Reviewerly/exHarmony huggingface dataset:
Fusing Measurements from Wi-Fi Emission-Based and Passive Radar Sensors for Short-Range Surveillance

In this work, we consider the joint use of different passive sensors for the localization and tracking of human targets and small drones at short ranges, based on the parasitic exploitation of Wi-Fi signals. Two different sensors are considered in this paper: (i) Passive Bistatic Radar (PBR) that exploits the Wi-Fi Access Point (AP) as an illuminator of opportunity to perform uncooperative target detection and localization and (ii) Passive Source Location (PSL) that uses radio frequency (RF) transmissions from the target to passively localize it, assuming that it is equipped with Wi-Fi devices. First, we show that these techniques have complementary characteristics with respect to the considered surveillance applications that typically include targets with highly variable motion parameters. Therefore, an appropriate sensor fusion strategy is proposed, based on a modified version of the Interacting Multiple Model (IMM) tracking algorithm, in order to benefit from the information diversity provided by the two sensors. The performance of the proposed strategy is evaluated against both simulated and experimental data and compared to the performance of the single sensors. The results confirm that the joint exploitation of the considered sensors based on the proposed strategy largely improves the positioning accuracy, target motion recognition capability and continuity in target tracking.
Seek-and-Hide: Adversarial Steganography via Deep Reinforcement Learning

The goal of image steganography is to hide a full-sized image, termed secret, into another, termed cover. Prior image steganography algorithms can conceal only one secret within one cover. In this paper, we propose an adaptive local image steganography (AdaSteg) system that allows for scale- and location-adaptive image steganography. By adaptively hiding the secret on a local scale, the proposed system makes the steganography more secured, and further enables multi-secret steganography within one single cover. Specifically, this is achieved via two stages, namely the adaptive patch selection stage and secret encryption stage. Given a pair of secret and cover, first, the optimal local patch for concealment is determined adaptively by exploiting deep reinforcement learning with the proposed steganography quality function and policy network. The secret image is then converted into a patch of encrypted noises, resembling the process of generating adversarial examples, which are further encoded to a local region of the cover to realize a more secured steganography. Furthermore, we propose a novel criterion for the assessment of local steganography, and also collect a challenging dataset that is specialized for the task of image steganography, thus contributing to a standardized benchmark for the area. Experimental results demonstrate that the proposed model yields results superior to the state of the art in both security and capacity.
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence

We take inspiration from the study of human explanation to inform the design and evaluation of interpretability methods in machine learning. First, we survey the literature on human explanation in philosophy, cognitive science, and the social sciences, and propose a list of design principles for machine-generated explanations that are meaningful to humans. Using the concept of weight of evidence from information theory, we develop a method for generating explanations that adhere to these principles. We show that this method can be adapted to handle high-dimensional, multi-class settings, yielding a flexible framework for generating explanations. We demonstrate that these explanations can be estimated accurately from finite samples and are robust to small perturbations of the inputs. We also evaluate our method through a qualitative user study with machine learning practitioners, where we observe that the resulting explanations are usable despite some participants struggling with background concepts like prior class probabilities. Finally, we conclude by surfacing design implications for interpretability tools in general.
Neural Modeling for Named Entities and Morphology (NEMO2)

Abstract Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically rich languages (MRLs) pose a challenge to this basic formulation, as the boundaries of named entities do not necessarily coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental questions, namely, what are the basic units to be labeled, and how can these units be detected and classified in realistic settings (i.e., where no gold morphology is available). We empirically investigate these questions on a novel NER benchmark, with parallel token- level and morpheme-level NER annotations, which we develop for Modern Hebrew, a morphologically rich-and-ambiguous language. Our results show that explicitly modeling morphological boundaries leads to improved NER performance, and that a novel hybrid architecture, in which NER precedes and prunes morphological decomposition, greatly outperforms the standard pipeline, where morphological decomposition strictly precedes NER, setting a new performance bar for both Hebrew NER and Hebrew morphological decomposition tasks.
T-Smart: Trust Model for Blockchain Based Smart Marketplace

With the innovation of embedded devices, the concept of smart marketplace came into&#x0D; existence. A smart marketplace is a platform on which participants can trade multiple resources,&#x0D; such as water, energy, bandwidth. Trust is an important factor in the trading platform, as the&#x0D; participants would prefer to trade with those peers who have a high trust rating. Most of the existing&#x0D; trust management models for smart marketplace only provide a single aggregated trust score for&#x0D; a participant. However, they lack the mechanism to gauge the level of commitment shown by a&#x0D; participant while trading a particular resource. This work aims to provide a fine-grained trust score&#x0D; for a participant with respect to each resource that it trades. Several parameters, such as resource&#x0D; availability, success rate, and turnaround time are used to gauge the participant’s level of commitment,&#x0D; specific to the resource being traded. Moreover, the effectiveness of the proposed model is validated&#x0D; through security analysis against ballot-stuffing and bad-mouthing attacks, along with simulationbased&#x0D; analysis and a comparison in terms of accuracy, false positive, false negative, computational&#x0D; cost and latency. The results indicate that the proposed trust model has 7% better accuracy, 30.13%&#x0D; lower computational cost and 31.74% less latency compared to the existing benchmark model.
Airbus ship detection from satellite imagery using frequency domain learning

Ship detection from remote sensing images has been a topic of interest that gradually gained attention over the years due to the wide variety of its applications in the field of maritime surveillance, such as oil discharge control, sea pollution monitoring, and harbour management. Even though there is an extensive amount of methods developed for ship detection, there are still several challenges that remain unsolved, especially in complex environments. These challenges include occlusions due to shadows, clouds, and fog. Nowadays, deep learning algorithms, especially Deep Convolutional Neural Networks (DCNNs), are considered as a powerful approach for automatically detecting ships in satellite imagery. In this paper, enhanced Faster R-CNN (FRCNN) model will be used to overcome the aforementioned unsolved challenges. The enhanced FRCNN, which combines high level features with low level features, will be trained and tested in the frequency domain using the publicly available satellite imagery dataset, Airbus Ship Detection, provided by Kaggle. The performance will be compared to the original FRCNN based on their Overall Accuracy (OA) and Mean Average Precision (mAP) metrics.
Leveraging DistilBERT for Summarizing Arabic Text: An Extractive Dual-Stage Approach

Towards tackling the phenomenon of textual information overload that is exponentially pumping with redundancy over the Internet, this paper investigates a solution depending on the Automatic Text Summarization (ATS) method.The idea of ATS is to assist, e.g., online readers, in getting a simplified version of texts for preserving their time/effort required to skim a given large body of text.However, ATS is deemed as one of the most complex NLP applications, particularly for the Arabic language that has not been intelligently developed like the other Indo-European languages.Thus, we present an extractivebased summarizer (ArDBertSum) for text written in Arabic, relying on the DistilBERT model.Besides, we propose a domain-specific sentence-clauses segmentater (SCSAR) to support our ArDBertSum in further shortening long/complex sentences.The results of our experiments illustrate that our ArDBertSum yields the best performance, compared with non-heuristic Arabic summarizers, in producing an acceptable quality of candidate summaries.These experiments have been conducted on EASC-dataset (along with our proposed dataset) to report on (1) a statistical evaluation utilizing ROUGE metrics and (2) a specific human-based evaluation.The human evaluation results revealed promising perceptions; however, further works are needed to ameliorate the coherence and punctuation of the automatic summaries.
The concept of streaming data processing of Russian satellite microwave radiometers of the MTVZA series based on IKI-Monitoring Center for Collective Use

Д. М. Ермаков и др.Концепция потоковой обработки данных российских спутниковых СВЧ-радиометров… for Collective Use.In general, the results of trial data processing indicate high competitiveness of MTVZA-GYa with well-known foreign analogues (SSMIS, AMSR-2).Further improving the accuracy of georeferencing and data calibration are currently the priority tasks.
Human Activity Classification Using Multilayer Perceptron

The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.
Towards a variational Jordan–Lee–Preskill quantum algorithm

Abstract Rapid developments of quantum information technology show promising opportunities for simulating quantum field theory in near-term quantum devices. In this work, we formulate the theory of (time-dependent) variational quantum simulation of the <?CDATA $1+1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:math> dimensional <?CDATA $\lambda \phi^4$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>λ</mml:mi> <mml:msup> <mml:mi>ϕ</mml:mi> <mml:mn>4</mml:mn> </mml:msup> </mml:math> quantum field theory including encoding, state preparation, and time evolution, with several numerical simulation results. These algorithms could be understood as near-term variational quantum circuit (quantum neural network) analogs of the Jordan–Lee–Preskill algorithm, the basic algorithm for simulating quantum field theory using universal quantum devices. Besides, we highlight the advantages of encoding with harmonic oscillator basis based on the Lehmann—Symanzik—Zimmermann reduction formula and several computational efficiency such as when implementing a bosonic version of the unitary coupled cluster ansatz to prepare initial states. We also discuss how to circumvent the ‘spectral crowding’ problem in the quantum field theory simulation and appraise our algorithm by both state and subspace fidelities.
Analyzing LDA and NMF Topic Models for Urdu Tweets via Automatic Labeling

Various topic modeling methods provide a means of understanding and analyzing content available on social media platforms like Twitter and Facebook in an unsupervised manner. However, despite several existing conventional techniques, they have had limited success when applied directly for filtering and quick comprehension of short-text contents due to text sparseness and noise. Thus, it always has been a challenging problem to discover reliable latent topics from online discussion texts that prevailed with low words co-occurrence and availability of large size social media benchmark datasets even for resource-rich languages. The existing literature lacks such work for Urdu text to unveil niche topics even with conventional topic models mainly due to lack of benchmark datasets, limited availability of pre-processing tools/ algorithms, and time and compute limitations on large size datasets. This work presents experiments with multiple approaches of topic modeling like Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF) on 0.8 million Urdu tweets. These tweets are collected through Twitter API by giving various hashtags as a query to avoid dominance of single topic in the dataset. In addition, we have pre-processed the text of the tweets, prepared the three variants of the collected dataset, and extracted multiple features to represent documents on different n-grams. Furthermore, all these techniques are compared and evaluated on the dataset variants using both qualitative and quantitative measures. We have also demonstrated the results of these approaches through visualization methods, graphs depicting tweets size per topic, word clouds, and hashtags analysis giving insights about algorithms performances on finalized topics. Observed results reveal that NMF outperformed aa the techniques with TF-IDF feature vectors on Urdu tweets text while LDA performed best with merging shorttext strategy into long pseudo documents.
TabLeX: A Benchmark Dataset for Structure and Content Information Extraction from Scientific Tables

Information Extraction (IE) from the tables present in scientific articles is challenging due to complicated tabular representations and complex embedded text. This paper presents TabLeX, a large-scale benchmark dataset comprising table images generated from scientific articles. TabLeX consists of two subsets, one for table structure extraction and the other for table content extraction. Each table image is accompanied by its corresponding LaTeX source code. To facilitate the development of robust table IE tools, TabLeX contains images in different aspect ratios and in a variety of fonts. Our analysis sheds light on the shortcomings of current state-of-the-art table extraction models and shows that they fail on even simple table images. Towards the end, we experiment with a transformer-based existing baseline to report performance scores. In contrast to the static benchmarks, we plan to augment this dataset with more complex and diverse tables at regular intervals.
A Novel Framework Incorporating Augmented Reality and Pedagogy for Improving Reading Comprehension in Special Education

Augmented Reality (AR) is an emerging technology thriving in recent years. The implementation of AR in education offers great opportunities to enhance educational environments achieving better learning outcomes. As students with learning disabilities struggle with reading comprehension, an AR learning environment provides them support to better understand texts they actually read. Even though few studies have tried to explore the impact of AR technology to reading comprehension for students with learning disabilities in Secondary Education, there is a lack of research grounded in the incorporation of learning theories and personalization technologies. The goal of this paper is to present an AR educational environment capable of supporting meaningful learning outcomes by taking into consideration each student special educational needs and learning style. The novelty of this study lies in the student-centered and personalized design, which leads to improved understanding, student interaction and self-learning.
Racing BIKE: Improved Polynomial Multiplication and Inversion in Hardware

BIKE is a Key Encapsulation Mechanism selected as an alternate candidate in NIST’s PQC standardization process, in which performance plays a significant role in the third round. This paper presents FPGA implementations of BIKE with the best area-time performance reported in literature. We optimize two key arithmetic operations, which are the sparse polynomial multiplication and the polynomial inversion. Our sparse multiplier achieves time-constancy for sparse polynomials of indefinite Hamming weight used in BIKE’s encapsulation. The polynomial inversion is based on the extended Euclidean algorithm, which is unprecedented in current BIKE implementations. Our optimized design results in a 5.5 times faster key generation compared to previous implementations based on Fermat’s little theorem.Besides the arithmetic optimizations, we present a united hardware design of BIKE with shared resources and shared sub-modules among KEM functionalities. On Xilinx Artix-7 FPGAs, our light-weight implementation consumes only 3 777 slices and performs a key generation, encapsulation, and decapsulation in 3 797 μs, 443 μs, and 6 896 μs, respectively. Our high-speed design requires 7 332 slices and performs the three KEM operations in 1 672 μs, 132 μs, and 1 892 μs, respectively.
Ordered physical human activity recognition based on ordinal classification

Human activity recognition (HAR) is a critical process for applications that focus on the classification of human physical activities such as jogging, walking, downstairs, and upstairs.Ordinal classification (OC) is a special type of supervised multi-class classification in which an inherent ordering among the classes exists, such as low, medium, and high.This study combines these two concepts and introduces an approach to "human activity recognition based on ordinal classification" (HAROC).In the proposed approach, ordinal classification is applied to human activity recognition where the physical activities can be ordered by using their signals' band power values.This is the first study that investigates the performance of the HAROC approach by combining the ordinal classification with eight different base learners.Besides, this study is also original in that it examines the effects of the demographic characteristics of the participants (i.e., sex, age, weight, and height) on the classification performance.The experiments carried out on a real-world dataset show that the proposed HAROC approach is an effective method for human activity recognition tasks.
A Study On Application Of Blockchain Technology To Control Counterfeit Drugs, Enhance Data Privacy and Improve Distribution In Online Pharmacy

Technology has always emerged to embolden the existing working process. On the one hand, it encourages transparency, accessibility and robustness in the system and on the other hand, it begets mitigation of the risks and allows us to detect, evaluate and eliminate vulnerability in the system. The most prominent technologies in todays’ world like Artificial Intelligence, Virtual/Augmented Reality, Automation, Cloud Computing are thriving to solve society’s problems and ensure the expedition in the process from its previous generation. Blockchain technology is no exception in providing the solution to eliminate the counterfeit markets across the globe and building trust among parties to do business without the fear of indulging or facing any unscrupulous business. Blockchain technology can ensure data privacy while improving supply chain transparency and reducing fraud. It can provide all stakeholders within a certain supply chain with access to the same information, potentially reducing errors. A decentralised application can pave the way to promulgate online pharmaceutical business where health information and each stakeholders’ data is not compromised. Against this backdrop the research conducted throws light on the challenges in the online medical drug and devices’ distribution and proposes a solution, an architectural design for blockchain technology in an online pharmaceutical platform to mitigate the counterfeit market while bringing efficiency to the ecosystem.
A deep learning‐based attack on text CAPTCHAs by using object detection techniques

Text-based CAPTCHAs have been widely deployed by many popular websites, and many have been attacked. However, most previous cracks were based on classification algorithms that typically rely on a series of preprocessing operations or on many training samples, thus making such attacks complicated and costly. In this study, a simple, generic, fast and end-to-end attack based on advanced object detection technologies is introduced. The proposed attack combines a feature extraction module, a character location and recognition module and a coordinate matching module. The experiments show that the attack can break a wide range of real-world text CAPTCHAs deployed by the 50 most popular websites on Alexa.com and that the method achieves a high attack accuracy with only 2000 samples at an attack speed of less than 0.10 s. The attack was also evaluated on four click-based CAPTCHAs that cannot be attacked in the end-to-end manner used by previous attacks, and the results demonstrated that within one step, the proposed approach achieves high success rates on both click-based CAPTCHAs and schemes based on large-scale character sets, such as Chinese character sets.
Classification of Breast Cancer Cell Images using Multiple Convolution Neural Network Architectures

Breast cancer is a malignant tumor that affects women.It is the most prevalent cancer in women, affecting about 10% of all women at any point in their lives.The development of breast cancer begins in the lobules or ducts of the cells.Early detection and prevention are the best ways to stop this cancer from spreading.In this study, five Convolution Neural Network (CNN) models are used to process image data of breast cells.AlexNet, InceptionV3, GoogLeNet, VGG19 and Xception models are used for the classification of Invasive Ductal Carcinoma, IDC and Non-Invasive Ductal Carcinoma (Non-IDC) cells.The models are trained and tested at different epochs to record the learning rate.It is observed from the study that with higher epochs, the data loss decreases and accuracy increases.The accuracy of InceptionV3 and Xception is 92.48% and 90.72% respectively.Likewise, VGG19 and AlexNet have fairly close accuracy of 94.83% and 96.74%.However, GoogLeNet dominates over the other implemented models with the highest accuracy of 97.80%.The GoogLeNet model performs with high accuracy and precision in detecting IDC cells responsible for breast cancer.
ODIST: Open World Classification via Distributionally Shifted Instances

In this work, we address the open-world classification problem with a method called ODIST(open world classification via distributionally shifted instances).This novel and straightforward method can create out-ofdomain instances from the in-domain training examples with the help of a pre-trained language model.Experimental results show that ODIST performs better than state-of-the-art decision boundary finding method.