Reviewerly/exHarmony huggingface dataset:
Building Goal-oriented Document-grounded Dialogue Systems

In this paper, we describe our systems for solving the two Doc2Dial shared task: knowledge identification and response generation.We proposed several pre-processing and postprocessing methods, and we experimented with data augmentation by pre-training the models on other relevant datasets.Our best model for knowledge identification outperformed the baseline by 10.5+ f1-score on the test-dev split, and our best model for response generation outperformed the baseline by 11+ SacreBleu score on the test-dev split.
Hand Gesture Recognition using TOF camera

Gesture recognition is an important topic in com-puter vision because of its wide range of applications, such as Human computer Interaction, sign language interpretation, and visual surveillance.3-D gesture recognition has attracted a lot of research interests in computer vision, pattern recognition, and human-computer interaction.The rising use of depth sensors greatly increases various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras.Conventional approach gesture con-trol in automobile industry etc possess certain challenges such as high computational power ,environmental lighting constraints or integrating voice control commands in noisy environments has also certain challenges,which can be solved and can be overcome by the use of Gesture Recognition Control using Time of flight camera which is fast and efficient.This paper aims at the use PicoZense ToF camera sensor and OpenCV libraries, we can easily overcome the challenges of recognising the gestures in low lighting conditions with noisy environment.
CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System

Yan Xu, Etsuko Ishii, Genta Indra Winata, Zhaojiang Lin, Andrea Madotto, Zihan Liu, Peng Xu, Pascale Fung. Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021). 2021.
Lexical Semantic Recognition

In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence.We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification/ classification and supersense tagging.Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation.As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE.Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.
Latent Mappings: Generating Open-Ended Expressive Mappings Using Variational Autoencoders

In many contexts, creating mappings for gestural interactions can form part of an artistic process.Creators seeking a mapping that is expressive, novel, and affords them a sense of authorship may not know how to program it up in a signal processing patch.Tools like Wekinator [1] and MIMIC [2] allow creators to use supervised machine learning to learn mappings from example input/output pairings.However, a creator may know a good mapping when they encounter it yet start with little sense of what the inputs or outputs should be.We call this an open-ended mapping process.Addressing this need, we introduce the latent mapping, which leverages the latent space of an unsupervised machine learning algorithm such as a Variational Autoencoder trained on a corpus of unlabelled gestural data from the creator.We illustrate it with Sonified Body, a system mapping full-body movement to sound which we explore in a residency with three dancers.
Jibes &amp; Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse

Online abuse and offensive language on social media have become widespread problems in today's digital age.In this paper, we contribute a Reddit-based dataset, consisting of 68, 159 insults and 51, 102 compliments targeted at individuals instead of targeting a particular community or race.Secondly, we benchmark multiple existing state-of-the-art models for both classification and unsupervised style transfer on the dataset.Finally, we analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.
Duluth at SemEval-2021 Task 11: Applying DeBERTa to Contributing Sentence Selection and Dependency Parsing for Entity Extraction

This paper describes the Duluth system that participated in SemEval-2021 Task 11, NLP Contribution Graph.It details the extraction of contribution sentences and scientific entities and their relations from scholarly articles in the domain of Natural Language Processing.Our solution uses deBERTa for multiclass sentence classification to extract the contributing sentences and their type, and dependency parsing to extract phrases from each sentence and format into subject-predicate-object triples.Our system ranked fifth of seven for Phase 1: end-to-end pipeline, sixth of eight for Phase 2 Part 1: phrases and triples, and fifth of eight for Phase 2 Part 2: triples extraction.
PENTINGNYA PENGUASAAN LITERASI TEKNOLOGI INFORMASI DAN KOMUNIKASI BAGI GURU MADRASAH IBTIDAIYAH DALAM MENYIAPKAN SISWA MENGHADAPI REVOLUSI INDUSTRY 4.0

In the era of the industrial revolution 4.0, it is a tough challenge for all Indonesian teachers. The quality of education is a measure of Indonesia's success in facing the era of the industrial revolution 4.0. A teacher at this time is required to be technologically literate because the quality of a teacher who is empty of technology will not be able to instill a "critical character" in students to become revolutionary humans. This study discusses the development of mastery of information and communication technology literacy at Madrasah Ibtidaiyah. Information and Communication Technology literacy includes the ability to use technology as a tool to research, organize, evaluate and communicate information, and have a fundamental understanding of ethical legal issues surrounding the access and use of information. There are seven elements of Information and Communication Technology literacy, namely: define, access, evaluate, manage, integrate, create, and communicate. Developing a learning framework in the 21st century that requires students to have skills, knowledge and abilities in the fields of technology, media and information, learning and innovation skills as well as life and career skills. The types of sources and learning media based on Information and Communication Technology that can be utilized by elementary school teachers in the learning process include: (1). Computer (2). LCD (3). Internet (4). Learning CD, (5) email (6). Power Point Presentation (7) Multimedia. In the learning process, using media is very important to increase interest and understanding of materials such as Augmented Reality-based learning media. Become a technology literate teacher and really need support from the government. Information and Communication Technology literacy can be done by participating in socialization, training, upgrading, seminars and workshops on Information and Communication Technology
Synthesis-guided Adversarial Scenario Generation for Gray-box Feedback Control Systems with Sensing Imperfections

In this paper, we study feedback dynamical systems with memoryless controllers under imperfect information. We develop an algorithm that searches for "adversarial scenarios", which can be thought of as the strategy for the adversary representing the noise and disturbances, that lead to safety violations. The main challenge is to analyze the closed-loop system's vulnerabilities with a potentially complex or even unknown controller in the loop. As opposed to commonly adopted approaches that treat the system under test as a black-box, we propose a synthesis-guided approach, which leverages the knowledge of a plant model at hand. This hence leads to a way to deal with gray-box systems (i.e., with known plant and unknown controller). Our approach reveals the role of the imperfect information in the violation. Examples show that our approach can find non-trivial scenarios that are difficult to expose by random simulations. This approach is further extended to incorporate model mismatch and to falsify vision-in-the-loop systems against finite-time reach-avoid specifications.
Regressing Location on Text for Probabilistic Geocoding

Text data are an important source of detailed information about social and political events.Automated systems parse large volumes of text data to infer or extract structured information that describes actors, actions, dates, times, and locations.One of these sub-tasks is geocoding: predicting the geographic coordinates associated with events or locations described by a given text.We present an end-to-end probabilistic model for geocoding text data.Additionally, we collect a novel data set for evaluating the performance of geocoding systems.We compare the model-based solution, called ELECTRo-map, to the current state-of-the-art open source system for geocoding texts for event data.Finally, we discuss the benefits of end-to-end model-based geocoding, including principled uncertainty estimation and the ability of these models to leverage contextual information.
InFillmore: Frame-Guided Language Generation with Bidirectional Context

We propose a structured extension to bidirectional-context conditional language generation, or "infilling," inspired by Frame Semantic theory (Fillmore, 1976).Guidance is provided through two approaches: (1) model fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive lexically constrained decoding that leverages frame semantic lexical units.Automatic and human evaluations confirm that frame-guided generation allows for explicit manipulation of intended infill semantics, with minimal loss in distinguishability from human-generated text.Our methods flexibly apply to a variety of use scenarios, and we provide an interactive web demo. 1
Vehicles Detection System at Different Weather Conditions

The importance of efficient vehicle detection (VD) is increased with the expansion of road networks and the number of vehicles in the Intelligent Transportation Systems (ITS). This paper proposes a system for detecting vehicles at different weather conditions such as sunny, rainy, cloudy and foggy days. The first step to the proposed system implementation is to determine whether the video’s weather condition is normal or abnormal. The Random Forest (RF) weather condition classification was performed in the video while the features were extracted for the first two frames by using the Gray Level Co-occurrence Matrix (GLCM). In this system, the background subtraction was applied by the mixture of Gaussian 2 (MOG 2) then applying a number of pre-processing operations, such as Gaussian blur filter, dilation, erosion, and threshold. The main contribution of this paper is to propose a histogram equalization technique for complex weather conditions instead of a Gaussian blur filter for improving the video clarity, which leads to increase detection accuracy. Based on the previous steps, the system defines each region in the frame expected to contain vehicles. Finally, Support Vector Machine (SVM) classifies the defined regions into a vehicle or not. As compared to the previous methods, the proposed system showed high efficiency of about 96.4% and ability to detect vehicles at different weather conditions.
Revisiting Pretraining with Adapters

Pretrained language models have served as the backbone for many state-of-the-art NLP results.These models are large and expensive to train.Recent work suggests that continued pretraining on task-specific data is worth the effort as pretraining leads to improved performance on downstream tasks.We explore alternatives to full-scale task-specific pretraining of language models through the use of adapter modules, a parameter-efficient approach to transfer learning.We find that adapter-based pretraining is able to achieve comparable results to task-specific pretraining while using a fraction of the overall trainable parameters.We further explore direct use of adapters without pretraining and find that the direct finetuning performs mostly on par with pretrained adapter models, contradicting previously proposed benefits of continual pretraining in full pretraining fine-tuning strategies.Lastly, we perform an ablation study on task-adaptive pretraining to investigate how different hyperparameter settings can change the effectiveness of the pretraining.
Experimental study of decoherence of the two-mode squeezed vacuum state via second harmonic generation

Decoherence remains one of the most serious challenges to the implementation of quantum technology. It appears as a result of the transformation over time of a quantum superposition state into a classical mixture due to the quantum system interacting with the environment. Since quantum systems are never completely isolated from their environment, decoherence therefore cannot be avoided in realistic situations. Decoherence has been extensively studied, mostly theoretically, because it has many important implications in quantum technology, such as in the fields of quantum information processing, quantum communication, and quantum computation. Here we report a novel experimental scheme on the study of decoherence of a two-mode squeezed vacuum state via its second harmonic generation signal. Our scheme can directly extract the decoherence of the phase-sensitive quantum correlation $\ensuremath{\langle}\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{a}\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{b}\ensuremath{\rangle}$ between two entangled modes $a$ and $b$. Such a correlation is the most important characteristic of a two-mode squeezed state. More importantly, this is an experimental study on the decoherence effect of a squeezed vacuum state, which has been rarely investigated.
Trading Complexity for Sparsity in Random Forest Explanations

Random forests have long been considered as powerful model ensembles in machine learning. By training multiple decision trees, whose diversity is fostered through data and feature subsampling, the resulting random forest can lead to more stable and reliable predictions than a single decision tree. This however comes at the cost of decreased interpretability: while decision trees are often easily interpretable, the predictions made by random forests are much more difficult to understand, as they involve a majority vote over hundreds of decision trees. In this paper, we examine different types of reasons that explain "why" an input instance is classified as positive or negative by a Boolean random forest. Notably, as an alternative to sufficient reasons taking the form of prime implicants of the random forest, we introduce majoritary reasons which are prime implicants of a strict majority of decision trees. For these different abductive explanations, the tractability of the generation problem (finding one reason) and the minimization problem (finding one shortest reason) are investigated. Experiments conducted on various datasets reveal the existence of a trade-off between runtime complexity and sparsity. Sufficient reasons - for which the identification problem is DP-complete - are slightly larger than majoritary reasons that can be generated using a simple linear- time greedy algorithm, and significantly larger than minimal majoritary reasons that can be approached using an anytime P ARTIAL M AX SAT algorithm.
A Novel Experimental Study and Analysis of Electrocoagulation Process for Textile Wastewater Treatment using Various Sensors with Integration of IoT Monitoring System

One of the basic requirements of living beings is water. Due to globalization, industries consume large amount of water and creates shortage of pure water. In addition, they pollute the existing fresh water resources. Therefore, it is essential to design an effective wastewater purification system. Electrochemical method namely, electrocoagulation paves the way for an effective wastewater purification system. This research focuses on the study and analysis of the textile wastewater purification, using electrocoagulation process. This novel experimental study and analysis was carried out using iron, aluminium and mild steel electrodes for batch, modified batch and continuous process respectively. In these electrocoagulation processes, three major parameters such as colour, turbidity and pH were sensed and monitored using IoT. Colour and Turbidity Removal efficiency were also calculated, which gives satisfying results.
Analogical Proportions: Why They Are Useful in AI

This paper presents a survey of researches in analogical reasoning whose building block are analogical proportions which are statements of the form “a is to b as c is to d”. They have been developed in the last twenty years within an Artificial Intelligence perspective. After discussing their formal modeling with the associated inference mechanism, the paper reports the main results obtained in various AI domains ranging from computational linguistics to classification, including image processing, I.Q. tests, case based reasoning, preference learning, and formal concepts analysis. The last section discusses some new theoretical concerns, and the potential of analogical proportions in other areas such as argumentation, transfer learning, and XAI.
A Framework for Exploring Churches/Monuments/Museums of Byzantine Cultural Influence Exploiting Immersive Technologies in Real-Time Networked Environments

The unique art that was developed in Byzantine times is widely accepted as a precursor to the Renaissance and is still evident in monuments either from or influenced by the Byzantium. Visiting such a site is a unique experience due to the lavishly painted interior for both tourists and scholars. Taking advantage of the emerging 5G technologies, cloud/fog computing, and Augmented/Mixed Reality mechanisms, a common smart device (e.g., smartphone, tablet) could be employed to give a better experience to the end-users. The proposed framework is intended to provide visitors with interpretative information regarding the visited monuments and their paintings. Under the framework introduced in this paper, camera input is uploaded to a cloud/fog computing infrastructure where appropriate algorithms and services provide monument and painting recognition and the mobile application retrieves and projects the related information. In addition, the designed immersive user interfaces assist visitors in contributing in cases of monuments and paintings for which no available information exists. This paper presents the state of the art in approaches for immersive experiences in Digital Culture, reviews the current image recognition approaches and their suitability for Byzantine paintings, proposes interaction techniques appropriately designed for observation and interaction of paintings, and discusses the overall framework architecture that is needed to support the described functionality while stressing the challenging issues of the above aspects thus paving the road for future work and providing guidelines for a test-bed implementation.
Metainferential Reasoning on Strong Kleene Models

Abstract Barrio et al. ( Journal of Philosophical Logic , 49 (1), 93–120, 2020) and Pailos ( Review of Symbolic Logic , 2020 (2), 249–268, 2020) develop an approach to define various metainferential hierarchies on strong Kleene models by transferring the idea of distinct standards for premises and conclusions from inferences to metainferences. In particular, they focus on a hierarchy named the $\mathbb {S}\mathbb {T}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>S</mml:mi> <mml:mi>T</mml:mi> </mml:math> -hierarchy where the inferential logic at the bottom of the hierarchy is the non-transitive logic ST but where each subsequent metainferential logic ‘says’ about the former logic that it is transitive. While Barrio et al. (2020) suggests that this hierarchy is such that each subsequent level ‘in some intuitive sense, more classical than’ the previous level, Pailos (2020) proposes an extension of the hierarchy through which a ‘fully classical’ metainferential logic can be defined. Both Barrio et al. (2020) and Pailos (2020) explore the hierarchy in terms of semantic definitions and every proof proceeds by a rather cumbersome reasoning about those semantic definitions. The aim of this paper is to present and illustrate the virtues of a proof-theoretic tool for reasoning about the $\mathbb {S}\mathbb {T}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>S</mml:mi> <mml:mi>T</mml:mi> </mml:math> -hierarchy and the other metainferential hierarchies definable on strong Kleene models. Using the tool, this paper argues that each level in the $\mathbb {S}\mathbb {T}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>S</mml:mi> <mml:mi>T</mml:mi> </mml:math> -hierarchy is non-classical to an equal extent and that the ‘fully classical’ metainferential logic is actually just the original non-transitive logic ST ‘in disguise’. The paper concludes with some remarks about how the various results about the $\mathbb {S}\mathbb {T}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>S</mml:mi> <mml:mi>T</mml:mi> </mml:math> -hierarchy could be seen as a guide to help us imagine what a non-transitive metalogic for ST would tell us about ST. In particular, it teaches us that ST is from the perspective of ST as metatheory not only non-transitive but also transitive.
Quantum Algorithm of Imperfect KB Self-organization Pt I: Smart Control-Information-Thermodynamic Bounds

The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed in Part I. The smart control design with guaranteed achievement of these tradeoff interrelations is main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect in Part I (autonomous robot in unpredicted control situations) and II (swarm robots with imperfect KB exchanging between “master - slaves”) introduced: a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.