Reviewerly/exHarmony huggingface dataset:
LifeMosaic: co-design of a personal informatics tool for youth

While a growing number of technologies offer personal data to the user, little is known about how such tools can be harnessed for and by adolescents. Prior work has focused on implementations, in which youth experience is subordinated to the prescribed aims of adults. Few studies have engaged with the concerns and motives of the young people themselves. Co-design can be a powerful method for exploring beyond such limits, informing design that reflects the voices and values of the designed-for population. This paper presents a case study exploring the design of a personal informatics app (LifeMosaic) by a group of 14 to 16-year-olds. LifeMosaic lets users set any focus they personally care about and track it using colours and stickers. The design was shaped through youth concerns around privacy, social support, flexibility and self-expression as well as the desire to support mental wellbeing. This work illustrates new opportunities and understandings for personal informatics with youth: framing the data as mediating transindividual meaning making.
Meta Learning for Causal Direction

The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data. Based on recent developments in meta learning as well as in causal inference, we introduce a novel generative model that allows distinguishing cause and effect in the small data setting. Using a learnt task variable that contains distributional information of each dataset, we propose an end-to-end algorithm that makes use of similar training datasets at test time. We demonstrate our method on various synthetic as well as real-world data and show that it is able to maintain high accuracy in detecting directions across varying dataset sizes.
The effect of image resolution in the human presence detection: A case study on real-world image data

The automated operation of robots and flying drones is coupled to high security requirements with respect to humans and environment. Sometimes, persons have to be detected from a long distance or high altitude to allow the autonomous system an adequate and timely response. State-of-the-art Convolutional Neural Networks (CNNs) enable high object detection rates for different image data but only within their respective training, validation and test datasets. Recent studies show the limited generalization ability of CNNs for unknown data, even with merely small image changes. A typical source of such problems is the varying resolution of input images and the inevitable scaling of them to match the input-layer size of the network model. While modern cameras are able to capture high-resolution images of humans also from a longer distance, the practical input-layer sizes of networks are comparably small. Hence, we investigate the reliability of a network architecture for human detection with respect to such input-scaling effects. The popular VisDrone dataset with its varying image resolution and many relatively small depictions of humans is surveyed as well as the high-resolution AgriDrone image data from an agricultural context. Our results show that the object detection rate depends on the image scaling factor as well as on the relative size of persons. An enlarged input-layer size of the network can only partially contribute to counteract the observed effects. In addition, the detection algorithm becomes computationally more expensive by the increased effort.
Peningkatan Rasa Percaya Diri Siswa Tunagrahita Melalui Pembelajaran Terintegrasi Semiotik Dengan Media Buku Pop Up

This service program is motivated by the lack of confidence in communicating retarded students in SLB C Autis Negeri Tuban. The lack of confidence in communication is caused by the lack of mastery and understanding of vocabulary. Therefore, by using a semiotic integrated learning design with pop-up book media it is hoped that it will increase the confidence of mentally retarded students, especially in communicating with others. This dedication program is carried out in the form of classroom action research involving 10 mild retarded students (C) and 5 moderate retarded students (C1). The team applies semiotic integrated learning with pop-up book media for six meetings. The results of this program indicate that there is an increase in confidence in mild and moderate retarded students at SLB C Autis Negeri Tuban. Confidence occurs through various processes, namely 1) self-performance, self-activity activities carried out in the learning process are the courage to appear in front of the class, tell stories, and answer questions. 2) there is a process of interaction and communication in learning activities between educators and students and fellow friends when the self-activity takes place. Thus, it can be concluded that through semiotic integrated learning with pop up book media can increase the confidence in communication retarded students.
HDR Synthesis Technology for Spaceborne CMOS Cameras Based on Virtual Digital TDI

Due to the fact that the traditional high dynamic range (HDR) imaging methods cannot be used for satellites, finding a way to generate HDR remote sensing images from the satellites has long been explored in the field of remote sensing imaging. In this article, a systematic method of synthesizing the HDR remote sensing images based on a new registration algorithm and virtual digital delay integration (TDI) technology is proposed. First, a series of original images are generated by the fast continuous shooting method through a push-broom spaceborne camera. Then, a new registration algorithm with high accuracy and high robustness is proposed in this article, which is used for image registration. Finally, an HDR multiframe image synthesis algorithm is used to generate high-quality and high signal-to-noise-ratio HDR images. This technology greatly improves the image information acquisition capabilities of digital TDI area scan cameras.
A SECURITY MODEL BASED ON BLOCKCHAIN SMART CONTRACTS FOR IMPROVE AUTHENTICATION ON THE INTERNET OF THINGS

IoT is one of the most important and profitable projects proposed by Kevin Ashton since 1999.One of the most critical issues in maintaining communication between things and protecting the data is the security issue of IoT.So far, different strategies have been made to maintain safe security.In this paper, we provide a Five-Layers model by adding two security layers based on BlockChain.We show that blockchain smart contracts are a solution to enhance IoT security and prevent infiltration into the network.
Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow–Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies.
Efficient Automated Reasoning About Sets and Multisets with Cardinality Constraints

When reasoning about container data structures that can hold duplicate elements, multisets are the obvious choice for representing the data structure abstractly. However, the decidability and complexity of constraints on multisets has been much less studied and understood than for constraints on sets. In this presentation, we outline an efficient decision procedure for reasoning about multisets with cardinality constraints. We describe how to translate, in linear time, multisets constraints to constraints in an extension of quantifier-free linear integer arithmetic, which we call LIA*. LIA* extends linear integer arithmetic with unbounded sums over values satisfying a given linear arithmetic formula. We show how to reduce a LIA* formula to an equisatisfiable linear integer arithmetic formula. However, this approach requires an explicit computation of semilinear sets and in practice it scales poorly even on simple benchmarks. We then describe a recent more efficient approach for checking satisfiability of LIA*. The approach is based on the use of under- and over-approximations of LIA* formulas. This way we avoid the space overhead and explicitly computing semilinear sets. Finally, we report on our prototype tool which can efficiently reason about sets and multisets formulas with cardinality constraints.
Sentiment Analysis of RSS Feeds on Sports News – A Case Study

With the advent of online social media, such as articles, websites, blogs, messages, posts, news channels, and by and large web content has drastically changed the way individuals take a glimpse at different things around them.Today, it's an everyday practice for some individuals to read the news on the web.Sentiment analysis (also called opinion mining) alludes to the utilization of natural language processing, content investigation, and computational linguistics to distinguish and separate subjective data in source materials.Sentiment analysis is broadly applied to online reviews, news feeds and social networking for a wide variety of applications, ranging from marketing to client services.Sentiment analysis emphasizes on the classification of textual data into positive, negative and neutral categories.This research is an endeavor to the case study that calculates news polarity or emotions on different sports feeds which may influence changes in sports news development patterns.The interest of this approach is to generate various text analytics that computes feelings from all pertinent ongoing sports news accessible out in the public domain.The significance and application value of sentiment analysis of RSS feeds in this study is to distinguish between positive feeds and negative feeds on sports that could affect readers or users minds in order to improve RSS feeds messaging broadcast among folks.The methodology utilizes the sentiment analysis techniques using two different online open-source sentiment analysis tools in Rich Site Summary (RSS) news feeds that have an influence on sports-related broadcast esteems.
The ill-posedness of derivative interpolation and regularized derivative interpolation for band-limited functions by sampling

Abstract In this paper, the ill-posedness of derivative interpolation is discussed, and a regularized derivative interpolation for band-limited signals is presented. The ill-posedness is analyzed by the Shannon sampling theorem. The convergence of the regularized derivative interpolation is studied by the combination of a regularized Fourier transform and the Shannon sampling theorem. The error estimation is given, and high-order derivatives are also considered. The algorithm of the regularized derivative interpolation is compared with derivative interpolation using some other algorithms.
Nonlinear Control Analysis of Quantized Congestion Notification in Data Center Networks

In data center networks, quantized congestion notification (QCN) has been ratified as the standard layer 2 congestion control mechanism.Since the congestion control algorithm of QCN conducts nonlinear switching between the rate increasing and decreasing stages, it is very difficult to analyze QCN theoretically.Previous works derive the stability condition of QCN using tools from linear control theory.However, the tools cannot reveal the reason why the queue at a congested link oscillates persistently when QCN is enabled.In this paper, we use the describing function method from nonlinear control theory to analyze queue oscillation.We show that unsuitable network configurations like more flows, a higher link capacity and a larger link delay will cause more severe oscillation of the queue.In particular, the impacts of the QCN parameters, such as the sampling probability and the constant in the active increase phase in QCN, on the oscillation magnitude of the queue are also analyzed.The simulation results validate the conclusion from our theoretical analysis well.
Application of Yokohama system for reporting breast Fine Needle Aspiration Cytology in correlation with histopathological and radiological findings

Background: Breast cancer is the leading cause of cancer deaths among women worldwide.Fine needle aspiration cytology (FNAC) and breast ultrasound are pivotal in characterization of a breast lump as benign and malignant lesion.The main objectives were to categorise the Breast FNAC samples according to new system of reporting and to assess the Risk of malignancy (ROM), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy for all categories.Methods: A Total of 900 FNAB cytology specimens from January 2017 to December 2018 were obtained.These were reclassified according to newly proposed IAC Yokohama system of reporting.The ROM, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and diagnostic yield of Breast FNAC and also of ultrasound were calculated.Result: Among 900 cases, 4.3% fall in category 1, 58%-category 2, 17.7%-category 3, 7.2%-category 4 and 12.8% -category 5, and correlated with corresponding radiological impression.Sensitivity, specificity, PPV, NPV and diagnostic accuracy of ultrasound were respectively 91.6%, 96.8%, 95.6%, 92.8%, 91.5% and those of FNAC respectively 94.59%, 98.9%, 98.59%, 95.74%, 96.97%.ROM is 5.0% for category 1, 1.2% for category 2, 12.5% for category 3, 93.65% for category 4 and 100% for category 5. Conclusion:IAC Yokohama system of reporting breast cytopathology serves as a common language to pathologist and clinician.Breast ultrasound is a non-invasive imaging-based technique and both these diagnostic tools can complement each other for patient diagnosis and management.
Academic Certification using Blockchain: Permissioned versus Permissionless Solutions

Understanding the challenges of implementing blockchain solutions is an important step towards scaling and adopting the technology.This paper analyses the adoption of blockchain technology in the management of academic certificates.In this use case, we identify certification providers that have adopted a permissionless approach and consortiums of academic institutions that are in the process of building permissioned networks.We explore the challenges faced by both approaches, and obtain information from competing projects to provide a preliminary approach for cost-benefit analysis that could potentially be applied for similar blockchain projects.For the management of academic certificates, we find that beyond the cost of implementing the technology there are additional elements of critical importance for adoption.For example, if blockchain-enabled certificates will replace notarised documents, how does the technology complement other forms of digital credentials, the ease of integration to existing administrative records within institutions and whether they are a viable first step towards a comprehensive, efficient and reliable system to share information among institutions.
An Anomaly Detection and Explainability Framework using Convolutional Autoencoders for Data Storage Systems

Anomaly detection in data storage systems is a challenging problem due to the high dimensional sequential data involved, and lack of labels. The state of the art for automating anomaly detection in these systems typically relies on hand crafted rules and thresholds which mainly allow to distinguish between normal and abnormal behavior of each indicator in isolation. In this work we present an end-to-end framework based on convolutional autoencoders which not only allows for anomaly detection on multivariate time series data, but also provides explainability. This is done by identifying similar historic anomalies and extracting the most influential indicators. These are then presented to relevant personnel such as system designers and architects, or to support engineers for further analysis. We demonstrate the application of this framework along with an intuitive interactive web interface which was developed for data storage system anomaly detection. We discuss how this framework along with its explainability aspects enables support engineers to effectively tackle abnormal behaviors, all while allowing for crucial feedback.
A Generic Approach for Efficient Detection of Vascular Structures

Vascular segmentation is often required in medical image analysis for various imaging modalities. Despite the rich literature in the field, the proposed methods need most of the time adaptation to the particular investigation and may sometimes lack the desired accuracy in terms of true positive and false positive detection rate. This paper proposes a general method for vascular segmentation based on locally connected filtering applied in a multiresolution scheme. The filtering scheme performs progressive detection and removal of the vessels from the image relief at each resolution level, by combining directional 2D-3D locally connected filters (LCF). An important property of the LCF is that it preserves (positive contrasted) structures in the image if they are topologically connected with other similar structures in their local environment. Vessels, which appear as curvilinear structures, can be filtered out by an appropriate LCF set-up which will minimally affect sheet-like structures. The implementation in a multiresolution framework allows dealing with different vessel sizes. The outcome of the proposed approach is illustrated on several image modalities including lung, liver and coronary arteries. It is shown that besides preserving high accuracy in detecting small vessels, the proposed technique is less sensitive with respect to noise and the presence of pathologies of positive-contrast appearance on the images. The detection accuracy is compared with a previously developed approach on the 20 patient database from the VESSEL12 challenge.
Geometric Feature Based Facial Emotion Recognition

Facial Emotion Recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential.Nowadays, emotional factors are important as classic functional aspects of customer purchasing behavior.Purchasing choices and decisions making are the result of a careful analysis of the product advantages and disadvantages and of affective and emotional aspects.This paper presents a novel method for human emotion classification and recognition.We generate seven referential faces suitable for each kind of facial emotion based on perfect face ratios and some classical averages.The basic idea is to extract perfect face ratios for emotional face and for each referential face as features and calculate the distance between them by using fuzzy hamming distance.To extract perfect face ratios, we use the point landmarks in the face then sixteen features will be extract.An experimental evaluation demonstrates the satisfactory performance of our approach on WSEFEP dataset (the Warsaw Set of Emotional Facial Expression Pictures dataset).It can be applied with any existing facial emotion dataset.The proposed algorithm will be a competitor of the other proposed relative approaches.The recognition rate reaches more than 93%.
Not Annoying the User for Better Password Choice: Effect of Incidental Anger Emotion on Password Choice

Literature often reports users’ experience of frustration, irritation [52], annoyance [42] with respect to security. With regards to password choice, annoyance due to complexity has been linked with weaker passwords [42]. We investigate the influence of incidental anger versus neutral emotion stimulus on password choice. We design a between-subject controlled lab experiment with N = 56 participants, with a GMail registration scenario. We employ standard video clips as mood induction protocol [59]. We measure password strength via zxcvbn and emotion via IBM’s Tone Analyzer and PANAS-X. We find that participants in the anger stimulus condition created significantly weaker passwords than those in the neutral stimulus condition, t(54) = 2.901, p = .005, with a near large effect size, g = .77. This study provides the empirical evidence of the effect of incidental anger emotion on password choice. Our findings are consequential for security because they suggest that if users feel frustration (which may arise from various sources including requirements for security compliance, human-computer interaction design, or any incidental life situation), the impact is likely a weaker security choice, that is, a risk-seeking rather than a risk-avoiding choice.
Information Retrieval from Software Bug Ontology Exploiting Formal Concept Analysis

Knowledge extraction and structuring is attaining importance in real world applications such as e-commerce, decision support, problem solving and semantic web. Extraction of knowledge from collection of text documents is based upon identification of semantic content. Ontology plays an important role in accessing and structuring information. Developing an ontology are at the core of new strategies which requires accurate domain knowledge. Identification of structural and logical concepts is a time-consuming process. This work presents an ontology-based retrieval approach, that visualizes and structure the data of software bug reports domain. It exploits formal concept analysis (FCA) to elicit conceptualizations from bug reports datasets and a hierarchical taxonomy is generated of extracted knowledge. A lattice diagram of concepts and relationships is constructed from concept-relationship matrix created by FCA. Ontology is constructed on fluent editor tool and knowledge is extracted with the help of small queries executed on a reasoner window. The proposed approach is evaluated on 21 bug reports of apache projects of jira repository. It can be concluded that information can be retrieved easily from ontology as compared to manual extraction of data.