Reviewerly/exHarmony huggingface dataset:
On Modeling Context from Objects with a Long Short-Term Memory for Indoor Scene Recognition

Recognizing indoor scenes is still regarded an open challenge on the Computer Vision field. Indoor scenes can be well represented by their composing objects, which can vary in angle, appearance, besides often being partially occluded. Even though Convolutional Neural Networks are remarkable for image-related problems, the top performances on indoor scenes are from approaches modeling the intricate relationship of objects. Knowing that Recurrent Neural Networks were designed to model structure from a given sequence, we propose representing an image as a sequence of object-level information in order to feed a bidirectional Long Short-Term Memory network trained for scene classification. We perform a Many-to-Many training approach, such that each element outputs a scene prediction, allowing us to use each prediction to boost recognition. Our method outperforms RNN-based approaches on MIT67, an entirely indoor dataset, while also improved over the most successful methods through an ensemble of classifiers.
Salient Object Detection by Spatiotemporal and Semantic Features in Real-Time Video Processing Systems

Object detection is significant for event analysis in various intelligent multimedia processing systems. Although there have been many studies conducting research in this area, effective and efficient object detection methods for video sequences are still much desired. In this article, we investigate salient object detection in real-time multimedia processing systems. Considering the intrinsic relationship between top-down and bottom-up saliency features, we present a new effective method for video salient object detection based on deep semantic and spatiotemporal cues. After extracting top-down semantic features for object perception by a 2-D convolutional network, we concatenate them with bottom-up spatiotemporal cues for motion perception extracted by a 3-D convolutional network. In order to combine these features effectively, we feed them into a 3-D deconvolutional network for feature-sharing learning between semantic features and spatiotemporal cues for the final saliency prediction. Additionally, we propose a novel Gaussian-like loss function with an L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization term for parameter learning. Experimental results show that the proposed salient object detection approach performs better in terms of both effectiveness and efficiency for video sequences compared with the state-of-the-art models.
Joint Source-Channel Coding of Gaussian sources over AWGN channels via Manifold Variational Autoencoders

In this paper, we give a new scheme for Joint Source-Channel Coding of Gaussian sources over AWGN channels. We present a novel VAE architecture that exploits the manifold nature of joint source-channel coding. Since the projection onto a complex manifold is a highly discontinuous function we split the encoder into two arms and a selector. This design combined with the power of data-driven deep neural networks allows us to efficiently train an encoder-decoder system end-to-end. The scheme achieves state of the art performance for five out of the seven configurations tested upon and match the state of the art for one of the other configurations for almost all channel conditions tested upon. Further, without providing any expert prior on the optimum JSCC encoder, the proposed method learns an encoding function that is similar to the ones proposed and found by the researchers in the past three decades via heuristic approaches or complex variational analysis for simple configurations. It is also able to find excellent encoders and decoders for those source channel dimensions where past works have been unable to find any good solutions. Finally, we show that the system is also robust to changes in channel conditions.
Unsupervised Outlier Detection in Appearance-Based Gaze Estimation

Appearance-based gaze estimation maps RGB images to estimates of gaze directions. One problem in gaze estimation is that there always exist low-quality samples (outliers) in which the eyes are barely visible. These low-quality samples are mainly caused by blinks, occlusions (e.g. by eye glasses), blur (e.g. due to motion) and failures of the eye landmark detection. Training on these outliers degrades the performance of gaze estimators, since they have no or limited information about gaze directions. It is also risky to give estimates based on these images in real-world applications, as these estimates may be unreliable. To solve this problem, we propose an algorithm that detects outliers without supervision. Based on the input images with only gaze labels, the proposed algorithm learns to predict a gaze estimates and an additional confidence score, which alleviates the impact of outliers during learning. We evaluated this algorithm on the MPIIGaze dataset and on an internal dataset. In cross-subject evaluation, our experimental results show that the proposed algorithm results in a better gaze estimator (8% improvement). The proposed algorithm is also able to reliably detect outliers during testing, with a precision of 0.71 when the recall is 0.63.
A Two-Stage Machine Learning Method for Highly-Accurate Drunk Driving Detection

Abnormal driving refers mostly to drunk driving, fatigue driving, and aggressive driving behaviors. This work addresses drunk driving detection, which actually can be extended to apply to detection of the other abnormal driving behaviors. The approach learns about the related on-board vehicle sensors for detecting drunk driving behaviors based on learning using certain drunk driving cues. In a previous work, we developed a Hidden Markov Model (HMM) method and applied it to each time series of the selected sensors measurements. The prediction accuracy was highest for the longitudinal acceleration, with a maximum of 79%. Here, we extend our early work that was based on HMMs and employ Recurrent Neural Networks that specialize in time series, where our testing results indicate accuracy percentages that go into the upper nineties.
Distributed Modelling Approaches for Data Privacy Preserving

Recently, machine learning has been developing rapidly. There is no doubt that data plays an important role in machine learning. However, it is hard to make full use of the data from a large amount of nodes to collaboratively train a good model with data privacy preserving. In this paper, we study and analyze several decentralized machine learning algorithms regarding to privacy protection, and propose a smart contract-based decentralized federated learning algorithm. We also propose a decentralized topology-based machine learning algorithm to solve the problems caused by star-topology network. Based on it, we further present a novel method of model aggregation based on distillation to break the conventional constrain of federated learning the models of different nodes shall have the same network structure. We also use several methods to generate synthetic dataset from raw dataset to train models with data privacy protected. Finally, we analyze and compare different distributed machine learning algorithms through the experiments.
Blockchain as a technology for the transition to a new digital economy

Abstract The modern economy is gradually developing and shifting into a virtual economic system. The new digital economy serves as the new model of economic development and redesigns businesses based on utilizing information in b2b relationships. New information and network technologies are becoming significant factors of a globalizing economy and contribute to the rapid transition of the economy from a real economy bound by boundaries to a network one connected by the internet, ensuring digital economy development and innovative business process formation. With the blockchain technology coming into play, the digital economy is set to take a step forward in globalizing the economy. This research explores how the blockchain technology has transformed the digital economy and how this technology can be exploited to make the digital economy an extra mile.
Drama patterns

The knowledge of drama building is somehow an inherent part of us. Albeit, it tends to be suppressed and forgotten. This is leveraged by a common, director or instructor supervised approach, which favors learning lines to produce them without making a mistake. But the essence of drama is in archetypal situations that tend to repeat over and over without ever being the same. These are known as patterns. The notion of a pattern itself indicates an intrinsic tendency of patterns to develop and compose into a greater whole. We need a way of recalling these patterns to build live and immersive drama plays. These patterns set frames within which the actors act, but they also give them a freedom to develop their roles in a natural and unaffected way in accordance with Stanislavski's approach. In this paper, we present the idea of drama patterns and report on its genesis and the experience we had with it so far. We bring a set of four drama patterns (plus one fundamental property) and their composition coming out of a real drama play. We also provide a glimpse of a more than a dozen of other drama patterns with examples of their occurrence. Drama patterns are aimed at the broadest audience without age or background limitations. Directors can use them to build their drama plays, actors can use them to self-organize, acting teachers can use them to teach acting (assuming it's being done through drama building), drama scholars might use them as a way of looking on drama plays and texts, and drama authors can use them to write dramas.
Recognition of Simple Handwritten Polynomials Using Segmentation with Fractional Calculus and Convolutional Neural Networks

This work introduces a method for recognizing handwritten polynomials using Convolutional Neural Networks (CNN) and Fractional Order Darwinian Particle Swarm Optimization (FODPSO). Segmentation of the input image is done with the FODPSO technique, which uses fractional derivative to control the rate of particle convergence. After segmentation, three CNN are used in the character recognition step: the first one classifies the individual symbols as numeric or non-numeric. The second network recognizes the numbers, while the third CNN recognize the non-numeric symbols. A heuristic procedure is used to build the polynomial, whose graph is finally plotted. A total of 264780 images containing symbols and numbers were used for training, validating, and testing the CNN, with an accuracy of approximately 99%.
AI Gauge: Runtime Estimation for Deep Learning in the Cloud

Major cloud providers, including IBM Cloud, Amazon Web Services, Microsoft Azure, and Google Cloud, offer services to train, debug, store, and deploy machine learning models at scale. For enhanced user experience in SLA-driven control, cost effective budgeting, elastic scaling, and efficient operations, estimating the runtime of training a machine learning model is important. We present AI Gauge, a cloud service to estimate runtime and cost for training deep learning models under different configuration options on the cloud. AI Gauge is designed using micro-service architecture and performs estimations based on machine learning models calibrated by an extensive and continuously populated job trace data-set. We show that AI Gauge can accurately predict the remaining time of running jobs based on its runtime progress (<; 10% relative error) and can accurately predict the total runtime for a job before it starts with 7-8% relative error on average.
Neighborhood Adaptive Graph Convolutional Network for Node Classification

Recently, graph convolutional neural network as an efficient and effective method has experienced significant attention and becomes the de facto method for learning node or graph representations. However, existing most methods use a fixed-order neighborhood information when integrating node representations for node classification on the graph. In this paper, we present a neighborhood adaptive graph convolutional network (NAGCN), a novel method to efficiently learn each node's representations. Particularly, we construct a convolutional kernel abstracted from the diffusion process, named as the neighborhood adaptive kernel to more precisely learn and integrate related neighborhood node information for each node. As a result, our proposed method can learn more useful information across the relevant near and distant neighbors according to the real applications. We also adopt a threshold mechanism on the constructed kernel to better reserve the most impact neighbor vertices for each node on the graph. Besides, one learnable feature refinement process is used in the model to obtain high-level node representations with sufficient expressive power. The model is also theoretically analyzed in terms of spectral convolution and message passing algorithm. Notably, extensive experiments demonstrate that our method can achieve better performance on node classification tasks compared to other related approaches.
Sentiment analysis of bengali online reviews written with english letter using machine learning approaches

In this paper, we have done sentiment analysis for English written Bengali words given in different online shops in Bangladesh. For this work we have chosen 4 latest mobile phone popular in Bangladesh. For this purpose we have used Multinomial naïve bayes classification system. The dataset that has been used for this work has been taken from some online shops websites that is been publicly available. The only data taken hasbeen related to selected popular cellphone sets. For better understanding and realistic understanding some common features were selected that people looks for. In this work 6 feature were selected. They are-Camera, Battery, Screen, Sounds, Design and Hardware/Software performance. We went for these feature related comments and reviews for constructing our dataset. 90% of them were trained and 10% of them were tested. They were tested in algorithm showed process. The programming tool has been used for this job was PYTHON .The algorithm calculates the positive and negative features to get the result of the reviews. Then the results are compared by graphical representation..
Deep Convolutional Network with Long Short-Term Memory Layers for Dynamic Gesture Recognition

The framework based on a convolutional neural network (CNN) with adding long short-term memory layers (LSTM) for recognizing hand gestures from a video stream in real-time is presented. A review and analysis of existing models relating to gesture recognition in deep learning are considered. The task was to perform hand gesture classification using deep convolutional neural networks and obtain a simple, precise and resource-efficient system for visual recognition of letters and digits in sign language. The model is stable to rather wide angles of hand rotation and independent of lighting due to the using of contour patterns. In experiments with CNN, 98.46% accuracy on the test dataset has been obtained.
A Robust Radio Frequency Fingerprint Identification Scheme for LFM Pulse Radars

Radar transmitter identification technology based on pulse descriptor word (PDW) is broadly used in military and civilian applications. However, as the complexity of the electromagnetic environment has increased, radar identification has been challenging. Radio frequency fingerprint (RFF) is an intrinsic hardware characteristic and has been widely employed for device identification. In this paper, we propose a robust RFF identification scheme for linear frequency modulation (LFM) pulse radars. The scheme includes a proposed piecewise curve fitting based denoising (PCFD) algorithm and a hybrid RFF identification algorithm. The PCFD algorithm can reduce the noise of LFM pulses without undermining RFF features. The hybrid RFF identification algorithm extracts both transient-based and modulation-based RFF features. Experimental results demonstrate that the proposed radar identification scheme can achieve a 100% identification accuracy when the SNR is about 0 dB.
A Qualitative Study on Framework Debugging

Features of frameworks, such as inversion of control and the structure of framework applications, require developers to adjust their programming and debugging strategies as compared to sequential programs. However, the benefits and challenges of framework debugging are not fully understood, and gaining this knowledge could provide guidance in debugging strategies and framework tool design. To gain insight into the framework application debugging process, we performed two human studies investigating how developers fix applications that use a framework API incorrectly. These studies focused on the Android Fragment class and the ROS framework. We analyzed the results of the studies using a mixed-methods approach, using techniques from qualitative approaches. Our analysis found that participants benefited from the structure of frameworks and the pre-made solutions to common problems in the domain. Participants encountered challenges with understanding frame-work abstractions, and had particular difficulty with inversion of control and object protocol issues. When compared to prior work on debugging, these results show that framework applications have unique debugging challenges.
Celebrating 20 Years of Computer-based Audio Gaming

We look back on two decades of academic research on audio games. During this time, a substantial amount of research has explored many facets of this special genre of computer games. However, despite many publications, there is a lack of review papers, which help delineate this growing research field. For this reason, we take one step back and investigate 20 years of audio game research by synthesizing a literature review adopting grounded theory methods. The resulting research map provides an overview of efforts into audio games with a special focus on how to design for audio games. We observed three important trends or tensions in audio game research. Firstly, audio games research depended heavily on technological advancements during the last two decades. Secondly, most studies about audio games were conducted with novices to audio games in lab situations, that is, based on artificial situations and not on real gamers and their genuine experience. Thirdly, the audio game design process per se has been greatly neglected in the literature so far. We conclude the paper by discussing design or research implications.
Multi-stream attentive generative adversarial network for dynamic scene deblurring

Image deblurring methods based on deep learning have been proved to be effective for dynamic scene deblurring. Nevertheless, the most existing methods usually fail to deal with severe and complex dynamic scene blurs. In this paper, we propose a novel multi-stream attentive generative adversarial network (MSA-GAN) for solving the problems. Firstly, we design a novel attentive guidance module for generating an attention map which can guide to pay more attention on the severe blur regions or objects and their edge or texture structures in the feature extraction and deblurring process. Secondly, we propose the multi-stream and multi-scale feature extraction strategy and design an attentive and multi-scale feature extraction module, in which we design a multi-scale residual block for extracting the multi-scale features and capturing more information of complex blurs. Thirdly, we propose a multi-scale feature fusion strategy and the strategy is implemented in the multi-stream and multi-scale feature fusion module through the designed gate structure, which can adaptively learn and fuse multi-scale features from different streams. Finally, The extensive experiments are performed on the GoPro dynamic scene dataset and real image data, and the experimental results both quantitatively and qualitatively have demonstrated that the proposed method outperform the recent state-of-the-art methods.
“I can’t believe #Ziggy #Stardust died”

Abstract Social networking sites (SNSs) have changed the way we mourn. Reactions on SNSs to celebrity death in particular have begun to attract the attention of both academic researchers and the broader media, yet so far linguistic studies thereof remain relatively rare. This study addresses this research gap by examining the pragmatics of Instagram posts labelled #bowie following the death of the musician David Bowie on 10 January 2016. It shows how Instagram users engage in affective stance-taking strategies ranging from disbelief to acceptance. It also suggests that the multimodality of Instagram posts functions as a means of combining grief and identity work within a fan community. The findings are relevant for an understanding of mediatised affect and offer further evidence of a renegotiation of norms of mourning online.
Survey of Offline Arabic Handwriting Word Recognition

The field of Arabic handwriting recognition and translation is currently experiencing rapid growth in terms of research, which is evident in the coverage of major conferences and journals that specialise in the area of handwriting recognition. Against this backdrop, a significant increase has been observed in the classification and features techniques used, as compared to some years back. Researchers have put in more efforts geared towards building a variety of databases for Arabic handwriting recognition. This article aims to provide a comprehensive survey of advances in Arabic offline handwriting recognition. We have been provided details of availability Arabic databases with limitation. Further, we focus on techniques of feature extraction and different variety of classification approaches such as ANN, HMM, SVM that used in Arabic handwriting recognition.
Entanglement and Entropy of a Three-Qubit System Interacting with a Quantum Spin Environment

Throughout this paper, we describe an analytical solution for a three-qubit system characterized by a finite temperature within a thermodynamic limit influenced by a quantum spin environment. As applications to the presented solution, we investigate the effect of the temperature, the coupling constant ϵ 0 within the spin- qubit system and an external magnetic field on the three-particles residual entanglement N a b c , the concurrence C ( ρ ) , the information entropy H ( σ Z ) and the linear entropy P P ( t ) . The results show an inverse relationship between the entanglement and entropy, where the degree of both is controlled by controlling the temperature T and the coupling constant ϵ 0 .
Dynamics-Enabled Safe Deep Reinforcement Learning: Case Study on Active Suspension Control

Reinforcement learning (RL) is in essence a trial-and-error process which involves exploratory actions. These explorations can lead to system constraint violations and physical system damages, impeding RL's use in many real-world engineered systems. In this paper, we develop a safe RL framework that integrates model-free learning with model-based safety supervision to bridge the gap. We exploit the underlying system dynamics and safety-related constraints to construct a safety set using recursive feasibility techniques. We then integrate the safety set in RL's exploration to guarantee safety while simultaneously preserving exploration efficiency by using the hit-and-run sampling. We design a novel efforts-to-remain-safe penalty to effectively guide RL to learn system constraints. We apply the proposed safe RL framework to the active suspension system in which actuation and state constraints are present due to ride comfort, road handling, and actuation limits. We show that the developed safe RL is able to learn a safe control policy safely while outperforming a nominal controller.