Reviewerly/exHarmony huggingface dataset:
Breast cancer classification using improved fuzzy c means algorithm

Abnormal growth in the breast tissue prompts to the strange cell development in the breast. The researchers typically research for the size of the tumour in a mammogram, because mammograms contain irregular measurements of large scale and smaller scale calcifications. The nearness of these irregular measures of calcium stores in the breast ought to never be ignored as these are indications of early breast malignancy. To decipher this statement in a mammogram precisely, the quality of the pictures ought to be at its incomparable. The proposed research work is conveyed out for examinations of different screening strategies to recognize the unique phases of breast malignancy. In India for every 4 minutes, the women are diagnosed with this disease. And a woman dies with this disease for every 13 minutes. This disease is prominent with the people living in the ruler area while comparing the people in the urban areas. Therefore, it is very important to find and treat this disease as early as possible. The breast tumour region, perimeter and breadth are assessed from mammogram picture databases. The Bits Errors Degree (BER),), Highest Indication to Clatter Percentage (PSNR) and Callous Tetragonal Inaccuracy (MSE) values are determined for both Abnormal and normal images. These analyses were used to approve the presence or absence of the disease and to support the evaluation process for finding the disease. This quality assessment is used to understand the reality on Earth for a specific diagnosis that is a specific type of chromatin in a carcinogenic core that may indicate an irregular protein sequence
Card Macth Circle: Innovative Learning Media on Social Science Learning in Grade IV Elementary School

Card Match Circle learning media is a learning media that is used to improve innovative and creative learning outcomes. This media is used because of the lack of availability of innovative learning media in elementary schools. This study aimed to develop learning media products in the form of picture cards in social studies learning in fourth-grade elementary school. This research design uses the ADDIE method, which only reaches the Implementation stage because the researchers only develop and carry out practicality tests. Sources of data in this study amounted to 15 people consisting of 4 experts, 1 teacher and 10 students. The data collection technique consisted of two data collections: the teacher's response, the student's response, the two validation sheets of linguists, media experts, and material experts. The research instrument used was in the form of a linguist validation questionnaire, a media expert validation questionnaire, a material expert validation questionnaire, a student response questionnaire, and a teacher response questionnaire. The data analysis technique is descriptive qualitative and descriptive quantitative data. Based on the data obtained from research from media experts, a total of 100%, material experts in total 100%, and linguists on average 65% and the overall result is an average value of 88%, and it can be concluded that the Card Match Circle media is very valid to be used as instructional media. The analysis results of teacher responses are a total average of 100%, and students are an average of 97% in total, and the overall results are an average of 98%. It can be concluded that the Card Match Circle learning media impacts student learning outcomes. With this media, it can facilitate students in learning. In addition, it makes learning not boring so that students are enthusiastic in following the lessons given by the teacher.
HashNET Blockchain Consensus for DLT Applications

Our modern world is becoming increasingly reliant on the processing, exchange and storage of information. This trend of so-called "digitalisation" is penetrating every pore of human civilisation, including nature, people, and machines; our economy and production, which must be as local as possible; and our global ecology. The use of information processing technology brings benefits to a wide range of endeavours. In this sense, many computing technologies and techniques must be used to achieve the goal of an integrated global service ecosystem, a Rainbow ecosystem of all hierarchical levels of computing.&#x0D; One of the most important new developments in recent years is the development of blockchain technology. A blockchain can solve many problems of persistent and traceable storage, as well as enable direct coordination, compensation, etc. Therefore, in the Dew-Fog-Cloud hierarchy, blockchain technology is a promising new approach to enable novel applications in a variety of fields, from social and educational to scientific and industrial.&#x0D; However, there are two important points in many implementations of the current blockchain which prevent them from being used for public service solutions. The first is the proof algorithm - the vast majority of proof-of-work algorithms do useless work and waste enormous amounts of energy. The second one is that proof-of-stake algorithm is not suitable for open public infrastructure.&#x0D; The HashNET algorithm, which uses proof-of-authority combined with master nodes to achieve distributed consensus and ensure trust, is explained in detail in this paper.&#x0D; As an example of future applications in science, education and society, we also briefly describe certificate validation and future application for scientific publications.
Imbalance Classification Based on Deep Learning and Fuzzy Support Vector Machine

Imbalanced data is widespread in the fields of medical diagnosis, information security and industrial production. Traditional classification methods can handle balanced data very well. However, when dealing with imbalanced classification, it will favor majority classes, which results in low classification performance. This paper proposes an imbalanced classification method based on deep feature representation, named DL-FSVM. DL-FSVM extracts feature information in the input space using a deep neural network (DNN) to ensure similarity within class and improve the separation between different classes. After obtaining the feature representation, oversampling is performed in this embedding space based on the center distance to enhance the balance of the data distribution. Fuzzy Support Vector Machine (FSVM) is used as the final classifier. Assigning higher misclassification costs to minority class samples through cost-sensitive learning. Experiments were performed on six real-world datasets. The experimental results show that DL-FSVM achieves promising classification performance in three evaluation metrics: G-means, F1-score and AUC.
Edge-based local push for personalized PageRank

Personalized PageRank (PPR) is a popular node proximity metric in graph mining and network research. Given a graph G=(V,E) and a source node $s \in V$, a single-source PPR (SSPPR) query asks for the PPR value $\vpi(u)$ with respect to s, which represents the relative importance of node u in the context of the source node s. Among existing algorithms for SSPPR queries, LocalPush is a fundamental method which serves as a cornerstone for subsequent algorithms. In LocalPush, a push operation is a crucial primitive operation, which distributes the probability at a node u to ALL u's neighbors via the corresponding edges. Although this push operation works well on unweighted graphs, unfortunately, it can be rather inefficient on weighted graphs. In particular, on unbalanced weighted graphs where only a few of these edges take the majority of the total weight among them, the push operation would have to distribute insignificant probabilities along those edges which just take the minor weights, resulting in expensive overhead. To resolve this issue, we propose the EdgePush algorithm, a novel method for computing SSPPR queries on weighted graphs. EdgePush decomposes the aforementioned push operations in edge-based push, allowing the algorithm to operate at the edge level granularity. Hence, it can flexibly distribute the probabilities according to edge weights. Furthermore, our EdgePush allows a fine-grained termination threshold for each individual edge, leading to a superior complexity over LocalPush. Notably, we prove that EdgePush improves the theoretical query cost of LocalPush by an order of up to O(n) when the graph's weights are unbalanced, both in terms of $\ell_1$-error and normalized additive error. Our experimental results demonstrate that EdgePush significantly outperforms state-of-the-art baselines in terms of query efficiency on large motif-based and real-world weighted graphs.
Natural Language Generation System for Knowledge Acquisition Based on Patent Database

Privacy concerns at the individual and public or private organizational levels are a crucial. Its importance is highly evident nowadays, with the development of advanced technology. This study proposes a system for text mining that analyzes characteristics related to language. This factor makes it possible to generate a fictitious system while analyzing the patent within a bird’s-eye view and presenting keywords to support an idea. By mapping each patent’s information and relationship to an n -dimensional space, one can search for similar patents employing cosine similarity. Quantitative and qualitative evaluation verified the usefulness of the system.
IDENTIFYING THE CHALLENGES IN THE FIELD OF COMPUTER SCIENCE IN DISTANCE LEARNING IN SECONDARY EDUCATION

The COVID-19 pandemic has left 87% of the world's student population affected by school closures (data source: UNESCO). While schools and colleges are still strategizing over reopening, how are the students responding to the new era of remote learning?To make remote learning successful, more needs to be invested in the digital, pedagogical, social, and emotional competencies required.Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. In response to school closures, educational organizations recommend the use of distance learning programs and open educational applications and platforms that schools and teachers can use to reach learners remotely and limit the disruption of education.It's time to reconsider the ways of teaching in education. The transition from face-to-face to remote learning is really difficult for students and teachers. There are challenges such as keeping them engaged, disciplined, and healthy to overcome this crisis in education.The purpose of this article is to identify these challenges and suggest adequate ways to address them in the field of Computer science studied in secondary school.A survey, conducted in the English Language School in Ruse, Bulgaria, presents how students feel about the complex Covid-19 situation and the change from face-to-face to remote learning. A comparison between different platforms for distance education is made. The survey results reveal important and interesting findings of students' attitude in distance learning, how a teacher can keep them engaged and how these difficulties can be overcome. The results are systematically organized and could be used for the future development of a new curriculum that adopted the principles of remote learning in secondary schools.
An analysis of over-sampling labeled data in semi-supervised learning with FixMatch

Most semi-supervised learning methods over-sample labeled data when constructing training mini-batches. This paper studies whether this common practice improves learning and how. We compare it to an alternative setting where each mini-batch is uniformly sampled from all the training data, labeled or not, which greatly reduces direct supervision from true labels in typical low-label regimes. However, this simpler setting can also be seen as more general and even necessary in multi-task problems where over-sampling labeled data would become intractable. Our experiments on semi-supervised CIFAR-10 image classification using FixMatch show a performance drop when using the uniform sampling approach which diminishes when the amount of labeled data or the training time increases. Further, we analyse the training dynamics to understand how over-sampling of labeled data compares to uniform sampling. Our main finding is that over-sampling is especially beneficial early in training but gets less important in the later stages when more pseudo-labels become correct. Nevertheless, we also find that keeping some true labels remains important to avoid the accumulation of confirmation errors from incorrect pseudo-labels.
A stochastic gradient descent approach with partitioned-truncated singular value decomposition for large-scale inverse problems of magnetic modulus data

Abstract We propose a stochastic gradient descent approach with partitioned-truncated singular value decomposition (SVD) for large-scale inverse problems of magnetic modulus data. Motivated by a uniqueness theorem in gravity inverse problem and realizing the similarity between gravity and magnetic inverse problems, we propose to solve the level-set function modeling the volume susceptibility distribution from the nonlinear magnetic modulus data. To deal with large-scale data, we employ a mini-batch stochastic gradient descent approach with random reshuffling when solving the optimization problem of the inverse problem. We propose a stepsize rule for the stochastic gradient descent according to the Courant–Friedrichs–Lewy condition of the evolution equation. In addition, we develop a partitioned-truncated SVD algorithm for the linear part of the inverse problem in the context of stochastic gradient descent. Numerical examples illustrate the efficacy of the proposed method, which turns out to have the capability of efficiently processing large-scale measurement data for the magnetic inverse problem. A possible generalization to the inverse problem of deep neural network is discussed at the end.
High-precision Hardware Oscillators Ensemble for GNSS Attack Detection

A wide gamut of important applications rely on global navigation satellite systems (GNSS) for precise time and positioning. Attackers dictating the GNSS receiver position and time solution are a significant risk, especially due to the inherent vulnerability of GNSS systems. A first line of defense, for a large number of receivers, is to rely on additional information obtained through the rich connectivity of GNSS enabled platforms. Network time can be used for direct validation of the GNSS receiver time; but this depends on network availability. To allow attack detection even when there are prolonged network disconnections, we present a method based on on-board ensemble of reference clocks. This allows the receiver to detect sophisticated attacks affecting the GNSS time solution, independently of the specific attack methodology. Results obtained with Chip-Scale Oven Compensated Oscillators (CS-OCXO) are promising and demonstrate the potential of embedded ensembles of reference clocks, detecting attacks causing modifications of the receiver time offset as low as 0.3us, with half the detection latency compared to related literature.
Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses

In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.
Emotion artificial intelligence

The digitally mediatised self is becoming intensively involved in emerging Emotion AI services. The purpose of this chapter is to conceptualise how Emotion AI recognises the quantified and mediatised self, and how the self reflects for emerging AI-driven media technologies? Three interrelated concepts interpret the changes, such as growing dependence on media technology, the disappearance of media into the lifeworld, and deep mediatisation. These concepts draw a digitalised-mediatised self with a quantified and representative entity. A metaphor of “water reflection” and the “Mirror Ritual” art project extends the theoretical considerations to present how self resonates with AI technology through emotion and love. The resulting conceptualisation proposes considering the competing future perspectives of the self with options of blended self-AI or AI-augmented self. The ultimate question is the well-being and satisfaction of the self, even if AI ethics detects controversial cases and biases due to cultural and moral codes. The chapter contributes to comprehensively understanding the available key terminology, development and art of the field.
Let Every Seat Be Perfect! A Case Study on Combining BIM and VR for Room Planning

When communicating indoor room design, professional designers normally rely on software like Revit to export walk-through videos for their clients. However, a lack of in-situ experience restricts the ultimate users from evaluating the design and hence provides limited feedback, which may lead to a rework after actual construction. In this case study, we explore empowering end-users by exposing rich design details through a Virtual Reality (VR) application based on building an information model. Qualitative feedback in our user study shows promising results. We further discuss the benefits of the approach and opportunities for future research.
Estimating uncertainty for the instrument transfer function measurement of 3D scanners

Spatial resolution is an important aspect of many optical instruments. It is defined as the ability of surface-topography measuring instruments to distinguish closely spaced surface features. Following convention, spatial resolution can be defined as the spatial frequency response of the instrument, known as the instrument transfer function (ITF). In this paper, we describe the step-artifact approach for estimating the ITF for 3D scanners, discuss step artifact characterization and validation approaches, and present a method to estimate the combined uncertainty of the ITF measurement. The approach is demonstrated using the EinScan-Pro 3D scanner. A step artifact is used for the measurement that takes advantage of the cleaving properties of a single-side polished silicon wafer. The uncertainty analysis includes simulations to estimate the contribution due to influencing factors such as the alignment of the step artifact to the measurement axis, the diffuse versus specular scattering properties of the step edge, and various processing parameter choices.
Designing Immersive Virtual Reality Environments for Supporting Patients at Home

Immersive virtual reality computer programs provide new experimental and treatment interventions that hold great promise for nursing. Immersive virtual reality uses sensory cues to represent real-world environments in a way that makes participants feel as if they are in a physical space different from the one in which they currently exist. As the acceptance of immersive virtual reality as a clinical and experimental tool has grown, so has the need to ensure that the context depicted in the environment mirrors both the sensory and the task requirements of the real-world situation. Here, we describe the use of nurse expert key informant group interviews to generate requirements that must be fulfilled in immersive virtual reality environments designed to evoke and engage participants in self-management tasks. An expert panel of four home care nurses participated in three sessions designed to elicit details of common home care challenges, frequency of variation, and typical participants. More than 20 potential scenarios were identified. The design team later used this information to create design requirements for two key scenarios and subsequently develop immersive virtual reality environments for use in research studies, mapping sensory and functional expectations to immersive virtual reality implementations. Challenges in mapping from key informant group findings to requirements are addressed.
Temperature and entanglement of the three-state quantum walk

Despite the coined quantum walk being a closed quantum system under a unitary evolution, its Hilbert space can be divided in two sub-spaces, which makes it possible for one to analyze one of the subsystems (the coin or the walker) as an open system in contact with a reservoir. In the present work we calculate the asymptotic reduced density matrix of the coin space of the three-state quantum walk in an infinite line, and use that result to analyze the entanglement between the chirality, and position space. We calculate the von Neumann entropy and the entanglement temperature per mean energy of the system in the asymptotic limit.
Procedural content generation using neuroevolution and novelty search for diverse video game levels

Procedurally generated video game content has the potential to drastically reduce the content creation budget of game developers and large studios. However, adoption is hindered by limitations such as slow generation, as well as low quality and diversity of content. We introduce an evolutionary search-based approach for evolving level generators using novelty search to procedurally generate diverse levels in real time, without requiring training data or detailed domain-specific knowledge. We test our method on two domains, and our results show an order of magnitude speedup in generation time compared to existing methods while obtaining comparable metric scores. We further demonstrate the ability to generalise to arbitrary-sized levels without retraining.
Automatic fall detection using Orbbec Astra 3D pro depth images

Falls particularly among the older population has always been a matter of concern. With the steady rise of small families, the elderly is very often left alone at home. Dedicated nurses or caretakers are quite expensive. Thus, intelligent monitoring systems with automatic fall detection systems installed at home or nursing homes could be a game changer in such applications. In this paper, a simple yet effective fall detection system based on computer vision. Novelty of this paper is that it uses the Yolo v2 network on the depth videos for extracting the subject from cluttered background. The robust performance of the YOLOv2 network ensures accurate subject detection and removes the need for any complicated fall detection algorithm. Fall detection is carried out using subject’s height to width ratio and fall velocity. These parameters are simple and easy to calculate and yet provide effective results. The input data is captured using the Orbbec Astra 3D camera.