Reviewerly/exHarmony huggingface dataset:
Smartphone-Based Activity Recognition in a Pedestrian Navigation Context

In smartphone-based pedestrian navigation systems, detailed knowledge about user activity and device placement is a key information. Landmarks such as staircases or elevators can help the system in determining the user position when located inside buildings, and navigation instructions can be adapted to the current context in order to provide more meaningful assistance. Typically, most human activity recognition (HAR) approaches distinguish between general activities such as walking, standing or sitting. In this work, we investigate more specific activities that are tailored towards the use-case of pedestrian navigation, including different kinds of stationary and locomotion behavior. We first collect a dataset of 28 combinations of device placements and activities, in total consisting of over 6 h of data from three sensors. We then use LSTM-based machine learning (ML) methods to successfully train hierarchical classifiers that can distinguish between these placements and activities. Test results show that the accuracy of device placement classification (97.2%) is on par with a state-of-the-art benchmark in this dataset while being less resource-intensive on mobile devices. Activity recognition performance highly depends on the classification task and ranges from 62.6% to 98.7%, once again performing close to the benchmark. Finally, we demonstrate in a case study how to apply the hierarchical classifiers to experimental and naturalistic datasets in order to analyze activity patterns during the course of a typical navigation session and to investigate the correlation between user activity and device placement, thereby gaining insights into real-world navigation behavior.
Sentiment Analysis on Images Using Convolutional Neural Network

Sentiment analysis has evolved to be of much greater use; it helps brands, political parties, governments, and anyone to help gauge the sentiment of the masses towards something, and to understand their needs; sentiment analysis has proven effective in decision and policy making. Sentiment analysis usually is limited to gauging sentiments from text. But to determine the sentiment depicted in images, we need to use convolutional neural networks (CNN), which use multilayer perceptions to detect unique features in the simplest and easiest method of sharing emotions and thoughts. Since image sharing on social media is images. Images very much popular; the use of images for sentiment analysis can enhance the results. In this paper, we present the use of sentiment analysis to filter out images with adult, violent content which is possibly dangerous to remain in the public domain; the applications using CNN can be so many. It can also be used in various social media platforms for automated tag predictions.
Quality of education as an indicator of the quality of life

The article presents ideas fundamental for the educational community of the determining role of the quality of education on the quality of a modern person and his/her various aspects of life. The authors turn to the origins of the idea of improving the quality of life in philosophical discourse, show the role of education in transforming a student's potency into his real achievements. The article describes the noospheric and acmeological guidelines for improving the quality of a person and his/her life, considering the degree of the culture of life. Having summarized the philosophical, pedagogical, sociological, and economic research, the authors describe the factors that affect the quality of life of subjects of education, systematize various views on the problem of the quality of education in domestic pedagogy, describe innovations that improve the quality of the educational process, and argue the need for their introduction into the practice of higher education. As a proof of the importance of improving the quality of education and its impact on the younger generation, the article presents the results of a reflective analysis of students derived from essays, questionnaires, and interviews that rank and independently describe the criteria for improving the quality of a person and life in conditions of high-quality higher education.
Design of bio-inspired binocular UAV detection system based on improved STC algorithm of scale transformation and occlusion detection

With the rapid development of drones, many problems have arisen, such as invasion of privacy and endangering security. Inspired by biology, in order to achieve effective detection and robust tracking of small targets such as unmanned aerial vehicles, a binocular vision detection system is designed. The system is composed of long focus and wide-angle dual cameras, servo pan tilt, and dual processors for detecting and identifying targets. In view of the shortcomings of spatio-temporal context target tracking algorithm that cannot adapt to scale transformation and easy to track failure in complex scenes, the scale filter and loss criterion are introduced to make an improvement. Qualitative and quantitative experiments show that the designed system can adapt to the scale changes and partial occlusion conditions in the detection, and meets the real-time requirements. The hardware system and algorithm both have reference value for the application of anti-unmanned aerial vehicle systems.
Content substantiation of the regional advanced training educational program “Kaizen Technology”

The relevance of the article is related to the need to modernize school education in Ukraine in the context of reform on the basis of the best world standards and models. The purpose of the article is to reveal the essence of Kaizen technology in secondary education, to substantiate the content of the regional educational program of teachers’ professional development on this basis. The research methods are theoretical analysis, synthesis, modeling, systematization, generalization and survey. The results of the research are following: the essence of the possibilities of Kaizen technology in secondary education is revealed, the complex of approaches to the program content development is determined and its content is substantiated. There are three modules such as “The essence and origins of Kaizen technology”, “Lean-education”, “Management in education based on Kaizen technology”. Conclusions: Kaizen technology is a long-term strategy of personal development, based on the creativity of the teacher; mastery of Kaizen is carried out in three stages; the updated content of teacher advanced training is based on a set of scientific approaches; the criterion for the effectiveness of Kaizen technology and its purpose is the professional readiness of teachers for creative and constructive professional activity and continuous self-development.
Normal-Bundle Bootstrap

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 28 July 2020Accepted: 12 February 2021Published online: 04 May 2021Keywordsprobabilistic learning, data manifold, dynamical systems, resampling, data augmentationAMS Subject Headings37M22, 53-08, 53A07, 62F40, 62G09Publication DataISSN (online): 2577-0187Publisher: Society for Industrial and Applied MathematicsCODEN: sjmdaq
On density estimation at a fixed point under local differential privacy

We consider non-parametric density estimation in the framework of local, both pure and approximate, differential privacy. In contrast to centralized privacy scenarios with a trusted curator, in the local setup anonymization must be guaranteed already on the individual data owners’ side and must therefore precede any data mining tasks. Thus, the published anonymized data should be compatible with as many statistical procedures as possible. We consider different mechanisms to establish pure and approximate differential privacy, respectively. We obtain minimax type results over Sobolev classes indexed by a smoothness parameter s>1∕2 for the mean squared error at a fixed point. In particular, we show that appropriately defined kernel density estimators can attain the optimal rate of convergence if the bandwidth parameter is correctly specified. Notably, the optimal convergence rate in terms of the sample size n is n−(2s−1)∕(2s+1) under pure differential privacy and thus deteriorated to the rate n−(2s−1)∕(2s) which holds both without privacy restrictions and under approximate differential privacy. Since the optimal choice of the bandwidth parameter depends on the smoothness s and is thus not accessible in practise, adaptive methods for bandwidth selection are necessary and must, in the local privacy framework, be performed based on the anonymized data only. We address this problem by means of variants of Lepski’s method tailored to the privacy setups at hand and obtain general oracle inequalities for private kernel density estimators. In the Sobolev case, the resulting adaptive estimators attain the optimal rates of convergence at least up to logarithmic factors. On the side, we discuss some critical issues related with the notion of approximate differential privacy.
Learning Latent Graph Dynamics for Deformable Object Manipulation.

Manipulating deformable objects, such as cloth and ropes, is a long-standing challenge in robotics: their large number of degrees of freedom (DoFs) and complex non-linear dynamics make motion planning extremely difficult. This work aims to learn latent Graph dynamics for DefOrmable Object Manipulation (G-DOOM). To tackle the challenge of many DoFs and complex dynamics, G-DOOM approximates a deformable object as a sparse set of interacting keypoints and learns a graph neural network that captures abstractly the geometry and interaction dynamics of the keypoints. Further, to tackle the perceptual challenge, specifically, object self-occlusion, G-DOOM adds a recurrent neural network to track the keypoints over time and condition their interactions on the history. We then train the resulting recurrent graph dynamics model through contrastive learning in a high-fidelity simulator. For manipulation planning, G-DOOM explicitly reasons about the learned dynamics model through model-predictive control applied at each of the keypoints. We evaluate G-DOOM on a set of challenging cloth and rope manipulation tasks and show that G-DOOM outperforms a state-of-the-art method. Further, although trained entirely on simulation data, G-DOOM transfers directly to a real robot for both cloth and rope manipulation in our experiments.
How to fool a black box machine learning based side-channel security evaluation

Machine learning and deep learning algorithms are increasingly considered as potential candidates to perform black box side-channel security evaluations. Inspired by the literature on machine learning security, we put forward that it is easy to conceive implementations for which such black box security evaluations will incorrectly conclude that recovering the key is difficult, while an informed evaluator / adversary will reach the opposite conclusion (i.e., that the device is insecure given the amount of measurements available).
F-mixup: Attack CNNs From Fourier Perspective

Recent research has revealed that deep neural networks are highly vulnerable to adversarial examples. In this paper, different from most adversarial attacks which directly modify pixels in spatial domain, we propose a novel black-box attack in frequency domain, named as f-mixup, based on the property of natural images and perception disparity between human-visual system (HVS) and convolutional neural networks (CNNs): First, natural images tend to have the bulk of their Fourier spectrums concentrated on the low frequency domain; Second, HVS is much less sensitive to high frequencies while CNNs can utilize both low and high frequency information to make predictions. Extensive experiments are conducted and show that deeper CNNs tend to concentrate more on the higher frequency domain, which may explain the contradiction between robustness and accuracy. In addition, we compared f-mixup with existing attack methods and observed that our approach possesses great advantages. Finally, we show that f-mixup can be also incorporated in training to make deep CNNs defensible against a kind of perturbations effectively.
Optical Flow-Based Weighted Magnitude and Direction Histograms for the Detection of Abnormal Visual Events Using Combined Classifier

Movement information of persons is a very vital feature for abnormality detection in crowded scenes. In this paper, a new method for detection of crowd escape event in video surveillance system is proposed. The proposed method detects abnormalities based on crowd motion pattern, considering both crowd motion magnitude and direction. Motion features are described by weighted-oriented histogram of optical flow magnitude (WOHOFM) and weighted-oriented histogram of optical flow direction (WOHOFD), which describes local motion pattern. The proposed method uses semi-supervised learning approach using combined classifier (KNN and K-Means) framework to detect abnormalities in motion pattern. The authors validate the effectiveness of the proposed approach on publicly available UMN, PETS2009, and Avanue datasets consisting of events like gathering, splitting, and running. The technique reported here has been found to outperform the recent findings reported in the literature.
Internet of Medical Things: Enabling Key Technologies

Across the World the Covid-19 pandemic has created a huge burden on the medical field. Technologies including Internet of Things (IoT) have been playing a vital role to create digital remote health services during a time of social distancing and isolation. IoT has rapidly expanded into the Healthcare field in recent years. The joining of Healthcare with IoT is named Internet of Medical Things (IoMT) and has significantly advanced many technologies with it. This paper is going to examine emerging technologies associated with IoMT as a whole and the embodiment of this technology including: Big-Data, Artificial Intelligence (AI), 5G, and Blockchain and how they are being utilized currently in the field. We will also present how these technologies can help combat the current pandemic.
A Geometric Analysis of Neural Collapse with Unconstrained Features

We provide the first global optimization landscape analysis of $Neural\;Collapse$ -- an intriguing empirical phenomenon that arises in the last-layer classifiers and features of neural networks during the terminal phase of training. As recently reported by Papyan et al., this phenomenon implies that ($i$) the class means and the last-layer classifiers all collapse to the vertices of a Simplex Equiangular Tight Frame (ETF) up to scaling, and ($ii$) cross-example within-class variability of last-layer activations collapses to zero. We study the problem based on a simplified $unconstrained\;feature\;model$, which isolates the topmost layers from the classifier of the neural network. In this context, we show that the classical cross-entropy loss with weight decay has a benign global landscape, in the sense that the only global minimizers are the Simplex ETFs while all other critical points are strict saddles whose Hessian exhibit negative curvature directions. In contrast to existing landscape analysis for deep neural networks which is often disconnected from practice, our analysis of the simplified model not only does it explain what kind of features are learned in the last layer, but it also shows why they can be efficiently optimized in the simplified settings, matching the empirical observations in practical deep network architectures. These findings could have profound implications for optimization, generalization, and robustness of broad interests. For example, our experiments demonstrate that one may set the feature dimension equal to the number of classes and fix the last-layer classifier to be a Simplex ETF for network training, which reduces memory cost by over $20\%$ on ResNet18 without sacrificing the generalization performance.
A two-level method for image denoising and image deblurring models using mean curvature regularization

The mean curvature (MC)-based image denoising and image deblurring models are used to enhance the quality of the denoised images and deblurred images respectively.These models are very efficient in removing staircase effect, preserving edges and other nice properties.However, high order derivatives appear in the Euler-Lagrange equations of the MCbased models which create problems in developing an efficient numerical algorithm.To overcome this difficulty, we present a robust and efficient Two-Level method for MC-based image denoising and image deblurring models.The Two-Level method is consist of solving one small problem and one large problem.The small problem is a nonlinear system, having high order derivative, on Level-I (image having small number of pixels).The large problem is one less expensive system, having low order derivative, on Level-II (image having large number of pixels).The derivation of the optimal regularization parameter of Level-II is studied and formula is presented.Numerical experiments on digital images are presented to exhibit the performance of the Two-Level method.
Scenario-Based Instruction: The Case of Foreign Language Training at Multidisciplinary University

Strong communication skills, in varied professional settings, have become an absolute necessity for young professionals. Therefore, university educators are challenged to provide novice specialists with the effective foreign language training that could bring the real professional world into the classroom. This study suggests an innovative method for teaching communication skills to students, through the use of real-life scenarios, drawn from the professional context. The core objective of the research was to define whether the scenario-based instruction could effectively enhance the development of professional communication skills of students in the context of university foreign language training. The data were collected through active observation and critical incidents technique, analyzed and summarized by means of descriptive statistics. The results of the study indicate the efficacy of scenario-based communication training in higher education. However, scenario-based instruction requires careful assessment of learners’ needs and expectations, thoughtful selection and design of teaching materials, and the development of an active learning environment.
Indexing the humanities knowledge in the Library Bibliographic Classification

Changing scientific knowledge entails the exclusion of some areas of research and inclusion of others into the main line of development of science, with the new publications revising well-established concepts and offering the new ones. Any rational reconstruction of the history of science has to identify the line of development that has resulted in the modern knowledge. The Soviet Library and Bibliographic Classification developed in the mid-20th century responded to the challenges of the epoch. The paradigm shift of the 1990s in this country resulted in the conceptual revision of scientific values and the emergence of new academic disciplines. That period gave rise to applied studies with their specific goals and values, standards and norms. In the recent period, the humanities studies proliferate to construct generalized interdisciplinary theory. The vast number of publications in the humanities knowledge suggests that the appropriate place for these studies within the structure of the national Library Bibliographic Classification has to be found.
Churn Prediction on Higher Education Data with Fuzzy Logic Algorithm

Colleges are an optional final stage of formal education. However, with time, the Management Section finds the fact that the student churn rate in the university scope becomes a problem. The purpose of this research is to predict whether the student will be churn or loyal in the future, the data will be taken from 2014. The analytical technique used in this research process is the Fuzzy method of C-Means. At a university, the variable that will be tested is the Length of students who are currently attending higher education at the University or campus, when the last student pays enrollment, the last payment period, and the student's total payment. The data used as many as 100 datasets were collected in this research, starting from 2014 to 2020. Of the 100 Datasets of informatics engineering, students gained 92% of loyal students, and 8% of students predicted to churn.
Minority Class Oriented Active Learning for Imbalanced Datasets

Active learning aims to optimize the dataset annotation process when resources are constrained. Most existing methods are designed for balanced datasets. Their practical applicability is limited by the fact that a majority of real-life datasets are actually imbalanced. Here, we introduce a new active learning method which is designed for imbalanced datasets. It favors samples likely to be in minority classes so as to reduce the imbalance of the labeled subset and create a better representation for these classes. We also compare two training schemes for active learning: (1) the one commonly deployed in deep active learning using model fine tuning for each iteration and (2) a scheme which is inspired by transfer learning and exploits generic pre-trained models and train shallow classifiers for each iteration. Evaluation is run with three imbalanced datasets. Results show that the proposed active learning method outperforms competitive baselines. Equally interesting, they also indicate that the transfer learning training scheme outperforms model fine tuning if features are transferable from the generic dataset to the unlabeled one. This last result is surprising and should encourage the community to explore the design of deep active learning methods.
Quantum Optimal Transport

We analyze a quantum version of the Monge--Kantorovich optimal transport problem. The quantum transport cost related to a Hermitian cost matrix $C$ is minimized over the set of all bipartite coupling states $\rho^{AB}$ with fixed reduced density matrices $\rho^A$ and $\rho^B$ of size $m$ and $n$. The minimum quantum optimal transport cost $\rT^Q_{C}(\rho^A,\rho^B)$ can be efficiently computed using semidefinite programming. In the case $m=n$ the cost $\rT^Q_{C}$ gives a semidistance if and only if $C$ is positive semidefinite and vanishes exactly on the subspace of symmetric matrices. Furthermore, if $C$ satisfies the above conditions, then $\sqrt{\rT^Q_{C}}$ induces a quantum analogue of the Wasserstein-2 distance. Taking the quantum cost matrix $C^Q$ to be the projector on the antisymmetric subspace, we provide a semi-analytic expression for $\rT^Q_{C^Q}$ for any pair of single-qubit states and show that its square root yields a transport distance on the Bloch ball. Numerical simulations suggest that this property holds also in higher dimensions. Assuming that the cost matrix suffers decoherence and that the density matrices become diagonal, we study the quantum-to-classical transition of the Earth mover's distance, propose a continuous family of interpolating distances, and demonstrate that the quantum transport is cheaper than the classical one. Furthermore, we introduce a related quantity -- the SWAP-fidelity -- and compare its properties with the standard Uhlmann--Jozsa fidelity. We also discuss the quantum optimal transport for general $d$-partite systems.