OpenRaiser/Intern-Atlas huggingface dataset:
Secure Evaluation of Knowledge Graph Merging Gain
Venue: arXiv cs.CR 2021
Year: 2021

Finding out the differences and commonalities between the knowledge of two parties is an important task. Such a comparison becomes necessary, when one party wants to determine how much it is worth to acquire the knowledge of the second party, or similarly when two parties try to determine, whether a collaboration could be beneficial. When these two parties cannot trust each other (for example, due to them being competitors) performing such a comparison is challenging as neither of them would be willing to share any of their assets. This paper addresses this problem for knowledge graphs, without a need for non-disclosure agreements nor a third party during the protocol. During the protocol, the intersection between the two knowledge graphs is determined in a privacy preserving fashion. This is followed by the computation of various metrics, which give an indication of the potential gain from obtaining the other parties knowledge graph, while still keeping the actual knowledge graph contents secret. The protocol makes use of blind signatures and (counting) Bloom filters to reduce the amount of leaked information. Finally, the party who wants to obtain the other's knowledge graph can get a part of such in a way that neither party is able to know beforehand which parts of the graph are obtained (i.e., they cannot choose to only get or share the good parts). After inspection of the quality of this part, the Buyer can decide to proceed with the transaction. The analysis of the protocol indicates that the developed protocol is secure against malicious participants. Further experimental analysis shows that the resource consumption scales linear with the number of statements in the knowledge graph.
Pedestrian Motion State Estimation From 2D Pose
Venue: arXiv cs.CV 2021
Year: 2021

Traffic violation and the flexible and changeable nature of pedestrians make it more difficult to predict pedestrian behavior or intention, which might be a potential safety hazard on the road. Pedestrian motion state (such as walking and standing) directly affects or reflects its intention. In combination with pedestrian motion state and other influencing factors, pedestrian intention can be predicted to avoid unnecessary accidents. In this paper, pedestrian is treated as non-rigid object, which can be represented by a set of two-dimensional key points, and the movement of key point relative to the torso is introduced as micro motion. Static and dynamic micro motion features, such as position, angle and distance, and their differential calculations in time domain, are used to describe its motion pattern. Gated recurrent neural network based seq2seq model is used to learn the dependence of motion state transition on previous information, finally the pedestrian motion state is estimated via a softmax classifier. The proposed method only needs the previous hidden state of GRU and current feature to evaluate the probability of current motion state, and it is computation efficient to deploy on vehicles. This paper verifies the proposed algorithm on the JAAD public dataset, and the accuracy is improved by 11.6% compared with the existing method.
Inferring Unobserved Events in Systems With Shared Resources and Queues
Venue: arXiv cs.DC 2021
Year: 2021

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to a variety of reasons, real-life systems often record only a subset of all events taking place. To understand and analyze the behavior and performance of processes with shared resources, we aim to reconstruct bounds for timestamps of events in a case that must have happened but were not recorded by inference over events in other cases in the system. We formulate and solve the problem by systematically introducing multi-entity concepts in event logs and process models. We introduce a partial-order based model of a multi-entity event log and a corresponding compositional model for multi-entity processes. We define PQR-systems as a special class of multi-entity processes with shared resources and queues. We then study the problem of inferring from an incomplete event log unobserved events and their timestamps that are globally consistent with a PQR-system. We solve the problem by reconstructing unobserved traces of resources and queues according to the PQR-model and derive bounds for their timestamps using a linear program. While the problem is illustrated for material handling systems like baggage handling systems in airports, the approach can be applied to other settings where recording is incomplete. The ideas have been implemented in ProM and were evaluated using both synthetic and real-life event logs.
Reasons behind growing adoption of Cloud after Covid-19 Pandemic and Challenges ahead
Venue: arXiv cs.DC 2021
Year: 2021

There are many sectors which have moved to Cloud and are planning aggressively to move their workloads to Cloud since the world entered Covid-19 pandemic. There are various reasons why Cloud is an essential irresistible technology and serves as an ultimate solution to access IT software and systems. It has become a new essential catalyst for Enterprise Organisations which are looking for Digital Transformation. Remote working is a common phenomenon now across all the IT companies making the services available all the time. Covid-19 has made cloud adoption an immediate priority for Organisation rather than a slowly approached future transformation. The benefits of Cloud lies in the fact that employees rather engineers of an enterprise are no more dependent on the closed hardware-based IT infrastructure and hence eliminates the necessity of working from the networked office premises. This has raised a huge demand for skilled Cloud specialist who can manage and support the systems running on cloud across different regions of the world. In this research, the reasons for growing Cloud adoption after pandemic Covid-19 has been described and the challenges which Organization will face is also explained. This study also details the most used cloud services during the pandemic considering Amazon Web Services as the cloud provider.
Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report
Venue: arXiv cs.AI 2021
Year: 2021

In this report, we present results reproductions for several core algorithms implemented in the OpenSpiel framework for learning in games. The primary contribution of this work is a validation of OpenSpiel's re-implemented search and Reinforcement Learning algorithms against the results reported in their respective originating works. Additionally, we provide complete documentation of hyperparameters and source code required to reproduce these experiments easily and exactly.
FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images
Venue: arXiv cs.CV 2021
Year: 2021

Keypoint detection and description is a commonly used building block in computer vision systems particularly for robotics and autonomous driving. However, the majority of techniques to date have focused on standard cameras with little consideration given to fisheye cameras which are commonly used in urban driving and automated parking. In this paper, we propose a novel training and evaluation pipeline for fisheye images. We make use of SuperPoint as our baseline which is a self-supervised keypoint detector and descriptor that has achieved state-of-the-art results on homography estimation. We introduce a fisheye adaptation pipeline to enable training on undistorted fisheye images. We evaluate the performance on the HPatches benchmark, and, by introducing a fisheye based evaluation method for detection repeatability and descriptor matching correctness, on the Oxford RobotCar dataset.
HIR: An MLIR-based Intermediate Representation for Hardware Accelerator Description
Venue: arXiv cs.AR 2021
Year: 2021

The emergence of machine learning, image and audio processing on edge devices has motivated research towards power efficient custom hardware accelerators. Though FPGAs are an ideal target for energy efficient custom accelerators, the difficulty of hardware design and the lack of vendor agnostic, standardized hardware compilation infrastructure has hindered their adoption. This paper introduces HIR, an MLIR-based intermediate representation (IR) to describe hardware accelerator designs. HIR combines high level language features, such as loops and multi-dimensional tensors, with programmer defined explicit scheduling, to provide a high-level IR suitable for DSL compiler pipelines without compromising control over the micro-architecture of the accelerator. HIR's explicit schedules allow it to express fine-grained, synchronization-free parallelism and optimizations such as retiming and pipelining. Built as a dialect in MLIR, it draws from best IR practices learnt from communities like those of LLVM. While offering rich optimization opportunities and a high level abstraction, HIR enables sharing of optimizations, utilities and passes with software compiler infrastructure. Our implementation shows that the code generation time of the HIR code generator is on average 1112x lower than that of Xilinx Vivado HLS on a range of kernels without a compromise on the quality of the generated hardware. We believe that these are significant steps forward in the design of IRs for hardware synthesis and in equipping domain-specific languages with a productive and performing compilation path to custom hardware acceleration.
UAV-Enabled Wireless Power Transfer: A Tutorial Overview
Venue: arXiv cs.IT 2021
Year: 2021

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) has recently emerged as a promising technique to provide sustainable energy supply for widely distributed low-power ground devices (GDs) in large-scale wireless networks. Compared with the energy transmitters (ETs) in conventional WPT systems which are deployed at fixed locations, UAV-mounted aerial ETs can fly flexibly in the three-dimensional (3D) space to charge nearby GDs more efficiently. This paper provides a tutorial overview on UAV-enabled WPT and its appealing applications, in particular focusing on how to exploit UAVs' controllable mobility via their 3D trajectory design to maximize the amounts of energy transferred to all GDs in a wireless network with fairness. First, we consider the single-UAV-enabled WPT scenario with one UAV wirelessly charging multiple GDs at known locations. To solve the energy maximization problem in this case, we present a general trajectory design framework consisting of three innovative approaches to optimize the UAV trajectory, which are multi-location hovering, successive-hover-and-fly, and time-quantization-based optimization, respectively. Next, we consider the multi-UAV-enabled WPT scenario where multiple UAVs cooperatively charge many GDs in a large area. Building upon the single-UAV trajectory design, we propose two efficient schemes to jointly optimize multiple UAVs' trajectories, based on the principles of UAV swarming and GD clustering, respectively. Furthermore, we consider two important extensions of UAV-enabled WPT, namely UAV-enabled wireless powered communication networks (WPCN) and UAV-enabled wireless powered mobile edge computing (MEC).
Expert Decision Support System for aeroacoustic source type identification using clustering
Venue: arXiv cs.SD 2021
Year: 2021

This paper presents an Expert Decision Support System for the identification of time-invariant, aeroacoustic source types. The system comprises two steps: first, acoustic properties are calculated based on spectral and spatial information. Second, clustering is performed based on these properties. The clustering aims at helping and guiding an expert for quick identification of different source types, providing an understanding of how sources differ. This supports the expert in determining similar or atypical behavior. A variety of features are proposed for capturing the characteristics of the sources. These features represent aeroacoustic properties that can be interpreted by both the machine and by experts. The features are independent of the absolute Mach number which enables the proposed method to cluster data measured at different flow configurations. The method is evaluated on deconvolved beamforming data from two scaled airframe half-model measurements. For this exemplary data, the proposed support system method results in clusters that mostly correspond to the source types identified by the authors. The clustering also provides the mean feature values and the cluster hierarchy for each cluster and for each cluster member a clustering confidence. This additional information makes the results transparent and allows the expert to understand the clustering choices.
Bounded Exhaustive Search of Alloy Specification Repairs
Venue: arXiv cs.SE 2021
Year: 2021

The rising popularity of declarative languages and the hard to debug nature thereof have motivated the need for applicable, automated repair techniques for such languages. However, despite significant advances in the program repair of imperative languages, there is a dearth of repair techniques for declarative languages. This paper presents BeAFix, an automated repair technique for faulty models written in Alloy, a declarative language based on first-order relational logic. BeAFix is backed with a novel strategy for bounded exhaustive, yet scalable, exploration of the spaces of fix candidates and a formally rigorous, sound pruning of such spaces. Moreover, different from the state-of-the-art in Alloy automated repair, that relies on the availability of unit tests, BeAFix does not require tests and can work with assertions that are naturally used in formal declarative languages. Our experience with using BeAFix to repair thousands of realworld faulty models, collected by other researchers, corroborates its ability to effectively generate correct repairs and outperform the state-of-the-art.
Transformers with Competitive Ensembles of Independent Mechanisms
Venue: arXiv cs.LG 2021
Year: 2021

An important development in deep learning from the earliest MLPs has been a move towards architectures with structural inductive biases which enable the model to keep distinct sources of information and routes of processing well-separated. This structure is linked to the notion of independent mechanisms from the causality literature, in which a mechanism is able to retain the same processing as irrelevant aspects of the world are changed. For example, convnets enable separation over positions, while attention-based architectures (especially Transformers) learn which combination of positions to process dynamically. In this work we explore a way in which the Transformer architecture is deficient: it represents each position with a large monolithic hidden representation and a single set of parameters which are applied over the entire hidden representation. This potentially throws unrelated sources of information together, and limits the Transformer's ability to capture independent mechanisms. To address this, we propose Transformers with Independent Mechanisms (TIM), a new Transformer layer which divides the hidden representation and parameters into multiple mechanisms, which only exchange information through attention. Additionally, we propose a competition mechanism which encourages these mechanisms to specialize over time steps, and thus be more independent. We study TIM on a large-scale BERT model, on the Image Transformer, and on speech enhancement and find evidence for semantically meaningful specialization as well as improved performance.
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations
Venue: arXiv cs.LG 2021
Year: 2021

We examine the general problem of inter-domain Gaussian Processes (GPs): problems where the GP realization and the noisy observations of that realization lie on different domains. When the mapping between those domains is linear, such as integration or differentiation, inference is still closed form. However, many of the scaling and approximation techniques that our community has developed do not apply to this setting. In this work, we introduce the hierarchical inducing point GP (HIP-GP), a scalable inter-domain GP inference method that enables us to improve the approximation accuracy by increasing the number of inducing points to the millions. HIP-GP, which relies on inducing points with grid structure and a stationary kernel assumption, is suitable for low-dimensional problems. In developing HIP-GP, we introduce (1) a fast whitening strategy, and (2) a novel preconditioner for conjugate gradients which can be helpful in general GP settings. Our code is available at https: //github.com/cunningham-lab/hipgp.
Individual risk profiling for portable devices using a neural network to process the cognitive reactions and the emotional responses to a multivariate situational risk assessment
Venue: arXiv cs.NE 2021
Year: 2021

In this paper, we are presenting a novel method and system for neuropsychological performance testing that can establish a link between cognition and emotion. It comprises a portable device used to interact with a cloud service which stores user information under username and is logged into by the user through the portable device; the user information is directly captured through the device and is processed by artificial neural network; and this tridimensional information comprises user cognitive reactions, user emotional responses and user chronometrics. The multivariate situational risk assessment is used to evaluate the performance of the subject by capturing the 3 dimensions of each reaction to a series of 30 dichotomous questions describing various situations of daily life and challenging the user's knowledge, values, ethics, and principles. In industrial application, the timing of this assessment will depend on the user's need to obtain a service from a provider such as opening a bank account, getting a mortgage or an insurance policy, authenticating clearance at work or securing online payments.
Passive Beamforming Design and Channel Estimation for IRS Communication System with Few-Bit ADCs
Venue: arXiv cs.IT 2021
Year: 2021

Utilizing intelligent reflecting surface (IRS) was proven to be efficient in improving the energy efficiency for wireless networks. In this paper, we investigate the passive beamforming and channel estimation for IRS assisted wireless communications with low-resolution analog-to-digital converters (ADCs) at the receiver. We derive the approximate achievable rate by using the Bussgang theorem. Based on the derived analytical achievable rate expression, we maximize the achievable rate by using semidefinite programming (SDP), branch-and-bound (BB), and gradient-based approaches. A maximum likelihood (ML) estimator is then proposed for channel estimation by considering the $\mathrm{1}$-bit quantization ADC. Numerical result shows that the proposed beamforming design and channel estimation method significantly outperforms the existing methods.
RoBERTa-wwm-ext Fine-Tuning for Chinese Text Classification
Venue: arXiv cs.CL 2021
Year: 2021

Bidirectional Encoder Representations from Transformers (BERT) have shown to be a promising way to dramatically improve the performance across various Natural Language Processing tasks [Devlin et al., 2019]. Meanwhile, progress made over the past few years by various Neural Net-work has also proved the effectiveness of Neural Network in the field of Natural Language Processing. In this project, RoBERTa-wwm-ext [Cui et al., 2019] pre-train language model was adopted and fine-tuned for Chinese text classification. The models were able to classify Chinese texts into two categories, containing descriptions of legal behavior and descriptions of illegal behavior. Four different models are also proposed in the paper. Those models will use RoBERTa-wwm-extas their embedding layer and feed the embedding into different neural networks. The motivation be-hind proposing these models is straightforward. By introducing complex output layer architecture, the overall performance of the models could be improved. All the models were trained on a data set derived from Chinese public court records, and the performance of different models were compared.The experiment shows that the performance of pro-posed models failed to beat the original RoBERTa-wwm-ext model in terms of accuracy and training efficiency.
Protocol-independent Detection of "Messaging Ordering" Network Covert Channels
Venue: arXiv cs.CR 2021
Year: 2021

Detection methods are available for several known covert channels. However, a type of covert channel that received little attention within the last decade is the "message ordering" channel. Such a covert channel changes the order of PDUs (protocol data units, i.e. packets) transferred over the network to encode hidden information. The advantage of these channels is that they cannot be blocked easily as they do not modify header content but instead mimic typical network behavior such as TCP segments that arrive in a different order than they were sent. Contribution: In this paper, we show a protocol-independent approach to detect message ordering channels. Our approach is based on a modified compressibility score. We analyze the detectability of message ordering channels and whether several types of message ordering channels differ in their detectability. Results: Our results show that the detection of message ordering channels depends on their number of utilized PDUs. First, we performed a rough threshold selection by hand, which we later optimized using the C4.5 decision tree classifier. We were able to detect message ordering covert channels with an accuracy and F1 score of >= 99.5% and a false-positive rate < 1% and < 0.1% if they use sequences of 3 or 4 PDUs, respectively. Simpler channels that only manipulate a sequence of two PDUs were detectable with an accuracy and F1 score of 94.5% and were linked to a false-positive rate of 5.19%. We thus consider our approach suitable for real-world detection scenarios with channels utilizing 3 or 4 PDUs while the detection of channels utilizing 2 PDUs should be improved further.
Formal Analysis of Composable DeFi Protocols
Venue: arXiv cs.CR 2021
Year: 2021

Decentralized finance (DeFi) has become one of the most successful applications of blockchain and smart contracts. The DeFi ecosystem enables a wide range of crypto-financial activities, while the underlying smart contracts often contain bugs, with many vulnerabilities arising from the unforeseen consequences of composing DeFi protocols together. In this paper, we propose a formal process-algebraic technique that models DeFi protocols in a compositional manner to allow for efficient property verification. We also conduct a case study to demonstrate the proposed approach in analyzing the composition of two interacting DeFi protocols, namely, Curve and Compound. Finally, we discuss how the proposed modeling and verification approach can be used to analyze financial and security properties of interest.
Perspectives on individual animal identification from biology and computer vision
Venue: arXiv cs.CV 2021
Year: 2021

Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations and propose how they might be addressed in the future.