OpenRaiser/Intern-Atlas huggingface dataset:
ExPAN(N)D: Exploring Posits for Efficient Artificial Neural Network Design in FPGA-based Systems
Venue: arXiv cs.AR 2020
Year: 2020

The recent advances in machine learning, in general, and Artificial Neural Networks (ANN), in particular, has made smart embedded systems an attractive option for a larger number of application areas. However, the high computational complexity, memory footprints, and energy requirements of machine learning models hinder their deployment on resource-constrained embedded systems. Most state-of-the-art works have considered this problem by proposing various low bit-width data representation schemes, optimized arithmetic operators' implementations, and different complexity reduction techniques such as network pruning. To further elevate the implementation gains offered by these individual techniques, there is a need to cross-examine and combine these techniques' unique features. This paper presents ExPAN(N)D, a framework to analyze and ingather the efficacy of the Posit number representation scheme and the efficiency of fixed-point arithmetic implementations for ANNs. The Posit scheme offers a better dynamic range and higher precision for various applications than IEEE $754$ single-precision floating-point format. However, due to the dynamic nature of the various fields of the Posit scheme, the corresponding arithmetic circuits have higher critical path delay and resource requirements than the single-precision-based arithmetic units. Towards this end, we propose a novel Posit to fixed-point converter for enabling high-performance and energy-efficient hardware implementations for ANNs with minimal drop in the output accuracy. We also propose a modified Posit-based representation to store the trained parameters of a network. Compared to an $8$-bit fixed-point-based inference accelerator, our proposed implementation offers $\approx46\%$ and $\approx18\%$ reductions in the storage requirements of the parameters and energy consumption of the MAC units, respectively.
Towards Real-Time Magnetic Dosimetry Simulations for Inductive Charging Systems
Venue: arXiv cs.CE 2020
Year: 2020

The exposure of a human by magneto-quasistatic fields from wireless charging systems is to be determined from magnetic field measurements in near real-time. This requires a fast linear equations solver for the discrete Poisson system of the Co-Simulation Scalar Potential Finite Difference (Co-Sim. SPFD) scheme. Here, the use of the AmgX library on NVIDIA GPUs is presented for this task. It enables solving the equation system resulting from an ICNIRP recommended human voxel model resolution of 2 mm in less than 0.5 seconds on a single NVIDIA Tesla V100 GPU.
Revisiting Neural Language Modelling with Syllables
Venue: arXiv cs.CL 2020
Year: 2020

Language modelling is regularly analysed at word, subword or character units, but syllables are seldom used. Syllables provide shorter sequences than characters, they can be extracted with rules, and their segmentation typically requires less specialised effort than identifying morphemes. We reconsider syllables for an open-vocabulary generation task in 20 languages. We use rule-based syllabification methods for five languages and address the rest with a hyphenation tool, which behaviour as syllable proxy is validated. With a comparable perplexity, we show that syllables outperform characters, annotated morphemes and unsupervised subwords. Finally, we also study the overlapping of syllables concerning other subword pieces and discuss some limitations and opportunities.
FedE: Embedding Knowledge Graphs in Federated Setting
Venue: arXiv cs.CL 2020
Year: 2020

Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples. Multi-Source KG is a common situation in real KG applications which can be viewed as a set of related individual KGs where different KGs contains relations of different aspects of entities. It's intuitive that, for each individual KG, its completion could be greatly contributed by the triples defined and labeled in other ones. However, because of the data privacy and sensitivity, a set of relevant knowledge graphs cannot complement each other's KGC by just collecting data from different knowledge graphs together. Therefore, in this paper, we introduce federated setting to keep their privacy without triple transferring between KGs and apply it in embedding knowledge graph, a typical method which have proven effective for KGC in the past decade. We propose a Federated Knowledge Graph Embedding framework FedE, focusing on learning knowledge graph embeddings by aggregating locally-computed updates. Finally, we conduct extensive experiments on datasets derived from KGE benchmark datasets and results show the effectiveness of our proposed FedE.
Word Embeddings for Chemical Patent Natural Language Processing
Venue: arXiv cs.CL 2020
Year: 2020

We evaluate chemical patent word embeddings against known biomedical embeddings and show that they outperform the latter extrinsically and intrinsically. We also show that using contextualized embeddings can induce predictive models of reasonable performance for this domain over a relatively small gold standard.
A Characterization of 3D Printability
Venue: arXiv cs.GR 2020
Year: 2020

Additive manufacturing technologies are positioned to provide an unprecedented innovative transformation in how products are designed and manufactured. Due to differences in the technical specifications of AM technologies, the final fabricated parts can vary significantly from the original CAD models, therefore raising issues regarding accuracy, surface finish, robustness, mechanical properties, functional and geometrical constraints. Various researchers have studied the correlation between AM technologies and design rules. In this work we propose a novel approach to assessing the capability of a 3D model to be printed successfully (a.k.a printability) on a specific AM machine. This is utilized by taking into consideration the model mesh complexity and certain part characteristics. A printability score is derived for a model in reference to a specific 3D printing technology, expressing the probability of obtaining a robust and accurate end result for 3D printing on a specific AM machine. The printability score can be used either to determine which 3D technology is more suitable for manufacturing a specific model or as a guide to redesign the model to ensure printability. We verify this framework by conducting 3D printing experiments for benchmark models which are printed on three AM machines employing different technologies: Fused Deposition Modeling (FDM), Binder Jetting (3DP), and Material Jetting (Polyjet).
Triclustering in Big Data Setting
Venue: arXiv cs.DC 2020
Year: 2020

In this paper, we describe versions of triclustering algorithms adapted for efficient calculations in distributed environments with MapReduce model or parallelisation mechanism provided by modern programming languages. OAC-family of triclustering algorithms shows good parallelisation capabilities due to the independent processing of triples of a triadic formal context. We provide the time and space complexity of the algorithms and justify their relevance. We also compare performance gain from using a distributed system and scalability.
Satisfying Increasing Performance Requirements with Caching at the Application Level
Venue: arXiv cs.SE 2020
Year: 2020

Application-level caching is a form of caching that has been increasingly adopted to satisfy performance and throughput requirements. The key idea is to store the results of a computation, to improve performance by reusing instead of recomputing those results. However, despite its provided gains, this form of caching imposes new design, implementation and maintenance challenges. In this article, we provide an overview of application-level caching, highlighting its benefits as well as the challenges and the issues to adopt it. We introduce three kinds of existing support that have been proposed, giving a broad view of research in the area. Finally, we present important open challenges that remain unaddressed, hoping to inspire future work on addressing them.
Power Allocation for Relayed OFDM with Index Modulation Assisted by Artificial Neural Network
Venue: arXiv cs.IT 2020
Year: 2020

In this letter, we propose a power allocation scheme for relayed orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. The proposed power allocation scheme replies on artificial neural network (ANN) and deep learning to allocate transmit power among various subcarriers at the source and relay nodes. The objective of the power allocation scheme is to minimize the overall transmit power under a set of constraints. Without loss of generality, we assume all subcarriers at source and relay nodes are independently distributed with different statistical distribution parameters. The relay node adopts the fixed-gain amplify-and-forward (FG AF) relaying protocol. We employ the adaptive moment estimation method (Adam) to implement back-propagation learning and simulate the proposed power allocation scheme. The analytical and simulation results show that the proposed power allocation scheme is able to provide comparable performance as the optimal solution but with lower complexity.
Transmitter and Receiver Window Designs for Orthogonal Time Frequency Space Modulation
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate the impacts of transmitter and receiver windows on the performance of orthogonal time-frequency space (OTFS) modulation and propose window designs to improve the OTFS channel estimation and data detection performance. In particular, assuming ideal pulse shaping filters at the transceiver, we derive the impacts of windowing on the effective channel and its estimation performance in the delay-Doppler (DD) domain, the total average transmit power and the effective noise covariance matrix. When the channel state information (CSI) is available at the transceiver, we analyze the minimum squared error (MSE) of data detection and propose an optimal transmitter window to minimize the detection MSE. The proposed optimal transmitter window is interpreted as a mercury/water-filling power allocation scheme, where the mercury is firstly filled before pouring water to pre-equalize the TF domain channels. When the CSI is not available at the transmitter but can be estimated at the receiver, we propose to apply a Dolph-Chebyshev (DC) window at either the transmitter or the receiver, which can effectively enhance the sparsity of the effective channel in the DD domain. Thanks to the enhanced DD domain channel sparsity, the channel spread due to the fractional Doppler is significantly reduced, which leads to a lower error floor in both channel estimation and data detection compared with that of rectangular window. Simulation results verify the accuracy of the obtained analytical results and confirm the superiority of the proposed window designs in improving the channel estimation and data detection performance over the conventional rectangular window design.
MLANE: Meta-Learning Based Adaptive Network Embedding
Venue: arXiv cs.SI 2020
Year: 2020

Most existing random walk based network embedding methods often follow only one of two principles, homophily or structural equivalence. In real world networks, however, nodes exhibit a mixture of homophily and structural equivalence, which requires adaptive network embedding that can adaptively preserve both homophily and structural equivalence for different nodes in different down-stream analysis tasks. In this paper, we propose a novel method called Meta-Learning based Adaptive Network Embedding (MLANE), which can learn adaptive sampling strategy for different nodes in different tasks by incorporating sampling strategy learning with embedding learning into one optimization problem that can be solved via an end-to-end meta-learning framework. In extensive experiments on real datasets, MLANE shows significant performance improvements over the baselines. The source code of MLANE and the datasets used in experiments and all the hyperparameter settings for baselines are available at https://github.com/7733com/MLANE.
Real-Time Variable-to-Fixed Lossless Source Coding of Randomly Arriving Symbols
Venue: arXiv cs.IT 2020
Year: 2020

We address the recently suggested problem of causal lossless coding of a randomly arriving source samples. We construct variable-to-fixed coding schemes and show that they outperform the previously considered fixed-to-variable schemes when traffic is high both in terms of delay and Age of Information by appealing to tools from queueing theory. We supplement our theoretical bounds with numerical simulations.
ExplanationLP: Abductive Reasoning for Explainable Science Question Answering
Venue: arXiv cs.AI 2020
Year: 2020

We propose a novel approach for answering and explaining multiple-choice science questions by reasoning on grounding and abstract inference chains. This paper frames question answering as an abductive reasoning problem, constructing plausible explanations for each choice and then selecting the candidate with the best explanation as the final answer. Our system, ExplanationLP, elicits explanations by constructing a weighted graph of relevant facts for each candidate answer and extracting the facts that satisfy certain structural and semantic constraints. To extract the explanations, we employ a linear programming formalism designed to select the optimal subgraph. The graphs' weighting function is composed of a set of parameters, which we fine-tune to optimize answer selection performance. We carry out our experiments on the WorldTree and ARC-Challenge corpus to empirically demonstrate the following conclusions: (1) Grounding-Abstract inference chains provides the semantic control to perform explainable abductive reasoning (2) Efficiency and robustness in learning with a fewer number of parameters by outperforming contemporary explainable and transformer-based approaches in a similar setting (3) Generalisability by outperforming SOTA explainable approaches on general science question sets.
XLVIN: eXecuted Latent Value Iteration Nets
Venue: arXiv cs.LG 2020
Year: 2020

Value Iteration Networks (VINs) have emerged as a popular method to incorporate planning algorithms within deep reinforcement learning, enabling performance improvements on tasks requiring long-range reasoning and understanding of environment dynamics. This came with several limitations, however: the model is not incentivised in any way to perform meaningful planning computations, the underlying state space is assumed to be discrete, and the Markov decision process (MDP) is assumed fixed and known. We propose eXecuted Latent Value Iteration Networks (XLVINs), which combine recent developments across contrastive self-supervised learning, graph representation learning and neural algorithmic reasoning to alleviate all of the above limitations, successfully deploying VIN-style models on generic environments. XLVINs match the performance of VIN-like models when the underlying MDP is discrete, fixed and known, and provides significant improvements to model-free baselines across three general MDP setups.
Performance Analysis of Scientific Computing Workloads on Trusted Execution Environments
Venue: arXiv cs.DC 2020
Year: 2020

Scientific computing sometimes involves computation on sensitive data. Depending on the data and the execution environment, the HPC (high-performance computing) user or data provider may require confidentiality and/or integrity guarantees. To study the applicability of hardware-based trusted execution environments (TEEs) to enable secure scientific computing, we deeply analyze the performance impact of AMD SEV and Intel SGX for diverse HPC benchmarks including traditional scientific computing, machine learning, graph analytics, and emerging scientific computing workloads. We observe three main findings: 1) SEV requires careful memory placement on large scale NUMA machines (1$\times$$-$3.4$\times$ slowdown without and 1$\times$$-$1.15$\times$ slowdown with NUMA aware placement), 2) virtualization$-$a prerequisite for SEV$-$results in performance degradation for workloads with irregular memory accesses and large working sets (1$\times$$-$4$\times$ slowdown compared to native execution for graph applications) and 3) SGX is inappropriate for HPC given its limited secure memory size and inflexible programming model (1.2$\times$$-$126$\times$ slowdown over unsecure execution). Finally, we discuss forthcoming new TEE designs and their potential impact on scientific computing.
MixNet for Generalized Face Presentation Attack Detection
Venue: arXiv cs.CV 2020
Year: 2020

The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises essential questions regarding its reliability. In the literature, several presentation attack detection algorithms are presented; however, they are still far behind from reality. The major problem with existing work is the generalizability against multiple attacks both in the seen and unseen setting. The algorithms which are useful for one kind of attack (such as print) perform unsatisfactorily for another type of attack (such as silicone masks). In this research, we have proposed a deep learning-based network termed as \textit{MixNet} to detect presentation attacks in cross-database and unseen attack settings. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category. Experiments are performed using multiple challenging face presentation attack databases such as SMAD and Spoof In the Wild (SiW-M) databases. Extensive experiments and comparison with existing state of the art algorithms show the effectiveness of the proposed algorithm.
An Approach to Preamble Collision Reduction in Grant-Free Random Access with Massive MIMO
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we study grant-free random access with massive multiple input multiple output (MIMO) systems. We first show that the performance of massive MIMO based grant-free random access is mainly decided by the probability of preamble collision. The implication of this is that although the number of antennas can be arbitrarily large, the (average) number of successful packet transmissions can be limited by the number of preambles. Then, we propose an approach to preamble collision reduction without increasing the number of preambles using the notion of superpositioned preambles (S-preambles). Since the channel estimation for conjugate beamforming can be carried out when unused S-preambles are known, a simple approach is derived to detect unused S-preambles and its performance is analyzed for a special case (superpositions with 2 preambles). Based on analysis and simulation results, it is confirmed that the proposed approach with S-preambles can increase the success probability with the same spectral efficiency as the conventional approach.
Early Warning of COVID-19 Hotspots using Mobility of High Risk Users from Web Search Queries
Venue: arXiv cs.SI 2020
Year: 2020

COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventions (e.g., lockdowns) through population density analysis. However, predicting the locations of potential outbreak occurrence is difficult using mobility data alone. Meanwhile, web search queries have been shown to be good predictors of the disease spread. In this study, we utilize a unique dataset of human mobility trajectories (GPS traces) and web search queries with common user identifiers (> 450K users), to predict COVID-19 hotspot locations beforehand. More specifically, web search query analysis is conducted to identify users with high risk of COVID-19 contraction, and social contact analysis was further performed on the mobility patterns of these users to quantify the risk of an outbreak. Our approach is empirically tested using data collected from users in Tokyo, Japan. We show that by integrating COVID-19 related web search query analytics with social contact networks, we are able to predict COVID-19 hotspot locations 1-2 weeks beforehand, compared to just using social contact indexes or web search data analysis. This study proposes a novel method that can be used in early warning systems for disease outbreak hotspots, which can assist government agencies to prepare effective strategies to prevent further disease spread.
Towards Empowering Diabetic Patients: A perspective on self-management in the context of a group-based education program
Venue: arXiv cs.CY 2020
Year: 2020

This paper provides a novel framework for maximizing the effectiveness of the Diabetes Group Education Program, which could be generalized in any similar problem context.
Semi-Disentangled Representation Learning in Recommendation System
Venue: arXiv cs.SI 2020
Year: 2020

Disentangled representation has been widely explored in many fields due to its maximal compactness, interpretability and versatility. Recommendation system also needs disentanglement to make representation more explainable and general for downstream tasks. However, some challenges slow its broader application -- the lack of fine-grained labels and the complexity of user-item interactions. To alleviate these problems, we propose a Semi-Disentangled Representation Learning method (SDRL) based on autoencoders. SDRL divides each user/item embedding into two parts: the explainable and the unexplainable, so as to improve proper disentanglement while preserving complex information in representation. The explainable part consists of $internal\ block$ for individual-based features and $external\ block$ for interaction-based features. The unexplainable part is composed by $other\ block$ for other remaining information. Experimental results on three real-world datasets demonstrate that the proposed SDRL could not only effectively express user and item features but also improve the explainability and generality compared with existing representation methods.
Enhancing Cloud Storage with Shareable Instances for Social Computing
Venue: arXiv cs.DC 2020
Year: 2020

Cloud storage plays an important role in social computing. This paper aims to develop a cloud storage management system for mobile devices to support an extended set of file operations. Because of the limit of storage, bandwidth, power consumption, and other resource restrictions, most existing cloud storage apps for smartphones do not keep local copies of files. This efficient design, however, limits the application capacities. In this paper, we attempt to extend the available file operations for cloud storage service to better serve smartphone users. We develop an efficient and secure file management system, Skyfiles, to support more advanced file operations. The basic idea of our design is to utilize cloud instances to assist file operations. Particularly, Skyfiles supports downloading, compressing, encrypting, and converting operations, as well as file transfer between two smartphone users' cloud storage spaces. In addition, we propose a protocol for users to share their idle instances. All file operations supported by Skyfiles can be efficiently and securely accomplished with either a self-created instance or shared instance.