OpenRaiser/Intern-Atlas huggingface dataset:
Online Self-Supervised Learning for Object Picking: Detecting Optimum Grasping Position using a Metric Learning Approach
Venue: arXiv cs.RO 2020
Year: 2020

Self-supervised learning methods are attractive candidates for automatic object picking. However, the trial samples lack the complete ground truth because the observable parts of the agent are limited. That is, the information contained in the trial samples is often insufficient to learn the specific grasping position of each object. Consequently, the training falls into a local solution, and the grasp positions learned by the robot are independent of the state of the object. In this study, the optimal grasping position of an individual object is determined from the grasping score, defined as the distance in the feature space obtained using metric learning. The closeness of the solution to the pre-designed optimal grasping position was evaluated in trials. The proposed method incorporates two types of feedback control: one feedback enlarges the grasping score when the grasping position approaches the optimum; the other reduces the negative feedback of the potential grasping positions among the grasping candidates. The proposed online self-supervised learning method employs two deep neural networks. : SSD that detects the grasping position of an object, and Siamese networks (SNs) that evaluate the trial sample using the similarity of two input data in the feature space. Our method embeds the relation of each grasping position as feature vectors by training the trial samples and a few pre-samples indicating the optimum grasping position. By incorporating the grasping score based on the feature space of SNs into the SSD training process, the method preferentially trains the optimum grasping position. In the experiment, the proposed method achieved a higher success rate than the baseline method using simple teaching signals. And the grasping scores in the feature space of the SNs accurately represented the grasping positions of the objects.
Formal Verification of Cyber-Physical Systems using Theorem Proving (Invited Paper)
Venue: arXiv cs.LO 2020
Year: 2020

Due to major breakthroughs in software and engineering technologies, embedded systems are increasingly being utilized in areas ranging from aerospace and next-generation transportation systems, to smart grid and smart cities, to health care systems, and broadly speaking to what is known as Cyber-Physical Systems (CPS). A CPS is primarily composed of several electronic, communication and controller modules and some actuators and sensors. The mix of heterogeneous underlying smart technologies poses a number of technical challenges to the design and more severely to the verification of such complex infrastructure. In fact, a CPS shall adhere to strict safety, reliability, performance and security requirements, where one needs to capture both physical and random aspects of the various CPS modules and then analyze their interrelationship across interlinked continuous and discrete dynamics. Often times however, system bugs remain uncaught during the analysis and in turn cause unwanted scenarios that may have serious consequences in safety-critical applications. In this paper, we introduce some of the challenges surrounding the design and verification of contemporary CPS with the advent of smart technologies. In particular, we survey recent developments in the use of theorem proving, a formal method, for the modeling, analysis and verification of CPS, and overview some real world CPS case studies from the automotive, avionics and healthtech domains from system level to physical components.
DeepLENS: Deep Learning for Entity Summarization
Venue: arXiv cs.IR 2020
Year: 2020

Entity summarization has been a prominent task over knowledge graphs. While existing methods are mainly unsupervised, we present DeepLENS, a simple yet effective deep learning model where we exploit textual semantics for encoding triples and we score each candidate triple based on its interdependence on other triples. DeepLENS significantly outperformed existing methods on a public benchmark.
Dependently Typed Knowledge Graphs
Venue: arXiv cs.FL 2020
Year: 2020

Reasoning over knowledge graphs is traditionally built upon a hierarchy of languages in the Semantic Web Stack. Starting from the Resource Description Framework (RDF) for knowledge graphs, more advanced constructs have been introduced through various syntax extensions to add reasoning capabilities to knowledge graphs. In this paper, we show how standardized semantic web technologies (RDF and its query language SPARQL) can be reproduced in a unified manner with dependent type theory. In addition to providing the basic functionalities of knowledge graphs, dependent types add expressiveness in encoding both entities and queries, explainability in answers to queries through witnesses, and compositionality and automation in the construction of witnesses. Using the Coq proof assistant, we demonstrate how to build and query dependently typed knowledge graphs as a proof of concept for future works in this direction.
Multiset Synchronization with Counting Cuckoo Filters
Venue: arXiv cs.DS 2020
Year: 2020

Set synchronization is a fundamental task in distributed applications and implementations. Existing methods that synchronize simple sets are mainly based on compact data structures such as Bloom filter and its variants. However, these methods are infeasible to synchronize a pair of multisets which allow an element to appear for multiple times. To this end, in this paper, we propose to leverage the counting cuckoo filter (CCF), a novel variant of cuckoo filter, to represent and thereafter synchronize a pair of multisets. The cuckoo filter (CF) is a minimized hash table that uses cuckoo hashing to resolve collisions. CF has an array of buckets, each of which has multiple slots to store element fingerprints. Based on CF, CCF extends each slot as two fields, the fingerprint field and the counter field. The fingerprint field records the fingerprint of element which is stored by this slot; while the counter field counts the multiplicity of the stored element. With such a design, CCF is competent to represent any multiset. After generating and exchanging the respective CCFs which represent the local multi-sets, we propose the query-based and the decoding-based methods to identify the different elements between the given multisets. The comprehensive evaluation results indicate that CCF outperforms the counting Bloom filter (CBF) when they are used to synchronize multisets, in terms of both synchronization accuracy and the space-efficiency, at the cost of a little higher time-consumption.
A Compiler Assisted Scheduler for Detecting and Mitigating Cache-Based Side Channel Attacks
Venue: arXiv cs.CR 2020
Year: 2020

Side channel attacks steal secret keys by cleverly leveraging information leakages and can, therefore, break encryption. Thus, detection and mitigation of side channel attacks is a very important problem, but the solutions proposed in the literature have limitations in that they do not work in a real-world multi-tenancy setting on servers, have high false positives, or have high overheads. In this work, we demonstrate a compiler guided scheduler, Biscuit, that detects cache-based side channel attacks for processes scheduled on multi-tenancy server farms. A key element of this solution involves the use of a cache-miss model which is inserted by the compiler at the entrances of loop nests to predict the cache misses of the corresponding loop. Such inserted library calls, or beacons, convey the cache miss information to the scheduler at run time, which uses it to co-schedule processes such that their combined cache footprint does not exceed the maximum capacity of the last level cache. The scheduled processes are then monitored for actual vs predicted cache misses, and when an anomaly is detected, the scheduler performs a search to isolate the attacker. We show that Biscuit is able to detect and mitigate Prime+Probe, Flush+Reload, and Flush+Flush attacks on OpenSSL cryptography algorithms with an F-score of 1, and also to detect and mitigate degradation of service on a vision application suite with an F-score of 0.9375. Under a no-attack scenario, the scheme poses low overheads (up to a maximum of 6 percent). In the case of an attack, the scheme ends up with less than 11 percent overhead and is able to reduce the degradation of service in some cases by 40 percent. With these many desirable features such as an ability to deal with multi-tenancy, its ability to detect attacks early, its ability to mitigate those attacks, and low runtime overheads, Biscuit is a practical solution.
Graph Clustering Via QUBO and Digital Annealing
Venue: arXiv cs.SI 2020
Year: 2020

This article empirically examines the computational cost of solving a known hard problem, graph clustering, using novel purpose-built computer hardware. We express the graph clustering problem as an intra-cluster distance or dissimilarity minimization problem. We formulate our poblem as a quadratic unconstrained binary optimization problem and employ a novel computer architecture to obtain a numerical solution. Our starting point is a clustering formulation from the literature. This formulation is then converted to a quadratic unconstrained binary optimization formulation. Finally, we use a novel purpose-built computer architecture to obtain numerical solutions. For benchmarking purposes, we also compare computational performances to those obtained using a commercial solver, Gurobi, running on conventional hardware. Our initial results indicate the purpose-built hardware provides equivalent solutions to the commercial solver, but in a very small fraction of the time required.
A working likelihood approach to support vector regression with a data-driven insensitivity parameter
Venue: arXiv cs.LG 2020
Year: 2020

The insensitive parameter in support vector regression determines the set of support vectors that greatly impacts the prediction. A data-driven approach is proposed to determine an approximate value for this insensitive parameter by minimizing a generalized loss function originating from the likelihood principle. This data-driven support vector regression also statistically standardizes samples using the scale of noises. Nonlinear and linear numerical simulations with three types of noises ($ε$-Laplacian distribution, normal distribution, and uniform distribution), and in addition, five real benchmark data sets, are used to test the capacity of the proposed method. Based on all of the simulations and the five case studies, the proposed support vector regression using a working likelihood, data-driven insensitive parameter is superior and has lower computational costs.
ROSE: Real One-Stage Effort to Detect the Fingerprint Singular Point Based on Multi-scale Spatial Attention
Venue: arXiv cs.CV 2020
Year: 2020

Detecting the singular point accurately and efficiently is one of the most important tasks for fingerprint recognition. In recent years, deep learning has been gradually used in the fingerprint singular point detection. However, current deep learning-based singular point detection methods are either two-stage or multi-stage, which makes them time-consuming. More importantly, their detection accuracy is yet unsatisfactory, especially in the case of the low-quality fingerprint. In this paper, we make a Real One-Stage Effort to detect fingerprint singular points more accurately and efficiently, and therefore we name the proposed algorithm ROSE for short, in which the multi-scale spatial attention, the Gaussian heatmap and the variant of focal loss are applied together to achieve a higher detection rate. Experimental results on the datasets FVC2002 DB1 and NIST SD4 show that our ROSE outperforms the state-of-art algorithms in terms of detection rate, false alarm rate and detection speed.
Pacemaker: Intermediate Teacher Knowledge Distillation For On-The-Fly Convolutional Neural Network
Venue: arXiv cs.CV 2020
Year: 2020

There is a need for an on-the-fly computational process with very low performance system such as system-on-chip (SoC) and embedded device etc. This paper presents pacemaker knowledge distillation as intermediate ensemble teacher to use convolutional neural network in these systems. For on-the-fly system, we consider student model using 1xN shape on-the-fly filter and teacher model using normal NxN shape filter. We note three points about training student model, caused by applying on-the-fly filter. First, same depth but unavoidable thin model compression. Second, the large capacity gap and parameter size gap due to only the horizontal field must be selected not the vertical receptive. Third, the performance instability and degradation of direct distilling. To solve these problems, we propose intermediate teacher, named pacemaker, for an on-the-fly student. So, student can be trained from pacemaker and original teacher step by step. Experiments prove our proposed method make significant performance (accuracy) improvements: on CIFAR100, 5.39% increased in WRN-40-4 than conventional knowledge distillation which shows even low performance than baseline. And we solve train instability, occurred when conventional knowledge distillation was applied without proposed method, by reducing deviation range by applying proposed method pacemaker knowledge distillation.
Shallow Discourse Annotation for Chinese TED Talks
Venue: arXiv cs.CL 2020
Year: 2020

Text corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chinese Language Technology and for Chinese-English translation, in the form of a set of TED talks (some originally given in English, some in Chinese) that have been annotated with discourse relations in the style of the Penn Discourse TreeBank, adapted to properties of Chinese text that are not present in English. The resource is currently unique in annotating discourse-level properties of planned spoken monologues rather than of written text. An inter-annotator agreement study demonstrates that the annotation scheme is able to achieve highly reliable results.
TEDL: A Text Encryption Method Based on Deep Learning
Venue: arXiv cs.CR 2020
Year: 2020

Recent years have seen an increasing emphasis on information security, and various encryption methods have been proposed. However, for symmetric encryption methods, the well-known encryption techniques still rely on the key space to guarantee security and suffer from frequent key updating. Aiming to solve those problems, this paper proposes a novel text encryption method based on deep learning called TEDL, where the secret key includes hyperparameters in deep learning model and the core step of encryption is transforming input data into weights trained under hyperparameters. Firstly, both communication parties establish a word vector table by training a deep learning model according to specified hyperparameters. Then, a self-update codebook is constructed on the word vector table with the SHA-256 function and other tricks. When communication starts, encryption and decryption are equivalent to indexing and inverted indexing on the codebook, respectively, thus achieving the transformation between plaintext and ciphertext. Results of experiments and relevant analyses show that TEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution. Especially, as a supplement to current encryption methods, the time-consuming process of constructing a codebook increases the difficulty of brute-force attacks while not degrade the communication efficiency.
On the Texture Bias for Few-Shot CNN Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Despite the initial belief that Convolutional Neural Networks (CNNs) are driven by shapes to perform visual recognition tasks, recent evidence suggests that texture bias in CNNs provides higher performing models when learning on large labeled training datasets. This contrasts with the perceptual bias in the human visual cortex, which has a stronger preference towards shape components. Perceptual differences may explain why CNNs achieve human-level performance when large labeled datasets are available, but their performance significantly degrades in lowlabeled data scenarios, such as few-shot semantic segmentation. To remove the texture bias in the context of few-shot learning, we propose a novel architecture that integrates a set of Difference of Gaussians (DoG) to attenuate high-frequency local components in the feature space. This produces a set of modified feature maps, whose high-frequency components are diminished at different standard deviation values of the Gaussian distribution in the spatial domain. As this results in multiple feature maps for a single image, we employ a bi-directional convolutional long-short-term-memory to efficiently merge the multi scale-space representations. We perform extensive experiments on three well-known few-shot segmentation benchmarks -- Pascal i5, COCO-20i and FSS-1000 -- and demonstrate that our method outperforms state-of-the-art approaches in two datasets under the same conditions. The code is available at: https://github.com/rezazad68/fewshot-segmentation
Stable Policy Optimization via Off-Policy Divergence Regularization
Venue: arXiv cs.LG 2020
Year: 2020

Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) are among the most successful policy gradient approaches in deep reinforcement learning (RL). While these methods achieve state-of-the-art performance across a wide range of challenging tasks, there is room for improvement in the stabilization of the policy learning and how the off-policy data are used. In this paper we revisit the theoretical foundations of these algorithms and propose a new algorithm which stabilizes the policy improvement through a proximity term that constrains the discounted state-action visitation distribution induced by consecutive policies to be close to one another. This proximity term, expressed in terms of the divergence between the visitation distributions, is learned in an off-policy and adversarial manner. We empirically show that our proposed method can have a beneficial effect on stability and improve final performance in benchmark high-dimensional control tasks.
Lightweight Inter-transaction Caching with Precise Clocks and Dynamic Self-invalidation
Venue: arXiv cs.DC 2020
Year: 2020

Distributed, transactional storage systems scale by sharding data across servers. However, workload-induced hotspots result in contention, leading to higher abort rates and performance degradation. We present KAIROS, a transactional key-value storage system that leverages client-side inter-transaction caching and sharded transaction validation to balance the dynamic load and alleviate workload-induced hotspots in the system. KAIROS utilizes precise synchronized clocks to implement self-invalidating leases for cache consistency and avoids the overhead and potential hotspots due to maintaining sharing lists or sending invalidations. Experiments show that inter-transaction caching alone provides 2.35x the throughput of a baseline system with only intra-transaction caching; adding sharded validation further improves the throughput by a factor of 3.1 over baseline. We also show that lease-based caching can operate at a 30% higher scale while providing 1.46x the throughput of the state-of-the-art explicit invalidation-based caching.
A Uniform Treatment of Aggregates and Constraints in Hybrid ASP
Venue: arXiv cs.LO 2020
Year: 2020

Characterizing hybrid ASP solving in a generic way is difficult since one needs to abstract from specific theories. Inspired by lazy SMT solving, this is usually addressed by treating theory atoms as opaque. Unlike this, we propose a slightly more transparent approach that includes an abstract notion of a term. Rather than imposing a syntax on terms, we keep them abstract by stipulating only some basic properties. With this, we further develop a semantic framework for hybrid ASP solving and provide aggregate functions for theory variables that adhere to different semantic principles, show that they generalize existing aggregate semantics in ASP and how we can rely on off-the-shelf hybrid solvers for implementation.
Danger Aware Vehicular Networking
Venue: arXiv cs.NI 2020
Year: 2020

IEEE 802.11p is one of the key technologies that enable Dedicated Short-Range Communications (DSRC) in intelligent transportation system (ITS) for safety on the road. The main challenge in vehicular communication is the large amount of data to be processed. As vehicle density and velocity increases, the data to be transmitted also increases. We proposed a protocol that reduces the number of messages transmitted at a vehicle according to the level of danger that the vehicle experiences. The proposed protocol measures inter-vehicle distance, as the representative of the danger of a vehicle, to determine the priority for transmission. Our results show that this prioritization of transmissions directly reduces the number of transmitters at a time, and hence results in higher performance in terms of key metrics--i.e., PDR, throughput, delay, probabilities of channel busy and collision.
Novel version of PageRank, CheiRank and 2DRank for Wikipedia in Multilingual Network using Social Impact
Venue: arXiv cs.SI 2020
Year: 2020

Nowadays, information describing navigation behaviour of internet users are used in several fields, e-commerce, economy, sociology and data science. Such information can be extracted from different knowledge bases, including business-oriented ones. In this paper, we propose a new model for the PageRank, CheiRank and 2DRank algorithm based on the use of clickstream and pageviews data in the google matrix construction. We used data from Wikipedia and analysed links between over 20 million articles from 11 language editions. We extracted over 1.4 billion source-destination pairs of articles from SQL dumps and more than 700 million pairs from XML dumps. Additionally, we unified the pairs based on the analysis of redirect pages and removed all duplicates. Moreover, we also created a bigger network of Wikipedia articles based on all considered language versions and obtained multilingual measures. Based on real data, we discussed the difference between standard PageRank, Cheirank, 2DRank and measures obtained based on our approach in separate languages and multilingual network of Wikipedia.
Finding Input Characterizations for Output Properties in ReLU Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Deep Neural Networks (DNNs) have emerged as a powerful mechanism and are being increasingly deployed in real-world safety-critical domains. Despite the widespread success, their complex architecture makes proving any formal guarantees about them difficult. Identifying how logical notions of high-level correctness relate to the complex low-level network architecture is a significant challenge. In this project, we extend the ideas presented in and introduce a way to bridge the gap between the architecture and the high-level specifications. Our key insight is that instead of directly proving the safety properties that are required, we first prove properties that relate closely to the structure of the neural net and use them to reason about the safety properties. We build theoretical foundations for our approach, and empirically evaluate the performance through various experiments, achieving promising results than the existing approach by identifying a larger region of input space that guarantees a certain property on the output.