Reviewerly/exHarmony huggingface dataset:
Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph

With the increasing number of deep learning applications, there is a growing demand for explanations. Visual explanations provide information about which parts of an image are relevant for a classifier's decision. However, highlighting of image parts (e.g., an eye) cannot capture the relevance of a specific feature value for a class (e.g., that the eye is wide open). Furthermore, highlighting cannot convey whether the classification depends on the mere presence of parts or on a specific spatial relation between them. Consequently, we present an approach that is capable of explaining a classifier's decision in terms of logic rules obtained by the Inductive Logic Programming system Aleph. The examples and the background knowledge needed for Aleph are based on the explanation generation method LIME. We demonstrate our approach with images of a blocksworld domain. First, we show that our approach is capable of identifying a single relation as important explanatory construct. Afterwards, we present the more complex relational concept of towers. Finally, we show how the generated relational rules can be explicitly related with the input image, resulting in richer explanations.
Paraconsistent Random Forest: An Alternative Approach for Dealing With Uncertain Data

Pattern recognition algorithms have introduced increasingly sophisticated solutions. However, many datasets are far from perfect; for example, they may include inconsistencies and have missing data, which may interfere with the classification process. Thus, the use of paraconsistent logic can provide a compelling quantitative analysis approach in classification algorithms because it deals directly with inaccurate, inconsistent and incomplete data. Paraconsistent logic is considered a nonclassical logic, which enables the processing of contradictory signals in its theoretical structure without invalidating the conclusions. In this context, the proposed approach aggregates the power of hybrid classifiers, the low noise susceptibility of the random forest approach and the robustness of paraconsistent logic to provide an intelligent treatment of contradictions and uncertainties in datasets. The proposed method is called paraconsistent random forest. The computational results demonstrated that paraconsistent random forest could classify several databases with satisfactory accuracy in comparison with state-of-the-art methods, namely, LDA, KNN, and SVM. Regarding imperfect datasets, the proposed approach significantly outperforms most of these methods in terms of prediction accuracy.
A Fuzz Testing Service for Assuring Smart Contracts

Smart contracts are code to keep agreements in the form of data or smart contracts residing in a blockchain system. Making data corruptions to these data records may lead to insert unwanted agreements, corrupt an existing one, or remove the latter ones mistakenly. This paper reports the progress of our progress in developing a fuzz testing service with client side support. It will present the overall approach of the testing services followed by a discussion on the road ahead.
Pull-Ups: Enhancing Suspension Activities in Virtual Reality with Body-Scale Kinesthetic Force Feedback

We present Pull-Ups, a suspension kit that can suggest a range of body postures and thus enables various exercise styles of users perceiving the kinesthetic force feedback by suspending their weight with arm exertion during the interaction. Pull-Ups actuates the user's body to move up to 15 cm by pulling his or her hands using a pair of pneumatic artificial muscle groups. Our studies informed the discernible kinesthetic force feedbacks that were then exploited for the design of kinesthetic force feedback in three physical activities: kitesurfing, paragliding, and space invader. Our final study on user experiences suggested that a passive suspension kit alone added substantially to users' perceptions of realism and enjoyment (all above neutral) with passive physical support, while sufficient active feedback can further level them up. In addition, we found that both passive and active feedback of the suspension kit significantly reduced motion sickness in simulated kitesurfing and paragliding compared to when no suspension kit (thus no feedback) was provided. This work suggests that a passive suspension kit is cost-effective as a home exercise kit, while active feedback can further level up user experience, though at the cost of the installation (e.g., an air compressor in our prototype).
INTEGRASI KETERAMPILAN ABAD 21 DALAM MODUL SOCIOLINGUISTICS: KETERAMPILAN 4C (COLLABORATION, COMMUNICATION, CRITICAL THINKING, DAN CREATIVITY)

Preparing 21st century students to be able to compete in the future, teachers around the world promote several skills to face the challenges of 21st century development. Students need 21st century skills which are known as 4C skills (critical thinking, collaboration, communication, and creativity). Integrating 21st century skills in the learning process effectively becomes very important. Teaching material as a source of learning is needed in this regard. One of the teaching materials is a module. The aim of this research is to develop an integrated Sociolinguistics module on 21st century skills that focus on 4C skills. It aims to find out the suitable of this module as teaching material for students in the English Education Department of STKIP Muhammadiyah Muara Bungo. This research method used research and development (R&amp;D). The research procedure was divided into two, namely preparation and implementation. This research was conducted to students in the fifth semester of the academic year 2019/2020 English Education Department of STKIP Muhammadiyah Muara Bungo. Data collection techniques used questionnaire and observation sheets as instrument. This research data analysis technique used quantitative data analysis techniques and qualitative data analysis techniques. The results showed the recapitulation of assessment aspects from various experts, sociolinguistics lecturer, student questionnaire sheets, and observations sheet. The following are the percentages of the result module eligibility which includes: the results of the material expert validation was 61.3% (good category); the results of the validation of media experts was 75.8% (excellent category); module self-evaluation results by lecturers was 90% (excellent category); field trial observation sheet results was 78.9% (excellent category); and the results of the student questionnaire sheet was 76.3% (excellent category). So the sociolinguistics module which is integrated with 4C skills is appropriate to be used in the learning process.
Immersive screens change attention width but not perception or decision-making performance in natural and basic tasks

In the last decades, a number of studies have examined people's perceptual and attentional capabilities using flat screen displays. The completion of studies using curved displays/screens has been neglected so far, despite their advantage of creating a more immersive and life-like experience. In two studies, we analysed possible performance differences between subjects' perceptual and attentional capabilities during a decision-making task whilst viewing life-size stimuli on large flat and curved immersive screens. In Study 1, participants performed an attention-demanding shape discrimination task. In Study 2, participants performed a more naturalistic football-specific discrimination task. Results of both studies revealed no differences in perception and decision making between screen conditions, but that attention can be directed across greater visual angles on immersive screens compared to flat screens. The findings suggest that attention can be directed across a larger visual angle on curved screens compared to flat screens probably because curved screens distort the image less than flat screens. This study has implications for the use of flat screens in studies that examine perceptual and attentional capabilities in the visual periphery.
Duet Robust Deep Subspace Clustering

Subspace clustering has long been recognized as vulnerable toward gross corruptions -- the corruptions can easily mislead the estimation of the underlying subspace structure. Recently, deep extensions of traditional subspace clustering methods have shown their great power to boost the clustering performance. However, deep learning methods are, in themselves, more prone to be affected by data corruptions. This motivates us to design specific robust extensions for deep subspace clustering methods. More precisely, we contribute a new robust deep framework called Duet Robust Deep Subspace Clustering (DRDSC). Our main idea is to explicitly model the corrupted patterns from both the data reconstruction perspective and the latent self-expression perspective with two regularization norms. Moreover, since the two involved norms are non-smooth, we implement a smoothing technique for these norms to facilitate the back-propagation of our proposed network. Experiments carried out on read-world vision tasks with different noise settings demonstrate the effectiveness of our proposed method.
Managing Security Risk

Health care data breaches are occurring at unprecedented rates, but breach causes are challenging to identify. The purpose of this exploratory study was to identify potential root causes associated with health care data breaches and to create a model of potential data breach factors to inform risk assessment and future predictive analysis. We considered organizational factors, business processes, and technological tools that may be associated with health care data breach occurrences. Using legal requirements, security industry frameworks, and health care standards, we developed a testable health care data breach model. This security model can inform managers who are working to conduct risk assessments, allocate resources, and minimize security risks.
Integration of lazy learning associative classification with kNN algorithm

With the improvement of web sources and the progression of science and innovation, a huge amount of data has been generated and stored. A proper and accurate classification system is in need for those datasets, so that its information can be utilized in the betterment of the society. In this paper, a new method for classification system is presented by integrating lazy learning associative classification and kNN algorithm. The proposed lazy learning associative classifier generates a good quality nearest neighbor class association rules based on the test query and predicts the class label. The experimental results shows that the propsed lazy kNN associate classifer shows betters accuracy when compared with existing lazy learning associative classier.
Vehicle Speed Monitoring using Convolutional Neural Networks

Recently, Computer Vision Techniques have been pushing the development of robust traffic monitoring systems. Such methods utilize images captured by video cameras to infer important traffic features, such as vehicle speed and traffic density. Frame Subtraction is currently the most used method to detect vehicles in a video stream, but there are scenarios where this method provides poor accuracy, given their struggle in handling disturbances caused by lighting changes, pedestrians in the scene, etc. In order to improve the accuracy of Traffic Monitoring Systems (TMS), this paper proposes a novel TMS design and implementation in which a Convolutional Neural Network is used to replace Frame Subtraction methods in the vehicles detection task. The results show up to 12% improvements on Vehicle Detection in comparison with Frame Subtraction-based systems, proving its effectiveness on challenging scenarios, while maintaining an error rate of 5% for speed detection.
Preserving nonclassical correlations in strongly unbalanced conditions

It is well known that optical losses represent the main obstacle to the real exploitation of quantum optical systems for quantum technology. Here we investigate to what extent the presence of unbalanced losses between the two parties of a mesoscopic twin-beam state can prevent or not the observation of nonclassical correlations. Moreover, we focus on the survival of nonclassicality in the presence of asymmetric lossy channels modeled according to specific statistical distributions.
Fine-Grained Product Categorization in E-commerce

E-commerce sites usually leverage taxonomies for better organizing products. The fine-grained categories, regarding the leaf categories in taxonomies, are defined by the most descriptive and specific words of products. Fine-grained product categorization remains challenging, due to blurred concepts of fine grained categories (i.e. multiple equivalent or synonymous categories), instable category vocabulary (i.e. the emerging new products and the evolving language habits), and lack of labelled data. To address these issues, we proposes a novel Neural Product Categorization model---NPC to identify fine-grained categories from the product content. NPC is equipped with a character-level convolutional embedding layer to learn the compositional word representations, and a spiral residual layer to extract the word context annotations capturing complex long range dependencies and structural information. To perform categorization beyond predefined categories, NPC categorizes a product by jointly recognizing categories from the product content and predicting categories from predefined category vocabularies. Furthermore, to avoid extensive human labors, NPC is able to adapt to weak labels, generated by mining the search logs, where the customers' behaviors naturally connect products with categories. Extensive experiments performed on a real e-commerce platform datasets illustrate the effectiveness of the proposed models.
Meta-Modelling Meta-Learning

Although artificial intelligence and machine learning are currently extremely fashionable, applying machine learning on real-life problems remains very challenging. Data scientists need to evaluate various learning algorithms and tune their numerous parameters, based on their assumptions and experience, against concrete problems and training data sets. This is a long, tedious, and resource expensive task. Meta-learning is a recent technique to overcome, i.e. automate this problem. It aims at using machine learning itself to automatically learn the most appropriate algorithms and parameters for a machine learning problem. As it turns out, there are many parallels between meta-modelling—in the sense of model-driven engineering—and meta-learning. Both rely on abstractions, the meta data, to model a predefined class of problems and to define the variabilities of the models conforming to this definition. Both are used to define the output and input relationships and then fitting the right models to represent that behaviour. In this paper, we envision how a meta-model for meta-learning can look like. We discuss possible variabilities, for what types of learning it could be appropriate for, how concrete learning models can be generated from it, and how models can be finally selected. Last but not least, we discuss a possible integration into existing modelling tools.
Classification of Normal and Leukemic Blast Cells in B-ALL Cancer Using a Combination of Convolutional and Recurrent Neural Networks

Acute Lymphocytic or Lymphoblastic Leukemia (ALL) is a virulent form of blood cancer that affects white blood cells and the bone-marrow–spongy tissue. At the start of ALL, immature white blood cells proliferate and replace healthy cells in the bone marrow. ALL progresses quickly and can be fatal within a few months if not treated. Computer assisted diagnosis and prognosis of ALL, therefore, has the potential to save many lives but requires high accuracy classification of malignant cells which is challenging due to the visual similarity between normal and malignant cells. In this work, we employ a custom-built deep learning model for the classification of immature lymphoblasts and normal cells. Our model is an ensemble of convolutional and recurrent neural networks. It also exploits the spectral features of the cells by using discrete cosine transform in conjunction with an RNN. The proposed classifier has been validated using multiple experiments. Our approach is able to achieve substantial performance gains when compared to, conventional, stand-alone CNN- and RNN-based methods. The highest accuracy achieved by our model is 86.6%.
From FE Combiners to Secure MPC and Back

Cryptographic combiners allow one to combine many candidates for a cryptographic primitive, possibly based on different computational assumptions, into another candidate with the guarantee that the resulting candidate is secure as long as at least one of the original candidates is secure. While the original motivation of cryptographic combiners was to reduce trust on existing candidates, in this work, we study a rather surprising implication of combiners to constructing secure multiparty computation protocols. Specifically, we initiate the study of functional encryption combiners and show its connection to secure multiparty computation. Functional encryption (FE) has incredible applications towards computing on encrypted data. However, constructing the most general form of this primitive has remained elusive. Although some candidate constructions exist, they rely on nonstandard assumptions, and thus, their security has been questioned. An FE combiner attempts to make use of these candidates while minimizing the trust placed on any individual FE candidate. Informally, an FE combiner takes in a set of FE candidates and outputs a secure FE scheme if at least one of the candidates is secure. Another fundamental area in cryptography is secure multi-party computation (MPC), which has been extensively studied for several decades. In this work, we initiate a formal study of the relationship between functional encryption (FE) combiners and secure multi-party computation (MPC). In particular, we show implications in both directions between these primitives. As a consequence of these implications, we obtain the following main results.
Online content moderation and the Dark Web: Policy responses to radicalizing hate speech and malicious content on the Darknet

De-listing, de-platforming, and account bans are just some of the increasingly common steps taken by major Internet companies to moderate their online content environments. Yet these steps are not without their unintended effects. This paper proposes a surface-to-Dark Web content cycle. In this process, malicious content is initially posted on the surface Web. It is then moderated by platforms. Moderated content does not necessarily disappear when major Internet platforms crackdown, but simply shifts to the Dark Web. From the Dark Web, malicious informational content can then percolate back to the surface Web through a series of three pathways. The implication of this cycle is that managing the online information environment requires careful attention to the whole system, not just content hosted on surface Web platforms per se. Both government and private sector actors can more effectively manage the surface-to-Dark Web content cycle through a series of discrete practices and policies implemented at each stage of the wider process.
An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere.

As the problem of drug abuse intensifies in the U.S., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use machine learning as a powerful tool for text classification and filtering. However, given the wide range of topics of Twitter users, tweets related to drug abuse are rare in most of the datasets. This imbalanced data remains a major issue in building effective tweet classifiers, and is especially obvious for studies that include abuse-related slang terms. In this study, we approach this problem by designing an ensemble deep learning model that leverages both word-level and character-level features to classify abuse-related tweets. Experiments are reported on a Twitter dataset, where we can configure the percentages of the two classes (abuse vs. non abuse) to simulate the data imbalance with different amplitudes. Results show that our ensemble deep learning models exhibit better performance than ensembles of traditional machine learning models, especially on heavily imbalanced datasets.
End-to-End Partial Convolutions Neural Networks for Dunhuang Grottoes Wall-Painting Restoration

In this paper, we focus on training a deep neural network to in-paint and restore the historical painting of Dunhuang Grottoes. Dunhuang Grottoes is more than 1000 years old and the wall-painting on the grottoes has suffered from various deterioration. The ground truth does not exist either. Furthermore, learning the style of the artists is not straight forward because the wall-paintings are created by thousands of artists over more 400-500 years. As the very first attempt to solve this problem, we propose an end-to-end image restoration model for Dunhuang wall-painting. The end-to-end image restoration model employ U-net with partially convoluational layers to construct, which is capable in restoring non-rigid deteriorated content given a loss content mask and a wall-painting image. To learn the various artists style from real data, the training set and validation set are collected by using a zooming-in-like and random cropping approach on the digital RGB images photographed on the healthy Grotto-painting. We also synthesize the deteriorated paintings from real data. To ensure the synthetic content in the masked region is consistent to the ground truths in term of texture, colors, artistic style and free of unnecessary noises, the loss function is in a hybrid form that comprises transition variation loss, content loss and style loss. Our contributions are of three folds: 1) proposed using partial convolutional U-net in restoring wall-paintings; 2) the method is tested in restoring highly non-rigid and irregular deteriorated regions; 3) two types of masks are designed for simulating deteriorations and experimental results are satisfactory.
A Study of User Interface with Wearable Devices Based on Computer Vision

Smart wearable devices are widely used in the field of healthcare. This article presents three approaches to human-computer interaction (HCI) via computer vision and hand gestures. They are timeline-user interface, virtual keyboard user interface, and handwritten digit user interface, respectively. These user interfaces are achieved by including the image process and machine learning, such as Convolutional Neuron Networks and AdaBoost. To evaluate the approaches, a prototype of the android device with a built-in color camera was employed. The result of the handwritten digit model was significant for high accuracy of 92.9%. In our view, the results emphasize the validity of our models.
BLOCK CHAIN ENABLED INTERNET OF THINGS

The internet of things constitutes for more and advanced opportunities paving way for a competitive advantage in numerous industrious in the prevailing and the upcoming markets. The internet of things and the technologies associated with it are not only creating alterations in the internetwork alone but also with the things that are connected to it. As the internet of things includes the generation and the analysis of the data as the important process, it finds difficulties in administering and protecting the data that are flowing from different levels. This in turn causes difficulties in the secure transmission of the information from its source to the authorized destination and the authorized person at the right time. So the above reasons bring in the capability of the block chain into the IOT, to improve the security as well as implant the transparency. The paper put forward a block chain enabled IOT for the pharmaceutical sector to elude the issue of counterfeit in the medicines.