Reviewerly/exHarmony huggingface dataset:
Single Image Super-Resolution: Depthwise Separable Convolution Super-Resolution Generative Adversarial Network

The super-resolution generative adversarial network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied by unpleasant artifacts. To further enhance the visual quality, we propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The method is based on depthwise separable convolution super-resolution generative adversarial network (DSCSRGAN). A new depthwise separable convolution dense block (DSC Dense Block) was designed for the generator network, which improved the ability to represent and extract image features, while greatly reducing the total amount of parameters. For the discriminator network, the batch normalization (BN) layer was discarded, and the problem of artifacts was reduced. A frequency energy similarity loss function was designed to constrain the generator network to generate better super-resolution images. Experiments on several different datasets showed that the peak signal-to-noise ratio (PSNR) was improved by more than 3 dB, structural similarity index (SSIM) was increased by 16%, and the total parameter was reduced to 42.8% compared with the original model. Combining various objective indicators and subjective visual evaluation, the algorithm was shown to generate richer image details, clearer texture, and lower complexity.
Federated Patient Hashing

Privacy concerns on sharing sensitive data across institutions are particularly paramount for the medical domain, which hinders the research and development of many applications, such as cohort construction for cross-institution observational studies and disease surveillance. Not only that, the large volume and heterogeneity of the patient data pose great challenges for retrieval and analysis. To address these challenges, in this paper, we propose a Federated Patient Hashing (FPH) framework, which collaboratively trains a retrieval model stored in a shared memory while keeping all the patient-level information in local institutions. Specifically, the objective function is constructed by minimization of a similarity preserving loss and a heterogeneity digging loss, which preserves both inter-data and intra-data relationships. Then, by leveraging the concept of Bregman divergence, we implement optimization in a federated manner in both centralized and decentralized learning settings, without accessing the raw training data across institutions. In addition to this, we also analyze the convergence rate of the FPH framework. Extensive experiments on real-world clinical data set from critical care are provided to demonstrate the effectiveness of the proposed method on similar patient matching across institutions.
Background Segmentation for Vehicle Re-identification

Vehicle re-identification (Re-ID) is very important in intelligent transportation and video surveillance. Prior works focus on extracting discriminative features from visual appearance of vehicles or using visual-spatio-temporal information. However, background interference in vehicle re-identification have not been explored. In the actual large-scale spatio-temporal scenes, the same vehicle usually appears in different backgrounds while different vehicles might appear in the same background, which will seriously affect the re-identification performance. To the best of our knowledge, this paper is the first to consider the background interference problem in vehicle re-identification. We construct a vehicle segmentation dataset and develop a vehicle Re-ID framework with a background interference removal (BIR) mechanism to improve the vehicle Re-ID performance as well as robustness against complex background in large-scale spatio-temporal scenes. Extensive experiments demonstrate the effectiveness of our proposed framework, with an average 9% gain on mAP over state-of-the-art vehicle Re-ID algorithms.
Generating Diverse Translation by Manipulating Multi-Head Attention

Transformer model (Vaswani et al. 2017) has been widely used in machine translation tasks and obtained state-of-the-art results. In this paper, we report an interesting phenomenon in its encoder-decoder multi-head attention: different attention heads of the final decoder layer align to different word translation candidates. We empirically verify this discovery and propose a method to generate diverse translations by manipulating heads. Furthermore, we make use of these diverse translations with the back-translation technique for better data augmentation. Experiment results show that our method generates diverse translations without a severe drop in translation quality. Experiments also show that back-translation with these diverse translations could bring a significant improvement in performance on translation tasks. An auxiliary experiment of conversation response generation task proves the effect of diversity as well.
Residual in Residual Based Convolutional Neural Network In-loop Filter for AVS3

Deep learning based video coding tools development has been an emerging topic recently. In this paper, we propose a novel deep convolutional neural network (CNN) based in-loop filter algorithm for the third generation of Audio Video Coding Standard (AVS3). Specifically, we first introduce a residual block based CNN model with global identity connection for the luminance in-loop filter to replace conventional rule-based algorithms in AVS3. Subsequently, the reconstructed luminance channel is deployed as textural and structural guidance for chrominance filtering. The corresponding syntax elements are also designed for the CNN based in-loop filtering. In addition, we build a large scale database for the learning based in-loop filtering algorithm. Experimental results show that our method achieves on average 7.5%, 16.9% and 18.6% BD-rate reduction under all intra (AI) configuration on common test sequences. In particular, the performance for 4K videos is 6.4%, 15.5% and 17.5% respectively. Moreover, under random access (RA) configuration, the proposed method brings 3.3%, 14.4%, and 13.6% BD-rate reduction separately.
Agile Transition and Adoption Frameworks, Issues and Factors: A Systematic Mapping

In order to adopt specific agile methods and to accommodate lean principles, many organizations need to tailor their processes. Different frameworks supporting and guiding the agile transition and adoption process exist in the literature. However, there is a demand for specific guidelines on the different circumstances influencing agile adoption. This paper provides a Systematic mapping of the available frameworks, issues and factors affecting a successful agile transition and adoption process. Twenty-eight primary studies have been considered. The results are presented according to the three research questions. Firstly, nine systematic approaches and frameworks for agile transition and adoption are analyzed. Secondly, different issues related to the agile transition and adoption process are gathered, compared and synthesized in the following five categories: pre-transformation activities, obstacles and results, stages and activities, agile practices and situational factors. Finally, a list of 154 situational factors affecting the agile transition and adoption process is proposed.
The Fast Maximum Distance to Average Vector (F-MDAV): An algorithm for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e393" altimg="si20.svg"><mml:mi>k</mml:mi></mml:math>-anonymous microaggregation in big data

The massive exploitation of tons of data is currently guiding critical decisions in domains such as economics or health. But serious privacy risks arise since personal data is commonly involved. k-Anonymous microaggregation is a well-known method that guarantees individuals’ privacy while preserving much of data utility. Unfortunately, methods like this are computationally expensive in big data settings, whereas the application domain of data might require an immediate response to make “life or death” decisions. Accordingly, this paper proposes five strategies to simplify the internal operations (such as distance calculations and element sorting) of the maximum distance to average vector algorithm, the de facto microaggregation standard. For the sake of its usability in large-scale databases, they, e.g., reduce the number of operations necessary to compute distances from 3m to 2m, where m is the number of attributes of the data set. Also, the complexity of sorting operations gets reduced from O(nlogn) to O(n) where n is the number of records. Through extensive experimentation over multiple data sets, we show that the new algorithm gets significantly faster. Interestingly, the speedup factor by each technique is not greater than 2, but the multiplicative effect of combining them all turns the algorithm four times faster than the original microaggregation mechanism. This remarkable speedup factor is achieved, literally, with no additional cost in terms of data utility, i.e., it does not incur greater information loss.
Parallel Implementation of K-Means Algorithm on FPGA

The K-means algorithm is widely used to find correlations between data in different application domains. However, given the massive amount of data stored, known as Big Data, the need for high-speed processing to analyze data has become even more critical, especially for real-time applications. A solution that has been adopted to increase the processing speed is the use of parallel implementations on FPGA, which has proved to be more efficient than sequential systems. Hence, this paper proposes a fully parallel implementation of the K-means algorithm on FPGA to optimize the system’s processing time, thus enabling real-time applications. This proposal, unlike most implementations proposed in the literature, even parallel ones, do not have sequential steps, a limiting factor of processing speed. Results related to processing time (or throughput) and FPGA area occupancy (or hardware resources) were analyzed for different parameters, reaching performances higher than 53 millions of data points processed per second. Comparisons to the state of the art are also presented, showing speedups of more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15573\times $ </tex-math></inline-formula> over a partially serial implementation.
Ki-67 Proliferation Index in Breast Cancer as a Function of Assessment Method: A NordiQC Experience

Immunohistochemical staining for Ki-67 is used to calculate a Ki-67 proliferation index (PI) that carries prognostic and predictive information in various cancers including breast carcinomas. Studies have documented challenges for observers to reproducibly estimate the Ki-67 PI. At present, no international consensus exists concerning scoring method (eg, hotspots vs. overall average, digital vs. manual counting) or even the definition of a Ki-67-positive cell. To clarify the approach to Ki-67 scoring and evaluation of the interobserver agreement among participants in the Nordic Immunohistochemical Quality Control (NordiQC) Breast Cancer Module, a study was set up on the basis of an online web module containing 15 digitized tissue microarray cores of breast carcinomas stained for Ki-67 in the NordiQC reference laboratory. All participants were invited to attend the study. In addition to Ki-67 scoring, they were asked to disclose their preferred method for Ki-67 estimation and their job title. For comparison, slides were analyzed using a Digital Image Analysis algorithm based on Virtual Double Staining. In total, 199 participants enrolled for the study. Overall, there was a good correlation in Ki-67 PIs among the participants, although results for some cores varied significantly. However, when applying a cutoff of 20%, a relatively low κ value of 0.52 was observed. Participants scoring in hotspots reported higher Ki-67 PIs than participants estimating an overall average, and, not surprisingly, participants who considered weak Ki-67 nuclear staining positive obtained higher Ki-67 PIs than those who did not. Ki-67 PI was not correlated to job title. The Virtual Double Staining algorithm obtained Ki-67 values close to the mean value of the human observers. Our study underlines the need for international standardization and guidelines in estimation of Ki-67 PI. Digital Image Analysis may be a useful tool in this process.
Balance resource allocation for spark jobs based on prediction of the optimal resource

Apache Spark provides a well-known MapReduce computing framework, aiming to fast-process big data analytics in data-parallel manners. With this platform, large input data are divided into data partitions. Each data partition is processed by multiple computation tasks concurrently. Outputs of these computation tasks are transferred among multiple computers via the network. However, such a distributed computing framework suffers from system overheads, inevitably caused by communication and disk I/O operations. System overheads take up a large proportion of the Job Completion Time (JCT). We observed that excessive computational resources incurs considerable system overheads, prolonging the JCT. The over-allocation of individual jobs not only prolongs their own JCTs, but also likely makes other jobs suffer from under-allocation. Thus, the average JCT is suboptimal, too. To address this problem, we propose a prediction model to estimate the changing JCT of a single Spark job. With the support of the prediction method, we designed a heuristic algorithm to balance the resource allocation of multiple Spark jobs, aiming to minimize the average JCT in multiple-job cases. We implemented the prediction model and resource allocation method in ReB, a Resource-Balancer based on Apache Spark. Experimental results showed that ReB significantly outperformed the traditional max-min fairness and shortest-job-optimal methods. The average JCT was decreased by around 10%-30% compared to the existing solutions.
Learning a deep predictive coding network for a semi-supervised 3D-hand pose estimation

In this paper we present a CNN based approach for a real time 3D-hand pose estimation from the depth sequence. Prior discriminative approaches have achieved remarkable success but are facing two main challenges: Firstly, the methods are fully supervised hence require large numbers of annotated training data to extract the dynamic information from a hand representation. Secondly, unreliable hand detectors based on strong assumptions or a weak detector which often fail in several situations like complex environment and multiple hands. In contrast to these methods, this paper presents an approach that can be considered as semi-supervised by performing predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision. The hand is modelled using a novel latent tree dependency model ( LDTM ) which transforms internal joint location to an explicit representation. Then the modeled hand topology is integrated with the pose estimator using data dependent method to jointly learn latent variables of the posterior pose appearance and the pose configuration respectively. Finally, an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose. Experiments on three challenging public datasets, ICVL, MSRA, and NYU demonstrate the significant performance of the proposed method which is comparable or better than state-of-the-art approaches.
On the Security Goals of White-Box Cryptography

We discuss existing and new security notions for white-box cryptography and comment on their suitability for Digital Rights Management and Mobile Payment Applications, the two prevalent use-cases of white-box cryptography. In particular, we put forward indistinguishability for white-box cryptography with hardware-binding (IND-WHW) as a new security notion that we deem central. We also discuss the security property of application-binding and explain the issues faced when defining it as a formal security notion. Based on our proposed notion for hardware-binding, we describe a possible white-box competition setup which assesses white-box implementations w.r.t. hardware-binding. Our proposed competition setup allows us to capture hardware-binding in a practically meaningful way.While some symmetric encryption schemes have been proven to admit plain white-box implementations, we show that not all secure symmetric encryption schemes are white-boxeable in the plain white-box attack scenario, i.e., without hardware-binding. Thus, even strong assumptions such as indistinguishability obfuscation cannot be used to provide secure white-box implementations for arbitrary ciphers. Perhaps surprisingly, our impossibility result does not carry over to the hardware-bound scenario. In particular, Alpirez Bock, Brzuska, Fischlin, Janson and Michiels (ePrint 2019/1014) proved a rather general feasibility result in the hardware-bound model. Equally important, the apparent theoretical distinction between the plain white-box model and the hardware-bound white-box model also translates into practically reduced attack capabilities as we explain in this paper.
Realtime index-free single source SimRank processing on web-scale graphs

Given a graph G and a node u ∈ G, a single source SimRank query evaluates the similarity between u and every node v ∈ G. Existing approaches to single source SimRank computation incur either long query response time, or expensive pre-computation, which needs to be performed again whenever the graph G changes. Consequently, to our knowledge none of them is ideal for scenarios in which (i) query processing must be done in realtime, and (ii) the underlying graph G is massive, with frequent updates. Motivated by this, we propose SimPush, a novel algorithm that answers single source SimRank queries without any pre-computation, and achieves significantly higher query speed than even the fastest known index-based solutions. Further, SimPush provides rigorous result quality guarantees, and its high performance does not rely on any strong assumption of the graph. Specifically, compared to existing methods, SimPush employs a radically different algorithmic design that focuses on (i) identifying a small number of nodes relevant to the query, and subsequently (ii) computing statistics and performing residue push from these nodes only. We prove the correctness of SimPush, analyze its time complexity, and compare its asymptotic performance with that of existing methods. Meanwhile, we evaluate the practical performance of SimPush through extensive experiments on 9 real datasets. The results demonstrate that SimPush consistently outperforms all existing solutions, often by over an order of magnitude. In particular, on a commodity machine, SimPush answers a single source SimRank query on a web graph containing over 133 million nodes and 5.4 billion edges in under 62 milliseconds, with 0.00035 empirical error, while the fastest index-based competitor needs 1.18 seconds.
Video-based augmented reality combining CT-scan and instrument position data to microscope view in middle ear surgery

Abstract The aim of the study was to develop and assess the performance of a video-based augmented reality system, combining preoperative computed tomography (CT) and real-time microscopic video, as the first crucial step to keyhole middle ear procedures through a tympanic membrane puncture. Six different artificial human temporal bones were included in this prospective study. Six stainless steel fiducial markers were glued on the periphery of the eardrum, and a high-resolution CT-scan of the temporal bone was obtained. Virtual endoscopy of the middle ear based on this CT-scan was conducted on Osirix software. Virtual endoscopy image was registered to the microscope-based video of the intact tympanic membrane based on fiducial markers and a homography transformation was applied during microscope movements. These movements were tracked using Speeded-Up Robust Features (SURF) method. Simultaneously, a micro-surgical instrument was identified and tracked using a Kalman filter. The 3D position of the instrument was extracted by solving a three-point perspective framework. For evaluation, the instrument was introduced through the tympanic membrane and ink droplets were injected on three middle ear structures. An average initial registration accuracy of 0.21 ± 0.10 mm (n = 3) was achieved with a slow propagation error during tracking (0.04 ± 0.07 mm). The estimated surgical instrument tip position error was 0.33 ± 0.22 mm. The target structures’ localization accuracy was 0.52 ± 0.15 mm. The submillimetric accuracy of our system without tracker is compatible with ear surgery.
AutoSegNet: An Automated Neural Network for Image Segmentation

Neural Architecture Search (NAS) has drawn significant attention as a tool for automatically constructing deep neural networks.The generated neural networks are mainly applied for image classification, and natural language processing.However, there are increasing demands for image segmentation in various areas, such as medical image processing, satellite image object location, and autopilot technology.We propose a NAS method called Automated Segmentation Network (AutoSegNet), targeting industrial and medical image segmentation.The search architectures are constructed by stacking the downsampling layer, the bridge layer, and the upsampling layer, which are explored by a recurrent neural network.Compared with other related methods for image segmentation, the proposed method has a small search space but can explore most of the-state-of-the-art supervised image segmentation models.We perform verification on two datasets, and the results show that AutoSegNet achieves superior segmentation results with clear and continuous segmented edges, as well as better image details.