A study on multidimensional evaluation of teaching using loose-leaf textbooks based on improved K-modes algorithm

Abstract: Loose-leaf textbook is a new type of textbook in the new era of education environment, which has better interactivity and flexibility of use than traditional textbooks. In order to improve effect analysis of teaching with loose-leaf textbooks, a multi-dimensional evaluation method of teaching with loose-leaf textbooks based on the improved K-modes algorithm is designed. In the process, the multi-dimensional evaluation system of teaching using loose-leaf textbooks is constructed by combining objective and subjective directions, and the corresponding index value calculation method is designed, and then the data processing of multi-dimensional evaluation based on the improved K-modes algorithm is optimised by introducing the error sum of squares and the co-presence rate to optimise the clustering process, so as to obtain a complete multi-dimensional evaluation method. Results show that the research method maintains the accuracy rate above81.2% when the number of sample points increases to 1200 when performing data classification accuracy analysis; when generating the multidimensional evaluation results, the research method generates realistic evaluation results for 8indicators. It shows that the research method can effectively carry out the multidimensional evaluation of teaching using loose-leaf textbooks, and can provide certain technical references to the education industry.


Entity relation joint extraction model combining pointer network and attention mechanism based on relative position embedding

Abstract: Extracting entity relations from unstructured text is an important step in constructing a knowledge graph, but most current methods are ineffective in dealing with the complex problem of overlapping entities. In this paper, we propose a novel entity relation joint extraction model. It extracts subjects through pointer annotation, fuses the extracted subjects with the sentence vector, then inputs them into an attention layer (Attention Mechanism Based on Relative Position Embedding, AMBRPE) to enhance feature expression ability. Under predefined relation conditions, the model extracts objects corresponding to the extracted subjects to generate relation triplets. And the model can effectively solve the problem of overlapping entities through hierarchical pointer annotation. In addition, we introduce an Adversarial Training Component (ATC) into the model, which generates adversarial samples for training and acts as a text data augmentation method to improve model generalization ability. On the public datasets NYT and WebNLG, we conducted extensive experiments and the results show that our model outperforms the cascaded binary tagging framework (CasRel) by 2.1 and 0.9 percentage points, respectively. Moreover, the effectiveness of the proposed model is verified through ablation experiments.


Efficiency study of VGG networks in autonomous driving tasks

Abstract: In this study, we aim to evaluate the applicability of the VGG 19 architecture-based neural network for autonomousdriving tasks. The objective is to demonstrate the effectiveness of employing this neural network to solve the problemand to compare the identification quality. Experimental methodology is utilized to compare the efficiency of different neural networks. During the experimentation, we observed a disparity in the efficiency of VGG with and without theinclusion of the attention mechanism. Presently, a variety of machine learning methods are increasingly used to addressdiverse problems. Image recognition holds particular significance among these challenges. The practical problemunderinvestigation in this study pertains to the identification of roads with varying curvatures and their correspondence tothevehicle’s steering angles for the purpose of autonomous vehicle control. This paper explores multiple types of neural networks commonly used for image recognition. Previous research has demonstrated the high efficiency of these neural networks in solving various tasks, while this evaluation focuses on the applicability of neural networks in solvingtheautonomous driving problems and a performance comparison of these methods. The experimental results indicatethat VGG 19 achieves the highest accuracy of all neural networks.


Factors Affecting the Happiness Index

Abstract: The happiness index is a system of measuring collective happiness as well as the well-being of a nation. The instrument measures life satisfaction and the condition of life among citizens because happiness is an indispensable human goal. A country’s happiness provides an important insight into its citizen’s well-being. For the citizens to be happy, the government must offer good governance supporting their well-being. A number of factors have a negative or positive impact on the happiness index of any given country. The factors that positively impact the happiness index include Gross Domestic Product, generosity, high life expectancy, generosity, and social support. Corruption negatively impacts the happiness index of a country. The government must ensure that they minimize the cases of corruption in the government so that citizens can have hope in the government, which increases their happiness. To ensure citizens are happy, the government ought to have a consistent gathering of happiness data on a large scale because it helps policymakers recognize deliverables that must be formed to enhance citizens’ well-being.


Research on brand differentiation strategies that can be implemented for e-commerce transformation - Using PDD as a case study

Abstract: In order to promote the transformation of China’s e-commerce industry, this study proposes a feasible strategy for brand differentiation based on the current status of China’s e-commerce, which is transitioning from high-speed growth to high-quality development. Under the guidance of a brand differentiation strategy, a brand differentiation strategy design system is constructed through case summaries and development analysis of PDD. Based on the theory of brand differentiation, this enables PDD to emerge as a leading company in a saturated and competitive market. When the market is highly homogenized, showcasing unique brand characteristics and differentiated features is the key to success. Through the theory of brand differentiation, combined with PDD’s brand development journey and differentiation strategy, we can identify the unique characteristics of the brand. This provides innovative ideas and serves as a reference for the transformation of China’s e-commerce.


Evolutionary constraints and imperfect ant mimics

Abstract: Adaptive evolution relies on both heritable variation and selection. Variation is the raw material upon which selection acts, so any mechanism that limits or prevents the generation of heritable variation reduces the power of selection to lead to adaptation. Such limitations are termed evolutionary constraints. While it is widely accepted that constraints play an important role in shaping evolutionary outcomes, their relative importance, as opposed to adaptation, in determining evolutionary outcomes remains a subject of debate. Evolutionary constraints are often evoked as the reason behind the persistence of inaccurate mimicry. Here, we compared the variation and accuracy of body-shape mimicry in ant-mimicking spiders with that of ant-mimicking insects, predicting greater constraints, and hence inaccuracy, in spiders mimicking ants, due to their evolutionary distance from the ant model. We found high inter-species variation in mimetic accuracy, but dorsally, no overall difference in mimetic accuracy between spider and insect mimics, which is inconsistent with a constraint causing inaccurate mimicry. Our study provides empirical evidence suggesting that imperfect mimicry in spiders and insects is predominantly shaped by adaptive processes rather than constraints or chance. Our findings contribute to our understanding of the mechanisms underlying evolutionary diversity and the processes that shape phenotypic outcomes.


Hybrid PSO-Jaya Algorithm for Solving Various Optimization Problems

Abstract: This article proposes a hybrid algorithm PSO-Jaya, based on two heuristic algorithms — PSO (Particle swarm optimization) and Jaya. The main idea of the PSO-Jaya hybrid algorithm is to use PSO for global research of the solutions space and when PSO ceases to improve the results, which indicates a possible hit in the local optimum, Jaya is connected for the best solution throughout space, taking into account the best solutions already found. This allows the hybrid algorithm to combine efficient exploration of large areas of the solution space with the ability to minimize the probability of hitting local optimum. Two problems are used to evaluate the effectiveness of the PSO-Jaya hybrid algorithm: functions optimization and training artificial neural network for classification task Glass Identification. In calculation tests, the PSO, Jaya, PSO-Jaya algorithms are compared in view of their mean, median, standard deviation and "best" minimum error. In this connection, 50 simulations-based test functions and 30 network simulations were fulfilled. The performance analysis the PSO and Jaya algorithms was carried out as well as the hybrid algorithm for test-based problems. In all test cases, the PSO-Jaya algorithm achieved the best performance in terms of convergence speed and ability to avoid local optimum.


A CRISPR collaboration compilation

Abstract: Major biopharma companies are partnering with pioneers of genome-editing technologies to realize the potential of this emerging precision medicine modality. Major biopharma companies are partnering with pioneers of genome-editing technologies to realize the potential of this emerging precision medicine modality.


Exploring Effective Models of Leadership in the Age of AI within Private Higher Education Institutions in England

Abstract: Artificial intelligence (AI) has gained significant interest and application in various fields and the impacts are evident in teaching, learning, and assessment. On one hand, AI can bring the much-needed revolution in higher education by assisting leaders and management to offer personalized teaching approaches that meet specific student needs, it can also enhance efficient feedback and the automation of administrative functions including assessment and grading. These capabilities make AI an important instrument in promoting efficiency in higher education and creating room for institutions to redirect their focus on other equally important aspects such as curriculum development. However, despite these advantages, AI comes with significant setbacks that adversely impact the quality of education. Particularly, various platforms allow students to do their assignments automatically thus negating their responsibility to research and learn. As such, institutions must put in place effective models of leadership and approaches that will ensure the use of AI in an ethical manner without compromising the quality of education.


Uncovering Media Bias in Eviction Reporting: A Comprehensive Analysis Utilising Sentiment Analysis Framework and Social Media Data

Abstract: This study investigates the prevalence of evictionsin South Africa and examines potential disparitiesbetween traditional media reporting and socialmedia discourse. Employing a sentiment analysisframework, we extend its application to comparethe reporting of evictions in newspaper articles(i.e. conventional media) and Twitter data (i.e.social media). Statistical machine-learningmethods are utilized to predict sentiment scoresfor both types of content, and a chi-square test isemployed to evaluate bias between news articlesand tweets. The test results reveal a significant biasin the sentiment distribution, suggesting that thedissimilarities observed between articles andtweets are not merely coincidental.


Pengembangan Literacy Digital Anak Usia Dini Melalui Pembelajaran Computer Kids

Abstract: The purpose of this study is to determine the developing digital literacy in early childhood through Computer Kids learning. This research uses descriptive qualitative methods. Data collection techniques through interviews, documentation, and observation. Data validity techniques use source triangulation and triangulation methods. Data analysis uses interactive data analysis, presentation of data, data reduction, and drawing conclusions. The results showed that Computer Kids learning at PAUD Inclusion Saymara was carried out once a week in the multimedia room. Before carrying out learning, educators make plans related to the material to be delivered. The learning materials for Computer kids include getting to know the hardware, the parts of the computer, turning the computer on and off. In addition, software that is suitable for early childhood is also introduced such as interactive games, Microsoft Paint, Microsoft Word where children can recognize shapes, letters, and numbers. Through this Computer Kids learning, children have the knowledge and skills to utilize digital media wisely, intelligently, meticulously, and precisely according to their age development.


A Novel Training Approach in Deep Spiking Neural Network Based on Fuzzy Weighting and Meta-heuristic Algorithm

Abstract: Abstract The challenge of supervised learning in spiking neural networks (SNNs) for digit classification from speech signals is examined in this study. Meta-heuristic algorithms and a fuzzy logic framework are used to train SNNs. Using gray wolf optimization (GWO), the features obtained from audio signals are reduced depending on the dispersion of each feature. Then, it combines fuzzy weighting system (FWS) and spike time-dependent flexibility (STDP) approach to implement the learning rule in SNN. The FWS rule produces a uniformly distributed random weight in the STDP flexibility window, so that the system requires fewer training parameters. Finally, these neurons are fed data to estimate the training weights and threshold values of the neurons using wild horse algorithm (WHO). With the parameters given, these rule weights are applied to appropriately display the class's share in extracting the relevant feature. The suggested network can classify speech signals into categories with 97.17% accuracy. The dataset was obtained using neurons operating at sparse biological rates below 600 Hz in the TIDIGITS test database. The suggested method has been evaluated on the IRIS and Trip Data datasets, where the classification results showed a 98.93% and 97.36% efficiency, respectively. Compared to earlier efforts, this study's results demonstrate that the strategy is both computationally simpler and more accurate. The accuracy of classification of digits, IRIS and Trip Data has increased by 4.9, 3.46 and 1.24%, respectively. The principal goal of this research is to improve the accuracy of SNN by developing a new high-precision training method.


Revolutionizing Saudi Healthcare: A Delphi Study on Identifying Competencies in Virtual Healthcare for Healthcare Professionals

Abstract: Background: Virtual care adoption accelerated during the COVID-19 pandemic, highlighting the need for healthcare professionals to develop relevant competencies. However, limited evidence exists on the core competencies required for quality virtual care delivery. Objective: This study aimed to identify the critical competencies physicians, nurses, and other health professionals need for adequate virtual care provision in Saudi Arabia using a Delphi method. Methods: A 3-round Delphi technique was applied with a panel of 42 experts, including policymakers, healthcare professionals, academicians, and telehealth specialists. In round 1, an open-ended questionnaire elicited competencies needed for virtual care. Competencies were distilled and rated for importance in rounds 2 and 3 until consensus was achieved. Results: Consensus emerged on 151 competencies across 33 domains. The most prominent domains were communication (15 competencies), professionalism (13), leadership (12), health informatics (5), digital literacy (5) and clinical expertise (11).


A First Approach to Quantum Logical Shape Classification Framework

Abstract: Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to device suitable applications. This article aims to propose for the first time an approach to use quantum logic in image processing at hand of a process for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables to initiate the principal component analysis. At hand of experiments, we explore the possibility to distinguish between different geometrical objects and discuss the results in terms of quantum logical interpretation.


PRODIGY: Probing Resilience Of Domain-wall Interactions at Gauge Theory

Abstract: We delve into the axion-dilaton model within the supergravity framework, with a specific focus on the intricacies of domain wall construction and stability. Employing holographic vitrification, we unravel the dynamics of domain wall formation in gauge theories featuring periodic vacuum structures. Our model, incorporating a QCD-like axion term and a stabilizing dilaton, undergoes scrutiny for conductivity variations under weak disorder. The investigation reveals the model's resilience, manifesting near-perfect conductivity under mild disorder conditions. However, the rigorous mathematical motivation for the holographic setup demands further elucidation. The scattered nature of our results prompts the necessity for a more systematic interpretation of QCD phenomena and conductivity transitions. This study contributes to the mathematical understanding of the axion-dilaton model's behavior, highlighting the imperative for a refined holographic framework and a more coherent interpretation of observed phenomena.


Detecting CSV File Dialects by Table Uniformity Measurement and Data Type Inference

Abstract: The human-readable simplicity with which the CSV format was devised, together with the absence of a standard that strictly defines this format, has allowed the proliferation of several variants in the dialects with which these files are written. The latter has meant that the exchange of information between data management systems, or between countries and regions, requires human intervention during the data mining and cleansing process. This has led to the development of various computational tools that aim to accurately determine the dialects of CSV files, in order to avoid data loss during data loading by a given system. However, current systems have limitations and make assumptions that need to be improved and/or extended. This paper proposes a method for determining CSV file dialects through table uniformity, a statistical approach based on table consistency and records dispersion measurement along with the detection of data type over each field. The new method has a 100% accuracy over a dataset with 147 sampling CSV files from a benchmark framework. Furthermore, the proposed method is accurate enough to determine dialects by reading only ten records, requiring more data to disambiguate those cases where the first records do not contain the necessary information to conclude with a dialect determination.


Image alignment-based patch distribution framework for anomaly detection

Abstract: Surface quality inspection is a crucial step in industrial manufacturing. However, it is challenging to collect an adequate quantity of abnormal samples in practice. Supervised methods require sample annotation, which is costly, so unsupervised methods that are high-speed and low-cost are more suitable for industrial applications. Among the current unsupervised methods, embedding-similarity methods haven shown excellent performance, but most of them do not preprocess the images and directly use convolutional neural networks to extract image features. While in actual scenarios, image can have some degree of offset or rotation due to machine variations. Therefore, this paper proposes a new patch distribution framework for anomaly detection, specifically a novel image alignment module is proposed to enhance the utility of the model. Image alignment reduces the dense distance between pixels during training, enabling more precise learning of the feature distribution of normal samples and reduce false positives during testing. In addition, in the feature extraction stage, the middle layer of the network is selected to extract features and establish embedding connections. This not only enhances the model’s precision but also reduces its memory requirements. Experiments on the publicly available datasets MVTec AD and BeanTech AD show that our proposed new framework achieves better performance than other baseline models.


Molecular components in the $J/\psi$ and the $\rho$-$\pi$ puzzle

Abstract: Abstract Motivated by the large branching fractions of $J/\psi \to f_0 (1710) \omega/f_0(1710) \phi$ and the light exotic candidates, we find that there may exist molecular states composed of $f_0(1710) \omega$ and $f_0 (1710) \phi$, which correspond to $X(2440)$ and $X(2680)$ observed in a few decades before. The branching fraction of $X(2440)$ and $X(2680)$ to various $PV$ channels and $KK\omega(\phi)$ channels are estimated in the molecular scenario. In addition, the large branching fractions of $J/\psi \to f_0 (1710) \omega/f_0(1710) \phi$ indicate the sizable molecular components in the $J/\psi$ state. Thus, we consider the $J/\psi$ as the supperposition of $c\bar{c}(1S)$, $f_0(1710) \omega$ and $f_0 (1710) \phi$ molecular states, and these molecular components have significant impact on the light hadron decays of $J/\psi$, which may shield light on the long standing $\rho-\pi$ puzzle. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.


Attention-guided hybrid transformer-convolutional neural network for underwater image super-resolution

Abstract: Underwater images suffer from localized distortion and blurred degradation of edge structures due to light absorption and scattering by water. However, existing super-resolution (SR) methods for underwater images cannot effectively solve the above problems and encounter model sizes that are too large. To this end, we propose an attention-guided hybrid transformer-CNN network (AHTCN) to improve the SR reconstruction of underwater images through the interaction of local and multiscale global information, as well as the long-range dependencies modeling capability. Specifically, AHTCN mainly consists of several cascaded transformer-CNN feature extraction blocks (TCFEB) and an image reconstruction module. In TCFEB, the designed attention-based channel separation mechanism can adaptively separate the weighted features while reducing the number of model parameters and then extract the local details and global structural information at different scales through the dual-stream structure. Moreover, we replace the feedforward layer in the transformer with the blueprint separable convolutional feedforward layer and propose an enhanced pyramid pooling transformer layer, which helps to strengthen the feature perception of the model. Experimental results demonstrate that AHTCN outperforms the state-of-the-art algorithms in terms of both subjective visual effects and objective quality assessment, while requiring fewer parameters.


Pengaruh Penggunaan Lensa Kontak Lunak Terhadap Kesehatan Mata Di Optik Padang

Abstract: The research results showed that respondents did not fully know the correct and correct procedure for using soft contact lenses, this can be seen from 28 respondents (62.2%) knowing the correct procedure for using soft contact lenses and 17 respondents (37.8%) not knowing the procedure for using lenses correct soft contact lenses, 26 respondents (57.8%) understand the impact of using soft contact lenses and 19 respondents (42.2%) do not understand the impact of using soft contact lenses, 29 respondents (64.4%) know about how to overcome the impact of using soft contact lenses. not in accordance with standards and as many as 16 respondents (35.6%) did not know about how to overcome the impact of using soft contact lenses that do not comply with standards,


Exploring ASR fine-tuning on limited domain-specific data for low-resource languages

Abstract: The majority of South Africa’s eleven languages are low-resourced, posing a major challenge to Automatic Speech Recognition (ASR) development. Modern ASR systems require an extensive amount of data that is extremely difficult to find for low-resourced languages. In addition, available speech and text corpora for these languages predominantly revolve around government, political and biblical content. Consequently, this hinders the ability of ASR systems developed for these languages to perform well especially when evaluating data outside of these domains. To alleviate this problem, the Icefall Kaldi II toolkit introduced new transformer model scripts, facilitating the adaptation of pre-trained models using limited adaptation data. In this paper, we explored the technique of using pre-trained ASR models in a domain where more data is available (government data) and adapted it to an entirely different domain with limited data (broadcast news data). The objective was to assess whether such techniques can surpass the accuracy of prior ASR models developed for these languages. Our results showed that the Conformer connectionist temporal classification (CTC) model obtained lower word error rates by a large margin in comparison to previous TDNN-F models evaluated on the same datasets. This research signifies a step forward in mitigating data scarcity challenges and enhancing ASR performance for low-resourced languages in South Africa.


Research on the Innovation and Development of Gravitational Wave Detection Technology

Abstract: According to Einstein’s general theory of relativity, gravity is described as the curvature of space-time caused by gravitational sources, and to prove the existence of gravitational waves, many scientific research institutes around the world have begun to build equipment to try to detect gravitational waves in the vast background signals of the universe. At present, gravitational waves have been detected by many scientific research institutions and scientists. Now, the detection of gravitational waves has shifted to the direction of high precision and accuracy. This paper starts with the laser interferometer, the most basic instrument for gravitational wave detection. It expounds on the latest development progress and key technologies of gravitational wave detection in the current physical world, including noise suppression and gravitational wave detection spacecraft projects. In terms of laser interferometers, this paper describes their principle and key technologies and puts forward the difficult problems that still need to be tackled and studied. In terms of noise suppression, this paper describes the noise and interference that may be generated and how to suppress the noise to avoid interfering with the accuracy of the experiment. It also points out the shortcomings of current noise suppression techniques. For gravitational wave detection spacecraft projects, this paper first focuses on several key projects in the world, then describes the technologies and shortcomings of spacecraft, and puts forward the direction of improvement and in-depth research on these technologies. This paper aims to summarize the key technologies of gravitational wave detection in the world today and point out the direction of its future development.


Advanced Machine Learning Techniques in Gomoku: Strategy,Implementation, and Analysis

Abstract: The strategic board game Gomoku has become a compelling domain for artificial intelligence (AI) research, particularly in developing and applying machine learning techniques. This paper comprehensively analyzes advanced machine learning strategies in Gomoku, focusing on logistic regression for board evaluation, neural networks for pattern recognition, and reinforcement learning for strategic gameplay. We discuss integrating these techniques in creating a sophisticated AI capable of high-level play and adaptability. Through this exploration, we highlight the potential of AI in strategic decision-making and its broader applications beyond board games.


A Comprehensive Review of Text Sentiment Analysis: A Survey of Traditional Methods and Deep Learning Approaches

Abstract: Text sentiment analysis is a crucial aspect within the realm of natural language processing. This paper, incorporating the latest research advancements, systematically reviews and summarizes mainstream methods in text sentiment analysis. It covers a spectrum of traditional text analysis techniques, including rule-based and dictionary-based methods, as well as machine learning approaches. The paper further delves into an in-depth discussion of text sentiment analysis methods based on deep learning. This encompasses Recurrent Neural Networks and their enhanced structures, Convolutional Neural Networks and their extended techniques, along with applications involving pre-trained models and transfer learning.


Briefly Explore the Combinatorial Game

Abstract: The paper talks about a series of games called combinatorial games. Combinatorial games are two-person games with perfect information, no chance moves, and a win-or-lose outcome. To be precise, a combinatorial game is a game that satisfies the following conditions. The paper also talks about the rules of the combinatorial game, such as how many players the game has and the rules of the game specify for both players and each position that moves to other positions a legal move. Then, the meaning of the P-positions and N-positions were introduced. After that, the paper talks about the typical combinatorial game- the game of Nim, another example of the combinatorial game, to illustrate the paper’s point of view. The paper talks about the whole rules of this example game, such as the number of players and the initial configuration of the piles/heaps. The author gives several strategies and talks about why these strategies can help the players win combinatorial games. The paper gives two cases to illustrate the game rules and give a better understanding after all. In the end, the paper gives a Python3 program for combinatorial games, making the ideas of combinatorial games more comprehensive.


Tools to Alleviate Teenager’s Anxiety in Learning ESL

Abstract: This paper explores a variety of different approaches teachers can take with technology assistance to alleviate the negative effects of learning anxiety, specifically in teenage students learning ESL. This paper focuses on online tools that teachers have used in studies before. Still, it includes offline tools teachers can use in the hopes that whatever the issue of learning anxiety may be, the educator and teacher understand the multitude of solutions available.


A Machine Learning Classification of Individuals with Mild Cognitive Impairment into Variants from Writing

Abstract: Introduction: Individuals with Mild Cognitive Impairment (MCI), a transitional stage between cognitively healthy aging and dementia, are characterized by subtle neurocognitive changes. Clinically, they can be grouped into two main variants, namely into patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). The distinction of the two variants is known to be clinically significant as they exhibit different progression rates to dementia. However, it has been particularly challenging to classify the two variants robustly. Recent research indicates that linguistic changes may manifest as one of the early indicators of pathology. Therefore, we focused on MCI’s discourse-level writing samples in this study. We hypothesized that a written picture description task can provide information that can be used as an ecological, cost-effective classification system between the two variants. Methods: We included one hundred sixty-nine individuals diagnosed with either aMCI or naMCI who received neurophysiological evaluations in addition to a short-written picture description task. Natural Language Processing (NLP) and BERT pre-trained Language Models were utilized to analyze the writing samples. Results: We showed that the written picture description task provided 90% overall classification accuracy for the best classification models, which performs better than cognitive measures. Discussion: Written discourses analyzed the AI models can automatically assess individuals with aMCI and naMCI and facilitate diagnosis, prognosis, therapy planning, and evaluation.


Using collaborative leadership at all levels to build people-oriented organizations

Abstract: Based on their nationwide work with the US Cooperative Extension Service, the authors have developed four concepts that leaders at all levels can use in creating people-oriented and effective organizations, groups, change teams, and a sense of community: (1) building collaborative leadership throughout the organization; (2) utilizing eight essential questions leaders need to answer; (3) developing a competency framework for leading people-oriented change; and (4) leveraging facilitation skills throughout an organization.


Research on extractive text abstract generation method for graph model based on TextRank

Abstract: The traditional TextRank algorithm is based on a graph model, and the extracted abstract generated by it does not deviate from the source text, but there may be issues such as polysemy. The BERT model can capture contextual semantics well. It can directly convert sentences in text into vector form, which can better solve the problem of polysemy. BiLSTM can obtain further information, IDCNN can better handle overfitting problems compared to traditional convolutional neural networks, and CRF can better solve the problem of sentence coherence. The model proposed in this article has been compared with other extraction based text summarization methods on the TTNews dataset, and experiments have shown that the method proposed in this article can obtain meaningful text summaries with significant optimization.


Predictive analytics and the collective dimensions of data protection

Abstract: This paper takes an interdisciplinary approach, combining legal studies, ethics and technical insights to shed light on the complex issues surrounding the regulation of predictive analytics. We argue that the individualised concept of regulation, shaped by the dogma of fundamental rights, is unable to adequately capture the implications of predictive analytics. We show that predictive analytics is a problem of collective privacy and informational power asymmetries, and conceptualise the form of data power at work in predictive analytics as ‘prediction power’. The unregulated prediction power of certain actors poses societal risks, especially if this form of informational power asymmetry is not normatively represented. The article analyses this legal lacuna in the light of recent case law of the European Court of Justice and new legislation at the EU level. To address these challenges, we develop the concept of ‘predictive privacy’ as a protected good based on collective interests.


Video anomaly detection via improved future frame prediction

Abstract: Video anomaly detection is extensively utilized across a variety of domains including public transportation, industrial production, city management, and military fields to mitigate risks and bolster enhance safety. To tackle the challenges associated with video anomaly detection in intricate environments, we propose a light but efficient framework that builds upon future frame prediction techniques. Our framework incorporates Convolutional Long Short-Term Memory (ConvLSTM), masked convolution, and attention mechanisms to enhance the detection accuracy. Furthermore, to simplify the model's complexity, we replace the convolutional layers in the network with depthwise separable convolutions (DSC). Through evaluation on public datasets such as CUHK Avenue, UCSD Peds1, and UCSD Peds2, our proposed network model exhibits both high accuracy and real-time performance.


Identification of Factors Influencing Educators Perception of Blended Learning and Triggering Adoption Decisions

Abstract: This study employs theoretical frameworks from the past to determine the factors that drive instructors to adopt blended learning (BL). Using a quantitative approach, the extended Technology Acceptance Model (TAM) was utilized to examine a sample size of 302 instructors from four multi-campus Lebanese universities that offer a business degree. The research findings indicate that ease of use has a positive impact on both attitude and usefulness towards BL. Furthermore, the perceived usefulness of BL positively influences the attitude and intention to use the system. To achieve greater adoption of BL, technical support should be a central component of educators' provision since it has a positive effect on the perceived ease of use. Additionally, subjective norms, such as positive image and usefulness, have a positive influence on educators, as they are influenced by important people in their lives, including their supervisors, as well as by the group's positive image.


Ultra-short-term forecast of distributed photovoltaic power based on satellite cloud image and LSTM model

Abstract: Abstract Photovoltaic (PV) power generation, with its volatile, intermittent, and random characteristics, and large-scale PV access pose a threat to grid stability. For this reason, predicting the photovoltaic output will help keep the grid safe and stable. On the basis of the influence of cloud groups on solar radiation, a very short-term forecast of distributed PV energy will be made using satellite cloud picture information to improve the forecast accuracy of PV energy production. The paper presents a method to predict distributed PV power at very short notice based on satellite clouds and a network model with Long Short-Term Memory (LSTM). First, extract a subset of meteorological and PV power data from the forecast area as training samples., and the abnormal part of the samples is cleaned by an isolated forest algorithm. Secondly, the occlusion feature is extracted from the satellite cloud image in the same period. Finally, the measured solar irradiance, meteorological information, and obscuration features are input into the LSTM network for prediction, and the photovoltaic power prediction results in the next 4 hours are obtained. The measured PV power of Jinghai Guangfu Power Station in Hefei, Anhui province on the 5th day was the training sample for the prediction of PV power on the 6th day. The prediction results show that the prediction error is 2.73% when a satellite cloud image is added, and 16.15% when a satellite cloud image is not added, and the prediction error is reduced by 13.42%.


The effect of PIQMAS application on the engagement processes of upper basic stage mathematics and science students

Abstract: New strategies, methods and projects for the learning of the mathematics and science disciplines are needed. One such project is the project for improving quality of mathematics and science (PIQMAS) education. In the present research, we consider the project implementation in Palestine. Specifically, we examine students’ engagement when implementing the project. To do that, we consider three components of engagement: the cognitive, the behavioral and the emotional. To collect the data, we used interviews and observations, which ensured the triangulation of data collection. We interviewed 13 science and mathematics teachers who carried out the project. We used deductive and inductive content analysis to analyze the interviews and observations transcripts. The research results indicated that the project impacted positively the three components of students’ engagement, which indicates the importance of incorporating new teaching methods, especially PIQMAS, in the teaching of science and mathematics. It is recommended to study different aspects of students’ learning in PIQMAS environment.


Modelling crypto markets by multi-agent reinforcement learning

Abstract: Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022. Unlike previous agent-based models (ABM) or multi-agent systems (MAS) which relied on zero-intelligence agents or single autonomous agent methodologies, our approach relies on endowing agents with reinforcement learning (RL) techniques in order to model crypto markets. This integration is designed to emulate, with a bottom-up approach to complexity inference, both individual and collective agents, ensuring robustness in the recent volatile conditions of such markets and during the COVID-19 era. A key feature of our model also lies in the fact that its autonomous agents perform asset price valuation based on two sources of information: the market prices themselves, and the approximation of the crypto assets fundamental values beyond what those market prices are. Our MAS calibration against real market data allows for an accurate emulation of crypto markets microstructure and probing key market behaviors, in both the bearish and bullish regimes of that particular time period.


Digital transformation in higher education

Abstract: Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings The process of digital transformation is accelerating among organizations and institutions within different settings. Firms' quest to build digital capability can be aided by following guidance provided by a suitable framework and taking necessary actions with regard to each of its dimensions. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.


Local unitary equivalence of arbitrary-dimensional multipartite quantum states

Abstract: Local unitary equivalence is an important ingredient for quantifying and classifying entanglement. Verifying whether or not two quantum states are local unitary equivalent is a crucial problem, where only the case of multipartite pure states is solved. For mixed states, however, the verification of local unitary equivalence is still a challenging problem. In this paper, based on the coefficient matrices of generalized Bloch representations of quantum states, we find a variety of local unitary invariants for arbitrary-dimensional bipartite quantum states. These invariants are operational and can be used as necessary conditions for verifying the local unitary equivalence of two quantum states. Furthermore, we extend the construction to the arbitrary-dimensional multipartite case. We finally apply these invariants to estimate concurrence, a vital entanglement measure, showing the practicability of local unitary invariants in characterizing entanglement.


Detecting DeFi securities violations from token smart contract code

Abstract: Abstract Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In recent years, DeFi has gained popularity and market capitalization. However, it has also been connected to crime, particularly various types of securities violations. The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses. This study aims to determine whether this problem is suited to a machine learning approach, namely, whether we can identify DeFi projects potentially engaging in securities violations based on their tokens’ smart contract code. We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projects’ tokens’ smart contract code (specifically, opcode-based features). Our final model was a random forest model that achieved an 80% F-1 score against a baseline of 50%. Notably, we further explored the code-based features that are the most important to our model’s performance in more detail by analyzing tokens’ Solidity code and conducting cosine similarity analyses. We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library, although this does not account for the entirety of our features. Another contribution of our study is a new dataset, comprising (a) a verified ground truth dataset for tokens involved in securities violations and (b) a set of legitimate tokens from a reputable DeFi aggregator. This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.


Interval reservoir computing: theory and case studies

Abstract: The time series data in many applications, for example, wind power and vehicle trajectory, show significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing the novel interval reservoir computing method. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using the recurrent neural network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in the RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying the condition that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented an experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. In addition, the confidence probability over the feasibility goes to 1 very quickly as the sample number increases.


Joint optimization of operational cost and carbon emission in multiple data center micro-grids

Abstract: As the internet data centers are mushrooming, the energy consumption and carbon emission of data centers are increasing rapidly. To cut down the electricity cost and carbon emission of the data centers, we proposed an optimization method to reduce the electricity cost and carbon emission in geo-distributed multiple data centers. In the proposed method, the carbon tax is considered in the overall operation cost to optimize the carbon emission. The spatial and temporal flexibility of computational workload is fully utilized by considering the difference in renewable energy power output, local electricity and carbon emission of multiple geo-distributed data centers to achieve a better performance. Furthermore, the nonlinear characteristics of the power loss of uninterruptible power supply (UPS) are considered in the optimization. To verify the proposed optimization method, simulation of six cases is carried out with realistic data, and results have proved the proposed method can reduce the operational costs by 4.93%–12.7% and decrease carbon emissions by up to 10%.


Tutt la tosse in una figura

Abstract: Cough is the most common cause of unscheduled paediatric visits. The figure in the article shows the main causes of paediatric coughs differentiated by duration, quality (dry or wet) and night-time coughing. With these few simple key anamnestic elements (and espe- cially if confirmed and reinforced by being able to see, even in a simple video, the child coughing) a diagnosis may be made with reasonable certainty, saving specialist visits and diagnostic tests that are unlikely useful and potentially harmful.


The Effect of Role Conflict, Role Ambiguity and Ethical Sensitivity on Auditor Performance with Intellectual Intelligence as a Moderating Variable

Abstract: This study aims to analyze the effect of role conflict, role ambiguity, and ethical sensitivity on auditor performance and intellectual intelligence as a moderating variable on the relationship between these factors and auditor performance. Data were collected from 200 respondents who were auditors at the Makassar City BPKP Office using a questionnaire. The data analysis technique used is multiple regression with a moderated regression analysis (MRA) approach. The results showed that role conflict and role ambiguity have a significant negative effect on auditor performance, while ethical sensitivity has a significant positive effect on their performance. In addition, intellectual intelligence moderates the relationship between role conflict and auditor performance so that the higher the intellectual intelligence of an auditor, the negative impact of role conflict on his performance will be reduced. However, intellectual intelligence does not moderate the relationship between role ambiguity and performance.


Self-Guided Robust Graph Structure Refinement

Abstract: Recent studies have revealed that GNNs are vulnerable to adversarial attacks. To defend against such attacks, robust graph structure refinement (GSR) methods aim at minimizing the effect of adversarial edges based on node features, graph structure, or external information. However, we have discovered that existing GSR methods are limited by narrowassumptions, such as assuming clean node features, moderate structural attacks, and the availability of external clean graphs, resulting in the restricted applicability in real-world scenarios. In this paper, we propose a self-guided GSR framework (SG-GSR), which utilizes a clean sub-graph found within the given attacked graph itself. Furthermore, we propose a novel graph augmentation and a group-training strategy to handle the two technical challenges in the clean sub-graph extraction: 1) loss of structural information, and 2) imbalanced node degree distribution. Extensive experiments demonstrate the effectiveness of SG-GSR under various scenarios including non-targeted attacks, targeted attacks, feature attacks, e-commerce fraud, and noisy node labels. Our code is available at https://github.com/yeonjun-in/torch-SG-GSR.


WildFake: A Large-scale Challenging Dataset for AI-Generated Images  Detection

Abstract: The extraordinary ability of generative models enabled the generation of images with such high quality that human beings cannot distinguish Artificial Intelligence (AI) generated images from real-life photographs. The development of generation techniques opened up new opportunities but concurrently introduced potential risks to privacy, authenticity, and security. Therefore, the task of detecting AI-generated imagery is of paramount importance to prevent illegal activities. To assess the generalizability and robustness of AI-generated image detection, we present a large-scale dataset, referred to as WildFake, comprising state-of-the-art generators, diverse object categories, and real-world applications. WildFake dataset has the following advantages: 1) Rich Content with Wild collection: WildFake collects fake images from the open-source community, enriching its diversity with a broad range of image classes and image styles. 2) Hierarchical structure: WildFake contains fake images synthesized by different types of generators from GANs, diffusion models, to other generative models. These key strengths enhance the generalization and robustness of detectors trained on WildFake, thereby demonstrating WildFake's considerable relevance and effectiveness for AI-generated detectors in real-world scenarios. Moreover, our extensive evaluation experiments are tailored to yield profound insights into the capabilities of different levels of generative models, a distinctive advantage afforded by WildFake's unique hierarchical structure.


ComFusion: Personalized Subject Generation in Multiple Specific Scenes  From Single Image

Abstract: Recent advancements in personalizing text-to-image (T2I) diffusion models have shown the capability to generate images based on personalized visual concepts using a limited number of user-provided examples. However, these models often struggle with maintaining high visual fidelity, particularly in manipulating scenes as defined by textual inputs. Addressing this, we introduce ComFusion, a novel approach that leverages pretrained models generating composition of a few user-provided subject images and predefined-text scenes, effectively fusing visual-subject instances with textual-specific scenes, resulting in the generation of high-fidelity instances within diverse scenes. ComFusion integrates a class-scene prior preservation regularization, which leverages composites the subject class and scene-specific knowledge from pretrained models to enhance generation fidelity. Additionally, ComFusion uses coarse generated images, ensuring they align effectively with both the instance image and scene texts. Consequently, ComFusion maintains a delicate balance between capturing the essence of the subject and maintaining scene fidelity.Extensive evaluations of ComFusion against various baselines in T2I personalization have demonstrated its qualitative and quantitative superiority.


Application of BiLSTM-CRF model with different embeddings for product name extraction in unstructured Turkish text

Abstract: Abstract Named entity recognition (NER) plays a pivotal role in Natural Language Processing by identifying and classifying entities within textual data. While NER methodologies have seen significant advancements, driven by pretrained word embeddings and deep neural networks, the majority of these studies have focused on text with well-defined grammar and structure. A significant research gap exists concerning NER in informal or unstructured text, where traditional grammar rules and sentence structure are absent. This research addresses this crucial gap by focusing on the detection of product names within unstructured Turkish text. To accomplish this, we propose a deep learning-based NER model which combines a Bidirectional Long Short-Term Memory (BiLSTM) architecture with a Conditional Random Field (CRF) layer, further enhanced by FastText embeddings. To comprehensively evaluate and compare our model’s performance, we explore different embedding approaches, including Word2Vec and Glove, in conjunction with the Bidirectional Long Short-Term Memory and Conditional Random Field (BiLSTM-CRF) model. Furthermore, we conduct comparisons against BERT to assess the efficacy of our approach. Our experimentation utilizes a Turkish e-commerce dataset gathered from the internet, where traditional grammatical and structural rules may not apply. The BiLSTM-CRF model with FastText embeddings achieved an F1 score value of 57.40%, a precision value of 55.78%, and a recall value of 59.12%. These results indicate promising performance in outperforming other baseline techniques. This research contributes to the field of NER by addressing the unique challenges posed by unstructured Turkish text and opens avenues for improved entity recognition in informal language settings, with potential applications across various domains.


Question Answering Over Spatio-Temporal Knowledge Graph

Abstract: Spatio-temporal knowledge graphs (STKGs) extend the concept of knowledge graphs (KGs) by incorporating time and location information. While the research community's focus on Knowledge Graph Question Answering (KGQA), the field of answering questions incorporating both spatio-temporal information based on STKGs remains largely unexplored. Furthermore, a lack of comprehensive datasets also has hindered progress in this area. To address this issue, we present STQAD, a dataset comprising 10,000 natural language questions for spatio-temporal knowledge graph question answering (STKGQA). Unfortunately, various state-of-the-art KGQA approaches fall far short of achieving satisfactory performance on our dataset. In response, we propose STCQA, a new spatio-temporal KGQA approach that utilizes a novel STKG embedding method named STComplEx. By extracting temporal and spatial information from a question, our QA model can better comprehend the question and retrieve accurate answers from the STKG. Through extensive experiments, we demonstrate the quality of our dataset and the effectiveness of our STKGQA method.


Training Language Model Agents without Modifying Language Models

Abstract: Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized functions. To facilitate the development of LLM agents, we present a novel paradigm of training LLM agents without modifying the LLM weights, which is particularly useful when the LLMs are difficult or inaccessible for modifications. Inspired by how humans continuously forge tools to adapt to real-world tasks, rather than change our biological structure to fit a static set of tools, we propose to progressively forge agent's functions to better solve the downstream tasks instead of modifying the LLM weights. By treating the functions as learnable `agent parameters' and leveraging the fundamental idea of model training in artificial intelligence, we develop AgentOptimizer that employs the LLM to update agents' functions and devise an agent training algorithm with two strategies, roll-back, and early-stop, to streamline the training process. With extensive experiments, we showcase that the agent training paradigm could significantly improve the performance of representative LLM agents in various downstream tasks. We also study the behavior of the agent training regarding aspects like the learning curve and domain transferability.


I Learn Better If You Speak My Language: Enhancing Large Language Model  Fine-Tuning with Style-Aligned Response Adjustments

Abstract: Fine-tuning large language models (LLMs) with a small data set for particular tasks is a widely encountered yet complex challenge. The potential for overfitting on a limited number of examples can negatively impact the model's ability to generalize and retain its original skills. Our research explores the impact of the style of ground-truth responses during the fine-tuning process. We found that matching the ground-truth response style with the LLM's inherent style results in better learning outcomes. Building on this insight, we developed a method that minimally alters the LLM's pre-existing responses to correct errors, using these adjusted responses as training targets. This technique enables precise corrections in line with the model's native response style, safeguarding the model's core capabilities and thus avoid overfitting. Our findings show that this approach not only improves the LLM's task-specific accuracy but also crucially maintains its original competencies and effectiveness.


Backward Lens: Projecting Language Model Gradients into the Vocabulary  Space

Abstract: Understanding how Transformer-based Language Models (LMs) learn and recall information is a key goal of the deep learning community. Recent interpretability methods project weights and hidden states obtained from the forward pass to the models' vocabularies, helping to uncover how information flows within LMs. In this work, we extend this methodology to LMs' backward pass and gradients. We first prove that a gradient matrix can be cast as a low-rank linear combination of its forward and backward passes' inputs. We then develop methods to project these gradients into vocabulary items and explore the mechanics of how new information is stored in the LMs' neurons.


ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic

Abstract: The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art models are partially trained on large Arabic texts, evaluating their performance in Arabic remains challenging due to the limited availability of relevant datasets. To bridge this gap, we present ArabicMMLU, the first multi-task language understanding benchmark for Arabic language, sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA), and is carefully constructed by collaborating with native speakers in the region. Our comprehensive evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models. Notably, BLOOMZ, mT0, LLama2, and Falcon struggle to achieve a score of 50%, while even the top-performing Arabic-centric model only achieves a score of 62.3%.


Privacy computing meets metaverse: Necessity, taxonomy and challenges

Abstract: Metaverse, the core of the next-generation Internet, is a computer-generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sustainable, interoperable, and data-sensitive characteristics. It cleverly blends the virtual and real worlds, allowing users to create, communicate, and transact in virtual form. With the rapid development of emerging technologies including augmented reality, virtual reality and blockchain, the metaverse system is becoming more and more sophisticated and widely used in various fields such as social, tourism, industry and economy. However, the high level of interaction with the real world also means a huge risk of privacy leakage both for individuals and enterprises, which has hindered the wide deployment of metaverse. Then, it is inevitable to apply privacy computing techniques in the framework of metaverse, which is a current research hotspot. In this paper, we conduct comprehensive research on the necessity, taxonomy and challenges when privacy computing meets metaverse. Specifically, we first introduce the underlying technologies and various applications of metaverse, on which we analyze the challenges of data usage in metaverse, especially data privacy. Next, we review and summarize state-of-the-art solutions based on federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs for different privacy problems in metaverse. Finally, we show the current security and privacy challenges in the development of metaverse and provide open directions for building a well-established privacy-preserving metaverse system.


Mask-up: Investigating Biases in Face Re-identification for Masked Faces

Abstract: AI based Face Recognition Systems (FRSs) are now widely distributed and deployed as MLaaS solutions all over the world, moreso since the COVID-19 pandemic for tasks ranging from validating individuals' faces while buying SIM cards to surveillance of citizens. Extensive biases have been reported against marginalized groups in these systems and have led to highly discriminatory outcomes. The post-pandemic world has normalized wearing face masks but FRSs have not kept up with the changing times. As a result, these systems are susceptible to mask based face occlusion. In this study, we audit four commercial and nine open-source FRSs for the task of face re-identification between different varieties of masked and unmasked images across five benchmark datasets (total 14,722 images). These simulate a realistic validation/surveillance task as deployed in all major countries around the world. Three of the commercial and five of the open-source FRSs are highly inaccurate; they further perpetuate biases against non-White individuals, with the lowest accuracy being 0%. A survey for the same task with 85 human participants also results in a low accuracy of 40%. Thus a human-in-the-loop moderation in the pipeline does not alleviate the concerns, as has been frequently hypothesized in literature. Our large-scale study shows that developers, lawmakers and users of such services need to rethink the design principles behind FRSs, especially for the task of face re-identification, taking cognizance of observed biases.


Parsimonious Generative Machine Learning for Non-Gaussian Tail Modeling  and Risk-Neutral Distribution Extraction

Abstract: In financial modeling problems, non-Gaussian tails exist widely in many circumstances. Among them, the accurate estimation of risk-neutral distribution (RND) from option prices is of great importance for researchers and practitioners. A precise RND can provide valuable information regarding the market's expectations, and can further help empirical asset pricing studies. This paper presents a parsimonious parametric approach to extract RNDs of underlying asset returns by using a generative machine learning model. The model incorporates the asymmetric heavy tails property of returns with a clever design. To calibrate the model, we design a Monte Carlo algorithm that has good capability with the assistance of modern machine learning computing tools. Numerically, the model fits Heston option prices well and captures the main shapes of implied volatility curves. Empirically, using S\&P 500 index option prices, we demonstrate that the model outperforms some popular parametric density methods under mean absolute error. Furthermore, the skewness and kurtosis of RNDs extracted by our model are consistent with intuitive expectations.


Imbalanced Data Clustering using Equilibrium K-Means

Abstract: Imbalanced data, characterized by an unequal distribution of data points across different clusters, poses a challenge for traditional hard and fuzzy clustering algorithms, such as hard K-means (HKM, or Lloyd's algorithm) and fuzzy K-means (FKM, or Bezdek's algorithm). This paper introduces equilibrium K-means (EKM), a novel and simple K-means-type algorithm that alternates between just two steps, yielding significantly improved clustering results for imbalanced data by reducing the tendency of centroids to crowd together in the center of large clusters. We also present a unifying perspective for HKM, FKM, and EKM, showing they are essentially gradient descent algorithms with an explicit relationship to Newton's method. EKM has the same time and space complexity as FKM but offers a clearer physical meaning for its membership definition. We illustrate the performance of EKM on two synthetic and ten real datasets, comparing it to various clustering algorithms, including HKM, FKM, maximum-entropy fuzzy clustering, two FKM variations designed for imbalanced data, and the Gaussian mixture model. The results demonstrate that EKM performs competitively on balanced data while significantly outperforming other techniques on imbalanced data. For high-dimensional data clustering, we demonstrate that a more discriminative representation can be obtained by mapping high-dimensional data via deep neural networks into a low-dimensional, EKM-friendly space. Deep clustering with EKM improves clustering accuracy by 35% on an imbalanced dataset derived from MNIST compared to deep clustering based on HKM.


Adaptive unscented Kalman filter methods for identifying time‐variant parameters via state covariance re‐updating

Abstract: Abstract The conventional parameter identification process generally assumes that parameters remain constant. However, under extreme loading conditions, structures may exhibit nonlinear behavior, and parameters could demonstrate time‐variant characteristics. The unscented Kalman filter (UKF), as an efficient online recursive estimator, is widely used for identifying parameters of nonlinear systems. Nevertheless, it exhibits limitations when attempting to identify time‐variant parameters. To address this issue, this paper proposes a covariance matching technique that produces an array of adaptive UKF algorithms. Firstly, the sensitivity parameter η is defined to identify the instant when the parameter change occurs, and its threshold is calculated based on the sensitivity parameter time history curve. Secondly, an adaptive forgetting factor is introduced to simultaneously update the innovation, cross, and state covariance matrices when the k th‐step sensitive parameter surpasses the threshold. Finally, a secondary correction forgetting factor (SCFF) is employed to further re‐update the state covariance values at the identified damage locations. This creative step enhances the adaptive capability and optimizes the identification accuracy of the proposed algorithms. Both the numerical simulations and shaking table test demonstrate that the proposed adaptive algorithms can efficiently identify the time‐variant stiffness‐type parameters, and accurately capture their time‐variant characteristics.


TSFIS-GWO: Metaheuristic-Driven Takagi-Sugeno Fuzzy System for Adaptive Real-Time Routing in WBANs

Abstract: Wireless body area network (WBAN) is an internet-of-things technology that facilitates remote patient monitoring and enables medical staff to administer timely treatments. One of the main challenges in designing WBANs is the routing problem, which is complicated due to dynamic changes in network topology and the limited resources of nodes. Several heuristic and metaheuristic methods have been presented to solve the routing problem in WBANs. Although metaheuristics outperform heuristics by producing higher-quality solutions, they cannot respond to real-time requests. This paper introduces a reactive routing protocol for WBANs that combines a fuzzy heuristic with a metaheuristic learning model. It utilizes a Takagi-Sugeno Fuzzy Inference System in conjunction with the Grey Wolf Optimizer (named TSFIS-GWO). The objective is to simultaneously benefit from the advantages of both approaches, namely, the effectiveness of metaheuristics for offline hyperparameter tuning and the quickness of fuzzy heuristics for real-time routing. At every round, the tuned fuzzy system takes multiple parameters of the current state of the nodes and links to construct the multi-hop routing tree under IEEE 802.15.6. To optimize the performance of the protocol for each WBAN, the fuzzy rules of the TSFIS model are automatically adjusted through a learning method based on GWO. This is done in accordance with the specific requirements of the application, and the tuning process takes place once before the protocol is applied. Simulation results in three applications demonstrate that the proposed TSFIS-GWO model is capable of providing real-time solutions while outperforming the existing methods in terms of application-specific performance measures.


Reconstruction techniques for quantum trees

Abstract: The inverse problem of recovery of a potential on a quantum tree graph from the Weyl matrix given at a number of points is considered. A method for its numerical solution is proposed. The overall approach is based on the leaf peeling method combined with Neumann series of Bessel functions (NSBF) representations for solutions of Sturm–Liouville equations. In each step, the solution of the arising inverse problems reduces to dealing with the NSBF coefficients. The leaf peeling method allows one to localize the general inverse problem to local problems on sheaves, while the approach based on the NSBF representations leads to splitting the local problems into two‐spectrum inverse problems on separate edges and reduces them to systems of linear algebraic equations for the NSBF coefficients. Moreover, the potential on each edge is recovered from the very first NSBF coefficient. The proposed method leads to an efficient numerical algorithm that is illustrated by numerical tests.


Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

Abstract: With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.


Improving Sentence Embeddings with an Automatically Generated NLI  Dataset

Abstract: Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing. This is also true for sentence embedding learning, where a decoder-based model, PromptEOL, has achieved the best performance on semantic textual similarity (STS) tasks. However, PromptEOL makes great use of fine-tuning with a manually annotated natural language inference (NLI) dataset. We aim to improve sentence embeddings learned in an unsupervised setting by automatically generating an NLI dataset with an LLM and using it to fine-tune PromptEOL. In experiments on STS tasks, the proposed method achieved an average Spearman's rank correlation coefficient of 82.21 with respect to human evaluation, thus outperforming existing methods without using large, manually annotated datasets.


How (un)ethical are instruction-centric responses of LLMs? Unveiling the  vulnerabilities of safety guardrails to harmful queries

Abstract: In this study, we tackle a growing concern around the safety and ethical use of large language models (LLMs). Despite their potential, these models can be tricked into producing harmful or unethical content through various sophisticated methods, including 'jailbreaking' techniques and targeted manipulation. Our work zeroes in on a specific issue: to what extent LLMs can be led astray by asking them to generate responses that are instruction-centric such as a pseudocode, a program or a software snippet as opposed to vanilla text. To investigate this question, we introduce TechHazardQA, a dataset containing complex queries which should be answered in both text and instruction-centric formats (e.g., pseudocodes), aimed at identifying triggers for unethical responses. We query a series of LLMs -- Llama-2-13b, Llama-2-7b, Mistral-V2 and Mistral 8X7B -- and ask them to generate both text and instruction-centric responses. For evaluation we report the harmfulness score metric as well as judgements from GPT-4 and humans. Overall, we observe that asking LLMs to produce instruction-centric responses enhances the unethical response generation by ~2-38% across the models. As an additional objective, we investigate the impact of model editing using the ROME technique, which further increases the propensity for generating undesirable content. In particular, asking edited LLMs to generate instruction-centric responses further increases the unethical response generation by ~3-16% across the different models.


A Data-Centric Approach To Generate Faithful and High Quality Patient  Summaries with Large Language Models

Abstract: Patients often face difficulties in understanding their hospitalizations, while healthcare workers have limited resources to provide explanations. In this work, we investigate the potential of large language models to generate patient summaries based on doctors' notes and study the effect of training data on the faithfulness and quality of the generated summaries. To this end, we develop a rigorous labeling protocol for hallucinations, and have two medical experts annotate 100 real-world summaries and 100 generated summaries. We show that fine-tuning on hallucination-free data effectively reduces hallucinations from 2.60 to 1.55 per summary for Llama 2, while preserving relevant information. Although the effect is still present, it is much smaller for GPT-4 when prompted with five examples (0.70 to 0.40). We also conduct a qualitative evaluation using hallucination-free and improved training data. GPT-4 shows very good results even in the zero-shot setting. We find that common quantitative metrics do not correlate well with faithfulness and quality. Finally, we test GPT-4 for automatic hallucination detection, which yields promising results.


Evaluating Publications' Keywords in Computer Science Education Research: A Bibliometric NLP Approach

Abstract: Abstract In the rapidly growing field of computer science education research (CSER), the collection of publication keywords represent the knowledge landscape of the research domain. This helps stakeholders to contextualize existing research topics, identify the strategic direction for future works, and shape the culture of this growing community. For the research studies published in CSER, the use of proper keywords will help to improve publication visibility in research networks. Publication visibility is defined as the share of traffic a study receives based on its ranking from Search Engine Result Pages (SERPs), which can be gained through indexing relevant keywords to help search engines to find the related studies. It is important to acknowledge that analyzing keyword selections in the context provided by the publication abstract, enables us to determine the keywords’ relevance to the research topics. However, evaluating the selection of publication keywords is an important but lesser-addressed issue. In this work, we utilized a bibliometric approach to scrutinize publication keywords in CSER publications from 2015 to 2020. We gathered the titles, abstracts, and keywords collectively from studies published in two major CS education conferences and journals. We also extracted the strategic directions from their mission statements. By applying the Natural Language Processing (NLP) technique, known as Term Frequency-Inverse Document Frequency (tf-idf), we aimed to answer: 1) What are the most prevalent research foci represented in CSER through the publication keywords? 2) Are publication keywords in alignment with the CSER publications’ stated strategic directions? 3) Do selected keywords in CSER publications adequately represent the research topics presented via publication abstracts? Our preliminary results suggest that, as expected, the most prevalent research foci represented by the most commonly used keywords of CSER were on teaching and learning and broadening participation, which is in alignment with the corresponding strategic directions. Our analysis suggests that the CSER community is putting efforts to formulate a diversified culture through teaching and learning for inclusivity. However, our results also suggest that there is a misalignment between keyword identification by authors and the topics presented in the abstracts. With our work, we hope to motivate scholars to carefully evaluate and select the keywords that will be used in the future for indexing publications to improve the research topic relevancy and publication visibility for broader impact.


Work in Progress: Development and Evaluation of Self-Contained, Shippable Outreach Experiments for Online Implementation in K-12 Classrooms

Abstract: Abstract Due to COVID-19, more students are transitioning to online classrooms. This poses a problem for STEM educators as students are limited in their ability to learn about the scientific process or scientific thinking through hands-on experimentation. Additionally, outreach programs that are designed to increase interest and participation in STEM face a challenge of not being able to enter the physical classroom for activities. To address these needs, a program was developed to create self-contained experimental kits that could be shipped to students’ homes. This allows students to gain the same outreach experience that they might have had in the classroom without leaving the safety of their home. Additionally, it removes the burden on parents of having to purchase outreach materials. The contents of the kits were designed with the following constraints: 1) experiments must be easy to complete with minimal instructions, 2) there must be a limited mess and 3) they must be easily and inexpensively shipped. With these constraints, four experiments were developed: analyzing the pH of household acids and bases using a red cabbage indicator (chemical engineering), building a popsicle-stick bridge (civil engineering), creating a drinking-straw prosthetic hand (biomedical engineering), and assembling an automatic LED night light (electrical engineering). The instructional guides to the kits include background information about the STEM topic, methods for completing the experiment, tables for data collection, and/or analysis questions about the data. To ensure the clarity of the instructions, recommendations were taken from a non-target audience to refine the materials. The kits were piloted on college-aged peers who have varying degrees of expertise in the subjects. Peers were asked to assess the clarity of the activity, provide feedback on the quality of the experiment, and complete questions for the pretest and posttest on the topic matter covered in the instructional guides. Results from this study will be used to refine the outreach kits prior to implementation with K-12 students. While originally designed to address the limitations in place due to COVID-19, this project can expand the outreach program to areas where in-person programs can be challenging. With considerate development, expansion to Appalachian regions can provide students with more exposure to STEM, while also allowing them to interact with engineering students from the University of Kentucky.


Using Agile and Active Learning in Software Development Curriculum

Abstract: Abstract This paper introduces a novel implementation of an agile software development process within an active learning paradigm in a semester long junior-level course for Computer Science majors. This Software Engineering course is centered around experiential learning of the entire software development lifecycle (SDLC) and applying a modified version of Scrum throughout. In order to mirror the real-world practice in a twice a week 85 minute class, we present many adjustments to Scrum for use in the classroom. We describe the implementation of the top six agile techniques used in industry (daily standup, sprint planning, retrospectives, sprint review, short iterations, planning poker) which focuses the learning experience on the most important components of agile development in addition to including top engineering practices used in industry. Additionally, we report extensions and variants for adapting this design to existing software engineering courses at other universities. Among these variants we propose adopting class-wide teams which is atypical at other universities for junior-level project courses.


Educating the Next Generation of Cybersecurity Experts

Abstract: Abstract The overall goal of the NSF Division of Undergraduate Education (DUE) S-STEM funded "Attracting and Cultivating Cybersecurity Experts and Scholars through Scholarships" (ACCESS) program is to increase Cybersecurity-related STEM degree completion of low-income, high-achieving undergraduate students with demonstrated financial need and to generate knowledge about academic success, retention, persistence, graduation, and career pathways of these students to improve the education of future STEM workers. Specifically, ACCESS aims to contribute towards addressing the tremendous governmental and industry need for highly skilled cybersecurity experts. Program objectives include: (1) increasing annual enrollment of students in the B.S. in Computer and Information Sciences programs with specialization in Cybersecurity; (2) enhancing curricular and extra-curricular student support services and activities for students; (3) strengthening the partnerships with computer and information technology employers; and (4) investigating the impact of the curricular and co-curricular activities on student success. While significant research has been done relative to student success, retention, and persistence to graduation in STEM fields, cybersecurity is a new field of study and factors affecting student recruitment, academic success, retention, persistence to graduation within this field are not known. In year 1, students were recruited, applications were evaluated, and scholarships were awarded to nine academically talented students, beginning fall 2020. Of these students, four are female (one is from an underrepresented minority population) and five are male (three are from underrepresented minority populations). The students engage in a set of co-curriculum activities, including participation in: outreach activities; technical and career development seminars; a cybersecurity-focused student organization; and potentially, cybersecurity undergraduate research and summer internship opportunities. The paper and poster describe the background of the ACCESS program, recruitment and selection of ACCESS scholarship recipients, project activities, and challenges presented by the COVID-19 pandemic.


Teaching in the Era of COVID-19: A Reinvented Course Project for an Ocean Engineering Course

Abstract: Abstract Experiential, team-based course projects, with an emphasis on designing and building physical products, are increasingly being adopted across many engineering disciplines, including wide use in ocean engineering courses. COVID-19 presents new challenges to pedagogies that rely heavily on physical production and face-to-face teamwork. While collaborative, hands-on projects, such as designing and building ROVs, have many documented educational gains—deepening content understanding and improving motivation, to name a few—these once beneficial activities are currently infeasible. The complications brought on by the pandemic necessitate the creation of new course projects that heed social distancing guidelines, minimize touch, and accommodate remote learners, all while continuing to enhance student learning. In the Fall of 2020, our small liberal arts university reopened its classrooms for in-person teaching and learning. While most students elected to return to the physical classroom, some chose to learn remotely, resulting in a large number of hybrid course offerings. The potential for a spike in COVID-19 cases in the campus community meant that courses could pivot to fully remote teaching and learning at any moment. In response to this new pedagogical framework, the semester-long course project for an upper-level ocean engineering course was reinvented. The project was inspired by Wired Magazine’s video series “5-Levels” in which experts explain a topic to a child, teenager, undergraduate, graduate student, and an expert in their field. This Fall, students worked individually to create a video series in which they explained a self-selected advanced topic in ocean engineering to three distinct audiences of their choosing. The success of the new course project is assessed through analysis of students’ videos, reflection papers, peer evaluations, and course surveys. More specifically, the aim of this work is to explore the efficacy of the project in meeting a variety of learning outcomes, including enhancing 21st century skills in audiovisual communication, and deepening the students’ knowledge of ocean engineering concepts. Finally, this paper shares lessons learned and provides recommendations for future implementations of this course project.


Upskilling to Meet Cloud Talent Needs

Abstract: Abstract Miami Dade College, based on demands in the workforce, has focused on creating emerging technology education, particularly Cloud technology. Business Cloud migration has accelerated over the last several years, and companies around the world are investing in their future with the cloud. With the increased demand for cloud-skilled professionals the last four years, we launched a cloud literacy initiative to meet cloud talent needs. This initiative aims to provide our students in the computing/IT fields with the knowledge, abilities and skills needed to accelerate their cloud-related learning. With the support of NSF ATE, we collaborated with Amazon Web Services (AWS) to create a new pathway for the next generation of cloud computing professionals. The course sequence was designed in conjunction with an AWS Educate team assisting in the design of course sequencing and degree plans to leverage their educational experience in teaching cloud technologies. The core curriculum designed for the cloud literacy initiative leveraged an existing pathway for an associate degree in networking technology, and then partially pulled classes from the design of the bachelor’s in information systems technology degree. The classes identified, used current offerings across our programs and included a focus on the supporting infrastructure of cloud systems: Databases, Linux OS, and Networking. With these three foundational classes that were cloudified, three cloud-focused courses based on industry certifications were developed: Cloud Essentials for AWS Cloud Practitioner and Cloud Infrastructure and Services for AWS Solutions Architect certifications with a capstone class completing the academic pathway. This new curriculum includes pedagogical changes to utilize project-based learning by incorporating resources and learning from multiple sources to best mimic real-world application, data, and design attributes. In addition to the associate degree, a college credit certificate in cloud computing was created to strengthen (re-)entering students in the workforce and dual enrollment students with credentials and employability skills by using high impact educational practices. Our cloud curriculum incorporates project-based learning approach, a real-world experience using the cloud technology. This poster shares strategies and pedagogical tools for teaching a cloud-focused curriculum for broader impact and student success.


Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

Abstract: When deploying workflows in cloud environments, the use of Spot Instances (SIs) is intriguing as they are much cheaper than on-demand ones. However, SIs are volatile and may be revoked at any time, which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling techniques. Although some scheduling methods for SIs have been proposed, most of them are no more applicable to the latest SIs, as they have evolved by eliminating bidding and simplifying the pricing model. This study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile SIs in cloud environments. Based on Monte Carlo simulation and list scheduling, a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this problem. With the Monte Carlo simulation framework, MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling, and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection criteria. Experimental results show that the performance of MCLS is more competitive compared with traditional algorithms.


MCARS-CC: A Salable Multicontext-Aware Recommender System

Abstract: Context-aware recommendation systems (CARSs) leverage contextual information, e.g., time, location, or mood, to generate more personalized recommendations with high accuracy; however, existing CARSs fall short in: 1) handling the high sparsity of data; 2) designing scalable solutions in real time; and 3) providing more personalized solutions with the current limited static contexts. This article proposes a multi-CARS based on consensus clustering (MCARS-CC) to solve these challenges. The item-based contextual information is acquired using explicit static and inferred contexts by applying sentiment analysis to the users’ reviews. The proposed model is experimented using contextual prefiltering and postfiltering techniques applied to two benchmark datasets, Yelp and TripAdvisor. The model is evaluated using mean absolute error (MAE), root-mean-squared error (RMSE), response time, precision, recall, and F-measure. The experimental results show that the proposed MCARS-CC model outperforms other baseline techniques using the accuracy and error-based metrics. Incorporating hypergraph partitioning algorithm (HGPA) could improve the MAE and RMSE by 25.96% and 8.94% (Yelp), respectively. Also, HGPA led to an 18.47% and 15.94% improvement ratio in terms of MAE and RMSE (TripAdvisor), respectively.


An efficient and secure certificateless aggregate signature scheme

Abstract: In recent years, the Internet of Things (IoT) has been developing rapidly and popularizing the world. Smart home is one of its important applications, the devices and household appliances are connected together through the network to provide intelligent services. However, malicious attacks often occur during the transmission of data information, resulting in data leakage or data changes. Therefore, it is necessary to ensure the integrity and availability of data. Based on this, we propose a new pairing-free certificateless aggregate signature (CLAS) scheme, which achieves efficient authentication of multiple messages and improves the verification rate. In addition, we demonstrate its security based on the hardness of the elliptic curve discrete logarithm problem in the random oracle model. Comparison with other CLAS schemes proves that our scheme has better security and higher efficiency through performance analysis.


Multilingual Character Recognition Dataset for Moroccan Official Documents

Abstract: This article focuses on the construction of a dataset for multilingual character recognition in Moroccan official documents. The dataset covers languages such as Arabic, French, and Tamazight and are built programmatically to ensure data diversity. It consists of sub-datasets such as Uppercase alphabet (26 classes), Lowercase alphabet (26 classes), Digits (9 classes), Arabic (28 classes), Tifinagh letters (33 classes), Symbols (14 classes), and French special characters (16 classes). The dataset construction process involves collecting representative fonts and generating multiple character images using a Python script, presenting a comprehensive variety essential for robust recognition models. Moreover, this dataset contributes to the digitization of these diverse official documents and archival papers, essential for preserving cultural heritage and enabling advanced text recognition technologies. The need for this work arises from the advancements in character recognition techniques and the significance of large-scale annotated datasets. The proposed dataset contributes to the development of robust character recognition models for practical applications.


A SYSTEMATIC LITERATURE REVIEW: RECURSIVE FEATURE ELIMINATION ALGORITHMS

Abstract: Recursive feature elimination (RFE) is a feature selection algorithm that works by gradually eliminating unimportant features. RFE has become a popular method for feature selection in various machine learning applications, such as classification and prediction. However, there is no systematic literature review (SLR) that discusses recursive feature elimination algorithms. This article conducts a SLR on RFE algorithms. The goal is to provide an overview of the current state of the RFE algorithm. This SLR uses IEEE Xplore, ScienceDirect, Springer, and Scopus (publish and publish) databases from 2018 to 2023. This SLR received 76 relevant papers with 49% standard RFEs, 43% strategy RFEs, and 8% modified RFEs. Research using RFE continues to increase every year, from 2018 to 2023. The feature selection method used simultaneously or for comparison is based on a filter approach, namely Pearson correlation, and an embedded approach, namely random forest. The most widely used machine learning algorithms are support vector machines and random forests, with 19.5% and 16.7%, respectively. Strategy RFE and modified RFE can be referred to as hybrid RFEs. Based on relevant papers, it is found that the RFE strategy is broadly divided into two categories: using RFE after other feature selection methods and using RFE simultaneously with other methods. Modification of the RFE is done by modifying the flow of the RFE. The modification process is divided into two categories: before the process of calculating the smallest weight criteria and after calculating the smallest weight criteria. Calculating the smallest weight criteria in this RFE modification is still a challenge at this time to obtain optimal results.