Reviewerly/exHarmony huggingface dataset:
Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings

Slope instabilities caused by heavy rainfall, man-made activity or earthquakes can be characterised by seismic events. To minimise mortality and infrastructure damage, a good understanding of seismic signal properties characterising slope failures is therefore crucial to classify seismic events recorded from continuous recordings effectively. However, there are limited contributions towards understanding the importance of feature selection for the classification of seismic signals from continuous noisy recordings from multiple channels/sensors. This paper first proposes a novel multi-channel event-detection scheme based on Neyman-Pearson lemma and Multi-channel Coherency Migration (MCM) on the stacked signal across multi-channels. Furthermore, this paper adapts graph-based feature weight optimisation as feature selection, exploiting the signal's physical characteristics, to improve signal classification. Specifically, we alternatively optimise the feature weight and classification label with graph smoothness and semidefinite programming (SDP). Experimental results show that with expert interpretation, compared with the conventional short-time average/long-time average (STA/LTA) detection approach, our detection method identified 614 more seismic events in five days. Furthermore, feature selection, especially via graph-based feature weight optimisation, provides more focused feature sets with less than half of the original number of features, at the same time enhancing the classification performance; for example, with feature selection, the Graph Laplacian Regularisation classifier (GLR) raised the rockfall and slide quake sensitivities to 92% and 88% from 89% and 85%, respectively.
Analysis on Early and Late Operation Results and Risk Factors of Elderly Acute Type-A Aortic Dissection.

To explore the early and late operation results and risk factors of elderly acute type-A aortic dissection.The regression analysis was conducted on the data of patients diagnosed with acute type-A aortic dissection in our hospital from January 2018 to January 2020, and a total of 98 patients aged over 70 years were included in the study. The patients were listed into the early operation group (a total of 51 patients operated within 3 days after admission) and the late operation group (a total of 47 patients operated within 10 days after admission) according to the time of operation. The operation results, postoperative complications and death were compared between the two groups, and the prognosis risk factors were analyzed through Logistic multi-factor regression.The operative time, aortic obstruction time and extracorporeal circulation time of the late operation group were all higher than those in the early operation group (p <0.05). The postoperative complications and mortality in the late operation group (12.77%) were higher than those in the early operation group (3.92%) (p < 0.05). The Logistic multi-factor regression showed that late operation (p=0.005, OR=4.213, 95% CI=1.567~11.201), postoperative acute renal insufficiency (p=0.028, OR=3.281, 95% CI=0.937~10.283), and postoperative pulmonary infection (p=0.033, OR=1.421, 95% CI=0.417~8.329) were risk factors affecting postoperative mortality (p <0.05).The early operation can effectively reduce the postoperative complications of elderly acute type-A aortic dissection, so early operation should be performed according to the conditions of patients and hospital.
Pembelajaran Daring Berbasis Pendekatan Studi Kasus di Era Digital

Today's digital age learning is not teacher-centered, it will be entirely student-centered. This is because all aspects of learning use a lot of supporting media such as learning videos, PowerPoint, and so on. Planning needs to be done in terms of the readiness of teachers and students to utilize information and communication technology, such as the internet from laptops and computers as well as applications via smartphones. The case study approach as an approach to online learning is an alternative way to provide ways to make learning more innovative and fun. In essence, the case study in question is seeking data information aimed at solving a particular problem or case in an attempt to provide a precise and accurate solution by considering the data obtained from the field. Therefore, the case study approach in online learning is expected to be a new breakthrough in distance learning which has limitations in monitoring students intensely because they can only see their body movements. The research method used is a qualitative research method with observation and interview techniques which are the main data collected during the research. Data analysis used by researchers is data reduction, data presentation, and verification. This study aims to provide insight into the educational component in managing ways in the digital era by using targeted technology. Apart from that, educators and students must be able to adapt to today's more digitalized environment.
Automation in the Laboratory and Liquid-Based Cytology

Automation in the laboratory is now increasingly used in modern laboratories. The automation includes receiving samples with barcoding, processing, embedding, microtomy and staining of the slides. The digitisation of the slides by whole slide imaging is also part of the automation. An artificial neural network or another expert system may help in the automated interpretation of the slides. The liquid-based cytology (LBC) is also part of automation that can process the fluid specimen. Presently two widely used techniques of LBC are available in the market: Thin Prep and SurePath. The present chapter discusses the various aspects of automation along with the principle of the two types of automated screening devices (BD Focal Point GS Imaging System and HOLOGIC ThinPrep Imaging System) in cervical cytology.
Experimenting Encoder-Decoder Architecture for Visual Image Captioning

Image captioning is an essential task in artificial intelligence that predicts the description of a given input image. In recent years, both computer vision and natural language processing witnessed huge advancement making it capable to extract high-level semantic information and process it to structure new sentences based on that. A myriad of research has been done discussing the use of deep learning in image caption tasks. This paper explores the various combination of CNN and RNN modules in Encoder-decoder architecture to find the best image caption generator. The different models were compared using BLEU and CIDEr metrics. The proposed model with fine-tuned parameters showed a BLEU as high as 67.2, 59.8, 53, 44.7, and CIDEr score was 46 with Restnet50v2 model as an encoder and GRU as a decoder. After a little model hyperparameter tuning on Batch size and learning rate, an improvement of 15% and 12.5% was achieved in CIDEr and BLEU-4score.
Leveraging the power of Natural Language Processing for Financial Intelligence System

In this study, we aim to exploit natural language processing (NLP) techniques to develop a financial intelligence system that understands and analyzes online news channels on the basis of companies filtered by specific keywords. The system enables us to immediately notify potential opportunities and threats that may arise for the relevant company portfolio and to take the necessary actions. The architecture can enrich portfolio management, increase a company's credit profitability, offer finance-specific functions and use time and resources effectively in collecting and evaluating information through various metrics. In this direction, we designated an infrastructure and addressed a wide variety of NLP issues to execute the system modules. Various NLP tasks such as text classification, text regression, impact measurement, and Named-Entity Recognition have been successfully solved with the latest techniques. Not only traditional machine learning techniques but also modern deep learning architectures such as RNN and Transformers have been utilized to solve financial tasks.
Anomaly Detection Using Smart Shirt and Machine Learning: A Systematic Review

In recent years, the popularity and use of Artificial Intelligence (AI) and significant investments in the Internet of Medical Things (IoMT) will be common for products such as smart socks, smart pants, and smart shirts. These products are known as Smart Textile or E-textile, which can monitor and collect signals our body emits. These signals allow it to extract anomalous components using Machine Learning (ML) techniques that play an essential role in this area. This study presents a Systematic Literature Review (SLR) on Anomaly Detection using ML techniques in Smart Shirt. The objectives of the SLR are: (i) identify machine learning techniques for anomaly detection in the smart shirt; (ii) identify the datasets used to train the ML algorithm; (iii) identify smart shirts or devices for acquiring vital signs; (iv) identify the performance metrics for evaluating the ML model; (v) types of ML being applied. The SLR selected eleven primary studies published between January/2017-May/2022. The results showed that six anomalies were identified, with the Fall anomaly being the most cited. The Support Vector Machines (SVM) algorithm is the most used. Most of the primary studies used public or private datasets. The Hexoskin smart shirt was most cited. The most used metric performance was Accuracy. Almost all primary studies presented a result above 90%, and all primary studies used the Supervisioned type of ML.
Confidence-Based Algorithm Parameter Tuning with Dynamic Resampling

This work presents an algorithm for tuning the parameters of stochastic search heuristics, the Robust Parameter Searcher (RPS). RPS is based on the Nelder-Mead Simplex algorithm and on confidence-based comparison operators. Whilst the latter algorithm is known for its robustness under noise in objective function evaluation, the confidence-based comparison endows the tuning algorithm with additional resilience against the intrinsic stochasticity which exists in the evaluation of performance of stochastic search heuristics. The proposed methodology was used to tune a Differential Evolution strategy for optimizing real-valued functions, with a limited function evaluation budget. In the computational experiments, RPS performed significantly better than other well-known tuning strategies from the literature.
Creation of a Legal Domain Corpus for the Belarusian Module in NooJ: Texts, Dictionaries, Grammars

This paper represents the procedure of creating a legal domain corpus in NooJ. It illustrates a primary analysis of the corpus including automatic text processing, emphasizing its main linguistic peculiarities, and analyzing prosodic characteristics of legal texts which is necessary for syntagmatic delimitation. It also depicts the compilation composition of three dictionaries in NooJ format: general, legal, and Belarusian-English-Russian dictionaries.
The Architecture of SANTI-Morf’s Guesser Module

SANTI-morf is a new morphological annotation system for Indonesian, implemented using Nooj [1, 2]. SANTI-morf is designed using multi-module pipeline architecture. The modules are the Annotator, the Improver, the Disambiguator, and the Guesser. The Guesser, as its name suggests, provides best guesses for words the Annotator fails to analyze. Due to the complexities of Indonesian morphology, multiple layers of rules are created to guess the morphological structures of unknown polymorphemic and monomorphemic words. These rules are incorporated into five morphological grammars, which are applied in a pipeline based on their priorities. In each grammar, there are two layers of rules. The first layer rules are prioritized, thus ending with a +UNAMB operator. The second layer rules only apply when the first layer rules fail to find any match. Thus, the rules are constructed without a +UNAMB operator. Reflecting on the complexity of this experiment, I therefore suggest an alternative to set priorities, whose method I simulate in this paper. I argue that using the proposed alternative, NooJ users can organize rules with multiple priorities in just one grammar file.
Analyzing Political Discourse: Finding the Frames for Guilt and Responsibility

This paper deals with the analysis of political discourse in Croatia, more precisely, it aims to determine how dissatisfaction is expressed with the attitudes represented by political rivals. We focus on the detection of linguistic means used to show disagreement with decisions or actions taken by parties or individuals considered political and/or ideological opponents. We are particularly interested in the means used by speakers to indicate that someone has failed to do something that is under his/her responsibility and is, therefore, guilty of this omission. In other words, we want to determine how the concept of responsibility is lexicalized, how it is signaled that there is a failure in someone’s responsibility, and, finally, that someone is therefore to be blamed for that omission or even transgression. For this purpose, we use a large corpus of texts, with over 127 million tokens, consisting of transcripts of plenary debates from the Croatian Parliament since 2003. We use NooJ for the construction of a set of rules that aim to detect the usage of the Croatian lexemes odgovornost [responsibility] and krivnja [guilt] in this corpus. Since Croatian is rich in terms of word formation, a set of rules is designed to capture the usage of derived words morphologically related to these nouns. In data analysis, we take into account the political orientation of MPs, i.e. their affiliation with left, right, or centrist parties, the usage of various linguistic constructions/frames related to responsibility and guilt as well as periods in which they were used.
Interactive Generalized Dirichlet Mixture Allocation Model

A lot of efforts have been put in recent times for research in the field of natural language processing. Extracting topics is undoubtedly one of the most important tasks in this area of research. Latent Dirichlet allocation (LDA) is a widely used model that can perform this task in an unsupervised manner efficiently. It has been proved recently that using priors other than Dirichlet can be advantageous in extracting better quality topics from the data. Hence, in our paper we introduce the interactive latent generalized Dirichlet allocation model to extract topics. The model infers better topics using little information provided by the users through interactive learning. We use a variational algorithm for efficient inference. The model is validated against text datasets based on extracting topics related to news categories and types of emotions to test its efficiency.
VinciDecoder: Automatically Interpreting Provenance Graphs into Textual Forensic Reports with Application to OpenStack

The operational complexity and dynamicity of clouds highlight the importance of automated solutions for explaining the root cause of security incidents. Most existing works rely on human analysts to interpret provenance graphs for root causes of security incidents. However, navigating and understanding a large and complex cloud-scale provenance graph can be very challenging for human analysts. Without such an understanding, cloud providers cannot effectively address the underlying security issues causing the incidents, such as vulnerabilities or misconfigurations. In this paper, we propose VinciDecoder, an automated approach for generating natural language forensic reports based on provenance graphs. Our main observation is that the way nodes and edges compose a path in provenance graphs is similar to how words compose a sentence in natural languages. Therefore, VinciDecoder leverages a novel combination of provenance analysis, natural language translation, and machine-learning techniques to generate forensic reports. We implement VinciDecoder on an OpenStack cloud testbed, and evaluate its performance based on real-world attacks. Our user study and experimental results demonstrate the effectiveness of our approach in generating high-quality reports (e.g., up to 0.68 BLEU score for precision).
Structural Composite Feature Triangulation for Visual Object Search

This paper proposes a new Structural Composite Feature Triangulation (SCFT) framework for Visual Object Search. Visual Object Search methods often use feature matching approaches which adopt Bag-of-Words (BoW) image representation and employ Geometric Verification (GV) to check the geometric consistency of the putative matching feature pairs between the query and the database image. However, these approaches are unable to handle multiple object instances in an image and they do not utilize the joint local structural information of the matching feature pairs to perform visual search. In view of this, this paper proposes the SCFT method to address these shortcomings. Its key contributions center on the formulation and proposal of a Structural Composite Feature Triangulation method to mine and detect composite structures from Delaunay Triangulation. The composite structures perform detection and localization of multiple object instances within an image. Experimental results on the Belgalogos dataset [1] and the Traffic sign dataset [2] demonstrate that the proposed method can detect multiple object instances, and the top retrieved images show that the proposed method is able to effectively retrieve the relevant images. The results show that SCFT achieves around 4% increase in mean Average Precision (mAP) when compared with that obtained by state-of the-art methods for the Belgalogos dataset.
Graph Adversarial Network with Bottleneck Adapter Tuning for Sign Language Production

Sign language production (SLP) task aims to convert spoken language sentences into corresponding sign language expressions such as sign pose sequences, animations or videos. Previous deep learning based SLP solutions mostly utilize encoder-decoder structure and lack the ability to obtain the contextual representation of sentences, resulting poor performance on unseen data. In order to tackle the problem, we propose a method called graph adversarial network with bottleneck adapter tuning (BatGANet). Specifically, we present a generalizable and parameter-efficient bottleneck adapter structure, to fine-tune a pre-trained language model and extract the contextual representation of spoken language text. Furthermore, we develop an adversarial training scheme based on spatial-temporal graph convolutional networks, which enables to captures the motion patterns of sign poses, and facilitates natural and realistic sign motion generation.
Real-Time Light Field Path Tracing

Light field rendering and displays are emerging technologies that produce more immersive visual 3D experiences than the conventional stereoscopic 3D technologies, as well as provide a more comfortable virtual or augmented reality (VR/AR) experience by mitigating the vergence–accommodation conflict. Path tracing photorealistic synthetic light fields in real time is extremely challenging, since it involves rendering a large amount of viewpoints for each frame. However, these viewpoints are often spatially very close to each other, especially in light field AR glasses or other near-eye light field displays. In this paper, we propose a practical real-time light field path tracing pipeline and demonstrate it by rendering a $$6\times 6$$ grid of 720p viewpoints at 18 frames per second on a single GPU, through utilizing denoising filters and spatiotemporal sample reprojection. In addition, we discuss how the pipeline can be scaled to yield higher-quality results if more parallel computing resources are available. We also show that our approach can be used to simultaneously serve multiple clients with varying light field grid sizes, with the quality remaining constant across clients.
Facial Landmarks Based Region-Level Data Augmentation for Gaze Estimation

Data augmentation (DA) is an effective technique and is widely used in various deep learning tasks (including gaze estimation). Appearance-based gaze estimation aims to directly learn a mapping from face images to gaze directions. Since subtle changes in eye regions are important for gaze estimation, direct data augmentation on faces is likely to damage key features in the eye region. We propose a facial landmarks based region-level data augmentation method. The method use facial landmarks to divide the face into eye regions and non-eye regions. Then we generate face images under different data augmentation methods by augmenting non-eye regions. We preserve the key features of eye regions. And the features of non-eye regions are augmented. We conduct experiments on the largest 2D dataset – GazeCapture. Comprehensive experiments show that the proposed method achieves promising results. Wide range of gaze estimation based application will be aspired from this work.
MaroBERTa: Multilabel Classification Language Model for Darija Newspaper

A large amount of valuable digital text, audio and video data is available on the web. Thus, a large application of machine learning based on Natural Language Processing (NLP) has taken advantage of these opportunities. Transformers, especially Bidirectional Encoder Representation from Transformers (BERT) based models, have become the state-of-the-art for downstream NLP tasks. Non-normalized languages such as Moroccan Arabic, also known as Darija, increases the complexity of natural language processing. Furthermore, Text written in Darija does not have a standard spelling, and there is a lack of resources, especially for multilabel classification. In this paper, we introduced a multilabel classification model for Moroccan Arabic (Darija) newspapers. Firstly, we created a dataset from 400.000 collected newspaper articles with their titles, written in darija and pre-trained our model: MaroBERTa. Secondly, we implemented a crowd-sourcing platform to help create a novel corpus called Darija Multilabel Dataset for News classification (DMDNews). This dataset contains 28 different classes representing the most frequent topics in Moroccan newspapers. Finally, we fine-tune MaroBERTa and two multilingual models (AraBERT and CAMelBert) for the multilabel classification task using the DMDNews. Experiments shows that our dedicated pretrained Darija model -MaroBERTa- outperforms the existing multilingual models despite of the large amount of data they have been trained on.
What does Data Visualization Reveal about Children’s Activities in RoboTutor?

We are in the era of promising mobile education. However, data analysis based on children's activities is still limited. This article used rural Tanzanian children’s activity log data in site 131 from November 2018 to January 2019 provided by the RoboTutor team and applied the method of Descriptive Data Analysis to roughly analyze children’s activity through data visualization. First, children may be more interested in story and literacy than math activities; second, children generally spend more time on tasks they cannot fully complete; finally, all children spend more than 60% of their time completing activities, usually in the morning or afternoon. Using this result can provide a more intuitive understanding of children’s activity patterns, which can be helpful for researchers to measure children's activity engagement.