Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Neural Network Optimization for Skin Disease Classification Forecast Analysis

Skin lesions are a prevalent condition that causes misery, many of which can be severe, for millions of individuals worldwide. Consequently, Deep learning seems to be an increasingly popular approach in recent years, and it may be a strong tool in difficult, earlier domains, specifically in health science, which is now dealing with a number of medical resources. In this paper, presented an interactive dermoscopy images diagnosis framework based on an gathering of intelligent deep learning model system for image classification to make advances their person accuracies within the prepare of classifying dermoscopy pictures into several classes such as melanoma, keratosis and nevus when we have not sufficient annotated images to train them on. We integrate the classification layer results for two distinct deep neural network designs to obtain excellent classification accuracy. More precisely, we combining robust convolutional neural networks (CNNs) into a unified structure, with the final classification relying on the weighted outcome of the respective CNNs by predictive ensemble methods and fine-tuning classifiers utilizing ISIC2019 images. Furthermore, the outliers and the substantial class imbalance are handled in order to improve the categorization of the disease. The experimental reveal that the framework produced result that are comparable to other models of conventional art. A substantial improvement in accuracy of 96.2 percentage indicated the efficiency of the proposed Predictive Ensemble Deep Convolutional Neural Networks Classifier (PE-DCNN Classifier) model and this study effectively built a system with all the important features.
Reactive particle swarm optimization based global energy balance protocol

In this paper, a robust energy efficient multi-support and integrated Wireless Multimedia Sensor Network (WMSN) platform, has been proposed. The proposed platform can be used for various ecological occurrence and detections such as the presence of plant diseases and insects in agriculture applications with high accuracy. Lifetime of WMSN has also been increased by using global energy balance protocol and particle swarm optimization. Finally, extensive experiment analysis is drawn between the existing and the proposed protocols and performance has been better than existing ones.
Augmenting Equivalent Mutant Dataset Using Symbolic Execution

Mutation testing aims to ensure that a test suite is capable of detecting real faults, by checking whether they can reveal (i.e., kill) small and arbitrary lexical changes made to the program (i.e., mutants). Some of these arbitrary changes may result in a mutant that is syntactically different but is semantically equivalent to the original program under test: such mutants are called equivalent mutants. Since program equivalence is undecidable in general, equivalent mutants pose a serious challenge to mutation testing. Given an unkilled mutant, it is not possible to automatically decide whether the cause is the weakness of test cases or the equivalence of the mutant. Recently machine learning has been adopted to train binary classification models for mutant equivalence. However, training such classification models requires a pool of equivalent mutants, the labelling for which involves a significant amount of human investigation. In this paper, we introduce two techniques that can be used to augment the equivalent mutant benchmarks. First, we propose a symbolic execution-based validation of mutant equivalence, instead of manual classification. Second, we introduce a synthesis technique for equivalent mutants: for a subset of mutation operators, the technique identifies potential mutation locations that are guaranteed to produce equivalent mutants. We compare these two techniques to MutantBench, a manually labelled equivalent mutant benchmark. For the 19 programs studied, MutantBench contains 462 equivalent mutants, whereas our technique is capable of generating 1,725 equivalent mutants automatically, of which 1,349 are new and unique. We further show that the additional equivalent mutants can lead to more accurate equivalent mutant classification models.
Considering the Role of Ion Transport in Diffuson‐Dominated Thermal Conductivity

Next‐generation thermal management requires the development of low lattice thermal conductivity materials, as observed in ionic conductors. For example, thermoelectric efficiency is increased when thermal conductivity is decreased. Detrimentally, high ionic conductivity leads to thermoelectric device degradation. Battery safety and design also require an understanding of thermal transport in ionic conductors. Ion mobility, structural complexity, and anharmonicity have been used to explain the thermal transport properties of ionic conductors. However, thermal and ionic transport are rarely discussed in direct comparison. Herein, the ionic conductivity of Ag+ argyrodites is found to change by orders of magnitude without altering the thermal conductivity. Thermal conductivity measurements and two‐channel lattice dynamics modeling reveal that the majority of Ag+ vibrations have a non‐propagating diffuson‐like character, similar to amorphous materials. It is found that high ionic mobility is not a requirement for diffuson‐mediated transport. Instead, the same bonding and structural traits that can lead to fast ionic conduction also lead to diffuson‐mediated transport. Bridging the fields of solid‐state ionics and thermal transport, it is proposed that a vibrational perspective can lead to new design strategies for functional ionic conducting materials. As a first step, the authors relate the so‐called Meyer–Neldel behavior in ionic conductors to phonon occupations.
Intrusion Detection System for Databases: A Hybrid Metaheuristic Clustering and Closed Sequential Pattern Mining Approach

In recent years, the creation of an Intrusion Detection System (IDS) for Database reliability has become an essential requisite. The paramount purpose of a contemporary IDS is to detect signature and anomaly based intrusive threats which abuse database prerogatives. To achieve the same, we present a hybrid metaheuristic clustering and closed sequential pattern mining based Intrusion Detection System (HMCSPM). In our proposed Database IDS, the signature detection capability is achieved using the ClaSP algorithm, which generates the data dependency rules. Subsequently the anomaly detection is done using the novel hybrid of Pathfinder and Gaining Sharing Knowledge clustering algorithm generating the role profiles analogous to user behaviour. An aggregation of the commonality and harmony indices are used as similarity measures to classify the incoming transactions as either malicious or non-malicious. Experimental assessment of the proposed technique reveals 96% accuracy on synthetically generated dataset in accordance to the TPC-C benchmark.
Pertinence of new communicating material paradigm: A first step towards wood mass marking

To highlight the interest of a new communicating material paradigm, this paper presents a first step towards the wood mass marking: We decide to use Nuclear Quadrupole Resonance and molecule assembling to identify wood material. We imagine a new method to create identification codes by using different marks. So we present our first test of wood mass marking using NQR as reader.
Poster: A gCov based new profiler, gMCov, for MC/DC and SC-MCC

In this paper, we propose and develop a real-time profiler to validate the strong coverage criteria such as Modified Condition/Decision Coverage (MC/DC) and Multiple Condition Coverage with Short Circuit (SC-MCC). We named our new tool as gMCov, which is a gCov based profiler. Currently, the existing gCov profiler gives line coverage and branch coverage. As we know, line and branch coverages are weak coverage criteria. Since there exists no profiler to produce the information of feasible sequence properties of either MC/DC or SC-MCC, it is important to have a tool that validates these coverage criteria based on their final scores. Hence, we proposed a new profiler i.e. gMCov which is a generalized tool that can be plugged with any test case generator. It requires a set of test cases and a program to produce the score (%) with a detailed report. The overhead of the gMCov execution time is considerable i.e., 0.68 (s) for MC/DC and 1.26 (s) for SC-MCC, thus proving its efficiency.
Ensemble Learning based Enzyme Family Classification using n-gram Feature

The human body has a huge variety of enzymes, each of which contributes to the acceleration of a certain kind of biochemical reaction. The majority of metabolic processes in living organisms move more slowly at moderate temperatures and pressures when these proteins are absent from the equation. Because of the strong relationship between the newly found enzyme molecule and the particular target on which it acts, its family must be identified as quickly as feasible. Experiments still have a long way to go before they can definitively differentiate between enzyme groups. There are six different kinds of enzymes that may be discovered in the human body. These include hydrolase, lyases, ligases, and isomerase. A brand-new method for identifying the enzyme family of proteins was developed by making use of a distance-based n-gram characteristic of pseudo amino acid composition. In order to do the analysis, we will employ a variety of equipment. Lerner's Support Vector Machine has several applications, some of which are K-Nearest Neighbor, Navier's Buyers, Random Forest, and Extra Tree (SVM). An estimation of the accuracy, precision, recall, and F1-score characteristics so they may be compared to other systems already in use.
Prediction of OPTIMIZED Stock Market Trends using Hybrid Approach Based on KNN and Bagging Classifier (KNNB)

Stock price prediction is a critical feature of financial time series forecasting, and it is of significant interest to equity investors, brokers, and applied scholars. The primary objective of this study is to find the best method for forecasting market capitalization. While analyzing a variety of tactics and factors, we noticed that random forest and support vector machine techniques were not adequately utilized. A more practical approach to forecasting stock movement with more accuracy is presented and discussed in this study. The first thing we looked at was the prior year's stock market statistics. For real-time analysis, the dataset was pre-processed and fine-tuned. As a result, we will concentrate on data preprocessing of the raw dataset in our article. Second, after preprocessing the data, we'll look at the results of using a hybrid model based on KNN and bagging classifier on the database. Furthermore, the proposed article investigates the application of the prediction system in real-world scenarios, as well as difficulties related to the correctness of the overall values provided. Stock market organizations will benefit greatly from good stock prediction since it will bring real-world solutions to the challenges that stock investors encounter.
Age-related hearing loss alters real-time dynamics of auditory cortical-brainstem coding during speech perception

Realtime modulation of brainstem frequency-following responses (FFRs) by online changes in cortical arousal state via the corticofugal (top-down) pathway has been demonstrated previously in younger humans and is more prominent in the presence of background noise. FFRs during high cortical arousal states also have a stronger relationship with speech behaviors. Aging is associated with overexaggerated auditory responses, which might reflect degraded inhibitory processing within the peripheral and ascending pathways, or changes in attentional control regulation via descending auditory pathways. Here, we tested the hypothesis that online corticofugal interplay is impacted in age-related hearing loss. We measured EEG in older adults with normal-hearing (NH) and hearing-loss (HL) while they actively performed speech identification tasks in different noise backgrounds. We measured α power to index online arousal states during task engagement and separate speech-FFRs according to fluctuations in cortical activity. We found c ortical α power was smaller in the HL than NH group. In NH listeners, α -FFRs modulation for clear speech also resembled that previously observed in noise among younger adults. Cortical-brainstem modulation was further diminished by noise and with HL in older adults. Lastly, neural decoding showed low-α FFRs were more predictive of behavioral measures and carried more stimulus-related information of speech in NH individuals. Our findings provide evidence that age-related hearing loss affects cortical-brainstem interplay in the auditory system. Aberrant modulation of brainstem speech processing exposes a new underlying mechanism for the listening difficulties faced by older adults when engaging in cocktail party-like conversations. Top-down (e.g., attention and arousal) effects on human brainstem responses have been controversial. Here, we show that cortical activity (indexed by EEG α -band amplitudes) is associated with trial-by-trial changes in brainstem speech representations measured via frequency-following responses. More critically, we show these cortical-subcortical interactions are particularly prominent during active speech listening tasks in normal-hearing older adults and differentially changed in older adults with hearing loss. Our study reveals cortical arousal state actively shapes brainstem speech representations and provide a potential new mechanism for older listeners ’ difficulties perceiving speech in cocktail party-like listening situations in the form of a miss-coordination between cortical and subcortical levels of auditory processing.
Interactive Exhibition Design Based on Precise Somatosensory Positioning Technology

Background: This paper shows more intelligential and humanized way of interactive exhibition in the background of display design developing more technically. Materials and Methods:Initially, this paper defines "precise somatosensory positioning technology" in exhibition spaces, introduces its content, application and effect. Based on the positioning technology and computer vision technology, then we discover the way of combination of somatosensory positioning technology and display art, , attached to other somatosensory detection methods. Results:This paper introduces the solution and principle of precise somatosensory positioning technology in exhibition, whereas wearable devices are introduced. Achievable result is provided. Conclusion:A method of combination of technology and art in exhibition field is introduced, exploring in the level of technology development and production application.
Urban green areas planning and development: An assessment of general urban plans of the City of Nis

Urban green areas have multiple roles in cities and to a large extent they determine the quality and livability of urban space. The process of urbanization has led to an increase in construction in cities and reduction of open public spaces, especially green areas. Planning of urban green areas is an integral part of urban planning, thus changes in planning and development of urban green areas can be observed through urban planning documents. The main question is whether the loss of green areas is a consequence of non-compliance with plans, or the reason for this lies in the method of planning of green areas. In order to answer this question the paper discusses: functions, standards and classification of urban green areas in general, and their use in two successive general urban plans of the city of Nis in the last twenty years, as a prerequisite for the development of green areas in accordance with growing need for green areas in the city.
Unconventional Protein Secretion Dependent on Two Extracellular Vesicles: Exosomes and Ectosomes

In addition to conventional protein secretion, dependent on the specific cleavage of signal sequences, proteins are secreted by other processes, all together called unconventional. Among the mechanisms operative in unconventional secretion, some are based on two families of extracellular vesicle (EVs), expressed by all types of cells: the exosomes (before secretion called ILVs) and ectosomes (average diameters ∼70 and ∼250 nm). The two types of EVs have been largely characterized by extensive studies. ILVs are assembled within endocytic vacuoles by inward budding of small membrane microdomains associated to cytosolic cargos including unconventional secretory proteins. The vacuoles containing ILVs are called multivesicular bodies (MVBs). Upon their possible molecular exchange with autophagosomes, MVBs undergo two alternative forms of fusion: 1. with lysosomes, followed by large digestion of their cargo molecules; and 2. with plasma membrane (called exocytosis), followed by extracellular diffusion of exosomes. The vesicles of the other type, the ectosomes, are differently assembled. Distinct plasma membrane rafts undergo rapid outward budding accompanied by accumulation of cytosolic/secretory cargo molecules, up to their sewing and pinching off. Both types of EV, released to the extracellular fluid in their complete forms including both membrane and cargo, start navigation for various times and distances, until their fusion with target cells. Release/navigation/fusion of EVs establish continuous tridimensional networks exchanging molecules, signals and information among cells. The proteins unconventionally secreted via EVs are a few hundreds. Some of them are functionally relevant (examples FADD, TNF, TACE), governing physiological processes and important diseases. Such proteins, at present intensely investigated, predict future discoveries and innovative developments, relevant for basic research and clinical practice.
Artificial Intelligence Assisted Topographic Mapping System for Endoscopic Submucosal Dissection Specimens

Background Endoscopic submucosal dissection (ESD), a minimally invasive surgery used to treat early gastrointestinal malignancies, has been widely embraced around the world. The gross reconstruction of ESD specimens can facilitate a more precise pathological diagnosis and allow endoscopists to explore lesions thoroughly. The traditional method of mapping is time-consuming and inaccurate. We aim to design a topographic mapping system via artificial intelligence to perform the job automatically. Methods The topographic mapping system was built using computer vision techniques. We enrolled 23 ESD cases at the Peking Union Medical College Hospital from September to November 2019. The reconstruction maps were created for each case using both the traditional approach and the system. Results Using the system, the time saved per case ranges from 34 to 3,336 s. Two approaches revealed no significant variations in the shape, size, or tumor area. Conclusion We developed an AI-assisted system that would help pathologists complete the ESD topographic mapping process rapidly and accurately.
Long-Term Outcomes for Chinese COPD Patients After PCI: A Propensity Score Matched, Double-Cohort Study

Objectives The aim of this study was to analyze long-term outcomes of Chinese coronary artery disease (CAD) patients with (and without) chronic obstructive pulmonary disease (COPD) after percutaneous coronary intervention (PCI). Background Chronic obstructive pulmonary disease is a chronic condition which often develops in conjunction with CAD. PCI is a core therapy for CAD, although we still need to understand CAD-COPD outcomes and to identify factors that influence prognoses, across ethnicities. Methods This double-cohort study involved 12,343 Chinese CAD patients who received PCI. Baseline characteristics were collected in two independent, specialty centers. Propensity-score matching was performed to control confounding factors, using a nearest neighbor matching method within a 0.02 caliper and on a propensity score scale of 0.1 for each center. Comorbid CAD-COPD cases were compared to non-COPD patients in terms of major adverse cardiac events (MACEs). Results Patients with COPD were generally older than those without COPD (65.4 ± 9.2 vs. 58.2 ± 10.3, p < 0.001). There were no significant differences in the end points between COPD and non-COPD groups after PCI (All p > 0.05); however, the incidence of MACEs increased after 450 days. Further subgroup analysis suggests that COPD is approximately four times more prevalent among those aged over 75 years (HR, 3.818; 95%CI, 1.10–13.29; p = 0.027) and those aged below 55 years (HR = 4.254; 95% CI, 1.55–11.72; p = 0.003). Conclusion Having COPD does not appear to have a significant impact on CAD outcomes 2 years after PCI, and beyond. However, an increasing number of MACEs was observed after 450 days, which suggests that there may be a double-stage effect of COPD on PCI prognosis. There is a need for focused comorbidity management, specifically for those aged below 55 years and above 75 years.
Clinical Values of the Identified Hub Genes in Systemic Lupus Erythematosus

Objective This study was conducted to identify the biomarkers and mechanisms associated with systemic lupus erythematosus(SLE) at a transcriptome level. Methods Microarray datasets were downloaded, and differentially expressed genes (DEGs) were identified. Enrichment and protein–protein interaction networks were analyzed, and hub genes were discovered. The levels of top 10 hub genes were validated by another dataset. The diagnostic accuracy of the hub genes was evaluated with the area under the curve of the receiver operating characteristic curve (ROC-AUC). The odds ratios (OR) and 95% confidence intervals (CI) of the relationship between clinical manifestations and hub genes were estimated with multivariable logistic regression. The relationships between the expression levels of the 10 identified hub genes and SLEDAI scores were subjected to linear correlation analysis. Changes in the expression levels of the hub genes during patient follow-up were examined through one-way repeated measures ANOVA. Results A total of 136 DEGs were identified. Enrichment analysis indicated that DEGs were primarily enriched in type I interferon-associated pathways. The identified hub genes were verified by the GSE65391 dataset. The 10 hub genes had good diagnostic performances. Seven (except IFI6, OAS1 and IFIT3) of the 10 hub genes were positively associated with SLEDAI. The combination models of IFIT3, ISG15, MX2, and IFIH1 were effective in diagnosing mucosal ulcers among patients with SLE. The expression levels of IRF7, IFI35, IFIT3, and ISG15 decreased compared with the baseline expression (not significantly). Conclusions In this work, the clinical values of the identified hub genes in SLE were demonstrated.