Reviewerly/exHarmony huggingface dataset:
PLS-SEM USING R: AN INTRODUCTION TO cSEM AND SEMinR

In this editorial, we continue our discussion on the use of PLS-SEM statistical applications by introducing a few useful packages implemented in R. R is a free and open-source programming software that is used for statistical computing and graphics.To the best of our knowledge, most established PLS-SEM statistical applications have undergone rigorous testing and development in the R environment before being compiled and packaged as a standalone software.This editorial presents two new and well-maintained PLS-SEM packages in R, namely SEMinR and cSEM, to assist readers who prefer to work in a syntax-based environment and seek more flexibility to test their research models.
Deep Visible and Thermal Camera-based Optimal Semantic Segmentation using Semantic Forecasting

Abstract Visible camera-based semantic segmentation and semantic forecasting are important perception tasks in autonomous driving. In semantic segmentation, the current frame's pixel level labels are estimated using the current visible frame. In semantic forecasting, the future frame's pixel-level labels are predicted using the current and the past visible frames and pixel-level labels. While reporting state-of-the-art accuracy, both of these tasks are limited by the visible camera's susceptibility to varying illumination, adverse weather conditions, sunlight and headlight glare etc. In this work, we propose to address these limitations using the deep sensor fusion of the visible and the thermal camera. The proposed sensor fusion framework performs both semantic forecasting as well as an optimal semantic segmentation within a multi-step iterative framework. In the first or forecasting step, the framework predicts the semantic map for the next frame. The predicted semantic map is updated in the second step, when the next visible and thermal frame is observed. The updated semantic map is considered as the optimal semantic map for the given visible-thermal frame. The semantic map forecasting and updating are iteratively performed over time. The estimated semantic maps contain the pedestrian behavior, the free space and the pedestrian crossing labels. The pedestrian behavior is categorized based on their spatial, motion and dynamic orientation information. The proposed framework is validated using the public KAIST dataset. A detailed comparative analysis and ablation study is performed using pixel-level classification and IOU error metrics. The results show that the proposed framework can not only accurately forecast the semantic segmentation map but also accurately update them.
Causal Algebras on Chain Event Graphs with Informed Missingness for System Failure

Graph-based causal inference has recently been successfully applied to explore system reliability and to predict failures in order to improve systems. One popular causal analysis following Pearl and Spirtes et al. to study causal relationships embedded in a system is to use a Bayesian network (BN). However, certain causal constructions that are particularly pertinent to the study of reliability are difficult to express fully through a BN. Our recent work demonstrated the flexibility of using a Chain Event Graph (CEG) instead to capture causal reasoning embedded within engineers’ reports. We demonstrated that an event tree rather than a BN could provide an alternative framework that could capture most of the causal concepts needed within this domain. In particular, a causal calculus for a specific type of intervention, called a remedial intervention, was devised on this tree-like graph. In this paper, we extend the use of this framework to show that not only remedial maintenance interventions but also interventions associated with routine maintenance can be well-defined using this alternative class of graphical model. We also show that the complexity in making inference about the potential relationships between causes and failures in a missing data situation in the domain of system reliability can be elegantly addressed using this new methodology. Causal modelling using a CEG is illustrated through examples drawn from the study of reliability of an energy distribution network.
Low-Quality Error Detection for Noisy Knowledge Graphs

The automatic construction of knowledge graphs (KGs) from multiple data sources has received increasing attention. The automatic construction process inevitably brings considerable noise, especially in the construction of KGs from unstructured text. The noise in a KG can be divided into two categories: factual noise and low-quality noise. Factual noise refers to plausible triples that meet the requirements of ontology constraints. For example, the plausible triple &lt;New_York, IsCapitalOf, America&gt; satisfies the constraints that the head entity “New_York” is a city and the tail entity “America” belongs to a country. Low-quality noise denotes the obvious errors commonly created in information extraction processes. This study focuses on entity type errors. Most existing approaches concentrate on refining an existing KG, assuming that the type information of most entities or the ontology information in the KG is known in advance. However, such methods may not be suitable at the start of a KG's construction. Therefore, the authors propose an effective framework to eliminate entity type errors. The experimental results demonstrate the effectiveness of the proposed method.
Sinhala Language Corpora and Stopwords from a Decade of Sri Lankan Facebook

This paper presents two colloquial Sinhala language corpora from the language efforts of the Data, Analysis and Policy team of LIRNEasia, as well as a list of algorithmically derived stopwords. The larger of the two corpora spans 2010 to 2020 and contains 28,825,820 to 29,549,672 words of multilingual text posted by 533 Sri Lankan Facebook pages, including politics, media, celebrities, and other categories; the smaller corpus amounts to 5,402,76 words of only Sinhala text extracted from the larger. Both corpora have markers for their date of creation, page of origin, and content type.
Preliminary Results of an Earthquake Early Warning System in Costa Rica

We analyze the performance of a prototype earthquake early warning system deployed at the National Seismological Network of Costa Rica in collaboration with the Swiss Seismological Service by presenting the real-time performance during six earthquakes (Mw 5.1-6.4) that took place during 2018 and 2019. We observe that, despite only limited efforts to optimize the existing network of 158 stations, for EEW purposes, the network density allows fast determination of source parameters using both the Virtual Seismologist and the Finite Fault Rupture Detector algorithms. Shallow earthquakes on or near-shore are routinely identified within 11–20 s of their occurrence. The warning times for the capital city of San Jose are of 43 s for epicenters located at 220 km, like for the Mw 6.4 Armuelles earthquake. On the other hand, during the time analyzed, the EEW system did not provide positive warning times for earthquakes at distances less than 40 km from San Jose. Even though large (Mw &amp;gt; 7) distant historical earthquakes have not caused heavy damage in San Jose, there is potential for developing an EEW system for Costa Rica, especially for the purposes of rapid earthquake notifications, disaster response management, and seismic risk mitigation.
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning.

To facilitate research in the direction of sample efficient reinforcement learning, we held the MineRL Competition on Sample Efficient Reinforcement Learning Using Human Priors at the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019). The primary goal of this competition was to promote the development of algorithms that use human demonstrations alongside reinforcement learning to reduce the number of samples needed to solve complex, hierarchical, and sparse environments. We describe the competition, outlining the primary challenge, the competition design, and the resources that we provided to the participants. We provide an overview of the top solutions, each of which use deep reinforcement learning and/or imitation learning. We also discuss the impact of our organizational decisions on the competition and future directions for improvement.
Discourse anaphoricity and first-person indexicality in emoji resolution

This paper proposes a formal semantic classification of emoji-text combinations, focusing on two core sets of emoji: face emoji and activity emoji. Based on different data sources (introspective intuitions, naturalistic Twitter examples, and experimental evidence), we argue that activity emoji (case study I) are essentially event descriptions that serve as separate discourse units (similar to free adjuncts) and connect to the accompanying text by virtue of suitable discourse relations. By contrast, face emoji (case study II) are expressive elements that are anchored to an attitude holder and comment on a proposition provided by the accompanying text. We conclude by revisiting emoji semantics from the perspective of formal gesture semantics: we probe interactions of emoji and texts that contain clausal negation, and conclude that emoji generally do not scope under negation; however, the appearance of such a scope relation arises when activity emoji are connected to the accompanying text by virtue of an Explanation discourse relation.
Decentralization of Credential Verification System using Blockchain

After successful completion of graduation, students receive the credits of the courses in the form of certificate issued by the respective University. A Student have to produce his/her documents to the employers or the authorities for employment or higher education. Today ,as the system is centralized all the data resides on the server which can be hacked or the data can be lost if the system crushes down. However, verifying a certificate by authorities, is a time-consuming process as there is an involvement of human resources ,for validating the details of the candidate from its University. Today , with the advancement in technologies and due to the easy availability of many efficient soft wares that have led to the forgery of credentials/certificates. The lack of anti-tampering mechanisms resulted in incidents where the forged graduation certificates are often found. Also ,in case certificates are out of place , applying for duplicate certificates and its issuance by the University consumes a lot of time. Use of blockchain technology in this process will make the system decentralized as blocks as cryptographically connected and all the nodes in the network shares the entire chain .Hence the proposed decentralized certificate verification system, uses blockchain technology incorporating all the essential features in developing a DAPP. This system is proposed to address the issue of certificate counterfeiting, faster certificate verification and issuance. Putting across all the issues, the system aims at addressing the problems and provide solutions to the current Certificate Issuance, verification and Validation Process.
Self-Motivated Communication Agent for Real-World Vision-Dialog Navigation

Vision-Dialog Navigation (VDN) requires an agent to ask questions and navigate following the human responses to find target objects. Conventional approaches are only allowed to ask questions at predefined locations, which are built upon expensive dialogue annotations, and inconvenience the real-word human-robot communication and cooperation. In this paper, we propose a Self-Motivated Communication Agent (SCoA) that learns whether and what to communicate with human adaptively to acquire instructive information for realizing dialogue annotation-free navigation and enhancing the transferability in real-world unseen environment. Specifically, we introduce a whether-to-ask (WeTA) policy, together with uncertainty of which action to choose, to indicate whether the agent should ask a question. Then, a what-to-ask (WaTA) policy is proposed, in which, along with the oracle’s answers, the agent learns to score question candidates so as to pick up the most informative one for navigation, and meanwhile mimic oracle’s answering. Thus, the agent can navigate in a self-Q&A manner even in real-world environment where the human assistance is often unavailable. Through joint optimization of communication and navigation in a unified imitation learning and reinforcement learning framework, SCoA asks a question if necessary and obtains a hint for guiding the agent to move towards the target with less communication cost. Experiments on seen and unseen environments demonstrate that SCoA shows not only superior performance over existing baselines without dialog annotations, but also competing results compared with rich dialog annotations based counterparts.
Hybrid classical-quantum approach to solve the heat equation using quantum annealers

The numerical solution of partial differential equations by discretization techniques is ubiquitous in computational physics. In this work we benchmark this approach in the quantum realm by solving the heat equation for a square plate subject to fixed temperatures at the edges and random heat sources and sinks within the domain. The hybrid classical-quantum approach consists in the solution on a quantum computer of the coupled linear system of equations that result from the discretization step. Owing to the limitations in the number of qubits and their connectivity, we use the Gauss-Seidel method to divide the full system of linear equations into subsystems, which are solved iteratively in block fashion. Each of the linear subsystems were solved using 2000Q and Advantage quantum computers developed by D-Wave Systems Inc. By comparing classical numerical and quantum solutions, we observe that the errors and chain break fraction are, on average, greater on the 2000Q system. Unlike the classical Gauss-Seidel method, the errors of the quantum solutions level off after a few iterations of our algorithm. This is partly a result of the span of the real number line available from the mapping of the chosen size of the set of qubit states. We verified this by using techniques to progressively shrink the range mapped by the set of qubit states at each iteration (increasing floating-point accuracy). As a result, no leveling off is observed. However, an increase in qubits does not translate to an overall lower error. This is believed to be indicative of the increasing length of chains required for the mapping to real numbers and the ensuing limitations of hardware.
The Meaning of “Explainability Fosters Trust in AI”

We provide a philosophical explanation of the relation between artificial intelligence (AI) explainability and trust in AI, providing a case for expressions, such as “explainability fosters trust in AI,” that commonly appear in the literature. This explanation considers the justification of the trustworthiness of an AI with the need to monitor it during its use. We discuss the latter by referencing an account of trust, called “trust as anti-monitoring,” that different authors contributed developing. We focus our analysis on the case of medical AI systems, noting that our proposal is compatible with internalist and externalist justifications of trustworthiness of medical AI and recent accounts of warranted contractual trust. We propose that “explainability fosters trust in AI” if and only if it fosters warranted paradigmatic trust in AI, i.e., trust in the presence of the justified belief that the AI is trustworthy, which, in turn, causally contributes to rely on the AI in the absence of monitoring. We argue that our proposed approach can intercept the complexity of the interactions between physicians and medical AI systems in clinical practice, as it can distinguish between cases where humans hold different beliefs on the trustworthiness of the medical AI and exercise varying degrees of monitoring on them. Finally, we discuss the main limitations of explainable AI methods and we argue that the case of “explainability fosters trust in AI” may be feasible only in a limited number of physician-medical AI interactions in clinical practice.
Could a Conversational AI Identify Offensive Language?

In recent years, we have seen a wide use of Artificial Intelligence (AI) applications in the Internet and everywhere. Natural Language Processing and Machine Learning are important sub-fields of AI that have made Chatbots and Conversational AI applications possible. Those algorithms are built based on historical data in order to create language models, however historical data could be intrinsically discriminatory. This article investigates whether a Conversational AI could identify offensive language and it will show how large language models often produce quite a bit of unethical behavior because of bias in the historical data. Our low-level proof-of-concept will present the challenges to detect offensive language in social media and it will discuss some steps to propitiate strong results in the detection of offensive language and unethical behavior using a Conversational AI.
Concept Drift Adaptation with Incremental–Decremental SVM

Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We introduce the Adaptive Incremental–Decremental SVM (AIDSVM), a model that adjusts the shifting window width using the Hoeffding statistical test. We evaluate AIDSVM performance on both synthetic and real-world drift datasets. Experiments show a significant accuracy improvement when encountering concept drift, compared with similar drift detection models defined in the literature. The AIDSVM is efficient, since it is not retrained from scratch after the shifting window slides.
Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval

The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches.However, neural models' dense representations are more suitable for re-ranking, due to their inefficiency.Sparse representations, either in symbolic or latent form, are more efficient with an inverted index.Taking the merits of the sparse and dense representations, we propose an ultra-high dimensional (UHD) representation scheme equipped with directly controllable sparsity.UHD's large capacity and minimal noise and interference among the dimensions allow for binarized representations, which are highly efficient for storage and search.Also proposed is a bucketing method, where the embeddings from multiple layers of BERT are selected/merged to represent diverse linguistic aspects.We test our models with MS MARCO and TREC CAR, showing that our models outperforms other sparse models.
Keyword Detection Based on RetinaNet and Transfer Learning for Personal Information Protection in Document Images

In this paper, a keyword detection scheme is proposed based on deep convolutional neural networks for personal information protection in document images. The proposed scheme is composed of key character detection and lexicon analysis. The first part is the key character detection developed based on RetinaNet and transfer learning. To find the key characters, RetinaNet, which is composed of convolutional layers featuring a pyramid network and two subnets, is exploited to detect key characters within the region of interest in a document image. After the key character detection, the second part is a lexicon analysis, which analyzes and combines several key characters to find the keywords. To train the model of RetinaNet, synthetic image generation and data augmentation are exploited to yield a large image dataset. To evaluate the proposed scheme, many document images are selected for testing, and two performance measurements, IoU (Intersection Over Union) and mAP (Mean Average Precision), are used in this paper. Experimental results show that the mAP rates of the proposed scheme are 85.1% and 85.84% for key character detection and keyword detection, respectively. Furthermore, the proposed scheme is superior to Tesseract OCR (Optical Character Recognition) software for detecting the key characters in document images. The experimental results demonstrate that the proposed method can effectively localize and recognize these keywords within noisy document images with Mandarin Chinese words.
Survey on Data Hiding Based on Block Truncation Coding

Data hiding technology has achieved many technological developments through continuous research over the past 20 years along with the development of Internet technology and is one of the research fields that are still receiving attention. In the beginning, there were an intensive amount of studies on digital copyright issues, and since then, interest in the field of secret communications has been increasing. In addition, research on various security issues using this technology is being actively conducted. Research on data hiding is mainly based on images and videos, and there are many studies using JPEG and BMP in particular. This may be due to the use of redundant bits that are characteristic of data hiding techniques. On the other hand, block truncation coding-based images are relatively lacking in redundant bits useful for data hiding. For this reason, researchers began to pay more attention to data hiding based on block-cutting coding. As a result, many related papers have been published in recent years. Therefore, in this paper, the existing research on data hiding technology of images compressed by block-cut coding among compressed images is summarized to introduce the contents of research so far in this field. We simulate a representative methodology among existing studies to find out which methods are effective through experiments and present opinions on future research directions. In the future, it is expected that various data hiding techniques and practical applications based on modified forms of absolute moment block truncation coding will continue to develop.
Air-Writing Character Recognition with Ultrasonic Transceivers

The interfaces between users and systems are evolving into a more natural communication, including user gestures as part of the interaction, where air-writing is an emerging application for this purpose. The aim of this work is to propose a new air-writing system based on only one array of ultrasonic transceivers. This track will be obtained based on the pairwise distance of the hand marker with each transceiver. After acquiring the track, different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNN), convolutional autoencoder (ConvAutoencoder), and convolutional LSTM have been evaluated for character recognition. It has been shown how these algorithms provide high accuracy, where the best result is extracted from the ConvLSTM, with 99.51% accuracy and 71.01 milliseconds of latency. Real data were used in this work to evaluate the proposed system in a real scenario to demonstrate its high performance regarding data acquisition and classification.