Reviewerly/exHarmony huggingface dataset:
Search Engines’ Visits and Users’ Behavior in Websites: Optimization of Users Engagement with the Content

In the new era of marketing, being at the top results of search engines constitutes one of the most competitive advantages to the organizations' overall online advertising strategy. In search engines, users type their search terms to cover their informational or purchasing needs and subsequently, search engines rank websites to the relevance of users' search terms. The higher are the rankings of the websites, the more is the percentage of visitors who explicitly come from search engines. Nevertheless this obvious one marketing advantage, there is no prior research evidence as regards the level of engagement between users and content, after they visit the websites from search engines' results. That is, users probably visit a website that comes at the top of search engines' results, however, they do not spend an amount of time, or they do not browse in several webpages inside of it and vice versa. Against this backdrop, the authors proceed into the construction of a methodology composed of the retrieval of web analytics datasets and the development of computational models with the purpose to evaluate users' engagement and content use within the websites. At the first stage, the authors proceed into the retrieval of web behavioral analytics at certain metrics for 125 sequential days as regards the time users are spending, the number of pageviews they are browsing, the percentage of immediate abandonments, and the percentage of traffic that explicitly comes from search engines. Following a data-driven methodological approach for the development of computational models, the fuzzy cognitive mapping at the descriptive modeling stage is adopted with the purpose to indicate the possible correlations between web analytics metrics. One step further, a corroborative and predictive model is proposed through the agent-based modeling method in order to compute the date ranges that resulted in the highest and the lowest engagements of users as regards the content of seven examined courseware websites. The proposed methodology and the results of this study work as a practical toolbox for decision makers while computing and evaluating through a data-driven way the level of engagement between visitors and the content they receive for online presence optimization on the web.
Deformable Kernel Convolutional Network for Video Extreme Super-Resolution

Video super-resolution, which attempts to reconstruct high-resolution video frames from their corresponding low-resolution versions, has received increasingly more attention in recent years. Most existing approaches opt to use deformable convolution to temporally align neighboring frames and apply traditional spatial attention mechanism (convolution based) to enhance reconstructed features. However, such spatial-only strategies cannot fully utilize temporal dependency among video frames. In this paper, we propose a novel deep learning based VSR algorithm, named Deformable Kernel Spatial Attention Network (DKSAN). Thanks to newly designed Deformable Kernel Convolution Alignment (DKC_Align) and Deformable Kernel Spatial Attention (DKSA) modules, DKSAN can better exploit both spatial and temporal redundancies to facilitate the information propagation across different layers. We have tested DKSAN on AIM2020 Video Extreme Super-Resolution Challenge to super-resolve videos with a scale factor as large as 16. Experimental results demonstrate that our proposed DKSAN can achieve both better subjective and objective performance compared with the existing state-of-the-art EDVR on Vid3oC and IntVID datasets.
Blood Glucose Prediction Based on Empirical Mode Decomposition and SSA-KELM

Predicting the blood glucose of type 1 diabetes patients in advance is of great significance to the prevention and treatment of diabetes. The prediction model used in this paper is based on the Sparrow Search Algorithm (SSA) of Empirical Mode Decomposition (EMD) to optimize the Kernel Extreme Learning Machine (KELM). In this work, EMD is used to decompose blood glucose values into different frequency sequences. Secondly, SSA- KELM is trained and each sub-sequence is predicted separately, and finally the prediction sequence is reconstructed to obtain the predicted value of blood glucose. The experimental results show that the SSA-KELM model has higher prediction accuracy than the ELM and PSO-KELM algorithms, and can be used for blood glucose prediction models.
Symmetric Quantile Quantizer Parameterization for the Laplacian Source: Qualification for Contemporary Quantization Solutions

In this paper, we consider the opportunities and constraints, which rest on quantization as a guiding principle for data representation and compression. In particular, we propose a novel model of Symmetric Quantile Quantizer (SQQ) and we describe in detail its parameterization. We suggest a simple method for offline precalculation of its parameters and we examine the inevitable loss of information introduced by SQQ, as an important part of bit optimization task at the traditional network level, which can be globally mapped out in many contemporary solutions. Our anticipation is that such precalculated values can be leveraged in deterministic quantization process. We highlight that this notice heavily relies on the fact that the values of interest are distributed according to the Laplacian distribution, which we consider in the paper. The basic difference of our SQQ and the previously established asymptotically optimal quantizer model, that is, Scalar Companding Quantizer (SCQ), is reflected in the fact that, in SCQ model, both decision thresholds and representation levels are determined in accordance with the specified compressor function, whereas in our SQQ model, a precedence of SCQ model for the straightforward decision thresholds calculation is used, while the representation levels are optimally determined for the specified decision thresholds and assumed Laplacian distribution. As a result, our SQQ outperforms SCQ in terms of signal-to-quantization noise ratio (SQNR). As stated in this paper, there are numerous indications to make us believe that appropriate quantizer parameterization will move us closer to an optimization in the amount of the transferred data in bits, which is strongly dependent on the amount of SQNR.
Graph and Centroid-based Word Clustering

With the recent exponential growth of text documents, a word clustering algorithm is an essential approach for making a reduction in a huge amount of text data and unsupervised feature selection on the domain of natural language processing. This paper proposes a novel method of the graph and centroid-based word clustering. The proposed method aims to automatically group similar words into the same cluster and handles noisy text and outliers. The proposed method applies the concepts of the hierarchical agglomerative clustering and K-means algorithm to find similar words according to the criterion of distance range on the co-occurrence graph. The small clusters and isolated words are also merged into another cluster. The experimental results demonstrate that the proposed method consistently and significantly outperforms state-of-the-art baselines in word clustering algorithms on the ground truth dataset. Besides, the proposed method is unsupervised learning and generic, which could be applied to various tasks of natural language processing and text mining.
Neural Networks Application on Human Skin Biophysical Impedance Characterizations

Artificial neural networks (ANNs) are basically the structures that perform input–output mapping. This mapping mimics the signal processing in biological neural networks. The basic element of biological neural network is a neuron. Neurons receive input signals from other neurons or the environment, process them, and generate their output which represents the input to another neuron of the network. Neurons can change their sensitivity to input signals. Each neuron has a simple rule to process an input signal. Biological neural networks have the property that signals are processed through many parallel connections (massively parallel processing). The activity of all neurons in these parallel connections is summed and represents the output of the whole network. The main feature of biological neural networks is that changes in the sensitivity of the neurons lead to changes in the operation of the entire network. This is called adaptation and is correlated with the learning process of living organisms. In this paper, a set of artificial neural networks are used for classifying the human skin biophysical impedance data.
ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results

This paper summarizes the ChaLearn Looking at People 2020 Challenge on Identity-preserved Human Detection (IPHD). For the purpose, we released a large novel dataset containing more than 112K pairs of spatiotemporally aligned depth and thermal frames (and 175K instances of humans) sampled from 780 sequences. The sequences contain hundreds of non-identifiable people appearing in a mix of in-the-wild and scripted scenarios recorded in public and private places. The competition was divided into three tracks depending on the modalities exploited for the detection: (1) depth, (2) thermal, and (3) depth-thermal fusion. Color was also captured but only used to facilitate the groundtruth annotation. Still the temporal synchronization of three sensory devices is challenging, so bad temporal matches across modalities can occur. Hence, the labels provided should considered “weak”, although test frames were carefully selected to minimize this effect and ensure the fairest comparison of the participants' results. Despite this added difficulty, the results got by the participants demonstrate current fully-supervised methods can deal with that and achieve outstanding detection performance when measured in terms of AP@0.50.
Handwritten Recognition: A survey

Handwritten recognition has received considerable attention in the domain of pattern recognition, image processing, over the last few decades. As a consequence of this research effort, several algorithms were developed using different techniques. Particularly, Deep Learning has shown a remarkable capability to handle handwritten recognition in very recent years. The well-known Deep learning techniques are the Convolutional Neuronal Networks (CNNs) and Recurrent Neuronal Networks (RNNs). This paper provides a survey of the most recent handwritten recognition systems. Thus, we present the most significant algorithms for handwritten character/word/text recognition by explaining the different approaches used in the recognition process and we compare them in terms of accuracy.
Analyzing and Controlling Inter-Head Diversity in Multi-Head Attention

Multi-head attention, a powerful strategy for Transformer, is assumed to utilize information from diverse representation subspaces. However, measuring diversity between heads’ representations or exploiting the diversity has been rarely studied. In this paper, we quantitatively analyze inter-head diversity of multi-head attention by applying recently developed similarity measures between two deep representations: Singular Vector Canonical Correlation Analysis (SVCCA) and Centered Kernel Alignment (CKA). By doing so, we empirically show that multi-head attention does diversify representation subspaces of each head as the number of heads increases. Based on our analysis, we hypothesize that there exists an optimal inter-head diversity with which a model can achieve better performance. To examine our hypothesis, we deeply inspect three techniques to control the inter-head diversity; (1) Hilbert-Schmidt Independence Criterion regularizer among representation subspaces, (2) Orthogonality regularizer, and (3) Drophead as zero-outing each head randomly in every training step. In our experiments on various machine translation and language modeling tasks, we show that controlling inter-head diversity leads to the best performance among baselines.
Evaluating Explainability Methods Intended for Multiple Stakeholders

Abstract Explanation mechanisms for intelligent systems are typically designed to respond to specific user needs, yet in practice these systems tend to have a wide variety of users. This can present a challenge to organisations looking to satisfy the explanation needs of different groups using an individual system. In this paper we present an explainability framework formed of a catalogue of explanation methods, and designed to integrate with a range of projects within a telecommunications organisation. Explainability methods are split into low-level explanations and high-level explanations for increasing levels of contextual support in their explanations. We motivate this framework using the specific case-study of explaining the conclusions of field network engineering experts to non-technical planning staff and evaluate our results using feedback from two distinct user groups; domain-expert telecommunication engineers and non-expert desk agent staff. We also present and investigate two metrics designed to model the quality of explanations - Meet-In-The-Middle (MITM) and Trust-Your-Neighbours (TYN). Our analysis of these metrics offers new insights into the use of similarity knowledge for the evaluation of explanations.
Sinhala Handwritten Character Recognition using Convolutional Neural Network

Handwritten character recognition is widely used for the English language. It is difficult to create a character recognition model for south Asian languages because of its shape and compound characters. Among other South Asian languages (e.g.: - Tamil, Hindi, Malayalam, etc.) Sinhala characters are unique, because of their shape, which are having mostly curves and dots. These unique characteristics make it difficult to create a model to recognize Sinhala's handwritten characters. Recognizing handwritten characters rather than typed characters is more complicated because the handwriting of each individual is varying from each other. Therefore the recognition of Sinhala handwritten character need to be improved. Convolutional Neural Network (CNN) is playing a vital role in character recognition by supporting the more efficient image classification. This research focuses on recognizing Sinhala handwritten characters using CNN. Google colaboratory platform is used for the experiment, and python programming language is used for the implementation part. In total, around 110,000 image data were used for the experiment. CNN's performance was evaluated by training and testing the dataset by increasing the number of character classes. When it reaches 100 character class it shows reasonable accuracy of 90.27%. The model was trained by 5 sets of different 100 character classes. Finally, the overall accuracy of 82.33% is achieved for 434 characters. This model outerformed than similar systems.
Image Dehazing Based on Haze Degree Classification

The existing technology of image dehazing neglects whether there is haze in an image and dehazes directly, which limits the efficiency of dehazing technology in real-world. Therefore, the classification of haze degree for image needs to be solved. This paper compares the features including brightness, haze factor, sharpness, etc. in clear and hazy images of different haze degree and obtain that different visual range images can be classified. And the Haze Image Classification Network is proposed which can classify an image to haze-free, haze-light or haze-dense. We test this method on our synthetic hazy dataset consisting of different visual range images and experiment results demonstrate the proposed idea performs well on accuracy, efficiency and real-time performance of hazy degree classification. We also train two dehazing model-haze-dense model and haze-light model for selecting and dehaze according to different haze degree. Compared to dehazing method without classification, doing haze degree classification before dehazing has got better performance on subjective and objective evaluation. The haze classification method and dehazing methods of this paper provide a good foundation for the following computer vision tasks, such as automatic driving, target detection, image recognition, etc.
Word Completion and Sequence Prediction in Bangla Language Using Trie and a Hybrid Approach of Sequential LSTM and N-gram

Autocompletion and sequence prediction is the basis of any assistance systems. When we step out to type something, it is much comforting to get a suggestion of the next word or even the full sentence before we type, which saves keystrokes of typing and reduce misspelling. Despite the several promising works in English language, little prior research in Bangla has shed light on this domain. In this paper, we proposed an integrated methodology of trie, sequential LSTM and N-gram for word completion and sequence prediction in Bangla language. The trie data structure was implemented to store Bangla vocabulary and retrieve the word from user-inputted prefix. For sequence prediction, we explored a hybrid approach of neural network and N-gram. This collaboration of sequential LSTM and N-gram reveals a better performance than any single model implementation. We evaluated this model with both small and large-scale Bangla datasets for better efficiency. The experiments show a promising outcome of our hybrid approach for word completion and sequence prediction. We believe that our framework leads to a profound impact on Bangla search engines, keyboards, and further researches based on recommendation systems.
Unsupervised Person Re-Identification Based on Measurement Axis

The main focus of unsupervised person re-identification is the clustering of unlabeled samples in the target domain. However, most existing studies neglected to mine the deep semantic information of the target domain and did not consider a better combination of the source domain and the target domain. In this letter, we not only consider the changes of the target domain within its own domain but also mine the deep semantic information of the images by designing a measurement axis component. Then, the deep semantic information mined by the axis is used as the judgment basis of hard negative samples. Moreover, a new loss function is designed in this work to improve the migration ability of the network. Experimental results on two person re-identification domains show that our technology accuracy outperforms the state of the art by a large margin.
Deep Learning Based Sinhala Optical Character Recognition (OCR)

With the advancement of computer technology during the last few years, researchers have integrated machine learning and deep learning techniques to analyse the textual representations on digital documents. As a result of that, people have tended to integrate Optical Character Recognition (OCR) technology to recognize printed texts into machine operable text for different character sets. Sinhala as an abugida script has its own writing system which is used to write Sinhala and Pali languages. With the complexities of the Sinhala script, it makes hard to develop an OCR system. When considering recent literature, most research groups try to reduce the complex nature of the Sinhala script with the support of computer science and Neural networks [1] , [2] . Tesseract is an open-source, deep-learning based OCR engine developed by Google [3] . Despite decades of research on the engineering aspects, our attempt was taken to improve the accuracy of Sinhala character recognition using deep learning mechanisms.
A Generative Adversarial Network-Based Image Denoiser Controlling Heterogeneous Losses

We propose a novel generative adversarial network (GAN)-based image denoising method that utilizes heterogeneous losses. In order to improve the restoration quality of the structural information of the generator, the heterogeneous losses, including the structural loss in addition to the conventional mean squared error (MSE)-based loss, are used to train the generator. To maximize the improvements brought on by the heterogeneous losses, the strength of the structural loss is adaptively adjusted by the discriminator for each input patch. In addition, a depth wise separable convolution-based module that utilizes the dilated convolution and symmetric skip connection is used for the proposed GAN so as to reduce the computational complexity while providing improved denoising quality compared to the convolutional neural network (CNN) denoiser. The experiments showed that the proposed method improved visual information fidelity and feature similarity index values by up to 0.027 and 0.008, respectively, compared to the existing CNN denoiser.
On the Hardness of PAC-learning Stabilizer States with Noise

We consider the problem of learning stabilizer states with noise in the Probably Approximately Correct (PAC) framework of Aaronson (2007) for learning quantum states. In the noiseless setting, an algorithm for this problem was recently given by Rocchetto (2018), but the noisy case was left open. Motivated by approaches to noise tolerance from classical learning theory, we introduce the Statistical Query (SQ) model for PAC-learning quantum states, and prove that algorithms in this model are indeed resilient to common forms of noise, including classification and depolarizing noise. We prove an exponential lower bound on learning stabilizer states in the SQ model. Even outside the SQ model, we prove that learning stabilizer states with noise is in general as hard as Learning Parity with Noise (LPN) using classical examples. Our results position the problem of learning stabilizer states as a natural quantum analogue of the classical problem of learning parities: easy in the noiseless setting, but seemingly intractable even with simple forms of noise.
Automatic Short Answer Grading Using Corpus-Based Semantic Similarity Measurements

In this paper, we have explored some unsupervised techniques for the task of Automatic Short Answer Grading. Three models are designed for the task in hand, with each model evaluating and grading students’ responses individually. The proposed method has shown quite promising correlation between scores generated by the method and that awarded by human scorers on a standard computer science dataset, with the overall correlation reaching the peak value at 0.805 which outperforms the state-of-the-art results reported on this dataset so far.
Video Quality Model for Space-Time Resolution Adaptation

Delivering voluminous amounts of video data through limited bandwidth channels is a challenge affecting billions of viewers. Accordingly, it is becoming more important to understand the perceptual effects that arise from various dimension reduction methodologies. Towards this direction, we propose a new video quality model that predicts the perceptual quality of videos undergoing varying levels of spatio-temporal subsampling and compression. The new model is established upon the natural statistics principle of videos, which leverage the fact that pristine videos obey statistical regularities that are disturbed by distortions. We found that there exist space-time paths between video frames that best preserve the statistical regularity inherent in the spatial structure of the video frames. The distribution features extracted from frame differences displaced in the direction of these paths correlate more highly with human subjective quality opinions than those from non-displaced frame differences. Given that non-displaced frame differences are widely utilized in video quality models, the improved efficiency of spatially and/or temporally displaced (possibly by more than one frame) frame differences, is an important finding that may significantly elevate the success of studies on temporal features and video quality.