Reviewerly/exHarmony huggingface dataset:
Undecidability of higher-order unification formalised in Coq

We formalise undecidability results concerning higher-order unification in the simply-typed λ-calculus with β-conversion in Coq. We prove the undecidability of general higher-order unification by reduction from Hilbert’s tenth problem, the solvability of Diophantine equations, following a proof by Dowek. We sharpen the result by establishing the undecidability of second-order and third-order unification following proofs by Goldfarb and Huet, respectively.
Automatic Synthetic Document Image Generation using Generative Adversarial Networks: Application in Mobile-Captured Document Analysis

In this paper, we propose a method using Generative Adversarial Networks for automatically synthesizing document images that are similar to real printed documents captured by mobile phone's camera in unconstrained environment. We focus on the simulation of image defects for unconstrained mobile image acquisition procedure (non-uniform illumination, defocusing, optical and mechanical deformations, vibrations, noise in electronic components,...). Our approach is proven to be low-cost as it only requires a collection of real document images without any annotation. Experimental results show the effectiveness of our approach to improve OCR (Optical Character Recognition) recognition rate in a mobile-captured document images framework. Although in this paper, we focus on modern printed document images, our proposed approach could be extended to another type of documents, including historical one.
Leadership Ideas Shaped by Digital Insights in Higher Education

The paper deals with the problem of exploiting the potential of digitalization at scale by organizations in the age of information technologies and advanced analytics of data. Being at the forefront of social progress, universities inspire managers, training staff and students to reset their working, thinking and leading initiatives and embrace digital transformation of the educational environment. The study aims to research the modern thought on transformational leadership befitting universities’ distinguished title of growth engines. The methodological framework comprises modern sociological, psychological, management and complex systems science theories of leadership. The paper analyzes organizational leadership models, explains their weak and strong points, formulates the terms of realizing the leadership phenomenon in the context of digitalization, studies the Financial University under the Government of the Russian Federation’s experience in introducing the elements of the ‘digital university’ model. The findings support the idea that generative leadership is attainable through inter-layer and inter-cluster interaction of all educational environment members. The paper suggests a mechanism of transforming hierarchical models into generative leadership models. The theoretic and practical significance is the validation of interconditionality of digitalization and a leadership ecosystem as a variety of communications, complexity of interrelations and integrity of functioning of educational leaders.
Citation Text Generation.

We introduce the task of citation text generation: given a pair of scientific documents, explain their relationship in natural language text in the manner of a citation from one text to the other. This task encourages systems to learn rich relationships between scientific texts and to express them concretely in natural language. Models for citation text generation will require robust document understanding including the capacity to quickly adapt to new vocabulary and to reason about document content. We believe this challenging direction of research will benefit high-impact applications such as automatic literature review or scientific writing assistance systems. In this paper we establish the task of citation text generation with a standard evaluation corpus and develop several strong baseline models. We provide extensive automatic and human evaluations to illustrate the successes and shortcomings of current text generation techniques for this task.
Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker

To access data stored in relational databases, users need to understand the database schema and write a query using a query language such as SQL. To simplify this task, text-to-SQL models attempt to translate a user's natural language question to corresponding SQL query. Recently, several generative text-to-SQL models have been developed. We propose a novel discriminative re-ranker to improve the performance of generative text-to-SQL models by extracting the best SQL query from the beam output predicted by the text-to-SQL generator, resulting in improved performance in the cases where the best query was in the candidate list, but not at the top of the list. We build the re-ranker as a schema agnostic BERT fine-tuned classifier. We analyze relative strengths of the text-to-SQL and re-ranker models across different query hardness levels, and suggest how to combine the two models for optimal performance. We demonstrate the effectiveness of the re-ranker by applying it to two state-of-the-art text-to-SQL models, and achieve top 4 score on the Spider leaderboard at the time of writing this article.
A comparative study of three new parallel models based on the PSO algorithm

Meta-heuristic PSO has limits, such as premature convergence and high running time, especially for complex optimization problems. In this paper, a description of three parallel models based on the PSO algorithm is developed, on the basis of combining two concepts: parallelism and neighborhood, which are designed according to three different approaches in order to avoid the two disadvantages of the PSO algorithm. The third model, SPM (Spherical-neighborhood Parallel Model), is designed to improve the obtained results from the two parallel NPM (Neighborhood Parallel Model) and MPM (Multi-PSO Parallel Model) models. The experimental results presented in this paper show that SPM model performed much better than both NPM and MPM models in terms of computing time and solution quality.
Human expression recognition using facial shape based Fourier descriptors fusion

Dynamic facial expression recognition has many useful applications in social networks, multimedia content analysis, security systems and others. This challenging process must be done under recurrent problems of image illumination and low resolution which changes at partial occlusions. This paper aims to produce a new facial expression recognition method based on the changes in the facial muscles. The geometric features are used to specify the facial regions i.e., mouth, eyes, and nose. The generic Fourier shape descriptor in conjunction with elliptic Fourier shape descriptor is used as an attribute to represent different emotions under frequency spectrum features. Afterwards a multi-class support vector machine is applied for classification of seven human expression. The statistical analysis showed our approach obtained overall competent recognition using 5-fold cross validation with high accuracy on well-known facial expression dataset.
Modal clustering asymptotics with applications to bandwidth selection

Density-based clustering relies on the idea of linking groups to some specific features of the probability distribution underlying the data.The reference to a true, yet unknown, population structure allows framing the clustering problem in a standard inferential setting, where the concept of ideal population clustering is defined as the partition induced by the true density function.The nonparametric formulation of this approach, known as modal clustering, draws a correspondence between the groups and the domains of attraction of the density modes.Operationally, a nonparametric density estimate is required and a proper selection of the amount of smoothing, governing the shape of the density and hence possibly the modal structure, is crucial to identify the final partition.In this work, we address the issue of density estimation for modal clustering from an asymptotic perspective.A natural and easy to interpret metric to measure the distance between density-based partitions is discussed, its asymptotic approximation explored, and employed to study the problem of bandwidth selection for nonparametric modal clustering.
Smart Phone Based Human Activity Recognition

Human Activity Recognition (HAR) is a field that uses collected data to classify different human actions. One simple and general approach to HAR is to use the sensor data from a mobile device to recognize different patterns behind complex motions. Early studies show promising results on simple activities using manually selected features from accelerometer readings. As newer publicly available datasets include more complex data and activities, manual feature selection have become cumbersome, impractical and face limitations in finding the optimal feature sets for HAR. In this paper, we present an empirical approach to defining models of 3D tensor data structures from 2D time series data obtained from multiple sensors on a smart phone, and a new Convolutional Neural Network (CNN) model, which uses the tensor data and performs automatic feature extraction and classification for HAR. We use the public benchmark dataset, MobiAct v2.0, to train and validate our model, which achieved an overall better performance in classifying 11 Activities of Daily Living (ADL) than the state-of-the-art approaches. Compared to the approach presented by Chatzaki et al. which has a very high rate of misclassifications for car-step out (CSO), car-step in (CSI), sit to stand (CHU), and stand to sit (SCH) classes, our proposed approach has 15% higher sensitivity for each of these activities with the optimal number of training epochs being only 25.
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study

Despite achieving remarkable performance on many image classification tasks, state-of-the-art machine learning (ML) classifiers remain vulnerable to small input perturbations. Especially, the existence of adversarial examples raises concerns about the deployment of ML models in safety- and security-critical environments, like autonomous driving and disease detection. Over the last few years, numerous defense methods have been published with the goal of improving adversarial as well as corruption robustness. However, the proposed measures succeeded only to a very limited extent. This limited progress is partly due to the lack of understanding of the decision boundary and decision regions of deep neural networks. Therefore, we study the minimum distance of data points to the decision boundary and how this margin evolves over the training of a deep neural network. By conducting experiments on MNIST, FASHION-MNIST, and CIFAR-10, we observe that the decision boundary moves closer to natural images over training. This phenomenon even remains intact in the late epochs of training, where the classifier already obtains low training and test error rates. On the other hand, adversarial training appears to have the potential to prevent this undesired convergence of the decision boundary.
Camera Calibration Using Projective Invariants of Sphere Images

Two novel linear camera calibration algorithms were investigated in this study.In general, an occluding contour circle of a sphere and the optical centre of a camera form a right circular cone, and the generalised eigenvectors of two right circular cones encode an infinity point that lies on two support planes containing the contour circle.In the image plane, a vanishing line can be determined by connecting two vanishing points.Furthermore, the direction of the diameter passing through the infinity point is orthogonal to the direction of its conjugate diameter; thus, a set of orthogonal vanishing points can be computed.The imaged circular points or orthogonal vanishing points provide constraints that enable the intrinsic parameters to be recovered completely.The results of simulations and comparison data obtained from actual experiments confirm the effectiveness and feasibility of the proposed algorithms.
An Improved Retrieval Method for Multi-Transaction Mode Consortium Blockchain

The traditional method of blockchain retrieval is to search the “Block File” in sequence from the “tail” to the “head” of the blockchain, which always takes a lot of time. How to reduce the retrieval time has been a hot issue in blockchain research. This paper proposes a fast retrieval method for the Multi-Transaction Mode Consortium Blockchain (MTMCB). Firstly, we create a “User Set” and “Block Name Set” cached in Redis. Then, according to the transaction participants and “Block Name Set”, we can get the relevant “Block Name List”, and quickly obtain the corresponding block files. On this basis, in order to meet the needs of rapid retrieval in large-scale systems, an improved retrieval algorithm based on a B+-tree data structure is proposed. Firstly, the block file information is put into different ordered sets according to the transaction participants, and the B+-tree index is established to quickly get the information of relevant block files by participants. Experimental results show that the improved method of Redis cache retrieval in this paper can greatly increase the efficiency of blockchain retrieval, and can settle some crucial problem in the blockchain application and popularization.
A method for error compensation of modeled annual energy production estimates introduced by intra-hour irradiance variability at PV power plants with a high DC to AC ratio

Financial models of PV power plants that are created during the development phase of a project are typically based upon simulations performed at hourly timesteps. Historical irradiance data used in these simulations, such as a Typical Meteorological Year, are created from average irradiances over hour-long periods. We find that when actual weather conditions include significant intra-hour variability in irradiance, the annual energy production estimates based on modeling in hourly timesteps will not account for all inverter clipping which occurs during moments of high irradiance. This effect is particularly significant for systems with high a DC to AC nameplate ratio. At some PV plants already in operation, this phenomenon has been found to result in overestimated production estimates of nearly 5%. In this work, the average amount of this error is quantified for locations within the United States, and a methodology is proposed for compensating for it in post processing. This methodology applies a variable discount to each hour of the year for hourly energy output simulations based on irradiance. The discount function is intended to account for the difference in useful insolation as computed from hourly average irradiance data versus sub-hourly sampling rates.
D3A energy exchange for a transactive grid

In June 2019, the Energy Web Foundation launched the first public, open source, energy enterprise blockchain, owned and managed by the community. Several energy corporates and startups have deployed commercial blockchain-based applications on the Energy Web Chain, while many others are testing and developing additional decentralized applications or dApps. This chapter explores the role of blockchain, or more precisely smart contracts, in creating a future transactive grid, focusing in particular on the potential early deployment of the D3A, an open source, customizable energy exchange, currently available as a simulation environment.
On the Effectiveness of Richardson Extrapolation in Machine Learning

Richardson extrapolation is a classical technique from numerical analysis that can improve the approximation error of an estimation method by combining linearly several estimates obtained from different values of one of its hyperparameters, without the need to know in details the inner structure of the original estimation method. The main goal of this paper is to study when Richardson extrapolation can be used within machine learning, beyond the existing applications to step-size adaptations in stochastic gradient descent. We identify two situations where Richardson interpolation can be useful: (1) when the hyperparameter is the number of iterations of an existing iterative optimization algorithm, with applications to averaged gradient descent and Frank-Wolfe algorithms (where we obtain asymptotically rates of $O(1/k^2)$ on polytopes, where $k$ is the number of iterations), and (2) when it is a regularization parameter, with applications to Nesterov smoothing techniques for minimizing non-smooth functions (where we obtain asymptotically rates close to $O(1/k^2)$ for non-smooth functions), and ridge regression. In all these cases, we show that extrapolation techniques come with no significant loss in performance, but with sometimes strong gains, and we provide theoretical justifications based on asymptotic developments for such gains, as well as empirical illustrations on classical problems from machine learning.
Security Architecture for Cloud-Based Command and Control System in IoT Environment

With the development of the fourth industrial technology, such as the Internet of Things (IoT) and cloud computing, developed countries including the U.S. are investigating the efficiency of national defense, the public sector and national innovation, and constructing the infrastructure for cloud computing environments through related policies. The Republic of Korea is enacting the related legislation and considering the fourth industrial technology in various fields. Particularly, it is considering the adaptation of the cloud to the command and control system in the national defense sector; hence, related research and pilot projects are being conducted. However, if the existing information system is converted to a cloud computing system by introducing IoT devices, existing security requirements cannot solve problems related to the security vulnerabilities of cloud computing. Therefore, to build a cloud-based secure command and control system, it is necessary to derive additional cloud computing-related security requirements that are lacking in the existing security requirements, and to build a secure national defense command and control system architecture based upon it. In this paper, we derive security requirements for a cloud-based command control system, propose a security architecture designed based thereupon, and implement a security architecture with an open-stack-based cloud platform, “OpenStack”.
PROSPECTS AND CHALLENGES OF ENTREPRENEURSHIP DEVELOPMENT IN THE KURDISTAN REGION OF IRAQ: AN OVERVIEW

Entrepreneurship as a driver of innovation and economic growth plays a key role in economic development and the appearance of knowledge and innovation-based economies. The most important effects of entrepreneurship development are increasing innovation, upgrading technology, increasing employment, producing technical knowledge, and generating income distribution at the community level, which can lead to increased national wealth and economic growth. At the moment, global developments are made entrepreneurship so important, and organizations need more innovation and entrepreneurship than ever before to adapt to new circumstances. In fact, in relation to entrepreneurship as the most important factor in economic development, organizations can gain more market share. The objectives of this study are to examine and overview the prospects and challenges of entrepreneurship in Iraq's Kurdistan region. In this regard, it can be said that with the human resources available and the valuable experiences available in the field of entrepreneurship around the world, it is necessary to plan for entrepreneurship development in the Kurdish Region of Iraq (KRI). It is revealed in this study Iraqi Kurdistan is determined can be one of the top entrepreneurial centers in the region with the scientific ability of specialists, and the potential of human resources, flexible structure and efficient technology. This paper recommended some strategies to reform and removing barriers to entrepreneurship development in the Kurdistan Region of Iraq by using factors such as entrepreneurship development and appropriate culture building, job training, setting up small business development centers, sponsoring entrepreneurs, establishing the legal framework for entrepreneurial activities, and providing spiritual support to entrepreneurs can lead to social, economic and industrial development and the declines of unemployment.
A Novel Zero-Watermarking Based Scheme for Copyright Protection of Gray scale Images

Zero-watermarking of digital images is a powerful method with respect to transparency in the watermarked image. However, robustness is still a challenging characteristic for researchers. The proposed method of zero-watermarking provides a novel solution for increasing robustness by obtaining resident features of gray scale image that are robust against common signal processing operations. The proposed solution is based on image scanning to produce NDD (Neighboring Distance Difference) profile. This scheme is used to extract image features for generating redundancy binary profile with the help of image scanning and identification of robust image areas for embedding a binary watermark. Redundant areas from binary profile show perceptually insignificant regions of gray scale image according to human visual system. Resident features from robust areas of image are collected to generate the zero watermarking binary key image using reversible XOR operation. The binary key is used for extraction of binary watermark. Experimental results of the proposed method have been compared with the results of various zero-watermarking schemes as well as traditional watermarking methods and found much better at slightly higher computational cost. The comparison analysis for testing robustness has been carried out against image processing attacks like Gaussian filtering, block average filtering, motion blur filtering, image resizing, image rotation, image compression and cropping. For each attack maximum correlated watermark from the set of recovered watermarks is selected to evaluate the performance of proposed zero-watermarking scheme. It has been recorded that perfect matching is observed between original and extracted watermarks for a number of signal processing attacks.
Social Media Origin Based Image Tracing Using Deep CNN

determining information regarding provenance of the digital images is challenging for image forensic scientific community as it employs tracing of the crucial yet hidden signatures associated with the images. Improving image content's history can be deliberated to mark an investigation for the assessment of the image by knowing its acquisition device or, model as the basis of investigations in certain direction. The fundamental approach for going back to image's provenance would involve revealing every subsequent postprocessing, like filtering or an interpolation achieved, applied on the uploaded image. This paper presents a model to uncover such distinct traces by exploiting Deep Learning (DL) based methods to examine the image origin in social networks, and particularly attempts to perceive from which social network the specific image has been downloaded. Here DL- based effective Convolutional Neural Networks (CNNs) is proposed to differentiate the image path in corresponding social media platform. In this work images uploaded/ downloaded from multiple social media paths are investigated to predict the image with correct class of media path. Along with source path identification, this work is expanded to carry out the social media distinction in spite of diverse JPEG quality factor.