nils-herrmann/clean_scirepeval huggingface dataset:
The Software Concordance: A User Interface for Advanced Software Documents

The Software Concordance is a hypermedia software development environment exploring how document technology and versioned hypermedia can improve software document management. The Software Concordance’s central tool is a document editor that integrates program analysis and hypermedia services for both source code and multimedia documentation in XML. The editor allows developers to embed inline multimedia documentation, including images and audio clips, into their program sources and to bind them to any program fragment. Web style hyperlinks are also supported. The developers are able to move seamlessly between source code and the documentation that describes its motivation, design, correctness and use without disrupting the integrated program analysis services, which include lexing, parsing and type checking. This paper motivates the need for environments like the Software Concordance, describes the key features and architecture of its document editor and discusses future work to improve its functionality.
The 3M syndrome.

3M syndrome (MIM 273750) is an autosomal recessive disorder characterized by pre- and post-natal growth retardation (<-4 SD), facial dysmorphism, large head circumference, normal intelligence and endocrine function. Skeletal changes include long slender tubular bones and tall vertebral bodies. There is no specific treatment. Up till now, mutations in either CUL7 or OBSL1 genes have been identified in this rare disorder. There are no clinical or radiological differences between patients with CUL7 or OBSL1 mutations. CUL7 appears to be the major gene responsible for 3M syndrome accounting for 77.5% of cases while OBSL1 mutations accounts for 16.3%. A few patients have no mutations in these genes suggesting the involvement of a third gene.
Modeling Network-Controlled Device-to-Device Communications in SimuLTE

In Long Term Evolution-Advanced (LTE-A), network-controlled device-to-device (D2D) communications allow User Equipments (UEs) to communicate directly, without involving the Evolved Node-B in data relaying, while the latter still retains control of resource allocation. The above paradigm allows reduced latencies for the UEs and increased resource efficiency for the network operator, and is therefore foreseen to support several services, from Machine-to-machine to vehicular communications. D2D communications introduce research challenges that might affect the performance of applications and upper-layer protocols, hence simulations represent a valuable tool for evaluating these aspects. However, simulating D2D features might pose additional computational burden to the simulation environment. To this aim, a careful modeling is required to reduce computational overhead. In this paper, we describe our modeling of network-controlled D2D communications in SimuLTE, a system-level LTE-A simulation library based on OMNeT++. We describe the core modeling choices of SimuLTE, and show how these allow an easy extension to D2D communications. Moreover, we describe in detail the modeling of specific problems arising with D2D communications, such as scheduling with frequency reuse, connection mode switching and broadcast transmission. We document the computational efficiency of our modeling choices, showing that simulation of D2D communications is not more complex than simulation of classical cellular communications of comparable scale. Results show that the heaviest computational burden of D2D communication lies in estimating the Sidelink channel quality. We show that SimuLTE allows one to evaluate the interplay between D2D communication and end-to-end performance of UDP- and TCP-based services. Moreover, we assess the accuracy of using a binary interference model for frequency reuse, and we evaluate the trade-off between speed of execution and accuracy in modeling the reception probability.
Proactive Threat Detection for Connected Cars Using Recursive Bayesian Estimation

Upcoming disruptive technologies around autonomous driving of connected cars have not yet been matched with appropriate security by design principles and lack approaches to incorporate proactive preventative measures in the wake of increased cyber-threats against such systems. In this paper, we introduce proactive anomaly detection to a use-case of hijacked connected cars to improve cyber-resilience. First, we manifest the opportunity of behavioral profiling for connected cars from recent literature covering related underpinning technologies. Then, we design and utilize a new data set file for connected cars influenced by the automatic dependent surveillance–broadcast surveillance technology used in the aerospace industry to facilitate data collection and sharing. Finally, we simulate the analysis of travel routes in real time to predict anomalies using predictive modeling. Simulations show the applicability of a Bayesian estimation technique, namely, Kalman filter. With the analysis of future state predictions based on the previous behavior, cyber-threats can be addressed with a vastly increased time window for a reaction when encountering anomalies. We discuss that detecting real-time deviations for malicious intent with the predictive profiling and behavioral algorithms can be superior in effectiveness than the retrospective comparison of known-good/known-bad behavior. When quicker action can be taken while connected cars encounter cyberattacks, more effective engagement or interception of command and control will be achieved.
A compact FPGA implementation of the hash function whirlpool

Recent breakthroughs in cryptanalysis of standard hash functions like SHA-1 and MD5 raise the need for alternatives. A credible alternative to for instance SHA-1 or the SHA-2 family of hash functions is Whirlpool. Whirlpool is a hash function that has been evaluated and approved by NESSIE and is standardized by ISO/IEC. To the best of our knowledge only one FPGA implementation of Whirlpool has been published to date. This implementation is designed for high throughput rates requiring a considerable amount of hardware resources. In this article we present a compact hardware implementation of the hash function Whirlpool. The proposed architecture uses an innovative state representation that makes it possible to reduce the required hardware resources remarkably. The complete implementation requires 1456 CLB-slices and, most notably, no block RAMs.
Creating Intelligent Business Systems by Utilising Big Data and Semantics

Increasing volumes of data are generated from a variety of industries, organizations and people. Such data is now seen as a key business asset, since analysing it allows business decision makers to make choices with confidence. This data is often “big data” – a data of such volume, velocity and variety that conventional processing techniques are not suitable. We research the specific challenge of variety, where data comprises a number of incompatible formats. Semantic technology, or the use of ontologies, is seen as a core approach to resolving this challenge and aligning the data generated from heterogeneous data sources. This paper lays down the issues which should be addressed and reviews the work which has been done in the area of integrating structured and unstructured data sources, with a special focus on the financial domain. We then chart a research plan in the area following Design Science research.
Random cell association and void probability in poisson-distributed cellular networks

This paper studied the fundamental modeling defect existing in Poisson-distributed cellular networks in which all base stations form a homogeneous Poisson point process (PPP) of intensity λB and all users form another independent PPP of intensity λU. The modeling defect, hardly discovered in prior works, is the void cell issue that stems from the independence between the distributions of users and BSs and “user-centric” cell association, and it could give rise to very inaccurate analytical results. We showed that the void probability of a cell under generalized random cell association is always bounded above zero and its theoretical lower bound is exp (-λU/λB) that can be achieved by large association weighting. An accurate expression of the void probability of a cell was derived and simulation results validated its correctness. We also showed that the associated BSs are essentially no longer a PPP such that modeling them as a PPP to facilitate the analysis of interference-related performance metrics may detach from reality if the BS intensity is not significantly large if compared with the user intensity.
Lessons Learned From Previous SSL/TLS Attacks - A Brief Chronology Of Attacks And Weaknesses

Since its introduction in 1994 the Secure Socket Layer (SSL) protocol (later renamed to Transport Layer Security (TLS)) evolved to the de facto standard for securing the transport layer. SSL/TLS can be used for ensuring data confidentiality, integrity and authenticity during transport. A main feature of the protocol is its flexibility. Modes of operation and security aims can easily be configured through different cipher suites. During its evolutionary development process several flaws were found. However, the flexible architecture of SSL/TLS allowed efficient fixes in order to counter the issues. This paper presents an overview on theoretical and practical attacks of the last 15 years, in chronological order and four categories: Attacks on the TLS Handshake protocol, on the TLS Record and Application Data Protocols, on the PKI infrastructure of TLS, and on various other attacks. We try to give a short ”Lessons Learned” at the end of each paragraph. SSL, TLS, Handshake Protocol, Record Layer, Public Key Infrastructures, Bleichenbacher Attack, Padding Oracles
PEDESTRIAN DETECTION AND TRACKING IN SURVEILLANCE VIDEO By PENNY CHONG

Pedestrian detection and tracking has many important applications in the security industry, pedestrian demographic analysis, and intelligent transportation system (ITS). In this project, we will develop a stable pedestrian detection and tracking algorithm. The Town Centre video frames and the hand annotated ground truth published by the University of Oxford are used as a benchmark. In experiment 1, we used Dalal and Triggs (2005) Support Vector Machines (SVM) classifier to detect pedestrians. In experiment 2, we trained our own cascade of boosted classifiers with Histogram of Oriented Gradients (HOG) feature for detection. Using Daimler training samples and INRIA training samples, we have trained two different detectors to perform pedestrian detection. The detector trained with Daimler training samples has outperformed the detector trained with INRIA training samples. For both experiments, the raw detection results are passed to the tracker. Our tracker uses Kalman filter to estimate the location of the pedestrians based on their track history. For data association, we employed the Hungarian algorithm. Overall, experiment 2 shows a more promising result as compared to experiment 1. Using raw detections from Daimler detector, our multiple object tracking accuracy (MOTA) value in experiment 2 had surpassed Benfold and Reid (2011) MOTA value by approximately 1%. However, it was observed that our algorithm suffers from a high number of misses due to occlusion. This is a common problem especially in crowded or semi-crowded environment. Thus to improve the detection or tracking results, one can opt to use a part-based detector instead of a full body detector to estimate the location of the pedestrians.
Ultrasound-Guided Genicular Nerve Block for Knee Osteoarthritis: A Double-Blind, Randomized Controlled Trial of Local Anesthetic Alone or in Combination with Corticosteroid.

Recently, several studies suggested that radiofrequency (RF) ablation of the genicular nerves is a safe and effective therapeutic procedure for intractable pain associated with chronic knee osteoarthritis (OA). Diagnostic genicular nerve block (GNB) with local anesthetic has been generally conducted before making decisions regarding RF ablation. Although GNB has been recently performed together with corticosteroid, the analgesic effects of corticosteroids for treating chronic pain remain controversial.



The current study aims to assess the effects of combining corticosteroids and local anesthesia during ultrasound-guided GNB in patients with chronic knee OA.



A randomized, double-blinded institutional study.



This study took place at Asan Medical Center in Seoul, Korea.



Forty-eight patients with chronic knee OA were randomly assigned to either the lidocaine alone group (n = 24) or lidocaine plus triamcinolone (TA) group (n = 24) before ultrasound-guided GNB. Visual analog scale (VAS), Oxford Knee Score (OKS), and global perceived effects (7-point scale) were assessed at baseline and at 1, 2, 4, and 8 weeks after the procedure.



The VAS scores were significantly lower in the lidocaine plus TA group than in the lidocaine alone group at both 2 (P < 0.001) and 4 (P < 0.001) weeks after GNB. The alleviation of intense pain in the lidocaine plus TA group was sustained up to 2 weeks after the procedure, in accordance with the definition of a minimal clinically important improvement. Although a similar intergroup difference in OKSs was observed at 4 weeks (P < 0.001), the clinical improvement in functional capacity lasted for only one week after the reassessment of OKSs, in accordance with a minimal important change. No patient reported any postprocedural adverse events during the follow-up period.



The emotional state of the patients, which might affect the perception of knee pain, was not evaluated. The follow-up period was 2 months; this period might be insufficient to validate the short-term effects of GNB.



Ultrasound-guided GNB, when combined with a local anesthetic and corticosteroid, can provide short-term pain relief. However, the clinical benefit of corticosteroid administration was not clear in comparison with local anesthesia alone. Given the potential adverse effects, corticosteroids might not be appropriate as adjuvants during a GNB for chronic knee OA.The study protocol was approved by our institutional review board (2012-0210), and written informed consent was obtained from all patients. 
Chronic pain, knee osteoarthritis, genicular nerve block, ultrasound, corticosteroid, local anesthetic, visual analog scale, Oxford Knee Score.
How not to do agile testing

When our team adopted extreme programming and Scrum, there were a number of challenges. Integrating an agile team in a "traditional" organization created friction and conflict within the team and with management. The team consisted of volunteers new to agile practices. We had difficulties with planning, estimation, task breakdown, managing requirements and working together in a collocated space. However, the biggest challenge the team encountered was not in any of these other areas, but in one that most of the team figured was simple: testing. This paper describes how difficult it was to actually get the various testing practices in place and functioning properly. In particular, the author discusses challenges we faced in coming to a common understanding of the practices, estimating testing effort, allocating the time required to do automated tests in a feature-driven culture, and getting a team of diverse individuals to understand how to apply principles that sound great but are challenging to implement
A time-delay neural network architecture for isolated word recognition

-A translation-invariant back-propagation network is described that performs better than a soph&ticated continuous acoustic parameter hidden Markov model on a noisy, lO0-speaker confusable vocabulary isolated word recognition task. The network's replicated architecture permits it to extract precise information from unaligned training patterns selected by a naive segmentation rule. Isolated word recognition, Network architecture, Constrained links, Time delays, Multiresolution learning, Multispeaker speech recognition, Neural networks.
UAV-based crop and weed classification for smart farming

Unmanned aerial vehicles (UAVs) and other robots in smart farming applications offer the potential to monitor farm land on a per-plant basis, which in turn can reduce the amount of herbicides and pesticides that must be applied. A central information for the farmer as well as for autonomous agriculture robots is the knowledge about the type and distribution of the weeds in the field. In this regard, UAVs offer excellent survey capabilities at low cost. In this paper, we address the problem of detecting value crops such as sugar beets as well as typical weeds using a camera installed on a light-weight UAV. We propose a system that performs vegetation detection, plant-tailored feature extraction, and classification to obtain an estimate of the distribution of crops and weeds in the field. We implemented and evaluated our system using UAVs on two farms, one in Germany and one in Switzerland and demonstrate that our approach allows for analyzing the field and classifying individual plants.
Fingerprint Liveness Detection in the Presence of Capable Intruders

Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system.
Let's Do the Time Warp: Multicomponent Nonlinear Motion Estimation in Differential SAR Tomography

In the differential synthetic aperture radar tomography (D-TomoSAR) system model, the motion history appears as a phase term. In the case of nonlinear motion, this phase term is no longer linear and, hence, cannot be retrieved by spectral estimation. We propose the “time warp” method that rearranges the acquisition dates such that a linear motion is pretended. The multicomponent generalization of time warp rewrites the D-TomoSAR system model to an (M + 1)-dimensional standard spectral estimation problem, where M indicates the user-defined motion model order and, hence, enables the motion estimation for all possible complex motion models. Both simulations and real data (from TerraSAR-X spotlight) examples demonstrate the applicability of the method and show that linear and periodic (seasonal) motion components can be separated and retrieved.
A Social Cognitive View of Self-Regulated Academic Learning

Researchers interested in academic self-regulated learning have begun to study processes that students use to initiate and direct their efforts to acquire knowledge and skill. The social cognitive conception of self-regulated learning presented here involves a triadic analysis of component processes and an assumption of reciprocal causality among personal, behavioral, and environmental triadic influences. This theoretical account also posits a central role for the construct of academic self-efficacy beliefs and three self-regulatory processes: self-observation, self-judgment, and self-reactions. Research support for this social cognitive formulation is discussed, as is its usefulness for improving student learning and academic achievement.
Smart healthcare monitoring: a voice pathology detection paradigm for smart cities

With the increasing demand for automated, remote, intelligent, and real-time healthcare services in smart cities, smart healthcare monitoring is necessary to provide improved and complete care to residents. In this monitoring, health-related media or signals collected from smart-devices/objects are transmitted and processed to cater to the need for quality care. However, it is challenging to create a framework or method to handle media-related healthcare data analytics or signals (e.g., voice/audio, video, or electroglottographic (EGG) signals) to meet the complex on-demand healthcare needs for successful smart city management. To this end, this paper proposes a cloud-oriented smart healthcare monitoring framework that interacts with surrounding smart devices, environments, and smart city stakeholders for affordable and accessible healthcare. As a smart city healthcare monitoring case study, a voice pathology detection (VPD) method is proposed. In the proposed method, two types of input, a voice signal and an EGG signal, are used. The input devices are connected to the Internet and the captured signals are transmitted to the cloud. The signals are then processed and classified as either normal or pathologic with a confidence score. These results are passed to registered doctors that make the final decision and take appropriate action. To process the signals, local features are extracted from the first-order derivative of the voice signal, and shape and cepstral features are extracted from the EGG signal. For classification, a Gaussian mixture model-based approach is used. Experimental results show that the proposed method can achieve VPD that is more than 93% accurate.
Pattern-Reconfigurable, Flexible, Wideband, Directive, Electrically Small Near-Field Resonant Parasitic Antenna

A pattern-reconfigurable, flexible, wideband, directive, electrically small near-field resonant parasitic (NFRP) antenna is presented. The antenna consists of a pair of Egyptian axe dipole NFRP elements, together with a pair of shaped metallic strips that act as the driven element and are fed by a coaxial cable. These NFRP and driven elements are designed to achieve compactness. Two pairs of p-i-n diodes are integrated into the driven element to enable the pattern reconfigurability. The antenna has two switchable directive endfire states, each pointed in direct opposition to the other. Examples of the evolution of the antenna are used to illustrate its operating principles. A prototype of the optimized design operating in a frequency range centered at 1.8 GHz was fabricated and measured. The simulation and experimental results are in good agreement. The antenna exhibits a wide ~13.1% impedance bandwidth and a ~4.42 dBi peak realized gain in both pattern-reconfigurable states while maintaining its electrically small size:  $\textit {ka} \sim 0.94$ . The flexibility of this antenna is demonstrated under different bending conditions by mounting it on cylinders with several different radii, and the results confirm that its performance characteristics are maintained under all of them.