nils-herrmann/clean_scirepeval huggingface dataset:
Characterization of the compact Hokuyo URG-04LX 2D laser range scanner

This paper presents a detailed characterization of the Hokuyo URG-04LX 2D laser range finder. While the sensor specifications only provide a rough estimation of the sensor accuracy, the present work analyzes issues such as time drift effects and dependencies on distance, target properties (color, brightness and material) as well as incidence angle. Since the sensor is intended to be used for measurements of a tubelike environment on an inspection robot, the characterization is extended by investigating the influence of the sensor orientation and dependency on lighting conditions. The sensor characteristics are compared to those of the Sick LMS 200 which is commonly used in robotic applications when size and weight are not critical constraints. The results show that the sensor accuracy is strongly depending on the target properties (color, brightness, material) and that it is consequently difficult to establish a calibration model. The paper also identifies cases for which the sensor returns faulty measurements, mainly when the surface has low reflectivity (dark surfaces, foam) or for high incidence angles on shiny surfaces. On the other hand, the repeatability of the sensor seems to be competitive with the LMS 200.
Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization

We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of graphs can be extended to graphs with positively and negatively weighted edges, giving signed spectral clustering methods, signed graph kernels and network visualization methods that apply to signed graphs. In particular, we review a signed variant of the graph Laplacian. We derive our results by considering random walks, graph clustering, graph drawing and electrical networks, showing that they all result in the same formalism for handling negatively weighted edges. We illustrate our methods using examples from social networks with negative edges and bipartite rating graphs.
Identifying Noun Product Features that Imply Opinions

Identifying domain-dependent opinion words is a key problem in opinion mining and has been studied by several researchers. However, existing work has been focused on adjectives and to some extent verbs. Limited work has been done on nouns and noun phrases. In our work, we used the feature-based opinion mining model, and we found that in some domains nouns and noun phrases that indicate product features may also imply opinions. In many such cases, these nouns are not subjective but objective. Their involved sentences are also objective sentences and imply positive or negative opinions. Identifying such nouns and noun phrases and their polarities is very challenging but critical for effective opinion mining in these domains. To the best of our knowledge, this problem has not been studied in the literature. This paper proposes a method to deal with the problem. Experimental results based on real-life datasets show promising results.
A Scalable, Lexicon Based Technique for Sentiment Analysis

Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much social media available on the web, sentiment analysis is now considered as a big data task. Hence the conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data available now a days. The main focus of the research was to find such a technique that can efficiently perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative and neutral in a fast and accurate manner. In the research, sentiment analysis was performed on a large data set of tweets using Hadoop and the performance of the technique was measured in form of speed and accuracy. The experimental results shows that the technique exhibits very good efficiency in handling big sentiment data sets.
Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks

Recognizing human activities in a sequence is a challenging area of research in ubiquitous computing. Most approaches use a fixed size sliding window over consecutive samples to extract features— either handcrafted or learned features—and predict a single label for all samples in the window. Two key problems emanate from this approach: i) the samples in one window may not always share the same label. Consequently, using one label for all samples within a window inevitably lead to loss of information; ii) the testing phase is constrained by the window size selected during training while the best window size is difficult to tune in practice. We propose an efficient algorithm that can predict the label of each sample, which we call dense labeling, in a sequence of human activities of arbitrary length using a fully convolutional network. In particular, our approach overcomes the problems posed by the sliding window step. Additionally, our algorithm learns both the features and classifier automatically. We release a new daily activity dataset based on a wearable sensor with hospitalized patients. We conduct extensive experiments and demonstrate that our proposed approach is able to outperform the state-of-the-arts in terms of classification and label misalignment measures on three challenging datasets: Opportunity, Hand Gesture, and our new dataset.
Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures

This study examines the role of Eigenvector selection and Eigenspace distance measures on PCA-based face recognition systems. In particular, it builds on earlier results from the FERET face recognition evaluation studies, which created a large face database (1,196 subjects) and a baseline face recognition system for comparative evaluations. This study looks at using a combinations of traditional distance measures (City-block, Euclidean, Angle, Mahalanobis) in Eigenspace to improve performance in the matching stage of face recognition. A statistically significant improvement is observed for the Mahalanobis distance alone when compared to the other three alone. However, no combinations of these measures appear to perform better than Mahalanobis alone. This study also examines questions of how many Eigenvectors to select and according to what ordering criterion. It compares variations in performance due to different distance measures and numbers of Eigenvectors. Ordering Eigenvectors according to a like-image difference value rather than their Eigenvalues is also considered.
Modelling Class Noise with Symmetric and Asymmetric Distributions

In classification problem, we assume that the samples around the class boundary are more likely to be incorrectly annotated than others, and propose boundaryconditional class noise (BCN). Based on the BCN assumption, we use unnormalized Gaussian and Laplace distributions to directly model how class noise is generated, in symmetric and asymmetric cases. In addition, we demonstrate that Logistic regression and Probit regression can also be reinterpreted from this class noise perspective, and compare them with the proposed models. The empirical study shows that, the proposed asymmetric models overall outperform the benchmark linear models, and the asymmetric Laplace-noise model achieves the best performance among all.
Shape and the Stereo Correspondence Problem

We examine the implications of shape on the process of finding dense correspondence and half-occlusions for a stereo pair of images. The desired property of the disparity map is that it should be a piecewise continuous function which is consistent with the images and which has the minimum number of discontinuities. To zeroth order, piecewise continuity becomes piecewise constancy. Using this approximation, we first discuss an approach for dealing with such a fronto-parallel shapeless world, and the problems involved therein. We then introduce horizontal and vertical slant to create a first order approximation to piecewise continuity. In particular, we emphasize the following geometric fact: a horizontally slanted surface (i.e., having depth variation in the direction of the separation of the two cameras) will appear horizontally stretched in one image as compared to the other image. Thus, while corresponding two images, N pixels on a scanline in one image may correspond to a different number of pixels M in the other image. This leads to three important modifications to existing stereo algorithms: (a) due to unequal sampling, existing intensity matching metrics must be modified, (b) unequal numbers of pixels in the two images must be allowed to correspond to each other, and (c) the uniqueness constraint, which is often used for detecting occlusions, must be changed to an interval uniqueness constraint. We also discuss the asymmetry between vertical and horizontal slant, and the central role of non-horizontal edges in the context of vertical slant. Using experiments, we discuss cases where existing algorithms fail, and how the incorporation of these new constraints provides correct results.
MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks. However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> and a discriminative model <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>. We treat <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> can produce numerous images that are similar to the training data; therefore, <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> can learn better representations of remotely sensed images using the training data provided by <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.
The complex network of global cargo ship movements.

Transportation networks play a crucial role in human mobility, the exchange of goods and the spread of invasive species. With 90 per cent of world trade carried by sea, the global network of merchant ships provides one of the most important modes of transportation. Here, we use information about the itineraries of 16 363 cargo ships during the year 2007 to construct a network of links between ports. We show that the network has several features that set it apart from other transportation networks. In particular, most ships can be classified into three categories: bulk dry carriers, container ships and oil tankers. These three categories do not only differ in the ships' physical characteristics, but also in their mobility patterns and networks. Container ships follow regularly repeating paths whereas bulk dry carriers and oil tankers move less predictably between ports. The network of all ship movements possesses a heavy-tailed distribution for the connectivity of ports and for the loads transported on the links with systematic differences between ship types. The data analysed in this paper improve current assumptions based on gravity models of ship movements, an important step towards understanding patterns of global trade and bioinvasion.
Actionable and Political Text Classification using Word Embeddings and LSTM

In this work, we apply word embeddings and neural networks with Long Short-Term Memory (LSTM) to text classification problems, where the classification criteria are decided by the context of the application. We examine two applications in particular. The first is that of Actionability, where we build models to classify social media messages from customers of service providers as Actionable or Non-Actionable. We build models for over 30 different languages for actionability, and most of the models achieve accuracy around 85%, with some reaching over 90% accuracy. We also show that using LSTM neural networks with word embeddings vastly outperform traditional techniques. Second, we explore classification of messages with respect to political leaning, where social media messages are classified as Democratic or Republican. The model is able to classify messages with a high accuracy of 87.57%. As part of our experiments, we vary different hyperparameters of the neural networks, and report the effect of such variation on the accuracy. These actionability models have been deployed to production and help company agents provide customer support by prioritizing which messages to respond to. The model for political leaning has been opened and made available for wider use.
A 45$^{\circ}$  Linearly Polarized Hollow-Waveguide 16 $\,\times$16-Slot Array Antenna Covering 71–86 GHz Band

A 45° linearly polarized hollow-waveguide 16 × 16- slot array antenna is proposed for point-to-point wireless communication in the 71-76 GHz and 81-86 GHz bands. The antenna is composed of an equally-split corporate-feed circuit and 2 × 2-element sub-arrays which radiate the 45 ° linear polarization. Low sidelobe characteristics are obtained in the E-plane by diagonal placement of the square antenna rotating by 45 degrees. To suppress cross polarization, a radiating narrow-slot pair is adopted. The sub-array is designed by a genetic algorithm and 25.7% reflection bandwidth for is obtained by decreasing the Q of two eigenmodes of the radiating element. A 16 × 16-element array is fabricated by diffusion bonding of thin copper plates. The total thickness is 3.2 mm which is less than 1 free space wavelength. A high gain of 32.9 dBi, high antenna efficiency of 86.6% and low sidelobe characteristics of -27.1-dB first sidelobe levels are measured at the center frequency of 78.5 GHz. Further, the broadband characteristics of reflection , show a gain of more than 31.4 dBi, and low cross polarization of less than -30 dB are achieved over the 71-86 GHz band.
On the wafer/pad friction of chemical-mechanical planarization (CMP) processes- Part II: experiments and applications

This paper presents the experimental validation and some application examples of the proposed wafer/pad friction models for linear chemical-mechanical planarization (CMP) processes in the companion paper. An experimental setup of a linear CMP polisher is first presented and some polishing processes are then designed for validation of the wafer/pad friction modeling and analysis. The friction torques of both the polisher spindle and roller systems are used to monitor variations of the friction coefficient in situ . Verification of the friction model under various process parameters is presented. Effects of pad conditioning and the wafer film topography on wafer/pad friction are experimentally demonstrated. Finally, several application examples are presented showing the use of the roller motor current measurement for real-time process monitoring and control.
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

We present the Python Materials Genomics (pymatgen) library, a robust, open-source Python library for materials analysis. A key enabler in highthroughput computational materials science efforts is a robust set of software tools to perform initial setup for the calculations (e.g., generation of structures and necessary input files) and post-calculation analysis to derive useful material properties from raw calculated data. The pymatgen library aims to meet these needs by (1) defining core Python objects for materials data representation, (2) providing a well-tested set of structure and thermodynamic analyses relevant to many applications, and (3) establishing an open platform for researchers to collaboratively develop sophisticated analyses of materials data obtained both from first principles calculations and experiments. The pymatgen library also provides convenient tools to obtain useful materials data via the Materials Project’s REpresentational State Transfer (REST) Application Programming Interface (API). As an example, using pymatgen’s interface to the Materials Project’s REST API and phasediagram package, we demonstrate how the phase and electrochemical stability of a recently synthesized material, Li4SnS4, can be analyzed using a minimum of computing resources. We find that Li4SnS4 is a stable phase in the LiSn-S phase diagram (consistent with the fact that it can be synthesized), but the narrow range of lithium chemical potentials for which it is predicted to be stable would suggest that it is not intrinsically stable against typical electrodes used in lithium-ion batteries.
Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images

In this letter, we propose a novel supervised change detection method based on a deep siamese convolutional network for optical aerial images. We train a siamese convolutional network using the weighted contrastive loss. The novelty of the method is that the siamese network is learned to extract features directly from the image pairs. Compared with hand-crafted features used by the conventional change detection method, the extracted features are more abstract and robust. Furthermore, because of the advantage of the weighted contrastive loss function, the features have a unique property: the feature vectors of the changed pixel pair are far away from each other, while the ones of the unchanged pixel pair are close. Therefore, we use the distance of the feature vectors to detect changes between the image pair. Simple threshold segmentation on the distance map can even obtain good performance. For improvement, we use a  $k$ -nearest neighbor approach to update the initial result. Experimental results show that the proposed method produces results comparable, even better, with the two state-of-the-art methods in terms of F-measure.
Design of emerging digital services: a taxonomy

26  4 September 2008  There has been a gigantic shift from a product based economy to one based on services, specifically digital services. From every indication it is likely to be more than a passing fad and the changes these emerging digital services represent will continue to transform commerce and have yet to reach market saturation. Digital services are being designed for and offered to users, yet very little is known about the design process that goes behind these developments. Is there a science behind designing digital services? By examining 12 leading digital services, we have developed a design taxonomy to be able to classify and contrast digital services. What emerged in the taxonomy were two broad dimensions; a set of fundamental design objectives and a set of fundamental service provider objectives. This paper concludes with an application of the proposed taxonomy to three leading digital services. We hope that the proposed taxonomy will be useful in understanding the science behind the design of digital services. European Journal of Information Systems (2008) 17, 505–517. doi:10.1057/ejis.2008.38
Speech translation: coupling of recognition and translation

In speech translation, we are faced with the problem of how to couple the speech recognition process and the translation process. Starting from the Bayes decision rule for speech translation, we analyze how the interaction between the recognition process and the translation process can be modelled. In the light of this decision rule, we discuss the already existing approaches to speech translation. None of the existing approaches seems to have addressed this direct interaction. We suggest two new methods, the local averaging approximation and the monotone alignments.
Attitude Determination from Vector Observations: Quaternion Estimation

Two recursive estimation algorithms, which use pairs of measured vectors to yield minimum variance estimates of the quaternion of rotation, are presented. The nonlinear relations between the direction cosine matrix and the quaternion are linearized, and a variant of the extended Kalman filter is used to estimate the difference between the quaternion and its estimate. With each measurement this estimate is updated and added to the whole quaternion estimate. This operation constitutes a full state reset in the estimation process. Filter tuning is needed to obtain a converging filter. The second algorithm presented uses the normality property of the quaternion of rotation to obtain, in a straightforward design, a filter which converges, with a smaller error, to a normal quaternion. This algorithm changes the state but not the covariance computation of the original algorithm and implies only a partial reset. Results of Monte-Carlo simulation runs are presented which demonstrate the superiority of the normalized quaternion.
Internet of Things (IoT) security

Network Security is one of the important concepts in data security as the data to be uploaded should be made secure. To make data secure, there exist number of algorithms like AES (Advanced Encryption Standard), IDEA (International Data Encryption Algorithm) etc. These techniques of making the data secure come under Cryptography. Involving lnternet of Things (IoT) in Cryptography is an emerging domain. IoT can be defined as controlling things located at any part of the world via Internet. So, IoT involves data security i.e. Cryptography. Here, in this paper we discuss how data can be made secure for IoT using Cryptography.
0.35mm pitch wafer level package board level reliability: Studying effect of ball de-population with varying ball size

Board level reliability studies have been performed on wafer level packages (WLP) of various die sizes with 0.35mm ball pitch, SAC405 solder alloy, and two solder ball sizes. The 0.35mm pitch test vehicles included 5mm × 5mm and 6mm × 6mm package sizes. All test vehicles had areas of depopulation in the ball array, but used the same design rules regardless of ball size. The parts were assembled at 3 different suppliers. All of the WLPs studied passed the minimum drop shock requirements of 30 drops. For board level temperature cycle, a minimum of 500 cycles was targeted as a passing result. The 220um ball size on 5mm × 5mm 188 pin WLP package failed at both the corner most solder balls and solder balls near the corner at all suppliers. Only one of three suppliers' corner balls passed using the 230um ball. For interior balls, the 220um ball size on 5mm × 5mm 188 pin WLP package failed temperature cycle at two of three suppliers, while two of three suppliers passed with 230um ball. For the larger 6mm × 6mm test vehicle, the corner most solder balls were also very sensitive to temperature cycle fatigue failures. The 220um ball size failed at the corner at all three suppliers. For interior balls, the 220um ball size failed temperature cycle at one of three suppliers, while two of three suppliers passed 500 cycles only marginally. This study shows that depopulated WLP ball arrays of 5mm × 5mm and 6mm × 6mm are sensitive to temperature cycle fatigue failures at 0.35mm pitch with SAC405 alloy.