Reviewerly/exHarmony huggingface dataset:
Learning to Generate Multi-Exposure Stacks With Cycle Consistency for High Dynamic Range Imaging

Inverse tone mapping aims at recovering the lost scene radiances from a single exposure image. With the successful use of deep learning in numerous applications, many inverse tone mapping methods use convolution neural networks in a supervised manner. As these approaches are trained with many pre-fixed high dynamic range (HDR) images, they fail to flexibly expand the dynamic ranges of images. To overcome this limitation, we consider a multiple exposure image synthesis approach for HDR imaging. In particular, we propose a pair of neural networks that represent mappings between images that have exposure levels one unit apart (stop-up/down network). Therefore, it is possible to construct two positive-feedback systems to generate images with greater or lesser exposure. Compared to previous works using the conditional generative adversarial learning framework, the stop-up/down network employs HDR friendly network structures and several techniques to stabilize the training processes. Experiments on HDR datasets demonstrate the advantages of the proposed method compared to conventional methods. Consequently, we apply our approach to restore the full dynamic range of scenes agilely with only two networks and generate photorealistic images in complex lighting situations.
Robust CAPTCHAs Towards Malicious OCR

Turing test was originally proposed to examine whether machine's behavior is indistinguishable from a human. The most popular and practical Turing test is CAPTCHA, which is to discriminate algorithm from human by offering recognition-alike questions. The recent development of deep learning has significantly advanced the capability of algorithm in solving CAPTCHA questions, forcing CAPTCHA designers to increase question complexity. Instead of designing questions difficult for both algorithm and human, this study attempts to employ the limitations of algorithm to design robust CAPTCHA questions easily solvable to human. Specifically, our data analysis observes that human and algorithm demonstrates different vulnerability to visual distortions: adversarial perturbation is significantly annoying to algorithm yet friendly to human. We are motivated to employ adversarially perturbed images for robust CAPTCHA design in the context of character-based questions. Four modules of multi-target attack, ensemble adversarial training, image preprocessing differentiable approximation, and expectation are proposed to address the characteristics of character-based CAPTCHA cracking. Qualitative and quantitative experimental results demonstrate the effectiveness of the proposed solution. We hope this study can lead to the discussions around adversarial attack/defense in CAPTCHA design and also inspire the future attempts in employing algorithm limitation for practical usage.
Efficient Hardware Post Processing of Anchor-Based Object Detection on FPGA

Object detection has been widely adopted in video analysis and image understanding. Anchor-based object detection has achieved good performance on the scale variation that is one long-standing problem for object detection. The postprocessing is an essential step of anchor-based object detection after convolutional neural networks (CNN) and it requires long computation time on CPU or GPU. In this paper, we propose an efficient FPGA solution using fixed-point representation for the postprocessing. The quantization error of fixed-point representation is mainly from the sigmoid function and the exponent function. In order to reduce the error, we implement the sigmoid function and exponent function on FPGA respectively employing piecewise non-linear approximation and "LUT and shifting" method. The performance of both functions has been demonstrated to realize 10-4 accuracy. In addition, the Non-Maximum Suppression (NMS) is also employed and implemented to reduce redundant objects bounding boxes. Based on these, a fast and resource-efficient accelerator for postprocessing is implemented on Intel Arria 10 FPGA. By using only about 1% of the FPGA hardware resources, our design can achieve about 111×, 50×, and 290× speedup compared to the software implementations on desktop CPU, GPU, and the embedded CPU inside FPGA, respectively.
HotDedup: Managing Hot Data Storage at Network Edge through Optimal Distributed Deduplication

The rapid growth of computing capabilities at network edge calls for efficient management frameworks that not only considers placing hot data on edge storage for best accessibility and performance, but also makes optimal utilization of edge storage space. In this paper, we solve a joint optimization problem by exploiting both data popularity (for optimal data access performance) and data similarity (for optimal storage space efficiency). We show that the proposed optimization is NP- hard and develop a 2⌈2Γ⌉ - 1 + ϵ-approximation algorithm by (i) making novel use of δ-similarity graph to capture pairwise data similarity and (ii) leveraging the k-MST algorithm to solve a Prize Collecting Steiner Tree problem on the graph. The proposed algorithm is prototyped using an open-source distributed storage system, Cassandra. We evaluate its performance extensively on a real-world testbed and with respect to real-world IoT datasets. The algorithm is shown to achieve over 55% higher edge service rate and reduces request response time by about 30%.
Trustworthy, Secure, and Privacy-aware Food Monitoring Enabled by Blockchains and the IoT

A large number of food scandals (e. g., falsely declared meat or non-compliance with hygiene regulations) are causing considerable concern to consumers. Although Internet of Things (IoT) technologies are used in the food industry to monitor production (e. g., for tracing the origin of meat or monitoring cold chains), the gathered data are not used to provide full transparency to the consumer. To achieve this, however, three aspects must be considered: a) The origin of the data must be verifiable, i. e., it must be ensured that the data originate from calibrated sensors. b) The data must be stored tamper-resistant, immutable, and open to all consumers. c) Despite this openness, the privacy of affected data subjects (e. g., the carriers) must still be protected. To this end, we introduce the SHEEPDOG architecture that “shepherds” products from production to purchase to enable a trustworthy, secure, and privacy-aware food monitoring. In SHEEPDOG, attribute-based credentials ensure trustworthy data acquisition, blockchain technologies provide secure data storage, and fine-grained access control enables privacy-aware data provision.
Sequentially spherical data modeling with hidden Markov models and its application to fMRI data analysis

Due to the reason that spherical data (i.e. L2 normalized vectors) are often involved with various real-life applications (such as anomaly detection, gesture recognition, intrusion detection in networks, gene expression data analysis, etc.), spherical data modeling has recently become an important research topic. In this work, we address the problem of modeling sequentially spherical data through continuous hidden Markov models (HMMs). Instead of adopting Gaussian mixture models (GMMs) as the emission distributions as in common continuous HMMs, we propose a continuous HMM by considering the mixture of von Mises–Fisher (VMF) distributions as its emission densities. Then, we systematically propose an effective method based on variational Bayes (VB) to learn the VMF-based HMM. The developed learning method has the following merits: (1) It is convergence-guaranteed; (2) It can be optimized with closed-form solutions. The proposed VMF–HMM with VB learning is validated by conducting experiments on both simulated sequential spherical data and a real application about fMRI data analysis.
Estimation of Quality Scores From Subjective Tests-Beyond Subjects’ MOS

Subjective tests for the assessment of the quality of experience (QoE) are typically run with a pool of subjects providing their opinion scores using a 5-point scale. The subjects’ mean opinion score (MOS) is generally assumed as the best estimation of the average score in the target population. Indeed, for a large enough sample, we may assume that the mean of the variations across the subjects approaches zero, but this is not the case for the limited number of subjects typically considered in subjective tests. In this paper, we propose an approach based on generalized linear models (GLMs) for estimation of the population average QoE. The motivating dataset is composed of the individual scores assigned by 25 subjects to a set of gaming videos evaluated under different resolutions and compression ratios. The approach recognizes the multinomial nature of the data and allows for correlation between scores of the same subject. The resulting estimated average QoE is shown to follow more credible patterns than the MOS, particularly for higher bitrates, for which the model estimates present more coherent behavior. Similar convincing results are found on a second dataset, showing the validity of the approach.
The evolutionary dynamics of social systems via reflexive transformation of external reality

One of the main challenges of the social sciences is to explain metasystem transitions from biological to social systems in the process of evolution. These transitions correspond to the emergence of the structure of the subject in which the external world is internalized as a symbolic image. This structure has the potential of rationally reflecting the external world and encoding it in human language. The structure of the subject was defined by Freud and Lacan within the framework of psychoanalysis and modeled by Vladimir Lefebvre using the algebra of simple relations. In that context, the binary oppositions of the Western (W) and Eastern (E) types of cognitive reflection generate not only opposite types of societies but also form the spatiotemporal pattern within a society as a whole underlying its homeostasis and internal dynamics. This opposition between Western and Eastern types is not identical to, but mirrors the probably genetically determined opposition between individuals with primarily selfish or altruistic behavior. The structure of the reflexive subject represents the basis of a sociotype that can be considered as the third component of biosocial being in addition to genotype and phenotype; it is defined a social form of the individual bonded by the social environment and social interactions. The transition to a different type of social relations and structure is based on an anticipated reflexive choice by an individual that is accompanied by a coherent response from the society. This change is defined as a “social transaction” and can be modeled by the logic of the transactional interpretation of quantum mechanics. In the history of human civilization, social transactions have resulted in the advancement and distribution of new knowledge and technologies, and in the formation, merging, splitting and decline and re-emergence of particular types of societies.
Dynamic Speed Warping: Similarity-Based One-shot Learning for Device-free Gesture Signals

In this paper, we propose a Dynamic Speed Warping (DSW) algorithm to enable one-shot learning for device-free gesture signals performed by different users. The design of DSW is based on the observation that the gesture type is determined by the trajectory of hand components rather than the movement speed. By dynamically scaling the speed distribution and tracking the movement distance along the trajectory, DSW can effectively match gesture signals from different domains that have a ten-fold difference in speeds. Our experimental results show that DSW can achieve a recognition accuracy of 97% for gestures performed by unknown users, while only use one training sample of each gesture type from four training users.
Adversarial behaviours in mixing coins under incomplete information

Criminals can launder crypto-currencies through mixing coins, whose original purpose is preservation of privacy in the presence of traceability. Therefore, it is essential to elaborately design mixing polices to achieve both privacy and anti-money laundering. Existing work on mixing policies relies on the knowledge of a blacklist. However, these policies are paralysed under the scenario where the blacklist is unknown or evolving. In this paper, we regard the above scenario as games under incomplete information where parties put down a deposit for the quality of coins, which is suitably managed by a smart contract in case of mixing bad coins. We extend the poison and haircut policies to incomplete information games, where the blacklist is updated after mixing. We prove the existence of equilibria for the improved polices, while it is known that there is no equilibria in the original poison and haircut policies, where blacklist is public known. Furthermore, we propose a seminal suicide policy: the one who mixes more bad coins will be punished by not having the deposit refunded. Thus, parties have no incentives to launder money by leveraging mixing coins. In effect, all three policies contrast money laundering while preserving privacy under incomplete information. Finally, we simulate and verify the validity of these policies.
DeepMapi: a Fully Automatic Registration Method for Mesoscopic Optical Brain Images Using Convolutional Neural Networks

Abstract The extreme complexity of mammalian brains requires a comprehensive deconstruction of neuroanatomical structures. Scientists normally use a brain stereotactic atlas to determine the locations of neurons and neuronal circuits. However, different brain images are normally not naturally aligned even when they are imaged with the same setup, let alone under the differing resolutions and dataset sizes used in mesoscopic imaging. As a result, it is difficult to achieve high-throughput automatic registration without manual intervention. Here, we propose a deep learning-based registration method called DeepMapi to predict a deformation field used to register mesoscopic optical images to an atlas. We use a self-feedback strategy to address the problem of imbalanced training sets (sampling at a fixed step size in nonuniform brains of structures and deformations) and use a dual-hierarchical network to capture the large and small deformations. By comparing DeepMapi with other registration methods, we demonstrate its superiority over a set of ground truth images, including both optical and MRI images. DeepMapi achieves fully automatic registration of mesoscopic micro-optical images, even macroscopic MRI datasets, in minutes, with an accuracy comparable to those of manual annotations by anatomists.
TGC20ReId: A dataset for sport event re-identification in the wild

Person re-identification (Re-ID) is the task of retrieving a person of interest taken from different cameras or from the same camera in different occasions. To address this challenging task, a large amount of labelled data is required both for testing and for learning. Such high quality annotated data is still rare for many Re-ID applications. In this paper, we introduce a novel dataset to evaluate Re-ID methods in complex real-world scenarios. In this case, we will be using a sporting event as the scenario. For this aim, participants in a 128 km night and day course were captured in five different recording points along the track and later manually identified and annotated. The dataset is evaluated using state-of-the-art techniques and wide array of experimental setups are considered, such as: day vs. night, motion blur, changes in clothing, etc. The evaluation suggests that the features present in the proposed dataset (time difference, unrestricted weather and illumination capture conditions, and the possibility of clothes changes between probe and gallery) pose a challenging scenario for future development in the Re-ID problem.
In-situ observation of tensile failure mode in cross-ply CFRP laminates using Talbot-Lau interferometry

Over the last decade, the use of X-ray inspection imaging is significantly grown for microcrack analysis of carbon fiber reinforced polymer (CFRP). In the current work, the microcracks generated in cross-ply CFRP laminates during the tensile test were examined using X-ray Talbot-Lau interferometry (TLI). Unlike conventional X-ray inspection, TLI provides three images in one shot, called attenuation contrast (AC), differential phase contrast (DPC), and dark-field contrast (DFC). During X-ray analysis, CFRP cross-ply laminate was subjected to monotonic tensile load and generated microcracks were recorded. Using DFC images, multiple transverse microcracks originated from the notch, and its propagation along the width during the tensile test was detected. Moreover, an optical microscope was used to find out the microcrack density and size of microcrack in CFRP cross-ply laminates.
Weakly Supervised Learning for Raindrop Removal on a Single Image

In this paper, we address a problem of view-disturbing raindrop removal on a single image. In existing methods to tackle this problem, machine learning based ones seem promising but require elaborate pairwise images, i.e., the raindrop-degraded image and the corresponding clean image of the same scene, for training. To overcome this drawback, we propose a weakly supervised learning based model in the absence of pairwise training examples, which needs only a collection of images with image-level annotations indicating the presence/absence of raindrops for training. Specifically, we train a raindrop detector for highlighting regions of raindrops in a multi-task learning manner. Then, we propose an attention-based generative network for raindrop removal and introduce a weighted preservation loss to retain the non-raindrop details. Specially, our model can be mixedly trained with pairwise and unpaired samples, which enables us to conveniently adapt the model to a new domain. Experiments verify the effect of the proposed method. Especially, using only weakly-supervised learning, our method can achieve comparable results with state-of-the-art strongly-supervised learning methods.
Enabling Live Migration of Containerized Applications Across Clouds

Live migration, the process of transferring a running application to a different physical location with minimal downtime, can provide many benefits desired by modern cloudbased systems. Furthermore, live migration between different cloud providers enables a new level of freedom for cloud users to move their workloads around for performance or business objectives without having to be tied down to any single provider. While this vision is not new, to-date, there are few solutions and proof-of-concepts that provide this capability. As containerized applications are gaining popularity, we focus on the design and implementation of live migration of containers across cloud providers. CloudHopper, our proof-of-concept live migration service for containers to hop around between Amazon Web Services, Google Cloud Platform, and Microsoft Azure is evaluated using a common web-based workload. CloudHopper is automated and supports pre-copy optimization, connection holding, traffic redirection, and multiple interdependent container migration. It is applicable to a broad range of application use cases.
Use of Twitter as an Educational Resource. Analysis of Concepts of Active and Trainee Teachers

The use of innovative tools is a key element in the improvement of the educational process, and consequently, the use of social networks as a means of training to improve not only the learning of students, but also to improve the expectations of the teacher. Therefore, in this research we tried to investigate the incidence of social networks as facilitators of resources and didactic/playful experiences. Starting from a qualitative methodology, a descriptive–interpretative study of the information from 31 interviews with both primary education teachers and future teachers who were in training and in their last year of initial training was approached. The results indicate that the interviewees confirmed the great usefulness of social networks as educational resources and for teacher communication. The possibilities of social networks within the educational field are overly broad. They become a way of interaction between teacher and student, encourage dialogue, share didactic resources, favor collaborative learning, and develop communication skills in all aspects. In short, these are results that have a special incidence and impact on the field and social development, since we are referring fundamentally to the improvements that are produced in interpersonal relations.
A blockchain based solution for the custody of digital files in forensic medicine

Despite the benefits that digital forensic medical evidence offers, the custody and sharing of such information remains an ongoing problem. While waiting for an optimal solution, both professionals and institutions must evaluate their options and choose the least disadvantageous among them. This paper proposes resolving the problem through an operational hybrid platform that uses a consensus mechanism to record a transparent history of access and prevent unauthorised users from modifying it. The digital evidence is encrypted and saved in an online file storage system, while the file properties are stored on a private implementation of the Hyperledger Fabric™ blockchain. The blockchain nodes allow access to the data through a dynamic consensus mechanism, and all operations (like uploads, views, or deletions) are continuously and permanently recorded on the blockchain. The network is safe and accessible through a dedicated application. All information is agreed upon and shared between the blockchain nodes to avoid single points of failure, and secure access to digital evidence is assured by combining cryptography and the blockchain consensus mechanism. The result is a secure and complete framework with which to upload, store and share digital forensic medical evidence. Despite some limitations, this proposal offers an implementable solution for the custody of digital evidence in forensic medicine that has been identified through existing and innovative technologies, the implementation of a proof of principle prototype, and benchmarks.
ProTO: Proactive Topology Obfuscation Against Adversarial Network Topology Inference

The topology of a network is fundamental for building network infrastructure functionalities. In many scenarios, enterprise networks may have no desire to disclose their topology information. In this paper, we aim at preventing attacks that use adversarial, active end-to-end topology inference to obtain the topology information of a target network. To this end, we propose a Proactive Topology Obfuscation (ProTO) system that adopts a detect-then-obfuscate framework: (i) a lightweight probing behavior identification mechanism based on machine learning is designed to detect any probing behavior, and then (ii) a topology obfuscation design is developed to proactively delay all identified probe packets in a way such that the attacker will obtain a structurally accurate yet fake network topology based on the measurements of these delayed probe packets, therefore deceiving the attacker and decreasing its appetency for future inference. We show that ProTO is very effective against active topology inference with minimum performance disruption. Experimental results under different evaluation scenarios show that ProTO is able to (i) achieve a detection rate of 99.9% with a false alarm of 3%, (ii) effectively disrupt adversarial topology inference and lead to the topology inferred by the attacker close to a fake topology, and (iii) result in an overall network delay performance degradation of 1.3% - 2.0%.