Reviewerly/exHarmony huggingface dataset:
Dense Incremental Metric-Semantic Mapping for Multiagent Systems via Sparse Gaussian Process Regression

In this article, we develop an online probabilistic metric-semantic mapping approach for mobile robot teams relying on streaming RGB-D observations. The generated maps contain full continuous distributional information about the geometric surfaces and semantic labels (e.g., chair, table, and wall). Our approach is based on online Gaussian process (GP) training and inference and avoids the complexity of GP classification by regressing a truncated signed distance function (TSDF) of the regions occupied by different semantic classes. Online regression is enabled through a sparse pseudo-point approximation of the GP posterior. To scale to large environments, we further consider spatial domain partitioning via a hierarchical tree structure with overlapping leaves. An extension to a multirobot setting is developed by having each robot execute its own online measurement update and then combine its posterior parameters via local weighted geometric averaging with those of its neighbors. This yields a distributed information processing architecture, in which the GP map estimates of all the robots converge to a common map of the environment while relying only on local one-hop communication. Our experiments demonstrate the effectiveness of the probabilistic metric-semantic mapping technique in 2-D and 3-D environments in both the single- and multirobot settings and in comparison to a deep TSDF neural network approach.
Accurate Assessment via Process Data

Accurate assessment of a student's ability is the key task of a test. Assessments based on final responses are the standard. As the infrastructure advances, substantially more information is observed. One of such instances is the process data that is collected by computer-based interactive items and contain a student's detailed interactive processes. In this paper, we show both theoretically and with simulated and empirical data that appropriately including such information in the assessment will substantially improve relevant assessment precision.
Claim Verification under Positive Unlabeled Learning

We extend evidence-aware claim verification to the context of positive-unlabeled (PU) learning. Existing works assume the truth and the falsity of the claims are known for training and form the task as a supervised learning problem. However, this assumption underestimates the difficulty of collecting false claims; we argue that claim verification is more challenging in the absence of negative labels. We consider a more practical setting, where only a comparatively small number of true claims are labeled and more claims remain unlabeled. Thus, we formulate the claim verification task as a PU learning problem. We decouple learning representation of claim-evidence pair from PU learning and adopt a pre-trained universal language model to encode claim-evidence pairs. We further propose to use the generative adversarial network (GAN) to capture the latent alignment between encoded claim-evidence pair and the truthfulness. We leverage the verification as part of the GAN by extending previous GAN based PU learning. We show that the proposed model achieves the best performance with a small amount of labeled data and is robust to the truthfulness prior estimation. We conduct a thorough analysis of the model selection. The proposed approach performs the best under two practical scenarios: (i) the unlabeled data is more than the labeled data; (ii) and the unlabeled positive data is more than the unlabeled negative data.
MTSAN: Multi-Task Semantic Attention Network for ADAS Applications

This paper presents a lightweight Multi-task Semantic Attention Network (MTSAN) to collectively deal with object detection as well as semantic segmentation aiding real-time applications of the Advanced Driver Assistance Systems (ADAS). This paper proposes a Semantic Attention Module (SAM) that introduces the semantic contextual clues from a segmentation subnet to guide a detection subnet. The SAM significantly boosts up the detection performance and computational cost by considerably decreasing the false alarm rate and it is completely independent of any other parameters. The experimental results show the effectiveness of each component of the network and demonstrate that the proposed MTSAN yields a better balance between accuracy and speed. Following the post-processing methods, the proposed module is tested and proved for its accuracy in the Lane Departure Warning System (LDWS) and Forward Collision Warning System (FCWS). In addition, the proposed lightweight network is deployable on low-power embedded devices to meet the requirements of the real-time applications yielding 10FPS @ 512 X 256 on NVIDIA Jetson Xavier and 15FPS @ 512 X 256 on Texas Instrument's TDA2x.
A NEAT Visualisation of Neuroevolution Trajectories

NeuroEvolution of Augmenting Topologies (NEAT) is a system for evolving neural network topologies along with weights that has proven highly effective and adaptable for solving challenging reinforcement learning tasks. This paper analyses NEAT through the lens of Search Trajectory Networks (STNs), a recently proposed visual approach to study the dynamics of evolutionary algorithms. Our goal is to improve the understanding of neuroevolution systems. We present a visual and statistical analysis contrasting the behaviour of NEAT, with and without using the crossover operator, when solving the two benchmark problems outlined in the original NEAT article: XOR and double-pole balancing. Contrary to what is reported in the original NEAT article, our experiments without crossover perform significantly better in both domains.
Software for the GeoSPARQL compliance benchmark

Checking the compliance of geospatial triplestores with the GeoSPARQL standard represents a crucial step for many users when selecting the appropriate storage solution. This publication presents the software which comprises the GeoSPARQL compliance benchmark — a benchmark which checks RDF triplestores for compliance with the requirements of the GeoSPARQL standard. Users can execute this benchmark within the HOBBIT benchmarking platform to quantify the extent to which the GeoSPARQL standard is implemented within the triplestore of interest. This enables users to make an informed decision when choosing an RDF storage solution and helps assess the general state of adoption of geospatial technologies on the Semantic Web.
Can intelligent agents improve data quality in online questiosnnaires? A pilot study

We explored the utility of chatbots for improving data quality arising from collection via sonline surveys. Three-hundred Australian adults sampled via Prolific Academic were randomized across chatbot-supported or unassisted online questionnaire conditions. The questionnaire comprised validated measures, along with challenge items formulated to be confusing yet aligned with the validated targets. The chatbot condition provided optional assistance with item clarity via a virtual support agent. Chatbot use and user satisfaction were measured through session logs and user feedback. Data quality was operationalized as between-group differences in relationships among validated and challenge measures. Findings broadly supported chatbot utility for online surveys, showing that most participants with chatbot access utilized it, found it helpful, and demonstrated modestly improved data quality (vs. controls). Absence of confusion for one challenge item is believed to have contributed to an underestimated effect. Findings show that assistive chatbots can enhance data quality, will be utilized by many participants if available, and are perceived as beneficial by most users. Scope constraints for this pilot study are believed to have led to underestimated effects. Future testing with longer-form questionnaires incorporating expanded item difficulty may further understanding of chatbot utility for survey completion and data quality.
Fast kNN Search in Weighted Hamming Space With Multiple Tables

Hashing methods have been widely used in Approximate Nearest Neighbor (ANN) search for big data due to low storage requirements and high search efficiency. These methods usually map the ANN search for big data into the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -Nearest Neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN) search problem in Hamming space. However, Hamming distance calculation ignores the bit-level distinction, leading to confusing ranking. In order to further increase search accuracy, various bit-level weights have been proposed to rank hash codes in weighted Hamming space. Nevertheless, existing ranking methods in weighted Hamming space are almost based on exhaustive linear scan, which is time consuming and not suitable for large datasets. Although Multi-Index hashing that is a sub-linear search method has been proposed, it relies on Hamming distance rather than weighted Hamming distance. To address this issue, we propose an exact <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN search approach with Multiple Tables in Weighted Hamming space named WHMT, in which the distribution of bit-level weights is incorporated into the multi-index building. By WHMT, we can get the optimal candidate set for exact <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN search in weighted Hamming space without exhaustive linear scan. Experimental results show that WHMT can achieve dramatic speedup up to 69.8 times over linear scan baseline without losing accuracy in weighted Hamming space.
Analysis of Multimodal Features for Speaking Proficiency Scoring in an Interview Dialogue

This paper analyzes the effectiveness of different modalities in automated speaking proficiency scoring in an online dialogue task of non-native speakers. Conversational competence of a language learner can be assessed through the use of multimodal behaviors such as speech content, prosody, and visual cues. Although lexical and acoustic features have been widely studied, there has been no study on the usage of visual features, such as facial expressions and eye gaze. To build an automated speaking proficiency scoring system using multi-modal features, we first constructed an online video interview dataset of 210 Japanese English-learners with annotations of their speaking proficiency. We then examined two approaches for incorporating visual features and compared the effectiveness of each modality. Results show the end-to-end approach with deep neural networks achieves a higher correlation with human scoring than one with handcrafted features. Modalities are effective in the order of lexical, acoustic, and visual features.
Hierarchical segment-channel attention network for explainable multichannel signal classification

Multichannel signal data collected from multiple sensors are widely used to monitor the status of various mechanical systems. Recently, deep neural networks have been successfully applied to multichannel signal data analysis because of their capability to learn discriminative features with minimum feature engineering. However, the latest deep neural networks for multichannel signal analysis lack explainability, which is essential for post hoc analysis in various fields. In this study, we propose an explainable neural network for the multichannel signal classification task. The proposed method is equipped with two levels of attention mechanisms –at the segment and channel levels– encouraging the model to focus on important parts in discriminating the status of a system. The derived attention probabilities facilitate interpretation of network behavior and thus can support post hoc analysis. To demonstrate the practicality and applicability of the proposed method, we conducted experiments on both simulated and real-world automobile data. The results confirmed that the proposed method is capable of accurately classifying multichannel signals and correctly identifying the critical segments and channels.
Clustering online handwritten mathematical expressions

To help human markers mark many answers in the form of online handwritten mathematical expressions (OHMEs), this paper proposes bag-of-features for clustering OHMEs. It consists of six levels of features from low-level pattern features to high-level symbolic and structural features obtained from a state-of-the-art OHME recognizer. Then, it introduces distance-based representation (DbR) to reduce the dimensionality of our proposed feature spaces. Moreover, it presents a method for combining the proposed features to improve the performance. Experiments using the k-means++ algorithm are conducted on a set of 3,150 OHMEs (Dset_50) and an answer dataset (Dset_Mix) of 200 OHMEs intermixed between real patterns and synthesized patterns for each of 10 questions. When the number of clusters is set as the true number of categories, the best purity around 0.99 is produced by bag-of-symbols with DbR for Dset_50, which is better than state-of-the-art methods for clustering offline patterns converted from their OHMEs. The combination of both low-level and high-level features with DbR achieves a purity of around 0.777, increases to more than 0.90 and reduce the marking cost by more than 0.35 point than manually marking OHME answers by adjusting the number of clusters for Dset_Mix.
An extended context-based entropy hybrid modeling for image compression

Recently deep learning has been introduced to the field of image compression. In this paper, we present a hybrid coding framework that combines entropy coding, deep learning, and traditional coding framework. In the base layer of the encoding, we use convolutional neural networks to learn the latent representation and importance map of the original image respectively. The importance map is then used to guide the bit allocation of the latent representation. A context model is also developed to help the entropy coding after the masked quantization. Another network is used to get a coarse reconstruction of the image in the base layer. The residual between the input and the coarse reconstruction is then obtained and encoded by the traditional BPG codec as the enhancement layer of the bit stream. We only need to train a basic model and the proposed scheme can realize image compression at different bit rates, thanks to the use of the traditional codec. Experimental results using the Kodak, Urban100 and BSD100 datasets show that the proposed scheme outperforms many deep learning-based methods and traditional codecs including BPG in MS-SSIM metric across a wide range of bit rates. It also exceeds some latest hybrid schemes in RGB444 domain on Kodak dataset in both PSNR and MS-SSIM metrics.
Deep Colormap Extraction From Visualizations

This article presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of ∼ 64K visualizations that cover a wide variety of data distributions, chart types, and colormaps. The network adopts an atrous spatial pyramid pooling module to capture color features at multiple scales in the input color histograms. We then classify the predicted colormap as discrete or continuous, and refine the predicted colormap based on its color histogram. Quantitative comparisons to existing methods show the superior performance of our approach on both synthetic and real-world visualizations. We further demonstrate the utility of our method with two use cases, i.e., color transfer and color remapping.
Hierarchical evolutionary classification framework for human action recognition using sparse dictionary optimization

Human action recognition using wearable sensors play a remarkable role in the province of Human-centric Computing. This paper accords a novel sparse representation based hierarchical evolutionary framework for classifying human activities. The main objective of this research is to propose a model for human action recognition that produces superior recognition results. This framework employs data from two inertial sensors namely an accelerometer and a gyroscope. Features like time and frequency-domain features were utilized in this work. This framework operates at multiple levels in the hierarchy, wherein, the output of one level is given as input to the next level in the hierarchy. A novel algorithm for deducing the hierarchical structure based on the input data called Hierarchical Architecture Design (HAD) algorithm is presented. We have also presented a novel Sparse Dictionary Optimization (SDO) algorithm for generating dictionaries that aid in the efficacious sparse representation-based classification. Finally, action recognition is done using the proposed Sparse Representation based Hierarchical (SRH) classifier. The performance analysis of the proposed system was conducted using University of Southern California Human Activity Dataset (USCHAD) and Human Activities and Postural Transitions (HAPT) Dataset. The proposed classification framework attained a very high F-score value of 98.01% and 93.51% for the USCHAD and HAPT datasets respectively.
Our (in)Secure Web: Understanding Update Behavior of Websites and Its Impact on Security

Software updates take an essential role in keeping IT environments secure. If service providers delay or do not install updates, it can cause unwanted security implications for their environments. This paper conducts a large-scale measurement study of the update behavior of websites and their utilized software stacks. Across 18 months, we analyze over 5.6M websites and 246 distinct client- and server-side software distributions. We found that almost all analyzed sites use outdated software. To understand the possible security implications of outdated software, we analyze the potential vulnerabilities that affect the utilized software. We show that software components are getting older and more vulnerable because they are not updated. We find that 95 % of the analyzed websites use at least one product for which a vulnerability existed .
Constructing of Semantically Dependent Patterns Based on SpaCy and StanfordNLP Libraries

The current stage of the tools development for processing Russian-language texts is associated with a weak elaboration of algorithms for identifying entities and dividing sentences into semantic, logically justified parts. In particular, how to determine an actor, actions performed by him/her, and the object(s) over which these actions are performed in a sentence. It is important to understand that the NLP algorithm should be designed in such a way that would identify the listed elements of the sentence not by the principle of a pre-compiled dictionary and the coincidence of parts of the sentence with it, but on the basis of highlighting universal dependencies. To this end, the team of authors have developed and tested algorithms based on the design of patterns universally describing various parts of a sentence: an actor, his/her actions and objects that these actions are directed to. To solve this problem, authors used the tools for parsing sentences into parts of speech and their dependencies (StanfordNLP); processing dependencies and identifying the tree structure of the sentence.
SinergyCloud: A simulator for evaluation of energy consumption in data centers and hybrid clouds

One of the significant limitations in the evaluation of hybrid clouds is the difficulty in validating new solutions by their deployment in real systems since replication of tests on large environments is usually highly expensive. To overcome these limitations, simulators have become popular for conducting preliminary tests. However, there is still a lack of simulation tools with easy-to-use code that allows handling diverse cloud scenarios and the simulation of energy consumption of all devices (hosts, switches, routers, and storage). To fill this gap, the simulator SinergyCloud has been developed to evaluate data centers in hybrid clouds. SinergyCloud allows evaluating diverse cloud scenarios, including energy consumption, workflow makespan, the completion time of tasks, and migrations of virtual machines, with a fine granularity of abstraction. SinergyCloud can handle the simulation of hybrid clouds with multiple data centers composed of hundreds of thousands of devices. Moreover, it is a Java-based, event-driven, and packet-level simulator, having a less steep learning curve than do other simulators. To show the feasibility of SinergyCloud, we performed accuracy and scalability analyses. Metrics values obtained by another simulator had less than 1% difference compared to SinergyCloud, demonstrating its accuracy. In terms of scalability, a scenario with 10,000 hosts was simulated in about 7 h using a typical personal computer. A comprehensive analysis of algorithms for the scheduling of virtual machines in a hybrid cloud is also presented, showing how to perform various evaluations.
“SME executives’ perceptions and the information security preparedness model”

Purpose Information security has increasingly been in the headlines as data breaches continue to occur at alarming rates. This paper aims to propose an Information Security Preparedness Model that was developed to examine how SME executives’ perceptions of security importance, implementation challenges and external influences impact their awareness and commitment to security preparedness. Design/methodology/approach Funded by the Department of Justice, a national survey of SME executives’ perceptions of information security preparedness was conducted. Using PLS-SEM, the survey responses were used to test the proposed Information Security Preparedness Model. Findings The results indicate that as perceptions of security importance and external influences increase, SME executives’ awareness and commitment to information security also increases. In addition, as implementation challenges increase, awareness and commitment to information security decreases. Finally, as security importance and awareness and commitment to information security increases, executives’ perception of security preparedness also increases. Research limitations/implications Executive perceptions of information security were measured and not the actual level of security. Further research that examines the agreement between executive perceptions and the true state of information security within the organization is warranted. Originality/value Prior information security studies using Roger’s (1975, 1983) Protection Motivation Theory have produced mixed results. This paper develops and tests the Information Security Preparedness Model to more fully explain SME executive’s perceptions of information security.