Reviewerly/exHarmony huggingface dataset:
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning

There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet, counterfactual explanations have become very popular in eXplainable AI (XAI) due to their asserted computational, psychological, and legal benefits. In contrast however, semi-factuals (which appear to be equally useful) have surprisingly received no attention. Most counterfactual methods address tabular rather than image data, partly because the non-discrete nature of images makes good counterfactuals difficult to define; indeed, generating plausible counterfactual images which lie on the data manifold is also problematic. This paper advances a novel method for generating plausible counterfactuals and semi-factuals for black-box CNN classifiers doing computer vision. The present method, called PlausIble Exceptionality-based Contrastive Explanations (PIECE), modifies all “exceptional” features in a test image to be “normal” from the perspective of the counterfactual class, to generate plausible counterfactual images. Two controlled experiments compare this method to others in the literature, showing that PIECE generates highly plausible counterfactuals (and the best semi-factuals) on several benchmark measures.
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems

Domain-specific intelligent systems are meant to help system users in their decision-making process. Many systems aim to simultaneously support different users with varying levels of domain expertise, but prior domain knowledge can affect user trust and confidence in detecting system errors. While it is also known that user trust can be influenced by first impressions with intelligent systems, our research explores the relationship between ordering bias and domain expertise when encountering errors in intelligent systems. In this paper, we present a controlled user study to explore the role of domain knowledge in establishing trust and susceptibility to the influence of first impressions on user trust. Participants reviewed an explainable image classifier with a constant accuracy and two different orders of observing system errors (observing errors in the beginning of usage vs. in the end). Our findings indicate that encountering errors early-on can cause negative first impressions for domain experts, negatively impacting their trust over the course of interactions. However, encountering correct outputs early helps more knowledgable users to dynamically adjust their trust based on their observations of system performance. In contrast, novice users suffer from over-reliance due to their lack of proper knowledge to detect errors.
2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots

As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mi>A</mi> </mrow> <mrow> <mi>∗</mi> </mrow> </msup> </math> algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling

Event schemas can guide our understanding and ability to make predictions with respect to what might happen next.We propose a new Event Graph Schema, where two event types are connected through multiple paths involving entities that fill important roles in a coherent story.We then introduce Path Language Model, an auto-regressive language model trained on event-event paths, and select salient and coherent paths to probabilistically construct these graph schemas.We design two evaluation metrics, instance coverage and instance coherence, to evaluate the quality of graph schema induction, by checking when coherent event instances are covered by the schema graph.Intrinsic evaluations show that our approach is highly effective at inducing salient and coherent schemas.Extrinsic evaluations show the induced schema repository provides significant improvement to downstream end-to-end Information Extraction over a state-of-the-art joint neural extraction model, when used as additional global features to unfold instance graphs. 1 Event Instance Graphs Path Language Model PER Transport Attack artifact attacker Transport
Identifying and Measuring Annotator Bias Based on Annotators’ Demographic Characteristics

Machine learning is recently used to detect hate speech and other forms of abusive language in online platforms.However, a notable weakness of machine learning models is their vulnerability to bias, which can impair their performance and fairness.One type is annotator bias caused by the subjective perception of the annotators.In this work, we investigate annotator bias using classification models trained on data from demographically distinct annotator groups.To do so, we sample balanced subsets of data that are labeled by demographically distinct annotators.We then train classifiers on these subsets, analyze their performances on similarly grouped test sets, and compare them statistically.Our findings show that the proposed approach successfully identifies bias and that demographic features, such as first language, age, and education, correlate with significant performance differences.
Rigorous measurement error correction

We review an experimental technique used to correct state preparation and measurement errors on gate-based quantum computers, and discuss its rigorous justification. Within a specific biased quantum measurement model, we prove that nonideal measurement of an arbitrary n-qubit state is equivalent to ideal projective measurement followed by a classical Markov process Γ acting on the output probability distribution. Measurement errors can be removed, with rigorous justification, if Γ can be learned and inverted. We show how to obtain Γ from gate set tomography (Blume-Kohout et al arXiv:1310.4492) and apply the rigorous correction technique to single IBM Q superconducting qubits.
Unified Ontology Implementation of Cloud Computing for Distributed Systems

The ability to provide massive data storage, applications, platforms plus many other services leads to make the number of clouds services providers been increased. Providing different types of services and resources by various providers implies to get a high level of complexity. This complexity leads to face many challenges related to security, reliability, discovery, service selection, and interoperability. In this review, we focus on the use of many technologies and methods for utilizing the semantic web and ontology in cloud computing and distributed system as a solution for these challenges. Cloud computing does not have an own search engine to satisfy the needs of the providers of the cloud service. Using ontology enhances the cloud computing self-motivated via an intelligent framework of SaaS and consolidating the security by providing resources access control. The use RDF and OWL semantic technologies in the modeling of a multi-agent system are very effective in increases coordination the interoperability. One of the most efficient proposed frameworks is building cloud computing marketplace that collects the consumer's requirements of cloud services provider and managing these needs and resources to provide quick and reliable services.
Efficient Evolutionary Search of Attention Convolutional Networks via Sampled Training and Node Inheritance

The performance of deep neural networks is heavily dependent on its architecture and various neural architecture search strategies have been developed for automated network architecture design. Recently, evolutionary neural architecture search (EvoNAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms. However, EvoNAS suffers from extremely high computational costs because a large number of performance evaluations are usually required in evolutionary optimization, and training deep neural networks is itself computationally very expensive. To address this issue, this article proposes a computationally efficient framework for the evolutionary search of convolutional networks based on a directed acyclic graph, in which parents are randomly sampled and trained on each mini-batch of training data. In addition, a node inheritance strategy is adopted so that the fitness of all offspring individuals can be evaluated without training them. Finally, we encode a channel attention mechanism in the search space to enhance the feature processing capability of the evolved neural networks. We evaluate the proposed algorithm on the widely used datasets, in comparison with 30 state-of-the-art peer algorithms. Our experimental results show that the proposed algorithm is not only computationally much more efficient but also highly competitive in learning performance.
Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words

Recently, people have been beginning paying more attention to the abstractive dialogue summarization task. Since the information flows are exchanged between at least two interlocutors and key elements about a certain event are often spanned across multiple utterances, it is necessary for researchers to explore the inherent relations and structures of dialogue contents. However, the existing approaches often process the dialogue with sequence-based models, which are hard to capture long-distance inter-sentence relations. In this paper, we propose a Topic-word Guided Dialogue Graph Attention (TGDGA) network to model the dialogue as an interaction graph according to the topic word information. A masked graph self-attention mechanism is used to integrate cross-sentence information flows and focus more on the related utterances, which makes it better to understand the dialogue. Moreover, the topic word features are introduced to assist the decoding process. We evaluate our model on the SAMSum Corpus and Automobile Master Corpus. The experimental results show that our method outperforms most of the baselines.
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey

In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user's needs in task-oriented dialogue systems.We focus on two core tasks, slot filling (SF) and intent classification (IC), and survey how neural based models have rapidly evolved to address natural language understanding in dialogue systems.We introduce three neural architectures: independent models, which model SF and IC separately, joint models, which exploit the mutual benefit of the two tasks simultaneously, and transfer learning models, that scale the model to new domains.We discuss the current state of the research in SF and IC, and highlight challenges that still require attention.
Synchronization Phenomena Investigation of A New Nonlinear Dynamical System 4-D by Gardano’s and Lyapunov’s Methods

Synchronization is one of the most important characteristics of dynamic systems.For this paper, the authors obtained results for the nonlinear systems controller for the custom Synchronization of two 4D systems.The findings have allowed authors to develop two analytical approaches using the second Lyapunov (Lyp) method and the Gardano method.Since the Gardano method does not involve the development of special positive Lyp functions, it is very efficient and convenient to achieve excessive system SYCR phenomena.Error is overcome by using Gardano and overcoming some problems in Lyp.Thus we get a great investigation into the convergence of error dynamics, the authors in this paper are interested in giving numerical simulations of the proposed model to clarify the results and check them, an important aspect that will be studied is Synchronization Complete hybrid SYCR and anti-Synchronization, by making use of the Lyapunov expansion analysis, a proposed control method is developed to determine the actual.The basic idea in the proposed way is to receive the evolution of between two methods.Finally, the present model has been applied and showing in a new attractor, and the obtained results are compared with other approximate results, and the nearly good coincidence was obtained.
Influence of COVID on the educational use of Social Media by students of Teaching Degrees

The situation caused by SARS-CoV-2 has resulted in the intensive use of technology in all fields, including education and interactions between students. This research seeks to identify changes in the way social media have been used for educational purposes by students of teaching degrees both before and after lockdown. Based on an online survey administered to a sample of 424 students from different Spanish universities in which it has been observed how their use has generally been very similar, although significant differences have been detected according to the kind of university. In this capacity, the use of social media for academic purposes has increased in universities offering traditional classroom-based courses, while it has remained the same in virtual universities which, due to their particular idiosyncrasies, have not been directly affected.
Improved naive Bayes classification algorithm for traffic risk management

Abstract Naive Bayesian classification algorithm is widely used in big data analysis and other fields because of its simple and fast algorithm structure. Aiming at the shortcomings of the naive Bayes classification algorithm, this paper uses feature weighting and Laplace calibration to improve it, and obtains the improved naive Bayes classification algorithm. Through numerical simulation, it is found that when the sample size is large, the accuracy of the improved naive Bayes classification algorithm is more than 99%, and it is very stable; when the sample attribute is less than 400 and the number of categories is less than 24, the accuracy of the improved naive Bayes classification algorithm is more than 95%. Through empirical research, it is found that the improved naive Bayes classification algorithm can greatly improve the correct rate of discrimination analysis from 49.5 to 92%. Through robustness analysis, the improved naive Bayes classification algorithm has higher accuracy.
Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis

Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides.
Constrained Language Models Yield Few-Shot Semantic Parsers

We explore the use of large pretrained language models as few-shot semantic parsers.The goal in semantic parsing is to generate a structured meaning representation given a natural language input.However, language models are trained to generate natural language.To bridge the gap, we use language models to paraphrase inputs into a controlled sublanguage resembling English that can be automatically mapped to a target meaning representation.Our results demonstrate that with only a small amount of data and very little code to convert into English-like representations, our blueprint for rapidly bootstrapping semantic parsers leads to surprisingly effective performance on multiple community tasks, greatly exceeding baseline methods also trained on the same limited data.
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph

Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer.The relations can be represented in terms of labels in knowledge graph (e.g., spouse) or text in text corpus (e.g., they have been married for 26 years).Existing models usually infer the answer by predicting the sequential relation path or aggregating the hidden graph features.The former is hard to optimize, and the latter lacks interpretability.In this paper, we propose Trans-ferNet, an effective and transparent model for multi-hop QA, which supports both label and text relations in a unified framework.Trans-ferNet jumps across entities at multiple steps.At each step, it attends to different parts of the question, computes activated scores for relations, and then transfer the previous entity scores along activated relations in a differentiable way.We carry out extensive experiments on three datasets and demonstrate that TransferNet surpasses the state-of-the-art models by a large margin.In particular, on MetaQA, it achieves 100% accuracy in 2-hop and 3-hop questions.By qualitative analysis, we show that TransferNet has transparent and interpretable intermediate results.
Representing Numbers in NLP: a Survey and a Vision

NLP systems rarely give special consideration to numbers found in text.This starkly contrasts with the consensus in neuroscience that, in the brain, numbers are represented differently from words.We arrange recent NLP work on numeracy into a comprehensive taxonomy of tasks and methods.We break down the subjective notion of numeracy into 7 subtasks, arranged along two dimensions: granularity (exact vs approximate) and units (abstract vs grounded).We analyze the myriad representational choices made by over a dozen previously published number encoders and decoders.We synthesize best practices for representing numbers in text and articulate a vision for holistic numeracy in NLP, comprised of design trade-offs and a unified evaluation. Benchmarking orProbing Tasks Downstream Abstract Grounded Applications Exact Simple Arithmetic (2+3=5) AWP (2 balls + 3 balls = 5 balls), Question Answering, Exact Facts (birds have two legs) Science Problems Approx Numeration ('2' = 2.0), Measurement (dogs weigh 50 lbs), Sarcasm Detection, Magnitude ('2' < '5') Numerical Language Modeling Numeral Categorization
Implicit Representations of Meaning in Neural Language Models

Does the effectiveness of neural language models derive entirely from accurate modeling of surface word co-occurrence statistics, or do these models represent and reason about the world they describe?In BART and T5 transformer language models, we identify contextual word representations that function as models of entities and situations as they evolve throughout a discourse.These neural representations have functional similarities to linguistic models of dynamic semantics: they support a linear readout of each entity's current properties and relations, and can be manipulated with predictable effects on language generation.Our results indicate that prediction in pretrained neural language models is supported, at least in part, by dynamic representations of meaning and implicit simulation of entity state, and that this behavior can be learned with only text as training data. 1
Explaining NLP Models via Minimal Contrastive Editing (MiCE)

Humans have been shown to give contrastive explanations, which explain why an observed event happened rather than some other counterfactual event (the contrast case).Despite the influential role that contrastivity plays in how humans explain, this property is largely missing from current methods for explaining NLP models.We present MIN-IMAL CONTRASTIVE EDITING (MICE), a method for producing contrastive explanations of model predictions in the form of edits to inputs that change model outputs to the contrast case.Our experiments across three tasks-binary sentiment classification, topic classification, and multiple-choice question answering-show that MICE is able to produce edits that are not only contrastive, but also minimal and fluent, consistent with human contrastive edits.We demonstrate how MICE edits can be used for two use cases in NLP system development-debugging incorrect model outputs and uncovering dataset artifacts-and thereby illustrate that producing contrastive explanations is a promising research direction for model interpretability.