Reviewerly/exHarmony huggingface dataset:
Real-time Embedded Demo System for Fall Detection under 15W Power

Fall is one of the major threats to the safety and life quality of elderly and patients. In this paper, an embedded demo system for real-time spatial-temporal fall detection on demo video is proposed. It is built upon MicroSoft Kinect V2 for 3D visual sensing, which is insensitive to illumination change. Meanwhile, NVIDIA Jetson AGX Xavier under 15W power serves as the embedded computing processor. To fit the proposed demo system, a novel real-time spatial-temporal fall detection approach based on deep learning technology is also proposed, with end-to-end running capacity. To our knowledge, this is the first spatial-temporal fall detection method in end-to-end way. In particular, it formulates spatial-temporal fall detection problem as a object detection like task. It first compresses the depth video clip captured by multi-scale temporal sliding window into a compact dynamic image, with fall's rich motion information. Consequently, fall is detected on dynamic image with YOLOv3-Tiny of high running efficiency and promising effectiveness from spatial perspective. Besides real-time live running capacity of our demo system, the experiments on 1 challenging datasets also verify the superiority of the proposed spatial-temporal fall detection approach.
Multi-level Multi-modal Feature Fusion for Action Recognition in Videos

Several multi-modal feature fusion approaches have been proposed in recent years in order to improve action recognition in videos. These approaches do not take full advantage of the multi-modal information in the videos, since they are biased towards a single modality or treat modalities separately. To address the multi-modal problem, we propose a Multi-Level Multi-modal feature Fusion (MLMF) for action recognition in videos. The MLMF projects each modality to shared and specific feature spaces. According to the similarity between the two modal shared features space, we augment the features in the specific feature space. As a result, the fused features not only incorporate the unique characteristics of the two modalities, but also explicitly emphasize their similarities. Moreover, the video's action segments differ in length, so the model needs to consider different-level feature ensembling for fine-grained action recognition. The optimal multi-level unified action feature representation is achieved by aggregating features at different levels. Our approach is evaluated in the EPIC-KITCHEN 100 dataset, and achieved encouraging results of action recognition in videos.
Face Clustering via Adaptive Aggregation of Clean Neighbors

Face clustering has been widely studied to solve the problem of data annotation in large-scale unlabeled face images. In recent years, state-of-the-art performance has been updated every year based on the application of Graph Convolutional Networks(GCN) in face clustering tasks. The existing GCN-based methods make each node accept the feature information from its neighbors, and then aggregate the neighbors' information with equal weights to learn enhanced feature embedding. However, rare attention has been paid to improving the quality of aggregated information. In this paper, we aim to make each node aggregate the feature information that is more conducive to clustering. The proposed novel method named Adaptive Aggregation of Clean Neighbors(AACN) has two stages of preparation before inputting the graph into GCN. Specifically, we first design a noise edge cleaner to remove the wrong neighbors of each node to ensure that they receive more accurate neighbor information. Then, we carefully allocate adaptive weights to the clean neighbors of each node, and make all nodes aggregate the received information via adaptive aggregation instead of mean aggregation. The two-stage preparation enables nodes to learn more robust features through the GCN module. Experiments on standard face clustering benchmark MS1M show that AACN has achieved state-of-the-art performance, significantly boosting the pairwise F-score from 92.79% to 93.72% on 584K unlabeled face images and from 83.99% to 86.41% on 5.21M unlabeled face images.
ASDFL: An adaptive super‐pixel discriminative feature‐selective learning for vehicle matching

Abstract There are a large number of cameras in modern transportation system that capture numerous vehicle images continuously. Therefore, automatic analysis of these vehicle images is helpful for traffic flow management, criminal investigations and vehicle inspections. Vehicle matching, which aims to determine whether two input images depict an identical vehicle, is one of the core tasks in vehicle analysis. Recent relevant studies have focused on local feature extraction instead of global extraction, since local details can provide crucial cues to distinguish between cars. However, these methods do not select local features; that is, they do not assign weights to local features. Therefore, in this research, we systematically study the vehicle matching task, and present a novel annotation‐free local‐based deep learning method called Adaptive super‐pixel discriminative feature‐selective learning (ASDFL) to address this issue. In ASDFL, vehicle images are segmented into clusters of super‐pixels of similar size by considering the location and colour similarities of pixels without using any component‐level annotation. These super‐pixels are deemed to be the virtual components of vehicles. Moreover, a convolutional neural network is used to extract the deep features of these virtual components. Thereafter, an instance‐specific mask generation module driven by the extracted global features is enhanced to produce a mask to select the most distinctive virtual components of each vehicle image pair in the feature space. Finally, the vehicle matching task is accomplished by classifying the selected virtual component features of each imaged vehicle pair. Extensive experiments on two popular vehicle identification benchmarks demonstrate that our method is 1.57% and 0.8% more accurate than the previous baselines in a vehicle matching task on the VeRi and VehicleID datasets, respectively, which demonstrates the effectiveness of our method.
A Three-Level Force-Directed Layout Algorithm Based on PageRank and Simulated Annealing

How to avoid visual confusion and effectively display network structure and features in network data analysis is a problem that has long plagued network visual analysis. The classical force-directed layout algorithm chooses a random layout for the initial layout, which leads to a high number of edge crossings and long layout time. In this paper, we design a three-level pre-positioning layout strategy based on a variant of the PageRank algorithm and an improved simulated annealing algorithm to achieve the division of node levels and layout positions. And we improve the cooling function in FR algorithm to further reduce layout time to complete the final layout. The experimental results show that compared with the classical algorithm, the algorithm in this paper can reduce the number of edge crossings as well as the iteration time of the algorithm, and effectively generate a high-quality network graph layout.
Evaluating the Project Management Methodology Adoption Process in the Sri Lankan Software Industry

Projects are the primary method of implementing change in an organization. Most software firms tend to use a Project Management Methodology (PMM) to deliver the product to the customer. However, there is no rational set of motives to select the desired PMM. Given this context, this study investigates the reasons for the adoption of PMM rather than using an internally defined set of processes. The study also evaluated the factors affecting the selection and implementation of the PMM. The study used multiple case study methods, and data were gathered by conducting six in-depth interviews with experienced management personnel in software organizations. The analysis concludes with a list of PMM adoption motives and how PMM selection, adoption and implementation are carried out in the software industry domain from a strategic point of view. The study investigated organizational transitions and change management from a complex managerial perspective, as well as cultural dimensions and individual and organizational social values. The study fills a gap in the literature. Also, the study derives a proper processual plan for the adoption motives, selection and adoption process of PMM, which should help inform future strategies in this field.
A Comparative Study of Clustering English News Articles Using Clustering Algorithms

The news informs us of what is going on in the world. People nowadays read their interesting news on news websites. There are numerous categories of news. Each newsreader has a different preference for news categories. Sportspeople prioritize sports news, whereas technology fans pay attention to the technology segment of the news. At the end of the day, each news category is important. Every day, a large amount of information is released on news websites. News sites usually categorize the news however, not all of the categories are published on those sites. Some categories are given higher attention by news outlets, while others receive less coverage. As a result, finding an appropriate category of news is tough. These issues make it difficult for newsreaders and content seekers to find relevant sections on news websites. The clustering of English news articles by relative category provides solutions to these issues. This research aims to use clustering algorithms to cluster news articles depending on the relevant domain/cluster. We consider five news categories: politics, sports, health, technology, and business. The data collected online was converted into a vector format using the term frequency-inverse document frequency (TF-IDF) vectorization. Then, on the body of the news and the news heading, the three clustering algorithms: Expectation-Maximization (EM), Simple K-means, and Hierarchical Clustering based on an agglomerative approach were applied individually. The Waikato Environment for Knowledge Analysis (WEKA) tool’s classes to clusters evaluation model are used to calculate the accuracy. The EM method had the maximum accuracy of 88.5% with the best results in terms of correctly clustered instances. The comparison between the heading of news and the body of news demonstrates that the body of news clustered the news items better than the heading of news.
Detecting Replay Attack on Voice-Controlled Systems using Small Neural Networks

Voice-control is becoming a common interface for many consumer IoT systems. Common threats to such systems include impersonation, replay, speech synthesis, and voice conversion attacks. Of these attacks, replay is the easiest to implement where a command is recorded and replayed. This paper explores the development of a lightweight intrusion detection neural network based on a recent command voice replay dataset. A lightweight model based on 1D Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) was proposed. The trained model was compared with baseline models based on Gaussian Mixture Models (GMM) using Constant Q Cepstral Coefficients (CQCC) and Mel-Frequency Cepstral Coefficient (MFCC). The proposed model outperformed the GMM models, and its size was significantly lower making it more feasible for embedded systems implementation.
Knowledge Discovery from the Statistical Analysis of On-Site Photovoltaic System Data

This paper presents some novel ideas and findings to assist data analysts and operators in discovering specific situations from the analysis of photovoltaic data collected in the field. The daily time series of solar irradiance and active power production are transformed into probability distribution functions (PDFs), then the skewness of the PDF is assessed as a potential indicator of orientation of the PV system in directions different from South. The relevant PDFs corresponding to bright days are identified by applying a clustering procedure. The combined calculation of skewness and correlation between solar irradiance and active power data is also used to discover specific cases in which high shadowing occurs in particular days of the year and at particular times. The analyses are carried out by using data collected on-site in PV systems installed at different locations.
Large Margin Training for Long Short-Term Memory Neural Networks in Neural Language Modeling

Language models based on Long Short-Term Memory (LSTM) neural networks have been widely applied to automatic speech recognition, natural language processing research. However, current LSTMs are trained by employing the cross-entropy loss function, which only considers the target category without considering the competing categories during the training processes. Thus, current training methods cannot fully exploit the discriminative information provided by the data labels. To tackle this problem, we propose a Large Margin Long Short-Term Memory Neural Network (LMLSTM) model in this paper. Our model employs the large margin discriminative principle as a heuristic term to navigate the convergence process during training. Our model has improved the discriminative ability of the original LSTM while maintaining the capability when generating sequential data. Our proposed large margin term was tested on the Penn Treebank corpus language modelling task. Experimental results demonstrate that the proposed LMLSTM model outperforms current LSTM models in terms of accuracy and perplexity without increasing the depth.
Deep Neural Architectures for Ethnicity Classification in Face Images

Ethnicity is a key metric of an individual’s identity, social cluster, physical behaviour and cultural association. Accurate ethnicity identification of humans is required in numerous fields like security, legislation, social analysis and psychology. Ethnicity classification using machine learning is a complex, non-trivial and multi-dimensional research problem due to the feature complexity, class imbalance and the absence of rich data sets. In this research study, we have trained and compared four state-of-the-art deep neural models and their ensemble architectures on the problem of ethnicity classification in large-scale image data. The empirical results demonstrate that these end-to-end deep learning models and their ensemble architectures perform well in learning complex ethnic features in facial images and classifying them. From the evaluated models, Ensemble Convolutional Neural network provided the highest classification performance with 78.9% accuracy. Also, we have tested six prominent pre-trained models using transfer learning for ethnicity classification while being able to achieve comparable results.
Performance Evaluation GUI for 50 MW Large Scale Solar PV System

Performance Ratio is one of important criteria to evaluate the achievement of Large Scale Solar Photovoltaic Power Plant. An establishment of graphic user interface tools for estimate the performance of Large Scale Solar Photovoltaic is developed for future economical and monitoring system purposes. This paper presents the performance evaluation system using Visual Basic software to create a graphic user interface platform and the comprehensive design of Large Scale Solar Photovoltaic system modeled using Matlab Simulink to evaluates the performance ratio using real-time data extracted from real-time 50 MW LSSPV power plant site. The data is extracted from real-time meteorological station and automatically generated from Large Scale Solar Photovoltaic plant and compared with PV syst designed report. The system is successfully simulated where the performance ratio of 50MW Kuala Ketil Large Scale Solar Photovoltaic modelled in Matlab Simulink provides a modulation index at VSC with 85% while the evaluation of PR based on GUI are obtained at 82% and 83% on March and April respectively. An established performance evaluation system for large-scale solar power plant is a crucial approach to measure the quality of the electricity generation from the plant.
A Small Target Detection Method Based on Feature Enhancement and Positioning Optimization

Feature Pyramid Network (FPN) is a basic but important component in target detection system. Together with target detection algorithms, such as SSD, Faster R-CNN and YOLO series, they have achieved good detection results for large targets with high resolutions, but the performance is less effective when it comes to detect small targets that contain relatively little semantic information. And small target detection is quite common in daily life, such as face recognition at long distances, traffic sign detection in automatic driving, etc. That means it is significant to break the bottleneck of target detection and get better accuracy performance. In this article, based on the FPN, we propose an improved network structure (IMFPN) from two aspects to get a better accuracy result in small target detection task. In the first aspect, we improve the feature map pyramid structure for feature enhancement, reduce the problem of information loss during feature map fusion and get the semantic information of multiscale feature maps. In the second aspect, we concentrate on the problem of information loss in the pooling process of feature maps, we propose an improved version of the PRRoI pooling method that combines RoI Pooling and RoI Align Pooling. And we also optimize the positioning of the frame through a new IoU calculation standard. Based on these above ideas and methods, we propose a small target detection method based on feature enhancement and positioning optimization.
Research on High Performance Word Segmentation Technology Based on Distributed Character Tree and Unknown Word Recognition

This paper summarizes the disadvantages of regular word segmentation and statistical word segmentation, then a high performance Chinese word segmentation algorithm based on distributed character tree and unknown word recognition is proposed, which not only solves the defect of dictionary dependence in regular word segmentation, but also makes up for the lack of high time complexity in statistical word segmentation. The main innovations of the algorithm include: in the preprocessing stage, defining the distributed character tree and creating the feature dictionary; In the stage of word segmentation, the concept of word-formation skewness is defined, and the judgment formula of unknown word is proposed. The experimental results show that the new method has improved the accuracy and recall rate, which is applicable.
A Hierarchical Bayes-Based Evolutionary Ensemble Classification Algorithm

Regarding a multi-label classification task, the different similarities between targeted categories are commonly overlooked in front of deep neural network models. Never-theless, decomposing the large-scale multi-label classification problem into a series of hierarchical sub-problems based on their similarity information can reduce the problem dimension and result in lower computational cost with competitive performance. This paper proposed a Hierarchical Bayes-based Evolutionary Ensemble (HBEE) classification algorithm that computes and utilises our new data-driven posterior-based class similarity to evolve a tree of weak classifiers. The posterior information is gathered from a reduced Bayes theorem, which is insensitive to imbalanced data amount and imbalanced inter-class similarities. Instead of doing gradient descent optimization on large scale parameters, a gradient-free optimization method, genetic technique, is adopted for a series of weak classifier's ensemble decision, which is extremely useful in industry when large scale gradient optimization method is not feasible.
Marine Predators Algorithm Based on Trigonometric Function

In the Marine Predators Algorithm (MPA), a set of solutions is randomly generated in a given search space and the optimal solution is searched through an iterative process, although MPA has shown good performance, it has not solved the problem of local optimal solutions and premature convergence caused by insufficient iterations of the search agent, and its optimization performance still has a lot of room for exploration. Therefore, this paper proposes a trigonometric function-based MPA (TF-MPA) algorithm. First, the trigonometric function is used to optimize the position update of the prey matrix; then the cosine function is used to define the inertia weight coefficient; finally, the sine function is used to define the nonlinear step size control parameters. The effectiveness of TF-MPA is tested on nine standard test functions, and the results show that the TF-MPA algorithm has faster convergence speed and higher stability than the traditional MPA algorithm and other improved algorithms. This proves the effectiveness of the algorithm.
Geo VISER: A Geospatial Data Visualization Platform

The analysis of geospatial data requires a very strong spatiotemporal awareness and an understanding of its specific data attribute index concept. Therefore, a geospatial data visualization platform, GeoVISER, is proposed to provide a fast, efficient and customized geospatial data visualization analysis solution. GeoVISER preprocesses complex geospatial data and realizes multidimensional customized visual analysis by filtering and clustering them through user-defined combined attribute fields. It creates visual elements such as points, lines, surfaces and volumes on tile maps and performs data linkage with secondary visual charts through interactive events. Interactively explore deeper relationships between geospatial data and its thematic attributes. Based on real geographic spatial point, line, plane and volume data, the basic rendering capability of the system was verified. In addition, the effectiveness of GeoVISER’s visualization analysis of geospatial data is verified by a case study of the US traffic accident data set.
Dynamic Policy Programming with Descending Regularization for Efficient Reinforcement Learning Control

In this work, a novel value function-based reinforcement learning (RL) approach, descending dynamic policy programming (DDPP) is proposed to address the issues of sample-efficiency and learning stability in control problems. Extended from the state-of-the-art Kullback-Leibler divergence regularized RL method dynamic policy programming (DPP) that punishes the over-large policy update during learning, DDPP employs a descending strategy of the parameters to dynamically control the penalty term. Evaluated by several benchmark control tasks in OpenAI gym, the proposed method successfully demonstrates its superiorities in both learning stability and sample-efficiency compared with the related baseline approaches and therefore indicates a great potential of the descending strategy of Kullback-Leibler divergence regularization towards more practical implementations using RL.
Pedestrian Re-identification after Enhancing Textural Features Based on Parallel Residual Network

Aiming at the availability of global features learned by residual network when it was trained to classify images, but was not sensitive to the classification of fine-grained images, a pedestrian re-identification method with enhancing textural features based on parallel residual network was proposed. First, a sub-hard example method was used to deal with the dataset of market1501, by this way the model could converge whiling maintaining good performance. Then structure a parallel network which was used to strengthen textural features, making the network paid more attention to the textural features of images and mixed features from multiple layers for final classification. Experimental results show that sub-hard examples method can improve the accuracy rate of model about 0.2% without changing its parameters, while the value rises to 1.2% if the parallel network is used jointing texture enhancement, with which increasing a few parameters. Improvements were also seen in CUHK03 and MSMT17, two large datasets, at 0.6% and 1.0%, respectively.