Reviewerly/exHarmony huggingface dataset:
Fall Prevention as Personal Learning and Changing Behaviors: Systems and Technologies

While living longer at home is the preferred option for the elderly, disease, accidents or general health decline might make this hard. Fall accidents, which are among the chief causes of unforced injury leading to hospitalization, must be viewed as a strong factor working against the possibilities for the elderly of living longer at home. Although many fall prevention programs have been developed and applied, and checklists and advice abound, most of these originate in the professional care area or depend on care professionals. In this paper, we present a fall prevention concept directed towards elderly people living at home, with the intention of providing a safer environment ensuring more years of doing this in a healthy manner. There are several obstacles for a successful implementation of fall prevention, many pending on the attitudes and thought systems of the elderly user community, including low risk awareness, technological stigmata, refusal to be associated with health problems and a general “not for me” world view. The paper presents an AI/IoT-driven learning environment for fall prevention which facilitates living independently and improved self-understanding to provide for easier everyday living. The central design idea is to lead the user through a learning process including acquiring knowledge of fall prevention, personal characteristics, taking fall prevention action and understanding the outcomes of changed behaviors. The concluding outcome is a discussion of a set of design variables for the effective implementation of fall prevention systems, including a holistic view of fall prevention and aging, open platform technologies, a flexible and adaptable module approach, feedback-based learning, IoT/sensor-driven learning, personalization and goal orientation and the dual use of technology avoiding technology stigmas.
TextEdge: Multi-oriented Scene Text Detection via Region Segmentation and Edge Classification

The semantic-segmentation-based scene text detection algorithms always use the bounding-box regions or their shrinks to represent the text pixels. However, the non-text pixel information in these regions easily results in the poor performance of text detection, because these semantic segmentation methods need accurate pixel-level annotated training data to achieve approving performance and they are sensitive to noise and interference. In this work, we propose a fully convolutional network (FCN) based method termed TextEdge for multi-oriented scene text detection. Compared with previous methods simply using bounding-box regions as a segmentation mask, TextEdge introduces the text-region edge map as a new segmentation mask. Edge information is more representative for text areas and is proved to be effective in improving detection performance. TextEdge is optimized in an end-to-end way with multi-task outputs: text and non-text classification, text-edge prediction and the text boundaries regression. Experiments on standard datasets demonstrate that the proposed method achieves state-of-the-art performance in both accuracy and efficiency. Specifically, it achieves an F-score of 0.88 on ICDAR 2013 dataset and 0.86 on ICDAR 2015 dataset.
Research on Face Super Resolution Reconstruction Based on DBGAN

Super-resolution reconstruction technology is an important research topic in many fields such as image processing and computer vision. This technology can be used widely for security monitoring, old image reconstruction, image compression transmission and other fields. In this paper, super-resolution image reconstruction is performed on a low-resolution image of four times magnification. We propose the dense convolutional networks used as a generator instead of residual networks, and set perceptual loss as the optimization goal. We use the VGG network feature map as the loss function instead of Mean Square Error, which combines the perceptual loss with the adversarial loss and is beneficial for compensating the shortcomings of previous methods that lack high frequency detail. Experimental results show that our method can produce clearer face images than the traditional methods. These reconstructed images have higher resolution and Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) than the images generated by the deep residual networks.
Hierarchical Attention Network for Action Segmentation

Temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video. Several attempts have been made to capture frame-level salient aspects through attention but they lack the capacity to effectively map the temporal relationships in between the frames as they only capture a limited span of temporal dependencies. To this end we propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time, thus improving the overall segmentation performance. The proposed hierarchical recurrent attention framework analyses the input video at multiple temporal scales, to form embeddings at frame level and segment level, and perform fine-grained action segmentation. This generates a simple, lightweight, yet extremely effective architecture for segmenting continuous video streams and has multiple application domains. We evaluate our system on multiple challenging public benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech Egocentric datasets and achieves state-of-the-art performance. The evaluated datasets encompass numerous video capture settings which are inclusive of static overhead camera views and dynamic, ego-centric head-mounted camera views, demonstrating the direct applicability of the proposed framework in a variety of settings.
Mammogram Segmentation Methods: A Brief Review

Being the prime reason, after skin cancer, of high mortality rate among women in present day, breast cancer requires correct diagnosis and precise treatment at its earliest stage. From the time of the advent of diagnosis tools, medical practitioners have left no stone unturned in their efforts of delivering timely medication to the patients; but often human error has resulted in either death due to dosage of medicines resulting from wrongly detected malignancies or due to negligence arising from not detecting the tumors at the right time. Hence, computer-aided diagnosis (CADx) has come into light as a key tool in statistically analyzing medical images obtained from various imaging machines and classifying the specimens into the categories of normal, benign, and malignant. A major step involved in it is the segmentation of the medical image into various regions and determining the required region-of-interest (ROI) from them. Automated image segmentation is quintessential today in order to extract the correct suspicious regions for diagnosis, instead of relying on erroneous human eye judgment. The following study aims to compare and analyze the effectiveness of some existing segmentation methods used to extract the ROIs for analysis of digital mammograms for breast cancer detection.
Repeatable determinism using non-random weight initialisations in smart city applications of deep learning

Modern Smart City applications draw on the need for requirements that are safe, reliable and sustainable, as such these applications have a need to utilise machine-learning mechanisms such that they are consistent with public liability. Machine and deep learning networks, therefore, are required to be in a form that is safe and deterministic in their development and also in their deployment. The viability of non-random weight initialisation schemes in neural networks make the network more deterministic in learning sessions which is a desirable property in safety critical systems where deep learning is applied to smart city applications and where public liability is a concern. The paper uses a variety of schemes over number ranges and gradients and achieved a 98.09% accuracy figure, + 0.126% higher than the original random number scheme at 97.964%. The paper highlights that in this case, it is the number range and not the gradient that is affecting the achieved accuracy most dominantly, although there can be a coupling of number range with activation functions used. Unexpectedly in this paper, an effect of numerical instability was discovered from run to run when run on a multi-core CPU. The paper also has shown the enforcement of consistent deterministic results on an multi-core CPU by defining atomic critical code regions, and that aids repeatable information assurance in model fitting (or learning sessions). That enforcement of consistent repeatable determinism has also a benefit to accuracy even for the random schemes, and a highest score of 98.29%, + 0.326% higher than the baseline was achieved. However, also the non-random initialisation scheme causes weight arrangements after learning to be more structured which has benefits for validation in safety critical applications.
Replay attack detection with raw audio waves and deep learning framework

Replay attacks present a significant threat to Automatic Speaker Verification systems as they can be easily mounted using everyday smart devices by any non-professional imposter. The ASVspoof 2017 challenge was an initiative to develop solutions to counteract such replay attacks. The proposed solution builds on the fact that all the distinguishing features between genuine and spoofed audio are not effectively captured by conventional feature extraction techniques. Hence we propose a 1D ConvNet system with raw audio waves as features to it. This approach is able to achieve an EER of 0.41% on development set and 5.29% on evaluation set and hence outperforming best submission to ASVspoof 2017 challenge which had EER of 3.95% and 6.73% on development and evaluation sets respectively.
Dtree2vec: A High-Accuracy and Dynamic Scheme for Real-Time Book Recommendation by Serialized Chapters and Local Fine-Grained Partitioning

With the rapid development of social computing technologies and online reading platforms, the proportion of e-books, especially online serialized novels, has been increasing.Identifying ways to add updated serialized books to readers' real-time recommendation lists has become an urgent problem to be solved.While serialized books are still underwritten and in the process of production, their features and categories can constantly evolve and change, lacking complete text content information and complete global scoring data.Therefore, this paper proposes a dynamic DTree2Vec scheme for serialized books that models text of varying degrees of completion to achieve unified semantic feature representation to measure the semantic relevance of books.At the same time, it ensures recommendation quality by tracking the update status of serialized books and by adding the subsequent chapter content in real time.This scheme establishes a dynamic hierarchical tree structure for serialized books and applies a cosine-type local reconstruction model to reconstruct new semantic features of books.In addition, the fine-grained factor of chapter partitioning is introduced to the reconstruction process to adjust the proportion of global and local features to better represent the semantic features of books.We analyzed effects of the content of serialized chapters on the recommendation results and used semantic features of the reconstruction to represent the content of serial novels in real time.The experiment results prove that the proposed DTree2Vec scheme can achieve higher degrees of recommendation accuracy when dealing with unfinished serialized books, effectively alleviating dynamic capture problems and real-time recommendations of book recommendations.
Cross-Generation Analysis of e-Book Consumers’ Preferences—A Prerequisite for Effective Management of Public Library

Nowadays, with the growing importance of providing digital services, public libraries offer a wide range of e-books. However, current studies show that reading preferences vary across countries, and identifying e-book demand is therefore difficult. For these reasons, this article focuses on different groups of e-book readers (pensioners, students, economically active) and on factors affecting their attitude to reading e-books, willingness to receive new library services and willingness to pay for them. For the purpose of this analysis, we are using unique data from an online questionnaire survey among readers of the Municipal Library of Prague in 2019 and own binary logistic models. The results show how e-book genres, information preferences and devices for reading e-books primarily affect selected groups of e-book readers. The unwillingness of e-book readers to pay for library services is also evident, especially among students. In the last section, we propose some practical implications that could help to library management and possibly attract more e-book readers from different groups.
Requirements Traceability Ontology to Support Requirements Management

research-article Share on Requirements Traceability Ontology to Support Requirements Management Authors: Adi Wibowo Petra Christian University, Indonesia Petra Christian University, IndonesiaView Profile , Joseph Davis The University of Sydney, Australia The University of Sydney, AustraliaView Profile Authors Info & Claims ACSW '20: Proceedings of the Australasian Computer Science Week MulticonferenceFebruary 2020 Article No.: 21Pages 1–9https://doi.org/10.1145/3373017.3373038Published:04 February 2020Publication History 0citation427DownloadsMetricsTotal Citations0Total Downloads427Last 12 Months77Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Automated Machine Learning based on Genetic Programming: a case study on a real house pricing dataset

Designing an effective machine learning model for prediction or classification problem is a tedious endeavor. Significant time and expertise are needed to customize the model for a specific problem. A significant way to reduce the complicated design is by using Automated Machine Learning (AML) that can intelligently optimize the best pipeline suitable for a problem or dataset. This paper demonstrates the utilization of an AML that has been developed with a meta-heuristic algorithm namely Genetic Programming (GP). Empirical experiment has been conducted to test the performances of AML on a real dataset of house prices in the area of Petaling Jaya, Selangor. The results show that the AML with GP able to produce the best pipeline of machine learning with high score of accuracy and minimal error.
A High Energy-Efficiency FPGA-Based LSTM Accelerator Architecture Design by Structured Pruning and Normalized Linear Quantization

LSTM (Long Short-Term Memory) is an artificial recurrent neural network (RNN) architecture and has been successfully applied to the areas where sequences of data need to be dealt with such as Natural Language Processing (NLP), speech recognition, etc. In this work, we explore an avenue to minimization of the LSTM inference part design based on FPGA for high performance and energy-efficiency. First, the model is pruned to create structured sparse features for the hardware-friendly purpose by using permuted block diagonal mask matrices. To further compress the model, we quantize the weights and activations following a normalized linear quantization approach. As a result, computational activities of the network are significantly deducted with an egligible loss on accuracy. Then a hardware architecture design has been devised to fully exploit the benefits of regular sparse structure. Having been implemented on Arria 10 (10AX115U4F45I3SG) FPGA running at 150 MHz, our accelerator demonstrates a peak performance of 2.22 TOPS at a power dissipation of 1.679 Watts. In comparison to the other FPGA-based LSTM accelerator designs previously reported, our approach achieves a 1.17-2.16x speedup in processing.
Cooperative Robot Control in Flexible Manufacturing Cells: Centralized vs. Distributed Approaches

This paper introduces a novel approach for the control of flexible manufacturing units by means of cooperatively interacting industrial robots. For fast adoption to the actual production requirements, we embed a learning module into the manufacturing cell. This learning module allows the robots to learn to solve the given task with respect to a given optimization objective. Simultaneously, the robots learn to efficiently cooperate and find an optimal collective behavior while solving the task. To this end, we develop two different control algorithms based on reinforcement learning. The first approach is based on a centralized agent which coordinates the learning behavior of the whole manufacturing cell. In the second approach, a learning agent is assigned to each robot allowing for more flexibility and reducing the state-action space of the reinforcement learning problem at hand. The approaches are applied to a laboratory testbed using two cooperating industrial robots which should learn to optimize the throughput of the manufacturing cell. A comparison of both approaches shows the improved performance of the decentralized learning agents compared to the centralized one both in goal achievement and learning speed.
Visual Domain Adaptation Exploiting Confidence-Samples

Domain adaptation methods are used to address a problem, in which train scenario (source domain) and test scenario (target domain) are different. The existing methods mainly perform adaptation via reducing domain discrepancy from the view of a probability distribution. However, the idea of probability distribution matching always leads to a complex optimization process. Thereby these methods are difficult to apply in some scenario like online application or fast perception in dynamic environments. In this paper, we propose a new and simple domain adaptation method that utilizes confidence- samples to facilitate the classifier training on the target domain. Here, the confidence-samples are a subset of the target samples, and they have very credibly predicted labels. In order to detect the samples, a Category Similarity Collaborative Representation (CSCR) is first developed, by which the raw labels of all target samples are predicted using the smallest projection error according to the law of category. After this, the confidence score of the raw predicted labels is evaluated by the energy context information of CSCR. Finally, the target samples with a high confidence score are selected. Because of the linearity of CSCR, our method avoids complex optimization for matching the probability distribution. Empirical studies on a standard dataset demonstrate the advantages of our method.
Key Problems and Solutions of the Application of Artificial Intelligence Technology

Google’s AlphaGo shocked the world by easily defeating Korean Go player Lee Shi-shi, thus setting off a new upsurge of artificial intelligence research and application. Currently, artificial intelligence technology is developing at a speed beyond imagination, and it has become the core and key to the leap-forward development of every industry. However, application of artificial intelligence technology also encounters many problems. Key problems of military application of artificial intelligence are analyzed emphatically, and solutions to those problems are introduced, as a reference to further research.
Application and Need-Based Architecture Design of Deep Neural Networks

This paper applies a hybrid evolutionary approach to a convolutional neural network (CNN) and determines the number of layers and filters based on the application and user need. It integrates compact genetic algorithm with stochastic gradient descent (SGD) for simultaneously evolving structure and parameters of the CNN. It defines an effectual string representation for combining structure and parameters of the CNN. The compact genetic algorithm helps in the evolution of network structure by optimizing the number of convolutional layers and number of filters in each convolutional layer. At the same time, an optimal set of weight parameters of the network is obtained using the SGD law. This approach amalgamates exploration in network space by compact genetic algorithm and exploitation in weight space with SGD in an effective manner. The proposed approach also incorporates user-defined parameters in the cost function in an elegant manner which controls the network structure and hence the performance of the network based on the users need. The effectiveness of the proposed approach has been demonstrated on four benchmark datasets, namely MNIST, COIL-100, CIFAR-10 and CIFAR-100. The obtained results clearly demonstrate the potential of the proposed approach by evolving architectures based on the nature of the application and the need of the user.
Enhanced Face Recognition Method Based On Local Binary Pattern and Principal Component Analysis For Efficient Class Attendance System

In this paper, an enhanced face recognition method is proposed to develop an efficient student attendance system. In the face recognition stage, enhanced local binary pattern (ELBP) is implemented by increasing the radius of the original local binary pattern (LBP) operator and principal component analysis (PCA) is used to extract the features from facial images. The ELBP outperforms the original LBP by suppressing the adverse influence of different illumination conditions and thus results in increased recognition rate. The test recognition rate on the Yale face database of the original LBP is 81.9%, while the ELBP with radius 2 to 5 pixels has achieved face recognition rate of 84.6% to 88.5%. The face images are recognized based on the classification results obtained from the combination of algorithms (PCA and ELBP). A user-friendly interface has been designed to improve the attendance marking process. Image pre-processing is performed to ensure better performance of the system. Lastly, the attendance of the recognized students is marked accordingly and saved in an excel sheet. Students who have not undergone pre-registration can register on the spot. Notification will be generated if a student signs in more than once. The recognition rate for high quality images in the attendance system is 100% whereas for low quality images it is 92.31%.
Camera Relocalization by Exploiting Multi-View Constraints for Scene Coordinates Regression

We propose a method for learning a scene coordinate regression model to perform accurate camera relocalization in a known environment from a single RGB image. Our method incorporates self-supervision for scene coordinates via multi-view geometric constraints to improve training. More specifically, we use an image-based warp error between different views of a scene point to improve the ability of the network to regress to the correct absolute scene coordinates of the point. For the warp error we explore both RGB values, and deep learned features, as the basis for the error. We provide a thorough analysis of the effect of each component in our framework and evaluate our method on both indoor and outdoor datasets. We show that compared to the coordinate regression model trained with single-view information, this multi-view constraint benefits the learning process and the final performance. It not only helps the networks converge faster compared to the model trained with single-view reprojection loss, but also improves the accuracy of the absolute pose estimation using a single RGB image compared to the prior art.