Reviewerly/exHarmony huggingface dataset:
SAAN: Semantic Attention Adaptation Network for Face Super-Resolution

Face super-resolution (Face SR) is a sub-domain of SR that reconstructs high-resolution face images from low-resolution ones. The prior knowledge of face is widely used for recovering more realistic facial details, which will increase the complexity of the network and introduce additional knowledge extraction procession both in the training and evaluating stage. To address the above issues, we propose to combine face semantic prior extraction and face SR with the attention adaptation model and design a Semantic Attention Adaptation Network (SAAN) for face SR. Specifically, we train the face semantic parsing network and face SR network jointly, by adopting the semantic attention adaptation (SAA) model to transfer the ability of extracting face prior knowledge to the SR network. Then our SR network can work independently in the testing stage without using the prior knowledge extraction network. To generate realistic face images, we also utilize GAN loss to enrich the texture with more details (i.e. SAAN-G). Extensive experiments on the benchmark dataset illustrate that our SAAN and SAAN-G improve the state-of-the-art both on quality and efficiency.
A Regional Regression Network for Monocular Object Distance Estimation

Monocular pipelines are convenient and cheap solutions for object distance estimation in 3D vision. Current methods for monocular object distance estimation either perform inaccurately or require heavy work. In this paper, we propose a network with R-CNN based structure to implement object detection and distance estimation simultaneously. To improve the estimation results, we add a shallow network to process camera extrinsic parameters and optimize feature processing structure. We train and validate our network on KITTI object dataset, and compare with other methods to show that our method is accurate and easy to train. To prove the generality of our method under other scenarios, we construct a dataset of surveillance scenes, and conduct similar experiments on this dataset.
Exploration Based Language Learning for Text-Based Games

This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. These games are of interest as they can be seen as a testbed for language understanding, problem-solving, and language generation by artificial agents. Moreover, they provide a learning setting in which these skills can be acquired through interactions with an environment rather than using fixed corpora. One aspect that makes these games particularly challenging for learning agents is the combinatorially large action space. Existing methods for solving text-based games are limited to games that are either very simple or have an action space restricted to a predetermined set of admissible actions. In this work, we propose to use the exploration approach of Go-Explore for solving text-based games. More specifically, in an initial exploration phase, we first extract trajectories with high rewards, after which we train a policy to solve the game by imitating these trajectories. Our experiments show that this approach outperforms existing solutions in solving text-based games, and it is more sample efficient in terms of the number of interactions with the environment. Moreover, we show that the learned policy can generalize better than existing solutions to unseen games without using any restriction on the action space.
Organizational Architecture, Resilience, and Cyberattacks

This article develops a unique model of organizational resilience architecture with an emphasis on the ways in which organizations respond to cyberattacks. The model elucidates the dynamics and approaches through which organizations mobilize and utilize expertise and resources to combat the effects of cyberattacks on normal business operations. Drawing on recent cases of cyberattacks against organizations, the article identifies a host of strategic and tactical responses victims used to aid recovery and return to daily activities. The responses are grouped into three stages to demonstrate the steps that organizations can take to enhance their resilience: Stage 1 focuses on proactive environmental scanning and locating potential threats and attacks, Stage 2 emphasizes neutralizing threats and attacks, and Stage 3 focuses on redesigning, upgrading, and updating human, technological, and financial resources. On this basis, the article sheds light on levels of organizational resilience and strategies for organizational design in withstanding cyberattacks and security breaches. The theoretical and practical implications of these findings are discussed.
Multi-Feature Encoder for Radar-Based Gesture Recognition

In this paper, a multi-feature encoder for gesture recognition based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system is proposed to extract the gesture characteristics, i.e., range, Doppler, azimuth and elevation, from the low-level raw data. The radar system updates the hand information for every measurement-cycle on all the scattering centers in its field of view, and our proposed encoder is devised to only focus on those essential scattering centers. After observing the hand over several measurement-cycles, we encode the gesture characteristics sequentially into a 2-D feature matrix, which is successively fed into a shallow convolutional neural network (CNN) for classification. For the purpose of distinguishing relevant gestures, the proposed multi-feature encoder is able to efficiently extract adequate information from a multi-dimensional feature space. Thus, the proposed approach is practical for industrial applications where the available dataset is mostly small-scale. The experimental results show that the proposed multi-feature encoder could guarantee a promising performance for a gesture dataset with 12 gestures.
Types of anti-phishing solutions for phishing attack

Abstract Nowadays, many people use Internet to do online activity. This scenario exposed them to danger in Internet which is phishing attack. In order to solve phishing attack, the anti-phishing solutions are needed. Based on our review, there are still lacks of articles that review on the types of anti-phishing solutions in detail. In this paper, a general idea of phishing attack and anti-phishing solutions is presented. The phishing attack can be classified into two categories which are social engineering and malware-based phishing attack. The anti-phishing solutions can be differentiating into two types which are phishing prevention and phishing detection. Compared to phishing prevention, the phishing detection is more important to solve the phishing attack. In phishing detection, there are two categories which are user awareness and software detection. The software detection has two methods which are traditional and automatic. There are two types for automatic method of software detection which are public phishing detection toolbars and academic phishing detection / classification schemes. Based on the comparison of all types of phishing detection, the academic phishing detection / classification schemes are more useful for phishing detection. For future work, we can do more research on the academic phishing detection / classification schemes that utilize deep learning to see its potential of accuracy to detect phishing websites.
MITIGATING DESIGN FIXATION WITH EVOLVING EXTENDED REALITY TECHNOLOGY: AN EMERGING OPPORTUNITY

Abstract Design fixation refers to the designers’ inability to avoid becoming stuck with preexisting ideas in order to generate new ones. With the recent fast advancements and developments, XR has emerged as a powerful promising technology that can shed new light on this issue. Consequently, this paper aims at: (1) investigating the underlying mechanisms of design fixation as reported in literature; (2) exploring the state-of-art in the use of XR technology in design; and (3) identifying ways to mitigate design fixation by employing XR technology.
Does Bitcoin Hedge Commodity Uncertainty?

This paper examines the connectedness between Bitcoin and commodity volatilities, including oil, wheat, and corn, during the period Oct. 2013–Jun. 2018, using time- and frequency-domain frameworks. The time-domain framework’s results show that the connectedness is 23.49%, indicating a low level of connection between Bitcoin and the commodity volatilities. Bitcoin contributes only 2.55% to the connectedness, while the wheat volatility index accounts for 12.51% of the total connectedness. The frequency connectedness shows that Bitcoin’s contribution to the total connectedness increases from high-frequency to low-frequency bands, and the total connectedness reaches up to 22.47%. It also indicates that Bitcoin is the spillover transmitter to the wheat volatility, while being the spillover receiver from the oil and corn volatilities. The findings suggest that Bitcoin could be a hedger for commodity volatilities.
Equilibria for games with combined qualitative and quantitative objectives

The overall aim of our research is to develop techniques to reason about the equilibrium properties of multi-agent systems. We model multi-agent systems as concurrent games, in which each player is a process that is assumed to act independently and strategically in pursuit of personal preferences. In this article, we study these games in the context of finite-memory strategies, and we assume players’ preferences are defined by a qualitative and a quantitative objective, which are related by a lexicographic order: a player first prefers to satisfy its qualitative objective (given as a formula of linear temporal logic) and then prefers to minimise costs (given by a mean-payoff function). Our main result is that deciding the existence of a strict $$\epsilon $$ Nash equilibrium in such games is 2ExpTime-complete (and hence decidable), even if players’ deviations are implemented as infinite-memory strategies.
Learning User Representations for Open Vocabulary Image Hashtag Prediction

In this paper, we introduce an open vocabulary model for image hashtag prediction - the task of mapping an image to its accompanying hashtags. Recent work shows that to build an accurate hashtag prediction model, it is necessary to model the user because of the self-expression problem, in which similar image content may be labeled with different tags. To take into account the user behaviour, we propose a new model that extracts a representation of a user based on his/her image history. Our model allows to improve a user representation with new images or add a new user without retraining the model. Because new hashtags appear all the time on social networks, we design an open vocabulary model which can deal with new hashtags without retraining the model. Our model learns a cross-modal embedding between user conditional visual representations and hashtag word representations. Experiments on a subset of the YFCC100M dataset demonstrate the efficacy of our user representation in user conditional hashtag prediction and user retrieval. We further validate the open vocabulary prediction ability of our model.
Can Deep Learning Recognize Subtle Human Activities?

Deep Learning has driven recent and exciting progress in computer vision, instilling the belief that these algorithms could solve any visual task. Yet, datasets commonly used to train and test computer vision algorithms have pervasive confounding factors. Such biases make it difficult to truly estimate the performance of those algorithms and how well computer vision models can extrapolate outside the distribution in which they were trained. In this work, we propose a new action classification challenge that is performed well by humans, but poorly by state-of-the-art Deep Learning models. As a proof-of-principle, we consider three exemplary tasks: drinking, reading, and sitting. The best accuracies reached using state-of-the-art computer vision models were 61.7%, 62.8%, and 76.8%, respectively, while human participants scored above 90% accuracy on the three tasks. We propose a rigorous method to reduce confounds when creating datasets, and when comparing human versus computer vision performance. Source code and datasets are publicly available.
A Lightweight High-Resolution Representation Backbone For Real-Time Keypoint-Based Object Detection

The keypoint based detectors are a relatively new object detection mechanism, avoiding the complicated computation related to anchor box and achieving state-of-the-art accuracy. However, inference speed is a major drawback of these detectors because of the heavy backbone network. In this paper, we design a novel lightweight backbone named DNet for keypoint-based detection and propose a real-time object detection network. In the backbone part, DNet is able to maintain high-resolution feature maps throughout the process and gradually extract and integrate features across scales. In the detection part, we detect a center keypoint and a pair of corners to predict the bounding boxes, and completely avoid the complicated computation related to anchor boxes. Compared with state-of-the-art real-time detectors, our network achieves superior performance with 30.0% AP on COCO benchmark at 21. 5ms. In addition, the experimental results show that our network is capable of running real-time on embedded devices.
An Analytical Framework for Trusted Machine Learning and Computer Vision Running with Blockchain

Machine learning algorithms often use data from databases that are mutable; therefore, the data and the results of machine learning cannot be fully trusted. Also, the learning process is often difficult to automate. A unified analytical framework for trusted machine learning has been presented in the literature to address both issues. It is proposed building a trusted machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts on blockchain are used to automate the machine learning process. However, in such a blockchain framework, data efficiency is a big concern, because it is very expensive to store a large amount of data on blockchain. On the other hand, machine learning-based computer vision systems often rely on a lot of data. Therefore, to fully leverage a blockchain-based machine learning framework for computer vision systems, data efficiency issues must be addressed. This paper investigates how to enhance data efficiency in such a framework to bring computer vision systems to the edge. It presents a three-step approach. First, a lightweight machine learning model is trained on the server layer. Second, the trained model is saved in a special binary data format for data efficiency. Finally, the streaming layer takes these binary data as input and scores incoming new data in an online fashion. Real-time semantic segmentation for autonomous driving is used as an example to demonstrate how this approach works. This paper makes the following contributions. First, it improves the analytical framework for trusted computer vision systems based on blockchain. Second, the real-time semantic segmentation example shows how data-efficient learning for computer vision can be performed on the edge.
Region of Interest-based Tamper Detection and Lossless Recovery Watermarking Scheme on MRI and X-ray Medical Images

Abstract The advancement of the technology has introduced many applications that will ease human daily process and improve the way people connect to each other. One of the applications that has been introduced to the user and vastly improve our life is the healthcare system. Health care system allows the medical practitioner to aware of the patient’s previous health condition and documents their current. It also allows the medical practitioner to communicate with each other or get some advice from their peers and colleague even if they are far from each other physically. To ensure the authenticity of an image when it is transfer through the health care system, watermarking has been implemented. Through the process of watermarking the images, the authenticity of the images can be ensured. In this study, a ROI-based tamper detection and recovery watermarking scheme has been introduced. The proposed scheme has been tested out on the MRI and XA images. The PSNR value for MRI is 49.09 for the sequential mode and 49.07 in parallel mode. On the other hand, PSNR for XA images is 52.68 in the sequential mode and 52.68 in the parallel mode.
Plastic Surgery: An Obstacle for Deep Face Recognition?

The impacts of plastic surgery on face recognition systems have been investigated in the past decade by many researchers. Diverse well-known face recognition approaches, e.g. based on PCA or LBP, have been bench-marked mostly on the web-collected IIITD plastic surgery face database. Generally, significant performance drops were reported when comparing facial images taken before and after plastic surgeries. On the one side, some researchers reported problems with said plastic surgery database, i.e. the presence of low quality images. On the other side, the applied methods no longer reflect the state-of-the-art in face recognition. This calls for evaluating the impact of plastic surgery on state-of-the-art deep face recognition systems anew considering high quality imagery of most relevant plastic surgeries.This work introduces the new Hochschule Darmstadt (HDA) plastic surgery database of facial images taken before and after surgery. This database vastly complies with the quality requirements defined by the International Civil Aviation Organization (ICAO) for electronic travel documents and comprises face images of the five most frequently applied facial plastic surgeries. The HDA plastic surgery database, the IIITD plastic surgery database, and a non-surgery database, i.e. ICAO-compliant subsets of the FRGCv2 and FERET datasets, are used for comparative verification and identification evaluations which are conducted using the commercial Cognitec FaceVACS system and the open-source ArcFace system. The obtained results suggest that the impact of plastic surgery on deep face recognition systems is less significant than that observed for previously benchmarked methods.
Phishing Detection Using Significant Feature Selection

Growth of cyber attacks is rapidly increasing in the entire world. To provide prevention from these attacks is a great challenge for the experts. Intruders are keep on adapting new methods and techniques to carry out their malicious goals. Phishing plays a dominant role in the field of web attacks and it has been used as a weapon by the attackers. In this paper we have given two algorithmic approaches to the problem of Phishing identification with reduced number of attributes. It makes this approach simple yet efficient. The first algorithm assigns weight to all attributes with respect to uniform resource locators. We have employed various analysis mechanism to identify significant role of selected attributes for the purpose of Phishing identification. The second approach takes former's output as input and classifies the uniform resource locators labeling as phishing or non phishing. The experimental work verifies that the approach for phishing detection proposed in this paper can attain a high accuracy in comparison to existing algorithms.
Reinforcing Cloud Environments via Index Policy for Bursty Workloads

In recent years, the amounts of network traffic targeted towards cloud data centers have fluctuated based on user requests. This traffic is bursty and requires a high degree of attention. Due to the variable nature of this traffic, some requests need to be re-allocated on-the-fly. Such circumstances result in performance degradations due to resource management. As appropriate solutions can be proposed only based on understanding the workload and the environment, Reinforcement Learning (RL) is a strategy that is predominantly used. Further, it has been shown that the Poisson arrival rates do not capture real-world burstiness. Thus, we mainly have a two-fold problem to address: (i) the traffic requires a new modelling approach that can characterize the burstiness, and (ii) balancing the load that can maximize the reward to the provider in such circumstances. In this paper, we propose a novel, yet simple traffic modelling that enables burst detection based on an index policy. We show that the throughput constraints play a crucial role in scheduling and our proposed RL technique produces reliable results in such a scenario. Our RL algorithm decides what instance of the request traffic needs to be processed so that the cloud provider can maximize its profit and the decisions made in hindsight are non-biased. We compare the proposed policy with two state-of-the-art approaches and draw key inferences as to why an index policy performs better in scenarios that demand RL. We observe over five times shorter average wait times while bursty workload crosses a saturation limit of 150% compared to conventional policies.
COMO: Efficient Deep Neural Networks Expansion With COnvolutional MaxOut

In this paper, we extend the classic MaxOut strategy, originally designed for Multiple Layer Preceptors (MLPs), into <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CO</u> nvolutional <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ax <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> ut (COMO) — a new strategy making deep convolutional neural networks wider with parameter efficiency. Compared to the existing solutions, such as ResNeXt for ResNet or Inception for VGG-alikes, COMO works well on both linear architectures and the ones with skipped connections and residual blocks. More specifically, COMO adopts a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">split-transform-merge</i> paradigm that extends the layers with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial resolution reduction</i> into multiple parallel splits. For the layer with COMO, each split passes the input feature maps through a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4D convolution operator</i> with independent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">batch normalization operators</i> for transformation, then merge into the aggregated output of the original sizes through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max-pooling</i> . Such a strategy is expected to tackle the potential classification accuracy degradation due to the spatial resolution reduction, by incorporating the multiple splits and max-pooling-based feature selection. Our experiment using a wide range of deep architectures shows that COMO can significantly improve the classification accuracy of ResNet/VGG-alike networks based on a large number of benchmark datasets. COMO further outperforms the existing solutions, e.g., Inceptions, ResNeXts, SE-ResNet, and Xception, that make networks wider, and it dominates in the comparison of accuracy versus parameter sizes.
New entanglement-assisted quantum MDS codes with length $$n=\frac{q^2+1}{5}$$

The entanglement-assisted stabilizer formalism can transform arbitrary classical linear codes into entanglement-assisted quantum error correcting codes. In this work, we construct some new entanglement-assisted quantum maximum distance separable codes with length $$n=\frac{q^2+1}{5}$$ from cyclic codes. Compared with all the previously known parameters with the same length, all of them have flexible parameters and larger minimum distance.