Reviewerly/exHarmony huggingface dataset:
Special Issue on Applications of Speech and Language Technologies in Healthcare

In recent years, the exploration and uptake of digital health technologies have advanced rapidly with a real potential impact to revolutionise healthcare delivery and associated industries [...]
Petroleum Geochemistry of the Central and Eastern Onshore Niger Delta Basin: Oil-Oil Correlation Using Biomarkers and Multi-Variate Statistics

Petroleum geochemistry of oils in several depobelts of the world has been well documented based on biomarkers. However, there is paucity of data regarding the use of multi-variate statistics in oil-oil correlation study, particularly in the Sub-Saharan African region. The purpose of the study was to gain an insight into the geochemical differences and identify their cause(s) through the application of bulk properties, biomarker indices and multi-variate statistics. Correlation of oils from eight wellheads of three stratigraphic units within the Central and Eastern onshore Niger Delta Basin fields (AG and AS) were undertaken by gas chromatography-flame ionization detector. Utilization of bulk properties, biomarker indices and cross plot parameters somewhat separated the mature AG1-4, AS3 and AS4 oils deposited in oxic/suboxicshaly environment from AS2 oil deposited in anoxic fluvio deltaic environment. In-reservoir alteration and migration differences were the major factors responsible for the observed slight geochemical variability in the studied oils. Nevertheless, multi-variate principal component analysis indicated that the Central and Eastern onshore Niger Delta Basin oils were principally generated from similar organic matter sources. The implication here was that the geochemical variations in the studied oils were not significant, a conclusion corroborated by Pearson correlation model data.
Language Report Estonian

Abstract This chapter gives a brief overview of Estonian LT tools and resources (Muischnek 2022; Liin et al. 2012). The Estonian language has only around one million speakers and so the market for Estonian LT products is also a small one. In general, the current situation of Estonian LT is acceptable for a small language, but far from perfect. The main force driving the development of Estonian LT has been the public sector and so the resources and tools developed by publicly funded projects are mainly open source. Nonetheless, during the last decade, the private sector has also engaged in creating tools and solutions for Estonian.
Language Report Latvian

Abstract Ten years ago, when META-NET conducted a study on Language Technology support for Europe’s languages, Latvian was assessed as a language with little or no support (Skadiņa et al. 2012). During the last decade, progress has been made in the development of language resources and tools for Latvian, particularly with respect to advanced datasets and language models, machine translation solutions, speech technologies, and technologies for natural language understanding and human-computer interaction. This chapter provides a summary of the current state of the Latvian language, the only official language of Latvia, in the digital environment and highlights the most important activities in the language technology field.
Language Report Slovak

Abstract For Slovak, all the fundamental NLP building blocks for basic applications exist, but they are often of lesser quality and lower accuracy than those of other languages. The availability of free and open tools and data is rather low, with most of the resources proprietary. Compared to neighbouring languages of similar levels of NLP development (Czech, Polish, Hungarian), Slovak is positioned toward the lower end of this group. Slovak language support by “big players” in the LT industry is comparable to other European languages with similar size; speech recognition and synthesis work acceptably while machine translation between Slovak and English is almost good enough to be used by professionals as a source for post-editing. Spell checkers, LT-assisted mobile phone input, OCR and lemmatised fulltext search are taken for granted, although their quality is significantly lacking compared to bigger European languages.
Language Report French

Abstract This chapter presents a survey of the current state of technologies for the automatic processing of the French language. It is based on a thorough analysis of existing tools and resources for French, and also provides an accurate presentation of the domain and its main stakeholders (Adda et al. 2022). The chapter documents the presence of French on the internet and describes in broad terms the existing technologies for the French language. It also spells out general conclusions and formulates recommendations for progress towards deep language understanding for French.
Breast cancer detection with upstream data fusion, machine learning, and automated registration: initial results

To develop an artificial intelligence algorithm for the detection of breast cancer by combining upstream data fusion (UDF), machine learning (ML), and automated registration, using digital breast tomosynthesis (DBT) and breast ultrasound (US).Our retrospective study included examinations from 875 women obtained between April 2013 and January 2019. Included patients had a DBT mammogram, breast US, and biopsy proven breast lesion. Images were annotated by a breast imaging radiologist. An AI algorithm was developed based on ML for image candidate detections and UDF for fused detections. After exclusions, images from 150 patients were evaluated. Ninety-five cases were used for training and validation of ML. Fifty-five cases were included in the UDF test set. UDF performance was evaluated with a free-response receiver operating characteristic (FROC) curve.Forty percent of cases evaluated with UDF (22/55) yielded true ML detections in all three images (craniocaudal DBT, mediolateral oblique DBT, and US). Of these, 20/22 (90.9%) produced a UDF fused detection that contained and classified the lesion correctly. FROC analysis for these cases showed 90% sensitivity at 0.3 false positives per case. In contrast, ML yielded an average of 8.0 false alarms per case.An AI algorithm combining UDF, ML, and automated registration was developed and applied to test cases, showing that UDF can yield fused detections and decrease false alarms when applied to breast cancer detection. Improvement of ML detection is needed to realize the full benefit of UDF.
Towards Improving Text Classification Tasks Based on Knowledge Graphs for Limited Labeled Data

Pre-trained Transformer models have become popular in various Natural Language Processing (NLP) tasks, following a two-step process of 'pre-training' and 'fine-tuning'.However, with the abundance of information on the web, some domain knowledge may be lacking.This can result in poor performance during the fine-tuning step when there is limited training data available.To address this issue in the case of limited data, we propose a knowledge graph-based data expansion method that enables the model to achieve good results even when there is limited data in the fine-tuning step.We extract entities in the text through Named Entity Recognition and then search for related information in the knowledge graph to expand the text's content.This allows the pretrained model to acquire more external knowledge and enhance its training.We used our data expansion method to conduct experiments on the following well-known models, i.e., BERT, RoBERTa, and GPT-3.Our experiments show that our approach can improve the accuracy of language models on text classification tasks when training data is limited.
Permacomputing Aesthetics: Potential and Limits of Constraints in Computational Art, Design and Culture

Permacomputing is a nascent concept and a community of practice centred around design principles that embrace limits and constraints as a positive thing in computational culture, and on creativity with scarce computational resources.As a result, permacomputing aims to provide a countervoice to digital practices that promote maximisation, hyper-consumption and waste.It seeks to encourage practices as an applied critique of contemporary computer technology that privileges maximalist aesthetics where more pixels, more frame rate, more computation and more power equals more potential at any cost and without any consequences.We believe that such a critical practice can be relevant to artists, designers and cultural practitioners working with computer and network technology who are interested in engaging with environmental issues.This is particularly relevant given the tendency in art, design and cultural production to rely on tools and techniques designed to maximise productivity and mass consumption.In this paper, we argue for the potential of permacomputing as a rich framework for exploring creative design constraints building on a long history of applying constraints in art, design and cultural practices.Because of the need to reconfigure the modes of production and organisation within computational practices, this calls for a different understanding of aesthetics, one that goes beyond the formal evaluation of how things look, but addresses how aesthetics can also be systems of relations, sensing and making sense that are already present in the process of making.We will also discuss the challenges faced by permacomputing practitioners, such as the complicated link with retro-computing, post-digital culture and nostalgia, as well as the problem of constraints in relation to the aesthetisation of poverty, and more generally what it means to work with self-imposed limits in a more privileged socioeconomic context.
Guided Learning of Human Sensor Models with Low-Level Grounding

Sensor data often lacks intuitive interpretability in its raw form, unlike language or image data.Furthermore, standard end-to-end training leaves little control over local representation learning.We postulate that guided local representation learning could be used to tackle both issues.In this paper we introduce a novel framework for sensor models which uses low-level grounding for guided learning of human sensor models.Our framework is amenable to different model architecture.We demonstrate our method on two different human activity datasets, one containing labels of low-level actions used in performing high-level activities, and one without any low-level labeling.We provide comprehensive analysis of our framework's performance across many low-level action subsets and demonstrate how it can be easily adapted to data with no low-level labeling.Our results demonstrate that low-level grounding can be used to improve both the interpretability and performance of sensor models.
TAVo: Tor Application Detection with Voting Critic

The Onion Router (TOR) Network, experiences an increasing number of users with an even more rapid increase in the volume of data usage.This leads to network congestion as well as a general decrease in end-user performance.TOR network is one of the solutions adopted by attackers that allows them to hide their identity through encryption.In this scenario, monitoring the applications and their traffic is a challenging task that can however bring relevant insights to network administrators and security teams.In this paper, we present a specially designed system to analyze the packet capture traffic of a network and classify each stream based on whether it is using the TOR network or not, as well as classifying the type of application using the TOR network.TOR Application detection with a Voting-classifier Critic (TAVo) uses a two-layer classifier that is combined with a specialized Critic model.The first layer aims at separating the Non-TOR from the TOR traffic, while the second layer determines the application that caused the TOR traffic.Whenever the second layer outputs a prediction with lower confidence the voting-classifier Critic is called to confirm or correct the predictions of these difficult cases.Through a set of experiments on a recent dataset, we show that TAVo has important advantages in terms of performance, achieving an average F1 score of 84% without the Critic model, and 91% with the Critic.Moreover, because the Critic model is only used for the cases where the base models face more difficulties, the overall system is efficient.
I3D Light - A Simple Motion Information Stream for I3D

Vision-based Human Activity Recognition (HAR) aims to recognize human activities based on the analysis of video data, and has extensive applications in modern industry and human life.Inflated 3D (I3D) is a deep learning architecture that is commonly used for action recognition by using two-stream video data: RGB stream and optical flow stream.I3D achieved great success on a variety of action recognition benchmarks.However, the use of optical flow incurs high computational cost, making the approach unsuitable for real-time applications.We propose an alternative simple motion information extractor to replace the optical flow branch and reduce the computational cost.It is a modified I3D that uses 128 frames of 112x112 images as input.The low spatial resolution and long temporal range of the proposed I3D RGB stream can reduce the spatial information and enhance the motion information.Experiments show that this simple motion stream can increase the accuracy of the original I3D spatial stream by 4.09% on the Kinetics 400 dataset.
PROSEDUR PEMERIKSAAN RADIOGRAFI SINUS PARANASAL (SPN) DENGAN KASUS SINUSITIS

Background : The radiographic procedure of the paranasal sinuses according to Long et al., (2016) uses Lateral projection, Caldwell Method, Parietoacanthial (Open Mouth and Closed Mouth) and Submentovertical. Caldwell method projection can be done with the image receptor 150 angled technique and the vertical image receptor technique by attaching the image receptor and propping the forehead with a soft bag. However, the radiographic examination procedure of the paranasal sinuses at the Radiology Installation of RSUD Karanganyar was carried out on the caldwell method projection, using a vertical image receptor technique, the patient's forehead and nose attach to the image receptor without a soft bag.&#x0D; Methods :The purpose of this study was to determine the radiographic examination procedure of the paranasal sinuses in cases of sinusitis and to find out the reasons for using the Caldwell Method with the patient's forehead and nose attached to the image receptor without a soft bag. This type of research is a qualitative with a case study approach This research was conducted by means of observation, documentation, and deepening with radiology specialists and radiographers. Observational data were processed and analyzed using open coding, interview data were processed, analyzed, and presented in the form of quotations so that conclusions could be drawn.&#x0D; Results : The results showed that the radiographic examination of the paranasal sinuses at the Radiology Installation of RSUD Karanganyar used a Lateral projection, Waters Closed Mouth, and Caldwell Method with a vertical image receptor technique and adjustment of the position of the forehead and nose against the image receptor.&#x0D; Conclusion : The reason the forehead and nose are positioned against the image receptors in the Caldwell Method projection is for convenience and to view the frontal sinus clearly without magnification.
Multihop Factual Claim Verification Using Natural Language Prompts

Claim verification based on factual evidence can become substantially difficult when the proof comprises several sentences, making it challenging for natural language processing (NLP) models to comprehend long-range dependencies.Inspired by the success of prompt learning in numerous NLP applications, in this paper, we introduce prompt learning for the multi-hop claim verification task.With extensive experiments, we achieve promising results using our proposed prompt-based method that leverages manually constructed prompts.By fine-tuning the language models with prompts, we have achieved an accuracy of 83.9% while obtaining an improved cross-domain generalization performance as well.Moreover, we conducted experiments in few-shot and zero-shot settings and obtained significantly better performance using prompt-based methods compared to traditional supervised learning techniques that are based on the fine-tuning paradigm to further demonstrate the effectiveness of prompt learning in claim verification.
Unsupervised Financial Fraud Detection Using Low-rank Recovery

Many outlier detection methods have been studied and applied to financial fraud detection problems in recent years.These traditional financial fraud detection problems (such as classification-based methods) often suffer performance degradation caused by imbalanced data.Even worse, the frauds often destroy the low dimensional structure of the sub-space on which the intrinsic samples are distributed, and the traditional financial fraud detection methods fail to study the low rankness prior in the intrinsic samples when outliers emerge.We give an effective unsupervised financial fraud detection algorithm based on a low-rank recovery method to solve these issues.Our work has several folds: (1) We adopt the Outlier Pursuit (OP) algorithm and its nonconvex variants, which can study the low rankness within the intrinsic samples and detect the outliers without supervision.(2) We solve OP and its non-convex variants by combining Alternating Direction Method Of Multipliers (ADMM) with Generalized Accelerating Iterative Algorithm (GAI).( 3) We evaluate the effectiveness of the OP-based methods and compare methods on three data sets, including the German Credit Data, Insurance Company (TIC) Benchmark, and IEEE-CIS fraud Detection data set.The related code is available at https://github.com/jzheng20/FinancialFraudDetectionViaOP.git.
Quantifying Path Smoothness in Video Object Tracking by Detection

Object detection and tracking are important areas of research in computer vision.Computer vision solutions to object detection are typically single-frame solutions.To perform tracking by detection, these solutions typically do object detection on a perframe basis, thus losing any temporal information from previous frames.Many multiobject tracking solutions report the average precision performance on video datasets, but they do not evaluate the temporal qualities of these solutions.In video, not only the detection of objects is important but the temporal motion attributes of an object's path, such as its velocity, acceleration, and jerk, are important as well.Many implementations of Object Tracking by Detection systems have run into the problem of motion smoothing for bounding box paths.This paper focuses on quantifying the smoothness of detected object paths within some temporal window.We propose using two smoothness metrics from the field of biokinematics and adapt them for use with detections.Finally, using these metrics, we evaluate the ground truth and two popular object detectors, at the time of experimentation (YOLOv3 and Retinanet), on the entire MOT17 dataset.The results show that the metrics are useful in determining object smoothness, and provide us with an additional approach to evaluate an algorithm's performance in object tracking.The experiments also demonstrate that YOLOv3 produces smoother bounding boxes than Retinanet.All supplemental graphs and data are shown in our appendix 1 .
PENGARUH FAKTOR EKSPOSI TERHADAP KUALITAS CITRA RADIOGRAFI PADA PEMERIKSAAN THORAX

Background: The exposure factor is one of the factors that influence and determine the quality and quantity of X-ray radiation required in making radiographic images. Every radiographic examination requires precision in the use of exposure factors in both conventional radiographic modalities and in the use of CR. Selection of the right exposure factor using both conventional radiography and Computed Radiography (CR) can produce radiographs with optimal results.&#x0D; Methods: This type of research is a library research or literature review by reviewing as many as 50 journals related to image quality on radiograph of thorax during the last 10 years. Then the researchers screened the journals related to the the effect of exposure factors on image quality radiograph of thorax in as many as 4 journals .&#x0D; Results: Image quality analysis is carried out based on image visualization (observation) parameters which include contrast and image sharpness. The exposure factors of 60 kV and 25 mAs in the image shows good gradations, namely the difference from bright white to perfect grayish black and the difference between the phantom object and the area around the object. When using a 60 kV tube voltage, you can see abnormalities in the ribs. Using exposure factors of 75 kV and 3.2 mAs and 65 kV and 8 mAs the resulting images have the highest contrast.&#x0D; Conclusions: Based on the review results, there are several exposure factors that can be used in thorax examination, including 55 kV and 8 mAs due to factor regulation. This exposure is the optimization of the PA thorax examination, the use of the 60 kV and 25 mAs exposure factors, radiographic images can be read and have met good image quality on the object and 60 kV produces maximum contrast, this is in accordance with the theory of using tube voltage to see abnormalities that occurs in the ribs. The highest contrast on the chest X-ray image was obtained using the exposure factors of 75 kV and 3.2 mAs and 65 kV and 8 mAs.
Scale-free network reveals relations between quantum computing and Barrow entropy

Abstract The scale-free network in the black hole horizon gives rise to Barrow entropy behavior, which can be visualized geometrically through the Farey diagram. We perform a detailed analysis to demonstrate the quantified values of Barrow entropy. We calculate the black hole entropy by using the Mertens function with the Farey sequence. We use the black hole network to create a register of quantum computers by applying controlled-NOT gates and use quantum entanglement for data transmission and retrieval. We propose a scenario of Hawking radiation according to the Farey background.
A Dive into Convolutional Neural Network and Understanding Its Functionalities

&lt;p&gt;A paper that consolidates basic theories of convolutional neural network and compares the performance of three CNN architectures on a dataset.&lt;/p&gt;
Exploring Distributed PV Power Measurements for Real-Time Potential Power Estimation in Utility-Scale PV Plants

&lt;p&gt;We explore the use of distributed PV power measurements for real-time short-term forecasting of the maximum potential power output of a utility-scale PV power plant, to support future incorporation of PV plants in automatic generation control (AGC) systems. PV plants operating under AGC may run in a curtailed state but must be capable of accurately forecasting their maximum potential non-curtailed power output, or potential high limit (PHL), 10-20 seconds in advance – despite variable environmental conditions. One forecast approach estimates PHL from a subset of inverters, designated reference inverters, which are never curtailed. We propose an alternative approach using in-situ I V tracers to measure maximum power point of reference modules distributed throughout a PV plant. This would allow for greater sampling of variations in irradiance, module temperature, soiling, and albedo, permitting better PHL prediction accuracy. To study potential benefits of this method, we analyzed a one-year data set from an approximately 100 MW PV power plant in the US Southeast, using non-curtailed string combiner outputs as a proxy for distributed module-level power measurements. We compare the reference inverter and distributed power measurement approaches. Results indicate that for highly flexible operation, substantial improvement in prediction accuracy can be achieved using distributed power measurements compared to using a low fraction of reference inverters. We review the study methodology, conclusions, and shortcomings and discuss prospective future work.&lt;/p&gt;