Miracle-dz/UniFAR-FTUs huggingface dataset:
Watch or warning? Perceptions, preferences, and usage of forecast information by members of the Canadian public

Environment Canada is responsible for monitoring weather conditions and developing associated forecast products for millions of Canadians. Although it is clear that these forecasts are reaching many different end‐users, it is less certain how these products are perceived or used by the Canadian public. This research investigated the way(s) in which individual end‐users in southern Ontario obtained, interpreted, and used weather forecasts in their every‐day decision‐making. Semi‐structured interviews (n = 35) and close‐ended questionnaires (n = 268) were conducted with residents from Huron and Perth Counties in Ontario, Canada, between October 2011 and March 2012 as part of a larger research project. It was found that most respondents used weather products for pragmatic reasons (e.g. deciding appropriate clothing or planning a trip). These individuals typically did not pay attention to ambient weather conditions unless they were inconvenient or threatening. Despite this, many respondents displayed relatively high levels of weather salience, in terms of familiarity with and awareness of local weather patterns. While most participants had a general understanding of the difference between a weather watch and a weather warning, a substantial portion of the sample were unable to differentiate between the two products. Lastly, many respondents also indicated that they trusted and valued Environment Canada weather forecasts, and they sought this information actively to help in their decision‐making.
From Laptop to Lambda: Outsourcing Everyday Jobs to Thousands of Transient Functional Containers

We present gg, a framework and a set of command-line tools that helps people execute everyday applications--e.g., software compilation, unit tests, video encoding, or object recognition--using thousands of parallel threads on a cloud-functions service to achieve near-interactive completion times. In the future, instead of running these tasks on a laptop, or keeping a warm cluster running in the cloud, users might push a button that spawns 10,000 parallel cloud functions to execute a large job in a few seconds from start. gg is designed to make this practical and easy. With gg, applications express a job as a composition of lightweight OS containers that are individually transient (lifetimes of 1-60 seconds) and functional (each container is hermetically sealed and deterministic). gg takes care of instantiating these containers on cloud functions, loading dependencies, minimizing data movement, moving data between containers, and dealing with failure and stragglers. We ported several latency-sensitive applications to run on gg and evaluated its performance. In the best case, a distributed compiler built on gg outperformed a conventional tool (icecc) by 2-5×, without requiring a warm cluster running continuously. In the worst case, gg was within 20% of the hand-tuned performance of an existing tool for video encoding (ExCamera).
Fuzzy Clustering of Maize Plant-Height Patterns Using Time Series of UAV Remote-Sensing Images and Variety Traits

The application of high-throughput phenotyping (HTP) techniques based on unmanned aerial vehicle (UAV) remote-sensing platforms to study large-scale population breeding opens the way to more efficient acquisition of dynamic phenotypic traits and provides new tools that should help close the gap between genotyping and traditional field-phenotyping methods. Toward this end we used a field UAV-HTP platform to deploy a RGB high-resolution camera to acquire time-series images. By using three-dimensional reconstructed point cloud models, we developed a repeatable processing workflow to extract plant height from time-series images. The plant height determined by the UAV-HTP platform correlated strongly with that measured manually. The plant heights estimated at various growth stages form temporal profiles that give insights into changes and trends in genotyping. Based on fuzzy c-means clustering analysis, we extract the typical dynamic patterns in phenotypic traits (i.e., plant height, average rate of growth of plant height, and rate of contribution of plant height) hidden in the temporal profiles. The fuzzy c-means clustering and set-intersection operation were first applied to analyze the temporal profile to identify how plant-height patterns change and to detect differences in phenotypic variability among the genotypes. The results revealed the capacity of UAV remote sensing to easily evaluate field traits on multiple timescales, for a few breeding plots or for 1000s of breeding plots.
Extension Framework for File Systems in User space

User file systems offer numerous advantages over their in-kernel implementations, such as ease of development and better system reliability. However, they incur heavy performance penalty. We observe that existing user file system frameworks are highly general; they consist of a minimal interposition layer in the kernel that simply forwards all low-level requests to user space. While this design offers flexibility, it also severely degrades performance due to frequent kernel-user context switching. This work introduces E XT FUSE, a framework for developing extensible user file systems that also allows applications to register “thin” specialized request handlers in the kernel to meet their specific operative needs, while retaining the complex functionality in user space. Our evaluation with two FUSE file systems shows that E XT FUSE can improve the performance of user file systems with less than a few hundred lines on average. E XT FUSE is available on GitHub.
Visual Analytics of Multilayer Networks Across Disciplines (Dagstuhl Seminar 19061)

This report documents the program and the outcomes of Dagstuhl Seminar 19061 "Visual Analytics of Multilayer Networks Across Disciplines". Networks, used to understand systems, often contain multiple types of nodes and/or edges. They are often flattened to a single network, even though real-world systems are more accurately modelled as a set of interacting networks, or layers, with different node and edge types. These are so-called multilayer networks. These networks are studied by researchers both in network visualization and in complex systems -- the domain from which the concept of multilayer networks has recently emerged. Moreover, researchers in various application domains study these systems, e.g. biology, digital humanities, sociology and journalism. These research areas have shown parallel individual developments. Therefore, one of the aims of the seminar was to bring together an interdisciplinary community of researchers and practitioners of different disciplines. This interdisciplinary community discussed existing solutions, open challenges and future research directions for visual analytics of multilayer networks across disciplines. The seminar was attended by researchers from information visualization, visual analytics, complex systems and application domains. The application domains covered digital humanities, social sciences, biological sciences, and in public health research (25% of attendees were from these fields). The seminar not only provided multiple application domains for the visualization experts, but also also provided the domains experts with different groups of visualization experts in breakouts sessions, to expose them to multiple approaches to solving their problems. Building on this close working relationship between the visualization and domain experts, working groups were defined to determine which are the important challenges for multilayer network visualization. A number of sub-topics were identified that require further research: A unifying visualization framework, Novel Visual Encodings, Analytic and Attributes, Interaction, Evaluation, Use Cases and Human Factors. The outcomes of the seminar should stimulate collaborative research on these topics between our community, complex networks, and wide range of application domains for the visual analytics of multilayer networks
Your Coflow has Many Flows: Sampling them for Fun and Speed

Coflow scheduling improves data-intensive application performance by improving their networking performance. State-of-the-art online coflow schedulers in essence approximate the classic Shortest-Job-First (SJF) scheduling by learning the coflow size online. In particular, they use multiple priority queues to simultaneously accomplish two goals: to sieve long coflows from short coflows, and to schedule short coflows with high priorities. Such a mechanism pays high overhead in learning the coflow size: moving a large coflow across the queues delays small and other large coflows, and moving similar-sized coflows across the queues results in inadvertent round-robin scheduling. We propose Philae, a new online coflow scheduler that exploits the spatial dimension of coflows, i.e., a coflow has many flows, to drastically reduce the overhead of coflow size learning. Philae pre-schedules sampled flows of each coflow and uses their sizes to estimate the average flow size of the coflow. It then resorts to Shortest Coflow First, where the notion of shortest is determined using the learned coflow sizes and coflow contention. We show that the sampling-based learning is robust to flow size skew and has the added benefit of much improved scalability from reduced coordinator-local agent interactions. Our evaluation using an Azure testbed, a publicly available production cluster trace from Facebook shows that compared to the prior art Aalo, Philae reduces the coflow completion time (CCT) in average (P90) cases by 1.50× (8.00×) on a 150-node testbed and 2.72× (9.78×) on a 900-node testbed. Evaluation using additional traces further demonstrates Philae's robustness to flow size skew.
Neural Network Model Assessment for Side-Channel Analysis

Leakage assessment of cryptographic implementations with side-channel analysis relies on two important assumptions: leakage model and the number of side-channel traces. In the context of proﬁled side-channel attacks, having these assumptions correctly deﬁned is a suﬃcient ﬁrst step to evaluate the security of a crypto implementation with template attacks. This method assumes that the features (leakages or points of interest) follow a univariate or multi-variate Gaussian distribution for the estimation of the probability density function. When trained machine learning or neural network models are employed as classiﬁers for proﬁled attacks, a third assumption must be taken into account that it the correctness of the trained model or learning parameters. It was already proved that convolutional neural networks have advantages for side-channel analysis like bypassing trace misalignments and defeating ﬁrst-order masking countermeasures in software implementations. However, if this trained model is incorrect and the test classiﬁcation accuracy is close to random guessing, the correctness of the two ﬁrst assumptions (number of traces and leakage model) will be insuﬃcient and the security of the target under evaluation can be overestimated. This could lead to wrong conclusions in leakage certiﬁcations. One solution to verify if the trained model is acceptable relies on the identifying of input features that the neural network considers as points of interest. In this paper, we implement the assessment of neural network models by using the proposed layer-wise activation path method. Our method is employed during the proﬁling phase as a tool to verify what the neural network is learning from side-channel traces and to support the optimization of hyper-parameters. The method is tested against masked AES implementation. One of the main results highlights the importance of L2 regularization for the automated points of interest selection from a neural network.
Non-blocking and self-routing properties of two-dimensional shuffle-exchange networks

A novel class of multistage interconnection networks known as two-dimensional shuffle-exchange networks is proposed. The two-dimensional shuffle-exchange network is an output-buffered network which possesses the non-blocking and self-routing properties under certain routing rules and a well-defined addressing scheme. Moreover the set of inputs can be divided into groups of N/4 inputs called contention groups, where N is the network size. The entire network is internally non-blocking if each contention group is conflict-free. Contention resolution can therefore be narrowed down and localized to individual contention groups, thereby reducing the time complexity of the contention resolution algorithm involved. The ATM switch constructed with the two-dimensional shuffle-exchange network therefore results in a reduction of both time and hardware complexity when compared with the one based on the traditional sort-banyan principle.
Clustering Methods in a Protein–Protein Interaction Network

With completion of a draft sequence of the human genome, the field of gen etics stands on the threshold of significant theoretical and practical advances. Crucial to furthering these investigations is a comprehensive understanding of the expression, function, and regulation of the proteins encoded by an organism. It has been observed that proteins seldom act as single isolated species in the performance of their functions; rather, proteins involved in the sam e cellular processes often interact with each other. Therefore, the functions of uncharacterized proteins can be predicted through comparison with the interactions of similar known proteins. A detailed examination of the protein-protein interaction (PPI) network can thus yield significant new un derstanding of protein function. Clustering is the process of grouping data objects into sets (cluste rs) which demonstrate greater similarity among objects in the same cluster than in different clusters . Clustering in the PPI network context groups together proteins which share a larger number of interactions. The results of this process can illuminate the structure of the PPI network and suggest possible functions for members of the cluster which were previously uncharacterized. This chapter will begin with a brief introduction of the properties of protein-p rotein interaction networks, including a review of the data which has been generated by both experimental and computational approaches. A variety of methods which have been employed to cluster these networks will then be presented. These approaches are broadly characterized as either distance-based or
Towards a Serverless Platform for Edge AI

This paper proposes a serverless platform for building and operating edge AI applications. We analyze edge AI use cases to illustrate the challenges in building and operating AI applications in edge cloud scenarios. By elevating concepts from AI lifecycle management into the established serverless model, we enable easy development of edge AI workflow functions. We take a deviceless approach, i.e., we treat edge resources transparently as cluster resources, but give developers fine-grained control over scheduling constraints. Furthermore, we demonstrate the limitations of current serverless function schedulers, and present the current state of our prototype.
SimpleENC and SimpleENCsmall - an Authenticated Encryption Mode for the Lightweight Setting

Block cipher modes of operation provide a way to securely encrypt using a block cipher, and different modes of operation achieve different tradeoffs of security, performance and simplicity. In this paper, we present a new authenticated encryption scheme that is designed for the lightweight cryptography setting, but can be used in standard settings as well. Our mode of encryption is extremely simple, requiring only a single block cipher primitive (in forward direction) and minimal padding, and supports streaming (online encryption). In addition, our mode achieves very strong security bounds, and can even provide good security when the block size is just 64 bits. As such, it is highly suitable for lightweight settings, where the lifetime of the key and/or overall amount encrypted may be high. Our new scheme can be seen as an improved version of CCM that supports streaming, and provides much better bounds.
On Deploying Secure Computing Commercially: Private Intersection-Sum Protocols and their Business Applications

In this work, we describe how to deploy a cryptographic secure computation protocol for routine use in industry. Based on our experience, we identify major preliminaries and enabling factors which we found to be critical to the successful deployment of such technology as a practical, and uniquely positioned method for solving the task at hand. The specific technical problem that we tackled is that of computing Private Intersection-Sum. In this setting two parties hold datasets containing user identifiers, and one of the parties additionally has an integer value associated with each of its user identifiers. The parties want to learn (1) the number of users they have in common and (2) the sum of the integer values associated with these users, without revealing any more information about their private inputs. Private Intersection-Sum is not an arbitrary question, but rather arose naturally and was concretely defined based on a given central business need: computing aggregate conversion rate (or effectiveness) of advertising campaigns. This problem has both great practical value and important privacy considerations, and represents a type of analysis that occurs surprisingly commonly. Among the factors that enabled our deployment, in this work we consider in more depth the technical issue of protocol choice and its performance implications. Specifically, we present a study involving three novel protocols for computing Private Intersection-Sum, which leverage three different basic protocol techniques including Random Oblivious Transfer, encrypted Bloom filters, and Diffie–Hellman style (Pohlig–Hellman specifically) double masking. We compare the three protocols under different instantiations of an additive homomorphic encryption, which is used as a building block in each protocol. We implement our constructions and compare their actual communication and computation overheads. Finally, we analyze the advantages of the DDH-based protocol which make it the solution of choice for our deployment setting.
Energy-Efficient Opportunistic Routing Protocol in Wireless Sensor Networks

The paper presents energy-efficiency opportunistic multicast routing protocol (EOMR) for the multicast energy consumption minimization problem in wireless sensor networks. The protocol divides the network into single grids, so each node determines their own coordinates according to the grid, which they locate in a certain period of time. The nodes only need to know the topology of their own grid, instead of the topology of the entire network. Nodes in the same grid determine the priority in light of the upstream and downstream links metrics, and use the opportunistic routing to send messages to the next hop. The destinations calculate the optimal path in accordance with the hops and links cost, and transmission acknowledgement to source along the optimal path selected. If all nodes in the current grid transmission messages failure, the neighbor grids are in charge of retransmission. The results of simulation show that, compared with traditional multicast routing, EOMR is effective in reducing the consumption of links cost, so that it improves the link reliability and reduces the delay.
Bringing CP, SAT and SMT together: Next Challenges in Constraint Solving (Dagstuhl Seminar 19062)

This report documents the program and the outcomes of Dagstuhl Seminar 19062 “Bringing CP, SAT and SMT together: Next Challenges in Constraint Solving”, whose main goals were to bring together leading researchers in the different subfields of automated reasoning and constraint solving, foster greater communication between these communities and exchange ideas about new research directions. Constraint solving is at the heart of several key technologies, including program analysis, testing, formal methods, compilers, security analysis, optimization, and AI. During the last two decades, constraint solving has been highly successful and transformative: on the one hand, SAT/SMT solvers have seen a significant performance improvement with a concomitant impact on software engineering, formal methods and security; on the other hand, CP solvers have also seen a dramatic performance improvement, with deep impact in AI and optimization. These successes bring new applications together with new challenges, not yet met by any current technology. The seminar brought together researchers from SAT, SMT and CP along with application researchers in order to foster cross-fertilization of ideas, deepen interactions, identify the best ways to serve the application fields and in turn help improve the solvers for specific domains. Seminar February 3–6, 2019 – http://www.dagstuhl. de/19062 2012 ACM Subject Classification Theory of computation → Logic, Theory of computation → Automated reasoning, Mathematics of computing → Solvers, Theory of computation → Constraint and logic programming, Hardware → Theorem proving and SAT solving, Software and its engineering → Formal methods, Software and its engineering → Software verification, Hardware → Functional verification
The role of references in scientific papers: cited papers as objects of research

Scientific papers are often cited because they were selected as objects of research, rather than because of their contents. This practice may substantially affect journal impact factors. Copyright The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com, Oxford University Press.
QZFS: QAT Accelerated Compression in File System for Application Agnostic and Cost Efficient Data Storage

Data compression can not only provide space efficiency with lower Total Cost of Ownership (TCO) but also enhance I/O performance because of the reduced read/write operations. However, lossless compression algorithms with high compression ratio (e.g. gzip) inevitably incur high CPU resource consumption. Prior studies mainly leveraged general-purpose hardware accelerators such as GPU and FPGA to offload costly (de)compression operations for application workloads. This paper investigates ASIC-accelerated compression in file system to transparently benefit all applications running on it and provide high-performance and cost-efficient data storage. Based on Intel R © QAT ASIC, we propose QZFS that integrates QAT into ZFS file system to achieve efficient gzip (de)compression offloading at the file system layer. A compression service engine is introduced in QZFS to serve as an algorithm selector and implement compressibility-dependent offloading and selective offloading by source data size. More importantly, a QAT offloading module is designed to leverage the vectored I/O model to reconstruct data blocks, making them able to be used by QAT hardware without incurring extra memory copy. The comprehensive evaluation validates that QZFS can achieve up to 5x write throughput improvement for FIO micro-benchmark and more than 6x costefficiency enhancement for genomic data post-processing over the software-implemented alternative.