Reviewerly/exHarmony huggingface dataset:
Ethical Conundrums and Virtual Humans

Abstract This paper explores ethical conundrums and virtual humans through building upon a post-Kantian framework, and one emerging from what is known as New Materialism. It begins by presenting the recent research and literature on virtual humans and suggesting that the central ethical conundrums that need to be examined are those of agency and values. The paper then argues that a combination of Luciano Floridi’s approach and one developed from New Materialism, namely modest ethics, offers a means of engaging with the ethical conundrums of virtual humans. It is argued that as yet there is little evidence for a democratic design process for virtual humans nor is there evidence about the possible impact virtual humans may have on a postdigital society. The paper concludes by suggesting that there need to be more open processes for debate which bring to light the values that are being built into these profound developments by the experts and focuses on using a modest ethics approach.
A unified neural network approach to e-commerce relevance learning

Result relevance scoring is critical to e-commerce search user experience. Traditional information retrieval methods focus on keyword matching and hand-crafted or counting-based numeric features, with limited understanding of item semantic relevance. We describe a highly-scalable feed-forward neural model to provide relevance score for (query, item) pairs, using only user query and item title as features, and both user click feedback as well as limited human ratings as labels. Several general enhancements were applied to further optimize eval/test metrics, including Siamese pairwise architecture, random batch negative co-training, and point-wise fine-tuning. We found significant improvement over GBDT baseline as well as several off-the-shelf deep-learning baselines on an independently constructed ratings dataset. The GBDT model relies on 10 times more features. We also present metrics for select subset combinations of techniques mentioned above.
An enhanced feature‐based sentiment analysis approach

Abstract In the last few years, online reviews where individuals express their thoughts, interests, experiences, and opinions have broadly spread over the internet. Sentiment analysis has evolved to analyze these online reviews and provide valuable insights for both individuals and organizations that may help them in making decisions. Unfortunately the performance of sentiment analysis process is affected by the nature of online reviews' content that may contain emoticons and negation words. Moreover, spam reviews have been written for the purpose of deceiving others. Therefore, there is a need to develop an approach that considers these issues. In this paper, an enhanced approach for sentiment analysis is proposed which aims to enhance the performance of classifying reviews based on their features and assigning accurate sentiment score to features. This enhanced approach is achieved by handling negation, detecting emoticons, and detecting spam reviews using a combination of different types of properties which leads to achieving better predictive performance. The proposed approach has been verified against three datasets of different sizes. The results indicate that the proposed approach achieves a maximum accuracy of about 99.06% in detecting spam reviews and a maximum accuracy of about 97.13% in classifying reviews. This article is categorized under: Algorithmic Development &gt; Text Mining Technologies &gt; Classification Technologies &gt; Machine Learning
Document-oriented versus Column-oriented Data Storage for Social Graph Data Warehouse

Graph data like social networks data are emerging and form a valuable source of information. Analyzing these graphs with OLAP-style operations provide a multi-dimensional and multi-level view of graph data which allows powerful analysis from different perspectives and multiple granularities. For this purpose, conceptual graph OLAP model was proposed in the literature. The key difference from traditional OLAP is that the measure in a graph cube is an aggregated graph. The question we are answering in this paper is how to store data of such graph cubes. We propose a column and a document store models. We compare the performance of the Roll up operation over these two databases. The Document oriented storage seems to be more suitable for Graph Cube.
Hadoop Distributed File System for Big data analysis

Hadoop is framework that is processing data with large volume that cannot be processed by conventional systems. Hadoop has management file system called Hadoop Distributed File System (HDFS) that has NameNode and DataNode where the data is divided into blocks based on the total size of dataset. In addition, Hadoop has MapReduce where the dataset is processed in Mapping phase and then reducing phase. Using Hadoop for big data analysis has been revealed important information that can be used for analytical purpose and enabling new products. Big data could be found in many different resources such as social networks, web server logs, broadcast audio streams and banking transactions. In this paper, we illustrated the main steps to setup Hadoop and MapReduce. The illustrated version in this work is the latest released of Hadoop 3.1.1 for big data analysis. A simplified pseudo code is provided to show the functionality of Map class and reduce class. The developed steps are applied with a given example that could be generalized with bigger data.
Classification Problem in Imbalanced Datasets

Classification is a data mining task.It aims to extract knowledge from large datasets.There are two kinds of classification.The first one is known as complete classification, and it is applied to balanced datasets.However, when it is applied to imbalanced ones, it is called partial classification or a problem of classification in imbalanced datasets, which is a fundamental problem in machine learning, and it has received much attention.Considering the importance of this issue, a large amount of techniques have been proposed trying to address this problem.These proposals can be divided into three levels: the algorithm level, the data level, and the hybrid level.In this chapter, we will present the classification problem in imbalanced datasets, its domains of application, its appropriate measures of performances, and its approaches and techniques.
Enhancing Temporal Action Localization with Transfer Learning from Action Recognition

Temporal localization of actions in videos has been of increasing interest in recent years. However, most existing approaches rely on complex architectures that are either expensive to train, inefficient at inference time, or require thorough and careful architecture engineering. Classical action recognition on pre-segmented clips, on the other hand, benefits from sophisticated deep architectures that paved the way for highly reliable video clip classifiers. In this paper, we propose to use transfer learning to leverage the good results from action recognition for temporal localization. We apply a network that is inspired by the classical bag-of-words model for transfer learning and show that the resulting framewise class posteriors already provide good results without explicit temporal modeling. Further, we show that combining these features with a deep but simple convolutional network achieves state of the art results on two challenging action localization datasets.
Uniaxial three-dimensional phase-shifting profilometry using a dual-telecentric structured light system in micro-scale devices

This paper presents a novel uniaxial microscopic 3D profilometry method using a structured light system with dual-telecentric lenses in micro-scale devices. Specifically, a telecentric lens can produce an orthographic view of an object and provide the exact size of objects in x and y-direction. An electronically focus-tunable lens attached to the projector rapidly and precisely changes the focal plane of the projected structured patterns, a camera captures the structured patterns from the same perspective, and a computational framework analyzes the relationship between captured structure images and the drive current of the electronically focus-tunable lens to obtain depth information for each pixel. By replacing the normal lens with telecentric lenses, the proposed method dodges perspective error and is easier for precision measurement. We developed a shadow-free coaxial 3D shape measurement system with dual telecentric lenses that achieved approximately 5.86 μm spatial resolution and 4.08 μm root-mean-square (RMS) error with a depth range of 1200 μm.
Monotonic alignments for summarization

Summarization is the task that creates a summary with the major points of the original document. Deep learning plays an important role in both abstractive and extractive summary generations. While a number of models show that combining the two gives good results, this paper focuses on a pure abstractive method to generate good summaries. Our model is a stacked RNN network with a monotonic alignment mechanism. Monotonic alignment has an advantage because it produces the context that is in the same sequence as the original document, at the same time eliminating repeating sequences. To obtain monotonic alignment, this paper proposes two energies that are calculated using only the previous alignment state. We use sub-word method to reduce the rate of producing OOVs(Out of Vocabulary). The dropout is used for generalization and the residual connection to overcome gradient vanishing. We experiment on CNN/daily new and Reddits dataset. Our method out-performs the previous models with monotonic alignment by 4 ROUGE-1 points and achieves the results comparable to state of the art.
Aesthetics, string figures, and the politics of the visible in science and education

This paper positions Aesthetics, specifically the politics of Aesthetics, as a frame for approaching traditionally conservative subfields of education, such as science education. Drawing primarily from Jacques Rancière's work on Aesthetics and Politics, and Donna Haraway's work with String Figures (SF), we outline the political stakes of engaging, transforming, and 'playing-with' the aesthetic dimensions of what science education (and education more broadly) might look like. In the first part of the article, we highlight the political potentiality of a turn toward aesthetics in (science) education, which includes a discussion of theory that supports this shift. We aim to, in part, disrupt the current distribution of the sensible to more deeply, and visibly, entangle science education with multi-species justice and a politics of equality writ large. In the second part of this article, we present our own string figure, an example of what we mean by a radical aesthetic shift in terms of what it allows students to see as visible, possible, and sensible in the world through science and education. We weave a string figure/SF story about our bee companions, who are/have been deeply affected by the Anthropocene, while simultaneously world-making in the Chthulucene. In concluding, we revisit our primary goal which is to open up new forms of political engagement in science education toward the goal of multi-species justice.
Decoloration of Multi-Color Fabric Images for Fabric Appearance Smoothness Evaluation by Supervised Image-to-Image Translation

For the poor adaptability of the two-dimensional image based fabric smoothness assessment methods for multi-color fabrics, three-dimensional imaging technologies were widely concerned in the area. In this paper, we suggest that the multi-color fabric smoothness assessment can be solved by the two-dimensional image based methods with the help of the proposed multi-color fabric decoloration method. The decoloration problem was solved by a paired image-to-image translation model built by conditional generative adversarial networks that was widely discussed in recent years. To train such model, we proposed a multi-color and white fabric image pairs generation method with a physically based fabric shading model. The experiment results show good performance of the proposed method on both generated image pairs and real multi-color fabric images. To quantitatively evaluate the performance of the decolored fabric images, a set of metrics was established based on the pixel wise image difference, fabric smoothness classification consistency, and manually fabric smoothness evaluation difference. The quantitative evaluation results demonstrated that the proposed method can achieve reasonable results for the multi-color fabric image decoloration.
Practical formulation of obstacle avoidance in the Task-Priority framework for use in robotic inspection and intervention scenarios

This paper presents a new formulation of a reactive obstacle avoidance algorithm, in the Task-Priority framework, delivering a practical solution for obstacle avoidance between vehicle-manipulator systems and complex environments. The presented concepts were implemented on an intervention autonomous underwater vehicle (I-AUV) and tested in an underwater pipe structure inspection and valve turning scenario, in a test tank, using GIRONA500 with an ECA 5E Micro manipulator. The obstacle avoidance is treated as an inequality (set-based) task, which takes into account all obstacles that are interacting with the robot links. Both the robot and the obstacles are represented by spheres to allow for analytical formulation. However, the environment is wrapped with spheres based on its actual geometry stored as an Octomap, hence it can be represented at different resolutions. Depending on the type of the mission performed by the robot we defined two modes of operation: (1) Navigation and Inspection, and (2) Intervention. For each mode, the algorithm takes into account different number of key points at the I-AUV and a different resolution of the environment representation. Typically, for the Intervention mode the resolution is higher, to allow for more precise motion. We also present an escape point strategy in case of the robot getting stuck between obstacles.
An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index

Ki-67 labeling index is a widely used biomarker for the diagnosis and monitoring of cancer. Many automated techniques have been proposed for evaluating Ki-67 index. In this paper, we introduce an integrated deep learning based approach. We use MobileUnet model for segmentation and classification and connected component based algorithm for the estimation of Ki-67 index in bladder cancer cases. The average F1 score is 0.92 and dice score is 0.96. The mean absolute error in the evaluated Ki-67 index is 2.1. We also explore possible pre-processing steps to generalize the segmentation model to at least one another type of cancer. Histogram matching and re-sizing improve the performance in breast cancer data by 12% in F1 score and 8% in dice score.
Frustratingly Easy Personalization for Real-time Affect Interpretation of Facial Expression

In recent years, researchers have developed technology to analyze human facial expressions and other affective data at very high time resolution. This technology is enabling researchers to develop and study interactive robots that are increasingly sensitive to their human interaction partners' affective states. However, typical interaction planning models and algorithms operate on timescales that are frequently orders of magnitude larger than the timescales at which real-time affect data is sensed. To bridge this gap between the scales of sensor data collection and interaction modeling, affective data must be aggregated and interpreted over longer timescales. In this paper we clarify and formalize the computational task of affect interpretation in the context of an interactive educational game played by a human and a robot, during which facial expression data is sensed, interpreted, and used to predict the interaction partner's gameplay behavior. We compare different techniques for affect interpretation, used to generate sets of affective labels for an interactive modeling and inference task, and evaluate how the labels generated by each interpretation technique impact model training and inference. We show that incorporating a simple method of personalization into the affect interpretation process - dynamically calculating and applying a personalized threshold for determining affect feature labels over time - leads to a significant improvement in the quality of inference, comparable to performance gains from other data pre-processing steps such as smoothing data via median filter. We discuss the implications of these findings for future development of affect-aware interactive robots and propose guidelines for the use of affect interpretation methods in interactive scenarios.
Speckle Noise Removal in Ultrasound Images Using a Deep Convolutional Neural Network and a Specially Designed Loss Function

The removal of speckle noise in ultrasound images has been the focus of a number of researches. Meanwhile, deep convolutional neural networks (DCNN) has been proved effective for various computer vision tasks, including image classification, segmentation and denoising. In this paper, we apply deep convolutional neural network to remove speckle noise in ultrasound images. Besides, a new hybrid loss function is specially designed for speckle noise removal, which can result in faster and more stable convergence during training. Experiments on synthetic and real Ultrasound images show that the proposed model outperforms other speckle reduction methods.
Cross-Lingual Argumentation Mining for Russian Texts

Argumentation mining refers to automatic extraction of arguments and their relations from texts. This field has been evolving rapidly in recent years, but there is almost no research for the Russian language. The present study is an attempt to overcome this gap. Firstly, we create the first argument-annotated corpus of Russian based on Argumentative Microtext Corpus and make it publicly available. Secondly, we study the importance of various feature types. Contextual and lexical features turn out to be the most significant. Thirdly, we evaluate the performance of various classifiers for argumentation mining. Bagging and XGBoost classifiers give the best results. Fourthly, we assess the possibility of using several machine translation systems (Google Translate, Yandex.Translate and Promt) for automatic creating of argument-annotated corpora. Google Translate appears to be the best system to reach this goal.
Topic Classification from Text Using Decision Tree, K-NN and Multinomial Naïve Bayes

One of the central motivations behind Natural Language Processing is detecting patterns. Given a text document, the task of identifying the context is known to be as topic classification. This paper explores the performance of three different classifiers namely Decision Tree, K-Nearest Neighbors and Multinomial Naive Bayes on a topic classification task (with six topic class). The evaluation is done on the basis of accuracy, precision, recall,and f1-score based results. Among those three aforementioned classifiers, we have selected the Multinomial Naïve Bayes a sour best model using which we achieved 91.8% accuracy.
Security Vulnerabilities of Smart Meters in Smart Grid

Integration of complex and high-speed electronic components in the state of art electric power system enhances the need for improved security infrastructure and resilience against invasive and non-invasive attacks on the smart grid. A modern smart grid system integrates a variety of instruments and standards to achieve cost-effective and time-effective energy measurement and management. As the fundamental component in the smart grid, the smart meter supports real-time monitoring, automatic control, and high-speed communication along with power consumption recording. However, the wide use of smart meters also increases privacy and security concerns. In this paper, we demonstrate the vulnerability of side-channel attacks on secure communication in smart grids for software-based and hardware-based implementations.
Taylor subsumes Scott, Berry, Kahn and Plotkin

The speculative ambition of replacing the old theory of program approximation based on syntactic continuity with the theory of resource consumption based on Taylor expansion and originating from the differential λ-calculus is nowadays at hand. Using this resource sensitive theory, we provide simple proofs of important results in λ-calculus that are usually demonstrated by exploiting Scott’s continuity, Berry’s stability or Kahn and Plotkin’s sequentiality theory. A paradigmatic example is given by the Perpendicular Lines Lemma for the Böhm tree semantics, which is proved here simply by induction, but relying on the main properties of resource approximants: strong normalization, confluence and linearity.
Deep Learned Nucleus Features for Breast Cancer Histopathological Image Analysis based on Belief Theoretical Classifier Fusion

Despite the significant progress in diagnosis and treatment, breast cancer still continues to be the most common deadly disease occurring among women in the world. Histopathological analysis is the standard method for breast cancer diagnosis. Manual inspection of histological samples is a highly specialized and time consuming task. Computer-Aided Diagnosis Systems (CAD) could overcome the limitations of manual analysis and assist pathologists to diagnose cancer more accurately and efficiently. Hence, we propose a novel approach that uses deep learned nucleus feature classifier fusion framework for breast cancer detection from histopathological images. Recently, convolutional neural networks (CNNs) have produced high recognition rates compared to hand crafted features but results in much higher complexity in network design. Moreover, designing CNN from scratch requires large amount of training data. So, we use pretrained CNNs for feature extraction. Extracted features are fused using norm pooling and fed to Support Vector Machine (SVM) for classification. Classification results using the different CNNs are fused using belief theory based fusion. Performance analysis is done using accuracy, sensitivity, specificity, receiver operating characteristics (ROC) curve and AUC (Area under the ROC curve) measures. Results comparable to the current state of the art is achieved.
Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection

This paper presents a probabilistic approach for the identifcation of abnormal behaviour in Activities of Daily Living (ADLs) from dense sensor data collected from 30 participants.The ADLs considered are related to preparing and drinking (i) tea, and (ii) coffee.Abnormal behaviour identifed in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident.The approach presented considers the temporal and sequential aspects of the actions that are part of each ADL and that vary between participants.The average and standard deviation for the duration and number of steps of each activity are calculated to defne the average time and steps and a range within which a behaviour could be considered as normal for each stage and activity.The Cumulative Distribution Function (CDF) is used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity in terms of time and steps.Analysis shows that CDF can provide precise and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute or consist of many steps.Finally, this approach could be used to train machine learning algorithms for abnormal behaviour detection.