Reviewerly/exHarmony huggingface dataset:
Cyberbullying in Poland: a case study of aggressive messages with emojis targeted at the community of hunters in urbanized society

The aim of the paper is to present the phenomenon of cyberbullying in Poland via emojis. Linguistic aggression is nowadays present in many spheres of human life and social media seem to be the main channel of cyberbullying outburst. One of the methods of expressing aggression is using emojis. Emojis have been developed to communicate emotions initially in a creative way. Unfortunately, the images have soon started to be used for cyberbullying. The research material consists of blogs and Facebook sites devoted to hunting. Hunting in urbanized societies is mainly understood as the unnecessary killing of innocent animals and many people having limited environmental knowledge perceive it stereotypically. The Polish Hunting Association members have been involved in protecting and restoring to their natural environments such species endangered with extinction as European bisons, beavers, moose, and Western capercaillie. However, people brought up far from nature in the idealized urban world very frequently resort to cyberbullying to stigmatize the community of hunters considered to be a community of ruthless and bloodthirsty killers only. The authors of the paper focus on the role of hunting and the language of aggression and emoticons used to express aggression and hatred against hunters for any reason.
Emotion Recognition from Partially Occluded Facial Images using Prototypical Networks

Facial expression recognition is a challenging task due to the variations and spontaneity of expressions in the real world scenarios. These variations include different head poses, occlusion of face regions, illumination changes and other noises in the image during capturing or transmission. This work aims at classifying five basic human emotions from partially occluded face images. Lack of base line datasets with occluded images is a hurdle to build a model which can generalize well. Meta-learning algorithms offer a solution to this problem. We have carried out research on leveraging meta-learning concepts with prototypical networks in our work to classify emotions in few-shot regime. We have used CMU Multi-PIE database, AffectNet database and images collected from Internet for training and testing purposes. The proposed method is named as MERO (meta-learning for emotion recognition under occlusion).
A Hybrid Learning approach for Sentiment Classification in Telugu Language

Sentiment or Opinion Analysis is a subset of Natural Language Processing which is employed in wide range of business verticals to disentangle, analyze, distinguish and comprehend the general opinion of user reviews, comments, feedback, news, and so on. Humans generate close to 2.5 quintillion bytes of data each day on internet and business leaders are tasked to derive hidden patterns and meaningful insights from this data to understand human behavior and take shrewd decisions. Since last decade, enormous amounts of text data on internet is generated for Indian languages. Researchers have shown inquisitiveness in deriving and analyzing this data to extract relevant information. To the best of our knowledge, there has been no requisite amount of research in classifying sentiment in Telugu language because of inadequate language resources and also being a regional language. This research paper illustrates a methodical approach which leverages lexicon based approach and machine learning in the field of sentiment analysis to classify the opinions in Telugu language. Firstly, by employing Lexicon based approach - Telugu SentiWordNet we identified the subjective sentences from the Telugu corpus. Secondly, by utilizing machine learning algorithms - SVM, Naïve Bayes and Random Forest we categorized the sentiment in the corpus. Our proposed methodology achieved highest accuracy of 85%.
Data Clustering Method Based on Improved Bat Algorithm With Six Convergence Factors and Local Search Operators

Clustering as an unsupervised learning method is a process of dividing a data object or observation object into a subset, that is to classify the data through observation learning instead of example learning without the guidance of the prior class label information.Bat algorithm (BA) is a swarm intelligence optimization algorithm inspired by bat's ultrasonic echo localization foraging behavior, but it has the disadvantages of being easily trapped into local minima and not being highly accurate.So an improved bat algorithm was proposed.In the global search, a Gaussian-like convergence factor is added, and five different convergence factors are proposed to improve the global optimization ability of the algorithm.In the local search, the hunting mechanism of the whale optimization algorithm (WOA) and the sine position updating strategy are adopted to improve the local optimization ability of the algorithm.This paper compares the clustering effect of the improved bat algorithm with bat algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, whale optimization algorithm and particle swarm optimization (PSO) algorithm on seven real data sets under six different convergence factors.The simulation results show that the clustering effect of the improved bat algorithm is superior to other intelligent optimization algorithms.
OrthoGaze: Gaze-based three-dimensional object manipulation using orthogonal planes

In virtual and augmented reality, gaze-based methods have been explored for decades as effective user interfaces for hands-free interaction. Though several well-known gaze-based methods exist for simple interactions such as selection, no solutions exist for 3D manipulation tasks requiring a higher degree of freedom (DoF). In this paper, we introduce OrthoGaze, a novel user interface that allows users to intuitively manipulate the three-dimensional position of a virtual object using only their eye or head gaze. Our approach makes use of three selectable, orthogonal planes, where each plane not only helps guide the user’s gaze in an arbitrary virtual space, but also allows for 2-DoF manipulations of object position. To evaluate our method, we conducted two user studies involving aiming and docking tasks in virtual reality to evaluate the fundamental characteristics of sustained gaze aiming and to determine which type of gaze-based control performs best when combined with OrthoGaze. Results showed that eye gaze was more accurate than head gaze for sustained aiming. Additionally, eye and head gaze-based control for 3D manipulations achieved 78% and 96% performance, respectively, in comparison with a hand-held controller. Subjective results also suggest that gaze-based manipulation can comprehensively cause more fatigue than controller-based. From the experimental results, we expect OrthoGaze to become an effective method for pure hands-free object manipulation in head-mounted displays.
Model-based contrastive explanations for explainable planning

An important type of question that arises in Explainable Planning is a contrastive question, of the form “Why action A instead of action B?”. These kinds of questions can be answered with a contrastive explanation that compares properties of the original plan containing A against the contrastive plan containing B. An effective explanation of this type serves to highlight the differences between the decisions that have been made by the planner and what the user would expect, as well as to provide further insight into the model and the planning process. Producing this kind of explanation requires the generation of the contrastive plan. This paper introduces domain-independent compilations of user questions into constraints. These constraints are added to the planning model, so that a solution to the new model represents the contrastive plan. We introduce a formal description of the compilation from user question to constraints in a temporal and numeric PDDL2.1 planning setting.
A Multi-Feature User Authentication Model Based on Mobile App Interactions

Knowledge-based authentication approaches such as the use of passwords and personal identification numbers (PINs) are the most common ways of authenticating users. The main problem with such approach is relying on simple authentication login credentials at the login stage, and assuming the user is still the same between access sessions makes applications and networks vulnerable to access by unauthorized users. Application-level access patterns on smartphone and tablet devices can be utilized to provide an approach for continuously authenticating and identifying users. This paper presents a user authentication and identification method based on mobile application access patterns, and throughout the paper we use a smart home environment as a motivating scenario. To enhance the classification process, many features have been extracted and utilized which considerably improved differentiating between users and eliminating similarities in the access usage patterns. The proposed model has been evaluated using two datasets, and the results show an ability to authenticate users with high accuracy in terms of low false positive, false negative, and equal error rates. Overall, the statistical analysis of the extracted multi-features and the results show that the feasibility of decision-making based on app interactions can lead to high accuracy.
Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy.
Overcoming the Grounding Bottleneck Due to Constraints in ASP Solving: Constraints Become Propagators

Answer Set Programming (ASP) is a well-known formalism for Knowledge Representation and Reasoning, successfully employed to solve many AI problems, also thanks to the availability of efficient implementations. Traditionally, ASP systems are based on the ground&amp;solve approach, where the grounding transforms a general input program into its propositional counterpart, whose stable models are then computed by the solver using the CDCL algorithm. This approach suffers an intrinsic limitation: the grounding of one or few constraints may be unaffordable from a computational point of view; a problem known as grounding bottleneck. In this paper, we develop an innovative approach for evaluating ASP programs, where some of the constraints of the input program are not grounded but automatically translated into propagators of the CDCL algorithm that work on partial interpretations. We implemented the new approach on top of the solver WASP and carried out an experimental analysis on different benchmarks. Results show that our approach consistently outperforms state-of-the-art ASP systems by overcoming the grounding bottleneck.
Coresets for Data-efficient Training of Machine Learning Models

Incremental gradient (IG) methods, such as stochastic gradient descent and its variants are commonly used for large scale optimization in machine learning. Despite the sustained effort to make IG methods more data-efficient, it remains an open question how to select a training data subset that can theoretically and practically perform on par with the full dataset. Here we develop CRAIG, a method to select a weighted subset (or coreset) of training data that closely estimates the full gradient by maximizing a submodular function. We prove that applying IG to this subset is guaranteed to converge to the (near)optimal solution with the same convergence rate as that of IG for convex optimization. As a result, CRAIG achieves a speedup that is inversely proportional to the size of the subset. To our knowledge, this is the first rigorous method for data-efficient training of general machine learning models. Our extensive set of experiments show that CRAIG, while achieving practically the same solution, speeds up various IG methods by up to 6x for logistic regression and 3x for training deep neural networks.
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness

Randomized smoothing, using just a simple isotropic Gaussian distribution, has been shown to produce good robustness guarantees against $\ell_2$-norm bounded adversaries. In this work, we show that extending the smoothing technique to defend against other attack models can be challenging, especially in the high-dimensional regime. In particular, for a vast class of i.i.d.~smoothing distributions, we prove that the largest $\ell_p$-radius that can be certified decreases as $O(1/d^{\frac{1}{2} - \frac{1}{p}})$ with dimension $d$ for $p > 2$. Notably, for $p \geq 2$, this dependence on $d$ is no better than that of the $\ell_p$-radius that can be certified using isotropic Gaussian smoothing, essentially putting a matching lower bound on the robustness radius. When restricted to {\it generalized} Gaussian smoothing, these two bounds can be shown to be within a constant factor of each other in an asymptotic sense, establishing that Gaussian smoothing provides the best possible results, up to a constant factor, when $p \geq 2$. We present experimental results on CIFAR to validate our theory. For other smoothing distributions, such as, a uniform distribution within an $\ell_1$ or an $\ell_\infty$-norm ball, we show upper bounds of the form $O(1 / d)$ and $O(1 / d^{1 - \frac{1}{p}})$ respectively, which have an even worse dependence on $d$.
Predicting Degrees of Technicality in Automatic Terminology Extraction

While automatic term extraction is a wellresearched area, computational approaches to distinguish between degrees of technicality are still understudied.We semi-automatically create a German gold standard of technicality across four domains, and illustrate the impact of a web-crawled general-language corpus on predicting technicality.When defining a classification approach that combines general-language and domain-specific word embeddings, we go beyond previous work and align vector spaces to gain comparative embeddings.We suggest two novel models to exploit general-vs.domain-specific comparisons: a simple neural network model with pre-computed comparative-embedding information as input, and a multi-channel model computing the comparison internally.Both models outperform previous approaches, with the multi-channel model performing best.
Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks

Multi-task learning (MTL) and transfer learning (TL) are techniques to overcome the issue of data scarcity when training state-of-theart neural networks.However, finding beneficial auxiliary datasets for MTL or TL is a time-and resource-consuming trial-and-error approach.We propose new methods to automatically assess the similarity of sequence tagging datasets to identify beneficial auxiliary data for MTL or TL setups.Our methods can compute the similarity between any two sequence tagging datasets, i.e. they do not need to be annotated with the same tagset or multiple labels in parallel.Additionally, our methods take tokens and their labels into account, which is more robust than only using either of them as an information source, as conducted in prior work.We empirically show that our similarity measures correlate with the change in test score of neural networks that use the auxiliary dataset for MTL to increase the main task performance.We provide an efficient, opensource implementation. 1
CLUZH at SIGMORPHON 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion

This paper describes the submission by the team from the Institute of Computational Linguistics, Zurich University, to the Multilingual Grapheme-to-Phoneme Conversion (G2P) Task of the SIGMORPHON 2020 challenge.The submission adapts our system from the 2018 edition of the SIGMORPHON shared task.Our system is a neural transducer that operates over explicit edit actions and is trained with imitation learning.It is well-suited for morphological string transduction partly because it exploits the fact that the input and output character alphabets overlap.The challenge posed by G2P has been to adapt the model and the training procedure to work with disjoint alphabets.We adapt the model to use substitution edits and train it with a weighted finitestate transducer acting as the expert policy.An ensemble of such models produces competitive results on G2P.Our submission ranks second out of 23 submissions by a total of nine teams.
A Lyapunov analysis for accelerated gradient methods: From deterministic to stochastic case

Recent work by Su, Boyd and Candes made a connection between Nesterov's accelerated gradient descent method and an Ordinary Differential Equation (ODE). We show that this connection can be extended to the case of stochastic gradients, and develop Lyapunov function based convergence rates proof for Nesterov's accelerated stochastic gradient descent. In the gradient case, we show Nesterov's method arises as a straightforward discretization of a modified ODE. Established Lyapunov analysis is used to recover the accelerated rates of convergence in both continuous and discrete time. Moreover, the Lyapunov analysis can be extended to the case of stochastic gradients. The result is a unified approach to acceleration in both continuous and discrete time, and in for both stochastic and full gradients.
A Decentralised Strategy for Heterogeneous AUV Missions via Goal Distribution and Temporal Planning

Heterogeneous multi-robot systems offer the potential to support complex missions, such as those needed for persistent autonomy in underwater domains. Such systems enable each robot to be optimised for specific tasks to better manage dynamic situations. In this context, temporal planning can generate plans to support the execution of multi-robot missions. However, the task distribution quality in the generated plans is often poor due to the strategies that existing planners employ to search for suitable actions, which do not tend to optimise task allocation. In this paper, we propose a new algorithm called the Decentralised Heterogeneous Robot Task Allocator (DHRTA) which enhances goal distribution by considering task spatial distribution, execution time, and the capabilities of the available robots. DHRTA is the first phase of our decentralised planning strategy which supports individual robot plan generation using temporal planners. Experiments illustrate the robustness of the approach and indicate improvements in plan quality by reducing the planning time, mission time and the rate of mission failures.
A Decentralized Car-Sharing Control Scheme Based on Smart Contract in Internet-of-Vehicles

Car sharing allows car owners to share their cars to tenants, making the control rights of vehicles to be frequently transferred among individuals. The existing control schemes for car shearing with centralized architecture are faced with several threatens, e.g., the single point of failure and lack of mutual trust. To this end, we propose a decentralized car-sharing control scheme by using blockchain and smart contracts. Massive base stations of Internet-of-Vehicles (IoV) deployed over wide areas are used to jointly build the distributed system with blockchain to replace the untrusted third-party server. Having the smart contract, access control procedures can be performed automatically by an arbitrary base station in the decentralized architecture. The scheme provides a secure platform for the interactions among vehicles, individuals and application providers to avoid some security issues. Several simulations are conducted to validate the feasibility and effectiveness of the proposed scheme.
Pure-Past Linear Temporal and Dynamic Logic on Finite Traces

We review PLTLf and PLDLf, the pure-past versions of the well-known logics on finite traces LTLf and LDLf, respectively. PLTLf and PLDLf are logics about the past, and so scan the trace backwards from the end towards the beginning. Because of this, we can exploit a foundational result on reverse languages to get an exponential improvement, over LTLf /LDLf , for computing the corresponding DFA. This exponential improvement is reflected in several forms of sequential decision making involving temporal specifications, such as planning and decision problems in non-deterministic and non-Markovian domains. Interestingly, PLTLf (resp., PLDLf ) has the same expressive power as LTLf (resp., LDLf ), but transforming a PLTLf (resp., PLDLf ) formula into its equivalent LTLf (resp.,LDLf) is quite expensive. Hence, to take advantage of the exponential improvement, properties of interest must be directly expressed in PLTLf /PLDLf .
A Lightweight Image Encryption Algorithm Based on Message Passing and Chaotic Map

The popularization of 5G and the development of cloud computing further promote the application of images. The storage of images in an untrusted environment has a great risk of privacy leakage. This paper outlines a design for a lightweight image encryption algorithm based on a message-passing algorithm with a chaotic external message. The message-passing (MP) algorithm allows simple messages to be passed locally for the solution to a global problem, which causes the interaction among adjacent pixels without additional space cost. This chaotic system can generate high pseudorandom sequences with high speed performance. A two-dimensional logistic map is utilized as a pseudorandom sequence generator to yield the external message sets of edge pixels. The external message can affect edge pixels, and then adjacent pixels interact with each other to produce an encrypted image. A MATLAB simulation shows the cipher-image performs fairly uniform distribution and has acceptable information entropy of 7.996749. The proposed algorithm reduces correlation coefficients from plain-image 1 to its cipher-image 0, which covers all of the plain-image characters with high computational efficiency (speed = 18.200374 Mbit/s). Theoretical analyses and experimental results prove the proposed algorithm’s persistence to various existing attacks with low cost.