nils-herrmann/clean_scirepeval huggingface dataset:
Autonomous vehicles safe-optimal trajectory selection based on big data analysis and predefined user preferences

Autonomous Vehicle (AV) or self-driving vehicle technology promises to provide many economical and societal benefits and impacts. Safety is on the top of these benefits. Trajectory or path planning is one of the essential and critical tasks in operating the autonomous vehicle. In this paper we are tackling the problem of trajectory planning for fully-autonomous vehicles. Our use cases are designed for autonomous vehicles in a cloud based connected vehicle environment. This paper presents a method for selecting safe-optimal trajectory in autonomous vehicles. Selecting the safe trajectory in our work mainly based on using Big Data mining and analysis of real-life accidents data and real-time connected vehicles' data. The decision of selecting this trajectory is done automatically without any human intervention. The human touches in this scenario could be only at defining and prioritizing the driving preferences and concerns at the beginning of the planned trip. Safety always overrides the ranked user preferences listed in this work. The output of this work is a safe trajectory that represented by the position, ETA, distance, and the estimated fuel consumption for the entire trip.
Mesotherapy using dutasteride-containing preparation in treatment of female pattern hair loss: photographic, morphometric and ultrustructural evaluation.

Treatment of female pattern hair loss (FPHL) is frustrating for both patients and doctors. Mesotherapy is a novel treatment for hair fall and its efficacy in FPHL has not been evaluated.



Evaluation of the efficacy and safety of mesotherapy using dutasteride-containing preparation in treatment of FPHL.



This study included 126 female patients with FPHL. They were classified into two groups; group I (86 patients) injected with dutasteride-containing preparation and group II (40 control patients) injected with saline. Patients received 12 sessions and were evaluated at the 18th week by: photographic assessment, hair pull test, hair diameter and patient self-assessment. Ultrastructural evaluation was done for three patients.



After mesotherapy with dutasteride-containing preparation, photographic improvement occurred in 62.8% of patients compared with 17.5% in control group (P < 0.05), mean number of epilated hairs was significantly decreased (P < 0.05), mean hair diameter was significantly increased (P < 0.05). Patient self-assessment showed statistically significant improvement compared with the controls (P < 0.05). There was a negative correlation between degree of improvement and duration of FPHL (P < 0.05). Side effects were minimal with no statistically significant difference between the two groups (P > 0.05). Ultrastructural examination of pretreated hairs revealed absent cuticle in one patient and focal destruction of the cuticle in the second patient, which reappeared in both after therapy.



We concluded that mesotherapy with dutasteride-containing preparation was effective, tolerable and minimally invasive treatment modality in FPHL with better response for shorter duration of the disease.
Spatiotemporal Analysis of Bacterial Diversity in Sediments of Sundarbans Using Parallel 16S rRNA Gene Tag Sequencing

The influence of temporal and spatial variations on the microbial community composition was assessed in the unique coastal mangrove of Sundarbans using parallel 16S rRNA gene pyrosequencing. The total sediment DNA was extracted and subjected to the 16S rRNA gene pyrosequencing, which resulted in 117 Mbp of data from three experimental stations. The taxonomic analysis of the pyrosequencing data was grouped into 24 different phyla. In general, Proteobacteria were the most dominant phyla with predominance of Deltaproteobacteria, Alphaproteobacteria, and Gammaproteobacteria within the sediments. Besides Proteobacteria, there are a number of sequences affiliated to the following major phyla detected in all three stations in both the sampling seasons: Actinobacteria, Bacteroidetes, Planctomycetes, Acidobacteria, Chloroflexi, Cyanobacteria, Nitrospira, and Firmicutes. Further taxonomic analysis revealed abundance of micro-aerophilic and anaerobic microbial population in the surface layers, suggesting anaerobic nature of the sediments in Sundarbans. The results of this study add valuable information about the composition of microbial communities in Sundarbans mangrove and shed light on possible transformations promoted by bacterial communities in the sediments.
ADPfusion Ecient Dynamic Programming over Sequence Data

ADPfusion combines the usual high-level, terse notation of Haskell with an underlying fusion framework. The result is a parsing library that allows the user to write algorithms in a style very close to the notation used in formal languages and reap the performance benefits of automatic program fusion. Recent developments in natural language processing and computational biology have lead to a number of works that implement algorithms that process more than one input at the same time. We provide an extension of ADPfusion that works on extended index spaces and multiple input sequences, thereby increasing the number of algorithms that are amenable to implementation in our framework. This allows us to implement even complex algorithms with a minimum of overhead, while enjoying all the guarantees that algebraic dynamic programming provides to the user.
Empirical Research on the efficiency of Floating PV systems compared with Overland PV Systems

The floating photovoltaic system is a new concept in energy technology to meet the needs of our time. The system integrates existing land based photovoltaic technology with a newly developed floating photovoltaic technology. K-water has already completed two floating photovoltaic systems that enable generation of 100kW and 500kW respectively. In this paper, the generation efficiency of floating and land photovoltaic systems were compared and analyzed. Floating PV has shown greater generation efficiency by over 10% compared with the general PV systems installed overland
Applying the learning rate adaptation to the matrix factorization based collaborative filtering

Matrix Factorization (MF) based Collaborative Filtering (CF) have proved to be a highly accurate and scalable approach to recommender systems. In MF based CF, the learning rate is a key factor affecting the recommendation accuracy and convergence rate; however, this essential parameter is difficult to decide, since the recommender has to keep the balance between the recommendation accuracy and convergence rate. In this work, we choose the Regularized Matrix Factorization (RMF) based CF as the base model to discuss the effect of the learning rate in MF based CF, trying to deal with the dilemma of learning rate tuning through learning rate adaptation. First of all, we empirically validate the affection caused by the change of the learning rate on the recommendation performance. Subsequently, we integrate three sophisticated learning rate adapting strategies into RMF, including the Deterministic Step Size Adaption (DSSA), the Incremental Delta Bar Delta (IDBD), and the Stochastic Meta Decent (SMD). Thereafter, by analyzing the characteristics of the parameter update in RMF, we further propose the Gradient Cosine Adaption (GCA). The experimental results on five public large datasets demonstrate that by employing GCA, RMF could maintain good balance between accuracy and convergence rate, especially with small learning rate values.
An automatic parameter identification method for a PMSM drive with LC-filter

This paper presents a method for stand-still identification of parameters in a permanent magnet synchronous motor (PMSM) fed from an inverter equipped with an three-phase LCtype output filter. Using a special random modulation strategy, the method uses the inverter for broad-band excitation of the PMSM fed through an LC-filter. Based on the measured current response, model parameters for both the filter (L, R, C) and the PMSM (L and R) are estimated: First, the frequency response of the system is estimated using Welch Modified Periodogram method and then an optimization algorithm is used to find the parameters in an analytical reference model that minimize the model error. To demonstrate the practical feasibility of the method, a fully functional drive including an embedded real-time controller has been built. In addition to modulation, data acquisition and control the whole parameter identification method is also implemented on the real-time controller. Based on laboratory experiments on a 22 kW drive, it it concluded that the embedded identification method can estimate the five parameters in less than ten seconds.
Development of whole-body humanoid “pneumat-BS” with pneumatic musculoskeletal system

The human musculoskeletal system is supposed to play an important role in doing various static and dynamic tasks. From this standpoint, some musculoskeletal humanoid robots have been developed in recent years. However, existing musculoskeletal robots did not have upper body with several DOFs to balance their bodies statically or did not have enough power to perform dynamic tasks. We think the musculoskeletal structure has two significant properties: whole-body flexibility and whole-body coordination. Using these two properties can enable us to make robots' performance better than before. In this study, we developed a humanoid robot with a musculoskeletal system that is driven by pneumatic artificial muscles. To demonstrate the robot's capability in static and dynamic tasks, we conducted two experiments. As a static task, we conducted a standing experiment using a simple feedback control and evaluated the stability by applying an impulse to the robot. As a dynamic task, we conducted a walking experiment using a feedforward controller with human muscle activation patterns and confirmed that the robot was able to perform the dynamic task.
Data driven prognostics using a Kalman filter ensemble of neural network models

This paper details the winning method in the IEEE GOLD category of the PHM psila08 Data Challenge. The task was to estimate the remaining useable life left of an unspecified complex system using a purely data driven approach. The method involves the construction of Multi-Layer Perceptron and Radial Basis Function networks for regression. A suitable selection of these networks has been successfully combined in an ensemble using a Kalman filter. The Kalman filter provides a mechanism for fusing multiple neural network model predictions over time. The essential initial stages of pre-processing and data exploration are also discussed.
3S-cart: A Lightweight, Interactive Sensor-Based Cart for Smart Shopping in Supermarkets

Nowadays, shopping has played a key role in our economic activity. It deserves investigation how to provide smart shopping by promptly interacting with customers in supermarkets. This paper proposes a sensor-based smart shopping cart (3S-cart) system by using the context-aware ability of sensors to detect the behavior of customers, and respond to them in real time. A prototype of 3S-cart is implemented by encapsulating modularized sensors in a box to be put on shopping carts. Thus, 3S-cart is lightweight and easy to deploy. We also demonstrate two supermarket applications by 3S-cart. In the sales-promotion application, each cart checks if its customer has interest in some products and shows sales information at once to increase the purchasing desire. In the product-navigation application, a customer asks the system to find an unhindered, shortest path to comfortably obtain the desired product. This paper contributes in exploiting the sensor technology to provide interactive shopping in supermarkets, and addressing the prototyping experience and potential applications of the proposed 3S-cart system.
An Overview of Process and Product Requirements for Next Generation Thin Film Electronics, Advanced Touch Panel Devices, and Ultra High Barriers

Roll-to-Roll (R2R) production of thin film based display components (e.g., active matrix TFT backplanes and touch screens) combine the advantages of the use > of inexpensive, lightweight, and flexible substrates with high throughput production. Significant cost reduction opportunities can also be found in terms of processing tool capital cost, utilized substrate area, and process gas flow when compared with batch processing systems. Applied Materials has developed a variety of different web handling and coating technologies/platforms to enable high volume R2R manufacture of thin film silicon solar cells, TFT active matrix backplanes, touch screen devices, and ultra-high barriers for organic electronics. The work presented in this chapter therefore describes the latest advances in R2R PVD processing and principal challenges inherent in moving from lab and pilot scale manufacturing to high volume manufacturing of flexible display devices using CVD for the deposition of active semiconductors layers, gate insulators, and high performance barrier/passivation layers. This chapter also includes brief description of the process and cost advantage of the use of rotatable PVD source technologies (primarily for use in flexible touch panel manufacture) and a summary of the current performance levels obtained for R2R processed amorphous silicon and IGZO TFT backplanes. Results will also be presented for barrier film for final device/frontplane encapsulation for display applications.
Evaluation of the factors that determine quality in higher education : an empirical study

The aim of this paper is to identify the quality determinants for education services provided by higher education institutions (HEIs) in Greece and to measure their relative importance from the students’ points of view.  A multi-criteria decision-making methodology was used for assessing the relative importance of quality determinants that affect student satisfaction. More specifically, the analytical hierarchical process (AHP) was used in order to measure the relative weight of each quality factor.  The relative weights of the factors that contribute to the quality of educational services as it is perceived by students was measured.  The research is based on the questionnaire of the Hellenic Quality Assurance Agency for Higher Education. This implies that the measured weights are related mainly to questions posed in this questionnaire. However, the applied method (AHP) can be used to assess different quality determinants.  The outcome of this study can be used in order to quantify internal quality assessment of HEIs. More specifically, the outcome can be directly used by HEIs for assessing quality as perceived by students.  The paper attempts to develop insights into comparative evaluations of quality determinants as they are perceived by students.
#∃SAT: Projected Model Counting

Model counting is the task of computing the number of assignments to variables V that satisfy a given propositional theory F . The model counting problem is denoted as #SAT. Model counting is an essential tool in probabilistic reasoning. In this paper, we introduce the problem of model counting projected on a subset of original variables that we call priority variables P ⊆ V. The task is to compute the number of assignments to P such that there exists an extension to non-priority variables V \ P that satisfies F . We denote this as #∃SAT. Projected model counting arises when some parts of the model are irrelevant to the counts, in particular when we require additional variables to model the problem we are counting in SAT. We discuss three different approaches to #∃SAT (two of which are novel), and compare their performance on different benchmark problems.
Feature Agglomeration Networks for Single Stage Face Detection

Recent years have witnessed promising results of face detection using deep learning. Despite making remarkable progresses, face detection in the wild remains an open research challenge especially when detecting faces at vastly different scales and characteristics. In this paper, we propose a novel simple yet effective framework of “Feature Agglomeration Networks” (FANet) to build a new single stage face detector, which not only achieves state-of-the-art performance but also runs efficiently. As inspired by Feature Pyramid Networks (FPN) [11], the key idea of our framework is to exploit inherent multi-scale features of a single convolutional neural network by aggregating higher-level semantic feature maps of different scales as contextual cues to augment lower-level feature maps via a hierarchical agglomeration manner at marginal extra computation cost. We further propose a Hierarchical Loss to effectively train the FANet model. We evaluate the proposed FANet detector on several public face detection benchmarks, including PASCAL face, FDDB and WIDER FACE datasets and achieved state-of-the-art results. Our detector can run in real time for VGA-resolution images on GPU.
Hydrodynamics of Two-Phase Flow in Gas-Liquid Cylindrical Cyclone Separators

This paper presents new experimental data and an improved mechanistic model for the Gas-Liquid Cylindrical Cyclone (GLCC) separator. The data were acquired utilizing a 3” ID laboratory-scale GLCC, and are presented along with a limited number of field data. The data include measurements of several parameters of the flow behavior and the operational envelope of the GLCC. The operational envelope defines the conditions for which there will be no liquid carry-over or gas carry-under. The developed model enables the prediction of the hydrodynamic flow behavior in the GLCC, including the operational envelope, equilibrium liquid level, vortex shape, velocity and holdup distributions and pressure drop across the GLCC. The predictions of the model are compared with the experimental data. These provide the state-of-the-art for the design of GLCC’s for the industry.  The gas-liquid separation technology currently used by the petroleum industry is mostly based on the vessel-type separator which is large, heavy and expensive to purchase and operate. This technology has not been substantially improved over the last several decades. In recent years the industry has shown interest in the development and application of alternatives to the vessel-type separator. One such alternative is the use of compact or in-line separators, such as the Gas-Liquid Cylindrical Cyclone (GLCC) separator. As can be seen in Fig. 1, the GLCC is an emerging class of vertical compact separators, as compared to the very mature technology of the vessel-type separator. D ev el op m en t GLCC’s FWKO Cyclones Emerging Gas Cyclones Conventional Horizontal and Vertical Separators Growth Finger Storage Slug Catcher Vessel Type Slug Catcher
Fuzzy based MPPT controller's role in extraction of maximum power in wind energy conversion system

The objective of the proposed scheme is to extract the maximum power at different wind turbine speed. In order to achieve this, MPPT controller is implemented on the rectifier side for the extraction of maximum power. On the inverter side normal closed loop PWM control is carried out. MPPT controller is implemented using fuzzy logic control technique. The fuzzy controller's role here is to track the speed reference of the generator. By doing so and keeping the generator speed at an optimal reference value, maximum power can be attained. This procedure is repeated for various wind turbine speeds, When the wind speed increases the real power generated by the PMSG based WECS increases with the aid of MPPT controller.
Enhancing situation awareness in power system control centers

Due to the growing size and complexity of modern grids, power system operators have to deal with several challenges to maintain the reliability of the electrical supply. Developing and maintaining a sufficient situation awareness is a key element in preserving this reliability. However, because numerous factors govern its formation in a power system control center, this can be a very complicated process. The aim of this paper is to demonstrate the impact of insufficient situation awareness on power systems operational reliability and provide an overview of best practices in supporting human operators. It first identifies the main sources of inadequate situation awareness in a control room, and then presents its impact on operational decision-making. Several measures for dealing with the problems related to situation awareness are discussed and their applicability to power systems is illustrated.
The Elderly as Victims of Crime , Abuse and Neglect

Surveys throughout the world have shown consistently that persons over 65 are far less likely to be victims of crime than younger age groups. However, many elderly people are unduly fearful about crime which has an adverse effect on their quality of life. This Trends and Issues puts this matter into perspective, but also discusses the more covert phenomena of abuse and neglect of the elderly. Our senior citizens have earned the right to live in dignity and without fear: the community as a whole should contribute to this process. Duncan Chappell Director
Comparison of commercial and next generation quantum key distribution: Technologies for secure communication of information

Battelle has been actively exploring emerging quantum key distribution (QKD) cryptographic technologies for secure communication of information with a goal of expanding the use of this technology by commercial enterprises in the United States. In QKD systems, the principles of quantum physics are applied to generate a secret data encryption key, which is distributed between two users. The security of this key is guaranteed by the laws of quantum physics, and this distributed key can be used to encrypt data to enable secure communication on insecure channels. To date, Battelle has studied commercially available and custom-built QKD systems in controlled laboratory environments and is actively working to establish a QKD Test Bed network to characterize performance in real world metropolitan (10-100 km) and long distance (>; 100 km) environments. All QKD systems that we have tested to date utilize a discrete variable (DV) binary approach. In this approach, discrete information is encoded onto a quantum state of a single photon, and binary data are measured using single photon detectors. Recently, continuous variable (CV) QKD systems have been developed and are expected to be commercially available shortly. In CV-QKD systems, randomly generated continuous variables are encoded on coherent states of weak pulses of light, and continuous data values are measured with homodyne detection methods. In certain applications for cyber security, the CV-QKD systems may offer advantages over traditional DV-QKD systems, such as a higher secret key exchange rate for short distances, lower cost, and compatibility with telecommunication technologies. In this paper, current CV- and DV-QKD approaches are described, and security issues and technical challenges fielding these quantum-based systems are discussed. Experimental and theoretical data that have been published on quantum key exchange rates and distances that are relevant to metropolitan and long distance network applications are presented. From an analysis of these data, the relative performance of the two approaches is compared as a function of distance and environment (free space and optical fiber). Additionally, current research activities are described for both technologies, which include network integration and methods to increase secret key distribution rates and distances.