Reviewerly/exHarmony huggingface dataset:
Enrichment of OntoSenseNet: Adding a Sense-annotated Telugu Lexicon

The paper describes the enrichment of OntoSenseNet- a verb-centric lexical resource for Indian Languages. This resource contains a newly developed Telugu-Telugu dictionary. It is important because native speakers can better annotate the senses when both the word and its meaning are in Telugu. Hence efforts are made to develop a soft copy of Telugu dictionary. Our resource also has manually annotated gold standard corpus consisting 8483 verbs, 253 adverbs and 1673 adjectives. Annotations are done by native speakers according to defined annotation guidelines. In this paper, we provide an overview of the annotation procedure and present the validation of our resource through inter-annotator agreement. Concepts of sense-class and sense-type are discussed. Additionally, we discuss the potential of lexical sense-annotated corpora in improving word sense disambiguation (WSD) tasks. Telugu WordNet is crowd-sourced for annotation of individual words in synsets and is compared with the developed sense-annotated lexicon (OntoSenseNet) to examine the improvement. Also, we present a special categorization (spatio-temporal classification) of adjectives.
Finding Word Sense Embeddings of Known Meaning

Word sense embeddings are vector representations of polysemous words – words with multiple meanings. These induced sense embeddings, however, do not necessarily correspond to any dictionary senses of the word. To overcome this, we propose a method to find new sense embeddings with known meaning. We term this method refitting, as the new embedding is fitted to model the meaning of a target word in an example sentence. The new lexically refitted embeddings are learnt using the probabilities of the existing induced sense embeddings, as well as their vector values. Our contributions are threefold: (1) The refitting method to find the new sense embeddings; (2) a novel smoothing technique, for use with the refitting method; and (3) a new similarity measure for words in context, defined by using the refitted sense embeddings. We show how our techniques improve the performance of the Adaptive Skip-Gram sense embeddings for word similarly evaluation; and how they allow the embeddings to be used for lexical word sense disambiguation.
Slice-Mask Based 3D Cardiac Shape Reconstruction from CT Volume

An accurate 3D ventricular model is essential for diagnosing and analyzing cardiovascular disease. It is challenging to obtain accurate patient-specific models on scarce data via widely accepted deep-learning methods. To fully use the characteristics of medical volume-based images, we present a slice-mask representation to better regress the parameters of the 3D model. A data synthesis strategy is proposed to alleviate the lack of training data by sampling in the constructed statistical shape model space and obtaining the corresponding slice-masks. We train the end-to-end structure by combining the segmentation and parametric regression modules. Furthermore, we establish a larger left ventricular CT dataset than before, which fills the gap in relevant data of the healthy population. Our method is evaluated on both synthetic data and real cardiac scans. Experiments demonstrate that our method can achieve advanced results in shape reconstruction and segmentation tasks. Code is publicly available at https://github.com/yuan-xiaohan/Slice-mask-based-3D-Cardiac-Shape-Reconstruction .
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-ensemble for Active Learning

Modern deep learning has achieved great success in various fields. However, it requires the labeling of huge amounts of data, which is expensive and labor-intensive. Active learning (AL), which identifies the most informative samples to be labeled, is becoming increasingly important to maximize the efficiency of the training process. The existing AL methods mostly use only a single final fixed model for acquiring the samples to be labeled. This strategy may not be good enough in that the structural uncertainty of a model for given training data is not considered to acquire the samples. In this study, we propose a novel acquisition criterion based on temporal self-ensemble generated by conventional stochastic gradient descent (SGD) optimization. These self-ensemble models are obtained by capturing the intermediate network weights obtained through SGD iterations. Our acquisition function relies on a consistency measure between the student and teacher models. The student models are given a fixed number of temporal self-ensemble models, and the teacher model is constructed by averaging the weights of the student models. Using the proposed acquisition criterion, we present an AL algorithm, namely student-teacher consistency-based AL (ST-CoNAL). Experiments conducted for image classification tasks on CIFAR-10, CIFAR-100, Caltech-256, and Tiny ImageNet datasets demonstrate that the proposed ST-CoNAL achieves significantly better performance than the existing acquisition methods. Furthermore, extensive experiments show the robustness and effectiveness of our methods.
Feature Selection and Extreme Learning Machine Tuning by Hybrid Sand Cat Optimization Algorithm for Diabetes Classification

It is vitally important to establish a system that is able to provide an early detection of diabetes as a stealth disease of modern era. In order to achieve this goal, this manuscript proposes a novel framework for feature selection and extreme learning machine (ELM) hyper-parameter optimization applied to diabetes diagnostics. Feature selection and hyper-parameter optimization are two of the most important challenges in the domain of machine learning and they both belong to the group of NP-hard challenges. An upgraded version of the newly suggested sand cat swarm optimization (SCSO) is developed to address these issues, and adapted for ELM hyper-parameter tuning and feature selection. A preliminary set of biases and weights are established using the proposed approach, as well as the optimal (sub-optimal) no. of neurons for the ELM hidden layer, as well as to establish initial set of biases and weights. Furthermore, each swarm individual also tries to select the most relevant features for classification tasks against a widely utilized diabetes dataset. The performance of proposed methods was compared to other well-known state-of-the-art swarm intelligence algorithms in terms of accuracy, precision, recall and f1 score. Experimental findings demonstrate that the improved SCSO is more efficient than other algorithms in addressing both challenges.
COLLIDER: A Robust Training Framework for Backdoor Data

Deep neural network (DNN) classifiers are vulnerable to backdoor attacks. An adversary poisons some of the training data in such attacks by installing a trigger. The goal is to make the trained DNN output the attacker’s desired class whenever the trigger is activated while performing as usual for clean data. Various approaches have recently been proposed to detect malicious backdoored DNNs. However, a robust, end-to-end training approach, like adversarial training, is yet to be discovered for backdoor poisoned data. In this paper, we take the first step toward such methods by developing a robust training framework, Collider, that selects the most prominent samples by exploiting the underlying geometric structures of the data. Specifically, we effectively filter out candidate poisoned data at each training epoch by solving a geometrical coreset selection objective. We first argue how clean data samples exhibit (1) gradients similar to the clean majority of data and (2) low local intrinsic dimensionality (LID). Based on these criteria, we define a novel coreset selection objective to find such samples, which are used for training a DNN. We show the effectiveness of the proposed method for robust training of DNNs on various poisoned datasets, reducing the backdoor success rate significantly.
An Examination of The Protagonist in Pidi Baiq's Novel Dilan: Dia Adalah Dilanku 1991

Novels are fictional stories that are created by human reflection on the environment and experiences. Within a novel, the author uses their aesthetic experience and creativity to arrange storylines and characters. Examining the characters within a contemporary novel can be intriguing as the main character may reflect the author's idealism and response to their own life. With this in mind, the researcher analyzes the novel "Dilan dia adalah Dilanku 1991" by Pidi Baiq, which has gained popularity among millennial youth. The novel follows the story of high school sweethearts, Dilan and Milea. The researcher focuses on the main characters, Dilan and Milea, and uses a descriptive qualitative method and content analysis to identify certain characteristics related to their characterizations. The study found that Dilan was a romantic, brave, stubborn yet intelligent male figure, while Milea was a loyal, loving, and firm woman.
Optimized Communication in Sharded Blockchain Networks

Sharding has established itself as a vital technology for significantly improving all three aspects of the blockchain trilemma (security, decentralization and scalability). In a sharded blockchain the network of nodes responsible for maintaining the blockchain is divided into separate groups called shards such that each shard can process transactions in parallel. In this paper we explore and improve the communication process that happens between nodes in a shard by making use of multi-signatures. Furthermore, a realistic simulation of the communication process between nodes in a shard using multi-signatures is implemented. This simulation is used to analyze and evaluate the performance of the different sharded network communication implementations.
Estimating the rupture directivity and source parameters of moderate to small earthquakes using the second seismic moment

Rupture processes of large earthquakes have been studied by seismic&amp;#160;waveform analysis&amp;#160;and directivity effects have also been observed in moderate and small earthquakes.&amp;#160;This effect leads to azimuthal and spectral variations in ground motion that can be used to estimate the fault plane orientation or a predominant rupture propagation direction in a particular region or during a seismic sequence. For moderate-to-strong events, directivity at low frequencies can result in potentially destructive pulses with large ground motions, while at high frequencies and for small-to-moderate events, the most pronounced effect is the shift in corner frequencies that results in high-frequency energy arriving in short time intervals.&amp;#160;It is therefore of the utmost importance to estimate the directivity effects in engineering applications and seismological studies of earthquake sources. While some methods appear to work well for high magnitude earthquakes, determining directivity and source parameters for small to moderate magnitude earthquakes remains a challenge.&amp;#160;One of the most common methods to estimate the directivity from moderate to small earthquakes relies on measuring the duration of the source pulse (the apparent source time function) at each location and then modeling it using a line source. Some approaches rely on the deconvolution of waveforms by an empirical Green&amp;#8217;s function (eGf), to overcome the problems associated with the presence of path and site effects.A promising approach for estimating the rupture directivity effect and associated source properties is based on the calculation of the second seismic moments. In this study we apply the method based on the calculation of the second seismic moments to estimate the rupture process and source parameters to study a Mw 4.7 earthquake that occurred in central Italy during the 2016 - 2017 seismic sequence recorded by the RAN (Rete Accelerometrica Nazionale) and RSN (Rete Sismica Nazionale) italian networks.We first used synthetic apparent source time functions calculated from a geometric source model obtained from a real event to test the robustness of the method. Then, we applied the second-seismic moment method and the approach based on high-frequency S wave amplitude variations versus source azimuths analysis with an empirical Green's function deconvolution approach and compare the results.&amp;#160;
Environmental data value stream as traceable linked data - Iliad Digital Twin of the Ocean case

In the distributed heterogeneous environmental data ecosystems, the number of data sources, volume and variances of derivatives, purposes, formats, and replicas are increasingly growing. In theory, this can enrich the information system as a whole, enabling new data value to be revealed via the combination and fusion of several data sources and data types, searching for further relevant information hidden behind the variety of expressions, formats, replicas, and unknown reliability. It is now visible how complex data alignment is, and even more, it is not always justified due to capacity and business issues. One of the challenging, but also most rewarding approaches is semantic alignment, which promises to fill the information gap of data discovery and joins. To formalise one, an inevitable enabler is an aligned, linked, and machine readable data model enabling the specification of relations between data elements generated information. The Iliad - digital twins of the ocean are cases of this kind, where in-situ data and citizen science observations are mixed with multidimensional environmental data to enable data science and what-if models implementation and to be integrated into even broader ecosystems like the European Digital Twin Ocean (EDITO) and European Data Spaces. An Ocean Information Model (OIM) that will enable traversals and profiles is the semantic backbone of the ecosystem. Defined as the multi-level ontology, it will explain data using well known generic (Darwin Core, WoT), spatio-temporal (SOSA/SSN, OGC Geo, W3C Time, QUDT, W3C RDF Data Cube, WoT) and domain (WORMS, AGROVOC) ontologies. Machine readability and unambiguity allow for both automated validation and some translations.On the other hand, efficient use of this requires yet another skill in data management and development besides GIS, ICT and domain expertise. In addition, as the semantics used in the data and metadata have not yet been stabilised on the implementation level, it introduces a few more flexibilities of data expression. Following the GEO data sharing and data management principles along with FAIR, CARE and TRUST, the environmental data is prepared for harmonisation. Furthermore, to ease the entry and to harmonise conventions, the authors introduce a multi-touchpoint data value chain API suite with an aligned approach to semantically enrich, entail and validate data sets such as observations streams in JSON or JSON-LD based on OIM, through storage and scientific data in NetCDF to exposing this semantically aligned data via the newly endorsed and already successful OGC Environmental Data Retrieval API. The practical approach is supported by a ready-to-use toolbox of components that presents portable tools to build and validate multi-source geospatial data integrations keeping track of the information added during mesh-up and predictions and what-if implementations.
Preference Study Environment of Group Study Room in University Library (A Case Study at Tishk International University Library)

This paper made an investigation at TIU library to indicate to where the group study rooms at the library provide a preference study environment for students, to identify students' satisfaction with current group study rooms situation and their preference for better group study rooms, and to identify factors in preference study environment in terms of size of rooms and furniture.The methodology was descriptive field survey, a questionnaire survey consisted of 20 students enrolled in the TIU to assess their satisfaction with current group study room, and a face to face interview with librarians to identify essential factors in preference study environment.Outcomes of this study provided suggestions in improving group study rooms at TIU library.The results showed that current size and number of group study rooms were not sufficient and students were using other spaces outside library for collaborative activities.The results also showed that although the group study rooms were crowded always, the students generally were not satisfied with current interior environment of group study rooms and they gave priority to number of rooms they liked most to see improved.Interview results showed that despite the reality that students' desire for collaborative activities is growing continually, current numbers of group study rooms are not sufficient, current old furniture and small size of rooms are not providing a preference study environment for students at TIU library.
Building a New IPv8 Bootstrapper and Network Discovery Strategy for Trusted Chain Identities

The Self-Sovereign Identity (SSI) strategy provides user identities that the SSI system cannot serve its purpose without considering user privacy at the network level. The existing systems work to the best of its ability but lack in allowing for a smooth transition of network input/output. This paper addresses the above issue by optimising the Network Discovery Strategy and creating a new Bootstrapper configuration.
Building a Recommender System Using Collaborative Filtering Algorithms and Analyzing its Performance

Recommender Systems (RS) systems help users to select items and recommend useful items to target customers who are interested in them, such as movies, music, books, and jokes. Traditional recommendation algorithms are primarily concerned with improving performance accuracy; as a result, these algorithms prefer to promote only popular products. Variability is also an important inaccurate number of personalized recommendations that suggest unfamiliar or different things. Multi objective development strategies, which magnify these contradictory measures simultaneously, are used to measure accuracy and variability. Existing algorithms have an important feature because they are not flexible enough to control competing targets. We suggest creating a recommendation system based on shared filtering. Instead of finding out the preferences and preferences of users openly, we can find out by publicly analyzing historical and real-time data. This is done through a process called matrix factorization. Matrix factorization algorithms work by decomposing the interactive matrix of the user object into a product of two rectangular matrices with a minimum size. This type of recommendation has the added advantage of finding invisible and unmeasured relationships that are not possible with standard content-based filters.
PILN: A posterior information learning network for blind reconstruction of lung CT images

Background and objective:Computer tomography (CT) imaging technology has played significant roles in the diagnosis and treatment of various lung diseases, but the degradations in CT images usually cause the loss of detailed structural information and interrupt the judgement from clinicians. Therefore, reconstructing noise-free, high-resolution CT images with sharp details from degraded ones is of great importance for the computer-assisted diagnosis (CAD) system. However, current image reconstruction methods suffer from unknown parameters of multiple degradations in actual clinical images. Methods:To solve these problems, we propose a unified framework, so called Posterior Information Learning Network (PILN), for blind reconstruction of lung CT images. The framework consists of two stages: Firstly, a noise level learning (NLL) network is proposed to quantify the Gaussian and artifact noise degradations into different levels. Inception-residual modules are designed to extract multi-scale deep features from the noisy image, and residual self-attention structures are proposed to refine deep features to essential representations of noise. Secondly, by taking the estimated noise levels as prior information, a cyclic collaborative super-resolution (CyCoSR) network is proposed to iteratively reconstruct the high-resolution CT image and estimate the blur kernel. Two convolutional modules are designed based on cross-attention transformer structure, named as Reconstructor and Parser. The high-resolution image is restored from the degraded image by the Reconstructor under the guidance of the predicted blur kernel, while the blur kernel is estimated by the Parser according to the reconstructed image and the degraded one. The NLL and CyCoSR networks are formulated as an end-to-end framework to handle multiple degradations simultaneously. Results:The proposed PILN is applied to the Cancer Imaging Archive (TCIA) dataset and the Lung Nodule Analysis 2016 Challenge (LUNA16) dataset to evaluate its ability in reconstructing lung CT images. Compared with the state-of-the-art image reconstruction algorithms, it can provide high-resolution images with less noise and sharper details with respect to quantitative benchmarks. Conclusions:Extensive experimental results demonstrate that our proposed PILN can achieve better performance on blind reconstruction of lung CT images, providing noise-free, detail-sharp and high-resolution images without knowing the parameters of multiple degradation sources.
Exploring Students’ Perception on Online EFL Learning through WhatsApp Application

The present study attempted to find out students’ perception in their learning English process through WhatsApp application. The study was conducted based on some facts that there are recently learning delivered by some teaching techniques because of Covid 19 pandemic. Due to the pandemic of Covid 19 has changed the system of education in the world, Indonesia is one of the countries that was influenced and applied distance learning system. The study was conducted in one of the private universities in Tangerang Selatan. There were 20 participants involving in this study who came from the second semester of academic year 2021-2022. To find some accurate data, the researchers collected a data through semi-structure interviews via Google Meet. The students were asked to join some learning activity such as speaking, writing, listening and reading. Then, they were interviewed about what they feel and their experiences during online learning. A qualitative study which focuses on a design narrative was used to get students’ point of view regarding learning English through WhatsApp application. The result showed that the students have a good perception of learning English using WhatsApp. The use of WhatsApp in learning English is very influential on students.
The Positive Influence of Family Ethics in Family Education of Adolescents in the New Era

Family education always occupies a crucial position in the education system of adolescents. In the process of adolescents' growth, family education will play an indispensable role, especially in the formation of behavior and character building. However, in the current stage of family education, there are still many problems, such as neglecting moral education, emotional education and family style influence, but instead emphasizing intellectual, material or verbal aspects. This paper will explore the positive influence of family style in family education of young people in the context of the new era.