Reviewerly/exHarmony huggingface dataset:
Collective Adaptation through Multi-Agents Ensembles

Modern software systems are becoming more and more socio-technical systems composed of distributed and heterogeneous agents from a mixture of people, their environment, and software components. These systems operate under continuous perturbations due to the unpredicted behaviors of people and the occurrence of exogenous changes in the environment. In this article, we introduce a notion of ensembles for which, systems with collective adaptability can be built as an emergent aggregation of autonomous and self-adaptive agents. Building upon this notion of ensemble, we present a distributed adaptation approach for systems composed by ensembles: collections of agents with their respective roles and goals. In these systems, adaptation is triggered by the run-time occurrence of an extraordinary circumstance, called issue. It is handled by an issue resolution process that involves agents affected by the issue to collaboratively adapt with minimal impact on their own preferences. Central to our approach is the implementation of a collective adaptation engine (CAE) able to solve issues in a collective fashion. The approach is instantiated in the context of a smart mobility scenario through which its main features are illustrated. To demonstrate the approach in action and evaluate it, we exploit the DeMOCAS framework, simulating the operation of an urban mobility scenario. We have executed a set of experiments with the goal to show how the CAE performs in terms of feasibility and scalability. With this approach, we are able to demonstrate how collective adaptation opens up new possibilities for tackling urban mobility challenges making it more sustainable respect to selfish and competitive behaviours.
Backlit images enhancement using global tone mappings and image fusion

The authors present a method for the enhancement of backlit images, i.e. images in which the main source of light is behind the photography subject. These images contain, simultaneously, very dark and very bright regions. In this situation, a single tone mapping function is unable to enhance the whole image. They propose the use of several such tone mappings, some of them enhancing the dark regions while others enhancing the bright regions, and then the combination of all these results using an image fusion algorithm. Qualitative and quantitative results confirm the validity of the proposed method.
Human-imperceptible Privacy Protection Against Machines

Privacy concerns with social media have recently been under the spotlight, due to a few incidents on user data leakage on social networking platforms. With the current advances in machine learning and big data, computer algorithms often act as a first-step filter for privacy breaches, by automatically selecting content with sensitive information, such as photos that contain faces or vehicle license plate. In this paper we propose a novel algorithm to protect the sensitive attributes against machines, meanwhile keeping the changes imperceptible to humans. In particular, we first conducted a series of human studies to investigate multiple factors that influence human sensitivity to the visual changes. We discover that human sensitivity is influenced by multiple factors, from low-level features such as illumination, texture, to high-level attributes like object sentiment and semantics. Based on our human data, we propose for the first time the concept of human sensitivity map. With the sensitivity map, we design a human-sensitivity-aware image perturbation model, which is able to modify the computational classification results of sensitive attributes while preserving the remaining attributes. Experiments on real world data demonstrate the superior performance of the proposed model on human-imperceptible privacy protection.
IPv6 Covert Channels in the Wild

The increasing diffusion of malware endowed with steganographic techniques requires to carefully identify and evaluate a new set of threats. The creation of a covert channel to hide a communication within network traffic is one of the most relevant, as it can be used to exfiltrate information or orchestrate attacks. Even if network steganography is becoming a well-studied topic, only few works focus on IPv6 and consider real network scenarios. Therefore, this paper investigates IPv6 covert channels deployed in the wild. Also, it presents a performance evaluation of six different data hiding techniques for IPv6 including their ability to bypass some intrusion detection systems. Lastly, ideas to detect IPv6 covert channels are presented.
Arcana imperii*

Abstract This paper reports the findings of a study on the mechanics of insult-retort adjacency pairs in Twitter interactions. The analysis concerns primarily the humorous retorts made by the pornographic entrepreneur Stormy Daniels, who has been pelted with politically-loaded misogynist insults, many of which qualify as slut-shaming. These acts of verbal aggression are the result of her involvement in a legal dispute with President Donald Trump and his former attorney. Based on a carefully collected corpus of public exchanges of tweets, our qualitative analysis achieves a few goals. First, it brings to focus a previously ignored function of witty and creative humour, including the self-deprecating variety, as a powerful rhetorical strategy that helps address insults with dignity and that displays the speaker’s intellectual superiority over the attacker and a good sense of humour, as evidenced by multiple users’ positive metapragmatic evaluations of Stormy Daniels’s retorts. Second, these findings carry vital practical implications for handling misogynist comments, including slut-shaming, online. Third, this study offers new insights into the workings of insults and retorts thereto, not only in multi-party interactions on social media, specifically on Twitter, but also through traditional channels of communication.
SchengenDB: A Data Protection Database Proposal

GDPR in Europe and similar regulations, such as the California CCPA, require new levels of privacy support for consumers. Most challenging to IT departments is the "right to be forgotten". Hence, an enterprise must ensure that ALL information about a specific consumer be deleted from enterprise storage, when requested. Since enterprises are internally heavily "siloed", sharing of information is usually accomplished by copying data between systems. This makes finding and deleting all copies of data on a particular consumer difficult. GDPR also requires the notion of purposes, which is an access control model orthogonal to the one customarily in SQL. Herein, we sketch an implementation of purposes and show how it fits within a conventional access control framework. We then propose two solutions to supporting GDPR in a DBMS. When a "green field" environment is present, we propose a solution which directly supports the process of ensuring GDPR compliance at enterprise-scale. Specifically, it is designed to store every fact about a consumer exactly once. Therefore, the right to be forgotten is readily supported by deleting that fact. On the other hand, when dealing with legacy systems in the enterprise, we propose a second solution which tracks all copies of personal information, so they can be deleted on request. Of course, this solution entails additional overhead in the DBMS. Once data leaves the DBMS, it is in some application. We propose "sandboxing" applications in a novel way that will prevent them from leaking data to the outside world when inappropriate. Lastly, we discuss the challenges associated with auditing and logging of data. This paper sketches the design of the above GDPR compliant facilities, which we collectively term SchengenDB.
A Community-Centered Design Framework for Robot-Assisted Feeding Systems

Robot-assisted feeding (RAF) systems offer enormous potential benefits to community-centered care-giving environments. However, developers of RAF technologies often focus on evaluating their standard transactional functionality, omitting the impact of such technologies in contexts that extend past the interaction of the robot and food receiver. RAF technologies have complex social, cultural and self-identity implications, since a "meal" extends well beyond the simple provisioning of nourishment. To better understand these implications we conducted a contextual inquiry in an assisted-living community with five potential care recipients and five caregivers, as well as interviews with fifteen domain experts including occupational therapists and feeding specialists. Based on our findings from these studies, we developed a new framework for RAF technologies that formulates this vital task as a community-centered relational service. We then use this framework to qualitatively and quantitatively assess three existing feeding systems and identify areas of improvement. Our work reveals new insights about stakeholders of RAF technologies and provides a roadmap for technology developers to better serve the needs of these stakeholders.
From Grayscale to Color: Quaternion Linear Regression for Color Face Recognition

Linear regression has shown an effective tool for face recognition in recent years.Most existing linear regression based methods are devised for grayscale image based face recognition and fail to exploit the color information of color face images.To extend linear regression for color images, we propose a novel color face recognition method by formulating the color face recognition problem as a quaternion linear regression model.The proposed quaternion linear regression classification (QLRC) algorithm models each color facial image as a quaternion signal and codes multiple channels of each query color image in a holistic manner.Thus, the correlation among distinct channels of each color image is well preserved and leveraged by QLRC to further improve the recognition performance.To further improve QLRC, we propose a quaternion collaborative representation optimized classifier (QCROC) which integrates QLRC and quaternion collaborative representation based classifier into a unified framework.The experiments on benchmark datasets demonstrate the efficacy of the proposed approaches for color face recognition.
Unsupervised Domain Adaptation for 3D Human Pose Estimation

Training an accurate 3D human pose estimator often requires a large amount of 3D ground-truth data which is inefficient and costly to collect. Previous methods have either resorted to weakly supervised methods to reduce the demand of ground-truth data for training, or using synthetically-generated but photo-realistic samples to enlarge the training data pool. Nevertheless, the former methods mainly require either additional supervision, such as unpaired 3D ground-truth data, or the camera parameters in multiview settings. On the other hand, the latter methods require accurately textured models, illumination configurations and background which need careful engineering. To address these problems, we propose a domain adaptation framework with unsupervised knowledge transfer, which aims at leveraging the knowledge in multi-modality data of the easy-to-get synthetic depth datasets to better train a pose estimator on the real-world datasets. Specifically, the framework first trains two pose estimators on synthetically-generated depth images and human body segmentation masks with full supervision, while jointly learning a human body segmentation module from the predicted 2D poses. Subsequently, the learned pose estimator and the segmentation module are applied to the real-world dataset to unsupervisedly learn a new RGB image based 2D/3D human pose estimator. Here, the knowledge encoded in the supervised learning modules are used to regularize a pose estimator without ground-truth annotations. Comprehensive experiments demonstrate significant improvements over weakly supervised methods when no ground-truth annotations are available. Further experiments with ground-truth annotations show that the proposed framework can outperform state-of-the-art fully supervised methods. In addition, we conducted ablation studies to examine the impact of each loss term, as well as with different amount of supervisions signal.
Aesthetic Attributes Assessment of Images

Image aesthetic quality assessment has been a relatively hot topic during the last decade. Most recently, comments type assessment (aesthetic captions) has been proposed to describe the general aesthetic impression of an image using text. In this paper, we propose Aesthetic Attributes Assessment of Images, which means the aesthetic attributes captioning. This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute. We introduce a new dataset named DPC-Captions which contains comments of up to 5 aesthetic attributes of one image through knowledge transfer from a full-annotated small-scale dataset. Then, we propose Aesthetic Multi-Attribute Network (AMAN), which is trained on a mixture of fully-annotated small-scale PCCD dataset and weakly-annotated large-scale DPC-Captions dataset. Our AMAN makes full use of transfer learning and attention model in a single framework. The experimental results on our DPC-Captions and PCCD dataset reveal that our method can predict captions of 5 aesthetic attributes together with numerical score assessment of each attribute. We use the evaluation criteria used in image captions to prove that our specially designed AMAN model outperforms traditional CNN-LSTM model and modern SCA-CNN model of image captions.
Technical Aspects of Artificial Intelligence: An Understanding from an Intellectual Property Perspective

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GAIN

Image inpainting, which aims to fill the missing holes of the images, is a challenging task because the holes may contain complicated structures or different possible layouts. Deep learning methods have shown promising performance in image inpainting but still, suffer from generating poor-structured artifacts when the holes are large and irregular. Some existing methods use edge inpainting to help image inpainting, with binary edge map obtained from image gradient. However, by only using the binary edge map, these methods discard the rich information in image gradient and thus leave some critical issues (e.g. , color discrepancy) unattended. In this paper, we propose Gradient Augmented Inpainting Network (GAIN), which uses image gradient information instead of edge information to facilitate image inpainting. Specifically, we formulate a multi-task learning framework which performs image inpainting and gradient inpainting simultaneously. A novel GAI-Block is designed to encourage the information fusion between the image feature map and the gradient feature map. Moreover, gradient information is also used to determine the filling priority, which can guide the network to construct more plausible semantic structures for the holes. Experimental results on public datasets CelebA-HQ and Places2 show that our proposed method outperforms state-of-the-art methods quantitatively and qualitatively.
Goal-Driven Sequential Data Abstraction

Automatic data abstraction is an important capability for both benchmarking machine intelligence and supporting summarization applications. In the former one asks whether a machine can `understand' enough about the meaning of input data to produce a meaningful but more compact abstraction. In the latter this capability is exploited for saving space or human time by summarizing the essence of input data. In this paper we study a general reinforcement learning based framework for learning to abstract sequential data in a goal-driven way. The ability to define different abstraction goals uniquely allows different aspects of the input data to be preserved according to the ultimate purpose of the abstraction. Our reinforcement learning objective does not require human-defined examples of ideal abstraction. Importantly our model processes the input sequence holistically without being constrained by the original input order. Our framework is also domain agnostic -- we demonstrate applications to sketch, video and text data and achieve promising results in all domains.
Design implications for adaptive augmented reality based interactive learning environment for improved concept comprehension in engineering paradigms

Augmented reality (AR) has tremendous potential as a teaching and learning tool in engineering education to enhance students' learning experience; it influences the students' spatial ability for real-time visualization. Furthermore, this helps to attain better concept comprehension pertaining to improved understanding of the topics. The present study provides evidence by developing an AR learning environment (ARLE) suitable for complicated theoretical topics of electronics engineering, which otherwise cannot be demonstrated using practical experiments. The idea of using different design variants; such as mobile and table-top for adaptive AR is also implemented. For this, 60 undergraduate students of electronics and electrical engineering were introduced to the ARLE system, in two different case studies. The first case validates the efficacy of using AR for learning the concept of stability in linear control systems through a questionnaire based survey where students reported the ARLE as an effective learning system for theoretical topics. Second case study provides comparative analysis for usability of two design variants of the ARLE in terms of manipulability and comprehensibility. Finally, the design implications for developed ARLE are discussed, which may be helpful for other researchers in creating learning environments for different courses of engineering education.
Agitation Monitoring and Prevention System for Dementia Caregiver Empowerment

Dementia caregiver burden associated with patient agitation is one of the most common reasons for the institutionalization of a person with dementia. We developed an integrative sensing, analytics, modeling, and intervention system that detects early signs of agitation and notifies the caregiver to intervene before escalation.
A Unified Learning-Based Framework for Light Field Reconstruction From Coded Projections

Light fields present a rich way to represent the 3D world by capturing the spatio-angular dimensions of the visual signal. However, the popular way of capturing light fields (LF) via a plenoptic camera presents a spatio-angular resolution trade-off. To address this issue, computational imaging techniques such as compressive light field and programmable coded aperture have been proposed, which reconstruct full sensor resolution LF from coded projections of the LF. Here, we present a unified learning framework that can reconstruct LF from a variety of multiplexing schemes with minimal number of coded images as input. We consider three light field capture schemes: heterodyne capture scheme with code placed near the sensor, coded aperture scheme with code at the camera aperture and finally the dual exposure scheme of capturing a focus-defocus pair where there is no explicit coding. Our algorithm consists of three stages: Firstly, we recover the all-in-focus image from the coded image. Secondly, we estimate the disparity maps for all the LF views from the coded image and the all-in-focus image. And finally, we render the LF by warping the all-in-focus image using the estimated disparity maps. We show that our proposed learning algorithm performs either on par with or better than the state-of-the-art methods for all the three multiplexing schemes. LF from focus-defocus pair is especially attractive as it requires no hardware modification and produces LF reconstructions that are comparable to the current state of the art learning-based view synthesis approaches from multiple images. Thus, our work paves the way for capturing full-resolution LF using conventional cameras such as DSLRs and smartphones.
TGG

Zero-shot and few-shot learning aim to improve generalization to unseen concepts, which are promising in many realistic scenarios. Due to the lack of data in unseen domain, relation modeling between seen and unseen domains is vital for knowledge transfer in these tasks. Most existing methods capture seen-unseen relationimplicitly via semantic embedding or feature generation, resulting in inadequate use of relation and some issues remain (e.g. domain shift). To tackle these challenges, we propose a Transferable Graph Generation (TGG ) approach, in which the relation is modeled and utilizedexplicitly via graph generation. Specifically, our proposed TGG contains two main components: (1) Graph generation for relation modeling. Anattention-based aggregate network and arelation kernel are proposed, which generate instance-level graph based on a class-level prototype graph and visual features. Proximity information aggregating is guided by a multi-head graph attention mechanism, where seen and unseen features synthesized by GAN are revised as node embeddings. The relation kernel further generates edges with GCN and graph kernel method, to capture instance-level topological structure while tackling data imbalance and noise. (2) Relation propagation for relation utilization. Adual relation propagation approach is proposed, where relations captured by the generated graph are separately propagated from the seen and unseen subgraphs. The two propagations learn from each other in a dual learning fashion, which performs as an adaptation way for mitigating domain shift. All components are jointly optimized with a meta-learning strategy, and our TGG acts as an end-to-end framework unifying conventional zero-shot, generalized zero-shot and few-shot learning. Extensive experiments demonstrate that it consistently surpasses existing methods of the above three fields by a significant margin.
Disentangled representation learning and residual GAN for age-invariant face verification

One of the challenges in cross-age face recognition and verification is to effectively model the facial aging process. Despite the rapid advances in face-related areas, it is still very difficult for existing methods to simultaneously achieve accurate facial feature preservation and reliable aging during aging modeling. Aiming to address this issue, we introduce a Disentangled Representation learning and Residual Generative Adversarial Network (DR-RGAN) that represents the facial features without age interference, which is achieved by explicitly disentangling the facial features and age variation. An encoder-decoder structured generator produces aging images from unstructured facial representations by using the age characteristics provided separately. It can thus take the disentangled representation to preserve personal identity for face verification. Considering that pixel-based errors may cause a loss of detail, a VGG based content loss is further equipped to preferably preserve facial features. The discriminator is trained to distinguish the real from generated faces, carry out identification prediction, and leverage an age estimator to boost the aging accuracy. It is beneficial for obtaining more photorealistic and desirable aging effects, as well as more consistent face verification results. Extensive experiments on the CACD and LFW datasets demonstrate that our DR-RGAN generates pleasing aging imageries and achieves a high accuracy of face verification.