nils-herrmann/clean_scirepeval huggingface dataset:
Towards Modelling Pedestrian-Vehicle Interactions: Empirical Study on Urban Unsignalized Intersection

The modelling and simulation of the interaction among vehicles and pedestrians during crosswalking is an open challenge for both research and practical computational solutions supporting urban/traffic decision makers and managers. The social cost of pedestrians’ risky behaviour pushes the development of a new generation of computational models integrating analytical knowledge, data and experience about the complex dynamics occurring in pedestrian/vehicle interactions, which are not completely understood despite recent efforts. This paper presents the results of a significant data gathering campaign realised at an unsignalized zebra crossing. The selected area of the city of Milan (Italy) is characterised by a significant presence of elderly inhabitants and pedestrian-vehicle risky interactions, testified by a high number of accidents involving pedestrians in the past years. The results concern the analysis of: (i) vehicular and pedestrian traffic volumes; (ii) level of service; (iii) pedestrian-vehicle interactions, considering the impact of ageing on crossing behaviour. Results showed that the phenomenon is characterised by three main phases: approaching, appraising (evaluation of the distance and speed of oncoming vehicles) and crossing. The final objective of the research is to support the development of a microscopic agent-based tool for simulating pedestrian behaviour at unsignalized crosswalks, focusing on the specific needs of the elderly pedestrians.
A General-Purpose Architecture for Replicated Metadata Services in Distributed File Systems

A large class of modern distributed file systems treat metadata services as an independent system component, separately from data servers. The availability of the metadata service is key to the availability of the overall system. Given the high rates of failures observed in large-scale data centers, distributed file systems usually incorporate high-availability (HA) features. A typical approach in the development of distributed file systems is to design and develop metadata services from the ground up, at significant cost in terms of complexity and time, often leading to functional shortcomings. Our motivation in this paper was to improve on this state of things by defining a general-purpose architecture for HA metadata services (which we call RMS) that can be easily incorporated and reused in new or existing file systems, reducing development time. Taking two prominent distributed file systems as case studies, PVFS and HDFS, we developed RMS variants that improve on functional shortcomings of the original HA solutions, while being easy to build and test. Our extensive evaluation of the RMS variant of HDFS shows that it does not incur an overall performance or availability penalty compared to the original implementation.
Falling for Fake News: Investigating the Consumption of News via Social Media

In the so called 'post-truth' era, characterized by a loss of public trust in various institutions, and the rise of 'fake news' disseminated via the internet and social media, individuals may face uncertainty about the veracity of information available, whether it be satire or malicious hoax. We investigate attitudes to news delivered by social media, and subsequent verification strategies applied, or not applied, by individuals. A survey reveals that two thirds of respondents regularly consumed news via Facebook, and that one third had at some point come across fake news that they initially believed to be true. An analysis task involving news presented via Facebook reveals a diverse range of judgement forming strategies, with participants relying on personal judgements as to plausibility and scepticism around sources and journalistic style. This reflects a shift away from traditional methods of accessing the news, and highlights the difficulties in combating the spread of fake news.
Safety net for a sectorless air traffic management concept

Today air traffic management divides the airspace into sectors. Controllers are responsible for one of these sectors and all the air traffic within. In a sectorless air traffic management concept the airspace is no longer divided into sectors but regarded as one piece. Controllers are assigned individual aircraft which they are responsible for from their entry into the airspace to their exit. This implies that the controllers have to manage flights which are not in the same geographic region but can be anywhere in the airspace and hence also in different traffic situations. Such a change of concept influences the way controllers work and calls for different support tools. Naturally, the question of safety arises with regard to standard operations as well as special situations. This discussion paper investigates the different safety aspects relevant in a sectorless concept. It highlights the differences between a conventional sectored and the proposed sectorless approach. We argue that most elements of the current safety net in air traffic management can also be applied to a sectorless air traffic management concept. Additional safety net elements are proposed together with examples of their application.
Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples

Machine learning (ML) models, e.g., state-of-the-art deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We then use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. After labeling 6,400 synthetic inputs to train our substitute (an order of magnitude smaller than the training set used by MetaMind), we find that their DNN misclassifies adversarial examples crafted with our substitute at a rate of 84.24%. We demonstrate that our strategy generalizes to many ML techniques like logistic regression or SVMs, regardless of the ML model chosen for the substitute. We instantiate the same attack against models hosted by Amazon and Google, using logistic regression substitutes trained with only 800 label queries. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19% and 88.94%. We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder. We conclude with experiments exploring why adversarial samples transfer between models.
Prediction of Primary Pupil Enrollment in Government School Using Data Mining Forecasting Technique

This research concentrates upon predictive analysis of enrolled pupil using forecasting based on data mining technique. The Microsoft SQL Server Data Mining Add-ins Excel 2007 was employed as a software mining tool for predicting enrollment of pupils. The time series algorithm was used for experiment analysis. U-DISE (Unified District Information System for Education) Dataset of Aurangabad district in Maharashtra (India) was obtained from SSA (Sarva Shiksha Abhiyan) and was used for analysis. Data mining for primary pupil enrollment research provides a feasible way to analyze the trend and to addresses the interest of pupil. The dataset was studied and analyzed to forecast registered pupils in government for upcoming years. We conclude that for upcoming year, pupil strength in government school will be dropped down and number of teachers will be supersizing in the said district.  Data mining, Microsoft SQL Server Data Mining Add-ins Excel 2007, Time series algorithm, U-DISE
Closing the functional and Performance Gap between SQL and NoSQL

Oracle release 12cR1 supports JSON data management that enables users to store, index and query JSON data along with relational data. The integration of the JSON data model into the RDBMS allows a new paradigm of data management where data is storable, indexable and queryable without upfront schema definition. We call this new paradigm Flexible Schema Data Management (FSDM). In this paper, we present enhancements to Oracle's JSON data management in the upcoming 12cR2 release. We present JSON DataGuide, an auto-computed dynamic soft schema for JSON collections that closes the functional gap between the fixed-schema SQL world and the schema-less NoSQL world. We present a self-contained query friendly binary format for encoding JSON (OSON) to close the query performance gap between schema-encoded relational data and schema free JSON textual data. The addition of these new features makes the Oracle RDBMS well suited to both fixedschema SQL and flexible-schema NoSQL use cases, and allows users to freely mix the two paradigms in a single data management system.
Trust and risk in e-government adoption

Citizen confidence in the competence of the government and the reliability of the technology used to implement egovernment initiatives is imperative to the wide-spread adoption of e-government. This study analyzes how citizens’ trust in technology and government affect their willingness to engage in e-government transactions. We proposes a model of egovernment trust composed of disposition to trust, institution-based trust (IBT), characteristic-based trust (CBT) and perceived risk. Data were collected via a survey of 214 citizens ranging in age from 14 to 83 old. The model was tested using Structural Equation Modeling techniques. Results indicate that disposition to trust positively affects IBT and CBT trusts, which in turn affect intentions to use an e-government service. CBT trust also affects negatively perceived risk, which affects use intentions as well. Implications for practice and research are discussed.
Real-time High Performance Anomaly Detection over Data Streams: Grand Challenge

Real-time analytics over data streams are crucial for a wide range of use cases in industry and research. Today's sensor systems can produce high throughput data streams that have to be analyzed in real-time. One important analytic task is anomaly or outlier detection from the streaming data. In many industry applications, sensing devices produce a data stream that can be monitored to know the correct operation of industry devices and consequently avoid damages by triggering reactions in real-time.
 While anomaly detection is a well-studied topic in data mining, the real-time high-performance anomaly detection from big data streams require special studies and well-optimized implementation. This paper presents our implementation of a real-time anomaly detection system over data streams. We outline details of our two separate implementations using the Java and C++ programming languages, and provide technical details about the data processing pipelines. We report experimental results and describe performance tuning strategies.
Joint wavelet-based spectrum sensing and FBMC modulation for cognitive mmWave small cell networks

Millimetre-wave (mmWave) 5G communications is an emerging technology to enhance the capacity of existing systems by thousand-fold improvement. Heterogeneous networks employing densely distributed small cells can optimise the available coverage and throughput of 5G systems. Efficiently utilising the spectrum bands by small cells is one of the approaches that will considerably increase the available data rate and capacity of the heterogeneous networks. This challenging task can be achieved by spectrum sensing capability of cognitive radios and new modulation techniques for data transmission. In this study, a wavelet-based filter bank is proposed for spectrum sensing and modulation in 5G heterogeneous networks. The proposed technique can mitigate the spectral leakage and interference by adapting the subcarriers according to cognitive information provided by wavelet packet based spectrum sensing (WPSS) and lowering sidelobes using wavelet-based filter bank multicarrier modulation. The performance improvement of WPSS compared with Fourier-based spectrum sensing is verified in terms of power spectral density comparison and probabilities of detection and false alarm. Meanwhile, the bit error rate performance demonstrates the superiority of the proposed wavelet-based system compared with its Fourier-based counterpart over the 60 GHz mmWave channel.
Homicides with mutilation of the victim's body.

Information on homicide offenders guilty of mutilation is sparse. The current study estimates the rate of mutilation of the victim's body in Finnish homicides and compares sociodemographic characteristics, crime history, life course development, psychopathy, and psychopathology of these and other homicide offenders. Crime reports and forensic examination reports of all offenders subjected to forensic examination and convicted for a homicide in 1995-2004 (n = 676) were retrospectively analyzed for offense and offender variables and scored with the Psychopathy Check List Revised. Thirteen homicides (2.2%) involved mutilation. Educational and mental health problems in childhood, inpatient mental health contacts, self-destructiveness, and schizophrenia were significantly more frequent in offenders guilty of mutilation. Mutilation bore no significant association with psychopathy or substance abuse. The higher than usual prevalence of developmental difficulties and mental disorder of this subsample of offenders needs to be recognized.
Design and implementation of a message-service oriented middleware for fog of things platforms

Fog of Things (FoT) is a new paradigm for design and implementation of Fog Computing platforms for the Internet of Things (IoT). The FoT proposal goes further than the Fog Computing in some directions: i) by using all the processing capacity of the network edge through performing data processing and service delivery on devices, gateways (very small servers) and small local servers; ii) by defining profiles for gateways and servers at the edge of the network, in order to define and implement IoT Services to be delivered in a distributed way; iii) by distributing these IoT Services also at the network edge through a message-service oriented middleware. In this paper, we propose FoT-MSOM, which is a message-service oriented middleware for the FoT paradigm, and we show how this middleware can be deployed on a Self-Organizing FoT-based platform (SOFT-IoT platform).
Calibrating Large-area Mask Projection Stereolithography for Its Accuracy and Resolution Improvements

Solid freeform fabrication (SFF) processes based on mask image projection such as digital micro-mirror devices (DMD) have the potential to be fast and inexpensive. More and more research and commercial systems have been developed based on such digital devices. However, a digital light processing (DLP) projector based on DMD has limited resolution and certain image blurring. In order to use a DLP projector in the large-area mask projection stereolithography, it is critical to plan mask images in order to achieve high accuracy and resolution. Based on our previous work on optimized pixel blending, we present a calibration method for capturing the non-uniformity of a projection image by a low cost off-the-shelf DLP projector. Our method is based on two calibration systems, a geometric calibration system that can calibrate the position, shape, size, and orientation of a pixel and an energy calibration system that can calibrate the light intensity of a pixel. Based on both results, the light intensity at various grayscale levels can be approximated for each pixel. Developing a library of such approximation functions is critical for the optimized pixel blending to generate a better mask image plan. Experimental results verify our calibration results.
A Microwave Microfluidic Sensor Based on a Dual-Mode Resonator for Dual-Sensing Applications

In this paper, we propose a novel microwave microfluidic sensor with dual-sensing capability. The sensor is based on a dual-mode resonator that consists of a folded microstrip line loaded with interdigital lines and a stub at the plane of symmetry. Due to the specific configuration, the resonator exhibits two entirely independent resonant modes, which allows simultaneous sensing of two fluids using a resonance shift method. The sensor is designed in a multilayer configuration with the proposed resonator and two separated microfluidic channels-one intertwined with the interdigital lines and the other positioned below the stub. The circuit has been fabricated using low-temperature co-fired ceramics technology and its performance was verified through the measurement of its responses for different fluids in the microfluidic channels. The results confirm the dual-sensing capability with zero mutual influence as well as good overall performance. Besides an excellent potential for dual-sensing applications, the proposed sensor is a good candidate for application in mixing fluids and cell counting.
Rank Correlation for Low-Rate DDoS Attack Detection: An Empirical Evaluation

A low-rate distributed denial of service (DDoS) attack has the ability to obscure its traffic because it is very similar to legitimate traffic. It can easily evade current detection mechanisms. Rank correlation measures can quantify significant differences between attack traffic and legitimate traffic based on their rank values. In this paper, we use two rank correlation measures, namely, Spearmen Rank Correlation (SRC) and Partial Rank Correlation (PRC) to detect low-rate DDoS attacks. These measures are empirically evaluated using three real-life datasets. Experimental results show that both measures can effectively discriminate legitimate traffic from attack traffic. We find that PRC performs better than SRC in detection of lowrate DDoS attacks in terms of spacing between malicious and legitimate traffic.
Deep learning at the shallow end: Malware classification for non-domain experts

Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors frommalware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.
Comparisons of centerline extraction methods for liver blood vessels in ImageJ and 3D slicer

In this paper, we introduce the centerline extraction processes of the liver vessel systems by using the plug-ins in ImageJ and 3D Slicer. The Skeletonize (2D/3D) plug-in in ImageJ is easy to use, and only based on the volumetric data. It performs fast and require no user interactions in terms of seed points selection. The VMTK toolkit in 3D slicer is more powerful. It uses the surface model and can provide more smooth and consistent results. Due to its complexities, it requires more computational resources and user specified seed points. Some possible improvements for these two methods are discussed.
Beyond the three-component model of organizational commitment.

This article offers a conceptual critique of the three-component model (TCM) of organizational commitment (Allen & Meyer, 1990) and proposes a reconceptualization based on standard attitude theory. The authors use the attitude-behavior model by Eagly and Chaiken (1993) to demonstrate that the TCM combines fundamentally different attitudinal phenomena. They argue that general organizational commitment can best be understood as an attitude regarding the organization, while normative and continuance commitment are attitudes regarding specific forms of behavior (i.e., staying or leaving). The conceptual analysis shows that the TCM fails to qualify as general model of organizational commitment but instead represents a specific model for predicting turnover. The authors suggest that the use of the TCM be restricted to this purpose and that Eagly and Chaiken's model be adopted as a generic commitment model template from which a range of models for predicting specific organizational behaviors can be extracted. Finally, they discuss the definition and measurement of the organizational commitment attitude. Covering the affective, cognitive, and behavioral facets of this attitude helps to enhance construct validity and to differentiate the construct from other constructs.
Review on fraud detection methods in credit card transactions

Cashless transactions such as online transactions, credit card transactions, and mobile wallet are becoming more popular in financial transactions nowadays. With increased number of such cashless transaction, number of fraudulent transactions are also increasing. Fraud can be distinguished by analyzing spending behavior of customers (users) from previous transaction data. If any deviation is noticed in spending behavior from available patterns, it is possibly of fraudulent transaction. To detect fraud behavior, bank and credit card companies are using various methods of data mining such as decision tree, rule based mining, neural network, fuzzy clustering approach, hidden markov model or hybrid approach of these methods. Any of these methods is applied to find out normal usage pattern of customers (users) based on their past activities. The objective of this paper is to provide comparative study of different techniques to detect fraud.
Low cost 60 GHz millimeter-wave microstrip patch antenna array using low-loss planar feeding scheme

A novel low cost feeding mechanism based upon new synthesized planar waveguide substrate integrated waveguide (SIW), is presented for microstrip antenna arrays (MPAs) at millimeter-wave (mmW) band to avoid feeding loss, specially the conduction loss. As an example, SIW-fed two dimensional (2D) 2×2 MPA array @ 60 GHz is introduced. The presented antenna is simulated by HFSS to validate its performance for mmW applications. The simulated results show a broadside gain of 11.71 dB within an operating impedance bandwidth of 500 MHz around a center frequency of 59.95 GHz. Also, the simulated radiation efficiency of the developed antenna is better than 85%, which is higher than that of conventional mmW microstrip antenna array.
Suicidal strangulation by double ligature: a case report.

This article reports a self-strangulation suicide in which a double complex group of ligatures was used. Although similar cases have been referenced in forensic literature, this case is notable because of the unusual method used by the victim. A 61-year-old man was found in a locked room with a double elaborate ligature comprising six wire clothes hangers completely encircling the neck and a black rubber band in a double loop. Autopsy also documented parallel superficial cut lesions in proximal forearm interpreted as hesitation marks. It stresses the importance of characteristics such as analysis of number of ligatures, position of the knots, number of knots and turns of ligature marks and the absence of any defense and relevant internal injuries inherent to suicide ligature strangulation cases.