Reviewerly/exHarmony huggingface dataset:
AutoAttacker: A reinforcement learning approach for black-box adversarial attacks

Recent research has shown that machine learning models are susceptible to adversarial examples, allowing attackers to trick a machine learning model into making a mistake and producing an incorrect output.Adversarial examples are commonly constructed or discovered by using gradient-based methods that require white-box access to the model.In most real-world AI system deployments, having complete access to the machine learning model is an unrealistic threat model.However, it is possible for an attacker to construct adversarial examples even in the black-box casewhere one assumes solely a query capability to the modelwith a variety of approaches each with its advantages and shortcomings.We introduce AutoAttacker, a novel reinforcement learning framework where agents learn how to operate around the blackbox model by querying it, to effectively extract the underlying decision behaviour, and to undermine it successfully.AutoAttacker is a first of its kind framework that uses reinforcement learning and assumes nothing about the differentiability or structure of the underlying function and is thus robust towards common defenses like gradient obfuscation or adversarial training.Finally, without differentiable output, as in binary classification, most methods cease to operate and require either an approximation of the gradient, or another approach altogether.Our approach, however, maintains the capability to function when the output descriptiveness diminishes.
Low-Cost VR Applications to Experience Real Word Places Anytime, Anywhere, and with Anyone

Low-cost VR applications in our understanding are applications that run on inexpensive hardware, such as mobile solutions based on a combination of smartphone and VR viewer, and that can be created with relatively low costs, efforts, and VR expertise involved. We present our approach for creating such low-cost applications of real world places, developed with the goal of putting the content creation into the hands of the domain experts rather than of VR experts. Since the target audience of such authors often consists of groups of people, our aim, furthermore, is to go beyond typical single user experiences by incorporating a joint VR component that allows users to not only use the applications anywhere and anytime but also together with anyone they want to share it with, resulting in new design decisions and challenges that need to be addressed. While our focus is on joint educational experiences, such as the example of an application to learn about fire ecology in the Ishi Wilderness in California used throughout this article, the approach can just as well be applied in business, entertainment, or social media oriented contexts.
Harnessing Higher-Order (Meta-)Logic to Represent and Reason with Complex Ethical Theories

The computer-mechanization of an ambitious explicit ethical theory, Gewirth’s Principle of Generic Consistency, is used to showcase an approach for representing and reasoning with ethical theories exhibiting complex logical features like alethic and deontic modalities, indexicals, higher-order quantification, among others. Harnessing the high expressive power of Church’s type theory as a meta-logic to semantically embed a combination of quantified non-classical logics, our work pushes existing boundaries in knowledge representation and reasoning. We demonstrate that intuitive encodings of complex ethical theories and their automation on the computer are no longer antipodes.
Hybrid Scheme of Image’s Regional Colorization Using Mask R-CNN and Poisson Editing

Image colorization is a creative process of reasonably adding colors on gray-scale images to generate well-pleasing colorized images. The existing colorization methods normally require user-supplied hints of color points and doodles, or handpicked color reference images for transferring colors, or diverse color images for predicting the colorized results; but the final colorized results generated by most of them may seem unnatural as a consequence of the unprofessional users' skills, inaccurately color transferring, or limited scale of color image collection. To overcome these limitations, a hybrid scheme consisting of two modules is proposed for images' region colorization by combining semantic segmentation and seamless fusion techniques in this paper. In the first module, the masks and category of input image's regions and background are derived from a Mask R-CNN model, and the corresponding reference images of each region are selected from a pre-classified color image database. In the second module, the background and various regions of an image are colorized by a U-Net model and a VGG model respectively. Then, the Poisson editing technique is applied for fusing all the colorized results to generate the final whole colorized image. The experiments show that our scheme can not only flexibly select the appropriate reference images for different regions of the image according their semantic information, but also effectively merge colorized results to generate a plausible colorful image. By reasonably combining different CNN-based models according to their superiority, our scheme avoids the limitation of failures caused by single-step methods, and achieves richer artistic visual effect compared with other existing methods.
Enabling deep learning for large scale question answering in Italian

The recent breakthroughs in the field of deep learning led to state-of-the-art results in several NLP tasks, such as Question Answering (QA). Unfortunately, the requirements of such neural QA systems are very strict due to the size of the involved training datasets. In cross-linguistic settings the se requirements are not satisfied as training datasets for QA over non-English texts are often not available. This represents the major barrier for a wide-spread adoption of neural QA methods in NLP applications. In this paper, the acquisition of a large scale dataset for an open-domain factoid question answering system in Italian is discussed. It is obtained by automatic translation and linguistic elicitation of an existing English dataset, i.e. the SQuAD question-answer pair corpus. Even though the quality of the resulting corpus for Italian might not be completely satisfying, our work allowed to generate more than 60 thousand question-answer pairs. In the paper the impact of this resource on the QA process over the Italian Wikipedia is studied, according to different training conditions and architectural constraints. A comparative evaluation against the English version, in line with standards in the SQuAD literature, is carried out. The outcomes show that the results achievable for Italian are below the state-of-the-art for English, but the ability of learning not to respond (i.e. the adoption of techniques for detecting question whose answers are simply not available, i.e. EMPTY set of answers) allows the system to pursue reasonable levels of precision. This make it already usable within realistic application scenarios. Finally, an error analysis is presented that suggests possible future research directions on still critical but highly beneficial enhancements, in view of concrete QA applications in Italian.
Virtual Reality Aided Tourism

VR technology evolves rapidly and deserve greater attention in many field of our life, so in tourism as well. We introduce different ways for application of VR in tourism. We also highlight the key components of development process which contributes to the success of a VR project. Finally we present our VR application which implements a virtual tour on a Mediterranean tourist destination. Through the introduction of our project we explain the previously mentioned guidelines.
Research on Collaborative Filtering Personalized Recommendation Algorithm Based on Deep Learning Optimization

In order to improve the accuracy of the project in the recommendation process, the article compares the existing content-based and collaborative filtering recommendation calculations, makes full use of the advantages of different algorithms, and proposes a collaborative filtering personalized recommendation algorithm based on deep learning optimization. Through the deep learning training of users' preferences, the corresponding parameters are adjusted to establish a personalized recommendation method. Through experimental verification, the proposed algorithm can effectively improve the quality of personalized recommendation.
Establishing Cyber Resilience in Embedded Systems for Securing Next-Generation Critical Infrastructure

The mass integration and deployment of intelligent technologies within critical commercial, industrial and public environments have a significant impact on business operations and society as a whole. Though integration of these critical intelligent technologies pose serious embedded security challenges for technology manufacturers which are required to be systematically approached, in-line with international security regulations.This paper establish security foundation for such intelligent technologies by deriving embedded security requirements to realise the core security functions laid out by international security authorities, and proposing microarchitectural characteristics to establish cyber resilience in embedded systems. To bridge the research gap between embedded and operational security domains, a detailed review of existing embedded security methods, microarchitectures and design practises is presented. The existing embedded security methods have been found ad-hoc, passive and strongly rely on building and maintaining trust. To the best of our knowledge to date, no existing embedded security microarchitecture or defence mechanism provides continuity of data stream or security once trust has broken. This functionality is critical for embedded technologies deployed in critical infrastructure to enhance and maintain security, and to gain evidence of the security breach to effectively evaluate, improve and deploy active response and mitigation strategies. To this end, the paper proposes three microarchitectural characteristics that shall be designed and integrated into embedded architectures to establish, maintain and improve cyber resilience in embedded systems for next-generation critical infrastructure.
Passive Observations of a Large DNS Service: 2.5 Years in the Life of Google

In 2009 Google launched its Public DNS service, which has since become the largest DNS service in existence. A common problem with public resolvers is that Content Delivery Networks (CDNs) struggle to map end user origin. The EDNS Client Subnet (ECS) extension allows resolvers to reveal part of a client's IP to authoritative name servers, helping CDNs pinpoint client origin. A side effect of ECS is that authoritative name server operators learn where in its network the public resolver handles queries. We leverage this side effect to study Google Public DNS (GPDNS). We perform a longitudinal analysis over data covering 2.5 years and 3.7 billion queries. Our study focuses on three aspects. First, we show that while GPDNS has PoPs in many countries, traffic is frequently routed out of country. This can reduce performance, and expose DNS requests to state level surveillance. We also show that end users are often served by a suboptimal PoP. Second, we show that end users switch to GPDNS en masse when their ISP resolver is unresponsive, and do not switch back. Finally, we also find that many e-mail providers configure GPDNS as resolver on their servers, causing serious privacy concerns due to information leakage.
Multi-Channel Multi-Loss Deep Learning Based Compression Model for Color Images

Lossy image compression aims to encode images with a low bit-rate representation while preserving a pleasant visual quality of decompressed images. By utilizing the manually designed features, the traditional compression may not be suitable for diverse image content and may cause visible artifacts under the low bit rate constraint. Recently, deep learning based methods, which can extract the compact representation of an image in an auto-encoder way, were proposed for image compression. Although satisfying the low bit-rate constraint, they also introduced the color bias problem due to the reconstruction and quantization errors. To overcome these problems, we proposed a deep learning framework to compress an image with two different settings. First, we use separate networks to compress intensity (Y) and color (Cb, Cr) channels. Second, to balance the bit rate and color preservation, we introduce an fusion network, which imports the redundant information from the intensity channel to the color channel. By leveraging the intensity information, the network would focus on disentangling the color-specific features and allow using fewer feature maps to encode the entire image color information. We evaluate the proposed method upon the Kodak image sets by the quantitative metrics (PSNR, SSIM, CM-SSIM). Also, the comparison with JPEG, JPEG2000, BPG and the deep learning based method are presented.
Community Radio as Amplification of Rural Knowledge Sharing

Community radio’s relationship with the farming communities has a long history in India. The earliest successful experiments in community broadcasting involved both farmers and agriculture. In terms of development communication, community radio in India represents a confluence of somewhat conflicting paradigms. While community radio is generally presented as a highly democratic, participatory medium, the way it is operationalized in India more closely aligns with the modernization/diffusion paradigm. In 1976, Joseph Ascroft observed the phenomenon of ‘interpersonal diffusion’ among farmers, whereby for each farmer trained in new techniques, three more would adopt the innovations. While this ‘interpersonal diffusion’ was by no means perfect, it was illustrative of the complex communication networks involved in the diffusion process. It also hints towards the ways in which community radio can act as a facilitator of these processes; as somewhat of an intersection between diffusion and participatory communication. Drawing on ethnographically inspired qualitative research conducted at a rural community radio station in South India, this article explores the role of community radio at the intersections of participatory development and diffusion. This article argues that community radio facilitates the sharing of technical information and innovations among farmers and contributes to amplifying existing knowledge communication systems. The implications of this article suggest that a focus on existing local knowledge communication and transfer systems could contribute to achieving broader development outcomes and further situating the role of community radio within development and social change initiatives.
Fast and Lightweight Image Super-Resolution Based on Dense Residuals Two-Channel Network

The existing advanced super-resolution methods with deepening or widening network demand high computational resources and memory consumption. It is difficult to directly apply them in practice. Therefore, we propose a fast and lightweight two-channel end-to-end network with fewer parameters and low computational complexity in this paper. The shallow channel mainly restores the general outline of the image, while the deep channel mainly learns the high-frequency texture information. And the deep channel combines the dense block and residual connection. The dense block increases data flow of network, while the residual connection reduces the number of parameters and speeds up the convergence of network. Moreover, we propose an enhanced network using group convolution, which significantly reduces the parameters and computational complexity with slight performance loss. Our model has many fewer parameters and operations, and it is evaluated on different datasets, outperforming the current representative methods in accuracy and run time.
The Epistle to Cangrande Through the Lens of Computational Authorship Verification

The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open.
Integrating Structural Symmetry and Local Homoplasy Information in Intuitionistic Fuzzy Clustering for Infrared Pedestrian Segmentation

Interferential background, boundary uncertainty, and noises are usually involved in infrared pedestrian imaging, which erect barrier for accurate segmentation. To counter the conundrum rising in these cases, we present a novel intuitionistic fuzzy clustering-based segmentation method, which integrates structural symmetry and local homoplasy information, for precise infrared pedestrian segmentation. Enlightened by the multiapplication and favorable performance of fuzzy clustering methods, intuitionistic fuzzy c-means (IFCM) is applied as the backbone of our segmentation method. The contributions of the proposed method mainly include two parts. First, motivated by potential target characteristics and tendency for clearer contour description, structural symmetry information is utilized, which is an intrinsic shape feature of objects and would be significant especially when the texture and details of the target are lost in infrared images. Further, a map that represents the probability of pixels belonging to the target is constructed in the form of ellipse symmetry region. Combined with the probability map, symmetry information is utilized to establish a novel dissimilarity function in fuzzy clustering. Second, local homoplasy information which is designed based on region similarity is introduced to suppress the intensity inhomogeneity and noises to further improve the performance of the proposed method. Finally, a dataset containing 500 infrared pedestrian images paired with corresponding pixel-wise annotation is constructed to verify segmentation effectiveness. The proposed SR-IFCM is compared with 12 state-of-the-art segmentation methods. The experimental results indicate that the proposed method outperforms the comparison methods and works better for infrared pedestrian segmentation.
Deconstructing Google Dataset Search

This essay examines the beta tool from Google, Google Dataset Search. The Google Dataset Search, announced in September 2018, is a search engine specific to finding research data published on the internet. The structure and methods of the search engine are examined, as well as the methods Google recommends to web developers to make it an effective tool across the World Wide Web. The column concludes with a discussion of the pros and cons of this tool in the research information landscape.
Data-Driven Elicitation of Quality Requirements in Agile Companies

Quality Requirements (QRs) are a key artifact to ensure the quality and success of a software system. Despite its importance, QRs have not reached the same degree of attention as its functional counterparts, especially in the context of trending software development methodologies like Agile Software Development (ASD). Moreover, crucial information that can be obtained from data sources of a project under development (e.g. JIRA, github,…) are not fully exploited, or even neglected, in QR elicitation activities. In this work, we present a data-driven approach to semi-automatically generate and document QRs in the context of ASD. We define an architecture focusing on the process and the artefacts involved. We validate and iterate on such architecture by conducting workshops in four companies of different size and profile. Finally, we present the implementation of such architecture, considering the feedback and outcomes of the conducted workshops.
Differentiated Learning Patterns with Mixed Reality

One of the critical challenges for teachers today is reaching the needs of an increasingly diverse student population. In order to address different types of learners, educators must begin by recognizing individual differences. Differentiated instruction with the use of emerging technology offers the opportunity for teachers to engage students in different ways, while also varying the rate of instruction, complexity levels, and teaching strategies to engage and challenge students. Mixed Reality (MR) is an emerging medium that offers unique affordances by combining physical and virtual worlds and allowing personalized user control of the hybrid reality. This paper provides an introduction to the Mixed Reality (MR) and Artificial Intelligence (AI) technologies and their possibilities for education. It also presents Clue: A mixed reality application to augment traditional classroom settings into accessible spaces for three main groups of learners: non-native English speakers, students with hearing disabilities and students with language-based learning disabilities.
Quantum state correction using a measurement-based feedforward mechanism

One of the weaknesses of quantum optical state postselection schemes is the low success probability. Typically there is a trade-off between amplifier properties such as success probability and output state fidelity. However, here we present a state comparison amplifier for optical coherent states, which features an active measurement and feedforward mechanism to correct for errors made during the initial amplification. The simple and relatively low latency mechanism allows us to correct for a binary phase alphabet. We demonstrate a significant simultaneous improvement in the amplifier characteristic parameters: output state fidelity, correct state fraction, and success probability. This demonstrates that nondeterministic quantum amplification can be enhanced significantly by measurement and feedforward.
Fast and Lightweight Execution Time Predictions for Spark Applications

Users and operators of cloud-based Spark clusters often require quick insights on how the execution time of an application is likely to be impacted by the resources allocated to the application, e.g., the number of Spark executor cores assigned, and the size of the data to be processed. Existing techniques typically require extensive prior executions of the application under various resource allocation settings and data sizes to obtain an accurate model. In this paper, we explore the accuracy of a model with less prior executions of the application. Such a model can be useful for situations where quick predictions are required and little cluster resources are available for building a model. We use logs from two executions of an application with small sample data and different resource settings and explore the accuracy of the predictions for other resource allocation settings and input data sizes.