nils-herrmann/clean_scirepeval huggingface dataset:
miRBase: tools for microRNA genomics

miRBase is the central online repository for microRNA (miRNA) nomenclature, sequence data, annotation and target prediction. The current release (10.0) contains 5071 miRNA loci from 58 species, expressing 5922 distinct mature miRNA sequences: a growth of over 2000 sequences in the past 2 years. miRBase provides a range of data to facilitate studies of miRNA genomics: all miRNAs are mapped to their genomic coordinates. Clusters of miRNA sequences in the genome are highlighted, and can be defined and retrieved with any inter-miRNA distance. The overlap of miRNA sequences with annotated transcripts, both protein- and non-coding, are described. Finally, graphical views of the locations of a wide range of genomic features in model organisms allow for the first time the prediction of the likely boundaries of many miRNA primary transcripts. miRBase is available at
Application of the functionally generated path technique to restore mandibular molars in bilateral group function occlusion.

The functionally generated path (FGP) is a static representation of the opposing cusps' dynamic eccentric movements from a centric position to achieve optimal articulation and occlusal harmony. When understood and appreciated, use of the FGP technique is a straightforward and practical method to achieve harmonious occlusal anatomy of restorations with the anterior determinant/anterior guidance, the posterior determinant/condylar guidance, existing occlusal and cuspal anatomy, and the neuromuscular system. Although the FGP technique is normally used in the fabrication of maxillary posterior indirect restorations, it is described and applied here in the fabrication of mandibular posterior restorations that maintained the patient's bilateral group function occlusion while eliminating the nonworking side and protrusive interferences. This novel procedure involved the use of a stone crib to intraorally construct a stone core that captured the FGP recording while simultaneously indexing to the contralateral and ipsilateral mandibular dentition. This technique lends additional stability to the stone core to minimize error during the mounting process.
A Tale of Many Cities: Universal Patterns in Human Urban Mobility

The advent of geographic online social networks such as Foursquare, where users voluntarily signal their current location, opens the door to powerful studies on human movement. In particular the fine granularity of the location data, with GPS accuracy down to 10 meters, and the worldwide scale of Foursquare adoption are unprecedented. In this paper we study urban mobility patterns of people in several metropolitan cities around the globe by analyzing a large set of Foursquare users. Surprisingly, while there are variations in human movement in different cities, our analysis shows that those are predominantly due to different distributions of places across different urban environments. Moreover, a universal law for human mobility is identified, which isolates as a key component the rank-distance, factoring in the number of places between origin and destination, rather than pure physical distance, as considered in some previous works. Building on our findings, we also show how a rank-based movement model accurately captures real human movements in different cities.
Using Microsoft Excel to model a tennis match

This paper demonstrates the use of Microsoft Excel to generate the probability of winning and mean length of the remainder of the match conditional on the state of the match. Previous models treat games, sets and matches independently. We show how a series of interconnected sheets can be used to repeat these results. We also set up a sheet which can give the required statistics using the full match, set and game score as the state. The exercise could form an interesting and useful teaching example, and allow students to investigate the properties of tennis scoring systems.
Classification using discriminative restricted Boltzmann machines

Recently, many applications for Restricted Boltzmann Machines (RBMs) have been developed for a large variety of learning problems. However, RBMs are usually used as feature extractors for another learning algorithm or to provide a good initialization for deep feed-forward neural network classifiers, and are not considered as a standalone solution to classification problems. In this paper, we argue that RBMs provide a self-contained framework for deriving competitive non-linear classifiers. We present an evaluation of different learning algorithms for RBMs which aim at introducing a discriminative component to RBM training and improve their performance as classifiers. This approach is simple in that RBMs are used directly to build a classifier, rather than as a stepping stone. Finally, we demonstrate how discriminative RBMs can also be successfully employed in a semi-supervised setting.
An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo.

This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.
Skin microbiota: overview and role in the skin diseases acne vulgaris and rosacea.

As the first barrier to environmental exposures, human skin has developed an integrated immune system to protect the inner body from chemical, physical or microbial insults. Microorganisms inhabiting superficial skin layers are known as skin microbiota and include bacteria, viruses, archaea and fungi. The microbiota composition is crucial in the instruction and support of the skin's immune system. Changes in microbiota can be due to individual, environmental or behavioral factors, such as age, climate, hygiene or antibiotic consumption, which can cause dysbiosis. The contribution of skin microbiota to disease development is known in atopic dermatitis, where there is an increase in Staphylococcus aureus. Culture-independent studies have enabled more accurate descriptions of this complex interplay. Microbial imbalance is associated with the development of various diseases. This review focuses on microbial imbalances in acne vulgaris and rosacea.
DESIGN OF AN ULTRA-WIDEBAND POWER DIVIDER VIA THE COARSE-GRAINED PARALLEL MICRO-GENETIC ALGORITHM

An ultra-wideband (UWB) power divider is designed in this paper. The UWB performance of this power divider is obtained by using a tapered microstrip line that consists of exponential and elliptic sections. The coarse grained parallel micro-genetic algorithm (PMGA) and CST Microwave Studio are combined to achieve an automated parallel design process. The method is applied to optimize the UWB power divider. The optimized power divider is fabricated and measured. The measured results show relatively low insertion loss, good return loss, and high isolation between the output ports across the whole UWB (3.1–10.6 GHz).
FOSTERING INTER-TEAM KNOWLEDGE SHARING EFFECTIVENESS IN AGILE SOFTWARE DEVELOPMENT

Agile methods have impacted on software development by providing several changes on how software is developed. Agile values, principles and practices foster intra-team knowledge sharing. However, they do not inform how to cope with that across organizational levels. This study seeks to understand inter-team knowledge sharing effectiveness in agile software development organizations that employ practices for this endeavor. Using grounded theory, we analyzed data from four Brazilian agile software organizations and got feedback from an expert in agile methods implementation. Our main contribution is a conceptual model that explains how effectiveness depends on applying purposeful practices, along with organizational conditions and stimuli, that foster knowledge sharing across agile software development teams. This understanding represents an innovative focus within agile context and provides what to consider when striving on this path. Yet, it yields opportunities for further studies in refining and extending the model to several organizational contexts. Scaling agility toward organizational level is the next horizon for agile software development. Cross-team knowledge sharing leverages this endeavor and also reflects the way agile software organizations are coping with enterprise agility and the consideration of knowledge as a resource for organizational competitiveness.
Underwater object detection and tracking based on multi-beam sonar image processing

A new framework capable of analyzing multi-beam sonar images for detecting and tracking underwater object using a BlueView (BV) Sonar is presented in this paper. This framework is applied to the design of an obstacle avoidance system for Unmanned Measurement Boat. The real-time sonar data flow collected by multi-beam sonar is expressed as an image and pre-processed by the system. According to the characteristics of sonar images, an improved Otsu method has been carried out to detect the object combining with the contour detection algorithm, with which the foreground object can be separated from background successfully. Then the object is tracked by a particle filter tracking method based on multi-feature adaptive fusion. Results obtained on real sonar data show that the proposed framework can detect and track the object accurately and robustly.
An Analysis of Transformations

In the analysis of data it is often assumed that observations y,, y,, ...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's. Inferences about the transformation and about the parameters of the linear model are made by computing the likelihood function and the relevant posterior distribution. The contributions of normality, homoscedasticity and additivity to the transformation are separated. The relation of the present methods to earlier procedures for finding transformations is discussed. The methods are illustrated with examples.
A model-based approach to security flaw detection of network protocol implementations

A lot of efforts have been devoted to the analysis of network protocol specification for reliability and security properties using formal techniques. However, faults can also be introduced during system implementation; it is indispensable to detect protocol implementation flaws, yet due to the black-box nature of protocol implementation and the unavailability of protocol specification most of the approaches resort to random or manual testing. In this paper we propose a model-based approach for security flaw detection of protocol implementation with a high fault coverage, measurability, and automation. Our approach first synthesizes an abstract behavioral model from a protocol implementation and then uses it to guide the testing process for detecting security and reliability flaws. For protocol specification synthesis we reduce the problem a trace minimization with a finite state machine model and an efficient algorithm is presented for state space reduction. Our method is implemented and applied to real network protocols. Guided by the synthesized model our testing tool reveals a number of unknown reliability and security issues by automatically crashing the implementations of the Microsoft MSN instant messaging (MSNIM) protocol. Analytical comparison between our model-based and prevalent syntax-based flaw detection schemes is also provided with the support of experimental results.
DIN: An efficient algorithm for detecting influential nodes in social graphs using network structure and attributes

Detecting influential nodes in social networks represents an essential issue for various applications to identify users that may maximize the influence of information in such networks. Several methods have been proposed to solve this problem often khown as influence maximization problem. However, most of them focused on the structure of the network and ignored the semantic aspect. Besides, these methods are parametric, they require the number k of influential elements in a deterministic manner. In this paper, we propose a parameterless algorithm called DIN (Detecting Influential Nodes in social networks) that combines the structure and the semantic aspect. The main idea of our proposal is to detect communities with overlap, modelize the semantic of each community then select influential elements. Experimental results on computer-generated artificial graphs demonstrate that DIN is efficient for identifying influential nodes, compared with two newly known proposals.
Deep Reinforcement Learning for Solving the Vehicle Routing Problem

We present an end-to-end framework for solving Vehicle Routing Problem (VRP) using deep reinforcement learning. In this approach, we train a single model that finds near-optimal solutions for problem instances sampled from a given distribution, only by observing the reward signals and following feasibility rules. Our model represents a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance. Our method is faster in both training and inference than a recent method that solves the Traveling Salesman Problem (TSP), with nearly identical solution quality. On the more general VRP, our approach outperforms classical heuristics on medium-sized instances in both solution quality and computation time (after training). Our proposed framework can be applied to variants of the VRP such as the stochastic VRP, and has the potential to be applied more generally to combinatorial optimization problems.
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as dataintensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing.
Multicategory Classification by Support Vector Machines

We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how two-class discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programming (QP) approaches based on Vapnik’s Support Vector Machine (SVM) can be combined to yield two new approaches to the multiclass problem. In LP multiclass discrimination, a single linear program is used to construct a piecewise-linear classification function. In our proposed multiclass SVM method, a single quadratic program is used to construct a piecewise-nonlinear classification function. Each piece of this function can take the form of a polynomial, a radial basis function, or even a neural network. For the k > 2-class problems, the SVM method as originally proposed required the construction of a two-class SVM to separate each class from the remaining classes. Similarily, k two-class linear programs can be used for the multiclass problem. We performed an empirical study of the original LP method, the proposed k LP method, the proposed single QP method and the original k QP methods. We discuss the advantages and disadvantages of each approach.
A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals.

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?
Energy Management of Fuel Cell/Battery/Supercapacitor Hybrid Power Sources Using Model Predictive Control

Well known as an efficient and eco-friendly power source, fuel cell, unfortunately, offers slow dynamics. When attached as primary energy source in a vehicle, fuel cell would not be able to respond to abrupt load variations. Supplementing battery and/or supercapacitor to the system will provide a solution to this shortcoming. On the other hand, a current regulation that is vital for lengthening time span of the energy storage system is needed. This can be accomplished by keeping fuel cell's and batteries' current slope in reference to certain values, as well as attaining a stable dc output voltage. For that purpose, a feedback control system for regulating the hybrid of fuel cell, batteries, and supercapacitor was constructed for this study. Output voltage of the studied hybrid power sources (HPS) was administered by assembling three dc-dc converters comprising two bidirectional converters and one boost converter. Current/voltage output of fuel cell was regulated by boost converter, whereas the bidirectional converters regulated battery and supercapacitor. Reference current for each converter was produced using Model Predictive Control (MPC) and subsequently tracked using hysteresis control. These functions were done on a controller board of a dSPACE DS1104. Subsequently, on a test bench made up from 6 V, 4.5 Ah battery and 7.5 V, 120 F supercapacitor together with a fuel cell of 50 W, 10 A, experiment was conducted. Results show that constructing a control system to restrict fuel cell's and batteries' current slope and maintaining dc bus voltage in accordance with the reference values using MPC was feasible and effectively done.
Analysis of charge-pump phase-locked loops

In this paper, we present an exact analysis for third-order charge-pump phase-locked loops using state equations. Both the large-signal lock acquisition process and the small-signal linear tracking behavior are described using this analysis. The nonlinear state equations are linearized for the small-signal condition and the z-domain noise transfer functions are derived. A comparison to some of the existing analysis methods such as the impulse-invariant transformation and s-domain analysis is provided. The effect of the loop parameters and the reference frequency on the loop phase margin and stability is analyzed. The analysis is verified using behavioral simulations in MATLAB and SPECTRE.
Unobservable Communication over Fully Untrusted Infrastructure

Keeping communication private has become increasingly important in an era of mass surveillance and statesponsored attacks. While hiding the contents of a conversation has well-known solutions, hiding the associated metadata (participants, duration, etc.) remains a challenge, especially if one cannot trust ISPs or proxy servers. This paper describes a communication system called Pung that provably hides all content and metadata while withstanding global adversaries. Pung is a key-value store where clients deposit and retrieve messages without anyone— including Pung’s servers—learning of the existence of a conversation. Pung is based on private information retrieval, which we make more practical for our setting with new techniques. These include a private multi-retrieval scheme, an application of the power of two choices, and batch codes. These extensions allow Pung to handle 103× more users than prior systems with a similar threat model.
BLE Beacon Based Patient Tracking in Smart Care Facilities

Patient tracking is an important component toward creating smart cities. In this demo we use Bluetooth Low Energy beacons and single board computers to track patients in the emerging field of smart care facilities. Our model utilizes a fixed scanner moving transmitter method for wireless tracking of patients through the facility. The data collected by all scanners is stored within a central database that is designed to be efficiently queried. We show how inexpensive components in conjunction with free open source software can be used to implement a patient tracking system. We focus on the pipeline between acquisition and display of the location data. Additionally, we also discuss the manipulation of the data required for usability, and optional filtering operations that improve accuracy.