Reviewerly/exHarmony huggingface dataset:
Leveraging Large Language Models for Literature Review Tasks - A Case Study Using ChatGPT

Literature reviews constitute an indispensable component of research endeavors; however, they often prove laborious and time-intensive. This study explores the potential of ChatGPT, a prominent large-scale language model, to facilitate the literature review process. By contrasting outcomes from a manual literature review with those achieved using ChatGPT, we ascertain the accuracy of ChatGPT's responses. Our findings indicate that ChatGPT aids researchers in swiftly perusing vast and heterogeneous collections of scientific publications, enabling them to extract pertinent information related to their research topic with an overall accuracy of 70%. Moreover, we demonstrate that ChatGPT offers a more economical and expeditious means of achieving this level of accuracy compared to human researchers. Nevertheless, we conclude that although ChatGPT exhibits promise in generating a rapid and cost-effective general overview of a subject, it presently falls short of generating a comprehensive literature overview requisite for scientific applications. Lastly, we propose avenues for future research to enhance the performance and utility of ChatGPT as a literature review assistant.
A Behavior-Based Fuzzy Control System for Mobile Robot Navigation: Design and Assessment

Decreasing the number of robot navigation accidents due to slow robot reaction times is still an important research topic. This paper proposes a methodology based on a behavior-based fuzzy control scheme for the path planning of a simulated mobile robot. The behaviors are designed based on fuzzy logic and are integrated to switch between them when necessary during navigation in narrow and unknown environments. To test the proposed methodology, we build a simulation environment in which a mobile robot can navigate in straightforward or curved scenarios by using only a Lidar sensor and the proposed behavior-based control scheme. We tested experiment conditions for each scenario, and we evaluated the performance of the vehicle by analyzing the minimum avoidance distance from the obstacles. The simulation results demonstrate that the proposed methodology can be used for a mobile robot to react fast enough to successfully avoid obstacles when navigating in narrow and unknown environments at different velocities.
Digitizing Musical Skills in Elementary School with the Use of Instagram

This paper examines the implementation of digital tools, specifically Instagram, to enhance musical competencies in primary education. The introduction of the LOGSE in 1990 brought positive changes to music education in schools, making it a mandatory subject at all educational stages. Moreover, specialized music teachers were introduced. Currently, under the LOMLOE, the Music subject is integrated with Visual Arts, forming a single area known as Artistic Education. This integration poses challenges in terms of curriculum development, evaluation criteria, content, and key competencies. In contrast, according to a 2019 UNESCO report than other countries allocate significantly more time to music education within their school schedules. This approach has been shown to facilitate the development of important cognitive, creative, and social skills. Despite the high interest in music, a significant percentage of individuals lack formal music training. While many people listen to music regularly, only a small fraction can play an instrument or sing in a choir. Addressing this gap, this intervention proposal aims to help students acquire musical competencies through motivating activities, specifically focusing on learning music notation through a melodic instrument (xylophone) using new technologies and social media platforms such as Instagram. The proposed intervention adopts a Flipped Classroom methodology. The intervention includes the creation of interactive tutorials using the Genially platform, allowing students to learn to interpret conventional music notation for instrumental songs on the xylophone. In conclusion, this intervention proposal aims to address the lack of musical competencies among students by leveraging digital tools and social media platforms.
alBERTUM: A Portuguese Search Engine for Scientific and Academic Language

alBERTUM is a Portuguese search engine designed for scientific and academic language. This paper provides an overview of alBERTUM’s data sources, model, architecture, and two core functions. The search engine uses a vast collection of scientific open data available in national repositories to provide bilingual terminology searches and offer a Portuguese academic writing assistant. Utilizing the deep learning model BERT, alBERTUM converts the available data and user queries into semantic vectors, enabling semantic searches for precise and contextually relevant results. The bilingual terminology search function is particularly valuable for professionals, researchers, and those working in fields that require cross-linguistic understanding, such as translation studies, linguistics, or international business. alBERTUM identifies translation equivalents in scientific documents that are not translations of each other. The academic writing assistant function seamlessly incorporates an European Portuguese academic phrase bank, which has also been developed by our team, into the search engine architecture. This integration empowers users, particularly students with limited academic vocabulary, to search for academic expressions based on their intended communicative function. The assistant not only provides contextual examples but also shows the communicative functions of these expressions, enhancing users’ understanding of their usage within an academic setting.
A Novel Optimization Algorithm for Smart Video Surveillance System and Change Object Detection

Security has been a significant concern in every aspect of life. Traditional surveillance systems require a huge amount of storage capacity to save recorded videos. Moreover, as these systems are not fully automated, object detection and change detection need a lot of computational power in real-time surveillance. Finding a specific event or object in the recorded videos becomes more tedious and time-consuming. Since the last decade, researchers and developers have been working on the optimization and improvement of surveillance systems. This paper mainly focuses on surveillance system optimization by presenting an algorithm that not only optimizes objects and change detection but also requires comparatively less time in searching for a particular object from the library of recorded videos. The paper also presents a software application that offers various unique features such as searching for a specific object and on-time notification on targeted object detection.
Use of Anomaly Detection and Object Detection as Basic Support in the Recognition of Outlier Data in Images

Despite significant advances in object detection in images, the ability to identify outlier objects in an image remains an unsolved problem. Its challenges arise due to the inherent variability of an outlier object, the limitations of the object detection approach, and the subjective nature of anomalies in different contexts. In addition, traditional object detection approaches focus on identifying previously known objects, which limits their effectiveness in detecting outlier objects. An comparative analysis based on anomaly detection and object detection is then proposed in this preliminary study, techniques that under training would allow learning a “normal” representation of the data, so that they can detect anomalies when reconstructing new data instances. If the reconstruction of a new data differs significantly from the “normal” data, it could indicate the presence of an outlier object.
Cloud Stateless Server Failover Prediction Using Machine Learning on Proactive System Metrics

Cloud computing, revered for its extraordinary scalability and elasticity, has revolutionized business operations by providing flexible resource options based on demand. However, this on-demand resource allocation poses distinct challenges. Due to the fluid nature of resource allocation and load distribution in the cloud, monitoring the health of servers using system metrics becomes problematic. This complexity can lead to unexpected server request failures and service interruptions due to resource insufficiencies, highlighting the need for more effective monitoring systems. Our research utilizes machine learning techniques to predict cloud server health based on resource usage and operational metrics, focusing specifically on stateless applications. Our study reveals that a Logistic Regression model trained on these system metrics delivers the most precise predictions. After hyperparameter tuning, the model exhibited robust performance, achieving a macro-averaged F1 Score of 97.7%. The paper outlines our methodology, findings, and the potential of this approach for cloud server health prediction.
An Open Dataset Storage Standard for 6G Testbeds

The emergence of sixth-generation (6G) networks has spurred the development of novel testbeds, including sub-THz networks, cell-free systems, and 6G simulators. To maximize the benefits of these systems, it is crucial to make the generated data publicly available and easily reusable by others. Although data sharing has become a common practice, a lack of standardization hinders data accessibility and interoperability. In this study, we propose the Dataset Storage Standard (DSS) to address these challenges by facilitating data exchange and enabling convenient processing script creation in a testbed-agnostic manner. DSS supports both experimental and simulated data, allowing researchers to employ the same processing scripts and tools across different datasets. Unlike existing standardization efforts such as SigMF and NI RF Data Recording API, DSS provides a broader scope by accommodating a common definition file for testbeds and is not limited to RF data storage. The dataset format utilizes a hierarchical structure, with a tensor representation for specific experiment scenarios. In summary, DSS offers a comprehensive and flexible framework for enhancing the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) in 6G testbeds, promoting open and efficient data sharing in the research community.
GAN-generated Synthetic Data and SVM-based Feature Selection for Improved Cardiovascular Disease Prediction

Early diagnosis requires cardiovascular disease forecasting. Past patient interests can improve machine learning predictions. This study uses a Generative Adversarial Network (GAN) to augment data to address the issue of insufficient datasets. Our objective is to use the GAN and feature selection (FS) to tackle the issue of class imbalance. The study assesses the efficacy of three different machine learning (ML) algorithms in predicting coronary artery disease (CAD). The UCI repository provided the Z-Alizadeh sani dataset for our analysis. A GAN was used to balance an imbalanced dataset. We used GAN to augment data and synthesise data to improve the models. Support Vector Machine (SVM) as feature selection tool was used to extract more effective attributes. We trained and tested CAD prediction using machine learning algorithms using 5-fold cross validation. We compared the machine-learning algorithm’s outcomes with and without GAN-based data enhancement and feature selection to evaluate its predicted accuracy. According to results GAN and feature selection improved accuracy, sensitivity, and specificity for all algorithms, suggesting that generated data and selected attributes can identify positive and negative cases.
Multi-Perspective Sentiment Analysis on Life Events with Sentiment Cause Identification

As social media plays an important role in people's interaction, extracting personal life events shared on social media becomes a hot research topic. However, identifying the sentiment of a life event is rarely discussed. In this work, we present a task of identifying the sentiment polarity of personal life events from social media posts. We construct the first human-annotated dataset called SentiLiveKB. Notably, the events described by a user may be her/his own experiences or the experiences of others. Analyzing sentiment polarity from the perspectives of the subject and the author is a widely-studied research topic. On the other hand, identifying the causes of sentiment polarity about the life event is essential for applications, such as personalized recommendation and memory recall assistance. To this end, we propose a sentiment cause-aware dual-channel graph con-volutional network (CauseDCGCN) with a graph augmentation mechanism to perform sentiment analysis on personal life events from multi-perspectives and identify the corresponding sentiment causes of the events. With two channels, the sentiment from the subject's and the author's perspectives can be identified simultaneously. In addition, the augmentation mechanism applied to both channels is beneficial for detecting the relations of sentiment words and the corresponding causes from different perspectives.
Learning and Identifying Human Behaviour Using Decision Trees

Recognition of activities in human-robot interaction typically assumes that humans follow a rational behaviour. However, how each person comes up with a plan depends on their own preferences, and usually, an optimal (rational) strategy is not always followed. This paper presents a method for learning human behaviours where the objective is to capture how the human selects the actions when solving a problem. To this end, we propose learning a Decision Tree that reflects a particular human behaviour using pairs state-action as training data where the state is represented by a set of automatically extracted features. A behaviour library is created with the learned decision trees, which are then used for identifying the behaviour followed by a person when executing a plan in a new situation. This identification is very useful for anticipating the person's needs and acting accordingly. Finally, we present an experimental evaluation with four different domains to test the validity of our approach.
Summary in Action: A Trade-Off Between Effectiveness and Efficiency in Multidimensional Relevance Estimation

In domain-specific search, effectiveness depends on considering multiple dimensions of relevance, beyond topicality, related to the domain involved. Estimating additional relevance dimensions can affect efficiency because the computation of relevance scores is time-consuming if performed on the full document, especially for query-dependent dimensions. Hence, this article introduces an approach for improving effectiveness in domain-specific search by considering multiple dimensions of relevance, namely topicality, correctness, and credibility. To address efficiency, we propose a re-ranking approach that estimates domain-specific relevance scores on document summaries, rather than full documents. We validate the proposed solution by performing the Ad-Hoc Retrieval task from the TREC 2020 Health Misinformation Track, a domain that crucially relies on the considered relevance dimensions. Our findings underscore the potential of our approach with respect to both effectiveness and efficiency.
RITA: A Phraseological Dataset of CEFR Assignments and Exams for Italian as a Second Language

This paper presents RITA (Resource for Italian Tests Assessment), a new dataset of academic exam texts written in Italian by second-language learners for obtaining the CEFR certification of proficiency level. In addition to the tests, RITA provides a variety of speech elements, annotations, and statistics, including phraseological units and their syntactic dependencies. The dataset consists of two corpora: one containing the task assignment and the other containing the texts elaborated by the learners in response to the assignment. This work describes the data collection and annotation process, structure, and statistics computed to facilitate the analysis of the phraseological text. RITA is a valuable resource for researchers and educators interested in Italian phraseology, language assessment, and natural language processing.
DeMaTO: An Ontology for Modeling Transactional Behavior in Decentralized Marketplaces

Blockchains are distributed ledger platforms that were originally envisioned for implementing digital assets and cryptocurrencies such as BITCOIN. Mainstream blockchain platforms such as Ethereumand Hyperledgersupport both "native" transactional behavior i.e., transferring cryptocurrency assets, as well as, other types of transactional behavior relevant to an emerging class of applications called decentralized applications or DApps. However, "non-native" transactional behavior is achieved in terms of user-defined programs, commonly referred to as smart contracts. Unfortunately, smart contracts have several known limitations, including the burden on both implementor and contract consumers, being prone to financially-costly errors, higher cost of execution, and overall difficulty with concurrency optimization. Further, there is a lack of standardization with respect to implementation. Consequently, this contributes to the difficulty of enabling interoperability across blockchains. In this paper, we propose an ontology DeMaTO for modeling transactional behavior on blockchains as a foundation for extending blockchain transaction primitives. As an application context, we focus on marketplace transactions because marketplaces are one of the most popular categories of DApps, i.e., decentralized marketplaces. This modeling of transactional behavior complements the modeling supported by existing ontologies that focus on the infrastructure layer. We illustrate how DeMaTO can be used in blockchain transaction modeling and its value with respect to blockchain queryability and transaction validation.
Service Clustering with Graph Embedding of Heterogeneous Networks

The API economy, characterized by the growing number of Web services, necessitates effective service discovery and management methods. A commonly used method is service clustering, categorizing Web services based on functionalities described in WSDL files. However, this approach is highly dependent on the description and susceptible to variations in naming conventions and languages employed by service providers. Therefore, we propose a novel method of service clustering that leverages service dependencies in composite services. We represent atomic services and their co-invocation relationships as a graph and apply graph embedding techniques for service clustering. Our method extends the existing node2vec technique by considering composite service contexts to embed features of atomic services more accurately. Experimental application of our method on a real-world Web service dataset demonstrated a 10% increase in clustering accuracy, as measured by the purity metric, compared to the traditional description-based method.
Aspect-Aware Multi-Criteria Recommendation Model with Aspect Representation Learning

Multi-criteria recommendation system refers to a recommendation system that takes multi-criteria or multiaspect ratings into consideration when learning user preferences. The core of the multi-criteria recommendation system is predicting the criteria ratings leading to overall rating estimation. The existing approaches for multi-criteria recommendation system rely on either 1) using collaborative filtering to predict the criteria ratings based on historical criteria ratings, or 2) extracting latent aspects and predicting ratings from the review. The latter approach ignores the explicit ratings and the inferred ratings from the review may not be accurate. In the former approach, historical criteria ratings can be scarce (users may only provide ratings to a few criteria), which could affect the prediction accuracy. In this work, we introduce a novel multi-criteria recommendation model that predicts the criteria ratings from the review and then uses an aggregation function to estimate the overall rating. Multi-criteria ratings are predicted by fine-tuning BERT (Bidirectional Encoder Representations from Transformer) with an added aspect representation layer. Finally, the overall ratings are computed using a deep neural network-based aggregation function. The performance of our proposed model is evaluated with three different datasets including Tripadvisor, RateBeer, and BeerAdvocate. Our method outperforms several competitive baselines.
Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom

We introduce a re-ranking model that augments the functionality of standard search engines to aid classroom search activities for children (ages 6–11). This model extends the known listwise learning-to-rank framework by balancing risk and reward. Doing so enables the model to prioritize Web resources of high educational alignment, appropriateness, and adequate readability by analyzing the URLs, snippets, and page titles of Web resources retrieved by a mainstream search engine. Experimental results demonstrate the value of considering multiple perspectives inherent to the classroom when designing algorithms that can better support children's information discovery.
Effective Entity Disambiguation in Low-Resource Languages: A Study of Icelandic

Entity disambiguation (ED) is integral to the task of entity linking (EL), the task of identifying named entities in text and linking them to their corresponding entries in a knowledge base (KB). In this paper, we present an effective and efficient ED system for Icelandic, using the Icelandic Wikipedia as a KB. We focus on disambiguation, the linking aspect of EL, assuming that an entity mention has already been located. We perform candidate generation using an alias table and Wikipedia search, and achieve candidate ranking through fine-grained entity typing and the use of an entity-aware Icelandic language model, IceLUKE. We study and compare the effects of different variations of candidate generation and candidate ranking, with the best approach reaching an accuracy of 95.2%. Our results highlight the importance of using an entity-aware language model in the candidate ranking step and show a minor improvement in using fine-grained entity typing to decrease the size of the candidate set before ranking.
Improving the Scalability of Collaborative Filtering Recommendation with Clustering Techniques

Nowadays, many online sales platforms make use of recommendation systems to retrieve the most suitable items for the user. Several recommendation techniques can be used, but in many cases, dealing with a huge number of users leads to a significant increase in costs, making them unaffordable to be used in commercial systems. Considering that serving the recommendation to users has to be as fast as possible to get the users' attention and, as far as we are aware, clustering has seen limited adoption in the realm of Deep Learning collaborative filtering techniques, we propose a novel clustering framework recommendation system that builds groups of users using a clustering technique based on users' genre preferences and predicts ratings using Machine Learning and Deep Learning techniques. The aim is to reduce the costs needed to generate the recommendation without decreasing the accuracy of the recommendation. The results will show that the clustering process not only improves the temporal and computational costs of the process but also decreases the error incurred by the recommendation system.