Reviewerly/exHarmony huggingface dataset:
Flavorium: An Exploration of Flavobacteria’s Living Aesthetics for Living Color Interfaces

Flavobacteria, which can be found in marine environments, are able to grow in highly organized colonies producing vivid iridescent colorations. While much is known about the biology of these organisms, their design potential as responsive media in user interfaces has not been explored. Our paper aims at bridging this gap by providing insights into the type, degree, and duration of change in Flavobacteria’s expression, i.e., their living aesthetics. We present a tool to capture and characterize these changes concerning form, texture and iridescent color. To support the long-term study of their living aesthetics, we designed Flavorium. This bio-digital artifact provides the necessary habitat conditions for Flavobacteria to thrive for a month. Granting insights into the responsive behavior of this organism, this work presents a design space, vocabulary, and application concepts to inspire HCI and design scholars to investigate the complex temporal qualities of living media for future user interfaces.
Integrating Python into a Physical Chemistry Lab

This paper shows a method for integrating computer programming into a standard physical chemistry laboratory sequence to augment student data analysis abilities and allow them to carry programming skills forward to other courses. The Python programming language is used, taking advantage of the pedagogical benefits of Jupyter notebooks, primarily the ability to intersperse instructions and interactive code cells. A series of five notebooks, plus one traditional script exercise, are designed to teach basic techniques (e.g., loops, assignments, data types), instrument interfacing, model fitting, and introductory quantum chemistry. The skills are directly applicable to laboratories the students perform during the hands-on portion of the courses. A survey of students who have completed the course show high confidence in their ability to learn new skills, and student comments reveal they have used these skills in a variety of other contexts.
Chatbots and virtual assistants: a bibliometric analysis

Purpose This paper attempts to provide significant information on the increased growth of literature of chatbots and virtual assistants. Technological changes are dynamic and keep changing at regular intervals. Therefore, it becomes highly crucial to review the performance of chatbots and virtual assistants. This paper aims to review the literature by eminent researchers in the form of authors, keywords and major contributing organizations. Design/methodology/approach Systematic literature review and bibliometric analysis is used to analyze the growth of literature on chatbots. A sum of articles has been extracted from Scopus database with selected keywords and with certain filters. VOSviewer software is used for analysis of data. A total of 130 documents are extracted from Scopus database from 2017 to 2021 (31st August). Findings This study provides a significant contribution to an existing literature and provides the understanding of research in the area of chatbots and virtual assistants. The authors with maximum number of citations are Yan, Zaho, Bengio, Weizenbaum, Song, Zhou and Maedche with jointly 180 citations. Research is been contributed by different countries where the United States is the country with highest number of documents published. The United States contributes 17% of the total production in the area of chatbots and virtual assistants. The analysis shows that the area is gaining momentum as contribution in this area is been increasing in last few years. Research limitations/implications The study shows that several branches of chatbots are also in mainstream like natural language processing, e-learning, behavioural research, conversational agents, virtual assistants, human–computer interaction, natural language and so on. It provides a wider scope to authors and researchers to gain useful insights. The bibliometric study will provide a broader spectrum in this area. Social implications The developments in technology and also the effect of COVID-19 pandemic is boosting the adoption of technology in different sectors. Deployment of technology will uplift the economy and social infrastructure. Society can avail different services from their comfort area and also in real time. This will help in reducing wastage of resources like people visiting offices for routine jobs which can be easily availed from their workplace. Society may access better services without much human interaction. Originality/value This paper adds significant contribution to the existing literature by analysing the published papers from Scopus database. The study contains new and significant information as this study covers all industries where chatbots and virtual assistants are being applied whereas in previous literature only specific industry has been taken into consideration.
Light Weight Authentication Scheme for Smart Home IoT Devices

In today’s world, the use of computer networks is everywhere, and to access the home network we use the Internet. IoT networks are the new range of these networks in which we try to connect different home appliances and try to give commands from a remote place. Access to any device over an insecure network invites various types of attacks. User authentication can be performed using some password or biometric technique. However, when it comes to authenticating a device, it becomes challenging to maintain data security over a secure network such as the Internet. Many encryptions and decryption algorithms assert confidentiality, and hash code or message authentication code MAC is used for authentication. Traditional cryptographic security methods are expensive in terms of computational resources such as memory, processing capacity, and power consumption. They are incompatible with the Internet of Things devices that have limited resources. Although automatic Device-to-Device communication enables new potential applications, the limited resources of the networks’ machines and devices impose various constraints. This paper proposes a home device authentication scheme when these are accessed from a remote place. An authentication device is used for the home network and controller device to control home appliances. Our scheme can prevent various attacks such as replay attacks, server spoofing, and man-in-the-middle attack. The proposed scheme maintains the confidentiality and authenticity of the user and devices in the network. At the same time, we check the system in a simulated environment, and the results show that the network’s performance does not degrade much in terms of delay, throughput, and energy consumed.
Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India

The challenges in applications of solar energy lies in its intermittency and dependency on meteorological parameters such as; solar radiation, ambient temperature, rainfall, wind-speed etc., and many other physical parameters like dust accumulation etc. Hence, it is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location. Machine learning (ML) models have gained importance and are widely used for prediction of solar power plant performance. In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for the first time. The performance of chosen ML algorithms is validated by field dataset of a 10kWp solar PV power plant in Eastern India region. Furthermore, a complete test-bed framework has been designed for data mining as well as to select appropriate learning models. It also supports feature selection and reduction for dataset to reduce space and time complexity of the learning models. The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting EML models. The proposed work is a generalized one and can be very useful for predicting the performance of large-scale solar PV power plants also.
Statistical analysis of social networks as a means of communication for children in educational institutions in Riohacha, La Guajira, Colombia

Technological advancements have impacted human life in various magnitudes, influencing the social, cultural, and educational aspects, particularly among children. The study aimed to examine the influence and impact of social networks concerning issues such as cyberbullying, sexting, grooming, and vamping, among others. Furthermore, different indicators of social network incidence in the school population were explored. According to the conclusions drawn, it was found that the use of social networks negatively affects the academic performance of children in Riohacha, generating social isolation, fear, and both visual and mental fatigue. It was also observed that most children in Riohacha spend between 3 to 4 hours a day on social networks and generally do not use them to carry out inappropriate behaviors. In most cases, they also do not exchange messages or videos not suitable for their age; when this occurs, those involved are removed from their contact lists.
Level by level image compression-encryption algorithm based on quantum chaos map

Informal availability of Internet and easy accessibility of network resources to a general public has created a new era of security design challenge for multimedia contents which is available in the form of image, text, video and audio etc. This paper presents a novel technique to employ a security mechanism for transmission of digital contents over public network. The proposed method uses a multilevel image encryption/decryption algorithm based on quantum chaos map and sparse sampling. In the initial phase an original image is divided into blocks of equal size and each block is further divided into sub-block using DWT technique. The pixels of the adjacent sub blocks are exchanged randomly by random matrix. Each frequency band of the blocks is encrypted by modulus function, and then combined each to get the new block. Next, pixels of the neighbor blocks are exchanged randomly by random matrix and subsequently each block is encrypted using modulus function. The result given in graphical and tabular form shows the correctness of the algorithm.
Fast Video Shot Transition Localization with Deep Structured Models

Detection of video shot transition is a crucial pre-processing step in video analysis. Previous studies are restricted on detecting sudden content changes between frames through similarity measurement and multi-scale operations are widely utilized to deal with transitions of various lengths. However, localization of gradual transitions are still under-explored due to the high visual similarity between adjacent frames. Cut shot transitions are abrupt semantic breaks while gradual shot transitions contain low-level spatial-temporal patterns caused by video effects, e.g. dissolve. In this paper, we propose a structured network aiming to detect these two shot transitions using targeted models separately. Considering speed performance trade-offs, we design the following framework. In the first stage, a light filtering module is utilized for collecting candidate transitions on multiple scales. Then, cut transitions and gradual transitions are selected from those candidates by separate detectors. To be more specific, the cut transition detector focus on measuring image similarity and the gradual transition detector is able to capture temporal pattern of consecutive frames, even locating the positions of gradual transitions. The light filtering module can rapidly exclude most of the video frames from further processing and maintain an almost perfect recall of both cut and gradual transitions. The targeted models in the second stage further process the candidates obtained in the first stage to achieve a high precision. With one TITAN GPU, the proposed method can achieve a 30 $$\times $$ real-time speed. Experiments on public TRECVID07 and RAI databases show that our method outperforms the state-of-the-art methods. To train a high-performance shot transition detector, we contribute a new database ClipShots, which contains 128636 cut transitions and 38120 gradual transitions from 4039 online videos. ClipShots intentionally collect short videos for more hard cases caused by hand-held camera vibrations, large object motions, and occlusion. The database is avaliable at https://github.com/Tangshitao/ClipShots .
Stronger Convergence Results for the Center-Based Fuzzy Clustering With Convex Divergence Measure

We present a novel alternative convergence theory of the fuzzy C-means (FCM) clustering algorithm with a super-class of the so-called “distance like functions” which emerged from the earlier attempts of unifying the theories of center-based clustering methods. This super-class does not assume the existence of double derivative of the distance measure with respect to the coordinate of the cluster representative (first coordinate in this formulation). The convergence result does not require the separability of the distance measures. Moreover, it provides us with a stronger convergence property comparable (same to be precise, but in terms of the generalized distance measure) to that of the classical FCM with squared Euclidean distance. The crux of the convergence analysis lies in the development of a fundamentally novel mathematical proof of the continuity of the clustering operator even in absence of the closed form upgrading rule, without necessitating the separability and double differentiability of the distance function and still providing us with a convergence result comparable to that of the classical FCM. The implication of our novel proof technique goes way beyond the realm of FCM and provides a general setup for convergence analysis of the similar iterative algorithms.
Text Image Deblurring Using Kernel Sparsity Prior

Previous methods on text image motion deblurring seldom consider the sparse characteristics of the blur kernel. This paper proposes a new text image motion deblurring method by exploiting the sparse properties of both text image itself and kernel. It incorporates the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm for regularizing the blur kernel in the deblurring model, besides the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> sparse priors for the text image and its gradient. Such a L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm-based model is efficiently optimized by half-quadratic splitting coupled with the fast conjugate descent method. To further improve the quality of the recovered kernel, a structure-preserving kernel denoising method is also developed to filter out the noisy pixels, yielding a clean kernel curve. Experimental results show the superiority of the proposed method. The source code and results are available at: https://github.com/shenjianbing/text-image-deblur.
Using Augmented Reality in Software Engineering Education? First insights to a comparative study of 2D and AR UML modeling

Although there has been much speculation about the potential of Augmented Reality (AR) in teaching for learning material, there is a significant lack of empirical proof about its effectiveness and implementation in higher education.We describe a software to integrate AR using the Microsoft Hololens into UML (Unified Modeling Language) teaching.Its user interface is laid out to overcome problems of existing software.We discuss the design of the tool and report a first evaluation study.The study is based upon effectiveness as a metric for students performance and components of motivation.The study was designed as control group experiment with two groups.The experimental group had to solve tasks with the help of the AR modeling tool and the control group used a classic PC software.We identified tendencies that participants of the experimental group showed more motivation than the control group.Both groups performed equally well.
Mechanics-aided digital image correlation for the investigation of piezoelectric and ferroelectric behaviour of a soft PZT

The strain response of a polarised PZT was characterised using Digital Image Correlation (DIC). The DIC algorithm is based on a global approach and regularises the displacement field using the balance equations of solid mechanics. The measurement error is reduced by correcting the displacements that are mechanically not admissible. A ferroelectric test showed that the standard deviations of strain fields remain mostly under 1.2 × 10−4 for an element size of 64 px. The standard deviation on the average value is 3 × 10−6. The strain field is homogeneous and its average value is consistent with the strain obtained from a CCD laser measurement device. The longitudinal strain, transverse strain and polarisation response of the PZT were measured for bipolar and unipolar loadings ranging from 50 to 5000 V/mm. Material properties were extracted from these measurements. This work shows the advantages of a novel 2D-DIC algorithm for piezoelectric strain characterisation.
Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments.

Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures? We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018), which we use to investigate the grammatical knowledge of three pretrained encoders, including the popular OpenAI Transformer (Radford et al., 2018) and BERT (Devlin et al., 2018). We fine-tune these encoders to do acceptability classification over CoLA and compare the models' performance on the annotated analysis set. Some phenomena, e.g. modification by adjuncts, are easy to learn for all models, while others, e.g. long-distance movement, are learned effectively only by models with strong overall performance, and others still, e.g. morphological agreement, are hardly learned by any model.
Data collection planning with non-zero sensing distance for a budget and curvature constrained unmanned aerial vehicle

Data collection missions are one of the many effective use cases of unmanned aerial vehicles (UAVs), where the UAV is required to visit a predefined set of target locations to retrieve data. However, the flight time of a real UAV is time constrained, and therefore only a limited number of target locations can typically be visited within the mission. In this paper, we address the data collection planning problem called the Dubins Orienteering Problem with Neighborhoods (DOPN), which sets out to determine the sequence of visits to the most rewarding subset of target locations, each with an associated reward, within a given travel budget. The objective of the DOPN is thus to maximize the sum of the rewards collected from the visited target locations using a budget constrained path between predefined starting and ending locations. The variant of the orienteering problem addressed here uses curvature-constrained Dubins vehicle model for planning the data collection missions for UAV. Moreover, in the DOPN, it is also assumed that the data, and thus the reward, may be collected from a close neighborhood sensing distance around the target locations, e.g., taking a snapshot by an onboard camera with a wide field of view, or using a sensor with a long range. We propose a novel approach based on the Variable Neighborhood Search (VNS) metaheuristic for the DOPN, in which combinatorial optimization of the sequence for visiting the target locations is simultaneously addressed with continuous optimization for finding Dubins vehicle waypoints inside the neighborhoods of the visited targets. The proposed VNS-based DOPN algorithm is evaluated in numerous benchmark instances, and the results show that it significantly outperforms the existing methods in both solution quality and computational time. The practical deployability of the proposed approach is experimentally verified in a data collection scenario with a real hexarotor UAV.
Trained Uniform Quantization for Accurate and Efficient Neural Network Inference on Fixed-Point Hardware

We propose a method of training quantization thresholds (TQT) for uniform symmetric quantizers using standard backpropagation and gradient descent. Contrary to prior work, we show that a careful analysis of the straight-through estimator for threshold gradients allows for a natural range-precision trade-off leading to better optima. Our quantizers are constrained to use power-of-2 scale-factors and per-tensor scaling of weights and activations to make it amenable for hardware implementations. We present analytical support for the general robustness of our methods and empirically validate them on various CNNs for ImageNet classification. We are able to achieve near-floating-point accuracy on traditionally difficult networks such as MobileNets with less than 5 epochs of quantized (8-bit) retraining. Finally, we present Graffitist, a framework that enables automatic quantization of TensorFlow graphs for TQT (available at this https URL ).
Unpacking the Infrastructuring Work of Patients and Caregivers around the World

Many healthcare systems around the world are noted as fragmented, complex and low-quality, leaving patients and caregivers with no choice but to engage in "infrastructuring work" to make it function for them. However, the work patients do to construct their functioning health service systems often remain invisible, with very little resources and technologies available to assist them. This workshop aims to bring together researchers, health practitioners, and patients to examine, discuss, and brainstorm ways to re-envision our healthcare service systems from the perspective of patients and caregivers. We aim to unpack the types of work that patients and caregivers do to reconfigure, reconstruct and adapt the healthcare infrastructure, and to brainstorm design solutions that can provide better infrastructuring assistance to them.
Multiscale image denoising using goodness-of-fit test based on EDF statistics

Two novel image denoising algorithms are proposed which employ goodness of fit (GoF) test at multiple image scales. Proposed methods operate by employing the GoF tests locally on the wavelet coefficients of a noisy image obtained via discrete wavelet transform (DWT) and the dual tree complex wavelet transform (DT-CWT) respectively. We next formulate image denoising as a binary hypothesis testing problem with the null hypothesis indicating the presence of noise and the alternate hypothesis representing the presence of desired signal only. The decision that a given wavelet coefficient corresponds to the null hypothesis or the alternate hypothesis involves the GoF testing based on empirical distribution function (EDF), applied locally on the noisy wavelet coefficients. The performance of the proposed methods is validated by comparing them against the state of the art image denoising methods.