Reviewerly/exHarmony huggingface dataset:
Recognition of Signature Using Neural Network and Euclidean Distance for Bank Cheque Automation

Handwritten signature recognition plays significant role in automatic document verification system in particularly bank cheque authorization. The proposed method focuses on A novel technique for offline signature recognition approach for bank cheque based on zonal features and regional features. These combined features are used to find genuinety of signature using Euclidean distance as a metric. Extensive experiments are carried out to exhibit the success of the recommended approach.
Pedestrian Re-Identification Monitoring System Based on Deep Convolutional Neural Network

The gradual establishment of large-scale distributed camera networks and the rapid development of ''Internet +'' have resulted in the recent popularization of massive video surveillance systems.As pedestrians are the key monitoring targets in video surveillance systems, many studies are focusing on pedestrian re-identification monitoring algorithms across cameras.At present, the pedestrian re-identification model is not only faced with the difficulty of training the network model due to the huge quantity difference between different types of training samples, but also needs to reduce the impact of the large difference in visual performance on the model identification accuracy.To solve these difficulties, this paper proposed a deep learning model and designed a system based on a deep convolutional neural network for pedestrian re-identification.In particular, we determined the difference between the system input neighborhoods in order to derive the local relationship between the two input images, thus reducing the effects of illumination and perspective.Furthermore, we employed focal loss to solve the phenomenon of sample imbalance in the pedestrian re-identification process in order to enhance the actual application potential of the model.The proposed method was implemented in our developed end-to-end monitoring system for pedestrian re-identification.The hardware component of the system design framework was composed of a digital matrix, streaming media storage server and a network high-speed dome, with the ability to extend to additional tasks in the future.Our approach reduces the effects of data imbalances and visual performance differences, with a score of 76.0% for rank-1 and 99.5% for rank-20 on large data sets (CUHK03), which is not only a significant improvement over the previous IDLA, but also superior to other existing approaches.
Medical and health systems

research-article Medical and health systems Share on Author: Daniel Sonntag View Profile Authors Info & Claims The Handbook of Multimodal-Multisensor Interfaces: Language Processing, Software, Commercialization, and Emerging DirectionsJuly 2019 Pages 423–476https://doi.org/10.1145/3233795.3233808Published:01 July 2019 4citation131DownloadsMetricsTotal Citations4Total Downloads131Last 12 Months10Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Corpus of Multimodal Interaction for Collaborative Planning

As autonomous systems become more commonplace, we need a way to easily and naturally communicate to them our goals and collaboratively come up with a plan on how to achieve these goals. To this end, we conducted a Wizard of Oz study to gather data and investigate the way operators would collaboratively make plans via a conversational ‘planning assistant’ for remote autonomous systems. We present here a corpus of 22 dialogs from expert operators, which can be used to train such a system. Data analysis shows that multimodality is key to successful interaction, measured both quantitatively and qualitatively via user feedback.
Situation Awareness and Conflict Resolution in Smart Home with Multiple Users

There are many research efforts on situation awareness in smart home with a single user. However, a harder problem is situation awareness when multiple users are present. We investigate the problem of recognizing multiuser activities using wearable devices in a home environment. The objective is to provide situation awareness so that a smart home can respond to the needs of its residents based on their activities. An important part of the research is to detect whether there is any service conflict resulting from the ongoing activities. If so, a conflict resolution algorithm needs to be applied so that an acceptable service by multiple users can be achieved. To verify both the activity detection and the conflict resolution algorithms, two experimental systems are constructed with different architecture alternatives to evaluate the algorithms and decide the most efficient implementation design.
Semi-supervised domain adaptation and collaborative deep learning for dual sentiment analysis

Sentiment classification is a much needed topic that has grabbed the interest of many researchers. Especially, classification of data from customer reviews on various commercial products has been an important source of research. A model called supervised dual sentiment analysis is used to handle the polarity shift problem that occurs in sentiment classification. Labeling the reviews is a tedious and time consuming process. Even, a classifier trained on one domain may not perform well on the other domain. To overcome these limitations, in this paper we propose semi-supervised domain adaptive dual sentiment analysis that train a domain independent classifier with few labeled data. Reviews are of varying length and hence, classification is more accurate if long term dependency between the words is considered. We propose a collaborative deep learning approach to the dual sentiment analysis. Long short term memory (LSTM) recurrent neural network is used to handle sequence prediction to classify the reviews more accurately. LSTM takes more time to extract features from the reviews. Convolution neural network is used before LSTM layers to extract features resulting in the reduction of training time compared to LSTM alone.
Construction, visualization and application of neutral zone classifiers

When the potential for making accurate classifications with a statistical classifier is limited, a neutral zone classifier can be constructed by adding a no-decision option as a classification outcome. We show how a neutral zone classifier can be constructed from a receiving operating characteristic (ROC) curve. We extend the ROC curve graphic to highlight important performance characteristics of a neutral zone classifier. Additional utility of neutral zone classifiers is illustrated by showing how they can be incorporated into the first stage of a two-stage classification process. At the first stage, a classification is attempted from easily collected or inexpensive features. If the classification falls into the neutral zone, additional relatively more expensive features can be obtained and used to make a definitive classification at the second stage. The methods discussed in the paper are illustrated with an application pertaining to prostate cancer.
An Ensemble Learning-Based Language Identification System

Speech recognition from multilingual voice signals is a challenging task. It is essential to distinguish the language of the spoken phrases prior to recognizing them. This is known as automatic language identification. Automatic language identification is very much important for multilingual countries like India as people often use more than a single language while talking. In this paper, a language identification system for seven different languages from the IIIT-H Indic Speech Databases is presented. We have used line spectral frequency-based features for modelling the languages. The highest accuracy of 99.71% has been obtained with ensemble learning-based classification technique.
Modeling Behavioral Aspects of Social Media Discourse for Moral Classification

Political discourse on social media microblogs, specifically Twitter, has become an undeniable part of mainstream U.S. politics. Given the length constraint of tweets, politicians must carefully word their statements to ensure their message is understood by their intended audience. This constraint often eliminates the context of the tweet, making automatic analysis of social media political discourse a difficult task. To overcome this challenge, we propose simultaneous modeling of high-level abstractions of political language, such as political slogans and framing strategies, with abstractions of how politicians behave on Twitter. These behavioral abstractions can be further leveraged as forms of supervision in order to increase prediction accuracy, while reducing the burden of annotation. In this work, we use Probabilistic Soft Logic (PSL) to build relational models to capture the similarities in language and behavior that obfuscate political messages on Twitter. When combined, these descriptors reveal the moral foundations underlying the discourse of U.S. politicians online, across differing governing administrations, showing how party talking points remain cohesive or change over time.
Runtime Verification and Vulnerability Testing of Smart Contracts

Smart contracts are programs that help in automating agreement between multiple parties involving no external trusted authority. Since smart contracts deal with millions of dollars worth of virtual coins, it is important to ensure that they execute correctly and are free from vulnerabilities. This work focuses on smart contracts in Ethereum blockchain, the most utilized platform for smart contracts so far. Our emphasis is mainly on two core areas. One involves the runtime verification of ERC20 tokens using K framework and the other involves the comparison of tools available for detecting the vulnerabilities in smart contract. The six core functions of ERC20, namely allowance(), approve(), total-supply(), balanceof(), transferfrom() and transfer() were considered for runtime verification. ERC20 contracts were tested with ERC20 standard and the results showed that only 30% in allowance() function, 50% in transferfrom() function, and 90% in transfer() function, were compliant to the standard. The other focus area involves the comparison of existing tool that could identify vulnerabilities in smart contract. Five tools were taken for the comparison, namely Oyente, Securify, Remix, Smartcheck and Mythril and were tested against 15 different vulnerabilities. Out of the 5 tools taken, Smartcheck was found to detect the highest number of vulnerabilities.
Monitoring open multi-agent systems: An aspect-oriented programming based approach

In Open Multi-Agent Systems (Open MAS), no central authority can control all the agents. The agents share some features like: heterogeneity, different aims and objectives, autonomy, self-interested that conflicts individual goals and limited trust. Also, agents, in Open MAS, can enter and leave a s ystem at any time. In order to ensure that the system carries out its goals, it must be observed and controlled. For that, it is necessary to define monitoring mechanisms for: (1) understanding and analyzing the system behavior, and (2) helping developers to review and examine, at run-time, agents’ communications and other different events (agents’ lifecycle, agent requested roles, performed actions by a given agent, etc.) generated during the system execution. In this paper, an AOP-based approach for monitoring Open MAS is proposed. The proposed approach is supported by RT-MTOMAS (Real-Time Monitoring Tool for Open Multi-Agent Systems), a new tool we developed for dealing with Open MAS monitoring issues. RT-MTOMAS provides, also, a behavioral model in order to illustrate agents’ interactions. The developed tool is illustrated through a concrete case study. We outlined its integration with MaDKit agent platform.
Towards a standardized annotation of rhyme judgments in Chinese historical phonology (and beyond)

Although rhyme analysis plays a crucial role in the reconstruction of Old Chinese phonology, the field has not yet developed a standardized annotation framework for rhyme judgments applied to Ancient Chinese texts. Building on initial attempts to standardize crosslinguistic data for the purpose of historical and typological language comparison (as part ofthe Cross-Linguistic Data Formats initiative), we present a proposal for consistent and transparent rhyme annotation. This proposal allows scholars to annotate the rhymes they identifyin historical texts in such a way that the judgments can be analyzed with computational toolsas well as conveniently inspected by scholars. Our framework is accompanied by softwaretools and exemplary datasets, which were annotated by various scholars, and reflect not onlyChinese, but also contemporary poetry in different languages. In the paper, we present theframework and also point to caveats and current insufficiencies in annotation. In doing so,we hope to inspire more scholars working on Old Chinese reconstruction to share theirjudgments, allowing others working in the field to improve, revise, and analyze them.
A Dual Approach to Verify and Train Deep Networks

This paper addressed the problem of formally verifying desirable properties of neural networks, i.e., obtaining provable guarantees that neural networks satisfy specifications relating their inputs and outputs (e.g., robustness to bounded norm adversarial perturbations). Most previous work on this topic was limited in its applicability by the size of the network, network architecture and the complexity of properties to be verified. In contrast, our framework applies to a general class of activation functions and specifications. We formulate verification as an optimization problem (seeking to find the largest violation of the specification) and solve a Lagrangian relaxation of the optimization problem to obtain an upper bound on the worst case violation of the specification being verified. Our approach is anytime, i.e., it can be stopped at any time and a valid bound on the maximum violation can be obtained. Finally, we highlight how this approach can be used to train models that are amenable to verification.
Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced pairwise conditional random field

Recently, fully convolutional network (FCN) has been successfully used to locate spliced regions in synthesized images. However, all the existing FCN-based algorithms use real-valued FCN to process each channel separately. As a consequence, they fail to capture the inherent correlation between color channels and the integrity of three channels. So, in this paper, quaternion fully convolutional network (QFCN) is proposed to generalize FCN to quaternion domain by replacing real-valued conventional blocks in FCN with quaternion conventional blocks. In addition, a new color image splicing localization algorithm is proposed by combining QFCNs and superpixel (SP)-enhanced pairwise conditional random field (CRF). QFCNs consider three different versions (QFCN32, QFCN16, and QFCN8) with different up-sampling layers. The SP-enhanced pairwise CRF is used to refine the results of QFCNs. Experimental results on three publicly available datasets demonstrate that the proposed algorithm outperforms the existing algorithms including some conventional algorithms and some deep learning-based algorithms.
Dynamic Visibility‐Driven Molecular Surfaces

Abstract Molecular surface representations are an important tool for the visual analysis of molecular structure and function. In this paper, we present a novel method for the visualization of dynamic molecular surfaces based on the Gaussian model. In contrast to previous approaches, our technique does not rely on the construction of intermediate representations such as grids or triangulated surfaces. Instead, it operates entirely in image space, which enables us to exploit visibility information to efficiently skip unnecessary computations. With this visibility‐driven approach, we can visualize dynamic high‐quality surfaces for molecules consisting of millions of atoms. Our approach requires no preprocessing, allows for the interactive adjustment of all properties and parameters, and is significantly faster than previous approaches, while providing superior quality.
A scene recognition algorithm based on deep residual network

Scene recognition is quite important in the field of robotics and computer vision. Aiming at providing high performance and universality of feature extraction, a convolutional neural network-based scene recognition model entitled Scene-RecNet is proposed. To reduce parameter space and improve the feature quality, deep residual network is introduced as the feature extractor. A feature adjustment layer composed of a convolutional layer and a fully connected layer is added after the feature extractor to further synthesize and compress the extracted features. Migration learning-based ‘pre-training and fine-tuning’ mode is used to train Scene-RecNet. The feature extractor is pre-trained by ImageNet, and the overall network performance is fine-tuned on specific data sets. Experiments show that comparing with other algorithms, the features obtained by Scene-RecNet have high generality and robustness, and Scene-RecNet can provide better scene classification accuracy rate.
Engaging Citizens with Urban Planning Using City Blocks, a Mixed Reality Design and Visualisation Platform

This paper looks at city scale visualisation during concept generation as a method to engage citizens with city planning. A Virtual Reality based platform is proposed, prototyped and piloted. The platform, City Blocks, utilises an abstracted physical design kit and a generated, explorable digital twin to visualise and analyse concepts. It is found that through the Virtual Reality based platform, later design stage visualisation of ideas can be brought to concept development and that citizens are able to reason and refine designs via an abstracted physical twin. The paper presents an analysis of the 149 city designs, including a comparison between citizens and experienced urban planners.
Enhancing scene perception using a multispectral fusion of visible–near‐infrared image pair

In this study, a method for fusing of visible (or standard RGB) with near-infrared (NIR) image pair for enhancing a hazy image is proposed. Better image enhancement in terms of contrast, sharpness, increased perception is realised by combining the components from both the spectra. While there are a number of applications that use NIR images, very few combine RGB and NIR information of the same scene taken from two separate imaging devices. Although NIR images are greyscaled in nature, they have intrinsic properties desirable in both colour and visible imagery of a number of scenes. Instances are increased the contrast between sky and clouds; shadowed and non-shadowed regions; and increased optical depth. These features are in most cases missed by a standard RGB camera and therefore, a fusion of such RGB–NIR image pairs is highly beneficial. The scheme for fusing these image pairs is realised by using a novel, fuzzy clustering algorithm along with wavelet transform so that not only contrast and sharpness are enhanced, but also the chromaticity of the original RGB image is retained. The proposed technique is well demonstrated for various outdoor scenes, and better results are obtained when compared against some of the state-of-the-art techniques proposed recently.
Selection-Based Subpixel-Shifted Images Super-Resolution

The subpixel-shifted (SPS) images acquisition method based on imaging system has the limitations of complex structure, difficult production and high cost.Therefore, this paper proposes an image super-resolution reconstruction method based on registration.In the first phase, the registration algorithm is used to select the SPS images.In order to improve the accuracy of the registration algorithm, a registration algorithm combining SIFT-FLANN and misregistration points elimination (SFME) is proposed.In the second phase, an interpolation of nonuniformly spaced samples based on pixel gray correction is proposed to get the high resolution (HR) image.Experiments show that the images selection method can obtain higher-precision SPS images, and the reconstruction method can reconstruct HR image with better visual and higher spatial resolution.Finally, the image magnification factor and the number of SPS images used in the reconstruction process are analyzed to describe the practical application value of the methods proposed in this paper.
Achilles’ heel of plug-and-Play software architectures: a grounded theory based approach

Through a set of well-defined interfaces, plug-and-play architectures enable additional functionalities to be added or removed from a system at its runtime. However, plug-ins can also increase the application’s attack surface or introduce untrusted behavior into the system. In this paper, we (1) use a grounded theory-based approach to conduct an empirical study of common vulnerabilities in plug-and-play architectures; (2) conduct a systematic literature survey and evaluate the extent that the results of the empirical study are novel or supported by the literature; (3) evaluate the practicality of the findings by interviewing practitioners with several years of experience in plug-and-play systems. By analyzing Chromium, Thunderbird, Firefox, Pidgin, WordPress, Apache OfBiz, and OpenMRS, we found a total of 303 vulnerabilities rooted in extensibility design decisions and observed that these plugin-related vulnerabilities were caused by 16 different types of vulnerabilities. Out of these 16 vulnerability types we identified 19 mitigation procedures for fixing them. The literature review supported 12 vulnerability types and 8 mitigation techniques discovered in our empirical study, and indicated that 5 mitigation techniques were not covered in our empirical study. Furthermore, it indicated that 4 vulnerability types and 11 mitigation techniques discovered in our empirical study were not covered in the literature. The interviews with practitioners confirmed the relevance of the findings and highlighted ways that the results of this empirical study can have an impact in practice.
BEARZ Attack FALCON: Implementation Attacks with Countermeasures on the FALCON Signature Scheme

Post-quantum cryptography is an important and growing area of research due to the threat of quantum computers, as recognised by the National Institute of Standards and Technology (NIST) recent call for standardisation. FALCON is a lattice-based signature candidate submitted to NIST, which has good performance but lacks in research with respect to implementation attacks and resistance. This research proposes the first fault attack analysis of FALCON and finds its lattice trapdoor sampler is as vulnerable to fault attacks as the GPV sampler used in alternative signature schemes. We simulate the post-processing component of this fault attack and achieve a 100% success rate at retrieving the private-key. This research then proposes an evaluation of countermeasures to prevent this fault attack and timing attacks of FALCON. We provide cost evaluations on the overheads of the proposed countermeasures which shows that FALCON has only up to 30% deterioration in performance of its key generation, and only 5% in signing, compared to runtimes without countermeasures.