Reviewerly/exHarmony huggingface dataset:
Digital evidence in fog computing systems

Fog Computing provides a myriad of potential societal benefits: personalised healthcare, smart cities, automated vehicles, Industry 4.0, to name just a few. The highly dynamic and complex nature of Fog Computing with its low latency communication networks connecting sensors, devices and actuators facilitates ambient computing at scales previously unimaginable. The combination of Machine Learning, Data Mining, and the Internet of Things, supports endless innovation in our data driven society. Fog computing incurs new threats to security and privacy since these become more difficult when there are an increased number of connected devices, and such devices (for example sensors) typically have limited capacity for in-built security. For law enforcement agencies, the existing models for digital forensic investigations are ill suited to the emerging fog paradigm. In this paper we examine the procedural, technical, legal, and geopolitical challenges associated with digital forensic investigations in Fog Computing. We highlight areas that require further development, and posit a framework to stimulate further consideration and discussion around the challenges associated with extracting digital evidence from Fog Computing systems.
Graph regularized virtual label regression for unsupervised feature selection

Feature selection is an important technique to deal with high-dimensional unlabeled data. Over the past decades, various unsupervised feature selection methods have been presented. However, some of these methods fail to consider the discriminative ability due to lack of label information or ignoring the local structure. To address this issue, we propose a novel unsupervised feature selection approach, called graph regularized virtual label regression (GVLR). In GVLR, we learn a virtual label matrix to guide the selection of features, which can ensure the selected features be more discriminative. Besides, we utilize graph Laplacian to preserve the geometric structure of the feature space. To improve the robustness of our model, we further adopt an ℓ2,1-norm constraint on the feature selection matrix and loss function. Finally, we incorporate the label regression and graph Laplacian into the subspace learning based feature selection framework. Thus, our method can enhance the discriminative ability of selected features, and preserve the local structural information of features simultaneously. Extensive experimental results on several public datasets show that the proposed GVLR method can obtain superior performance in comparison with some state-of-the-art feature selection algorithms.
Can Blockchain be Trusted in Industry 4.0? Study of a Novel Misleading Attack on Bitcoin

After Bitcoin’s emergence, blockchain found its way to many industries, including Fintech, energy, and manufacturing. Blockchains consensus algorithms, like Nakamoto’s, are mechanisms to probabilistically guarantee that a transaction is not undone after confirmation. This mechanism requires that no one’s computational power exceeds 50% of the network power. However, recent attacks on blockchains have raised serious questions about their security and whether they can be trusted to be employed in critical infrastructure and Industry 4.0. In this article, we introduce a new category of blockchain attacks which we call “misleading-attacks.” In this type of attack, a fraction of network power is misled so that the attacker reaches her goal. The technique is most effective when miners are rational and algorithm-oriented, similar to machines/agents in future Industry 4.0 or industrial Internet of Things. Moreover, this technique has the potential to be used in inventing new attacks, or can be used in combination with other known attacks. We first analyze a case in which the attacker uses misleading techniques to prevent her newly mined block from becoming orphaned. We show that the proposed technique can push the attack success probability up by 16.42%. In a case study, we demonstrate how the technique promotes the success rate from 29.02% to 45.44%. Initiating the attack will be profitable if the attacker’s power is more than 24% of the network power. By combining this novel technique with bribery attack, we show how the cost of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guaranteed variable-rate bribing with commitment</i> strategy can be drastically reduced.
Automatic Generation of Multiple-choice Cloze-test Questions for Lao Language Learning

We propose a framework and a set of distractor generation strategy for multiple-choice cloze test question generation for low-resource language such as Lao. We hypothesize that a neural model trained with high predictive performance on MC cloze test could serve as question filters to provide the instructors with a model-verified pre-selected question set for easy question selection and adaptation. Therefore we evaluate a BERT baseline model to analyze the neural language model's predictive performance on MC cloze test. We observe that our distractor generation strategies can increase the problem difficulty for adjective and adverb question answering significantly more than for the others. The neural model could be a good question filter for close word class questions of conjunctions and preposition by achieving high accuracy and true positive rate. Its performance needs to be further improved for answering open word class questions such as adjectives and adverbs.
Reasoning about “reasoning about reasoning”: semantics and contextual equivalence for probabilistic programs with nested queries and recursion

Metareasoning can be achieved in probabilistic programming languages (PPLs) using agent models that recursively nest inference queries inside inference queries. However, the semantics of this powerful, reflection-like language feature has defied an operational treatment, much less reasoning principles for contextual equivalence. We give formal semantics to a core PPL with continuous distributions, scoring, general recursion, and nested queries. Unlike prior work, the presence of nested queries and general recursion makes it impossible to stratify the definition of a sampling-based operational semantics and that of a measure-theoretic semantics—the two semantics must be defined mutually recursively. A key yet challenging property we establish is that probabilistic programs have well-defined meanings: limits exist for the step-indexed measures they induce. Beyond a semantics, we offer relational reasoning principles for probabilistic programs making nested queries. We construct a step-indexed, biorthogonal logical-relations model. A soundness theorem establishes that logical relatedness implies contextual equivalence. We demonstrate the usefulness of the reasoning principles by proving novel equivalences of practical relevance—in particular, game-playing and decisionmaking agents. We mechanize our technical developments leading to the soundness proof using the Coq proof assistant. Nested queries are an important yet theoretically underdeveloped linguistic feature in PPLs; we are first to give them semantics in the presence of general recursion and to provide them with sound reasoning principles for contextual equivalence.
PDR-Net: Progressive depth reconstruction network for color guided depth map super-resolution

Low spatial resolution is a common problem for depth maps captured by low-cost consumer depth cameras. Depth map super-resolution (DMSR) can improve the quality of depth maps, but it is an ill-posed problem with many challenges. This paper proposes a progressive depth reconstruction network (PDR-Net) to further enhance the performance of DMSR. Specifically, we design an adaptive feature recombination module to recombine depth and color guidance features. We generate sufficient information from the recombined features with the proposed multi-scale feature fusion module, in which multi-scale feature distillation and joint attention mechanism are employed. We learn high frequency compensations for each up-interpolating and reconstruct corresponding high resolution depth maps in the proposed progressive depth reconstruction module. Experimental results with benchmark datasets verified the proposed method's superiority over the state-of-the-art DMSR methods.
Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation

With the rapid development of Internet technology, how to mine and analyze massive amounts of network information to provide users with accurate and fast recommendation information has become a hot and difficult topic of joint research in industry and academia in recent years. One of the most widely used social network recommendation methods is collaborative filtering. However, traditional social network-based collaborative filtering algorithms will encounter problems such as low recommendation performance and cold start due to high data sparsity and uneven distribution. In addition, these collaborative filtering algorithms do not effectively consider the implicit trust relationship between users. To this end, this paper proposes a collaborative filtering recommendation algorithm based on graphsage (GraphSAGE-CF). The algorithm first uses graphsage to learn low-dimensional feature representations of the global and local structures of user nodes in social networks and then calculates the implicit trust relationship between users through the feature representations learned by graphsage. Finally, the comprehensive evaluation shows the scores of users and implicit users on related items and predicts the scores of users on target items. Experimental results on four open standard datasets show that our proposed graphsage-cf algorithm is superior to existing algorithms in RMSE and MAE.
Digitally transferable ownership of mobility-as-a-service systems using blockchain and smart contracts

Mobility-as-a-service (MaaS) has promised to integrate multiple service providers to deliver a multimodal mobility service to commuters through a seamless digital platform. An efficient digital network architecture is imperative to achieve secure and reliable interactions among the involved parties. From the service provider’s perspective, by defining the mobility ownership for customers, MaaScan bring a personalized transport system to complement or even fully replace the concept of private vehicle ownership. This paper paves the initial steps to develop a distributed architecture to realize a MaaS digital network using blockchain technology. Therefore, a comprehensive blockchain-enabled MaaS platform with related industries is illustrated in this paper. To do so, first, we define crypto-tickets as means of ownership of the service and devise a straightforward smart contract to be executed in the blockchain network, which allows the customers to exchange the service ownership in a privacy-preserved manner. Then, we evaluate our blockchain-based ownership scheme’s efficacy against the traditional membership plans using a simplified simulation instance in MATLAB. Finally, we discuss the blockchain potentials in personalizing the ownership of the service, congestion management, and data trading between various stakeholder of a comprehensive MaaS platform.
Traffic Anomaly Prediction System Using Predictive Network

Anomaly anticipation in traffic scenarios is one of the primary challenges in action recognition. It is believed that greater accuracy can be obtained by the use of semantic details and motion information along with the input frames. Most state-of-the art models extract semantic details and pre-defined optical flow from RGB frames and combine them using deep neural networks. Many previous models failed to extract motion information from pre-processed optical flow. Our study shows that optical flow provides better detection of objects in video streaming, which is an essential feature in further accident prediction. Additional to this issue, we propose a model that utilizes the recurrent neural network which instantaneously propagates predictive coding errors across layers and time steps. By assessing over time the representations from the pre-trained action recognition model from a given video, the use of pre-processed optical flows as input is redundant. Based on the final predictive score, we show the effectiveness of our proposed model on three different types of anomaly classes as Speeding Vehicle, Vehicle Accident, and Close Merging Vehicle from the state-of-the-art KITTI, D2City and HTA datasets.
A New Method of Solving UAV Trajectory Planning Under Obstacles and Multi-Constraint

Multi-constraint trajectory planning for unmanned aerial vehicles (UAVs) has been widely used in military and civil fields.The existing path planning methods, such as swarm intelligence algorithm and graph-based algorithm, cannot incorporate the flying time and UAV kinematic model into evolution.To overcome such disadvantage, a method of solving trajectory planning under obstacles and multi-constraint is investigated in this paper.Firstly, the flying time is discretized as a certain number of Chebyshev points which are the optimized moments of control variable, and they can reduce the computational burden.The process of solution is divided into multi-phase, i.e., two points as a phase to generate the trajectory.Then, angular velocity is taken as control variable, and function of angular velocity is solved by cubic spline interpolation.Besides, functions of angle and position are obtained by integration.The results are substituted into the model consisted by particle swarm optimization (PSO) and the UAV kinematic model to optimize.On this basis, the angular velocity, angle and position are calculated according to the allocated moments.Finally, Monte-Carlo simulation and comparison with existed method are carried out in obstacle environment.All the results illustrate that the multi-phase method can calculate the kinematic parameters of UAV accurately and plan smooth trajectories.Meanwhile, the proposed method is easier to meet the complicated constraints than the single-phase method.In addition, the dimensionality of solution is also enriched effectively.
A Deep Dual Adversarial Network for Cross-Domain Recommendation

Data sparsity is a common issue for most recommender systems and can severely degrade the usefulness of a system. One of the most successful solutions to this problem has been cross-domain recommender systems. These frameworks supplement the sparse data of the target domain with knowledge transferred from a source domain rich with data that is in some way related. However, there are three challenges that, if overcome, could significantly improve the quality and accuracy of cross-domain recommendation: 1) ensuring latent feature spaces of the users and items are both maximally matched; 2) taking consideration of user-item relationship and their interaction in modelling user preference; 3) enabling a two-way cross-domain recommendation that both the source and the target domains benefit from a knowledge exchange. Hence, in this paper, we propose a novel deep neural network called Dual Adversarial network for Cross-Domain Recommendation (DA-CDR). By training the shared encoders with a domain discriminator via dual adversarial learning, the latent feature spaces for both the users and items are maximally matched between the source and target domains. The domain-specific encoders are applied with an orthogonal constraint to ensure that any domain-specific features are properly extracted and work as supplement to the shared features. Allowing the two domains to collaboratively benefit from each other results in better recommendations for both domains. Extensive experiments with real-world datasets on six tasks demonstrate that DA-CDR significantly outperforms seven state-of-the-art baselines in terms of recommendation accuracy.
RF Transmitter Identification Using Combined Siamese Networks

RF transmitter identification is facing a big challenge since the increasing use of wireless devices in recent years. Traditional methods for identification are implemented by choosing specific rapid fingerprints manually, which fails to distinguish threats from unknown or rogue transmitters in a few shots under the complex electromagnetic environment. In order to solve this problem, a combined Siamese networks learning method for RF transmitter identification (CSNTI) is proposed in this work by considering both the data augmentation and the classical fingerprints of RF signals. The proposed method is composed of a series of classifiers trained by Siamese networks. Each classifier is utilized to distinguish one transmitter from others. Based on the special structure of Siamese networks, the number of training samples for each classifier increases greatly, which is efficient for data augmentation and takes full use of the limited RF transmitter signals. Then, the softmax procedure is followed to normalize the output of all classifiers. A criterion for unknown transmitters’ identification is proposed. Numerical experiments for basic classification and new devices identification with eight known transmitters and three unknown transmitters are validated. Results of compared methods suggest that the highest accuracy across a broad range of conditions is achieved by the proposed method.
Digital learning for disadvantaged students during COVID-19

The digital learning instituted in Hungary in the spring of 2020 to halt the spread of the coronavirus there arguably encountered stumbling blocks as regards disadvantaged students. However, we have no information about the details, and mitigating these disadvantages is therefore fraught. The aim of our research is to shed light on the experience of digital learning among disadvantaged students. We analysed responses to an online questionnaire completed by teachers at 48 of Hungary’s after-school programmes (ASPs) who were in contact with over 1000, mostly disadvantaged children. We discussed the questionnaire-based analysis with ten ASP representatives in online workshops. In addition to ICT devices and Internet access, the lack of a learning space is also worth considering in promoting the establishment of objective conditions. In maintaining contact with parents, it is recommended that communication habits that differ from those of the middle class should be taken into account. Developing time management and other skills necessary for independent learning (e.g. reading comprehension, digital literacy) as well as alleviating the psychological burden represent a central task in similar situations. Our recommendations may be of use in the event of future school closures and in terms of facilitating digital learning among disadvantaged students.
VeriDedup: A Verifiable Cloud Data Deduplication Scheme With Integrity and Duplication Proof

Data deduplication is a technique to eliminate duplicate data in order to save storage space and enlarge upload bandwidth, which has been applied by cloud storage systems.However, a cloud storage provider (CSP) may tamper user data or cheat users to pay unused storage for duplicate data that are only stored once.Although previous solutions adopt message-locked encryption along with Proof of Retrievability (PoR) to check the integrity of deduplicated encrypted data, they ignore proving the correctness of duplication check during data upload and require the same file to be derived into same verification tags, which suffers from brute-force attacks and restricts users from flexibly creating their own individual verification tags.In this paper, we propose a verifiable deduplication scheme called VeriDedup to address the above problems.It can guarantee the correctness of duplication check and support flexible tag generation for integrity check over encrypted data deduplication in an integrative way.Concretely, we propose a novel Tag-flexible Deduplication-supported Integrity Check Protocol (TDICP) based on Private Information Retrieval (PIR) by introducing a novel verification tag called note set, which allows multiple users holding the same file to generate their individual verification tags and still supports tag deduplication at the CSP.Furthermore, we make the first attempt to guarantee the correctness of data duplication check by introducing a novel User Determined Duplication Check Protocol (UDDCP) based on Private Set Intersection (PSI), which can resist a CSP from providing a fake duplication check result to users.Security analysis shows the correctness and soundness of our scheme.Simulation studies based on real data show the efficacy and efficiency of our proposed scheme and its significant advantages over prior arts.
MAENet: Multiple Attention Encoder–Decoder Network for Farmland Segmentation of Remote Sensing Images

With the rapid development of computer vision, semantic segmentation as an important part of the technology has made some achievements in different applications. However, in the farmland segmentation scenario of remote sensing images, the capability of common semantic segmentation methods in restoring the farmland edge and identifying narrow farmland ridges needs to be improved. Therefore, in this letter a semantic segmentation method–multiple attention encoder–decoder network (MAENet)–for farmland segmentation is proposed. The design of a dual-pooling efficient channel attention (DPECA) module and its embedment in the backbone to improve the efficiency of feature extraction is described; secondly, a dual-feature attention (DFA) module is proposed to extract contextual information of high-level features; finally, a global-guidance information upsample (GIU) module is added to the decoder to reduce the influence of redundant information on feature fusion. We use three self-made farmland image datasets representing UAV data to train MAENet and compare them with other methods. The results show that the performances of segmentation and generalization of MAENet are improved compared with other methods. The MIoU and Kappa coefficient in the farmland multi-classification test set can reach 93.74% and 96.74%.
Using the Fisher Vector Approach for Cold Identification

In this paper, we present a computational paralinguistic method for assessing whether a person has an upper respiratory tract infection (i.e. cold) using their speech. Having a system that can accurately assess a cold can be helpful for predicting its propagation. For this purpose, we utilize Mel-frequency Cepstral Coefficients (MFCC) as audio-signal representations, extracted from the utterances, which allowed us to fit a generative Gaussian Mixture Model (GMM) that serves to produce an encoding based on the Fisher Vector (FV) approach. Here, we use the URTIC dataset provided by the organizers of the ComParE Challenge 2017 of the Interspeech Conference. The classification is done by a linear kernel Support Vector Machines (SVM); owing to the high imbalance of classes on the training dataset, we opt for undersampling the majority class, that is, to reduce the number of samples to those of the minority class. We find that applying Power Normalization (PN) and Principal Component Analysis (PCA) on the Fisher vector features is an effective strategy for the classification performance. We get better performance than that of the Bag-of-Audio-Words approach reported in the paper of the challenge.