bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating face recognition with different texture descriptions and convolution neural network
Authors: Al-Hameed Wafaa Mohammed Saeed Hamzah , B. Mohammed Marwan
Published: January 2023

Extracting the remarkable attributes of the image objects is an issue of ongoing research special in the face recognition problem. This paper presents two directions. The first is a comparison between the local binary patterns (LBP) and its modified center symmetric LBP drawn from localized facial expressions and due to the efficiency, K-nearest neighbor (KNN) and the support vector machine (SVM) techniques play significant roles in this research used to implement the proposed system efficiently. The second direction proposes an efficient architecture by depending on deep learning convolution neural network (CNN) to implement face recognition. Such a design consists of two parts: a convolutional learning feature model and a classification model. The first one learns the important feature,while the second part produces a score class for each sample input. Many experiments are implemented on the known dataset once for the number of nearest neighbors (K value), and then decrease the number of expression samples for each individual the other time. The cross-validation method is used to provide a true picture of the accuracy of the face recognition system. In all experiment results, the center symmetric LBP with KNN outperforms the classic LBP. While significant progress in the results accuracy recognition ratio of the CNN model compared with other methods used.
Weed detection by using image processing
Authors: Bidve Vijaykumar , Mane Sulakshana , Tamkhade Pradip , Pakle Ganesh
Published: January 2023

In agricultural regions, the procedure of weed removal is crucial. Weed removal in the classic way, takes longer and requires greater physical effort. The idea is to eliminate weeds from agricultural fields automatically. The proposed study uses a deep learning algorithm to detect weeds growing between crops. Deep learning method also known as deep learning is used to analyse the main properties of agricultural photographs. Weeds and crops have been identified using the dataset. Convolutional neural network (CNN) uses a completely attached surface with rectified linear units (RELU) to differentiate weed and crop. It extracts features of crop using deep learning. The CNN uses features of proceeded image to extract region of interest (ROI). A deep learning network features are used to identify crop. In total of 1280 images are used for testing the system, and 10 images are used to find the confidence score.
Requirement engineering problems impacting the quality of software in Sub-Saharan Africa
Authors: Quansah Andrew , Emmanuel Asiamah , Kyeremateng Bright , Ntow Kesse Esther
Published: January 2023

Poor software quality has led to tremendous financial losses, necessitating the goal of this study. This study aimed to find out the major cause of poor quality of software and propose solutions to mitigate the problem. Histogram analysis was conducted using data from software development firms’ online applications used to track all defects and issues for each project, which are logged under a unique project ID. The requirement engineering stage was found to produce the most problems that directly or indirectly impact software quality. The capability maturity model integration, prototyping, ISO 9001, Walkthroughs, and Formal Inspections were proposed as solutions that could be used to mitigate the software quality problems that arise from the requirement engineering stage in the software development life cycle.
A combination of machine learning based natural language processing with technical analysis for stock trading
Authors: Meesad Phayung , Boonmatham Sukanchalika
Published: January 2023

Stock price analysis appropriately is a challenging area of research as many factors directly affect stock prices. As a result, so not easy to analyze to identify stock trading signals appropriately. The proposed approach builds a framework for classifying stock trading signals by combining natural language processing with technical analysis. The dataset implemented focuses on corporate news and stock indicators from 01-01-2019 to 31-12-2021 from the eight corporates of the Thai Industry Group Index and Sector Index. Two traditional machine learning models, multilayer perceptron (MLP) and support vector machine (SVM), and four deep learning models, Bidirectional GRU (BiGRU), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and long short-term memory (LSTM) used for comparison purposes. The training model classifies daily trading signals into three classes: buy, sell, and hold-after that, the model’s efficiency evaluates by measuring accuracy, precision, recall, and F1-score. For the results, classification average efficiency in all models showed that the BiGRU model obtained higher average accuracy (0.93), precision (0.93), recall (0.93), and F1-score (0.92) than other models. Therefore, the BiGRU model was appropriate for our experiment and was applied to determine daily trading signals for analyzing investment returns.
Service quality model analysis on the acceptance of information system users’ behavior
Authors: Fiati Rina , Widowati Widowati , Mutiara Kusumo Nugraheni Dinar
Published: January 2023

Website technology have created both opportunities and challenges for higher education. Information systems as online learning medium need to pay attention to access, quality and user needs in order to improve the quality of e-learning services. The research objective is to determine user acceptance of the system. The service quality method as identification in solving problems. The research focus on the analysis of five dimensions namely measurable, reliability, responsiveness, assurance and empathy. The research was conducted at the research college at Muria Kudus University. The results state that the assessment model of a system on the website can be completed properly. The level of effectiveness is carried out with respondents as users through the distribution of questionnaires. The results of the analysis with a statistical correlation performance test of 0.985 were declared accepted with a validity level of 97% indicating that the success of the system implemented was from the acceptance side. The higher the empathy with service quality and performance expectations, the greater the student's intention to receive online education services. This research is a reference for developing information systems on e-learning.
Efficient hardware implementation for lightweight Loong algorithm using FPGA
Authors: Subhi Ibrahim Marwa , Amer Abbas Yasir , Hussein Ali Mudhafar
Published: January 2023

Recently low-resource devices such as radio frequency identification (RFID), internet of things (IoT), and wireless sensor networks (WSN) using lightweight cryptography (LWC) to protect devices. Created or design low-resource devices with a lightweight cryptographic technique should take into account important factors such as the battery life and the amount of data to be processed. This paper provides a new hardware designed for Loong lightweight cryptographic algorithm that takes into account the previously described constraints. The new hardware architecture for Loong algorithm with resource sharing to reduce system designed. The proposed approach is implemented using ISE Xilinx V14.7 using Virtex 4 field programmable gate array (FPGA) platform. The synthesis analysis for ISE showed the throughput of 851.264 Mbps with efficiency of 2.282 Mbps/slice, and a power consumption of 0.193 Watt. The implementation designed show the all-algorithms size consists of 373 slices, and the maximum possible operating frequency is 212.816 MHz. To the best of our knowledge, this is the first time that Loong algorithm has been implemented on FPGA using very high-speed integrated circuit hardware description language (VHDL).
Utilization of deep learning and semantic analysis for opinion mining in information extraction: a review
Authors: Susmitha Mekala , Razia Shaik
Published: January 2023

In concern of the increasing availability and popularity of the opinion information sources at a different platform like individual blogs, online feedback, and social network are proliferating and gaining new opportunities and challenges that can be actively exploited using information technology to seek and comprehend people's opinions. In today's field people or entrepreneurs before taking any decision they must be considering the opinion of peoples or information networks. Most of them express the view or opinion through social media platform like Tweeter, Facebook, or blogs on the internet. Therefore, it is essential to analyze to automatically analyze the immense amount of social data available on the internet. Deep learning (DL) has appeared as an influential machine learning (ML) method that studies the properties of different layers or data and provides more advanced predictive results. The study of DL, along with success in many other practical fields, has been widely used in opinion and emotion analysis in recent years. This review explores new challenges in brainstorming and facilitates DL study and the use of semantic analysis. The analysis focuses mainly on methods that try to solve new challenges identified by empathy programs, compared to those already in place. It also conducts extensive research on its current uses in emotion analysis.
An improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS)
Authors: Mleih Al-Sbou Ahed , Abd Rahim Noor Hafhizah
Published: January 2023

In recent years, information overload has become a phenomenon where it makes people difficult to filter relevant information. To address issues such as high-dimensional data, cold start, and data sparsity, semi-autoencoder is one of the unsupervised deep learning methods used in the recommendation systems. It is particularly useful for reducing data dimensions, capturing latent representations, and flexibly reconstructing various parts of input data. In this article, we propose an improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS) framework. This method aims to show better performance of the hybrid collaborative recommendation via semi-autoencoder (HRSA) technique. Two novel elements for iHSARS’s architecture have been introduced. The first element is an increase sources of side information of the input layer, while the second element is the number of hidden layers has been expanded. To verify the improvement of the model, MovieLens-100K and MovieLens-1M datasets have been applied to the model. The comparison between the proposed model and different state-of-the-art methods has been carried using mean absolute error (MAE) and root mean square error (RMSE) metrics. The experiments demonstrate that our framework improved the efficiency of the recommendation system better than others.
An abbreviated review of deep learning-based image classification models
Authors: Talal Abbood Zaman , Nasser Al-Turfi Mohammed , Kamil Adday Almajmaie Layth
Published: January 2023

Image classification is an extensively researched sub-fields of computer vision implemented in face recognition, self-driving, medical image segmentation, biological identification, and others. Traditional models of image classification require manual construction of feature extraction techniques and classification accuracy which are closely associated with these utilized techniques. During the rapid progress of multimedia technologies, the number of images that require classification got bigger, and this led to making image classification more complicated, hence, the manual construction of feature extraction techniques consumes more time and provides lower accuracy. In the recent decade, deep learning-based models have appeared in various applications. These models hold the merits of an effective extraction of image features, low-weight features filtering, a large capacity for processing, and higher classification speed and accuracy. Thus, lots of researchers have attempted to utilize deep learning algorithms, especially convolutional neural networks (CNNs) for image classification. Therefore, this paper concentrates on providing an abbreviated review of deep learning-based image classification models, by covering the recently utilized deep learning algorithms, comparing various related works and benchmark datasets mentioned in this paper, and summarizing the fundamental analysis and discussion.
Street-crimes modelled arms recognition technique employing deep learning and quantum deep learning (SMARTED)
Authors: Atif Ali Shah Syed , Abdel-Wahab Ahmed , Ageelani Nasir , Najeeb Najeeb
Published: January 2023

An increase in population causes loopholes in controlling law and order situations. One of the threatening aspects of peace is the availability of weapons to the general public. As a result, many dangerous situations arise, most notably street crimes. Traditional methods are not sufficient to deal with such situations. Consequently, the police and other security concerns need serious technological reforms to prevent such situations. In modern technology tools, deep learning has made great improvements in various areas of daily life, especially in object detection. This paper presents an efficient technique for detecting weapons from closed-circuit television (CCTV) cameras, videos, or images. Upon the detection of the weapon, the concerned person is automatically informed to take the necessary action; without human intervention. For the first time, RetinaNet has been employed to detect weapons in real-time scenarios. RetinaNet has shown remarkable improvement in this domain, by achieving an average of 90% accuracy in real-time scenarios. With the emergence of quantum computing, many computer environments saw a revolution. Thus, we have also utilized quantum computing technology for real-time weapons detection using quantum deep learning. In this paper, quantum inspired RetinaNet (QIR-Net) is developed for weapons detection and amazing results are observed.
Real-time recognition of American sign language using long-short term memory neural network and hand detection
Authors: Mohamed Abdulhamied Reham , M. Nasr Mona , N. Abdul Kader Sarah
Published: January 2023

Sign language recognition is very important for deaf and mute people because it has many facilities for them, it converts hand gestures into text or speech. It also helps deaf and mute people to communicate and express mutual feelings. This paper's goal is to estimate sign language using action detection by predicting what action is being demonstrated at any given time without forcing the user to wear any external devices. We captured user signs with a webcam. For example; if we signed “thank you”, it will take the entire set of frames for that action to determine what sign is being demonstrated. The long short-term memory (LSTM) model is used to produce a real-time sign language detection and prediction flow. We also applied dropout layers for both training and testing dataset to handle overfitting in deep learning models which made a good improvement for the final result accuracy. We achieved a 99.35% accuracy after training and implementing the model which allows the deaf and mute communicate more easily with society.
Indonesian perceptions on online learning amidst COVID-19: a Twitter sentiment analysis
Authors: Abduh Muhammad , Hamka Muhammad , Taniredja Tukiran , Zainuddin Almuntaqo , Najib Habiby Wahdan
Published: January 2023

The objective of this research is to uncover Indonesians' perceptions of online learning during the COVID-19 pandemic by determining the polarity of language texts (positive, neutral, or negative) compiled from Twitter. The data required to reveal the Indonesian people's opinion on online learning during the COVID-19 pandemic is a tweet on Twitter with the hashtag #Pembelajaran daring (Online learning); #Pembelajaran jarak jauh (distance learning); #Belajar dari rumah (learning from home); #Belajar di rumah (learning in the home) (learning at home). The time frame for collecting these tweets is March 2020 to November 2021. The data was then analyzed using lexicon analysis and analytical tools that used Part of Speech Tagging. According to the results, 77.58% of the tweets are positive, 17.97% are negative, and the remainder are neutral. People prefer to refer to learning support, teachers, schools, education, students, and distance learning. Distance learning is the most positively received category among online learning. However, learning support is the most widely discussed topic among the general public. The overwhelming positive sentiment across all categories suggests that the majority of Indonesians have high hopes for online learning during the pandemic.
Single-phase grid-connected power control in dq synchronous reference frame with space vector modulation using FPGA
Authors: Salam Al-Khayyat Ali , Al-Safi Amean , Jameel Hameed Mustafa
Published: January 2023

This paper presents the performance of controlling the active and reactive power of single-phase grid connected inverter by dq synchronous reference frame and space vector modulation (SVM) which is implemented by using field programmable gate array (FPGA) due to the flexibility they offer in comparison to other digital signal processors, hence it become the standard instruments for implementing various types of pulse width modulation (PWM) digital signal processors (DSPs). The SVM method has used because of its superiority in terms of harmonic distortion, switching losses and utilization of direct current (DC) bus. The synchronization is achieved by using second order generalized integrator (SOGI) due to its simple structure, certain filtering ability and frequency adaptability, in which the phase locked loop (PLL) ensures high performance for single-phase inverter. The active and reactive inverter current has been controlled and decoupled from each other and the dynamic response has been improved and became fast with proper feed-forward and decoupling grid voltage term. The system has simulated by MATLAB and the result shows robust control of injected grid current, active and reactive power control and DC link voltage control.
Improved grey wolf optimizer for multiple unmanned aerial vehicles task allocation
Authors: Wang Yu , Luo Qifang , Zhou Yongquan
Published: January 2023

Grey wolf optimizer (GWO) is a metaheuristic optimization algorithm proposed in 2014, which has already been applied in many fields. However, there are still two problems in GWO: i) during the optimization process, there are three leading wolves to lead the population for search, resulting in poor population diversity and ii) because of its position updated equation which not only brings strong convergence ability but also makes it easily fall into local optimal. In this paper, to overcome this, the following contributions were made: i) an improved GWO (IGWO) with two strategies was proposed to solve the above problems and ii) for verifying the effectiveness of IGWO, it was applied in solving multiple UAVs task allocation problems. The experimental results show that IGWO can solve this problem well and suit for large-scale complex examples.
A survey: medical health record data security based on interplanetary file system and blockchain technologies
Authors: Abbas Al-Kaabi Rana , A. Abdullah Alharith
Published: January 2023

The adoption of modern health records is growing more mature, yet security issues always accompany it. Interplanetary file system (IPFS) and blockchain are developing technologies with decentralization, distributed fault tolerance, and trustworthiness. Using IPFS and blockchain technology to tackle medical health record data security issues is a very promising trend, and it is presently being utilized to secure medical health record data security. This article first explains the idea of IPFS and highlights the classification of existing IPFS and blockchain techniques before briefly discussing distributed ledger to tackle the existing medical health record data security challenges and faults. Finally, to preserve medical health records, a new medical health record storage architectural model based on IPFS and blockchain technologies is presented.
Analysis of facial emotion recognition rate for real-time application using NVIDIA Jetson Nano in deep learning models
Authors: Dudekula Usen , Nalluri Purnachand
Published: January 2023

Detecting facial emotion expression is a classic research problem in image processing. Face expression detection can be used to help human users monitor their stress levels. Perceiving an individual's failure to communicate specific looks might help analyze early psychological disorders. several issues like lighting changes, rotations, occlusions, and accessories persist. These are not simply traditional image processing issues, yet additionally, action units that make gathering activity of facial acknowledgment troublesome look information, and order of the demeanor. In this study, we use Xception taking into account Xception and convolution neural network (CNN), which is easy to focus on incredible parts like the face, and visual geometric group (VGG-19) used to extract the facial feature using the OpenCV framework classifying the image into any of the basic facial emotions. NVIDIA Jetson Nano has a high video handling outline rate. Accomplishing preferable precision over the recently evolved models on software. The average accuracies for standard data set CK+,” on NVIDIA Jetson Nano, the accuracy rate is 97.1% in the Xception model in the convolutional neural network, 98.4% in VGG-19, and real-time environment accuracy using OpenCV, accuracy rate is 95.6%.
Sophisticated CPBIS methods applied for FBISODATA clustering algorithm using with real time image database
Authors: Ramaraj Muniappan , Sabareeswaran Dhandapani , Vijayalaksmi V. , Jothish Chembath , Thangarasu N. , Manivasagam Govindaraj
Published: January 2023

Data mining is a process of mining hidden information to the previously unknown data and theoretically useful unknown information from a large amount of genuine data to be stored in a database. Image mining is a part of data mining with used as a predictive measure to identify with the age of the tiger. This research work is mainly focused on, to identify with the age of the Tiger using data mining techniques. This research work incorporates with which those domains of image processing and data mining to predict the age of the tiger using different kinds of color images are used. The fuzzy iterative self-organizing data analysis (FISODATA) clustering method requires more predefined parameters tofind the maximum number of iterations, the minimum number of points in the cluster, and smallest amount of distance with the centers of the clusters. The key undertaking of the studies of diverse colors mechanism is to decide the age of the tiger; the usage of shade action pixel primarily based on image segmentation; the usage of facts that are used in the mining techniques. However, the more matrix components to be measuring the processing time, retrieval time, accuracy, and blunders fee with the aid of using producing better performance.
Optimal tuning of PID controller for speed control of DC motor using equilibrium optimizer
Authors: Damilola Fajuke Ibukun , Raji Atanda
Published: January 2023

The efficient and smooth operation of direct current (DC) motors is of high significance in various industrial applications. This study proposed a recent optimization technique, named equilibrium optimizer (EO) for optimal tuning of the parameters of proportional integral derivative (PID) controller to provide accurate and robust control in DC motors. Mathematical modelling of the motor was carried out in Simulink environment using basic electric machine theory. Conventional Ziegler-Nichols (ZN) technique is initially designed to tune the PID controller parameters, an optimization model based on the minimization of integrated absolute error and named EO-PID is formulated and subsequently implemented to optimize the tuned parameters obtained using the ZN-PID controller. Simulation results showed that there is a significant reduction in terms of rise time, settling time, overshoot and mean square error using the proposed EO-PID tuning model as compared to the ZN-PID technique. The result also showed that the proposed EO-PID model gives a better dynamic performance as it eliminates the oscillatory response common with most conventional control techniques. The results of this study have established EO as an effective optimization technique for optimal tuning of PID controller parameters, and can therefore be used by machine operators for various industrial applications.
Cloud-based control systems: a systematic literature review
Authors: Wijaya Santo , Ramadhan Arief , Andhika Andhika
Published: January 2023

Control systems and computer science are two distinct and important fields of engineering. The development of cloud computing in computer science has become an enabler for the widely used controller in control systems to migrate to the cloud and has created a new field of research in cloud-based control systems (CCS). The paper used the systematic literature review approach to obtain insight into current CCS research. The objectives include a review in areas such as the demographics, topics of the research, evaluation method, and application domain. To that end, systematic literature review (SLR) has been conducted. The study obtained 64 primary studies from 581 articles. The CCS has a distinct characteristic; despite the fact that the cloud and network dynamics system, when coupled with the controlled plant, is inherently nonlinear, research efforts have used linear models with optimal control to approach it successfully in a limited case of control objectives. Furthermore, cloud-centric and cloud-fog network architecture approaches are considered in the studies—whereas, the quantitative method mainly uses simulation and discussion. Finally, the SLR summarizes open challenges for CCS in the future.
Application of artificial intelligence techniques in the intensive care unit
Authors: Ray Prabhudutta , Sharma Sachin , Raval Raj
Published: January 2023

Intensive care unit deals with data that are dynamic in nature like real time measurement of health condition to laboratory test data that are continuously changes accordingly with time. Artificial intelligence (AI’s) potential ability to perform complex pattern analyses using large volumes of data. Generated pattern discovers the new symptoms of the disease in the Intensive care units (ICUs), helps the doctors to prescribe the new drug discovery which is helpful to intelligent use. Currently research work has been focused in the ICU making more efficient clinical workflow by generation of high-risk patterns from improved high volumes of data. Emerging area of AI in the ICU includes mortality prediction, uses of powerful sensors, new drug discovery, prediction of length of stay and legal role in uses of drugs for severity of disease. This review focuses latest application of AI drugs and other relevant issues for the ICU.
Apolipoprotein Proteomics for Residual Lipid-Related Risk in Coronary Heart Disease
Authors: Clarke Robert , Von Ende Adam , Schmidt Lukas E. , Yin Xiaoke , Hill Michael , Hughes Alun D. , Pechlaner Raimund , Willeit Johann , Kiechl Stefan , Watkins Hugh , Theofilatos Konstantinos , Hopewell Jemma C. , Mayr Manuel ,   on behalf of the PROCARDIS Consortium
Published: January 2023

Background:

Recognition of the importance of conventional lipid measures and the advent of novel lipid-lowering medications have prompted the need for more comprehensive lipid panels to guide use of emerging treatments for the prevention of coronary heart disease (CHD). This report assessed the relevance of 13 apolipoproteins measured using a single mass-spectrometry assay for risk of CHD in the PROCARDIS case-control study of CHD (941 cases/975 controls).

Methods:

The associations of apolipoproteins with CHD were assessed after adjustment for established risk factors and correction for statin use. Apolipoproteins were grouped into 4 lipid-related classes [lipoprotein(a), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides] and their associations with CHD were adjusted for established CHD risk factors and conventional lipids. Analyses of these apolipoproteins in a subset of the ASCOT trial (Anglo-Scandinavian Cardiac Outcomes Trial) were used to assess their within-person variability and to estimate a correction for statin use. The findings in the PROCARDIS study were compared with those for incident cardiovascular disease in the Bruneck prospective study (n=688), including new measurements of Apo(a).

Results:

Triglyceride-carrying apolipoproteins (ApoC1, ApoC3, and ApoE) were most strongly associated with the risk of CHD (2- to 3-fold higher odds ratios for top versus bottom quintile) independent of conventional lipid measures. Likewise, ApoB was independently associated with a 2-fold higher odds ratios of CHD. Lipoprotein(a) was measured using peptides from the Apo(a)-kringle repeat and Apo(a)-constant regions, but neither of these associations differed from the association with conventionally measured lipoprotein(a). Among HDL-related apolipoproteins, ApoA4 and ApoM were inversely related to CHD, independent of conventional lipid measures. The disease associations with all apolipoproteins were directionally consistent in the PROCARDIS and Bruneck studies, with the exception of ApoM.

Conclusions:

Apolipoproteins were associated with CHD independent of conventional risk factors and lipids, suggesting apolipoproteins could help to identify patients with residual lipid-related risk and guide personalized approaches to CHD risk reduction.
Computational Language Modeling and the Promise of In Silico Experimentation
Authors: Jain Shailee , Vo Vy A. , Wehbe Leila , Huth Alexander G.
Published: January 2023

Language neuroscience currently relies on two major experimental paradigms: controlled experiments using carefully hand-designed stimuli, and natural stimulus experiments. These approaches have complementary advantages which allow them to address distinct aspects of the neurobiology of language, but each approach also comes with drawbacks. Here we discuss a third paradigm—in silico experimentation using deep learning-based encoding models—that has been enabled by recent advances in cognitive computational neuroscience. This paradigm promises to combine the interpretability of controlled experiments with the generalizability and broad scope of natural stimulus experiments. We show four examples of simulating language neuroscience experiments in silico and then discuss both the advantages and caveats of this approach.
Citation metrics covary with researchers’ assessments of the quality of their works
Authors: Aksnes Dag W. , Piro Fredrik Niclas , Fossum Lone Wanderås
Published: January 2023

For a long time, citation counts have been used to measure scientific impact or quality. Do such measures align with researchers’ assessments of the quality of their work? In this study, we address this issue by decomposing the research quality concept into constituent parts and analyzing their correspondence with citation measures. The focus is on individual publications, their citation counts and how the publications are rated by the authors themselves along quality dimensions. Overall, the study shows a statistically significant relationship for all dimensions analyzed: solidity, novelty/originality, scientific importance and societal impact. The highest correlation is found for scientific importance. However, it is not very strong, but we find distinct gradients when publications are grouped by quality scores. This means that the higher the researchers rate their work, the more they are cited. The results suggest that citation metrics have low reliability as indicators at the level of individual articles, but at aggregated levels, the validity is higher, at least according to how authors perceive quality.
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering
Authors: Siriwardhana Shamane , Weerasekera Rivindu , Wen Elliott , Kaluarachchi Tharindu , Rana Rajib , Nanayakkara Suranga
Published: January 2023

Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint training of the retriever and generator components of RAG for the task of domain adaptation in ODQA. We propose RAG-end2end, an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. In addition, we introduce an auxiliary training signal to inject more domain-specific knowledge. This auxiliary signal forces RAG-end2end to reconstruct a given sentence by accessing the relevant information from the external knowledge base. Our novel contribution is that, unlike RAG, RAG-end2end does joint training of the retriever and generator for the end QA task and domain adaptation. We evaluate our approach with datasets from three domains: COVID-19, News, and Conversations, and achieve significant performance improvements compared to the original RAG model. Our work has been open-sourced through the HuggingFace Transformers library, attesting to our work’s credibility and technical consistency.
On the Role of Negative Precedent in Legal Outcome Prediction
Authors: Valvoda Josef , Cotterell Ryan , Teufel Simone
Published: January 2023

Every legal case sets a precedent by developing the law in one of the following two ways. It either expands its scope, in which case it sets positive precedent, or it narrows it, in which case it sets negative precedent. Legal outcome prediction, the prediction of positive outcome, is an increasingly popular task in AI. In contrast, we turn our focus to negative outcomes here, and introduce a new task of negative outcome prediction. We discover an asymmetry in existing models’ ability to predict positive and negative outcomes. Where the state-of-the-art outcome prediction model we used predicts positive outcomes at 75.06 F1, it predicts negative outcomes at only 10.09 F1, worse than a random baseline. To address this performance gap, we develop two new models inspired by the dynamics of a court process. Our first model significantly improves positive outcome prediction score to 77.15 F1 and our second model more than doubles the negative outcome prediction performance to 24.01 F1. Despite this improvement, shifting focus to negative outcomes reveals that there is still much room for improvement for outcome prediction models.

https://github.com/valvoda/Negative-Precedent-in-Legal-Outcome-Prediction
Meta-Learning a Cross-lingual Manifold for Semantic Parsing
Authors: Sherborne Tom , Lapata Mirella
Published: January 2023

Localizing a semantic parser to support new languages requires effective cross-lingual generalization. Recent work has found success with machine-translation or zero-shot methods, although these approaches can struggle to model how native speakers ask questions. We consider how to effectively leverage minimal annotated examples in new languages for few-shot cross-lingual semantic parsing. We introduce a first-order meta-learning algorithm to train a semantic parser with maximal sample efficiency during cross-lingual transfer. Our algorithm uses high-resource languages to train the parser and simultaneously optimizes for cross-lingual generalization to lower-resource languages. Results across six languages on ATIS demonstrate that our combination of generalization steps yields accurate semantic parsers sampling ≤10% of source training data in each new language. Our approach also trains a competitive model on Spider using English with generalization to Chinese similarly sampling ≤10% of training data.1
Impacts of Mountain Topography and Background Flow Conditions on the Predictability of Thermally Induced Thunderstorms and the Associated Error Growth
Authors: Wu Pin-Ying , Takemi Tetsuya
Published: January 2023

Thermally induced thunderstorm simulations were conducted with the Weather Research and Forecasting (WRF) Model in an idealized configuration to investigate the associated error growth and predictability. We conducted identical twin experiments with different topography and background winds to assess the impacts of these factors. The results showed that mountain topography restrains error growth at the early stage of convection development. This topographic effect is sensitive to mountain geometry and background winds: it was more noticeable in cases with higher and narrower mountains and difficult to see without background wind. The topographic effect and its sensitivity resulted from the different natures of convection initiation. However, the topographic effect became less apparent when moist convection continued growing and triggered rapid error growth. The predictability of thunderstorms is then limited at the timing after the convective activity reached its maximum. A smaller initial error or starting a simulation at a later time did not break this timing of predictability limit. Mountain topography also limitedly affected the timing of the maximum convective activity and the predictability limit. In contrast, background flows changed the convection evolution and the following predictability. The predictability limit assessed by rainfall suggested other aspects of the topographic effect. The domain-scale rainfall distribution and the intense accumulated rainfall can be adequately captured in the presence of mountains.
Entrainment in a Simulated Supercell Thunderstorm. Part III: The Influence of Decreased Environmental Humidity and General Effects upon Precipitation Efficiency
Authors: Jo Enoch , Lasher-Trapp Sonia
Published: January 2023

Entrainment is a key process that can modulate the intensity of supercells, and a better understanding of its impact could help improve forecasts of thunderstorms and the precipitation they produce. In Part III of this series, the three distinct mechanisms of entrainment identified during the mature stage of idealized supercell thunderstorms in Part I (overturning “ribbons” of horizontal vorticity, “disorganized turbulent eddies,” and the “storm-relative airstream”) are examined as the absolute humidity of the environment is decreased. The existence of these mechanisms in a more realistic simulated storm environment is also established. Entrainment is calculated as fluxes of air across the storm core surface; passive fluid tracers help determine the resulting dilution of the storm updraft. Model microphysical rates are used to examine the direct impacts of entrainment on hydrometeors within the storm updraft as well as precipitation that falls to the ground. Results show that as mixed-layer humidity decreases, the “ribbons” and turbulent eddy mechanisms decrease in intensity, but their effects on precipitation production change little. With decreasing humidity in the 3–4 km AGL layer, the storm-relative airstream entrains less humid low-level air into the storm core, decreasing the vertical mass flux, and therefore the precipitation produced by the storm. When the humidity in the mid- to upper troposphere (4–20 km AGL) is decreased, precipitation is significantly reduced, but not due to the effects of the entrained air. Rather, enhanced evaporation and sublimation of falling precipitation decreases the overall precipitation efficiency of the storm.
Multiscale Processes Leading to Heavy Precipitation in the Eastern Nepal Himalayas
Authors: Hirata Hidetaka , Fujinami Hatsuki , Kanamori Hironari , Sato Yota , Kato Masaya , Kayastha Rijan B. , Shrestha Madan L. , Fujita Koji
Published: January 2023

The processes underlying heavy rainfall in the higher elevations of the Himalayas are still not well known despite their importance. Here, we examine the detailed process causing a heavy rainfall event, observed by our rain gauge network in the Rolwaling valley, eastern Nepal Himalayas, using ERA5 and a regional cloud-resolving numerical simulation. Heavy precipitation (112 mm day−1) was observed on 8 July 2019 at Dongang (2790 m above sea level). Most of the precipitation (81 mm) occurred during 1900–2300 local time (LT). The synoptic-scale environment is characterized by a monsoon low pressure system (LPS) over northeastern India. The LPS lifted moisture upward from the lower troposphere and then horizontally transported it into the eastern Nepal Himalayas within the middle troposphere, increasing the content of the water vapor around Dongang. A mesoscale convective system passed over Dongang around the time of the intense precipitation. The numerical simulation showed that surface heat fluxes prevailed under the middle tropospheric (∼500 hPa) southeasterly flow associated with the LPS around a mountain ridge on the upwind side of Dongang until 1900 LT, enhancing convective instability. Topographic lifting led to the release of the enhanced instability, which triggered the development of a mesoscale precipitation system. The southeasterly flow pushed the precipitation system northward, which then passed over Dongang during 2000–2200 LT, resulting in heavy precipitation. Thus, we conclude that the heavy precipitation came from the multiscale processes such as three-dimensional moisture transport driven by the LPS and the diurnal variation in heat fluxes from the land surface.

Significance Statement

Precipitation in the Himalayas is closely related to the hydrological cycle, floods, and landslide disasters in South Asia. Thus, elucidating the features of precipitation in the Himalayas is important. This study explored multiscale processes leading to a heavy precipitation event that was observed on 8 July 2019 at Dongang in the Rolwaling valley of the eastern Nepal Himalayas. We identified new processes producing heavy precipitation in the Himalayas: the three-dimensional synoptic-scale moisture transport driven by a monsoon low pressure system and the effect of the diurnal variation in heat fluxes from the land surface on the development and movement of a mesoscale precipitation system causing heavy precipitation. These findings broaden our understanding of heavy precipitation in the Himalayas.
Designing For Academic Resilience in Hands-On Courses in Times of Crisis: Two Models for Supporting Hands-On Online Learning Drawn From the COVID-19 Pandemic
Authors: Schell Julie A.
Published: January 2023

Expanding on academic continuity planning research in higher education, this article presents two models for transitioning hands-on coursework online. Integrating precedent, case study, and autoethnography methods, the article analyzes higher education leadership and faculty decision-making within the context of the onset of the COVID-19 pandemic at The College of Fine Arts at The University of Texas at Austin. When COVID-19 closed campuses worldwide, 98% of over 1,200 College of Fine Arts class sections were happening in-person; most of those courses required hands-on, applied learning, which is challenging to translate online. With few exceptions, these courses not only continued, but they also demonstrated academic resilience—the ability to survive, adapt, and grow. Academic continuity and planning researchers have reached consensus that institutions need to support high-quality online coursework to effectively manage disruptions; the problem presented in this article is that extant academic continuity models too often conceptualize faculty, students, and staff as a single user with a set of common characteristics and needs related to online learning. Such generalized conceptualizations lead to academic continuity planning strategies and tactics that do not account for the variegated complexities involved in online hands-on education.
Unraveling the Sexual Victimization of Sex Workers: A Latent Class Analysis Through the Lens of Environmental Criminology
Authors: Miin Miin Chai April , Chopin Julien , Molnar Lorena , Beauregard Eric
Published: January 2023

Past research on violence against sex workers has contributed to our understanding of this phenomenon yet, often do not offer concrete preventative measures. The current study aims to investigate this issue through an environmental criminology perspective, and to identify measures that can be implemented to decrease violence through a situational crime prevention framework. Our sample consist of 402 French sex workers who experienced violent victimization (1990–2018). Latent class analysis revealed a four-class solution: (1) indoor/low-moderate guardianship, (2) outdoor/low guardianship, (3) mobile/low guardianship, and (4) mobile/moderate guardianship. Actionable crime prevention methods to mitigate the risk of violence suggested for each of the classes included pre-screening clients, installing panic buttons/closed-circuit television, offering self-defense and conflict management courses, and working in tandem.
Is indigenisation a social justice risk? Exploring indigenisation of social work in Africa through the lens of universalism versus relativism of social work ethics and values
Authors: Ekoh Prince Chiagozie
Published: January 2023

With the indigenisation of social work gathering momentum, the lens of universalism and relativism can highlight some critical social justice concerns. Sociocultural beliefs around human rights and gender equality, reproductive health and traditional fostering, among others, pose unprecedented dilemmas for social workers in Africa and indigenous societies globally. Furthermore, the blind pursuit of indigenisation can reinforce discriminatory and oppressive beliefs and practices. To rectify this, developing ethical decision-making screens and additional research promoting indigenous cultural competency within the social justice goal of social work is needed.
Psychological stress and strain in laboratory animal professionals – a systematic review
Authors: Rumpel Sonja , Kempen Regina , Merle Roswitha , Thoene-Reineke Christa
Published: January 2023

Laboratory animal professionals (LAP) are faced with various situations and tasks influencing their mental well-being. A systematic review has been conducted to investigate whether there are specific stressors for LAP and which moderators are relevant for the development of psychological strain. A comprehensive search following PRISMA Guidelines was carried out in June 2021. Results include 12 studies and have been summarized qualitatively in narrative synthesis and tabular presentation. Available literature indicates that LAP are facing stressors but does not allow for conclusions on specific stressful job duties other than euthanasia.

Signs of strain are present in LAP. Specifically, participants in qualitative studies reported acute symptoms, while chronic manifestations were in focus in quantitative studies. Although a wide variety of moderating factors have been investigated, only social support has been rated as relevant by multiple qualitative and quantitative studies without contrasting results.

According to current data, there is a risk for psychological strain in LAP. However, there is limited understanding of specific stressors and data on moderators is diverse. Further studies that focus on domain-specific knowledge and clearly distinguish stressors from moderators are necessary to set up institutional programmes addressing psychological strain in LAP.