Miracle-dz/UniFAR-FTUs huggingface dataset:
Creative Cognition as a Bandit Problem

This paper characterizes creative cognition as a multi-armed bandit problem involving a trade-off between exploration and exploitation in sequential decisions from experience taking place in novel uncertain environments. Creative cognition implements an efficient learning process in this kind of dynamic decision. Special emphasis is put on the optimal sequencing of divergent and convergent behavior by showing that divergence must be inhibited at one point to converge toward creative behavior so that excessive divergence is counterproductive. We test this hypothesis in two behavioral experiments, using both novel and well-known tasks and precise measures of individual differences in creative potential in middle and high school students. Results in both studies confirmed that a task-dependent mix of divergence and convergence predicted high performance in a production task and better satisfaction in a consumption task, but exclusively in novel uncertain environments. These predictions were maintained after controlling for gender, personality, incentives, and other factors. As hypothesized, creative cognition was shown to be necessary for high performance under the appropriate conditions. However, it was not necessary for getting high grades in a traditional school system.
Pulse Train Control Strategy for Autonomous Three-Phase Voltage Source Inverter in Stationary Reference Frame

Pulse train (PT) control strategy is proposed for autonomous three-phase voltage source inverter (3ph-VSI) in stationary reference frame. Compared with classical control strategies for 3ph-VSI system, the proposed PT control strategy of 3ph-VSI utilizes the combination of two different duty cycles to approximate the desired duty cycles of 3ph-VSI. The load voltage can therefore be regulated. Without error amplifier and its corresponding compensation circuit, the proposed control scheme features fast transient performance and simple operation. The control loop of the proposed control strategy is simple to design. The operation principle and structure of the proposed PT-controlled 3ph-VSI are introduced. Its implementation and control parameter design are presented as well. The effects of the control parameter on the performance of the proposed control method are verified by simulation results. Besides, the fault-tolerance analysis of the circuit parameters of the proposed control scheme is provided. A 1.0-kVA experimental prototype of 3ph-VSI is built to verify the proposed control technique. Experimental results of the proposed control technique are provided to compare with conventional Ziegler–Nichols PI control method, which show that the steady-state, load transient and output frequency transient performance of the proposed PT-controlled 3ph-VSI can be maintained over a wide output power range of 0–1000 VA.
Evaluation of Distribution Line Fault Probability and Line Reinforcement Method in Typhoon Events

Aiming at the problem of distribution line fault in typhoon events, a method for evaluating the fault probability of distribution line before the arrival of typhoon and a reinforcement method are proposed. Firstly, based on historical wind speed data, the wind speed hybrid prediction model is used to predict the local wind speed in 24 hours after the upcoming typhoon. Secondly, the fault model of component is used to calculate the distribution line fault probability of IEEE 33-bus distribution system. Finally, the corresponding reinforcement measures of the distribution lines are proposed and the power flow of the power distribution system before and after reinforcement is compared. This method can provide reference for emergency early warning and disaster prevention and mitigation during typhoons in the distribution network of southeast China.
Semi-supervised Learning of Visual Causal Macrovariables

Discovery of causally related concepts is one of the key challenges in extracting knowledge from observational data. Lower-dimensional “causal macrovariables” represent concepts which preserve all relevant causal information in high-dimensional systems. Existing causal macrovariable discovery algorithms are limited by assumptions about known and controllable interventions. We propose a variational autoencoder-inspired architecture with regularization terms for semi-supervised causal macrovariable discovery. These terms impose domain knowledge regarding visual causal concepts to differentiate between correlation and causation. Experiments on both synthetic and real-world datasets with known causal dynamics show that our method can discover more concise and precise causal macrovariables than unsupervised methods.
A Survey of FPGA Optimization Methods for Data Center Energy Efficiency

This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGAs energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the European Code of Conduct for Data Center Energy Efficiency. The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector.
Experimental Data Management Platform for Materials Informatics

We are promoting the research and development of materials informatics, which realizes the efficiency and sophistication of material research by utilizing a huge amount of experimental data. In order to accelerate new material research and development, it is important to predict the results of new ma-terial development by unimplemented experiments from past experimental data, and for that purpose, a large amount of experimental data generated through various material research processes is used for business. The challenge of material research is to reduce the time for search and acquire data from enormous experiment data. In this paper, we show the experimental da-ta management platform by describing the research and development work-flow with a unified data model. Through prototyping and evaluating the pro-posed technique, we confirmed that the time required for system construction and data acquisition of required experimental data can be reduced by an average of 94.4% in the target work.
Splicing Characterization and Isoform Switch Events in Human Keratinocytes Carrying Oncogenes from High-Risk HPV-16 and Low-Risk HPV-84

Infection of epithelial cells with high-risk HPV (HR-HPV) types, followed by expression of virus oncogenic proteins (E5, E6, and E7), leads to genomic imbalance, suppression of tumor inhibitors, and induction of oncogenes. Low-risk HPV (LR-HPV) may slow the rate at which cervical cancer spreads to an invasive stage since co-infection with LR-HPV is linked to a decreased risk of future invasive cancer than infection with HR-HPV alone. We then propose that cancer-progressing changes may be distinguished through identifying the functional differences between LR-HPV and HR-HPV. Lentiviral strategies were followed to establish HaCaT cells with constitutive expression of HPV oncogenes. RNAseq experiments were designed to analyze the transcriptome modulations caused by each of the E5, E6, and E7 oncogenes of HPV-16 and HPV-84 in HaCaT cells. We identified enhanced RNA degradation, spliceosome, and RNA polymerase pathways related to mRNA processing. ATTS (alternative transcription termination site) was discovered to be more prevalent in cells with HPV-16E5 than HPV-84E5. In HPV-16E6-infected cells, ATTS gain was significantly higher than ATTS loss. Cells with HPV-16E7 had more isoforms with intron retention (IR) than those with HPV-84E7. We identified switches in ADAM10, CLSPN, and RNPS1 that led to greater expression of the coding isoforms in HR-HPV. The results of this work highlight differences between LR-HPV and HR-HPV in mRNA processing. Moreover, crucial cervical cancer-related switch events were detected.
A Self-Powered Rectifier-Less Series-Synchronized Switch Harvesting on Inductor (S-SSHI) Interface Circuit for Flutter-Based Piezoelectric Energy Harvesters

Energy harvesting from flow-induced vibrations has been a hot spot in recent years. In this study, a flutter-based piezoelectric energy harvester (FPEH) connected with a self-powered rectifier-less S-SSHI interface circuit is working at the limit cycle oscillation (LCO) state to efficiently harvest wind-induced vibration energy. First, an FPEH is designed, and the theoretical model is derived. The dynamic response of the FPEH is tested and measured in a wind tunnel, and results show that flutters start at the wind speed of 7.3 m/s. Meanwhile, the root mean square (RMS) output voltage increases with the increase of the wind speed which is also proved by the numerical simulations and the experiment. A self-powered optimized series synchronized switch harvesting on inductor circuit (SP-OSSHI) is proposed to efficiently harvest the electrical energy according to the output characteristic from flutter. The proposed circuit reduces the number of components and the circuit size by improving the positive and negative peak detection switches, which reduces the internal energy loss and thus improves the energy harvesting efficiency. The energy harvester is verified by the experiment, and a maximum output power of 36 μ W is obtained.
Improved multiple sclerosis diagnosis with advanced deep learning techniques

White matter pathologies analysis is important for segmenting multiple sclerosis (MS) lesions in MRI slices. Myelin damage affects motor function, sensory conflicts, paralysis, and vision. Extracting an optimistic number of hidden characteristic features induces high feature maps and reduces redundancy and dimensionality. A framework based on Hybrid Deep Convolutional Neural Networks (HD‐CNN) is presented to classify MS based on the acquired optimistic feature characteristic. First, Chaotic Leader‐Selective Particle Swarm Optimization uses the pixel intensity ratio of the targeted region to extract the most hidden white matter features. Second, Refined Slime Mold categorizes hidden features and ensures that the necessary features are selected effectively for a better segmentation result. An effective inbuilt Maximum Bidirectional Gradient Classifier accurately classifies MS lesions by detecting white matter spots in the target region. Finally, we will evaluate the proposed HD‐CNN framework using the industry‐standard University Medical Centre Ljubljana (UMCL) and international symposium on biomedical imaging (ISBI) 2015 datasets as benchmarks. It reaches 98.56%, 93.15%, and 98.44% accuracy for FLAIR, T1W, and T2W, respectively. Our HD‐CNN improves MS classification over other state‐of‐the‐art techniques, compared with accuracy, F‐score, Recall, Precision, Dice, and Jaccard indexes values.
The Model of Organizational Performance Based on Employees’ Dynamic Capabilities–Verification During Crisis Caused by Black Swan Event

The article concerns the potential influence of employees’ dynamic capabilities on the performance of entire organization, which operates in crisis caused by Black Swan event. It is the expansion of job performance model based on employees’ dynamic capabilities, proposing the possibility of translating the positive influence of those capabilities onto entire organization and underlining the importance of employees’ dynamic capabilities during crisis within organization. Based on literature analysis, the shape of the amended model is proposed, in which employees’ dynamic capabilities influence organizational performance through elements of the original model (person-job fit, work motivation, job satisfaction, work engagement and job performance), and additional ones: person-organization fit, person-supervisor fit. The proposed model is empirically verified based on the sample of 1160 organization operating in Poland, Italy and USA during an active wave of COVID-19 pandemic (which is an example of Black Swan event). The results obtained using path analysis confirmed that employees’ dynamic capabilities indeed influence organizational performance of organizations operating in crisis caused by Black Swan event through elements proposed in the model.
MINING: Multi-Granularity Network Alignment Based on Contrastive Learning

Network alignment aims to discover nodes in different networks belonging to the same identity. In recent years, the network alignment problem has aroused significant attentions in both industry and academia. However, the continuous exploding of network data brings two challenges in solving the network alignment problem, i.e., large network scale and scarce labeled data. To bridge this gap, in this paper we propose a novel approach termed as <underline>M</underline>ulti-granular<underline>I</underline>ty <underline>N</underline>etwork al<underline>I</underline>gnment based on co<underline>N</underline>trastive learnin<underline>G</underline> (MINING). Specifically, in MINING, we first design multi-granularity alignment framework to solve the issue of large network scale. Then, we design intra- and inter-network contrastive learning to solve the issue of scarce labeled data. Moreover, we provide theoretical proofs to demonstrate the effectiveness of MINING. Finally, we conduct extensive experiments on the benchmark datasets of Facebook-Twitter, AMiner-LinkedIn and DBpedia<inline-formula><tex-math notation="LaTeX">$_{\text{ZH}}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mtext>ZH</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="zhang-ieq1-3273782.gif"/></alternatives></inline-formula>-DBpedia<inline-formula><tex-math notation="LaTeX">$_{\text{EN}}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mtext>EN</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="zhang-ieq2-3273782.gif"/></alternatives></inline-formula>, and results show that MINING can averagely achieve 15.93% higher <inline-formula><tex-math notation="LaTeX">$\operatorname{Hits@}k$</tex-math><alternatives><mml:math><mml:mrow><mml:mo form="prefix">Hits@</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq3-3273782.gif"/></alternatives></inline-formula> and 14.82% higher <inline-formula><tex-math notation="LaTeX">$\operatorname{MRR@}k$</tex-math><alternatives><mml:math><mml:mrow><mml:mo form="prefix">MRR@</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq4-3273782.gif"/></alternatives></inline-formula> compared with the state-of-the-art methods.
CALIBRATION OF C-TOWN NETWORK USING HARMONY SEARCH ALGORITHM

This study adopts the Harmony Search (HS) meta-heuristic algorithm (Geem et al., 2001; Kim et al., 2001) to calibrate a hydraulic simulation model (EPANET) for C-Town. The algorithm conceptualizes a musical process of searching for a perfect state of harmony (optimal solution) and allows a random search without initial values, thus removing the necessity for information on derivatives. The calibration procedure is implemented in a manner that the sum of errors between two data sets: the measured SCADA data and the values derived from the HS algorithm. The calibration process is implemented into three stages – preprocess, rough tuning, and fine tuning. In the preprocess stage the original SCADA data and pump control rules are modified to complement missing data and operation rules. In the rough and fine tuning stages the C-town network is divided into five sub-networks based on supply zones. Pipe roughness factors in each sub-network are calibrated by pipe diameters which are divided into five groups. Therefore, total number of decision variables in each sub-network is 168 nodal water demand factors and 5 pipe roughness coefficients. The entire C-town network is also calibrated using each already calibrated sub-network as the initial populations. It is found that the simple network in the downstream end can be calibrated separately; thus, the network division does accelerate the convergence speed. Several parameters of the HS algorithm such as HMCR and PAR can be adjusted to find better calibration results
Valorization of Second-Grade Date Fruit Byproducts and Nonstandard Sweet Potato Tubers to Produce Novel Biofortified Functional Jam

Byproducts of second-grade dates and sweet potato tubers of noncommercial standard are produced along with the main product and are just as important as the main product but cannot be sold in the open market, as they may not be considered acceptable by consumers. Such byproducts can be valorized through the manufacture of a wide range of functional food products with high market appeal, such as jams. The research approach of this study included measuring antioxidant activity, total flavonoids, polyphenols, physicochemical and color indices, pH, and total sugar, as well as conducting a sensory evaluation, of mixed jams composed of different ratios of date jam (DFJ) to sweet potato jam (SPJ), namely, DP1 (80:20), DP2 (70:30), DP3 (60:40), and DP4 (50:50). To date, no other studies have considered producing mixed jam from dates and sweet potato byproducts. The sensory evaluation results indicated that jam DP4 (consisting of 50% date and 50% sweet potato) had the maximum overall acceptability. This investigation reveals the potential of using mixed byproducts in jams as natural functional ingredients, suggesting the economic value of valorization byproducts as low-cost ingredients to expand the properties, nutritional value, antioxidant content, and overall acceptability of jams. The discovered optimal mixed fruit jam has significant potential for further development as a commercial product.
Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network

In this research, we demonstrate a Deep Convolutional Neural Network-based classification model for the detection of monkeypox. Monkeypox can be difficult to diagnose clinically in its early stages since it resembles both chickenpox and measles in symptoms. The early diagnosis of monkeypox helps doctors cure it more quickly. Therefore, pre-trained models are frequently used in the diagnosis of monkeypox, because the manual analysis of a large number of images is labor-intensive and prone to inaccuracy. Therefore, finding the monkeypox virus requires an automated process. The large layer count of convolutional neural network (CNN) architectures enables them to successfully conceptualize the features on their own, thereby contributing to better performance in image classification. The scientific community has recently articulated significant attention in employing artificial intelligence (AI) to diagnose monkeypox from digital skin images due primarily to AI’s success in COVID-19 identification. The VGG16, VGG19, ResNet50, ResNet101, DenseNet201, and AlexNet models were used in our proposed method to classify patients with monkeypox symptoms with other diseases of a similar kind (chickenpox, measles, and normal). The majority of images in our research are collected from publicly available datasets. This study suggests an adaptive k-means clustering image segmentation technique that delivers precise segmentation results with straightforward operation. Our preliminary computational findings reveal that the proposed model could accurately detect patients with monkeypox. The best overall accuracy achieved by ResNet101 is 94.25%, with an AUC of 98.59%. Additionally, we describe the categorization of our model utilizing feature extraction using Local Interpretable Model-Agnostic Explanations (LIME), which provides a more in-depth understanding of particular properties that distinguish the monkeypox virus.
APPLICATION OF DIGITAL TOOLS TO MAGNIFY LEARNING: PAKISTANI PRIVATE HIGH SCHOOLS PERSPECTIVE

Digital integration facilitates to enhance learning and stimulates students to attain success in their academics as well as enriches the quality of education. This enhanced learning process employed a questionnaire to survey the teaching staff and the students of Pakistan Private High Schools respectively. Digital approaches were extensively used by both the respondents and their responses divulged utilization, motivations or discontinuation of digital tools. The teaching staff used textbooks, and other media to enhance the learning process. On contrary, student survey demonstrated their perceptions regarding digitalized education that influenced their learning, their preferred digital tool, and their progress. Eighty-one percent students agreed that digital integration enhanced their learning. Moreover, their use and inclination for digital tools proposed that students are in favour of resources that are used by teaching staff to enhance learning. Interestingly, the outcomes revealed that technology facilitated student’s learning because it increases communication skills, accessibility and inclusion to learn independently. Digital integration, monitoring and evaluation will offer a strategic planning process and help in developing institutions prevailing learning procedures.
An inventory system for perishable commodities with random lifetime

In this study we assume an inventory system for perishable commodities in which the lifetimes of the items stored are i.i. d. random variable with finite mean. We utilize the analogy between this inventory system and a queueing system with impatient customers, to study the process of the lost demand, the death of the unused items and the number of items in the system.
Prediction of Liver Abnormality using Machine Learning

Cancer according to the American Society of Clinical Oncology journal was first described back in 1600 B.C. and has been prevalent ever since. The technological advancement in the field of medical sciences aided with advancement in the field of machine learning and deep learning has brought us to a situation today, where a subject can be informed of the dangers or the possibility of possessing an infected liver. In the prediction model, different enzymes were studied, and appropriate ratios were to determine the stability of the hepatocytes in the liver. The data was employed by different Machine Learning algorithms and based on their accuracy levels the final prediction has been made using the most appropriate algorithm. In an attempt to take the model to the next level, a few more algorithms were employed and explored the dataset even more. The results of each algorithm are compared using ROC graphs and ROC AUC SCORE to achieve a better model for this prediction model. Each algorithm is given by certain hyper-parameters which would increase the fitting nature more towards the best. The most important features calculated by each algorithm are mentioned and used accordingly to calculate the results.