Reviewerly/exHarmony huggingface dataset:
Platform for Studying Self-Repairing Auto-Corrections in Mobile Text Entry based on Brain Activity, Gaze, and Context

Auto-correction is a standard feature of mobile text entry. While the performance of state-of-the-art auto-correct methods is usually relatively high, any errors that occur are cumbersome to repair, interrupt the flow of text entry, and challenge the user's agency over the process. In this paper, we describe a system that aims to automatically identify and repair auto-correction errors. This system comprises a multi-modal classifier for detecting auto-correction errors from brain activity, eye gaze, and context information, as well as a strategy to repair such errors by replacing the erroneous correction or suggesting alternatives. We integrated both parts in a generic Android component and thus present a research platform for studying self-repairing end-to-end systems. To demonstrate its feasibility, we performed a user study to evaluate the classification performance and usability of our approach.
Acoustic and Visual Approaches to Adversarial Text Generation for Google Perspective

Google's Perspective API was introduced to help detect and classify toxic comments in online platforms. Adversarial machine learning attacks can decrease the effectiveness of Perspective in identifying toxic comments. We have shown in our previous study that by applying a semantic-based attack to a surrogate model trained with just 10,000 queries could produce adversarial examples which evade Perspective 25% of the time. In this paper, we propose two new approaches to generate adversarial text to evade Google's Perspective, one based on acoustic similarity and the other based on visual similarity. We tested the success rate of obfuscation in Google Perspective using the adversarial texts generated through the proposed approaches and showed that Google Perspective misclassified the generated texts 33% and 72.5% of the time for the visual-based and acoustic-based approaches, respectively. The study aims to broaden the understanding of adversarial text generation and to improve the robustness for online toxic comment detection for a safe online community.
Modeling USAR Maps for the Collection of Information on the State of the Environment

There is a problem associated with various natural and man-made emergencies. The consequences of which can be colossal destruction in densely populated territories. Therefore, USAR teams, which include rescue robots, need to act quickly. However, due to the unpredictability of the situation, rescuers are faced with a number of difficulties due to which expensive equipment can suffer. To prevent the latter and optimize the work of robots, it is necessary to conduct testing in an environment close to reality. For many reasons, this cannot be done in real life, so there is a need to recreate the danger situations in format USAR in a simulation environment, designed to develop algorithms for the interaction of robots with a simulated environment.
The Role of AR/ VR in an IoT connected digital enterprise for smart education

The Internet of Things (IoT) is a hot topic among IT and other fields such as Business, Applied Media. Augmented and Virtual reality (AR/ VR) hits a new dimension with the IoT's ability to connect and utilize digital information from various other physical devices. With the help of specialized software and hardware AR / VR overlays digital content seamlessly into the real world. With its unique user experience AR/ VR enables the entire gamut of functions like R&D, manufacturing, production and field services. The aspects of smart education that will help the educators and learners are discussed here. In this research work a new prototype is explained for the purpose of AR in IoT for all people including the people with learning disability.
Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming

Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how its expressiveness makes it possible to perform deductive and abductive reasoning tasks in domains featuring, for example, constraints involving both dense time and dense fluents.
Fibre direction strain measurement in a composite ply under pure bending using Digital Volume Correlation and Micro-focus Computed Tomography

This paper presents an experimental demonstration and validation of high-resolution three-dimensional experimental strain measurement using Digital Volume Correlation (DVC) on Carbon Fibre-Reinforced Polymers, via through-thickness strain analysis under a state of pure bending. To permit the application of DVC to displacements and/or strain measurements parallel to the fibre direction in well-aligned unidirectional materials at high volume fractions, a methodology was developed for the insertion of sparse populations of 400 nm BaTiO 3 particles within the matrix to act as displacement trackers (i.e. fiducial markers). For this novel material system, measurement sensitivity and noise are considered, along with the spatial filtering intrinsic to established DVC data processing. In conjunction with Micro-focus Computed Tomography, the technique was applied to a simple standard specimen subjected to a four-point flexural test, which resulted in a linear strain distribution through the beam thickness. The high-resolution, fibre-level strain distributions (imaged at a voxel resolution of ∼0.64 µm) were compared against the classical beam theory (Euler–Bernoulli) in incrementally decreasing averaging schemes and different sub-set sizes. Different sampling and averaging strategies are reported, showing that DVC outputs can be obtained that are in very good agreement with the analytical solution. A practical lower limit for the spatial resolution of strain is discerned for the present materials and methods. This study demonstrates the effectiveness of DVC in measuring local strains parallel to the fibre direction, with corresponding potential for calibration and validation of micromechanical models predicting various fibre-dominated damage mechanisms.
Texture and colour region separation based image retrieval using probability annular histogram and weighted similarity matching scheme

Content-based image retrieval (CBIR) uses primitive image features for retrieval of similarimages from a dataset. Generally, researchers extract these visual features fromthe whole image. Therefore, the extracted features contain overlappedinformation of texture, colour, and shape features, and it is a criticalchallenge in the field of CBIR. This problem can be overcome by extracting thecolour features from the colour as well as shape and texture features from theintensity dominant part only. In this study, the authors have proposed aniterative algorithm to separate colour and texture dominant part of the imageinto two different images. Here, a combination of edge maps and gradients hasbeen used to achieve separate colour and texture images. Further,scale-invariant feature transform and 2D dual-tree complex wavelet transform hasbeen realised to extract unique shape and texture features from the textureimage. Simultaneously, a probability-based semantic centred annular histogramhas been suggested to extract unique colour features from the colour image.Finally, a novel weighted distance-based feature comparison scheme has beenproposed for similarity matching and retrieval. All the image retrievalexperiments have been carried out on seven standard datasets and demonstratedsignificant improvements over other state-of-arts CBIR systems
Lexical Data Augmentation for Text Classification in Deep Learning

This paper presents our work on using part-of-speech focused lexical substitution for data augmentation (PLSDA) to enhance the prediction capabilities and the performance of deep learning models. This paper explains how PLSDA uses part-of-speech information to identify words and make use of different augmentation strategies to find semantically related substitutions to generate new instances for training. Evaluations of PLSDA is conducted on a variety of datasets across different text classification tasks. When PLSDA is applied to four deep learning models, results show that classifiers trained with PLSDA achieve 1.3% accuracy improvement on average.
3D Technologies and Applications in Sign Language

Millions of people suffering from partial or complete hearing loss use variants of sign language to communicate with each other or hearing people in their everyday life. Thus, it is imperative to develop systems to assist these people by removing the barriers that affect their social inclusion. These systems should aim towards capturing sign language in an accurate way, classifying sign language to natural words and representing sign language by having avatars or synthesized videos execute the exact same moves that convey a meaning in the sign language. This chapter reviews current state-of-the-art approaches that attempt to solve sign language recognition and representation and analyzes the challenges they face. Furthermore, this chapter presents a novel AI-based solution to the problem of robust sign language capturing and representation, as well as a solution to the unavailability of annotated sign language datasets before limitations and directions for future work are discussed.
Employing Deep Learning Methods for Predicting Helpful Reviews

E-commerce dominates a large part of the world's economy with many websites dedicated to selling products online. The vast majority of e-commerce websites provide their customers with the ability to express their opinions about the products/services they purchase. These reviews represent a rich source of information about the users' experiences, which is of great benefit to both the producer and the consumer. In This paper we present a set of machine/deep learning models, especially using Recurrent Convolutional Neural Network (RCNN) to predict the helpfulness of reviews. Mainly, two approaches are used: a supervised learning approach and a semi-supervised approach. The latter is a unique aspect of our work and it takes advantage of a large number of unlabeled reviews. The results show that both approaches are better than existing approaches. Moreover, the results show that the second approach has a remarkably better performance compared with the first one, which is in accordance with recent trends in machine/deep learning that focus on benefiting from the huge amount of unlabeled data to enhance the performance of supervised models.
Witness Maps and Applications

We introduce the notion of Witness Maps as a cryptographic notion of a proof system. A Unique Witness Map (UWM) deterministically maps all witnesses for an $$\mathbf {NP}$$ statement to a single representative witness, resulting in a computationally sound, deterministic-prover, non-interactive witness independent proof system. A relaxation of UWM, called Compact Witness Map (CWM), maps all the witnesses to a small number of witnesses, resulting in a "lossy" deterministic-prover, non-interactive proof-system. We also define a Dual Mode Witness Map (DMWM) which adds an "extractable" mode to a CWM. Our main construction is a DMWM for all $$\mathbf {NP}$$ relations, assuming sub-exponentially secure indistinguishability obfuscation ($${i\mathcal {O}}$$), along with standard cryptographic assumptions. The DMWM construction relies on a CWM and a new primitive called Cumulative All-Lossy-But-One Trapdoor Functions (C-ALBO-TDF), both of which are in turn instantiated based on $${i\mathcal {O}}$$ and other primitives. Our instantiation of a CWM is in fact a UWM; in turn, we show that a UWM implies Witness Encryption. Along the way to constructing UWM and C-ALBO-TDF, we also construct, from standard assumptions, Puncturable Digital Signatures and a new primitive called Cumulative Lossy Trapdoor Functions (C-LTDF). The former improves up on a construction of Bellare et al. (Eurocrypt 2016), who relied on sub-exponentially secure $${i\mathcal {O}}$$ and sub-exponentially secure OWF. As an application of our constructions, we show how to use a DMWM to construct the first leakage and tamper-resilient signatures with a deterministic signer, thereby solving a decade old open problem posed by Katz and Vaikunthanathan (Asiacrypt 2009), by Boyle, Segev and Wichs (Eurocrypt 2011), as well as by Faonio and Venturi (Asiacrypt 2016). Our construction achieves the optimal leakage rate of $$1 - o(1)$$.
Fracture attribute and topology characteristics of a geothermal reservoir: Southern Negros, Philippines

Running header -Fracture attributes and topology of a geothermal reservoir Supplementary material: Slopes, coefficient of determination, and AIC values of the cumulative frequency versus length and aperture
3D-BoxSup: Positive-Unlabeled Learning of Brain Tumor Segmentation Networks From 3D Bounding Boxes

Accurate segmentation is one of the essential tasks when working with medical images. Recently, deep convolutional neural networks achieved state-of-the-art performance in many segmentation benchmarks. Regardless of the network architecture, the deep learning-based segmentation methods view the segmentation problem as a supervised task that requires a relatively large number of annotated images. Acquiring a large number of annotated medical images is time-consuming, and high-quality segmented images (i.e., strong labels) by human experts are expensive to collect. In this paper, we propose a method that achieves competitive accuracy from a ``weakly annotated'' image where the weak annotation is obtained via a 3D bounding box denoting an object of interest. Our method, called ``3D-BoxSup'', employs a positive-unlabeled learning framework to learn segmentation masks from 3D bounding boxes. Specially, we consider the pixels outside of the bounding box as positively-labeled data and the pixels inside the bounding box as unlabeled data. Our method can suppress the negative effects of pixels residing between the true segmentation mask and the 3D bounding box and produce accurate segmentation masks. We apply our method to segment brain tumor. The experimental results on the BraTS 2017 dataset demonstrate the effectiveness of our method.
Content-based image retrieval using block truncation coding based on edge quantization

In this paper, we propose an effective image retrieval approach using block truncation coding compressed data stream based on edge-based quantization (EQBTC). First, an image is compressed into corresponding quantisers and a bitmap image by EQBTC. Then, the quantisers are used for colour feature extraction, whereby the bitmap image and grey image are used for luminance and edge feature extraction. Subsequently, two image features, the colour histogram feature (CHF) and the overall structure feature (OSF), are computed to measure the similarity between two images using a specific distance metric computation. The results presented in this paper demonstrate that the proposed model is superior to the block truncation coding image retrieval scheme and some earlier proposed methods.
Empirical estimation of daily global solar radiation with contrasting seasons of rain and drought characterize over tropical China

Despite a number of publications which have used readily available meteorological data to estimate daily Rs in a tropical environment with distinct dry and wet seasons, relatively few studies have focused on the dry and wet seasons independently. In this study, we compared 11 widely used empirical Rs estimation models using routinely measured meteorological data from the period 1957–2013 between the wet season and the dry season at 11 locations in tropical China. In terms of 4 statistical evaluation indicators, the top 3 performing sunshine-based models were, in rank order, M4, M5, and M2 over the entire year and M5, M2, and M1 (A-P model) during the dry and wet seasons. Meanwhile, the top 3 performing temperature-based models were, in rank order, M11, M10, and M7 over the entire year, M9, M10, and M11 in the dry season, and M11, M9, and M8 in the wet season. A comparison of the reported results revealed that the empirical models calibrated in the dry season performed better than in other seasons. For the temperature-based models, it was essential to develop the M6 (H-S), M7, and M9 models in the dry and wet seasons independently, eventhough their coefficients exhibited little significant difference between the dry and wet seasons according to ANOVA (P ≤ 0.5). This study revealed that daily Rs estimations could be developed with high accuracy during the dry and wet seasons independently in regions with contrasting seasons of rain and drought using only temperature data.
Strong Replica Symmetry in High-Dimensional Optimal Bayesian Inference

We consider generic optimal Bayesian inference, namely, models of signal reconstruction where the posterior distribution and all hyperparameters are known. Under a standard assumption on the concentration of the free energy, we show how replica symmetry in the strong sense of concentration of all multioverlaps can be established as a consequence of the Franz–de Sanctis identities; the identities themselves in the current setting are obtained via a novel perturbation coming from exponentially distributed "side-observations" of the signal. Concentration of multioverlaps means that asymptotically the posterior distribution has a particularly simple structure encoded by a random probability measure (or, in the case of binary signal, a non-random probability measure). We believe that such strong control of the model should be key in the study of inference problems with underlying sparse graphical structure (error correcting codes, block models, etc) and, in particular, in the rigorous derivation of replica symmetric formulas for the free energy and mutual information in this context.
Identity theft: a small step towards big financial crimes

Purpose This paper aims to study the concept of identity fraud and how these identity thefts can actually lead to financial crime. These crimes which usually were done in the traditional way now have taken leaps with the increase in the use of cyber world. Design/methodology/approach Several research papers, articles and newsfeeds were referred to study the concept, growth, scope, effect and impact of identity theft. It was also found that identity theft is the most common type of cybercrimes. Findings Identity theft though a simple crime but if not taken care of can lead to multiple crimes which can affect not only individuals but also companies. And when these crimes impact companies, they can actually hamper the economy as a whole. Practical implications Information for the same is not available very easily, so the study is solely based on secondary data. Social implications Identity theft effects an individual not only financially but also mentally and socially; thus, these effect each and every one in the said economy. Originality/value This paper is an original work of the authors, and it is for the use of students, educators and academicians.
Feature Selection: Multi-source and Multi-view Data Limitations, Capabilities and Potentials

Feature Selection (FS) is a crucial step in high-dimensional and big data analytics. It mitigates the `curse of dimensionality' by removing redundant and irrelevant features. Most FS algorithms use a single source of data and struggle with heterogeneous data, yet multi-source (MS) and multi-view (MV) data are rich and valuable knowledge sources. This paper reviews numerous, emerging FS techniques for both these data types. The major contribution of this paper is to underscore uses and limitations of these heterogeneous methods concurrently, by summarising their capabilities and potentials to inform key areas of future research, especially in numerous applications.
Substream-Centric Maximum Matchings on FPGA

Developing high-performance and energy-efficient algorithms for maximum matchings is becoming increasingly important in social network analysis, computational sciences, scheduling, and others. In this work, we propose the first maximum matching algorithm designed for FPGAs; it is energy-efficient and has provable guarantees on accuracy, performance, and storage utilization. To achieve this, we forego popular graph processing paradigms, such as vertex-centric programming, that often entail large communication costs. Instead, we propose a substream-centric approach, in which the input stream of data is divided into substreams processed independently to enable more parallelism while lowering communication costs. We base our work on the theory of streaming graph algorithms and analyze 14 models and 28 algorithms. We use this analysis to provide theoretical underpinning that matches the physical constraints of FPGA platforms. Our algorithm delivers high performance (more than 4× speedup over tuned parallel CPU variants), low memory, high accuracy, and effective usage of FPGA resources. The substream-centric approach could easily be extended to other algorithms to offer low-power and high-performance graph processing on FPGAs.
An Effective Model-Based Trust Collaborative Filtering for Explainable Recommendations

Nowadays, many companies through the world wide web like YouTube, Netflix, Aliexpress and Amazon, provide personalized services as recommendations. Recommender systems use the related information about products or services to suggest the most relevant of them to particular users. The recommendation is usually made based on the prediction of the users’ constraints and interests. Despite that the most of the existing recommender systems give accurate recommendation results, they do not provide explainable recommendation support. Therefore, in this paper we propose a new effective model-based trust collaborative filtering for explainable recommendations that aims not only to improve the quality of recommendation but also to provide an efficient support for explainable recommendations based on trustworthiness modeling. Our solution can both ensure how the item is recommended and why it is recommended. The proposed method is evaluated on Amazon Instant Video dataset in terms of RMSE.
Interactive Attention Model Explorer for Natural Language Processing Tasks with Unbalanced Data Sizes

Conventional attention visualization tools compromise either the readability or the information conveyed when documents are lengthy, especially when these documents have imbalanced sizes. Our work strives toward a more intuitive visualization for a subset of Natural Language Processing tasks, where attention is mapped between documents with imbalanced sizes. We extend the flow map visualization to enhance the readability of the attention-augmented documents. Through interaction, our design enables semantic filtering that helps users prioritize important tokens and meaningful matching for an in-depth exploration. Case studies and informal user studies in machine comprehension prove that our visualization effectively helps users gain initial understandings about what their models are "paying attention to." We discuss how the work can be extended to other domains, as well as being plugged into more end-to-end systems for model error analysis.