Miracle-dz/UniFAR-FTUs huggingface dataset:
Vibrational and dielectric properties of monolayer transition metal dichalcogenides

First-principles studies of two-dimensional transition metal dichalcogenides have contributed considerably to the understanding of their dielectric, optical, elastic, and vibrational properties. The majority of works to date focus on a single material or physical property. Here we use a single first-principles methodology on the whole family of systems, to investigate in depth the relationships between different physical properties, the underlying symmetry and the composition of these materials, and observe trends. We compare to bulk counterparts to show strong interlayer effects in triclinic compounds. Previously unobserved relationships between these monolayer compounds become apparent. These trends can then be exploited by the materials science, nanoscience, and chemistry communities to better design devices and heterostructures for specific functionalities.
Data Scraping from Google Play Store and Visualization of its Content for Analytics

There are millions of applications uploaded by the developers on the daily basis. Without any check and balance millions of users download these applications. Theses duplicated applications damage the users trust on Google play store and can grab the confidential information of user. There is no more information provided by developers on the front end of the application that can define the legitimacy of the application. In this paper, by using a Google-play-scraper build a Google play store dataset with all categories of games. Scraping at least 550 applications of each category of games in free and respectively in paid applications by using Google play scraper, cumulatively scrape the 3600 paid applications and 10k free applications of all categories in games. The categories of these games’ applications use respectively are Word, Trivia, Simulation, Sports, Strategy, Racing, Role_Playing, Puzzle, Music, Educational, Card, Casino, Casual, Board, Action, Adventure, and Arcade. On each application on Google play store, scrape maximum 70 attributes, but use four attributes for analysis in this paper that is Installs, Advertisements support, InApplicationPurcahses and Ratings. In this paper, visualizing the InAppPurchase rate of free and paid applications, Percentage of the advertisement support in free and paid applications, Ratings of free and paid application with histogram, Installs of free and paid application with a histogram with all categories of games application. To check the relationship in between attributes also, visualize them in CIRCOS. This visualization is more helpful for game developers in the development phases, also for the users of the game’s application for the selection of the game that they want to play.
Validating the Accuracy of Imaged-Based Research into the Uncanny Valley: An Experimental Proposal

The uncanny valley phenomenon has be researched for the past 15 years, attempting to prove its validity with limited success. Researchers have been trying to recreate Masahiro Mori's hypothesised function [1] through a variety of experiments using images of real robots and images created from morphing humans and robots. Although some of these experiments have provided results supporting Mori's hypothesis, there is no solid confirmation of their legitimacy when it comes to real human-robot interaction. This paper examines the methods and subsequent results of studies to draw conclusions regarding the validity of experimental data into Mori's hypothesis. These conclusions lead us to propose an Augmented Reality(AR) experiment designed to verify the results of previous experimentation.
An Improved Calibration Technique for Polarization Images

Polarization imaging can be used to improve the image quality by reducing the effects of the unwanted light reflection, enhancing the quality of images taken in non-ideal conditions, such as foggy weather, or reconstructing the 3D form of an object based on the shape from polarization. Reconstructing a 3D form requires the acquisition of multiple images and inference of the polarization parameters, such as the degree of polarization and angle of polarization, from these experimental data. The light polarization extraction process should begin by performing the photometric calibration to improve the performance of the polarize instrument measurements by reducing the measurement errors and improving the consistency between measurements. We propose an algorithm based on the kernel ridge regression method to estimate both the polarization parameters and the measurement (angle) errors in the placement of the polarizer. The algorithm was tested on four different sets of images containing different objects based on their responses to light and polarization. The algorithm that uses the mixed kernel function gives the best results.
Combining Transfer Learning And Segmentation Information with GANs for Training Data Independent Image Registration

Registration is an important task in automated medical image analysis. Although deep learning (DL) based image registration methods out perform time consuming conventional approaches, they are heavily dependent on training data and do not generalize well for new images types. We present a DL based approach that can register an image pair which is different from the training images. This is achieved by training generative adversarial networks (GANs) in combination with segmentation information and transfer learning. Experiments on chest Xray and brain MR images show that our method gives better registration performance over conventional methods.
SAC-Net: Spatial Attenuation Context for Salient Object Detection

This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects. Our key idea is to adaptively propagate and aggregate the image context features with variable attenuation over the entire feature maps. To achieve this, we design the spatial attenuation context (SAC) module to recurrently translate and aggregate the context features independently with different attenuation factors and then to attentively learn the weights to adaptively integrate the aggregated context features. By further embedding the module to process individual layers in a deep network, namely SAC-Net, we can train the network end-to-end and optimize the context features for detecting salient objects. Compared with 29 state-of-the-art methods, experimental results show that our method performs favorably over all the others on six common benchmark data, both quantitatively and visually.
Blockchain-Based Traffic Event Validation and Trust Verification for VANETs

Sharing traffic information on the vehicular network can help in the implementation of intelligent traffic management, such as car accident warnings, road construction notices, and driver route changes to reduce traffic congestion earlier. In the future, in the case of autonomous driving, traffic information will be exchanged more frequently and more immediately. Once the exposed traffic incident is incorrect, the driving route will be misleading, and the driving response may be in danger. The blockchain ensures the correctness of data and tampers resistance in the consensus mechanism, which can solve such similar problems. This paper proposes a proof-of-event consensus concept applicable to vehicular networks rather than proof-of-work or proof-of-authority approaches. The traffic data are collected through the roadside units, and the passing vehicles will verify the correctness when receiving the event notification. In addition, a two-phase transaction on blockchain is introduced to send warning messages in appropriate regions and time periods. The simulation results show that the proposed mechanism can effectively feedback the correctness of traffic events and provide traceable events with trust verification.
Detection of False Data Injection Attacks in Smart Grid Utilizing ELM-Based OCON Framework

False data injection (FDI) attacks, as a new class of cyberattacks, bring a severe threat to the security and reliable operation of the smart grid by damaging the state estimation of the power system. To address this issue, an extreme learning machine (ELM)-based one-class-one-network (OCON) framework is proposed for detecting the FDI attacks in this paper. Under this framework, to effectively detect bus-based FDI attacks and identify the bus node being attacked, the subnets of state identification layer in OCON adopt the ELM algorithm to accurately divide the false data and the normal data. After that, a global layer is employed to analyze whether the bus node associated with its corresponding subnet is attacked by false data utilizing the results from the state identification layer. Finally, in order to improve the resilience of the power system, a prediction recovery strategy is proposed to remedy the detected false data by exploiting the spatial correlation of power data. The proposed framework is tested on the IEEE 14 bus system using real load data from New York independent system operator. The simulation results demonstrate that the proposed framework not only accurately recognizes the multiple bus nodes under the FDI attacks but also efficiently recovers the data injected by false data.
How does priority setting for resource allocation happen in commissioning dental services in a nationally led, regionally delivered system: a qualitative study using semistructured interviews with NHS England dental commissioners

Objectives To understand approaches to priority setting for healthcare service resource allocation at an operational level in a nationally commissioned but regionally delivered service. Design Qualitative study using semistructured interviews and a Framework analysis. Setting National Health Service dentistry commissioning teams within subregional offices in England. Participants All 31 individuals holding the relevant role (dental lead commissioner in subregional offices) were approached directly and from this 14 participants were recruited, with 12 interviews completed. Both male and female genders and all regions were represented in the final sample. Results Three major themes arose. First, ‘Methods of priority setting and barriers to explicit approaches’ was a common theme, specifically identifying the main methods as: perpetuating historical allocations, pressure from politicians and clinicians and use of needs assessments while barriers were time and skill deficits, a lack of national guidance and an inflexible contracting arrangements stopping resource allocation. Second, ‘Relationships with key stakeholders and advisors’ were discussed, showing the important nature of relationships with clinical advisors but variation in the quality of these relationships was noted. Finally, ‘Tensions between national and local responsibilities’ were illustrated, where there was confusion about where power and autonomy lay. Conclusions Commissioners recognised a need for resource allocation but relied on clinical advice and needs assessment in order to set priorities. More explicit priority setting was prevented by structure of the commissioning system and standard national contracts with providers. Further research is required to embed and simplify adoption of tools to aid priority setting.
A Novel Quality and Reliability-Based Approach for Participants’ Selection in Mobile Crowdsensing

With the advent of mobile crowdsensing, we now have the possibility of tapping into the sensing capabilities of smartphones carried by citizens every day for the collection of information and intelligence about cities and events. Finding the best group of crowdsensing participants that can satisfy a sensing task in terms of data types required, while satisfying the quality, time, and budget constraints is a complex problem. Indeed, the time-constrained and location-based nature of crowdsensing tasks, combined with participants’ mobility, render the task of participants’ selection, a difficult task. In this paper, we propose a comprehensive and practical mobile crowdsensing recruitment model that offers reliability and quality-based approach for selecting the most reliable group of participants able to provide the best quality possible for the required sensory data. In our model, we adopt a group-based approach for the selection, in which a group of participants (gathered into sites) collaborate to achieve the sensing task using the combined capabilities of their smartphones. Our model was implemented using MATLAB and configured using realistic inputs such as benchmarked sensors’ quality scores, most widely used phone brands in different countries, and sensory data types associated with various events. Extensive testing was conducted to study the impact of various parameters on participants’ selection and gain an understanding of the compromises involved when deploying such process in practical MCS environments. The results obtained are very promising and provide important insights into the different aspects impacting the quality and reliability of the process of mobile crowdsensing participants’ selection.
Persistence of various food contaminating bacteria in various food samples of Solan city

Food is consumed to provide nutritional support for the body. Food is a source of nutrients therefore it also provides suitable growth medium to pathogenic organisms for its proliferation as a result food get contaminated which can cause consumer illness. Unsanitary handling of the food by the some of the vendors has been commonly found to be the source of contamination. In the present study from 12 food samples 30 bacterial strains were isolated. Some of the isolates showed characteristic growth/pigment over Nutrient Agar, MacConkey Agar, Bismuth sulphite agar and EMB Agar. Further, biochemical identification showed that isolate OJ was E. coli, isolate PJ was S. aureus, isolate CJ was Salmonella sp. and isolate SJ was P. aeruginosa. Further, antibiotic susceptibility test results showed that minimum zone of inhibition about around 12mm showed by Salmonella sp. against Gentamicin. The isolate S. aureus was found resistant to Aztreonam and Penicillin whereas E. coli isolate was resistant to Cefotaxime and Ampicillin. On the other hand, P. aeruginosa was found resistant to Cefotaxime, Aztreonam and Cefoperazone. Staphylococcus aureus was found Coagulase positive while all other bacteria were Coagulase negative. P. aeruginosa and Salmonella sp. was found positive for proteolytic activity. Only Pseudomonas aeruginosa showed haemolytic activity. So, overall Pseudomonas aeruginosa showed maximum virulent traits alongwith antibiotic resistance. Therefore, it is necessary to monitor microbial community in the different food samples specially pathogenic bacteria by the govt. authority as the presence of pathogenic and multi-drug resistant bacteria may be a cause of epidemic.
Contrasting Changes in Vegetation Growth due to Different Climate Forcings over the Last Three Decades in the Selenga-Baikal Basin

The Selenga-Baikal Basin, a transboundary river basin between Mongolia and Russia, warmed at nearly twice the global rate and experienced enhanced human activities in recent decades. To understand the vegetation response to climate change, the dynamic spatial-temporal characteristics of the vegetation and the relationships between the vegetation dynamics and climate variability in the Selenga-Baikal Basin were investigated using the Normalized Difference Vegetation Index (NDVI) and gridded temperature and precipitation data for the period of 1982 to 2015. Our results indicated that precipitation played a key role in vegetation growth across regions that presented multiyear mean annual precipitation lower than 350 mm, although its importance became less apparent over regions with precipitation exceeding 350 mm. Because of the overall temperature-limited conditions, temperature had a more substantial impact on vegetation growth than precipitation. Generally, an increasing trend was observed in the growth of forest vegetation, which is heavily dependent on temperature, whereas a decreasing trend was detected for grassland, for which the predominant growth-limiting factor is precipitation. Additionally, human activities, such as urbanization, mining, increased wildfires, illegal logging, and livestock overgrazing are important factors driving vegetation change.
Spatial-spectral cube matching frame for spectral CT reconstruction

Spectral computed tomography (CT) reconstructs the same scanned object from projections of multiple narrow energy windows, and it can be used for material identification and decomposition. However, the multi-energy projection dataset has a lower signal-noise-ratio (SNR), resulting in poor reconstructed image quality. To address this thorny problem, we develop a spectral CT reconstruction method, namely spatial-spectral cube matching frame (SSCMF). This method is inspired by the following three facts: (i) human body usually consists of two or three basic materials implying that the reconstructed spectral images have a strong sparsity; (ii) the same basic material component in a single channel image has similar intensity and structures in local regions. Different material components within the same energy channel share similar structural information; (iii) multi-energy projection datasets are collected from the subject by using different narrow energy windows, which means images reconstructed from different energy-channels share similar structures. To explore those information, we first establish a tensor cube matching frame (CMF) for a BM4D denoising procedure. Then, as a new regularizer, the CMF is introduced into a basic spectral CT reconstruction model, generating the SSCMF method. Because the SSCMF model contains an L0-norm minimization of 4D transform coefficients, an effective strategy is employed for optimization. Both numerical simulations and realistic preclinical mouse studies are performed. The results show that the SSCMF method outperforms the state-of-the-art algorithms, including the simultaneous algebraic reconstruction technique, total variation minimization, total variation plus low rank, and tensor dictionary learning.
Utilizing Deep Architecture Networks of VAE in Software Fault Prediction

No matter how experienced the programmers are, it is very hard for them to avoid software fault in software projects. How to predict fault in order to reduce risk and enhance the reliability of software is an important challenge in software engineering. With successful application of deep learning in the field of image processing, voice and natural language, it is time to study how to apply deep learning technology in the field of software fault prediction. In this paper, we adopt deep learning technique of Variational Autoencoder(VAE) for software fault prediction. VAE has the ability to generate new samples according to the distribution of original data, which has been used to generate new images. How to use VAE to predict fault of software is studied in this paper. As we known, there is a problem of imbalanced data in software fault prediction. There always exist less data to indicate failure module and much more data to represent non-failure module. How to classify the data in this situation is an issue worthy of study. In this paper, we utilize the ability of generating new samples to produce failure data in order to balance the failure and non-failure samples. We design the structure of VAE by MLP to fit for the data dimension of software fault and the model of VAE is realized on GPU TITAN X. Five typical classifiers are adopted to verify that our idea of using VAE is effective for software fault prediction in practice.
Necessity of Charge Measurement for Field Prediction of Transmission Lines

For field prediction of transmission lines, usually current information is concerned. However, charge, which is the dual field source of current, is necessary to be measured as well as the current in near-field conditions. Although charge could be derived from current theoretically, the error of charge may be much larger than that of current. Such an error transfer effect is considered in this paper and studied on a typical case.
Magnetic Augmented Self-sensing Flexible Electroadhesive Grippers

Current electroadhesion (EA) grippers cannot be used to lift objects with limited contact areas. Current electromagnetic adhesion (MA) grippers cannot be used to grip nonferrous materials. Here, we combine EA and MA into a monolithic, electrically controllable, flexible, and hybrid electro/magneto-adhesive (E/MA), to offer EA grippers augmented with magnetoadhesion and MA grippers augmented with electroadhesion. This E/MA was achieved by exploiting two intertwined coplanar coils inspired by Tesla's flat bifilar coil. By appropriate electrical connections an electromagnetic field is generated – driving magnetic attraction—or an electric field is generated—delivering electroadhesion—or both fields can be generated simultaneously. As a result, the E/MA grippers cannot only be used to lift ferrous and nonferrous materials, but also low-density objects, such as office paper clips. We also present a customized capacitance measurement method to enable an autonomous material handling system without embedding external sensors. E/MA grippers have the potential to expand the capabilities and impact of soft robotics and industrial gripper systems.
A Blockchain-Based Offloading Approach in Fog Computing Environment

With the development of mobile computing technology, there has higher demand for computation resource in mobile applications than before. Fog computing has emerged as a promising infrastructure to provide elastic resources at the proximity of mobile users. Mobile users can offload some computations from the mobile devices to the nearby Fog servers so as to release the workloads of mobile devices, and therefore improve mobile users' quality of experience. However, mobile users may mistakenly offload their computations to the nearby Fog servers which have been injected by some attackers, and therefore induce some privacy and security issues. As most of mobile devices have natural mobility feature, it is very necessary to check the veracity of a Fog server in a very short time before doing computation offloading. In view of this challenge, we bring blockchain technique into Fog environment so as to verify each Fog server's authenticity and propose a blockchain-based offloading approach in this paper. Concretely, the proposed approach constantly maintains a set of candidate authorized Fog servers by leveraging blockchain technology, and the offloading decision could be made in a real-time fashion. Extensive experimental results have demonstrated our method's feasibility and efficiency.
Research on Short-Term Load Forecasting Approach for Smart Grid

To solve the problems including improving the precision of power load forecasting, speeding up the convergence speed, improving the possibility of falling into the local minimum and the single optimization process, a short-term forecasting model for the power supply load for smart grid system is established in this article. The particle swarm optimization(PSO) is used to initialize the reverse neural network, and the improved genetic algorithm(GA) is also introduced. In the crossing process, the optimal value of parent generation is integrated with the next generation population to optimize the network weight and improve the model performance. Then, by using the advantages of high nonlinearity of neural network and PCA reduction dimension principle, the prediction results are obtained through Matlab simulation. The results show that the approach proposed in this paper is reasonable and effective for short-term load forecasting.