Miracle-dz/UniFAR-FTUs huggingface dataset:
THE PAN-ALPINE CONSERVATION STRATEGY FOR THE LYNX

This Pan-Alpine Conservation Strategy for the Lynx (PACS) bases on two ideas: (1) no Alpine country can host a viable lynx population in isolation all regional populations will be transboundary; and (2) international co-operation is essential for the conservation of shared populations, and even more to solve the mutual problems. The goal is to re-establish and maintain, in co-existence with people, a vital lynx population covering the whole of the Alpine arc. This general goal can be split in four objectives: (1) The lynx populations in Slovenia and Switzerland maintain their vitality and must be helped to expand. (2) The populations in Slovenia and Switzerland are joined through colonisation of the area in between (Alps of Austria, Germany, Italy and Liechtenstein). (3) This unified population in the central Alps is allowed to expand to the north-east (Austria) and the south-west (France, Italy). (4) Gene flow is assured between the Alpine sub-populations and the population of Slovenia and Croatia, the population of the Jura Mountains and the population of the Bohemian/Bavarian forest. THE PAN-ALPINE CONSERVATION STRATEGY FOR THE LYNX Anja Molinari-Jobin, Paolo Molinari, Christine Breitenmoser-Würsten, Manfred Wölfl, Cvetko Stanisa, Michael Fasel, Philippe Stahl, Jean-Michel Vandel, Luca Rotelli, Petra Kaczensky, Thomas Huber, Miha Adamic, Iztok Koren and Urs Breitenmoser Convention on the Conservation of European Wildlife and Natural Habitats (Bern Convention) Nature and environment, No. 130 Council of Europe Publishing French edition: La stratégie panalpine pour la conservation du lynx
A Model-Based Automated Debug Process

As components, boards and systems become more and more complex; the cost of troubleshooting failures is getting greater and greater. Test strategies try to detect and diagnose failures early on in the test process (component and structural tests), but inevitably some defects are not detected until functional testing is done on the board. Finding these defects usually requires an “expert”, with engineering level skills in both hardware and software. Depending on the complexity of the product, it could take several months (even years) to develop this level of expertise. During the initial product ramp, this expertise is usually most needed and often unavailable. Debug time is usually very long and scrap rates are generally high.
Assur ' s-groups-based simulation for teaching kinematics of planar linkages

In this work we present a simulation tool based on the method of Assur's groups to support teaching activities in the field of the kinematics of planar linkages. The tool is embedded in SIMULINK ad uses the block-oriented approach that is central both in SIMULINK modeling and in Assur's method. For each component useful for simulation according to Assur's method a SIMULINK masked block has been developed using subsystems and embedded MATLAB functions. Simple graphics is ready to use. Several examples are provided.
Automatic/Control Processing and Attention.

Abstract : Automatic/controlled processing theory is reviewed with emphasis on applications to research on attention. Automatic/controlled processing theory assumes that human performance is the result of two qualitatively different processes; automatic and controlled processing. Automatic processing is a fast, parallel process not limited by short term memory. Automatic processing uses little subject effort, permits little direct subject control, but requires extensive and consistent training to develop. Controlled processing is a comparatively slow, serial process limited by short term memory. Controlled processing requires subject effort, permits a large degree of subject control, but needs little training to develop. Attention paradigms discussed include selective attention, focused attention, and attentional capacity. Conclusions from the application of automatic and controlled processing determines performance; paradigms suggest that: (1) performance differs to the degree that automatic or controlled processing determines performance; (2) performance improves with extensive consistent practice; (3) automatic processes are difficult to control; and (4) capacity reductions primarily harm controlled processing. The development of automatic processing is examined, and performance is seen to improve as a function of consistent executions.
Fluid: A Blockchain based Framework for Crowdsourcing

Recently, crowdsourcing has emerged as a new computing paradigm to solve problems that need human intrinsic, such as image annotation. However, there are two limitations in existing crowdsourcing platforms, i.e. non-transparent incentive mechanism and isolated profiles of workers, which harms the interests of both requesters and workers. Meanwhile, Blockchain technology introduces a solution to build a transparent, immutable data model in the Byzantine environment. Moreover, Blockchain systems (e.g. Ethereum) can also support the Tuning-complete script called smart contracts. Thus, we are motivated to use the feature of the transparent data model and smart contract in Blockchain to address the two limitations. Based on the proposed solutions, we have designed a Blockchain based framework which supports foundations of general crowdsourcing platforms. In addition, our framework also has following novel features: (1) it provides the transparent incentive mechanisms; (2) it supports a trusted worker's profile sharing in a cross-platform mode.
Fractal: A General-Purpose Graph Pattern Mining System

In this paper we propose Fractal, a high performance and high productivity system for supporting distributed graph pattern mining (GPM) applications. Fractal employs a dynamic (auto-tuned) load-balancing based on a hierarchical and locality-aware work stealing mechanism, allowing the system to adapt to different workload characteristics. Additionally, Fractal enumerates subgraphs by combining a depth-first strategy with a from scratch processing paradigm to avoid storing large amounts of intermediate state and, thus, improves memory efficiency. Regarding programmer productivity, Fractal presents an intuitive, expressive and modular API, allowing for rapid compositional expression of many GPM algorithms. Fractal-based implementations outperform both existing systemic solutions and specialized distributed solutions on many problems - from frequent graph mining to subgraph querying, over a range of datasets.
Helios: An Adaptive and Query Workload-driven Partitioning Framework for Distributed Graph Stores

In this paper, we present a novel adaptive and workload-driven partitioning framework, named Helios, aiming to achieve low-latency and high-throughput online queries in distributed graph stores. As each workload typically contains popular or similar queries, our partitioning method uses the existing workload to capture active vertices and edges which are frequently visited and traversed respectively. This information is used to heuristically improve the quality of partitions either by avoiding the concentration of active vertices in a few partitions proportional to their visit frequencies or by reducing the probability of the cut of active edges proportional to their traversal frequencies. In order to assess the impact of Helios on a graph store, and to show how easily the approach can be plugged on top of the system, we exploit it in a distributed, graph-based RDF store. The query engine of the store exploits Helios to reduce or eliminate data communication for future queries and balance the load among nodes. We evaluate the store by using realistic query workloads over an RDF dataset. Our results demonstrate the ability of Helios to handle varying query workloads with minimum overhead while maintaining the quality of partitions over time, along with being scalable by increasing either the data size or the number of computing nodes.
RaSQL: Greater Power and Performance for Big Data Analytics with Recursive-aggregate-SQL on Spark

Thanks to a simple SQL extension, Recursive-aggregate-SQL (RaSQL) can express very powerful queries and declarative algorithms, such as classical graph algorithms and data mining algorithms. A novel compiler implementation allows RaSQL to map declarative queries into one basic fixpoint operator supporting aggregates in recursive queries. A fully optimized implementation of this fixpoint operator leads to superior performance, scalability and portability. Thus, our RaSQL system, which extends Spark SQL with the before-mentioned new constructs and implementation techniques, matches and often surpasses the performance of other systems, including Apache Giraph, GraphX and Myria.
LiveNature: Ambient Display and Social Robot-Facilitated Multi-Sensory Engagement for People with Dementia

The wellbeing of people with dementia in long-term care facilities is hindered, as they spend most of their time alone with little engagement in meaningful activities and an absence of pleasant sensory stimulation. We designed an interactive system called LiveNature that adopts a novel combined approach involving an ambient display unit and an interactive robotic sheep, to offer long-term access and to engage people with dementia in long-term care facilities in rewarding experiences. LiveNature aims to provide holistic multi-sensory engagement to provoke positive emotions, increase social bonding, and restore attentiveness and communication. The design was implemented within a Dutch nursing home. An evaluation of the user experience and the effectiveness of the design was conducted in a real-life setting with nine participants, five family members, two caregivers and four volunteers, using observational rating scales and semi-structured interviews. The results of the rating scales and the findings from qualitative data showed evidence of enhanced positive engagement.
Schemas and Types for JSON Data: From Theory to Practice

The last few years have seen the fast and ubiquitous diffusion of JSON as one of the most widely used formats for publishing and interchanging data, as it combines the flexibility of semistructured data models with well-known data structures like records and arrays. The user willing to effectively manage JSON data collections can rely on several schema languages, like JSON Schema, JSound, and Joi, as well as on the type abstractions offered by modern programming and scripting languages like Swift or TypeScript. The main aim of this tutorial is to provide the audience (both researchers and practitioners) with the basic notions for enjoying all the benefits that schema and types can offer while processing and manipulating JSON data. This tutorial focuses on four main aspects of the relation between JSON and schemas: (1) we survey existing schema language proposals and discuss their prominent features; (2) we analyze tools that can infer schemas from data, or that exploit schema information for improving data parsing and management; and (3) we discuss some open research challenges and opportunities related to JSON data.
Peering through the Dark: An Owl's View of Inter-job Dependencies and Jobs' Impact in Shared Clusters

Shared multi-tenant infrastructures have enabled companies to consolidate workloads and data, increasing data-sharing and cross-organizational re-use of job outputs. This same resource- and work-sharing has also increased the risk of missed deadlines and diverging priorities as recurring jobs and workflows developed by different teams evolve independently. To prevent incidental business disruptions, identifying and managing job dependencies with clarity becomes increasingly important. Owl is a cluster log analysis and visualization tool that (i) extracts and visualizes job dependencies derived from historical job telemetry and data provenance data sets, and (ii) introduces a novel job valuation algorithm estimating the impact of a job on dependent users and jobs. This demonstration showcases Owl's features that can help users identify critical job dependencies and quantify job importance based on jobs' impact.
FamilySong: Designing to Enable Music for Connection and Culture in Internationally Distributed Families

FamilySong aims to connect internationally distributed family groups via synchronized music-listening. It fosters feelings of togetherness and mutual belonging, that is, connection and culture. We conceptualize it as a domestic Media Space with no live audio or video. It emphasizes intimacy without intrusion, and lives within an existing ecology of interactive technologies. The design journey includes both autobiographical and research-through-design components with similarly-structured family groups (very young children with parents located in the United States and grandparents in Ecuador). Parents, children and grandparents all participated both in the moment and in subsequent interaction grounded in the FamilySong experience. Grandparents took the lead in expressing the importance of the values and goals they saw embedded in the system. This work presents a design that illuminates what it means to connect like a family in which values, needs and priorities are interdependent, and joy and delight are important.
Oblivious dimension reduction for k-means: beyond subspaces and the Johnson-Lindenstrauss lemma

We show that for n points in d-dimensional Euclidean space, a data oblivious random projection of the columns onto m∈ O((logk+loglogn)ε−6log1/ε) dimensions is sufficient to approximate the cost of all k-means clusterings up to a multiplicative (1±ε) factor. The previous-best upper bounds on m are O(logn· ε−2) given by a direct application of the Johnson-Lindenstrauss Lemma, and O(kε−2) given by [Cohen et al.-STOC’15].
BlueThinking, a programming tool for the development of executive functions at childhood

Research shows that teaching computer programming to children help them develop important 21st century skills such as planning, problem solving, and flexibility of thinking. However, teaching these skills to young children is not an easy task as the existing tools in the market do not seem to be well adapted for them. The aim of this work is to present BlueThinking, an inclusive application to learn programming at early stages. A preliminary evaluation with 5 and 6 years old children was carried out to get the first impressions of the application and detect possible issues. The results show that the degree of children satisfaction with the application was high. Therefore, we believe BlueThinking could be easily used with young children to help them developing the aforementioned skills as well as to introduce them in a subtle way to the world of computer programming.
CFS: A Distributed File System for Large Scale Container Platforms

We propose CFS, a distributed file system for large scale container platforms. CFS supports both sequential and random file accesses with optimized storage for both large files and small files, and adopts different replication protocols for different write scenarios to improve the replication performance. It employs a metadata subsystem to store and distribute the file metadata across different storage nodes based on the memory usage. This metadata placement strategy avoids the need of data rebalancing during capacity expansion. CFS also provides POSIX-compliant APIs with relaxed semantics and metadata atomicity to improve the system performance. We performed a comprehensive comparison with Ceph, a widely-used distributed file system on container platforms. Our experimental results show that, in testing 7 commonly used metadata operations, CFS gives around 3 times performance boost on average. In addition, CFS exhibits better random-read/write performance in highly concurrent environments with multiple clients and processes.
A Survey of Users' Expectations Towards On-body Companion Robots

Being as a robotic companion is an extensive application of on-body robots; yet, as an emerging type of robots, few previous works focus on the design of on-body companion robots from the users' perspective, remaining users' expectations towards this type of robots unclear. To assist designers in the design process of on-body companion robots, we surveyed users' expectations towards on-body companion robots (n=215) by a questionnaire constituting of questions on factors that may affect robot acceptance, including robot functionality, robot appearance, and robot social ability. Based on the survey results, we stated design guidelines for the design of on-body companion robots supporting designers with insights into users. To demonstrate how to design on-body companion robots based on our findings, we organized a workshop with experienced designers to develop a conceptual on-body companion robot, and they proposed Bubo, an example prototype of on-body companion robot.
Biometric Mirror: Exploring Ethical Opinions towards Facial Analysis and Automated Decision-Making

Facial analysis applications are increasingly being applied to inform decision-making processes. However, as global reports of unfairness emerge, governments, academia and industry have recognized the ethical limitations and societal implications of this technology. Alongside initiatives that aim to formulate ethical frameworks, we believe that the public should be invited to participate in the debate. In this paper, we discuss Biometric Mirror, a case study that explored opinions about the ethics of an emerging technology. The interactive application distinguished demographic and psychometric information from people's facial photos and presented speculative scenarios with potential consequences based on their results. We analyzed the interactions with Biometric Mirror and media reports covering the study. Our findings demonstrate the nature of public opinion about the technology's possibilities, reliability, and privacy implications. Our study indicates an opportunity for case study-based digital ethics research, and we provide practical guidelines for designing future studies.
Data Pipelines for User Group Analytics

User data is becoming increasingly available in various domains ranging from the social Web to electronic patient health records (EHRs). User data is characterized by a combination of demographics (e.g., age, gender, life status) and user actions (e.g., posting a tweet, following a diet). Domain experts rely on user data to conduct large-scale population studies. Information consumers, on the other hand, rely on user data for routine tasks such as finding a book club and getting advice from look-alike patients. User data analytics is usually based on identifying group-level behaviors such as "teenage females who watch Titanic" and "old male patients in Paris who suffer from Bronchitis. " In this tutorial, we review data pipelines for User Group Analytics (UGA). These pipelines admit raw user data as input and return insights in the form of user groups. We review research on UGA pipelines and discuss approaches and open challenges for discovering, exploring, and visualizing user groups. Throughout the tutorial, we will illustrate examples in two key domains: "the social Web" and "health-care".
Exploring the Data Wilderness through Examples

Exploration is one of the primordial ways to accrue knowledge about the world and its nature. As we accumulate, mostly automatically, data at unprecedented volumes and speed, our datasets have become complex and hard to understand. In this context exploratory search provides a handy tool for progressively gather the necessary knowledge by starting from a tentative query that hopefully leads to answers at least partially relevant and that can provide cues about the next queries to issue. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user or the analyst circumvent query languages by using examples as input. This shift in semantics has led to a number of methods receiving as query a set of example members of the answer set. The search system then infers the entire answer set based on the given examples and any additional information provided by the underlying database. In this tutorial, we present an excursus over the main example-based methods for exploratory analysis, show techniques tailored to different data types, and provide a unifying view of the problem. We show how different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data.
iQCAR: inter-Query Contention Analyzer for Data Analytics Frameworks

Resource interferences caused by concurrent queries is one of the key reasons for unpredictable performance and missed workload SLAs in cluster computing systems. Analyzing these inter-query resource interactions is critical in order to answer time-sensitive questions like 'who is creating resource conflicts to my query'. More importantly, diagnosing whether the resource blocked times of a 'victim' query are caused by other queries or some other external factor can help the database administrator narrow down the many possibilities of query performance degradation. We introduce iQCAR, an inter-Query Contention Analyzer, that attributes blame for the slowdown of a query to concurrent queries. iQCAR models the resource conflicts using a multi-level directed acyclic graph that can help administrators compare impacts from concurrent queries, identify most contentious queries, resources and hosts in an online execution for a selected time window. Our experiments using TPCDS queries on Apache Spark show that our approach is substantially more accurate than other methods based on overlap time between concurrent queries.
Entity Matching Meets Data Science: A Progress Report from the Magellan Project

Entity matching (EM) finds data instances that refer to the same real-world entity. In 2015, we started the Magellan project at UW-Madison, joint with industrial partners, to build EM systems. Most current EM systems are stand-alone monoliths. In contrast, Magellan borrows ideas from the field of data science (DS), to build a new kind of EM systems, which is an ecosystem of interoperable tools. \em This paper provides a progress report on the past 3.5 years of Magellan, focusing on the system aspects and on how ideas from the field of data science have been adapted to the EM context. We argue why EM can be viewed as a special class of DS problems, and thus can benefit from system building ideas in DS. We discuss how these ideas have been adapted to build \pymatcher\ and \cloudmatcher, EM tools for power users and lay users. These tools have been successfully used in 21 EM tasks at 12 companies and domain science groups, and have been pushed into production for many customers. We report on the lessons learned, and outline a new envisioned Magellan ecosystem, which consists of not just on-premise Python tools, but also interoperable microservices deployed, executed, and scaled out on the cloud, using tools such as Dockers and Kubernetes.
Capturing and Querying Structural Provenance in Spark with Pebble

Analyzing and debugging Spark processing pipelines is a tedious task which typically involves a lot of engineering effort. The task becomes even more complex when the pipelines process nested data. Provenance solutions that track the derivation process of individual data items assist data engineers while debugging these pipelines. However, state-of-the-art solutions do not precisely track nested data items. We demonstrate Pebble, a system for capturing and querying a new type of provenance on nested data in Spark called structural provenance. It captures access and modification of top-level as well as nested data items, and allows querying the provenance of nested items based on tree-pattern-matching. Implemented as a standalone library on top of Apache Spark, it seamlessly leverages the underlying infrastructure for scalability. Through the graphical user interface implemented in a Jupyter notebook we showcase ten debugging scenarios of Spark programs on real-world datasets.