Reviewerly/exHarmony huggingface dataset:
Distant Supervision for Sentiment Attitude Extraction

News articles often convey attitudes between the mentioned subjects, which is essential for understanding the described situation.In this paper, we describe a new approach to distant supervision for extracting sentiment attitudes between named entities mentioned in texts.Two factors (pair-based and frame-based) were used to automatically label an extensive news collection, dubbed as RuAttitudes.The latter became a basis for adaptation and training convolutional architectures, including piecewise max pooling and full use of information across different sentences.The results show that models, trained with RuAttitudes, outperform ones that were trained with only supervised learning approach and achieve 13.4% increase in F1score on RuSentRel collection. 1
CyberSPL: A Framework for the Verification of Cybersecurity Policy Compliance of System Configurations Using Software Product Lines

Cybersecurity attacks affect the compliance of cybersecurity policies of the organisations. Such disadvantages may be due to the absence of security configurations or the use of default configuration values of software products and systems. The complexity in the configuration of products and systems is a known challenge in the software industry since it includes a wide range of parameters to be taken into account. In other contexts, the configuration problems are solved using Software Product Lines. This is the reason why in this article the framework Cybersecurity Software Product Line (CyberSPL) is proposed. CyberSPL is based on a methodology to design product lines to verify cybersecurity policies according to the possible configurations. The patterns to configure the systems related to the cybersecurity aspects are grouped by defining various feature models. The automated analysis of these models allows us to diagnose possible problems in the security configurations, reducing or avoiding them. As support for this proposal, a multi-user and multi-platform solution has been implemented, enabling setting a catalogue of public or private feature models. Moreover, analysis and reasoning mechanisms have been integrated to obtain all the configurations of a model, to detect if a configuration is valid or not, including the root cause of problems for a given configuration. For validating the proposal, a real scenario is proposed where a catalogue of four different feature models is presented. In this scenario, the models have been analysed, different configurations have been validated, and several configurations with problems have been diagnosed.
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks

The performance of deep network learning strongly depends on the choice of the non-linear activation function associated with each neuron. However, deciding on the best activation is non-trivial and the choice depends on the architecture, hyper-parameters, and even on the dataset. Typically these activations are fixed by hand before training. Here, we demonstrate how to eliminate the reliance on first picking fixed activation functions by using flexible parametric rational functions instead. The resulting Pade Activation Units (PAUs) can both approximate common activation functions and also learn new ones while providing compact representations. Our empirical evidence shows that end-to-end learning deep networks with PAUs can increase the predictive performance. Moreover, PAUs pave the way to approximations with provable robustness.
A Study of The Convolutional Neural Networks Applications

At present, deep learning is widely used in a broad range of arenas. A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image classification; Second, in natural language processing, that is, the fields of speech recognition and text classification.
Policy Distillation and Value Matching in Multiagent Reinforcement Learning

Multiagent reinforcement learning (MARL) algorithms have been demonstrated on complex tasks that require the coordination of a team of multiple agents to complete. Existing works have focused on sharing information between agents via centralized critics to stabilize learning or through communication to improve performance, but do not generally consider how information can be shared between agents to address the curse of dimensionality in MARL. We posit that a multiagent problem can be decomposed into a multi-task problem where each agent explores a subset of the state space instead of exploring the entire state space. This paper introduces a multiagent actor-critic algorithm for combining knowledge from homogeneous agents through distillation and value-matching that outperforms policy distillation alone and allows further learning in discrete and continuous action spaces.
Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Data imbalance during the training of deep networks can cause the network to skip directly to learning minority classes. This paper presents a novel framework by which to train segmentation networks using imbalanced point cloud data. PointNet, an early deep network used for the segmentation of point cloud data, proved effective in the point-wise classification of balanced data; however, performance degraded when imbalanced data was used. The proposed approach involves removing between-class data point imbalances and guiding the network to pay more attention to majority classes. Data imbalance is alleviated using a hybrid-sampling method involving oversampling, as well as undersampling, respectively, to decrease the amount of data in majority classes and increase the amount of data in minority classes. A balanced focus loss function is also used to emphasize the minority classes through the automated assignment of costs to the various classes based on their density in the point cloud. Experiments demonstrate the effectiveness of the proposed training framework when provided a point cloud dataset pertaining to six objects. The mean intersection over union (mIoU) test accuracy results obtained using PointNet training were as follows: XYZRGB data (91%) and XYZ data (86%). The mIoU test accuracy results obtained using the proposed scheme were as follows: XYZRGB data (98%) and XYZ data (93%).
Deep Learning: Current State

Deep learning, a derived from machine learning, has grown into widespread usage with applications as diverse as cancer detection, elephant spotting, and game development. The number of published studies shows an increasing interest by researchers because of its demonstrated ability to achieve high performance in the solution of complex problems, the wide availability of data and computing resources, and the groundbreaking development of effective algorithms. This paper reviews the current state of deep learning. It includes a revision of basic concepts, such as the operations of feed forward and backpropagation, the use of convolution to extract features, the role of the loss function, and the optimization and learning processes; the survey of main stream techniques, in particular convolutional, recurrent, recursive, deep belief, deep generative, generative adversarial, and variational auto-enconder neural networks; the description of an ample array of applications organized by the type of technique employed; and the discussion of some of its most intriguing open problems.
Flow-Motion and Depth Network for Monocular Stereo and Beyond

We propose a learning-based method <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> that solves monocular stereo and can be extended to fuse depth information from multiple target frames. Given two unconstrained images from a monocular camera with known intrinsic calibration, our network estimates relative camera poses and the depth map of the source image. The core contribution of the proposed method is threefold. First, a network is tailored for static scenes that jointly estimates the optical flow and camera motion. By the joint estimation, the optical flow search space is gradually reduced resulting in an efficient and accurate flow estimation. Second, a novel triangulation layer is proposed to encode the estimated optical flow and camera motion while avoiding common numerical issues caused by epipolar. Third, beyond two-view depth estimation, we further extend the above networks to fuse depth information from multiple target images and estimate the depth map of the source image. To further benefit the research community, we introduce tools to generate realistic structure-from-motion datasets such that deep networks can be well trained and evaluated. The proposed method is compared with previous methods and achieves state-of-the-art results within less time. Images from real-world applications and Google Earth are used to demonstrate the generalization ability of the method.
Recognising rights for robots: Can we? Will we? Should we?

This article considers the law’s response to the emergence of robots and artificial intelligence (AI), and whether they should be considered as legal persons and accordingly the bearers of legal rights. We analyse the regulatory issues raised by robot rights through three questions: (i) could robots be granted rights? (ii) will robots be granted rights? and (iii) should robots be granted rights? On the question of whether we can recognise robot rights we examine how the law has treated different categories of legal persons and non-persons historically, finding that the concept of legal personhood is fluid and so arguably could be extended to include robots. However, as can be seen from the current debate in Intellectual Property (IP) law, AI and robots have not been recognised as the bearers of IP rights despite their ability to create and innovate, suggesting that the answer to the question of whether we will grant rights to robots is less certain. Finally, whether we should recognise rights for robots will depend on the intended purpose of regulatory reform.
Monocular 3D Object Detection via Geometric Reasoning on Keypoints

Monocular 3D object detection is well-known to be a challenging vision task due to the loss of depth information; attempts to recover depth using separate image-only approaches lead to unstable and noisy depth estimates, harming 3D detections. In this paper, we propose a novel keypoint-based approach for 3D object detection and localization from a single RGB image. We build our multi-branch model around 2D keypoint detection in images and complement it with a conceptually simple geometric reasoning method. Our network performs in an end-to-end manner, simultaneously and interdependently estimating 2D characteristics, such as 2D bounding boxes, keypoints, and orientation, along with full 3D pose in the scene. We fuse the outputs of distinct branches, applying a reprojection consistency loss during training. The experimental evaluation on the challenging KITTI dataset benchmark demonstrates that our network achieves state-of-the-art results among other monocular 3D detectors.
Evaluating Semantic Feature Representations to Efficiently Detect Hate Intent on Social Media

Detecting malicious intent behavior such as sharing hate speech has become an important challenge for social networking platforms. The method of automated hate speech detection for social media posts is often challenged by the complexity of capturing the context of the user expression with potential hate intent. We hypothesize that semantic features can help enrich the context representation of word senses in a social media post for machine learning algorithms. This paper presents a novel empirical study of diverse semantic features for hate speech classification task on social media posts. Specifically, we present an extensive empirical analysis, where we test the features of vector space model representation for corpus-based semantics, neural word embedding representation for distributional semantics, and declarative knowledge patterns from external knowledge base for domain semantics. Our experimental results show that ensembling the diverse feature representations improves the efficiency of hateful behavior classification in contrast to the case of a single type of feature representation. Results on two popular Twitter datasets for the hate speech detection task showed a consistent performance gain for the classification models that were based on the hybrid feature representation (absolute gain in F1 score up to 3.0%). The application of the proposed method of combining diverse feature representations can help in improving social media analytics systems for monitoring human behavior.
H‐WordNet: a holistic convolutional neural network approach for handwritten word recognition

Segmentation of handwritten words into isolated characters and their recognition are challenging due to the presence of high variability and cursiveness in Indian scripts. The complex shapes and availability of numerous atomic character classes, compound characters, modifiers, ascendants, and descendants make the recognition task even more difficult. A holistic approach effectively tackles such issues by avoiding the character-level segmentation and the earlier holistic methods have been mostly developed using multi-stage machine learning architecture. In this study, a deep convolutional neural network-based holistic method termed ‘H-WordNet’ is proposed for handwritten word recognition. The H-WordNet model includes merely four convolutional layers and one fully connected layer to effectively classify the word images', which lead to a significant reduction in parameters. The efficacy of different pooling operations with the proposed model is investigated. The main purpose of this study is to avoid the need for handcrafted feature extraction and obtain a more stable and generalised system for word recognition. The proposed model is evaluated using a standard handwritten Bangla word database (CMATERdb2.1.2), which contains 18000 Bangla word images of 120 different categories and it obtained a higher recognition accuracy of 96.17% when compared to recent state-of-the-art methods.
An artificial-intelligence-based method for assessing service quality: insights from the prosthodontics sector

Purpose The prosthodontics sector is facing major challenges because of scientific and technological advances that imply a clearer definition of lines of action and decision making processes. Measuring quality of service in this sector is a complex decision problem since the perceptions of three main players need to be considered: patients, dentists and dental technicians. This study sought to develop an artificial-intelligence-based (AI-based) method for assessing service quality in the dental prosthesis sector. Design/methodology/approach Using strategic options development and analysis (SODA), which is grounded on cognitive mapping, and the measuring attractiveness by a categorical based evaluation technique (MACBETH), a constructivist decision support system was designed to facilitate the assessment of service quality in the dental prosthesis sector. The system was tested, and the results were validated both by the members of an expert panel and by the vice-president of the Portuguese association of dental prosthesis technicians. Findings The methodological process developed in this study is extremely versatile and its practical application facilitated the development of an empirically robust evaluation model in this study context. Specifically, the profile analyses carried out in actual clinics allowed the cases in which improvements are needed to be identified. Originality/value Although already applied in the fields of AI and decision making, no prior work reporting the use of SODA and MACBETH for assessing service quality in the prosthodontics sector has been found.
Cloud computing ecosystem model: evaluation and role clusters

Abstract Several significantly different attempts have been undertaken to visualize the complex and opaque cloud computing ecosystem by a descriptive model. Recently, scholars have developed a new role-based cloud ecosystem model ( Pa ssau C loud Computing E cosystem Model (PaCE model)) based on a systematic analysis and synthesis of the previous models. However, its scientific evaluation is still outstanding. The main goal is therefore to conduct an evaluation regarding its structural equivalence and completeness compared to the real cloud business according to the design science research paradigm. The second goal is to demonstrate the model’s usefulness by using it as an instrument for the identification of role clusters, which are frequently covered by organizations. To this end, a cross-sectional analysis is conducted, in which 758 organizations are manually analysed and matched with the model’s current roles. As a result, roles have been newly added, removed, broken down into sub-roles, abstracted to a meta-role and renamed. The cluster analysis unveiled five role clusters. Whereas some roles were predominantly found as single role, further roles were exclusively or very frequently found in combination with others. Overall, the revised model creates, compared to the common rather technically orientated market overviews by market research institutes, a detailed categorization schema helping to understand and investigate the organizations belonging to the cloud ecosystem.
A Novel GSP Auction Mechanism for Dynamic Confirmation Games on Bitcoin Transactions

Bitcoin is gaining ground in recent years. In the Bitcoin system, miners provide computing power to confirm transactions in pursuit of transaction fees, while users compete by bidding transaction fees for faster confirmation. This process is in essence analogous to online ad auctions, where advertisers bid for more prominent ad slots. Therefore, inspired by ad auction research, we propose to apply the Generalized Second Price (GSP) auction mechanism in the dynamic confirmation game on Bitcoin transactions. Our model is targeted to deal with the problems caused by instability and low efficiency in the currently-adopted Generalized First Price (GFP) auction model in Bitcoin confirmation games. Besides, we use the “rank-by-cost” rule to replace the “rank-by-fee” rule, where each transaction’s cost is calculated by the user-submitted fee and the waiting time. Aiming to probe users’ equilibrium strategy, we first discuss the GSP game with complete information under synchronous submissions, and show that it has the Locally Envy-Free equilibrium. Then, we study the GSP game with incomplete information under asynchronous submissions, and define two types of strategies, i.e., the Farsighted Balanced (FB) strategy and the Instant Balanced (IB) strategy. The FB strategy is in line with users’ practical needs of determining fees so as to maximize the long-term payoffs; however it cannot generate a stable equilibrium. Alternatively, the IB strategy focuses on the instant payoff maximization, and if all users follow the IB strategy, their equilibrium fees can finally converge to a stable profile. Finally, we design computational experiments to validate our theoretical models and analysis. Our research findings indicate that this novel GSP mechanism is superior to the currently adopted GFP mechanism. Besides, the convergence of the GSP game under the IB strategy has also been illustrated by the computational experiments.
Cursive-Text: A Comprehensive Dataset for End-to-End Urdu Text Recognition in Natural Scene Images

Reading text in natural scene images is an active research area in the fields of computer vision and pattern recognition as text detection, text recognition and script identification are required. In this data article, a comprehensive dataset for Urdu text detection and recognition in natural scene images is presented and analysed. To develop the dataset, more than 2500 natural scene images were captured using a digital camera and a built-in mobile phone camera. Three separate datasets for isolated Urdu character images, cropped word images and end-to-end text spotting were developed. The isolated Urdu character and cropped word images dataset contain a much larger number of samples than existing Arabic natural scene text datasets. The Urdu text spotting dataset contains images with Urdu, English and Sindhi text instances. However, the focus has been given to the Urdu text instances. The ground truths for each image in the isolated character, cropped word or text spotting datasets are provided separately. The proposed datasets can be used to perform Urdu text detection and recognition or end-to-end recognition in natural scenes. These datasets can also be helpful to develop Arabic and Persian natural scene text detection and recognition systems, as Urdu is a derived language of these scripts and has many similar letters. The datasets can also be helpful to develop multi-language translation systems, which can facilitate foreign tourists to read and translate multilingual text in natural scene images. To evaluate the datasets, state-of-the-art machine learning and deep neural networks were used to build the text detection and recognition models, where the best classification accuracies are achieved. To the best of the authors' knowledge, this is the first dataset proposed for Urdu text detection, recognition or end-to-end text recognition in natural scene images. The aim of this data article is to present a benchmark work in the field of document analysis and recognition. Computer Science Computer Vision and Pattern Recognition Tables Figures Images Text Files Using a digital camera with a 20 megapixels (MP) sensor, an iPhone with a 12 MP back camera and a Samsung mobile with a 16MP back camera. Raw Analyzed Environmental factors such as illuminations, blurring and lighting conditions were considered while capturing images. The focus was given to the text within an image. The images in the dataset were obtained from the advertisement banners, sign-boards along the road side and streets, shop name boards, text written on the passing vehicles and walls. The images provided in this dataset were collected in different cities of Sindh, Pakistan. Summarized data are hosted with the article. The datasets and their related files are hosted in a Mendeley public data repository. DOI: https://data.mendeley.com/datasets/k5fz57zd9z/1 URL: http://dx.doi.org/10.17632/k5fz57zd9z.1.
Application of MCDM using PROMETHEE II Technique in the Case of Social Media Selection for Online Businesses.

Abstract Internet media has become one of the means of product promotion that has very good prospects today. The research aims to recommend the right social media for online businesses. The data collection method was conducted by interview and questionnaire to 300 respondents who conducted online business in Pematangsiantar city by random sampling. Based on the results of interviews and questionnaires obtained assessment criteria namely security (C1), application features (C2), community (C3), ease of access (C4) and response speed (C5). The alternatives used in this research are Facebook (A1), Instagram (A2), Line (A3) and WhatsApp (A4). The settlement method applied is a decision support system with the PROMETHEE II algorithm. The results of the algorithm show that the right alternative for doing online business is Facebook (A1) with a net flow of 0.25 and followed by WhatsApp (A4) with a net flow of ¬0.1. The results of the study are expected to provide recommendations in conducting online business.