Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Spam Filtering Technology Based on New Mutual Information Feature Selection Algorithm

Aiming at the deficiency of traditional mutual information algorithm in feature selection, this paper proposes a weighted naive Bayesian algorithm based on improved mutual information, called imi-wnb algorithm. In the feature selection stage, the word frequency factor and the difference factor between classes are introduced to improve the traditional mutual information algorithm to achieve feature dimension reduction. In the process of classification, the value of IMI is introduced to weight the attributes of naive Bayes algorithm, which partly eliminates the influence of conditional independence assumption of naive Bayes algorithm on classification, and improves the efficiency and stability of spam classification.
DTaPO: Dynamic Thermal-Aware Performance Optimization for Dark Silicon Many-Core Systems

Future many-core systems need to handle high power density and chip temperature effectively. Some cores in many-core systems need to be turned off or ‘dark’ to manage chip power and thermal density. This phenomenon is also known as the dark silicon problem. This problem prevents many-core systems from utilizing and gaining improved performance from a large number of processing cores. This paper presents a dynamic thermal-aware performance optimization of dark silicon many-core systems (DTaPO) technique for optimizing dark silicon a many-core system performance under temperature constraint. The proposed technique utilizes both task migration and dynamic voltage frequency scaling (DVFS) for optimizing the performance of a many-core system while keeping system temperature in a safe operating limit. Task migration puts hot cores in low-power states and moves tasks to cooler dark cores to aggressively reduce chip temperature while maintaining high overall system performance. To reduce task migration overhead due to cold start, the source core (i.e., active core) keeps its L2 cache content during the initial migration phase. The destination core (i.e., dark core) can access it to reduce the impact of cold start misses. Moreover, the proposed technique limits tasks migration among cores that share the last level cache (LLC). In the case of major thermal violation and no cooler cores being available, DVFS is used to reduce the hot cores temperature gradually by reducing their frequency. Experimental results for different threshold temperatures show that DTaPO can keep the average system temperature below the thermal limit. Affirmatively, the execution time penalty is reduced by up to 18% compared with using only DVFS for all thermal thresholds. Moreover, the average peak temperature is reduced by up to 10.8°C. In addition, the experimental results show that DTaPO improves the system’s performance by up to 80% compared to optimal sprinting patterns (OSP) and reduces the temperature by up to 13.6°C.
Distillation of the material point method cell crossing error leading to a novel quadrature‐based C0 remedy

The material point method (MPM) is a hybrid particle‐mesh scheme conceptually placed between mesh‐based and mesh‐free methods, combining aspects of both and well suited to solving large deformation engineering problems involving history‐dependent material models. A unique drawback of the MPM is the cell crossing error whereby material points crossing grid cells suddenly produce spurious stress oscillations leading to significant errors. After distilling the necessary conditions required for the cell crossing error, a novel quadrature‐based C0 enhancement scheme is proposed. The partitioned quadrature material point method (PQMPM) effectively mitigates the cell crossing error while retaining the linear Lagrangian basis and Dirac density function of the original MPM, rendering it easily integrable into existing MPM codebases. One and two‐dimensional examples illustrate the elimination of the cell crossing error, improvement of solution accuracy (particularly reliable stress prediction), local application of the scheme across dynamically varying arbitrary spatial and temporal regions and the reasonable additional computational costs against increased accuracy. Contrasting existing enhanced MPM schemes, the PQMPM only incurs additional computational cost exactly when and where cell crossing errors would otherwise occur and minimally diffuses local effects.
Knowledge Structures and Components of Rural Resilience in the 2010s: Conceptual Development and Implications

Resilience is being widely adopted as a comprehensive analytical framework for understanding sustainability dynamics, despite the conceptual challenges in developing proxies and indicators for researchers and policy makers. In our study, we observed how the concept of resilience undergoes continued extension within the rural resilience literature. We comprehensively reviewed rural resilience literature using keyword co-occurrence network (KCN) analysis and a systematic review of shortlisted papers. We conducted the KCN analysis for 1186 papers to characterize the state of the rural resilience literature, and systematically reviewed 36 shortlisted papers to further examine how rural resilience analysis and its assessment tools are helping understand the complexity and interdependence of rural social-ecological systems, over three three-year periods from 2010 to 2018. The results show that the knowledge structure built by the high frequency of co-occurrence keywords remains similar over the three-year periods, including climate change, resilience, vulnerability, adaptation, and management, whereas the components of knowledge have greatly expanded, indicating an increased understanding of rural system dynamics. Through the systematic review, we found that developing resilience assessment tools is often designed as a process to strengthen adaptive capacity at the household or community level in response to global processes of climate change and economic globalization. Furthermore, community resilience is found to be an interesting knowledge component that has characterized rural resilience literature in the 2010s. Based on our study, we summarized conceptual characteristics of rural resilience and discussed the challenges and implications for researchers and policy makers.
The Carbon Footprint Methodology in CFOOD Project

In the paper, the research on the process of optimizing the carbon footprint to obtain the low-carbon products is presented. The optimization process and limits were analyzed based on the CFOOD project co-financed by the Polish Research and Development Agency. In the article, the carbon footprint (CF) testing methods with particular emphasis on product life cycle assessment (LCA) are discussed. The main problem is that the energy received from the energy-meters per the production stage is not directly represented in the raw data set obtained from the factory because many machines are connected to a single measurement point. In the paper, we show that in some energy-demanding production stages connected with cooling processes the energy used for the same stage and similar production can differ even 25-40%. That is why the energy optimization in the production can be very demanding.
A Summary of Research on Intelligent Dialogue Systems

Dialogue systems have always been an important research direction of natural language processing. In recent years, the potential and commercial value of dialogue systems have attracted people’s attention, and technologies such as intelligent customer service and chatbots have gradually become popular. First, it outlines the concept and classification of the dialog system; then introduces the constituent modules of the dialog system, and mainly studies the key technologies and mainstream ideas; finally, summarizes the problems encountered at this stage and looks forward to the future research direction.
Graded YSZ/Al2O3 hot corrosion resistant coating with enhanced thermal shock resistance

In order to reduce the interfacial thermal stress, a novel functionally graded YSZ/Al2O3 nanocoating (YSZ/Al2O3 FGC) which is hot corrosion resistant was developed with the sols of ZrO2, Y2O3 and AlOOH in this paper. SEM and XRD analyses indicated that the YSZ/Al2O3 FGC was about 4.2 μm in thickness and composed of t′-YSZ and γ-Al2O3 nanoparticles. Hot corrosion tests showed that the YSZ/Al2O3 FGC remarkably restrained the infiltration of the molten NaVO3 salt into the YSZ substrate. A thermal shock test at 700 °C showed that the thermal shock resistance of the YSZ/Al2O3 FGC was enhanced greatly compared with that of the Al2O3 coating. The improvement of the YSZ/Al2O3 FGC in the thermal cycle lifetime could be attributed to its compositionally graded structure, which decreased the thermal stress due to the thermal expansion coefficient mismatch between the YSZ substrate and the Al2O3 coating.
USING TRANSFER LEARNING FOR MALWARE CLASSIFICATION

Abstract. In this paper, we propose a malware classification framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets. In recent years there has been a significant increase in the number and variety of malwares, which amplifies the need to improve automatic detection and classification of the malwares. Nowadays, neural network methodology has reached a level that may exceed the limits of previous machine learning methods, such as Hidden Markov Models and Support Vector Machines (SVM). As a result, convolutional neural networks (CNNs) have shown superior performance compared to traditional learning techniques, specifically in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture for malware classification. The malicious binary files are represented as grayscale images and a deep neural network is trained by freezing the pre-trained VGG16 layers on the ImageNet dataset and adapting the last fully connected layer to the malware family classification. Our evaluation results show that our approach is able to achieve an average of 98% accuracy for the MALIMG dataset.
Investigation of the micropolarities in reverse micelles of Triton X-100 in mixed solvents of benzene and n-hexane

The micropolarities in Triton X-100 reverse micelles were invetigated in the mixed solvent of 30% (v/v) benzene and 70% (v/v) n-hexane, by the use of methyl orange (MO) and 1-methyl-8-oxyquinolinium betaine (QB) as absorption probes, Information about the distribution of the probe, water, and solvent molecule in the aggregates was obtained. MO is located in the polar core of the reverse micelles, but is not solubilized in the water pools, whereas QB transfers from the polar cores into the water pools when the latter are formed. In dry solutions, solubilized MO exists in two spectrophotomctrically distinguishable states
Media Attention and Regulatory Efficiency of Corporate Violations: Evidence from China

We examine the influence of media attention on regulatory efficiency of corporate violations in restrictive media environment. Using a hand-collected sample of corporate violations in China during 1998-2018, we find that fraudulent firms accompanied by more negative media attention are associated with a shorter duration of the violation being investigated and enforced. The effect is not diminished by the political connections of the fraudulent firms and is robust to accounting for potential confounding factors. We interpret our findings to suggest that by playing both an informational intermediary role and a pressure-exerting role in influencing regulatory efficiency, the media can serve as an effective governance mechanism even in markets with strict media control.
A New Adaptive Filter for Eliminating Salt and Pepper Noise

Salt-and-pepper noise can substantially degrade the appearance of images and make further processing difficult. This paper develops a new filter for eliminating different levels of salt-and-pepper noise efficiently without degrading important image details. The filter uses a variable window size. After identifying distorted pixels, if there is at least one or more undistorted pixels are encountered in the processing window, the updated value of the distorted pixel is calculated by the weighted mean of undistorted pixels when a window size is 3×3 and replaced by the mean value of the undistorted pixels with the highest frequency distribution when a window size is larger. This filter is applied twice in order to sufficiently remove high noise levels. The comparison results with other denoising filters indicate that the developed filter has superior or comparable denoising capability in terms of visual appearance and objective measures.
Using functional neuroimaging to advance entrepreneurial cognition research

ABSTRACT This article advances current understandings of why and how functional neuroimaging can enrich the study of entrepreneurship. We discuss the foundations of this cross-disciplinary research area and its evolving boundaries, focusing on explaining and providing actionable insights on how two of the most widely used brain-imaging methods can be leveraged for use in entrepreneurship research. We provide guidelines aimed to equip entrepreneurship scholars with the fundamentals needed to design and evaluate research involving these neuroscience methods. In so doing, we delineate examples related to entrepreneurial cognition and propose several ways in which this domain of research can be enhanced with neuroimaging.
THE IMPACT FACTORS ON PROJECT MANAGEMENT SUCCESS IN GLOBAL TEAMS: THE CASE OF COMPANY “X” PROJECTS

Nowadays, with globalization and the development of information technology, global teams are one of the fastest growing types of teams. Communication technology capabilities have significantly reduced the cost of team management and remote collaboration. Current management science has a broad theoretical basis for managing teams that are physically located in one place, but there is much less understanding of how leaders can effectively manage global teams. Company X is a global management consulting, technology services and outsourcing company. All company projects have virtual teams and participants from different countries The goal of this research is to identify and study the factors that influence the success of global project team management and operations in Company X. The research provides answers to the following questions: 1) Which factors impact the project management success at global company X? ; 2) What is the impact of a team member’s role and experience on project management success at company X? ; 3) What are the cultural differences within the project teams? The data was collected from a survey of two global teams (n=31) at global company X. Team members represent three cultures: Finnish, Indian and Latvian. The analyzed factors were communication, remote communication tools, roles and rules, and trust and cooperation in a multicultural environment. Communication, remote communication tools, and roles and rules showed a statistically significant impact on project management success in global teams. The current role of team members has a negative effect on project management success. No statistically significant differences between cultures were found which supports Cultural Convergence Theory which suggests that two cultures will be more and more like each other as their interactions increase.
Angle-dependent oscillations in valence-band photoemission intensity of C60

High-resolution angle-resolved photoelectron spectroscopy (ARPES) has been performed to investigate valence-band photoemission in C60 single crystals, which were cleaved in ultra-high-vacuum systems. The results present angle-dependent modulations in intensities of valence-band photoemission of the C60 single crystal. The observed intensity modulations are caused by polarization influence and photoelectron diffraction (PED) effects, of which the latter is also responsible for the similar oscillations produced by varying incident photon energies. The calculated results indicate that only photoelectrons scattered by the top hemisphere of C60 molecules dominate the angle-dependent interference modulations.
Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data

Location-based social media have facilitated us to bridge the gap between virtual and physical worlds through the exploration of human online dynamics from a geographic perspective. This study uses a large collection of geotagged photos from Flickr to investigate the complexity of spatial interactions at the country level. We adopted three levels of administrative divisions in mainland China—province, city, and county—as basic geographic units and established three types of topology—province–province network, city–city network, and county–county network—from the extracted user movement trajectories. We conducted the scaling analysis based on heavy-tailed distribution statistics including power law exponents, goodness of fit index, and ht-index, by which we characterized a great complexity of the trajectory lengths, spatial distribution of geotagged photos, and the related metrics of built networks. The great complexity indicates the highly imbalanced ratio of populated-to-unpopulated areas or large-to-small flows between areas. More interestingly, all power law exponents were around 2 for the networks at various spatial and temporal scales. Such a recurrence of scaling statistics at multiple resolutions can be regarded a statistical self-similarity and could thus help us to reveal the fractal nature of human mobility patterns.
COMPARATIVE EVALUATION OF FORECAST ACCURACIES FOR ARIMA, EXPONENTIAL SMOOTHING AND VAR

While various linear and nonlinear forecasting models exist, multivariate methods like VAR, Exponential smoothing, and Box-Jenkins’ ARIMA methodology constitute the widely used methods in time series. This paper employs series of Turkish private consumption, exports and GDP data ranging between 1998: Q1 and 2017: Q4 to analyze the forecast performance of the three models using measures of accuracy such as RMSE, MAE, MAPE, Theil’s U1 and U2. Seasonal decomposition and ADF unit root tests were performed to obtain new deseasonalized series and stationarity, respectively. Results offer preference for the use of ARIMA in forecasting, having performed better than VAR and exponential smoothing in all scenarios. Additionally, VAR model provided better forecast accuracy than exponential smoothing on all measures of accuracy except on Thiel’s U2 whose VAR values were not computed. Cautionary use of ARIMA for forecasting is recommended.
Neutron Star Properties: Quantifying the Effect of the Crust–Core Matching Procedure

The impact of the equation of state (EoS) crust-core matching procedure on neutron star (NS) properties is analyzed within a meta-modeling approach. Using a Taylor expansion to parametrize the core equation of state (EoS) and the SLy4 crust EoS, we create two distinct EoS datasets employing two matching procedures. Each EoS describes cold NS matter in a β equilibrium that is thermodynamically stable and causal. It is shown that the crust-core matching procedure affects not only the crust-core transition but also the nuclear matter parameter space of the core EoS, and thus the most probable nuclear matter properties. An uncertainty of as much as 5% (8%) on the determination of low mass NS radii (tidal deformability) is attributed to the complete matching procedure, including the effect on core EoS. By restricting the analysis, imposing that the same set of core EoS is retained in both matching procedures, the uncertainty on the NS radius drops to 3.5% and below 1.5% for 1.9M⊙. Moreover, under these conditions, the crust-core matching procedure has a strong impact on the Love number k2, of almost 20% for 1.0M⊙ stars and 7% for 1.9M⊙ stars, but it shows a very small impact on the tidal deformability Λ, below 1%.
Collagen polymer and magnetic collagen nanocomposite recycled from waste to reduce polluted water toxicity

The public concern for industrial pollution is urging for an economical solution to remove industrial effluent that increases the risk of human cancers. Currently, there are a few successful techniques implemented for dye removal; however, little has been done in the field of using reusable biological material extracted from waste. We propose a new procedure to fabricate biocompatible magnetic collagen for dye removal. Our study was conducted on crystal violet (CV) removal from industrial effluent using fish scales collagen. In this study, removal of dye by adsorption on various types of collagen was studied using soft, coarse and magnetic collagen nanocomposite. The magnetic collagen nanocomposite was synthesized by coprecipitation method followed by mixing of iron nanoparticles with collagen. Characterization of the prepared materials was performed using transmission electron microscopy (TEM) and Fourier transform infrared spectroscopy (FTIR). Soft collagen, coarse collagen and magnetic collagen nanocomposite were proved to adsorb crystal violet dye at concentrations up to 1000 ppm. Uptake capacity was 92% at 100 ppm of CV while cytotoxicity was reduced to 15%. The ability of collagen and magnetic collagen nanocomposite to adsorb the dye and thus reducing the polluted water cytotoxicity, as well as accumulating under the action of the magnetic field was proved experimentally.
Projected Changes in Abrupt Shifts Between Dry and Wet Extremes Over China Through an Ensemble of Regional Climate Model Simulations

The dry‐wet abrupt alternation (DWAA) event, which is defined as the phenomenon of dry (or wet) spells abruptly following wet (or dry) spells, magnifies the influence of individual wet and dry events. The dynamic evolution of DWAA events has not been studied for different climate zones of China that is particularly susceptible to dry and wet extremes. This study explores the future changes in the abrupt alternations between dry and wet extremes across 10 climate divisions of China, with a thorough assessment of dry and wet conditions using the Standardized Precipitation Evapotranspiration Index (SPEI). We take advantage of an ensemble of regional climate model simulations including the Providing Regional Climate Impacts for Studies (PRECIS) experiment and five CORDEX East Asia experiments to produce high‐resolution climate information for a baseline period of 1975–2004 and a future period of 2069–2098. Our findings disclose that a total of 70% of China's land area suffered from the DWAA events at least once during 1975–2004. The wet‐dry alternation event is projected to become more frequent in summer, and a prominent increase in the number of dry‐wet alternation events is expected to occur in spring over most parts of China. Moreover, an increasing number of DWAA events with intensified magnitude is projected to strike the North China Plain dominated by warm temperature and humid zone, which is the most densely populated region of the country and is also the largest agriculture production area. Our findings also reveal a strong positive correlation between DWAA and heavy rainfall. The 95th percentile rainfall event contributes most to the wet‐dry alternation event for most climate divisions of China.
Method for removing valuable components from technogenic solutions by the example of rare earth elements

The improvement of the effectiveness of separation of similar rare earth elements in terms of properties, permitting cost reduction of individual REE and their oxides, and extending the possibility of their application, becomes a topical problem. The method for removing valuable components from technogenic solutions is going significance now days. The article examines the possibility of rare earth elements removal and separation by adsorptive bubble methods with the use of surfactant and experimental justification of distribution factors and separation factors. The above-mentioned factors of rare earth elements in the process of ion flotation depending on aqueous phase pH with the determination of the forms of recovered compounds were studied.