Spam campaign detection, analysis, and formalization

Les courriels Spams (courriels indésirables ou pourriels) imposent des coûts annuels extrêmement lourds en termes de temps, d’espace de stockage et d’argent aux utilisateurs privés et aux entreprises. Afin de lutter efficacement contre le problème des spams, il ne suffit pas d’arrêter les messages de spam qui sont livrés à la boîte de réception de l’utilisateur. Il est obligatoire, soit d’essayer de trouver et de persécuter les spammeurs qui, généralement, se cachent derrière des réseaux complexes de dispositifs infectés, ou d’analyser le comportement des spammeurs afin de trouver des stratégies de défense appropriées. Cependant, une telle tâche est difficile en raison des techniques de camouflage, ce qui nécessite une analyse manuelle des spams corrélés pour trouver les spammeurs. Pour faciliter une telle analyse, qui doit être effectuée sur de grandes quantités des courriels non classés, nous proposons une méthodologie de regroupement catégorique, nommé CCTree, permettant de diviser un grand volume de spams en des campagnes, et ce, en se basant sur leur similarité structurale. Nous montrons l’efficacité et l’efficience de notre algorithme de clustering proposé par plusieurs expériences. Ensuite, une approche d’auto-apprentissage est proposée pour étiqueter les campagnes de spam en se basant sur le but des spammeur, par exemple, phishing. Les campagnes de spam marquées sont utilisées afin de former un classificateur, qui peut être appliqué dans la classification des nouveaux courriels de spam. En outre, les campagnes marquées, avec un ensemble de quatre autres critères de classement, sont ordonnées selon les priorités des enquêteurs. Finalement, une structure basée sur le semiring est proposée pour la représentation abstraite de CCTree. Le schéma abstrait de CCTree, nommé CCTree terme, est appliqué pour formaliser la parallélisation du CCTree. Grâce à un certain nombre d’analyses mathématiques et de résultats expérimentaux, nous montrons l’efficience et l’efficacité du cadre proposé.

Essays on market design and regulation in electricity systems

This thesis discusses market design and regulation in electricity systems, focusing on the information exchange of the regulated grid firm and the generation firms as well as the regulation of the grid firm. In the first chapter, an economic framework is developed to consistently analyze different market designs and the information exchange between the grid firm and the generation firms. Perfect competition between the generation firms and perfect regulation of the grid firm is assumed. A numerical algorithm is developed and its feasibility demonstrated on a large-scale problem. The effects of different market designs for the Central Western European (CWE) region until 2030 are analyzed. In the second chapter, the consequences of restricted grid expansion within the current market design in the CWE region until 2030 are analyzed. In the third chapter the assumption of efficient markets is modified. The focus of the analysis is then, whether and how inefficiencies in information availability and processing affect different market designs. For different parameter settings, nodal and zonal pricing are compared regarding their welfare in the spot and forward market. In the fourth chapter, information asymmetries between the regulator and the regulated firm are analyzed. The optimal regulatory strategy for a firm, providing one output with two substitutable inputs, is defined. Thereby, one input and the absolute quantity of inputs is not observable for the regulator. The result is then compared to current regulatory approaches.

HSP70 abundance and antioxidant capacity in feeding and fasting gray seal pups: suckling is associated with higher levels of key cellular defenses

Heat shock proteins (HSPs) and antioxidants are key cellular defenses against stress. Seals routinely undergo protracted fasting, which is normally associated with physiological stress in other animals. We tested the hypotheses that (1) relative HSP70 protein abundance is higher in liver and blubber of fasting relative to suckling wild gray seal pups; (2) differences in HSP70 are mirrored in tissue superoxide dismutase (SOD) and catalase activity, as well as glutathione levels; (3) extracellular HSP70 correlates with hepatic and blubber HSP70 abundance; and (4) protein carbonylation, an index of oxidative damage, is lower in tissues with higher levels of these cellular stress markers. In contrast to our expectation, suckling pups had higher relative HSP70 abundance and glutathione levels in liver and blubber and higher hepatic catalase activity. Plasma HSP70 did not correlate with liver or blubber abundance of the protein. Suckling pups did not experience greater protein carbonylation, suggesting that cellular protective mechanisms prevent protein damage despite an apparent increase in cellular stress. SOD activity was not affected by nutritional state, but in blubber tissue, it was positively correlated with blubber thickness. Greater requirements for antioxidants and HSPs in suckling pups or in animals with thicker blubber could arise from rapid protein synthesis, high metabolic fuel availability, and/or exposure to lipophilic toxins. Developmental and nutritional changes in cellular defenses have important implications for gray seals’ susceptibility to additional stress exposure.

Effects of mass and body composition on fasting fuel utilisation in grey seal pups (Halichoerus grypus Fabricius): an experimental study using supplementary feeding

This study used supplementary feeding to test the hypothesis that fuel partitioning during the postweaning fast in grey seal pups is affected by size and composition of energy reserves at weaning, and by extra provisioning. Mass and body composition changes were measured during suckling and fasting to investigate the effect of natural differences in energy reserves at weaning on subsequent allocation of fat and protein to energy use. We fed seven pups for 5 days after weaning, to investigate the effect of increased fuel availability, and particularly protein, on fuel utilisation. After correcting for protein used during the moult, the proportional contribution of fat was 86–99% of total energy use. Pups with greater energy reserves, i.e. those that were heavier and fatter at weaning, had higher rates of fat and energy use. There was no significant relationship between adiposity at weaning and proportional contribution of fat to energy use, perhaps due to a limited sample size or range of body masses and adiposity. Supplemented individuals used energy, specifically fat, much faster and utilised proportionally less of their endogenous protein by departure than non-supplemented individuals. Fat metabolism contributed a similar percentage to daily energy use in both groups. These findings show that pups spare protein, even when energy use is dramatically increased. Pups that receive greater maternal provisioning and lay down more protein may have increased survival chances at sea. This study highlights the importance of protein reserves in first year survival of grey seal pups.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

ISA Training: Hair and beauty trainees take advantage of technology

ISA Training based in south Wales is taking steps to upskill its staff and learners in digital literacies in order to make most effective use of technology in delivery and assessment of courses in hairdressing, beauty therapy and business management. To increase the flexibility and efficiency of its provision, the company has adopted the Learning Assistant e-portfolio system.

A Sense of Wonder: Enhancing Access to Folktales through Task and Facet Analysis

Discusses the approach taken in Phase 1 of a three-phase project Folktales, Facets and FRBR [funded by a grant from OCLC/ALISE]. This project works with the special collection of folktales at the Center for Children’s Books (CCB) at the University of Illinois at Urbana-Champaign, and the scholars who use this collection. The project aims to enhance the effectiveness and efficiency of folktale access through deep understanding of user needs. Phase 1 included facet analysis of the bibliographic records for a sample of 100 folktale books in the CCB, and task analysis of interviews with four CCB-affiliated faculty. Describes the information tasks, information seeking obstacles, and desired features for a discovery and access tool related to folktales for this initial group of scholarly users of folktales.

The fisheries of the Kyoga lakes and the likely impact on them by human activities in the catchment

A review of the historical development of the fishery resources of the Kyoga basin shows that the fishery based on the alien fishes which surplanted the indigenous species in the 1960's and early 1970's has been evolving. The record catches of the 19705 and early 1980's were mainly influenced by increased nutrient supply and enlarged lake volume. The sudden collapse in the fishery particularly of the Nile perch in the mid 1980's was most probably imposed by bad fishing regimes. However, future trends in the fisheries of the Kyoga basin will most likely be controlled by nutrient availability and dynamics as well as by the impact of mans activities in the Kyoga catchment.

Effects of density dependence, zooplankton and temperature on blue whiting Micromesistius poutassou growth

Blue whiting Micromesistius poutassou mean total length at age in the north-east Atlantic Ocean was found to vary by around ±6% during the period 2004–2011 and mean mass at age by ±22% during the years 1981–2013. Linear modelling provided strong evidence that these phenotypic growth variations can be explained by trophic conditions, mainly negative density dependence and also food availability, and a negative long-term temperature effect on asymptotic siz

Scheduling of track inspection and maintenance activities in railroad networks

The U.S. railroad companies spend billions of dollars every year on railroad track maintenance in order to ensure safety and operational efficiency of their railroad networks. Besides maintenance costs, other costs such as train accident costs, train and shipment delay costs and rolling stock maintenance costs are also closely related to track maintenance activities. Optimizing the track maintenance process on the extensive railroad networks is a very complex problem with major cost implications. Currently, the decision making process for track maintenance planning is largely manual and primarily relies on the knowledge and judgment of experts. There is considerable potential to improve the process by using operations research techniques to develop solutions to the optimization problems on track maintenance. In this dissertation study, we propose a range of mathematical models and solution algorithms for three network-level scheduling problems on track maintenance: track inspection scheduling problem (TISP), production team scheduling problem (PTSP) and job-to-project clustering problem (JTPCP). TISP involves a set of inspection teams which travel over the railroad network to identify track defects. It is a large-scale routing and scheduling problem where thousands of tasks are to be scheduled subject to many difficult side constraints such as periodicity constraints and discrete working time constraints. A vehicle routing problem formulation was proposed for TISP, and a customized heuristic algorithm was developed to solve the model. The algorithm iteratively applies a constructive heuristic and a local search algorithm in an incremental scheduling horizon framework. The proposed model and algorithm have been adopted by a Class I railroad in its decision making process. Real-world case studies show the proposed approach outperforms the manual approach in short-term scheduling and can be used to conduct long-term what-if analyses to yield managerial insights. PTSP schedules capital track maintenance projects, which are the largest track maintenance activities and account for the majority of railroad capital spending. A time-space network model was proposed to formulate PTSP. More than ten types of side constraints were considered in the model, including very complex constraints such as mutual exclusion constraints and consecution constraints. A multiple neighborhood search algorithm, including a decomposition and restriction search and a block-interchange search, was developed to solve the model. Various performance enhancement techniques, such as data reduction, augmented cost function and subproblem prioritization, were developed to improve the algorithm. The proposed approach has been adopted by a Class I railroad for two years. Our numerical results show the model solutions are able to satisfy all hard constraints and most soft constraints. Compared with the existing manual procedure, the proposed approach is able to bring significant cost savings and operational efficiency improvement. JTPCP is an intermediate problem between TISP and PTSP. It focuses on clustering thousands of capital track maintenance jobs (based on the defects identified in track inspection) into projects so that the projects can be scheduled in PTSP. A vehicle routing problem based model and a multiple-step heuristic algorithm were developed to solve this problem. Various side constraints such as mutual exclusion constraints and rounding constraints were considered. The proposed approach has been applied in practice and has shown good performance in both solution quality and efficiency.

Modelling the relationship between biomechanics and performance of young sprinting swimmers

The aim of this study was to compute a swimming performance confirmatory model based on biomechanical parameters. The sample included 100 young swimmers (overall: 12.3 ± 0.74 years; 49 boys: 12.5 ± 0.76 years; 51 girls: 12.2 ± 0.71 years; both genders in Tanner stages 1-2 by self-report) participating on a regular basis in regional and national-level events. The 100 m freestyle event was chosen as the performance indicator. Anthropometric (arm span), strength (throwing velocity), power output (power to overcome drag), kinematic (swimming velocity) and efficiency (propelling efficiency) parameters were measured and included in the model. The path-flow analysis procedure was used to design and compute the model. The anthropometric parameter (arm span) was excluded in the final model, increasing its goodness-of-fit. The final model included the throw velocity, power output, swimming velocity and propelling efficiency. All links were significant between the parameters included, but the throw velocity-power output. The final model was explained by 69% presenting a reasonable adjustment (model's goodness-of-fit; x(2)/df = 3.89). This model shows that strength and power output parameters do play a mediator and meaningful role in the young swimmers' performance.

Protein encapsulation and release from PEO-b-polyphosphoester templated calcium carbonate particles

International audience

A study of ignorance: suffering and freedom in early Buddhist teachings and parallels in modern neuroscience

What might early Buddhist teachings offer neuroscience and how might neuroscience inform contemporary Buddhism? Both early Buddhist teachings and cognitive neuroscience suggest that the conditioning of our cognitive apparatus and brain plays a role in agency that may be either efficacious or non-efficacious. Both consider internal time to play a central role in the efficacy of agency. Buddhism offers an approach that promises to increase the efficacy of agency. This approach is found in five early Buddhist teachings that are re-interpreted here with a view to explaining how they might be understood as a dynamic basis for ‘participatory will’ in the context of existing free will debates and the neuroscientific work of Patrick Haggard (et al.). These perspectives offer Buddhism and neuroscience a basis for informing each other as the shared themes of: (1) cognition is dynamic and complex/aggregate based, (2) being dynamic, cognition lacks a fixed basis of efficacy, and (3) efficacy of cognition may be achieved by an understanding of the concept of dynamic: as harmony and efficiency and by means of Buddha-warranted processes that involve internal time.

Résultats de la mise en oeuvre des réseaux REPHY et SRN – Zones côtières de la Manche orientale et de la baie sud de la Mer du Nord – Bilan de l’année 2015

The coastal zone of the Nord – Pas de Calais / Picardie showed dysfonctioning patterns of the ecosystem considered to be link to human activities along shores. These results in regular massive development of species, such as the phytoplanktonic seaweed, Phaeocystis sp. which life cycle was partly linked to nutrients availability and consequently to anthropogenic inputs. As part of the evaluation of the influence of continental inputs on the marine environment (nitrates, phosphates,…) and on potential eutrophication processes, of the estimation of the efficiency of the sewage treatments plants in the possible elimination of dumpings and in order to establish a long-term survey to follow up the change in coastal waters quality, the regional nutrients monitoring network was implemented by Ifremer in collaboration with the Agence de l'Eau Artois-Picardie in 1992 in order to complete the REPHY (Phytoplankton and Phycotoxins) monitoring programme. This study reports the main results for the year 2015 in terms of temporal change of the main physico-chemical and biological parameters characteristic of water masses sampled along three transects offshore Dunkerque, Boulogne-sur-Mer and the Bay of Somme.

Traditional part-time grazing: a more sustainable sheep milk and cheese production in the Basque Country

[EN] The concept of sustainability when referring to food production rests, in general, on 3 main aspects: 1) respect for the environment; 2) economic and social benefits for all involved in production; and 3) production of sufficient quantity of quality food at an accessible price. In this contribution we focus on the main aspects of the traditional sheep's milk and cheese production (under the Denomination of Origin Idiazabal Cheese) in the Basque Country that contribute primarily to its sustainability. It is based on the local latxa or carranzana breeds of sheep, adapted to the mountainous terrain. The sheepherder takes advantage of local resources to reduce management costs by combining indoor dry forage and concentrates with outdoor grazing throughout lactation, according to local pasture availability, and thus avoiding having to buy large amounts of feed. This system facilitates recycling of manure, fertilising pastures and forest at the same time. Use of local breeds helps maintain biodiversity of sheep breeds. Cheese is produced industrially (44.5% of the total cheese produced in 2008) from milk of many flocks, or artisanally (38.3%) by the sheepherders with the milk from their own flocks. Transforming their own milk into cheese is advantageous for the following reasons: 1) higher economic returns as compared to selling the milk to cheese factories because cheese price directly sold to consumers is more competitive than industrial cheese sold in supermarkets; 2) increases the value of women's work (over 80% of the cheese makers are women) in the community and their self-esteem; 3) it creates rural jobs and contributes to rural development; 4) we have demonstrated both with experimental and commercial flocks that part-time grazing allows the sheepherder to obtain high yields of milk, and cheese, of high nutritional and functional quality. Currently a less sustainable, intensive sheep's milk production with foreign, imported breeds kept indoors constantly is gaining favour among milk producers because of its perceived higher economic profitability.