Reviewerly/exHarmony huggingface dataset:
Performance Investigation of Parallel Immune Plasma Algorithm

The increasing power and common usage of the shared memory based systems gained the researchers’ attentions to the development of parallel algorithms or parallelization of the existing algorithms for utilizing the computational capabilities of these systems. In this study, Immune Plasma algorithm for short IP Algorithm or IPA that models the steps of a medical method called convalescent plasma or immune plasma treatment was parallelized and solving capabilities of the parallel IPA was investigated by using a set of numerical benchmark problems. The results of the experimental studies showed that parallelization of IPA contributes to the convergence performance and qualities of the final solutions while decreasing the execution time compared to the serial implementation of the same algorithm.
UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model

Recovering dense human poses from images plays a critical role in establishing an image-to-surface correspondence between RGB images and the 3D surface of the human body, serving the foundation of rich real-world applications, such as virtual humans, monocular-to-3d reconstruction. However, the popular DensePose-COCO dataset relies on a sophisticated manual annotation system, leading to severe limitations in acquiring the denser and more accurate annotated pose resources. In this work, we introduce a new 3D human-body model with a series of decoupled parameters that could freely control the generation of the body. Furthermore, we build a data generation system based on this decoupling 3D model, and construct an ultra dense synthetic benchmark UltraPose, containing around 1.3 billion corresponding points. Compared to the existing manually annotated DensePose-COCO dataset, the synthetic UltraPose has ultra dense image-to-surface correspondences without annotation cost and error. Our proposed UltraPose provides the largest benchmark and data resources for lifting the model capability in predicting more accurate dense poses. To promote future researches in this field, we also propose a transformer-based method to model the dense correspondence between 2D and 3D worlds. The proposed model trained on synthetic UltraPose can be applied to real-world scenarios, indicating the effectiveness of our benchmark and model. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Putting collation of text witnesses on a formal basis

Abstract This article presents an approach to the collation problem of text witnesses which is correct for all distance functions and is applicable to the alignment of any types of tokens, be it letters, words, sentences, or paragraphs. In contrast to the existing approaches, which all are heuristics in nature, we specify a formal model in the form of an integer linear program (ILP) that formally describes the optimization problem. For a special case that usually occurs in practice, we then transfer the ILP into an efficient algorithm that optimally solves the alignment problem. In the second part of the article, we apply our approach to the alignment of paragraphs of text witnesses—an alignment problem that has received little attention in the literature so far—and compare the results of our approach, which we have called TSaligner, with those of CollateX.
Automatic Channel Pruning with Hyper-parameter Search and Dynamic Masking

Modern deep neural network models tend to be large and computationally intensive. One typical solution to this issue is model pruning. However, most current model pruning algorithms depend on hand crafted rules or need to input the pruning ratio beforehand. To overcome this problem, we propose a learning based automatic channel pruning algorithm for deep neural network, which is inspired by recent automatic machine learning (Auto ML). A two objectives' pruning problem that aims for the weights and the remaining channels for each layer is first formulated. An alternative optimization approach is then proposed to derive the channel numbers and weights simultaneously. In the process of pruning, we utilize a searchable hyper-parameter, remaining ratio, to denote the number of channels in each convolution layer, and then a dynamic masking process is proposed to describe the corresponding channel evolution. To adjust the trade-off between accuracy of a model and the pruning ratio of floating point operations, a new loss function is further introduced. Extensive experimental results on benchmark datasets demonstrate that our scheme achieves competitive results for neural network pruning.
Improved SinGAN's Performance by Changing the Activation Function

Generative adversarial nets (GANs) perform well on a variety of tasks, but rely on large datasets and expensive computer‐based learning. SinGAN was proposed as a GANs that overcomes this problem, but its super‐resolution performance for large‐scale natural images was not very good. In this study, we aimed to improve the performance of SinGAN by changing the activation function. As a result of the verification, it was found that applying RSwish to the generator and Swish to the discriminator can generate an image with less deterioration as a whole than the default. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Is neural machine translation approach accurate enough for coding assistance?

Coding assistance with deep learning is an emerging concern that has recently attracted much attention in the software development community. To integrate coding assistance with deep learning compactly, we focus on neural machine translation (NMT), which allows users to translate natural language descriptions into expressions in a programming language such as Python. A rising problem here is the limited availability of parallel corpora, which is essential to train better NMT models.
Fuzzy Randomized Load Balancing for Cloud Computing

In cloud computing different resources are grouped to work collectively to service users from remote locations. The cloud system’s responsibility is to route the user request to such resources, which are less utilized to achieve the least response time. This concept of distributing the workload from cloud users transparently among all the resources is called load balancing. In this paper, we discuss various algorithms for support load balancing. We propose a Fuzzy-based Randomized Load Balancing(FRLB) model that overcomes shortcomings of throttled load balancing. The simulation results are analyzed and compared to other existing load balancing algorithms proving better efficiency over them.
$\mathcal{LATA}{-}$ Label Attention Transformer Architectures for ICD-10 Coding of Unstructured Clinical Notes

Effective code assignment for patient clinical records in a hospital plays a significant role in the process of standardizing medical records, mainly for streamlining clinical care delivery, billing, and managing insurance claims. The current practice employed is manual coding, usually carried out by trained medical coders, making the process subjective, error-prone, inexact, and time-consuming. To alleviate this cost-intensive process, intelligent coding systems built on patients' structured electronic medical records are critical. Classification of medical diagnostic codes, like ICD-10, is widely employed to categorize patients' clinical conditions and associated diagnoses. In this work, we present a neural model <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{LATA}$</tex> , built on Label Attention Transformer Architectures for automatic assignment of ICD-10 codes. Our work is benchmarked on the CodiEsp dataset, a dataset for automatic clinical coding systems for multilingual medical documents, used in the eHealth CLEF 2020-Multilingual Information Extraction Shared Task. The experimental results reveal that the proposed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{LATA}$</tex> variants outperform their basic BERT counterparts by 33-49% in terms of standard metrics like precision, recall, F1-score and mean average precision. The label attention mechanism also enables direct extraction of textual evidence in medical documents that map to the clinical ICD-10 diagnostic codes.
Why Many Benchmarks Might Be Compromised

Benchmarking experiments often draw strong conclusions but lack information about the environmental influences like the hardware used to deploy the investigated system. Fairness and repeatability of these benchmarks are at least questionable. Developing for or migrating applications to the cloud or DevOps environments often requires performance testing, either for ensuring quality-of-service or for choosing the correct service parameters when deciding for a cloud offering. While building a benchmarking pipeline for cloud functions, the typical assumption is that a CPU scales the resources linearly to the used utilization. Due to heat generation, noise and other constraints, this is not the case due to the trade off between efficiency and performance. To investigate this trade off and its implications, we set up some experiments in order to evaluate the influence of these factors for benchmark results. We solely focus on Intel CPUs. Beginning with the second generation (Sandy Bridge), Intel uses their own scaling driver intel_pstate. Our results show that different settings for this scaling driver have a significant impact on the measured performance and therefore on the linear regression models we computed using LINPACK benchmarks. These benchmarks are executed at different CPU utilization points. An active intel_pstate scaling driver with enabled turbo boost and powersave governor reached a R2 of 0.7349, whereas the performance governor shows a significantly better, ideal determination coefficient with 0.9999 on a machine used in the benchmarks. Therefore, we propose a methodology for system calibration to ensure fair and repeatable benchmarks.
Cyberattacks as “state of exception” reconceptualizing cybersecurity from prevention to surviving and accommodating

Purpose This paper aims to outline how destructive communication exemplified by ransomware cyberattacks destroys the process of organization, causes a “state of exception,” and thus constitutes organization. The authors build on Agamben's state of exception and translate it into communicative constitution of organization (CCO) theory. Design/methodology/approach A significant increase of cyberattacks have impacted organizations in recent times and laid organizations under siege. This conceptual research builds on illustrative cases chosen by positive deviance case selection (PDCS) of ransomware attacks. Findings CCO theory focuses mainly on ordering characteristics of communication. The authors aim to complement this view with a perspective on destructive communication that destroys the process of organization. Based on illustrative cases, the authors conceptualize a process model of destructive CCO. Practical implications The authors expand thoughts about a digital “corporate immune system” to question current offensive cybersecurity strategies of deterrence and promote resilience approaches instead. Originality/value Informed by destructive communication of cyberattacks, this theory advancement supports arguments to include notions of disorder into CCO theory. Furthermore, the paper explains where disruptions like cyberattacks may trigger sensemaking and change to preserve stability. Finally, a novel definition of ‘destructive CCO’ is provided: Destructive Communication Constitutes Organization by disrupting and destroying its site and surface while triggering sensemaking and becoming part of sensemaking itself.
Overview of Tencent Multi-modal Ads Video Understanding

Multi-modal Ads Video Understanding Challenge is the first grand challenge aiming to comprehensively understand ads videos. Our challenge includes two tasks: video structuring and multi-label classification. Video structuring asks the participants to accurately predict both the scene boundaries and the multi-label categories of each scene based on a fine-grained and ads-related category hierarchy. This task will advance the foundation of comprehensive ads video understanding, which has a significant impact on many applications in ads, such as video recommendation and user behavior analysis. This paper presents an overview of the video structuring task in our grand challenge, including the background of ads videos, an elaborate description of this task, our proposed dataset, the evaluation protocol, and our baseline model. By ablating the key components of our baseline, we would like to reveal the main challenges of this task and provide useful guidance for future research of this area.
Designing Privacy for Collaborative Music Making in Virtual Reality

Privacy is needed to schedule personal activities and social interaction, and to prepare and create wonderful things before confidently sharing with others. How to build privacy in virtual reality remains an open question, though it is potentially as important as it is in reality. This paper presents two studies to build privacy in VR with a focus on providing sonic privacy to aid collaborative music making (CMM). The first study uses soundproof walls to provide sonic privacy, the second study applies augmented sound attenuation as a way to produce sonic privacy to aid collaborative music making. Results show privacy is needed and both ways can produce sonic privacy. Privacy provided in the first study is binary and discrete, whilst privacy provided in the latter is continuous, step-less and adjustable. By comparing and discussing between these two approaches, we propose a methodological approach to build privacy to aid collaboration in shared virtual reality. In particular, three channels for building privacy have been identified and three implications have been proposed for designing privacy for collaborative tasks in shared virtual environments, collaborative music making in particular.
Identity Based Linkable Ring Signature with Logarithmic Size

Anonymity is an inevitable and sensitive matter of concern, especially in the age where people are willing to use digital devices and Internet to deal with almost all things in their work and daily life. To support anonymity, modern cryptography is one of the most suitable choices in the algorithm level. Particularly, in the scenarios, such as e-voting, crypto-currency, and smart grid etc., a cryptographic primitive, called linkable ring signature, has shown its ability to handle anonymity problems. However, signature schemes will introduce additional costs to those e-commerce systems, so that they should be as efficient as possible. On the other side, a signature scheme that requires the public key infrastructure (PKI) brings much unnecessary inconvenience to its users, since typically, users of the aforementioned systems are not familiar with cryptographic skills and the system establishers are trusted by them in some sense. As a result, an identity-based (ID-based) signature scheme with small size fulfills the visible requirements of e-commerce systems. In this paper, we proposed an ID-based linkable ring signature scheme with logarithmic size from pairing and elliptic curve discrete logarithm problem (ECDLP), and gave all the security proofs in detail. Besides that, the scheme needs no trusted setup, except that the key generation center knows the secret key of each user and it is a property of ID-based cryptography in nature.
Sensor Placement for Globally Optimal Coverage of 3D-Embedded Surfaces

We carry out a structural and algorithmic study of a mobile sensor coverage optimization problem targeting 2D surfaces embedded in a 3D workspace. The investigated settings model multiple important applications including camera net- work deployment for surveillance, geological monitoring/survey of 3D terrains, and UVC-based surface disinfection for the prevention of the spread of disease agents (e.g., SARS-CoV-2). Under a unified general "sensor coverage" problem, three concrete formulations are examined, focusing on optimizing visibility, single-best coverage quality, and cumulative quality, respectively. After demonstrating the computational intractability of all these formulations, we describe approximation schemes and mathematical programming models for near-optimally solving them. The effectiveness of our methods is thoroughly evaluated under realistic and practical scenarios.
Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling on-the-Fly for Moving Object Detection

Moving object detection is critical for automated video analysis in many vision-related tasks, such as surveillance tracking, video compression coding, etc. Robust Principal Component Analysis (RPCA), as one of the most popular moving object modelling methods, aims to separate the temporally-varying (i.e., moving) foreground objects from the static background in video, assuming the background frames to be low-rank while the foreground to be spatially sparse. Classic RPCA imposes sparsity of the foreground component using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> -norm, and minimizes the modeling error via <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _2$</tex-math></inline-formula> -norm. We show that such assumptions can be too restrictive in practice, which limits the effectiveness of the classic RPCA, especially when processing videos with dynamic background, camera jitter, camouflaged moving object, etc. In this paper, we propose a novel RPCA-based model, called Hyper RPCA, to detect moving objects on the fly. Different from classic RPCA, the proposed Hyper RPCA jointly applies the maximum correntropy criterion (MCC) for the modeling error, and Laplacian scale mixture (LSM) model for foreground objects. Extensive experiments have been conducted, and the results demonstrate that the proposed Hyper RPCA has competitive performance for foreground detection to the state-of-the-art algorithms on several well-known benchmark datasets.
A Note About Entropy and Inconsistency in Evidence Theory

Information content is classically measured by entropy measures in probability theory, that can be interpreted as a measure of internal inconsistency of a probability distribution. While extensions of Shannon entropy have been proposed for quantifying information content of a belief function, other trends have been followed which rather focus on the notion of consistency between sets. Relying on previous general entropy measures of probability, we propose in this paper to establish some links between the different measures of internal inconsistency of a belief functions. We propose a general formulation which encompasses inconsistency measures derived from Shannon entropy as well as those derived from the N-consistency family of measures.
Ship Detection and Recognition in Optical Remote Sensing Images Based on Scale Enhancement Rotating Cascade R-CNN Networks

Ship detection and recognition in remote sensing images have important significance in military and civilian applications. Traditional methods have insufficient generalization ability in complicated scenes. The Faster R-CNN-based methods cannot predict the orientation of the ship. The R2CNN-based methods can predict the orientation of the ship but not considerate the scale of object in classification. In order to solve the problems mentioned above, this article proposes a scale enhancement rotating Cascade R-CNN network (SER-Cascade). Using the multistage network of rotating Cascade R-CNN, the output of the previous stage is fed to current stage, which can effectively regress the orientation of the ship. To improve the recognition performance of multi-class ships, a novel RoI pooling method is proposed in this article, in which the scale information is enhanced and context information is reserved. To evaluate the proposed networks, a dataset named HR-SHIP-15 that currently contains 15 categories of ship targets has been produced for ship recognition. Experiments are conducted on HR-SHIP-15 dataset, and the results verify that the proposed method has state-of-the-art performance.
Small Ship Detection via Deformable Convolutional Network

Though widely studied, target detection in synthetic aperture radar (SAR) image is still a challenging problem. The classical convolutional neural network (CNN) samples spatial locations with the fixed geometric structure, and hence is incapable of learning the representations of varied-scale ships. It's difficult to locate multi-scale targets accurately in the complex scenes. On the other hand, to apply the classical models in SAR image, we need to duplicate the single-channel image to 3-channel one. The preprocess could not introduce the additional semantic information yet producing feature redundancy. To solve these problems, we introduced a new method for ship detection. We deployed the deformable convolutional block to learn features of ships with various scales at arbitrary locations. Different from the preceding works, the former shallow feature maps are also employed to enhance the representations of targets, especially small ships. In addition, the group normalization strategy is configured to alleviate internal covariate shift and accelerate the convergence. There is no need to make a tradeoff between the scale of model architecture and the batch size. Multiple comparative experiments on SSDD dataset are performed to demonstrate the advantage of proposed methods.
Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks

In safety-critical applications like medical diagnosis, certainty associated with a model’s prediction is just as important as its accuracy. Consequently, uncertainty estimation and reduction play a crucial role. Uncertainty in predictions can be attributed to noise or randomness in data (aleatoric) and incorrect model inferences (epistemic). While model uncertainty can be reduced with more data or bigger models, aleatoric uncertainty is more intricate. This work proposes a novel approach that interprets data uncertainty estimated from a self-supervised task as noise inherent to the data and utilizes it to reduce aleatoric uncertainty in another task related to the same dataset via data augmentation. The proposed method was evaluated on a benchmark medical imaging dataset with image reconstruction as the self-supervised task and segmentation as the image analysis task. Our findings demonstrate the effectiveness of the proposed approach in significantly reducing the aleatoric uncertainty in the image segmentation task while achieving better or on-par performance compared to the standard augmentation techniques.