Reviewerly/exHarmony huggingface dataset:
Error Correction Coding for One-Bit Quantization With CNN-Based AutoEncoder

Recently, a deep learning based error correction coding scheme is proposed to compensate for the severe distortion due to one-bit quantization. However, the fully-connected (FC) layers aided autoencoder is too heavy, resulting in high storage cost. In this letter, a novel convolutional autoencoder named ECCNet is introduced to lighten the scheme. Additionally, the soft quantization function is introduced to overcome the gradient mismatch. The squeeze and excitation (SE) block is applied for further performance boosting. Simulations show that the BER performance of the proposed ECCNet outperforms the previous state-of-the-art method under 16-QAM modulation with fewer parameters required. Furthermore, the proposed autoencoder design has impressive robustness in near Gaussian multi-path fading channels.
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization

The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist's reasoning and conclusions, and it also aids the referring physician in confirming or excluding certain diagnoses.A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report.These tasks include acquisition of salient content from the report and generation of a concise, easily consumable IMPRESSIONS section.Prior research on radiology report summarization has focused on single-step end-to-end models -which subsume the task of salient content acquisition.To fully explore the cascade structure and explainability of radiology report summarization, we introduce two innovations.First, we design a two-step approach: extractive summarization followed by abstractive summarization.Second, we additionally break down the extractive part into two independent tasks: extraction of salient (1) sentences and (2) keywords.Experiments on English radiology reports from two clinical sites show our novel approach leads to a more precise summary compared to single-step and to two-stepwith-single-extractive-process baselines with an overall improvement in F1 score of 3-4%.
Zero-shot Cross-lingual Transfer is Under-specified Optimization

Pretrained multilingual encoders enable zeroshot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language.We postulate that this high variance results from zero-shot cross-lingual transfer solving an under-specified optimization problem.We show that any linear-interpolated model between the source language monolingual model and source + target bilingual model has equally low source language generalization error, yet the target language generalization error reduces smoothly and linearly as we move from the monolingual to bilingual model, suggesting that the model struggles to identify good solutions for both source and target languages using the source language alone.Additionally, we show that zero-shot solution lies in non-flat region of target language error generalization surface, causing the high variance.
VibroAuth: Authentication with Haptics Based Non-visual, Rearranged Keypads to Mitigate Shoulder Surfing Attacks

PIN (Personal Identification Number) code entry is a widely used authentication method used on smartphones, ATMs, etc. However, it is typically subject to shoulder surfing attacks where, a bystander may observe the user's keypad during PIN code entry. To mitigate this issue, we present a novel method that uses non-visual keypads for entering PIN codes where, the numbers on the keypad are invisible and rearranged. The numbers are rearranged by shifting the rows/columns of the keypad and we use haptic patterns to privately convey the keypad layout information to the user. The results of our first study with 22 participants indicated that the participants could learn the haptic patterns relatively fast and use the non-visual keypads with high accuracy. Next, a security experiment with 12 participants discusses the effectiveness of our method in mitigating shoulder surfing attacks.
Language-Agnostic Knowledge Representation for a Truly Multilingual Semantic Web

As the Internet users’ communities and the information on the web are increasing exponentially, that too associated with a wide range of different cultures and languages; meaningful search in a multilingual setting is required. It is crucial to develop a simple to use, granular, and adaptable knowledge representation system that could constitute the core of Semantic Web. It should be well-prepared for multilingual and multicultural settings by virtue of internationalizing the code. In this work, a model for ontology-based language-agnostic Semantic Web application architecture is offered which is independent of today’s heterogeneous encoding of language resources and provides smooth exploitation. The suggested system architecture enables a quick and meaningful search for information independent of the end user’s preferred language and cultural conventions, an easy extension of the application with new information, as well as its easy localization for new language contexts. The model’s usability is proven by a prototype application simulation in the domain of Indian biodiversity.
From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer

We study the task of toxic spans detection, which concerns the detection of the spans that make a text toxic, when detecting such spans is possible.We introduce a dataset for this task, TOXICSPANS, which we release publicly.By experimenting with several methods, we show that sequence labeling models perform best.Moreover, methods that add generic rationale extraction mechanisms on top of classifiers trained to predict if a post is toxic or not are also surprisingly promising.Finally, we use TOXICSPANS and systems trained on it, to provide further analysis of state-of-the-art toxic to non-toxic transfer systems, as well as of human performance on that latter task.Our work highlights challenges in finer toxicity detection and mitigation.Gold Spans (set of character offsets) Post {55, 56, 57, 58, 59, 60} What if his opinion is that most other commenters are idiots?:-) {80, 81, 82,
A Cognitive-Driven Ordinal Preservation for Multimodal Imbalanced Brain Disease Diagnosis

The multimodal imbalanced data learning problems are becoming increasingly common in the real world, especially in brain disease diagnosis. Although multimodal data provide complementary information for decision making, it can also lead the model to be more sensitive to the adverse effects of imbalance. The existing imbalance learning methods are mostly based on single modality. In this article, we design a cognitive driven ordinal preservation model optimized in both feature and sample levels for multimodal imbalanced data. At the feature level, we project the original data into the label space by a second-order Laplacian manifold for better capturing the minor changes and preserving the discriminative information among samples. At the sample level, we derive the class-specific self-paced learning, simulating human cognitive mechanism, to drive data participating in learning from balance subset to the whole set, which can reduce the negative effects of imbalance on the learning model. Meanwhile, we impose the group sparse constraint on the projection matrix to embed the latent relationship pattern among different modalities, and theoretically prove its convergence. The proposed method is applied to multimodal brain disease diagnosis, including Alzheimer’s disease (AD) and epilepsy. The experimental results show that our method outperforms the existing imbalance and fusion algorithms.
Digital Gaps Influencing the Online Learning of Rural Students in Secondary Education: A Systematic Review

The digital gaps that were amplified due to the COVID-19 pandemic deepened more in vulnerable areas in different parts of the world. The present work aims to analyze the study of digital gaps that influence online learning of students from rural areas in secondary education, based on a systematic review of the literature. It was developed using the PRISMA methodology, the scientific information was retrieved from the Web of Science and ERIC databases in a period of three months. In the searches it was taken as inclusion criteria; open access, year of publication 2017 - 2021, English and Spanish language, rural secondary education level and exclusion; articles whose studies were carried out in higher education, in specific areas, related to research policies, evaluation, studies before 2017 and articles from systematic reviews. The results and findings emphasize that digital literacy levels are scarce and limited in terms of the skills developed by students and teachers to achieve digital competencies, in addition to restricted access due to technological, economic, and coverage gaps of families. The conclusions are aimed at strengthening urgent educational policies in addition to the integration of the school curriculum.
An Encoder-Decoder Approach to Offline Handwritten Mathematical Expression Recognition with Residual Attention

Unlike handwritten numeral recognition and handwritten text recognition, the recognition of handwritten mathematical expressions is more difficult because of their complex two-dimensional spatial structure. Since the “watch, attend and parse (WAP)” method was proposed in 2017, encoder-decoder models have made significant progress on handwritten mathematical expression recognition. Our model is improved based on the WAP [1] model. In this paper, the attention module is reasonably added to the encoder to make the extracted features are more informative. The new network is called Dense Attention Network (DATNet), which allows for an adequate extraction of the structural information from handwritten mathematical expressions. To prevent the model from overfitting during the training process, we use label smoothing [2] as the loss. Experiments showed that our model (DATWAP) improved WAP expression recognition from 48.4%/46.8%/45.1% to 54.72%/52.83%/52.54% on the CROHME 2014/2016/2019 test sets.
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection

Summary While deep learning models have seen recent high uptake in the geosciences, and are appealing in their ability to learn from minimally processed input data, as ‘black box’ models they do not provide an easy means to understand how a decision is reached, which in safety-critical tasks especially can be problematical. An alternative route is to use simpler, more transparent ‘white box’ models, in which task-specific feature construction replaces the more opaque feature discovery process performed automatically within deep learning models. Using data from the Groningen Gas Field in the Netherlands, we build on an existing logistic regression model by the addition of four further features discovered using Elastic Net driven data mining within the catch22 time series analysis package. We then evaluate the performance of the augmented logistic regression model relative to a deep (CNN) model, pre-trained on the Groningen data, on progressively increasing noise-to-signal ratios. We discover that, for each ratio, our logistic regression model correctly detects every earthquake, while the deep model fails to detect nearly 20% of seismic events, thus justifying at least a degree of caution in the application of deep models, especially to data with higher noise-to-signal ratios.
Real-Time Multiple Pedestrian Tracking With Joint Detection and Embedding Deep Learning Model for Embedded Systems

This paper proposes an improvement to the multi-object tracking system framework based on the image inputs.By analyzing the role and performance of each block in the original multi-objects tracking system, the blocks of the original system are reconstructed to enhance the efficiency and yield a faster processing speed suiting the real-time applications.In the proposed method, the first two parts of the multiobject tracking system are merged into a single neural network designed for object detection and feature extraction.A new object association judgment method and JDE inspired prediction head are included in order to achieve a better and an outstanding association effect resulting in the overall improvement of the original system by 45.2%.The enhanced method is aimed at the application of smart roadside units and uses fixedviewpoint image input to achieve multi-object tracking on embedded platforms.The proposed method is implemented on the NVIDIA Jetson AGX Xavier embedded platform.The NVIDIA TensorRT software development kit is used to accelerate the neural network.The overall performance of the proposed system yields better efficiency compared to that of the original SDE design and the overall computing performance achieve up to 14-26 images per second, making it ideal for the real-time smart roadside unit applications.
Formal Methods for Trusted Space Autonomy: Boon or Bane?

Trusted Space Autonomy is challenging in that space systems are complex artifacts deployed in a high stakes environment with complicated operational settings. Thus far these challenges have been met using the full arsenal of tools: formal methods, informal methods, testing, runtime techniques, and operations processes. Using examples from previous deployments of autonomy (e.g. the Remote Agent Experiment on Deep Space One, Autonomous Sciencecraft on Earth Observing One, WATCH on MER, IPEX, AEGIS on MER, MSL, and M2020, and the M2020 Onboard planner), we discuss how each of these approaches have been used to enable successful deployment of autonomy. We next focus on relatively limited use of formal methods (both prior to deployment and runtime methods). From the needs perspective, formal methods may represent the best chance for reliable autonomy. Testing, informal methods, and operations accommodations do not scale well with increasing complexity of the autonomous system as the number of text cases explodes and human effort for informal methods becomes infeasible. However from the practice perspective, formal methods have been limited in their application due to: difficulty in eliciting formal specifications, challenges in representing complex constraints such as metric time and resources, and requiring significant expertise in formal methods to apply properly to complex, critical applications. We discuss some of these challenges as well as the opportunity to extend formal and informal methods into runtime validation systems.
Learning I/O Variables from Scientific Software’s User Manuals

Scientific software often involves many input and output variables. Identifying these variables is important for such software engineering tasks as metamorphic testing. To reduce the manual work, we report in this paper our investigation of machine learning algorithms in classifying variables from software’s user manuals. We identify thirteen natural-language features, and use them to develop a multi-layer solution where the first layer distinguishes variables from non-variables and the second layer classifies the variables into input and output types. Our experimental results on three scientific software systems show that random forest and feedforward neural network can be used to best implement the first layer and second layer respectively.
Enhancement of Crowd Logistics Model in an E-Commerce Scenario Using Blockchain-Based Decentralized Application

Globalization and developments in technology have contributed to the growth of numerous industries around the globe which are creating major impacts on today's supply chains. The supply chain has drastically changed under the open-ended influence of globalization. The need for different types of mobilities is increasing due to urbanization and population growth, rapid development in the E-commerce industry, and the growing expectation of customers. Crowd logistics is one of these techniques that is gaining rapid attention in the logistics industries and many start-ups have started using this method in their business models. This paper has investigated the crowd logistics and the challenges like user trust, data safety and security, security of the financial transactions for both the customer and the crowd and tracking service quality. Using the Blockchain technology, an e-commerce crowd logistics conceptual model is proposed. Moreover, through different scenarios the capabilities of the proposed model besides the detailed flow of the business processes have been discussed. The conceptual model of a Blockchain-based crowd logistics has used the functionalities of Blockchain such as the smart contracts and the DApps and especially has increased the flexibility of the crowd logistics system.
Building a Knowledge-Intensive, Intent-Lean, Question Answering Chatbot in the Telecom Industry - Challenges and Solutions

Artificial Intelligence (AI) is one of the most emerging technologies of the past decade, leading the way towards human and machine interactions in terms of efficiency, accuracy and the overall value gained. This paper describes an intent-lean AI chatbot solution that handles user queries posed in natural language upon business related documents of a single-domain of Hellenic Telecommunications Organization S.A. (HTO). Unlike other traditional chatbot solutions that strictly rely on intent identification, our approach infers the implicit user need in order to provide the most relative documents and text snippets within, as the proper answer. To do this, we proceeded with a custom implementation based on Elasticsearch engine and most common NLP techniques tailored to our needs; i.e., tokenization, lowercase filtering, stop words removal, stemming, fuzzy searching, synonyms, etc. The main challenges as well as the architectural models that thrive to overcome them are being described in detail. Finally, the effectiveness of the proposed solution is being measured and the identified features for improvement are being presented.
Multibranch Feature Difference Learning Network for Cross-Spectral Image Patch Matching

Cross-spectral image patch matching is still challenging due to significant nonlinear differences between image patches. Recently, image patch matching methods based on feature relation learning have attracted increasing attention and achieved good performance. However, we find that the metric learning methods based on feature difference cannot comprehensively and effectively extract useful discriminative information between image patch pairs by only adopting two branches network structure. Therefore, we propose a novel multi-branch feature difference learning network (MFD-Net). Specifically, we build a multi-branch parallel feature difference extraction network, which can capture richer and more discriminative feature difference information and achieve significant improvements on matching tasks. Furthermore, we propose a combined metric network composed of a master metric network module and multiple branch metric network modules, which promotes the forward update of network weights and reduces the similarity of features extracted by each feature difference extraction module with negligible increase in inference time. Extensive experimental results show that the proposed MFD-Net achieves superior performances on cross-spectral image patch matching and single spectral image patch matching.
Round-Optimal Multi-party Computation with Identifiable Abort

Secure multi-party computation (MPC) protocols that are resilient to a dishonest majority allow the adversary to get the output of the computation while, at the same time, forcing the honest parties to abort. Aumann and Lindell introduced the enhanced notion of security with identifiable abort, which still allows the adversary to trigger an abort but, at the same time, it enables the honest parties to agree on the identity of the party that led to the abort. More recently, in Eurocrypt 2016, Garg et al. showed that, assuming access to a simultaneous message exchange channel for all the parties, at least four rounds of communication are required to securely realize non-trivial functionalities in the plain model. Following Garg et al., a sequence of works has matched this lower bound, but none of them achieved security with identifiable abort. In this work, we close this gap and show that four rounds of communication are also sufficient to securely realize any functionality with identifiable abort using standard and generic polynomial-time assumptions. To achieve this result we introduce the new notion of bounded-rewind secure MPC that guarantees security even against an adversary that performs a mild form of reset attacks. We show how to instantiate this primitive starting from any MPC protocol and by assuming trapdoor-permutations. The notion of bounded-rewind secure MPC allows for easier parallel composition of MPC protocols with other (interactive) cryptographic primitives. Therefore, we believe that this primitive can be useful in other contexts in which it is crucial to combine multiple primitives with MPC protocols while keeping the round complexity of the final protocol low.
Computer Vision Based Method for Shadow Detection

Shadows in images and videos can cause the failure of computer vision techniques’ performance, such as segmentation, tracking, object detection, and recognition. Therefore, we propose a unique shadow detection for images and videos using artificial intelligence techniques. Our system is effective and can process real-time frames with different textures and the formality of shadows. This proposed method aims to improve the performance of the assistive devices that are provided for visually impaired people. We ran the proposed algorithm on different scenarios and different settings. An overall accuracy of 92% was achieved as a promising result.
Annotated 3D-Models of Cuneiform Tablets

The dataset<sup>1</sup> consists of 3D scans of one cuneiform tablet from Haft Tappeh Iran and one cuneiform tablet of the Hilprecht Collection as well as 3D annotations on these 3D meshes, including metadata. The 3D annotations were created with the annotation software Annotorious<sup>2</sup> on 2D renderings and reprojected to the original 3D model. The respective 2D renderings and annotations in 2D are also part of this data publication. The annotations might be used in machine learning tasks for character recognition, linguistic studies, or visualization in Assyriology. We publish these data in different formats and give guidance on how to use them in different usage scenarios and with several software applications. The data serve as the basis for a detailed description, reasoning, and elaboration of a recommendation for the state-of-the-art handling of 3D data in cuneiform research. The data is stored as an archive on Zenodo and may serve as an example for replication by similar 3D scanning.