scholarly journals Computing top-k temporal closeness in temporal networks

2022 ◽  
Author(s):  
Lutz Oettershagen ◽  
Petra Mutzel
Keyword(s):  
Real World ◽  
High Probability ◽  
Transitive Closure ◽  
Closeness Centrality ◽  
Data Sets ◽  
Temporal Networks ◽  
Real World Data ◽  
World Data ◽  
Temporal Domain ◽  
Vertex Sets

AbstractThe closeness centrality of a vertex in a classical static graph is the reciprocal of the sum of the distances to all other vertices. However, networks are often dynamic and change over time. Temporal distances take these dynamics into account. In this work, we consider the harmonic temporal closeness with respect to the shortest duration distance. We introduce an efficient algorithm for computing the exact top-k temporal closeness values and the corresponding vertices. The algorithm can be generalized to the task of computing all closeness values. Furthermore, we derive heuristic modifications that perform well on real-world data sets and drastically reduce the running times. For the case that edge traversal takes an equal amount of time for all edges, we lift two approximation algorithms to the temporal domain. The algorithms approximate the transitive closure of a temporal graph (which is an essential ingredient for the top-k algorithm) and the temporal closeness for all vertices, respectively, with high probability. We experimentally evaluate all our new approaches on real-world data sets and show that they lead to drastically reduced running times while keeping high quality in many cases. Moreover, we demonstrate that the top-k temporal and static closeness vertex sets differ quite largely in the considered temporal networks.

scholarly journals Hfinger: Malware HTTP Request Fingerprinting

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 507
Author(s):  
Piotr Białczak ◽  
Wojciech Mazurczyk
Keyword(s):  
Real World ◽  
Network Traffic ◽  
Experimental Evaluation ◽  
Data Sets ◽  
Real World Data ◽  
Malicious Software ◽  
Default Mode ◽  
World Data ◽  
Effectiveness Analysis ◽  
Http Protocol

Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. It extracts information from the parts of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8–34 times lower than existing tools. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times. As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools.

Comparison of M2M Traffic Models Against Real World Data Sets

2018 ◽  
Author(s):  
Marco Sansoni ◽  
Giuseppe Ravagnani ◽  
Daniel Zucchetto ◽  
Chiara Pielli ◽  
Andrea Zanella ◽  
...  
Keyword(s):  
Real World ◽  
Data Sets ◽  
Real World Data ◽  
Traffic Models ◽  
World Data

Activities for Students: As the Ball Rolls: A Quadratic Investigation Using Multiple Representations

2009 ◽  
Vol 103 (1) ◽  
pp. 62-68
Author(s):  
Kathleen Cage Mittag ◽  
Sharon Taylor
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Physical Model ◽  
Real World ◽  
Graphing Calculator ◽  
Data Sets ◽  
Real World Data ◽  
World Data ◽  
Hands On ◽  
Modeling Data

Using activities to create and collect data is not a new idea. Teachers have been incorporating real-world data into their classes since at least the advent of the graphing calculator. Plenty of data collection activities and data sets exist, and the graphing calculator has made modeling data much easier. However, the authors were in search of a better physical model for a quadratic. We wanted students to see an actual parabola take shape in real time and then explore its characteristics, but we could not find such a hands-on model.

scholarly journals Comparing ICP variants on real-world data sets

2013 ◽  
Vol 34 (3) ◽  
pp. 133-148 ◽  
Author(s):  
François Pomerleau ◽  
Francis Colas ◽  
Roland Siegwart ◽  
Stéphane Magnenat
Keyword(s):  
Real World ◽  
Data Sets ◽  
Real World Data ◽  
World Data

Verification of statistical oncological endpoints on encrypted data: Confirming the feasibility of real-world data sharing without the need to reveal protected patient information.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18725-e18725
Author(s):  
Ravit Geva ◽  
Barliz Waissengrin ◽  
Dan Mirelman ◽  
Felix Bokstein ◽  
Deborah T. Blumenthal ◽  
...  
Keyword(s):  
Data Sharing ◽  
Real World ◽  
Clinical Decision Making ◽  
Homomorphic Encryption ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Raw Data ◽  
Encrypted Data ◽  
World Data

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]

scholarly journals Accurate prediction of chemical shifts for aqueous protein structure on “Real World” data

2020 ◽  
Vol 11 (12) ◽  
pp. 3180-3191 ◽  
Author(s):  
Jie Li ◽  
Kochise C. Bennett ◽  
Yuchen Liu ◽  
Michael V. Martin ◽  
Teresa Head-Gordon
Keyword(s):  
Protein Structure ◽  
Chemical Shift ◽  
Real World ◽  
Chemical Shifts ◽  
Accurate Prediction ◽  
Data Sets ◽  
Real World Data ◽  
World Data ◽  
Nmr Chemical Shifts

UCBShift predicts NMR chemical shifts of proteins that exceeds accuracy of other popular chemical shift predictors on real-world data sets.

DETERMINING THE DIMENSIONS OF VARIABLES IN PHYSICS ALGEBRAIC EQUATIONS

2005 ◽  
Vol 14 (01n02) ◽  
pp. 25-41 ◽  
Author(s):  
C. W. LIEW ◽  
JOEL A. SHAPIRO ◽  
D. E. SMITH
Keyword(s):  
Large Class ◽  
Real World ◽  
Constraint Propagation ◽  
Data Sets ◽  
Algebraic Equations ◽  
Tutoring System ◽  
Real World Data ◽  
Tutoring Systems ◽  
World Data ◽  
Algebraic Solutions

This paper describes work on methods that evaluate algebraic solutions to word problems in physics. Many current tutoring systems rely on substantial scaffolding and consequently require students to completely describe every variable used in the solution. A heuristic, based on constraint propagation, capable of inferring the description of variables (i.e., the possible dimensions and physics concepts) is shown to be highly reliable on three real world data sets, one covering a few problems with a small number of student answers and two others covering a large class of problems (~100) with a large number of student answers (~11,000). The heuristic uniquely determines the dimensions of all the variables in 91–92% of the equation sets. By asking the student for dimension information about one variable, an additional 3% of the sets can be determined. An ITS tutoring system can use this heuristic to reason about a student's answers even when the scaffolding and context are removed.

CO-CLUSTERING BIPARTITE WITH PATTERN PRESERVATION FOR TOPIC EXTRACTION

2008 ◽  
Vol 17 (01) ◽  
pp. 87-107 ◽  
Author(s):  
TIANMING HU ◽  
CHEW LIM TAN ◽  
YONG TANG ◽  
SAM YUAN SUNG ◽  
HUI XIONG ◽  
...  
Keyword(s):  
Real World ◽  
Data Sets ◽  
Real World Data ◽  
Topic Extraction ◽  
World Data ◽  
Search Results ◽  
Word Clustering ◽  
Two Sides ◽  
Clustering Validation ◽  
Single Cluster

The duality between document and word clustering naturally leads to the consideration of storing the document dataset in a bipartite. With documents and words modeled as vertices on two sides respectively, partitioning such a graph yields a co-clustering of words and documents. The topic of each cluster can then be represented by the top words and documents that have highest within-cluster degrees. However, such claims may fail if top words and documents are selected simply because they are very general and frequent. In addition, for those words and documents across several topics, it may not be proper to assign them to a single cluster. In other words, to precisely capture the cluster topic, we need to identify those micro-sets of words/documents that are similar among themselves and as a whole, representative of their respective topics. Along this line, in this paper, we use hyperclique patterns, strongly affiliated words/documents, to define such micro-sets. We introduce a new bipartite formulation that incorporates both word hypercliques and document hypercliques as super vertices. By co-preserving hyperclique patterns during the clustering process, our experiments on real-world data sets show that better clustering results can be obtained in terms of various external clustering validation measures and the cluster topic can be more precisely identified. Also, the partitioned bipartite with co-preserved patterns naturally lends itself to different clustering-related functions in search engines. To that end, we illustrate such an application, returning clustered search results for keyword queries. We show that the topic of each cluster with respect to the current query can be identified more accurately with the words and documents from the patterns than with those top ones from the standard bipartite formulation.

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