Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosis of malignant melanoma using a neural network

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991, with approximately 80 percent of patients expected to survive five years [1], Fortunately, if detected early, even malignant melanoma may be treated successfully. Thus, in recent years, there has been a rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma [2]. In this thesis, a novel neural network approach for the automated distinction of melanoma from three benign categories of tumors which exhibit melanoma-like characteristics is presented. The approach is based on devising new and discriminant features which are used as inputs to an artificial neural network for classification of tumor images as malignant or benign. Promising results have been obtained using this method on real skin cancer images.
Constraining dust properties in circumstellar envelopes of C-stars in the Magellanic Clouds: Optical constants and grain size of carbon dust

We present a new approach aimed at constraining the typical size and optical properties of carbon dust grains in Circumstellar envelopes (CSEs) of carbon-rich stars (C-stars) in the Small Magellanic Cloud (SMC). To achieve this goal, we apply our recent dust growth description, coupled with a radiative transfer code to the CSEs of C-stars evolving along the TP-AGB, for which we compute spectra and colors. Then we compare our modeled colors in the near- and mid-infrared (NIR and MIR) bands with the observed ones, testing different assumptions in our dust scheme and employing several data sets of optical constants for carbon dust available in the literature. Different assumptions adopted in our dust scheme change the typical size of the carbon grains produced. We constrain carbon dust properties by selecting the combination of grain size and optical constants which best reproduces several colors in the NIR and MIR at the same time. The different choices of optical properties and grain size lead to differences in the NIR and MIR colors greater than two magnitudes in some cases. We conclude that the complete set of observed NIR and MIR colors are best reproduced by small grains, with sizes between $\sim$0.035 and $\sim$0.12~$\mu$m, rather than by large grains between $\sim0.2$ and $0.7$~$\mu$m. The inability of large grains to reproduce NIR and MIR colors seems independent of the adopted optical data set. We also find a possible trend of the grain size with mass-loss and/or carbon excess in the CSEs of these stars.
Optimization of a polyphenol extraction method for sweet orange pulp (Citrus sinensis L.) to identify phenolic compounds consumed from sweet oranges

The consumption of sweet oranges has been linked to several health benefits, many of which are attributed to hesperidin, a flavanone that is present in high amounts in these fruits. However, other phenolic compounds can contribute to the bioactivity of sweet orange. To link those effects to their phenolic profile, the complete characterization of the phenolic profile is mandatory. Although many studies have profiled the phenolic composition of orange juices, their pulps, which retain phenolic compounds, are overlooked. This fact is particularly relevant because dietary guidelines recommend the consumption of whole fruits. Therefore, this study aimed to develop a specific method for the optimal extraction of phenolics from orange pulp and to use this method to characterize these fruits grown at different locations by HPLC-ESI-MS/MS. The extraction conditions that reported the highest total polyphenol content (TPC) and hesperidin contents were 20 mL/g, 55 °C, and 90% methanol. The extraction time and number of sequential steps were further evaluated and optimized as 20 min and two extraction steps, respectively. Although lower extraction rates were achieved when using ethanol as the extraction solvent, high TPC and hesperidin yields were obtained, suggesting the potential use of this methodology to produce phenolic-rich extracts for the food industry. By applying the optimized methodology and analyzing the extracts by HPLC-ESI-MS/MS, geographic cultivation regions were demonstrated to affect the phenolic profiles of oranges. In short, we developed a quick, easy-to-perform methodology that can be used to extract orange phenolics from pulp for their identification and quantification and to evaluate the factors that affect the phenolic profile in sweet orange pulps.
Effects of Electroacupuncture on Expression of D1 Receptor (D1R), Phosphorylation of Extracellular-Regulated Protein Kinase 1/2 (p-ERK1/2), and c-Fos in the Insular Cortex of Ketamine-Addicted Rats

Background The aim of this study was to investigate the effects of electroacupuncture (EA) on expression of the D1 receptor (D1R), phosphorylation of extracellular-regulated protein kinase 1/2 (p-ERK1/2) and c-Fos in the insular cortex (IC) of ketamine-addicted rats. Material/Methods Sprague-Dawley rats were randomly divided into 7 groups: the normal group, the normal saline (NS) group, the ketamine (Ket) group, the U0126+Ket group, the SCH23390+Ket group, the Ket+acupoints EA (EA1) group, and the Ket+ non-acupoints EA (EA2) group. We used immunohistochemistry to detect the expression of D1R, p-ERK1/2, and c-Fos. We also used Nissl staining techniques to study the morphology of IC neurons. Results Our study demonstrated that the ketamine group had sparsely distributed neurons, large intracellular vacuoles, nuclei shift, and unclear nucleolus. The number of Nissl-positive (neuronal) cells in the ketamine group were decreased than in the normal group. Our results also indicated that there was significantly lower expression of D1R, p-ERK1/2, and c-Fos in the IC of the U0126+Ket group, SCH23390+Ket group, and Ket+EA1 group as compared with that of the Ket group. Conclusions Ketamine addiction induces c-Fos overexpression in the IC by increasing the expression of D1R and p-ERK1/2. Acupoints EA downregulate D1R and p-ERK1/2 by reducing the overexpression of c-Fos.
Yet Another BGP Archive Forensic Analysis Tool Using Hadoop and Hive

A large volume of continuously growing BGP data files can raise two technical challenges regarding scalability and manageability. Due to the recent development of the open-source distributed computing infrastructure, Hadoop, it becomes feasible to handle a large amount of data in a scalable manner. In this paper, we present a new Hadoop-based BGP tool (BGPdoop) that provides the scale-out performance as well as the extensible and agile analysis capability. In particular, BGPdoop realizes a query-based BGP record exploration function using Hive on the partitioned BGP data structure, which enables flexible and versatile analytics of BGP archive files. From the experiments for the scalability with a Hadoop cluster of 20 nodes, we demonstrate that BGPdoop achieves 5 times higher performance and the user-defined analysis capability by expressing diverse BGP routing analytics in Hive queries.
Secure Wireless Body Area Network (WBAN) Communication Method Using New Random Key Management Scheme

Wireless Body Area Networks (WBANs) have an important role in healthcare. So, the security of WBANs becomes a challenging issue. High performance encryption method and efficient key management scheme are required for securing WBAN communications. This study presents and evaluates an efficient key management scheme and efficient encryption method for improving WBAN security. We proposed a new random key management scheme. The proposed method utilized Advanced Encryption Standard (AES)-256 to encrypt the bio signals. Simulation results show that the proposed method has advantages compared to other secure WBAN communication methods
Research on the Technologies of Byzantine System

Nowadays,in order to resolve the reliability problem in an enlarging distributed system,Byzantine fault tolerant system has researched popularly for its ability of tolerating arbitrary faults.In this paper,the definitions of Byzantine system and the estimation methods of improving the performance of Byzantine system are introduced.After that,some unresolved problems and some future development trends will be indicated.Finally,after analyzing the status of studies,several conclusions are drawn: 1) The cost of running a Byzantine system is still much higher than non-Byzantine system.Plans to increase the performance and decrease the overhead are need to be explored in further study.2) While detecting Byzantine faults and proactive recovery can keep Byzantine system from breaking down,they still have some drawbacks.How to eliminate the drawbacks should be studied.3) Different applications require different aspect of optimization.How to make practical Byzantine systems are needed to be studied.
A bodily user interface for VR-CAVE

The main purpose of this research is to explore how body movement can increase a user’s sense of presence in the virtual reality cave (VR-CAVE). Traditionally, in architectural applications, there is no interaction between users and VR-CAVE. Visual perception is the major way of presentation. The users feel a new sense of presence. Therefore, in order to increase user’s sense of presence, we designed a bodily user interface as our controller which utilized user’s body movement to interact with VR. The contribution is that not only can a user easily or effortlessly control and navigate VR space but also VR navigation will directly link to the experience of walking in a physical space, which provides a strong sense of presence to user.
Feedback Driven Pipelining

Extracting performance from Chip Multiprocessors requires that the application be parallelized. A common software technique to parallelize loops is pipeline parallelism in which the programmer/compiler splits each loop iteration into stages and each stage runs on a certain number of cores. It is important to choose the number of cores for each stage carefully because the core-to-stage allocation determines performance and power consumption. Finding the best core-to-stage allocation for an application is challenging because the number of possible allocations is typically quite large, and the best allocation changes with the input set and machine configuration. This paper proposes Feedback-Directed Pipelining (FDP), a software framework that chooses the core-tostage allocation at run-time. FDP first maximizes the performance of the workload and then saves power by reducing the number of active cores, without impacting performance. Our evaluation of FDP on a real 8-core SMP system (2x Core2Quad) shows that FDP provides an average speedup of 4.2x which is significantly higher than the 2.3x speedup obtained with a practical profile-based allocation. We also show that FDP is robust to changes in machine configuration and input set variation.
A COMPARATIVE STUDY ON OPEN SOURCE CLOUD COMPUTING FRAMEWORKS

Cloud computing is a computing technique where sharing of resources rather than personal system or servers for applications. Cloud is used as a metaphor for Web applications. Cloud computing is a web based computing such as database, server applications etc. Main goal of cloud computing is high performance computing power. Virtualization is a technique to improve computing power. This paper aim a relative study on different frameworks used in cloud computing.
Mesh Generation for Modeling and Simulation of Carbon Sequestration Process.

Recently, randomly close-packed Voronoi meshes have been proposed for simulating pervasive fracture processes in materials and structures by allowing fractures to grow only at the interelement faces of the polyhedral cells. The polyhedral cells are formulated as finite elements. A new meshing tool is presented here for creating randomly close-packed Voronoi meshes in nonconvex domains with internal surfaces. Applications using these meshes include blast and impact response of engineered structures as well as hydraulic fracturing in geostructures and the design of CO2 sequestration processes to maintain the integrity of a reservoir caprock that contains preexisting fractures and joints. Our meshing approach is based on creating a random cloud of n points whose locations are determined by solving a maximal Poisson-disk sampling problem over nonconvex domains with internal surfaces, required points, and multiple regions in contact. A novel constrained Delaunay algorithm is then utilized to generate Poisson-disk triangulations using O(n) time and memory. The required Voronoi mesh is constructed by retrieving the dual of the triangular mesh. Each phase (sampling, triangulation, Voronoi meshing) of our algorithm utilizes local operations th facilitate parallel implementations. An example of the use of this meshing tool for a fracture simulation is given.
Suppression of miR-21-3p enhances TRAIL-mediated apoptosis in liver cancer stem cells by suppressing the PI3K/Akt/Bad cascade via regulating PTEN

Background TNF-related apoptosis-inducing ligand (TRAIL) functions as a selective apoptosis-inducing ligand in cancer cells with normal cells remaining unaffected; however, resistance limits its anticancer properties. Cancer stem cells (CSCs) are involved in the treatment of resistant cancer cases including liver cancer (LC). The aim of this study was to look into the approaches for increasing the sensitivity of liver cancer stem cells (LCSCs) toward TRAIL. Methodology PLC, HepG2 and Huh7 LC cell lines were used in this study. Quantitative reverse transcription PCR (qRT-PCR) analysis was done for evaluating the expression of miR-21-3b. Fluorescent-activated cell-sorting equipment was used for separation and identification of LCSCs and non-LCSCs. The cells were transfected with RNA along with miR-21-3p mimics, anti- miR-21-3p, miR-NC and the phosphatase and tensin homologue (PTEN) siRNA. MTT assay for cell viability, Luciferase assay for luciferase activity, Western blots for the expression of proteins and flow cytometry for the measurement of ROS and apoptosis, respectively, were carried out. Tumor xenografts nude mice were used for tumor growth in vivo. Results We found that miR-21-3p was overexpressed in LCSCs compared to non-LCSCs and that the suppression of miR-21-3p along with anti-miR-21-3p enhanced the sensitivity of LCSCs to TRAIL-mediated apoptosis. We further found that miR-21-3p regulated the expression of PTEN in Huh7-LCSCs directly and that the suppression of miR-21-3p enhanced the levels of PTEN. The study confirmed that inhibition of the PI3K/Akt/Bad signaling pathway was involved in enhancing TRAIL-mediated apoptosis of LC cells. Conclusion The study suggested that overexpression of miR-21-3p suppresses the sensitivity to TRAIL in LCSCs. This study concludes that the suppression of miR-21-3p is a potential approach for enhancing the sensitivity of LC cells toward TRAIL by PI3K/Akt/Bad cascade via the miR-21-3p/PTEN axis.
Speech emotion recognition using acoustic features

A speech emotion recognition algorithm was developed based on the statistical and temporal features of the acoustic parameters for discriminating between emotions.The system first extracted the basic prosody parameters and spectral parameters,then used a PNN(probabilistic neural network) to model the statistic features and a HMM(hidden Markov model) to model the temporal features.The sum and product rules were used to combine the probabilities from each group of features for the final decision.Experiments on the Chinese speech corpus showed how the statistical and temporal features tend to reflect different aspects of emotions.The accuracy rate obtained by feature combination is higher than that by each group alone,reaching a maximum of(92.9%.)
Texture and Shape based Object Detection Strategies

Objectives: To identify the Objects individually in an image. Methods/Statistical Analysis: The texture based object detection is done based on the texture available over the surface of the object and the shape based object detection is done based on the outline of the objects in an image. These two methods are used for the object detection in an image. Findings: Both of the methods prove to have their own advantages and limitations; so based on the applications the appropriate method can be applied. Applications: Biometric recognition, surveillance, Medical Analysis.
Why Language Matters For Theory Of Mind

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Application of SSL in constructing virtual private network

The principle of SSL protocol and VPN technology was analyzed,and the background of SSL VPN was introduced.Through the analysis of the simplest form of SSL VPN——Web reverse proxy,essence of SSL VPN was revealed,and an end-to-end security scheme of SSL VPN was proposed.At last,a comparative analysis between SSL VPN and IPSec VPN was made.
Existe relación entre el Clima Organizacional y la Satisfacción Laboral en la División de Distribución Norte de la CFE

En el presente estudio se analiza la relacion de Clima Organizacional y la Satisfaccion Laboral en la Division de Distribucion Norte de Comision Federal de Electricidad [CFE] con los resultados de la encuesta aplicada durante el ano 2012.Reorganizamos las 84 preguntas del cuestionario original, reduciendolo en 14 dimensiones de Clima Organizacional con 64 preguntas y una dimension de Satisfaccion Laboral con 20 preguntas. Posteriormente se analizan los datos, evaluando la consistencia del cuestionario mediante el Coeficiente Alpha de Cronbach de cada una de las dimensiones, resultando en promedio 0.808 el cual se considera elevado. Tambien se analiza la relacion del Clima Organizacional con la Satisfaccion Laboral mediante un Modelo de Regresion Simple cuya R cuadrada fue de 0.772, llegandose a la conclusion de que existe relacion entre las dimensiones de Clima Organizacional con la Satisfaccion Laboral.
Stochastic models for resource sharing in wireless networks

Next-generation wireless networks will likely evolve from cellular and small-scale home networks to large, inter-connected networks that form the backbone for low-cost internet access. A de? ning characteristic of wireless networks is that all nodes share the same medium for their transmissions, and consequently, simultaneous transmissions from nearby nodes will interfere with each other. The resulting performance issues can be mitigated by regulating node activity. Various mechanisms exist for regulating node access to the wireless medium, which can be categorized into scheduled-access and random-access algorithms. The former involve a centralized entity that controls the behavior of all nodes, while random-access constitutes a class of randomized, distributed algorithms. Random-access algorithms are popular for their simplicity, and their distributed nature makes them well-suited for large, dynamic wireless networks. Scheduled- access networks generally have better performance since the presence of an omniscient controller allows for coordination between nodes, but entail higher implementation complexity. One well-known algorithm for centralized access is MaxWeight scheduling, which is popular for its ability to achieve maximum stability and throughput optimality in a wide variety of scenarios. The distinguishing characteristic of MaxWeight policies is that these require solving the maximum weighted independent set problem of the underlying interference graph. The maximum- stability guarantees however rely on the premise that the system consists of a fi? xed set of fl ows, while in reality the collection of active ows dynamically varies. In Chapters 2 and 3 we demonstrate that in the presence of ow-level dynamics the algorithm may no longer be throughput-optimal, and we identify two causes for the instability: (i) failure to fully exploit rate variations; and (ii) spatial ine? ciency. In Chapter 2 we consider the MaxWeight scheduling algorithm in a single downlink scenario with varying transmission rates. We identify a simple necessary and su? cient condition for stability, and show that MaxWeight policies may fail to provide maximum stability. The intuitive explanation is that these policies tend to favor ows with large backlogs, so that the rate variations of ows with smaller backlogs are not fully exploited.
Integration Of Big Data And Cloud Computing To Detect Black Money Check Rotation With Range Aggregate Queries

A Cloud is expanding from application aggregation and sharing to data aggregation and utilization. To make use of data tens of terabytes and tens of beta bytes of data to be handled. These massive amounts of data are called as a big data. Range-aggregate queries are to apply a certain aggregate function on all tuples within given query ranges. Fast RAQ first divides big data into independent partitions with a balanced partitioning algorithm, and then generates a local estimation sketch for each partition. When a range-aggregate query request arrives, Fast RAQ obtains the result directly by summarizing local estimates from all partitions & Collective Results are provided. Data Mining can process only Structured Data only. Big Data Approach is spoken all over the Paper. They insist of Three Tier Architecture, 1. Big Data implementation in Multi System Approach, 2. Application Deployment - Banking / Insurance. 3. Extraction of Useful information from Unstructured Data. We implement this Project for Banking Domain. There will be Two Major Departments. 1. Bank Server for Adding New Clients and maintaining their Accounts. Every User while Registration has to provide their aadhar card as a ID Proof to create Account in any Bank. 2. Accounts Monitoring Sever will monitor every users and their Account Status in different Banks. This Server will retrieve users who maintain & Transact more than Rs. 50,000 / Annum in all 3 Accounts in different Banks using the same ID Proof. Map & Reduce is achieved.
Impact of human resource management on business performance: A review of literature

In this paper the influence of human resource management (HRM) on business performance is analyzed. The main goal was to thoroughly and systematically examine literature in the domain of HRM, and business performance that resulted in a concise review paper. A large body of literature addresses the HRM-business performance link, thus there was plenty of room for an extensive analysis of various articles. To be more precise, two hundred and twelve (212) articles were analyzed from which 187 were removed through several steps in the review process. The findings indicate that there is a positive relationship between HRM practices and overall business performance. In addition, HRM has a positive influence on employee wellbeing, productivity, and organizational climate. However, there is a possibility that good HRM practices are not the cause of good business performance, but vice-versa. This issue is addressed, and discussed further in this present article. Based on the gathered information, and obtained results, it is safe to suggest that this systematic review paper has a significant contribution to the existing body of literature. In addition, the paper can be used as a starting point for future research in the domain of HRM practices and their impact on business performance.