Reviewerly/exHarmony huggingface dataset:
A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image

In medical imaging, chromosome straightening plays a significant role in the pathological study of chromosomes and in the development of cytogenetic maps. Whereas different approaches exist for the straightening task, typically geometric algorithms are used whose outputs are characterized by jagged edges or fragments with discontinued banding patterns. To address the flaws in the geometric algorithms, we propose a novel framework based on image-to-image translation to learn a pertinent mapping dependence for synthesizing straightened chromosomes with uninterrupted banding patterns and preserved details. In addition, to avoid the pitfall of deficient input chromosomes, we construct an augmented dataset using only one single curved chromosome image for training models. Based on this framework, we apply two popular image-to-image translation architectures, U-shape networks and conditional generative adversarial networks, to assess its efficacy. Experiments on a dataset comprised of 642 real-world chromosomes demonstrate the superiority of our framework, as compared to the geometric method in straightening performance, by rendering realistic and continued chromosome details. Furthermore, our straightened results improve the chromosome classification by 0.98%-1.39 % mean accuracy.
Agile-Based Education for Teaching an Agile Requirements Engineering Methodology for Knowledge Management

The importance of the role that knowledge management (KM) plays in the success of knowledge-intensive organizations has long been acknowledged. However, requirements engineering (RE) methods for KM solutions still lack maturity, as do the educational programs in this domain. In this paper, an agile-based educational method for teaching RE for KM solutions, specifically an agile KM RE methodology (AKM-REM), is proposed. The paper reports a teaching experience, in which 17 teams of graduate students learned and implemented AKM-REM, each in a different real-life case study. Data were collected via class observations and from the students’ submitted projects and underwent qualitative content analysis. The findings demonstrate the manner in which AKM-REM creates a culture of collaboration, trust, and reflection, fostering the dynamic and flexible organization that is required in today’s competitive business environment. The contribution of this paper is fourfold. First, it presents an extension of the research on agile-based RE for KM solutions as a specialized area. Second, it presents an extension of an existing method KM-REM along the agile principles. Third, it provides means for agile-based education for analyzing and addressing KM challenges effectively. Finally, it describes the importance of agile KM RE in the context of sustainability.
SensPick: Sense Picking for Word Sense Disambiguation

Word sense disambiguation (WSD) methods identify the most suitable meaning of a word with respect to the usage of that word in a specific context. Neural network-based WSD approaches rely on a sense-annotated corpus since they do not utilize lexical resources. In this study, we utilize both context and related gloss information of a target word to model the semantic relationship between the word and the set of glosses. We propose SensPick, a type of stacked bidirectional Long Short Term Memory (LSTM) network to perform the WSD task. The experimental evaluation demonstrates that SensPick outperforms traditional and state-of-the-art models on most of the benchmark datasets with a relative improvement of 3.5% in F-1 score. While the improvement is not significant, incorporating semantic relationships brings SensPick in the leading position compared to others.
A Model for Rapid Selection and COVID-19 Prediction with Dynamic and Imbalanced Data

The COVID-19 pandemic is threatening our quality of life and economic sustainability. The rapid spread of COVID-19 around the world requires each country or region to establish appropriate anti-proliferation policies in a timely manner. It is important, in making COVID-19-related health policy decisions, to predict the number of confirmed COVID-19 patients as accurately and quickly as possible. Predictions are already being made using several traditional models such as the susceptible, infected, and recovered (SIR) and susceptible, exposed, infected, and resistant (SEIR) frameworks, but these predictions may not be accurate due to the simplicity of the models, so a prediction model with more diverse input features is needed. However, it is difficult to propose a universal predictive model globally because there are differences in data availability by country and region. Moreover, the training data for predicting confirmed patients is typically an imbalanced dataset consisting mostly of normal data; this imbalance negatively affects the accuracy of prediction. Hence, the purposes of this study are to extract rules for selecting appropriate prediction algorithms and data imbalance resolution methods according to the characteristics of the datasets available for each country or region, and to predict the number of COVID-19 patients based on these algorithms. To this end, a decision tree-type rule was extracted to identify 13 data characteristics and a discrimination algorithm was selected based on those characteristics. With this system, we predicted the COVID-19 situation in four regions: Africa, China, Korea, and the United States. The proposed method has higher prediction accuracy than the random selection method, the ensemble method, or the greedy method of discriminant analysis, and prediction takes very little time.
Introducing the Concept of “Digital-Agent Signatures”: How SSI Can Be Expanded for the Needs of Industry 4.0.

Qualified digital/electronic signatures from human employees or legal entities can be used to generate sub-signatures for AI-powered software or robots/bots at the moment the dedicated tasks are being executed. This makes it not only possible to determine who gave the order, but also to store milestones of the respective order on a decentralised digital ledger (Blockchain). Smart Contracts can map further relevant functions such as the processing of contract-relevant data (e.g. logistics or insurance-related data), especially if, for example, different departments of one company or more companies are involved in the process. Furthermore, modern encryption technologies ensure that only the information can be read by those persons or companies for whom it is intended. Approaches such as “pruning” can offer the possibility to store private information only up to certain block height, after which only the proof of the transaction per se remains. This procedure could help Industry 4.0 processes not only being conducted efficiently but above all within a comprehensible legal framework. It can go even one step further, through digital-agents with appropriate classification clarifying the authorisation levels of additional AI-agents and generating such sub(sub)-signatures, on behalf of their original principals, to initiate further (needed) operating steps, which are recorded with the aid of specific Blockchain technologies.
How Companies Can Benefit from Brilliant Failures

Overview:Failure is integral to growth and progress, yet companies and individuals struggle to accept it. A company’s culture is one of the most important influences for accepting and learning from failures. We define brilliant failure and distinguish brilliant failures from mistakes, avoidable failures, and epic failures. We identify best practices so companies can understand the value of failure and use what they learn to pivot and achieve success. We present seven major drivers that can impact a company’s ability to fail brilliantly.
Dual attention per-pixel filter network for spatially varying image deblurring

Spatially varying motion deblurring has recently witnessed substantial progress due to the development of deep neural network. However, most existing CNN-based methods involve two major shortcomings: (1) The CNN weights are space-sharing, and these methods thus ignore the properties of complex spatially variant blurs which vary from pixel to pixel in natural blurry images. (2) Stacked convolution layers with a large kernel or recurrent neural networks (RNNs) cannot capture the global contextual dependence of features, they thus cannot exploit the relationship between different blur pixels at a distance. To solve these problems, we propose a new dual attention per-pixel filter network (DAPFN). First, we develop a multiscale per-pixel filter network (MSPFN) to learn a specific deblurring mapping for different blur pixels, which predicts the per-pixel spatially adaptive convolution kernel for each blur pixel in the input blurry image of different scales and restores the clean pixel by performing channel-wise spatially adaptive convolution with the local neighborhood pixels. Second, we develop a dual attention enhanced residual network (DAERN) to capture the global contextual dependence of the blurry images, which introduces a dual attention (DA) module consisting of the spatial self-attention module (SSA) and channel self-attention module (CSA). The fusion of the two attention modules helps to further improve the deblurring performance. Third, we propose a new receptive field selection (RFS) block to learn the nonlinear characteristics of spatially variant blurs, which enables the adaptive fusing of the features with different receptive fields and effectively enhances the network nonlinear representation ability. The experimental results on GOPRO dataset indicate that the average PSNR and SSIM of the proposed method reached 31.8455 and 0.9231, respectively. The results of extensive experiments pertaining to spatially varying image deblurring demonstrate that the proposed method outperforms the state-of-the-art image deblurring methods.
ElectroPaper: Design and Fabrication of Paper-Based Electronic Interfaces for the Water Environment

The fabrication of underwater devices is necessary for the exploration of water environments and interactions in the Human–Computer Interaction (HCI) field. However, there are fewer approaches to support prototyping used in water environments. The existing prototype methods lack systematic waterproof treatments and provide insufficient software for balance and buoyancy analysis. To address these limitations, we present ElectroPaper, a new approach for the design and fabrication of prototypes used in water environments (surface or beneath) with paper-based electronic interfaces with a crease layer, hardware distribution layer, and hollow-out layer to support physical properties, such as waterproofing, foldability, and conformability. The approach includes a computational design tool for assisting in balance analysis, three-dimensional (3D) model unfolding, and circuit drawing. We describe the design and fabrication process and provide several example applications to illustrate the feasibility and utility of our approach. ElectroPaper provides an inexpensive and effective medium for the fabrication of customized digital prototypes for water environment use.
Comparison of Artificial Neural Network, Linear Regression and Support Vector Machine for Prediction of Solar PV Power

Solar Photo voltaic (PV) system's usage is increasing day by day as a substitute of energy considering about the environmental factors. But at the same time its performance have a huge impact. Due to the uncertainty of solar Photo voltaic (PV) power outputs, an accurate method of predicting the output power is required. Thus, a proper estimation of the solar power must be done. Predicting the solar power will help in optimal planning of PV units in generating or transmission, scheduling of other generators by considering the predicted values etc. This paper deals with a comparison between Machine Learning algorithms on Day-Ahead forecasting of the Solar Photo-voltaic output considering the weather parameters (which include wind speed, humidity, radiance and temperature). Artificial Neural Network, Linear Regression and Support Vector Machine have been considered and a conclusion is drawn based on the results obtained. The objective of the paper is to predict the Solar Power at Amal Jyothi College of Engineering, Kanjirapally, Kottayam. To provide real time values weather station has been used. The daily forecast results will help to improve the forecast accuracy which will eventually help in its proper utilization.
Research on Reading Recognition Method of Pointer Meters Based on Deep Learning Combined with Rotating Virtual Pointer

There are still a large number of pointer meters in substations, which mainly rely on manual inspection to detect the readings of pointer meters. In order to solve the above problems, this paper proposes a pointer meter keypoints detection method based on deep learning combined with virtual pointer rotation to determine the meter readings. This method first uses the deep learning algorithm to train data set of pointer meters and obtain the keypoints information of pointer meters. According to the keypoints information, we can further determine the center of rotation and radius of the pointer meter, then the real position of the pointer meter can be determined. Finally, according to the meter range information, as well as the real pointer angle and the meter range angle, we can calculate the readings of the pointer meter. The experiment results show that the proposed method can quickly and accurately obtain the pointer position, and further identify the meter readings. The proposed method can reduce the uncertainty caused by manual inspection and have a great impact on intelligent development of substations.
Blockchain based Patients' detail management System

In the data technology revolution, electronic medical records are a standard way to store patients' information in hospitals. Although some hospital systems using server-based patient detail management systems, they need a large amount of storage to store all the patients' medical reports, therefore affecting the scalability. At the same time, they are facing several difficulties, such as interoperability concerns, security and privacy issues, cyber-attacks to the centralized storage and maintaining adhering to medical policies. Proposed Flexi Medi is a private blockchain based patient detail management system which is expected to address the above problems. Solution proposes a distributed secure ledger to permits efficient system access and systems retrieval, which is secure and immutable. The improved consensus mechanism achieves the consensus of the data without large energy utilization and network congestion. Moreover, Flexi Medi achieves high data security principles based on a combination of hybrid access control mechanism, public key cryptography, and a secure live health condition monitoring mechanism. The proposed solution results in successfully deployed smart contracts according to the roles of the system, real time patient health monitoring with more scalable and access controlled system. The overall objective of this solution is to bring the entire medical industry into a common platform using a decentralized approach to store, share medical details while eliminating the need to maintain printed medical records.
Improving English as a foreign language–learning performance using mobile devices in unfamiliar environments

In this study, the participants learned English as a foreign language (EFL) in the classroom and then worked on five learning tasks to apply their newly learned knowledge to unfamiliar environments. The participants took photos of people, objects, situations or scenarios and described them in detail using a mobile learning system. Familiarization strategies were developed for the participants to increase their knowledge of strange environments. We aimed to test the effectiveness of the familiarization strategies on learning performance in the mobile-assisted language-learning context. To this end, an experiment was carried out with fifty participants who were assigned either to the experimental or control groups. The participants in the former used the familiarization strategies, whereas the participants in the latter did not. We compared the learning performance of the participants in the two groups based on the scores of pre- and posttests with phrase translation, sentence translation and writing items. We also compared the EFL writing quality of the two groups on five tasks with respect to the amount, vocabulary, grammar, content, origination and creativity dimensions. The results of the pre/posttest comparison showed that the experimental group outperformed the control group on the writing item only. The results showed that the writing quality of the experimental group on the five tasks was significantly better than that of the control group in all of the six dimensions. In addition, all of the participants had positive perceptions of the usefulness of our learning system to assist their EFL learning in unfamiliar environments. The results suggest that familiarization strategies can significantly improve EFL learning performance in unfamiliar environments.
Comparative Monte Carlo Analysis of Background Estimation Algorithms for Unmanned Aerial Vehicle Detection

Background estimation algorithms are important in UAV (Unmanned Aerial Vehicle) vision tracking systems. Incorrect selection of an algorithm and its parameters leads to false detections that must be filtered by the tracking algorithm of objects, even if there is only one UAV within the visibility range. This paper shows that, with the use of genetic optimization, it is possible to select an algorithm and its parameters automatically. Background estimation algorithms (CNT (CouNT), GMG (Godbehere-Matsukawa-Goldberg), GSOC (Google Summer of Code 2017), MOG (Mixture of Gaussian), KNN (K–Nearest Neighbor–based Background/Foreground Segmentation Algorithm), MOG2 (Mixture of Gaussian version 2), and MEDIAN) and the reference algorithm of thresholding were tested. Monte Carlo studies were carried out showing the advantages of the MOG2 algorithm for UAV detection. An empirical sensitivity analysis was presented that rejected the MEDIAN algorithm.
DroRun: Drone Visual Interactions to Mediate a Running Group

Running with others is motivational but finding running partners is not always accessible due to constraints such as being in remote locations. We present a novel concept of augmenting remote runners' experience by mediating a running group using drone-projected visualisations. As an initial step, a team of five interaction designers apply a user-centred design approach to scope out possible visual prototypes that can mediate a running group. The design team specifically focused on visual prototypes through a series of workshops. We report on the impressions of the visual prototypes from six potential users and present future directions to this project.
On the Use of Fibonacci Sequences for Detecting Injection Attacks in Cyber Physical Systems

Cyber Physical Systems are characterized by a strong interaction among networking, sensing, and control functionalities. Moreover, the recent advent of Internet of Things extended their information sharing capability. However, the interaction between Internet and Cyber Physical Systems requires increased efforts for guaranteeing the security of connected systems. In the industrial field, the problem becomes more complex due to the need of protecting a large attack surface while guaranteeing system availability and real-time response to the detection of threats. In this contribution, we deal with the injection of tampered data into the communication channel with the aim of modifying the status of the physical system. To cope with this attack, we design a secure control system able to detect the injection of tampered data by coding the output of the measurement systems. The proposed approach is based on the use of permutation matrices, whose scheme varies upon a secret pattern obtained exploiting the Fibonacci p-sequences. The detection strategy is compliant with the time delay constraints typical of a Cyber Physical System. An analysis of the security performances of the proposed system is presented along with the experimental proof of its effectiveness.
Subjective And Objective Quality Assessment Of Mobile Gaming Video

Nowadays, with the vigorous expansion and development of gaming video streaming techniques and services, the expectation of users, especially the mobile phone users, for higher quality of experience is also growing swiftly. As most of the existing research focuses on traditional video streaming, there is a clear lack of both subjective study and objective quality models that are tailored for quality assessment of mobile gaming content. To this end, in this study, we first present a brand new Tencent Gaming Video dataset containing 1293 mobile gaming sequences encoded with three different codecs. Second, we propose an objective quality framework, namely Efficient hard-RAnk Quality Estimator (ERAQUE), that is equipped with (1) a novel hard pairwise ranking loss, which forces the model to put more emphasis on differentiating similar pairs; (2) an adapted model distillation strategy, which could be utilized to compress the proposed model efficiently without causing significant performance drop. Extensive experiments demonstrate the efficiency and robustness of our model.
Human Activity Recognition System from Different Poses with CNN

In the principle of Human Activity Recognition, a variety of real-life implementations are available using different types of sensors such as fitness monitoring, day-life monitoring, health monitoring, etc. Especially for the elders, sensor-based applications are not feasible due to many reasons such as carrying a mobile phone or gadgets. In this paper, we focused on CCTV videos and camera images to detect human poses using HAAR Feature-based Classifier and recognize the activities of the human using the Convolutional Neural Network (CNN) Classifier. Our Human Activity Recognition System was trained using our own collected dataset which is composed of 5648 images. The approach accomplished an efficacious detection accuracy of 99.86% and recognition accuracy of 99.82% with approximately 22 frames/second after 20 epochs.
Educational Association Mining on the Use of Media Platforms for e- Learning

The education sector is always facing challenges related to various teaching and learning aspects. With continuous advancements in education technology, students, as well as staff, constantly face issues related to upgrading their knowledge to the next level. During the situation of COVID-19 pandemic, many educational organizations were forced to adopt these technologies very rapidly in order to keep themselves on track and not to lose their students involvement. This research paper has shown the effect of mobile technology and its role in the e-learning process during the online mode of study. A survey was conducted having 206 participants. The questionnaire was having 27 attributes. The research has used association rule mining technique such as CN2 Rule Induction. Principal Component Analysis was used to reduce the components and the gradient approach was used to optimize the results. The study shows a high impact on learning with the usage of mobile phones and better internet infrastructure providing learning anywhere at any time.