Reviewerly/exHarmony huggingface dataset:
Symmetrically Stacked Long Short-Term Memory Networks for Fall Event Recognition Using Compact Convolutional Neural Networks-Based Tracker

In recent years, the advancement of pattern recognition algorithms, specifically the deep learning-related techniques, have propelled a tremendous amount of researches in fall event recognition systems. It is important to detect a fall incident as early as possible, whereby a slight delay in providing immediate assistance can cause severe unrecoverable injuries. One of the main challenges in fall event recognition is the imbalanced training data between fall and no-fall events, where a real-life fall incident is a sporadic event that occurs infrequently. Most of the recent techniques produce a lot of false alarms, as it is hard to train them to cover a wide range of fall situations. Hence, this paper aims to detect the exact fall frame in a video sequence, as such it will not be dependent on the whole clip of the video sequence. Our proposed approach consists of a two-stage module where the first stage employs a compact convolutional neural network tracker to generate the object trajectory information. Features of interest will be sampled from the generated trajectory paths, which will be fed as the input to the second stage. The next stage network then models the temporal dependencies of the trajectory information using symmetrical Long Short-Term Memory (LSTM) architecture. This two-stage module is a novel approach as most of the techniques rely on the detection module rather than the tracking module. The simulation experiments were tested using Fall Detection Dataset (FDD). The proposed approach obtains an expected average overlap of 0.167, which is the best performance compared to Multi-Domain Network (MDNET) and Tree-structured Convolutional Neural Network (TCNN) trackers. Furthermore, the proposed 3-layers of stacked LSTM architecture also performs the best compared to the vanilla recurrent neural network and single-layer LSTM. This approach can be further improved if the tracker model is firstly pre-tuned in offline mode with respect to a specific type of object of interest, rather than a general object.
Multi-layer Perceptron based Comparative Analysis between CNTFET and Quantum Wire FET for Optimum Design Performance

A novel classification technique is applied for identifying carbon nanotube FET and quantum wire FET based on their electrical characteristics and percentage error is estimated using multi-layer perceptron analysis to justify the accuracy of computation. Two different cross-validation methods, namely decision table and multilayer perceptron (MLP) are applied on same data set of both the devices, and results speak about higher accuracy when MLP is performed. Also, for different testing-training set of data, MLP performs far better than conventional decision table approach; when correlation coefficient, mean absolute error, root mean squared error, relative absolute error and root relative squared error are computed. For comparative study, similar geometrical configuration, and equivalent biasing arrangement of both the devices are assumed, and identical number of iterations is performed for equal subsets. Results speak supremacy of MLP technique applied for classification and identification of nanometric devices based on their electronic attributes.
Clustering

This chapter will discuss the unsupervised machine learning technique known as clustering and its main approaches and use cases. After presenting typical application areas for the tourism industry, the mathematical principle of clustering will be explained. Various techniques for representing differences between cases or clusters will be introduced, and major methods used to form clusters based on these differences will be presented (i.e., single linkage, complete linkage, average linkage, and centroid). Subsequently, the three most widely applied clustering approaches will be described. First, major concepts of hierarchical clustering, like divisive and agglomerative techniques, will be highlighted. Second, the partitioning technique k-means will be introduced, and, third, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) will be discussed. By using real tourism data and the data science platform RapidMiner, the practical demonstration will then explain step-by-step how clustering approaches can be executed. After employing typical processes for data transformation and normalization, RapidMiner processes for k-means, hierarchical clustering, and DBSCAN will be shown, and the clustering results will be discussed. Lastly, a tourism case applying k-means and DBSCAN to identify points of interest based on uploaded photo data extracted from the platform Flickr will conclude the chapter.
Age-Invariant Adversarial Feature Learning for Kinship Verification

Kinship verification aims to determine whether two given persons are blood relatives. This technique can be leveraged in many real-world scenarios, such as finding missing people, identification of kinship in forensic medicine, and certain types of interdisciplinary research. Most existing methods extract facial features directly from given images and examine the full set of features to verify kinship. However, most approaches are easily affected by the age gap among faces, with few methods taking age into account. This paper accordingly proposes an Age-Invariant Adversarial Feature learning module (AIAF), which is capable of factoring in full facial features to create two uncorrelated components, i.e., identity-related features and age-related features. More specifically, we harness a type of adversarial mechanism to make the correlation between these two components as small as possible. Moreover, to pay different attention to identity-related features, we present an Identity Feature Weighted module (IFW). Only purified identity features are fed into the IFW module, which can assign different weights to the features according to their importance in the kinship verification task. Experimental results on three public popular datasets demonstrate that our approach is able to capture useful age-invariant features, i.e., identity features, and achieve significant improvements compared with other state-of-the-art methods on both small-scale and large-scale datasets.
Blockchain Development and Corporate Performance in China: The Role of Ownership

This research investigates the short-term and long-term performance and volatility of publicly traded firms engaged in blockchain business. In particular, it examines how ownership structure impacts performance and volatility in such firms. We manually collected the data of Chinese A-listed companies participating in blockchain development during 2013–2018 as samples, and find that both short- and long-term performance and volatility significantly decrease among these firms after involvement in blockchain business. Ownership concentration has a positive correlation with stock returns, return on assets, and volatilities, whereas the state as a controlling shareholder strengthens these positive links.
A Course-Focused Dual Curriculum For Image Captioning

We propose a curriculum learning captioning method to caption fetal ultrasound images by training a model to dynamically transition between two different modalities (image and text) as training progresses. Specifically, we propose a course-focused dual curriculum method, where a course is training with a curriculum based on only one of the two modalities involved in image captioning. We compare two configurations of the course-focused dual curriculum; an image-first course-focused dual curriculum which prepares the early training batches primarily on the complexity of the image information before slowly introducing an order of batches for training based on the complexity of the text information, and a text-first course-focused dual curriculum which operates in reverse. The evaluation results show that dynamically transitioning between text and images over epochs of training improves results when compared to the scenario where both modalities are considered in equal measure in every epoch.
Improving Depth Estimation Using Map-Based Depth Priors

Accurate scene depth is fundamental for robot scene understanding as it adds spatial reasoning. However, accurate scene depth often comes at the cost of expensive additional depth sensors. In this letter, we propose to use map-based depth data as an additional input instead of expensive depth sensors. Such an approach is especially appealing in autonomous driving since map-based depth is commonly available from high-definition maps. To validate this approach, we propose a mapping method that works with common autonomous driving datasets and allows for precise localization using a mix of GNSS-INS and image-based techniques. Furthermore, we propose an entirely learnable three-stage network that handles foreground-background mismatches between the map-based prior depth and the actual scene. Finally, we validate the performance of our method in comparison to several baseline methods and SOTA depth completion methods receiving map-based depth as an input. Our method significantly outperforms these methods both in quantitative and qualitative results. Moreover, our method achieves better metric-scale predictions compared to image-only approaches.
Local Linear Approximation Algorithm for Neural Network

This paper aims to develop a new training strategy to improve efficiency in estimation of weights and biases in a feedforward neural network (FNN). We propose a local linear approximation (LLA) algorithm, which approximates ReLU with a linear function at the neuron level and estimate the weights and biases of one-hidden-layer neural network iteratively. We further propose the layer-wise optimized adaptive neural network (LOAN), in which we use the LLA to estimate the weights and biases in the LOAN layer by layer adaptively. We compare the performance of the LLA with the commonly-used procedures in machine learning based on seven benchmark data sets. The numerical comparison implies that the proposed algorithm may outperform the existing procedures in terms of both training time and prediction accuracy.
Quality Optimization of Live Streaming Services over HTTP with Reinforcement Learning

Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.7\times$</tex> .
A robust cross-ethnic digital handwriting recognition method based on deep learning

With the development of deep learning technology, handwritten character recognition has become the basic hotspot of research. At the same time, the recognition of handwriting in minority languages is gradually worthy of attention. In this paper, a robust and novel improved deep convolutional neural network method is proposed to recognize normal digital handwriting images and minority digital handwritten images (Yi language). To improve the recognition accuracy of handwritten fonts, there are four improvements in our work. Firstly, separable convolution is adopted in the network to cope with the problem of large parameters. Secondly, data augmentation techniques (Gaussian noise, color perturbation, and random rotation) are utilized to improve the expression ability of the dataset and the generalization capabilities of the model. Finally, the shortcut block and the weight initialization are introduced in our network for the problems of network degradation and gradient saturation. Based on deep learning technology, this paper studies the classification and recognition of handwritten digits, and improves the accuracy of the recognition in numbers such as invoices in financial bills. The recognition accuracy of 99.82% has been achieved on MNIST dataset, and 98.79% on the self-building dataset of Yi handwritten digits (YHDD).
BGGMM-HMT based locally optimum image watermark detector in high-order NSST difference domain

Imperceptibility, robustness and data payload are three main requirements of any image watermarking systems to guarantee desired functionalities, but there is a tradeoff among them from the information-theoretic perspective. How to achieve this balance is a major challenge. In this paper, we propose a new statistical image watermarking scheme, which is based on the high-order difference coefficients in nonsubsampled Shearlet transform (NSST) domain and the bounded generalized Gaussian mixture model-based hidden Markov tree (BGGMM-HMT). In the watermark embedding process, we use a nonlinear embedding approach to hide the digital watermark into the robust high-order difference coefficients, which can achieve better imperceptibility. In the watermark detection process, high-order difference coefficients are accurately modeled by using BGGMM-HMT, where the distribution characteristics of high-order difference coefficients can be captured through BGGMM, and the scale dependencies of high-order difference coefficients can be captured through HMT. Statistical model parameters are then estimated by combining the approach of minimizing the higher bound on data negative log-likelihood function and upward–downward algorithm. Finally, an image watermark detector based on BGGMM-HMT is developed using the locally optimum (LO) decision rule. For the proposed detector, the receiver operating characteristic (ROC) expression is derived in detail. We evaluate the proposed scheme from different aspects and compare it with the state-of-the-art schemes. After a large number of experimental tests, the encouraging results obtained prove the effectiveness of our watermarking scheme.
Face Recognition Technology Improving Using Convolutional Neural Networks

The principles of the face recognition technology building, which are based on the architecture of convolutional neural networks, are considered. Attention is paid to such stages of recognition technology as detection, feature extraction, identification and verification. Also, neural network learning methods, cost functions are reviewed. FaceNet and OpenFace architectures were chosen for further analysis. The OpenFace architecture has been modified through the classifier learning method. TensorFlow and Keras libraries, LFW and Pin Faces network learning datasets were used to build the neural network structure. The ORL Faces dataset was used for testing. The accuracy of training reached 98%. The improvement after modification on the test dataset reached 17%. It was decided to use a modified architecture for implementation in the face recognition system under development.
Low-Bitwidth Convolutional Neural Networks for Wireless Interference Identification

Wireless interference identification (WII) is critical for non-cooperative communication systems in both civilian and military scenarios. Recently, deep learning (DL) based WII methods have been proposed with impressive performances. However, these methods did not consider the quantization problem for DL based methods of WII, which is an indispensable process when deploying deep neural networks into hardware units. This paper addresses the problem of training quantized convolutional neural networks (CNNs), with low-precision weights as well as activations for WII. Optimizing a low-bit width network is very challenging due to the non-differentiable quantization function. To mitigate the difficulty of training, we propose three effective approaches. Firstly, we propose to train the quantized network with the guidance of the full-precision counterpart. The quantized network can learn from the full-precision counterpart. Unfortunately, training an extra full-precision network to assist a quantized model is cumbersome and computationally expensive. To this end, we further propose a training mechanism which makes use of the manually designed probability distributions to provide a virtual full-precision counterpart for guiding the training of the quantization network without extra computational cost. Thirdly, to make the gradient back-propagates more easily, we propose novel auxiliary output modules, which can be seamlessly incorporated into the proposed quantization networks. Experimental results validate the effectiveness of the proposed methods. Furthermore, it is shown that training 3-bit and 4-bit precision networks with the proposed methods leads to performance improvement as compared to their full precision counterparts with standard network architectures.
Towards AI application marketplaces - an interview with Dorian Selz

Abstract This interview with the CEO from Squirro reports on how a search engine in the enterprise context evolves with artificial intelligence (AI) towards a cognitive search engine and ultimately towards an electronic marketplace for information objects. Using several examples, Dorian Selz describes the opportunities of Squirro‘s low-code in creating user-specific information objects from internal and external data sources as well as in deriving probabilities and projections. This not only has the potential to improve existing business processes, but also sheds light on new predictive capabilities. The example shows potential elements of future AI marketplaces, such as models, app directories and connectors.
Content-based Image Retrieval based on Convolutional Neural Networks

Images can communicate a service, brand or product. Moreover, images provide depth and context to a description or story and give a much more intense experience than writing alone. Image retrieval is the highest searching performance process, especially in large databases. Search by content is a powerful tool for many industries. Images are more complicated than texts. Each image has important features like color, texture, and edges. Searching by part of image is termed content-based image retrieval. Convolution neural network achieved high accuracies in images feature extraction and classification. For high-performance image retrieval, we proposed an intelligent model using CNN. The model was applied on Cifar10 and Mnist datasets. The proposed model achieved 92.9% and 99.8% accuracies for Cifar10 and Mnist datasets respectively. The model achieved the highest accuracies and best processing performance compared by State-of-arts-models.
On the adaptation of foreign students

Over the recent years, internationalization of the educational process draws heightened interests to foreign students, which in turn, entails a surge of research dedicated to the problems of their adaptation. The goal of this article lies in analyzing the cross-cultural adaptation of foreign students from non-CIS states who study in Russia. The subject of this article is the targeted work of the academic staff on the adaptation of foreign students. Methodological framework is comprised of the following approaches: individual, personality-oriented, activity-oriented, humanistic, and cross-cultural, which takes into account the ethnic and cultural-specific peculiarities of foreign students. The relevance of this topic is justified by the globalization trends of international educational space, as well as strategic tasks of the Russian higher education &amp;ndash; stimulate the export of educational services, attract foreign students, and promote the integration of foreign specialists in the Russian job market. The scientific novelty lies in determination of the factors that either contribute to or obstruct the adaptation of foreign students in the Russian universities, as well as the agents of adaptation. On the example of Moscow State University of Technology "STANKIN", the author analyzes the peculiarities of adaptation process, as well as the factors that improve the living and learning conditions of foreign citizens in the new sociocultural environment, increase the level of multicultural interaction within the framework of the educational institution. It is established that the adaptation of foreign students depends on the psychophysiological, educational-cognitive, sociocultural and casual factors, which are closely interrelated. The problems faced by foreign students are associated with submerging to a new sociocultural and educational environment of the university; therefore, the process of their adaptation should be organized, targeted, and integrated.