Reviewerly/exHarmony huggingface dataset:
Character Recognition from Handwritten Image Using Convolutional Neural Networks

Character recognition from handwritten images is of great interest in the pattern recognition research community for their good application in many areas. To implement the system, it requires two steps, viz., feature extraction followed by character recognition based on any classification algorithm. Convolutional neural network (CNN) is an excellent feature extractor and classifier. The performance of a CNN for a particular application depends on the parameters used in the network. In this article, a CNN is implemented for the MNIST dataset with appropriate parameters for training and testing the system. The system provides accuracy up to 98.85%, which is better with respect to others. It also takes very low amount of time for training the system.
A Joint Deep Recommendation Framework for Location-Based Social Networks

Location-based social networks, such as Yelp and Tripadvisor, which allow users to share experiences about visited locations with their friends, have gained increasing popularity in recent years. However, as more locations become available, the need for accurate systems able to present personalized suggestions arises. By providing such service, point-of-interest recommender systems have attracted much interest from different societies, leading to improved methods and techniques. Deep learning provides an exciting opportunity to further enhance these systems, by utilizing additional data to understand users’ preferences better. In this work we propose Textual and Contextual Embedding-based Neural Recommender (TCENR), a deep framework that employs contextual data, such as users’ social networks and locations’ geo-spatial data, along with textual reviews. To make best use of these inputs, we utilize multiple types of deep neural networks that are best suited for each type of data. TCENR adopts the popular multilayer perceptrons to analyze historical activities in the system, while the learning of textual reviews is achieved using two variations of the suggested framework. One is based on convolutional neural networks to extract meaningful data from textual reviews, and the other employs recurrent neural networks. Our proposed network is evaluated over the Yelp dataset and found to outperform multiple state-of-the-art baselines in terms of accuracy, mean squared error, precision, and recall. In addition, we provide further insight into the design selections and hyperparameters of our recommender system, hoping to shed light on the benefit of deep learning for location-based social network recommendation.
Learning cross‐modal visual‐tactile representation using ensembled generative adversarial networks

In this study, the authors study a deep learning model that can convert vision into tactile information, so that different texture images can be fed back to the tactile signal close to the real tactile sensation after training and learning. This study focuses on the classification of different image visual information and its corresponding tactile feedback output mode. A training model of ensembled generative adversarial networks is proposed, which has the characteristics of simple training and stable efficiency of the result. At the same time, compared with the previous methods of judging the tactile output, in addition to subjective human perception, this study also provides an objective and quantitative evaluation system to verify the performance of the model. The experimental results show that the learning model can transform the visual information of the image into the tactile information, which is close to the real tactile sensation, and also verify the scientificity of the tactile evaluation method.
Break-glass Encryption

“Break-glass” is a term used in IT healthcare systems to denote an emergency access to private information without having the credentials to do so. In this paper we introduce the concept of break-glass encryption for cloud storage, where the security of the ciphertexts – stored on a cloud – can be violated exactly once, for emergency circumstances, in a way that is detectable and without relying on a trusted party. Detectability is the crucial property here: if a cloud breaks glass without permission from the legitimate user, the latter should detect it and have a proof of such violation. However, if the break-glass procedure is invoked by the legitimate user, then semantic security must still hold and the cloud will learn nothing. Distinguishing that a break-glass is requested by the legitimate party is also challenging in absence of secrets. In this paper, we provide a formalization of break-glass encryption and a secure instantiation using hardware tokens. Our construction aims to be a feasibility result and is admittedly impractical. Whether hardware tokens are necessary to achieve this security notion and whether more practical solutions can be devised are interesting open questions.
Assessing visual STM in infants and adults: eye movements and pupil dynamics reflect memory maintenance

Though previous work has demonstrated substantial short-term memory (STM) development in the first year of life (e.g., Ross-Sheehy, S., Oakes, L. M., & Luck, S. J. 2003. The development of visual short-term memory capacity in Infants. Child Development, 74(6), 1807–1822. doi:10.1046/j.1467-8624.2003.00639.x), limitations in infant-based procedures make it difficult to assess the continuity of STM mechanisms from infancy though adulthood. The goal of the current work is to present data collected from both infants and adults using a novel STM assessment task. Eye-tracking was used to capture gaze and pupillometry in 5-, 8- and 11-month-old infants and adults (n = 176). Infant data replicate and extend previous findings demonstrating clear evidence of STM improvement from 5- to 11-months-of-age. Analysis of adult data reveal qualitatively similar patterns of responding, and additionally reveal several behavioural markers of STM accuracy including pupil dilation, saccade count, and switching. These results suggest that change-detection tasks recruit similar memory mechanisms in both infants and adults, and further suggest that visual dynamics may provide important new continuous measures of STM from infancy through adulthood.
Detection of Breast Cancer Using Machine Learning Support Vector Machine Algorithm

A woman majorly suffers due to breast cancer which is due to hormone imbalance. It leads to huge death in recent years. Early detection of the breast cancer is more important to prevent human lives. Image Processing plays an important to classify and detect the same. So this paper proposes machine learning based cancer classification using support vector machine with Wisconsin breast cancer data set.
Toward the Use of Upper-Level Ontologies for Semantically Interoperable Systems: An Emergency Management Use Case

In the context of globalization and knowledge management, information technologies require an ample need of unprecedented levels of data exchange and sharing to allow collaboration between heterogeneous systems. Yet, understanding the semantics of the exchanged data is one of the major challenges. Semantic interoperability can be ensured by capturing knowledge from diverse sources by using ontologies and align these latter by using upper-level ontologies to come up with a common shared vocabulary. In this paper, we aim in one hand to investigate the role of upper-level ontologies as a mean for enabling the formalization and integration of heterogeneous sources of information and how it may support interoperability of systems. On the other hand, we present several upper-level ontologies and how we chose and then used basic formal ontology (BFO) as an upper-level ontology and common core ontology (CCO) as a mid-level ontology to develop a modular ontology that defines emergency responders’ knowledge starting from firefighters’ module for a solution to the semantic interoperability problem in emergency management.
Fractal dimension and its translation into a model of gold spatial proxy

Unraveling the spatial distribution of mineral deposits is an essential part of exploration and important for developing a better understanding of ore-forming processes. In this study, gold assay results (in g/t) of twelve boreholes completed from Mysore Block and eleven boreholes completed from Nabhapur North Block, Central Lode System, Gadag Gold Field (GGF), India, were analyzed using fractal analysis. Both the fractal correlation integral and rescaled range analysis were used to measure the fractal dimension from the gold-concentration values. The fractal dimension values obtained from the fractal correlation integral were used to better constrain the possible auriferous fluid-flow pathways, whilst results obtained from the rescaled range analysis were used to describe the behavior of auriferous veins across the boreholes. The fractal dimension values were found to vary from 0.014 to 3.0, with the higher values taken to indicate the presence of well-developed interconnected fault-fracture networks representing steep permeable fluid-flow pathways. The lower fractal dimension values were taken to indicate unfavorable fluid-flow pathways. In other words, higher-grade gold ores were deposited along the structures represented by steep fractal gradients. Hurst exponent (H) values obtained from the rescaled range analysis were found to vary from 0.50 to 0.96, satisfying the condition 0.5 < H < 1. In summation, the mineralized veins displaying fractal behavior are persistent across the boreholes from both the Mysore and Nabhapur gold deposits, which suggests these deposits are targets for gold exploration.
KMI-Coling at SemEval-2019 Task 6: Exploring N-grams for Offensive Language detection

In this paper, we present the system description of Offensive language detection tool which is developed by the KMI_Coling under the OffensEval Shared task. The OffensEval Shared Task was conducted in SemEval 2019 workshop. To develop the system, we have explored n-grams up to 8-gram and trained three different namely A, B and C systems for three different subtasks within the OffensEval task which achieves 79.76%, 87.91% and 44.37% accuracy respectively. The task was completed using the dataset provided to us by the OffensEval organisers was the part of OLID dataset. It consists of 13,240 tweets extracted from twitter and were annotated at three levels using crowdsourcing.
The Financial Market Effects of Cryptocurrency Energy Usage

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Cryptography in Blockchain

There is a vast multitude of different hashing and cryptography algorithms out there, and more are developed every year. Blockchain technology, and crypto-currencies specifically, raised many concerns in different institutions. Users want to be sure that their assets are adequately protected by the best available algorithms, but some institutions around the world are opposed to that, or enforce their own standards. Ideally, every platform would allow its users to choose any algorithm they want, but in most situations this is, unfortunately, not the case. In this paper we present a review of the most popular blockchain platforms and the options they provide, and compare their cryptographic strength.
Refining the Threat Calculus of Technology Threat Avoidance Theory

The number of people using fitness devices and mobile health applications creates unprecedented amounts of health-related fitness data. In the United States, healthcare regulations do not consider the data that these devices collect as protected health information when no covered entity is involved; therefore, the law does not provide such data with the same legal protections as an individual’s health records. Thus, users must ensure that they keep their data safe from potential data breaches and malicious activities. In this study, we analyze users’ motivations to implement safeguards to protect their private health-related fitness data. To test user motivation, we issued wearable activity tracking devices and an associated online health fitness data account to students. We instructed the students about how to use the fitness device and how the device connected to the user’s phone and Web-based application. We then had them complete a survey to determine how they form their threat perceptions and other factors influencing their avoidance motivations for computer-security incidents. With the exception of safeguard cost and privacy concerns, results support a revised threat calculus in the TTAT model and the original model constructs.
Topic Shift Detection in Online Discussions using Structural Context

Topic shift occurs frequently in online discussions, and automatically detecting topic shift can help to better capture the main clues and obtain relevant answers from large number of comments. Traditional topic-shift detection methods calculate text similarity and have limited success because they ignore semantic relatedness. In this paper, we propose a new topic shift detection model that uses conversational structure to enrich the context information and word embedding to build the semantic associations for each comment - post pair. Experiments show that the proposed model leads to better performance in terms of precision, recall, and F1 score.
Where the User Does Look When Reading Phishing Mails – An Eye-Tracking Study

To detect phishing mails, various strategies based on a reliable cryptography-based security framework exist. Nevertheless, the user themselves still provide a greater opportunity for phishing attacks. Therefore, it is crucial to understand how the user deals with phishing mails when confronted with them. This study limits itself to visual stimuli of phishing mails and therefore uses an eye-tracking procedure to determine the gaze behavior. Twenty-one different mails were used for this experiment, of which fourteen were phishing mails. The task of the users was to decide whether it was a phishing mail or a real mail. For the evaluation, the individual mails were provided with Areas of Interest (AOIs). This is similar to the usual components of a mail that would be attachment, body, footer, header and signature. Thereafter, three artificial groups were formed. There was one group with a low score of correct answers, one with a middle score and one with a high score. These three groups were then compared and showed differences in processing time. This led to the assumption that knowledge and time are two important factors in recognizing phishing mails.
Lightly-supervised Representation Learning with Global Interpretability

We propose a lightly-supervised approach for information extraction, in particular named entity classification, which combines the benefits of traditional bootstrapping, i.e., use of limited annotations and interpretability of extraction patterns, with the robust learning approaches proposed in representation learning. Our algorithm iteratively learns custom embeddings for both the multi-word entities to be extracted and the patterns that match them from a few example entities per category. We demonstrate that this representation-based approach outperforms three other state-of-the-art bootstrapping approaches on two datasets: CoNLL-2003 and OntoNotes. Additionally, using these embeddings, our approach outputs a globally-interpretable model consisting of a decision list, by ranking patterns based on their proximity to the average entity embedding in a given class. We show that this interpretable model performs close to our complete bootstrapping model, proving that representation learning can be used to produce interpretable models with small loss in performance. This decision list can be edited by human experts to mitigate some of that loss and in some cases outperform the original model.
A Comparative Study of Different CNN Models in City Detection Using Landmark Images

Navigation assistance using different local Landmarks is an emerging research field now-a-days. Landmark images taken from different camera angles are being vividly used alongside the GPS (Global Positioning System) data to determine the location of the user and help user with navigation. However, determining the location of the user by recognizing the landmarks from different images, without the help of GPS, can be a worthy research trend to explore. Hence, in this paper, we have conducted a comparative study of 3 different popular CNN models, namely - Inception V3, MobileNet and ResNet50, and they have achieved an overall accuracy of 99.7%, 99.5% and 99.7% respectively while determining cities using landmark images.
The Gaussian Process Autoregressive Regression Model (GPAR)

Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have limited representational power. We present the Gaussian Process Autoregressive Regression (GPAR) model, a scalable multi-output GP model that is able to capture nonlinear, possibly input-varying, dependencies between outputs in a simple and tractable way: the product rule is used to decompose the joint distribution over the outputs into a set of conditionals, each of which is modelled by a standard GP. GPAR’s efficacy is demonstrated on a variety of synthetic and real-world problems, outperforming existing GP models and achieving state-of-the-art performance on established benchmarks.
Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively

Knowledge compilation techniques translate propositional theories into equivalent forms to increase their computational tractability. But, how should we best present these propositional theories to a human? We analyze the standard taxonomy of propositional theories for relative interpretability across three model domains: highway driving, emergency triage, and the chopsticks game. We generate decision-making agents which produce logical explanations for their actions and apply knowledge compilation to these explanations. Then, we evaluate how quickly, accurately, and confidently users comprehend the generated explanations. We find that domain, formula size, and negated logical connectives significantly affect comprehension while formula properties typically associated with interpretability are not strong predictors of human ability to comprehend the theory.
The Optical Subsystem for the Empty Containers Recognition and Sorting in a Reverse Vending Machine

A reverse vending machine (RVM) is a machine where people can return empty beverage containers like plastic bottles and cans for recycling taking back a deposit or refund amount. Reverse vending machines are a key part of container deposit systems in Europe and the United States. Waste recognition and sorting in RVM machines can be performed by any of the following procedures: by determining the container material (e.g. using the IR-spectrometer), by recognition of the container type by its shape, or by the barcode identification. These three basic control-procedures make any attempt of the fraud completely impossible. But at the same time, it makes the RVM too expensive. With the modern computer vision technologies, we can design another kind of efficient and non-expensive RVM having the same functionality using energy-efficient IoT MCUs. In this paper, some approaches in computer vision and image processing and their application to the problem of automatic recognition of empty recyclable containers (bottles and cans) and detecting fraud were considered. The list of the available methods and frameworks was shortened because SoC and IoT controllers have memory and computational restrictions. The RVM's task is the classification of the image inside the RVM by three possible classes: PET bottle, aluminum can or fraud (everything that doesn't match PET bottle or can), even if cans or bottles are twisted or jammed. Finally, the performance of image recognition procedures in Python and C ++ languages was analyzed and some methods of efficient image processing and RVM structure enhancements to achieve competitive advantages were proposed.
Associative Memories With Synaptic Delays

In this paper, we introduce a new concept of associative memories in which synaptic connections of the self-organizing neural network learn time delays between input sequence elements. Synaptic connections represent both the synaptic weights and expected delays between the network inputs. This property of synaptic connections facilitates recognition of time sequences and provides context-based associations between sequence elements. Characteristics of time delays are learned and are updated each time an input sequence is presented. There are no separate learning and testing modes typically used in other neural networks, as the network starts to predict the next input element as soon as there is no expected input signal. The network generates output signals useful for associative recall and prediction. These output signals depend on the presented input context and the knowledge stored in the graph. Such a mode of operation is preferred for the organization of episodic memories used to store the observed episodes and to recall them if a sufficient context is provided. The associative sequential recall is useful for the operation of working memory in a cognitive agent. Test results demonstrate that the network correctly recognizes the input sequences with variable delays and that it is more efficient than other recently developed sequential memory networks based on associative neurons.
Blind digital modulation classification for STBC‐OFDM system in presence of CFO and channels estimation errors

Here, the authors propose a robust blind digital modulation classification (BDMC) algorithm for space time block coding (STBC)-based MIMO-OFDM system in the presence of carrier frequency offset (CFO) and channel estimation errors. Previous papers published on the topic of modulation identification were limited to single-carrier systems operating over frequency-flat channels. The problem of joint channel and CFO estimation in conjunction with blind digital modulation classification for STBC-OFDM in frequency selective channel and in the presence of the impulsive noise has not been addressed before to the best of their knowledge. To cope with performance degradation of BDMC due to CFO and channels errors, they propose joint semi-blind CFO and channels estimation methods. Higher order statistics (HOS), used for feature extraction, are combined with pattern recognition methods to solve the modulation identification problem. The main contribution of their work is the development of estimators, the study of their impacts on the blind classification capability, and the use of simulations to demonstrate the superior performance of the proposed algorithms.
Robust Poisson Multi-Bernoulli Filter With Unknown Clutter Rate

In Bayesian multi-target tracking (MTT), knowledge of clutter intensity is required for effective multi-target state estimation. In this paper, we propose an online multi-target filter that can operate under background with unknown clutter intensity. Our solution is based on the Poisson multi-Bernoulli mixture (PMBM) filter that jointly estimating the multi-target state and clutter rate. The unknown clutter rate is modeled as Gamma distribution, consequently, the derived PMBM recursion that adapts for unknown clutter intensity remains closed. Moreover, we adopt a Gibbs sampler to find the finite number of global hypotheses, then the multi-Bernoulli mixture is approximated by a multi-Bernoulli distribution based on a simple fusion strategy. The derived Poison multi-Bernoulli (PMB) filter has a similar form with labeled multi-Bernoulli filter (LMB) but has a straightforward prediction step. Simulations conducted for linear-Gaussian models are presented to verify that the proposed algorithm can adapt to the background with unknown clutter intensity and yield reliable tracking performance.