Inferring Unusual Crowd Events From Mobile Phone Call Detail Records

The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behaviors in urban environments. Cities can leverage such knowledge in order to provide better services (e.g., public transport planning, optimized resource allocation) and safer cities. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we provide a methodology that is able to detect unusual events from CDR data that typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10 times higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.Comment: 18 pages, 6 figure

The Need for Compliance Verification in Collaborative Business Processes

Compliance constrains processes to adhere to rules, standards, laws and regulations. Non-compliance subjects enterprises to litigation and financial fines. Collaborative business processes cross organizational and regional borders implying that internal and cross regional regulations must be complied with. To protect customs’ data, European enterprises must comply with the EU data privacy regulation (general data protection regulation - GDPR) and each member state’s data protection laws. An example of non-compliance with GDPR is Facebook, it is accused for breaching subscriber trust. Compliance verification is thus essential to deploy and implement collaborative business process systems. It ensures that processes are checked for conformance to compliance requirements throughout their life cycle. In this paper we take a proactive approach aiming to discuss the need for design time preventative compliance verification as opposed to after effect runtime detective approach. We use a real-world case to show how compliance needs to be analyzed and show the benefits of applying compliance check at the process design stag

Aligning Monitoring and Compliance Requirements in Evolving Business Networks

Dynamic business networks (BNs) are intrinsically characterised by change. Compliance requirements management, in this context, may become particularly challenging. Partners in the network may join and leave the collaboration dynamically and tasks over which compliance requirements are specified may be consequently delegated to new partners or backsourced by network participants. This paper considers the issue of aligning the compliance requirements in a BN with the monitoring requirements they induce on the BN participants when change (or evolution) occurs. We first provide a conceptual model of BNs and their compliance requirements, introducing the concept of monitoring capabilities induced by compliance requirements. Then, we present a set of mechanisms to ensure consistency between the monitoring and compliance requirements when BNs evolve, e.g. tasks are delegated or backsourced in-house. Eventually, we discuss a prototype implementation of our framework, which also implements a set of metrics to check the status of a BN in respect of compliance monitorability

The Need for Compliance Verification in Collaborative Business Processes

Compliance constrains processes to adhere to rules, standards, laws and regulations. Non-compliance subjects enterprises to litigation and financial fines. Collaborative business processes cross organizational and regional borders implying that internal and cross regional regulations must be complied with. To protect customs’ data, European enterprises must comply with the EU data privacy regulation (general data protection regulation - GDPR) and each member state’s data protection laws. An example of non-compliance with GDPR is Facebook, it is accused for breaching subscriber trust. Compliance verification is thus essential to deploy and implement collaborative business process systems. It ensures that processes are checked for conformance to compliance requirements throughout their life cycle. In this paper we take a proactive approach aiming to discuss the need for design time preventative compliance verification as opposed to after effect runtime detective approach. We use a real-world case to show how compliance needs to be analyzed and show the benefits of applying compliance check at the process design stag

Secondary syphilis presenting as pseudolymphoma of the skin.

Secondary syphilis most commonly presents with a papulosquamous eruption that involves the palms, soles, and mucous membranes. The papulonodular variant has only been described 11 times in the literature. We describe a case of papulonodular secondary syphilis presenting as an atypical lymphoid hyperplasia suggestive of cutaneous lymphoma

Wireless Sensor Network using DRINA

On demand routing protocols give climbable and price effective solutions for transferring packets in mobile spontaneous networks (MANET). A wireless detector network could be a assortment of distributed nodes to watch and additionally to transmit their information from detector network to a sink node. In wireless detector network, detector nodes area unit set close to every different and additionally act with one another through information routing. In wireless detector network, the information routing takes place in non-aggregated manner would force a lot of energy. Energy conservation is that the major issue in wireless detector network. During this work we have a tendency to propose jury-rigged information routing with in-network aggregation formula which may address this energy consumption issue. It uses information aggregation technique and it will be effective in routing. so information aggregation is beneficial for increasing information accuracy, elimination of information redundancy, and reduction of communication load alongside reducing energy consumption

GoCo: planning expressive commitment protocols

Acknowledgements We gratefully thank those who shared their code with us. Special thanks to Ugur Kuter. We thank the anonymous reviewers, and also acknowledge with gratitude the reviewers at ProMAS’11, AAMAS’13, AAAI’13, and AAMAS’15, where preliminary parts of this work appeared. FM thanks the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the support within process numbers 306864/2013-4 under the PQ fellowship and 482156/2013-9 under the Universal project programs. NYS acknowledges support of the AUB University Research Board Grant Number 102853 and the OSB Grant OFFER_C1_2013_2014.Peer reviewe

Fructose-2,6-Bisphosphate Synthesis by 6-Phosphofructo-2-Kinase/Fructose-2,6-Bisphosphatase 4 (PFKFB4) Is Required for the Glycolytic Response to Hypoxia and Tumor Growth

Fructose-2,6-bisphosphate (F2,6BP) is a shunt product of glycolysis that allosterically activates 6-phosphofructo-1-kinase (PFK-1) resulting in increased glucose uptake and glycolytic flux to lactate. The F2,6BP concentration is dictated by four bifunctional 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatases (PFKFB1-4) with distinct kinase:phosphatase activities. PFKFB4 is over-expressed in human cancers, induced by hypoxia and required for survival and growth of several cancer cell lines. Although PFKFB4 appears to be a rational target for anti-neoplastic drug development, it is not clear whether its kinase or phosphatase activity is required for cancer cell survival. In this study, we demonstrate that recombinant human PFKFB4 kinase activity is 4.3-fold greater than its phosphatase activity, siRNA and genomic deletion of PFKFB4 decrease F2,6BP, PFKFB4 over-expression increases F2,6BP and selective PFKFB4 inhibition in vivo markedly reduces F2,6BP, glucose uptake and ATP. Last, we find that PFKFB4 is required for cancer cell survival during the metabolic response to hypoxia, presumably to enable glycolytic production of ATP when the electron transport chain is not fully operational. Taken together, our data indicate that the PFKFB4 expressed in multiple transformed cells and tumors functions to synthesize F2,6BP. We predict that pharmacological disruption of the PFKFB4 kinase domain may have clinical utility for the treatment of human cancers

Anomaly Detection in Social Media Using Recurrent Neural Network

© 2019, Springer Nature Switzerland AG. In today’s information environment there is an increasing reliance on online and social media in the acquisition, dissemination and consumption of news. Specifically, the utilization of social media platforms such as Facebook and Twitter has increased as a cutting edge medium for breaking news. On the other hand, the low cost, easy access and rapid propagation of news through social media makes the platform more sensitive to fake and anomalous reporting. The propagation of fake and anomalous news is not some benign exercise. The extensive spread of fake news has the potential to do serious and real damage to individuals and society. As a result, the detection of fake news in social media has become a vibrant and important field of research. In this paper, a novel application of machine learning approaches to the detection and classification of fake and anomalous data are considered. An initial clustering step with the K-Nearest Neighbor (KNN) algorithm is proposed before training the result with a Recurrent Neural Network (RNN). The results of a preliminary application of the KNN phase before the RNN phase produces a quantitative and measureable improvement in the detection of outliers, and as such is more effective in detecting anomalies or outliers against the test dataset of 2016 US Presidential Election predictions

Targeting cancer metabolism: a therapeutic window opens

Genetic events in cancer activate signalling pathways that alter cell metabolism. Clinical evidence has linked cell metabolism with cancer outcomes. Together, these observations have raised interest in targeting metabolic enzymes for cancer therapy, but they have also raised concerns that these therapies would have unacceptable effects on normal cells. However, some of the first cancer therapies that were developed target the specific metabolic needs of cancer cells and remain effective agents in the clinic today. Research into how changes in cell metabolism promote tumour growth has accelerated in recent years. This has refocused efforts to target metabolic dependencies of cancer cells as a selective anticancer strategy.Burroughs Wellcome FundSmith Family FoundationStarr Cancer ConsortiumDamon Runyon Cancer Research FoundationNational Institutes of Health (U.S.