Reviewerly/exHarmony huggingface dataset:
Performance Analyses of AES and 3DES Algorithms for Encryption of Satellite Images

As the size of the data increases, securing the data has become an important concern as much as storing and transferring. Especially, the sensitive satellite images obtained from earth observation satellites should be transferred to the grand stations by preventing the intruders from accessing the images. For this purpose, cryptography algorithms are used to encrypt the satellite images and these images are transferred to ground station as ciphered image. In this study, AES and 3DES, which are symmetric block encryption algorithms, were used to encrypt three different size satellite images and the performance comparisons of these algorithms were performed by considering some metrics such as histogram, correlation coefficient, NPCR, UACI, PSNR, and computational time. In the simulations, AES and 3DES showed very close results in terms of histogram, correlation coefficient, NPCR, UACI and PSNR metrics for all three satellite images. However, it was observed that AES had performed better in both encryption and decryption of satellite images than 3DES in terms of the computational time criteria, and it had spent almost twice less time than 3DES.
When Deep Classifiers Agree: Analyzing Correlations Between Learning Order and Image Statistics

Although a plethora of architectural variants for deep classification has been introduced over time, recent works have found empirical evidence towards similarities in their training process. It haswrapfig been hypothesized that neural networks converge not only to similar representations, but also exhibit a notion of empirical agreement on which data instances are learned first. Following in the latter works’ footsteps, we define a metric to quantify the relationship between such classification agreement over time, and posit that the agreement phenomenon can be mapped to core statistics of the investigated dataset. We empirically corroborate this hypothesis across the CIFAR10, Pascal, ImageNet and KTH-TIPS2 datasets. Our findings indicate that agreement seems to be independent of specific architectures, training hyper-parameters or labels, albeit follows an ordering according to image statistics.
Exemplar Guided Cross-Spectral Face Hallucination via Mutual Information Disentanglement

Recently, many Near infrared-visible (NIR-VIS) heterogeneous face recognition (HFR) methods have been proposed in the community. But it remains a challenging problem because of the sensing gap along with large pose variations. In this paper, we propose an Exemplar Guided Cross-Spectral Face Hallucination (EGCH) to reduce the domain discrepancy through disentangled representation learning. For each modality, EGCH contains a spectral encoder as well as a structure encoder to disentangle spectral and structure representation, respectively. It also contains a traditional generator that reconstructs the input from the above two representations, and a structure generator that predicts the facial parsing map from the structure representation. Besides, mutual information minimization and maximization are conducted to boost disentanglement and make representations adequately expressed. Then the translation is built on structure representations between two modalities. Provided with the transformed NIR structure representation and original VIS spectral representation, EGCH is capable to produce high-fidelity VIS images that preserve the topology structure of the input NIR while transfer the spectral information of an arbitrary VIS exemplar. Extensive experiments demonstrate that the proposed method achieves more promising results both qualitatively and quantitatively than the state-of-the-art NIR-VIS methods.
Pruning of Convolutional Neural Networks using ising Energy Model

Pruning is one of the major methods to compress deep neural networks. In this paper, we propose an Ising energy model within an optimization framework for pruning convolutional kernels and hidden units. This model is designed to reduce redundancy between weight kernels and detect inactive kernels/hidden units. Our experiments using ResNets, AlexNet, and SqueezeNet on CIFAR-10 and CIFAR-100 datasets show that the proposed method on average can achieve a pruning rate of more than 50% of the trainable parameters with approximately < 10% and < 5% drop of Top-1 and Top-5 classification accuracy, respectively.
Blockchain and Smart Contracts as New Governance Tools for the Sharing Economy

The last financial downturn has imposed severe changes on the labour market leading to the disappearance of a number of traditional jobs and social securities. Among the diverse effects induced by those recent modifications to the socio-economic fabric, we have seen the emergence of the sharing economy. The sharing economy can be defined as an economic system where entities can share services and goods, for a fee or a payment via shared platforms or peer-to-peer networks (P2P). The idea behind the rise of the sharing economy is related to sustainability and social justice, and the promotion of the democratisation of goods and resources.
Assessment method for Scrum culture within the Development team

Agile methods have become the most adopted within the software industry as they offer a faster return on investment, better software quality, and higher customer satisfaction in comparison with the traditional approaches. However, the agile method is regarded as a cultural shift where values need to be institutionalized within the development team prior methodology implementation. Many organization adopting agile method without considering its values which lead to undesired outcomes. Despite this, the current literature lacks attempts in assessing agile team culture readiness. Therefore, this research develops the Value, Goal, Question, and metric (VGQM) assessment method to assess agile team culture readiness within the development team. The VGQM includes metrics that selected purposely to evaluate agile team values. The VGQM is based on the Goal, Question, and Metric (GQM) paradigm concept. The VGQM was applied in a software company as a case study for evaluation purposes. The outcome of the case study shows the VGQM capability to assess agile values and identify gaps as areas for improvement.
Mining Tourists’ Opinions on Popular Indian Tourism Hotspots using Sentiment Analysis and Topic Modeling

User-generated content is an exploration area of interest with regards to web 2.0. The development of social networks and community-based websites have changed the manner in which individuals utilize the Internet. It makes individuals no longer restricted to pursuing the data given by professional channels, but to making individual profiles, producing personalized content, or sharing photographs, recordings, blogs, and so forth. This sort of data comprises the current online user-generated content. With the continuous development of the travel industry, the quantity of online travel review websites has also increased. Indian Tourism is popular for its rich culture and diversity and hence Government of India has increased the number of new tourist destinations to expand their popularity and presence. Researchers have proposed various studies to increase tourism network using Big Data. Techniques of Sentiment Analysis along with Topic Modelling have been used to unearth patterns and observations from online reviews. This paper aims to mine reviews of 10 popular travel destinations in India. Using sentiment analysis technique, the proposed research work has explored the polarity of various reviews extracted from TripAdvisor. Data collection was done by using the web framework Scrapy to acquire more than 10,000 reviews for these destinations. This paper also analyzes the result of doing Topic Modeling on reviews for individual destinations. Results conclude that Joy is the most common emotion in all the visitor’s experiences. Indian tourism decision quality can be improved by the help of the results from this study.
Wavelength conversion for single-photon polarization qubits through continuous-variable quantum teleportation

A quantum internet connects remote quantum processors that need to interact and exchange quantum signals over a long distance through photonic channels. However, these quantum nodes operate at the wavelength ranges unsuitable for long-distance transmission. Therefore, quantum wavelength conversion to telecom bands is crucial for long-distance quantum networks based on optical fiber. Here, we propose wavelength conversion devices for single-photon polarization qubits using continuous-variable quantum teleportation that can efficiently convert qubits between near-infrared (780--795 nm suitable for interacting with atomic quantum nodes) and telecom wavelength (1300--1500 nm suitable for long-distance transmission). The teleportation uses entangled photon fields (i.e., nondegenerate two-mode squeezed state) that can be generated by four-wave mixing in a rubidium atomic gas using a diamond configuration of atomic transitions. The entangled fields can be emitted in two orthogonal polarizations with locked relative phase, making them especially suitable for interfacing with single-photon polarization qubits. Our work may pave the way for the realization of long-distance quantum networks.
Estimation of Visual Features of Viewed Image From Individual and Shared Brain Information Based on FMRI Data Using Probabilistic Generative Model

This paper presents a method for estimation of visual features based on brain responses measured when subjects view images. The pro-posed method estimates visual features of viewed images by using both individual and shared brain information from functional magnetic resonance imaging (fMRI) data when subjects view images. To extract an effective latent space shared by multiple subjects from high dimensional fMRI data, a probabilistic generative model that can provide a prior distribution to the space is introduced into the proposed method. Also, the extraction of a robust feature space with respect to noise for the individual information becomes feasible via the proposed probabilistic generative model. This is the first contribution of our method. Furthermore, the proposed method constructs a decoder transforming brain information into visual features based on collaborative use of both estimated spaces for individual and shared brain information. This is the second contribution of our method. Experimental results show that the proposed method improves the estimation accuracy of the visual features of viewed images.
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables

Recognizing human activities from multi-channel time series data collected from wearable sensors has become an important practical application of machine learning. A serious challenge comes from the presence of coherent activities or body movements, such as movements of the head while walking or sitting, since signals representing these movements are mixed and interfere with each other. Basic multi-label classification typically assumes independence within the multiple activities. This is oversimplified and reduces modeling power even when using state-of-the-art deep learning methods. In this paper, we investigate this new problem, which we name “Coherent Human Activity Recognition (Co-HAR)”, that keeps complete conditional dependency between the multiple labels. Additionally, we treat Co-HAR as a dense labelling problem that classifies each sample on a time step with multiple coherent labels to provide high-fidelity and duration-sensitive support to high-precision applications. To explicitly model conditional dependency, a novel condition-aware deep architecture “Conditional-UNet” is developed to allow for multiple dense labeling for Co-HAR. We also contribute a first-of-its-kind Co-HAR dataset for head gesture recognition associated with a user's activity, walking or sitting, to the research community. Extensive experiments on this dataset show that our model outperforms state-of-the-art deep learning methods and achieves up to 92% accuracy on context-based head gesture classification.
Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems

Markov chain Monte Carlo (MCMC) approaches are traditionally used for uncertainty quantification in inverse problems where the physics of the underlying sensor modality is described by a partial differential equation (PDE). However, the use of MCMC algorithms is prohibitively expensive in applications where each log-likelihood evaluation may require hundreds to thousands of PDE solves corresponding to multiple sensors; i.e., spatially distributed sources and receivers perhaps operating at different frequencies or wavelengths depending on the precise application. We show how to mitigate the computational cost of each log-likelihood evaluation by using several randomized techniques and embed these randomized approximations within MCMC algorithms. The resulting MCMC algorithms are computationally efficient methods for quantifying the uncertainty associated with the reconstructed parameters. We demonstrate the accuracy and computational benefits of our proposed algorithms on a model application from diffuse optical tomography where we invert for the spatial distribution of optical absorption.
Beyond Beauty: Towards a Deeper Understanding of Aesthetics in HCI

The word aesthetics, as used in Human-Computer Interaction (HCI), tends to refer to visual characteristics of an interface. Furthermore, it is broadly taken to mean beauty, which, while a significant aspect of aesthetics, is not its only concern. Unfortunately, HCI tends to hold a narrow-sighted view of the topic that often ignores a rich history of discourse. Aesthetics is a key concern of philosophy, considering our perception of the natural and artefactual world. In more recent times, it has grown to consider all of our sensory perceptions of the world around us, where our encounters with everyday objects and environments are two areas of interest. Here, I explain how HCI describes aesthetics and give an overview of philosophical approaches to aesthetics, show where some common ground lies between the two, and suggest how aesthetic categorisations could work for artefacts in HCI.
Recovery of 2D and 3D Layout Information through an Advanced Image Stitching Algorithm using Scanning Electron Microscope Images

Image stitching describes the process of reconstruction of a high-resolution image by combining multiple images. Using a scanning electron microscope as the image source, individual images will show patterns in a nm dimension, whereas the combined image may cover an area of several mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The recovery of the physical layout of modern semiconductor products manufactured in advanced technologies nodes down to 22 nm requires a perfect stitching process with no deviation with respect to the original design data, as any stitching error will result in failures during the reconstruction of the electrical design. In addition, the recovery of the complete design requires the acquisition of all individually layers of a semiconductor device. The layers represent a 3D structure with interconnections defining error limits on the stitching error for each individual scanned image mosaic. An advanced stitching and alignment process is presented, enabling a true geometrical layout recovery in nanoscale dimensions, which is also applied to and evaluated for other use cases from biological applications.
Comparative Study Of Google Search Engine Optimization Algorithms: Panda, Penguin and Hummingbird

Thousands of websites are hosted daily all over the world. However, very few of them actually make an impact on the world wide web and attract valuable web traffic. In this scenario, Search Engine Qptimization(SEO) has become a very significant factor to increase the ranking of such websites by improving the quality of content and thereby increasing visibility for relevant searches. There are various page ranking algorithms present today to help with this aspect of SEO and this paper is going to present a comparative analysis of some of the popular Google SEO algorithms such as Panda, Penguin and Hummingbird which help in detecting low quality websites thereby affecting their ranking on the search engine results page(SERP).
Cgan-Net: Class-Guided Asymmetric Non-Local Network for Real-Time Semantic Segmentation

By introducing various non-local blocks to capture the long-range dependencies, remarkable progress has been achieved in semantic segmentation recently. However, the improvement in segmentation accuracy usually comes at the price of significant reductions in network efficiency, as non-local block usually requires expensive computation and memory cost for dense pixel-to-pixel correlation. In this paper, we introduce a Class-Guided Asymmetric Non-local Network (CGAN-Net) to enhance the class-discriminability in learned feature map, while maintaining real-time efficiency. The key to our approach is to calculate the dense similarity matrix in coarse semantic prediction maps, instead of the high-dimensional latent feature map. This is not only computationally and memory efficient, but helps to learn query-dependent global context. Experiments conducted on Cityscape and CamVid demonstrate the compelling performance of our CGAN-Net. In particular, our network achieves 76.8% mean IoU on the Cityscapes test set with a speed of 38 FPS for 1024×2048 images on a single Tesla V100 GPU.
SeekSuspect

It is crucial for the police department to automatically determine if suspects are present in the criminal database, sometimes based on the informant's visual memory alone. FaceFetch [15] is a state-of-the-art face retrieval system capable of retrieving an envisioned face from a large-scale database. Although FaceFetch can retrieve images effectively, it lacks sophisticated techniques to produce results efficiently. To this end, we propose SeekSuspect, a faster interactive suspect retrieval framework, which introduces several optimization algorithms to FaceFetch's framework. We train and test our system on a real-world dataset curated in collaboration with a metropolitan police department in India. Results reveal that SeekSuspect beats FaceFetch and can be employed by law enforcement agencies to retrieve suspects.
Mitigation of the Effects of Network Outage on Video QoE Using a Sender Buffer

With the growth of multimedia applications and the mobile Internet, quality sense and quality expectation of the end-user are rising rapidly. A small notable distortion in the multimedia applications may degrade the degree of delight of the user, who is very considerate of the video Quality of Experience (QoE). During live streaming, a network outage may result in video freezes and video jumps. To dampen the impact of a network outage on the video QoE, we propose the use of a well-sized sender buffer. We present the concept, derive key analytical relations, and perform a set of subjective tests. Based on those, we report a significant enhancement of user ratings due to the proposed sender buffer in the presence of network outages.
Examining the Historical Development of Techno-Scientific Biomedical Communication in Russia

The article focuses on the shift from traditional to techno-scientific biomedical discourse in health-related media communication. The Russian case should be put in context of successes and failures of governmental policies and campaigns, as well as diverse changes in social life that took place in the last decade. To this end, we developed a method for the reconstruction of the timeline that triggered the ongoing techno-scientific progress in Russia based on the decade-long history of healthcare media reports. The dataset includes news reports collected using health and medicine-related keywords from two prominent Russian newspapers ("Rossiyskaya Gazeta" and "Kommersant", from 2010 to 2019). The method employs text-mining tools including topic modelling (Latent Dirichlet Allocation-based), term statistics and graphs of keyphrase relationships to analyze the dynamics of media communication in healthcare during the study period. The results of the study confirm that before 2015 the coverage of health-associated topics includes criminal incidents and the "modernization" of the state healthcare system. After 2016, we detected the spread of a new discourse related to drugs elabeling, new drugs, networks and databases development.
Investigating Awareness and usage of Electronic Resources by the Library Users of Selected Colleges of Solapur University

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Determining the Target Point of the Mid-Air Pinch Gesture

Pinching is a common gesture primarily used for zooming on mobile devices, and previous studies considered utilizing it as a mid-air gesture in AR/VR. As opposed to touch screens, there is no physical contact point between the display and the fingers in mid-air pinching, which means the positional relationship between the target point for zooming and the users' finger movement in mid-air pinching could be different from that of touch screens. In this study, we investigated the relationship in mid-air pinching to estimate the target point from the hand posture, and found that the point was significantly off towards the thumb and away from the index finger (approximately 7% offset). This finding contributes to a more accurate mid-air zooming.
The HisClima database: historical weather logs for automatic transcription and information extraction

Knowing the weather and atmospheric conditions from the past can help weather researchers to generate models like the ones used to predict how weather conditions are likely to change as global temperatures continue to rise. Many historical weather records are available from the past registered on a systemic basis. Historical weather logs were registered in ships, when they were on the high seas, recording daily weather conditions such as: wind speed, temperature, coordinates, etc. These historical documents represent an important source of knowledge with valuable information to extract climatic information of several centuries ago. This paper presents a database for researching about the capability of state-of-the-art handwritten text recognition systems to extract relevant information from this source of knowledge. This database is composed mainly by handwritten tables that contain mostly numerical information. The problems on these documents are explained and baseline experiments are introduced on them.
FlakeFlagger: Predicting Flakiness Without Rerunning Tests

This is an extended abstract that describes the code and data artifact [1] of our paper “FlakeFlagger: Predicting Flakiness Without Rerunning Tests” [2]. The goal of our artifact is to let researchers reproduce our experiment on our provided flaky dataset or reuse our tool on different flaky tests datasets.