Reviewerly/exHarmony huggingface dataset:
Adversarial Hash-Code Learning for Remote Sensing Image Retrieval

Hashing, a useful solution for approximate nearest neighbor (ANN) search, is popular for large-scale image retrieval. In this paper, we presents a deep supervised hashing model for remote sensing image retrieval (RSIR) in the framework of generative adversarial networks (GAN), named GAN-assist Hashing (GAAH). First, to learn the compact and useful hash codes from the images, we define a novel loss function for the generator. The loss function mainly consists of classification, similarity, and bits entropy terms. The classification term makes the hash code is discriminative, the similarity term constrains the binary code is similarity preserving, and the bits entropy term assures the learned code is low-error in the quantization. Second, we construct the unique "true" matrix with the uniform distribution as the input of discriminator to limit the leaned hash codes are bit balanced. The final hash code is learned by a minimax optimization. The positive experimental results on a ground-truth remote sensing image archive validate the usefulness of our GAAH model. Compare with the popular deep hashing methods, our GAAH achieves improved performance.
A Technique for Mixed Reality Remote Collaboration using 360 Panoramas in 3D Reconstructed Scenes

Mixed Reality (MR) remote collaboration provides an enhanced immersive experience where a remote user can provide verbal and nonverbal assistance to a local user to increase the efficiency and performance of the collaboration. This is usually achieved by sharing the local user's environment through live 360 video or a 3D scene, and using visual cues to gesture or point at real objects allowing for better understanding and collaborative task performance. While most of prior work used one of the methods to capture the surrounding environment, there may be situations where users have to choose between using 360 panoramas or 3D scene reconstruction to collaborate, as each have unique benefits and limitations. In this paper we designed a prototype system that combines 360 panoramas into a 3D scene to introduce a novel way for users to interact and collaborate with each other. We evaluated the prototype through a user study which compared the usability and performance of our proposed approach to live 360 video collaborative system, and we found that participants enjoyed using different ways to access the local user's environment although it took them longer time to learn to use our system. We also collected subjective feedback for future improvements and provide directions for future research.
Improving Low Resource Turkish Speech Recognition with Data Augmentation and TTS

One of the major problems faced by speech recognition researchers is the lack of data. In this paper, our objective is to compare alternative solutions to lack of data. Some experiments are conducted with very limited training data to see the effects of data augmentation and speech synthesis on speech recognition. Speed and volume perturbations are applied in this study. Besides data augmentation, synthetic speech is generated by using two different speech synthesis methods. In first speech synthesis approach, Google Translate Text to Speech (gTTS) is used as speech synthesizer. In second speech synthesis approach, an end-to-end Turkish TTS system is trained by us. Finally, we examined the effects of all these alternative methods on speech recognition for low resource languages. Our results demonstrate that some data augmentation or speech synthesis techniques work well to improve speech recognition for low resource languages. In this study, 14.8% relative Word Error Ratio (WER) improvement is obtained by using combination of augmented and synthetic data.
Opposing the Power of Lies, Bullshit and Fake News: the Value of Truth

Abstract ‘Post-truth’ politics poses a serious challenge to the values of truth, and consequently trust. Sections of mainstream political parties and the media do not appear to have basic knowledge or insight into processes that underpin our institutions, and we are confronted with the proliferation of lies, fake news and bullshit—and profound ignorance. We will explore the distinctions between truth and truthfulness, and lies and deceit to centrally argue that truth has considerable intrinsic and instrumental value that should be protected and respected. Truth is invaluable to the integrity of the person, institution and nation, and a climate of trust to the proper functioning of democracy. While it may be expedient to distort or ignore the truth, we value truth in ways that cannot be reduced to its practical value. This is because it is very closely related to trust, sincerity and integrity. Indifference to truth, and the cynical espousing of lies, fake news or bullshit can be destabilising and harmful, as Brexit (the United Kingdom’s process of withdrawal from the European Union) is painfully revealing. Deception constitutes a limit to our will: how can we make informed choices if the information we receive is inaccurate, false or untrue? As our information ecosystem becomes increasingly complex, unpredictable and balkanised, educators have a vital role in helping an informed public navigate what it encounters online.
An Emerging Decentralized Services Computing Paradigm for Big Data Governance: A Position Paper

With the explosion of “big data” in the past decade, exploring and mining the value hidden in the data has already generated a lot of innovative applications, especially the recent advances of AI applications. The data governance, including activities of data creation, sharing, exchange, management, analytics, tracing, and accounting, has drawn a lot of attentions. Services computing establishes the foundation of current data governance, typically in a centralized fashion, e.g., the cloud-based storage services and analytic services. However, the potential values of big data distributed on the Internet are far away from being adequately explored. Considering the infrastructure revolution made by the blockchain, in this position article, we try to rethink a new data governance fashion that is built upon the blockchain-based decentralized services computing paradigm. The core principle is that data owners are able to publish their data as a set of services that can be deployed independently from the application systems where the data were born. Meanwhile, data owners can define service rules/policies where their data should be stored and how the data can be shared, and keep governing the whole lifecycle record of how their data are actually used. Similar to existing services computing paradigm, data users can search, discover, integrate, and analyze the data in a decentralized fashion. With this perspective, we try to discuss some key insights and enumerate several related new technologies and open challenges, in terms of programmability, interoperability, and intelligence.
Avatar Type Affects Performance of Cognitive Tasks in Virtual Reality

Current consumer virtual reality applications typically represent the user by an avatar comprising a simple head/torso and decoupled hands. In the prior work of Steed et al. it was shown that the presence or absence of an avatar could have a significant impact on the cognitive load of the user. We extend that work in two ways. First they only used a full-body avatar with articulated arms, so we add a condition with hands-only representation similar to the majority of current consumer applications. Second we provide a real-world benchmark so as to start to get at the impact of using any immersive system. We validate the prior results: real and full body avatar performance on a memory task is significantly better than no avatar. However the hands only condition is not significantly different than either these two extremes. We discuss why this might be, in particular we discuss the potential for a individual variation in response to the embodiment level.
SRG: Snippet Relatedness-Based Temporal Action Proposal Generator

Recent temporal action proposal generation approaches have suggested integrating segment- and snippet score-based methodologies to produce proposals with high recall and accurate boundaries. In this paper, different from such a hybrid strategy, we focus on the potential of the snippet score-based approach. Specifically, we propose a new snippet score-based method, named Snippet Relatedness-based Generator (SRG), with a novel concept of "snippet relatedness". Snippet relatedness represents which snippets are related to a specific action instance. To effectively learn this snippet relatedness, we present "pyramid non-local operations" for locally and globally capturing long-range dependencies among snippets. By employing these components, SRG first produces a 2D relatedness score map that enables the generation of various temporal intervals reliably covering most action instances with high overlap. Then, SRG evaluates the action confidence scores of these temporal intervals and refines their boundaries to obtain temporal action proposals. On THUMOS-14 and ActivityNet-1.3 datasets, SRG outperforms state-of-the-art methods for temporal action proposal generation. Furthermore, compared to competing proposal generators, SRG leads to significant improvements in temporal action detection.
Web service based credit card fraud detection by applying machine learning techniques

Financial fraud in the credit card transaction environment refers to the unauthorized use of card transactions through multiple platforms by stealing the user credentials to gain advantages fraudulently. In order to curb this problem, an accurately financial fraud detection method is necessary to implement; since this type of fraud causes financial loss with security breach. It is observed in literature that application of machine learning techniques yields satisfactory result for fraud detection in the credit card operations and hence, it has been an important area of research in the domain of digital transactions. In this study, we have proposed the application of commonly applied classification techniques such as Decision tree (DT), Extreme learning machine (ELM), K-Nearest neighbor (K-NN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) to find the accuracy for the fraud detection. Further, we have proposed a model by hybridizing DT, SVM and K-NN models, in which the prediction accuracy has improved significantly. Apart from this, two web services Simple Object Access Protocol (SOAP) and Representational State Transfer (REST) have been applied in this study for efficient exchange of data across heterogeneous platform.
Website defacement and routine activities: considering the importance of hackers’ valuations of potential targets

Although a relatively simple form of hacking, website defacement can have severe consequences both for the websites that are attacked and the reputation of their owners. However, criminological research has yet to fully explore the causes and correlates of website defacement. We consider whether variables derived from routine activity theory can be applied to understanding website defacement. Specifically, using a sample of websites that were targeted by hackers in 2017 across the world, we examine the relationship between a country’s structural characteristics and the frequency of website defacement reported for the country. We find that website defacements are less likely to occur in the presence of capable guardianship (strong military presence) and more likely to occur when certain measures of target suitability are present. Additionally, using hackers’ self-reported valuations of potential targets, we separate defacements into two groups, and examine whether websites targeted for political reasons have different correlates than websites targeted for recreational reasons. Findings reveal that recreational defacements are deterred by capable guardianship (strong military presence) and are influenced by certain measures of target suitability while political defacements are not.
What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues

Grounding a pronoun to a visual object it refers to requires complex reasoning from various information sources, especially in conversational scenarios.For example, when people in a conversation talk about something all speakers can see, they often directly use pronouns (e.g., it) to refer to it without previous introduction.This fact brings a huge challenge for modern natural language understanding systems, particularly conventional contextbased pronoun coreference models.To tackle this challenge, in this paper, we formally define the task of visual-aware pronoun coreference resolution (PCR) and introduce VisPro, a large-scale dialogue PCR dataset, to investigate whether and how the visual information can help resolve pronouns in dialogues.We then propose a novel visual-aware PCR model, VisCoref, for this task and conduct comprehensive experiments and case studies on our dataset.Results demonstrate the importance of the visual information in this PCR case and show the effectiveness of the proposed model.
A BPSO-based method for high-utility itemset mining without minimum utility threshold

High-utility itemset mining is used to obtain high utility itemsets by taking into account both the quantity as well as the utility of each item, which have not been considered in frequent itemset mining. Many algorithms compute high utility itemsets by setting a minimum utility threshold in advance. However, determining the minimum utility threshold is not easy. Too high or too low a threshold may result in incorrect high utility itemsets. In this paper, we propose a method based on binary particle swarm optimization to optimize the search for high utility itemsets without setting the minimum utility threshold beforehand. Instead, the application of the minimum utility threshold is performed as a post-processing step. Experiments on five datasets indicate that the proposed method is better than existing methods in finding high utility itemsets, and the time to obtain those itemsets is faster than that with setting the minimum utility threshold first.
Visual Semantic Information Pursuit: A Survey

Visual semantic information comprises two important parts: the meaning of each visual semantic unit and the coherent visual semantic relation conveyed by these visual semantic units. Essentially, the former one is a visual perception task while the latter corresponds to visual context reasoning. Remarkable advances in visual perception have been achieved due to the success of deep learning. In contrast, visual semantic information pursuit, a visual scene semantic interpretation task combining visual perception and visual context reasoning, is still in its early stage. It is the core task of many different computer vision applications, such as object detection, visual semantic segmentation, visual relationship detection, or scene graph generation. Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual semantic information pursuit methods. Surprisingly, a comprehensive review for this exciting area is still lacking. In this survey, we present a unified theoretical paradigm for all these methods, followed by an overview of the major developments and the future trends in each potential direction. The common benchmark datasets, the evaluation metrics and the comparisons of the corresponding methods are also introduced.
Immersive Media and Their Future

Drawing on the deeply engaging characteristics of virtual reality (VR), augmented reality (AR), and mixed reality (MR), we refer to these three concepts collectively as "immersive media" (IM). IM are often thought of as new concepts, which may hold true in their integrated, holistic forms. However, the technological and psychological aspects of IM have long been explored, both in the media industry and in education. Through an analysis of the evolution of established media (EM), that is, cinema, TV, radio, and video games, we explore the extent to which past processes in EM development resemble the current trajectory of IM evolution. We infer that with time, IM are likely to join EM and, consequently, they should not be considered solely as a passing trend. An immediate practical implication is that educators should not neglect allocating resources and time to research the potential of IM technology for learning.
Attribute-Driven Spontaneous Motion in Unpaired Image Translation

Current image translation methods, albeit effective to produce high-quality results in various applications, still do not consider much geometric transform. We in this paper propose the spontaneous motion estimation module, along with a refinement part, to learn attribute-driven deformation between source and target domains. Extensive experiments and visualization demonstrate effectiveness of these modules. We achieve promising results in unpaired-image translation tasks, and enable interesting applications based on spontaneous motion.
Domain Intersection and Domain Difference

We present a method for recovering the shared content between two visual domains as well as the content that is unique to each domain. This allows us to map from one domain to the other, in a way in which the content that is specific for the first domain is removed and the content that is specific for the second is imported from any image in the second domain. In addition, our method enables generation of images from the intersection of the two domains as well as their union, despite having no such samples during training. The method is shown analytically to contain all the sufficient and necessary constraints. It also outperforms the literature methods in an extensive set of experiments.
Insulator Segmentation for Power Line Inspection Based on Modified Conditional Generative Adversarial Network

There are a large number of insulators on the transmission line, and insulator damage will have a major impact on power supply security. Image-based segmentation of the insulators in the power transmission lines is a premise and also a critical task for power line inspection. In this paper, a modified conditional generative adversarial network for insulator pixel-level segmentation is proposed. The generator is reconstructed by encoder-decoder layers with asymmetric convolution kernel which can simplify the network complexity and extract more kinds of feature information. The discriminator is composed of a fully convolutional network based on patchGAN and learns the loss to train the generator. It is verified in experiments that the proposed method has better performances on mIoU and computational efficiency than Pix2pix, SegNet, and other state-of-the-art networks.
LEAP

Despite a crucial role in providing public health services, Community Health Workers (CHWs) remain disadvantaged in receiving effective skill-building opportunities. Due to the lack of health experts and appropriate infrastructure, it becomes challenging to provide training on a regular basis. Our aim is to investigate opportunities for designing technology-supported collaborative learning to compensate for the limited availability of instructors. We designed a mobile learning-based peer-led educational intervention, and conducted an eight week long between-group study with 120 CHWs across four districts of Delhi, India. We found that CHWs were able to participate and use the system on their own leading to significant knowledge gains and increased desire to learn. With little guidance, CHWs exhibited benefits of collaborative learning in terms of positive interdependence on each other and use of interpersonal skills. The informal peer learning environment encouraged CHWs to have discourses on deeper societal aspects e.g. their role in society.
Structural block driven enhanced convolutional neural representation for relation extraction

In this paper, we propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning. Specifically, we detect the essential sequential tokens associated with entities through dependency analysis, named as a structural block, and only encode the block on a block-wise and an inter-block-wise representation, utilizing multi-scale CNNs. This is to 1) eliminate the noisy from irrelevant part of a sentence; meanwhile 2) enhance the relevant block representation with both block-wise and inter-block-wise semantically enriched representation. Our method has the advantage of being independent of long sentence context since we only encode the sequential tokens within a block boundary. Experiments on two datasets i.e., SemEval2010 and KBP37, demonstrate the significant advantages of our method. In particular, we achieve the new state-of-the-art performance on the KBP37 dataset; and comparable performance with the state-of-the-art on the SemEval2010 dataset.
ED2: A Case for Active Learning in Error Detection

State-of-the-art approaches formulate error detection as a semi-supervised classification problem. Recent research suggests that active learning is insufficiently effective for error detection and proposes the usage of neural networks and data augmentation to reduce the number of these user-provided labels. However, we can show that using the appropriate active learning strategy, it is possible to outperform the more complex models that rely on data augmentation. To this end, we propose a multi-classifier approach with two-stage sampling for active learning. This intuitive and neat sampling method chooses the most promising cells across rows and columns for labeling. On three datasets, ED2 achieves state-of-the-art detection accuracy while for large datasets, the required number of user labels is lower by one order of magnitude compared to the state of the art.
Anytime Multi-arm Task and Motion Planning for Pick-and-Place of Individual Objects via Handoffs

Automation applications are pushing the deployment of many high DoF manipulators in warehouse and manufacturing environments. This has motivated many efforts on optimizing manipulation tasks involving a single arm. Coordinating multiple arms for manipulation, however, introduces additional computational challenges arising from the increased DoFS, as well as the combinatorial increase in the available operations that many manipulators can perform, including handoffs between arms. The focus here is on pick-and-place tasks, which require a sequence of handoffs to be executed, so as to achieve computational efficiency, asymptotic optimality and practical anytime performance. The paper leverages recent advances in multi-robot motion planning for high DoF systems to propose a novel multi-modal extension of the dRRT* algorithm. The key insight is that, instead of naively solving a sequence of motion planning problems, it is computationally advantageous to directly explore the composite space of the integrated multi-arm task and motion planning problem, given input sets of possible pick and handoff configurations. A proof sketch regarding the asymptotic optimality of the method is included in this work given the sampling of additional picks and handoffs over time. The evaluation shows that the approach finds initial solutions fast and improves their quality over time. It also succeeds in finding solutions to harder problem instances relative to alternatives and can scale effectively as the number of robots increases.