Reviewerly/exHarmony huggingface dataset:
MicroBERT: Effective Training of Low-resource Monolingual BERTs through Parameter Reduction and Multitask Learning

Transformer language models (TLMs) are critical for most NLP tasks, but they are difficult to create for low-resource languages because of how much pretraining data they require.In this work, we investigate two techniques for training monolingual TLMs in a low-resource setting: greatly reducing TLM size, and complementing the masked language modeling objective with two linguistically rich supervised tasks (part-of-speech tagging and dependency parsing).Results from 7 diverse languages indicate that our model, MicroBERT, is able to produce marked improvements in downstream task evaluations relative to a typical monolingual TLM pretraining approach.Specifically, we find that monolingual MicroBERT models achieve gains of up to 18% for parser LAS and 11% for NER F1 compared to a multilingual baseline, mBERT, while having less than 1% of its parameter count.We conclude reducing TLM parameter count and using labeled data for pretraining low-resource TLMs can yield large quality benefits and in some cases produce models that outperform multilingual approaches.
Empathetic and Emotionally Positive Conversation Systems with an Emotion-specific Query-Response Memory

Emotional conversation systems generate responses for the input queries considering the speaker's emotions in a conversation.Existing emotional conversation systems output emotional responses according to either a given emotion or the user's emotion reflected in the input queries.Following a given emotion may lead to an emotional drift between the given emotion and the conversation state, and following only the user's emotion may aggravate the user's negative feelings if users suffer from a negative mood.In this paper, we propose to generate empathetic responses catering to the user's emotions while leading the conversation to be emotionally positive.Particularly, by abstracting the conversation corpus, we extract and store the different responding strategies for different users' emotions and conversational topics into a memory.We encourage positive emotions in conversation via a sentiment evaluator.We model the memory outputs with a Gaussian mixture distribution and sample a final responding strategy from the distribution.The strategy acts as a condition to a transformer model to generate responses.The experiments verify our model surpasses the baseline methods in appropriateness, diversity, and generating emotionally positive responses.
Leveraging World Knowledge in Implicit Hate Speech Detection

Warning: This paper contains content that may be offensive or disturbing.While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems.This paper presents the first attempt to apply Entity Linking (EL) techniques to both explicit and implicit hate speech detection, where we show that such real world knowledge about entity mentions in a text does help models better detect hate speech, and the benefit of adding it into the model is more pronounced when explicit entity triggers (e.g., rally, KKK) are present.We also discuss cases where real world knowledge does not add value to hate speech detection, which provides more insights into understanding and modeling the subtleties of hate speech.
It’s Time to Reason: Annotating Argumentation Structures in Financial Earnings Calls: The FinArg Dataset

With the goal of reasoning on the financial textual data, we present in this paper, a novel approach for annotating arguments, their components and relations in the transcripts of earnings conference calls (ECCs).The proposed scheme is driven from the argumentation theory at the micro-structure level of discourse.We further conduct a manual annotation study with four annotators on 136 documents.By that, we obtained inter-annotator agreement of α U = 0.70 for argument components and α = 0.81 for argument relations.The final created corpus, with the size of 804 documents, as well as the annotation guidelines are publicly available for researchers in the domains of computational argumentation, finance and FinNLP.
Are Multilingual Sentiment Models Equally Right for the Right Reasons?

Multilingual NLP models provide potential solutions to the digital language divide, i.e., crosslanguage performance disparities.Early analyses of such models have indicated good performance across training languages and good generalization to unseen, related languages.This work examines whether, between related languages, multilingual models are equally right for the right reasons, i.e., if interpretability methods reveal that the models put emphasis on the same words as humans.To this end, we provide a new trilingual, parallel corpus of rationale annotations for English, Danish, and Italian sentiment analysis models and use it to benchmark models and interpretability methods.We propose rank-biased overlap as a better metric for comparing input token attributions to human rationale annotations.Our results show: (i) models generally perform well on the languages they are trained on, and align best with human rationales in these languages; (ii) performance is higher on English, even when not a source language, but this performance is not accompanied by higher alignment with human rationales, which suggests that language models favor English, but do not facilitate successful transfer of rationales.
Neuro-fuzzy systems for daily solar irradiance classification and PV efficiency forecasting

Considering the impact of photovoltaic installations and the fact that their performance depends on the type of day, this paper presents a classifier that makes use of fuzzy logic to classify daily irradiance profiles as a human would do. To do this, the system must be linguistically interpretable, so the classifier must be simple enough, but without losing accuracy. This is why the article combines the use of data mining and supervised learning algorithms to obtain an initial system and then exploits simplification techniques such as the concept of fuzzy classifiers with incomplete rule bases, as well as fuzzy tabular simplification of rules to obtain a compact and simple final system. The classifier obtained handles the ambiguity presented by the daily irradiance profiles with precision. Once the system has been obtained, a large number of days in southern Spain are classified, analysing the performances of a photovoltaic plant obtained in each of the classes. Then, a neuro-fuzzy system is designed to predict the performance of the photovoltaic installation, considering the type of day, the maximum ambient temperature reached during the day, and the degradation of the installation over time, proving its usefulness in alerting about anomalous behaviour of the system.
Intellectual Design of Bomb Identification and Defusing Robot based on Logical Gesturing Mechanism

Expanding human wants coincide with the development of ever more sophisticated technologies. The majority of this study focuses on robotic arms, which are used daily in the job done to satisfy these objectives and make life simpler. Mechanical limbs can interact with humans or be programmed to follow specific instructions. Law enforcement officers face a high-stakes, high-precision challenge while neutralizing explosives, since any misstep might result in serious injury or death. An idea derived from wireless bomb disposal robots is offered as a solution to the issue. Through using wireless control module, the robot will dispose of the explosive. A transmitter makes up the control module, while a receiver makes up the Robot itself. An ARDUINO UNO R3 board connects to the receiver module. Input from the transmitter controls the Robot's actions. An LCD screen is connected to a wireless video receiver in the control module, so you can watch what the camera captures on the screen. The robot may be operated with a wave of the hand thanks to a sensor module linked to an Arduino Nano microcontroller. The hand gesture based robot is better option to pick up the explosive items and diffusing
NFT Marketplace for Geospatial Data Using Blockchain

Rights that can be transferred to digital assets like pictures, videos, or music are known as non-fungible tokens (NFTs). The phenomenon and its marketplaces have grown dramatically since early 2021. NFT marketplaces are becoming more prevalent since a few years ago. Most of them store digital materials on centralized systems (files). This study makes a recommendation for a secure trading environment for non-fungible tokenized digital assets. Users will be able to create new digital assets and exchange them for Ethereum-based currency. Also, the IPFS protocol-based decentralized file system's technological potential for more secure digital asset storage is examined. In this approach, the problem of file storage is attempted to be solved. It also represents an effort to promote the usage of blockchain technology. In a typical storage system, the user and provider sign a digitalized contract that gives the user legal recourse against the provider should their data be lost, compromised, or sold to a third party for services that they do not supply.Geospatial Data can be safely stored and secured by the user. To make the storage system more secure, two ERC rules will be used.The files are distributedly stored using this technology's decentralized network, which prohibits unauthorized access to the storage system. The blockchain project relies on hashing to function. Every block in a blockchain creates a hash using the SHA 256 hashing algorithm.
A Comparative Analysis of Optimizing Medical Insurance Prediction Using Genetic Algorithm and Other Machine Learning Algorithms

In this paper, an analysis is conducted on different machine learning strategies to predict medical insurance charges using demographic and health-related information on individuals. Each algorithm was trained and tested on a preprocessed dataset, and the performance of various models is compared, including linear regression, random forest, gradient boosting, LassoLarsCV, and a model created with automated machine learning using TPOT, which makes use of genetic programming for optimization. Conclusively, the results bring to light that the TPOT-generated model, which is a combination of LassoLarsCV and GradientBoostingRegressor, performed better than the other models, attaining a root mean square error of 0.0686 and an accuracy of 87.45% on the test set. These findings suggest that automated machine learning techniques and metaheuristic optimization, as was used in TPOT, can bring improvement to the performance of existing medical insurance cost prediction models.
Multiscale Attention Network for Detection and Localization of Image Splicing Forgery

Image forgery detection and localization has become a research topic of increasing interest with the enormous spread of manipulated images on the Web. Most previous methods related to image forgery focus on one particular attribute or neglect the importance of multi-scale information. Such approaches are considered not suitable for detecting and locating multi-scale splicing forgeries. This paper presents a novel approach that exploits residual attention and integrates multi-scale local and global information to improve detection accuracy. In the proposed method, we first aggregate multi-level convolutional feature maps extracted by the encoder to enrich the feature representations and improve the ability of the model to locate multi-scale forged areas. Then, we design a residual attention block (RAB) to purify the features, which enhances the response of task-related regions and suppresses noise information. Furthermore, a global feature mining block (GFMB) is proposed to capture the long-range dependencies between different regions of the image, enabling the model to handle complex tampering scenarios effectively. The multi-scale splicing forgery regions are precisely detected and located by utilizing the proposed method. The extensive experiments are conducted on three benchmark datasets, CASIA, COLUMB, and NIST’16. Specifically, our model achieves the F1 score of 84.3%, 87.9%, and 80.8% on CASIA, COLUMB, and NIST’16 test sets, respectively, outperforming state-of-the-art methods.
Comparative study on the variations of quantum approximate optimization algorithms to the Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also being used heavily as a vehicle to study the feasibility of the quantum computing paradigm for this class of problems. In this work, we formulate the symmetric TSP as an optimization problem, which we solve using the quantum approximate optimization algorithm (QAOA) approach. By adopting an improved qubit encoding strategy and a layerwise learning optimization protocol, we obtain numerical results on the gate-based digital quantum simulator for the 3-, 4-, and 5-city TSPs. Specifically, we focus on three QAOA mixer designs to evaluate their performances in terms of numerical accuracy and optimization cost. Based on our results, we propose that a well-balanced QAOA mixer design is more prominent on gate-based simulators or realistic quantum devices in the near future. In addition, we study the sensitivity of TSP graph properties such as graph skewness and penalty weight in the TSP-QAOA simulation. Overall, our results prove digital quantum simulation is a powerful candidate for obtaining the optimal solution to the TSP.
A Lightweight Concept Drift Detection Method for On-Device Learning on Resource-Limited Edge Devices

A practical issue of edge AI systems is that data distributions of trained dataset and deployed environment may differ due to noise and environmental changes over time. Such a phenomenon is known as a concept drift, and this gap degrades the performance of edge AI systems and may introduce system failures. To address this gap, retraining of neural network models triggered by concept drift detection is a practical approach. However, since available compute resources are strictly limited in edge devices, in this paper we propose a fully sequential concept drift detection method in cooperation with an on-device sequential learning technique of neural networks. In this case, both the neural network retraining and the proposed concept drift detection are done only by sequential computation to reduce computation cost and memory utilization. Evaluation results of the proposed approach shows that while the accuracy is decreased by 3.8% 4.3% compared to existing batch-based detection methods, it decreases the memory size by 88.9%-96.4% and the execution time by 1.3%-83.8%. As a result, the combination of the neural network retraining and the proposed concept drift detection method is demonstrated on Raspberry Pi Pico that has 264kB memory.
Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment

The growth of online media platforms, particularly multimedia-rich social networks such as YouTube, has resulted in a demand for efficient data collection and analysis techniques. One of the critical data elements for multimedia-rich social platforms is the video transcript, which is not readily available through social platforms. Traditional methods for transcript generation are time-consuming and are challenged by the vast amount of data. This study proposes a methodology that leverages parallel computing and the Python multiprocessing library to improve the speed of transcript collection from YouTube. The methodology utilizes YouTube’s Transcript API to extract YouTube-generated transcripts and OpenAI’s Whisper model to generate transcriptions on videos without native YouTube transcriptions. Additionally, the Googletrans Translation API was used to translate transcriptions from non-English videos. The results showed a significant improvement in processing time and performance, enabling researchers to conduct various studies on a larger scope of YouTube data with ease. With parallel processing, the YouTube Transcript API showed a 2100.88% performance increase, the Whisper model showed a 29.45% improvement, and the Googletrans API showed a 738.46% increase compared to the sequential processing baseline using the same process. The total time consumption was reduced by 25.54% from 105.64 hours to 78.66 hours. The methodology developed in this study is not limited to YouTube and can be applied to other social media platforms, making it a versatile solution for data collection and analysis.
EcoAssist: A no-code platform to train and deploy custom YOLOv5 object detection models

EcoAssist is a tkinter based graphical user interface (GUI) which is designed to enable object detection for novices.The package allows the user to annotate images, train and deploy custom models, and post-process imagery without having to write code (Figure 1).Installation is automated and dependencies will be installed in a virtual environment to avoid conflicts.Annotation is done via the open-source package labelImg (Heartexlabs, 2022) and postprocessing features include folder separation, detection visualization, cropping, label creation, and exporting results to CSV files.It will automatically run on NVIDIA or Apple Silicon GPU and is available for Microsoft Windows, Apple macOS, and Linux.The main target audience is ecologists working with motion triggered camera traps, although it is not limited to detect animals as object detection models are generic.The package is developed to be used in conjunction with Timelapse (Greenberg et al., 2019) (an image analyzer for camera traps) and MegaDetector (Beery et al., 2019) (a trained object detection model to localize animals in camera trap imagery).More features are planned and will be added in the future.
A Clustering Extension of HUEPs for the Analysis of Performance Anomalies in Robots

AbstractErrors in Cyber-Physical Systems present a major problem given the current state of technological complexity. Self-diagnosis can contribute to address it, being the standpoint of the present paper. Hence, an application of exploratory Machine Learning models to assess the functioning of robot software in order to identify anomalies that lead to low performance is proposed. More precisely, Hybrid Unsupervised Exploratory Plots (HUEPs) are extended through density-based clustering techniques, that are applied together with unsupervised exploratory projection models. As a result, intuitive and informative visualizations of software performance are obtained, supporting the monitoring and anomaly detection tasks. The proposed clustering extension of HUEPs is thoroughly validated on a massive and up-to-date open dataset, obtaining promising results.Keywords: Anomaly detectionexploratory analysisrobot softwareperformancemachine learningclustering Notes1 European Commission. Key Enabling Technologies (KETs) Observatory. URL: https://knowledge4policy.ec.europa.eu/publication/key-enablingtechnologies-kets-observatory_en2 International Federation of Robotics. IFR presents the World Robotics 2021 reports. URL: https://ifr.org/ifr-press-releases/news/robot-sales-riseagain
Hepatocellular Carcinoma Recognition from Ultrasound Images by Fusing Convolutional Neural Networks at Decision Level

The Hepatocellular Carcinoma (HCC) is the most often met malignant tumor of the liver. It develops from cirrhosis, after a parenchyma restructuring phase, at the end of which dysplastic nodules result that can transform into HCC. Nowadays, HCC diagnosis is usually performed through needle biopsy, also through medical imaging, but most of the methods are invasive and/or expensive. Ultrasonography is the best option for screening, while computerized techniques must be developed as well to derive subtle information. In our previous research, we experimented with conventional techniques, based on advanced texture analysis methods and traditional classifiers, as well as with deep learning techniques, aiming to perform HCC recognition with maximum accuracy. We combined the CNN architectures with each other, respectively with the conventional techniques, at classifier level. In this work, we combined existing and original CNN architectures at decision level, we assessed the corresponding performance, and we compared it with our previous results.
Free-Weight Exercise Activity Recognition using Deep Residual Neural Network based on Sensor Data from In-Ear Wearable Devices

Human activity recognition (HAR) is a popular study area in the current era of the Internet of Things and artificial intelligence. HAR approaches have been successfully applied in many real-world scenarios, such as sports tracking and assessment and remote healthcare for the elderly. Over the past decade, studies on HAR employing wearable sensors have been explored operating various wearable devices, including smartwatches, smart shoes, and other intelligent wearables. The advancement of sensor technology has directed the combining of inertial sensors into ear-worn devices, making it possible to record physical movements privately. In this study, we proposed a deep residual network named ResNeXt, for identifying free- weight exercises using sensor data from smart in-ear devices. To consider the performance of the proposed model and other deep learning models, we conducted experiments using a publicly available dataset called ERICA, which collected free-weight exercise activities from inertial sensors of both an in-ear device and a dumbbell. Our experimental results demonstrated that the suggested ResNeXt network surpassed other deep learning models, gaining the highest F1-score of 96.67% and 99.75% using inertial data from in-ear sensors and dumbbell sensors, respectively
Applications and challenges of SA in real-life scenarios

Sentiment analysis has benefited from the availability of lexicons and benchmark datasets created over decades of research. However, its applications to the real world are a driving force for research in SA. This chapter describes some of these applications and related challenges in real-life scenarios. In this chapter, we focus on five applications of SA: health, social policy, e-commerce, digital humanities, and other areas of NLP. This chapter is intended to equip an NLP researcher with the 'what', 'why', and 'how' of applications of SA: what is the application about, why it is important and challenging, and how current research in SA deals with the application. We note that, while the use of deep-learning techniques is a popular paradigm that spans these applications, challenges around privacy and selection bias of datasets is a recurring theme across several applications.
A comparison of two performance portability metrics

Summary The rise in the demand for new performance portability frameworks for heterogeneous computing systems has brought with it a number of proposals of workable metrics for evaluating the performance portability of applications. The aim of this article is twofold. First, we analyze the underlying principles of the criteria and definition of the revised Ⴔ metric and show that these principles are partially correct and suffer from a lack of a solid and clear performance portability model. We prove mathematically and demonstrate it practically that the principles are only correct for the architectural efficiency approach based on throughputs but are incorrect for the popular application efficiency approach based on run‐times. Second, we are examining whether the Ⴔ and metrics meet the requirements of consistency, proportionality, and lossless information. We use examples from the scientific literature to show the reader that the Ⴔ metric loses information while the metric and other metrics do not lose information.