Reviewerly/exHarmony huggingface dataset:
Marketing of academic health libraries 2.0: a case study

Purpose The advent of Web 2.0 in libraries persuades the librarians to adopt new ways to communicate, determine, and satisfy the needs of the users. The paper aims to discuss this issue. Design/methodology/approach A 30-question questionnaire was given to 30 undergraduate medical students of Vardhman Mahavir Medical College and a 10-question questionnaire was given to the librarian, to find out: the marketing and promotional strategies employed by the library; determine the awareness and satisfaction level of the users; prepare library profile, customer profile and market profile; and perform SWOT analysis. User responses were coded and processed using GNU PSPP software. Findings From the library profile of Vardhman Mahavir Medical College library, it can be concluded that the library has the potential to offer better services and products to its users. From the customer profile of Vardhman Mahavir Medical College library, it is concluded that the most of its users are young male undergraduate students who use the library more on weekly basis. From the market profile of Vardhman Mahavir Medical College library, it is concluded that the library has not invested in the marketing and has no promotion strategy or marketing strategy for its products and services. Also, the library has only one digital promotional activity. From the SWOT analysis of Vardhman Mahavir Medical College library, it is concluded that the library does not use any of the social platforms to market its products and services. Most of the users are unaware of the services and products offered by the library. There are many opportunities for the library to work upon and improve the quality of products and services being offered to its users. Research limitations/implications The major limitation of the present study is its small sample size. It is very difficult to conduct surveys in health libraries because of the busy schedule of the undergraduate medical students and their reluctance to fill out lengthy questionnaires. However, this small sample size only made it possible to conduct the SWOT analysis on the basis of the users’ survey successfully. Further, the small sample size helped to take into account all the opportunities stated by the users which would not have been possible if a larger sample size was taken. Originality/value This study is one of a kind which provides an overview of marketing research of an academic health library of New Delhi (India) with a special focus on library profile, market profile, customer profile and SWOT analysis. It addresses the gaps in the literature by studying marketing in the context of academic health libraries in the digital environment.
Quantum algorithms and approximating polynomials for composed functions with shared inputs

We give new quantum algorithms for evaluating composed functions whose inputs may be shared between bottom-level gates. Let f be a Boolean function and consider a function F obtained by applying f to conjunctions of possibly overlapping subsets of n variables. If f has quantum query complexity Q(f), we give an algorithm for evaluating F using [MATH HERE]) quantum queries. This improves on the bound of [MATH HERE]) that follows by treating each conjunction independently, and is tight for worst-case choices of f. Using completely different techniques, we prove a similar tight composition theorem for the approximate degree of f.By recursively applying our composition theorems, we obtain a nearly optimal O(n1−2−d) upper bound on the quantum query complexity and approximate degree of linear-size depth-d AC0 circuits. As a consequence, such circuits can be PAC learned in subexponential time, even in the challenging agnostic setting. Prior to our work, a subexponential-time algorithm was not known even for linear-size depth-3 AC0 circuits. We also show that any substantially faster learning algorithm will require fundamentally new techniques.
Computational Curriculum for MatSE Undergraduates and the Influence on Senior Classes

Urbana-Champaign and he is currently working in the Schleife's
Jitter Estimation with High Accuracy for Oscillator-Based TRNGs

Ring oscillator-based true random number generators (RO-based TRNGs) are widely used to provide unpredictable random numbers for cryptographic systems. The unpredictability of the output numbers, which can be measured by entropy, is extracted from the jitter of the oscillatory signal. To quantitatively evaluate the entropy, several stochastic models have been proposed, all of which take the jitter as a key input parameter. So it is crucial to accurately estimate the jitter in the process of entropy evaluation. However, several previous methods have estimated the jitter with non-negligible error, which would cause the overestimation of the entropy. In this paper, we propose a jitter estimation method with high accuracy. Our method aims at eliminating the quantization error in previous counter-based jitter estimation methods and finally can estimate the jitter with the error smaller than $$1\%$$ . Furthermore, for the first time, we give a theoretical error bound for our jitter estimation. The error bound confirms the $$1\%$$ error level of our method. As a consequence, our method will significantly help to evaluate the entropy of RO-based TRNGs accurately. Finally, we present the application of our jitter estimation method on a practical FPGA device and provide a circuit module diagram for on-chip implementation.
Domain Adaptation for Reinforcement Learning on the Atari

Deep Reinforcement learning is a powerful machine learning paradigm that has had significant success across a wide range of control problems. This success often requires long training times to achieve. Observing that many problems share similarities, it is likely that much of the training done could be redundant if knowledge could be efficiently and appropriately shared across tasks. In this paper we demonstrate a novel adversarial domain adaptation approach to transfer state knowledge between domains and tasks on the Atari game suite. We show how this approach can successfully transfer across very different visual domains of the Atari platform. We focus on semantically related games that involve returning a ball with the user controlled agent. Our experiments demonstrate that our method reduces the number of samples required to successfully train an agent to play an Atari game.
Multichannel Semantic Segmentation with Unsupervised Domain Adaptation

Most contemporary robots have depth sensors, and research on semantic segmentation with RGBD images has shown that depth images boost the accuracy of segmentation. Since it is time-consuming to annotate images with semantic labels per pixel, it would be ideal if we could avoid this laborious work by utilizing an existing dataset or a synthetic dataset which we can generate on our own. Robot motions are often tested in a synthetic environment, where multichannel (e.g., RGB + depth + instance boundary) images plus their pixel-level semantic labels are available. However, models trained simply on synthetic images tend to demonstrate poor performance on real images. In order to address this, we propose two approaches that can efficiently exploit multichannel inputs combined with an unsupervised domain adaptation (UDA) algorithm. One is a fusion-based approach that uses depth images as inputs. The other is a multitask learning approach that uses depth images as outputs. We demonstrated that the segmentation results were improved by using a multitask learning approach with a post-process and created a benchmark for this task.
Estimation of Vector Autoregressive Parameters and Granger Causality From Noisy Multichannel Data

Objective: The objective of this paper is to estimate the parameters of a multivariate autoregressive process from noisy multichannel data. Methods: Using a multivariate generalization of the Cadzow method, we propose a new method for estimating autoregressive parameters from noisy data: the nonlinear Cadzow method. Results: We show that our method outperforms existing multivariate methods such as higher order Yule-Walker method and Kalman EM method on simulated data. We apply our method to estimation of Granger causality from noisy data and again obtain superior results compared to previous methods. Finally, when applied to experimental local field potential data from monkey somatosensory and motor cortical areas, our method produces results consistent with cortical physiology. Conclusion: The proposed nonlinear Cadzow method outperforms existing methods in obtaining denoised estimates of multivariate autoregressive parameters. Significance: Since multichannel recordings have become commonplace in biomedical applications ranging from discovering functional connectivity in the brain to speech data analysis and these recordings are inevitably contaminated by measurement noise, we believe our method has the potential for significant impact.
Hierarchical Planning Guided by Genetic Algorithms for Multiple HAPS in a Time-Varying Environment

A hierarchical task planning structure is favorable for its capability to accommodate constraints at different abstraction levels and also for the similarity of its planning approach as a human. This structure is adopted for the task planning for multiple HAPS. However, the combinatorial search problem grows with the presence of multiple agents. This work proposes a method to guide the decomposition of the tasks down the hierarchy with genetic algorithm in order to find quality plans within limited time.
An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching

Recently, reversible image transformation (RIT) technology has attracted considerable attention because it is able not only to generate stego-images that look similar to target images of the same size, but also to recover the secret image losslessly. Therefore, it is very useful in image privacy protection and reversible data hiding in encrypted images. However, the amount of accessorial information, for recording the transformation parameters, is very large in the traditional RIT method, which results in an abrupt degradation of the stego-image quality. In this paper, an improved RIT method for reducing the auxiliary information is proposed. Firstly, we divide secret and target images into non-overlapping blocks, and classify these blocks into K classes by using the K-means clustering method. Secondly, we match blocks in the last (K-T)-classes using the traditional RIT method for a threshold T, in which the secret and target blocks are paired with the same compound index. Thirdly, the accessorial information (AI) produced by the matching can be represented as a secret segment, and the secret segment can be hided by patching blocks in the first T-classes. Experimental results show that the proposed strategy can reduce the AI and improve the stego-image quality effectively.
Mr Fantastic Meets The Invisible Man: An Illusion of Invisible Finger Stretching

Our brain continually integrates bottom-up sensory signals to create a coherent experience of the body. This bodily experience is also constrained by top-down knowledge of body appearance. However, the extent of these constraints has been challenged. Here, we explore top-down limits on body ownership with the invisible finger stretching illusion, in which synchronous visuotactile stimulation applied to the real fingers and an area of empty space elicits the illusion of owning elongating fingers. The results demonstrate that it is possible to experience stretchy fingers like Mr Fantastic without visual stimuli of a fake hand, even if we do not actually feel invisible like The Invisible Man.
Implementation of Digital Signature Algorithm by using Elliptical Curve p-192

The public key cryptographic algorithms considered unsuitable for Internet of Things due to use of longer keys, stressful calculations and consumption of battery resources for 16bit processers. This paper presents a novel algorithm based on elliptical curve for securing communication between internet of things by use of single coordinate system of Elliptical curve cryptography. Though lengdre symbol or single coordinate system exist in mathematics literature, it’s use for securing IoT devices is fairly new. In the proposed system IoT device transfer one coordinate of public key with extra bit allowing to save 50 % of bandwidth and storage for IoT devices.
Modeling and Simulation Studies of Complex Biological Systems for Precision Medicine and Healthcare

Here present three articles on the private preservation of genome or electronic health record (EHR) data. Three articles focus on the algorithm developing for the analysis of GWAS and EHR-based phenotyping as well as electrocardiogram (ECG)-based disease recognition and classification. Here also include an article developing an algorithm for repositioning of old drugs for their new applications. The articles in this special section proposed several computational model and simulation methods to address diverse medical and healthcare issues which will be helpful to the promotion of the cross-disciplinary researches on the translational medicine and healthcare.
Moderate Environmental Variation Across Generations Promotes the Evolution of Robust Solutions

Previous evolutionary studies demonstrated how robust solutions can be obtained by evaluating agents multiple times in variable environmental conditions. Here we demonstrate how agents evolved in environments that vary across generations outperform agents evolved in environments that remain fixed. Moreover, we demonstrate that best performance is obtained when the environment varies at a moderate rate across generations, that is, when the environment does not vary every generation but every N generations. The advantage of exposing evolving agents to environments that vary across generations at a moderate rate is due, at least in part, to the fact that this condition maximizes the retention of changes that alter the behavior of the agents, which in turn facilitates the discovery of better solutions. Finally, we demonstrate that moderate environmental variations are advantageous also from an evolutionary computation perspective, that is, from the perspective of maximizing the performance that can be achieved within a limited computational budget.
Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation

Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and require long training time. To overcome this issue, we introduce a network structure for volumetric data without 3D convolutional layers. The main idea is to include maximum intensity projections from different directions to transform the volumetric data to a sequence of images, where each image contains information of the full data. We then apply 2D convolutions to these projection images and lift them again to volumetric data using a trainable reconstruction algorithm.The proposed network architecture has less storage requirements than network structures using 3D convolutions. For a tested binary segmentation task, it even shows better performance than the 3D U-net and can be trained much faster.
The Use of 360-Degree Video to Provide an Alternative Media Approach to Paralympic Sports

This paper describes exploratory research that consists of the production and testing of a 360-degree video-prototype aiming to provide a closer perspective to Paralympic athletes’ training universe, exemplified by the wheelchair basketball. Media coverage of Paralympic sports has been changing but still represents athletes as heroes due to the overcoming of odds related to the impairment and not to the sports results. The present work considers immersive journalism and the use of virtual reality (VR) technologies as the path to an alternative framing where the athletes are portrayed as players facing a hard training routine and having sporting goals as the non-disabled ones. The paper presents a brief description of prototype production and details the user study conducted in the aftermath with four focus groups. Feedback provided by participants indicates the suitability of the 360-degree video, when experienced with a headset and headphones, for the framing proposed and also points out narrative strategies that can be useful not only in Paralympic sports’ storytelling but in the conception of diverse narratives for 360-degree video.
An Empirical Study of Steganography and Steganalysis of Color Images in the JPEG Domain

This paper tackles the problem of JPEG steganography and steganalysis for color images, a problem that has rarely been studied so far and which deserves more attention. After focusing on the 4:4:4 sampling strategy, we propose to modify for each channel the embedding rate of J-UNIWARD and UERD steganographic schemes in order to arbitrary spread the payload between the luminance and the chrominance components while keeping a constant message size for the different strategies. We also compare our spreading payload strategy w.r.t. two strategies: (i) the concatenation of the cost map (CONC) or (ii) equal embedding rates (EER) among channels. We then select good candidates within the feature sets designed either for JPEG or color steganography. Our conclusions are threefold: (i) the GFR or DCTR features sets, concatenated on the three channels offer better performance than ColorSRMQ1 for JPEG Quality Factor (QF) of 75 and 95 but ColorSRMQ1 is more sensitive for QF = 100, (ii) the CONC or EER strategies are suboptimal, and (iii) depending of the quality factors and the embedding schemes, the empirical security is maximized when between 33% (QF = 100, UERD) and 95% (QF = 75, J-UNIWARD) of the payload is allocated to the luminance channel.
Internationalization of the educational services market through development of the system of remote education

Purpose The purpose of this paper is to study the possibilities and potential barriers on the path of internationalization of the educational services market through development of the system of remote education. Design/methodology/approach In order to determine the influence of development of the system of remote education on the process of internationalization of the educational services market, the authors use the method of regression analysis. A regression curve is built, containing the model of paired linear regression and the value of determination coefficient. The research object is the market of higher educational services – as it is subject to internationalization and distribution of remote education. The research is performed based on the 2017 data by the example of countries from various regions of the world that show different rates and levels of socio-economic development but that achieved the largest success in development of the system of remote education – the USA, the UK, Brazil, China, South Korea, Italy, Germany, India, Malaysia, Australia, South Africa and Russia. Due to the absence of the official international statistics on the issue of internationalization of the educational services market, the proprietary method is used for determining its level. Findings It is concluded that remote education has a slight influence on the process of internationalization of the educational services market in the countries of the world, though it has large potential in the sphere of stimulation of internationalization of the educational services market, as it allows providing educational services of equal quality and equal cost for domestic and foreign students. Originality/value The developed and presented model of managing the development of the remote education system of a modern university in the interests of stimulating internationalization of the educational services market allows overcoming these barriers and opening the existing potential of remote education in the sphere of stimulation of internationalization of the educational services market.
Anomaly Detection in Crowded Scene by Pedestrians Behaviour Extraction using Long Short Term Method: A Comprehensive Study

With the expansion of worldwide security concerns and a consistently expanding requirement for successful checking of open places, i.e. air terminals, railroad stations, shopping centres, crowded sports fields, army bases or smart healthcare facilities such as daily activity monitoring and fall detection in old people's homes is increasing very rapidly.The visual occlusions and ambiguities in crowded scenes, usage of suitable method and in addition the perplexing practices and scene semantics make the investigation a challenging task.This research demonstrates comprehensive and critical analysis of crowd scene involves in object detection, tracking, feature extraction and learning from visual surveillance which helps to recognize behavioural pattern.This research refers scene understanding as scene layout, i.e. finding streets, structures, side-walks, vehicles turning, person on foot intersection and scene status such as crowd congestion, split, merge etc.The significance of the proposed comprehensive review to create crowd administration procedures and help the development of the group or people, to maintain a strategic distance from the group calamities and guarantee general society security.Based on the observation of previous research in three aspects, i.e. review based on methods, frameworks and critical existing results analysis, this research propose a framework for anomaly detection in crowded scene using LSTM (long Short-Term Method).Proposed comprehensive review is expected to contribute significantly for the investigation of behavior pattern analysis in computer vision research domains.
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images

The human ability to recognize objects is impaired when the object is not shown in full. Minimal are the smallest regions of an image that remain recognizable for humans. Ullman et al. 2016 show that a slight modification of the location and size of the visible region of the minimal image produces a sharp drop in human recognition accuracy. In this paper, we demonstrate that such drops in accuracy due to changes of the visible region are a common phenomenon between humans and existing state-of-the-art deep neural networks (DNNs), and are much more prominent in DNNs. We found many cases where DNNs classified one region correctly and the other incorrectly, though they only differed by one row or column of pixels, and were often bigger than the average human minimal image size. We show that this phenomenon is independent from previous works that have reported lack of invariance to minor modifications in object location in DNNs. Our results thus reveal a new failure mode of DNNs that also affects humans to a much lesser degree. They expose how fragile DNN recognition ability is for natural images even without adversarial patterns being introduced. Bringing the robustness of DNNs in natural images to the human level remains an open challenge for the community.