Reviewerly/exHarmony huggingface dataset:
Syntax Role for Neural Semantic Role Labeling

Semantic role labeling (SRL) is dedicated to recognizing the semantic predicate-argument structure of a sentence. Previous studies in terms of traditional models have shown syntactic information can make remarkable contributions to SRL performance; however, the necessity of syntactic information was challenged by a few recent neural SRL studies that demonstrate impressive performance without syntactic backbones and suggest that syntax information becomes much less important for neural semantic role labeling, especially when paired with recent deep neural network and large-scale pre-trained language models. Despite this notion, the neural SRL field still lacks a systematic and full investigation on the relevance of syntactic information in SRL, for both dependency and both monolingual and multilingual settings. This paper intends to quantify the importance of syntactic information for neural SRL in the deep learning framework. We introduce three typical SRL frameworks (baselines), sequence-based, tree-based, and graph-based, which are accompanied by two categories of exploiting syntactic information: syntax pruning-based and syntax feature-based. Experiments are conducted on the CoNLL-2005, -2009, and -2012 benchmarks for all languages available, and results show that neural SRL models can still benefit from syntactic information under certain conditions. Furthermore, we show the quantitative significance of syntax to neural SRL models together with a thorough empirical survey using existing models.
Joint Detection and Matching of Feature Points in Multimodal Images

In this work, we propose a novel Convolutional Neural Network (CNN) architecture for the joint detection and matching of feature points in images acquired by different sensors using a single forward pass. The resulting feature detector is tightly coupled with the feature descriptor, in contrast to classical approaches (SIFT, etc.), where the detection phase precedes and differs from computing the descriptor. Our approach utilizes two CNN subnetworks, the first being a Siamese CNN and the second, consisting of dual non-weight-sharing CNNs. This allows simultaneous processing and fusion of the joint and disjoint cues in the multimodal image patches. The proposed approach is experimentally shown to outperform contemporary state-of-the-art schemes when applied to multiple datasets of multimodal images. It is also shown to provide repeatable feature points detections across multi-sensor images, outperforming state-of-the-art detectors. To the best of our knowledge, it is the first unified approach for the detection and matching of such images.
Identification of malachite and alteration minerals using airborne AVIRIS-NG hyperspectral data

Hyperspectral remote sensing technique is a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. The purpose of present study is to identify, delineate and map the altered zones by AVIRIS-NG Airborne hyperspectral data and validate the output by field survey and analysis of lab spectra. Major lithologies of the study area includes the presence of dolomite, banded iron formation, orthoquartzite, grey phyllite, granites and carbonaceous phyllite. Hydrothermal altered zones are delineated in the study area based on Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral data. FLAASH atmospheric correction was applied to hyperspectral data followed by minimum noise fraction, pixel purity index, n-dimensional visualisation and spectral angle mapper (SAM). SAM classified image was used for identification of altered minerals such as kaolinite, talc, kaosmec, sensitive to the shortwave infrared, whereas iron oxides such as goethite and limonite were distinguished in Visible and Near Infrared region. With the help of alteration minerals, Malachite mineral stains in the study area were identified. Representative rock samples are collected from field and lab spectra generated between wavelength region of 350–2500 nm. ASD spectra and image spectra compared with the USGS spectral library minerals which shows good spectral match with the collected samples. Therefore, it can be concluded that AVIRIS-NG hyperspectral data is useful in identification, delineation and mapping of altered and weathered minerals.
Simulated annealing for symbolic regression

Symbolic regression aims to hypothesize a functional relationship involving explanatory variables and one or more dependent variables, based on examples of the desired input-output behavior. Genetic programming is a meta-heuristic commonly used in the literature to achieve this goal. Even though Symbolic Regression is sometimes associated with the potential of generating interpretable expressions, there is no guarantee that the returned function will not contain complicated constructs or even bloat. The Interaction-Transformation (IT) representation was recently proposed to alleviate this issue by constraining the search space to expressions following a simple and comprehensive pattern. In this paper, we resort to Simulated Annealing to search for a symbolic expression using the IT representation. Simulated Annealing exhibits an intrinsic ability to escape from poor local minima, which is demonstrated here to yield competitive results, particularly in terms of generalization, when compared with state-of-the-art Symbolic Regression techniques, that depend on population-based meta-heuristics, and committees of learning machines.
Scaling Federated Learning for Fine-Tuning of Large Language Models

Federated learning (FL) is a promising approach to distributed compute, as well as distributed data, and provides a level of privacy and compliance to legal frameworks. This makes FL attractive for both consumer and healthcare applications. However, few studies have examined FL in the context of larger language models and there is a lack of comprehensive reviews of robustness across tasks, architectures, numbers of clients, and other relevant factors. In this paper, we explore the fine-tuning of large language models in a federated learning setting. We evaluate three popular models of different sizes (BERT, ALBERT, and DistilBERT) on a number of text classification tasks such as sentiment analysis and author identification. We perform an extensive sweep over the number of clients, ranging up to 32, to evaluate the impact of distributed compute on task performance in the federated averaging setting. While our findings suggest that the large sizes of the evaluated models are not generally prohibitive to federated training, we found that not all models handle federated averaging well. Most notably, DistilBERT converges significantly slower with larger numbers of clients, and under some circumstances, even collapses to chance level performance. Investigating this issue presents an interesting direction for future research.
Stealthy Targeted Data Poisoning Attack on Knowledge Graphs

A host of different KG embedding techniques have emerged recently and have been empirically shown to be very effective in accurately predicting missing facts in a KG, thus improving its coverage and quality. Unfortunately, embedding techniques can fall prey to adversarial data poisoning attack. In this form of attack, facts may be added to or deleted from a KG, called performing perturbations, that results in the manipulation of the plausibility of target facts in a KG. While recent works confirm this intuition, the attacks considered there ignore the risk of exposure. Intuitively, an attack is of limited value if it is highly likely to be caught, i.e., exposed. To address this, we introduce a notion of the exposure risk and propose a novel problem of attacking a KG by means of perturbations where the goal is to maximize the manipulation of the target fact's plausibility while keeping the risk of exposure under a given budget. We design a deep reinforcement learning-based framework, called RATA, that learns to use low-risk perturbations without compromising on the performance, i.e., manipulation of target fact plausibility. We test the performance of RATA against recently proposed strategies for KG attacks, on two different benchmark datasets and on different kinds of target facts. Our experiments show that RATA achieves state-of-the-art performance even while using a fraction of the risk.
Robust and Online Vehicle Counting at Crowded Intersections

In this paper, we propose an online movement-specific vehicle counting system to realize robust traffic flow analysis at crowded intersections. Our proposed framework adopts PP-YOLO as the vehicle detector and adapts the Deep-Sort algorithm to perform multi-object tracking. In order to realize online and robust vehicle counting, we further adopt a shape-based movement assignment strategy to differentiate movements and carefully designed spatial constraints to effectively reduce false-positive counts. Our proposed framework achieves the overall S1-score of 0.9467, ranking the first in the AICITY2021-track1 challenge.
SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

Abstract This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms’ performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child’s first name, birth date, date of baptism, father's first/last name, mother’s first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.
<scp>Banners:</scp> Binarized Neural Networks with Replicated Secret Sharing

Binarized Neural Networks (BNN) provide efficient implementations of Convolutional Neural Networks (CNN). This makes them particularly suitable to perform fast and memory-light inference of neural networks running on resource-constrained devices. Motivated by the growing interest in CNN-based biometric recognition on potentially insecure devices, or as part of strong multi-factor authentication for sensitive applications, the protection of BNN inference on edge devices is rendered imperative. We propose a new method to perform secure inference of BNN relying on secure multiparty computation. While preceding papers offered security in a semi-honest setting for BNN or malicious security for standard CNN, our work yields security with abort against one malicious adversary for BNN by leveraging on Replicated Secret Sharing (RSS) for an honest majority with three computing parties. Experimentally, we implement Banners on top of MP-SPDZ and compare it with prior work over binarized models trained for MNIST and CIFAR10 image classification datasets. Our results attest the efficiency of Banners as a privacy-preserving inference technique.
Cross-Referencing Method for Scalable Public Blockchain

We previously proposed a cross-referencing method for enabling multiple peer-to-peer network domains to manage their own public blockchains and periodically exchanging the state of the latest fixed block in the blockchain with hysteresis signatures among all the domains via an upper network layer. In this study, we evaluated the effectiveness of our method from three theoretical viewpoints: decentralization, scalability, and tamper resistance. We show that the performance of the entire system can be improved because transactions and blocks are distributed only inside the domain. We argue that the transaction processing capacity will increase to 56,000 transactions per second, which is as much as that of a VISA credit card system. The capacity is also evaluated by multiplying the number of domains by the average reduction in transaction-processing time due to the increase in block size and reduction in the block-generation-time interval by domain partition. For tamper resistance, each domain has evidence of the hysteresis signatures of the other domains in the blockchain. We introduce two types of tamper-resistance-improvement ratios as evaluation measures of tamper resistance for a blockchain and theoretically explain how tamper resistance is improved using our cross-referencing method. With our method, tamper resistance improves as the number of domains increases. The proposed system of 1,000 domains are 3-10 times more tamper-resistant than that of 100 domains, and the capacity is 10 times higher. We conclude that our method enables a more scalable and tamper-resistant public blockchain balanced with decentralization.
Visual Analysis of Electronic Densities and Transitions in Molecules

Abstract The study of electronic transitions within a molecule connected to the absorption or emission of light is a common task in the process of the design of new materials. The transitions are complex quantum mechanical processes and a detailed analysis requires a breakdown of these processes into components that can be interpreted via characteristic chemical properties. We approach these tasks by providing a detailed analysis of the electron density field. This entails methods to quantify and visualize electron localization and transfer from molecular subgroups combining spatial and abstract representations. The core of our method uses geometric segmentation of the electronic density field coupled with a graph‐theoretic formulation of charge transfer between molecular subgroups. The design of the methods has been guided by the goal of providing a generic and objective analysis following fundamental concepts. We illustrate the proposed approach using several case studies involving the study of electronic transitions in different molecular systems.
Masked Face Detection using Multi-Graph Convolutional Networks

In epidemic situations, as in the current COVID-19 pandemic, wearing face-masks is one of the most effective practices imposed to protect people lives. This will be one of the daily-life routines for a prolonged period, especially in public areas. Therefore, there is a demand to provide an efficient face detection method to help in dealing with such abnormal situations where people wearing masks are under monitoring. In this paper, we propose a deep learning model based on multi-graph convolutional networks (MGCN) to accurately detect people wearing masks. Unlike conventional GCNs, the proposed model includes many convolutional filters to produce multi-graph structure in which we use a 4D facet tensor as an input function and a convergence layer to capture multiple face expressions. This multi-graph version of spectral convolution transforms the extracted facial relief and generalizes image frequencies using graph rows and columns eigenvalues. The proposed architecture is simple yet efficient with several layers, including multi-graph convolutional, max pooling, dropout and softmax. We evaluate the performance of masked-faces detection on the publicly available real-world masked face dataset (RWMFD). The experimental results show an accuracy of 97.9%, which proves the efficiency of our proposed model in detecting people wearing facemasks.
Impact of Spatial and Temporal Information on Video Quality and Compressibility

Spatial Information (SI) and Temporal Information (TI) are frequently-used metrics to classify the spatiotemporal complexity of video content. However, they are mostly used on original video sources, and their impact on actual encoding efficiency is not known. In this paper, we propose a method to determine the compressibility of video sources, that is, how good video quality can be under a given bitrate constraint. We show how various aggregations of SI and TI correlate with compressibility scores obtained from a public dataset of H.264/HEVCN P9 content. We observe that the minimum TI value as well as an existing criticality metric from the literature are good indicators for compressibility, as judged by subjective ratings as well as VMAF and P.1204.3 objective scores.
MotionCompass

Wireless security cameras are integral components of security systems used by military installations, corporations, and, due to their increased affordability, many private homes. These cameras commonly employ motion sensors to identify that something is occurring in their fields of vision before starting to record and notifying the property owner of the activity. In this paper, we discover that the motion sensing action can disclose the location of the camera through a novel wireless camera localization technique we call MotionCompass. In short, a user who aims to avoid surveillance can find a hidden camera by creating motion stimuli and sniffing wireless traffic for a response to that stimuli. With the motion trajectories within the motion detection zone, the exact location of the camera can be then computed. We develop an Android app to implement MotionCompass. Our extensive experiments using the developed app and 18 popular wireless security cameras demonstrate that for cameras with one motion sensor, MotionCompass can attain a mean localization error of around 5 cm with less than 140 seconds. This localization technique builds upon existing work that detects the existence of hidden cameras, to pinpoint their exact location and area of surveillance.
IIMDR: Intelligence Integration Model for Document Retrieval

With the rise of digital documents on the internet, the demand for accurate search results has been a challenging task and many notable algorithms have been used to cater to such queries. However, with the increasingly fast-paced discoveries in machine learning and artificial intelligence, many of the algorithms have been outdated and no longer used. It is necessary to display only the relevant content that the user queried for. In this paper, IIMDR: Intelligence Integration Model for Document Retrieval is proposed. Jaccard Similarity is used to calculate word similarity in search result documents. Experiments using the proposed model on RCV dataset show that the proposed framework results in improved performance by providing more relevant documents to the user query. An accuracy of 86.78% on the RCV1 dataset has been achieved with this method.
Characteristic Parameters of Epoch Deep Learning to Predict Covid-19 Data in Indonesia

Abstract This study aims to predict Covid-19 data in Indonesia using LSTM machines learning and GRU using python. As a comparison, two datasets from other countries which have strong correlation were used. The dataset is of the ourworldindata.org page. The results of the LSTM model with epoch 15, RMSE 68,417 require rapid processing time and better accuracy than GRU with epoch 400, RMSE 90,173. The results from Covid-19 data processing in Indonesia have a robust correlation with Covid-19 data in Azerbaijan, Bangladesh, Bhutan, Cape Verde, Curacao, Slovenia, South Africa, and Thailand. The epoch characteristics of LSTM and GRU are a challenge since the amount of Covid-19 data is relatively minor.
Swarm Differential Privacy for Purpose-Driven Data-Information-Knowledge-Wisdom Architecture

Privacy protection has recently been in the spotlight of attention to both academia and industry. Society protects individual data privacy through complex legal frameworks. The increasing number of applications of data science and artificial intelligence has resulted in a higher demand for the ubiquitous application of the data. The privacy protection of the broad Data-Information-Knowledge-Wisdom (DIKW) landscape, the next generation of information organization, has taken a secondary role. In this paper, we will explore DIKW architecture through the applications of the popular swarm intelligence and differential privacy. As differential privacy proved to be an effective data privacy approach, we will look at it from a DIKW domain perspective. Swarm Intelligence can effectively optimize and reduce the number of items in DIKW used in differential privacy, thus accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW. The proposed approach is demonstrated through the application of personalized data that is based on the open-sourse IRIS dataset. This experiment demonstrates the efficiency of Swarm Intelligence in reducing computing complexity.
The Effect of VR Gaming on Discomfort, Cybersickness, and Reaction Time

The capabilities of commercial virtual reality (VR) systems can be utilized to implement a wide spectrum of interaction and locomotion methods. However, immersion in VR is often accompanied by undesirable physical sensations that undermine the VR gaming experience. Presenting the results of a user study (N=20) comparing the physiological and cognitive aftereffects of three different VR games with game mechanics centered around natural interaction, this paper attempts to shed light on the impact of different content factors (e.g., game genre, visual environment, interaction method) on user comfort. While moving away from cybersickness as the primary measure of VR-induced discomfort through investigating a broader set of physical symptoms, such as device-related discomfort, muscle fatigue, and pain, we also include a discussion on the complexities of measuring cybersickness for physically demanding games. Further focusing on cognitive performance, we discuss preliminary findings regarding different effects of common VR game interactions (pick and place, slicing, and shooting) on reaction time.
What are Table Cartograms Good for Anyway? An Algebraic Analysis

Abstract Unfamiliar or esoteric visual forms arise in many areas of visualization. While such forms can be intriguing, it can be unclear how to make effective use of them without long periods of practice or costly user studies. In this work we analyze the table cartogram—a graphic which visualizes tabular data by bringing the areas of a grid of quadrilaterals into correspondence with the input data, like a heat map that has been “area‐ed” rather than colored. Despite having existed for several years, little is known about its appropriate usage. We mend this gap by using Algebraic Visualization Design to show that they are best suited to relatively small tables with ordinal axes for some comparison and outlier identification tasks. In doing so we demonstrate a discount theory‐based analysis that can be used to cheaply determine best practices for unknown visualizations.