nils-herrmann/clean_scirepeval huggingface dataset:
Connectivity of the uniform random intersection graph

A uniform random intersection graph G(n, m, k) is a random graph constructed as follows. Label each of n nodes by a randomly chosen set of k distinct colours taken from some finite set of possible colours of size m. Nodes are joined by an edge if and only if some colour appears in both their labels. These graphs arise in the study of the security of wireless sensor networks, in particular when modelling the network graph of the well known key predistribution technique due to Eschenauer and Gligor. The paper determines the threshold for connectivity of the graph G(n, m, k) when n → ∞ in many situations. For example, when k is a function of n such that k ≥ 2 and m = ⌊nα⌋ for some fixed positive real number α then G(n, m, k) is almost surely connected when lim inf kn/m logn > 1, and G(n, m, k) is almost surely disconnected when lim sup kn/m logn < 1.
Implantable batteryless device for on-demand and pulsatile insulin administration

Many implantable systems have been designed for long-term, pulsatile delivery of insulin, but the lifetime of these devices is limited by the need for battery replacement and consequent replacement surgery. Here we propose a batteryless, fully implantable insulin pump that can be actuated by a magnetic field. The pump is prepared by simple-assembly of magnets and constituent units and comprises a drug reservoir and actuator equipped with a plunger and barrel, each assembled with a magnet. The plunger moves to noninvasively infuse insulin only when a magnetic field is applied on the exterior surface of the body. Here we show that the dose is easily controlled by varying the number of magnet applications. Also, pump implantation in diabetic rats results in profiles of insulin concentration and decreased blood glucose levels similar to those observed in rats treated with conventional subcutaneous insulin injections.
RPRep: A Robust and Privacy-Preserving Reputation Management Scheme for Pseudonym-Enabled VANETs

Establishing trust and reputation for evaluation ofmessage reliability is key to the vehicular ad hoc networks (VANETs).Most of the previous reputationmanagement systems focus on the effectiveness of the reputationmanagement system in handling the liars who send false service messages. However, these reputation management systems have two drawbacks. One is that they are vulnerable to tactical attacks such as self-promoting attacks and bad-mouthing attacks. The other is that they may violate location privacy because they assume every vehicle communicates with a unique ID. Our research particularly investigates the robustness against these tactical attacks, as well as the preservation of privacy by integrating trust management with the pseudonym technique. To resist the tactical attacks in VANETs, we present a reputation model which builds both service reputation and feedback reputation. Moreover, we apply the information entropy and the majority rule to the reputation accumulation algorithms to counter false feedback. To defend the reputation link attack during pseudonym changes, we propose hidden-zone strategy and k-anonymity strategy.The simulation results show that our scheme is robust to these tactical attacks and preserves privacy against the reputation link attack during the pseudonym changes.
Managing multiple generations: scenarios from the workplace.

The current nursing workforce is composed of multigenerational staff members creating challenges and at times conflict for managers.



Generational cohorts are defined and two multigenerational scenarios are presented and discussed using the ACORN imperatives and Hahn's Five Managerial Strategies for effectively managing a multigenerational staff.



Communication and respect are the underlying key strategies to understanding and bridging the generational gap in the workplace.



Embracing and respecting generational differences can bring strength and cohesiveness to nursing teams on the managerial or unit level.
Some Potential Issues with the Security of HTML5 IndexedDB

The new HTML5 standard provides much more access to client resources, such as user location and local data storage. Unfortunately, this greater access may create new security risks that potentially can yield new threats to user privacy and web attacks. One of these security risks lies with the HTML5 client-side database. It appears that data stored on the client file system is unencrypted. Therefore, any stored data might be at risk of exposure. This paper explains and performs a security investigation into how the data is stored on client local file systems. The investigation was undertaken using Firefox and Chrome web browsers, and Encase (a computer forensic tool), was used to examine the stored data. This paper describes how the data can be retrieved after an application deletes the client side database. Finally, based on our findings, we propose a solution to correct any potential issues and security risks, and recommend ways to store data securely on local file systems.
Large scale web-content classification

Web classification is used in many security devices for preventing users to access selected web sites that are not allowed by the current security policy, as well for improving web search and for implementing contextual advertising. There are many commercial web classification services available on the market and a few publicly available web directory services. Unfortunately they mostly focus on English-speaking web sites, making them unsuitable for other languages in terms of classification reliability and coverage. This paper covers the design and implementation of a web-based classification tool for TLDs (Top Level Domain). Each domain is classified by analysing the main domain web site, and classifying it in categories according to its content. The tool has been successfully validated by classifying all the registered it. Internet domains, whose results are presented in this paper.
Restraint asphyxiation in excited delirium.

Eleven cases of sudden death of men restrained in a prone position by police officers are reported. Nine of the men were hogtied, one was tied to a hospital gurney, and one was manually held prone. All subjects were in an excited delirious state when restrained. Three were psychotic, whereas the others were acutely delirious from drugs (six from cocaine, one from methamphetamine, and one from LSD). Two were shocked with stun guns shortly before death. The literature is reviewed and mechanisms of death are discussed.
Explanations for command recommendations : an experimental study

Recently, evaluation of a recommender system has been beyond evaluating just the algorithm. In addition to accuracy of algorithms, user-centric approaches evaluate a system’s e↵ectiveness in presenting recommendations, explaining recommendations and gaining users’ confidence in the system. Existing research focuses on explaining recommendations that are related to user’s current task. However, explaining recommendations can prove useful even when recommendations are not directly related to user’s current task. Recommendations of development environment commands to software developers is an example of recommendations that are not related to the user’s current task, which is primarily focussed on programming, rather than inspecting recommendations. In this dissertation, we study three di↵erent kinds of explanations for IDE commands recommended to software developers. These explanations are inspired by the common approaches based on literature in the domain. We describe a lab-based experimental study with 24 participants where they performed programming tasks on an open source project. Our results suggest that explanations a↵ect users’ trust of recommendations, and explanations reporting the system’s confidence in recommendation a↵ects their trust more. The explanation with system’s confidence rating of the recommendations resulted in more recommendations being investigated. However, explanations did not a↵ect the uptake of the commands. Our qualitative results suggest that recommendations, when not user’s primary focus, should be in context of his task to be accepted more readily.
Stochastic optimization using a trust-region method and random models

In this paper, we propose and analyze a trust-region model-based algorithm for solving unconstrained stochastic optimization problems. Our framework utilizes random models of an objective function f(x), obtained from stochastic observations of the function or its gradient. Our method also utilizes estimates of function values to gauge progress that is being made. The convergence analysis relies on requirements that these models and these estimates are sufficiently accurate with sufficiently high, but fixed, probability. Beyond these conditions, no assumptions are made on how these models and estimates are generated. Under these general conditions we show an almost sure global convergence of the method to a first order stationary point. In the second part of the paper, we present examples of generating sufficiently accurate random models under biased or unbiased noise assumptions. Lastly, we present some computational results showing the benefits of the proposed method compared to existing approaches that are based on sample averaging or stochastic gradients.
The Process of Cultural Competence in the Delivery of Healthcare Services: a model of care.

Several models of service care delivery have emerged to meet the challenges of providing health care to our growing multi-ethnic world. This article will present Campinha-Bacote's model of cultural competence in health care delivery: The Process of Cultural Competence in the Delivery of Healthcare Services. This model views cultural competence as the ongoing process in which the health care provider continuously strives to achieve the ability to effectively work within the cultural context of the client (individual, family, community). This ongoing process involves the integration of cultural awareness, cultural knowledge, cultural skill, cultural encounters, and cultural desire.
Research on Logistics Distribution Center Location Model and Evaluation Under Electronic Commerce

Modern logistics combines transportation, storage, loading and unloading, handling, carrying, packaging, processing, distribution, freight forwarders, information industry and socialized production of new services together, the development of economies scale and the reduction of transportation cost makes people pay attention to logistics distribution center gradually. Location of logistics distribution center plays a very important position in the logistics system, belongs to the logistics management strategy layer. In this paper, we investigate the multiple attribute decision making problems for evaluating the logistics distribution center location model with 2-tuple linguistic information. We extended the TOPSIS model to solve the evaluation problems of the logistics distribution center location model with 2-tuple linguistic information. Finally, a numerical example for evaluating the logistics distribution center location model with 2-tuple linguistic information is used to illustrate the applicability and effectiveness of the proposed model.
A RGBD SLAM algorithm combining ORB with PROSAC for indoor mobile robot

In order to enhance the instantaneity of SLAM for indoor mobile robot, a RGBD SLAM method based on Kinect was proposed. In the method, oriented FAST and rotated BRIEF(ORB) algorithm was combined with progressive sample consensus(PROSAC) algorithm to execute feature extracting and matching. More specifically, ORB algorithm which has better property than many other feature descriptors was used for extracting feature. At the same time, ICP algorithm was adopted for coarse registration of the point clouds, and PROSAC algorithm which is superior than RANSAC in outlier removal was employed to eliminate incorrect matching. To make the result more accurate, pose-graph optimization was achieved based on g2o framework. In the end, a 3D volumetric map which can be directly used to the navigation of robots was created.
Cross-Linked Gold-Nanoparticle Membrane Resonators as Microelectromechanical Vapor Sensors.

We report a novel approach for the detection of volatile compounds employing electrostatically driven drumhead resonators as sensing elements. The resonators are based on freestanding membranes of alkanedithiol cross-linked gold nanoparticles (GNPs), which are able to sorb analytes from the gas phase. Under reduced pressure, the fundamental resonance frequency of a resonator is continuously monitored while the device is exposed to varying partial pressures of toluene, 4-methylpentan-2-one, 1-propanol, and water. The measurements reveal a strong, reversible frequency shift of up to ∼10 kHz, i.e., ∼5% of the fundamental resonance frequency, when exposing the sensor to toluene vapor with a partial pressure of ∼20 Pa. As this strong shift cannot be explained exclusively by the mass uptake in the membrane, our results suggest a significant impact of analyte sorption on the pre-stress of the freestanding GNP membrane. Thus, our findings point to the possibility of designing highly sensitive resonators, which utilize sorption induced changes in the membrane's pre-stress as primary transduction mechanism.
Comparative proteomic analysis of methyl jasmonate-induced defense responses in different rice cultivars.

Jasmonate is an important endogenous chemical signal that plays a role in modulation of plant defense responses. To understand its mechanisms in regulation of rice resistance against the fungal pathogen Magnaporthe oryzae, comparative phenotype and proteomic analyses were undertaken using two near-isogenic cultivars with different levels of disease resistance. Methyl-jasmonate (MeJA) treatment significantly enhanced the resistance against M. oryzae in both cultivars but the treated resistant cultivar maintained a higher level of resistance than the same treated susceptible cultivars. Proteomic analysis revealed 26 and 16 MeJA-modulated proteins in resistant and susceptible cultivars, respectively, and both cultivars shared a common set of 13 proteins. Cumulatively, a total of 29 unique MeJA-influenced proteins were identified with many of them known to be associated with plant defense response and ROS accumulation. Consistent with the findings of proteomic analysis, MeJA treatment increased ROS accumulation in both cultivars with the resistant cultivar showing higher levels of ROS production and cell membrane damage than the susceptible cultivar. Taken together, our data add a new insight into the mechanisms of overall MeJA-induced rice defense response and provide a molecular basis of using MeJA to enhance fungal disease resistance in resistant and susceptible rice cultivars.
Comparative analysis of preoperative diagnostic values of HRCT and CBCT in patients with histologically diagnosed otosclerotic stapes footplates

This prospective case review was performed with the aim to compare and asses the diagnostic values of cone-beam computed tomography (CBCT) and high-resolution computed tomography (HRCT) in the preoperative evaluation of otosclerosis. A total of 43 patients with histologically confirmed stapedial otosclerosis, who underwent unilateral stapedectomies were analyzed. Preoperative temporal bone CBCT and HRCT scans were performed in all cases. Both CBCT and HRCT imaging were characterized by a slice thickness of 0.4–0.625 mm and multiplanar image reconstruction. Histopathologic examination of the removed stapes footplates was performed in all cases. Findings of CBCT and HRCT were categorized according to the modified Marshall’s grading system (fenestral or retrofenestral lesions). Histopathologic results were correlated with multiplanar reconstructed CBCT and HRCT scans, respectively. Negative control groups for CBCT (n = 36) and HRCT (n = 27) examinations consisted of patients, who underwent CBCT imaging due to various dental disorders or HRCT analysis due to idiopathic sudden sensorineural hearing loss. Histologically active foci of otosclerosis (n = 31, 72 %) were identified by both CBCT and HRCT in all cases with a sensitivity of 100 %. However, CBCT could not detect histologically inactive otosclerosis (n = 12, 23 %; sensitivity 0 %). In contrast, HRCT showed inactive otosclerosis with a sensitivity of 59.3 %. According to CBCT results, no retrofenestral lesions were found and the overall sensitivity for hypodense lesions was 61.37 %. In conclusion, CBCT is a robust imaging method in the detection of histologically active fenestral hypodense foci of otosclerosis with high sensitivity and radiologic specificity. In the light of these results, HRCT still remains the basic imaging method in the preoperative diagnosis of otosclerosis, since it has much greater sensitivity and specificity in the detection of retrofenestral hypodense lesions and histologically inactive otosclerotic foci in the oval window niche.
Ocean energy power take-off using oscillating paddle

This paper presents an ocean energy power take-off system using paddle like wave energy converter (WEC), magnetic gear and efficient power converter architecture. As the WEC oscillates at a low speed of about 5-25 rpm, the direct drive generator is not an efficient design. To increase the generator speed a cost effective flux focusing magnetic gear is proposed. Power converter architecture is discussed and integration of energy storage in the system to smooth the power output is elaborated. Super-capacitor is chosen as energy storage for its better oscillatory power absorbing capability than battery. WEC is emulated in hardware using motor generator set-up and energy storage integration in the system is demonstrated.
Multi-classification of skin diseases for dermoscopy images using deep learning

Skin diseases are very common in our daily life. Due to the similar appearance of skin diseases, automatic classification through lesion images is quite a challenging task. In this paper, a novel multi-classification method based on convolutional neural network (CNN) is proposed for dermoscopy images. A CNN network with nested residual structure is designed first, which can learn more information than the original residual structure. Then, the designed network are trained through transfer learning. With the trained network, 6 kinds of lesion diseases are classified, including nevus, seborrheic keratosis, psoriasis, seborrheic dermatitis, eczema and basal cell carcinoma. The experiments are conducted on six-classification and two-classification tasks, and with the accuracies of 65.8% and 90% respectively, our method greatly outperforms other 4 state-of-the-art networks and the average of 149 professional dermatologists.
Genetic algorithms and engineering optimization

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Repairing post burn scar contractures with a rare form of Z-plasty.

Although many precautions have been introduced into early burn management, post burn contractures are still significant problems in burn patients. In this study, a form of Z-plasty in combination with relaxing incision was used for the correction of contractures.



Preoperatively, a Z-advancement rotation flap combined with a relaxing incision was drawn on the contracture line. Relaxing incision created a skin defect like a rhomboid. Afterwards, both limbs of the Z flap were incised. After preparation of the flaps, advancement and rotation were made in order to cover the rhomboid defect. Besides subcutaneous tissue, skin edges were closely approximated with sutures.



This study included sixteen patients treated successfully with this flap. It was used without encountering any major complications such as infection, hematoma, flap loss, suture dehiscence or flap necrosis. All rotated and advanced flaps healed uneventfully. In all but one patient, effective contracture release was achieved by means of using one or two Z-plasty. In one patient suffering severe left upper extremity contracture, a little residual contracture remained due to inadequate release.



When dealing with this type of Z-plasty for mild contractures, it offers a new option for the correction of post burn contractures, which is safe, simple and effective.
Energy Theft in the Advanced Metering Infrastructure

Global energy generation and delivery systems are transitioning to a new computerized “smart grid”. One of the principle components of the smart grid is an advanced metering infrastructure (AMI). AMI replaces the analog meters with computerized systems that report usage over digital communication interfaces, e.g., phone lines. However, with this infrastructure comes new risk. In this paper, we consider adversary means of defrauding the electrical grid by manipulating AMI systems. We document the methods adversaries will use to attempt to manipulate energy usage data, and validate the viability of these attacks by performing penetration testing on commodity devices. Through these activities, we demonstrate that not only is theft still possible in AMI systems, but that current AMI devices introduce a myriad of new vectors for achieving it.
Extracting Top-K Insights from Multi-dimensional Data

OLAP tools have been extensively used by enterprises to make better and faster decisions. Nevertheless, they require users to specify group-by attributes and know precisely what they are looking for. This paper takes the first attempt towards automatically extracting top-k insights from multi-dimensional data. This is useful not only for non-expert users, but also reduces the manual effort of data analysts. In particular, we propose the concept of insight which captures interesting observation derived from aggregation results in multiple steps (e.g., rank by a dimension, compute the percentage of measure by a dimension). An example insight is: ``Brand B's rank (across brands) falls along the year, in terms of the increase in sales''. Our problem is to compute the top-k insights by a score function. It poses challenges on (i) the effectiveness of the result and (ii) the efficiency of computation. We propose a meaningful scoring function for insights to address (i). Then, we contribute a computation framework for top-k insights, together with a suite of optimization techniques (i.e., pruning, ordering, specialized cube, and computation sharing) to address (ii). Our experimental study on both real data and synthetic data verifies the effectiveness and efficiency of our proposed solution.