Reviewerly/exHarmony huggingface dataset:
Feature Re-Learning with Data Augmentation for Video Relevance Prediction

Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. Thanks to the increasing availability of pre-trained image and video convolutional neural network models, deep visual features are widely used for video content representation. However, as how two videos are relevant is task-dependent, such off-the-shelf features are not always optimal for all tasks. Moreover, due to varied concerns including copyright, privacy and security, one might have access to only pre-computed video features rather than original videos. We propose in this paper feature re-learning for improving video relevance prediction, with no need of revisiting the original video content. In particular, re-learning is realized by projecting a given deep feature into a new space by an affine transformation. We optimize the re-learning process by a novel negative-enhanced triplet ranking loss. In order to generate more training data, we propose a new data augmentation strategy which works directly on frame-level and video-level features. Extensive experiments in the context of the Hulu Content-based Video Relevance Prediction Challenge 2018 justify the effectiveness of the proposed method and its state-of-the-art performance for content-based video relevance prediction.
A Hybrid Data Hiding Method for Strict AMBTC Format Images with High-Fidelity

With the rapid development of smartphones, cloud storage, and wireless communications, protecting the security of compressed images through data transmission on the Internet has become a critical contemporary issue. A series of data hiding methods for AMBTC compressed images has been proposed to solve this problem. However, most of these methods either change the file size of the final compressed code or exchange the order of two quantization values in some blocks. To reverse this situation, this paper proposes a data hiding method for strict AMBTC format images using a hybrid strategy: replacement, matrix encoding, and symmetric quantization value embedding for three block types i.e., smooth blocks, less complex blocks and highly complex blocks. According to the hybrid strategy, an efficient data hiding order is designed to achieve higher-fidelity. Experimental results show that our proposed method provides an excellent balance between image quality and hiding capacity and has no error blocks in the final stego-compressed code.
Imagining Mundane Futures

When people move country, they experience new social, infrastructural, and ambient contingencies, which enables them to imagine otherwise unknowable possible futures ‘at home’. In this article, we mobilise a design anthropological approach to show how collaboration with temporary migrants can generate understandings that generate insights regarding future sustainable products in emerging economies. We draw on research with temporary Indonesian student migrants in Australia, which explored how they envisioned their possible domestic futures through their changing laundry practices.
Deep fractal residual network for fast and accurate single image super resolution

Recently, deep convolutional neural networks (CNNs) have achieved excellent performance improvement in Single Image Super-Resolution (SISR) tasks. However, it is still difficult for most of the existing CNN-based models to learn and make full use of adequate hierarchical features, which is crucial to ideally recover both high-frequency and low-frequency information. Generally, many networks tend to incorporate numerous parameters aiming to improve their learning capability, which demands more storage and is less applicable to mobile systems. To address this issue, we propose a novel method for SISR by using a deep fractal residual network (DFRN) composed of a series of fractal blocks containing multiple paths for feature extraction. The fractal blocks learn and combine different hierarchical features to generate finer features favoring the reconstruction of high resolution (HR) images. Moreover, we integrate both local and global residual learning to preserve the low-level features and decrease the difficulty of training. Finally, we propose a weight-sharing version with fewer parameters to reduce the space complexity while keeping comparable performance. Experimental results demonstrate that DFRN has the capability of learning different hierarchical features and outperforms other state-of-the-art methods for SISR by reconstructing low resolution (LR) images into the HR images quickly and accurately.
An Improved Faster R-CNN for Devices Detection in Railway 4C System

As main devices in railway 4C system, accurate detection of insulators is essential to guarantee stable operation of whole railway network. There have been kinds of methods based on traditional image processing methods proposed in last several years. However, few of these methods meet requirement of the task due to complexity of the real environment. Deep learning has been developed rapidly in recent years and has been practically applied to computer vision domain. Detection algorithm based on these thoughts has replaced conventional methods that utilize hand-engineered features, becoming the most prevalent option in the community. Inspired by effectivity of two-stage methods, we propose a novel detection framework for detecting insulators in railway 4C system via improving the state-of-art two-stage framework Faster R-CNN. The main contribution of this framework is to apply FPN to Faster R-CNN to generate feature pyramids for both region proposal and detection. In order to obtain better performance, RoIAlign is utilized instead of RoIPool for avoiding misalignment. Experimental results reveal that this improved Faster R-CNN has remarkable performance on testing set in terms of accuracy. This framework is more robust for detecting objects at different scales, compared with original Faster R-CNN framework.
The Memory Palace

Many accounts and devices require only infrequent authentication by an individual, and thus authentication secrets should be both secure and memorable without much reinforcement. Inspired by people's strong visual-spatial memory, we introduce a novel system to help address this problem: the Memory Palace. The Memory Palace encodes authentication secrets as paths through a 3D virtual labyrinth navigated in the first-person perspective. We ran two experiments to iteratively design and evaluate the Memory Palace. In the first, we found that visual-spatial secrets are most memorable if navigated in a 3D first-person perspective. In the second, we comparatively evaluated the Memory Palace against Android's 9-dot pattern lock along three dimensions: memorability after one week, resilience to shoulder surfing, and speed. We found that relative to 9-dot, complexity-controlled secrets in the Memory Palace were significantly more memorable after one week, were much harder to break through shoulder surfing, and were not significantly slower to enter.
The dynamic reconfiguration approach for fault-tolerance web service composition based on multi-level VCSOP

The dynamism of web service on the internet brings new needs to build autonomous services. In practical environments, Web services could occur many incidents with unacceptable information and generate a large number of undesirable solutions. Thus, it becomes an additionally urgent challenge on how to adjust service-based applications to meet highly dynamic environments and fast-changing business requirements. A self-reconfigurable application becomes a necessity to produce an adequate web service process. To cope with this hindrance, the present paper proposes a sensor and repair-based algorithm to achieve reliability at the component level before a system crash occurs. We adopt a new sensor method based on project management rules to detect the faulty region on a workflow. After that, we applied the repair algorithm: such as a single service (SSR) and multiple service reconfiguration (MSR) that systematically search to find a new nearby solution. In MSR algorithm we referred to a new multi-level VCSOP with Harmony Search (HS) metaheuristic to quickly recover the faulty region without system crash. The results show that our proposed method could recover the original instance of the faulty region without user integration and in a very acceptable time regarding the recomposing process.
SemKeyphrase: An Unsupervised Approach to Keyphrase Extraction from MOOC Video Lectures

The Massive Open Online Courses (MOOCs) have emerged as a great resource for learners. Numerous challenges remain to be addressed in order to make MOOCs more useful and convenient for learners. One such challenge is how to automatically extract a set of keyphrases from MOOC video lectures that can help students quickly identify a suitable knowledge without spending too much time and expedite their learning process. In this paper, we propose SemKeyphrase, an unsupervised cluster-based approach for keyphrase extraction from MOOC video lectures. SemKeyphrase incorporates a new ranking algorithm, called PhaseRank, that involves two phases on ranking candidate keyphrases. Experiment results on a real-world dataset of MOOC video lectures show that our proposed approach outperforms the state-of-the-art methods by 16% or more in terms of F1 score.
From free to fee: Exploring the factors that influence the askers' switching behavior on online Q&amp;A platforms

ABSTRACT More and more social Q&amp;A platforms are launching new business models to monetize online knowledge. Many users of the traditional free Q&amp;A are gradually adopting the payment‐based Q&amp;A services. Previous studies have focused more on askers' participation behaviors in the free Q&amp;A context, while few of them explored the switching behavior of askers from free‐ to payment‐based Q&amp;A platforms. Based on the push‐pull‐mooring (PPM) model, this paper aims at exploring the influencing factors of askers' switching behavior using the critical incident technique. The classification scheme of the factors that influence the askers' switching behaviors includes 5 categories and 16 subcategories. We also use the entropy‐weight method to calculate the weight of each factor. The findings suggest that the psychological characteristics, cognitive needs, and financial incentives have great impacts on askers' switching behavior in the Q&amp;A context.
Secure decentralized decisions to enhance coordination in consolidated hospital systems

Shared decision making has become a crucial solution to build a consolidated healthcare system. While there is some research in the healthcare literature discussing the advantages and disadvantages of shared decision making, its efficiency has not been addressed quantitatively. In this paper, we propose a Decentralized Patients Assignment System (DPAS) as a universal decentralized decision making architecture. It utilizes the blockchain technology, machine learning, and integer programing to enhance coordination among healthcare providers and patients in consolidated hospital systems. To test the efficiency of the proposed DPAS, a prototype system is developed using an Agent-based model and Ethereum and is compared to the current practice of central referral systems in consolidated hospital systems. The agent-based model consists of four agents including patients, physicians, hospitals, and miners interacting within a decentralized system. The proposed system highlights the importance of interoperability and consensus among healthcare agents in the decision making process. The results demonstrate the DPAS efficiency in decreasing computational time and rejection rates for patients transfer.
Emoticons and Their Effects on Sentiment Analysis of Twitter Data

Several social media sites have come to become a growing source of data and information. The recent years have witnessed a massive and unprecedented growth of the World Wide Web. Usage of other social networking sites, Web Forums, and Blogs has also seen an ever-increasing trend. Twitter and Facebook, in particular, have become well-known means of communication over the Internet where millions of users share their opinions, reviews, experiences, thoughts, feelings, and preferences on different aspects of products or services, on a daily basis. This large volume of user-generated content contains sentiment-based sentences, which are expressions that describe the mood of the writer or his/her opinion towards a particular person/entity. This has resulted in the use of tweets, SMS messages, and other short informal texts being used in Sentiment Analysis (SA). In addition to text, emoticons are increasingly being used by people (specially youngsters) to express their feelings/sentiments that otherwise cannot be adequately communicated in words. Majority of the present SA systems do not consider emoticons as a part of analysis. However, emoticons are strong indicators of sentiments and can be considered as cues for analyzing sentiments. The main objective of this work is to determine whether emoticons can be used as reliable cues in SA, based on a comparison between SA conducted on tweets with emoticons and without emoticons
Video Abnormal Event Detection Based on ELM

To the problem of poor real-time detection of abnormal behaviors of the crowd, low recognition rate of classification algorithm and less characteristic quantity, the characteristics of crowd movement based on optical flow field and Harris corner are presented. Firstly, the optical flow vector of the crowd in video is extracted by the Lucas-Kanade optical flow method, the average kinetic energy and the direction entropy are calculated, and the distance potential energy of the crowd is extracted using the Harris corner detection algorithm. Secondly, the Extreme Learning Machine (ELM) and Support Vector Machine (SVM) are used to establish the classification model for the crowd. The experimental results show that by comparing the two classification algorithms, the time reduction of the ELM is 1.6ms (nearly a quarter) when the recognition rate is close to the SVM (the recognition rate of ELM is 96.92%, and that of SVM is 96.15%) and the characteristics of crowd movement based on optical flow field and Harris corner have higher robustness.
A-DSP: An Adaptive Join Algorithm for Dynamic Data Stream on Cloud System

The join operations, including both equi and non-equi joins, are essential to the complex data analytics in the big data era. However, they are not inherently supported by existing DSPEs (Distributed Stream Processing Engines). The state-of-the-art join solutions on DSPEs rely on either complicated routing strategies or resource-inefficient processing structures, which are susceptible to dynamic workload, especially when the DSPEs face various join predicate operations and skewed data distribution. In this paper, we propose a new cost-effective stream join framework, named A-DSP (Adaptive Dimensional Space Processing), which enhances the adaptability of real-time join model and minimizes the resource used over the dynamic workloads. Our proposal includes: 1) a join model generation algorithm devised to adaptively switch between different join schemes so as to minimize the number of processing task required; 2) a load-balancing mechanism which maximizes the processing throughput; and 3) a lightweight algorithm designed for cutting down unnecessary migration cost. Extensive experiments are conducted to compare our proposal against state-of-the-art solutions on both benchmark and real-world workloads. The experimental results verify the effectiveness of our method, especially on reducing the operational cost under pay-as-you-go pricing scheme.
A Real-Time Lazy Eye Correction Method for Low Cost Webcams

Lazy eye, or Amblyopia, affects 3.2% of the adult population, Amblyopia can reduce the clarity of vision within the affected eye of the sufferer, causing sufferers to obtain side-effects such as a drift, in the weaker eye, from the point of focus. Eye-tracking systems are becoming more prevalent within topic areas such as human-computer interaction and action assistance such as road sign detection. The accuracies of eye-tracking systems must be ensured to be of highest caliber. Therefore, the impact of Amblyopia upon the accuracies of eye-tracking must be considered and discussed. This paper discusses the effects of Amblyopia on the accuracy of a recent eye tracking system and presents a real-time correction method, which implements the analysis of collected calibration data from the user, using nearest neighbor calibration points for calculation of predicted drift at the current focus of a user. The experiments we conducted on participants suffering from Amblyopia show promising results of our method.
Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering

Recommendation systems often use side information to both alleviate problems, such as the cold start problem and data sparsity, and increase prediction accuracy. One such piece of side information, which has been widely investigated in addressing such challenges, is trust. However, the difficulty in obtaining explicit relationship data has led researchers to infer trust values from other means such as the user-to-item relationship. This paper proposes a model to improve prediction accuracy by applying the trust relationship between the user and item ratings. Two approaches to implement trust into prediction are proposed: one involves the use of estimated trust, and the other involves the initial trust. The efficiency of the proposed method is verified by comparing the obtained results with four well-known methods, including the state-of-the-art deep learning-based method of neural graph collaborative filtering (NGCF). The experimental results demonstrate that the proposed method performs significantly better than the NGCF, and the three other matrix factorization methods, namely, the singular value decomposition (SVD), SVD++, and the social matrix factorization (SocialMF).
Extending AR Interaction through 3D Printed Tangible Interfaces in an Urban Planning Context

Embedding conductive material into 3D printed objects enables non-interactive objects to become tangible without the need to attach additional components. We present a novel use for such touch-sensitive objects in an augmented reality (AR) setting and explore the use of gestures for enabling different types of interaction with digital and physical content. In our demonstration, the setting is an urban planning scenario. The multi-material 3D printed buildings consist of thin layers of white plastic filament and a conductive wireframe to enable touch gestures. Attendees can either interact with the physical model or with the mobile AR interface for selecting, adding or deleting buildings.
Multivocal literature reviews in software engineering: Preliminary findings from a tertiary study

Background: In recent years, studies involving Grey Literature (GL) have been growing and attracting the attention of researchers in software engineering (SE). One of the sources of GL refers to content produced by professionals based on their practical experiences? Recent researches in the SE states that GL can complement areas of research that are not yet clearly defined in the scientific literature. In this context, the Multivocal Literature Review (MLR), a form of Systematic Literature Review (SLR) with the inclusion of GL, emerges. Goal: Provide preliminary work about the current research involving MLR studies? First, we investigate the motivation of the researchers to include GL in review studies; and second, we examine how GL was included in the studies. Method: A tertiary study was conducted to search MLR studies published between 2009 to April of 2019. Results: The main motivations for including GL in review studies are: lack of academic research on the topic, emerging research on this topic, and complementary evidence in the GL? Internet articles and white papers were the main sources of GL data used. Conclusions: The conducting of MLR studies is still in its early stages; we have identified only 12 secondary studies. The MLR studies were conducted using guidelines for performing SLRs. What we consider to be a threat to the validity of these studies, since guidelines to conduct SLR studies do not provide recommendations for quality analysis and synthesis of primary studies, including GL.
A Cognitive Reformation

Although cognitive science started in the 1970s as a multidisciplinary field with the goal of becoming an interdisciplinary one over time, it is now dominated by cognitive psychology. The question becomes whether this matters, and if it does, what should cognitive scientists do about it? I propose that the multidisciplinarity and interdisciplinarity of cognitive science does matter because it leads to potential generation of new ideas, models, and methods. I offer a few recommendations for reforming cognitive science based, in part, on the recent 41st annual meeting of the Cognitive Science Society that sought to reopen cognitive science to minority disciplines comprising cognitive science.
Blind Realistic Blur Assessment Based on Discrepancy Learning

Blur is one of the most common distortions that degrade natural images. This stimulates the blossom of sharpness assessment metrics. Existing sharpness metrics possess good performance for evaluating simulated blur, but are limited for the more common realistic blur that are introduced during image capture and processing in real life. To this end, we propose an effective Realistic Blur Assessment method (RBA) based on discrepancy learning. First, motivated by the fact that the distortion-free reference images are usually unavailable in practice, but the Human Visual System (HVS) can still accurately perceive image sharpness by quantifying the perceptual discrepancy between the distorted image and the hallucinated reference image in mind, we propose to train a discrepancy generation model to automatically generate the discrepancy map from the distorted image analogous to the HVS. This is achieved by using a deep neural network with rich training images. With the discrepancy map, two sharpness-aware features, i.e. sparse representation based entropy of primitive and content-guided variation of power, are then extracted to severally quantify spatial visual information amount and spectral power. Finally, the two features are integrated to produce the overall sharpness score. Extensive experiments demonstrate the superiority of the proposed method over the state-of-the-arts.
BodyHub

While mobile technologies are moving closer to our body and novel wearable gadgets and smart textile interfaces emerge, current approaches are often expensive individual solutions for specific applications and lack reconfiguration possibilities. With this work, we introduce BodyHub, a modular wearable approach that allows users to realize their own smart garment applications by arranging and configuring exchangeable functional modules. To address individual user requirements and preferences, we developed a comprehensive repertoire of input and output modules that can be placed freely onto slide-in sockets which are imprinted in the textile by using 3D printing. Further, we developed a smartphone companion app that facilitates the creation of user-defined system functions without any programming skills. BodyHub thereby allows the creation of personalized wearable solutions by the users themselves and also supports ad-hoc assemblies for interface design explorations in research labs. To demonstrate the range of possible applications, we describe real-world use-cases from the areas of work life, shopping, mobility, and gaming.