Reviewerly/exHarmony huggingface dataset:
Scala step-by-step: soundness for DOT with step-indexed logical relations in Iris

The metatheory of Scala’s core type system—the Dependent Object Types (DOT) calculus—is hard to extend, like the metatheory of other type systems combining subtyping and dependent types. Soundness of important Scala features therefore remains an open problem in theory and in practice. To address some of these problems, we use a semantics-first approach to develop a logical relations model for a new version of DOT, called guarded DOT (gDOT) . Our logical relations model makes use of an abstract form of step-indexing , as supported by the Iris framework, to model various forms of recursion in gDOT. To demonstrate the expressiveness of gDOT, we show that it handles Scala examples that could not be handled by previous versions of DOT, and prove using our logical relations model that gDOT provides the desired data abstraction. The gDOT type system, its semantic model, its soundness proofs, and all examples in the paper have been mechanized in Coq.
PaRoT: A Practical Framework for Robust Deep Neural Network Training

Deep Neural Networks (DNNs) are finding important applications in safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the environment correctly and robustly is necessary for safe operation. Raising unique challenges for assurance due to their black-box nature, DNNs pose a fundamental problem for regulatory acceptance of these types of systems. Robust training—training to minimize excessive sensitivity to small changes in input—has emerged as one promising technique to address this challenge. However, existing robust training tools are inconvenient to use or apply to existing codebases and models: they typically only support a small subset of model elements and require users to extensively rewrite the training code. In this paper we introduce a novel framework, PaRoT, developed on the popular TensorFlow platform, that greatly reduces the barrier to entry. Our framework enables robust training to be performed on existing DNNs without rewrites to the model. We demonstrate that our framework’s performance is comparable to prior art, and exemplify its ease of use on off-the-shelf, trained models and its testing capabilities on a real-world industrial application: a traffic light detection network.
Fixing Social Media with the Blockchain

In today's social media context, many criticalities have emerged, particularly concerning the security of users' personal data and the unbalanced redistribution of the value generated. This work, analyzing 40 emerging platforms, investigates how Blockchain technology is reshaping the social media scenario through transparency, value redistribution, ownership awareness, decentralization of data, and censorship resistance.
Trace2TAP

Two common approaches for automating IoT smart spaces are having users write rules using trigger-action programming (TAP) or training machine learning models based on observed actions. In this paper, we unite these approaches. We introduce and evaluate Trace2TAP, a novel method for automatically synthesizing TAP rules from traces (time-stamped logs of sensor readings and manual actuations of devices). We present a novel algorithm that uses symbolic reasoning and SAT-solving to synthesize TAP rules from traces. Compared to prior approaches, our algorithm synthesizes generalizable rules more comprehensively and fully handles nuances like out-of-order events. Trace2TAP also iteratively proposes modified TAP rules when users manually revert automations. We implemented our approach on Samsung SmartThings. Through formative deployments in ten offices, we developed a clustering/ranking system and visualization interface to intelligibly present the synthesized rules to users. We evaluated Trace2TAP through a field study in seven additional offices. Participants frequently selected rules ranked highly by our clustering/ranking system. Participants varied in their automation priorities, and they sometimes chose rules that would seem less desirable by traditional metrics like precision and recall. Trace2TAP supports these differing priorities by comprehensively synthesizing TAP rules and bringing humans into the loop during automation.
Big data analytics of crime prevention and control based on image processing upon cloud computing

Aim: Current crime behavior observation has the problem of not being real time, thus criminal behavior cannot be promptly controlled.To improve the control of criminal behavior, this study was based on cloud computing image processing, and adopted data mining for criminal behavior.Methods: This study obtained many criminal behavior characteristics through data collection and combined the rapid response capability of cloud computing to adopt data processing.In addition, to improve the accuracy of criminal behavior recognition, the identification method for criminal behaviors in selected populations was studied, and the image processing technology was combined to identify individual crimes and subject segmentation.Results: Our work used statistical methods to collect the characteristics of criminal behavior, and we designed experiments to verify the effectiveness of the algorithm.The experimental research shows that the algorithm has high accuracy in identifying abnormal behavior. Conclusion:The research shows that the accuracy of the algorithm for identifying abnormal behavior is relatively high, and it has high practical value, which can meet the accuracy and real-time requirements of security systems.
Usability Studies of an Egocentric Vision-Based Robotic Wheelchair

Motivated by the need to improve the quality of life for the elderly and disabled individuals who rely on wheelchairs for mobility, and who may have limited or no hand functionality at all, we propose an egocentric computer vision based co-robot wheelchair to enhance their mobility without hand usage. The robot is built using a commercially available powered wheelchair modified to be controlled by head motion. Head motion is measured by tracking an egocentric camera mounted on the user’s head and faces outward. Compared with previous approaches to hands-free mobility, our system provides a more natural human robot interface because it enables the user to control the speed and direction of motion in a continuous fashion, as opposed to providing a small number of discrete commands. This article presents three usability studies, which were conducted on 37 subjects. The first two usability studies focus on comparing the proposed control method with existing solutions while the third study was conducted to assess the effectiveness of training subjects to operate the wheelchair over several sessions. A limitation of our studies is that they have been conducted with healthy participants. Our findings, however, pave the way for further studies with subjects with disabilities.
An Adversarial Attacks Resistance-based Approach to Emotion Recognition from Images using Facial Landmarks

Emotion recognition has become an increasingly important area of research due to the increasing number of CCTV cameras in the past few years. Deep network-based methods have made impressive progress in performing emotion recognition-based tasks, achieving high performance on many datasets and their related competitions such as the ImageNet challenge. However, deep networks are vulnerable to adversarial attacks. Due to their homogeneous representation of knowledge across all images, a small change to the input image made by an adversary might result in a large decrease in the accuracy of the algorithm. By detecting heterogeneous facial landmarks using the machine learning library Dlib we hypothesize we can build robustness to adversarial attacks. The residual neural network (ResNet) model has been used as an example of a deep learning model. While the accuracy achieved by ResNet showed a decrease of up to 22%, our proposed approach has shown strong resistance to an attack and showed only a little (<; 0.3%) or no decrease when the attack is launched on the data. Furthermore, the proposed approach has shown considerably less execution time compared to the ResNet model.
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition

Image recognition with prototypes is considered an interpretable alternative for black box deep learning models. Classification depends on the extent to which a test image “looks like” a prototype. However, perceptual similarity for humans can be different from the similarity learned by the classification model. Hence, only visualising prototypes can be insufficient for a user to understand what a prototype exactly represents, and why the model considers a prototype and an image to be similar. We address this ambiguity and argue that prototypes should be explained. We improve interpretability by automatically enhancing visual prototypes with quantitative information about visual characteristics deemed important by the classification model. Specifically, our method clarifies the meaning of a prototype by quantifying the influence of colour hue, shape, texture, contrast and saturation and can generate both global and local explanations. Because of the generality of our approach, it can improve the interpretability of any similarity-based method for prototypical image recognition. In our experiments, we apply our method to the existing Prototypical Part Network (ProtoPNet). Our analysis confirms that the global explanations are generalisable, and often correspond to the visually perceptible properties of a prototype. Our explanations are especially relevant for prototypes which might have been interpreted incorrectly otherwise. By explaining such ‘misleading’ prototypes, we improve the interpretability and simulatability of a prototype-based classification model. We also use our method to check whether visually similar prototypes have similar explanations, and are able to discover redundancy. Code is available at https://github.com/M-Nauta/Explaining_Prototypes.
Benchmarking Meaning Representations in Neural Semantic Parsing

Meaning representation is an important component of semantic parsing.Although researchers have designed a lot of meaning representations, recent work focuses on only a few of them.Thus, the impact of meaning representation on semantic parsing is less understood.Furthermore, existing work's performance is often not comprehensively evaluated due to the lack of readily-available execution engines.Upon identifying these gaps, we propose UNIMER, a new unified benchmark on meaning representations, by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines.The resulting unified benchmark contains the complete enumeration of logical forms and execution engines over three datasets × four meaning representations.A thorough experimental study on UNIMER reveals that neural semantic parsing approaches exhibit notably different performance when they are trained to generate different meaning representations.Also, program alias and grammar rules heavily impact the performance of different meaning representations.Our benchmark, execution engines and implementation can be found on: https: //github.com/JasperGuo/Unimer.
Sketch-Guided Object Localization in Natural Images

We introduce a novel problem of localizing all the instances of an object (seen or unseen during training) in a natural image via sketch query. We refer to this problem as sketch-guided object localization. This problem is distinctively different from the traditional sketch-based image retrieval task where the gallery set often contains images with only one object. The sketch-guided object localization proves to be more challenging when we consider the following: (i) the sketches used as queries are abstract representations with little information on the shape and salient attributes of the object, (ii) the sketches have significant variability as they are hand-drawn by a diverse set of untrained human subjects, and (iii) there exists a domain gap between sketch queries and target natural images as these are sampled from very different data distributions. To address the problem of sketch-guided object localization, we propose a novel cross-modal attention scheme that guides the region proposal network (RPN) to generate object proposals relevant to the sketch query. These object proposals are later scored against the query to obtain final localization. Our method is effective with as little as a single sketch query. Moreover, it also generalizes well to object categories not seen during training and is effective in localizing multiple object instances present in the image. Furthermore, we extend our framework to a multi-query setting using novel feature fusion and attention fusion strategies introduced in this paper. The localization performance is evaluated on publicly available object detection benchmarks, viz. MS-COCO and PASCAL-VOC, with sketch queries obtained from ‘Quick, Draw!’. The proposed method significantly outperforms related baselines on both single-query and multi-query localization tasks.
Antibunched photon-pair source based on photon blockade in a nondegenerate optical parametric oscillator

Nonclassical light sources, such as correlated photon-pairs, play an important role in quantum optics and quantum information processing systems. This study proposes a process to generate antibunched photon-pairs in a nondegenerate optical parametric oscillator. It is found that when the parameters of the system satisfy certain conditions, the generated photons in subharmonic modes exhibit a strong antibunching behavior and are strongly correlated with one another. In particular, the average photon-pair number is resonantly enhanced. It is also observed that the conventional photon blockade contributes to this phenomenon. In addition, it is interesting to note that fundamental mode photons can blockade the subharmonic mode photons. We refer to this phenomenon as a heterogeneous photon blockade.
People as Scene Probes

By analyzing the motion of people and other objects in a scene, we demonstrate how to infer depth, occlusion, lighting, and shadow information from video taken from a single camera viewpoint. This information is then used to composite new objects into the same scene with a high degree of automation and realism. In particular, when a user places a new object (2D cut-out) in the image, it is automatically rescaled, relit, occluded properly, and casts realistic shadows in the correct direction relative to the sun, and which conform properly to scene geometry. We demonstrate results (best viewed in supplementary video) on a range of scenes and compare to alternative methods for depth estimation and shadow compositing.
Ultra High Fidelity Deep Image Decompression With l<sub>∞</sub>-Constrained Compression

We propose a novel asymmetric image compression system of light ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -constrained predictive encoding and heavy-duty CNN-based soft decoding. The system achieves superior rate-distortion performances over the best of existing image compression methods, including BPG, WebP, FLIF and recent CNN codecs, in both ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> error metrics, for bit rates near or above the threshold of perceptually transparent reconstruction. These remarkable coding gains are made by deep learning for compression artifact removal. A restoration CNN is designed to map a lossy compressed image to its original. Its unique strength is to enforce a tight error bound on a per pixel basis. As such, no small distinctive structures of the original image can be dropped or distorted, even if they are statistical outliers that are otherwise sacrificed by mainstream CNN restoration methods.
Journalism that tells the future: possibilities and journalistic scenarios for augmented reality

The Gartner Hype Cycle for Emerging Technologies highlights “augmented human technologies” as one of their emerging trends, while the Future Today Institute alludes to the potential of extended reality (digitally manipulated environments encompassing virtual and augmented reality) for the development of new applications with great informative value. Based on this, the current study brings together in-depth interviews with experts who analyze the possibilities of augmented reality (AR) in journalism, including its weaknesses and some proposals for new journalistic scenarios. The study concludes that, according to experts, the use of AR in daily systems and applications is key to establishing this technology. In addition, experts stress the need to carry out sociological and ethical studies of its impact, as well as experimental research on the use of augmented senses in humans, including both visual and haptic augmentation and hearing. Resumen El Gartner hype cycle for emerging technologies destaca las “tecnologías humanas aumentadas” como una de las tendencias emergentes; mientras que el Future Today Institute alude al potencial de la realidad extendida (entornos manipulados digitalmente que abarca la realidad virtual y aumentada) para el desarrollo de nuevas aplicaciones de gran valor informativo. Partiendo de ello, el estudio reúne entrevistas en profundidad a expertos que analizan las posibilidades de la realidad aumentada (RA) en el periodismo y sus debilidades, y propone nuevos escenarios a nivel periodístico. Según los expertos, el uso de RA en sistemas y aplicaciones de uso diario es el punto clave para asentar esta tecnología. Además, inciden en la necesidad de realizar estudios sociológicos y éticos de su impacto, así como investigaciones experimentales sobre el uso de sentidos aumentados en humanos, tanto el aumento visual como la háptica y la audición.
Internalizing representation independence with univalence

In their usual form, representation independence metatheorems provide an external guarantee that two implementations of an abstract interface are interchangeable when they are related by an operation-preserving correspondence. If our programming language is dependently-typed, however, we would like to appeal to such invariance results within the language itself, in order to obtain correctness theorems for complex implementations by transferring them from simpler, related implementations. Recent work in proof assistants has shown that Voevodsky's univalence principle allows transferring theorems between isomorphic types, but many instances of representation independence in programming involve non-isomorphic representations. In this paper, we develop techniques for establishing internal relational representation independence results in dependent type theory, by using higher inductive types to simultaneously quotient two related implementation types by a heterogeneous correspondence between them. The correspondence becomes an isomorphism between the quotiented types, thereby allowing us to obtain an equality of implementations by univalence. We illustrate our techniques by considering applications to matrices, queues, and finite multisets. Our results are all formalized in Cubical Agda, a recent extension of Agda which supports univalence and higher inductive types in a computationally well-behaved way.
Measuring Correlation-to-Causation Exaggeration in Press Releases

Press releases have an increasingly strong influence on media coverage of health research; however, they have been found to contain seriously exaggerated claims that can misinform the public and undermine public trust in science.In this study we propose an NLP approach to identify exaggerated causal claims made in health press releases that report on observational studies, which are designed to establish correlational findings, but are often exaggerated as causal.We developed a new corpus and trained models that can identify causal claims in the main statements in a press release.By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22% of press releases made exaggerated causal claims from correlational findings in observational studies.Furthermore, universities exaggerated more often than journal publishers by a ratio of 1.5 to 1. Encouragingly, the exaggeration rate has slightly decreased over the past 10 years, despite the increase of the total number of press releases.More research is needed to understand the cause of the decreasing pattern.
Benchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation

A fundamental ability of humans is to utilize commonsense knowledge in language understanding and question answering. In recent years, many knowledge-enhanced Commonsense Question Answering (CQA) approaches have been proposed. However, it remains unclear: (1) How far can we get by exploiting external knowledge for CQA? (2) How much potential of knowledge has been exploited in current CQA models? (3) Which are the most promising directions for future CQA? To answer these questions, we benchmark knowledge-enhanced CQA by conducting extensive experiments on multiple standard CQA datasets using a simple and effective knowledge-to-text transformation framework. Experiments show that: (1) Our knowledge-to-text framework is effective and achieves state-of-the-art performance on CommonsenseQA dataset, providing a simple and strong knowledge-enhanced baseline for CQA; (2) The potential of knowledge is still far from being fully exploited in CQA — there is a significant performance gap from current models to our models with golden knowledge; and (3) Context-sensitive knowledge selection, heterogeneous knowledge exploitation, and commonsense-rich language models are promising CQA directions.
Person Tracking and Reidentification for Multicamera Indoor Video Surveillance Systems

For practical use, the relevance of indoor surveillance from multiple cameras to track the movement of people and reidentify them in video sequences is constantly increasing. This is a complex task due to the effect of uneven illumination, background inhomogeneity, overlap, uncertainty of the trajectories of people, and the similarity of their visual features. The article presents an approach to track people by video sequences and reidentify them in multicamera video surveillance systems that are used indoors. At the first step, people are detected using a YOLO v4 convolution neural network (CNN) and described by a rectangular area. Further, the search for the face area and the calculation of its features are carried out, which in the developed method are used when accompanying a person in a video sequence and during his intercamera reidentification. This approach improves the accuracy of tracking with a complex movement trajectory and multiple intersections of people with similar characteristics. The search for faces is carried out on the detected areas based on the multitasking MTCNN, and the MobileFaceNetwork model is used to form the vector of the features of the face. Human features are generated using a modified CNN based on ResNet34 and an HSV color tone channel histogram. The correspondence between people on different frames is established based on the analysis of the spatial coordinates of faces and people, as well as their CNN features, using the Hungarian algorithm. To ensure the accuracy of intercamera tracking, reidentification is performed based on the facial features. Five test video sequences of different numbers of people captured indoors with a fixed video camera were used to test and compare different approaches. The obtained experimental results confirmed the strength of the characteristics of the proposed approach.
Deep-CAPTCHA: A Deep Learning Based CAPTCHA Solver for Vulnerability Assessment

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