Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed aggregative games for Euler–Lagrange systems with system parameter uncertainties

In this paper, an aggregative game of Euler–Lagrange (EL) systems is studied, where the parameters of the EL systems are not available. To seek the Nash equilibrium of the game, a novel distributed Nash equilibrium seeking algorithm is proposed, where the system parameters are not used in the feedback control. Moreover, a Lyapunov function is constructed such that EL players are proved to exponentially converge to the Nash equilibrium of the game. Finally, an example in the electricity market is provided to illustrate our result.
Evaluating ELT Materials with Specific Reference to Colleges of Applied Sciences General Foundation Programme in Oman: Towards a Viable checklist

The main question and purpose of this study was to pinpoint a sustainable method on developing an evaluation checklist for teaching materials in the English language programmes. The answer of the main question is rejoined through three sub-questions about the sources for the checklist, identifying the design guidelines and offering a validation method for the developed checklist. Design-based research methodology was utilized through three main phases: analysis and exploration, design and construction and evaluation and reflection where the last phase comprised cycles of formative review of the developed checklist. The participants were purposefully sampled from the six Colleges of Applied Sciences in Oman and other higher education institutions. A report is written after each cycle of formative review (expert review, one –to-one, small group, and field testing) with the recommended changes which led to four revisions and redesigns of the checklist prototype. The results of this study were fourfold. First, a conceptual framework was designed that can be used to develop checklists for the evaluation of teaching materials in the English language programmes. Second, a verified checklist is developed that can be used as both an evaluation & selection instrument as well as a professional development tool. Third, formative review is perceived to be a powerful validation tool for reviewing the developed teaching materials evaluation checklists. Finally, guidelines on how to develop teaching materials evaluation checklists are yielded through the different phases of this study. The use of design-based research facilitated the design and the assessment of the checklist which indicates the necessity of such methodology in the complicated educational milieus with its focus on research, design and setting in unison. Besides the practical results, findings comprised adding new visualization of sources, content and use of teaching materials evaluation checklists. It is concluded that teaching materials evaluation is the main contributor to the students’ progression, the practitioners’ professional development and the success of the English language programmes as a whole.
Long-Term Performance of the Magmaris Drug-Eluting Bioresorbable Metallic Scaffold in All-Comers Patients’ Population

Background: The long-term efficacy and safety of bioresorbable vascular scaffolds (BVS) in real world clinical practice including Magmaris need to be elucidated to better understand performance of this new and evolutive technology. The aim of this study was to evaluate long-term performance of Magmaris, drug-eluting bioresorbable metallic scaffold, in all-comers patients’ population. Methods: We included in this prospective registry first 54 patients (54 ± 11 years; male 46) treated with Magmaris, with at least 30 months of follow-up. Diabetes mellitus and acute coronary syndrome were present in 33 (61%) and 30 (56%) of the patients, respectively. Patients were followed for device- and patient-oriented cardiac events during a median follow-up of 47 months (DOCE–cardiac death, target vessel myocardial infarction, and target lesion revascularization; POCE–all cause death, any myocardial infarction, any revascularization). Results: Event-free survivals for DOCE and POCE were 86.8% and 79.2%, respectively. The rate of DOCE was 7/54 (13%), including in total target vessel myocardial infarction in two patients (4%), target lesion revascularization in six patients (11%), and no cardiac deaths. The rate of POCE was 11/54 (21%), including in total any myocardial infarctions in 3 patients (6%), any revascularization in 11 patients (20%), and no deaths. Definite Magmaris thrombosis occurred in two patients (3.7%), and in-scaffold restenosis developed in five patients (9.3%). Variables associated with DOCE were implantation of ≥2 Magmaris BVS (HR: 5.4; 95%CI: 1.21–24.456; p = 0.027) and total length of Magmaris BVS ≥ 40 mm (HR: 6.4; 95%CI: 1.419–28.855; p = 0.016), whereas previous PCI was the only independent predictor of POCE (HR: 7.4; 95%CI: 2.216–24.613; p = 0.001). Conclusions: The results of the long-term clinical outcome following Magmaris implantation in patients with complex clinical and angiographic features were acceptable and promising. Patients with multi-BVS and longer multi-BVS in lesion implantation were associated with worse clinical outcome.
Rate of Change of Frequency Estimation for Power Systems Using Interpolated DFT and Kalman Filter

This paper presents a new method for estimating the rate of change of frequency (RoCoF) of voltage or current signals measured using instrument transformers. The method is demonstrably superior to currently available methods in the literature, in terms of estimation latency and estimation error. The estimation is performed in two steps. In the first step, the analog voltage or current signal obtained from an instrument transformer is statistically processed using interpolated discrete Fourier transform (IDFT) in order to obtain the means and variances of the signal parameters. These means and variances are then given as inputs to the second step, in which Kalman filtering (KF) is used to find the final RoCoF estimate. Accurate mathematical expressions for the means and variances of signal parameters have been derived and used in the second step, which is the main reason behind the superior performance of the method. The applicability of the method has been demonstrated on a benchmark power system model.
Diagnostic Accuracy of Loop-Mediated Isothermal Amplification Assay for Group B Streptococcus Detection in Recto-Vaginal Swab: Comparison with Polymerase Chain Reaction Test and Conventional Culture

A rapid method for obtaining group B streptococcus (GBS) screening results has been required in the obstetric field. We aimed to determine the diagnostic performance of the Loop-Mediated Isothermal Amplification (LAMP) assay is acceptable compared to the existing polymerase chain reaction (PCR) assay. The study involved 527 pregnant women aged 19 to 44 years. Rectovaginal swabs were collected between 35 and 37 weeks of gestation or prior to impending preterm births or term labor without GBS screening. We presented the diagnostic performance of the LAMP assay with a 95% confidence interval (CI) compared to the PCR and microbiological culture. In total, 115 (21.8%), 115 (21.8%) and 23 (4.4%) patients showed positive results using the LAMP, PCR assay and microbiological culture method, respectively. The LAMP assay showed 100% sensitivity (95% CI, 96.8–100.0), 100% specificity (95% CI, 99.1–100.0) and 100% diagnostic accuracy (95% CI, 99.3–100.0) with the reference being the PCR assay. Meanwhile, the LAMP assay showed 87.0% sensitivity (95% CI, 71.0–100.0), 81.2% specificity (95% CI, 77.6–84.7), and 81.4% diagnostic accuracy (95% CI, 78.0–84.8) with the microbiological culture as a reference. This study presented the LAMP assay as an acceptable method for GBS screening with a similar performance to the existing PCR method.
The Short-Term Opening of Cyclosporin A-Independent Palmitate/Sr2+-Induced Pore Can Underlie Ion Efflux in the Oscillatory Mode of Functioning of Rat Liver Mitochondria

Mitochondria are capable of synchronized oscillations in many variables, but the underlying mechanisms are still unclear. In this study, we demonstrated that rat liver mitochondria, when exposed to a pulse of Sr2+ ions in the presence of valinomycin (a potassium ionophore) and cyclosporin A (a specific inhibitor of the permeability transition pore complex) under hypotonia, showed prolonged oscillations in K+ and Sr2+ fluxes, membrane potential, pH, matrix volume, rates of oxygen consumption and H2O2 formation. The dynamic changes in the rate of H2O2 production were in a reciprocal relationship with the respiration rate and in a direct relationship with the mitochondrial membrane potential and other indicators studied. The pre-incubation of mitochondria with Ca2+(Sr2+)-dependent phospholipase A2 inhibitors considerably suppressed the accumulation of free fatty acids, including palmitic and stearic acids, and all spontaneous Sr2+-induced cyclic changes. These data suggest that the mechanism of ion efflux from mitochondria is related to the opening of short-living pores, which can be caused by the formation of complexes between Sr2+(Ca2+) and endogenous long-chain saturated fatty acids (mainly, palmitic acid) that accumulate due to the activation of phospholipase A2 by the ions. A possible role for transient palmitate/Ca2+(Sr2+)-induced pores in the maintenance of ion homeostasis and the prevention of calcium overload in mitochondria under pathophysiological conditions is discussed.
Extracting Age-Related Stereotypes from Social Media Texts

Age-related stereotypes are pervasive in our society, and yet have been under-studied in the NLP community. Here, we present a method for extracting age-related stereotypes from Twitter data, generating a corpus of 300,000 over-generalizations about four contemporary generations (baby boomers, generation X, millennials, and generation Z), as well as “old” and “young” people more generally. By employing word-association metrics, semi-supervised topic modelling, and density-based clustering, we uncover many common stereotypes as reported in the media and in the psychological literature, as well as some more novel findings. We also observe trends consistent with the existing literature, namely that definitions of “young” and “old” age appear to be context-dependent, stereotypes for different generations vary across different topics (e.g., work versus family life), and some age-based stereotypes are distinct from generational stereotypes. The method easily extends to other social group labels, and therefore can be used in future work to study stereotypes of different social categories. By better understanding how stereotypes are formed and spread, and by tracking emerging stereotypes, we hope to eventually develop mitigating measures against such biased statements.
HyperDet3D: Learning a Scene-conditioned 3D Object Detector

As shown in Table 1, we detail the per-category results on the SUN RGB-D [5] dataset. We observe the obvious improvements on category bed, bookshelf, nightstand and toilet which are more conditioned on its corresponding scenes (beddroom, washroom, etc.). The exception is the bathtub category with performance drop of -4.9%, which might be attributed to the extremely scarcity of its annotations in the dataset (the least one in the statistical distribution shown in the paper [5]).
Maya Archaeology Reconstruction: From Scan to Tour

There’s an ongoing tension between the preservation of cultural heritage sites and the need for usable land. As a result, there have been several efforts to preserve these sites using current advances in scanning, modeling, and visualization technologies - notably RGB-D cameras, scene reconstruction pipeline, and Virtual Reality (VR). However, these individual technologies have mostly been developed independently, and little effort has been dedicated to integrating them. This paper presents an evolution of an existing system for reconstructing and displaying cultural heritage sites in Virtual Reality environments. To achieve this, we develop a pipeline to (i) track the cameras using visual-inertial SLAM, (ii) perform a 3D reconstruction using registered depth and RGB data, (iii) facilitate loading and displaying the reconstruction in VR, and (iv) create virtual voice-guided tours. We discuss the details of this automated pipeline and demonstrate its ability to take an unskilled user from data capture to an immersive virtual tour without the need for 3rd party or command-line tools by designing a desktop GUI interface. We hope that this automated scanning and reconstruction pipeline will help the digitization and education of cultural heritage sites.
SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots

We conduct benchmark evaluation on the test sets of three publicly available datasets: SUIM [5], UFO-120 [7], and MUED [9]. SVAM-Net is jointly supervised on 3025 training instances of SUIM and UFO-120; their test sets contain an additional 110 and 120 instances, respectively. These datasets contain a diverse collection of natural underwater images with important object categories such as fish, coral reefs, humans, robots, wrecks/ruins, etc. Besides, MUED dataset contains 8600 images in 430 groups of conspicuous objects; although it includes a wide variety of complex backgrounds, the images lack diversity in terms of object categories and water-body types. Moreover, MUED provides bounding-box annotations only. Hence, to maintain consistency in our quantitative evaluation, we select 300 diverse groups and perform pixel-level annotations on those images.
Named Entity Recognition to Detect Criminal Texts on the Web

This paper presents a toolkit that applies named-entity extraction techniques to identify information related to criminal activity in texts from the Polish Internet. The methodological and technical assumptions were established following the requirements of our application users from the Border Guard. Due to the specificity of the users’ needs and the specificity of web texts, we used original methodologies related to the search for desired texts, the creation of domain lexicons, the annotation of the collected text resources, and the combination of rule-based and machine-learning techniques for extracting the information desired by the user. The performance of our tools has been evaluated on 6240 manually annotated text fragments collected from Internet sources. Evaluation results and user feedback show that our approach is feasible and has potential value for real-life applications in the daily work of border guards. Lexical lookup combined with hand-crafted rules and regular expressions, supported by text statistics, can make a decent specialized entity recognition system in the absence of large data sets required for training a good neural network.
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference

Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), has been one of the central tasks in Artificial Intelligence (AI) and Natural Language Processing (NLP). RTE between the two pieces of texts is a crucial problem, and it adds further challenges when involving two different languages, i.e., in the cross-lingual scenario. This paper proposes an effective transfer learning approach for cross-lingual NLI. We perform experiments on English-Hindi language pairs in the cross-lingual setting to find out that our novel loss formulation could enhance the performance of the baseline model by up to 2%. To assess the effectiveness of our method further, we perform additional experiments on every possible language pair using four European languages, namely French, German, Bulgarian, and Turkish, on top of XNLI dataset. Evaluation results yield up to 10% performance improvement over the respective baseline models, in some cases surpassing the state-of-the-art (SOTA). It is also to be noted that our proposed model has 110M parameters which is much lesser than the SOTA model having 220M parameters. Finally, we argue that our transfer learning-based loss objective is model agnostic and thus can be used with other deep learning-based architectures for cross-lingual NLI.
Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification

We present EpidBioBERT, a biosurveillance epidemiological document tagger for disease surveillance over PADI-Web system. Our model is trained on PADI-Web corpus which contains news articles on Animal Diseases Outbreak extracted from the web. We train a classifier to discriminate between relevant and irrelevant documents based on their epidemiological thematic feature content in preparation for further epidemiology information extraction. Our approach proposes a new way to perform epidemiological document classification by enriching epidemiological thematic features namely disease, host, location and date, which are used as inputs to our epidemiological document classifier. We adopt a pre-trained biomedical language model with a novel fine tuning approach that enriches these epidemiological thematic features. We find these thematic features rich enough to improve epidemiological document classification over a smaller data set than initially used in PADI-Web classifier. This improves the classifiers ability to avoid false positive alerts on disease surveillance systems. To further understand information encoded in EpidBioBERT, we experiment the impact of each epidemiology thematic feature on the classifier under ablation studies. We compare our biomedical pre-trained approach with a general language model based model finding that thematic feature embeddings pre-trained on general English documents are not rich enough for epidemiology classification task. Our model achieves an F1-score of 95.5% over an unseen test set, with an improvement of +5.5 points on F1-Score on the PADI-Web classifier with nearly half the training data set.
gaHealth: An English–Irish Bilingual Corpus of Health Data

Machine Translation is a mature technology for many high-resource language pairs. However in the context of low-resource languages, there is a paucity of parallel data datasets available for developing translation models. Furthermore, the development of datasets for low-resource languages often focuses on simply creating the largest possible dataset for generic translation. The benefits and development of smaller in-domain datasets can easily be overlooked. To assess the merits of using in-domain data, a dataset for the specific domain of health was developed for the low-resource English to Irish language pair. Our study outlines the process used in developing the corpus and empirically demonstrates the benefits of using an in-domain dataset for the health domain. In the context of translating health-related data, models developed using the gaHealth corpus demonstrated a maximum BLEU score improvement of 22.2 points (40%) when compared with top performing models from the LoResMT2021 Shared Task. Furthermore, we define linguistic guidelines for developing gaHealth, the first bilingual corpus of health data for the Irish language, which we hope will be of use to other creators of low-resource data sets. gaHealth is now freely available online and is ready to be explored for further research.
ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification

Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks. However, the increasing complexity of such models turns them into “black boxes”, creating uncertainty about their internal operation and decision-making. Tsetlin Machine (TM) employs human-interpretable conjunctive clauses in propositional logic to solve complex pattern recognition problems and has demonstrated competitive performance in various NLP tasks. In this paper, we propose ConvTextTM, a novel convolutional TM architecture for text classification. While legacy TM solutions treat the whole text as a corpus-specific set-of-words (SOW), ConvTextTM breaks down the text into a sequence of text fragments. The convolution over the text fragments opens up for local position-aware analysis. Further, ConvTextTM eliminates the dependency on a corpus-specific vocabulary. Instead, it employs a generic SOW formed by the tokenization scheme of the Bidirectional Encoder Representations from Transformers (BERT). The convolution binds together the tokens, allowing ConvTextTM to address the out-of-vocabulary problem as well as spelling errors. We investigate the local explainability of our proposed method using clause-based features. Extensive experiments are conducted on seven datasets, to demonstrate that the accuracy of ConvTextTM is either superior or comparable to state-of-the-art baselines.
The Bulgarian Event Corpus: Overview and Initial NER Experiments

The paper describes the Bulgarian Event Corpus (BEC). The annotation scheme is based on CIDOC-CRM ontology and on the English Framenet, adjusted for our task. It includes two main layers: named entities and events with their roles. The corpus is multi-domain and mainly oriented towards Social Sciences and Humanities (SSH). It will be used for: extracting knowledge and making it available through the Bulgaria-centric Knowledge Graph; further developing an annotation scheme that handles multiple domains in SSH; training automatic modules for the most important knowledge-based tasks, such as domain-specific and nested NER, NEL, event detection and profiling. Initial experiments were conducted on standard NER task due to complexity of the dataset and the rich NE annotation scheme. The results are promising with respect to some labels and give insights on handling better other ones. These experiments serve also as error detection modules that would help us in scheme re-design. They are a basis for further and more complex tasks, such as nested NER, NEL and event detection.
Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network

Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website.