nils-herrmann/clean_scirepeval huggingface dataset:
MEMS mirror for low cost laser scanners

Concept and design of a low cost two-axes MEMS scanning mirror with an aperture size of 7 millimetres for a compact automotive LIDAR sensor is presented. Hermetic vacuum encapsulation and stacked vertical comb drives are the key features to enable a large tilt angle of 15 degrees. A tripod MEMS mirror design provides an advantageous ratio of mirror aperture and chip size and allows circular laser scanning.
Open-Set Language Identification

We present the first open-set language identification experiments using one-class classification models. We first highlight the shortcomings of traditional feature extraction methods and propose a hashing-based feature vectorization approach as a solution. Using a dataset of 10 languages from different writing systems, we train a One-Class Support Vector Machine using only a monolingual corpus for each language. Each model is evaluated against a test set of data from all 10 languages and we achieve an average F-score of 0.99, demonstrating the effectiveness of this approach for open-set language identification.
Electrical Contact Resistance Theory for Anisotropic Conductive Films Considering Electron Tunneling and Particle Flattening

This study models the electrical contact resistance (ECR) between two surfaces separated by an anisotropic conductive film. The film is made up of an epoxy with conductive spherical particles(metallic) dispersed within. In practical situations the particles are often heavily loaded and will undergo severe plastic deformation and may essentially be flattened out. In between the particles and the surfaces there may also be an ultra-thin insulating film (consisting of epoxy) which causes considerable electrical resistance between the surfaces. In the past this effect has been neglected and the predicted ECR was much lower than that measured experimentally. This added resistance is considered using electron tunneling theory. The severe plastic deformation of the spherical particles is modeled using a new expanded elasto-plastic spherical contact model. This work also investigates the effect of compression of the separating epoxy film on the electrical contact resistance. The model finds that the high experimental ECR measurements can be accounted for by including the existence of a thin insulating film through the electron tunneling model
Contextual Time Series Change Detection

Time series data are common in a variety of fields ranging from economics to medicine and manufacturing. As a result, time series analysis and modeling has become an active research area in statistics and data mining. In this paper, we focus on a type of change we call contextual time series change (CTC) and propose a novel two-stage algorithm to address it. In contrast to traditional change detection methods, which consider each time series separately, CTC is defined as a change relative to the behavior of a group of related time series. As a result, our proposed method is able to identify novel types of changes not found by other algorithms. We demonstrate the unique capabilities of our approach with several case studies on real-world datasets from the financial and Earth science domains.
Red tide detection and tracing using MODIS fluorescence data : A regional example in SW Florida coastal waters

Near real-time data from the MODIS satellite sensor was used to detect and trace a harmful algal bloom (HAB), or red tide, in SW Florida coastal waters from October to December 2004. MODIS fluorescence line height (FLH in W m 2 Am 1 sr ) data showed the highest correlation with near-concurrent in situ chlorophyll-a concentration (Chl in mg m ). For Chl ranging between 0.4 to 4 mg m 3 the ratio between MODIS FLH and in situ Chl is about 0.1 W m 2 Am 1 sr 1 per mg m 3 chlorophyll (Chl=1.255 (FLH 10), r =0.92, n =77). In contrast, the band-ratio chlorophyll product of either MODIS or SeaWiFS in this complex coastal environment provided false information. Errors in the satellite Chl data can be both negative and positive (3–15 times higher than in situ Chl) and these data are often inconsistent either spatially or temporally, due to interferences of other water constituents. The red tide that formed from November to December 2004 off SW Florida was revealed by MODIS FLH imagery, and was confirmed by field sampling to contain medium (10 to 10 cells L ) to high (>10 cells L ) concentrations of the toxic dinoflagellate Karenia brevis. The FLH imagery also showed that the bloom started in midOctober south of Charlotte Harbor, and that it developed and moved to the south and southwest in the subsequent weeks. Despite some artifacts in the data and uncertainty caused by factors such as unknown fluorescence efficiency, our results show that the MODIS FLH data provide an unprecedented tool for research and managers to study and monitor algal blooms in coastal environments. D 2005 Elsevier Inc. All rights reserved.
Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction

Bankruptcy prediction has been a subject of interests for almost a century and it still ranks high among hottest topics in economics. The aim of predicting financial distress is to develop a predictive model that combines various econometric measures and allows to foresee a financial condition of a firm. In this domain various methods were proposed that were based on statistical hypothesis testing, statistical modelling (e.g., generalized linear models), and recently artificial intelligence (e.g., neural networks, Support Vector Machines, decision tress). In this paper, we propose a novel approach for bankruptcy prediction that utilizes Extreme Gradient Boosting for learning an ensemble of decision trees. Additionally, in order to reflect higher-order statistics in data and impose a prior knowledge about data representation, we introduce a new concept that we refer as to synthetic features. A synthetic feature is a combination of the econometric measures using arithmetic operations (addition, subtraction, multiplication, division). Each synthetic feature can be seen as a single regression model that is developed in an evolutionary manner. We evaluate our solution using the collected data about Polish companies in five tasks corresponding to the bankruptcy prediction in the 1st, 2nd, 3rd, 4th, and 5th year. We compare our approach with the reference methods. ∗Corresponding author, Tel.: (+48) 71 320 44 53. Email addresses: maciej.zieba@pwr.edu.pl (Maciej Zięba ), sebastian.tomczak@pwr.edu.pl (Sebastian K. Tomczak), jakub.tomczak@pwr.edu.pl (Jakub M. Tomczak)  to Expert Systems with Applications April 4, 2016
Determinants of Mobile Apps' Success: Evidence from the App Store Market

GUNWOONG LEE is a Ph.D. candidate of Information Systems at the W. P. Carey School of Business, Arizona State University. His research interests include digital content management in mobile platforms, information and communication technology for development, and technologydriven healthcare innovations. His research has appeared in major conferences and journals including Decision Support Systems, International Conference on Information Systems, and America Conference on Information Systems. He has consulting experience with Korea Association of Game Industry, Korea Stock Exchange, and other companies and government agencies.
DESIGN OF A HUMAN-LIKE RANGE OF MOTION HIP JOINT FOR HUMANOID ROBOTS

For a humanoid robot to have the versatility of humans, it needs to have similar motion capabilities. This paper presents the design of the hip joint of the Tactical Hazardous Operations Robot (THOR), which was created to perform disaster response duties in human-structured environments. The lower body of THOR was designed to have a similar range of motion to the average human. To accommodate the large range of motion requirements of the hip, it was divided into a parallel-actuated universal joint and a linkage-driven pin joint. The yaw and roll degrees of freedom are driven cooperatively by a pair of parallel series elastic linear actuators to provide high joint torques and low leg inertia. In yaw, the left hip can produce a peak of 115.02 [Nm] of torque with a range of motion of -20° to 45°. In roll, it can produce a peak of 174.72 [Nm] of torque with a range of motion of -30° to 45°. The pitch degree of freedom uses a Hoeken’s linkage mechanism to produce 100 [Nm] of torque with a range of motion of -120° to 30°.
A novel axial flux switched reluctance motor with multi-level air gap geometry

The switched reluctance motor (SRM) is a promising drive solution for electric vehicle propulsion thanks to its simple, rugged structure, satisfying performance and low price. Among other SRMs, the axial flux SRM (AFSRM) is a strong candidate for in-wheel drive applications because of its high torque/power density and compact disc shape. In this paper, a four-phase 8-stator-pole 6-rotor-pole double-rotor AFSRM is investigated for an e-bike application. A series of analyses are conducted to reduce the torque ripple by shaping the rotor poles, and a multi-level air gap geometry is designed with specific air gap dimensions at different positions. Both static and dynamic analyses show significant torque ripple reduction while maintaining the average electromagnetic output torque at the demanded level.
Sketch drawing by NAO humanoid robot

This paper demonstrates the sketch drawing capability of NAO humanoid robot. Two redundant degrees of freedom elbow yaw (RElbowYaw) and wrist yaw (RWristYaw) of the right hand have been sacrificed because of their less contribution in drawing. The Denavit-Hartenberg (DH) parameters of the system has been defined in order to measure the working envelop of the right hand as well as to achieve the inverse kinematic solution. A linear transformation has been used to transform the image points with respect to real world coordinate system and novel 4 point calibration technique has been proposed to calibrate the real world coordinate system with respect to NAO end effector.
Business Analytics: Radical Shift or Incremental Change?

Business analytics systems are seen by many to be a growing source of value and competitive advantage for businesses. However, it is not clear if increasingly advanced analytical capabilities create opportunities for radical change in business or just represent an incremental improvement to existing systems. What are the key questions that researchers should be focusing on to improve our understanding of analytics? And are IS programs teaching students the right things to be successful in this environment? This panel aims to take stock of technological possibilities, practical experience and leading research to assess the current state and future direction of business analytics. In doing so, it will bring together senior researchers and industry representatives to share the leading challenges, opportunities and good practice that they see.
Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies

Excess bodyweight, expressed as increased body-mass index (BMI), is associated with the risk of some common adult cancers. We did a systematic review and meta-analysis to assess the strength of associations between BMI and different sites of cancer and to investigate differences in these associations between sex and ethnic groups.



We did electronic searches on Medline and Embase (1966 to November 2007), and searched reports to identify prospective studies of incident cases of 20 cancer types. We did random-effects meta-analyses and meta-regressions of study-specific incremental estimates to determine the risk of cancer associated with a 5 kg/m2 increase in BMI.



We analysed 221 datasets (141 articles), including 282,137 incident cases. In men, a 5 kg/m2 increase in BMI was strongly associated with oesophageal adenocarcinoma (RR 1.52, p<0.0001) and with thyroid (1.33, p=0.02), colon (1.24, p<0.0001), and renal (1.24, p <0.0001) cancers. In women, we recorded strong associations between a 5 kg/m2 increase in BMI and endometrial (1.59, p<0.0001), gallbladder (1.59, p=0.04), oesophageal adenocarcinoma (1.51, p<0.0001), and renal (1.34, p<0.0001) cancers. We noted weaker positive associations (RR <1.20) between increased BMI and rectal cancer and malignant melanoma in men; postmenopausal breast, pancreatic, thyroid, and colon cancers in women; and leukaemia, multiple myeloma, and non-Hodgkin lymphoma in both sexes. Associations were stronger in men than in women for colon (p<0.0001) cancer. Associations were generally similar in studies from North America, Europe and Australia, and the Asia-Pacific region, but we recorded stronger associations in Asia-Pacific populations between increased BMI and premenopausal (p=0.009) and postmenopausal (p=0.06) breast cancers.



Increased BMI is associated with increased risk of common and less common malignancies. For some cancer types, associations differ between sexes and populations of different ethnic origins. These epidemiological observations should inform the exploration of biological mechanisms that link obesity with cancer.
Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

There is a trend in the scientific community to model and solve complex optimization problems by employing natural metaphors. This is mainly due to inefficiency of classical optimization algorithms in solving larger scale combinatorial and/or highly non-linear problems. The situation is not much different if integer and/or discrete decision variables are required in most of the linear optimization models as well. One of the main characteristics of the classical optimization algorithms is their inflexibility to adapt the solution algorithm to a given problem. Generally a given problem is modelled in such a way that a classical algorithm like simplex algorithm can handle it. This generally requires making several assumptions which might not be easy to validate in many situations. In order to overcome these limitations more flexible and adaptable general purpose algorithms are needed. It should be easy to tailor these algorithms to model a given problem as close as to reality. Based on this motivation many nature inspired algorithms were developed in the literature like genetic algorithms, simulated annealing and tabu search. It has also been shown that these algorithms can provide far better solutions in comparison to classical algorithms. A branch of nature inspired algorithms which are known as swarm intelligence is focused on insect behaviour in order to develop some meta-heuristics which can mimic insect's problem solution abilities. Ant colony optimization, particle swarm optimization, wasp nets etc. are some of the well known algorithms that mimic insect behaviour in problem modelling and solution. Artificial Bee Colony (ABC) is a relatively new member of swarm intelligence. ABC tries to model natural behaviour of real honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new intelligent search algorithms. In this chapter an extensive review of work on artificial bee algorithms is given. Afterwards, development of an ABC algorithm for solving generalized assignment problem which is known as NP-hard problem is presented in detail along with some comparisons. It is a well known fact that classical optimization techniques impose several limitations on solving mathematical programming and operational research models. This is mainly due to inherent solution mechanisms of these techniques. Solution strategies of classical optimization algorithms are generally depended on the type of objective and constraint
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning

Although neural networks are well suited for sequential transfer learning tasks, the catastrophic forgetting problem hinders proper integration of prior knowledge. In this work, we propose a solution to this problem by using a multi-task objective based on the idea of distillation and a mechanism that directly penalizes forgetting at the shared representation layer during the knowledge integration phase of training. We demonstrate our approach on a Twitter domain sentiment analysis task with sequential knowledge transfer from four related tasks. We show that our technique outperforms networks fine-tuned to the target task. Additionally, we show both through empirical evidence and examples that it does not forget useful knowledge from the source task that is forgotten during standard fine-tuning. Surprisingly, we find that first distilling a human made rule based sentiment engine into a recurrent neural network and then integrating the knowledge with the target task data leads to a substantial gain in generalization performance. Our experiments demonstrate the power of multi-source transfer techniques in practical text analytics problems when paired with distillation. In particular, for the SemEval 2016 Task 4 Subtask A (Nakov et al., 2016) dataset we surpass the state of the art established during the competition with a comparatively simple model architecture that is not even competitive when trained on only the labeled task specific data.
The Power of Good: A Leader's Personal Power as a Mediator of the Ethical Leadership-Follower Outcomes Link

The study's goal was to examine the socially responsible power use in the context of ethical leadership as an explanatory mechanism of the ethical leadership-follower outcomes link. Drawing on the attachment theory (Bowlby, 1969/1982), we explored a power-based process model, which assumes that a leader's personal power is an intervening variable in the relationship between ethical leadership and follower outcomes, while incorporating the moderating role of followers' moral identity in this transformation process. The results of a two-wave field study (N = 235) that surveyed employees and a scenario experiment (N = 169) fully supported the proposed (moderated) mediation models, as personal power mediated the positive relationship between ethical leadership and a broad range of tested follower outcomes (i.e., leader effectiveness, follower extra effort, organizational commitment, job satisfaction, and work engagement), as well as the interactive effects of ethical leadership and follower moral identity on these follower outcomes. Theoretical and practical implications are discussed.
A LTE antenna design with metal-bezel structure for mobile phone application

This paper shows a LTE antenna design with metal-bezel structure for mobile phone application. For this antenna design, it overcomes the limitation of metal industrial design to achieve LTE all band operation by using two of metal-bezel as antenna radiators (radiator1 and 2). The radiator1 uses a tunable element at feed point and connects an inductor at antenna ground terminal to generate three resonate modes at 700∼960 MHz and 1710–2170 MHz. The radiator2 includes a part of metal-bezel and an extended FPC, which depends on a capacitive coupling effect between metal-bezel to provide the high band resonate mode at 2300–2700 MHz. Using this mechanism, the mobile phone can easily realize wideband LTE antenna design and has an attractive industrial design with metal-bezel structure.
Mobile controlled multipurpose rescue robot using DTMF technology

Rescue operations play an important role in disaster management and in most of the cases rescue operation are challenged by the conditions where human intervention is highly unlikely allowed, in such cases a device which can replace human limitations with advanced technology in robotics and humanoids which can track or follow a route to find the targets. In this paper we use Cellular mobile communication technology as communication channel between the transmitter and the receiving robot device. A phone is established between the transmitter mobile phone and the one on robot with a DTMF decoder which receives the motion control commands from the keypad via mobile phone. The implemented system is built around on the ARM7 LPC2148. It processes the information came from sensors and DTMF module and send to the motor driver bridge to control the motors to change direction and position of the robot. This system is designed to use best in the conditions of accidents or incidents happened in coal mining, fire accidents, bore well incidents and so on.
Automatic lung field segmentation using novel feature extraction and unsupervised learning

Obtaining accurate and automated lung field segmentation is a challenging step in the development of Computer-Aided Diagnosis (CAD) system. In this paper, fully automatic lung field segmentation is proposed. Initially, novel features are extracted by considering spatial interaction of the neighbouring pixels. Then constrained non-negative matrix factorization (CNMF) factorized the data matrix obtained from the visual appearance model into basis and coefficient matrices. Initial lung segmentation is achieved by applying unsupervised learning on the coefficient matrix. 2-D region growing operation removes trachea and bronchi appearing in the initial lung segmentation. The experimental results on different database shows that the proposed method produces significant DSC 0.973 as compared to the existing lung segmentation algorithms.
Joint Global and Co-Attentive Representation Learning for Image-Sentence Retrieval

In image-sentence retrieval task, correlated images and sentences involve different levels of semantic relevance. However, existing multi-modal representation learning paradigms fail to capture the meaningful component relation on word and phrase level, while the attention-based methods still suffer from component-level mismatching and huge computation burden. We propose a Joint Global and Co-Attentive Representation learning method (JGCAR) for image-sentence retrieval. We formulate a global representation learning task which utilizes both intra-modal and inter-modal relative similarity to optimize the semantic consistency of the visual/textual component representations. We further develop a co-attention learning procedure to fully exploit different levels of visual-linguistic relations. We design a novel softmax-like bi-directional ranking loss to learn the co-attentive representation for image-sentence similarity computation. It is capable of discovering the correlative components and rectifying inappropriate component-level correlation to produce more accurate sentence-level ranking results. By joint global and co-attentive representation learning, the latter benefits from the former by producing more semantically consistent component representation, and the former also benefits from the latter by back-propagating the contextual information. Image-sentence retrieval is performed as a two-step process in the testing stage, inheriting advantages on both effectiveness and efficiency. Experiments show that JGCAR outperforms existing methods on MSCOCO and Flickr30K image-sentence retrieval tasks.
A Dissipativity Approach to Safety Verification for Interconnected Systems

We propose a computational method for verifying a state-space safety constraint of a network of interconnected dynamical systems satisfying a dissipativity property. We construct an invariant set as the sublevel set of a Lyapunov function comprised of local storage functions for each subsystem. This approach requires only knowledge of a local dissipativity property for each subsystem and the static interconnection matrix for the network, and we pose the safety verification as a sum-of-squares feasibility problem. In addition to reducing the computational burden of system design, we allow the safety constraint and initial conditions to depend on an unknown equilibrium, thus offering increased flexibility over existing techniques.