Reviewerly/exHarmony huggingface dataset:
Harmony: A Scheduling Framework Optimized for Multiple Distributed Machine Learning Jobs

We introduce Harmony, a new scheduling framework that executes multiple Parameter-Server ML training jobs together to improve cluster resource utilization. Harmony coordinates a fine-grained execution of co-located jobs with complementary resource usages to avoid contention and to efficiently share resources between the jobs. To resolve the memory pressure due to the increased number of simultaneous jobs, Harmony uses a data spill/reload mechanism optimized for multiple jobs with the iterative execution pattern. Our evaluation shows that Harmony improves cluster resource utilization by up to 1.65×, resulting in a reduction of the mean ML training job time by about 53%, and makespan, the total time to process all given jobs, by about 38%, compared to the traditional approaches that allocate dedicated resources to each job.
H2HI-Net: A Dual-Branch Network for Recognizing Human-to-Human Interactions From Channel-State Information

Recognizing human activities is considered a vital research challenge because of its essential significance for improving human–machine collaboration in the Internet of Things environments. The present deep learning (DL) literature focused on studying human activities (HAs) from one subject, with several schemes differing in the recognition method and sensing strategy. However, few research interests have been dedicated to situations where numerous individuals interact to perform common activities. This challenge is termed human-to-human interaction (H2HI) recognition. This study addresses the H2HI problem by a novel device-free DL model, named H2HI-NET, for modeling the HA representation of the Channel State Information of Wireless Fidelity devices. In H2HI-NET, a bi-directional temporal learning module is introduced to capture temporal representation from historical and future information. Simultaneously, the residual spatial learning module is designed to combine residual learning and transformer network capabilities for the efficient extraction of complex spatial features of HAs. The experimental evaluations reveal the efficiency of the H2HI-NET with 96.39% accuracy overcoming cutting-edge studies.
Resonant scanning design and control for fast spatial sampling

Two-dimensional, resonant scanners have been utilized in a large variety of imaging modules due to their compact form, low power consumption, large angular range, and high speed. However, resonant scanners have problems with non-optimal and inflexible scanning patterns and inherent phase uncertainty, which limit practical applications. Here we propose methods for optimized design and control of the scanning trajectory of two-dimensional resonant scanners under various physical constraints, including high frame-rate and limited actuation amplitude. First, we propose an analytical design rule for uniform spatial sampling. We demonstrate theoretically and experimentally that by expanding the design space, the proposed designs outperform previous designs in terms of scanning range and fill factor. Second, we show that we can create flexible scanning patterns that allow focusing on user-defined Regions-of-Interest (RoI) by modulation of the scanning parameters. The scanning parameters are found by an optimization algorithm. In simulations, we demonstrate the benefits of these designs with standard metrics and higher-level computer vision tasks (LiDAR odometry and 3D object detection). Finally, we experimentally implement and verify both unmodulated and modulated scanning modes using a two-dimensional, resonant MEMS scanner. Central to the implementations is high bandwidth monitoring of the phase of the angular scans in both dimensions. This task is carried out with a position-sensitive photodetector combined with high-bandwidth electronics, enabling fast spatial sampling at [Formula: see text] Hz frame-rate.
Multi-Level Privacy Preserving K-Anonymity

k-anonymity is a well-known definition of privacy, which guarantees that any person in the released dataset cannot be distinguished from at least k-1 other individuals. In the protection model, the records are anonymized through generalization or suppression with a fixed value of k. Accordingly, each record has the same level of anonymity in the published dataset. However, different people or items usually have inconsistent privacy requirements. Some records need extra protection while others require a relatively low level of privacy constraint. In this paper, we propose Multi-Level Privacy Preserving K-Anonymity, an advanced protection model based on k-anonymity, which divides records into different groups and requires each group to satisfy its respective privacy requirement. Moreover, we present a practical algorithm using clustering techniques to ensure the property. The evaluation on a real-world dataset confirms that the proposed method has the advantages of offering more flexibility in setting privacy parameters and providing higher data utility than traditional k-anonymity.
TabAug: Data Driven Augmentation for Enhanced Table Structure Recognition

Table Structure Recognition is an essential part of end-to-end tabular data extraction in document images. The recent success of deep learning model architectures in computer vision remains to be non-reflective in table structure recognition, largely because extensive datasets for this domain are still unavailable while annotating new data is expensive and time-consuming. Traditionally, in computer vision, these challenges are addressed by standard augmentation techniques that are based on image transformations like color jittering and random cropping. As demonstrated by our experiments, these techniques are not effective for the task of table structure recognition. In this paper, we propose TabAug, a re-imagined Data Augmentation technique that produces structural changes in table images through replication and deletion of rows and columns. It also consists of a data-driven probabilistic model that allows control over the augmentation process. To demonstrate the efficacy of our approach, we perform experimentation on ICDAR 2013 dataset where our approach shows consistent improvements in all aspects of the evaluation metrics, with cell-level correct detections improving from 92.16% to 96.11% over the baseline.
MS-Transformer: Introduce multiple structural priors into a unified transformer for encoding sentences

Transformers have been widely utilized in recent NLP studies. Unlike CNNs or RNNs, the vanilla Transformer is position-insensitive, and thus is incapable of capturing the structural priors between sequences of words. Existing studies commonly apply one single mask strategy on Transformers for incorporating structural priors while failing at modeling more abundant structural information of texts. In this paper, we aim at introducing multiple types of structural priors into Transformers, proposing the Multiple Structural Priors Guided Transformer (MS-Transformer) that transforms different structural priors into different attention heads by using a novel multi-mask based multi-head attention mechanism. In particular, we integrate two categories of structural priors, including the sequential order and the relative position of words. For the purpose of capturing the latent hierarchical structure of the texts, we extract these information not only from the word contexts but also from the dependency syntax trees. Experimental results on three tasks show that MS-Transformer achieves significant improvements against other strong baselines.
Unrolling Particles: Unsupervised Learning of Sampling Distributions

Particle filtering is used to compute nonlinear estimates of complex systems. It samples trajectories from a chosen distribution and computes the estimate as a weighted average of them. Easy-to-sample distributions often lead to degenerate samples where only one trajectory carries all the weight, negatively affecting the resulting performance of the estimate. While much research has been done on the design of appropriate sampling distributions that would lead to controlled degeneracy, in this paper our objective is to learn sampling distributions. Leveraging the framework of algorithm unrolling, we model the sampling distribution as a multivariate normal, and we use neural networks to learn both the mean and the covariance. We carry out unsupervised training of the model to minimize weight degeneracy, relying only on the observed measurements of the system. We show in simulations that the resulting particle filter yields good estimates in a wide range of scenarios.
Generator Versus Segmentor: Pseudo-healthy Synthesis

This paper investigates the problem of pseudo-healthy synthesis that is defined as synthesizing a subject-specific pathology-free image from a pathological one. Recent approaches based on Generative Adversarial Network (GAN) have been developed for this task. However, these methods will inevitably fall into the trade-off between preserving the subject-specific identity and generating healthy-like appearances. To overcome this challenge, we propose a novel adversarial training regime, Generator versus Segmentor (GVS), to alleviate this trade-off by a divide-and-conquer strategy. We further consider the deteriorating generalization performance of the segmentor throughout the training and develop a pixel-wise weighted loss by muting the well-transformed pixels to promote it. Moreover, we propose a new metric to measure how healthy the synthetic images look. The qualitative and quantitative experiments on the public dataset BraTS demonstrate that the proposed method outperforms the existing methods. Besides, we also certify the effectiveness of our method on datasets LiTS. Our implementation and pre-trained networks are publicly available at https://github.com/Au3C2/Generator-Versus-Segmentor.
CaregiverMatcher: graph neural networks for connecting caregivers of rare disease patients

Rare diseases affect a growing number of individuals. One key problem for patients and their caregivers is the difficulty in reaching experts and associations competent on a particular disease. As a consequence, caregivers, often family members of the patient, learn much about the disease from their own experience. CaregiverMatcher is a proof of concept providing a smart solution to build a network of caregivers, linked by a matching mechanism based on graph neural networks. The caregivers and their experience with rare diseases are described by node features. Associations and care centers are invited to share their knowledge on the platform.
SiamPolar: Semi-supervised realtime video object segmentation with polar representation

Video object segmentation (VOS) is an essential part of autonomous vehicle navigation. The real-time speed is very important for the autonomous vehicle algorithms along with the accuracy metric. In this paper, we propose a semi-supervised real-time method based on the Siamese network using a new polar representation. The input of bounding boxes is initialized rather than the object masks, which are applied to the video object detection tasks. The polar representation could reduce the parameters for encoding masks with subtle accuracy loss so that the algorithm speed can be improved significantly. An asymmetric siamese network is also developed to extract the features from different spatial scales. Moreover, the peeling convolution is proposed to reduce the antagonism among the branches of the polar head. The repeated cross-correlation and semi-FPN are designed based on this idea. The experimental results on the DAVIS-2016 dataset and other public datasets demonstrate the effectiveness of the proposed method.
Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-trained Language Models

Public security vulnerability reports (e.g., CVE reports) play an important role in the maintenance of computer and network systems. Security companies and administrators rely on information from these reports to prioritize tasks on developing and deploying patches to their customers. Since these reports are unstructured texts, automatic information extraction (IE) can help scale up the processing by converting the unstructured reports to structured forms, e.g., software names and versions [] and vulnerability types []. Existing works on automated IE for security vulnerability reports often rely on a large number of labeled training samples [, , ]. However, creating massive labeled training set is both expensive and time consuming. In this work, for the first time, we propose to investigate this problem where only a small number of labeled training samples are available. In particular, we investigate the performance of fine-tuning several state-of-the-art pre-trained language models on our small training dataset. The results show that with pre-trained language models and carefully tuned hyperparameters, we have reached or slightly outperformed the state-of-the-art system [] on this task. Consistent with previous two-step process of first fine-tuning on main category and then transfer learning to others as in [], if otherwise following our proposed approach, the number of required labeled samples substantially decrease in both stages: 90% reduction in fine-tuning from 5758 to 576, and 88.8% reduction in transfer learning with 64 labeled samples per category. Our experiments thus demonstrate the effectiveness of few-sample learning on NER for security vulnerability report. This result opens up multiple research opportunities for few-sample learning for security vulnerability reports, which is discussed in the paper. Our implementation for few-sample vulnerability entity tagger in security reports could be found at https://github.com/guanqun-yang/FewVulnerability.
A survey on parallel computing for traditional computer vision

Summary The applications of computer vision (CV) are continuously increasing along with the enormous demand for real‐time data processing. This visual data processing is done with various compute‐intensive image/video processing algorithms that may belong to traditional approaches or deep learning approaches. This article aims to provide a survey of state‐of‐the‐art hardware platforms and software frameworks for parallel implementation of traditional CV applications. The article discusses various options for hardware platforms for centralized‐computing architecture and edge‐computing architecture, and various software frameworks that can be used to leverage the hardware. This discussion is based on a systematic survey of studies/works that show the use of various hardware platforms and software frameworks in order to achieve real‐time processing for CV algorithms. Based on the survey, some possible future directions are also discussed.
Towards a Novel Strategic Scheme for Web Crawler Design Using Simulated Annealing and Semantic Techniques

In this era the reflexive response to any question that pops in the mind, is to ask Google. And it is fascinating to witness the speed at which Google is able to give us so many answers with multiple interpretations despite the usage of broken language during type-searching. The module that powers google to provide so many responses is known as a web crawler. Just as the name suggests, it crawls through the web using the hyperlinks or URLs present on each page. The most vital unit of a search engine is the web crawler. When a particular topic is to be searched for, a focused web crawler is put to work. The internet has become the hub of resources of late and the efficiency of web crawlers is extensively put to test. This paper is built over two baseline papers that suggest models for focused web crawling- one for bioinformatics and the other in relevance to building a database for academicians. A multi module hybridized approach which initially classifies the URL database using decision tree and the optimizes with simulated annealing - a meta heuristic optimization algorithm and domain ontology has been devised to lift the efficiency of the focused web crawler. The proposed approach is seen to have an accuracy of 90.12% and an F-measure of 91.62%.
Single task fine-tune BERT for text classification

Over the past decades, natural language processing (NLP) has been a hot topic in many fields, e.g., sentiment analysis and news topic classification. As a very powerful language pre-training model, Bidirectional Encoder Representations from Transformers (BERT) has achieved promising results in many language understanding tasks including text classification. However, fine-tune BERT to adapt different text classification task efficiently is a critical problem that needs improvement. In this paper, a general solution is proposed for BERT fine-tuning on single text classification task. Compared with other traditional fine-tune strategies without any pre-training step, the performance of BERT is boosted by pre-training withintask data. Moreover, the proposed solution obtains superior results on six widely-used text classification datasets.
A New Salp Swarm Algorithm for the Numerical Optimization Problems Based on An Elite Opposition-based Learning

An Elite Opposition-based Learning Salp Swarm Algorithm (EOSSA) for the numerical optimization problems is introduced in this article. An attempt is made to introduce a hybrid model of two new paradigms: Salp Swarm Algorithm (SSA) and Elite Opposition-based Learning (EOL). SSA is a novel population-based meta-heuristic optimization algorithm with excellent edges while solving real-world optimization problems. EOL is a novel scheme in intelligent computing with advantages of economic and practical in solving optimization problems. The objective of this hybridization is to augment the exploration and evaluation aptitude of the original SSA. The obtained result of the proposed strategy is evaluated on the different configurations of well-known global benchmark functions and compared with the development of standard SSA. The experimental results show that EOSSA is competitive to the traditional SSA approaches.
Learning Embedding for Knowledge Graph Completion with Hypernetwork

Link prediction in Knowledge Graph, also called knowledge completion, is a significant problem in graph mining and has many applications for large companies. The more accurate the link prediction results will bring satisfaction, reduce and avoid risks, and commercial benefits. Almost all state-of-the-art models focus on the deep learning approach, especially using convolutional neural networks (CNN). By analysing the strengths and weaknesses of the CNN based models, we proposed a better model to improve the performance of the link prediction task. Specifically, we apply a CNN with specific filters generated through the Hypernetwork architecture. Moreover, we increase the depth of the model more than baseline models to help learn more helpful information. Experimental results show that the proposed model gets better results when compared to CNN-base models.
Guidance Module Network for Video Captioning

Video captioning has been a challenging and significant task that describes the content of a video clip in a single sentence. The model of video captioning is usually an encoder-decoder. We find that the normalization of extracted video features can improve the final performance of video captioning. Encoder-decoder model is usually trained using teacher-enforced strategies to make the prediction probability of each word close to a 0-1 distribution and ignore other words. In this paper, we present a novel architecture which introduces a guidance module to encourage the encoder-decoder model to generate words related to the past and future words in a caption. Based on the normalization and guidance module, guidance module net (GMNet) is built. Experimental results on commonly used dataset MSVD show that proposed GMNet can improve the performance of the encoder-decoder model on video captioning tasks.
A qualitative study of architectural design issues in DevOps

Abstract Software architecture is critical in succeeding with Development and Operations (DevOps). However, designing software architectures that enable and support DevOps (DevOps‐driven software architectures) is a challenge for organizations. We assert that one of the essential steps towards characterizing DevOps‐driven architectures is to understand architectural design issues raised in DevOps. At the same time, some of the architectural issues that emerge in the DevOps context (and their corresponding architectural practices or tactics) may stem from the context (i.e., domain) and characteristics of software organizations. To this end, we conducted a mixed‐methods study that consists of a qualitative case study of two teams in a company during their DevOps transformation and a content analysis of Stack Overflow and DevOps Stack Exchange posts to understand architectural design issues in DevOps. Our study found eight specific and contextual architectural design issues faced by the two teams and classified architectural design issues discussed in Stack Overflow and DevOps Stack Exchange into 11 groups. Our aggregated results reveal that the main characteristics of DevOps‐driven architectures are being loosely coupled and prioritizing deployability, testability, supportability, and modifiability over other quality attributes. Finally, we discuss some concrete implications for research and practice.
ROBOTune: High-Dimensional Configuration Tuning for Cluster-Based Data Analytics

Spark is popular for its ability to enable high-performance data analytics applications on diverse systems. Its great versatility is achieved through numerous user- and system-level options, resulting in an exponential configuration space that, ironically, hinders data analytics’s optimal performance. The colossal complexity is caused by two main issues: the high dimensionality of configuration space and the expensive black-box configuration-performance relationship. In this paper, we design and develop a robust tuning framework called ROBOTune that can tackle both issues and tune Spark applications quickly for efficient data analytics. Specifically, it performs parameter selection through a Random Forests based model to reduce the dimensionality of analytics configuration space. In addition, ROBOTune employs Bayesian Optimization to overcome the complex nature of the configuration-performance relationship and balance exploration and exploitation to efficiently locate a globally optimal or near-optimal configuration. Furthermore, ROBOTune strengthens Latin Hypercube Sampling with caching and memoization to enhance the coverage and effectiveness in the generation of sample configurations. Our evaluation results demonstrate that ROBOTune finds similar or better performing configurations than contemporary tuning tools like BestConfig and Gunther while improving on search cost by 1.59 × and 1.53 × on average and up to 2.27 × and 1.71 × , respectively.
Vulnerabilities of quantum cryptography on geometrically uniform coherent states

It is shown that the quantum cryptography protocol on geometrically uniform coherent states, which uses the restriction for unambiguous discrimination of a set of symmetric coherent states and allows resisting an unambiguous state discrimination attack (USD attack), is not secure to a number of other attacks. The key length formula from the work of S.N. Molotkov [JETP Lett., 101 (8), 579 (2015)] and the key generation rate for a number of constructive attacks of the eavesdropper are compared and it is shown that the key generation rate in this work is significantly overestimated. This leads to the fact that the distributed key is not secret.