nils-herrmann/clean_scirepeval huggingface dataset:
A Framework for End-to-End Evaluation of 5G mmWave Cellular Networks in ns-3

The growing demand for ubiquitous mobile data services along with the scarcity of spectrum in the sub-6 GHz bands has given rise to the recent interest in developing wireless systems that can exploit the large amount of spectrum available in the millimeter wave (mmWave) frequency range. Due to its potential for multi-gigabit and ultra-low latency links, mmWave technology is expected to play a central role in 5th Generation (5G) cellular networks. Overcoming the poor radio propagation and sensitivity to blockages at higher frequencies presents major challenges, which is why much of the current research is focused at the physical layer. However, innovations will be required at all layers of the protocol stack to effectively utilize the large air link capacity and provide the end-to-end performance required by future networks.
 Discrete-event network simulation will be an invaluable tool for researchers to evaluate novel 5G protocols and systems from an end-to-end perspective. In this work, we present the first-of-its-kind, open-source framework for modeling mmWave cellular networks in the ns-3 simulator. Channel models are provided along with a configurable physical and MAC-layer implementation, which can be interfaced with the higher-layer protocols and core network model from the ns-3 LTE module to simulate end-to-end connectivity. The framework is demonstrated through several example simulations showing the performance of our custo mmmWave stack.
Zero-Day Attack Identification in Streaming Data Using Semantics and Spark

Intrusion Detection Systems (IDS) have been in existence for many years now, but they fall short in efficiently detecting zero-day attacks. This paper presents an organic combination of Semantic Link Networks (SLN) and dynamic semantic graph generation for the on the fly discovery of zero-day attacks using the Spark Streaming platform for parallel detection. In addition, a minimum redundancy maximum relevance (MRMR) feature selection algorithm is deployed to determine the most discriminating features of the dataset. Compared to previous studies on zero-day attack identification, the described method yields better results due to the semantic learning and reasoning on top of the training data and due to the use of collaborative classification methods. We also verified the scalability of our method in a distributed environment.
On effect size.

The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qualities of effect sizes. Our definition of effect size is general and subsumes many existing definitions of effect size. We define effect size as a quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest. Our definition of effect size is purposely more inclusive than the way many have defined and conceptualized effect size, and it is unique with regard to linking effect size to a question of interest. Additionally, we review some important developments in the effect size literature and discuss the importance of accompanying an effect size with an interval estimate that acknowledges the uncertainty with which the population value of the effect size has been estimated. We hope that this article will facilitate discussion and improve the practice of reporting and interpreting effect sizes.
RF MEMS Actuated Reconfigurable Reflectarray Patch-Slot Element

This paper describes the design of a reconfigurable reflectarray element using commercially available radio frequency micro-electromechanical system (RF MEMS) switches. The element consists of a microstrip patch on the top surface and a slot with an actuated variable length in the ground plane. RF MEMS switches are mounted on the slot to electronically vary the slot length by actuating the switches and thus obtaining the desired phase response. Waveguide measurements and high frequency structure simulator (HFSS) simulations are used to characterize the reflectarray element. The four MEMS switches element gives 10 independent states with a phase swing of 150 deg and a loss variation from 0.4 dB to 1.5 dB at 2 GHz (more switches can provide larger phase shift). The loss is mainly attributed to the dielectric loss and the conductor loss, which occur due to the relatively strong electric fields in the substrate region below the patch and the large currents on the top surface of the patch, respectively, close to the patch resonance. Detailed analysis is performed to characterize the effect of the switches by taking into consideration the switch model and wire bonding effects.
Measuring information systems success: models, dimensions, measures, and interrelationships

30 December 2006  12 July 2007 2nd Revision: 16 December 2007 3rd Revision: 29 April 2008  15 May 2008  Since DeLone and McLean (D&M) developed their model of IS success, there has been much research on the topic of success as well as extensions and tests of their model. Using the technique of a qualitative literature review, this research reviews 180 papers found in the academic literature for the period 1992–2007 dealing with some aspect of IS success. Using the six dimensions of the D&M model – system quality, information quality, service quality, use, user satisfaction, and net benefits – 90 empirical studies were examined and the results summarized. Measures for the six success constructs are described and 15 pairwise associations between the success constructs are analyzed. This work builds on the prior research related to IS success by summarizing the measures applied to the evaluation of IS success and by examining the relationships that comprise the D&M IS success model in both individual and organizational contexts.  (2008) 17, 236–263. doi:10.1057/ejis.2008.15
Discriminant validity of well-being measures.

The convergent and discriminant validities of well-being concepts were examined using multitrait-multimethod matrix analyses (D. T. Campbell & D. W. Fiske, 1959) on 3 sets of data. In Study 1, participants completed measures of life satisfaction, positive affect, negative affect, self-esteem, and optimism on 2 occasions 4 weeks apart and also obtained 3 informant ratings. In Study 2, participants completed each of the 5 measures on 2 occasions 2 years apart and collected informant reports at Time 2. In Study 3, participants completed 2 different scales for each of the 5 constructs. Analyses showed that (a) life satisfaction is discriminable from positive and negative affect, (b) positive affect is discriminable from negative affect, (c) life satisfaction is discriminable from optimism and self-esteem, and (d) optimism is separable from trait measures of negative affect.
ARTIST: The Android Runtime Instrumentation Toolkit

Smartphones are becoming more and more ubiquitous in the modern world, entrusted with such sensitive information as the user's location and banking data. Since Android is the most widespread smartphone platform, reliable and versatile means for Android application analysis are of great importance. Most of the existing code instrumentation approaches for Android suffer from two important shortcomings: the need for root access and limited support for the new Android Runtime(ART). WeaimtofillthisgapbyproposingARTIST, the Android Runtime Instrumentation Toolkit1. ARTIST is a framework that allows analysts to easily monitor the execution of Java and native code using native instrumentation techniques. ARTIST, to the best of our knowledge, is the first tool allowing monitoring of both native and Java code with the same instrumentation technique. ARTIST provides two methods to locate instrumentation targets. First, it can parse OAT executable files in memory to find classes and methods of interest. This allows monitoring a specific set of Java methods. Second, ARTIST can locate internal structures of the Android Runtime in memory. Monitoring function pointers found in these allows the user to track specific interactions of Java code with the Android Runtime. We evaluate the applicability of native instrumentation for Java code using a set of the most popular Android apps. The results show that over 80% of the tested Java methods are targetable using this approach. The performance impact, estimated with the CaffeineMark benchmark suite, does not exceed 20% and therefore can be considered generally acceptable.
Open source social media intelligence for enabling government applications: extended abstract

Open-source social media intelligence (OSSMInt) is a field comprising of techniques and applications to analyze and mine open-source social media data for extracting actionable information and useful insights. The focus of the work presented in this paper is on novel applications and techniques of OSSMInt in the government sector. I propose and develop several novel use-cases and applications around OSSMInt for government and broadly divide them into three categories: identification, prediction, and response applications. In particular, I present solutions, tools and techniques for analyzing data from micro-blogging website to analyze citizen complaints and grievances in the public sector [response]. The research presented in this paper also describes my work on analyzing data from Twitter micro-blogging website to early forecast a civil unrest and protest [prediction]. Furthermore, I build various applications around identification and detection that are useful for the government and security analysts. I demonstrate the application of OSSMInt for identifying religious conflicts within society by mining public opinions on Tumblr website and fill the gaps of offline surveys. The study presented in this paper propose solutions for enabling law enforcement agencies to detect, prevent and combat online radicalization and extremism (content, users, and communities) by mining data from Tumblr, Twitter and YouTube websites [identification]. Furthermore, I also propose and build an application for detecting secret message exchanged in an adversarial communication and capture the obfuscated terms in messages. The central component of my proposed solution approach is the application of information retrieval and machine learning based techniques and algorithms. The study consists of experimenting with a diverse range of machine learning algorithms such as unsupervised, semi-supervised and supervised learning (k-NN, SVM, naive Bayes, random forest and decision tree) based algorithms. Based on the experimental results, I observe that there are several untapped opportunities as well as technical challenges in exploiting open-source social media data by the government for intelligence gathering. Furthermore, social media is a rich source of information for building security informatics based applications by mining the content, users, links and relationships on social media platforms.
Bidirectional Decoder Networks for Attention-Based End-to-End Offline Handwriting Recognition

Recurrent neural networks that can be trained end-to-end on sequence learning tasks provide promising benefits over traditional recognition systems. In this paper, we demonstrate the application of an attention-based long short-term memory decoder network for offline handwriting recognition and analyze the segmentation, classification and decoding errors produced by the model. We further extend the decoding network by a bidirectional topology together with an integrated length estimation procedure and show that it is superior to unidirectional decoder networks. Results are presented for the word and text line recognition tasks of the RIMES handwriting recognition database. The software used in the experiments is freely available for academic research purposes.
Efficacy of Occupational Therapy Task-oriented Approach in Upper Extremity Post-stroke Rehabilitation.

There is a need for more effective rehabilitation methods for individuals post-stroke. Occupational Therapy Task-Oriented (TO) approach has not been evaluated in a randomized clinical trial. The purpose of this study was to evaluate functional and impairment efficacies of TO approach on the more-affected Upper Extremity (UE) of persons post-stroke. A randomized single-blinded cross-over trial recruited 20 participants post-stroke (mean chronicity = 62 months) who demonstrated at least 10° active more-affected shoulder flexion and abduction and elbow flexion-extension. Participants were randomized into immediate (n = 10) and delayed intervention (n = 10) groups. Immediate group had 6 weeks of 3 hr/week TO intervention followed by 6 weeks of no-intervention control. Delayed intervention group underwent the reversed order.  Test (WMFT). Impairment measures included UE Active Range of Motion (AROM) and handheld dynamometry strength. Measurements were obtained at baseline, cross over, and end of the study. TO intervention showed statistically higher functional change scores. COPM performance and satisfaction scores were 2.83 and 3.46 units greater respectively (p < .001), MAL amount of use and quality of use scores were 1.1 and 0.87 units greater, respectively (p < .001), WMFT time was 8.35 seconds faster (p = .009). TO impairment outcomes were not significantly larger than control ones. TO approach appears to be an effective UE post-stroke rehabilitation approach inducing clinically meaningful functional improvements. More studies are needed with larger samples and specific stroke chronicity and severity.
Leaky-Wave Theory, Techniques, and Applications: From Microwaves to Visible Frequencies

Leaky waves have been among the most active areas of research in microwave engineering over the second half of the 20th century. They have been shown to dominate the near-field of several open wave-guiding structures, of great interest to tailor their radiation, guidance and filtering properties. The elegant theoretical analyses and deep physical insights in this area, developed in an era in which computational resources were limited, represent a fundamental scientific legacy that is still extremely relevant in today's engineering society and beyond. In this regard, the relevance of leaky-wave concepts has been increasingly recognized in recent times over a broader scientific community, including optics and physics societies. In this paper, after revisiting the fundamental concepts of leaky-wave theory, we discuss and connect different relevant research activities in which leaky-wave concepts have been applied, with the goal of facilitating multidisciplinary interactions on these topics. In addition to the canonical microwave applications of leaky waves, particular attention is devoted to a few areas of interest in modern optics, such as directive optical antennas, extraordinary optical transmission, and embedded scattering eigenvalues, in which leaky waves play a fundamental role.
Camera Augmented Mobile C-Arm (CAMC): Calibration, Accuracy Study, and Clinical Applications

Mobile C-arm is an essential tool in everyday trauma and orthopedics surgery. Minimally invasive solutions, based on X-ray imaging and coregistered external navigation created a lot of interest within the surgical community and started to replace the traditional open surgery for many procedures. These solutions usually increase the accuracy and reduce the trauma. In general, they introduce new hardware into the OR and add the line of sight constraints imposed by optical tracking systems. They thus impose radical changes to the surgical setup and overall procedure. We augment a commonly used mobile C-arm with a standard video camera and a double mirror system allowing real-time fusion of optical and X-ray images. The video camera is mounted such that its optical center virtually coincides with the C-arm's X-ray source. After a one-time calibration routine, the acquired X-ray and optical images are coregistered. This paper describes the design of such a system, quantifies its technical accuracy, and provides a qualitative proof of its efficiency through cadaver studies conducted by trauma surgeons. In particular, it studies the relevance of this system for surgical navigation within pedicle screw placement, vertebroplasty, and intramedullary nail locking procedures. The image overlay provides an intuitive interface for surgical guidance with an accuracy of <;1 mm, ideally with the use of only one single X-ray image. The new system is smoothly integrated into the clinical application with no additional hardware especially for down-the-beam instrument guidance based on the anteroposterior oblique view, where the instrument axis is aligned with the X-ray source. Throughout all experiments, the camera augmented mobile C-arm system proved to be an intuitive and robust guidance solution for selected clinical routines.
A data mining approach for diagnosis of coronary artery disease

Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08% accuracy is achieved, which is higher than the known approaches in the literature.
Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms

The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.
Groupware walkthrough: adding context to groupware usability evaluation

Discount usability evaluation methods have recently been introduced as a way to assess groupware systems. However, one criticism of these techniques is that they do not make use of information about users and their work contexts. To address this problem, we developed groupware walkthrough, a new usability inspection technique for groupware. The technique is a substantive modification of cognitive walkthrough to include consideration for the complexities of teamwork. The two components of groupware walkthrough are a task model for identifying and analysing real-world collaborative tasks, and a walkthrough process for assessing a system's support for those tasks. Groupware walkthrough is a low-cost technique that can identify collaboration-specific usability problems and can find problems that would not be revealed through other inspection methods
ETS gene fusions in prostate cancer: from discovery to daily clinical practice.

In 2005, fusions between the androgen-regulated transmembrane protease serine 2 gene, TMPRSS2, and E twenty-six (ETS) transcription factors were discovered in prostate cancer.



To review advances in our understanding of ETS gene fusions, focusing on challenges affecting translation to clinical application.



The PubMed database was searched for reports on ETS fusions in prostate cancer.



Since the discovery of ETS fusions, novel 5' and 3' fusion partners and multiple splice isoforms have been reported. The most common fusion, TMPRSS2:ERG, is present in approximately 50% of prostate-specific antigen (PSA)-screened localized prostate cancers and in 15-35% of population-based cohorts. ETS fusions can be detected noninvasively in the urine of men with prostate cancer, with a specificity rate in PSA-screened cohorts of >90%. Reports from untreated population-based cohorts suggest an association between ETS fusions and cancer-specific death and metastatic spread. In retrospective prostatectomy cohorts, conflicting results have been published regarding associations between ETS fusions and cancer aggressiveness. In addition to serving as a potential biomarker, tissue and functional studies suggest a specific role for ETS fusions in the transition to carcinoma. Finally, recent results suggest that the 5' and 3' ends of ETS fusions as well as downstream targets may be targeted therapeutically.



Recent studies suggest that the first clinical applications of ETS fusions are likely to be in noninvasive detection of prostate cancer and in aiding with difficult diagnostic cases. Additional studies are needed to clarify the association between gene fusions and cancer aggressiveness, particularly those studies that take into account the multifocal and heterogeneous nature of localized prostate cancer. Multiple promising strategies have been identified to potentially target ETS fusions. Together, these results suggest that ETS fusions will affect multiple aspects of prostate cancer diagnosis and management.
Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving

The virtual validation of automated driving functions requires meaningful simulation models of environment perception sensors such as radar, lidar, and cameras. There does not yet exist an unrivaled standard for perception sensor models, and radar especially lacks modeling approaches that consistently produce realistic results. In this paper, we present measurements that exemplify challenges in the development of meaningful radar sensor models. We highlight three major challenges: multi-path propagation, separability, and sensitivity of radar cross section to the aspect angle. We also review previous work addressing these challenges and suggest further research directions towards meaningful automotive radar simulation models.