nils-herrmann/clean_scirepeval huggingface dataset:
Discrimination Between Silicone Oil Droplets and Protein Aggregates in Biopharmaceuticals: A Novel Multiparametric Image Filter for Sub-visible Particles in Microflow Imaging Analysis

Accurate monitoring of the sub-visible particle load in protein biopharmaceuticals is increasingly important to drug development. Manufacturers are expected to characterize and control sub-visible protein particles in their products due to their potential immunogenicity. Light obscuration, the most commonly used analytical tool to count microscopic particles, does not allow discrimination between potentially harmful protein aggregates and harmless pharmaceutical components, e.g. silicone oil, commonly present in drug products. Microscopic image analysis in flow-microscopy techniques allows not only counting, but also classification of sub-visible particles based on morphology. We present a novel approach to define software filters for analysis of particle morphology in flow-microscopic images enhancing the capabilities of flow-microscopy. Image morphology analysis was applied to analyze flow-microscopy data from experimental test sets of protein aggregates and silicone oil suspensions. A combination of four image morphology parameters was found to provide a reliable basis for automatic distinction between silicone oil droplets and protein aggregates in protein biopharmaceuticals resulting in low misclassification errors. A novel, custom-made software filter for discrimination between proteinaceous particles and silicone oil droplets in flow-microscopy imaging analysis was successfully developed.
A Movie Recommender System: MOVREC

Now a day’s recommendation system has changed the style of searching the things of our interest. This is information filtering approach that is used to predict the preference of that user. The most popular areas where recommender system is applied are books, news, articles, music, videos, movies etc. In this paper we have proposed a movie recommendation system named MOVREC. It is based on collaborative filtering approach that makes use of the information provided by users, analyzes them and then recommends the movies that is best suited to the user at that time. The recommended movie list is sorted according to the ratings given to these movies by previous users and it uses K-means algorithm for this purpose. MOVREC also help users to find the movies of their choices based on the movie experience of other users in efficient and effective manner without wasting much time in useless browsing. This system has been developed in PHP using Dreamweaver 6.0 and Apache Server 2.0. The presented recommender system generates recommendations using various types of knowledge
A facial wearable robot with eyelid gating mechanism for supporting eye blink

Facial paralysis causes loss of control over facial muscles such as the orbicularis oculi which closes the eyelid. People with facial paralysis thus are not able to blink which could cause dry eye and/or permanent damage to the cornea. Also, during the convalescent phase of facial paralysis, there is possibility to get Synkinesis because of inappropriate rehabilitation. However, few devices for rehabilitation or tools for activity of daily life for them are available. We propose a novel approach for supporting eyelid gating movement with facial wearable robotic technology. This robot has novel eyelid gating mechanism made of silicon, and provides suitable support for eyelid gating movement using its deformation, triggered by wink detection on healthy side. We control the deformation designed to eyelid movement with simple rotation movement. We evaluated the proposed system with 2 healthy participants and verified the feasibility of eyelid gating support for people with facial paralysis.
Impact of battery energy storage on power system with high wind penetration

The penetration of renewable resources in power system has been increasing in recent years. Many of these resources are uncontrollable and variable in nature, wind in particular, are relatively unpredictable. At high penetration levels, volatility of wind power production could cause problems for power system to maintain system security and reliability. One of the solutions being proposed to improve reliability and performance of the system is to integrate energy storage devices into the network. In this paper, unit commitment and dispatch schedule in power system with and without energy storage is examined for different level of wind penetration. Battery energy storage (BES) is considered as an alternative solution to store energy. The SCUC formulation and solution technique with wind power and BES is presented. The proposed formulation and model is validated with eight-bus system case study. Further, a discussion on the role of BES on locational pricing, economic, peak load shaving, and transmission congestion management had been made.
Are Genetic Algorithms Function Optimizers?

Genetic Algorithms (GAs) have received a great deal of attention regarding their poten­ tial as optimization techniques for complex functions. The level of interest and success in this area has led to a number of improvements to GA-based function optimizers and a good deal of progress in characterizing the kinds of functions that are easy/hard for GAs to optim­ ize. With all this activity, there has been a natural tendency to equate GAs with function optimization. However, the motivating context of Holland's initial GA work was the design and implementation of robust adaptive systems. In this paper we argue that a proper under­ standing of GAs in this broader adaptive systems context is a necessary prerequisite for understanding their potential application to any problem domain. We then use these insights to better understand the strengths and limitations of GAs as function optimizers.
A hierarchical similarity based job recommendation service framework for university students

When people want to move to a new job, it is often difficult since there is too much job information available. To select an appropriate job and then submit a resume is tedious. It is particularly difficult for university students since they normally do not have any work experience and also are unfamiliar with the job market. To deal with the information overload for students during their transition into work, a job recommendation system can be very valuable. In this research, after fully investigating the pros and cons of current job recommendation systems for university students, we propose a student profiling based re-ranking framework. In this system, the students are recommended a list of potential jobs based on those who have graduated and obtained job offers over the past few years. Furthermore, recommended employers are also used as input for job recommendation result re-ranking. Our experimental study on real recruitment data over the past four years has shown this method’s potential.
Forecasting methods for cloud hosted resources, a comparison

The emergence of cloud management systems, and the adoption of elastic cloud services enable dynamic adjustment of cloud hosted resources and provisioning. In order to effectively provision for dynamic workloads presented on cloud platforms, an accurate forecast of the load on the cloud resources is required. In this paper, we investigate various forecasting methods presented in recent research, identify and adapt evaluation metrics used in literature and compare forecasting methods on prediction performance. We investigate the performance gain of ensemble models when combining three of the best performing models into one model. We find that our 30th order Auto-regression model and Feed-Forward Neural Network method perform the best when evaluated on Google's Cluster dataset and using the provision specific metrics identified. We also show an improvement in forecasting accuracy when evaluating two ensemble models.
Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data

This paper investigates the problems of outliers and/or noise in surface segmentation and proposes a statistically robust segmentation algorithm for laser scanning 3-D point cloud data. Principal component analysis (PCA)-based local saliency features, e.g., normal and curvature, have been frequently used in many ways for point cloud segmentation. However, PCA is sensitive to outliers; saliency features from PCA are nonrobust and inaccurate in the presence of outliers; consequently, segmentation results can be erroneous and unreliable. As a remedy, robust techniques, e.g., RANdom SAmple Consensus (RANSAC), and/or robust versions of PCA (RPCA) have been proposed. However, RANSAC is influenced by the well-known swamping effect, and RPCA methods are computationally intensive for point cloud processing. We propose a region growing based robust segmentation algorithm that uses a recently introduced maximum consistency with minimum distance based robust diagnostic PCA (RDPCA) approach to get robust saliency features. Experiments using synthetic and laser scanning data sets show that the RDPCA-based method has an intrinsic ability to deal with outlier- and/or noise-contaminated data. Results for a synthetic data set show that RDPCA is 105 times faster than RPCA and gives more accurate and robust results when compared with other segmentation methods. Compared with RANSAC and RPCA based methods, RDPCA takes almost the same time as RANSAC, but RANSAC results are markedly worse than RPCA and RDPCA results. Coupled with a segment merging algorithm, the proposed method is efficient for huge volumes of point cloud data consisting of complex objects surfaces from mobile, terrestrial, and aerial laser scanning systems.
Proactive Drone-Cell Deployment: Overload Relief for a Cellular Network Under Flash Crowd Traffic

This paper is concerned with providing radio access network (RAN) elements (supply) for flash crowd traffic demands. The concept of multi-tier cells [heterogeneous networks (HetNets)] has been introduced in 5G network proposals to alleviate the erratic supply–demand mismatch. However, since the locations of the RAN elements are determined mainly based on the long-term traffic behavior in 5G networks, even the HetNet architecture will have difficulty in coping up with the cell overload induced by flash crowd traffic. In this paper, we propose a proactive drone-cell deployment framework to alleviate overload conditions caused by flash crowd traffic in 5G networks. First, a hybrid distribution and three kinds of flash crowd traffic are developed in this framework. Second, we propose a prediction scheme and an operation control scheme to solve the deployment problem of drone cells according to the information collected from the sensor network. Third, the software-defined networking technology is employed to seamlessly integrate and disintegrate drone cells by reconfiguring the network. Our experimental results have shown that the proposed framework can effectively address the overload caused by flash crowd traffic.
Fully automated assessment of the severity of Parkinson's disease from speech

For several decades now, there has been sporadic interest in automatically characterizing the speech impairment due to Parkinson's disease (PD). Most early studies were confined to quantifying a few speech features that were easy to compute. More recent studies have adopted a machine learning approach where a large number of potential features are extracted and the models are learned automatically from the data. In the same vein, here we characterize the disease using a relatively large cohort of 168 subjects, collected from multiple (three) clinics. We elicited speech using three tasks - the sustained phonation task, the diadochokinetic task and a reading task, all within a time budget of 4 minutes, prompted by a portable device. From these recordings, we extracted 1582 features for each subject using openSMILE, a standard feature extraction tool. We compared the effectiveness of three strategies for learning a regularized regression and find that ridge regression performs better than lasso and support vector regression for our task. We refine the feature extraction to capture pitch-related cues, including jitter and shimmer, more accurately using a time-varying harmonic model of speech. Our results show that the severity of the disease can be inferred from speech with a mean absolute error of about 5.5, explaining 61% of the variance and consistently well-above chance across all clinics. Of the three speech elicitation tasks, we find that the reading task is significantly better at capturing cues than diadochokinetic or sustained phonation task. In all, we have demonstrated that the data collection and inference can be fully automated, and the results show that speech-based assessment has promising practical application in PD. The techniques reported here are more widely applicable to other paralinguistic tasks in clinical domain.
How does customer self-construal moderate CRM value creation chain?

Most of the existing literature on CRM value chain creation has focused on the effect of customer satisfaction and customer loyalty on customer profitability. In contrast, little has been studied about the CRM value creation chain at individual customer level and the role of self-construal (i.e., independent self-construal and interdependent self-construal) in such a chain. This research aims to construct the chain from customer value to organization value (i.e., customer satisfaction ? customer loyalty ? patronage behavior) and investigate the moderating effect of self-construal. To test the hypotheses suggested by our conceptual framework, we collected 846 data points from China in the context of mobile data services. The results show that customer’s self-construal can moderate the relationship chain from customer satisfaction to customer loyalty to relationship maintenance and development. This implies firms should tailor their customer strategies based on different self-construal features.
Overcoming Catastrophic Forgetting by Incremental Moment Matching

Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task. Here, we propose a method, i.e. incremental moment matching (IMM), to resolve this problem. IMM incrementally matches the moment of the posterior distribution of the neural network which is trained on the first and the second task, respectively. To make the search space of posterior parameter smooth, the IMM procedure is complemented by various transfer learning techniques including weight transfer, L2-norm of the old and the new parameter, and a variant of dropout with the old parameter. We analyze our approach on a variety of datasets including the MNIST, CIFAR-10, Caltech-UCSDBirds, and Lifelog datasets. The experimental results show that IMM achieves state-of-the-art performance by balancing the information between an old and a new network.
Marine harmful algal blooms, human health and wellbeing: challenges and opportunities in the 21st century.

Microalgal blooms are a natural part of the seasonal cycle of photosynthetic organisms in marine ecosystems. They are key components of the structure and dynamics of the oceans and thus sustain the benefits that humans obtain from these aquatic environments. However, some microalgal blooms can cause harm to humans and other organisms. These harmful algal blooms (HABs) have direct impacts on human health and negative influences on human wellbeing, mainly through their consequences to coastal ecosystem services (fisheries, tourism and recreation) and other marine organisms and environments. HABs are natural phenomena, but these events can be favoured by anthropogenic pressures in coastal areas. Global warming and associated changes in the oceans could affect HAB occurrences and toxicity as well, although forecasting the possible trends is still speculative and requires intensive multidisciplinary research. At the beginning of the 21st century, with expanding human populations, particularly in coastal and developing countries, mitigating HABs impacts on human health and wellbeing is becoming a more pressing public health need. The available tools to address this global challenge include maintaining intensive, multidisciplinary and collaborative scientific research, and strengthening the coordination with stakeholders, policymakers and the general public. Here we provide an overview of different aspects of the HABs phenomena, an important element of the intrinsic links between oceans and human health and wellbeing.
Multi-level Simulation of Internet of Things on Smart Territories

In this paper, a methodology is presented and employed for simulating the Internet of Things (IoT). The requirement for scalability, due to the possibly huge amount of involved sensors and devices, and the heterogeneous scenarios that might occur, impose resorting to sophisticated modeling and simulation techniques. In particular, multi-level simulation is regarded as a main framework that allows simulating large-scale IoT environments while keeping high levels of detail, when it is needed. We consider a use case based on the deployment of smart services in decentralized territories. A two level simulator is employed, which is based on a coarse agent-based, adaptive parallel and distributed simulation approach to model the general life of simulated entities. However, when needed a finer grained simulator (based on OMNeT++) is triggered on a restricted portion of the simulated area, which allows considering all issues concerned with wireless communications. Based on this use case, it is confirmed that the ad-hoc wireless networking technologies do represent a principle tool to deploy smart services over decentralized countrysides. Moreover, the performance evaluation confirms the viability of utilizing multi-level simulation for simulating large scale IoT environments.
Network models in neuroscience

Danielle S. Bassett, Perry Zurn, and Joshua I. Gold Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104 Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104 Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104 Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104 Department of Philosophy, American University, Washington, DC, 20016 Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104 and To whom correspondence should be addressed: dsb@seas.upenn.edu
Analysis of security attacks in a smart home networks

This paper presents an analytical review about smart home network, their working and technologies from a security standpoint. It highlights various security flaws in a different smart home networks. It clearly explain, why a smart home is such an easy target for the attackers, it also discuss the role of interfaces in a smart home network by describing the scenario in which smart home network can be easily trapped. It also explains various types of home automation techniques along with the attacks which are quite more vulnerable to the smart home networks. As there of now there are many vulnerabilities which are exploiting the smart home network but no strong defensive mechanism has been developed till yet. It also indicates the basic components of a smart home network and the possible attacks which can be happen on a smart home networks.
Approaches to Identifying Synthetic Lethal Interactions in Cancer

Targeting synthetic lethal interactions is a promising new therapeutic approach to exploit specific changes that occur within cancer cells. Multiple approaches to investigate these interactions have been developed and successfully implemented, including chemical, siRNA, shRNA, and CRISPR library screens. Genome-wide computational approaches, such as DAISY, also have been successful in predicting synthetic lethal interactions from both cancer cell lines and patient samples. Each approach has its advantages and disadvantages that need to be considered depending on the cancer type and its molecular alterations. This review discusses these approaches and examines case studies that highlight their use.
BIDMach: Large-scale Learning with Zero Memory Allocation

This paper describes recent work on the BIDMach toolkit for large-scale machine learning. BIDMach has demonstrated single-node performance that exceeds that of published cluster systems for many common machine-learning task. BIDMach makes full use of both CPU and GPU acceleration (through a sister library BIDMat), and requires only modest hardware (commodity GPUs). One of the challenges of reaching this level of performance is the allocation barrier. While it is simple and expedient to allocate and recycle matrix (or graph) objects in expressions, this approach is too slow to match the arithmetic throughput possible on either GPUs or CPUs. In this paper we describe a caching approach that allows code with complex matrix (graph) expressions to run at massive scale, i.e. multi-terabyte data, with zero memory allocation after initial start-up. We present a number of new benchmarks that leverage this approach.
Detection of Brain Tumor Using K-Means Clustering

A tumor is a mass of tissue that's formed by an accumulation of abnormal cells. Normally, the cells in your body age, die, and are replaced by new cells. With cancer and other tumors, something disrupts this cycle. Tumor cells grow, even though the body does not need them, and unlike normal old cells, they don't die. As this process goes on, the tumor continues to grow as more and more cells are added to the mass. Image processing is an active research area in which medical image processing is a highly challenging field. Brain tumor analysis is done by doctors but its grading gives different conclusions which may vary from one doctor to another. In this project, it provides a foundation of segmentation and edge detection, as the first step towards brain tumor grading. Current segmentation approaches are reviewed with an emphasis placed on revealing the advantages and disadvantages of these methods for medical imaging applications. There are dissimilar types of algorithm were developed for brain tumor detection. Comparing to the other algorithms the performance of fuzzy c-means plays a major role. The patient's stage is determined by this process, whether it can be cured with medicine or not. Also we study difficulty to detect Mild traumatic brain injury (mTBI) the current tools are qualitative, which can lead to poor diagnosis and treatment and to overcome these difficulties, an algorithm is proposed that takes advantage of subject information and texture information from MR images. A contextual model is developed to simulate the progression of the disease using multiple inputs, such as the time post injury and the location of injury. Textural features are used along with feature selection for a single MR modality.