Reviewerly/exHarmony huggingface dataset:
Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data

Bayesian networks have been widely used in many scientific fields for describing the conditional independence relationships for a large set of random variables. This letter proposes a novel algorithm, the so-called p-learning algorithm, for learning moral graphs for high-dimensional Bayesian networks. The moral graph is a Markov network representation of the Bayesian network and also the key to construction of the Bayesian network for constraint-based algorithms. The consistency of the p-learning algorithm is justified under the small- n, large- p scenario. The numerical results indicate that the p-learning algorithm significantly outperforms the existing ones, such as the PC, grow-shrink, incremental association, semi-interleaved hiton, hill-climbing, and max-min hill-climbing. Under the sparsity assumption, the p-learning algorithm has a computational complexity of O(p 2 ) even in the worst case, while the existing algorithms have a computational complexity of O(p 3 ) in the worst case.
Text Classification Research Based on Improved Word2vec and CNN

In view of the traditional classification algorithm, the problem of high feature dimension and data sparseness often occurs when text classification of short texts. This paper proposes a text feature combining neural network language model word2vec and document topic model Latent Dirichlet Allocation (LDA). Represents a matrix model. The matrix model can not only effectively represent the semantic features of the words but also convey the context features and enhance the feature expression ability of the model. The feature matrix was input into the convolutional neural network (CNN) for convolution pooling, and text classification experiments were performed. The experimental results show that the proposed matrix model has better classification effect than the traditional text classification methods based on word2vec and CNN. In the text classification accuracy rate, recall rate and F1 three evaluation indicators increased by 8.4%, 8.9% and 8.6%.
A new data encoding based on maximum to minimum histogram in reversible data hiding

The dual stego images based on data hiding has become popular in recent days. Since the embedding rate in dual stego images is very high. This paper proposes a new data encoding method based on maximum to the minimum histogram in reversible data hiding. This method estimates the encoded intensity from the message intensity and embeds the encoded intensity in the cover image. The stego image quality decreases the intensity of the hidden data which is high. The intensity of the data must be kept low to maintain a high-quality stego image. It is achieved using data encoding. Therefore, the proposed method uses new data encoding which converts the higher histogram data to lower intensity and the lower histogram data to higher intensity during the data embedding process. In the extraction process, the encoded intensities are decoded to message intensities to obtain the original data. The proposed data hiding approach has improved in terms of image quality (PSNR), structure similarity index measurements and embedding rate.
An Outlier Fuzzy Detection Method Using Fuzzy Set Theory

Outlier mining task is to discover some unusual objects, and however, most existing methods and their mining results lack pertinence. To address the pertinence of outlier results, we propose a novel outlier detection approach, namely, FOD, which aims at finding anomalies in full dimensions that lack pertinence and understandability. Our key idea is to use fuzzy constraint technology to prune irrelevant objects for outlier detection, during which the nearness measure theory in fuzzy mathematics is used for detecting similarities between objects and constraint information. FOD finds outlier by searching sparse subspace, where genetic algorithms can be extended and incorporated into FOD such that an optimum solution of an anomaly is discovered. While constructing a sparse subspace, we present the sparse threshold concept to describe the sparse levels of data objects in a subspace, where data objects are regarded as anomalies. Then, we demonstrate the effectiveness and scalability of our method on synthetic and UCI datasets. The experiment evaluations reveal that our fuzzy constraint-based outlier detection is superior to two existing full dimensional algorithms. Moreover, FOD algorithm also improves the accuracy of outlier detection.
A Hybrid Countermeasure-Based Fault-Resistant AES Implementation

A Fault-Resistant scheme has been proposed to secure the Advanced Encryption Standard (AES) against Differential Fault Analysis (DFA) attack. In this paper, a hybrid countermeasure has been presented in order to protect a 32-bits AES architecture proposed for resource-constrained embedded systems. A comparative study between the most well-known fault detection schemes in terms of fault detection capabilities and implementation cost has been proposed. Based on this study, we propose a hybrid fault resistant scheme to secure the AES using the parity detection for linear operations and the time redundancy for SubBytes operation. The proposed scheme is implemented on the Virtex-5 Xilinx FPGA board in order to evaluate the efficiency of the proposed fault-resistant scheme in terms of area, time costs and fault coverage (FC). Experimental results prove that the countermeasure achieves a FC with about 98,82% of the injected faults detected during the 32-bits AES process. The area overhead of the proposed countermeasure is about 14% and the additional time delay is about 13%.
Real-Time Exploration of Large Spatiotemporal Datasets Based on Order Statistics

In recent years sophisticated data structures based on datacubes have been proposed to perform interactive visual exploration of large datasets. While powerful, these approaches overlook the important fact that aggregations used to produce datacubes do not represent the actual distribution of the data being analyzed. As a result, these methods might produce biased results as well as hide important features in the data. In this paper, we introduce the Quantile Datacube Structure (QDS) that bridges this gap by supporting interactive visual exploration based on order statistics. To achieve this, QDS makes use of an efficient non-parametric distribution approximation scheme called p-digest and employs a novel datacube indexing scheme that reduces the memory usage of previous datacube methods. This enables interactive slicing and dicing while accurately approximating the distribution of quantitative variables of interest. We present two case studies that illustrate the ability of QDS to not only build order statistics based visualizations interactively but also to perform event detection on very large datasets. Finally, we present extensive experimental results that validate the effectiveness of QDS regarding memory usage and accuracy in the approximation of order statistics for real-world datasets.
Revision in Continuous Space: Fine-Grained Control of Text Style Transfer.

Typical methods for unsupervised text style transfer often rely on two key ingredients: 1) seeking the explicit disentanglement of the content and the attributes, and 2) troublesome adversarial learning. In this paper, we show that neither of these components is indispensable. We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer. Our method consists of three key components: a variational auto-encoder (VAE), some attribute predictors (one for each attribute), and a content predictor. The VAE and the two types of predictors enable us to perform gradient-based optimization in the continuous space, which is mapped from sentences in a discrete space, to find the representation of a target sentence with the desired attributes and preserved content. Moreover, the proposed method naturally has the ability to simultaneously manipulate multiple fine-grained attributes, such as sentence length and the presence of specific words, when performing text style transfer tasks. Compared with previous adversarial learning based methods, the proposed method is more interpretable, controllable and easier to train. Extensive experimental studies on three popular text style transfer tasks show that the proposed method significantly outperforms five state-of-the-art methods.
Toward Multimodal Sentiment Analysis of Historic Plays: A Case Study with Text and Audio for Lessing’s Emilia Galotti

We present a case study as part of a work-in-progress project about multimodal sentiment analysis on historic German plays, taking Emilia Galotti by G. E. Lessing as our initial use case. We analyze the textual version and an audio version (audiobook). We focus on ready-to-use sentiment analysis methods: For the textual component, we implement a naive lexicon-based approach and another approach that enhances the lexicon by means of several NLP methods. For the audio analysis, we use the free version of the Vokaturi tool. We compare the results of all approaches and evaluate them against the annotations of a human expert, which serves as a gold standard. For our use case, we can show that audio and text sentiment analysis behave very differently: textual sentiment analysis tends to predict sentiment as rather negative and audio sentiment as rather positive. Compared to the gold standard, the textual sentiment analysis achieves accuracies of 56% while the accuracy for audio sentiment analysis is only 32%. We discuss possible reasons for these mediocre results and give an outlook on further steps we want to pursue in the context of multimodal sentiment analysis on historic plays.
Knowledge Hypergraphs: Extending Knowledge Graphs Beyond Binary Relations.

Knowledge graphs store facts using relations between two entities. In this work, we address the question of link prediction in knowledge hypergraphs where relations are defined on any number of entities. While techniques exist (such as reification) that convert non-binary relations into binary ones, we show that current embedding-based methods for knowledge graph completion do not work well out of the box for knowledge graphs obtained through these techniques. To overcome this, we introduce HSimplE and HypE, two embedding-based methods that work directly with knowledge hypergraphs. In both models, the prediction is a function of the relation embedding, the entity embeddings and their corresponding positions in the relation. We also develop public datasets, benchmarks and baselines for hypergraph prediction and show experimentally that the proposed models are more effective than the baselines.
Unsupervised Controllable Text Generation with Global Variation Discovery and Disentanglement.

The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent space. However, on discrete data such as text, it is unclear if unsupervised learning can discover similar latent space that allows controllable manipulation. In this work, we find that sequence VAEs trained on text fail to properly decode when the latent codes are manipulated, because the modified codes often land in holes or vacant regions in the aggregated posterior latent space, where the decoding network fails to generalize. Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex. Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text. Empirically, our method outperforms unsupervised baselines and strong supervised approaches on text style transfer, and is capable of performing more flexible fine-grained control over text generation than existing methods.
CHOReVOLUTION: Automating the Realization of Highly–Collaborative Distributed Applications

CHOReVOLUTION is a platform for the tool-assisted development and execution of scalable applications that leverage the distributed collaboration of services specified through service choreographies. It offers an Integrated Development and Runtime Environment (IDRE) comprising a wizard-aided development environment, a system monitoring console, and a back-end for managing the deployment and execution of the system on the cloud. We describe the platform by using a simple example and evaluate it against two industrial use cases in the domain of Smart Mobility & Tourism and Urban Traffic Coordination.
Fast, Furious and Insecure: Passive Keyless Entry and Start Systems in Modern Supercars

The security of immobiliser and Remote Keyless Entry systems has been extensively studied over many years. Passive Keyless Entry and Start systems, which are currently deployed in luxury vehicles, have not received much attention besides relay attacks. In this work we fully reverse engineer a Passive Keyless Entry and Start system and perform a thorough analysis of its security. Our research reveals several security weaknesses. Specifically, we document the use of an inadequate proprietary cipher using 40-bit keys, the lack of mutual authentication in the challenge-response protocol, no firmware readout protection features enabled and the absence of security partitioning. In order to validate our findings, we implement a full proof of concept attack allowing us to clone a Tesla Model S key fob in a matter of seconds with low cost commercial off the shelf equipment. Our findings most likely apply to other manufacturers of luxury vehicles including McLaren, Karma and Triumph motorcycles as they all use the same system developed by Pektron.
A Multi-agent System to Manage Users and Spaces in a Adaptive Environment System

This paper, deals with the actual problem of manage user preferences and local specifications on an IoT adaptive system, namely using a multi agent system to achieve a Smart Environment System. On a new era of interaction between persons and physical spaces, users want those spaces smartly adapt to their preferences in a transparent way. To achieve that, new approaches are needed. In this project we develop a multi agent system architecture with different layers to achieve a solution that entails all the proposed objectives.
LIA: Latently Invertible Autoencoder with Adversarial Learning

Generative Adversarial Networks (GANs) play an increasingly important role in machine learning. However, there is one fundamental issue hindering their practical applications: the absence of capability for encoding real-world samples. The conventional way of addressing this issue is to learn an encoder for GAN via Variational Auto-Encoder (VAE). In this paper, we show that the entanglement of the latent space for the VAE/GAN framework poses the main challenge for encoder learning. To address the entanglement issue and enable inference in GAN we propose a novel algorithm named Latently Invertible Autoencoder (LIA). The framework of LIA is that an invertible network and its inverse mapping are symmetrically embedded in the latent space of VAE. The decoder of LIA is first trained as a standard GAN with the invertible network and then the partial encoder is learned from a disentangled autoencoder by detaching the invertible network from LIA, thus avoiding the entanglement problem caused by the random latent space. Experiments conducted on the FFHQ face dataset and three LSUN datasets validate the effectiveness of LIA/GAN.
Meta-Graph Based Attention-Aware Recommendation over Heterogeneous Information Networks

Heterogeneous information network (HIN), which involves diverse types of data, has been widely used in recommender systems. However, most existing HINs based recommendation methods equally treat different latent features and simply model various feature interactions in the same way so that the rich semantic information cannot be fully utilized. To comprehensively exploit the heterogeneous information for recommendation, in this paper, we propose a Meta-Graph based Attention-aware Recommendation (MGAR) over HINs. First of all, MGAR utilizes rich meta-graph based latent features to guide the heterogeneous information fusion recommendation. Specifically, in order to discriminate the importance of latent features generated by different meta-graphs, we propose an attention-based feature enhancement model. The model enables useful features and useless features contribute differently to the prediction, thus improves the performance of the recommendation. Furthermore, to holistically exploit the different interrelation of features, we propose a hierarchical feature interaction method which consists three layers of second-order interaction to mine the underlying correlations between users and items. Extensive experiments show that MGAR outperforms the state-of-the-art recommendation methods in terms of RMSE on Yelp and Amazon Electronics.
Technology at/of the border

In a time of increased (forced) migration, the borders of many countries are not only experienced physically. We are witnessing new ways in which borders are being created, re-created and evolved.
Query Performance Prediction for Pseudo-Feedback-Based Retrieval

The query performance prediction task (QPP) is estimating retrieval effectiveness in the absence of relevance judgments. Prior work has focused on prediction for retrieval methods based on surface level query-document similarities (e.g., query likelihood). We address the prediction challenge for pseudo-feedback-based retrieval methods which utilize an initial retrieval to induce a new query model; the query model is then used for a second (final) retrieval. Our suggested approach accounts for the presumed effectiveness of the initially retrieved list, its similarity with the final retrieved list and properties of the latter. Empirical evaluation demonstrates the clear merits of our approach.
Local Matrix Approximation based on Graph Random Walk

How to decompose a large global matrix into many small local matrices has been recently researched a lot for matrix approximation. However, the distance computation in matrix decomposition is a challenging issue, as no prior knowledge about the most appropriate feature vectors and distance measures are available. In this paper, we propose a novel scheme for local matrix construction without involving distance computation. The basic idea is based on the application of convergence probabilities of graph random walk. At first, a user-item bipartite graph is constructed from the global matrix. After performing random walk on the bipartite graph, we select some user-item pairs as anchors. Then another random walk with restart is applied to construct the local matrix for each anchor. Finally, the global matrix approximation is obtained by averaging the prediction results of local matrices. Our experiments on the four real-world datasets show that the proposed solution outperforms the state-of-the-art schemes in terms of lower prediction errors and higher coverage ratios.
Zero-shot image classification using coupled dictionary embedding

Zero-shot learning (ZSL) is a framework to classify images that belong to unseen visual classes using their semantic descriptions about the unseen classes. We develop a new ZSL algorithm based on coupled dictionary learning. The core idea is to enforce the visual features and the semantic attributes of an image to share the same sparse representation in an intermediate embedding space, modeled as the shared input space of two sparsifying dictionaries. In the ZSL training stage, we use images from a number of seen classes for which we have access to both the visual and the semantic attributes to train two coupled dictionaries that can represent both the visual and the semantic feature vectors of an image using a single sparse vector. In the ZSL testing stage and in the absence of labeled data, images from unseen classes are mapped into the attribute space by finding the joint-sparse representations using solely the visual dictionary via solving a LASSO problem. The image is then classified in the attribute space given semantic descriptions of unseen classes. We also provide attribute-aware and transductive formulations to tackle the “domain-shift” and the “hubness” challenges for ZSL, respectively. Experiments on four primary datasets using VGG19 and GoogleNet visual features, are provided. Our performances using VGG19 features are 91.0%, 48.4%, and 89.3% on the SUN, the CUB, and the AwA1 datasets, respectively. Our performances on the SUN, the CUB, and the AwA2 datasets are 57.0%,49.7%, and 71.7%, respectively, when GoogleNet features are used. Comparison with existing methods demonstrates that our method is effective and compares favorably against the state-of-the-art. In particular, our algorithm leads to decent performance on the all four datasets.2
Security in Smart Toys: A Systematic Review of Literature

Smart toys are traditional toys embedded with technological intelligence that enhances the toys functionalities to match the need of children in an ever-advancing technological savvy generation. Due to some weaknesses present in their embedded features, the sale and the use of this type of product have been driving some great concernments related to cyber-security. Therefore, this systematic review mapped the current research areas in smart toy security, collect data and show the available works on the subject in question. For conducting this survey, we used electronic resources such as ACM Digital Library, Science Direct (Elsevier) and Scopus in the period between 2014 to 2018. This research protocol was developed by the research question, data sources and search strategies, data extraction and synthesis. After completing all these processes, we selected and analyzed a range of 26 studies from an initial set of 731. We considered the suggestions presented in the analyzed studies about keeping children’s data secured while using smart toys and we came to the conclusion that there is a significant growth in the studies about it, although there is not any study implementing a security model in these toys or a guideline of how to use it safely.
Function evaluations upto 1e+12 and large population sizes assessed in distance-based success history differential evolution for 100-digit challenge and numerical optimization scenarios (DISHchain 1e+12)

This paper describes a competition entry for the "100-Digit Challenge, and Four Other Numerical Optimization Competitions" at The Genetic and Evolutionary Computation Conference (GECCO) 2019, by assessing the function evaluations up to 1e+12, and large population sizes in Distance-based Success History Differential Evolution for the 100-Digit Challenge and Numerical Optimization Scenarios (DISHchain 1e+12).
An Anonymous Protocol for Member Privacy in a Consortium Blockchain

A consortium blockchain is that multiple groups of authorized members that share one ledger. Transaction validation and membership authentication are executed by trusted nodes which create and store full block (a.k.a full node). Other lightweight nodes which stored only header block request validating transaction to full node. Therefore, because the lightweight node requesting of validation transaction and sharing ledger of members between groups in consortium blockchain creates a privacy problem. In this paper, we propose an anonymous protocol based on a credential system for privacy in a consortium blockchain. This solves the problem of computation overhead and privacy in the consortium blockchain.
Making the Case for Human-Aware Navigation in Warehouses

This work addresses the performance of several local planners for navigation of autonomous pallet trucks in the presence of humans in a simulated warehouse as well as a complementary approach developed within the ILIAD project. Our focus is to stress the open problem of a safe manoeuvrability of pallet trucks in the presence of moving humans. We propose a variation of ROS navigation stack that includes in the planning process a model of the human robot interaction.