Reviewerly/exHarmony huggingface dataset:
Unsupervised Feature Selection Based on Matrix Factorization with Redundancy Minimization

Unsupervised feature selection (UFS) based on matrix factorization (MF) is an efficient technique for dimensionality reduction in image processing task. Most MF based methods learn cluster indicator matrix and bases matrix, and exploit the bases matrix for feature selection via ranking weights. Since correlated features tend to have similar rankings and these methods select the top ranked features with large correlations, the selected features might contain redundant information. Toward this end, we propose a novel matrix factorization with redundancy minimization method, in which performing MF and removing redundant features are incorporated into a coherent model. To reduce the redundancy, we define a regularization to penalize the high-correlated features. The effective $$\ell _{2,p}$$-norm $$(0 < p \le 1)$$ imposed on bases matrix is suitable for feature selection. Experimental results on image datasets validate the effectiveness of the proposed approach.
Investigation of the dynamic variability in the amount of contaminants in urbanized atmosphere

Abstract The dynamic variability in the amount of contaminants in urban atmosphere and the mathematical models which allow describing the given variability have been analyzed. The models of the seasonal variation in the amount of solid particles in the atmosphere of Volgograd were developed based on Fourier series. The seasonal index was determined. A dynamic equation characterizing the seasonal variability in the amount of solid particles in the atmosphere of Volgograd was obtained applying the method of analytical seasonal adjustment.
Front surface geometry modeling of remotely operated vehicle (ROV) body observation class

Sunda Strait of Indonesia needs a technology to find out information on the bottom of Sunda Strait, and the suitable technology is ROV. The existing ROVs have limitations; those are in design structure and hydrodynamic response. An early research on design concepts has been conducted to develop existing ROVs. This research has a purpose to develop existing ROVs and to continue preliminary research on design concepts of the ROVs, and also to obtain the best front surface geometry modeling of the ROV body. The method used was the Granville method which determines parametric profile on ROV body which had streamlined body geometry. The result of the research was to obtain K1(curvature at xm) and rn (radius of the curvature at front end part) modeling, so that the best front surface geometry modeling curve on the body of the ROV Observation Class can be found.
Design Of A New Thresholder Using For Spiking Photonic Neuron

In this paper, an ultrafast spiking photonic neuron is illustrated with a new thresholder that based on a designed Sagnac loop. The novel all-optical thresholder (AOT) is internally passive with unified outside pump source in a highly nonlinear of hollow-core photonic crystal fiber(HC-PCF) loop. It can eliminate noise accumulation and results in robust and efficient information processing and achieves a high extinction ratio in our simulation. This neuron works in a low power state.
Detecting Faults in Inner-Product Masking Scheme - IPM-FD: IPM with Fault Detection

Side-channel analysis and fault injection attacks are two typical threats to cryptographic implementations, especially in modern embedded devices. Thus there is an insistent demand for dual side-channel and fault injection protections. As it is known, masking scheme is a kind of provable countermeasures against side-channel attacks. Recently, inner product masking (IPM) was proposed as a promising higher-order masking scheme against side-channel analysis, but not for fault injection attacks. In this paper, we devise a new masking scheme named IPM-FD. It is built on IPM, which enables fault detection. This novel masking scheme has three properties: the security orders in the word-level probing model, bit-level probing model, and the number of detected faults. IPM-FD is proven secure both in the word-level and in the bit-level probing models, and allows for end-to-end fault detection against fault injection attacks. Furthermore, we illustrate its security order by linking it to one defining parameters of linear code, and show its implementation cost by applying IPM-FD to AES-128.
Adaptive Multi-Strategy Observation of Kernelized Correlation Filter for Visual Object Tracking

Visual object tracking leads a vital role in multiple fields such as intelligent surveillance system, intelligent transportation system, human-computer interaction, behavior analysis, and intelligent driving assistance. In recent years, research of object tracking tends to focus on improving accuracy. Kernelized Correlation Filter (KCF) is considered as a baseline algorithm for real-time object tracking in term of high computation speed and accuracy by using correlation efficiently in the Frequency domain. However, correlation filter-based tracker is still prone to model drift due to incorrect predictions. This condition caused by varied appearance model especially in fast motion and motion blur. We proposed a new concept of KCF based tracker by adding confidence score scheme to detect tracker loss. Our tracker also introduces observation model with adaptive multi-strategy to find the lost target. We test the proposed method using OTB100 data that has strong characteristics in fast motion and motion blur. The result demonstrates that the proposed method was capable of recovering the lost target. The proposed tracker achieves better performance compared to the existing tracker in term of 0.887 in accuracy and 0.895 success rate.
Depth Estimated History Image based Appearance Representation for Human Action Recognition

Depth of a human action scene is important to distinguish human action from its background. Therefore, it helps to describe the appearance of the action. In this work, depth history image (DHI) is proposed by estimating depth from action frames. The DHIs are then applied to AlexNet to finetune the weights of the pre-trained deep learning architecture. To recognize the closely related actions, DHI alone is not sufficient. The 3D projected planes are extracted and trained separately on AlexNet for this purpose. Two types of projected planes are extracted in this work such as XT plane or side view and YT plane or top view of the action videos. The scores from both the learning techniques are fused to provide the final recognition score. The proposed HAR technique is evaluated on well established KTH and Weizmann human action datasets and the results suggest that the proposed HAR is better compared to most of the state-of-the-art methods.
Software Design for Depiction and Calculation of Curves Area on Cartesian Coordinates

Abstract The area which is limited by the curve can be calculated using certain integral function. The process of calculating integral functions is quitely complex and extensive because it is requires to control several rules of integration. In addition, the process of calculating a definite integral function for several equations such as a circle equation requires an example and substitution. This learning software performs various completion processes. This process begin with the process of reading and checking input data. The process is continued by describe the function of the input curve. The process is ended by calculate the area using the certain integral function. This learning software will display an image of the function curve which is inputted and the results of the calculation of the area using the certain integral function. The software also provides facilities for storing input and image data from curve functions in the *.bmp image format.
Applying Information Theory and Bayesian Inference to Paleoenvironmental Interpretation

Abstract Gathering and analyzing data in the most unbiased and reproducible fashion is crucial for successful scientific research. However, scientists are influenced by prior experiences and tend to introduce biases in data interpretation when solutions are nonunique. In Earth science, a key task is the reconstruction of paleoenvironmental conditions, which relies on measurable changes in certain attributes of the resultant landscapes or the sedimentary record. The attributes in use commonly have multiple explanations, which may result in biased interpretation. To mitigate the problem, we propose a general conceptual framework for linking the observed attribute changes to their possible causes based on Shannon's information theory. This framework further serves as a hypothesis generator. We then present a rigorous procedure for paleoenvironmental interpretation based on the multiple‐hypotheses method and Bayesian inference. Application examples illustrate the benefits of this procedure in paleoenvironmental reconstructions and in understanding environmental signal propagation through sediment routing systems.
Safe laboratory practices &amp; procedures introduced to the students through an augmented reality application

The adoption of digital technology in both learning and teaching process, and the evolution of information technologies are opening new perspectives for the use of augmented reality (AR) and virtual reality (VR). Thus, these technologies can facilitate the assistance of learners working on complex learning situation by using AR and enable the implementation of VR trainings, thus improving their efficiency. However, integrating these new tools into the existing learning processes remains complex, due to the technological aspects and the data continuum to be implemented, through the identification of use cases and the associated gains and by the diversity actors and experts to involve in this process: the expert of the main field, the designer and the IT developer. In this paper we aim to develop an AR application by following a developing process that will led to the planned results. The proposed application will be dictated for learners in higher education context, especially newbies that will practice their first experiment in the laboratory e.g." biology, chemistry" by giving them an interactive environment to understand the safety procedure followed during experiments in laboratories.
An Optimal Composition Strategy for Knowledge Service Component Based on Flexible Tracking Particle Swarm Algorithm

It is urgent to combine knowledge resources with manufacturing business processes to form a knowledge service in the cloud mode, so as to provide intelligent support for business activities in product development process. The main challenge of knowledge resource service, however, is how to rapidly construct the complex resource service system and respond promptly to the changeable service requirements in the business process, which is similar to the software system modeling using a component in software engineering. This paper is concerned with an optimal composition framework (OCF) of knowledge resource service, including service decomposition, component encapsulation, and optimal composition. Firstly, the typical business processes are decomposed into the dynamic knowledge element (DKE), and all kinds of knowledge resources and service behaviors are encapsulated into the reusable resource service components (RSC). Then, a multicomponent optimal composition mathematical model is presented, which transforms the problem of component composition into a multiobjective optimization problem. On this basis, a heuristic algorithm with the adaptive mutation probability is introduced to composite the multigranularity service component dynamically and robustly. Finally, the case of component composition for maintenance resource service is studied and the simulation results are provided to verify the efficacy of the proposed model and algorithms.
Touchscreen Behavioural Biometrics Authentication in Self-contained Mobile Applications Design

The article presents the research connected with developing a mobile touchscreen behavioural biometrics solution that may be applicable for authentication and improving transaction security of financial services. The article aims to present the research approach and a literature review that identified research gaps and performed a critical analysis of previous results. The goal is to suggest possible improvements over the existing methods in the literature. The motivation, methodology and main problem statements of the aforementioned research are presented, focusing on the characteristics of behavioural biometrics methods. The main contribution of the article consists of the literature review focused on the characteristics of the approaches used, differences in results caused by the evaluation criteria of the research processes and their comparability. Based on it insights are derived which can be used to build touchscreen based authentication method and validate the results.
Modification of SWIR Face Images for their Comparison by Distributed Cumulative Histogram Features

The following topics are dealt with: statistical analysis; feature extraction; software reliability; user interfaces; time series; fractals; Bayes methods; Monte Carlo methods; astrometry; astronomical image processing.
A scalable model for real‐time venue recommendations using MapReduce

Summary The popular social networks such as Facebook, Twitter, and Foursquare closely monitor user activities to recommend different services and events. Among others, venue recommendation proposes users the most appropriates venues based on the user preferences. It offers facility to the user to mark the check‐ins when a venue is visited. The traditional venue recommendation systems have opted collaborative filtering to propose recommendations. However, collaborative filtering overlooked certain critical issues, including real‐time recommendations, cold start, and scalability, for venue recommendations. Moreover, real‐time physical factors such as distance from the venue are also not considered in traditional venue recommendation systems. Furthermore, parsing and processing of huge volume of unstructured data is the main challenge for conventional recommender systems, particularly when dealing with real‐time recommendations. For efficient scaling, significant computational and storage resources for recommendation systems are desired. This article proposes a Real‐Time Venue Recommendation (RTVR) model that utilizes cloud‐based MapReduce framework to process, compare, mine, and manage large data sets for generating recommendations. The results showed that the proposed model has improved accuracy for real‐time recommendations. The proposed RTVR is more scalable as it exploits a cloud‐based architecture. Moreover, the proposed techniques are verified using formal verification methods .
Pure Consciousness in the Context of Formal Logic

The subject of the research is the dynamics of noesis of pure consciousness and the rules of formal logic. The goal was to establish the foundations for the synthesis of pure consciousness and ordinary reason. The methodological basis was the theory of pure consciousness postulated by E. Husserl in his "Ideas for pure phenomenology and phenomenological philosophy. Book one. A general introduction to pure phenomenology". The research also featured I. Kant's "Critique of Pure Reason", in particular his reflections on the "foundations of pure reason" and the formal logic of Aristotle. Results. If we split the emerging experience of pure consciousness into interoceptive and exteroceptive, it means that the pure contemplation of things is not as pure as E. Husserl believed it to be, since the reason with its invariable logical operations is always added to the noesis procedure. This leads us to a less idealized understanding of phenomenology as a philosophical trend, even though Husserl’s work used classical laws of "contemplative intuition". The results can be applied in the field of epistemology and the theory of phenomenological knowledge. Findings. The subject of knowledge, even after the phenomenological reduction is completed, is still connected with rational activity. Such a vision of the phenomenological layer could eliminate the very possibility of the appearance of "pure entities" since we do not completely abandon our everyday "natural setting". However, we believe that in order to encounter anything at all in the "epoch" state, it is necessary to continue to keep in touch with reason, since otherwise there is a risk of falling into somnambulism. Reason, although it enters the layer of pure knowledge, does not lose its formal logical abilities. Logical laws are connected with the operation of noesis, thereby creating a general experience of things of an interoceptive or exteroceptive nature.
Knowledge acquisition for reconstruction and identification of dimensionless groups by means of case based reasoning

When searching with Data Mining Techniques to identify or find out dimensionless groups (DGs) in technical literature, it is possible to meet errors/faults/omissions concerning both, the form and the content of such groups. In the present study, a methodological framework has been developed in terms of a logical flow chart, including 11 activity stages and 7 decision nodes, to acquire/process/store/retrieve knowledge for reconstruction and identification of these groups. Case Based Reasoning (CBR), especially modified to meet the needs of this work, has been used for tracing causality paths by similarity and making correction suggestions. Two case examples are presented to prove the functionality of the proposed methodology.
Keyphrase Generation with Word Attention

Keyphrase generation aims to generate several words that can simply summarize the semantics of the article, which is the basis of many natural language processing tasks. Although most previous approaches have achieved good results, they neglect the independent word information in the source text. Previous models use attention mechanism to calculate the relationship between the encoder RNN hidden states and the target side. However hidden state $$h_t$$ is the summarization of the first t words as a subsequence in the source sentence. We, in this paper, propose a novel sequence-to-sequence model called WordRNN, which can capture word level representations in the source text. Our model can enrich the expression of the source text by directly promoting the pure word level information. Moreover, we use fuse gate and simply concat operation to combine the subsequence level and word level contextual information. Experiment results demonstrate that our approach achieves higher performance than the state-of-the-art methods.
Efficient Hardware Implementation of Switching Median Filter for extraction of Extremely High Impulse Noise Corrupted Images

Images acquired by any imaging system suffers from arbitrary variation in the intensity values, abrupt changes in the illumination and low contrast. Such images lack utilizable information and suffers from visual interpretability. Subsidiary information from such images are extracted by removing the noise, sharpening contrast and detection of the edges by using several filtering techniques. Median filtering is one such non linear based method which removes range isolated noise like salt and pepper noise while preserving the edge information. But median filtering fails when the image is corrupted by extremely high impulse noise. Switching based median filtering is able to perform the noise removal in the case of high intensity impulse noise. This brief introduces an efficient parallel architecture for implementation of switching based median filtering technique using Field Programmable Gate Array (FPGA) prototyping. The results are analysed based on the hardware requirement, power consumption, and speed of the architecture. The FPGA result is validated by implementing the above algorithm in MATLAB when the images are corrupted by noise models.
Learning with Incomplete Labels for Multi-label Image Annotation Using CNN and Restricted Boltzmann Machines

Multi-label image annotation based on convolutional neural networks (CNN) has seen significant improvements in recent years. One problem, however, is that it is difficult to prepare complete labels for the training images and usually training data has missing or incomplete labels. Restricted Boltzmann Machines (RBM) can explore the co-occurrence distribution of the labels and estimate the missing labels efficiently. Hence we intend to propose a novel learning model for multi-label image annotation with missing labels based on CNNs, which aims to regenerate the missing labels for an image by learning the generative model of labels using an RBM. Firstly, label sets are reconstructed by the pre-trained RBM model which is trained on data with some missing labels. Then the reconstructed label sets are used as a teacher signal to train the CNN. The effectiveness of the proposed approach is confirmed by comparing the performance with baseline CNNs using various performance evaluation metrics on two different data sets. Experimental results prove that our RBM-CNN formulation exceeds the performance of the baseline CNN.
Lifting-based fast and low memory DWT computation for IoT platform

The segmented fractional wavelet filter (SFrWF) allows the use of the fractional wavelet filter (FrWF) on line segments, thus can easily be implemented on low-memory devices and sensor nodes to compute the discrete wavelet transform of images. However, the complexity of SFrWF is relatively high due to the use of overlap and add method used in it for avoiding the boundary discontinuities at the segment boundaries. The high complexity of SFrWF makes it unsuitable for low-cost visual sensors used in wireless visual sensor networks (WVSNs)/Internet of things (IoT). In this paper, a lifting-based implementation of SFrWF with 9/7 filter-bank is presented with the aim to reduce it's computational complexity. The proposed lifting based implementation requires fewer computations than SFrWF. Furthermore, the proposed implementation uses an alternative approach to avoid the border discontinuities at segment boundaries. Evaluation results show that for high-resolution ( 2048×2048) images, the complexity of proposed implementation is approximately 40% less than that of SFrWF, without any additional memory requirement.
Manipulating with Base-ten Blocks and Understanding of Their Visual Representations on Addition with Carrying and Subtraction with Borrowing

Base-ten blocks are widely used in teaching number and operation domains. So, the purpose of this study is to identify how students perform with base-ten blocks related to addition with carrying and subtraction with borrowing and students’ understanding the visual representations in textbooks about base-ten blocks. For this purpose, a total of eight students were selected, four for each of the 2nd and 3rd graders. The test questions are two items related to addition with carrying and subtraction with borrowing, presented in mathematics textbooks. The research contents are as follow. First, each student was provided with sufficient base-ten blocks and the addition and subtraction without the textbook was calculated using base-ten blocks. Second, the students' understanding of the visual representation presented in the textbook was confirmed by showing the relevant base-ten blocks of the textbook and explaining what the drawing represents and what they are doing. As a result of the analysis, some characteristics of students' manipulation of base-ten blocks for addition and subtraction were identified, and some students showed difficulties in understanding base-ten blocks presented in the textbook. Through the discussion of the research results, some implications for teaching and learning mathematics were drawn.