nils-herrmann/clean_scirepeval huggingface dataset:
SDN-based security framework for the IoT in distributed grid

Software-defined networking (SDN), is evolving as a new paradigm for the next generation of network architecture. The separation of control plane and data plane within SDN, brings the flexibility to manage, configure, secure, and optimize network resources using dynamic software programs. From a security point of view SDN has the ability to collect information from the network devices and allow applications to program the forwarding devices, which unleashes a powerful technology for proactive and smart security policy. These functions enable the integration of security tools that can be used in distributed scenarios, unlike the traditional security solutions based on a static firewall programmed by an administrator such as Intrusion Detection and Prevention System (IDS/IPS). This network programmability may be integrated to create a new communication platform for the Internet of Things (IoT). In this paper, we present our preliminary study that is focused on the understanding of an effective approach to build a cluster network using SDN. By using network virtualization and OpenFlow technologies to generate virtual nodes, we simulate a prototype system of over 500 devices controlled by SDN, and it represents a cluster. The results show that the network devices are only able to forward the packets by predefined rules on the controller. For this reason, we propose a method to control the IP header at the application-level to overcome this problem using Opflex within SDN architecture.
Cybersecurity for Critical Infrastructures: Attack and Defense Modeling

Disruption of electric power operations can be catastrophic on national security and the economy. Due to the complexity of widely dispersed assets and the interdependences among computer, communication, and power infrastructures, the requirement to meet security and quality compliance on operations is a challenging issue. In recent years, the North American Electric Reliability Corporation (NERC) established a cybersecurity standard that requires utilities' compliance on cybersecurity of control systems. This standard identifies several cyber-related vulnerabilities that exist in control systems and recommends several remedial actions (e.g., best practices). In this paper, a comprehensive survey on cybersecurity of critical infrastructures is reported. A supervisory control and data acquisition security framework with the following four major components is proposed: (1) real-time monitoring; (2) anomaly detection; (3) impact analysis; and (4) mitigation strategies. In addition, an attack-tree-based methodology for impact analysis is developed. The attack-tree formulation based on power system control networks is used to evaluate system-, scenario -, and leaf-level vulnerabilities by identifying the system's adversary objectives. The leaf vulnerability is fundamental to the methodology that involves port auditing or password strength evaluation. The measure of vulnerabilities in the power system control framework is determined based on existing cybersecurity conditions, and then, the vulnerability indices are evaluated.
Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors

Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures.



We used wearable sensors to detect close-range interactions ("contacts") between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders.



Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.
A Multimodel Methodology for Qualitative Model Engineering

Qualitative models arising in artificial intelligence domain often concern real systems that are difficult to represent with traditional means. However, some promise for dealing with such systems is offered by research in simulation methodology. Such research produces models that combine both continuous and discrete-event formalisms. Nevertheless, the aims and approaches of the AI and the simulation communities remain rather mutually ill understood. Consequently, there is a need to bridge theory and methodology in order to have a uniform language when either analyzing or reasoning about physical systems. This article introduces a methodology and formalism for developing multiple, cooperative models of physical systems of the type studied in qualitative physics. The formalism combines discrete-event and continuous models and offers an approach to building intelligent machines capable of physical modeling and reasoning.
Transforming Auto-Encoders

The artificial neural networks that are used to recognize shapes typically use one or more layers of learned feature detectors that produce scalar outputs. By contrast, the computer vision community uses complicated, hand-engineered features, like SIFT [6], that produce a whole vector of outputs including an explicit representation of the pose of the feature. We show how neural networks can be used to learn features that output a whole vector of instantiation parameters and we argue that this is a much more promising way of dealing with variations in position, orientation, scale and lighting than the methods currently employed in the neural networks community. It is also more promising than the handengineered features currently used in computer vision because it provides an efficient way of adapting the features to the domain.
Adversarial examples in the physical world

Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. Adversarial examples pose security concerns because they could be used to perform an attack on machine learning systems, even if the adversary has no access to the underlying model. Up to now, all previous work have assumed a threat model in which the adversary can feed data directly into the machine learning classifier. This is not always the case for systems operating in the physical world, for example those which are using signals from cameras and other sensors as an input. This paper shows that even in such physical world scenarios, machine learning systems are vulnerable to adversarial examples. We demonstrate this by feeding adversarial images obtained from cell-phone camera to an ImageNet Inception classifier and measuring the classification accuracy of the system. We find that a large fraction of adversarial examples are classified incorrectly even when perceived through the camera.
3D-scattering center detection of automotive targets using 77 GHz UWB radar sensors

3D scattering center analysis enables to derive radar system specifications for future automotive applications. Synthetic aperture radar (SAR) and SAR in combination with digital beam forming (DBF) will be presented to extract the location of scattering centers at 77 GHz. Furthermore, a mechanical scanning antenna with high directivity is used to compare the reflections of different automotive targets for different elevation and aspect angles.
Learning Ordinal Relationships for Mid-Level Vision

We propose a framework that infers mid-level visual properties of an image by learning about ordinal relationships. Instead of estimating metric quantities directly, the system proposes pairwise relationship estimates for points in the input image. These sparse probabilistic ordinal measurements are globalized to create a dense output map of continuous metric measurements. Estimating order relationships between pairs of points has several advantages over metric estimation: it solves a simpler problem than metric regression, humans are better at relative judgements, so data collection is easier, ordinal relationships are invariant to monotonic transformations of the data, thereby increasing the robustness of the system and providing qualitatively different information. We demonstrate that this frame-work works well on two important mid-level vision tasks: intrinsic image decomposition and depth from an RGB image. We train two systems with the same architecture on data from these two modalities. We provide an analysis of the resulting models, showing that they learn a number of simple rules to make ordinal decisions. We apply our algorithm to depth estimation, with good results, and intrinsic image decomposition, with state-of-the-art results.
Simulation & performance study of wireless sensor network (WSN) using MATLAB

A wireless sensor network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. Different approaches have used for simulation and modeling of SN (Sensor Network) and WSN. Traditional approaches consist of various simulation tools based on different languages such as C, C++ and Java. In this paper, MATLAB (7.6) Simulink was used to build a complete WSN system. Simulation procedure includes building the hardware architecture of the transmitting nodes, modeling both the communication channel and the receiving master node architecture. Bluetooth was chosen to undertake the physical layer communication with respect to different channel parameters (i.e., Signal to Noise ratio, Attenuation and Interference). The simulation model was examined using different topologies under various conditions and numerous results were collected. This new simulation methodology proves the ability of the Simulink MATLAB to be a useful and flexible approach to study the effect of different physical layer parameters on the performance of wireless sensor networks.
SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity

Verbs play a critical role in the meaning of sentences, but these ubiquitous words have received little attention in recent distributional semantics research. We introduce SimVerb-3500, an evaluation resource that provides human ratings for the similarity of 3,500 verb pairs. SimVerb-3500 covers all normed verb types from the USF free-association database, providing at least three examples for every VerbNet class. This broad coverage facilitates detailed analyses of how syntactic and semantic phenomena together influence human understanding of verb meaning. Further, with significantly larger development and test sets than existing benchmarks, SimVerb-3500 enables more robust evaluation of representation learning architectures and promotes the development of methods tailored to verbs. We hope that SimVerb-3500 will enable a richer understanding of the diversity and complexity of verb semantics and guide the development of systems that can effectively represent and interpret this meaning.
Frequency and Quality of Social Networking Among Young Adults: Associations With Depressive Symptoms, Rumination, and Corumination.

Two studies examined associations between social networking and depressive symptoms among youth. In Study 1, 384 participants (68% female; mean age = 20.22 years, SD = 2.90) were surveyed. In Study 2, 334 participants (62% female; M age = 19.44 years, SD = 2.05) were surveyed initially and 3 weeks later. Results indicated that depressive symptoms were associated with quality of social networking interactions, not quantity. There was some evidence that depressive rumination moderated associations, and both depressive rumination and corumination were associated with aspects of social networking usage and quality. Implications for understanding circumstances that increase social networking, as well as resulting negative interactions and negative affect are discussed.
Fast Author Name Disambiguation in CiteSeer

Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative machine learning framework for solving the name disambiguation problem: a blocking method retrieves candidate classes of authors with similar names and a clustering method, DBSCAN, clusters papers by author. The distance metric between papers used in DBSCAN is calculated by an online active selection support vector machine algorithm (LASVM), yielding a simpler model, lower test errors and faster prediction time than a standard SVM. We prove that by recasting transitivity as density reachability in DBSCAN, transitivity is guaranteed for core points. For evaluation, we manually annotated 3,355 papers yielding 490 authors and achieved 90.6% pairwise-F1 metric. For scalability, authors in the entire CiteSeer dataset, over 700,000 papers, were readily disambiguated.
Mindfulness training and stress reactivity in substance abuse: results from a randomized, controlled stage I pilot study.

Stress is important in substance use disorders (SUDs). Mindfulness training (MT) has shown promise for stress-related maladies. No studies have compared MT to empirically validated treatments for SUDs. The goals of this study were to assess MT compared to cognitive behavioral therapy (CBT) in substance use and treatment acceptability, and specificity of MT compared to CBT in targeting stress reactivity. Thirty-six individuals with alcohol and/or cocaine use disorders were randomly assigned to receive group MT or CBT in an outpatient setting. Drug use was assessed weekly. After treatment, responses to personalized stress provocation were measured. Fourteen individuals completed treatment. There were no differences in treatment satisfaction or drug use between groups. The laboratory paradigm suggested reduced psychological and physiological indices of stress during provocation in MT compared to CBT. This pilot study provides evidence of the feasibility of MT in treating SUDs and suggests that MT may be efficacious in targeting stress.
CloudWatcher: Network security monitoring using OpenFlow in dynamic cloud networks (or: How to provide security monitoring as a service in clouds?)

Cloud computing is becoming a popular paradigm. Many recent new services are based on cloud environments, and a lot of people are using cloud networks. Since many diverse hosts and network configurations coexist in a cloud network, it is essential to protect each of them in the cloud network from threats. To do this, basically, we can employ existing network security devices, but applying them to a cloud network requires more considerations for its complexity, dynamism, and diversity. In this paper, we propose a new framework, CloudWatcher, which provides monitoring services for large and dynamic cloud networks. This framework automatically detours network packets to be inspected by pre-installed network security devices. In addition, all these operations can be implemented by writing a simple policy script, thus, a cloud network administrator is able to protect his cloud network easily. We have implemented the proposed framework, and evaluated it on different test network environments.
Event-Driven Headline Generation

We propose an event-driven model for headline generation. Given an input document, the system identifies a key event chain by extracting a set of structural events that describe them. Then a novel multi-sentence compression algorithm is used to fuse the extracted events, generating a headline for the document. Our model can be viewed as a novel combination of extractive and abstractive headline generation, combining the advantages of both methods using event structures. Standard evaluation shows that our model achieves the best performance compared with previous state-of-the-art systems.
Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparation (pairing images across cameras) and allows the understanding of what the network learned. Second, and most notably, the training data consists of a synthetic 100K instance dataset, SOMAset, created by photorealistic human body generation software. Synthetic data represents a good compromise between realistic imagery, usually not required in re-identification since surveillance cameras capture low-resolution silhouettes, and complete control of the samples, which is useful in order to customize the data w.r.t. the surveillance scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on recent re-identification benchmarks, outperforms all competitors, matching subjects even with different apparel. The combination of synthetic data with Inception architectures opens up new research avenues in re-identification.
Source Camera Identification Using Large Components of Sensor Pattern Noise

Digital image forensics has attracted a lot of attention recently for its role in identifying the origin of digital image. Although different forensic approaches have been proposed, one of the most popular approaches is to rely on the imaging sensor pattern noise, where each sensor pattern noise uniquely corresponds to an imaging device and serves as the intrinsic fingerprint. The correlation-based detection is heavily dependent upon the accuracy of the extracted pattern noise. In this work, we discuss the way to extract the pattern noise, in particular, explore the way to make better use of the pattern noise. Unlike current methods that directly compare the whole pattern noise signal with the reference one, we propose to only compare the large components of these two signals. Our detector can better identify the images taken by different cameras. In the meantime, it needs less computational complexity.
EIO: Error Handling is Occasionally Correct

The reliability of file systems depends in part on how well they propagate errors. We develop a static analysis technique, EDP, that analyzes how file systems and storage device drivers propagate error codes. Running our EDP analysis on all file systems and 3 major storage device drivers in Linux 2.6, we find that errors are often incorrectly propagated; 1153 calls (13%) drop an error code without handling it. We perform a set of analyses to rank the robustness of each subsystem based on the completeness of its error propagation; we find that many popular file systems are less robust than other available choices. We confirm that write errors are neglected more often than read errors. We also find that many violations are not cornercase mistakes, but perhaps intentional choices. Finally, we show that inter-module calls play a part in incorrect error propagation, but that chained propagations do not. In conclusion, error propagation appears complex and hard to perform correctly in modern systems.
Jersey number detection in sports video for athlete identification

Athlete identification is important for sport video content analysis since users often care about the video clips with their preferred athletes. In this paper, we propose a method for athlete identification by combing the segmentation, tracking and recognition procedures into a coarse-to-fine scheme for jersey number (digital characters on sport shirt) detection. Firstly, image segmentation is employed to separate the jersey number regions with its background. And size/pipe-like attributes of digital characters are used to filter out candidates. Then, a K-NN (K nearest neighbor) classifier is employed to classify a candidate into a digit in “0-9” or negative. In the recognition procedure, we use the Zernike moment features, which are invariant to rotation and scale for digital shape recognition. Synthetic training samples with different fonts are used to represent the pattern of digital characters with non-rigid deformation. Once a character candidate is detected, a SSD (smallest square distance)-based tracking procedure is started. The recognition procedure is performed every several frames in the tracking process. After tracking tens of frames, the overall recognition results are combined to determine if a candidate is a true jersey number or not by a voting procedure. Experiments on several types of sports video shows encouraging result.
Using Rules to Analyse Bio-medical Data: A Comparison between C4.5 and PCL

For easy comprehensibility, rules are preferrable to non-linear kernel functions in the analysis of bio-medical data. In this paper, we describe two rule induction approaches—C4.5 and our PCL classifier—for discovering rules from both traditional clinical data and recent gene expression or proteomic profiling data. C4.5 is a widely used method, but it has two weaknesses, the single coverage constraint and the fragmentation problem, that affect its accuracy. PCL is a new rule-based classifier that overcomes these two weaknesses of decision trees by using many significant rules. We present a thorough comparison to show that our PCL method is much more accurate than C4.5, and it is also superior to Bagging and Boosting in general.
The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction

This paper reports on the Event StoryLine Corpus (ESC) v0.9, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news documents spread in time and clustered around a single seminal event or topic. In addition to describing the dataset, we also report on three baselines systems whose results show the complexity of the task and suggest directions for the development of more robust systems.