nils-herrmann/clean_scirepeval huggingface dataset:
Cat swarm algorithm in wireless sensor networks for optimized cluster head selection: a real time approach

The life time extension in the wireless sensor network (WSN) is the major concern in real time application, if the battery attached with the sensor node life is not optimized properly then the network life fall short. A protocol using a new evolutionary technique, cat swarm optimization (CSO), is designed and implemented in real time to minimize the intra-cluster distances between the cluster members and their cluster heads and optimize the energy distribution for the WSNs. We analyzed the performance of WSN protocol with the help of sensor nodes deployed in a field and grouped in to clusters. The novelty in our proposed scheme is considering the received signal strength, residual battery voltage and intra cluster distance of sensor nodes in cluster head selection with the help of CSO. The result is compared with the well-known protocol Low-energy adaptive clustering hierarchy-centralized (LEACH-C) and the swarm based optimization technique Particle swarm optimization (PSO). It was found that the battery energy level has been increased considerably of the traditional LEACH and PSO algorithm.
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction

Fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization. Supervised learning methods have proven to be effective for this task. However, in many domains, the lack of labeled data hinders the learning of a precise extraction model. In this case, unsupervised domain adaptation methods are desired to transfer knowledge from the source domain to any unlabeled target domain. In this paper, we develop a novel recursive neural network that could reduce domain shift effectively in word level through syntactic relations. We treat these relations as invariant “pivot information” across domains to build structural correspondences and generate an auxiliary task to predict the relation between any two adjacent words in the dependency tree. In the end, we demonstrate state-ofthe-art results on three benchmark datasets.
Discriminative multinomial Naïve Bayes for network intrusion detection

This paper applies discriminative multinomial Naïve Bayes with various filtering analysis in order to build a network intrusion detection system. For our experimental analysis, we used the new NSL-KDD dataset, which is considered as a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. We perform 2 class classifications with 10-fold cross validation for building our proposed model. The experimental results show that the proposed approach is very accurate with low false positive rate and takes less time in comparison to other existing approaches while building an efficient network intrusion detection system.
Multiple regression of cost data: use of generalised linear models.

Choosing an appropriate method for regression analyses of cost data is problematic because it must focus on population means while taking into account the typically skewed distribution of the data. In this paper we illustrate the use of generalised linear models for regression analysis of cost data.



We consider generalised linear models with either an identity link function (providing additive covariate effects) or log link function (providing multiplicative effects), and with gaussian (normal), overdispersed poisson, gamma, or inverse gaussian distributions. These are applied to estimate the treatment effects in two randomised trials adjusted for baseline covariates. Criteria for choosing an appropriate model are presented.



In both examples considered, the gaussian model fits poorly and other distributions are to be preferred. When there are variables of prognostic importance in the model, using different distributions can materially affect the estimates obtained; it may also be possible to discriminate between additive and multiplicative covariate effects.



Generalised linear models are attractive for the regression of cost data because they provide parametric methods of analysis where a variety of non-normal distributions can be specified and the way covariates act can be altered. Unlike the use of data transformation in ordinary least-squares regression, generalised linear models make inferences about the mean cost directly.
Deep Learning for Real-Time Crime Forecasting and its Ternarization

Real-time crime forecasting is important. However, accurate prediction of when and where the next crime will happen is difficult. No known physical model provides a reasonable approximation to such a complex system. Historical crime data are sparse in both space and time and the signal of interests is weak. In this work, we first present a proper representation of crime data. We then adapt the spatial temporal residual network on the well represented data to predict the distribution of crime in Los Angeles at the scale of hours in neighborhood-sized parcels. These experiments as well as comparisons with several existing approaches to prediction demonstrate the superiority of the proposed model in terms of accuracy. Finally, we present a ternarization technique to address the resource consumption issue for its deployment in real world. This work is an extension of our short conference proceeding paper [Wang et al, .
Exercise in Computer Vision A Comparison of Thresholding Methods – NTNU –

This paper contains a comparison of common, simple thresholding methods. Basic thresholding, two-band thresholding, optimal thresholding (Calvard Riddler), adaptive thresholding, and p-tile thresholding is compared. The different thresholding methods has been implemented in the programming language c, using the image analysis library Xite. The program sources should accompany this paper. 1 Methods of thresholding Basic thresholding. Basic thresholding is done by visiting each pixel site in the image, and set the pixel to maximum value if its value is above or equal to a given threshold value and to the minimum value if the threshold value is below the pixels value. Basic thresholding is often used as a step in other thresholding algorithms. Implemented by the function threshold in thresholding.h Band thresholding. Band thresholding is similar to basic thresholding, but has two threshold values, and set the pixel site to maximum value if the pixels intensity value is between or at the threshold values, else it it set to minimum. Implemented by the function bandthresholding2 in thresholding.h P-tile thresholding. P-tile is a method for choosing the threshold value to input to the “basic thresholding” algorithm. P-tile means “Percentile”, and the threshold is chosen to be the intensity value where the cumulative sum of pixel intensities is closest to the percentile. Implemented by the function ptileThreshold in thresholding.h Optimal thresholding. Optimal thresholding selects a threshold value that is statistically optimal, based on the contents of the image. Algorithm, due to Calvard and Riddler:
Dermoscopic Features of Pigmented Fungiform Papillae of the Tongue.

Fungiform papillae of the tongue are small projections involved in taste function that are typically located on the lateral aspects and tip of the tongue. They are called fungiform papillae because of their close resemblance to a fungus. In fair-skinned individuals, they are usually pink or red, but in dark-skinned individuals, they are frequently pigmented and are considered a variant of normal oral pigmentation. We report a case of pigmented fungiform papillae of the tongue (PFPT) and describe its dermoscopic features, which have been rarely reported in the literature. A 30-year-old black woman with no personal or family history of interest was evaluated for a pigmented lesion with no apparent cause on the tongue. The lesion had appeared in adolescence but had remained stable and asymptomatic . The patient was not taking any regular medication, did not smoke, had not had dental treatment in the area of
A bluetooth based sophisticated home automation system using smartphone

This paper represents a reliable, compact, fast and low cost smart home automation system, based on Arduino (microcontroller) and Android app. Bluetooth chip has been used with Arduino, thus eliminating the use of personal computers (PCs). Various devices such as lights, DC Servomotors have been incorporated in the designed system to demonstrate the feasibility, reliability and quick operation of the proposed smart home system. The entire designed system has been tested and it is seen capable of running successfully and perform the desired operations, such as switching functionalities, position control of Servomotor, speed control of D.C motor and light intensity control (Via Voltage Regulation).
Experimental Design for Learning Causal Graphs with Latent Variables

We consider the problem of learning causal structures with latent variables using interventions. Our objective is not only to learn the causal graph between the observed variables, but to locate unobserved variables that could confound the relationship between observables. Our approach is stage-wise: We first learn the observable graph, i.e., the induced graph between observable variables. Next we learn the existence and location of the latent variables given the observable graph. We propose an efficient randomized algorithm that can learn the observable graph using O(d log2 n) interventions where d is the degree of the graph. We further propose an efficient deterministic variant which uses O(log n+ l) interventions, where l is the longest directed path in the graph. Next, we propose an algorithm that uses only O(d2 log n) interventions that can learn the latents between both nonadjacent and adjacent variables. While a naive baseline approach would require O(n2) interventions, our combined algorithm can learn the causal graph with latents using O(d log2 n+ d2 log (n)) interventions.
Safety Approach to Otoplasty: A Surgical Algorithm.

An algorithm was developed through an evolution of refinements in surgical technique with the goal to minimize risk and morbidity in otoplasty. Key principles were avoidance of cartilage incisions and transections and the use of multiple surgical techniques to distribute the "surgical load" evenly among these techniques. The present retrospective study was designed to test safety and efficacy of the concept in 100 consecutive patients and to discuss the results in light of the literature. Data detailing the surgery, preoperative, and postoperative period were extracted from the record and during patient interviews. Patients were contacted to complete a questionnaire to rate the postoperative pain and their satisfaction with the final outcome on a 6-point visual analog scale (VAS). An expert and a lay panel assessed preoperative and postoperative frontal-view photographs, using the same VAS. Pain in the postoperative was rated as minor (pain level VAS average score, 2.33) and patients' satisfaction was excellent (satisfaction level VAS average score, 1.82). The assessment by the panels of expert and lay evaluators paralleled these outcomes with a postoperative average VAS score of 1.69 and 1.87, respectively. Cartilage incision and transection can be effectively avoided in otoplasty. Even distribution of the surgical load among multiple techniques avoids the problems associated with "overload" of a single technique. The innovative technique of cortical mastoid drill-out is described. High satisfaction with the results, excellent patient comfort, and a favorable safety profile are associated with the present algorithm.
Optimal Approximation of Signal Priors

In signal restoration by Bayesian inference, one typically uses a parametric model of the prior distribution of the signal. Here, we consider how the parameters of a prior model should be estimated from observations of uncorrupted signals. A lot of recent work has implicitly assumed that maximum likelihood estimation is the optimal estimation method. Our results imply that this is not the case. We first obtain an objective function that approximates the error occurred in signal restoration due to an imperfect prior model. Next, we show that in an important special case (small gaussian noise), the error is the same as the score-matching objective function, which was previously proposed as an alternative for likelihood based on purely computational considerations. Our analysis thus shows that score matching combines computational simplicity with statistical optimality in signal restoration, providing a viable alternative to maximum likelihood methods. We also show how the method leads to a new intuitive and geometric interpretation of structure inherent in probability distributions.
Control system with sinusoidal PWM three-phase inverter with a frequency scalar control of induction motor

The principles of implementation of the control system with sinusoidal PWM inverter voltage frequency scalar and vector control induction motor are reviewed. Comparisons of simple control system with sinusoidal PWM control system and sinusoidal PWM control with an additional third-harmonic signal and gain modulated control signal are carried out. There are shown the maximum amplitude and actual values phase and line inverter output voltage at the maximum amplitude of the control signals. Recommendations on the choice of supply voltage induction motor electric drive with frequency scalar control are presented.
Selective Jamming of LoRaWAN using Commodity Hardware

Long range, low power networks are rapidly gaining acceptance in the Internet of Things (IoT) due to their ability to economically support long-range sensing and control applications while providing multi-year battery life. LoRa is a key example of this new class of network and is being deployed at large scale in several countries worldwide. As these networks move out of the lab and into the real world, they expose a large cyber-physical attack surface. Securing these networks is therefore both critical and urgent. This paper highlights security issues in LoRa and LoRaWAN that arise due to the choice of a robust but slow modulation type in the protocol. We exploit these issues to develop a suite of practical attacks based around selective jamming. These attacks are conducted and evaluated using commodity hardware. The paper concludes by suggesting a range of countermeasures that can be used to mitigate the attacks.
Real-time safe trajectory generation for quadrotor flight in cluttered environments

We address the problem of real-time generation of smooth and collision-free trajectories for autonomous flight of a quadrotor through 3-D cluttered environments. Our approach starts by generating a sequence of variable sized 3-D free space grids as the virtual flight corridor using an OctoMap-based environment representation and search-based planning method. The key contribution is a quadratic programming-based formulation for generating multi-segment polynomial trajectories that are entirely fit within the corridor, and thus collision-free. Our formulation also allows incorporating higher-order dynamical constraints to ensure that the trajectory is feasible for the platform. A novel non-iterative constraint relaxation method is also proposed for implicit optimization of segment duration. The proposed algorithm runs real-time onboard our quadrotor experimental testbed. Both simulation and online experimental results are presented for performance verification.
Shoulder strength imbalances as injury risk in handball.

This study was conducted to analyze whether internal (IR) and external (ER) rotator shoulder muscles weakness and/or imbalance collected through a preseason assessment could be predictors of subsequent shoulder injury during a season in handball players. In preseason, 16 female elite handball players (HPG) and 14 healthy female nonathletes (CG) underwent isokinetic IR and ER strength test with use of a Con-Trex® dynamometer in a seated position with 45° shoulder abduction in scapular plane, at 60, 120 and 240°/s in concentric and at 60°/s in eccentric, for both sides. An imbalanced muscular strength profile was determined using -statistically selected cut-offs from CG values. For HPG, all newly incurred shoulder injuries were reported during the season. There were significant differences between HPG and CG only for dominant eccentric IR strength, ER/IR ratio at 240°/s and for IRecc/ERcon ratio. In HPG, IR and ER strength was higher, and ER/IR ratios lower for dominant than for nondominant side. The relative risk was 2.57 (95%CI: 1.60-3.54; P<0.05) if handball players had an imbalanced muscular strength profile. In youth female handball players IR and ER muscle strength increases on the dominant side without ER/IR imbalances; and higher injury risk was associated with imbalanced muscular strength profile.
The effect of agriculture on the primary production in Lake Beskie (Poland) as recorded in the stratigraphy of fossil pigments

Analysis of fossil pigments deposited in the bottom sediments of Lake Beskie, was used to assess changes in the primary productivity during the past years. Three characteristic periods of lake development were distinguished. These periods correspond with a transformation in the lake's catchment area induced by the development of agriculture. A first period was characterized by intensive inflow of allochthonous matter into the lake, due to agriculture in the catchment area, favouring soil erosion. This erosion and the subsequent increase in mineral fertilization resulted in decrease of sorption ability of the soil. This in turn led to increased leaching of nutrients into the lake which resulted in increased primary production and hypolimnetic anoxia. These high oxygen deficits were characterized by a development of photosynthetic bacteria of the genus Chlorobium, and an intensification of the lake's enrichment, mainly with phosphorus. In a final period organic fertilizers (manure) were used in the catchment area. A noticeable improvement of sorption ability of the soil occurred, migration of nutrients to the lake was inhibited, and primary productivity decreased.
RFID- based supply chain traceability system

In this paper we propose a real solution for gathering information throughout the entire pig meat supply chain. The architecture consists of a a complex identification system based on RFID tags that transmits data to a distributed database during all phases of the production process. The specific work environment required identifying a suitable technology for implementation in the supply chain and the best possible organization. The aim of this work is to keep track of all the information generated during meat processing, not only for traceability purposes but chiefly for enhancing and optimizing production. All information generated by the traceability system will be collected in a central database accessible by end users thtough a public dedicated web interface.
EV charging stations and modes: International standards

In recent years, Electric vehicles (EVs) are receiving significant attention as an environmental-sustainable and cost-effective substitute of vehicles with internal combustion engine, for the solution of the dependence from fossil fuels and for the saving of Green-House Gasses emission The present paper deals with an overview on different types of EVs charging stations and a comparison between the related European and American Standards. The work includes also a summary on possible types of Energy Storage Systems (ESSs), that are important for the integration of EVs fast charging stations of the last generation in smart grids. Finally a brief analysis on the possible electrical layout for the ESS integration in EVs charging system, proposed in literature, is reported.
Influences on purposeful implementation of ICT into the classroom: An exploratory study of K-12 teachers

Teachers are increasingly required to incorporate information and communications technologies (ICT) into the modern classroom. The implementation of ICT into the classroom should not be seen as merely an add-on, however, but should be included with purpose; meaningfully implemented based on pedagogy. The aim of this study is to explore potential factors that might predict purposeful implementation of ICT into the classroom. Using an online survey, skills in and beliefs about ICT were assessed, as well as the teaching and learning beliefs of forty-five K-12 teachers. Hierarchical multiple regression revealed that competence using ICT and a belief in the importance of ICT for student outcomes positively predicted purposeful implementation of ICT into the classroom, while endorsing more traditional content-based learning was a negative predictor. These three predictors explained 47% of the variance in purposeful implementation of ICT into the classroom. ICT competence was unpacked further with correlations. This revealed that there is a relationship between teachers having ICT skills that can personalize, engage, and create an interactive atmosphere for students and purposeful implementation of ICT into the classroom. Based on these findings, suggestions are made of important focal areas for encouraging teachers to purposefully implement ICT into their classrooms.
Classification of Mandibular Condylar Fractures.

There is no universal gold standard classification system for mandibular condylar process fractures. A clinically relevant mandibular condyle classification system should be easy to understand, and be easy to recall, for implementation into the management of a condylar fracture. An accurate appreciation of the location of the mandibular condylar fracture assists with the determination of either an operative or nonoperative management regimen.
Trace elements determination in seawater by ICP-MS with on-line pre-concentration on a Chelex-100 column using a ‘standard’ instrument setup.

Trace element determination in seawater is analytically challenging due to the typically very low concentrations of the trace elements and the potential interference of the salt matrix. A common way to address the challenge is to pre-concentrate the trace elements on a chelating resin, then rinse the matrix elements from the resin and subsequently elute and detect the trace elements using inductively coupled plasma mass spectrometry (ICP-MS). This technique typically involves time-consuming pre-treatment of the samples for 'off-line' analyses or complicated sample introduction systems involving several pumps and valves for 'on-line' analyses. As an alternative, the following method offers a simple method for 'on-line' analyses of seawater by ICP-MS. As opposed to previous methods, excess seawater was pumped through the nebulizer of the ICP-MS during the pre-concentration step but the gas flow was adjusted so that the seawater was pumped out as waste without being sprayed into the instrument. Advantages of the method include: •Simple and convenient analyses of seawater requiring no changes to the 'standard' sample introduction system except from a resin-filled micro-column connected to the sample tube. The 'standard' sample introduction system refers to that used for routine digest-solution analyses of biota and sediment by ICP-MS using only one peristaltic pump; and•Accurate determination of the elements V, Mn, Co, Ni, Cu, Zn, Cd and Pb in a range of different seawater matrices verified by participation in 6 successive rounds of the international laboratory intercalibration program QUASIMEME.