Reviewerly/exHarmony huggingface dataset:
Safe Path Planning with Multi-Model Risk Level Sets

This paper investigates the safe path planning problem for an autonomous vehicle operating in unstructured, cluttered environments. While some objects may be accurately with canonical perception algorithms, other objects and clutter may be harder to track. We present an approach that combines two methods of risk assessment: for objects with reliable tracking, we use a Gaussian Process (GP) regulated risk map to describe the risk map information; for unknown objects that we fail to accurately track, we compute a Dynamic Risk Density (DRD) from the overall occupancy and velocity field from LiDAR scan snapshots. Several methods are proposed for combining the GP risk map and DRD, and the resultant hybrid risk map is used for the proposed safe path planning algorithm. Experimental results on an autonomous buggy show that the hybrid risk map is able to yield a safe path planner to navigate the autonomous testbed within the cluttered environments.
Smartphone Health Biomarkers: Positive Unlabeled Learning of In-the-Wild Contexts

There has recently been increased interest in context-aware mobile sensing applications due to the ubiquity of sensor-rich smartphones. Our DARPA-funded Warfighter Analytics for Smartphone Healthcare (WASH) project is exploring passive assessment methods using smartphone biomarkers and context-specific tests. Our envisioned context-specific assessments require accurate recognition of specific smartphone user contexts. Existing context datasets were either scripted or in-the-wild. Scripted datasets have accurate context labels but user behaviors are not realistic. In-the-wild datasets have realistic user behaviors but often have wrong or missing labels. We introduce a novel coincident data gathering study design in which data were gathered for the same contexts using both a scripted and in-the-wild study. We then propose positive unlabeled context learning (PUCL), a transductive method to transfer knowledge from highly accurate labels of the scripted dataset to the less accurate in-the-wild dataset. Our PUCL approach for context recognition outperforms state-of-the-art methods with an increase of over 3% in balanced accuracy.
Syntactic Completeness of Proper Display Calculi

A recent strand of research in structural proof theory aims at exploring the notion of analytic calculi (i.e., those calculi that support general and modular proof-strategies for cut elimination) and at identifying classes of logics that can be captured in terms of these calculi. In this context, Wansing introduced the notion of proper display calculi as one possible design framework for proof calculi in which the analyticity desiderata are realized in a particularly transparent way. Recently, the theory of properly displayable logics (i.e., those logics that can be equivalently presented with some proper display calculus) has been developed in connection with generalized Sahlqvist theory (a.k.a. unified correspondence). Specifically, properly displayable logics have been syntactically characterized as those axiomatized by analytic inductive axioms , which can be equivalently and algorithmically transformed into analytic structural rules so the resulting proper display calculi enjoy a set of basic properties: soundness, completeness, conservativity, cut elimination, and the subformula property. In this context, the proof that the given calculus is complete w.r.t. the original logic is usually carried out syntactically , i.e., by showing that a (cut-free) derivation exists of each given axiom of the logic in the basic system to which the analytic structural rules algorithmically generated from the given axiom have been added. However, so far, this proof strategy for syntactic completeness has been implemented on a case-by-case base and not in general. In this article, we address this gap by proving syntactic completeness for properly displayable logics in any normal (distributive) lattice expansion signature. Specifically, we show that for every analytic inductive axiom a cut-free derivation can be effectively generated that has a specific shape, referred to as pre-normal form .
Immersive Learning Environments at Scale: Constraints and Opportunities

The expansion of online education into massive open online courses (MOOCs) and equipment have created a unique opportunity for delivering immersive learning experiences at scale. However, although the inclusivity of the MOOC ecosystem can be commended, many online courses lack key benefits associated with traditional classroom environments: immersive, engaging, and team-driven learning opportunities. Immersive learning environments (ILEs) address these educational gaps but has not been able to operate at the broad scale that MOOCs offer. Importantly, ILEs address opportunities missing from MOOC systems, they add unique learning opportunities that would also be missing in a traditional classroom. The inclusion of this virtual reality technology is pivotal topic for educational research. This theoretical paper will briefly define immersive learning environments and the potential benefits of incorporating immersive learning environments into scalable educational systems. We will also consider developers constraints on creating these online ecosystem and suggested strategies for overcoming them.
Accurate Modulation Classification with Reusable-Feature Convolutional Neural Network

Deep learning (DL) nowadays activates the advancement of multiple research fields, from signal processing to computer vision and communication. Compared with traditional machine learning (ML) algorithms, some DL architectures such as convolutional neural network (CNN) allow learning feature automatically without expert knowledge in a specific domain. Inspired by the notable achievement of DL for general image classification, this work studies an efficient CNN-based approach for automatic modulation classification which is recognized as the fundamental task in several modern communication systems. Besides the architecture organized by several blocks of parallel asymmetric convolutional layers, the proposed network is specialized by two mechanisms: skip-connection for the prevention of vanishing gradient and reusable-feature depth-concatenation for an optimal feature utilization via accumulating the deep feature at multi-scale representation. According performance evaluation, our method gains the 24-modulation classification rate of 85.10% at +10 dB SNR on the most challenging and large-scale DeepSig: RadioML (version 2018.01A) dataset.
Elan: Towards Generic and Efficient Elastic Training for Deep Learning

Showing a promising future in improving resource utilization and accelerating training, elastic deep learning training has been attracting more and more attention recently. Nevertheless, existing approaches to provide elasticity have certain limitations. They either fail to fully explore the parallelism of deep learning training when scaling out or lack an efficient mechanism to replicate training states among different devices.To address these limitations, we design Elan, a generic and efficient elastic training system for deep learning. In Elan, we propose a novel hybrid scaling mechanism to make a good trade-off between training efficiency and model performance when exploring more parallelism. We exploit the topology of underlying devices to perform concurrent and IO-free training state replication. To avoid the high overhead of start and initialization, we further propose an asynchronous coordination mechanism. Powered by the above innovations, Elan can provide high-performance (~1s) migration, scaling in and scaling out support with negligible runtime overhead (<3‰). For elastic training of ResNet-50 on ImageNet, Elan improves the time to solution by 20%. For elastic scheduling, with the help of Elan, resource utilization is improved by 21%+ and job pending time is reduced by 43%+.
An Evaluation of Neural Machine Translation and Pre-trained Word Embeddings in Multilingual Neural Sentiment Analysis

One of the main elements in several application domains, such as policy making, addresses the scope of public opinion analysis. The latter is recently realized through sentiment analysis and Natural Language Processing, for identifying and extracting subjective information from raw texts. An additional challenge refers to the exploitation and correlation of data from different languages, in order to analyse sentiments by considering all available information in a holistic way. This paper investigates the impact of Neural Machine Translation in sentiment analysis, based on the translation of text for which neural sentiment analysis in English has been already applied and is applied again on the translated German and Greek texts. The latter is performed through the same Neural Network models that were used in the original language, in order to analyse the differences between the performed neural sentiment analysis on the source and the target languages. The outcomes of the proposed approach have a twofold interpretation. While, it is concluded that modern Neural Machine Translation tools are mature enough to provide high accuracy translations and have minor impact on multilingual sentiment analysis, on the other hand it is shown that additional research should be performed on the multilingualism that pre-trained Word Embeddings can provide.
Improving the state-of-the-art in Thai semantic similarity using distributional semantics and ontological information

Research into semantic similarity has a long history in lexical semantics, and it has applications in many natural language processing (NLP) tasks like word sense disambiguation or machine translation. The task of calculating semantic similarity is usually presented in the form of datasets which contain word pairs and a human-assigned similarity score. Algorithms are then evaluated by their ability to approximate the gold standard similarity scores. Many such datasets, with different characteristics, have been created for English language. Recently, four of those were transformed to Thai language versions, namely WordSim-353, SimLex-999, SemEval-2017-500, and R&amp;G-65. Given those four datasets, in this work we aim to improve the previous baseline evaluations for Thai semantic similarity and solve challenges of unsegmented Asian languages (particularly the high fraction of out-of-vocabulary (OOV) dataset terms). To this end we apply and integrate different strategies to compute similarity, including traditional word-level embeddings, subword-unit embeddings, and ontological or hybrid sources like WordNet and ConceptNet. With our best model, which combines self-trained fastText subword embeddings with ConceptNet Numberbatch, we managed to raise the state-of-the-art, measured with the harmonic mean of Pearson on Spearman ρ , by a large margin from 0.356 to 0.688 for TH-WordSim-353, from 0.286 to 0.769 for TH-SemEval-500, from 0.397 to 0.717 for TH-SimLex-999, and from 0.505 to 0.901 for TWS-65.
Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference

In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important. Human behavior is naturally rich and diverse. Cost/reward learning, as an efficient way to learn and represent human behavior, has been successfully applied in many domains. Most of traditional inverse reinforcement learning (IRL) algorithms, however, cannot adequately capture the diversity of human behavior since they assume that all behavior in a given dataset is generated by a single cost function. In this paper, we propose a probabilistic IRL framework that directly learns a distribution of cost functions in continuous domain. Evaluations on both synthetic data and real human driving data are conducted. Both the quantitative and subjective results show that our proposed framework can better express diverse human driving behaviors, as well as extracting different driving styles that match what human participants interpret in our user study.
Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks

Outliers are observations suspected of not having been generated by the underlying process of the remaining data. Many applications require a way of identifying interesting or unusual patterns in multivariate time series (MTS), now ubiquitous in many applications; however, most outlier detection methods focus solely on univariate series. We propose a complete and automatic outlier detection system covering the pre-processing of MTS data that adopts a dynamic Bayesian network (DBN) modeling algorithm. The latter encodes optimal inter and intra-time slice connectivity of transition networks capable of capturing conditional dependencies in MTS datasets. A sliding window mechanism is employed to score each MTS transition gradually, given the DBN model. Two score-analysis strategies are studied to assure an automatic classification of anomalous data. The proposed approach is first validated in simulated data, demonstrating the performance of the system. Further experiments are made on real data, by uncovering anomalies in distinct scenarios such as electrocardiogram series, mortality rate data, and written pen digits. The developed system proved beneficial in capturing unusual data resulting from temporal contexts, being suitable for any MTS scenario. A widely accessible web application employing the complete system is publicly available jointly with a tutorial.
Representing Spatial Object Relations as Parametric Polar Distribution for Scene Manipulation Based on Verbal Commands

Understanding spatial relations is a key element for natural human-robot interaction. Especially, a robot must be able to manipulate a given scene according to a human verbal command specifying desired spatial relations between objects. To endow robots with this ability, a suitable representation of spatial relations is necessary, which should be derivable from human demonstrations. We claim that polar coordinates can capture the underlying structure of spatial relations better than Cartesian coordinates and propose a parametric probability distribution defined in polar coordinates to represent spatial relations. We consider static spatial relations such as left of, behind, and near, as well as dynamic ones such as closer to and other side of, and take into account verbal modifiers such as roughly and a lot. We show that adequate distributions can be derived for various combinations of spatial relations and modifiers in a sample-efficient way using Maximum Likelihood Estimation, evaluate the effects of modifiers on the distribution parameters, and demonstrate our representation's usefulness in a pick-and-place task on a real robot.
Copyright protection scheme for color images using extended visual cryptography

Most of the existing visual cryptography based copyright protection schemes create random looking shares which can create suspicion of some secret information being shared. In order to handle this issue, a copyright protection scheme based on curvelet transform and extended visual cryptography is proposed for color images. Robustness of the scheme against various attacks is achieved by using inter-layer low and middle frequency coefficients obtained by applying discrete curvelet transform on R,G, and B components of host image. The curvelet inter-layer coefficients are used to create a master share. This master share and the watermark are used to create the ownership share by using the codebook. XOR-superimposition of master and ownership shares retrieve the watermark to prove the copyright. Security of the scheme is achieved by creating meaningful ownership shares; and by using Baker’s Map for scrambling watermark and transformed host image. The scheme is robust to withstand several image processing attacks as well as provides better imperceptibility. Effectiveness of the scheme is shown by comparing it with the existing copyright protection schemes.
Cyber Situation Awareness Monitoring and Proactive Response for Enterprises on the Cloud

The cloud model allows many enterprises able to outsource computing resources at an affordable price without having to commit the expense upfront. Although the cloud providers are responsible for the security of the cloud, there are still many security concerns due to inherently complex model the cloud providers operate on (e.g.,multi-tenancy). In addition, the enterprises whose services have migrated into the cloud have a preference for their own cybersecurity situation awareness capability on top of the security mechanisms provided by the cloud providers. In this way, the enterprises can monitor the performance of the security offerings of the cloud and have a choice to decide and select potential response strategies more appropriate to the enterprise in the presence of the attack where the defense provided by the cloud doesn't work for them. However, some response strategies, such as Moving Target Defense (MTD) techniques shown to be effective to secure cloud, cannot be deployed by the enterprise themselves. In this paper, we propose a framework that enables better collaboration between enterprises and cloud providers. Our proposed framework, which offers more in-depth security analysis based on the set of most advanced security metrics, allows the security experts of the enterprise to obtain better situational awareness in the cloud. With better and more effective situation awareness of cloud security, our framework can support better decision-making and further allows to deploy more appropriate threat responses to protect the outsourced resources. We also propose a secure protocol which can facilitate more secure communication between the enterprises and cloud provider. Using our proposed secure protocol, which is based on authentication and key exchange mechanism, the enterprises can send a secure request to the cloud provider to perform a selected defensive strategy.
Effectiveness of the News Text Classification Test Using the Naïve Bayes’ Classification Text Mining Method

Abstract This study used a quantitative approach created by the author to describe the accuracy of the classification machine learning for news text. In this report we compare the results of the accuracy values obtained using the Naïve Bayes method with other methods to see the effectiveness of the method used. The resulting accuracy value has not reached its maximum and thus it could still be restructured and re-evaluated into a better model. In this case the writer tried to increase the precision value produced in order to make this machine able to predict news that contains sarcasm through modification of the set threshold value. After the threshold value was changed to 0.3, the accuracy value decreased to 61% but the precision value increased to 77% and the error value in the prediction of false positive headlines also increased significantly and only produced 89 errors in predicting sarcasm. The ROC Curve test showed that this machine learning model could still be improved by trying other text preprocessing methods such as the bigrams, tidytext, lemmatization methods, so that the machine will become smarter at predicting the resulting vectors and increase the value of precision and accuracy obtained.
A fast robot path planning algorithm based on bidirectional associative learning

Fast path planning in unknown environment is important to reduce the loss of human and material resources. To reduce planning time while obtaining a short path, this paper proposes a Bidirectional Associative Learning Algorithm (BALA). In the proposed algorithm, an episode is defined as a bidirectional movement between the start point and the target point. The planning process in the BALA is divided into three stages: early stage, medium stage and end stage. In the early stage, the attraction of the target point is adopted to instruct the robot to select action. This strategy not only helps the robot avoid blind search, but also provides the search scope that may contain the global shortest path for the subsequent episodes. In the medium stage, we propose an action selection strategy based on the experience guidance, where the experience obtained in the obverse and reverse movements is used alternately to improve the learning efficiency of the robot. In the end stage, a strong connectivity relationship between nodes is defined. Planning by this relationship, the length of the final planned path will be the shortest based on the experience the robot obtains. The comparison results with Q-Learning and its improved algorithm reveal that the BALA demonstrates desirable and stable performance in planning efficiency in any environment, and it can well balance the planning time and path length. Additionally, the practicability of the proposed algorithm is validated on Turtlebot3 burger robot.
Enabling Secure and Versatile Packet Inspection With Probable Cause Privacy for Outsourced Middlebox

Middlebox is an intermediary network equipment which can be outsourced to remote cloud servers for low-cost and customizable network services, such as load balancer and intrusion detection. A fundamental function of the middlebox is packet inspection, where both the packet header and payload are extracted and analyzed based on inspection rules. However, as the packet may contain sensitive individual or organizational information, it may raise severe privacy concerns without proper countermeasures. In this article, we propose a secure and versatile packet inspection scheme for outsourced middlebox. The proposed scheme builds upon two non-collusion cloud servers, where the first server conducts the inspection task over the ciphertext domain and the second reveal the inspection results. By doing so, the proposed scheme achieves versatile inspection functionalities: range-query-based header inspection and token-based payload inspection, while preserving the privacy of packet header, payload, and inspection rules. Moreover, we identify and address two challenging issues in the state-of-the-art literatures. First, we tailor the design of mis-operation resistant searchable homomorphic encryption (MR-SHE) and somewhat homomorphic encryption in the two-server model, to resist <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offline dictionary attack on payload headers</i> . Second, we propose a key management mechanism with compelled access for the middlebox, to achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fine-grained probable cause privacy</i> . We also conduct extensive experiments and compare the results with existing schemes to demonstrate the feasibility of the proposed scheme.
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes

We consider a scenario in which an autonomous vehicle equipped with a downward facing camera operates in a 3D environment and is tasked with searching for an unknown number of stationary targets on the 2D floor of the environment. The key challenge is to minimize the search time while ensuring a high detection accuracy. We model the sensing field using a multi-fidelity Gaussian process that systematically describes the sensing information available at different altitudes from the floor. Based on the sensing model, we design a novel algorithm called Expedited Multi-Target Search (EMTS) that (i) addresses the coverage-accuracy trade-off: sampling at locations farther from the floor provides wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection. We rigorously analyze the algorithm and establish formal guarantees on the target detection accuracy and the detection time. We illustrate the algorithm using a simulated multi-target search scenario.