Reviewerly/exHarmony huggingface dataset:
Extracting salient features from convolutional discriminative filters

Convolutional neural networks (CNN) have been widely used in various tasks, largely due to their ability to efficiently extract n-gram features for text analysis and document representation. In this paper, we intend to insight the CNN model regarding its capability on text analysis. Vanilla CNNs do have weaknesses when it comes to the representation and feature extraction. Duplicate filters are inevitable with vanilla CNNs, which reduces the discriminative power of the representations. In addition, the current pooling operations either limit the CNN to the local optimum (i.e., max pooling) or they do not consider the importance of all features (i.e., mean pooling). In this paper, we propose two modules for vanilla CNNs to overcome these shortcomings. The first equips the CNN with discriminative filters (distinct filters with maximised divergence) and the second provides the ability to comprehensively extract all salient features. Specifically, our model increases the discriminative power of the model by maximizing the distance between different filters, and a novel global pooling mechanism for feature extraction. Validation tests against state-of-the-art baselines on five benchmark classification datasets achieve the competitive performance of our proposed model. Furthermore, visualization on upgrade filters and pooling features verify our hypothesis that the proposed model can receive discriminative filters and salient features.
Generation of Synthetic Images of Randomly Stacked Object Scenes for Network Training Applications

Image recognition algorithms based on deep learning have been widely developed in recent years owing to their capability of automatically capturing recognition features from image datasets and constantly improving the accuracy and efficiency of the image recognition process. However, the task of training deep learning networks is time-consuming and expensive because large training datasets are generally required, and extensive manpower is needed to annotate each of the images in the training dataset to support the supervised learning process. This task is particularly arduous when the image scenes involve randomly stacked objects. The present work addresses this issue by developing a synthetic training dataset generation method based on OpenGL and the Bullet physics engine which can automatically generate annotated synthetic datasets by simulating the freefall of a collection of objects under the force of gravity. Rigorous statistical comparison of a real image dataset of staked scenes with a synthetic image dataset generated by the proposed approach demonstrates that the two datasets exhibit no significant differences. Moreover, the object detection performances obtained by three popular network architectures trained using the synthetic dataset generated by the proposed approach are demonstrated to be much better than the results of training conducted using a synthetic dataset generated by a conventional cut and paste approach, and these performances are also competitive with the results of training conducted using a dataset composed of real images.
Temporal Causal Modelling on Large Volume Enterprise Data

Structural Causal Modelling (SCM) with its intervention analysis is one of the promising modelling approach that assists in data driven decision making. SCM not only overcomes the black box modelling associated with most of the classification algorithms but also gives enterprises an opportunity to perform intervention analysis without having to perform randomized controlled experiments. But the large volume of enterprises' data pose challenges in learning the causal structure as existing algorithms are not suitable to learn from data present in Distributed File System (DFS). Hence algorithm presented in this paper, proposes a novel variation to PC-Stable algorithm to efficiently learn the causal structure from data present in DFS - thus enabling temporal causal modelling on large volume time-series data. The proposed learning algorithm is used to determine the causal story associated with churn in telecommunication industry and flight delay in airline industry. Our model identifies and quantifies the respective causal factors for unfavourable events churn and flight delay.
Decentralized Network Governance: Blockchain Technology and the Future of Regulation

The advancements in the digital domain, for example Blockchain technology, Big Data and machine learning, are increasingly shaping the lives of individuals, groups, organisation, and societies. These developments call for effective governance to protect the basic interests and needs of individuals and groups. Simultaneously, the very nature of governance is changing. Increasingly, policy-making moves away from top-down governance by the state towards more horizontal modes of governance. This paper reviews the literature on governance theory in order to conceptualise governance as a mode of decentralized networked regulation. The paper will argue that dominant modes of governance are inadequate to describe governance in the digital domain and are poorly equipped to conceptualize novel forms of governance such as decentralized autonomous organizations (DAOs). Therefore, a novel mode of governance is proposed based on the regulation of new power relationships between the state and actors in the digital domain. This model further explores the role that Blockchain can play in what we term decentralized network governance.
Chapter 6. Complimenting behavior in Spanish political discourse on Twitter

This study explores complimenting behavior in interactions between Spanish politicians and citizens on Twitter. It adopts a corpus-based methodology, using both quantitative and qualitative analysis to identify the topics which give rise to compliments being paid, the linguistic strategies adopted by users in their complimentary replies to politicians, and the functions of such compliments. The results show that users frequently pay compliments on the basis of performance and ability, and combine explicit and implicit compliments with congratulatory messages, requests, and advice. Finally, in terms of social functions, compliments in this context appear to enhance both the affiliative and the autonomy face of the interactants.
A Detect method for deepfake video based on full face recognition

In recent years, with the continuous upgrading of computer hardware and the continuous development of deep learning technology, new multimedia tampering tools can make it easier for people to tamper with faces in videos. Tampered videos produced by these new tools may hardly be detected by human, so we need effective method to detect these face-tampered videos. Current popular video face tampering technologies mainly include Deepfake technology based on self-encoder and Face2face technology based on computer graphics. In this paper, we propose a new method for tamper video detection based on the full faces. Facenet algorithm is used here to compare the similarity between real and fake video faces. Finally, in the experimental part, the results showed a significant effect.
Deep Reinforcement Learning based Node Pairing Scheme in Edge-Chain for IoT Applications

Nowadays, the Internet of Things (IoT) is playing an important role in our life. This inevitably generates mass data and requires a more secure transmission. As blockchain technology can build trust in a distributed environment and ensure the data traceability and tamper resistance, it is a promising way to support IoT data transmission and sharing. In this paper, edge computing is considered to provide adequate resources for end users to offload computing tasks in the blockchain enabled IoT system, and the node pairing problem between end users and edge computing servers is researched with the consideration of wireless channel quality and the service quality. From the perspective of the end users, the objective optimization is designed to maximize the profits and minimize the payments for completing the tasks and ensuring the resource limits of the edge servers at the same time. The deep reinforcement learning (DRL) method is utilized to train an intelligent strategy, and the policy gradient based node pairing (PG-NP) algorithm is proposed. Through a deep neural network, the well-trained policy matched the system states to the optimal actions. The REINFORCE algorithm with baseline is applied to train the policy network. According to the training results, as the comparison strategies are max-credit, max-SINR, random and max-resource, the PG-NP algorithm performs about 57% better than the second-best method. And testing results show that PGNP also has a good generalization ability which is negatively correlated with the training performance to a certain extend.
Accelerating DNN Training Through Selective Localized Learning

Training Deep Neural Networks (DNNs) places immense compute requirements on the underlying hardware platforms, expending large amounts of time and energy. We propose LoCal+SGD, a new algorithmic approach to accelerate DNN training by selectively combining localized or Hebbian learning within a Stochastic Gradient Descent (SGD) based training framework. Back-propagation is a computationally expensive process that requires 2 Generalized Matrix Multiply (GEMM) operations to compute the error and weight gradients for each layer. We alleviate this by selectively updating some layers' weights using localized learning rules that require only 1 GEMM operation per layer. Further, since localized weight updates are performed during the forward pass itself, the layer activations for such layers do not need to be stored until the backward pass, resulting in a reduced memory footprint. Localized updates can substantially boost training speed, but need to be used judiciously in order to preserve accuracy and convergence. We address this challenge through a Learning Mode Selection Algorithm, which gradually selects and moves layers to localized learning as training progresses. Specifically, for each epoch, the algorithm identifies a Localized→SGD transition layer that delineates the network into two regions. Layers before the transition layer use localized updates, while the transition layer and later layers use gradient-based updates. We propose both static and dynamic approaches to the design of the learning mode selection algorithm. The static algorithm utilizes a pre-defined scheduler function to identify the position of the transition layer, while the dynamic algorithm analyzes the dynamics of the weight updates made to the transition layer to determine how the boundary between SGD and localized updates is shifted in future epochs. We also propose a low-cost weak supervision mechanism that controls the learning rate of localized updates based on the overall training loss. We applied LoCal+SGD to 8 image recognition CNNs (including ResNet50 and MobileNetV2) across 3 datasets (Cifar10, Cifar100, and ImageNet). Our measurements on an Nvidia GTX 1080Ti GPU demonstrate upto 1.5× improvement in end-to-end training time with ~0.5% loss in Top-1 classification accuracy.
GSA-GAN: Global Spatial Attention Generative Adversarial Networks

This paper proposes a solution to translating the visible images into infrared images, which is challenging in computer vision. Our solution belongs to unsupervised learning, which has recently become popular in image-to-image translation. However, existing methods do not produce satisfactory results because (1) most existing methods are mainly used in entertainment scenarios with single scenes and low complexity. The problem solved by this article is more diverse and more complicated. (2) The infrared response of objects depends not only on itself but also on the current environment, and existing methods cannot correlate long-range dependent objects. In this paper, We propose Global Spatial Attention (GSA), which enhances dependence between long-range objects and improves the synthesized image quality. Compared with other methods, GSA can save more space and time. Moreover, we introduce the idea of subspace learning into the neural network to make training more stable. Our method takes unpaired visible images and infrared images for training, which are easy to collect. Experimental results show that our method can generate high-quality infrared images from visible images and outperforms state-of-the-art methods.
Machete

We present Machete, a straightforward segmenter one can use to isolate custom gestures in continuous input. Machete uses traditional continuous dynamic programming with a novel dissimilarity measure to align incoming data with gesture class templates in real time. Advantages of Machete over alternative techniques is that our segmenter is computationally efficient, accurate, device-agnostic, and works with a single training sample. We demonstrate Machete’s effectiveness through an extensive evaluation using four new high-activity datasets that combine puppeteering, direct manipulation, and gestures. We find that Machete outperforms three alternative techniques in segmentation accuracy and latency, making Machete the most performant segmenter. We further show that when combined with a custom gesture recognizer, Machete is the only option that achieves both high recognition accuracy and low latency in a video game application.
Datasets for Machine Reading Comprehension: A Literature Review

Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence. Datasets play an important role while describing or building an algorithm for machine reading comprehension. The type of answers we required from developed algorithm depends on datasets. The datasets are classified into two types, namely datasets with extractive answers and datasets with descriptive answers. This article summarize both datasets with an example of each type to get a better insight into datasets in machine reading comprehension and which datasets to use depending on the requirements.
Mediated Trust – A Theoretical Framework to Address the Trustworthiness of Technological Trust Mediators

This paper introduces the new theoretical concept of technology mediated trust, or in short, mediated trust to capture and analyze the role of complex techno-social assemblages in trust production and distrust management. Societies are increasingly reliant on digital technologies to collect data; sort, rank and filter users and information in order to ease communication, commerce, or collaboration. This paper considers the impact of these digital practices on the interpersonal and institutional logics of trust production. The first part of the paper argues that globalization and digitalization unleashed a trust crisis, as the traditional institutional and interpersonal logics are not attuned to produce trust and manage distrust at the scale and speed of global networks of communication, media, finance, etc. In the second part, the paper describes how digital intermediation changes the traditional logics of interpersonal trust formation, and creates new trust mediating services. It also considers how old institutional forms of trust production are affected by the fact that these institutions employ digital technologies in their processes, and describes new, technological forms of institutional trust production. Finally, the paper asks the question: why should we trust these technological trust mediators? The analysis concludes at best, it is impossible to establish the trustworthiness trust mediators, at worst, we have no reason to trust them.
A Lightweight Transformer with Convolutional Attention

Neural machine translation (NMT) goes through rapid development because of the application of various deep learning techs. Especially, how to construct a more effective structure of NMT attracts more and more attention. Transformer is a state-of-the-art architecture in NMT. It replies on the self-attention mechanism exactly instead of recurrent neural networks (RNN). The Multi-head attention is a crucial part that implements the self-attention mechanism, and it also dramatically affects the scale of the model. In this paper, we present a new Multi-head attention by combining convolution operation. In comparison with the base Transformer, our approach can reduce the number of parameters effectively. And we perform a reasoned experiment. The result shows that the performance of the new model is similar to the base model.
Synthesis-based resolution of feature interactions in cyber-physical systems

The feature interaction problem arises when two or more independent features interact with each other in an undesirable manner. Feature interactions remain a challenging and important problem in emerging domains of cyber-physical systems (CPS), such as intelligent vehicles, unmanned aerial vehicles (UAVs) and the Internet of Things (IoT), where the outcome of an unexpected interaction may result in a safety failure. Existing approaches to resolving feature interactions rely on priority lists or fixed strategies, but may not be effective in scenarios where none of the competing feature actions are satisfactory with respect to system requirements. This paper proposes a novel synthesis-based approach to resolution, where a conflict among features is resolved by synthesizing an action that best satisfies the specification of desirable system behaviors in the given environmental context. Unlike existing resolution methods, our approach is capable of producing a desirable system outcome even when none of the conflicting actions are satisfactory. The effectiveness of the proposed approach is demonstrated using a case study involving interactions among safety-critical features in an autonomous drone.
Video super resolution using convolutional neural network and image fusion techniques

Recently, huge progress has been achieved in the field of single image super resolution which augments the resolution of images. The idea behind super resolution is to convert low-resolution images into high-resolution images. SRCNN (Single Resolution Convolutional Neural Network) was a huge improvement over the existing methods of single-image super resolution. However, video super-resolution, despite being an active field of research, is yet to benefit from deep learning. Using still images and videos downloaded from various sources, we explore the possibility of using SRCNN along with image fusion techniques (minima, maxima, average, PCA, DWT) to improve over existing video super resolution methods. Video Super-Resolution has inherent difficulties such as unexpected motion, blur and noise. We propose Video Super Resolution – Image Fusion (VSR-IF) architecture which utilizes information from multiple frames to produce a single high- resolution frame for a video. We use SRCNN as a reference model to obtain high resolution adjacent frames and use a concatenation layer to group those frames into a single frame. Since, our method is data-driven and requires only minimal initial training, it is faster than other video super resolution methods. After testing our program, we find that our technique shows a significant improvement over SCRNN and other single image and frame super resolution techniques.
A Compressed domain Based Robust and Imperceptible Digital Video Watermarking Scheme

The paper described a new MPEG-2 compressed domain based imperceptible, blind, robust, and secure digital video watermarking method. The scheme exploited the intra coding part of MPEG compression algorithm for inserting the watermark image as copyright information. Three cryptographic keys are used to extract the watermark. This process further enhances the security of overall watermarking algorithm. Therefore, proper extraction would never possible without negotiating the unique keys. The strength of the proposed algorithm evaluated the robustness by applying wide variety of frame-based attacks to measure grading of resemblance and divergence between the extracted and original watermark respectively. The dominance of the projected technique is to obtain the admirable result for robustness and perceptibility both. The perceptibility is maintained as the scheme doesn't require to change the motion vectors resultant from DPCM process while encoding the video through MPEG-2 compression standard.
Breast Regions Segmentation Based on U-net++ from DCE-MRI Image Sequences

Abstract Background analysis of breast cancer can depict the progress and states of the tumour, which is based on the whole breast segmentation from MRI images. The focus of this paper is to construct a pipeline for breast region segmentation for the possibility of breast cancer automatic diagnosis by using MRI image serials. Studies of breast region segmentation based on traditional and deep learning methods have undergone several years, but most of them have not achieved a satisfactory consequence for the following background analysis. In this paper, we proposed a novel pipeline for whole breast region segmentation method based on U-net++, that can achieve a better result compared with the traditional U-net model which is the most common used medical image analysis model and achieve a better IoU than CNN models. We have evaluated the U-net++ model with tradition U-net, our experiments demonstrate that the U-net++ with deep supervision achieves a higher IoU over U-net model.
Nonparametric Maximum Likelihood Methods for Binary Response Models With Random Coefficients

The venerable method of maximum likelihood has found numerous recent applications in nonparametric estimation of regression and shape constrained densities. For mixture models the nonparametric maximum likelihood estimator (NPMLE) of Kiefer and Wolfowitz plays a central role in recent developments of empirical Bayes methods. The NPMLE has also been proposed by Cosslett as an estimation method for single index linear models for binary response with random coefficients. However, computational difficulties have hindered its application. Combining recent developments in computational geometry and convex optimization, we develop a new approach to computation for such models that dramatically increases their computational tractability. Consistency of the method is established for an expanded profile likelihood formulation. The methods are evaluated in simulation experiments, compared to the deconvolution methods of Gautier and Kitamura and illustrated in an application to modal choice for journey-to-work data in the Washington DC area. Supplementary materials for this article are available online.
Multi-Cyclic Requirement Engineering for Educational and Industrial Models in Software Development

In software development, requirement elicitation (RE) is quite challenging in software industry to meet the customer requirements and for successful project delivery. In educational projects, requirements are taken both in natural and formal languages. In software industry, commercially viable methods are adopted as per demands of variety of novel software architectures and practices. This paper covers perspectives of teachers, students and industrial requirement engineers in RE as per their level of understanding and practices. We have proposed bi-signaling requirement engineering model (BSRE) to engineer the requirement process. Secondly, Requirement Engineering Design Model is proposed based on the Multi-cyclic approach to overcome the gap between client and related stakeholders. We address the challenges of existing methodologies for RE elicitation, by introducing two new methods. First, the bi-signaling RE that connects requirements engineer to the stakeholders through the management of logged issues, while the requirement engineering design model iteratively elicit requirements from multiple stakeholders in multiple cycles.