Reviewerly/exHarmony huggingface dataset:
Resource-Efficient Computing in Wearable Systems

We propose two optimization techniques to minimize memory usage and computation while meeting system timing constraints for real-time classification in wearable systems. Our method derives a hierarchical classifier structure for Support Vector Machine (SVM) in order to reduce the amount of computations, based on the probability distribution of output classes occurrences. Also, we propose a memory optimization technique based on SVM parameters, which results in storing fewer support vectors and as a result requiring less memory. To demonstrate the efficiency of our proposed techniques, we performed an activity recognition experiment and were able to save up to 35% and 56% in memory storage when classifying 14 and 6 different activities, respectively. In addition, we demonstrated that there is a trade-off between accuracy of classification and memory savings, which can be controlled based on application requirements.
Blockchain’s future: can the decentralized blockchain community succeed in creating standards?

Abstract Nakamoto proposed a new solution to transact value via the internet. And since 2009, blockchain technology has expanded and diversified. It has, however, proven to be inefficient in the way it achieves its outcomes, especially through the proof of work protocol. Other developers are promoting alternative methods but, as yet, none has superseded proof of work. The competing protocols illuminate a key feature of the blockchain community, namely, its inability to create consensus in a decentralized community. Because of this lack of consensus, the formation of standards is particularly difficult to achieve. At best standards are contested sites, and we examine three such sites where some form of agreement over standards will be essential if blockchain is to evolve successfully. These three sites are blockchain governance, smart contracts, and interoperability of blockchains. We argue that because standards’ formation is a contested and contingent process, the blockchain community will persist in creating difficulties and barriers for itself until it is able to resolve internal conflicts.
Multi-Timescale Context Encoding for Scene Parsing Prediction

Predicting the future is a crucial ability for intelligence systems. It is of great importance for many real-world applications, such as autonomous driving, which need scene parsing to understand the environment. Recent research has shown that predicting in semantic level is more effective than segmenting the predicted RGB frames. In order to label pixels in future frames correctly, the rich contextual dependencies should be exploit which existing methods paid less attention to. Therefore, we propose a novel network which catches both the short-term and long-term relations of observed frames for future scene parsing. Specifically, we introduce an attention mechanism to model semantic interdependencies between consecutive frames and a modified convolutional LSTM to model the correlations among all the input frames. Experiments validate that our approach outperforms other state-of-the-art methods on the large-scale Cityscapes dataset.
Quantitative Assessment of Inheritance Hierarchies for Aspect Oriented Software Development using a proposed Aspect Inheritance Reusability Model

Aspect Oriented Software Development (AOSD) is a promising methodology for efficiently capturing the crosscutting functionalities (concerns) into modular units called aspects, thereby reducing maintainability, and increasing reusability and ease of software evolution. Since inheritance of classes and aspects plays a vital role in defining the units of encapsulation, it is essential that the impact of introducing inheritance in AOSD need to be quantitatively captured using core design level metrics and mapped onto higher level quality attributes. In this research work, a new set of metrics have been proposed to quantify the impact of using multi-level inheritance hierarchies in an Aspect Oriented software. A model that captures the reusability using inheritance of aspects has been used for defining and applying the proposed metrics and later relates them onto higher level quality attributes. Further, the proposed metrics and model have been applied to AspectJ versions of an SOA based case study application. Based on the measurement, it was inferred that aspectization has improved the higher level quality attributes of reusability, modularity and maintainability of the case study over its versions.
Rotation-Aware Discriminative Scale Space Tracking

Although discriminative correlation filters (DCF) based methods have achieved desirable performance in visual tracking, most existing methods still fail to cope with significant target rotations in challenging scenarios. This paper proposes a rotation-aware DCF-based tracking method that efficiently estimates the target rotation and exploits the estimated rotation in translation and scale models. The proposed method reformulates the discriminative scale space tracker (DSST) to estimate target location and rotation, simultaneously. Then, the estimated rotation integrates into the scale estimation process, effectively. While the proposed method can estimate all target rotations, it uses only three target models in each frame to have desirable tracking speed. Experimental results demonstrate that the proposed method outperforms the average precision and success rate of the DSST up to 3.9% and 5.3% on 21 relevant video sequences, respectively.
Video Text Detection with Fully Convolutional Network and Tracking

Scene text in videos carries rich semantic information that is of great value in various content-based video applications. In this paper, we propose an effective fully convolutional network model for detecting text in videos based on a novel refine block structure. The model hierarchically exploits low-level features from earlier convolutions to refine high-level semantic features, thereby fusing multi-resolution features extracted from the frame to generate high-resolution semantic feature maps for better capturing widely varied appearances of video text. We further complement the individual-frame detection with an efficient correlation filter based text tracking mechanism, and enhance the overall detection performance by matching and combining detection and tracking results. Experiments on public scene text video datasets demonstrate the state-of-the-art performance of the proposed method.
Image Classification using small Convolutional Neural Network

With an upsurge in the rate of data production, pervasive usage of cameras for automation and surveillance and the requirement of visual input for artificially intelligent devices all across the globe, there has been a rapid increase in the mass of image data being generated today. This gives rise to the essentiality of automated image processing required to simplify image related tasks. Automated image processing bridges the gap between the human visual system and the pixel level data of images. Deep Convolution Neural Networks are being deployed expansively to analyze, detect and classify images for a diverse number of tasks. These neural networks, similar to the human neural network, contain neurons with learnable weights and biases, which are trained to identify and classify different objects or features across the image. This paper presents a functional implementation of image recognition using a small convolutional neural network, proposing less complexity and yielding good classification accuracy for all tested data sets.
Path Planning of UAV-UGV Heterogeneous Robot System in Road Network

The previous research on path planning of the UAV-UGV heterogeneous robot system plan paths for both UAV and UGV without considering the UGV’s moving range or plan only the UAV’s path based on the given UGV’s path. In reality, the UGV should be restricted to drive in the road network, and the given UGV’s path is not necessarily the best UGV’s path. In the heterogeneous package delivery system considered in this paper, the UGV’s path was restricted to the road network and the UAV’s and UGV’s paths were optimized simultaneously to get the optimized paths. This paper proposed a two-stage strategy to solve the path planning problem by a hybrid algorithm of modified ant colony optimization and genetic algorithm. The simulation results show that the proposed method is feasible.
Semi-dense and robust image registration by shift adapted weighted aggregation and variational completion

This work presents a novel approach for both stereo and optical flow that deals with large displacements, depth/motion discontinuities and occlusions. The proposed method comprises two main steps. First, a novel local stereo matching algorithm is presented, whose main novelty relies in the block-matching aggregation step. We adopt an adaptive support weights approach in which the weight distribution favors pixels that share the same displacement with the reference one. State-of-the-art methods make the weight function depend only on image features. On the contrary, the proposed weight function depends additionally on the tested shift, by giving more importance to those pixels in the block-matching with smaller cost, as these are supposed to have the tested displacement. Moreover, the method is embedded into a pyramidal procedure to locally limit the search range, which helps to reduce ambiguities in the matching process and saves computational time. Second, the non-dense local estimation is filtered and interpolated by means of a new variational formulation making use of intermediate scale estimates of the local procedure. This permits to keep the fine details estimated at full resolution while being robust to noise and untextured areas using estimates at coarser scales. The introduced variational formulation as well as the block-matching algorithm are robust to illumination changes. We test our algorithm for both stereo and optical flow public datasets showing competitive results.
Two-Stage Model for Social Relationship Understanding from Videos

Social relationship understanding from videos bears an enormous potential for social media analysis. However, most existing researches only explored spatio-temporal features from videos, ignoring rich contextual information hidden in semantic objects. In this paper, we propose a Two-Stage Model (TSM), first introducing object information for social relationship understanding from videos, to our best knowledge. In the first stage, rich and robust representation for social relationship is obtained by the extraction of both spatio-temporal features and semantic objects information. In the second stage, we utilize a propagated knowledge graph to capture the interaction between semantic objects and video scenes. Specially, an attention mechanism is employed to measure the effectiveness of each semantic object on different scenes. Extensive experiments demonstrate our TSM achieves the state-of-the-art performance on the dataset SRIV.
Special Issue: Immersive Systems

Immersive systems purposefully change or enhance the user’s perception of reality. They include such categories as virtual reality (VR), augmented reality (AR), mixed reality (VR/AR), gamified appl...
Deep Learning Based Object Recognition Using Physically-Realistic Synthetic Depth Scenes

Recognizing objects and estimating their poses have a wide range of application in robotics. For instance, to grasp objects, robots need the position and orientation of objects in 3D. The task becomes challenging in a cluttered environment with different types of objects. A popular approach to tackle this problem is to utilize a deep neural network for object recognition. However, deep learning-based object detection in cluttered environments requires a substantial amount of data. Collection of these data requires time and extensive human labor for manual labeling. In this study, our objective was the development and validation of a deep object recognition framework using a synthetic depth image dataset. We synthetically generated a depth image dataset of 22 objects randomly placed in a 0.5 m × 0.5 m × 0.1 m box, and automatically labeled all objects with an occlusion rate below 70%. Faster Region Convolutional Neural Network (R-CNN) architecture was adopted for training using a dataset of 800,000 synthetic depth images, and its performance was tested on a real-world depth image dataset consisting of 2000 samples. Deep object recognizer has 40.96% detection accuracy on the real depth images and 93.5% on the synthetic depth images. Training the deep learning model with noise-added synthetic images improves the recognition accuracy for real images to 46.3%. The object detection framework can be trained on synthetically generated depth data, and then employed for object recognition on the real depth data in a cluttered environment. Synthetic depth data-based deep object detection has the potential to substantially decrease the time and human effort required for the extensive data collection and labeling.
Empirical Analysis of Affect of Merged Issues on GitHub

Pull-request based workflows are popular trends of modern software development platform such as GitHub. A pull-request notifies other developers that new changes are proposed, a code review process follows the pull-request that may be merged in the main branch if other developers accept the changes. Many factors influence the acceptance of pull-requests. Since open source software is based on collaboration, it is essential to discover how the affect expressed by developer discussing pull-request issues, namely how they collaborate, influences the acceptance of the pull-request proposed. In this study we analysed the relations with the affect expressed in pull-request issues'comments and whether an issue is merged in the main branch or not. We focused on pull-request issues and we found that issues with higher level anger, sadness, arousal and valence are less likely to be merged while issues with higher level of valence, joy are more likely to be merged. Positive affect indicates a good collaboration environment, and our finding shows that this healthy collaboration is likely to increase the acceptance of pull-requests.
Oboyob: A sequential-semantic Bengali image captioning engine

Understanding the context with generation of textual description from an input image is an active and challenging research topic in computer vision and natural language processing. However, in the case of Bengali language, the problem is still unexplored. In this paper, we address a standard approa ch for Bengali image caption generation though subsampling the machine translated dataset. Later, we use several pre-processing techniques with the state-of-the-art CNN-LSTM architecture-based models. The experiment is conducted on standard Flickr-8K dataset, along with several modifications applied to adapt with the Bengali language. The training caption subsampled dataset is computed for both Bengali and English languages for further experiments with 16 distinct models developed in the entire training process. The trained models for both languages are analyzed with respect to several caption evaluation metrics. Further, we establish a baseline performance in Bengali image captioning defining the limitation of current word embedding approaches compared to internal local embedding.
On-device facial verification using NUF-Net model of deep learning

The major problems for Convolutional Neural Networks (CNNs) on devices with low processing power, such as Raspberry Pi 3, are the processing time and the model storage space. This paper presents the development of the NUF-Net to be an efficient CNNs for on-device processing for face detection and verification systems. The development of the NUF-Net architecture was focused on reducing the number of parameters, the size of the network and the processing time, to ensure suitability on small devices. The NUF-Net demonstrated an accuracy of 99.13% for the LFW dataset with 7.1 million parameters, which is approximately 25% of that of the Inception-ResNet-V1 model. When the NUF-Net with-Residual technique was applied to an Automatic Door (AutoDoor) as a replacement for the Fingerprint Door Lock, the NUF-Net-with-Residual was able to perform detection and confirmation of faces on a Raspberry Pi 3 within 1–1.5 s, providing a recognition accuracy of 91.18% with a sample of 68 people, which is close to the Inception-ResNet-V1, with multiply-accumulation operations equal to 10.12 million FLOPs which is about four times faster than the Inception-ResNet-V1 (46.86 million FLOPs).
On the expressive power of collective attacks

In this paper, we consider SETAFs due to Nielsen and Parsons, an exten-sion of Dung’s abstract argumentation frameworks that allow for collective attacks.We first provide a comprehensive analysis of the expressiveness of SETAFs un-der conflict-free, naive, stable, complete, admissible and preferred semantics. Ouranalysis shows that SETAFs are strictly more expressive than Dung AFs. Towardsa uniform characterization of SETAFs and Dung AFs we provide general resultson expressiveness which take the maximum degree of the collective attacks intoaccount. Our results show that, for eachk>0, SETAFs that allow for collectiveattacks ofk+1 arguments are more expressive than SETAFs that only allow forcollective attacks of at mostkarguments.
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

Much human and computational effort has aimed to improve how deep reinforcement learning (DRL) algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by different families of DRL algorithms. Sources of friction include the onerous computational requirements, and general logistical and architectural complications for running DRL algorithms at scale. We lessen this friction, by (1) training several algorithms at scale and releasing trained models, (2) integrating with a previous DRL model release, and (3) releasing code that makes it easy for anyone to load, visualize, and analyze such models. This paper introduces the Atari Zoo framework, which contains models trained across benchmark Atari games, in an easy-to-use format, as well as code that implements common modes of analysis and connects such models to a popular neural network visualization library. Further, to demonstrate the potential of this dataset and software package, we show initial quantitative and qualitative comparisons between the performance and representations of several DRL algorithms, highlighting interesting and previously unknown distinctions between them.
Early Detection of Injuries in MLB Pitchers from Video

Injuries are a major cost in sports. Teams spend millions of dollars every year on players who are hurt and unable to play, resulting in lost games, decreased fan interest and additional wages for replacement players. Modern convolutional neural networks have been successfully applied to many video recognition tasks. In this paper, we introduce the problem of injury detection/prediction in MLB pitchers and experimentally evaluate the ability of such convolutional models to detect and predict injuries in pitches only from video data. We conduct experiments on a large dataset of TV broadcast MLB videos of 20 different pitchers who were injured during the 2017 season. We experimentally evaluate the model's performance on each individual pitcher, how well it generalizes to new pitchers, how it performs for various injuries, and how early it can predict or detect an injury.
REVnet: Bring Reviewing Into Video Captioning for a Better Description

Recently, the task of automatically generating a textual description of a video is attracting increasing interest. The attention-based encoder-decoder framework has been extensively applied in this domain. However, compared with other captioning tasks, such as image captioning, video captioning is more challenging because semantic information among frames is hard to be extracted. In this paper, we propose a reviewing network (REVnet) to reconstruct the previous hidden state, which is combined with the conventional encoder-decoder framework. REVnet brings backward flow into the caption generation process, which encourages the hidden state embedding more information and enables the semantics of the generated sentence more coherent. Furthermore, REVnet can regularize the attention mechanism within the framework, which encourages the model better utilizing the semantic information extracted from multiple different frames. Our experimental results on benchmark datasets demonstrate that our proposed REVnet has a significant improvement over the baseline method. Furthermore, we use a reinforcement learning method to finetune the model, and get better results than the state-of-the-art methods.
A Two-Layer-Consensus Based Blockchain Architecture for IoT

Due to its immutable and transparent nature, Blockchain (BC) provided a decentralized solution to overcome security and resource challenges of centralized Internet of Things (IoT). However, its integration with IoT is limited by scalability and expensive computation. To this end, we propose a two-layer-consensus based BC architecture optimized for IoT requirements. Low resource devices attached to the server and with users and other nodes form Base-Layer. There is a high-scalable and fully decentralized blockchain performing basic functions maintaining in Base-Layer, myriad blocks are mined and submitted every round, but only one block selected by Top-Layer can be recorded. Top-Layer with another high-security blockchain is formed by special nodes running a Non-byzantine fault tolerance algorithm to determine the accounting rights in random. Analysis and evaluation show that consensus has better fault tolerance and increases scalability.
Fast Approximation of Empirical Entropy via Subsampling

Empirical entropy refers to the information entropy calculated from the empirical distribution of a dataset. It is a widely used aggregation function for knowledge discovery, as well as the foundation of other aggregation functions such as mutual information. However, computing the exact empirical entropy on a large-scale dataset can be expensive. Using a random subsample, we can compute an approximation of the empirical entropy efficiently. We derive probabilistic error bounds for the approximation, where the error bounds reduce in a near square root rate with respect to the subsample size. We further study two applications which can benefit from the error-bounded approximation: feature ranking and filtering based on mutual information. We develop algorithms to progressively subsample the dataset and return correct answers with high probability. The sample complexity of the algorithms is independent of data size. The empirical evaluation of our algorithms on large-scale real-world datasets demonstrates up to three orders of magnitude speedup over exact methods with \errrate\ error.
Min-Max Statistical Alignment for Transfer Learning

A profound idea in learning invariant features for transfer learning is to align statistical properties of the domains. In practice, this is achieved by minimizing the disparity between the domains, usually measured in terms of their statistical properties. We question the capability of this school of thought and propose to minimize the maximum disparity between domains. Furthermore, we develop an end-to-end learning scheme that enables us to benefit from the proposed min-max strategy in training deep models. We show that the min-max solution can outperform the existing statistical alignment solutions, and can compete with state-of-the-art solutions on two challenging learning tasks, namely, Unsupervised Domain Adaptation (UDA) and Zero-Shot Learning (ZSL).