Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequential-Mining-Based Vulnerable Branches Identification for the Transmission Network Under Continuous Load Redistribution Attacks

Load redistribution (LR) attacks represent a major cyber threat to the power grid that can result in branch overloads and even cascading failures in the transmission network by injecting false data to load bus power injection and line power flow measurements. While the mechanism and defense strategies have been well studied for single-occurrence (i.e., discrete) LR attacks, this paper focuses on a yet unexplored category of LR attacks, namely, the continuous LR (CLR) attack, which continuously injects time-varying false data to one or more bus and branch measurements within the system for a certain period of time. To cope with the potentially ever-changing targeted branches (i.e., the branches under attack) and system vulnerability features caused by CLR attacks, this paper takes a different route from the traditional LR defense and mitigation approaches. We propose a novel correlation chain model to capture the vulnerability correlations among branches under the CLR attack. Then, a dynamic sequential-mining algorithm (DSMA) with top-k structure is proposed based on the specific nature of the CLR attack, to mine the historical SCADA measurement database, extract vulnerable branch sequences, and identify the most suitable branches to defend preventively. Simulation studies are conducted on IEEE RTS-96 system to evaluate the effectiveness of the proposed approach in terms of increasing attack costs for the attackers and reducing the potential impacts of CLR attacks.
A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery

Accurate landslide detection and mapping are essential for land use planning, management/assessment, and geo-disaster risk mitigation as well as post-disaster reconstructions. Till now, visual interpretation and field survey are still the most widely adopted techniques for landslide mapping, which are often criticized labor-intensive, time-consuming, and costly. With the rapid advancement of artificial intelligence, deep-learning-based approach for landslide detection and mapping has drawn great attention for its significant advantages over the traditional techniques. However, lack of sufficient training samples has constrained the application of deep-learning-based approach in landslide detection from satellite images for a long time. The present study aimed to examine the feasibility of a new deep-learning-based approach to intelligently detect and map earthquake-triggered landslides from single-temporal RapidEye satellite images. Specifically, the proposed approach consists of three steps. First of all, a standard data preprocessing workflow to automatically generate training samples was designed and some data augmentation strategies were implemented to alleviate the lack of training samples. Then, a cascaded end-to-end deep learning network, namely LandsNet, was constructed to learn various features of landslides. Finally, the identified landslide maps were further optimized with morphological processing. Experiments in two spatially independent earthquake-affected regions showed our proposed approach yielded the best F1 value of about 86.89%, which was about 7% and 8% higher than that obtained by ResUNet and DeepUNet, respectively. Comparative studies on the feasibility and robustness of the proposed approach with ResUNet and DeepUNet demonstrated its strong application potentials in the emergency response of natural disasters.
Joint Attention Mechanisms for Monocular Depth Estimation With Multi-Scale Convolutions and Adaptive Weight Adjustment

Monocular depth estimation is a fundamental problem for various vision applications, and is therefore gaining increasing attention in the field of computer vision. Though a great improvement has been made thanks to the rapid progress of deep convolutional neural networks, depth estimation of the object at finer details remains an unsatisfactory issue, especially in complex scenes that has rich structure information. In this article, we proposed a deep end-to-end learning framework with the combination of multi-scale convolutions and joint attention mechanisms to tackle this challenge. Specifically, we firstly elaborately designed a lightweight up-convolution to generate multi-scale feature maps. Then we introduced an attention-based residual block to aggregate different feature maps in joint channel and spatial dimension, which could enhance the discriminant ability of feature fusion at finer details. Furthermore, we explored an effective adaptive weight adjustment strategy for the loss function to further improve the performance, which adjusts the weight of each loss term during training without additional hyper-parameters. The proposed framework was evaluated using challenging NYU Depth v2 and KITTI datasets. Experimental results demonstrated that the proposed approach is superior to most of the state-of-the-art methods.
Wanted dead or alive: a state-space mark–recapture–recovery model incorporating multiple recovery types and state uncertainty

We developed a state-space mark–recapture–recovery model that incorporates multiple recovery types and state uncertainty to estimate survival of an anadromous fish species. We apply the model to a dataset of outmigrating juvenile steelhead trout (Oncorhynchus mykiss (Walbaum, 1792)) tagged with passive integrated transponders, recaptured during outmigration, and recovered on bird colonies in the Columbia River basin (2008–2014). Recoveries on bird colonies are often ignored in survival studies because the river reach of mortality is often unknown, which we model as a form of state uncertainty. Median outmigration survival from release to the lower river (river kilometre 729 to 75) ranged from 0.27 to 0.35, depending on year. Recovery probabilities were frequently ≥0.20 in the first river reach following tagging, indicating that one out of five fish that died in that reach was recovered on a bird colony. Integrating dead recovery data provided increased parameter precision, estimation of where birds consumed fish, and survival estimates across larger spatial scales. More generally, these modeling approaches provide a flexible framework to integrate multiple sources of tag recovery data into mark–recapture studies.
Finger Joint Detection Method in Hand X-ray Radiograph Images Using Statistical Shape Model and Support Vector Machine

The number of rheumatoid arthritis (RA) patients is about 700,000 in Japan. The modified total sharp score (mTSS) calculated from hand X-ray image is a standard diagnosis method of RA progression but can be time-consuming for physicians. Computer-aided diagnostic (CAD) system is expected to improve the diagnostic quality of RA patients. We have previously proposed a CAD system, which can detect finger joint positions using support vector machine and estimate mTSS using support vector regression. This study improves the finger joint detection accuracy by incorporating with statistical shape model, which statistically models individual variety of spatial relationship among finger joints. Experimental results on radiographic images of the hands of 90 RA patients showed that the finger joints were detected with an accuracy of 94.5%, which is higher than 90.6% of the accuracy with the previous method.
A Robust Spatiotemporal Forecasting Framework for Photovoltaic Generation

Deployment of PV generation has been recognized as one of the promising measures taken for mitigating the environmental issues worldwide. To seamlessly integrate PV and other renewables, accurate prediction is imperative to ensure the reliability and economy of the power system. Distinguished from most existing methods, this work presents a novel robust spatiotemporal deep learning framework that can generate the PV forecasts for multiple regions and horizons simultaneously considering corrupted samples. Within this framework, the Convolutional Long Short-Term Memory Neural Network is employed to exploit the temporal trends and spatial correlations of the PV measurements. Besides, given the collected PV measurements might be subject to various data contaminations, the correntropy criterion is integrated to give the unbiased parameter estimation and robust spatiotemporal forecasts. The performance of the proposed correntropy-based deep convolutional recurrent model is evaluated on the synthetic solar PV dataset recorded in 56 locations in U.S. offered by NREL. The comparative study is conducted against benchmarks over different sample contamination types and levels. Experimental results show that the proposed model can achieve the highest robustness among the rivals.
Understanding Education Difficulty During COVID-19 Lockdown: Reports on Malaysian University Students’ Experience

The COVID-19 pandemic has struck the world and forced countries to go into lockdown including education sector. Students have been staying in hostels or houses, unable to go to university campuses. This situation has left university administrators no choice, but to have an online learning channel. Malaysian universities in particular have gone through many challenges to bring their online learning system up and ready to resume education process. However, students have found themselves caught in this situation (pure online learning) with no plan or readiness. Literature reviews showed that students encountered some challenges that could not be easily resolved. This study explored the challenges encountered by students of a government-linked university. This university is one of the largest in Malaysia with over 10 campuses across the country. This study collected 284 valid answers. The findings show that respondents lacked full readiness in this situation physically, environmentally, and psychologically with some differences in perspectives according to their gender, age, and residing state. Respondents were concerned about the implications of lockdown on their performance. The findings of this study indicate that a sudden switch to a pure online alternative creates considerable challenges to students who have no plans to be physically apart from classes. The findings also indicate that the current blended learning process which uses online learning as a support mechanism for face-to-face learning has faced a considerable challenge to replace it, particularly with unprepared students.
Patients Requiring Conversion to General Anesthesia during Endovascular Therapy Have Worse Outcomes: A Post Hoc Analysis of Data from the SAGA Collaboration

BACKGROUND AND PURPOSE: Endovascular therapy for acute ischemic stroke is often performed with the patient under conscious sedation. Emergent conversion from conscious sedation to general anesthesia is sometimes necessary. The aim of this study was to assess the functional outcome in converted patients compared with patients who remained in conscious sedation and to identify predictors associated with the risk of conversion. MATERIALS AND METHODS: Data from 368 patients, included in 3 trials randomizing between conscious sedation and general anesthesia before endovascular therapy (SIESTA, ANSTROKE, and GOLIATH) constituted the study cohort. Twenty-one (11%) of 185 patients randomized to conscious sedation were emergently converted to general anesthesia. RESULTS: Absence of hyperlipidemia seemed to be the strongest predictor of conversion to general anesthesia, albeit a weak predictor (area under curve = 0.62). Sex, hypertension, diabetes, smoking status, atrial fibrillation, blood pressure, size of the infarct, and level and side of the occlusion were not significantly associated with conversion to general anesthesia. Neither age (mean age, 71.3 ± 13.8 years for conscious sedation versus 71.6 ± 12.3 years for converters, P = .58) nor severity of stroke (mean NIHSS score, 17 ± 4 versus 18 ± 4, respectively, P = .27) were significantly different between converters and those who tolerated conscious sedation. The converters had significantly worse outcome with a common odds ratio of 2.67 (P = .015) for a shift toward a higher mRS score compared with the patients remaining in the conscious sedation group. CONCLUSIONS: Patients undergoing conversion had significantly worse outcome compared with patients remaining in conscious sedation. No factor was identified that predicted conversion from conscious sedation to general anesthesia.
Efficacy of “High in” Nutrient Specific Front of Package Labels—A Retail Experiment with Canadians of Varying Health Literacy Levels

Background: In 2018, Health Canada, the Federal department responsible for public health, put forward a regulatory proposal to introduce regulations requiring a “High in” front-of-package label (FOPL) on foods that exceed predetermined thresholds for sodium, sugars, or saturated fat. This study evaluated the efficacy of the proposed FOPL as a quick and easy tool for making food choices that support reduction in the intakes of these nutrients. Methods: Consumers (n = 625) of varying health literacy levels (HL) were assigned to control (current labeling with no FOPL) or one of four FOPL designs. They completed six shopping tasks, designed to control for internal motivations. Efficacy was measured with correct product selection and response time (seconds) to make food choices using repeated measures statistical modeling, adjusting for HL, task type, and task order. Eye-tracking and structured interviews were used to gather additional insights about participants’ choices. Results: Overall, FOPL was significantly more effective than current labeling at helping consumers of varying HL levels to identify foods high in nutrients of concern and make healthier food choices. All FOPL were equally effective. Conclusions: “High in” FOPL can be effective at helping Canadians of varying HL levels make more informed food choices in relation to sugars, sodium, and saturated fat.
Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN

Traditional approaches for job shop scheduling problems are ill-suited to deal with complex and changeable production environments due to their limited real-time responsiveness. Based on disjunctive graph dispatching, this work proposes a deep reinforcement learning (DRL) framework, that combines the advantages of real-time response and flexibility of a deep convolutional neural network (CNN) and reinforcement learning (RL), and learns behavior strategies directly according to the input manufacturing states, thus is more appropriate for practical order-oriented manufacturing problems. In this framework, a scheduling process using a disjunction graph is viewed as a multi-stage sequential decision-making problem and a deep CNN is used to approximate the state-action value. The manufacturing states are expressed as multi-channel images and input into the network. Various heuristic rules are used as available actions. By adopting the dueling double Deep $Q$ -network with prioritized replay (DDDQNPR), the RL agent continually interacts with the scheduling environment through trial and error to obtain the best policy of combined actions for each decision step. Static computational experiments are performed on 85 JSSP instances from the well-known OR-Library. The results indicate that the proposed algorithm can obtain optimal solutions for small scale problems, and performs better than any single heuristic rule for large scale problems, with performances comparable to genetic algorithms. To prove the generalization and robustness of our algorithm, the instances with random initial states are used as validation sets during training to select the model with the best generalization ability, and then the performance of the trained policy on scheduling instances with different initial states is tested. The results show that the agent is able to get better solutions adaptively. Meanwhile, some studies on dynamic instances with random processing time are performed and experiment results indicate that out method can achieve comparable performances in dynamic environment in the short run.
Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach

Melanoma is considered as one of the fatal cancer in the world, this form of skin cancer may spread to other parts of the body in case that it has not been diagnosed in an early stage. Thus, the medical field has known a great evolution with the use of automated diagnosis systems that can help doctors and even normal people to determine a certain kind of disease. In this matter, we introduce a hybrid method for melanoma skin cancer detection that can be used to examine any suspicious lesion. Our proposed system rely on the prediction of three different methods: A convolutional neural network and two classical machine learning classifiers trained with a set of features describing the borders, texture and the color of a skin lesion. These methods are then combined to improve their performances using majority voting. The experiments have shown that using the three methods together, gives the highest accuracy level.
Interactive Applied Graph Chatbot with Semantic Recognition

Companies and small medium businesses (UMKM) need to interact with customers to increase their customer retention rate. One of the ways is to use chatbot. Aside from being cost-effective, this method is also very effective and very easy for companies to use. To make an easy and effective chatbot requires a combination of two scientific fields, artificial intelligence and software engineering. This study has the following features. 1) Affective sentiment analysis, this feature is inspired by Russel’s Circumplex Model. Adjective words will be mapped in a matrix with values based on the Russell Circumplex Model. This model will pay attention on the adjective words, polarity, and affection degree of a sentence. 2) Conjunction sentiment analysis, consider of free way of interaction nowdays, a sentiment analysis system need to determine the sentiment value in multilevel sentences. This multilevel sentence has one or more conjunctions. This conjunction sentiment system will break sentences based on conjunction. The fractional sentence will be processed by affective sentiment analysis. The system then considers the nature of the conjunction to determine the sentiment of the whole sentence. 3) Graph Chatbot, this feature is used to make it easy for companies to modify and generate their bots. This bot will interact with customers like humans, based on a graph chat map that has been created. Chatbot graphs were developed using javascript library Vue js to make it easier to manipulate the visual graph. The system produces satisfying accuracy. Graph chatbot can handle procedural conversations very well. The flow of conversation in accordance with the graph that has been defined. Graph chatbot has 100% accuracy and successfully responds to all user conversations. The sentiment method has 63% accuracy.
Discovering of Game AIs’ Characters Using a Neural Network based AI Imitator for AI Clustering

In game AI research, most work aims at building a more powerful AI but few are reported on explaining AI’s behavior or revealing the characters of AI. Previous efforts in training a human-like AI via style learning implies that AI may behave with human characteristics. However, the early work treated human playstyle as a whole instead of identifying the difference among various AIs. In this paper, we focus on finding out manlike characters of individual AIs, and clustering AIs according to their characters. We propose a Neural Network based game AI imitator to imitate AIs’ behavior and find that some AIs are easier to imitate than others. Based on this observation we define the term imitability to describe the difficulty of imitation and cluster the AIs into two categories according to their imitability. Through statically analyzing, we find that AIs with lower imitability are generally farseeing with a global perspective while the other group are nearsighted and narrow-minded. The AIs hard to imitate also perform better when fighting with others. Upon the above semantic analysis of the clustering results, we conclude that the imitability can be used to identify AIs’ character.
Brucella Genomics: Macro and Micro Evolution

Brucella organisms are responsible for one of the most widespread bacterial zoonoses, named brucellosis. The disease affects several species of animals, including humans. One of the most intriguing aspects of the brucellae is that the various species show a ~97% similarity at the genome level. Still, the distinct Brucella species display different host preferences, zoonotic risk, and virulence. After 133 years of research, there are many aspects of the Brucella biology that remain poorly understood, such as host adaptation and virulence mechanisms. A strategy to understand these characteristics focuses on the relationship between the genomic diversity and host preference of the various Brucella species. Pseudogenization, genome reduction, single nucleotide polymorphism variation, number of tandem repeats, and mobile genetic elements are unveiled markers for host adaptation and virulence. Understanding the mechanisms of genome variability in the Brucella genus is relevant to comprehend the emergence of pathogens.
Multiscale Transfer Spectral Entropy for Quantifying Corticomuscular Interaction

Corticomuscular coupling reflects nonlinear interactions and multi-layer neural information transmission between the motor cortex and effector muscle in the sensorimotor system. Transfer spectral entropy (TSE) method has been used to describe corticomuscular coupling within single scale. As an extension of TSE, multiscale transfer spectral entropy (MSTSE) is proposed in this paper to depict multi-layer of neural information transfer between two coupling signals. The reliability and effectiveness of MSTSE were verified on data generated by nonlinear numerical models and those of a force tracking task. Compared with TSE, MSTSE is more robust to the embedding dimension and performs optimally in the detection of the coupling properties. Further analysis of the physiological signals showed that the MSTSE provided more detailed band characteristics than the single scale TSE measurement. MSTSE indicates significant coupling scattered in alpha, beta and low gamma bands during the force tracking task. Besides, the coupling strength in the descending direction of the beta band was significantly higher than that in the ascending direction. This study constructs multi-scale coupling information to provide a new perspective for exploring corticomuscular interaction.
Evaluation of three different types of nitrogen, heavy metals, and endocrine disruptors in water and sediments of Wenyu River

ABSTRACT Concentrations of NO3−(nitrate), NO2−(nitrite), NH4+(ammonia nitrogen), Mn, Fe, Ba, As, Se, Cd, Pb, Cr6+, estrone (E1), estradiol (E2), ethinylestradiol (EE2), estriol (E3), bisphenol A (BPA), nonylphenol (NP) and 4-tert-octylphenol (OP) had been estimated in water and sediments of Wenyu River. Using single factor, ecological risk assessment, geoaccumulation index, human health risk assessment and multivariate statistical analysis, we described the current situation of river contamination, human health risks of different age groups and possible sources of pollutants as well as their transformation characteristics. The results showed that main contaminants were NH4+, NO3−, NO2−, EE2 and E3, E1 in water, while sediments have been slightly polluted by heavy metals, whose order of polluting degree is Zn>Cr6+>Cd>Cu. Residents in the study area are exposed to the risk, and EE2 made the greatest contribution to it. E1, As, and NO3- also affect negatively on the health of the residents, and NO2- only do harm to juveniles. Human activities and industrial production are main sources of contaminants, also they can transform into each other between water and sediments. Heavy metal ions can be easily precipitated by Ferric oxide or hydroxide, E2 and EE2 are also transformed into E3 in natural environment.