Reviewerly/exHarmony huggingface dataset:
Control in belief space with temporal logic specifications using vision-based localization

We present a solution for operating a vehicle without global positioning infrastructure while satisfying constraints on its temporal behavior, and on the uncertainty of its position estimate. The proposed solution is an end-to-end framework for mapping an unknown environment using aerial vehicles, synthesizing a control policy for a ground vehicle in that environment, and using a quadrotor to localize the ground vehicle within the map while it executes its control policy. This vision-based localization is noisy, necessitating planning in the belief space of the ground robot. The ground robot’s mission is given using a language called Gaussian Distribution Temporal Logic (GDTL), an extension of Boolean logic that incorporates temporal evolution and noise mitigation directly into the task specifications. We use a sampling-based algorithm to generate a transition system in the belief space and use local feedback controllers to break the curse of history associated with belief space planning. To localize the vehicle, we build a high-resolution map of the environment by flying a team of aerial vehicles in formation with sensor information provided by their onboard cameras. The control policy for the ground robot is synthesized under temporal and uncertainty constraints given the semantically labeled map. Then the ground robot can execute the control policy given pose estimates from a dedicated aerial robot that tracks and localizes the ground robot. The proposed method is validated using two quadrotors to build a map, followed by a two-wheeled ground robot and a quadrotor with a camera for ten successful experimental trials.
Fiber bundle imaging resolution enhancement using deep learning

We propose a deep learning based method to estimate high-resolution images from multiple fiber bundle images. Our approach first aligns raw fiber bundle image sequences with a motion estimation neural network and then applies a 3D convolution neural network to learn a mapping from aligned fiber bundle image sequences to their ground truth images. Evaluations on lens tissue samples and a 1951 USAF resolution target suggest that our proposed method can significantly improve spatial resolution for fiber bundle imaging systems.
Revising recurrent neural networks from a granular perspective

In this paper, we formally deduce a new computational model, with a recurrent structure, by means of data granulation. The proposed scheme can be regarded as an Echo State Network (ESN), with an additional granular layer. ESNs have been recently revisited in the context of deep learning. In view of such a state-of-the-art, and coherently with the concept of data granulation, the aim herein is to propose a more efficient and transparent structure. The stability of the proposed scheme is formally discussed. The performance is shown by means of several benchmarks against the state-of-the-art methods. The proposed architecture exhibits a lower computational cost and a higher accuracy.
"The Printer is Telling Me about Itself"

The increasing complexity of cyber-physical systems poses special challenges for users to be able to appropriate and apply such technologies within their practice. Classic tools to support appropriation have usually taken the form of written manuals or video tutorials. However, recent research regarding appropriation infrastructures and sociable technologies has suggested that appropriation support functionality can be integrated directly into software and cyber-physical systems. The problem confronting this kind of support is the adequate visualization of the information and the provision of user interfaces that could offer the necessary basis for appropriation. Based on the example of 3D printing, we examine how projection mapping-as an innovative form of visualization-can be used as a user interface for hardware-related appropriation support. Through reflections upon the design and evaluation of a projection-based 3D-printer system, we provide insights that extend the notion of appropriation support to encompass projection mapping and that can contribute to the future development of projection-based human-machine interfaces.
Why SIEM is Irreplaceable in a Secure IT Environment?

The aim of the publication is to brief on the importance of a SIEM (Security Information and Event Management) solution. Its benefits but also taking time to reflect on this system drawbacks. All of which is intended for those who are looking into cybersecurity solution that will learn from entire IT infrastructure and be able to identify anomalies, like cyberattacks. Depending on the region and market, enterprise priorities tends to be different, but all mainly take into consideration TCO (Total Cost of Ownership), which in SIEM case is a key metric. If company/organization is serious about deploying a SIEM, then another key security technology they should think about is SOC. If deployed correctly than SOC (Security Operations Centre) is a full framework of technologies, people and processes to act like a well-oiled machine that identifies, protects, detects, responds and recovers from all security related incidents.
SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference

We present SherLIiC, a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) ~960k unlabeled InfCands, and (ii) ~190k typed textual relations between Freebase entities extracted from the large entity-linked corpus ClueWeb09. Each InfCand consists of one of these relations, expressed as a lemmatized dependency path, and two argument placeholders, each linked to one or more Freebase types. Due to our candidate selection process based on strong distributional evidence, SherLIiC is much harder than existing testbeds because distributional evidence is of little utility in the classification of InfCands. We also show that, due to its construction, many of SherLIiC’s correct InfCands are novel and missing from existing rule bases. We evaluate a large number of strong baselines on SherLIiC, ranging from semantic vector space models to state of the art neural models of natural language inference (NLI). We show that SherLIiC poses a tough challenge to existing NLI systems.
An Improved Approach for Complex Activity Recognition in Smart Homes

The essence of intelligent assistive technologies in smart homes can be outlined as their ability to enhance the user experience in many ways. A way to accomplish this goal is to make such systems aware and knowledgeable about user interactions in the context of the given environment. In the context of smart homes, the ability of such intelligent systems to understand and analyze human behavior in the context of Activities of Daily Living is very essential. Modern day software-based solutions for development of such intelligent systems, should not only be able to sense human movements through Internet of Things based technologies but they should also be able to analyze these components of user interactions in a manner that facilitates optimal utilization of system resources, increases software productivity, improves system interoperability, reduces development and maintenance costs and allows both internal and external reusability of the solutions. Therefore, this paper discusses a study on the ‘Complex Activity Recognition Algorithm’ [1] and proposes a systematic approach to improve this algorithm for better performance and system optimization. The improved version of this algorithm has been tested on several activities and the results obtained uphold the relevance for implementation of the same in different real-time environments.
Prediction of Transaction Confirmation Time in Ethereum Blockchain Using Machine Learning

An Ethereum transaction is defined as the method by which the external world interacts with Ethereum. More and more users are getting involved in cryptocurrencies like Ethereum and Bitcoin. With a sudden increase in the number of transactions happening every second and the capital involved in those transactions, there is a need for the users to able to predict whether a transaction would be confirmed and if yes, then how much time would it take for it to be confirmed. This paper aims to use modern machine learning techniques to propose a model that would be able to predict the time frame within which a miner node will accept and include a transaction to a block. The paper also explores the impact of imbalanced data on our chosen classifiers-Bayes, Random Forest and Multi-Layer Perceptron (MLP) with SoftMax output and the alternative performance measures to optimally handle the imbalanced nature of the dataset.
Fast Dynamic Vehicle Detection in Road Scenarios Based on Pose Estimation with Convex-Hull Model

Dynamic vehicle detection is of great significance for the safety of autonomous vehicles and the formulation of subsequent driving strategies. A pose-estimation algorithm, namely, the pose estimation with convex-hull model (PE-CHM), is proposed in this paper, and introduced in the dynamic vehicle detection system. In PE-CHM, the convex hull of the object's point-clouds is first extracted and the most fitted bounding box is determined by a multifactor objective function. Next, the center position of the target is inferred according to the location and direction of the target. With the obtained bounding box and the position inference, the pose of the target is determined, which reduces the interference of the missing contour on pose estimation. Finally, three experiments were performed to validate the performance of the proposed PE-CHM method. Compared with several typical model-based methods, PE-CHM can implement dynamic vehicle detection faster, which reduces the amount of calculation on the basis of ensuring detection efficiency.
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling

Debjit Paul, Mittul Singh, Michael A. Hedderich, Dietrich Klakow. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop. 2019.
Network Analysis of Recurring YouTube Spam Campaigns

As the popularity of content sharing websites has increased, they have become targets for spam, phishing and the distribution of malware. On YouTube, the facility for users to post comments can be used by spam campaigns to direct unsuspecting users to malicious third-party websites. In this paper, we demonstrate how such campaigns can be tracked over time using network motif profiling, i.e. by tracking counts of indicative network motifs. By considering all motifs of up to five nodes, we identify discriminating motifs that reveal two distinctly different spam campaign strategies, and present an evaluation that tracks two corresponding active campaigns.
Entangled thermal mixed states for multi-qubit systems

We derive the entangled thermal mixed states by using the formalism of phase states for a finite-dimensional algebra of a multi-qubit system in contact with an independent thermal environment of absolute temperature [Formula: see text]. Thermal entangled states describing the multi-qubit system in equilibrium with the thermal bath are a special kind of mixed states that exhibit genuine multipartite correlation. We define the unitary phase operators for a multipartite system of non-interacting qubits. Entangled density matrices are derived for qubits interacting via an Hermitian Hamiltonian of Heisenberg type [Formula: see text]. By assuming that the noisy interaction of the entangled qubit ensemble with the bath is governed by a local Hamiltonian [Formula: see text], we show that the entangled phase states can be decohered. When the multi-qubits entangled system reaches the equilibrium with the thermal bath, the decohered mixed states are identified with entangled thermal states. The thermal mixed states for bipartite and multipartite systems are explicitly expressed and their bipartite entanglement properties are investigated.
EyeSeeThrough: Unifying Tool Selection and Application in Virtual Environments

In 2D interfaces, actions are often represented by fixed tools arranged in menus, palettes, or dedicated parts of a screen, whereas 3D interfaces afford their arrangement at different depths relative to the user and the user can move them relative to each other. In this paper, we introduce EyeSeeThrough as a novel interaction technique that utilizes eye-tracking in VR. The user can apply an action to an intended object by visually aligning the object with the tool at the line-of-sight, and then issue a confirmation command. The underlying idea is to merge the two-step process of 1) selection of a mode in a menu and 2) applying it to a target, into one unified interaction. We present a user study where we compare the method to the baseline two-step selection. The results of our user study showed that our technique outperforms the two step selection in terms of speed and comfort. We further developed a prototype of a virtual living room to demonstrate the practicality of the proposed technique.
Intelligent design and implementation of blockchain and Internet of things–based traffic system

With the continuous development of Internet of things, all kinds of smart systems are quickly evolving to make our day-to-day life smoother and safer. Like many other sectors, transportation has entered a period of rapid change. Intelligent Traffic System is one of the fastest-growing fields within the smart systems, which is expected to increase road safety, mitigate traffic congestion, and enable fuel efficiency. The main functionalities of Intelligent Traffic System are as follows: (1) monitoring real-time traffic conditions in specific areas, (2) locating traffic emergencies (i.e. traffic accidents) in specific areas, and (3) dynamic monitoring and managing the continuous use in public transit services (i.e. change in car lanes) that may lead to changes in macro traffic conditions. This article will use the above-mentioned functionalities of the Intelligent Traffic System as underlying simulative scenarios, to design and to implement a smart transportation system based on Internet of things and blockchain—both share inherent distributed technology characteristics—combining both Internet of things sensor nodes and distributed ledger technology, to (1) record the changes in intelligent transportation systems and (2) set up a credit-token mechanism for paying the use and misuses in public transit services accordingly. The Intelligent Traffic System described in this article is intended to be used as experimental project only, given the terms and conditions as depicted in the simulated scenario. In real-life traffic scenarios, it may generate more complex system and data security issues, which will be elaborated and analyzed at the end of this article. Intelligent Traffic System is a comprehensive smart system; it can significantly change and reinvent the wheel for traffic conditions. Based on the system development as discussed in this article, there are still a lot of demands and challenges that need to be addressed in the future. Such topic scope will be explored in depth in our subsequent research.
Theoretical Analysis of Adversarial Learning: A Minimax Approach

In this paper, we propose a general theoretical method for analyzing the risk bound in the presence of adversaries. Specifically, we try to fit the adversarial learning problem into the minimax framework. We first show that the original adversarial learning problem can be transformed into a minimax statistical learning problem by introducing a transport map between distributions. Then, we prove a new risk bound for this minimax problem in terms of covering numbers under a weak version of Lipschitz condition. Our method can be applied to multi-class classification and popular loss functions including the hinge loss and ramp loss. As some illustrative examples, we derive the adversarial risk bounds for SVMs and deep neural networks, and our bounds have two data-dependent terms, which can be optimized for achieving adversarial robustness.
Neural Relational Inference with Fast Modular Meta-learning

Graph neural networks (GNNs) are effective models for many dynamical systems consisting of entities and relations. Although most GNN applications assume a single type of entity and relation, many situations involve multiple types of interactions. Relational inference is the problem of inferring these interactions and learning the dynamics from observational data. We frame relational inference as a modular meta-learning problem, where neural modules are trained to be composed in different ways to solve many tasks. This meta-learning framework allows us to implicitly encode time invariance and infer relations in context of one another rather than independently, which increases inference capacity. Framing inference as the inner-loop optimization of meta-learning leads to a model-based approach that is more data-efficient and capable of estimating the state of entities that we do not observe directly, but whose existence can be inferred from their effect on observed entities. To address the large search space of graph neural network compositions, we meta-learn a proposal function that speeds up the inner-loop simulated annealing search within the modular meta-learning algorithm, providing two orders of magnitude increase in the size of problems that can be addressed.
Parity Games: Zielonka's Algorithm in Quasi-Polynomial Time

Calude, Jain, Khoussainov, Li, and Stephan (2017) proposed a quasi-polynomial-time algorithm solving parity games. After this breakthrough result, a few other quasi-polynomial-time algorithms were introduced; none of them is easy to understand. Moreover, it turns out that in practice they operate very slowly. On the other side there is Zielonka's recursive algorithm, which is very simple, exponential in the worst case, and the fastest in practice. We combine these two approaches: we propose a small modification of Zielonka's algorithm, which ensures that the running time is at most quasi-polynomial. In effect, we obtain a simple algorithm that solves parity games in quasi-polynomial time. We also hope that our algorithm, after further optimizations, can lead to an algorithm that shares the good performance of Zielonka's algorithm on typical inputs, while reducing the worst-case complexity on difficult inputs.
Content-Based Image Retrieval using Convolutional Neural Networks

Searching a collection of images that have similarities with input images, without knowing the name of the image, makes a search system that applies the concept of content-based image retrieval (CBIR), is very necessary. In general, CBIR systems use visual features such as color, image edge, texture, and suitability of names in input images with images in the database. The method for classification is convolutional neural networks (CNN), while retrieval with cosine similarity. Dataset is divided into 5 masterclasses, each masterclass has 5 subclasses. The class used for retrieval is a masterclass, where the images of each large class are combined images of subclasses in the large class. From the experiments, we found that the CNN method has succeeded in supporting the retrieval task, by classifying image classes.
Marginalized maximum a posteriori estimation for the four‐parameter logistic model under a mixture modelling framework

The four‐parameter logistic model (4PLM) has recently attracted much interest in various applications. Motivated by recent studies that re‐express the four‐parameter model as a mixture model with two levels of latent variables, this paper develops a new expectation–maximization (EM) algorithm for marginalized maximum a posteriori estimation of the 4PLM parameters. The mixture modelling framework of the 4PLM not only makes the proposed EM algorithm easier to implement in practice, but also provides a natural connection with popular cognitive diagnosis models. Simulation studies were conducted to show the good performance of the proposed estimation method and to investigate the impact of the additional upper asymptote parameter on the estimation of other parameters. Moreover, a real data set was analysed using the 4PLM to show its improved performance over the three‐parameter logistic model.
Semi-supervised Learning by Disentangling and Self-ensembling over Stochastic Latent Space

The success of deep learning in medical imaging is mostly achieved at the cost of a large labeled data set. Semi-supervised learning (SSL) provides a promising solution by leveraging the structure of unlabeled data to improve learning from a small set of labeled data. Self-ensembling is a simple approach used in SSL to encourage consensus among ensemble predictions of unknown labels, improving generalization of the model by making it more insensitive to the latent space. Currently, such an ensemble is obtained by randomization such as dropout regularization and random data augmentation. In this work, we hypothesize – from the generalization perspective – that self-ensembling can be improved by exploiting the stochasticity of a disentangled latent space. To this end, we present a stacked SSL model that utilizes unsupervised disentangled representation learning as the stochastic embedding for self-ensembling. We evaluate the presented model for multi-label classification using chest X-ray images, demonstrating its improved performance over related SSL models as well as the interpretability of its disentangled representations.
Privacy-Preserving Distributed Average Consensus based on Additive Secret Sharing

One major concern of distributed computation in networks is the privacy of the individual nodes.To address this privacy issue in the context of the distributed average consensus problem, we propose a general, yet simple solution that achieves privacy using additive secret sharing, a tool from secure multiparty computation.This method enables each node to reach the consensus accurately and obtains perfect security at the same time.Unlike differential privacy based approaches, there is no trade-off between privacy and accuracy.Moreover, the proposed method is computationally simple compared to other techniques in secure multiparty computation, and it is able to achieve perfect security of any honest node as long as it has one honest neighbour under the honest-but-curious model, without any trusted third party.
Considerations for the Conduction and Interpretation of FAIRness Evaluations

The FAIR principles were received with broad acceptance in several scientific communities. However, there is still some degree of uncertainty on how they should be implemented. Several self-report questionnaires have been proposed to assess the implementation of the FAIR principles. Moreover, the FAIRmetrics group released 14, general-purpose maturity for representing FAIRness. Initially, these metrics were conducted as open-answer questionnaires. Recently, these metrics have been implemented into a software that can automatically harvest metadata from metadata providers and generate a principle-specific FAIRness evaluation. With so many different approaches for FAIRness evaluations, we believe that further clarification on their limitations and advantages, as well as on their interpretation and interplay should be considered.