Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Protein in Cancer Diagnosis Using Effective Classification and Feature Selection Technique

Cancer is the ailment where some cells in the body develop abnormally and wreck the opposite surrounding cells and their normal functions. Most cancers spread throughout the human body. Prediction of the protein using genes dataset causing cancer is of terrific significance in cancer prognosis and discovery of the drug. The DNA microarray approach concurrently monitor hundreds of gene expression viable. Using this massive quantity of Genes information, the prediction of protein causing cancer is explored. In this paper diverse information mining strategies are used that are expected to predict the protein causing cancer. The first stage of retrieving the records is characteristic extraction where Brute Force Algorithm is used in which the crucial features are extracted from the genes dataset which improvises the device performance, then the Fuzzy C Means the clustering approach is used to divide the gene dataset into numerous clusters while predicting the new patients the machine verify to which cluster it belong and examine the facts most effective with records of that precise cluster therefore the performance of the system is improvised and KNN algorithm is used for predicting protein. The protein prediction might help the clinical sector in the diagnosis of most cancer and additionally help to discover the drug for the protein causing cancer.
C2 AT2HUB: Long-term Characterization of Robots based on Human Child's Personality Development

Inadequate variety of personalities for communication robots may cause unnatural interaction with them and reduction in attachment. This paper proposes C<sup>2</sup> AT<sup>2</sup> HUB, where communication robots are characterized by changing tendency of affect transition based on long-term interaction with users. In C<sup>2</sup> AT<sup>2</sup> HUB, robots' affect is defined as two types; “interpersonal affect” and “emotions”, and transition of each type of affect is adjusted by history of users' actions to robots in order to characterize robots gradually. Evaluation experiment revealed our approach characterizes robots naturally and improves impression of robots.
Position Determination for Moving Transmitter Using Single Station

A common case in location problem is that the terminal is unstable or moving. The classical methods construct equations related to the measurements observed at the individual receivers first, and then solves the problem to localize the source. In this paper, we focus on the moving transmitter localization problem using only one station. The thinking of inverse synthesis aperture is implied, which takes advantage of the motion model to increase the equivalent aperture for higher accuracy. The proposed method formulates the problem through the angle of arrival and Doppler frequency measurements that are obtained from independent observations. After pseudo linearization by ignoring higher order noise terms, the estimator of position and velocity is easily solved through weighted least squares method. The bias reduction (BR) is utilized for lower bias and better root-mean-square error (RMSE). Using the result of BR as an initial solution, the maximum likelihood estimator (MLE) is realized through Gauss–Newton iteration. Theoretical analysis verifies that the BR estimator is efficient, which has Cramér-Rao Lower Bound (CRLB) accuracy if the noise level is mild. While MLE attends CRLB in higher noise situation albeit the possible divergence problem. Simulations indicate the feasibility and validate the advocated performance. The RMSE and bias match the theory very well.
Capacity Analysis of A MEO Satellite Constellation System

Aiming to calculate the system capacity of a given satellite constellation, this paper proposes a time-varying traffic model based on varying traffic with varying time-zone and focuses on a MEO satellite constellation with global coverage. Using a load balanced routing algorithm, this paper analyzes the system capacity with time slicing method. The time-varying traffic model makes analysis more reliable and effective by comparison with equal traffic model.
Relative Fatigue Life Analysis for Bearing of the Robot of Feeding System In Naval Gun's Ammunition

Two-dimensional digital filtering method is used to simulate the machining surface of the thin walled rotary table bearing of the robot of feeding system in naval gun's ammunition. Taking individual contact pair as the research object, the surface stress distribution is solved by multigrid method under the condition of elastohydrodynamic lubrication. The subsurface stress is solved by ANSYS. Based on the stress life model of Zaresky, the effects of the rough surface parameters, such as RMS(root mean square root), skewness, and kurtosis on relative fatigue life of the thin walled rotary table bearing are studied. The relationships between the RMS, skewness, kurtosis and the relative fatigue life of the bearing are obtained.
Detection of Minerals Through the Processing of Satellite Images

Currently, the city of Cerro de Pasco is officially in emergency due to the high level of contamination of its natural resources caused by the open pit mine that exists in the middle of the city and something more dangerous, air pollution by waste toxic substances that pile up inside and around the city. In this sense, this research proposes and develops the detection of the percentage of lead sulfide and zinc oxide, scattered in the city, using the digital processing of multispectral satellite images. As a result of this mineral detection, it is obtained that more than a quarter of the area of the city, it contains minerals scattered in urban areas.
Directional Reference Signal Design for 5G Millimeter Wave Cellular Systems

Millimeter wave (mmWave) frequency bands offer a new frontier for cellular wireless systems to enable multigigabit communication. However, the availability and reliability of mmWave links are significantly limited due to its propagation characteristics. To mitigate the impact of such links and provide uninterrupted network services, the study of changing link condition is important. This paper proposes a reference signal (RS) transmission scheme for a mmWave cellular system using highly directional antenna beams. The aim of RS is to estimate the channel condition, which can be used for various purposes, e.g., initial access, beam alignment, and channel state information measurement. In particular, the objective of our proposed scheme is to enable efficient RS transmission in terms of angular coverage while ensuring a fair number of RS reception at all UEs within cell coverage. In addition, we provide a framework for studying the tradeoff between RS transmission overhead and RS measurement accuracy. Through theoretical analysis and simulation, we show that the proposed scheme achieves higher efficiency, especially in a small-cell scenario with smaller number of UE presence.
Distributed Memory Sparse Inverse Covariance Matrix Estimation on High-Performance Computing Architectures

We consider the problem of estimating sparse inverse covariance matrices for high-dimensional datasets using the ℓ<sub>1</sub>-regularized Gaussian maximum likelihood method. This task is particularly challenging as the required computational resources increase superlinearly with the dimensionality of the dataset. We introduce a performant and scalable algorithm which builds on the current advancements of second-order, maximum likelihood methods. The routine leverages the intrinsic parallelism in the linear algebra operations and exploits the underlying sparsity of the problem. The computational bottlenecks are identified and the respective subroutines are parallelized using an MPI-OpenMP approach. Experiments conducted on a 5,320 node Cray XC50 system at the Swiss National Supercomputing Center show that, in comparison to the state-of-the-art algorithms, the proposed routine provides significant strong scaling speedup with ideal scalability up to 128 nodes. The developed framework is used to estimate the sparse inverse covariance matrix of both synthetic and real-world datasets with up to 10 million dimensions.
Runtime Data Management on Non-Volatile Memory-based Heterogeneous Memory for Task-Parallel Programs

Non-volatile memory (NVM) provides a scalable solution to replace DRAM as main memory. Because of relatively high latency and low bandwidth of NVM (comparing with DRAM), NVM often pairs with DRAM to build a heterogeneous main memory system (HMS). Deciding data placement on NVM-based HMS is critical to enable future NVM-based HPC. In this paper, we study task-parallel programs, and introduce a runtime system to address the data placement problem on NVM-based HMS. Leveraging semantics and execution mode of task-parallel programs, we efficiently characterize memory access patterns of tasks and reduce data movement overhead. We also introduce a performance model to predict performance for tasks with various data placements on HMS. Evaluating with a set of HPC benchmarks, we show that our runtime system achieves higher performance than a conventional HMS-oblivious runtime (24% improvement on average) and two state-of-the-art HMS-aware solutions (16% and 11% improvement on average, respectively).
Detection and characterization of ESBL-producing Escherichia coli expressing mcr-1 from dairy cows in China

Objectives To investigate the prevalence and molecular characteristics of ESBL-producing Escherichia coli (ESBL-EC) in faecal samples from dairy cows in China. Methods In total, 651 faecal samples were collected from cows distributed among the 10 provinces of China. Potential ESBL-EC isolates were cultured on selective medium. The clonal relatedness of the ESBL-EC isolates was assessed using MLST. WGS was conducted on 3 mcr-positive isolates and 14 additional randomly selected ESBL-EC isolates. Southern blot, S1-PFGE and conjugation were performed for mcr-1-carrying isolates. The genetic environment of the pMCR-JLF4 plasmid was also analysed. Results In total, 290 unique ESBL-EC isolates were detected from 284 cows (43.6%). Alleles of CTX-M were observed in 94.1% (273/290) of all isolates. The most prevalent genotypes observed in this study were blaCTX-M-14, blaCTX-M-15, blaCTX-M-17 and blaCTX-M-55. Differentiation of 79 STs with a polyclonal structure was accomplished using MLST. Clonal complex 10 was the most prevalent major complex detected here. Furthermore, the mcr-1 gene was detected in three isolates. The complete sequence of the mcr-1-containing pMCR-JLF4 was determined. The plasmid was 66.7 kb in length, with a genetic structure of nikA-nikB-mcr-1-pap2. Conjugation analysis confirmed that the mcr-1 gene in pMCR-JLF4 was transferable without the assistance of the ISApl1 gene. Conclusions The data presented here suggest high prevalence of ESBL-EC in Chinese cow farms. Furthermore, it was clearly demonstrated that commensal E. coli strains can be reservoirs of blaCTX-M genes, potentially contributing to the dissemination and transfer of the mcr-1 gene to pathogenic bacteria among cows.
Investigation of factors related to the occurrence of osteochondral lesions of the talus by 3D bone morphology of the ankle

Aims The aims of this study were to evaluate the morphology of the ankle in patients with an osteochondral lesion of the talus using 3D CT, and to investigate factors that predispose to this condition. Patients and Methods The study involved 19 patients (19 ankles) who underwent surgery for a medial osteochondral lesion (OLT group) and a control group of 19 healthy patients (19 ankles) without ankle pathology. The mean age was significantly lower in the OLT group than in the control group (27.0 vs 38.9 years; p = 0.02). There were 13 men and six women in each group. 3D CT models of the ankle were made based on Digital Imaging and Communications in Medicine (DICOM) data. The medial malleolar articular and tibial plafond surface, and the medial and lateral surface area of the trochlea of the talus were defined. The tibial axis‐medial malleolus (TMM) angle, the medial malleolar surface area and volume (MMA and MMV) and the anterior opening angle of the talus were measured. Results The mean TMM angle was significantly larger in the OLT group (34.2°, sd 4.4°) than in the control group (29.2°, sd 4.8°; p = 0.002). The mean MMA and MMV were significantly smaller in the OLT group than in the control group (219.8 mm2, sd 42.4) vs (280.5 mm2, sd 38.2), and (2119.9 mm3, sd 562.5) vs (2646.4 mm3, sd 631.4; p < 0.01 and p = 0.01, respectively). The mean anterior opening angle of the talus was significantly larger in the OLT group than in the control group (15.4°, sd 3.9°) vs (10.2°, sd 3.6°; p < 0.001). Conclusion 3D CT measurements showed that, in patients with a medial osteochondral lesion of the talus, the medial malleolus opens distally, the MMA and MMV are small, and the anterior opening angle of the talus is large. This suggests that abnormal morphology of the ankle predisposes to the development of osteochondral lesions of the talus. Cite this article: Bone Joint J 2018;100‐B:1487‐90.
Sybil Detection in Social-Activity Networks: Modeling, Algorithms and Evaluations

Detecting fake accounts (sybils) in online social networks (OSNs) is vital to protect OSN operators and their users from various malicious activities. Typical graph-based sybil detection (a mainstream methodology) assumes that sybils can make friends with only a limited (or small) number of honest users. However, recent evidences showed that this assumption does not hold in real-world OSNs, leading to low detection accuracy. To address this challenge, we explore users' activities to assist sybil detection. The intuition is that honest users are much more selective in choosing who to interact with than to befriend with. We first develop the social and activity network (SAN), a two-layer hyper-graph that unifies users' friendships and their activities, to fully utilize users' activities. We also propose a more practical sybil attack model, where sybils can launch both friendship attacks and activity attacks. We then design Sybil SAN to detect sybils via coupling three random walk-based algorithms on the SAN, and prove the convergence of Sybil SAN. We develop an efficient iterative algorithm to compute the detection metric for Sybil SAN, and derive the number of rounds needed to guarantee the convergence. We use "matrix perturbation theory" to bound the detection error when sybils launch many friendship attacks and activity attacks. Extensive experiments on both synthetic and real-world datasets show that Sybil SAN is highly robust against sybil attacks, and can detect sybils accurately under practical scenarios, where current state-of-art sybil defenses have low accuracy.
Object Tracking via Boosted Cascade of Simple Features and Coarse and Fine Structural Local Sparse Appearance Models

Sparse coding technique is effectively implemented in visual tracking applications to find the position of target in different frames. A large number of methods will choose holistic appearance of the target.it is more risky under occlusion. In this project, we develop a robust tracking algorithm based Haar features and Adaboost classifier for fast detection and a coarse and fine structural local sparse appearance model for the case of occlusion. The proposed method creates a dictionary for a target by using some templates. The averaging and pooling operations extract consistent appearance of object parts, thereby helping for tracking the target accurately. This paper compare the accuracy and running time of sparse coding based occlusion detection model and the proposed work to highlights the performance of the proposed frame work.
Facial Expression Recognition by Calculating Euclidian Distance for Eigen Faces Using PCA

In this paper the proposed methodology is to recognize Facial expressions of human being by using Eigen Faces. For recognition of human expression, in this approach we consider the following steps: face detection, facial feature extraction and then facial expression classification. The proposed system is based on calculating the Euclidian distance for the Eigen Faces. In this approach we will consider all the seven basic emotions which are classified as happy, anger, sad, fear, and disgust, surprised, neutral. Here 50 facial expression images are considered and trained by Eigen Faces. Finally the featured Eigen Faces are compared with the test image. In our approach face recognition is done by “Principal component analysis (PCA)”. The experimental result gives 98.5% recognition rates for each emotion.
A Novel Error Diffusion Algorithm for Halftoning Greyscale Image Using Pull Based Method

Halftoning is essentially required to print image efficiently. The objective of this vital stage of printing image processing pipeline, is to reduce the colour levels of an image without losing its original perception. It is also used for efficient storage and transmission purpose. Fundamental techniques of halftoing are as: (1) Dithering, (2) Error Diffusion, (3) Iterative algorithm. In error diffusion technique, it is a challenge to reduce the error. Proposed algorithm defines a circular filter that considers error of neighbours of a pixel for processing. It pulls the error of the neighbouring pixels, sums it to the pixel and processes the pixel. Visual perception of halftone image by proposed algorithm quite satisfactory and in experiment, we demonstrated the performance of six existing algorithms and compared it with proposed one. The mean square error (MSE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structure similarity index map (SSIM) of output image are 1.1535e+04, 107.3990, 7.5108, 0.1221 respectively.
Commercial Demand Response Programs in Bidding of a Technical Virtual Power Plant

Regarding the potentials of activating commercial consumers in demand response programs, this paper proposes a new framework for energy scheduling of an active distribution network based on the concept of technical virtual power plant (TVPP), considering operational constraints of distribution network. The TVPP enables presence of commercial buildings and other distributed energy resources in day-ahead (DA) electricity market, as a price maker agent. In this regard, a bilevel optimization framework is designed to optimize the bidding strategy of TVPP in the DA market with the main goal of maximizing TVPP profit. The upper-level problem maximizes the TVPP profit, while in the lower level, market is cleared from independent system operator viewpoint. Using Karush—Kuhn–Tucker optimality conditions and strong duality theory, the proposed bilevel problem is transformed into mixed integer linear programming problem. Implementing the model on the Roy Billinton Test System (RBTS) test system demonstrates the applicability of the proposed model.
Multi-label Object Attribute Classification using a Convolutional Neural Network

Objects of different classes can be described using a limited number of attributes such as color, shape, pattern, and texture. Learning to detect object attributes instead of only detecting objects can be helpful in dealing with a priori unknown objects. With this inspiration, a deep convolutional neural network for low-level object attribute classification, called the Deep Attribute Network (DAN), is proposed. Since object features are implicitly learned by object recognition networks, one such existing network is modified and fine-tuned for developing DAN. The performance of DAN is evaluated on the ImageNet Attribute and a-Pascal datasets. Experiments show that in comparison with state-of-the-art methods, the proposed model achieves better results.
Identifying Socio-Cognitive Structures in Online Knowledge Building Communities Using Cohesion Network Analysis

Online knowledge building communities (OKBCs) prove beneficial in informal learning settings. Extending their use to the instructional design of formal learning environments requires identification methods of the central, intermediate and peripheral community layers. This study proposes such a social learning analytics method, i.e., an automated discourse analysis based on Natural Language Processing. The method was applied to the dialog produced by N = 1,990 participants in 20 blogger communities. Centrality criteria based on Cohesion Network Analysis were highly consistent and could successfully identify the socio-cognitive layers of the analyzed OKBCs. Ongoing research proposes instructional design based on formal learnersâ€™ interactions with OKBCs.
Characterizing Sources of Variability in Zebrafish Embryo Screening Protocols

There is a need for fast, efficient, and cost-effective hazard identification and characterization of chemical hazards. This need is generating increased interest in the use of zebrafish embryos as both a screening tool and an alternative to mammalian test methods. A Collaborative Workshop on Aquatic Models and 21st Century Toxicology identified the lack of appropriate and consistent testing protocols as a challenge to the broader application of the zebrafish embryo model. The National Toxicology Program established the Systematic Evaluation of the Application of Zebrafish in Toxicology (SEAZIT) initiative to address the lack of consistent testing guidelines and identify sources of variability for zebrafish-based assays. This report summarizes initial SEAZIT information-gathering efforts. Investigators in academic, government, and industry laboratories that routinely use zebrafish embryos for chemical toxicity testing were asked about their husbandry practices and standard protocols. Information was collected about protocol components including zebrafish strains, feed, system water, disease surveillance, embryo exposure conditions, and endpoints. Literature was reviewed to assess issues raised by the investigators. Interviews revealed substantial variability across design parameters, data collected, and analysis procedures. The presence of the chorion and renewal of exposure media (static versus static-renewal) were identified as design parameters that could potentially influence study outcomes and should be investigated further with studies to determine chemical uptake from treatment solution into embryos. The information gathered in this effort provides a basis for future SEAZIT activities to promote more consistent practices among researchers using zebrafish embryos for toxicity evaluation.