Reviewerly/exHarmony huggingface dataset:
CompGen: generation of fast JIT compilers in a multi-language VM

The first Futamura projection enables compilation and high performance code generation of user programs by partial evaluation of language interpreters. Previous work has shown that online partial evaluation can yield the same peak performance as a specialized JIT compiler. However, this comes with the downside of additional compile time: Online partial evaluation of language interpreters has to specialize interpreter code on the fly to the dynamic types used at run time to create efficient target code. As a result, the time spent on partial evaluation itself is a significant contributor to the overall compile time of a method.
Accelerated-YOLOv3 for Ship Detection from SAR Images

Synthetic Aperture Radar (SAR) imagery has been widely used in many maritime applications due to its high resolution, wide coverage, and real-time monitoring characteristics. Nevertheless, the size of SAR images is significantly large for real-time application. In recent years, High-Performance Computing (HPC)-related methods have been used to improve the precision and detection rate of SAR imagery analysis. In this paper, motivated by the state-of-the-art real time object detection You Only Look Once version 3 (YOLOv3), an enhanced GPU-based deep learning method has been proposed, namely Accelereated-YOLOv3 (A-YOLOv3), to detect ships from the SAR images. A-YOLOv3 aims to reduce the computational time with relatively competitive detection accuracy by constructing a new architecture with less layers and channels. The proposed A-YOLOv3 architecture achieves Average Precision (AP) of 97.4% on the Expand Diversified SAR Ship Detection Dataset (EDSSDD).
Long-Horizon Motion Planning for Autonomous Vehicle Parking Incorporating Incomplete Map Information

This paper presents a hierarchical motion planning approach that can provide real-time parking plans for autonomous vehicles with limited memory. Through combining a high-level route planner that searches for collision-free routes given traffic and obstacle information and a low level motion planner that considers vehicle dynamics, our approach generates smooth trajectories with reasonable parking behaviors rapidly with very low memory consumption. This hierarchical approach allows for online path repairing and replanning when newly detected obstacles that were not indicated on the offline map obstruct the original planned trajectory. It employs a fast clearance checking procedure to obtain a practical indicator of repairability as well as heuristic guidance for rapid trajectory repairing, and utilizes the high-level route planner to conduct real-time replanning when trajectory repairing is deemed to be difficult. Performance analysis on parking tasks in simulation environments demonstrates the advantages of the proposed approach in terms of both trajectory quality and planning time.
Wukong+G: Fast and Concurrent RDF Query Processing Using RDMA-Assisted GPU Graph Exploration

RDF graph has been increasingly used to store and represent information shared over the Web, including social graphs and knowledge bases. With the increasing scale of RDF graphs and the concurrency level of SPARQL queries, current RDF systems are confronted with inefficient concurrent query processing on massive data parallelism. The situation becomes more severe in the face of data-intensive queries (aka heavy query), which usually lead to suboptimal response time (latency) as well as throughput collapse. In this article, we present Wukong+G, the first graph-based distributed RDF query processing system that efficiently exploits the hybrid parallelism of CPU and GPU. Wukong+G is made fast and concurrent with four key designs. First, Wukong+G tames massive random memory accesses in graph exploration by efficiently mapping data between CPU and GPU for latency hiding, including a set of techniques like query-aware prefetching, pattern-aware pipelining and fine-grained swapping. Second, Wukong+G scales up by introducing a GPU-friendly RDF store to support RDF graphs exceeding GPU memory size, by using techniques like predicate-based grouping, pairwise caching and look-ahead replacing to narrow the gap between host and device memory scale. Third, Wukong+G scales out through a communication layer that decouples the transferring process for query metadata and intermediate results, and further leverages both native and GPUDirect RDMA to enable efficient communication on a CPU/GPU cluster. Finally, Wukong+G simultaneously runs multiple queries on a single GPU to improve overall throughput and fully exploits hardware heterogeneity (CPU/GPU) by scheduling a single query on CPU and GPU adaptively. We have implemented Wukong+G by extending a state-of-the-art distributed RDF store (i.e., Wukong) with distributed GPU support. Evaluation on a heterogeneous CPU/GPU cluster with RDMA-capable network shows that Wukong+G outperforms Wukong by up to 9.0× (from 2.3×) and scales well on 10 GPU cards for heavy queries. Wukong+G can also improve both latency and throughput by more than one order of magnitude when facing hybrid workloads.
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition

The Transformer architecture has been well adopted as a dominant architecture in most sequence transduction tasks including automatic speech recognition (ASR), since its attention mechanism excels in capturing long-range dependencies. While models built solely upon attention can be better parallelized than regular RNN, a novel network architecture, SRU++, was recently proposed. By combining the fast recurrence and attention mechanism, SRU++ exhibits strong capability in sequence modeling and achieves near-state-of-the-art results in various language modeling and machine translation tasks with improved compute efficiency. In this work, we present the advantages of applying SRU++ in ASR tasks by comparing with Conformer across multiple ASR benchmarks and study how the benefits can be generalized to long-form speech inputs. On the popular LibriSpeech benchmark, our SRU++ model achieves 2.0% / 4.7% WER on test-clean / test-other, showing competitive performances compared with the state-of-the-art Conformer encoder under the same set-up. Specifically, SRU++ can surpass Conformer on long-form speech input with a large margin, based on our analysis.
Rule-based data augmentation for knowledge graph embedding

Knowledge graph (KG) embedding models suffer from the incompleteness issue of observed facts. Different from existing solutions that incorporate additional information or employ expressive and complex embedding techniques, we propose to augment KGs by iteratively mining logical rules from the observed facts and then using the rules to generate new relational triples. We incrementally train KG embeddings with the coming of new augmented triples, and leverage the embeddings to validate these new triples. To guarantee the quality of the augmented data, we filter out the noisy triples based on a propagation mechanism during the validation. The mined rules and rule groundings are human-understandable, and can make the augmentation procedure reliable. Our KG augmentation framework is applicable to any KG embedding models with no need to modify their embedding techniques. Our experiments on two popular embedding-based tasks (i.e., entity alignment and link prediction) show that the proposed framework can bring significant improvement to existing KG embedding models on most benchmark datasets.
Leveraging Fine-grained Structured Sparsity for CNN Inference on Systolic Array Architectures

The high computational complexity of convolutional neural networks (CNNs) has motivated many studies of accelerating CNN inference on field-programmable gate arrays (FPGAs). Among these, designs that feature systolic arrays can effectively leverage the parallelism in CNNs while acheiving good placement and routing quality. Weight sparsity – the presence of zeros in CNN weights – can further reduce the number of necessary multiply-accumulate (MAC) operations in CNNs, but has yet resulted in performance gain on systolic arrays. In this work, we propose a novel fine-grained structured weight sparsity pattern, showcase a processing element (PE) design that leverages this sparsity pattern, and develop a systolic array CNN inference accelerator that targets an Intel Arria 10 GX1150 FPGA. When evaluated on ResNet-50 and VGG-16 that are trained and pruned on the ImageNet dataset, our accelerator achieves 2.26 TOPs/s and 1.21 TOPs/s, respectively, on the MAC operations, while keeping the top-l accuracy degradation within 5%. These results translate to $2.86\times$ and $1.75\times$ speed-up compared to a dense systolic array baseline.
Automatic generation of graphical game assets using GAN

This paper presents an application of the Generative Adversarial Networks (GAN) approach to automatically generate realistic-looking fantasy and science fiction game icons. In this study, we explored ways to avoid the manual drawing of graphics, instead producing synthetic images indistinguishable from artist-made ones. We developed the method based on multiple experiments and commonly known GAN improvement techniques.
Sparse Multilevel Roadmaps for High-Dimensional Robotic Motion Planning

Sparse roadmaps are important to compactly represent state spaces, to determine problems to be infeasible and to terminate in finite time. However, sparse roadmaps do not scale well to high-dimensional planning problems. In prior work, we showed improved planning performance on high-dimensional planning problems by using multilevel abstractions to simplify state spaces. In this work, we generalize sparse roadmaps to multilevel abstractions by developing a novel algorithm, the sparse multilevel roadmap planner (SMLR). To this end, we represent multilevel abstractions using the language of fiber bundles, and generalize sparse roadmap planners by using the concept of restriction sampling with visibility regions. We argue SMLR to be probabilistically complete and asymptotically near-optimal by inheritance from sparse roadmap planners. In evaluations, we outperform sparse roadmap planners on challenging planning problems, in particular problems which are high-dimensional, contain narrow passages or are infeasible. We thereby demonstrate sparse multilevel roadmaps as an efficient tool for feasible and infeasible high-dimensional planning problems.
Extension-Based Semantics for Incomplete Argumentation Frameworks

Incomplete Argumentation Frameworks (IAFs) have been defined to incorporate some qualitative uncertainty in abstract argumentation: information such as "I am not sure whether this argument exists" or "I am not sure whether this argument attacks that one" can be expressed. Reasoning with IAFs is classically based on a set of completions, i.e. standard argumentation frameworks that represent the possible worlds encoded in the IAF. The number of these completions may be exponential with respect to the number of arguments in the IAF. This leads, in some cases, to an increase of the complexity of reasoning, compared to the complexity of standard AFs. In this paper, we follow an approach that was initiated for Partial AFs (a subclass of IAFs), which consists in defining new forms of conflict-freeness and defense, the properties that underly the definition of Dung's semantics for AFs. We generalize these semantics from PAFs to IAFs. We show that, among three possible types of admissibility, only two of them satisfy some desirable properties. We use them to define two new families of extension-based semantics. We study the properties of these semantics, and in particular we show that their complexity remains the same as in the case of Dung's AFs. Finally, we propose a logical encoding of these semantics, that paves the way to the development of SAT-based solvers for reasoning with our new semantics for IAFs.
Prerequisites for the effective fight against hate crimes

Abstract Hate crimes poison societies by threatening individual rights, human dignity and equality. They effect private lives, or even victims’ life and limb. Due to their ripple effect, they terrify whole communities, reinforce tensions between social groups, ultimately jeopardising peaceful coexistence. No society is immune from the signs of hatred, but whether they get tamed or whether prejudices are deepened, depends on the social measures that are applied vis-à-vis the phenomenon. The state’s reaction creates norms and will informs society about the current acceptable standards. European expectations help forming these. Standards developed by the European Court of Human Rights include the obligations to ensure that hate against social groups as a motivation is considered an aggravating circumstance or leads to penalty enhancement. States must also ensure that national investigation authorities show special vigilance to explore and unmask the bias motives behind hate crimes. Such European expectations still leave a wide room of manoeuvre to respond to hate crimes efficiently and dissuasively. But irrespectively of the national codification method, for legal provisions to reach the desired outcome, certain social preconditions must be met. For hate crime laws or provisions to work, states must reach a certain level of maturity from the viewpoint of democracy, fundamental rights in general and the rule of law, where guaranteeing judicial independence is an absolute minimum.
Towards Bridging Video and Language by Caption Generation and Sentence Localization

Various video understanding tasks (classification, tracking, action detection, etc.) have been extensively studied in the multimedia and computer vision communities over the recent years. While these tasks are important, we think that bridging video and language is a more natural and intuitive way to interact with videos. Caption generation and sentence localization are two representative tasks for connecting video and language, and my research is focused on these two tasks. In this extended abstract, I present approaches for tackling each of these tasks by exploiting fine-grained information in videos, together with ideas about how these two tasks can be connected. So far, my work have demonstrated that these two tasks share a common foundation, and by connecting them to form a cycle, video and language can be more closely bridged. Finally, several challenges and future directions will be discussed.
A Sentence-Level Hierarchical BERT Model for Document Classification with Limited Labelled Data

The emergence of transformer models like BERT means that deep learning language models can achieve reasonably good performance in document classification with few labelled instances. However, there is a lack of evidence for the utility of applying BERT-like models on long document classification in few-shot scenarios. This paper introduces a long-text-specific model—the Hierarchical BERT Model (HBM)—that learns sentence-level features of a document and works well in few-shot scenarios. Evaluation experiments demonstrate that HBM can, with only 50 to 200 labelled instances, achieve higher document classification performance than existing state-of-the-art methods, especially when documents are long. Also, as an extra benefit of HBM, the salient sentences identified by a HBM are useful as explanations for document classifications. A user study demonstrates that highlighting these salient sentences is an effective way to speed up the document annotation required in interactive machine learning approaches like active learning.
Cross-Domain Transfer for Ship Instance Segmentation in SAR Images

Considering insufficient data and difficulty of labeling in Synthetic Aperture Radar (SAR) images, we propose a method for SAR ship instance segmentation based on cross-domain transfer learning. Compared with optical images, transfer learning in SAR images faces the difficulties of insufficient data to pre-train and lacking detail features. The proposed method, containing sample transfer module and knowledge transfer module, simulates images from optics to SAR and pre-train the ship detection part of the instance segmentation network with simulation images. In addition, we design a Res-Pyramid network to prevent the deep network from being unable to extract efficient features of SAR images. The method proposed combines the content of the optics and the style of the SAR and incorporates multiscale features in backbone, which improves performance in ship instance segmentation in SAR images. Experiments show that it has achieved 1.3 and 1.1 points higher Average Precision (AP) in detection and segmentation tasks on SAR dataset of HRSID when using cross-domain transfer learning, which has exceeded state-of-the-art methods.
Using Ontologies to aid Knowledge Sharing in HCI Design

Developing interactive systems is a challenging task that involves concerns related to the human-computer interaction (HCI), such as usability and user experience. Therefore, HCI design is a core issue when developing such systems. It often involves people with different backgrounds (e.g., Arts, Software Engineering, Design), which makes knowledge transfer a challenging issue. Ontologies have been acknowledged as a successful approach to represent domain knowledge and support knowledge-based solutions. Hence, in this work, we propose to explore ontologies to represent structured knowledge and improve knowledge sharing in HCI design. We briefly present the Human-Computer Interaction Design Ontology (HCIDO), a reference ontology that addresses HCI design aspects that connect HCI and Software Engineering concerns. By making knowledge related to the HCI design domain explicit and structured, HCIDO has helped us to develop KTID, a tool that aims to support capturing and sharing useful knowledge to aid in HCI design. Preliminary results indicate that the tool may be particularly useful for novice HCI designers.
The Game of Video Game Objects

While looking to the future, we have overlooked what is right before us. With new technology, haptics, rendering, virtual reality, we have spent much energy discussing immersion and presence, thinking sometimes about current technology, but often about hypothetical perfect experiences or future perfect technology.
ElearnChain: A privacy-preserving consortium blockchain system for e-learning educational records

Nowadays, some educational institutions provide online multimedia learning resources(MLR) and bring a series of challenges in the insecurity of digital diplomas, unacceptable digital diplomas between different institutions, and difficulty searching for high-quality multimedia learning resources. The blockchain’s immutability and privacy provide solutions for the circulation of e-learning educational records, but existing research cannot be directly applied. This paper aims to solve these issues. We propose an ElearnChain, which is a privacy-preserving consortium blockchain system for e-learning educational records. Furthermore, we implement a proof-of-concept for a tamper-proof and traceable privacy-preserving consortium blockchain system.
A novel image deblocking approach within a graph framework

Since JPEG compression is achieved via independent and coarse quantization of discrete cosine transformation coefficients in each block, its results generally exhibit visually annoying blocking artifacts and Gibbs phenomena at low bitrates. Moreover, the existing image deblocking algorithms are basically processed under the classical image topology structure. In this paper, we present a novel image blocking artifact-free methodology within a graph framework. A graph is a compatible tool. Most of the existing image deblocking models can be improved by transforming images or image patches into graph signals. To verify this viewpoint, we focus on improving the existing mainstream total-variation-based and constrained non-convex low-rank image deblocking models. Furthermore, we provide numerical algorithms and the corresponding fast workarounds. The introduction of a graph renders the pixel representation more flexible and adaptive, and can improve the computational efficiency of the constrained non-convex low-rank image deblocking approach. In addition, twelve standard images are used to test the performance of the proposed models. The experiments show that in comparison to several existing famous models, our models achieve competitive results in terms of deblocking and calculation speed.
Probabilistic Inference in Planning for Partially Observable Long Horizon Problems

For autonomous service robots to successfully perform long horizon tasks in the real world, they must act intelligently in partially observable environments. Most Task and Motion Planning approaches assume full observability of their state space, making them ineffective in stochastic and partially observable domains that reflect the uncertainties in the real world. We propose an online planning and execution approach for performing long horizon tasks in partially observable domains. Given the robot’s belief and a plan skeleton composed of symbolic actions, our approach grounds each symbolic action by inferring continuous action parameters needed to execute the plan successfully. To achieve this, we formulate the problem of joint inference of action parameters as a Hybrid Constraint Satisfaction Problem (H-CSP) and solve the H-CSP using Belief Propagation. The robot executes the resulting parameterized actions, updates its belief of the world and replans when necessary. Our approach is able to efficiently solve partially observable tasks in a realistic kitchen simulation environment. Our approach outperformed an adaptation of the state-of-the-art method across our experiments.
Progress toward a comprehensive teaching approach to the FAIR data principles

We evaluate recent efforts to further the effective teaching of FAIR data principles by examining existing and developing educational frameworks focused upon FAIR, training initiatives that have informed teaching on FAIR skills' topics, and a number of key sources for discovering FAIR training materials and how much those sources provide descriptive information about the materials. FAIR4S, providing a coherent description of skills and competencies, is analyzed by target audience using the description of actors found in a European Open Science Cloud ecosystem report and by comparison of the coverage and extent of description of educational and training materials available from the list of sources for finding such materials. Our analysis elucidates the importance of linking resources to FAIR-related educational frameworks, providing consistent descriptions of them using a community-based metadata scheme, and developing an instructor community of practice where ideas and methods can be shared on how to teach FAIR data skills.
Autonomous Vehicle Motion Planning via Recurrent Spline Optimization

Trajectory planning in dynamic environments can be decomposed into two sub-problems: 1) planning a path to avoid static obstacles, 2) then planning a speed profile to avoid dynamic obstacles. This is also called path-speed decomposition. In this work, we present a novel approach to solve the first sub-problem, motion planning with static obstacles. From an optimization perspective, motion planning for autonomous vehicles can be viewed as non-convex constrained nonlinear optimization, which requires a good enough initial guess to start and is often sensitive to algorithm parameters. We formulate motion planning as convex spline optimization. The convexity of the formulated problem makes it able to be solved fast and reliably, while guaranteeing a global optimum. We then reorganize the constrained spline optimization into a recurrent formulation, which further reduces the computational time to be linear in the optimization horizon size. The proposed method can be applied to both trajectory generation and motion planning problems. Its effectiveness is demonstrated in challenging scenarios such as tight lane changes and sharp turns.