Reviewerly/exHarmony huggingface dataset:
Landmarks and regions: a robust approach to data extraction

We propose a new approach to extracting data items or field values from semi-structured documents. Examples of such problems include extracting passenger name, departure time and departure airport from a travel itinerary, or extracting price of an item from a purchase receipt. Traditional approaches to data extraction use machine learning or program synthesis to process the whole document to extract the desired fields. Such approaches are not robust to for- mat changes in the document, and the extraction process typically fails even if changes are made to parts of the document that are unrelated to the desired fields of interest. We propose a new approach to data extraction based on the concepts of landmarks and regions. Humans routinely use landmarks in manual processing of documents to zoom in and focus their attention on small regions of interest in the document. Inspired by this human intuition, we use the notion of landmarks in program synthesis to automatically synthesize extraction programs that first extract a small region of interest, and then automatically extract the desired value from the region in a subsequent step. We have implemented our landmark based extraction approach in a tool LRSyn, and show extensive valuation on documents in HTML as well as scanned images of invoices and receipts. Our results show that the our approach is robust to various types of format changes that routinely happen in real-world settings
Snoopy: A Webpage Fingerprinting Framework With Finite Query Model for Mass-Surveillance

Internet users are vulnerable to privacy attacks despite the use of encryption. Webpage fingerprinting, an attack that analyzes encrypted traffic, can identify the webpages visited by a user. The key challenges in performing mass-scale webpage fingerprinting arise from (i) the sheer number of combinations of user behavior and preferences to account for, and; (ii) the bound on the number of website queries imposed by the defense mechanisms (e.g., DDoS defense) deployed at the website. These constraints preclude the use of conventional data-intensive ML-based techniques. In this work, we propose Snoopy, a first-of-its-kind framework, that performs webpage fingerprinting for a large number of users visiting a website. Snoopy caters to the generalization requirements of mass-surveillance while complying with a bound on the number of website accesses (finite query model) for traffic sample collection. We show that Snoopy achieves <inline-formula><tex-math notation="LaTeX">$\approx 90\%$</tex-math></inline-formula> accuracy when evaluated on most websites, across various browsing contexts. A simple ensemble of Snoopy and an ML-based technique achieves <inline-formula><tex-math notation="LaTeX">$\approx 97\%$</tex-math></inline-formula> accuracy while adhering to the finite query model, in cases when Snoopy alone does not perform well.
Hand Gesture Recognition Using a Dual Axis Millimeter-Wave Interferometric-Doppler Radar and Convolutional Neural Networks

We demonstrate hand gesture classification based on measurements obtained from a 40 GHz dual axis interferometric-Doppler radar. Using a dual axis, three-element receiver, velocity is measured in three dimensions (two angular, one radial). We compare the results of four millimeter-wave receiver topologies: a system providing two Doppler responses; two Doppler responses plus their corresponding interferometric response; three Doppler responses; and three Doppler responses plus their two corresponding interferometric responses. In almost every case, the system with three micro-Doppler and two interferometric responses performed the best, having a total classification accuracy of 99.7% over all of the test set measurements.
Everest: A Top-K Deep Video Analytics System

The impressive accuracy of deep neural networks (DNNs) has created great demands on practical analytics over video data. Although efficient and accurate, the latest video analytic systems have not supported analytics beyond selection and aggregation queries. In data analytics, Top-K is a very important analytical operation that enables analysts to focus on the most important entities. In this demonstration, we present Everest, the first system that supports efficient and accurate Top-K video analytics. Everest ranks and identifies the most interesting frames/clips from videos with probabilistic guarantees. Furthermore, it supports user-defined functions to rank frames/clips based on different semantics using different deep vision models. Everest leverages techniques from computer vision, uncertain databases, and Top-K query processing to return results quickly.
Forgery Attack on An Outsourced Attribute-Based Signature Scheme

Attribute-based signatures have been widely used in many applications including anonymous authentication and fine-grained access control. Unfortunately, existing attribute-based signature schemes have high computational complexity which usually makes them unsuitable for resource-constrained devices. One approach to get around this problem is outsourcing heavy computation through cloud servers. But then, we have to take extra measures to ensure that the server performs computations honestly and that it cannot forge any user’s signature. Recently, Liu et al. proposed an outsourced attribute-based signature scheme. The goal of this paper is to show that this signature is vulnerable against forgery attack so that an attacker can forge a signature without the knowledge of the secret keys. We further present an improved method and show that it resolves the attack.
Presenting method to schedule tasks in the cloud computing environment using the whale optimization algorithm

Cloud computing, the distributed computing template, is a model for providing easy access based on the user demand through changeable and configurable computing resources with the slightest need to manage the help of a heterogeneous computing system in its geographically distributed form for sharing. Task scheduling in different heterogeneous physical machines and also efficient scheduling of tasks from a program in the available resources are the key factors in achieving the high performance. The main problem of task scheduling involves assigning the functions of a program to the appropriate processors and arranging work functions in each resource. One of the crucial challenges with a significant impact on the performance of such systems is the optimal scheduling. For the purpose of study, the whale optimization algorithm for a high-performance optimal scheduling was studied in different heterogeneous environments. This meta-heuristic algorithm can find the optimal answer with great accuracy while taking advantage of the relationships between the whales. To reduce time, both the total execution time and also the maximum execution time of every task were minimized. Through this, the highest level of time consumption for the task execution would be reduced. The results show that for a large set of functions (400 and more), the total time obtained is much less than the time for the base study. As a result, for the set of1000 functions, the given improvements through the suggested method is 24.26 in the best case and 24.85 in the average mode.
NoSQL Database Performance Diagnosis through System Call-level Introspection

Since its emergence, NoSQL databases have firmly established themselves as an indispensable software component of modern cloud-native applications. However, it also becomes increasingly challenging to perform critical management tasks such as troubleshooting unexpected performance problems. This is due to the ever-increasing diversity and specialization of NoSQL databases that make it difficult to observe the internal activities. To address these challenges, we have designed and built a technique for introspecting NoSQL databases. Our technique traces system call sequences of key operations under controlled workloads and filters scaling patterns from constant components. Novel algorithms are developed to uncover repeating patterns of system calls from massive amounts of traces and filter out background noise with high efficiency. The evaluation shows that our technique can greatly enhance the visibility into the NoSQL databases enabling us to diagnose performance problems or gain insights into internal activities.
Intent-based System Design with Efficient Search Using Partial-Order Reduction

Intent-based design (IBD) methods are used for automatically designing system configurations from service requirements. IBD methods based on graph rewriting can receive flexible requirement definitions as input, which are not confined to a specific format, and flexibly respond to the environment and user demands.However, current graph-rewriting-based methods require a large amount of time to discover a concrete system configuration. Therefore, we propose a graph-rewriting-based IBD method that can discover system configurations more efficiently while maintaining the advantages of current graph-rewriting-based methods by introducing a pruning method that focuses on a single unit in the requirements to be resolved.This paper includes a description of a specific procedure of the proposed method, proof that pruning does not reduce the number of variations of generated system configurations, and experimental results of comparing the proposed method with a graph-rewriting-based method without pruning. The results indicate that the proposed method can reduce computation time by 4-10 times compared with the method without pruning.
A Versatile Systolic Array for Transposed and Dilated Convolution on FPGA

Many modern CNNs feature complex architecture topologies with different layer types. One of these special layers is a fractionally-strided or transposed convolution (T-CONV) layer [1] , which is an up-sampling layer that uses trained weights to produce enlarged high-resolution feature maps. An atrous or dilated convolution (D-CONV) layer is another special layer that maintains the resolution and coverage of feature maps by expanding the receptive fields of convolution filters as discussed in [2] . Both T-CONV and D-CONV layers can be naïvely implemented as normal convolution (N-CONV) layers by inserting S ′ − 1 zeros between adjacent pixels of the input feature maps (FMs) for T-CONV or d − 1 zeros between adjacent values of the filters for D-CONV, where S ′ is T-CONV stride and d is D-CONV dilation rate. This approach, however, leads to a huge underutilization of computation resources due to the introduced zero MAC operations.
Comparison of Neural Network Models for LDA Inferring

One of the most reliable methods to find topics for a document is Latent Dirichlet Allocation (LDA) which is a generative statistical model, but with the growing amount of data, this method can be time consuming. This problem can be solved by utilizing neural network to learn from LDA and train model for faster processing time. This study aims to inspect further on how accurate different neural network models can be when learn from LDA. The neural network models that are used to compare in this work are dense neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), and bidirectional GRU (BiGRU). From the experiment, it shows that BiGRU and RNN are good alternative to learn from LDA when compare to DNN, RNN has the best test accuracy on 15 topics at 0.8833, comparing to Dense 3 at 0.8807, Dense 2 at 0.8798, and BiGRU at 0.8767, while BiGRU has the best test accuracy on 20 topics at 0.8727, comparing to Dense 2 at 0.8704, RNN at 0.8664, and Dense 3 at 0.8642. If the topic is more than 35 topics, Dense 2 outperform other techniques including Dense 3 as well.
Probabilistic Analysis of Search Performance of Differential Evolution Algorithm in Low-Dimensional Case

In this paper, a method of establishing and analyzing the probability model of its mutation and crossoperation process for differential evolution (DE) algorithm is proposed; especially, the escape ability and further optimization ability of individuals trapped in the local optimal neighborhood are deduced in detail, and the characteristic curves of the influence of key parameters such as population size, scaling factor, and crossfactor on the search performance of the algorithm are obtained. It provides a theoretical reference for the application of the algorithm.
Diffuse Kalman filtering with linear constraints on the state parameters

We give a general proof, based on Piet de Jong’s diffuse Kalman filter, that imposing linear constraints on state smoothing is still feasible under diffuse initialization. We also offer simple derivations of existing identities related to the diffuse Kalman filter and smoother.
An Investigation into the Prevalence of Reflection Techniques in Distributed Microsoft .Net NuGet Artefacts

This paper investigates the prevalence of reflection and reflective techniques in the NuGet distribution mechanism and applies the results of the prevalence into a general discussion on the prevalence of reflective techniques onto the Microsoft .Net ecosystem as whole. The results show a over 15% utilization in over 6 million NuGets which suggests that reflective techniques are a very valuable technique in the .Net ecosystem. The results also show and discuss the number of overall NuGets utilizing reflection, unique NuGets utilizing reflection, and the most popular reflective references in said NuGets. The paper then concludes with a discussion on the advantages and disadvantages of utilizing reflection, and the value of learning and utilizing the technique for developers both at the initial stages of their career and the later ones.
A data optimization and processing method for complex micro‐electronics system in the <scp>IoT</scp> environment

With the rapid development of network technologies nowadays, the Internet of Things (IoT) has become a comment trend in most situations. In this trend, the complex micro‐electronics system is the most benefited one. Generally, the traditional evaluation and design of the micro‐electronics system is to establish an approximate model based on the statistical theory with a large error probability. Now, with the benefits of IoT, new methods can be applied. In this paper, we mainly use the IoT enabled Back Propagation (BP) neural network to establish the reliability evaluation and analysis model of micro‐electronic complex system. Firstly, the life cycle of micro‐electronic components and mechanical parts obeys exponential distribution and Weibull distribution in the IoT environment respectively. Then the reliability of each piece under different working time and the overall reliability of the system is calculated according to the failure rate of each part. Secondly, the numerical simulation results show that the error of the reliability value between the BP reliability evaluation model and the basic reliability model is very small, which verifies the accuracy and feasibility of the model, and predicts the reliability of the system. The research shows that this method can predict the overall reliability from the reliability of components, improve the accuracy of reliability evaluation, solve the problem that it is difficult to obtain the analytical expression of distribution model based on statistical theory, and reduce the complexity of the model.
IsiXhosa Named Entity Recognition Resources

Named entity recognition has been one of the most widely researched natural language processing technologies over the past two decades. For the South African languages, however, relatively little research and development work has been done. This changed with the release of the NCHLT named entity annotated resources, a collection of named entity annotated data and Conditional Random Field-based named entity recognisers for ten of the official languages. In this work, we provide a detailed description and linguistic analysis of the named entity (NE) annotated data for the agglutinative isiXhosa language, by analysing the morphosyntactic features relevant to the three main types of NE, viz. person, location, and organisation. From the data, we identify suffix and capitalisation features that may be good predictors of the different NE types. Based on these features, we describe the named entity recogniser and feature set developed as part of the NCHLT release. The recogniser has high precision, 0.9713 overall, but relatively low recall, 0.7409, especially for person names, 0.5963, resulting in an overall F-score of 0.8406. Although there are various avenues to improve the named entity recogniser, this is a significant release for a historically under-resourced language.
Variational Graph Autoencoder with Mutual Information Maximization for Graph Representations Learning

Graph neural network (GNN) is a powerful representation learning framework for graph-structured data. Some GNN-based graph embedding methods, including variational graph autoencoder (VGAE), have been presented recently. However, existing VGAE-based methods typically focus on reconstructing the adjacent matrix, i.e. topological structure, instead of the node features matrix, this strategy makes graphical features difficult to be fully learned, which weakens and restricts the capacity of a generative network to learn higher-quality representations. To address the issue, we use a contrastive estimator on the representation mechanism, i.e. on the encoding process under the framework of VGAE. In particular, we maximize the mutual information (MI) between encoded latent representation and node attributes which acts as a regularizer forcing the encoder to select the most informative with respect to the node attributes. Additionally, we also solve another key question how to effectively estimate the mutual information by drawing samples from the joint and marginal, and explain why the maximization of MI can contribute to the encoder obtaining more node feature information. Ultimately, extensive experiments on three citation networks and four web-age networks show that our method outperforms contemporary popular algorithms (such as DGI) on node classifications and clustering tasks, and the best result is an [Formula: see text] increase on node clustering task.
A Novel Undeniable (t, n)-Threshold Signature with Cheater Identification

A digital signature is one of the most widely used cryptographic primitives in asymmetry cryptography. According to the security requirements in different symmetry or asymmetry network models, various digital signatures have been developed in the literature. To protect the right of the signer, Chaum and Antrepen first introduced the concept of an undeniable signature, where interactive protocols are needed for the verification process. Besides, a signer can, also, perform a disavowal protocol to prove that they did not sign the message. On the other hand, threshold cryptography is, usually, used to protect the system from a single point of failure. In a (t,n)-threshold signature scheme, as long as t people in the group of n people participate, the signature can be smoothly signed. By combining these two features, an undeniable threshold signature enjoys the advantages from both sides. After our survey, we found that the existing undeniable threshold signature schemes are either insecure or apply impractical assumptions. Thus, in this manuscript, we aim at designing a novel and provably secure undeniable threshold signature scheme. The proposed scheme is formally proven to be unforgeable and invisible. Besides, our scheme supports cheater identification, which allows one to find the cheater, when a signing protocol fails. Moreover, the proposed scheme can be performed without the help of trusted third parties or secure cryptographic modules, which would be more practical when our scheme is deployed in real-world applications.
Adaptive Threshold Sampling

Sampling is a fundamental problem in computer science and statistics. However, for a given task and stream, it is often not possible to choose good sampling probabilities in advance. We derive a general framework for adaptively changing the sampling probabilities via a collection of thresholds. In general, adaptive sampling procedures introduce dependence amongst the sampled points, making it difficult to compute expectations and ensure estimators are unbiased or consistent. Our framework address this issue and further shows when adaptive thresholds can be treated as if they were fixed thresholds which samples items independently. This makes our adaptive sampling schemes simple to apply as there is no need to create custom estimators for the sampling method. Using our framework, we derive new samplers that can address a broad range of new and existing problems including sampling with memory rather than sample size budgets, stratified samples, multiple objectives, distinct counting, and sliding windows. In particular, we design a sampling procedure for the top-K problem where, unlike in the heavy-hitter problem, the sketch size and sampling probabilities are adaptively chosen.
Carnivalesque Theory and Social Networks

Carnivalesque theory has been used as a model and a structure in the works carried out in many fields such as communication, literature, and sociology. In fact, Carnivalesque appears in many environments/areas, particularly in the social networks, which are the manifestation of social life. This chapter examines social networks in the context of carnivalesque theory to reveal facts of carnivalesque in Twitter. Content analysis technique was used in the research. Research data came from 10 Twitter accounts which have a maximum number of followers in Turkey. These data were analyzed and examined in terms of grotesque, dialogism, carnival laughter, upside-down world, marketplace, and marketplace speech belonging to the carnivalesque theory. According to the findings, the structure of Twitter, which is one of the most popular social networks in Turkey, is largely similar to the structure of the carnival and features of carnivalesque theory.
Exploring participant interaction with interpersonal touch musical interfaces

There is growing research on the benefits of interpersonal touch as part of our social lives. Interpersonal touch musical interfaces (ITMIs) are devices that turn a participant's skin into a touch sensitive interface to control music. These devices could have a huge impact on normalizing interpersonal touch interactions as we emerge from restrictions brought on by COVID 19. There is little research that outlines the design of these devices or offers a framework for their implementation. This research hopes to encourage interpersonal touch by examining how participants interact with each other when wearing an ITMI device. It will do this by considering the experiences of a group of participants engaging with a novel body-worn ITMI. This design framework will illuminate the effect of these devices and be useful to interaction designers and researchers who are examining new ways to encourage interpersonal touch in performance and public art contexts.
Sparx: Distributed Outlier Detection at Scale

There is no shortage of outlier detection (OD) algorithms in the literature, yet a vast body of them are designed for a single machine. With the increasing reality of already cloud-resident datasets comes the need for distributed OD techniques. This area, however, is not only understudied but also short of public-domain implementations for practical use. This paper aims to fill this gap: We design Sparx, a data-parallel OD algorithm suitable for shared-nothing infrastructures, which we specifically implement in Apache Spark. Through extensive experiments on three real-world datasets, with several billions of points and millions of features, we show that existing open-source solutions fail to scale up; either by large number of points or high dimensionality, whereas Sparx yields scalable and effective performance. To facilitate practical use of OD on modern-scale datasets, we open-source Sparx under the Apache license at https://tinyurl.com/sparx2022.