nils-herrmann/clean_scirepeval huggingface dataset:
Detecting Algorithmically Generated Domain-Flux Attacks With DNS Traffic Analysis

Recent botnets such as Conficker, Kraken, and Torpig have used DNS-based "domain fluxing" for command-and-control, where each Bot queries for existence of a series of domain names and the owner has to register only one such domain name. In this paper, we develop a methodology to detect such "domain fluxes" in DNS traffic by looking for patterns inherent to domain names that are generated algorithmically, in contrast to those generated by humans. In particular, we look at distribution of alphanumeric characters as well as bigrams in all domains that are mapped to the same set of IP addresses. We present and compare the performance of several distance metrics, including K-L distance, Edit distance, and Jaccard measure. We train by using a good dataset of domains obtained via a crawl of domains mapped to all IPv4 address space and modeling bad datasets based on behaviors seen so far and expected. We also apply our methodology to packet traces collected at a Tier-1 ISP and show we can automatically detect domain fluxing as used by Conficker botnet with minimal false positives, in addition to discovering a new botnet within the ISP trace. We also analyze a campus DNS trace to detect another unknown botnet exhibiting advanced domain-name generation technique.
CV-PCR: a context-guided value-driven framework for patent citation recommendation

Patent citation recommendation and prior patent search, critical for patent filing and patent examination, have become increasingly difficult due to the rapidly growing number of patents. Unlike paper citations that focus on reference comprehensiveness, patent citations tend to be more parsimonious and refer only to those prior patents bearing significant technological and/or economic value, as they define the scope of the citing patent and thus have significant legal and economic implications. Based on the insight that patent citations are important information reflecting the value of cited patents to the citing patent, we propose a heterogeneous patent citation-bibliographic network that combines patent citations (reflecting value relation) and bibliographic information (reflecting similarity relation) together. From this network, we extract various features that reflect the value of a prior patent to a query patent with regard to the context of the query patent such as its assignee, classifications, etc. We then propose a two-stage framework for patent citation recommendation. Our idea is that by exploiting those context-specific value measures of candidate patents to the query patent, the proposed framework is able to make effective patent citation recommendations. We evaluate the proposed context-guided value-driven framework using a collection of 1.8M U.S. patents. Experimental results validate our ideas and show that those value-driven features are very effective and significantly outperform two state-of-the-art methods in terms of both the precision and recall rates.
Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network

Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool very frequently used for brain 8 imaging. The classification of MRI images of normal and pathological brain conditions pose a challenge from 9 technological and clinical point of view, since MR imaging focuses on soft tissue anatomy and generates a large 10 information set and these can act as a mirror reflecting the conditions of the brain. A new approach by 11 integrating wavelet entropy based spider web plots and probabilistic neural network is proposed for the 12 classification of MRI brain images. The two step method for classification uses (1) wavelet entropy based spider 13 web plots for the feature extraction and (2) probabilistic neural network for the classification. The spider web 14 plot is a geometric construction drawn using the entropy of the wavelet approximation components and the areas 15 calculated are used as feature set for classification. Probabilistic neural network provides a general solution to 16 the pattern classification problems and the classification accuracy is found to be 100%. 17 Magnetic Resonance Imaging (MRI), Wavelet Transformation, Entropy, Spider Web Plots, 18 Probabilistic Neural Network 19
Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media

Many current Natural Language Processing [NLP] techniques work well assuming a large context of text as input data. However they become ineffective when applied to short texts such as Twitter feeds. To overcome the issue, we want to find a related newswire document to a given tweet to provide contextual support for NLP tasks. This requires robust modeling and understanding of the semantics of short texts. The contribution of the paper is two-fold: 1. we introduce the Linking-Tweets-toNews task as well as a dataset of linked tweet-news pairs, which can benefit many NLP applications; 2. in contrast to previous research which focuses on lexical features within the short texts (text-to-word information), we propose a graph based latent variable model that models the inter short text correlations (text-to-text information). This is motivated by the observation that a tweet usually only covers one aspect of an event. We show that using tweet specific feature (hashtag) and news specific feature (named entities) as well as temporal constraints, we are able to extract text-to-text correlations, and thus completes the semantic picture of a short text. Our experiments show significant improvement of our new model over baselines with three evaluation metrics in the new task.
ARTEMIS : A Vision for Remote Triage and Emergency Management Information Integration

This paper describes the design of an automated triage and emergency management information system. The prototype system is capable of monitoring and assessing physiological parameters of individuals, transmitting pertinent medical data to and from multiple echelons of medical service, and providing filtered data for command and control applications. The system employs wireless networking, portable computing devices, and reliable messaging technology as a framework for information analysis, information movement, and decision support capabilities. The embedded medical model and physiological status assessment are based on input from humans and a pulse oximetry device. The physiological status determination methodology follows NATO defined guidelines for remote triage and is implemented using an approach based on fuzzy logic. The approach described can be used in both military and civilian
A New Triple-Band Circular Ring Patch Antenna With Monopole-Like Radiation Pattern Using a Hybrid Technique

Investigation results of a new conical-shape radiation-pattern antenna based on a hybrid technique are presented. A circular-ring patch antenna excited with the dominant mode TM02 is integrated with a monopole antenna, offering two distinct operating bands. The coupling effect of the higher-order modes of the monopole and the circular-ring patch create a third operating broad band. Using this hybrid resonant configuration, the expected ripples in the E-plane pattern due to the monopole higher-order modes are alleviated over the third band, providing a good radiation performance. The measurement results performed for the proposed structure represent a good agreement with the simulated ones.
A Novel Distributed Intrusion Detection System for Vehicular Ad Hoc Networks

In the new interconnected world, we need to secure vehicular cyber-physical systems (VCPS) using sophisticated intrusion detection systems. In this article, we present a novel distributed intrusion detection system (DIDS) designed for a vehicular ad hoc network (VANET). By combining static and dynamic detection agents, that can be mounted on central vehicles, and a control center where the alarms about possible attacks on the system are communicated, the proposed DIDS can be used in both urban and highway environments for real time anomaly detection with good accuracy and response time.
Voice Loops as Cooperative Aids in Space Shuttle Mission Control

In domains like air traffic management, aircraft carrier operations, and space mission control, practitioners coordinate their activities through voice loops that allow communication among groups of people who are spatially separate. Voice loops have evolved into essential coordination support tools for experienced practitioners in space shuttle mission control, as well as other domains. We describe how voice loops support the coordination of activities and cognitive processes in event-driven domains like space shuttle mission control. We discuss how the loops help flight controllers synchronize their activities and integrate information, and how they facilitate directed communication and support the negotiation of interruptions. In addition, we suggest factors like attentional cues, implicit protocols, and the structure and features of the loops, which might govern the success of voice loops in the mission control domain. Our results should provide insight into the important functions that should be considered in the development of systems intended to support cooperative work.
Graph Matching : Theoretical Foundations , Algorithms , and Applications

Graphs are a powerful and versatile tool useful in various subfields of science and engineering. In many applications, for example, in pattern recognition and computer vision, it is required to measure the similarity of objects. When graphs are used for the representation of structured objects, then the problem of measuring object similarity turns into the problem of computing the similarity of graphs, which is also known as graph matching. In this paper, similarity measures on graphs and related algorithms will be reviewed. Applications of graph matching will be demonstrated giving examples from the fields of pattern recognition and computer vision. Also recent theoretical work showing various relations between different similarity measures will be discussed.
Teaching Cybersecurity with DeterLab

The DETER project aims to advance cybersecurity research and education. Over the past seven years, the project has focused on improving and redefining the methods, technology, and infrastructure for developing cyberdefense technology. The project's research results are put into practice by DeterLab, a public, free-for-use experimental facility available to researchers and educators worldwide. Educators can use DeterLab's exercises to teach cybersecurity technology and practices. This use of DeterLab provides valuable feedback on DETER innovations and helps grow the pool of cybersecurity innovators and cyberdefenders.
22 kV SiC Emitter turn-off (ETO) thyristor and its dynamic performance including SOA

Ultra-high voltage (>10 kV) power devices based on SiC are gaining significant attentions since Si power devices are typically at lower voltage levels. In this paper, a world record 22kV Silicon Carbide (SiC) p-type ETO thyristor is developed and reported as a promising candidate for ultra-high voltage applications. The device is based on a 2cm2 22kV p type gate turn off thyristor (p-GTO) structure. Its static as well as dynamic performances are analyzed, including the anode to cathode blocking characteristics, forward conduction characteristics at different temperatures, turn-on and turn-off dynamic performances. The turn-off energy at 6kV, 7kV and 8kV respectively is also presented. In addition, theoretical boundary of the reverse biased safe operation area (RBSOA) of the 22kV SiC ETO is obtained by simulations and the experimental test also demonstrated a wide RBSOA.
Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform

Online media offers opportunities to marketers to deliver brand messages to a large audience. Advertising technology platforms enables the advertisers to find the proper group of audiences and deliver ad impressions to them in real time. The recent growth of the real time bidding has posed a significant challenge on monitoring such a complicated system. With so many components we need a reliable system that detects the possible changes in the system and alerts the engineering team. In this paper we describe the mechanism that we invented for recovering the representative metrics and detecting the change in their behavior. We show that this mechanism is able to detect the possible problems in time by describing some incident cases.
Computation and validation of rail-to-earth potential for diode-grounded DC traction system at Taipei Rapid Transit System

In this paper we present a case study of frequent surges of unusually high rail-to-earth potential values at Taipei Rapid Transit System. The rail potential values observed and the resulting stray current flow associated with the diode-ground DC traction system during operation are contradictory to the moderate values on which the grounding of the DC traction system design was based. Thus we conducted both theoretical study and field measurements to obtain better understanding of the phenomenon, and to develop a more accurate algorithm for computing the rail-to-earth potential of the diode-ground DC traction systems.
Adolescent obesity and risk for subsequent major depressive disorder and anxiety disorder: prospective evidence.

To assess whether adolescent obesity is associated with risk for development of major depressive disorder (MDD) or anxiety disorder. Obesity has been linked to psychosocial difficulties among youth.



Analysis of a prospective community-based cohort originally from upstate New York, assessed four times over 20 years. Participants (n = 776) were 9 to 18 years old in 1983; subsequent assessments took place in 1985 to 1986 (n = 775), 1991 to 1994 (n = 776), and 2001 to 2003 (n = 661). Using Cox proportional hazards analysis, we evaluated the association of adolescent (age range, 12-17.99 years) weight status with risk for subsequent MDD or anxiety disorder (assessed at each wave by structured diagnostic interviews) in males and females. A total of 701 participants were not missing data on adolescent weight status and had > or = 1 subsequent assessments. MDD and anxiety disorder analyses included 674 and 559 participants (free of current or previous MDD or anxiety disorder), respectively. Adolescent obesity was defined as body mass index above the age- and gender-specific 95th percentile of the Centers for Disease Control and Prevention growth reference.



Adolescent obesity in females predicted an increased risk for subsequent MDD (adjusted hazard ratio (HR) = 3.9; 95% confidence interval (CI) = 1.3, 11.8) and for anxiety disorder (HR = 3.8; CI = 1.3, 11.3). Adolescent obesity in males was not statistically significantly associated with risk for MDD (HR = 1.5; CI = 0.5, 3.5) or anxiety disorder (HR = 0.7; CI = 0.2, 2.9).



Females obese as adolescents may be at increased risk for development of depression or anxiety disorders.
Big Data Deep Learning: Challenges and Perspectives

Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. In this paper, we provide a brief overview of deep learning, and highlight current research efforts and the challenges to big data, as well as the future trends.
The social sharing of emotion (SSE) in online social networks: A case study in Live Journal

Social Sharing of Emotion (SSE) occurs when one person shares an emotional experience with another and is considered potentially beneficial. Though social sharing has been shown prevalent in interpersonal communication, research on its occurrence and communication structure in online social networks is lacking. Based on a content analysis of blog posts (n = 540) in a blog social network site (Live Journal), we assess the occurrence of social sharing in blog posts, characterize different types of online SSE, and present a theoretical model of online SSE. A large proportion of initiation expressions were found to conform to full SSE, with negative emotion posts outnumbering bivalent and positive posts. Full emotional SSE posts were found to prevail, compared to partial feelings or situation posts. Furthermore, affective feedback predominated to cognitive and provided emotional support, empathy and admiration. The study found evidence that the process of social sharing occurs in Live Journal, replicating some features of face to face SSE. Instead of a superficial view of online social sharing, our results support a prosocial and beneficial character to online SSE.
ConvNet-Based Localization of Anatomical Structures in 3-D Medical Images

Localization of anatomical structures is a prerequisite for many tasks in a medical image analysis. We propose a method for automatic localization of one or more anatomical structures in 3-D medical images through detection of their presence in 2-D image slices using a convolutional neural network (ConvNet). A single ConvNet is trained to detect the presence of the anatomical structure of interest in axial, coronal, and sagittal slices extracted from a 3-D image. To allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling is applied. After detection, 3-D bounding boxes are created by combining the output of the ConvNet in all slices. In the experiments, 200 chest CT, 100 cardiac CT angiography (CTA), and 100 abdomen CT scans were used. The heart, ascending aorta, aortic arch, and descending aorta were localized in chest CT scans, the left cardiac ventricle in cardiac CTA scans, and the liver in abdomen CT scans. Localization was evaluated using the distances between automatically and manually defined reference bounding box centroids and walls. The best results were achieved in the localization of structures with clearly defined boundaries (e.g., aortic arch) and the worst when the structure boundary was not clearly visible (e.g., liver). The method was more robust and accurate in localization multiple structures.
Spatial parallel soft robotic architectures

In this paper we present a class of soft-robotic systems with superior load bearing capacity and expanded degrees of freedom. Spatial parallel soft robotic systems utilize spatial arrangement of soft actuators in a manner similar to parallel kinematic machines. In this paper we demonstrate that such an arrangement of soft actuators enhances stiffness and yield dramatic motions. The current work utilizes tri-chamber actuators made from silicone rubber to demonstrate the viability of the concept.
Power Supply for a High-Voltage Application

In this paper, the guidelines to design a high-voltage power converter based on the hybrid series parallel resonant topology, PRC-LCC, with a capacitor as output filter are established. As a consequence of the selection of this topology, transformer ratio, and therefore secondary volume, is reduced. The mathematical analysis provides an original equivalent circuit for the steady-state and dynamical behavior of the topology. A new way to construct high-voltage transformers is also proposed, pointing out the advantages and establishing an original method to evaluate the stray components of the transformer before construction. The way to make compatible the characteristics of both, topology and transformer is illustrated in the frame of a practical application. To demonstrate the feasibility of this solution, a high-voltage, high-power prototype is assembled and tested with good performance and similar behavior to the one predicted by the models. Experimental results are shown on this particular.
What does this Emoji Mean? A Vector Space Skip-Gram Model for Twitter Emojis

Emojis allow us to describe objects, situations and even feelings with small images, providing a visual and quick way to communicate. In this paper, we analyse emojis used in Twitter with distributional semantic models. We retrieve 10 millions tweets posted by USA users, and we build several skip gram word embedding models by mapping in the same vectorial space both words and emojis. We test our models with semantic similarity experiments, comparing the output of our models with human assessment. We also carry out an exhaustive qualitative evaluation, showing interesting results.
Planar high-frequency contactless inductive position sensor

This paper presents a low cost, precise and reliable inductive absolute position measurement system. It is suitable for rough industrial environments, offers a high inherent resolution (0.1 % to 0.01 % of antenna length), can measure target position over a wide measurement range and can potentially measure multiple target locations. The position resolution is improved by adding two additional finer pitched receive channels. The sensor works on principles similar to contactless resolvers. It consists of a rectangular antenna PCB and a passive LC resonance target. A mathematical model and the equivalent circuit of this kind of sensor is explained in detail. Such sensors suffer from transmitter to receiver coil capacitive crosstalk, which results in a phase sensitive offset. This crosstalk will be analyzed by a mathematical model and will be verified by measurements. Moreover, the mechanical transducer arrangement, the measurement setup and measured results will be presented.
A prospective ten-year clinical trial of porcelain veneers.

The aim of this in vivo study was to evaluate the clinical performance of porcelain veneers after 5 and 10 years of clinical service.



A single operator placed porcelain laminates on 87 maxillary anterior teeth in 25 patients. All restorations were recalled at 5 years and 93% of the restorations at 10 years. Clinical performance was assessed in terms of esthetics, marginal integrity, retention, clinical microleakage, caries recurrence, fracture, vitality, and patient satisfaction. Failures were recorded either as "clinically unacceptable but repairable" or as "clinically unacceptable with replacement needed".



Porcelain veneers maintained their esthetic appearance after 10 years of clinical service. None of the veneers were lost. The percentage of restorations that remained "clinically acceptable" (without need for intervention) significantly decreased from an average of 92% (95 CI: 90% to 94%) at 5 years to 64% (95 CI: 51% to 77%) at 10 years. Fractures of porcelain (11%) and large marginal defects (20%) were the main reason for failure. Marginal defects were especially noticed at locations where the veneer ended in an existing composite filling. At such vulnerable locations, severe marginal discoloration (19%) and caries recurrence (10%) were frequently observed. Most of the restorations that present one or more "clinically unacceptable" problems (28%) were repairable. Only 4% of the restorations needed to be replaced at the 10-year recall.



It was concluded that labial porcelain veneers represent a reliable, effective procedure for conservative treatment of unesthetic anterior teeth. Occlusion, preparation design, presence of composite fillings, and the adhesive used to bond veneers to tooth substrate are covariables that contribute to the clinical outcome of these restorations in the long-term.