Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Kinship Verification with Large Age Variation Using Deep Linear Metric Learning

Facial appearance affects how humans interact with each other. It is also how relatives are visually identified to determine how social interactions proceed. Humans can identify kin relations based only on the face. Intrinsically, giving the ability to detect kin relations to computers can improve their usefulness in our daily lives. This research proposes a solution to the kinship verification problem with a novel non-context-aware approach. The approach is validated using a dataset with large age variation upon which is applied the proposed Deep Linear Metric Learning (DLML). The method leverages multiple deep learning architectures trained with massive facial datasets. The knowledge acquired on traditional facial recognition tasks is re-purposed to feed a linear metric learning model. The proposed method was able to achieve better performance than other context-aware methods on tests that are inherently more difficult than ones used on previous methods with the UB Kinface dataset. The results show that the method can use the knowledge of deep learning architectures trained to perform mainstream facial recognition tasks with massive datasets to solve kinship verification on the UB Kinface database with robustness towards large age differences present on the dataset. The method also offers enhanced applicability when compared to previous methods in real-world situations, because it removes the necessity of knowing/detecting and treating large age variations to perform kinship verification. Additional tests were also performed at the KinFaceW-I and KinFaceW-II datasets to further assess performance of the method. 
Self-Supervised Segmentation and Source Separation on Videos

Semantic segmentation of images [11, 3] and sound source separation in audio [8, 4, 1] are two important and popular tasks in the computer vision and computational audition communities. Traditional approaches have relied on large, labeled datasets, but recent work has leveraged the natural correspondence between vision and sound to apply supervised learning without explicit labels. In this paper, we develop a neural network model for visual object segmentation and sound source separation that learns from natural videos through self-supervision. The model is an extension of recently proposed work that maps image pixels to sounds [9]. This paper is a workshop edit of Rouditchenko et al. 2019 [5]. In the Mix-and-Separate framework proposed in [9], neural networks are trained on videos through selfsupervision to perform image segmentation and sound source separation. However, following training, the model could only be applied to videos with synchronized audio, limiting their use in real applications where synchronized data are not available. Here we seek to enable a system that can perform segmentation and separation tasks using test input containing only video frames or sound mixtures. We introduce a learning approach that disentangles concepts learned by neural networks, enabling independent inference of images and audio mixtures without needing to combine visual and auditory features. We evaluate performance on image-only and audio-only tasks, which was not possible using the previous model. Furthermore, we substantially extend the scale of previous work [9] by training on a video dataset of naturally occurring audio-visual events with 28 event categories and over 4000 videos [6]. The results show that we can achieve promising semantic segmentation and source source separation performance.
Domain Selective Precoding in 3-D Massive MIMO Systems

Three-dimensional multiple input multiple output (3-D MIMO) with a large number of active antennas equipped in a uniformly rectangular antenna array has a significant potential in improving the system capacity. In this paper, we present an efficient two-dimensional (2-D) downlink precoding scheme for single-cell 3-D massive MIMO systems. Considering instantaneous channel state information at the base station, we show that either the elevation or azimuth domain can be used for interference cancellation. We divide the interference into two components, one of which is canceled in the elevation domain, while the other is canceled in the azimuth domain. Based on this domain selective (DS) strategy, two DS precoding algorithms are proposed for the single-path scenario based on the zero forcing and signal-to-leakage-plus-noise ratio criteria. Next, we extend our DS precoding scheme to a multi-path scenario. In the proposed algorithms, the precoding vectors of different users are determined in parallel, so as to reduce the computational complexity. Simulation results are provided to demonstrate that the proposed algorithms can achieve better spectral efficiency performance with a low computational complexity.
Parallelised Algorithm of Isolated Minterm Detection for Logic Function Simplification

This study compares serial and parallel implementation of isolated minterm detection algorithms. Isolated minterms are detected from 10 different functions. Each function has different sized Karnaugh maps (Kmaps). Both serial and parallel implementations detected same isolated minterms but parallel implementation of algorithm runs faster than serial one. We presented “Finding isolated minterms in simplification of logic functions” in our previous work [1]. In this study, we developed the algorithm, parallelised this algorithm and compare computation times on different benchmarks. 10 different sized functions are tested with both algorithms. Results revealed that parallelised algorithms works faster than serial algorithm especially for big sized logic functions.
Massive Dimensionality Reduction for the Left Ventricular Mesh

Statistical emulation is a promising approach for the translation of cardio-mechanical modelling into the clinical practice. However, a key challenge is to find a low-dimensional representation of the heart, or, for the specific purpose of diagnosing the risk of heart attacks, the left-ventricle of the heart. We consider the problem of dimensionality reduction of the left ventricular mesh, in which we investigate three classes of techniques: principal component analysis (PCA), deep learning (DL) methods based on auto-encoders, and a parametric model from the cardio-mechanical literature. Our finding is that PCA performs as well as the computationally more expensive DL methods, and both outperform the state-of-the-art parametric model.
Multi-Scale Proposal Regression Network for Temporal Action Proposal Generation

Temporal action detection, as a branch of video analysis, aims to locate the time points when the actions start and end, and classify the actions occurred in videos into correct categories. Generating high-quality proposals is a key step in temporal action detection task. In this paper, we introduce a novel network, named multi-scale proposal regression network (MPRN), for temporal action proposal generation. First, we take encoding visual features as input and predict action scores for time points, in order to group them to generate rough proposals. Then, we regress the proposal’s boundaries to obtain more precise proposals via our multi-scale proposal regression network. Compared with SSN and TURN, our multi-scale regression segments are characterized by flexible boundaries. Experiments show that 1) Our method is better than other proposal generation methods on THUMOS-14 dataset and ActivityNet-v1.3 dataset. 2) The effectiveness of our method is due to its own architecture, not the selection of visual feature encoders. 3) Our proposal generation method can generate temporal proposals for unseen action classes, which shows the good generalization ability of our proposal generation method.
High-precision Positioning and Deformation Monitoring Analysis of BD/GPS Based on Improved Kalman Filter Fusion

Abstract The real time kinematic of global positioning system (GPS-RTK) provides precise positioning for bridge deformation monitoring and monitoring bridge health status. In order to solve the problem that the satellite signal is vulnerable to the influence of the positioning environment when monitoring bridges in the valleys and urban buildings[1], and the problem that Kalman fusion algorithm is difficult to detect the divergence caused by interruption or wrong data[2]. This paper proposes an improved Kalman filter fusion position method based on the BD/GPS fusion positioning. This improved algorithm introduces the environmental information. The confidence level is calculated with membership function by defining the confidence region of sensor, so as to the fusion weight coefficient is determined. This paper analyzes the performance of BD/GPS positioning in bridge monitoring through comparing the traditional fusion method with the improved fusion method. Experiments show that the improved algorithm eliminates the problem of error divergence; the average number of visible satellites in BD/GPS fusion positioning is increased by 7 compared with that of GPS single system positioning, the GDOP value is reduced by 21.83%, and the positioning error is reduced by 2.51 cm. The feasibility of all-weather monitoring in the mountains, buildings and other areas is verified, and millimeter accuracy is provided, which greatly improves the performance of bridge deformation monitoring.
Methods in Computational Design and Optimization

Computational design allows creation of functional objects by using accurate computational models and 3D printing technologies. As a result, not only is customization of functional objects possible; but it also eliminates the need for expert domain knowledge required to intuitively model the functionality. Shape representation and parameterization, combined with numerical simulation, are used to create an accurate model of the real world response of an object. Then, to achieve the desired functionality, mathematical optimization is used to efficiently search over parameterized space for possible variations and physical energy spaces of an object’s design. By using a structured method to search over these parametric spaces, i.e., using nonlinear and often constraint multi-objective optimization methods, we guarantee the most optimal design of the object. This optimized design is 3D print ready and functional. My work has focused on developing optimization schemes for computational design problems, namely – walking automata, bistable structures, and contact sound spectrum design. Such problems are variations of physical energy minimization of designs, and I propose ways to optimize them. For walking automata, starting with an initial unstable non-walking linkage configuration, I develop a sampling based optimization scheme to search the highly nonlinear space of possibly stable walking automata. Further, improvements in the efficiency of the optimization strategy are gained by learning a space of valid linkage configurations. Linkage chains can also be used for creating bistable structures. I quantify static stability of the linkage structure through the geometry of the physical energy and develop a non-linear
Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on Support Vector Machine

As an important part of power system, power transformer plays an irreplaceable role in the process of power transmission. Diagnosis of transformer’s failure is of significance to maintain its safe and stable operation. Frequency response analysis (FRA) has been widely accepted as an effective tool for winding deformation fault diagnosis, which is one of the common failures for power transformers. However, there is no standard and reliable code for FRA interpretation as so far. In this paper, support vector machine (SVM) is combined with FRA to diagnose transformer faults. Furthermore, advanced optimization algorithms are also applied to improve the performance of models. A series of winding fault emulating experiments were carried out on an actual model transformer, the key features are extracted from measured FRA data, and the diagnostic model is trained and obtained, to arrive at an outcome for classifying the fault types and degrees of winding deformation faults with satisfactory accuracy. The diagnostic results indicate that this method has potential to be an intelligent, standardized, accurate and powerful tool.
Intrusion Detection Systems

Intrusion Detection System(IDS) is an activity based network security system. As the logical supplement of usual passive policy, it is the important part of whole security system. This paper analyzes IDS , presents the mechanisms of IDS, and categorizes IDS from two different points of view. Then it discusses the existing IDS, at last ends with a discussion of the method and the promising future.
Multiple collaborative filtering recommendation algorithms for electronic commerce information

ABSTRACT The information filtering recommendation algorithm has poor security, low efficiency and user satisfaction, and high network energy consumption. A multiple collaborative filtering recommendation algorithm for e-commerce information based on user preferences is proposed. In the time series data of the original e-commerce information, using the sliding window for feature extraction, the trend value characteristics of the information variable will be obtained, and the suspected outfielders will be determined based on the trend feature value. Using the resulting eigenvalues for secondary detection, the abnormal data points are further judged, and the abnormal data therein is filtered using a filter. The filtering results are substituted into the information filtering recommendation based on the user’s preferences. According to the user’s preference knowledge, the user’s preference space matrix is constructed to complete the user similarity calculation, and the nearest neighbors with the same or similar preferences are obtained. The demand of the target user is predicted using the nearest neighbor and preference knowledge, and the multiple collaborative filtering of the electronic commerce information is completed. Experiments show that the user satisfaction of this algorithm is in the range of 91%–99%, with high recommendation efficiency and security, low network energy consumption, and strong practicality.
DSMC-SPARTA implementation of majorant collision frequency scheme

The direct simulation Monte Carlo (DSMC) method takes a stochastic approach to solving the Boltzmann equation, and is commonly used to model rareﬁed gas ﬂows. The simulation domain is split into cells, in which computational representative particles mimic the real ﬂow. Since millions to billions of particles need to be tracked, these models need to be both accurate and computationally e ﬃ cient. Even with high-ﬁdelity collision models, the overall accuracy of the DSMC outcome strongly depends on its ability to predict the correct number of collision events per simulated time step, i.e. the collision frequency. The two most popular approaches for this purpose are the no-time-counter (NTC) and majorant collision frequency (MCF) schemes. While the NTC scheme is designed to reproduce the average collision time for a su ﬃ ciently large sample sizes, the MCF scheme has the advantage of reproducing the exact Poisson distribution of collision time as well as the mean collision time with a reduced sample size. Both schemes have linear complexity of O(N), with N being the number of particles. In this work, we implement the MCF scheme in SPARTA, an open source DSMC solver. A series of numerical tests are performed to illustrate the e ﬃ ciency and accuracy of both the schemes. Various benchmarks highlighting unsteady, compression, and expansion problems are studied.
A review of the artificial neural network surrogate modeling in aerodynamic design

Artificial neural network surrogate modeling with its economic computational consumption and accurate generalization capabilities offers a feasible approach to aerodynamic design in the field of rapid investigation of design space and optimal solution searching. This paper reviews the basic principle of artificial neural network surrogate modeling in terms of data treatment and configuration setup. A discussion of artificial neural network surrogate modeling is held on different objectives in aerodynamic design applications, various patterns of realization via cutting-edge data technique in numerous optimizations, selection of network topology and types, and other measures for improving modeling. Then, new frontiers of modern artificial neural network surrogate modeling are reviewed with regard to exploiting the hidden information for bringing new perspectives to optimization by exploring new data form and patterns, e.g. quick provision of candidates of better aerodynamic performance via accumulated database instead of random seeding, and envisions of more physical understanding being injected to the data manipulation.
Influence of spectrum not-matching on calibration precision of remote sensor

The spectrum not-matching of a calibration source with target source can affect remote sensor measurement precision in the calibration of remote sensor.The measuring relative errors caused by the spectrum not-matching was analyzed quantitively and the relation between the measuring errors and the bandwidth of remote sensor was given.The simulated results indicate that,for the remote sensor with bandwidth 20 nm,the error for spectrum not-matching of calibration source can be negligible;for the moderate resolution remote sensor and whole band remote sensor,the measuring relative error is 7.9% when the bandwidth is 300 nm,which shows that selecting a calibration source with spectrum matching is important.
Simulation of intelligent information-measuring systems of thermophysical properties of materials and products

The article contains a formalized description of functioning of intelligent information measuring system for non-destructive testing of materials and products thermal properties. System simulation has been fulfilled, and as a result of it the mathematical models for transformation of measurement data in the intelligent system in Mathcad software during thermophysical measurements. The mathematical pattern of decision-making in the intelligent system allows to improve the accuracy of parameter determination of thermophysical properties of tested materials and the efficiency of the functioning system.
Multi Attribute Objective Similarity Measure for High Dimensional Data Streams

: Retrieval of data from different data sources is an aggressive concept with respect to multi-attribute relations present in uncertain high-dimensional data. Similarity in between pair of objects outside data relations i.e explicitly or inside data relations i.e implicitly. Extensible and Classification by Pattern Based Hierarchical Clustering (ECPBHC) is used to extract relational data from different web oriented categorical data sets based on users behavior and analysis with different attributes relations. So that improves multi objective data relations with different attributes for data retrieval from data sources is aggressive and important concept to view data relations in different dimensions. We propose Enhanced Multi Feature Sub set Selection Clustering (EMFSSC) approach which is extension to EMFSSC for multi object attributes relations. We also propose Perato Optimization approach to represent optimized multi-attribute relations based on similarity measure. This approach mainly proposes on documents to retrieve multi objective attribute relations on multi view of data representation. Theoretical and empirical study is conducted to support this problem for different attribute relations.
Classification of Radiolucency in Dental X-Ray Image

Health is the greatest gift to any human being. Growth of the nation depends on the health of every individual. To maintain health and hygiene, a human being must eat good food. For eating healthy food teeth plays an important role. The teeth being a small part of the body plays a very critical part in digestion. Many times due to time constraints patient cannot go to the hospital at right time or wants to get a second opinion. So, in this aspect, the proposed system is created to diagnose the status of the tooth automatically. The anticipated system takes x-ray images as input and classifies the output as a category of radiolucency that it falls under. The classification of the tooth is done by using Multi-Layer Perceptron (MLP), SMO, KNN. Features are extracted using both, Spatial and Frequency domain. Classification is done using Weka tool.
A Novel Boolean Expression based Algorithm to find all possible Simple Paths between two nodes of a Graph

A novel algorithm has been proposed to find all possible simple paths between any two given nodes of a graph which is a NP-hard problem. First a novel approach to represent a graph using Boolean operators has been structured. The unique Boolean expression is used to find all possible paths between any two nodes. The analysis of Boolean expression based representation of a graph reveals that the problem is in NP-hard. Further a necessary and sufficient condition is given to show that the problem is not NP-complete. A detail theoretical analysis and experimental results has been given in support of its ingenuity. Keywords: Boolean Transformation, Counting problems, Graph enumeration, NP-completeness, Satisfiability, Undecidability.
Making good suggestions in analytics-based early alert systems

Purpose The purpose of this paper is to answer the following questions: On which early alert system suggestions are students more likely to act? What factors drive students’ decisions to act on early alert system recommendations? Design/methodology/approach This study examined whether students’ behaviour changed after receiving the results of an early alert system (CDR). In the middle of a semester, 423 students with varying levels of English proficiency were invited to try the CDR and complete a questionnaire that asked about their perception of the tool and whether they planned to act on the recommendations they received. Findings Results suggested that students mainly planned to take the assessment-related recommendations provided through the CDR to improve their assessment performance. Results also suggested that student anxiety and student ability affected the likelihood that students would act on the recommendations. Practical implications These findings provide useful insights for early alert system designers to establish a system that generates useful recommendations for students. Originality/value The findings of this study contribute to the development of early alert systems. Designers can now realise what suggestions can be effectively offered to students.
Survey On Sentiment Analysis in Hindi Language

The recent improvement in web technologies and increasing user-generated content (UGC) in Hindi on the web is the inspiration behind the sentiment analysis in the language. This UGC can turn out to be extremely helpful for researchers, governments and organization to learn what’s on public mind, to make sound decisions. Sentiment Analysis is a natural language processing task that mine information from various content structures such as reviews, news, and blogs and classify them on the basis of their polarity as positive, negative or neutral. In this paper, the authors have tried to investigate the on-going research in the field of sentiment analysis, particularly in the Hindi language.