Reviewerly/exHarmony huggingface dataset:
Smart Educational Innovation Leads to University Competitiveness

The paper aims at identifying the boundaries and fundamentals of university competitiveness built on development and application of smart educational innovations. The study goes through a literature analysis for the purpose of stating the art of the main objective—the linkage between educational innovation and university competitiveness. Educational innovation is analyzed from its application prism in higher education in the main fields of its manifestation. Competitiveness is analyzed from the point of view of its importance for universities and encompasses the most popular global rankings. As a result, the paper shortlists the main educational innovation types and possible existing metrics, measuring university competitiveness as well as critically analyzes the higher education rankings worldwide. The study brings valuable insights for the audience engaged in education, higher education and university management as well as to all aspects of educational innovation application for potential stakeholders. The results may be of use in implementing regulations for stimulating and further development of theoretical and applied models for measuring higher education competitiveness and application of education innovation in higher education.
Weight enumerators for nonbinary asymmetric quantum codes and their applications

In 1997, Shor and Laflamme defined weight enumerators for quantum error-correcting codes and derived a MacWilliams identity. Recently, we proposed two new notions, the double weight enumerators and complete weight enumerators, for binary quantum codes. Now we will extend these results to nonbinary quantum codes. Based on the MacWilliams identities for these enumerators, we explicitly determine the Singleton-type and Hamming-type bounds for arbitrary nonbinary asymmetric quantum codes.
Counterexample-driven genetic programming without formal specifications

Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints in order to generate the training cases used to evaluate the evolving programs. It has also been extended to combine formal constraints and user-provided training data to solve symbolic regression problems. Here we show how the ideas underlying CDGP can also be applied using only user-provided training data, without formal specifications. We demonstrate the application of this method, called "informal CDGP," to software synthesis problems.
Training Multi-Layer Perceptron Using Harris Hawks Optimization

In this paper, Harris hawks optimization (HHO) algorithm has been proposed as an up-to-date meta-heuristic algorithm for training multi-layer perceptron (MLP). The performance of the HHO-based MLP trainer was tested by employing five standard data sets (XOR, Balloon, Iris, Breast Cancer and Heart). The results were compared with those obtained with the sine cosine algorithm (SCA). Comparative statistical results showed that using HHO algorithm as a trainer is more effective and has a higher rate of classification ability.
An adaption mechanism for the error threshold of XCSF

Learning Classifier System (LCS) is a class of rule-based learning algorithms, which combine reinforcement learning (RL) and genetic algorithm (GA) techniques to evolve a population of classifiers. The most prominent example is XCS, for which many variants have been proposed in the past, including XCSF for function approximation. Although XCSF is a promising candidate for supporting autonomy in computing systems, it still must undergo parameter optimization prior to deployment. However, in case the later deployment environment is unknown, a-priori parameter optimization is not possible, raising the need for XCSF to automatically determine suitable parameter values at run-time. One of the most important parameters is the error threshold, which can be interpreted as a target bound on the approximation error and has to be set according to the approximated function. To enable XCSF to reliably approximate functions unknown at design-time, we propose the use of an error threshold, which is adapted at run-time based on the currently achieved approximation error. Our experimental evaluation shows that the adaption mechanism automatically adjusts the error threshold to a suitable value. On six different target functions, XCSF with an adaptive error threshold leads to a worst-case approximation error that is 11.5% higher than the error achieved with the best-suited static threshold.
Evolutionary super-resolution

Super-resolution increases the resolution of an image. Using evolutionary optimization, we optimize the noise injection of a super-resolution method for improving the results. More generally, our approach can be used to optimize any method based on noise injection.
Cognitive Computing Technologies, Products, and Applications

Cognitive computing has made industries and business organizations to operate in a different paradigm with respect to the use of technology right from carrying business operations to high-level decision-making strategy. The ability of human experts in any field to think and make right decisions varies from person to person which creates the demand and necessary requirement of a high skilled person in an industry, but it becomes difficult for any human when it comes to obtaining useful insights to carry out business operations and to take right decisions from a huge amount of data that gets generated every day. Different technologies and platforms are necessary to process almost petabytes of data and make proper use of it to obtain patterns and insights.
A Comparative Study of Sequence Tagging Methods for Domain Knowledge Entity Recognition in Biomedical Papers

Named entity recognition has been extensively studied in the past decade. The state-of-the-art models, trained on general text such as Wikipedia articles and newsletters, have achieved F_1>0.90. Entity types are focused on people, location, organization, etc. However, entity recognition from domain-specific text, in particular research papers, is still challenging. In this paper, we perform a comparative study of sequence tagging (ST) methods on this task using a manually curated corpus from biomedical papers on Lyme disease. Each model we compare consists of a ST and a non-ST classification component. In this pilot study, we freeze the non-ST classifier to study how the ST component performs with variants of the conditional random field (CRF) and bidirectional long short-term memory (BiLSTM). The results shed light on the importance of pre-trained word embeddings such as ELMo and the residual unit. The attention mechanism and enriched features do not seem to boost the performance in recognizing entity mentions and their positions, which is likely to be caused by the relatively small training sample. We plan to improve the model by increasing the training corpus size and trying different combinations of features.
Effect handlers in Haskell, evidently

Algebraic effect handlers offer an alternative to monads to incorporate effects in Haskell. In recent work Xie _et al._ show how to give semantics to effect handlers in terms of plain polymorphic lambda calculus through _evidence translation_. Besides giving precise semantics, this translation also allows for potentially more efficient implementations. Here we present the first implementation of this technique as a library for effect handlers in Haskell. We show how the design naturally leads to a concise effect interface and how evidence translation enables evaluating _tail resumptive_ operations _in-place_. We give detailed benchmark results where our library performs well with respect to other approaches.
Investigating exploration techniques for ACS in discretized real-valued environments

One way of dealing with the real-valued input signal is to discretize it. This might influence the process of learning the environmental model by the ACS2 agent. A more sophisticated method of selecting action can be applied to increase the speed of gaining knowledge by determining the most valuable regions of the input-space. This paper compares four ACS2 biasing exploration techniques applied across four real-valued environments. A new class of benchmark problem (inverted pendulum) and an agent modification - Optimistic Initial Quality (OIQ) are introduced for ACS2 both with promising outcomes.
MA<sup>3</sup>: Model Agnostic Adversarial Augmentation for Few Shot learning

Despite the recent developments in vision-related problems using deep neural networks, there still remains a wide scope in the improvement of generalizing these models to unseen examples. In this paper, we explore the domain of few-shot learning with a novel augmentation technique. In contrast to other generative augmentation techniques, where the distribution over input images are learnt, we propose to learn the probability distribution over the image transformation parameters which are easier and quicker to learn. Our technique is fully differentiable which enables its extension to versatile data-sets and base models. We evaluate our proposed method on multiple base-networks and 2 data-sets to establish the robustness and efficiency of this method. We obtain an improvement of nearly 4% by adding our augmentation module without making any change in network architectures. We also make the code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> readily available for usage by the community.
Evolving deep autoencoders

Autoencoders have seen wide success in domains ranging from feature selection to information retrieval. Despite this success, designing an autoencoder for a given task remains a challenging undertaking due to the lack of firm intuition on how the backing neural network architectures of the encoder and decoder impact the overall performance of the autoencoder. In this work we present a distributed system that uses an efficient evolutionary algorithm to design a modular autoencoder. We demonstrate the effectiveness of this system on the tasks of manifold learning and image denoising. The system beats random search by nearly an order of magnitude on both tasks while achieving near linear horizontal scaling as additional worker nodes are added to the system.
Learning TBox With a Cascaded Anchor-Free Network for Vehicle Detection

Vehicle detection, the process of identifying vehicles as axis-aligned bounding boxes in still images, is widely used to estimate the range, time-to-collision, and motion of autonomous vehicles (AVs). Bounding boxes, while convenient, are too coarse to adapt well to vehicle shape and pose variations. In this work, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBox</i> (Trapezoid & Box), a novel fine-grained representation useful for both localization and recognition that extends the bounding box by restricting the spatial extent of a vehicle to a set of keypoints and indicating semantically significant local areas using subclasses. In contrast to the previous monolithic models, we propose a cascaded anchor-free architecture to estimate the bounding box and TBox. One subnetwork uses a stacked hourglass network to detect each vehicle as a pair of corners without using anchors. Specifically, it learns corner affinity fields, enabling it to perform robust corner grouping. The other subnetwork estimates a TBox as a set of keypoints. This subnetwork utilizes the bounding box results to avoid ambiguous keypoint associations and reuses existing features to reduce the number of parameters. We also propose a multitask learning strategy for training the cascaded model that implicitly integrates the global context with local details, introducing improvements for both tasks. During testing, a refinement algorithm explicitly uses robust local keypoints to correct possible global box errors, ensuring tight geometric representations for nearby critical vehicles. The experiments show that our method outperforms existing anchor-free detectors for vehicle detection and achieves better performance on the TBox task while using a small model.
Breast Cancer Detection Using Deep Learning and Machine Learning: A Comparative Analysis

Nowadays, breast cancer is one of the most frequently diagnosed deadly cancers in women. The cancer leads to uncontrolled growth of cell, usually present in form of a tumor or lump in the affected area accompanied by skin hardness, redness, and irritation. In this paper, an attempt has been made to detect breast cancer using deep neural network using Wisconsin breast cancer dataset. Further, the results are compared with machine learning techniques like support vector machine and linear regression.
Multiple-Input, Multilayer-Perception-Based Classification of Traces From Side-Channel Attacks

Side-channel analysis concerns cryptanalytic attacks that exploit the physical environment of cryptographic modules by analyzing leaking emanations and obtaining the encryption key. We propose a multiple-input, multilayer-perceptron-based method for classifying power traces, in which a probability is assigned to each class, indicating the likelihood that each trace corresponds to the label for further recovering the key.
Moving Object Detection based on Deep Atrous Spatial Features for Moving Camera

Moving object detection in a moving camera is a challenging task. The task is not only challenging due to a change in the pose of a camera. But the challenges posed by moving objects add on such as illumination changes, dynamic background, shadows, camouflaged, and static moving objects. This paper proposed moving object detection using a moving camera. The algorithm is developed using the deep learning technique. The deep dilated features are extracted using a dilated convolution kernel. The features are expanded using a bilinear interpolation based network. The proposed algorithm is compared with the top-ranked algorithms in the moving object detection field on the change detection dataset.
Relation Aware Attention Model for Uncertainty Detection in Text

Uncertainty in text is an important linguistic phenomenon that is relevant in many areas of natural language processing. In this paper, we present a neural approach towards detecting uncertainty cues in texts. We have explored a series of neural network architectures and evaluated the models with respect to three different data sources belonging to domains such as bio-medical texts, privacy policies, and product reviews. Our preliminary analysis showed that the relation aware attention models outperform the existing baseline systems across all the domains. We have also observed for domain specific texts incorporating character level embeddings significantly improves the performance.
Combining decision making and dynamical systems for monitoring and executing manipulation tasks

Abstract In this paper, we propose a unified framework for online task scheduling, monitoring, and execution that integrates reconfigurable behavior trees, a decision-making framework with integrated low-level control functionalities, and reactive motion generation with stable dynamical systems. In this way, we realize a flexible and reactive system capable of coping with unexpected variations in the executive context without penalizing modularity, expressiveness, and readability of humans. The framework is evaluated in a simulated sorting task showing promising results in terms of flexibility regarding task scheduling and robustness to external disturbances.
Hasten Slowly

Responding to the climate crisis and a considerable downturn in planetary well-being due to human consumption patterns, The Lions' Gate, on-campus, urban, permaculture-inspired, interactive community gardens project aims to affect changes in attitude toward the natural world by leveraging potentials within the university set-up. Specifically, this work details the development of an interactive storytelling chair that will sit central to goings-on in the garden space as both a technological and social hub that propagates sustainability-based conversations.
Dirichlet-tree multinomial mixtures for clustering microbiome compositions

Studying the human microbiome has gained substantial interest in recent years, and a common task in the analysis of these data is to cluster microbiome compositions into subtypes. This subdivision of samples into subgroups serves as an intermediary step in achieving personalized diagnosis and treatment. In applying existing clustering methods to modern microbiome studies including the American Gut Project (AGP) data, we found that this seemingly standard task, however, is very challenging in the microbiome composition context due to several key features of such data. Standard distance-based clustering algorithms generally do not produce reliable results as they do not take into account the heterogeneity of the cross-sample variability among the bacterial taxa, while existing model-based approaches do not allow sufficient flexibility for the identification of complex within-cluster variation from cross-cluster variation. Direct applications of such methods generally lead to overly dispersed clusters in the AGP data and such phenomenon is common for other microbiome data. To overcome these challenges, we introduce Dirichlet-tree multinomial mixtures (DTMM) as a Bayesian generative model for clustering amplicon sequencing data in microbiome studies. DTMM models the microbiome population with a mixture of Dirichlet-tree kernels that utilizes the phylogenetic tree to offer a more flexible covariance structure in characterizing within-cluster variation, and it provides a means for identifying a subset of signature taxa that distinguish the clusters. We perform extensive simulation studies to evaluate the performance of DTMM and compare it to state-of-the-art model-based and distance-based clustering methods in the microbiome context, and carry out a validation study on a publicly available longitudinal data set to confirm the biological relevance of the clusters. Finally, we report a case study on the fecal data from the AGP to identify compositional clusters among individuals with inflammatory bowel disease and diabetes. Among our most interesting findings is that enterotypes (i.e., gut microbiome clusters) are not always defined by the most dominant species as previous analyses had assumed, but can involve a number of less abundant OTUs, which cannot be identified with existing distance-based and method-based approaches.
Dynamic Anchor Selection for Improving Object Localization

Anchor boxes act as potential object localization candidates allow single-stage detectors to achieve real-time performance, at the cost of localization accuracy when compared to state-of-the-art two-stage detectors. Therefore, correct selection of the scale and aspect ratio associated with an anchor box is crucial for detector performance. In this work, we propose a novel architecture called DANet for improving the localization performance of single-stage object detectors, while maintaining real-time inference. The proposed network achieves this by predicting (1) the combination of aspect ratio and scale per feature map based on object density and (2) localization confidence per anchor box. We evaluate the proposed network using the benchmark dataset. On the MS COCO dataset, DANet achieves 30.9% AP at 51.8 fps using ResNet-18 and 45.3% AP at 7.4 fps using ResNeXt-101. The code and models will be available at https://github.com/PS06/AnchorNet.
Chinese Calligraphy Character Image Recognition and Its Applications in Web and Wechat Applet Platform

Calligraphy is an important part of traditional Chinese culture. Authentic calligraphy works are usually preserved on paper, bamboo slips and stone tablets, which are easy to be destroyed and not easily appreciated by most calligraphy lovers anytime and anywhere. Therefore, in order to facilitate the preservation and appreciation of calligraphy works, digital technology and computer technology are used for digital storage, management and service.
A Novel Sketch Recognition Model based on Convolutional Neural Networks

Deep neural networks have been widely used for visual recognition tasks based on real images as they have proven their efficiency. Unlike real images, sketches exhibit a high level of abstraction as they lack the rich features that the real images contain such as various colors, backgrounds, and environmental detail. Despite all of these shortages and being drawn with just a few strokes, they are still meaningful enough to encompass an appropriate level of meaning. The efficiency of deep neural networks on sketch recognition has been relatively less studied compared to the visual recognition of real images. To experiment with the efficiency of deep neural networks on sketch recognition, a novel sketch recognition model based on Convolutional Neural Networks is proposed in this study. The proposed model consisted of 21 layers and was tuned in an automated manner to find out the best-optimized model. In order to reveal the proposed model's efficiency in terms of predicting the classes of the given sketches, the model was evaluated on a gold standard sketch dataset, namely, Quick, Draw!. According to the experimental result, the proposed model's accuracy was calculated as high as 89.53% which outperformed the related work on the same dataset. The key findings that were obtained during the conducted experiments were discussed to shed light on future studies.
End-to-End Verifiable E-Voting Trial for Polling Station Voting

On 2 May 2019, during United Kingdom local elections, an e-voting trial was conducted in Gateshead, using a touch-screen end-to-end verifiable e-voting system.This was the first test of its kind in the United Kingdom, and it presented a case study to envisage the future of e-voting.
A Primer on Opinion Mining: The Growing Research Area

The Web is extensively being used all around the world and holds valuable, vast and unstructured information about public opinions which can be analysed to obtain knowledge. Most of the people express their opinions about any product, service, event or topic through social media sites, blogs, channels, forums and review sites. Opinions expressed by others are important, and people, companies and organizations are heavily using opinions of individuals for decision making, which produced interest in extracting the individual’s opinion or sentiment. Thus, the everyday growing information on the Web about public opinions makes opinion mining a relevant field to extract the opinions or sentiments of others. Opinion mining evaluates the opinions, attitudes and emotions of people about some particular event or topic, and identifies the opinion polarity as positive, negative or neutral in data available from World Wide Web (WWW). In this paper, we have discussed and analysed the worthy work done by various researchers in the area of opinion mining and its classification techniques. This survey also highlights the basic concepts of opinion mining, opinion mining framework, opining mining techniques or approaches, opinion mining tools used to mine the opinions, opinion mining applications, and research challenges or issues faced in mining the opinions. The intent of this research paper is to discuss the previous work done on the hybrid opinion mining classification techniques and compare them with the base classifiers. The analysis shows that hybrid classification techniques give superior performance than existing classification techniques.