Miracle-dz/UniFAR-FTUs huggingface dataset:
Few-shot Food Recognition via Multi-view Representation Learning

This article considers the problem of few-shot learning for food recognition. Automatic food recognition can support various applications, e.g., dietary assessment and food journaling. Most existing works focus on food recognition with large numbers of labelled samples, and fail to recognize food categories with few samples. To address this problem, we propose a Multi-View Few-Shot Learning (MVFSL) framework to explore additional ingredient information for few-shot food recognition. Besides category-oriented deep visual features, we introduce ingredient-supervised deep network to extract ingredient-oriented features. As general and intermediate attributes of food, ingredient-oriented features are informative and complementary to category-oriented features, and thus they play an important role in improving food recognition. Particularly in few-shot food recognition, ingredient information can bridge the gap between disjoint training categories and test categories. To take advantage of ingredient information, we fuse these two kinds of features by first combining their feature maps from their respective deep networks and then convolving combined feature maps. Such convolution is further incorporated into a multi-view relation network, which is capable of comparing pairwise images to enable fine-grained feature learning. MVFSL is trained in an end-to-end fashion for joint optimization on two types of feature learning subnetworks and relation subnetworks. Extensive experiments on different food datasets have consistently demonstrated the advantage of MVFSL in multi-view feature fusion. Furthermore, we extend another two types of networks, namely, Siamese Network and Matching Network, by introducing ingredient information for few-shot food recognition. Experimental results have also shown that introducing ingredient information into these two networks can improve the performance of few-shot food recognition.
An Attention-based Rumor Detection Model with Tree-structured Recursive Neural Networks

Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors. For modeling non-sequential structure, we first represent the diffusion of microblog posts with propagation trees, which provide valuable clues on how a claim in the original post is transmitted and developed over time. We then present a bottom-up and a top-down tree-structured models based on Recursive Neural Networks (RvNN) for rumor representation learning and classification, which naturally conform to the message propagation process in microblogs. To enhance the rumor representation learning, we reveal that effective rumor detection is highly related to finding evidential posts, e.g., the posts expressing specific attitude towards the veracity of a claim, as an extension of the previous RvNN-based detection models that treat every post equally. For this reason, we design discriminative attention mechanisms for the RvNN-based models to selectively attend on the subset of evidential posts during the bottom-up/top-down recursive composition. Experimental results on four datasets collected from real-world microblog platforms confirm that (1) our RvNN-based models achieve much better rumor detection and classification performance than state-of-the-art approaches; (2) the attention mechanisms for focusing on evidential posts can further improve the performance of our RvNN-based method; and (3) our approach possesses superior capacity on detecting rumors at a very early stage.
A Practical Learning-based Approach for Viewer Scheduling in the Crowdsourced Live Streaming

Scheduling viewers effectively among different Content Delivery Network (CDN) providers is challenging owing to the extreme diversity in the crowdsourced live streaming (CLS) scenarios. Abundant algorithms have been proposed in recent years, which, however, suffer from a critical limitation: Due to their inaccurate feature engineering or naive rules, they cannot optimally schedule viewers. To address this concern, we put forward LTS (Learn to Schedule), a novel scheduling algorithm that can adapt to the dynamics from both viewer traffics and CDN performance. In detail, we first propose LTS-RL, an approach that schedules CLS viewers based on deep reinforcement learning (DRL). Since LTS-RL is trained in an end-to-end way, it can automatically learn scheduling algorithms without any pre-programmed models or assumptions about the environment dynamics. At the same time, to practically deploy LTS-RL, we then use the decision tree and imitation learning to convert LTS-RL into a more light-weighted and interpretable model, which is denoted as Fast-LTS. After the extensive evaluation of the real data from a leading CLS platform in China, we demonstrate that our proposed model (both LTS-RL and Fast-LTS) can improve the average quality of experience (QoE) over state-of-the-art approaches by 8.71--15.63%. At the same time, we also demonstrate that Fast-LTS can faithfully convert the complicated LTS-RL with slight performance degradation (< 2%), while significantly reducing the decision time (×7--10).
VTR 8

Developing Field-programmable Gate Array (FPGA) architectures is challenging due to the competing requirements of various application domains and changing manufacturing process technology. This is compounded by the difficulty of fairly evaluating FPGA architectural choices, which requires sophisticated high-quality Computer Aided Design (CAD) tools to target each potential architecture. This article describes version 8.0 of the open source Verilog to Routing (VTR) project, which provides such a design flow. VTR 8 expands the scope of FPGA architectures that can be modelled, allowing VTR to target and model many details of both commercial and proposed FPGA architectures. The VTR design flow also serves as a baseline for evaluating new CAD algorithms. It is therefore important, for both CAD algorithm comparisons and the validity of architectural conclusions, that VTR produce high-quality circuit implementations. VTR 8 significantly improves optimization quality (reductions of 15% minimum routable channel width, 41% wirelength, and 12% critical path delay), run-time (5.3× faster) and memory footprint (3.3× lower). Finally, we demonstrate VTR is run-time and memory footprint efficient, while producing circuit implementations of reasonable quality compared to highly-tuned architecture-specific industrial tools—showing that architecture generality, good implementation quality, and run-time efficiency are not mutually exclusive goals.
Designing Mid-Air Haptic Gesture Controlled User Interfaces for Cars

We present advancements in the design and development of in-vehicle infotainment systems that utilize gesture input and ultrasonic mid-air haptic feedback. Such systems employ state-of-the-art hand tracking technology and novel haptic feedback technology and promise to reduce driver distraction while performing a secondary task therefore cutting the risk of road accidents. In this paper, we document design process considerations during the development of a mid-air haptic gesture-enabled user interface for human-vehicle-interactions. This includes an online survey, business development insights, background research, and an agile framework component with three prototype iterations and user-testing on a simplified driving simulator. We report on the reasoning that led to the convergence of the chosen gesture-input and haptic-feedback sets used in the final prototype, discuss the lessons learned, and give hints and tips that act as design guidelines for future research and development of this technology in cars.
An Efficient Service Function Chaining Placement Algorithm in Mobile Edge Computing

Mobile Edge Computing (MEC) is a promising network architecture that pushes network control and mobile computing to the network edge. Recent studies propose to deploy MEC applications in the Network Function Virtualization (NFV) environment. The mobile network service in NFV is deployed as a Service Function Chaining (SFC). In the dynamic and resource-limited mobile network, SFC placement aiming at optimizing resource utilization is a challenging problem. In this article, we solve the SFC placement problem in the MEC-NFV environment. We formulate the SFC placement problem as a weighted graph matching problem, including two sub-problems: a graph matching problem and an SFC mapping problem. To efficiently solve the graph matching problem, we propose a Linear Programming–(LP) based approach to calculate the similarity between VNFs and physical nodes. Based on the similarity, we design a Hungarian-based algorithm to solve the SFC mapping problem. Evaluation results show that our proposed LP-based solutions outperform the heuristic algorithms in terms of execution time and resource utilization.
A Blockchain-based Iterative Double Auction Protocol using Multiparty State Channels

Current implementations of iterative double auction rely on a trusted third-party to handle the auction process. This imposes the risk of single point of failures and monopoly. We tackle this problem by proposing a novel decentralized and trustless blockchain-based framework for iterative double auction. Our design extends the state channel technologies to enable a double auction process among trading parties, while minimizing the blockchain transactions. Then we provide a formal development of the framework and prove its security against adversaries. Finally, we develop a proof-of-concept implementation of our framework, on which we conduct experiments to demonstrate its feasibility and practicality.
Effect of flowering phenology and habitat on pollen limitation in Erica multiflora

Pollination can be a major factor influencing reproductive success in plants. Pollination may be influenced by variability in pollen production and vector activity associated with environmental heterogeneity in time and space. These factors were addressed experimentally using hand pollinations of Erica multiflora L. , a shrub occurring in the western Mediterranean Basin that exhibits a long flowering period. Variation in pollen limitation was considered three times (early, peak, and late) over the flowering season and between two habitats (shrubland and pine forest). The breeding system and the pollination vectors were also studied by hand pollination and exclusion experiments. Xenogamy and natural pollination (control) treatments produced more seed and fruit set than geitonogamy and autogamy treatments. Insect visitors were less abundant at the end of the flowering period and in the pine forest habitat. Pollen limitation was not different between habitats, but it changed over the flowering period. Fruit s.
Three Perceptual Dimensions for Specular and Diffuse Reflection

Previous research investigated the perceptual dimensionality of achromatic reflection of opaque surfaces, by using either simple analytic models of reflection or measured reflection properties of a limited sample of materials. Here, we aim to extend this work to a broader range of simulated materials. In a first experiment, we used sparse multidimensional scaling techniques to represent a set of rendered stimuli in a perceptual space that is consistent with participants’ similarity judgments. Participants were presented with one reference object and four comparisons, rendered with different material properties. They were asked to rank the comparisons according to their similarity to the reference, resulting in an efficient collection of a large number of similarity judgments. To interpret the space individuated by multidimensional scaling, we ran a second experiment in which observers were asked to rate our experimental stimuli according to a list of 30 adjectives referring to their surface reflectance properties. Our results suggest that perception of achromatic reflection is based on at least three dimensions, which we labelled “Lightness,” “Gloss,” and “Metallicity,” in accordance with the rating results. These dimensions are characterized by a relatively simple relationship with the parameters of the physically based rendering model used to generate our stimuli, indicating that they correspond to different physical properties of the rendered materials. Specifically, “Lightness” relates to diffuse reflections, “Gloss” to the presence of high contrast sharp specular highlights, and “Metallicity” to spread out specular reflections.
A Survey and Classification of Software-Defined Storage Systems

The exponential growth of digital information is imposing increasing scale and efficiency demands on modern storage infrastructures. As infrastructure complexity increases, so does the difficulty in ensuring quality of service, maintainability, and resource fairness, raising unprecedented performance, scalability, and programmability challenges. Software-Defined Storage (SDS) addresses these challenges by cleanly disentangling control and data flows, easing management, and improving control functionality of conventional storage systems. Despite its momentum in the research community, many aspects of the paradigm are still unclear, undefined, and unexplored, leading to misunderstandings that hamper the research and development of novel SDS technologies. In this article, we present an in-depth study of SDS systems, providing a thorough description and categorization of each plane of functionality. Further, we propose a taxonomy and classification of existing SDS solutions according to different criteria. Finally, we provide key insights about the paradigm and discuss potential future research directions for the field.
Edge2vec

Graph embedding, also known as network embedding and network representation learning, is a useful technique which helps researchers analyze information networks through embedding a network into a low-dimensional space. However, existing graph embedding methods are all node-based, which means they can just directly map the nodes of a network to low-dimensional vectors while the edges could only be mapped to vectors indirectly. One important reason is the computational cost, because the number of edges is always far greater than the number of nodes. In this article, considering an important property of social networks, i.e., the network is sparse, and hence the average degree of nodes is bounded, we propose an edge-based graph embedding (edge2vec) method to map the edges in social networks directly to low-dimensional vectors. Edge2vec takes both the local and the global structure information of edges into consideration to preserve structure information of embedded edges as much as possible. To achieve this goal, edge2vec first ingeniously combines the deep autoencoder and Skip-gram model through a well-designed deep neural network. The experimental results on different datasets show edge2vec benefits from the direct mapping in preserving the structure information of edges.
Dynamic Precision Autotuning with TAFFO

Many classes of applications, both in the embedded and high performance domains, can trade off the accuracy of the computed results for computation performance. One way to achieve such a trade-off is precision tuning—that is, to modify the data types used for the computation by reducing the bit width, or by changing the representation from floating point to fixed point. We present a methodology for high-accuracy dynamic precision tuning based on the identification of input classes (i.e., classes of input datasets that benefit from similar optimizations). When a new input region is detected, the application kernels are re-compiled on the fly with the appropriate selection of parameters. In this way, we obtain a continuous optimization approach that enables the exploitation of the reduced precision computation while progressively exploring the solution space, thus reducing the time required by compilation overheads. We provide tools to support the automation of the runtime part of the solution, leaving to the user only the task of identifying the input classes. Our approach provides a significant performance boost (up to 320%) on the typical approximate computing benchmarks, without meaningfully affecting the accuracy of the result, since the error remains always below 3%.
Oblivious Resampling Oracles and Parallel Algorithms for the Lopsided Lovász Local Lemma

The Lovász Local Lemma (LLL) shows that, for a collection of “bad” events B in a probability space that are not too likely and not too interdependent, there is a positive probability that no events in B occur. Moser and Tardos (2010) gave sequential and parallel algorithms that transformed most applications of the variable-assignment LLL into efficient algorithms. A framework of Harvey and Vondrák (2015) based on “resampling oracles” extended this to sequential algorithms for other probability spaces satisfying a generalization of the LLL known as the Lopsided Lovász Local Lemma (LLLL). We describe a new structural property that holds for most known resampling oracles, which we call “obliviousness. ” Essentially, it means that the interaction between two bad-events B, B′ depends only on the randomness used to resample B and not the precise state within B itself. This property has two major consequences. First, combined with a framework of Kolmogorov (2016), it leads to a unified parallel LLLL algorithm, which is faster than previous, problem-specific algorithms of Harris (2016) for the variable-assignment LLLL and of Harris and Srinivasan (2014) for permutations. This gives the first RNC algorithms for rainbow perfect matchings and rainbow Hamiltonian cycles of Kn. Second, this property allows us to build LLLL probability spaces from simpler “atomic” events. This gives the first resampling oracle for rainbow perfect matchings on the complete s-uniform hypergraph Kn(s) and the first commutative resampling oracle for Hamiltonian cycles of Kn.
Processing Grid-format Real-world Graphs on DRAM-based FPGA Accelerators with Application-specific Caching Mechanisms

Graph processing is one of the important research topics in the big-data era. To build a general framework for graph processing by using a DRAM-based FPGA board with deep memory hierarchy, one of the reasonable methods is to partition a given big graph into multiple small subgraphs, represent the graph with a two-dimensional grid, and then process the subgraphs one after another to divide and conquer the whole problem. Such a method (grid-graph processing) stores the graph data in the off-chip memory devices (e.g., on-board or host DRAM) that have large storage capacities but relatively small bandwidths, and processes individual small subgraphs one after another by using the on-chip memory devices (e.g., FFs, BRAM, and URAM) that have small storage capacities but superior random access performances. However, directly exchanging graph (vertex and edge) data between the processing units in FPGA chip with slow off-chip DRAMs during grid-graph processing leads to limited performances and excessive data transmission amounts between the FPGA chip and off-chip memory devices. In this article, we show that it is effective in improving the performance of grid-graph processing on DRAM-based FPGA hardware accelerators by leveraging the flexibility and programmability of FPGAs to build application-specific caching mechanisms, which bridge the performance gaps between on-chip and off-chip memory devices, and reduce the data transmission amounts by exploiting the localities on data accessing. We design two application-specific caching mechanisms (i.e., vertex caching and edge caching) to exploit two types of localities (i.e., vertex locality and subgraph locality) that exist in grid-graph processing, respectively. Experimental results show that with the vertex caching mechanism, our system (named as FabGraph) achieves up to 3.1× and 2.5× speedups for BFS and PageRank, respectively, over ForeGraph when processing medium graphs stored in the on-board DRAM. With the edge caching mechanism, the extension of FabGraph (named as FabGraph+) achieves up to 9.96× speedups for BFS over FPGP when processing large graphs stored in the host DRAM.
Cyanide as a feeding stimulant for the southern army worm, Spodoptera eridania

ABSTRACT. 1 All instars of Spodoptera eridania larvae grow as well or better when cyanide is present in their diet as when it is absent. Concentrations up to 0.05% stimulate feeding in first to fourth instar larvae. Concentrations from 0.1% to 1.0% stimulate feeding in fifth and sixth instar larvae. 2 Three‐day‐old sixth instar larvae pre‐exposed to cyanide are completely resistant to its acutely toxic effects, but previously unexposed larvae suffer reversible symptoms of poisoning when feeding on a diet containing 1.0% KCN. 3 A 1.0% dietary KCN exposure during the sixth instar reduces ecdysis to 17% adult emergence and completely inhibits oviposition. 4 Cyanide concentrations from 0.5% to 1.0% in the diet, although effecting increased growth rates, induce necrotic lesions in larval mid‐gut epithelial cells. 5 Thiocyanate, one of the in vivo cyanide metabolites, at 0.5% in the diet reduces pupation to 23%, delays and reduces adult emergence to 20% and inhibits oviposition. 6 The preferred host plant of S.eridania is the lima bean, Phaseolus lunatus, probably due to its content of the cyanogenic glycoside linamarin. Dietary valine has no effect on the southern armyworm feeding and growth behaviour (Long & Brattsten, 1982) but dietary cyanide does. The lima bean is known to contain up to 31 ppm cyanide in some varieties.
Fog in the Clouds

Internet of Things (IoT) has emerged as a huge paradigm shift by connecting a versatile and massive collection of smart objects to the Internet, coming to play an important role in our daily lives. Data produced by IoT devices can generate a number of computational tasks that cannot be executed locally on the IoT devices. The most common solution is offloading these tasks to external devices with higher computational and storage capabilities, usually provided by centralized servers in remote clouds or on the edge by using the fog computing paradigm. Nevertheless, in some IoT scenarios there are remote or challenging areas where it is difficult to connect an IoT network to a fog platform with appropriate links, especially if IoT devices produce a lot of data that require processing in real-time. To this purpose, in this article, we propose to use unmanned aerial vehicles (UAVs) as fog nodes. Although this idea is not new, this is the first work that considers power consumption of the computing element installed on board UAVs, which is crucial, since it may influence flight mission duration. A System Controller (SC) is in charge of deciding the number of active CPUs at runtime by maximizing an objective function weighing power consumption, job loss probability, and processing latency. Reinforcement Learning (RL) is used to support SC in its decisions. A numerical analysis is carried out in a use case to show how to use the model introduced in the article to decide the computation power of the computing element in terms of number of available CPUs and CPU clock speed, and evaluate the achieved performance gain of the proposed framework.