Reviewerly/exHarmony huggingface dataset:
A 1.4GHz 20.5Gbps GZIP decompression accelerator in 14nm CMOS featuring dual-path out-of-order speculative Huffman decoder and multi-write enabled register file array

A 33,464μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GZIP decompression accelerator is fabricated in 14nm CMOS, achieving industry-leading 20.5Gbps throughput. The design features out-of-order speculative Huffman decoder to break the fundamental serial dependency resulting in 69% higher decode throughput. The hybrid dual-path decoder provides 2.3× higher performance with multi-write enabled register-file array increasing decompression throughput by up to 41%. The arithmetic-architecture-circuit co-optimized design operates at 1.4GHz at 750mV, 25°C with peak measured energy-efficiency of 1.86pJ/code at 280mV, 2.7× higher than previously reported implementations.
Causal Analysis of the Unsatisfying Experience in Realtime Mobile Multiplayer Games in the Wild

There are anecdotal evidences that realtime mobile multiplayer games (RMMGs), which require realtime interactions, suffer from unsatisfying experience in the wild. This paper presents thefirst measurement results of such experience based on 12 million real game sessions from a toptier RMMG. We observe that 13% of the game sessions suffer from at least one location resynchronization, and 7.12% have been aborted abnormally before the end of the game. This paper thus proposes ExCause, a general causal analysis framework to systematically analyze historical game session records to 1) obtain context factors that cause unsatisfying RMMG experience, and 2) recommend adjustments with quantified expectation of QoE improvement, by applying the potential outcome framework. The recommendations suggested by ExCause can reduce the number of location resynchronization by 95.1%, from 1.323 to 0.065 on average. Furthermore, ExCause enables us to rectify some misperceptions from previous correlation-based studies.
Periodic Action Temporal Localization Method Based on Two-Path Architecture for Product Counting in Sewing Video

Automatically product counting in the handmade process plays a vital role in the manufacturing industry, especially at the sewing industry. Nevertheless, there is currently a few methods to count the product number in the hand sewing process from surveillance video automatically. Due to the sewing procedure is a cyclical action, the product counting in hand sewing process is regarded as periodic action temporal localization and counting problem. In this paper, in order to solve this problem, we propose a novel two-path method, based on pose estimation and region-based convolutional neural network. The pose estimation method is used to obtain the trajectory information of human joint points, and the periodic action is located by detecting the periodic changes in joint trajectory. An effective two-threshold method is proposed to locate each action and count the number of periodic action from the trajectory information. To more accurately localization, we use a convolutional neural network to predict whether the workbench is empty or not. We fuse the results of joint trajectory and the status of the workbench to adjust the final periodic action localization and counting the number. To verify the proposed method, we built a new video database collected in the real sewing industry. The experimental results show that the proposed method is effective and constructive at periodic action localization and counting in the video for the sewing industry.
The Transdisciplinary Nature of Virtual Space

This paper presents a transdisciplinary view on virtual space, through a description of how different domains of knowledge inform the concepts of virtuality and space. The aim is to show how these different perspectives come together in the virtual space that facilitates combining science and technology with cultural aspects coming from arts and other domains of knowledge. The argument leads to two models of the understanding of virtual space. The first model is an explanation of virtual space as a hybrid that has emerged from both nature (represented by sciences) and culture (represented by arts). The second model puts the observer in the center, exploring the physical-virtual space through an embodied interaction. The contribution of this paper is twofold. First, it presents virtual space as a platform for transdisciplinary work, exposing its underlying processes from both theoretical and practical point of view. Second, it introduces a model for the way transdisciplinarity can inform the understanding of virtuality that is taking increasing part of our everyday lives as well as variety of knowledge production in form of advanced visualizations, simulations and virtual reality approaches.
Rectification of Camera-Captured Document Images with Mixed Contents and Varied Layouts

This paper focuses on the rectification of camera-captured document images with varied layouts of mixed contents. Document images acquired via cameras, including smartphones, are typically plagued by perspective, geometric, and/or rotational distortion that hinders document analysis processes. In this paper, we propose an approach to camera-captured image rectification of text and non-text regions that handles perspective, geometric and rotational distortions present in planar and curled documents, extending a state-of-the-art content-based rectification method. We define surface projections via a three-tiered local transformation model, in which primary curved surface projections are formed from individual text regions, and secondary and tertiary surface projections are formed from non-text regions, resulting in a 'patchwork' combination of surfaces spanning the document image. This transformation model allows us to process document images with varied layouts of mixed contents, including large images and graphics, that also contain some justified text. Experiments and comparisons with a state-of-the-art content-based rectification approach on the public IUPR dataset demonstrate the value of the proposed approach on two levels: 1) a significantly improved rectification performance using standard optical character recognition metrics, along with increased document readability, and 2) an improved range of applicability, i.e. ability to correct document images showing various layouts and content types.
Data Clustering using Variational Learning of Finite Scaled Dirichlet Mixture Models

We introduce a variational Bayesian learning framework for finite Scaled Dirichlet mixture model. The variational inference estimates the parameters without the computational cost of conventional Bayesian methods. Furthermore, our model avoids including additional model selection criterion by automatically determining the optimal number of components as a part of the inference process. The proposed model is tested with real-life data clustering problems to demonstrate its effectiveness.
A Residential Client-side Perspective on SSL Certificates

SSL certificates are a core component of the public key infrastructure that underpins encrypted communication in the Internet. In this paper, we report the results of a longitudinal study of the characteristics of SSL certificate chains presented to clients during secure web (HTTPS) connection setup. Our data set consists of 23B SSL certificate chains collected from a global panel consisting of over 2M residential client machines over a period of 6 months. The data informing our analyses provide perspective on the entire chain of trust, including root certificates, across a wide distribution of client machines. We identify over 35M unique certificate chains with diverse relationships at all levels of the PKI hierarchy. We report on the characteristics of valid certificates, which make up 99.7% of the total corpus. We also examine invalid certificate chains, finding that 93% of them contain an untrusted root certificate and we find they have shorter average chain length than their valid counterparts. Finally, we examine two unintended but prevalent behaviors in our data: the deprecation of root certificates and secure traffic interception. Our results support aspects of prior, scan-based studies on certificate characteristics but contradict other findings, highlighting the importance of the residential client-side perspective.
Approximate and Exact Selection on GPUs

We present a novel algorithm for parallel selection on GPUs. The algorithm requires no assumptions on the input data distribution, and has a much lower recursion depth compared to many state-of-the-art algorithms. We implement the algorithm for different GPU generations, always using the respectively-available low-level communication features, and assess the performance on server-line hardware. The computational complexity of our SampleSelect algorithm is comparable to specialized algorithms designed for - and exploiting the characteristics of - pleasant data distributions. At the same time, as the SampleSelect does not work on the actual values but the ranks of the elements only, it is robust to the input data and can complete significantly faster for adversarial data distributions. Additionally to the exact SampleSelect, we address the use case of approximate selection by designing a variant that radically reduces the computational cost while preserving high approximation accuracy.
A New Evaluation Model for Information Security Risk Management of SCADA Systems

Supervisory control and data acquisition (SCADA) systems are becoming increasingly susceptible to cyber-physical attacks on both physical and cyber layers of critical information infrastructure. Failure Mode and Effects Analysis (FMEA) have been widely used as a structured method to prioritize all possible vulnerable areas (failure modes) for design review of security of information systems. However, traditional RPN based FMEA has some inherent problems. Besides, there is a lacking of application of FMEA for security in SCADAs under vague and uncertain environment. Thus, the main purpose of this study was to propose a new evaluation model, which not only intends to recover above mentioned problems, but also intends to evaluate, prioritize and correct security risk of SCADA system’s threat modes. A numerical case study was also conducted to demonstrate that the proposed new evaluation model is not only capable of addressing FMEA’s inherent problems but also is best suited for a semi-quantitative high level analysis of a secure SCADA’s failure modes in the early design phases.
A Fuzzy Constraint Based Outlier Detection Method

With a huge amount of data generated every second, it has become important to remove data anomalies. Outliers are the extreme value that deviates from other observations in data. We propose a novel outlier detection method; FCBODM (Fuzzy Constraint based Outlier Detection Method) that takes into account of fuzzy constraint and background knowledge to discover the outliers in a dataset. Our key idea is to use fuzzy constraint technology wherein we used nearness measure theory in fuzzy mathematics for finding similarities between data objects and background information. It helps in finding more meaningful outliers. Our novel approach can be integrated with traditional outlier detection methods to improve the outlier ranking. In order to validate and demonstrate the effectiveness and scalability of our method we experimented it on real and semantically meaningful datasets.
Inspecting a Machine Learning Based Clinical Risk Calculator: A Practical Perspective

Health is reaching a point where machines are more accurate than humans, or at least as accurate but with less effort, in more and more applications. However, accuracy alone is not enough, explanation and understanding is equally important to clinicians, governments, and patients. Possibly leading to loss of health benefits potentially realized through increasingly accurate algorithms. However, various techniques exist for auditing machine learning systems via insightful visualisations. Modelling best practices, parallel computations and open source technologies facilitate implementation of these techniques. This paper leverages several of these methods to increase interpretability for a black-box clinical risk calculator, hopefully opening the door to a better adoption of modern machine learning pipelines in the healthcare sector.
Learning with Per-Sample Side Information

Learning from few samples is a major challenge for parameter-rich models such as deep networks. In contrast, people can learn complex new concepts even from very few examples, suggesting that the sample complexity of learning can often be reduced. We describe an approach to reduce the number of samples needed for learning using per-sample side information. Specifically, we show how to speed up learning by providing textual information about feature relevance, like the presence of objects in a scene or attributes in an image. We also give an improved generalization error bound for this case. We formulate the learning problem using an ellipsoid-margin loss, and develop an algorithm that minimizes this loss effectively. Empirical evaluation on two machine vision benchmarks for scene classification and fine-grain bird classification demonstrate the benefits of this approach for few-shot learning.
Analyzing Eye Gaze of Users with Learning Disability

This paper investigates eye gaze movements of children with different reading abilities due to learning disability. We analyzed eye gaze fixations using cluster analysis and velocity-based fixation classification algorithms. We used a non-invasive, screen mounted, low-cost eye tracker to collect data from 30 participants including students with learning disabilities. We analyzed raw gaze points, fixations and saccades, and noted that the density of gaze points around fixation. Total number of fixations and regressions (backward gaze movement) were statistically significantly correlated to the reading ability of students. We concluded that by analyzing different eye movements for a representative reading task, we can detect early signs of learning disabilities such as dyslexia.
Low-cost multi-view pose tracking using active markers

Vision-based motion tracking systems have important applications in robotics and human motion analysis as well as in the entertainment industry. In this paper, we present an overview of a high-precision low-cost motion tracking system that combines active pulsed markers with multi-view pose reconstruction. While many passive-marker tracking systems impose restrictions on marker placement for correct marker identification, active pulsed LEDs allow for rigid body tracking with arbitrary marker placement as well as marker tracking on unknown articulated and deformable bodies. Individual markers are tracked via a path search method, which is robust against short temporary occlusions. Once detected, a rigid body can be tracked via a minimal number of marker observations from a single view or distributed across multiple views, even if the position of each marker could not be reconstructed. Additionally, IMU data can be fused with optical marker tracking to improve precision and robustness against marker occlusions.
An Enhanced MoBayesShrink Thresholding for Medical Image Denoising

The digital image carries a lot of pictorial information which has now become one of the chief ways of communication in this generation. The transmission of media especially image is often corrupted by the Gaussian noise due to various issues. This noise is needed to be removed. Image denoising technique is applied to remove such noise and make it of high quality. This additive Gaussian noise can be removed using wavelet denoising technique. This paper presents a better method that controls the threshold (T) adaptively to eliminate noise. Experimental results of the proposed method are better than the existing denoising algorithm Such as NSTISM(19), SPBIDM(18), BayesShrink, NormalShrink, and ModifiedBayesShrink.
An Adaptive Bully Algorithm for Leader Elections in Distributed Systems

Leader election is a classical problem in distributed system applications. There are many leader election algorithms, but we focus here on Bully Algorithm (BA). The main drawback of BA algorithm is the high number of messages passing. In BA algorithm, the message passing has order O ( $$n^2$$ ) that increases heavy traffic on the network. In this paper, an Adaptive BA (ABA) is proposed to reduce the number of messages and make the leader election operation more flexible and safer. The proposed algorithm is based on the Highest Process Identification (HPI) and the Next HPI (NHPI) to facilitate the leader election operation. Moreover, the repetition of the leader election is stopped when the candidate coordinator fails. Our analytical equations show that the ABA algorithm is more efficient rather than BA algorithm, in both, the number of message passing and the latency, and the message passing complexity decreased to O(n).