Identifying reusable services from collaborative activities using activity theory (AT): The Activity-Based Service Identification Framework (ASIF)

Qualitative data

Identifying reusable services from collaborative activities using activity theory (AT): The Activity-Based Service Identification Framework (ASIF)

Qualitative data

LandmarkMiner

High-confidence network landmarks are the basis of IP geolocation. However, existing landmarks acquisition methods had weakness such as high time cost and insufficient landmarks number. For this, LandmarkMiner, a street-level network landmarks mining method, is proposed based on service identification and domain name association. First, LandmarkMiner trains classifiers using the scanning results of IPs with known hosting service type, identifies the hosting service type of target IPs using the trained classifiers, and obtains the classified IPs’ domain names using DNS. Then, according to institutional names, a database associating institutional name with possible domain names is built by statistical relationship, which is obtained between the known institutional names and their domain names. Finally, geographical location of IP's domain name after classification is matched in the database and online maps, thereby obtaining landmarks and evaluating reliability of them. LandmarkMiner has mined 9,423 reliable street-level landmarks from 304M IPs in 18 cities. Comparing with existing methods, LandmarkMiner increases the number of reliable street-level landmarks significantly and can be applied in different network connectivity conditions.

ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network

The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work.

Unravelling Diverse Values of Ecosystem Services: A Socio-Cultural Valuation Using Q Methodology in Messenia, Greece

People perceive the importance of benefits from ecosystem services in different ways, depending on their values, beliefs, and needs. Acknowledging and integrating this diversity into decision-making processes can support informed natural resource management. Our empirical study unpicks the multiple ways stakeholder groups perceive the benefits derived from wetland ecosystem services (WES) in the area surrounding the “Gialova” coastal wetland in Messenia, Greece. The inhabitants from this region benefit from a range of WES, and most livelihoods are closely linked to agriculture and tourism. We aim to understand the patterns in commonly held stakeholder views on WES using “Q methodology”, a participatory mixed-methods approach. We identified five distinct perspectives on WES from a sample of 32 stakeholders. Alongside diverse perceptions of the relative importance of different WES, we observed a range of explanations of why certain WES are important and analyzed these through the lens of “value pluralism”. This identified tension between relational and instrumental values. Such analyses move beyond ecosystem service identification towards an understanding of value justifications and conflicts, and can support the deliberation of conflicted views, and policy design in alignment with people’s values.