nils-herrmann/clean_scirepeval huggingface dataset:
A Two-Phase Safe Vehicle Routing and Scheduling Problem: Formulations and Solution Algorithms

We propose a two phase time dependent vehicle routing and scheduling optimization model that identifies the safest routes, as a substitute for the classical objectives given in the literature such as shortest distance or travel time, through (1) avoiding recurring congestions, and (2) selecting routes that have a lower probability of crash occurrences and non-recurring congestion caused by those crashes. In the first phase, we solve a mixed-integer programming model which takes the dynamic speed variations into account on a graph of roadway networks according to the time of day, and identify the routing of a fleet and sequence of nodes on the safest feasible paths. Second phase considers each route as an independent transit path (fixed route with fixed node sequences), and tries to avoid congestion by rescheduling the departure times of each vehicle from each node, and by adjusting the sub-optimal speed on each arc. A modified simulated annealing (SA) algorithm is formulated to solve both complex models iteratively, which is found to be capable of providing solutions in a considerably short amount of time. In this paper, speed (and travel time) variation with respect to the hour of the day is calculated via queuing models (i.e., M/G/1) to capture the stochasticity of travel times more accurately unlike the most researches in this area, which assume the speed on arcs to be a fixed value or a time dependent step function. First, we demonstrate the accurate performance of M/G/1 in estimation and predicting speeds and travel times for those arcs without readily available speed data. Crash data, on the other hand, is obtained for each arc. Next, 24 scenarios, which correspond to each hour of a day, are developed, and are fed to the proposed solution algorithms. This is followed by evaluating the routing schema for each scenario where the following objective * Corresponding author.  E-mail address: Aschkan@ufl.edu functions are utilized: (1) the minimization of the traffic delay (maximum congestion avoidance), and (2) the minimization of the traffic crash risk, and (3) the combination of two objectives. Using these objectives, we identify the safest routes, as a substitute for the classical objectives given in the literature such as shortest distance or travel time, through (1) avoiding recurring congestions, and (2) selecting routes that have a lower probability of crash occurrences and non-recurring congestion caused by those crashes. This also allows us to discuss the feasibility and applicability of our model. Finally, the proposed methodology is applied on a benchmark network as well as a small real-world case study application for the City of Miami, Florida. Results suggest that in some instances, both the travelled distance and travel time increase in return for a safer route, however, the advantages of safer route can outweigh this slight increase.
Frontal Fibrosing Alopecia: Role of Dermoscopy in Differential Diagnosis

Frontal fibrosing alopecia (FFA) is more common in postmenopausal women, but it can occur in younger women. Some authors consider FFA to be a distinct frontal variant of lichen planopilaris. From a clinical point of view, this relatively uncommon condition is characterized by progressive frontotemporal recession due to inflammatory destruction of hair follicles. Dermoscopy can be very useful, as the differential diagnosis between traction alopecia, alopecia areata, FFA and cicatricial marginal alopecia may be difficult. It is not clear whether or not treatment alters the natural history of the disease - the disease stabilized with time in most of the patients with or without continuing treatment. Here we report a case of a 50-year-old woman with FFA and discuss the relevance of dermoscopy in the differential diagnosis of this disease.
Spondylometaphyseal dysplasia (Kozlowski type): case report.

Kozlowski syndrome is the most common type of spondylometaphyseal dysplasia (SMD). It is characterized by short stature (130 to 150 cm), pectus carinatum, limited elbow and hip movement, mild bowleg deformity, and curvature of the spinal column. Children with Kozlowski dwarfism usually are not recognized at birth, since they have normal clinical features, weight, and size. This article reports the dental treatment and oral findings of a 14-year-old female patient with Kozlowski dwarfism.
Lateral stopper LOW VOLTAGE MEMS ANALOG MICROMIRROR ARRAYS WITH HIDDEN VERTICAL COMB-DRIVE ACTUATORS

We report on a novel, polysilicon surface-micromachined one-dimensional (1-D) analog micromirror array. Large continuous DC scan angle (23.6° optical) and low operating voltage (6V) have been achieved using vertical comb-drive actuators. The actuators and torsion springs are placed underneath the mirror (137 × 120 μm) to achieve high fill-factor (91%). The measured resonant frequency of the mirror ranges from 3.4 kHz to 8.1 kHz. The static scanning characteristics show good uniformity (<±3.2%) for a 1 × 10 array with a mirror pitch of 150 μm. The measured DC scanning characteristics and resonant frequencies agree well with theoretical values. This micromirror array has applications in optical wavelength-division multiplexed (WDM) routers and wavelength-selective crossconnect (WSXC).
Towards Mutation Analysis for Lustre Programs

Mutation analysis is usually used to provide indication of the fault detection ability of a test set. It is mainly used for unit testing evaluation. This paper describes mutation analysis principles and their adaptation to the Lustre programming language. Alien-V, a mutation tool for Lustre is presented. Lesar modelchecker is used for eliminating equivalent mutant. A first experimentation to evaluate Lutess testing tool is summarized.
Information elaboration and team performance : Examining the psychological origins and environmental contingencies

Information elaboration enables functionally diverse teams to transform their breadth of knowledge resources into actionable solutions to complex problems. The current study advances information elaboration theory and research in two ways. First, we identify how team ability and social motivation composition characteristics provide the psychological origins of complex information processing efforts. Second, we identify environmental turbulence as an important boundary condition, clarifying when information elaboration benefits team performance and when it does not. These ideas were tested in a sample of 4-person self-managed teams (N = 68) which were functionally diverse and performed a cooperative strategic decision-making task. Results indicate that cognitive ability equips teams with the ‘‘can do’’ ability for complex elaboration efforts through emergent team mental models, whereas low preferences for self-reliance provide the ‘‘will do’’ motivation for in-depth information exchange through collective leadership. In turn, teams benefited from information elaboration in turbulent but not stable
MEMS Lorentz force magnetic sensor based on a balanced torsional resonator

We demonstrate an x-axis Lorentz force sensor (LFS) for electronic compass applications. The sensor is based on a 30 μm thick torsional resonator fabricated in a process similar to that used in commercial MEMS gyroscopes. The sensor achieved a resolution of 210 nT/√Hz with a DC supply voltage of 2 V and driving power consumption of 1 mW. Bias instability was measured as 60 nT using the minimum Allan deviation. This mechanically-balanced torsional resonator also confers the advantage of low acceleration sensitivity; the measured response to acceleration is below the sensor's noise level.
Network function virtualization: Challenges and opportunities for innovations

Network function virtualization was recently proposed to improve the flexibility of network service provisioning and reduce the time to market of new services. By leveraging virtualization technologies and commercial off-the-shelf programmable hardware, such as general-purpose servers, storage, and switches, NFV decouples the software implementation of network functions from the underlying hardware. As an emerging technology, NFV brings several challenges to network operators, such as the guarantee of network performance for virtual appliances, their dynamic instantiation and migration, and their efficient placement. In this article, we provide a brief overview of NFV, explain its requirements and architectural framework, present several use cases, and discuss the challenges and future directions in this burgeoning research area.
Quality Risk in Outsourcing : Noncontractible Product Quality and Private Quality Cost Information

This article addresses the concept of quality risk in outsourcing. Recent trends in outsourcing extend a contract manufacturer’s (CM’s) responsibility to several functional areas, such as research and development and design in addition to manufacturing. This trend enables an original equipment manufacturer (OEM) to focus on sales and pricing of its product. However, increasing CM responsibilities also suggest that the OEM’s product quality is mainly determined by its CM. We identify two factors that cause quality risk in this outsourcing relationship. First, the CM and the OEM may not be able to contract on quality; second, the OEM may not know the cost of quality to the CM. We characterize the effects of these two quality risk factors on the firms’ profits and on the resulting product quality. We determine how the OEM’s pricing strategy affects quality risk. We show, for example, that the effect of noncontractible quality is higher than the effect of private quality cost information when the OEM sets the sales price after observing the product’s quality. We also show that committing to a sales price mitigates the adverse effect of quality risk. To obtain these results, we develop and analyze a three-stage decision model. This model is also used to understand the impact of recent information technologies on profits and product quality. For example, we provide a decision tree that an OEM can use in deciding whether to invest in an enterprise-wide quality management system that enables accounting of quality-related activities across the supply chain.
Lactobacillus iners: Friend or Foe?

The vaginal microbial community is typically characterized by abundant lactobacilli. Lactobacillus iners, a fairly recently detected species, is frequently present in the vaginal niche. However, the role of this species in vaginal health is unclear, since it can be detected in normal conditions as well as during vaginal dysbiosis, such as bacterial vaginosis, a condition characterized by an abnormal increase in bacterial diversity and lack of typical lactobacilli. Compared to other Lactobacillus species, L. iners has more complex nutritional requirements and a Gram-variable morphology. L. iners has an unusually small genome (ca. 1 Mbp), indicative of a symbiotic or parasitic lifestyle, in contrast to other lactobacilli that show niche flexibility and genomes of up to 3-4 Mbp. The presence of specific L. iners genes, such as those encoding iron-sulfur proteins and unique σ-factors, reflects a high degree of niche specification. The genome of L. iners strains also encodes inerolysin, a pore-forming toxin related to vaginolysin of Gardnerella vaginalis. Possibly, this organism may have clonal variants that in some cases promote a healthy vagina, and in other cases are associated with dysbiosis and disease. Future research should examine this friend or foe relationship with the host.
Standardized Usability Questionnaires: Features and Quality Focus

For the last few decades more than twenty standardized usability questionnaires for evaluating software systems have been proposed. These instruments have been widely used in the assessment of usability of user interfaces. They have their own characteristics, can be generic or address specific kinds of systems and can be composed of one or several items. Some comparison or comparative studies were also conducted to identify the best one in different situations. All these issues should be considered while choosing a questionnaire. In this paper, we present an extensive review of these questionnaires considering their key features, some classifications and main comparison studies already performed. Moreover, we present the result of a detailed analysis of all items being evaluated in each questionnaire to indicate those that can identify users’ perceptions about specific usability problems. This analysis was performed by confronting each questionnaire item (around 475 items) with usability criteria proposed by quality standards (ISO 9421-11 and ISO/WD 9241-112) and classical quality ergonomic criteria.
Smart Small Cell with Hybrid Beamforming for 5G: Theoretical Feasibility and Prototype Results

In this article, we present a real-time 3D hybrid beamforming approach for 5G wireless networks. One of the key concepts in 5G cellular systems is the small cell network, which settles the high mobile traffic demand and provides uniform user-experienced data rates. The overall capacity of the small cell network can be enhanced with the enabling technology of 3D hybrid beamforming. This study validates the feasibility of 3D hybrid beamforming, mostly for link-level performance, through the implementation of a real-time testbed using a SDR platform and fabricated antenna array. Based on the measured data, we also investigate system-level performance to verify the gain of the proposed smart small cell system over LTE systems by performing system-level simulations based on a 3D ray-tracing tool.
Deep Web crawling: a survey

Deep Web crawling refers to the problem of traversing the collection of pages in a deep Web site, which are dynamically generated in response to a particular query that is submitted using a search form. To achieve this, crawlers need to be endowed with some features that go beyond merely following links, such as the ability to automatically discover search forms that are entry points to the deep Web, fill in such forms, and follow certain paths to reach the deep Web pages with relevant information. Current surveys that analyse the state of the art in deep Web crawling do not provide a framework that allows comparing the most up-to-date proposals regarding all the different aspects involved in the deep Web crawling process. In this article, we propose a framework that analyses the main features of existing deep Web crawling-related techniques, including the most recent proposals, and provides an overall picture regarding deep Web crawling, including novel features that to the present day had not been analysed by previous surveys. Our main conclusion is that crawler evaluation is an immature research area due to the lack of a standard set of performance measures, or a benchmark or publicly available dataset to evaluate the crawlers. In addition, we conclude that the future work in this area should be focused on devising crawlers to deal with ever-evolving Web technologies and improving the crawling efficiency and scalability, in order to create effective crawlers that can operate in real-world contexts.
A Facial-Expression Monitoring System for Improved Healthcare in Smart Cities

Human facial expressions change with different states of health; therefore, a facial-expression recognition system can be beneficial to a healthcare framework. In this paper, a facial-expression recognition system is proposed to improve the service of the healthcare in a smart city. The proposed system applies a bandlet transform to a face image to extract sub-bands. Then, a weighted, center-symmetric local binary pattern is applied to each sub-band block by block. The CS-LBP histograms of the blocks are concatenated to produce a feature vector of the face image. An optional feature-selection technique selects the most dominant features, which are then fed into two classifiers: a Gaussian mixture model and a support vector machine. The scores of these classifiers are fused by weight to produce a confidence score, which is used to make decisions about the facial expression’s type. Several experiments are performed using a large set of data to validate the proposed system. Experimental results show that the proposed system can recognize facial expressions with 99.95% accuracy.
Few-Shot Human Motion Prediction via Meta-learning

Human motion prediction, forecasting human motion in a few milliseconds conditioning on a historical 3D skeleton sequence, is a long-standing problem in computer vision and robotic vision. Existing forecasting algorithms rely on extensive annotated motion capture data and are brittle to novel actions. This paper addresses the problem of few-shot human motion prediction, in the spirit of the recent progress on few-shot learning and meta-learning. More precisely, our approach is based on the insight that having a good generalization from few examples relies on both a generic initial model and an effective strategy for adapting this model to novel tasks. To accomplish this, we propose proactive and adaptive meta-learning (PAML) that introduces a novel combination of model-agnostic meta-learning and model regression networks and unifies them into an integrated, end-to-end framework. By doing so, our meta-learner produces a generic initial model through aggregating contextual information from a variety of prediction tasks, while effectively adapting this model for use as a task-specific one by leveraging learningto-learn knowledge about how to transform few-shot model parameters to many-shot model parameters. The resulting PAML predictor model significantly improves the prediction performance on the heavily benchmarked H3.6M dataset in the small-sample size regime.
Composite Differential Search Algorithm

Differential search algorithm (DS) is a relatively new evolutionary algorithm inspired by the Brownian-like random-walkmovement which is used by an organism to migrate. It has been verified to be more effective than ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA-ES. In this paper, we propose four improved solution search algorithms, namely “DS/rand/1,” “DS/rand/2,” “DS/current to rand/1,” and “DS/current to rand/2” to search the new space and enhance the convergence rate for the global optimization problem. In order to verify the performance of different solution search methods, 23 benchmark functions are employed. Experimental results indicate that the proposed algorithm performs better than, or at least comparable to, the original algorithm when considering the quality of the solution obtained. However, these schemes cannot still achieve the best solution for all functions. In order to further enhance the convergence rate and the diversity of the algorithm, a composite differential search algorithm (CDS) is proposed in this paper. This new algorithm combines three new proposed search schemes including “DS/rand/1,” “DS/rand/2,” and “DS/current to rand/1” with three control parameters using a random method to generate the offspring. Experiment results show that CDS has a faster convergence rate and better search ability based on the 23 benchmark functions.
Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training

Automatic generation of natural language from images has attracted extensive attention. In this paper, we take one step further to investigate generation of poetic language (with multiple lines) to an image for automatic poetry creation. This task involves multiple challenges, including discovering poetic clues from the image (e.g., hope from green), and generating poems to satisfy both relevance to the image and poeticness in language level. To solve the above challenges, we formulate the task of poem generation into two correlated sub-tasks by multi-adversarial training via policy gradient, through which the cross-modal relevance and poetic language style can be ensured. To extract poetic clues from images, we propose to learn a deep coupled visual-poetic embedding, in which the poetic representation from objects, sentiments \footnoteWe consider both adjectives and verbs that can express emotions and feelings as sentiment words in this research. and scenes in an image can be jointly learned. Two discriminative networks are further introduced to guide the poem generation, including a multi-modal discriminator and a poem-style discriminator. To facilitate the research, we have released two poem datasets by human annotators with two distinct properties: 1) the first human annotated image-to-poem pair dataset (with $8,292$ pairs in total), and 2) to-date the largest public English poem corpus dataset (with $92,265$ different poems in total). Extensive experiments are conducted with 8K images, among which 1.5K image are randomly picked for evaluation. Both objective and subjective evaluations show the superior performances against the state-of-the-art methods for poem generation from images. Turing test carried out with over $500$ human subjects, among which 30 evaluators are poetry experts, demonstrates the effectiveness of our approach.
Adaptive neural controller for redundant robot manipulators and collision avoidance with mobile obstacles

The paper presents a new controller approach applied to redundant robot manipulators constrained by mobile obstacles. The proposed controller is constructed in task space by using optimization strategy, in order to achieve a good trajectory tracking of the end effector even if the obstacles are fixed or mobile. The criterion to be optimized is chosen as the sum of the joint displacements energy and the internal needed, only its structure. All unknown functions in the robot dynamical model, written in extended Cartesian space, are carried out using multilayer perceptron (MLP) neural networks. The adaptation laws of the neural parameters are obtained via closed loop stability analysis of the system (Lyapunov approach). In order to evaluate the proposed controller performance a 3 DOF robot manipulator evolving in a vertical space constrained by a mobile obstacle is used. The obtained results show its effectiveness. & 2011 Elsevier B.V. All rights reserved.
Adaptive mobile activity recognition system with evolving data streams

Mobile activity recognition focuses on inferring current user activities by leveraging sensory data available on today's sensor rich mobile phones. Supervised learning with static models has been applied pervasively for mobile activity recognition. In this paper, we propose a novel phone-based dynamic recognition framework with evolving data streams for activity recognition. The novel framework incorporates incremental and active learning for real-time recognition and adaptation in streaming settings. While stream evolves, we refine, enhance and personalise the learning model in order to accommodate the natural drift in a given data stream. Extensive experimental results using real activity recognition data have evidenced that the novel dynamic approach shows improved performance of recognising activities especially across different users. & 2014 Elsevier B.V. All rights reserved.