nils-herrmann/clean_scirepeval huggingface dataset:
Surgical anatomy of the superior gluteal nerve and landmarks for its localization during minimally invasive approaches to the hip.

The superior gluteal nerve (SGN) is vulnerable to damage during total hip arthroplasty and various pelvic surgeries. Recently introduced minimally invasive approaches to the hip show promise for less muscle trauma compared to conventional approaches. However, the risk of damaging the SGN has not been well documented for such alternative approaches. Therefore, we aimed to investigate the anatomic course of the SGN and to define anatomical landmarks that may be used by surgeons during minimally invasive approaches to the hip. Twenty-eight gluteal regions from 14 formalin-fixed cadavers were dissected and the course and the distances of the SGN and its branches to the tip of the greater trochanter (GT) were measured. The landmarks for standardizing the course of the SGN included the posterior inferior iliac spine (PIIS), GT, and a line (PIIS-GT) connecting these two points. The exit of the SGN was found to be at the medial one third of the PIIS-GT line and 5.4 cm from the GT. Two branching patterns were noted. The branches of the SGN were distributed lateral to the PIIS-GT line. On the basis of our study, the safe zone for the SGN was smaller than previously reported. Posterior, lateral, or anterolateral minimally invasive approaches to the hip should take into account the point of exit of the SGN and the area of distribution of its branches. A minimally invasive anterolateral approach may particularly compromise branches to the tensor fasciae latae muscle. Localization of the SGN and its branches using the anatomic landmarks defined in this study may decrease surgical morbidity.
Text mining facebook status updates for sentiment classification

In recent years, text mining and sentiment analysis have received great attention due to the abundance of opinion data that exist in social networks such as Facebook, Twitter, etc. Sentiments are projected on these media using texts for expressing feelings such as friendship, social support, anger, happiness, etc. Existing sentiment analysis studies tend to identify user behaviors and state of minds but remain insufficient due to complexities in conveyed texts. In this research paper, we focus on the usage of text mining for sentiment classification. Illustration is performed on Tunisian users' statuses on Facebook posts during the “Arabic Spring” era. Our aim is to extract useful information, about users' sentiments and behaviors during this sensitive and significant period. For that purpose, we propose a method based on Support Vector Machine (SVM) and Naïve Bayes. We also construct a sentiment lexicon, based on the emoticons, interjections and acronyms', from extracted statuses updates. Moreover, we perform some comparative experiments between two machine learning algorithms SVM and Naïve Bayes through a training model for sentiment classification.
Detection of Atrial Fibrillation using model-based ECG analysis

Atrial fibrillation (AF) is an arrhythmia that can lead to several patient risks. This kind of arrhythmia affects mostly elderly people, in particular those who suffer from heart failure (one of the main causes of hospitalization). Thus, detection of AF becomes decisive in the prevention of cardiac threats. In this paper an algorithm for AF detection based on a novel algorithm architecture and feature extraction methods is proposed. The aforementioned architecture is based on the analysis of the three main physiological characteristics of AF: i) P wave absence ii) heart rate irregularity and iii) atrial activity (AA). Discriminative features are extracted using model-based statistic and frequency based approaches. Sensitivity and specificity results (respectively, 93.80% and 96.09% using the MIT-BIH AF database) show that the proposed algorithm is able to outperform state-of-the-art methods.
Activity Maximization by Effective Information Diffusion in Social Networks

In a social network, even about the same information the excitement between different users are different. If we want to spread a piece of new information and maximize the expected total amount of excitement, which seed users should we choose? This problem indeed is substantially different from the renowned influence maximization problem and cannot be tackled using the existing approaches. In this paper, motivated by the demand in a few interesting applications, we model the novel problem of activity maximization, and tackle the problem systematically. We first analyze the complexity and the approximability of the problem. We develop an upper bound and a lower bound that are submodular so that the Sandwich framework can be applied. We then devise a polling-based randomized algorithm that guarantees a data dependent approximation factor. Our experiments on four real data sets clearly verify the effectiveness and scalability of our method, as well as the advantage of our method against the other heuristic methods.
Large-Margin Classification in Hyperbolic Space

Representing data in hyperbolic space can effectively capture latent hierarchical relationships. With the goal of enabling accurate classification of points in hyperbolic space while respecting their hyperbolic geometry, we introduce hyperbolic SVM1, a hyperbolic formulation of support vector machine classifiers, and elucidate through new theoretical work its connection to the Euclidean counterpart. We demonstrate the performance improvement of hyperbolic SVM for multi-class prediction tasks on real-world complex networks as well as simulated datasets. Our work allows analytic pipelines that take the inherent hyperbolic geometry of the data into account in an end-to-end fashion without resorting to ill-fitting tools developed for Euclidean space.
Contributions to High-Performance Big Data Computing

Our project is at Interface Big Data and HPC -High-Performance Big Data computing and this paper describes a collaboration between 7 collaborating Universities at Arizona State, Indiana (lead), Kansas, Rutgers, Stony Brook, Virginia Tech, and Utah. It addresses the intersection of High-performance and Big Data computing with several different application areas or communities driving the requirements for software systems and algorithms. We describe the base architecture including the HPC-ABDS, High-Performance Computing enhanced Apache Big Data Stack, and an application use case study identifying key features that determine software and algorithm requirements. We summarize middleware including Harp-DAAL collective communication layer, Twister2 Big Data toolkit and pilot jobs. Then we present the SPIDAL Scalable Parallel Interoperable Data Analytics Library and our work for it in core machine-learning, image processing and the application communities, Network science, Polar Science, Biomolecular Simulations, Pathology and Spatial systems. We describe basic algorithms and their integration in end-to-end use cases.
Rapid assessment of well-being: The Short Depression-Happiness Scale (SDHS).

The purpose of this paper was: first, to develop the short six-item form of the Depression-Happiness Scale; and second, to examine evidence of reliability and validity for the short form. Three studies are presented. In the first study, principal components analysis is reported and used to select six items to compose the short form of the scale. In the second study, re-analyses of data from three previous studies are presented which confirm that the short scale has good psychometric properties of internal consistency reliability, test-retest reliability, and convergent and discriminant validity. In the third study, the short form is found to have a single component structure and convergent validity with measures of depression, happiness and personality.
Phenotypic Characterization of a Genetically Diverse Panel of Mice for Behavioral Despair and Anxiety

Animal models of human behavioral endophenotypes, such as the Tail Suspension Test (TST) and the Open Field assay (OF), have proven to be essential tools in revealing the genetics and mechanisms of psychiatric diseases. As in the human disorders they model, the measurements generated in these behavioral assays are significantly impacted by the genetic background of the animals tested. In order to better understand the strain-dependent phenotypic variability endemic to this type of work, and better inform future studies that rely on the data generated by these models, we phenotyped 33 inbred mouse strains for immobility in the TST, a mouse model of behavioral despair, and for activity in the OF, a model of general anxiety and locomotor activity.



We identified significant strain-dependent differences in TST immobility, and in thigmotaxis and distance traveled in the OF. These results were replicable over multiple testing sessions and exhibited high heritability. We exploited the heritability of these behavioral traits by using in silico haplotype-based association mapping to identify candidate genes for regulating TST behavior. Two significant loci (-logp >7.0, gFWER adjusted p value <0.05) of approximately 300 kb each on MMU9 and MMU10 were identified. The MMU10 locus is syntenic to a major human depressive disorder QTL on human chromosome 12 and contains several genes that are expressed in brain regions associated with behavioral despair.



We report the results of phenotyping a large panel of inbred mouse strains for depression and anxiety-associated behaviors. These results show significant, heritable strain-specific differences in behavior, and should prove to be a valuable resource for the behavioral and genetics communities. Additionally, we used haplotype mapping to identify several loci that may contain genes that regulate behavioral despair.
FromDaDy: Spreading Aircraft Trajectories Across Views to Support Iterative Queries

When displaying thousands of aircraft trajectories on a screen, the visualization is spoiled by a tangle of trails. The visual analysis is therefore difficult, especially if a specific class of trajectories in an erroneous dataset has to be studied. We designed FromDaDy, a trajectory visualization tool that tackles the difficulties of exploring the visualization of multiple trails. This multidimensional data exploration is based on scatterplots, brushing, pick and drop, juxtaposed views and rapid visual design. Users can organize the workspace composed of multiple juxtaposed views. They can define the visual configuration of the views by connecting data dimensions from the dataset to Bertin's visual variables. They can then brush trajectories, and with a pick and drop operation they can spread the brushed information across views. They can then repeat these interactions, until they extract a set of relevant data, thus formulating complex queries. Through two real-world scenarios, we show how FromDaDy supports iterative queries and the extraction of trajectories in a dataset that contains up to 5 million data.
“ Proof-of-Work ” Proves Not to Work version 0 . 1

A frequently proposed method of reducing unsolicited bulk email (“spam”) is for senders to pay for each email they send. Proof-ofwork schemes avoid charging real money by requiring senders to demonstrate that they have expended processing time in solving a cryptographic puzzle. We attempt to determine how difficult that puzzle should be so as to be effective in preventing spam. We analyse this both from an economic perspective, “how can we stop it being cost-effective to send spam”, and from a security perspective, “spammers can access insecure end-user machines and will steal processing cycles to solve puzzles”. Both analyses lead to similar values of puzzle difficulty. Unfortunately, realworld data from a large ISP shows that these difficulty levels would mean that significant numbers of senders of legitimate email would be unable to continue their current levels of activity. We conclude that proof-of-work will not be a solution to the problem of spam.
Contact force based compliance control for a trotting quadruped robot

In these paper, a compliance control strategy based on force control for quadruped trot gait locomotion over unperceived rough terrain. To avoid interference of environmental disturbance and unknown model characters, a sliding mode based controller is designed to control the position/posture of the robot torso. The force distribution and control modules calculate desired contact force by eliminating interaction force between the stance legs. Simulations and experimental results demonstrate the effectiveness of our compliance control strategy as the quadruped robot successfully trotting over rough terrain.
Simultaneous robot-world and hand-eye calibration

1 Simultaneous Robot-World and Hand-Eye Calibration Fadi Dornaika and Radu Horaud, Member, IEEE Abstract|Recently, Zhuang, Roth, & Sudhakar  proposed a method that allows simultaneous computation of the rigid transformations from world frame to robot base frame and from hand frame to camera frame. Their method attempts to solve a homogeneous matrix equation of the form AX = ZB. They use quaternions to derive explicit linear solutions for X and Z. In this short paper, we present two new solutions that attempt to solve the homogeneous matrix equation mentioned above: (i) a closed-form method which uses quaternion algebra and a positive quadratic error function associated with this representation and (ii) a method based on non-linear constrained minimization and which simultaneously solves for rotations and translations. These results may be useful to other problems that can be formulated in the same mathematical form. We perform a sensitivity analysis for both our two methods and the linear method developed by Zhuang et al. . This analysis allows the comparison of the three methods. In the light of this comparison the non-linear optimization method, which solves for rotations and translations simultaneously, seems to be the most stable one with respect to noise and to measurement errors. Keywords| hand/eye calibration, robot/world calibration, quaternion algebra.
Multiband Handset Antenna Combining a PIFA, Slots, and Ground Plane Modes

A multiband handset antenna combining a PIFA and multiple slots on a ground plane is presented. It is shown by means of simulations that the slots on the ground plane have a double function: to tune the ground plane resonance at low frequencies (f ¿ 900 MHz) and to act as parasitic radiators at high frequencies (f ¿ 1800 MHz). A prototype is designed and built featuring a behavior suitable for low frequencies (GSM850 and GSM900) and for high frequencies spanning from DCS1800 to Bluetooth, and including, for instance, PCS1900, UMTS2000, and other possible systems. Reflection coefficient, efficiency, and radiation patterns are measured and compared with a design without slots to prove the advantages of the slotted ground plane. The component effect is investigated to determine critical areas where the placement is not recommended. Besides, the effect of the slot of the ground plane on SAR is investigated, by discussing the effect of the ground plane and slot modes for two phone positions. The total antenna volume of the proposed design is 40 × 15 × 6 mm3.
Combined Phase-Shift and Frequency Modulation of a Dual-Active-Bridge AC–DC Converter With PFC

This paper presents a combined phase-shift and frequency modulation scheme of a dual-active-bridge (DAB) ac- dc converter with power factor correction (PFC) to achieve zero voltage switching (ZVS) over the full range of the ac mains voltage. The DAB consists of a half bridge with bidirectional switches on the ac side and a full bridge on the dc side of the isolation transformer to accomplish single-stage power conversion. The modulation scheme is described by means of analytical formulas, which are used in an optimization procedure to determine the optimal control variables for minimum switch commutation currents. Furthermore, an ac current controller suitable for the proposed modulation scheme is described. A loss model and measurements on a 3.3-kW electric vehicle battery charger to connect to the 230 Vrms / 50-Hz mains considering a battery voltage range of 280-420 V validate the theoretical analysis.
Learning Bound for Parameter Transfer Learning

We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied to another objective task. Then, we introduce the notion of the local stability and parameter transfer learnability of parametric feature mapping, and thereby derive a learning bound for parameter transfer algorithms. As an application of parameter transfer learning, we discuss the performance of sparse coding in selftaught learning. Although self-taught learning algorithms with plentiful unlabeled data often show excellent empirical performance, their theoretical analysis has not been studied. In this paper, we also provide the first theoretical learning bound for self-taught learning.
Probabilistic slope stability analysis by finite elements

The paper investigates the probability of failure of a cohesive slope using both simple and more advanced probabilistic analysis tools. The influence of local averaging on the probability of failure of a test problem is thoroughly investigated. In the simple approach, classical slope stability analysis techniques are used, and the shear strength is treated as a single random variable. The advanced method, called the random finite element method (RFEM), uses elastoplasticity combined with random field theory. The RFEM method is shown to offer many advantages over traditional probabilistic slope stability techniques, because it enables slope failure to develop naturally by “seeking out” the most critical mechanism. Of particular importance in this work, is the conclusion that simplified probabilistic analysis, in which spatial variability is ignored by assuming perfect correlation, can lead to unconservative estimates of the probability of failure. This contradicts the findings of other investigators using classical slope stability analysis tools.
Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world

Through time, we have seen mobile phones transform into multifaceted devices, adapted to meet and exceed our everyday needs. These needs range from something as personal as a health care manager to something as purely analytical as an environment monitor. In effect, mobile phones have come into our lives, making life easier, smarter, and more efficient. In this article we discuss mobile sensing and cloud computing separately and in detail, then combine the two concepts to form the singular idea of mobile cloud sensing. We will also give an intuitive architectural description of mobile cloud sensing, along with discussions about each of its individual building blocks. There are limitations to mobile cloud sensing today, but with the emergence of 5G coupled with the analysis of big data, we can address the current issues at hand. We believe that with the advent of mobile cloud sensing, 5G, and big data analysis, our lives will continue to see an increase in overall quality.
A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating decision-tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to traditional decision-tree algorithms such as C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.
Digitally Controlled Oscillator ( DCO )-Based Architecture for RF Frequency Synthesis in a Deep-Submicrometer CMOS Process

A novel digitally controlled oscillator (DCO)-based architecture for frequency synthesis in wireless RF applications is proposed and demonstrated. It deliberately avoids any use of an analog tuning voltage control line. Fine frequency resolution is achieved through high-speed dithering. Other imperfections of analog circuits are compensated through digital means. The presented ideas enable the employment of fully-digital frequency synthesizers using sophisticated signal processing algorithms, realized in the most advanced deep-submicrometer digital CMOS processes which allow almost no analog extensions. They also promote costeffective integration with the digital back-end onto a single silicon die. The demonstrator test chip has been fabricated in a digital 0.13m CMOS process together with a DSP, which acts as a digital baseband processor with a large number of digital gates in order to investigate noise coupling. The phase noise is112 dBc/Hz at 500-kHz offset. The close-in spurious tones are below 62 dBc, while the far-out spurs are below 80 dBc. The presented ideas have been incorporated in a commercial Bluetooth transceiver.
Detecting Anomalous Network Traffic with Self-organizing Maps

Integrated Network-Based Ohio University Network Detective Service (INBOUNDS) is a network based intrusion detection system being developed at Ohio University. The Anomalous Network-Traffic Detection with Self Organizing Maps (ANDSOM) module for INBOUNDS detects anomalous network traffic based on the Self-Organizing Map algorithm. Each network connection is characterized by six parameters and specified as a six-dimensional vector. The ANDSOM module creates a Self-Organizing Map (SOM) having a two-dimensional lattice of neurons for each network service. During the training phase, normal network traffic is fed to the ANDSOM module, and the neurons in the SOM are trained to capture its characteristic patterns. During real-time operation, a network connection is fed to its respective SOM, and a “winner” is selected by finding the neuron that is closest in distance to it. The network connection is then classified as an intrusion if this distance is more than a pre-set threshold.
C
1-Almost Periodic Solutions of BAM Neural Networks with Time-Varying Delays on Time Scales

On a new type of almost periodic time scales, a class of BAM neural networks is considered. By employing a fixed point theorem and differential inequality techniques, some sufficient conditions ensuring the existence and global exponential stability of C1-almost periodic solutions for this class of networks with time-varying delays are established. Two examples are given to show the effectiveness of the proposed method and results.