Reviewerly/exHarmony huggingface dataset:
Unsupervised Slot Schema Induction for Task-oriented Dialog

Carefully-designed schemas describing how to collect and annotate dialog corpora are a prerequisite towards building task-oriented dialog systems.In practical applications, manually designing schemas can be error-prone, laborious, iterative, and slow, especially when the schema is complicated.To alleviate this expensive and time consuming process, we propose an unsupervised approach for slot schema induction from unlabeled dialog corpora.Leveraging in-domain language models and unsupervised parsing structures, our data-driven approach extracts candidate slots without constraints, followed by coarse-to-fine clustering to induce slot types.We compare our method against several strong supervised baselines, and show significant performance improvement in slot schema induction on MultiWoz and SGD datasets.We also demonstrate the effectiveness of induced schemas on downstream applications including dialog state tracking and response generation.
Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data

In the context of data labeling, NLP researchers are increasingly interested in having humans select rationales, a subset of input tokens relevant to the chosen label.We conducted a 332-participant online user study to understand how humans select rationales, especially how different instructions and user interface affordances impact the rationales chosen.Participants labeled ten movie reviews as positive or negative, selecting words and phrases supporting their label as rationales.We varied the instructions given, the rationaleselection task, and the user interface.Participants often selected about 12% of input tokens as rationales, but selected fewer if unable to drag over multiple tokens at once.Whereas participants were near unanimous in their data labels, they were far less consistent in their rationales.The user interface affordances and task greatly impacted the types of rationales chosen.We also observed large variance across participants.
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner

Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive.Structured explanations, called entailment trees, were recently suggested as a way to explain and inspect a QA system's answer.In order to better generate such entailment trees, we propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR).Our model is able to explain a given hypothesis by systematically generating a stepby-step explanation from textual premises.The IRGR model iteratively searches for suitable premises, constructing a single entailment step at a time.Contrary to previous approaches, our method combines generation steps and retrieval of premises, allowing the model to leverage intermediate conclusions, and mitigating the input size limit of baseline encoder-decoder models.We conduct experiments using the EN-TAILMENTBANK dataset, where we outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300% gain in overall correctness.
Ablation Study of a Person Re-Identification on a Mobile Robot Using a Depth Camera

In this paper, we describe an ablation study for a person re-identification API on a mobile robot, for a closed-world setting, using only the IR gray value image of a depth camera. Previously, we have trained the state-of-the-art neural network for person re-identification with common parameters and methods. The resulting real-time application reached as closed-world setting a rank-1-accuracy of 94.78% and a mAP of 68.16%. Now, we focused on increasing the accuracy by removing and adjusting the image processing pipeline of our dataset. By these adjustments, we have reached a rank-1-accuracy of 98.56% and a mAP of 79.05%.
New Frontiers of Information Extraction

This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources.In particular, this tutorial will provide audience with a systematic introduction to recent advances in IE, by addressing several important research questions.These questions include (i) how to develop a robust IE system from a small amount of noisy training data, while ensuring the reliability of its prediction?(ii) how to foster the generalizability of IE through enhancing the system's cross-lingual, crossdomain, cross-task and cross-modal transferability?(iii) how to support extracting structural information with extremely fine-grained and diverse labels?(iv) how to further improve IE by leveraging indirect supervision from other NLP tasks, such as Natural Language Generation (NLG), Natural Language Inference (NLI), Question Answering (QA) or summarization, and pre-trained language models?(v) how to acquire knowledge to guide inference in IE systems?We will discuss several lines of frontier research that tackle those challenges, and will conclude the tutorial by outlining directions for further investigation.
IISERB Brains at SemEval-2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English

This paper describes the system and the resulted models submitted by our team "IISERB Brains" to SemEval-2022 Task 6 competition.We participated in the three sub-tasks for English language datasets.Our submitted models use BERT-based classifiers along with data augmentation.We ranked 19th out of 43 teams for sub-task A, 8th rank out of 22 teams for sub-task B, and 13th rank out of 16 teams for sub-task C. In this paper, we describe details of our submissions, related evaluation and additional experiments conducted post the termination of the shared task.Our code and used additional resources are present in GitHub 1 for reproducibility.Authors with equal contributions are marked by *.
IU-AutoSVD++: An Item–User Features-Based Recommender System Using Contractive Autoencoder and Matrix Factorization

Matrix factorization is a successful approach in recommender systems that is used largely to provide adequate recommendations to users. In the last years, many approaches based on deep learning, such as autoencoders, were used or combined with other methods to extract nonlinear relationships between items. But most of these models do not include user’s information side in the rating process. In the present article, we have proposed a new model IU-AutoSVD++ combining the matrix factorization and the contractive autoencoder in order to include item features and user side information. Experiments results prove that our model performs better than many baselines models.
Towards Explainable Deep Neural Networks (xDNN)

In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers a clearly explainable internal architecture that can outperform the existing methods, requires very little computational resources (no need for GPUs) and short training times (in the order of seconds). The proposed approach, xDNN is using prototypes. Prototypes are actual training data samples (images), which are local peaks of the empirical data distribution called typicality as well as of the data density. This generative model is identified in a closed form and equates to the pdf but is derived automatically and entirely from the training data with no user- or problem-specific thresholds, parameters or intervention. The proposed xDNN offers a new deep learning architecture that combines reasoning and learning in a synergy. It is non-iterative and non-parametric, which explains its efficiency in terms of time and computational resources. From the user perspective, the proposed approach is clearly understandable to human users. We tested it on some well-known benchmark data sets such as iRoads and Caltech-256. xDNN outperforms the other methods including deep learning in terms of accuracy, time to train and offers a clearly explainable classifier. In fact, the result on the very hard Caltech-256 problem (which has 257 classes) represents a world record.
Review on Soft Computing in Data Analysis

The ability to access, store, and process enormous volumes of data has significantly expanded due to technological advancements in computation, data storage, networks, and sensors. Large-scale data processing is becoming an increasingly important thing for both research and business. Clients, who are typically domain experts, face an enormous challenge and require assistance in handling huge amount of data's. Soft computing can indeed be characterised as a science of thought and logic that aids in navigating complex systems. This article is about the use of soft computing techniques to support data analysis in an intelligent manner.
A Unified Weighted MMD For Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) recognizes unlabeled domain data by using the classifier learned from another domain. Previous works mainly focus on domain-level alignment that usually ignores the class-level information, resulting in the samples of different classes being too close to be classified correctly. To tackle this challenge, we design a unified weighted maximum mean discrepancy (MMD) metric method, that measures the differences in empirical distributions of two domains by calculating the weights of different sample pairs adaptively. The unified weighted MMD method is proposed which combines the class-level alignment with domain-level alignment, making full use of intra-domain, inter-domain, intra-class, and inter-class information with adaptive weights, and it is easy to implement. Experiment results demonstrate that our method can obtain superior results from two standard UDA datasets Office-31 and ImageCLEF-DA, compared with other UDA approaches.
Designing an Intelligent Teaching System of Chinese as a Foreign Language under the Internet Background

To overcome the limitations of the traditional teaching system and improve learners’ language learning efficiency by incorporating personalized allocation of learning resources and learning evaluation into the Chinese as a foreign language teaching system (CFLTS). This paper aims to deeply study the overall design of the intelligent teaching system (ITS) for CFLTS. Firstly, this paper develops an ITS for CFLTS. It focuses on the intelligence teaching system’s structural planning and structural design. The system functional structure and the specific characteristics and recommendations module constitute a two-way teaching quality evaluation module. The elite teaching optimization algorithm based on the feedback mechanism is adopted. The students are divided into excellent and poor students after the feedback mechanism is added to split the student stage. Next, they dynamically adjust the scale of excellent and poor students, conduct feedback communication between poor students and teachers, and strengthen the convergence ability. Finally, self-learning for good students is arranged by performing an excellent local search and including the feedback stage to broaden the range of learning methods and improve the ability to find global teaching resources. The completion of all these steps ensures that the design of an ITS for CFLTS is now complete. According to the experimental results, the ITS proposed in this work has a higher level of overall stability, effectively improves students’ learning efficiency and teaching quality, and can better meet the needs of Chinese language learners.
A Statistical English Syntax Analysis Model Based on Linguistic Evaluation Information

Language evaluation research currently focuses on the analysis of scholars from various native language backgrounds, whereas the local grammatical characteristics of other groups, particularly English language learners, are discussed less frequently. Local grammar offers a new perspective for analyzing the meaning characteristics of evaluation languages from the point of view of the people who employ them. In order to provide context for this paper, past research on local syntax is reviewed. The language model generates text that can be analyzed to determine the model’s aggressiveness when perturbed. To evaluate the method’s precision and efficacy, we compared the aggressiveness of pretrained models under various conditions using an English database. The results demonstrate that the method is capable of automatically and effectively evaluating the aggressiveness of language models. We then examine the scales of model parameters and the relationships between words in the training corpus.
Multiple Hypothesis Tracking with Merged Bounding Box Measurements Considering Occlusion

A new approach for multi-target tracking in an occlusion environment is presented. In pedestrian tracking using a video camera, pedestrains must be tracked accurately and continuously in the images. However, in a crowded environment, the conventional tracking algorithm has a problem in that tracks do not continue when pedestrians are hidden behind the foreground object. In this study, we propose a robust tracking method for occlusion that introduces a degeneration hypothesis that relaxes the track hypothesis which has one measurement to one track constraint. The proposed method relaxes the hypothesis that one measurement and multiple trajectories are associated based on the endpoints of the bounding box when the predicted trajectory is approaching, therefore the continuation of the tracking is improved using the measurement in the foreground. A numerical evaluation using MOT (Multiple Object Tracking) image data sets is performed to demonstrate the effectiveness of the proposed algorithm.
Smart Cyberbullying Detection with Machine Learning

The usage of social media has become very common in day-to-day life. However, it has been found that few people are misbehaving with others, by means of vulgar words. These have negative impacts on the targeted persons which are called as cyberbullying. In lieu of the same, in this paper cyberbullying content is detected and appropriate actions are taken against the person who bullies others. We are using social media platform “Reddit” to detect cyberbullying content and have developed rules to automatically detect the same. In our paper, we have used “Ensemble Learning”, an algorithm of machine learning, through which we will be detecting the language patterns used by bullies and victims. Ensemble method is unique in that it combines multiple base models to output an optimal predictive model. Therefore, for the base models, we have used Bow, TFIDF and SVM, and then we have combined them through the ensemble method “Voting” in order to produce a much better proven optimal predictive model.
Embedded U-Net Liver Segmentation Model based on Multi-scale Semantic Information-MSN-Net Model

In this paper, the problem of liver segmentation in liver CT images is studied, and the MSN-Net model is proposed. The single-hop connection in the traditional U-Net model is replaced by an embedded structure in the MSN-Net model. The embedded structure is a novel skip-connection architecture between the encoding path and the decoding path. The convolution operation is added to the structure to alleviate the semantic gap caused by the traditional skip connection; in addition, the high-dimensional-low-dimensional feature fusion strategy is used in the embedded structure, which combines the feature maps from high-dimensional and low-dimensional features. The feature maps are combined to enhance the feature expression ability of CT images and extract richer semantic information. The MSN-Net model is subjected to ablation experiments and compared with other current state-of-the-art methods. The experimental results show that the MSN-Net model proposed in this paper has superior segmentation performance.
All-photonic quantum simulators with spectrally disordered ensembles of emitters

We model an all-photonic quantum simulator that consists of a solid-state coupled-cavity array with integrated ensembles of color centers. We quantify conditions for polariton creation that overcome the setbacks of moderate spectral dispersion.
Using Vector Proximity for NLP Analysis of Specialized Texts

The paper considers some latest approaches to develop a machine translation system to handle texts specialized in construction industry both in Russian and English. Graph theory is the basis for automatic sentence analysis and representation of its structure. At the first stage, the sentence is represented by a line graph. The article discusses how to assign attributes to each word in accordance with its prospective belonging to different parts of speech for their subsequent processing using graph convolutional neural networks (GCN). The initial features of each element of the sentence are compared with the calculated values obtained, and by means of certain parameters for the compiled vectors, they are classified as parts of speech. The paper contains parts of the software listing for the computer implementation of the proposed technology.
Embedded Parameter Information in Conditional Generative Adversarial Networks for Compressor Airfoil Design

The use of conditional generative adversarial networks has become popular with the advancement of computer power, and in particular graphics processing units. Large hardware and software companies such as Google, IBM, and Facebook have been experimenting in this field for over a decade for facial recognition, image classification and pattern recognition, and deep fake facial and object design. One essential key to their success is access to large quantities of tagged and classified images, with millions of images typically being used. In contrast, relatively few similar advances have been seen in the engineering sector, mainly because engineering analyses that produce suitable images are often very expensive processes that absorb a considerable amount of effort to generate. In addition, feeding synthetically generated image data back into traditional engineering workflows is not entirely straightforward. In this paper we show how adversarial networks can be used if order [Formula: see text] images are available and focused on the problem in hand. In particular we show how such images can then be augmented with histograms and glyphs to enhance the image content with pictorial representations of numerical data. This is shown to significantly assist the network training process on our data when used in contexts where numerical data categorizing individual images are available and numerical performance measures of products must be predicted. Crucially, it allows for follow-on analyses without the need to use artificially generated flow fields.
Pelatihan presentasi menggunakan Microsoft Power Point pada SMP Patra Mandiri 2 Palembang

Learning media is a tool used by teachers to convey subject matter to students or media used by students to deliver assignments given by the teacher. In this training activity, computer-based learning media were introduced for presentations using Microsoft PowerPoint, which students of SMP Patra Mandiri 2 Palembang could use to present assignments given by the teacher to the front of the class with attractive shapes and appearances. Community Service Activities (PKM) in the form of training are carried out for two days, in which students are required to be able to make presentation materials using Microsoft PowerPoint and present the material they made in front of the computer laboratory room. This activity is carried out in direct and interactive practice, where students directly practice the material provided by the PKM Team, and students can also have direct discussions with the PKM Team. Before implementing the training activities, students were given a written knowledge test to determine the level of students' understanding of the training material to be delivered. Only 20% of the student participants often made materials using Microsoft PowerPoint. After two days of implementing this training activity, an evaluation was carried out regarding the knowledge and abilities of the student participants in creating and presenting material using Microsoft PowerPoint. From the evaluation results, it can be seen that all student participants have succeeded in making presentation materials using Microsoft PowerPoint and some student participants are also able to present the material in front of the class
Low-cost on-line monitoring system for agriculture based on raspberry pi zero

Abstract Based on data from the National Defense Agency, the trend of conversion of agricultural land in the 1990s reached around 30,000 ha/year. However, in 2011 there was an increase of 110,000 ha/year and reached 150,000 ha/year in 2019. In fact, with the growing human population, the need for food is increasing from year to year. On the other hand, the slow regeneration of farmers and the lack of dissemination &amp; technological innovation to increase agricultural productivity are challenges in realizing food security. In line with the ongoing industrial revolution in the industrial era 4.0, it has brought very significant changes, not only the type of technology that has shifted, but more importantly a change in mindset in this industrial era. This has a real impact on the direction of national development and has an impact on the agricultural system. The agricultural system is no longer just a cultivation activity, but agriculture is also part of the industrial system which is characterized by a transformation, both in terms of raw materials, economy, social and environment. The development of modern agriculture is marked by mindset changing of using biological and non-biological resources by the needs. The precise use of resources in agricultural production systems is then called precision agriculture. Precision agriculture is an agricultural concept with a systems approach towards low-input, high-efficiency, and sustainable agriculture. In a previous study, a CNC-based precision farming system (Farming-BOT) was developed. The device could perform vegetable crop cultivation automatically. To properly observe the development of cultivation, an online monitoring device based on the minicomputer was developed. The Raspberry Pi Zero, equipped with a 5MP camera and a temperature &amp; humidity sensor, is used. The device is placed on the top of the Farming-BOT device. Image data, temperature, and humidity are sent to the cloud database periodically every day. Users can access the data through the website and Android application. Based on the testing result, with a stable internet connection, data on environmental conditions can be monitored perfectly.
Sentiment Thesaurus, Synset and Word2Vec Based Improvement in Bigram Model for Classifying Product Reviews

Classifying product reviews is one of the tasks in Natural Language Processing by which the sentiment of the reviewer towards a product can be identified. This identification is useful for the growth of the business by increasing the number of satisfied customers through product quality improvement. Bigram models are more popular in performing this classification since it considers the occurrence of two words consecutively in the reviews. In the existing works on bigram models, semantically similar words to the words present in bigrams are not considered. As the reviewers use different words with the same meaning to express their feeling, we proposed improved bigram models in which semantically similar words to the words in bigrams are also used for classifying the reviews. In the proposed models, sentiment polarity thesaurus is constructed by including sentiment words and their synonyms. The combinations of constructed thesaurus, Synset and Word2Vec are used for extracting synonyms for the words in the reviews. Performance of the proposed models is compared with the traditional bigram model and state-of-the-art methods. It is observed from the results that our models are able to achieve better performance than traditional model and recent methods.