their trading activity on a single coexisting marketplace, typically the one with the highest trading volume. User migration is swift and trading volumes of migrating users recover quickly. Thus, although individual marketplaces might appear fragile, coordinated user migration guarantees overall systemic resilience.
X-reality technologies for museums: a comparative evaluation based on presence and visitors experience through user studies

Virtual museum systems, based on different X-reality technologies, has begun to spread, as they represent decisive tools to promote exhibitions and reaching out to audiences. Although budgetary considerations have so far limited the choice of technologies a wide range of possible technological options are available today at low cost. This paper provides the results of an empirical study, with the aim to determine the most appropriate technologies to satisfy the visitors’ expectation and maximise their likelihood to repeat and recommend the experience. The study focuses on the comparison of the performance of five VM systems for visualise digital reproduction of archaeological finds, based on different technologies (i.e., PC desktop, holographic display, 3D stereoscopic projection, head mounted display and mobile Augmented Reality). The results provide useful insight for the development of VM systems, in order to maximize the visitor experience in terms of presence and ability to activate an experience economy perspective.
A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management

Unmanned aerial vehicles are instrumental in monitoring and analyzing information and searching for people in disaster relief scenarios. In this paper, path planning is constructed as a multiobjective optimization problem with constraints in a three-dimensional terrain disaster scenario. The objective functions involve the distance and risk of the path, which are calculated based on Bézier theory. The constraints include the turning angle and flight altitude. To solve this problem in an effective and efficient manner, a differential evolution algorithm that is based solely on the knee point is proposed, in which the knee solution would guide the search direction of the algorithm. According to the minimal Manhattan distance approach, the algorithm can quickly identify an optimal solution to generating a smooth path for decision-makers. Experimental results have confirmed the superiority of the proposed algorithm, and the rankings of the minimal Manhattan distance approach are consistent with multicriteria decision-making methods.
Blockchains for Government

Blockchain is the technology used by developers of cryptocurrencies, like Bitcoin, to enable exchange of financial “coins” between participants in the absence of a trusted third party to ensure the transaction, such as is typically done by governments. Blockchain has evolved to become a generic approach to store and process data in a highly decentralized and secure way. In this article, we review blockchain concepts and use cases, and discuss the challenges in using them from a governmental viewpoint. We begin with reviewing the categories of blockchains, the underlying mechanisms, and why blockchains can achieve their security goals. We then review existing known governmental use cases by regions. To show both technical and deployment details of blockchain adoption, we study a few representative use cases in the domains of healthcare and energy infrastructures. Finally, the review of both technical details and use cases helps us summarize the adoption and technical challenges of blockchains.
Towards Facial Recognition Problem in COVID-19 Pandemic

In epidemic situations such as the novel coronavirus (COVID-19) pandemic, face masks have become an essential part of daily routine life.The face mask is considered as a protective and preventive essential of everyday life against the coronavirus.Many organizations using a fingerprint or card-based attendance system had to switch towards a face-based attendance system to avoid direct contact with the attendance system.However, face mask adaptation brought a new challenge to already existing commercial biometric facial recognition techniques in applications such as facial recognition access control and facial security checks at public places.In this paper, we present a methodology that can enhance existing facial recognition technology capabilities with masked faces.We used a supervised learning approach to recognize masked faces together with in-depth neural network-based facial features.A dataset of masked faces was collected to train the Support Vector Machine classifier on state-of-the-art Facial Recognition Feature vector.Our proposed methodology gives recognition accuracy of up to 97% with masked faces.It performs better than exiting devices not trained to handle masked faces.
A Pragmatic Approach to Membership Inferences on Machine Learning Models

Membership Inference Attacks (MIAs) aim to determine the presence of a record in a machine learning model's training data by querying the model. Recent work has demonstrated the effectiveness of MIA on various machine learning models and corresponding defenses have been proposed. However, both attacks and defenses have focused on an adversary that indiscriminately attacks all the records without regard to the cost of false positives or negatives. In this work, we revisit membership inference attacks from the perspective of a pragmatic adversary who carefully selects targets and make predictions conservatively. We design a new evaluation methodology that allows us to evaluate the membership privacy risk at the level of individuals and not only in aggregate. We experimentally demonstrate that highly vulnerable records exist even when the aggregate attack precision is close to 50% (baseline). Specifically, on the MNIST dataset, our pragmatic adversary achieves a precision of 95.05% whereas the prior attack only achieves a precision of 51.7%.
FSS-GCN: A graph convolutional networks with fusion of semantic and structure for emotion cause analysis

Most existing methods capture semantic information by using attention mechanism or joint learning, ignoring inter-clause dependency. However, inter-clause dependency contains richer structural information that is useful to alleviate information loss so as to understand text deeply. To tackle this problem, we construct a graph suited to encode dependency information at clause level. Based on this graph, we propose a graph convolutional network over the inter-clause dependency to fuse the semantics and structural information, which automatically learns how to selectively attend the relevant clauses useful for emotion cause analysis. Intuitively, our model can be understood as a method that narrows focus from global structure to local structure by continuously injecting structural constricts into networks. Our proposed method is evaluated on two public datasets in different languages (Chinese and English). Experimental results demonstrate that our model achieves superior performance compared to the existing methods, in which the graph convolution structure is found to be effective for emotion cause analysis. We further conduct experiments to confirm the ability of our model to capture long-distance information in terms of semantic and structural information.1
Improved Cryptanalysis of UOV and Rainbow

The contributions of this paper are twofold. First, we simplify the description of the Unbalanced Oil and Vinegar scheme (UOV) and its Rainbow variant, which makes it easier to understand the scheme and the existing attacks. We hope that this will make UOV and Rainbow more approachable for cryptanalysts. Second, we give two new attacks against the UOV and Rainbow signature schemes; the intersection attack that applies to both UOV and Rainbow and the rectangular MinRank attack that applies only to Rainbow. Our attacks are more powerful than existing attacks. In particular, we estimate that compared to previously known attacks, our new attacks reduce the cost of a key recovery by a factor of $$2^{17}$$ , $$2^{53}$$ , and $$2^{73}$$ for the parameter sets submitted to the second round of the NIST PQC standardization project targeting the security levels I, III, and V respectively. For the third round parameters, the cost is reduced by a factor of $$2^{20}$$ , $$2^{40}$$ , and $$2^{55}$$ respectively. This means all these parameter sets fall short of the security requirements set out by NIST.
Character Region Attention for Text Spotting

A scene text spotter is composed of text detection and recognition modules. Many studies have been conducted to unify these modules into an end-to-end trainable model to achieve better performance. A typical architecture places detection and recognition modules into separate branches, and a RoI pooling is commonly used to let the branches share a visual feature. However, there still exists a chance of establishing a more complimentary connection between the modules when adopting recognizer that uses attention-based decoder and detector that represents spatial information of the character regions. This is possible since the two modules share a common sub-task which is to find the location of the character regions. Based on the insight, we construct a tightly coupled single pipeline model. This architecture is formed by utilizing detection outputs in the recognizer and propagating the recognition loss through the detection stage. The use of character score map helps the recognizer attend better to the character center points, and the recognition loss propagation to the detector module enhances the localization of the character regions. Also, a strengthened sharing stage allows feature rectification and boundary localization of arbitrary-shaped text regions. Extensive experiments demonstrate state-of-the-art performance in publicly available straight and curved benchmark dataset.
Differentiable Automatic Data Augmentation

Data augmentation (DA) techniques aim to increase data variability, and thus train deep networks with better generalisation. The pioneering AutoAugment automated the search for optimal DA policies with reinforcement learning. However, AutoAugment is extremely computationally expensive, limiting its wide applicability. Followup works such as Population Based Augmentation (PBA) and Fast AutoAugment improved efficiency, but their optimization speed remains a bottleneck. In this paper, we propose Differentiable Automatic Data Augmentation (DADA) which dramatically reduces the cost. DADA relaxes the discrete DA policy selection to a differentiable optimization problem via Gumbel-Softmax. In addition, we introduce an unbiased gradient estimator, RELAX, leading to an efficient and effective one-pass optimization strategy to learn an efficient and accurate DA policy. We conduct extensive experiments on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets. Furthermore, we demonstrate the value of Auto DA in pre-training for downstream detection problems. Results show our DADA is at least one order of magnitude faster than the state-of-the-art while achieving very comparable accuracy. The code is available at https://github.com/VDIGPKU/DADA .
PV park site selection for utility-scale solar guides combining GIS and power flow analysis: A case study on a Swedish municipality

Utility-scale solar photovoltaic (PV) parks have dominated the international market for the past few years. However, in some countries, like Sweden, utility-scale PV is on the verge to economic viability. Using existing infrastructure in a resource-efficient manner could be a crucial strategy for a successful implementation at scale. In this study, a new methodology for a utility-scale solar guide is developed by studying the hosting capacity in the local grid and identifying land appropriate for PV parks. The method is applied on a rural municipality in Sweden (512 km2) with a local distribution grid (5,000 customers). The impact on the grid, if connecting a PV park to a substation, was analyzed through power flow simulations and the geographical assessment was done using multi-criteria analysis with a Boolean approach. Three different sizes of PV parks, 1, 3, and 5 MWp, were analyzed. Results showed that 3.7% of the studied area is qualified for locating 1 MWp PV parks. However, if introducing a maximum distance threshold to the nearest substation that can host the PV generation from the park, the potential is further reduced (e.g., to 1% for a 750 m threshold). Furthermore, parts of the grid can host PV parks of 3 and 5 MWp, but only near urban areas, where qualified land is lacking. The results highlight that the proposed methodology can function as a tool in the dialog between utility companies, municipalities, PV companies, land-owners and other stakeholders in order to find resource- and system-efficient locations for PV parks.
Phishing Detection on Ethereum via Learning Representation of Transaction Subgraphs

With the widespread application of blockchain in the financial field, blockchain security also faces huge challenges brought by cybercrimes, especially phishing scams. It forces us to explore more efficient countermeasures and perspectives for better solution. Since graph modeling provides rich information for possible downstream tasks, we use a surrounding graph to model the transaction data of a target address, aiming to analyze the identity of an address by defining its transaction pattern on a high-level structure. In this paper, we propose a graph-based classification framework on Ethereum. Firstly we collect the transaction records of some verified phishing addresses and the same number of normal addresses. Secondly we form a set of subgraphs, each of which contains a target address and its surrounding transaction network in order to represent the original address on graph-level. Lastly, based on the analysis of the Ether flow of the phishing scam cycle, we propose an improved Graph2Vec, and make classification prediction on the subgraphs we built. The experimental results show that our framework has achieved a great competitiveness in the final classification task, which also indicate the potential value of phishing detection on Ethereum via learning the representation of transaction network.
Rough volatility of Bitcoin

Recent studies have found that the log-volatility of asset returns exhibits roughness. This study investigates roughness or the anti-persistence of Bitcoin volatility. Using multifractal detrended fluctuation analysis, we obtain the generalized Hurst exponent of the log-volatility increments and find that the generalized Hurst exponent is less than 1/2, which indicates rough log-volatility increments. Furthermore, we find that the generalized Hurst exponent is not constant. This observation indicates that the log-volatility has a multifractal property. Using shuffled time series of the log-volatility increments, we infer that the source of multifractality partly derives from the distributional property.
Dual Convolutional LSTM Network for Referring Image Segmentation

We consider referring image segmentation. It is a problem at the intersection of computer vision and natural language understanding. Given an input image and a referring expression in the form of a natural language sentence, the goal is to segment the object of interest in the image referred by the linguistic query. To this end, we propose a dual convolutional LSTM (ConvLSTM) network to tackle this problem. Our model consists of an encoder network and a decoder network, where ConvLSTM is used in both encoder and decoder networks to capture spatial and sequential information. The encoder network extracts visual and linguistic features for each word in the expression sentence, and adopts an attention mechanism to focus on words that are more informative in the multimodal interaction. The decoder network integrates the features generated by the encoder network at multiple levels as its input and produces the final precise segmentation mask. Experimental results on four challenging datasets demonstrate that the proposed network achieves superior segmentation performance compared with other state-of-the-art methods.
Weakly Supervised Object Detection in Artworks

We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This, combined with the efficiency of our multiple-instance learning method, enables one to learn new classes on-the-fly from globally annotated databases, avoiding the tedious task of manually marking objects. We show on several databases that dropping the instance-level annotations only yields mild performance losses. We also introduce a new database, IconArt, on which we perform detection experiments on classes that could not be learned on photographs, such as Jesus Child or Saint Sebastian. To the best of our knowledge, these are the first experiments dealing with the automatic (and in our case weakly supervised) detection of iconographic elements in paintings. We believe that such a method is of great benefit for helping art historians to explore large digital databases.
Automatic translation of spoken English based on improved machine learning algorithm

Due to the complexity of English machine translation technology and its broad application prospects, many experts and scholars have invested more energy to analyze it. In view of the complex and changeable English forms, the large difference between Chinese and English word order, and insufficient Chinese-English parallel corpus resources, this paper uses deep learning to complete the conversion between Chinese and English. The research focus of this paper is how to use language pairs with rich parallel corpus resources to improve the performance of Chinese-English neural machine translation, that is, to use multi-task learning to train neural machine translation models. Moreover, this research proposes a low-resource neural machine translation method based on weight sharing, which uses the weight-sharing method to improve the performance of Chinese-English low-resource neural machine translation. In addition, this study designs a control experiment to analyze the effectiveness of this study model. The research results show that the model proposed in this paper has a certain effect.
Consensus guided incomplete multi-view spectral clustering

Incomplete multi-view clustering which aims to solve the difficult clustering challenge on incomplete multi-view data collected from diverse domains with missing views has drawn considerable attention in recent years. In this paper, we propose a novel method, called consensus guided incomplete multi-view spectral clustering (CGIMVSC), to address the incomplete clustering problem. Specifically, CGIMVSC seeks to explore the local information within every single-view and the semantic consistent information shared by all views in a unified framework simultaneously, where the local structure is adaptively obtained from the incomplete data rather than pre-constructed via a k-nearest neighbor approach in the existing methods. Considering the semantic consistency of multiple views, CGIMVSC introduces a co-regularization constraint to minimize the disagreement between the common representation and the individual representations with respect to different views, such that all views will obtain a consensus clustering result. Experimental comparisons with some state-of-the-art methods on seven datasets validate the effectiveness of the proposed method on incomplete multi-view clustering.
Exact simulation of Gaussian boson sampling in polynomial space and exponential time

This work introduces a classical algorithm that can simulate the output of a Gaussian Boson Sampling device in time exponential in the number of photons detected and in only polynomial space in the number of modes/photons. The algorithm takes advantage of the fact that the marginal Wigner functions of Gaussian states can be easily calculated.