Reviewerly/exHarmony huggingface dataset:
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering

Intent Detection is a crucial component of Dialogue Systems wherein the objective is to classify a user utterance into one of the multiple pre-defined intents.A pre-requisite for developing an effective intent identifier is a training dataset labeled with all possible user intents.However, even skilled domain experts are often unable to foresee all possible user intents at design time and for practical applications, novel intents may have to be inferred incrementally on-the-fly from user utterances.Therefore, for any real-world dialogue system, the number of intents increases over time and new intents have to be discovered by analyzing the utterances outside the existing set of intents.In this paper, our objective is to i) detect known intent utterances from a large number of unlabeled utterance samples given a few labeled samples and ii) discover new unknown intents from the remaining unlabeled samples.Existing SOTA approaches address this problem via alternate representation learning and clustering wherein pseudo labels are used for updating the representations and clustering is used for generating the pseudo labels.Unlike existing approaches that rely on epoch-wise cluster alignment, we propose an end-to-end deep contrastive clustering algorithm that jointly updates model parameters and cluster centers via supervised and self-supervised learning and optimally utilizes both labeled and unlabeled data.Our proposed approach outperforms competitive baselines on five public datasets for both settings: (i) where the number of undiscovered intents is known in advance, and (ii) where the number of intents is estimated by an algorithm.We also propose a human-in-the-loop variant of our approach for practical deployment which does not require an estimate of new intents and outperforms the end-to-end approach.
Long-term Control for Dialogue Generation: Methods and Evaluation

Current approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic.In this work, we focus on constrained long-term dialogue generation, which involves more fine-grained control and requires a given set of control words to appear in generated responses.This setting requires a model to not only consider the generation of these control words in the immediate context, but also produce utterances that will encourage the generation of the words at some time in the (possibly distant) future.We define the problem of constrained long-term control for dialogue generation, identify gaps in current methods for evaluation, and propose new metrics that better measure long-term control.We also propose a retrieval-augmented method that improves performance of long-term controlled generation via logit modification techniques.We show through experiments on three task-oriented dialogue datasets that our metrics better assess dialogue control relative to current alternatives and that our method outperforms state-of-theart constrained generation baselines. 1
MarSan at SemEval-2022 Task 6: iSarcasm Detection via T5 and Sequence Learners

The paper describes SemEval-2022's shared task "Intended Sarcasm Detection in English and Arabic."This task includes English and Arabic tweets with sarcasm and non-sarcasm samples and irony speech labels.The first two subtasks predict whether a text is sarcastic and the ironic category the sarcasm sample belongs to.The third one is to find the sarcastic sample from a sarcastic sample and its non-sarcastic paraphrase.Deep neural networks have recently achieved highly competitive performance in many tasks.Combining deep learning with language models has also resulted in acceptable accuracy.Inspired by this, we propose a novel deep learning model on top of language models.On top of T5, the architecture uses an encoder module of the transformer, followed by LSTM and attention to utilizing past and future information, concentrating on informative tokens.Due to the success of the proposed model, we used the same architecture with a few modifications to the output layer in all three subtasks.
PiCkLe at SemEval-2022 Task 4: Boosting Pre-trained Language Models with Task Specific Metadata and Cost Sensitive Learning

This paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language Detection.Patronizing and Condescending Language (PCL) refers to language used with respect to vulnerable communities that portrays them pitifully and is reflective of a sense of superiority.Task 4 involved binary classification (Subtask 1) and multi-label classification (Subtask 2) of Patronizing and Condescending Language (PCL).For our system, we experimented with fine-tuning different transformer-based pre-trained models including BERT, DistilBERT, RoBERTa and AL-BERT.Further, we have used token separated metadata to improve our model by helping it contextualize different communities with respect to PCL.We faced the challenge of class imbalance, which we solved by experimenting with different class weighting schemes.Our models were effective in both subtasks, with the best performance coming out of models with Effective Number of Samples (ENS) class weighting and token separated metadata in both subtasks.For subtask 1 and subtask 2, our best models were finetuned BERT and RoBERTa models respectively.
Generating Complement Data for Aspect Term Extraction with GPT-2

Aspect Term Extraction (ATE) is the task of identifying the word(s) in a review text toward which the author express an opinion.A major challenges for ATE involve data scarcity that hinder the training of deep sequence taggers to identify rare targets.To overcome these issues, we propose a novel method to better exploit the available labeled data for ATE by computing effective complement sentences to augment the input data and facilitate the aspect term prediction.In particular, we introduce a multistep training procedure that first obtains optimal complement representations and sentences for training data with respect to a deep ATE model.Afterward, we fine-tune the generative language model GPT-2 to allow complement sentence generation at test data.The REIN-FORCE algorithm is employed to incorporate different expected properties into the reward function to perform the fine-tuning.We perform extensive experiments on the benchmark datasets to demonstrate the benefits of the proposed method that achieve the state-of-the-art performance on different datasets.
DH-FBK at SemEval-2022 Task 4: Leveraging Annotators’ Disagreement and Multiple Data Views for Patronizing Language Detection

The subtle and typically unconscious use of patronizing and condescending language (PCL) in large-audience media outlets undesirably feeds stereotypes and strengthens power-knowledge relationships, perpetuating discrimination towards vulnerable communities.Due to its subjective and subtle nature, PCL detection is an open and challenging problem, both for computational methods and human annotators.In this paper we describe the systems submitted by the DH-FBK team to SemEval-2022 Task 4, aiming at detecting PCL towards vulnerable communities in English media texts.Motivated by the subjectivity of human interpretation, we propose to leverage annotators' uncertainty and disagreement to better capture the shades of PCL in a multi-task, multi-view learning framework.Our approach achieves competitive results, largely outperforming baselines and ranking on the top-left side of the leaderboard on both PCL identification and classification.Noticeably, our approach does not rely on any external data or model ensemble, making it a viable and attractive solution for real-world use.
Resilience of Named Entity Recognition Models under Adversarial Attack

Named entity recognition (NER) is a popular language processing task with wide applications.Progress in NER has been noteworthy, as evidenced by the F1 scores obtained on standard datasets.In practice, however, the end-user uses an NER model on their dataset out-of-the-box, on text that may not be pristine.In this paper we present four modelagnostic adversarial attacks to gauge the resilience of NER models in such scenarios.Our experiments on four state-of-the-art NER methods with five English datasets suggest that the NER models are over-reliant on case information and do not utilise contextual information well.As such, they are highly susceptible to adversarial attacks based on these features.
Multiformer: A Head-Configurable Transformer-Based Model for Direct Speech Translation

Transformer-based models have been achieving state-of-the-art results in several fields of Natural Language Processing.However, its direct application to speech tasks is not trivial.The nature of this sequences carries problems such as long sequence lengths and redundancy between adjacent tokens.Therefore, we believe that regular self-attention mechanism might not be well suited for it.Different approaches have been proposed to overcome these problems, such as the use of efficient attention mechanisms.However, the use of these methods usually comes with a cost, which is a performance reduction caused by information loss.In this study, we present the Multiformer, a Transformer-based model which allows the use of different attention mechanisms on each head.By doing this, the model is able to bias the self-attention towards the extraction of more diverse token interactions, and the information loss is reduced.Finally, we perform an analysis of the head contributions, and we observe that those architectures where all heads relevance is uniformly distributed obtain better results.Our results show that mixing attention patterns along the different heads and layers outperforms our baseline by up to 0.7 BLEU.
Incorporating Centering Theory into Neural Coreference Resolution

In recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset.However, how coreference resolvers can benefit from discourse coherence is still an open question.In this paper, we propose to incorporate centering transitions derived from centering theory in the form of a graph into a neural coreference model.Our method improves the performance over the SOTA baselines, especially on pronoun resolution in long documents, formal well-structured text, and clusters with scattered mentions.
Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings

Word embeddings have advanced the state of the art in NLP across numerous tasks. Understanding the contents of dense neural representations is of utmost interest to the computational semantics community. We propose to focus on relating these opaque word vectors with human-readable definitions, as found in dictionaries This problem naturally divides into two subtasks: converting definitions into embeddings, and converting embeddings into definitions. This task was conducted in a multilingual setting, using comparable sets of embeddings trained homogeneously.
LastResort at SemEval-2022 Task 4: Towards Patronizing and Condescending Language Detection using Pre-trained Transformer Based Models Ensembles

This paper presents our solutions for Task4 at SemEval2022: Patronizing and Condescending Language Detection.This shared task contains two sub-tasks.The first sub-task is a binary classification task whose goal is to predict whether a given paragraph contains any form of patronising or condescending language(PCL).For the second sub-task, given a paragraph, we have to find which PCL categories express the condescension.Here we have a total of 7 overlapping sub-categories for PCL.Our proposed solution uses BERT based ensembled models with hard voting and techniques applied to take care of class imbalances.Our paper describes the system architecture of the submitted solution and other experiments that we conducted.Our best performing models achieve an F1 score of 59.4 and 15.7 on sub-tasks 1 and 2 respectively.
A New approach to Recognize Human Face Under Unconstrained Environment

Human face is considered as one of the most useful traits in biometrics, and it has been widely used in education, security, military and many other applications. However, in most of currently deployed face recognition systems ideal imaging conditions are assumed; to capture a fully featured images with enough quality to perform the recognition process. As the unmasked face will have a considerable impact on the numbers of new infections in the era of COVID-19 pandemic, a new unconstrained partial facial recognition method must be developed. In this research we proposed a mask detection method based on HOG (Histogram of Gradient) features descriptor and SVM (Support Vector Machine) to determine whether the face is masked or not, the proposed method was tested over 10000 randomly selected images from Masked Face-Net database and was able to correctly classify 98.73% of the tested images. Moreover, and to extract enough features from partially occluded face images, a new geometrical features extraction algorithm based on Contourlet transform was proposed. The method achieved 97.86% recognition accuracy when tested over 4784 correctly masked face images from Masked Face-Net database. Keywords: Facial Recognition, Unconstraint conditions, masked faces, HOG, Support Vector Machine.
Candlestick Pattern Classification Using Feedforward Neural Network

Investment in the capital market can help boost a country’s economic growth. Without a doubt, in investing, a technical analysis of the condition of the stock is needed at that time. One of the technical analyses that can be done is to look at the historical data of stocks. Candlestick charts can summarize historical data that contain price value for Open, High, Low, and Close (OHLC) in the form of a chart. A group of candlesticks will form a pattern that can help investors to see whether the stock is trending up or down. The number of candlestick patterns and the manual determination of candlestick patterns may take time and effort. Feedforward Neural Network (FNN) is one of the algorithms that can help map the input and output of a given dataset. This study aims to implement FNN to classify candlestick patterns found in historical stock data. The test results show that the accuracy for each model scenario does not guarantee whether all patterns can be properly recognized. This is mainly caused by an imbalanced dataset and the classification process cannot be done properly. Testing with the original data has an accuracy of above 85% on each stock, but the average F1-score is below 45%. Further experiments using random under-sampling and Synthetic Minority Oversampling Technique (SMOTE) result in decreased accuracy value, where the lowest is 59% in PT Bukit Asam Tbk share, and an increased average F1-score, but less than 15%. Keywords: Candlestick patterns, feedforward neural network, investment, historical data, OHLC, SMOTE, stocks.
Analysis on the coupling degree of water source ecological environment based on 3S technology

In order to overcome the problems of low classification accuracy, high error and long time in traditional water source eco-environmental coupling model analysis method, a water source ecological environment coupling degree analysis based on 3S technology is proposed. According to the classification results of water source ecological environment features, the water source ecological environment evaluation matrix is constructed, and all indexes are normalised. The index weight is estimated by calculating the information entropy of the evaluation index, and the water source ecological environment coupling degree model is constructed according to the index weight to complete the analysis of water source ecological environment coupling degree. The experimental results show that the accuracy of surface feature classification is always above 97%, the relative error rate of water source ecological environment coupling analysis is always below 3.9%, and the average analysis time is only 0.62 s.
Usability Measurement: Chatbot as a Pedagogical Support for Learning French Grammar

The use of technology media in the field of education is increasingly popular.Along with the needs of learning media that do not depend on space and time, the use of artificial intelligence is expected to be a support for language learning as well.This study discusses the use of artificial intelligence based on Chatbot and named ChatJours which is used as a media to support learning in French through telegram media.This research used a quantitative method and data were collected using a questionnaire containing questions about the usability system (SUS).In this study the aim is to see how useful this Chatbot technology media is as a pedagogical support.The results of the study, which were assessed using a Likert scale, showed that from the questionnaire filled in by the respondents, the overall score was 76%, which indicates that the Chatbot media is suitable to be used as a supporting medium for learning French grammar.
PolyDot Coded Privacy Preserving Multi-Party Computation at the Edge

We investigate the problem of privacy preserving distributed matrix multiplication in edge networks using multi-party computation (MPC). Coded multi-party computation (CMPC) is an emerging approach to reduce the required number of workers in MPC by employing coded computation. Existing CMPC approaches usually combine coded computation algorithms designed for efficient matrix multiplication with MPC. We show that this approach is not efficient. We design a novel CMPC algorithm; PolyDot coded MPC (PolyDot-CMPC) by using PolyDot codes. We exploit "garbage terms" that naturally arise when polynomials are constructed in the design of PolyDot-CMPC to reduce the number of workers needed for privacy-preserving computation. We show that entangled polynomial codes, which are consistently better than PolyDot codes in coded computation setup, are not necessarily better than PolyDot-CMPC in MPC setting.
Application of artificial intelligence-based video image processing technology in security industry

In the field of security, video image processing technology plays an important role. An efficient security system based on video processing technology can effectively improve the efficiency of public security, transportation, justice, retail and other industries. Nowadays, with the development of information technology, video image processing technology can gradually replace manual labor in the field of security. This paper analyzes the point processing, area processing and frame processing in video image processing technology in detail, and conducts in-depth investigation and research on the application of video image processing technology in the field of security.
Early alert for sleep deprivation using mobile sensor data fusion

In recent years, with the widespread use of digital technology and intelligent gadgets, an enormous amount of data are being generated at an alarmingly rapid rate. The quickly growing technologies and data generated by mobile and Internet of Things devices are tremendous. These data can be used to achieve sleep deprivation in users using machine learning . Multi-modal sensors and technology to detect activity, sleep, and circadian rhythms have become increasingly widespread. For the first time, large-scale sleep tracking is conceivable. In addition to large-scale epidemiological research, multi-sensor data can be used for wellness applications such as sleep coaching for chronic disease patients. Individuals, health, and research must overcome several fundamental obstacles before new technologies may ultimately benefit. We examine existing sleep-monitoring technology from data gathering through mobile data sensors to machine learning mode. We also analyze the current and emerging sensing technologies. • An early alert of sleep deprivation using mobile sensor data to monitor sleep deprivation. • The Deep neural network model is developed to accumulate sensor fusion. • The categorization of sleep situations has been shown based on user data and learning. • Our model can provide monitoring throughout the night.
A Survey of Estonian Video Game Industry Needs

Designing a video game design and development curriculum in higher education is a challenging task. Information about the needs of the respective industry certainly helps. In this paper, we have surveyed Estonian video game development companies to determine their current needs when it comes to knowledge areas, software tools, languages, abilities, and contextual fluencies. The survey is based on a similar survey conducted a decade ago and this paper compares the current results with those found earlier.&#x0D; &#x0D; Compared to the prior survey, we have found significant differences in the rated importance of knowledge in optimization, version control technologies, the C, C++, and C# programming languages, and the time management ability for video game development companies looking to hire university graduates. We have also extended the previous survey to include a contemporary selection of game design and development tools. Based on that, we have determined a strong need for graduates with skills specifically in Unity and Unreal Engine game engines, Photoshop raster image editing software, and Git version control software.&#x0D; &#x0D; While most of our results are largely consistent with the previous research, our added survey items like visual languages and game engines bring the results to the modern context. This allows curriculum designers and managers to see the differences regarding the landscape of industry needs for their graduates and thus make more informed decisions in their work.
WIFLF: An approach independent of the target project for cross‐project defect prediction

Abstract Cross‐project defect prediction (CPDP) is used to build defect prediction models when data from the target project are not enough. There has been several approaches to improve the performance of CPDP, such as feature transformation and instance selection methods. However, existing techniques are strongly dependent on the target data to reduce the distribution discrepancy between source and target projects. That is, the performance of these methods is determined by the effectiveness of feature transformation or the similarity between two projects. Additionally, when there is a large amount of source data that needs to be matched with target data, it will take much time and reduce the efficiency of model construction. Therefore, it is vital to explore a target project‐agnostic approach to build CPDP models. This paper presents a Weighted Isolation Forest with class Label information Filter (WIFLF) to relieve the issues above. Four groups of datasets from AEEEM, Relink and PROMISE Data Repository are used to conduct CPDP models. Besides, WIFLF is compared with 12 approaches. The experimental results indicate that WIFLF significantly outperforms all the baselines. Specifically, WIFLF with random forest significantly improves the performance over the baselines on average by at least 14.64% and 4.90% with respect to Skewed F‐Measure and G‐Measure , respectively.