Reviewerly/exHarmony huggingface dataset:
Development of a methodology for building an information security system in the corporate research and education system in the context of university autonomy

The development of computing tools and technologies of corporate networks has expanded the range of educational and information services in corporate research and education networks (CRES). CRES belong to critical cybernetic information systems (CCIS) built on the basis of open network models. In the early 80s of the 20th century, this approach did not consider the need to build a security system, which does not allow it to provide the required level of protection against modern hybrid threats. The transition to autonomy in decision-making, education and university management all over the world places requirements to ensuring the required quality of service (QoS) of CRES clients. CRES users include university administration, faculty, students and support personnel of educational services in higher education institutions. One of the main criteria for QoS is information security. However, there is no general approach to building integrated information security in CRES, which would provide the required level of security.The methodology is based on the concept of synthesizing a synergistic model of threats to CCIS, improved models of CRES infrastructure, an intruder, assessing the current state of information security (IS) and improved method of investment in the CRES IS. It is shown that the basis of the synergistic model is a three-level model of strategic security management, which provides a synergistic effect in the context of simultaneous threats to information security, cybersecurity and security of information. In contrast to the known, such an approach provides for the determination of qualitatively new and previously unknown emergent properties of the information security system, taking into account the means used to create it. The application of the methodology in practice through the development and implementation of new solutions to provide security services allows for the required level of information security in CRES. The proposed information security service mechanisms are built on hybrid cryptosystems based on crypto-code structures with flawed codes.
AI meets Angry Birds

Could this be the year that AI is going to surpass human performance in playing the popular video game Angry Birds? The organizers of the annual AIBIRDS competition discuss the challenges involved.
Nepali Speech Recognition using RNN-CTC Model

This paper presents a Neural Network based Nepali Speech Recognition model.RNN (Recurrent Neural Networks) is used for processing sequential audio data.CTC (Connectionist Temporal Classification) [1] technique is applied allowing RNN to train over audio data.CTC is a probabilistic approach of maximizing the occurrence probability of the desired labels from RNN output.After processing through RNN and CTC layers, Nepali text is obtained as output.This paper also defines a character set of 67 Nepali characters required for transcription of Nepali speech to text.
A Simple BERT-Based Approach for Lexical Simplification

Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple BERT-based LS approach that makes use of the pre-trained unsupervised deep bidirectional representations BERT. Despite being entirely unsupervised, experimental results show that our approach obtains obvious improvement than these baselines leveraging linguistic databases and parallel corpus, outperforming the state-of-the-art by more than 11 Accuracy points on three well-known benchmarks.
No Permanent

Xiaochuang Han, Eunsol Choi, Chenhao Tan. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Maximum Likelihood Estimation for Learning Populations of Parameters

Consider a setting with $N$ independent individuals, each with an unknown parameter, $p_i \in [0, 1]$ drawn from some unknown distribution $P^\star$. After observing the outcomes of $t$ independent Bernoulli trials, i.e., $X_i \sim \text{Binomial}(t, p_i)$ per individual, our objective is to accurately estimate $P^\star$. This problem arises in numerous domains, including the social sciences, psychology, health-care, and biology, where the size of the population under study is usually large while the number of observations per individual is often limited. Our main result shows that, in the regime where $t \ll N$, the maximum likelihood estimator (MLE) is both statistically minimax optimal and efficiently computable. Precisely, for sufficiently large $N$, the MLE achieves the information theoretic optimal error bound of $\mathcal{O}(\frac{1}{t})$ for $t < c\log{N}$, with regards to the earth mover's distance (between the estimated and true distributions). More generally, in an exponentially large interval of $t$ beyond $c \log{N}$, the MLE achieves the minimax error bound of $\mathcal{O}(\frac{1}{\sqrt{t\log N}})$. In contrast, regardless of how large $N$ is, the naive plug-in estimator for this problem only achieves the sub-optimal error of $\Theta(\frac{1}{\sqrt{t}})$.
Upward Book Embeddings of st-Graphs

We study k-page upward book embeddings (kUBEs) of st-graphs, that is, book embeddings of single-source single-sink directed acyclic graphs on k pages with the additional requirement that the vertices of the graph appear in a topological ordering along the spine of the book. We show that testing whether a graph admits a kUBE is NP-complete for k >= 3. A hardness result for this problem was previously known only for k = 6 [Heath and Pemmaraju, 1999]. Motivated by this negative result, we focus our attention on k=2. On the algorithmic side, we present polynomial-time algorithms for testing the existence of 2UBEs of planar st-graphs with branchwidth b and of plane st-graphs whose faces have a special structure. These algorithms run in O(f(b)* n+n^3) time and O(n) time, respectively, where f is a singly-exponential function on b. Moreover, on the combinatorial side, we present two notable families of plane st-graphs that always admit an embedding-preserving 2UBE.
Chaotic Time Series Prediction Using a Novel Echo State Network Model with Input Reconstruction, Bayesian Ridge Regression and Independent Component Analysis

This paper presents a novel Echo State Network (ESN) model for chaotic time series prediction, which consists of three steps including input reconstruction, dimensionality reduction and regression. First, phase-space reconstruction is used to reconstruct the original ‘attractor’ of the input time series. Then, Independent Component Analysis (ICA) is used to identify independent components, reduce dimensionality and overcome multicollinearity problem of the reconstructed input matrix. Finally, Bayesian Ridge Regression provides accurate predictions thanks to its regularization effect to avoid over-fitting and its robustness to noise owing to its probabilistic strategy. Our experimental results show that our model significantly outperforms other ESN models in predicting both artificial and real-world chaotic time series.
SepNE: Bringing Separability to Network Embedding

Many successful methods have been proposed for learning low dimensional representations on large-scale networks, while almost all existing methods are designed in inseparable processes, learning embeddings for entire networks even when only a small proportion of nodes are of interest. This leads to great inconvenience, especially on super-large or dynamic networks, where these methods become almost impossible to implement. In this paper, we formalize the problem of separated matrix factorization, based on which we elaborate a novel objective function that preserves both local and global information. We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes. By implementing separability, our algorithm reduces the redundant efforts to embed irrelevant nodes, yielding scalability to super-large networks, automatic implementation in distributed learning and further adaptations. We demonstrate the effectiveness of this approach on several real-world networks with different scales and subjects. With comparable accuracy, our approach significantly outperforms state-of-the-art baselines in running times on large networks.
Cyberbullying Detection in Hindi-English Code-Mixed Language Using Sentiment Classification

Cyberbullying is one of the radical emerging problems with the advancements in the Internet, connecting people around the globe by social media networks. Existing studies mostly focus only on cyberbullying detection in the English language, thus the main objective of this paper is to develop an approach to detect cyberbullying in Hindi-English code-mixed language (Hinglish) which is exorbitantly used by Indian users. Due to the unavailability of Hinglish dataset, we created the Hinglish Cyberbullying Comments (HCC) labeled dataset consisting of comments from social media networks such as Instagram and YouTube. We also developed eight different machine learning models for sentiment classification in-order to automatically detect incidents of cyberbullying. Performance measures namely accuracy, precision, recall and f1 score are used to evaluate these models. Eventually, a hybrid model is developed based on top performers of these eight baseline classifiers which perform better with an accuracy of 80.26% and f1-score of 82.96%.
Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces

We introduce an approach to model surface properties governing bounces in everyday scenes. Our model learns end-to-end, starting from sensor inputs, to predict post-bounce trajectories and infer two underlying physical properties that govern bouncing - restitution and effective collision normals. Our model, Bounce and Learn, comprises two modules -- a Physics Inference Module (PIM) and a Visual Inference Module (VIM). VIM learns to infer physical parameters for locations in a scene given a single still image, while PIM learns to model physical interactions for the prediction task given physical parameters and observed pre-collision 3D trajectories. To achieve our results, we introduce the Bounce Dataset comprising 5K RGB-D videos of bouncing trajectories of a foam ball to probe surfaces of varying shapes and materials in everyday scenes including homes and offices. Our proposed model learns from our collected dataset of real-world bounces and is bootstrapped with additional information from simple physics simulations. We show on our newly collected dataset that our model out-performs baselines, including trajectory fitting with Newtonian physics, in predicting post-bounce trajectories and inferring physical properties of a scene.
Soft Computing Simulations of Chaotic Systems

Soft computing strategies are drawing widespread interest in engineering and science fields, particularly so because of their capacity to reason and learn in a domain of inherent uncertainty, approximation, and unpredictability. However, soft computing research devoted to finite precision effects in chaotic system simulations is still in a nascent stage, and there are ample opportunities for new discoveries. In this paper, we consider the error that is due to finite precision in the simulation of chaotic systems. We present a generalized version of the lower bound error using an arbitrary number of natural interval extensions. The lower bound error has been used to simulate a chaotic system with lower and upper bounds. The width of this interval does not diverge, which is an advantage compared to other techniques. We illustrate our approach on three systems, namely the logistic map, the Singer map and the Chua circuit. Moreover, we validate the method by calculating the largest Lyapunov exponent.
Model Recognition as Planning

Given a partially observed plan execution, and a set of possible planning models (models that share the same state variables but different action schemata), model recognition is the task of identifying the model that explains the observation. The paper formalizes this task and introduces a novel method that estimates the probability of a STRIPS model to produce an observation of a plan execution. This method builds on top of off-the-shelf classical planning algorithms and it is robust to missing actions and intermediate states in the observation. The effectiveness of the method is tested in three experiments, each encoding a set of different STRIPS models and all using empty-action observations: (1) a classical string classification task; (2) identification of the model that encodes a failure present in an observation; and (3) recognition of a robot navigation policy.
Determination and adjustment of optimal defocus level for fringe projection systems

The sinusoidal fringe pattern is widely used in fringe projection profilometry. Too much or too little defocusing will affect the quality of sinusoidal fringe patterns and consequently jeopardize the accuracy of measurement results. This paper proposes a method to quantify and ascertain the defocus level by simulations and experiments. By simulating the defocus pattern with a Gaussian low-pass filter, the optimum defocus level of the fringe pattern is determined so that the projected fringe pattern is closer to the sinusoidal function. Then, a method is proposed to adjust the projector to make the projected pattern in the optimal defocus degree. Experiments show the feasibility and the validity of the proposed method, and the accuracy is improved up to 9.9%, compared with the focus-projected pattern.
Application Scenario of Artificial Intelligence Technology in Higher Education

Since the emergence of artificial intelligence, it has experienced more than 50 years of exploration and change, showing a vigorous vitality. Science and Technology Promote Industrial Revolution and Industrial Revolution Promote Educational Reform. With the continuous development of artificial intelligence technology, in order to comply with the actual needs of colleges and universities to train innovative talents and teach students in accordance with their aptitude, it is bound to be the general trend to apply artificial intelligence to higher education. This paper mainly analyses the current situation of AI development, designs the overall framework of AI technology integration in the field of higher education, and points out the future development direction of AI in combination with higher education, such as enhancing the application of virtual reality technology, personalized learning, assistant teaching, building intelligent cloud schools, etc., which provides a new way of thinking for the information construction of higher education.
A New Method to Study the Periodic Solutions of the Ordinary Differential Equations Using Functional Analysis

In this paper, a new theorems of the derived numbers method to estimate the number of periodic solutions of first-order ordinary differential equations are formulated and proved. Approaches to estimate the number of periodic solutions of ordinary differential equations are considered. Conditions that allow us to determine both upper and lower bounds for these solutions are found. The existence and stability of periodic problems are considered.
Machine learning for solar trackers

A new approach for solar tracking, based on deep learning techniques, is being studied and tested using Tensorflow, an open source machine learning framework. Tensorflow makes the implementation more flexible and increases the development capabilities. Tensorflow facilitates the neural network implementation on several kinds of devices (embedded and mobile devices, mini computers, etc.). Furthermore, Tensorflow supports different types of neural networks which can be tuned and retrained for particular purposes. The presented results are promising, since the retrained networks correctly identify the Sun and the target, with this information the system can be controlled to properly track the Sun’s apparent trajectory without further information.
Ten Years of EARS

The first easy Approach to Requirements Syntax (EARS) paper was published 10 years ago.1 (For more information, see "Introduction to the Easy Approach to Requirements Syntax.") As the original authors, we were initially unsure about publishing because we thought that EARS was too simple: almost too obvious to be of value. It turns out that it is this simplicity that is perhaps the greatest strength of EARS. Here we reflect on 10 years of EARS and share some lessons learned. Experienced EARS practitioners provide case studies to illustrate some applications of the approach. (For more information, see "The Easy Approach to Requirements Syntax for Agile Cyberphysical System Development," "Applying the Easy Approach to Requirements Syntax to Regulatory Requirements in the Nuclear Energy Domain," and "Intel Corporation.")
An Efficient Premiumness and Utility-Based Itemset Placement Scheme for Retail Stores

In retail stores, the placement of items on the shelf space significantly impacts the sales of items. In particular, the probability of sales of a given item is typically considerably higher when it is placed in a premium (i.e., highly visible/easily accessible) slot as opposed to a non-premium slot. In this paper, we address the problem of maximizing the revenue for the retailer by determining the placement of the itemsets in different types of slots with varied premiumness such that each item is placed at least once in any of the slots. We first propose the notion of premiumness of slots in a given retail store. Then we discuss a framework for efficiently identifying itemsets from a transactional database and placing these itemsets by mapping itemsets with different revenue to slots with varied premiumness for maximizing retailer revenue. Our performance evaluation on both synthetic and real datasets demonstrate that the proposed scheme indeed improves the retailer revenue by up to 45% w.r.t. a recent existing scheme.
Adolescents’ perceptions of social media writing: Has non-standard become the new standard?

Abstract The present study examines adolescents’ attitudes and perceptions with respect to writing practices on social media. It reports the findings of a survey conducted among 168 Flemish high school students with various socio-demographic profiles. The survey examines linguistic attitudes and awareness of sociolinguistic patterns in computer-mediated communication, as well as relevant language skills. Moreover, the present paper uniquely combines the study of both adolescents’ perceptions and their production of informal online writing, as the participants’ responses to the survey are compared to their peers’ actual online writing practices. The respondents appear to have a fairly accurate intuition with respect to age and gender patterns in social media writing, but much less so with respect to education-related patterns. Furthermore, while typical chatspeak phenomena are easily identified as such, ordinary spelling mistakes often are not. Strikingly, the teenagers do not claim a high standard language proficiency, although they do state to care about standard language use in formal contexts. Finally, some significant differences were found between participants with distinct socio-demographic profiles, e. g. girls and highly educated teenagers appear to be more sensitive to the potential negative connotations of linguistic features and that sensitivity seems to increase with age.
Relating Labelled and Label-Free Bunched Calculi in BI Logic

In this paper we study proof translations between labelled and label-free calculi for the logic of Bunched Implications ( $$\mathrm {BI}$$ ). We first consider the bunched sequent calculus $$\mathrm {LBI}$$ and define a labelled sequent calculus, called $$\mathrm {GBI}$$ , in which labels and constraints reflect the properties of a specifically tailored Kripke resource semantics of $$\mathrm {BI}$$ with two total resource composition operators and explicit internalization of inconsistency. After showing the soundness of $$\mathrm {GBI}$$ w.r.t. our specific Kripke frames, we show how to translate any $$\mathrm {LBI}$$ -proof into a $$\mathrm {GBI}$$ -proof. Building on the properties of that translation we devise a tree property that every $$\mathrm {LBI}$$ -translated $$\mathrm {GBI}$$ -proof enjoys. We finally show that any $$\mathrm {GBI}$$ -proof enjoying this tree property (and not only $$\mathrm {LBI}$$ -translated ones) can systematically be translated to an $$\mathrm {LBI}$$ -proof.
Latent Part-of-Speech Sequences for Neural Machine Translation

Xuewen Yang, Yingru Liu, Dongliang Xie, Xin Wang, Niranjan Balasubramanian. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Multiple-image encryption based on angular-multiplexing holography with quick response code and spiral phase keys

In this research, a novel optical multiple-image encryption method based on angular-multiplexing holography, quick response (QR) code, and spiral phase keys is proposed. With this method, images are transformed into QR codes and subsequently encrypted into a series of encrypted holograms using an angular-multiplexing technique. The encrypted holograms can only be decrypted when the hologram is illuminated with a duplicate of the reference beam and correct fingerprint and spiral phase plate (SPP) keys. The multiplexing performance and key sensitivity of fingerprint and SPP order were both analyzed, showing the high strength of the security of our proposed method.
LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired

Currently, the visually impaired rely on either a sighted human, guide dog, or white cane to safely navigate. However, the training of guide dogs is extremely expensive, and canes cannot provide essential information regarding the color of traffic lights and direction of crosswalks. In this paper, we propose a deep learning based solution that provides information regarding the traffic light mode and the position of the zebra crossing. Previous solutions that utilize machine learning only provide one piece of information and are mostly binary: only detecting red or green lights. The proposed convolutional neural network, LYTNet, is designed for comprehensiveness, accuracy, and computational efficiency. LYTNet delivers both of the two most important pieces of information for the visually impaired to cross the road. We provide five classes of pedestrian traffic lights rather than the commonly seen three or four, and a direction vector representing the midline of the zebra crossing that is converted from the 2D image plane to real-world positions. We created our own dataset of pedestrian traffic lights containing over 5000 photos taken at hundreds of intersections in Shanghai. The experiments carried out achieve a classification accuracy of 94%, average angle error of 6.35 degrees, with a frame rate of 20 frames per second when testing the network on an iPhone 7 with additional post-processing steps.