OpenRaiser/Intern-Atlas huggingface dataset:
Phoenix: Towards Persistently Secure, Recoverable, and NVM Friendly Tree of Counters
Venue: arXiv cs.CR 2019
Year: 2019

Emerging Non-Volatile Memories (NVMs) bring a unique challenge to the security community, namely persistent security. As NVM-based memories are expected to restore their data after recovery, the security metadata must be recovered as well. However, persisting all affected security metadata on each memory write would significantly degrade performance and exacerbate the write endurance problem. Moreover, recovery time can increase significantly (up to hours for practical memory sizes) when security metadata are not updated strictly. Counter trees are used in state-of-the-art commercial secure processors, e.g., Intel's Safe Guard Extension (SGX). Counter trees have a unique challenge due to the inability to recover the whole tree from leaves. Thus, to ensure recoverability, all updates to the tree must be persisted, which can be tens of additional writes on each write. The state-of-art scheme, Anubis, enables recoverability but incurs an additional write per cache eviction, i.e., reduces lifetime to approximately half. Additionally, Anubis degrades performance significantly in many cases. In this paper, we propose Phoenix, a practical novel scheme which relies on elegantly reproducing the cache content before a crash, however with minimal overheads. Our evaluation results show that Phoenix reduces persisting security metadata overhead writes from 87\% extra writes (for Anubis) to less than write-back compared to an encrypted system without recovery, thus improving the NVM lifetime by 2x. Overall Phoenix performance is better than the baseline, unlike Anubis which adds 7.9\% (max of 35\%) performance overhead.
Performance Evaluation of VDI Environment
Venue: arXiv cs.DC 2019
Year: 2019

Virtualization technology is widely used for sharing the abilities of computer systems by splitting the resources among various virtual PCs. In traditional computer systems, the utilization of hardware is not maximized and the resources are not fully consumed. By using the virtualization technology, the maximum utilization of computer system hardware is possible. Today, many organizations especially educational institutes are adopting virtualization technology to minimize the costs and increase the flexibility, responsiveness, and efficiency. In this paper, we built a virtual educational lab in which we successfully implemented Virtual Desktop Infrastructure (VDI) and created more than 30 virtual desktops. The virtual desktops are created on the Citrix XenServer hypervisor using the Citrix XenDesktop. Finally, we used benchmarking software tool called Login VSI in order to measure the performance of the virtual desktop infrastructure and the XenServer hypervisor. The results have shown that the system performs well with 30 virtual desktops without reaching the saturation point.
Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges
Venue: arXiv cs.DC 2019
Year: 2019

Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.
A Memetic Algorithm Based on Breakout Local Search for the Generalized Travelling Salesman Problem
Venue: arXiv cs.NE 2019
Year: 2019

The Travelling Salesman Problem (TSP) is one of the most popular Combinatorial Optimization Problem. It is well solicited for the large variety of applications that it can solve, but also for its difficulty to find optimal solutions. One of the variants of the TSP is the Generalized TSP (GTSP), where the TSP is considered as a special case which makes the GTSP harder to solve. We propose in this paper a new memetic algorithm based on the well-known Breakout Local Search (BLS) metaheuristic to provide good solutions for GTSP instances. Our approach is competitive compared to other recent memetic algorithms proposed for the GTSP and gives at the same time some improvements to BLS to reduce its runtime.
Thermodynamic Computing
Venue: arXiv cs.CY 2019
Year: 2019

The hardware and software foundations laid in the first half of the 20th Century enabled the computing technologies that have transformed the world, but these foundations are now under siege. The current computing paradigm, which is the foundation of much of the current standards of living that we now enjoy, faces fundamental limitations that are evident from several perspectives. In terms of hardware, devices have become so small that we are struggling to eliminate the effects of thermodynamic fluctuations, which are unavoidable at the nanometer scale. In terms of software, our ability to imagine and program effective computational abstractions and implementations are clearly challenged in complex domains. In terms of systems, currently five percent of the power generated in the US is used to run computing systems - this astonishing figure is neither ecologically sustainable nor economically scalable. Economically, the cost of building next-generation semiconductor fabrication plants has soared past $10 billion. All of these difficulties - device scaling, software complexity, adaptability, energy consumption, and fabrication economics - indicate that the current computing paradigm has matured and that continued improvements along this path will be limited. If technological progress is to continue and corresponding social and economic benefits are to continue to accrue, computing must become much more capable, energy efficient, and affordable. We propose that progress in computing can continue under a united, physically grounded, computational paradigm centered on thermodynamics. Herein we propose a research agenda to extend these thermodynamic foundations into complex, non-equilibrium, self-organizing systems and apply them holistically to future computing systems that will harness nature's innate computational capacity. We call this type of computing "Thermodynamic Computing" or TC.
Bipolar Morphological Neural Networks: Convolution Without Multiplication
Venue: arXiv cs.NE 2019
Year: 2019

In the paper we introduce a novel bipolar morphological neuron and bipolar morphological layer models. The models use only such operations as addition, subtraction and maximum inside the neuron and exponent and logarithm as activation functions for the layer. The proposed models unlike previously introduced morphological neural networks approximate the classical computations and show better recognition results. We also propose layer-by-layer approach to train the bipolar morphological networks, which can be further developed to an incremental approach for separate neurons to get higher accuracy. Both these approaches do not require special training algorithms and can use a variety of gradient descent methods. To demonstrate efficiency of the proposed model we consider classical convolutional neural networks and convert the pre-trained convolutional layers to the bipolar morphological layers. Seeing that the experiments on recognition of MNIST and MRZ symbols show only moderate decrease of accuracy after conversion and training, bipolar neuron model can provide faster inference and be very useful in mobile and embedded systems.
Dancing to Music
Venue: arXiv cs.CV 2019
Year: 2019

Dancing to music is an instinctive move by humans. Learning to model the music-to-dance generation process is, however, a challenging problem. It requires significant efforts to measure the correlation between music and dance as one needs to simultaneously consider multiple aspects, such as style and beat of both music and dance. Additionally, dance is inherently multimodal and various following movements of a pose at any moment are equally likely. In this paper, we propose a synthesis-by-analysis learning framework to generate dance from music. In the analysis phase, we decompose a dance into a series of basic dance units, through which the model learns how to move. In the synthesis phase, the model learns how to compose a dance by organizing multiple basic dancing movements seamlessly according to the input music. Experimental qualitative and quantitative results demonstrate that the proposed method can synthesize realistic, diverse,style-consistent, and beat-matching dances from music.
A Method to Model Conditional Distributions with Normalizing Flows
Venue: arXiv cs.LG 2019
Year: 2019

In this work, we investigate the use of normalizing flows to model conditional distributions. In particular, we use our proposed method to analyze inverse problems with invertible neural networks by maximizing the posterior likelihood. Our method uses only a single loss and is easy to train. This is an improvement on the previous method that solves similar inverse problems with invertible neural networks but which involves a combination of several loss terms with ad-hoc weighting. In addition, our method provides a natural framework to incorporate conditioning in normalizing flows, and therefore, we can train an invertible network to perform conditional generation. We analyze our method and perform a careful comparison with previous approaches. Simple experiments show the effectiveness of our method, and more comprehensive experimental evaluations are undergoing.
Response Prediction for Low-Regret Agents
Venue: arXiv cs.GT 2019
Year: 2019

Companies like Google and Microsoft run billions of auctions every day to sell advertising opportunities. Any change to the rules of these auctions can have a tremendous effect on the revenue of the company and the welfare of the advertisers and the users. Therefore, any change requires careful evaluation of its potential impacts. Currently, such impacts are often evaluated by running simulations or small controlled experiments. This, however, misses the important factor that the advertisers respond to changes. Our goal is to build a theoretical framework for predicting the actions of an agent (the advertiser) that is optimizing her actions in an uncertain environment. We model this problem using a variant of the multi-armed bandit setting where playing an arm is costly. The cost of each arm changes over time and is publicly observable. The value of playing an arm is drawn stochastically from a static distribution and is observed by the agent and not by us. We, however, observe the actions of the agent. Our main result is that assuming the agent is playing a strategy with a regret of at most $f(T)$ within the first $T$ rounds, we can learn to play the multi-armed bandits game (without observing the rewards) in such a way that the regret of our selected actions is at most $O(k^4(f(T)+1)\log(T))$, where $k$ is the number of arms.
Recurrent Instance Segmentation using Sequences of Referring Expressions
Venue: arXiv cs.CV 2019
Year: 2019

The goal of this work is to segment the objects in an image that are referred to by a sequence of linguistic descriptions (referring expressions). We propose a deep neural network with recurrent layers that output a sequence of binary masks, one for each referring expression provided by the user. The recurrent layers in the architecture allow the model to condition each predicted mask on the previous ones, from a spatial perspective within the same image. Our multimodal approach uses off-the-shelf architectures to encode both the image and the referring expressions. The visual branch provides a tensor of pixel embeddings that are concatenated with the phrase embeddings produced by a language encoder. Our experiments on the RefCOCO dataset for still images indicate how the proposed architecture successfully exploits the sequences of referring expressions to solve a pixel-wise task of instance segmentation.
Soft Error Resilience and Failure Recovery for Continuum Dynamics Applications
Venue: arXiv cs.DC 2019
Year: 2019

The persistently growing resilience concerns of large-scale computing systems today require not only generic fault tolerance approaches, but also application-level resilience, due to demanding efficiency and various domain-specific requirements. Scientific applications within a particular domain generally comply with domain conservation laws, which can be leveraged as an error detection criterion to study the resilience of this domain of applications sharing similar program characteristics. However, it is challenging to achieve application resilience: (a) how to identify the invariants of a given domain of applications, knowing the conservation laws, and (b) how to utilize the invariants to efficiently detect and recover from failures in application runs. In this work, we target several continuum dynamics software packages, FleCSALE [1] and CODY [2] (with intrinsic invariants during computation), study their resilience to soft errors online (injected using an open-source fault injector), and investigate the opportunities for non-intrusive and lightweight failure recovery (checksum-based invariant checking). We propose a checksum-retry approach to achieve our goals, and experimental results on a virtualized platform with extensive fault injection campaigns demonstrate the effectiveness and efficiency of the proposed approach.
Plankton: Scalable network configuration verification through model checking
Venue: arXiv cs.NI 2019
Year: 2019

Network configuration verification enables operators to ensure that the network will behave as intended, prior to deployment of their configurations. Although techniques ranging from graph algorithms to SMT solvers have been proposed, scalable configuration verification with sufficient protocol support continues to be a challenge. In this paper, we show that by combining equivalence partitioning with explicit-state model checking, network configuration verification can be scaled significantly better than the state of the art, while still supporting a rich set of protocol features. We propose Plankton, which uses symbolic partitioning to manage large header spaces and efficient model checking to exhaustively explore protocol behavior. Thanks to a highly effective suite of optimizations including state hashing, partial order reduction, and policy-based pruning, Plankton successfully verifies policies in industrial-scale networks quickly and compactly, at times reaching a 10000$\times$ speedup compared to the state of the art.
Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation
Venue: arXiv cs.CL 2019
Year: 2019

Emotion detection in text is an important task in NLP and is essential in many applications. Most of the existing methods treat this task as a problem of single-label multi-class text classification. To predict multiple emotions for one instance, most of the existing works regard it as a general Multi-label Classification (MLC) problem, where they usually either apply a manually determined threshold on the last output layer of their neural network models or train multiple binary classifiers and make predictions in the fashion of one-vs-all. However, compared to labels in the general MLC datasets, the number of emotion categories are much fewer (less than 10). Additionally, emotions tend to have more correlations with each other. For example, the human usually does not express "joy" and "anger" at the same time, but it is very likely to have "joy" and "love" expressed together. Given this intuition, in this paper, we propose a Latent Variable Chain (LVC) transformation and a tailored model -- Seq2Emo model that not only naturally predicts multiple emotion labels but also takes into consideration their correlations. We perform the experiments on the existing multi-label emotion datasets as well as on our newly collected datasets. The results show that our model compares favorably with existing state-of-the-art methods.
Symbol-pair Weight Distributions of Some Linear Codes
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we investigate the symbol-pair weight distributions of MDS codes and simplex codes over finite fields. Furthermore, the result shows that all the nonzero codewords of simplex codes have the same symbol $b$-weight and rearrangement entries of codewords in simplex code may induce different symbol-pair weight. In addition, the symbol $b$-weight distributions of variation simplex codes over certain finite fields are derived.
Experience Sharing Between Cooperative Reinforcement Learning Agents
Venue: arXiv cs.LG 2019
Year: 2019

The idea of experience sharing between cooperative agents naturally emerges from our understanding of how humans learn. Our evolution as a species is tightly linked to the ability to exchange learned knowledge with one another. It follows that experience sharing (ES) between autonomous and independent agents could become the key to accelerate learning in cooperative multiagent settings. We investigate if randomly selecting experiences to share can increase the performance of deep reinforcement learning agents, and propose three new methods for selecting experiences to accelerate the learning process. Firstly, we introduce Focused ES, which prioritizes unexplored regions of the state space. Secondly, we present Prioritized ES, in which temporal-difference error is used as a measure of priority. Finally, we devise Focused Prioritized ES, which combines both previous approaches. The methods are empirically validated in a control problem. While sharing randomly selected experiences between two Deep Q-Network agents shows no improvement over a single agent baseline, we show that the proposed ES methods can successfully outperform the baseline. In particular, the Focused ES accelerates learning by a factor of 2, reducing by 51% the number of episodes required to complete the task.
Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set
Venue: arXiv cs.LG 2019
Year: 2019

Zellner (1988) modeled statistical inference in terms of information processing and postulated the Information Conservation Principle (ICP) between the input and output of the information processing block, showing that this yielded Bayesian inference as the optimum information processing rule. Recently, Alemi (2019) reviewed Zellner's work in the context of machine learning and showed that the ICP could be seen as a special case of a more general optimum information processing criterion, namely the Predictive Information Bottleneck Objective. However, Alemi modeled the model training step in machine learning as using training and test data sets only, and did not account for the use of a validation data set during training. The present note is an attempt to extend Alemi's information processing formulation of machine learning, and the predictive information bottleneck objective for model training, to the widely-used scenario where training utilizes not only a training but also a validation data set.
Robot navigation and target capturing using nature-inspired approaches in a dynamic environment
Venue: arXiv cs.AI 2019
Year: 2019

Path Planning and target searching in a three-dimensional environment is a challenging task in the field of robotics. It is an optimization problem as the path from source to destination has to be optimal. This paper aims to generate a collision-free trajectory in a dynamic environment. The path planning problem has sought to be of extreme importance in the military, search and rescue missions and in life-saving tasks. During its operation, the unmanned air vehicle operates in a hostile environment, and faster replanning is needed to reach the target as optimally as possible. This paper presents a novel approach of hierarchical planning using multiresolution abstract levels for faster replanning. Economic constraints like path length, total path planning time and the number of turns are taken into consideration that mandate the use of cost functions. Experimental results show that the hierarchical version of GSO gives better performance compared to the BBO, IWO and their hierarchical versions.
Spatial Feature Extraction in Airborne Hyperspectral Images Using Local Spectral Similarity
Venue: arXiv cs.CV 2019
Year: 2019

Local spectral similarity (LSS) algorithm has been developed for detecting homogeneous areas and edges in hyperspectral images (HSIs). The proposed algorithm transforms the 3-D data cube (within a spatial window) into a spectral similarity matrix by calculating the vector-similarity between the center pixel-spectrum and the neighborhood spectra. The final edge intensity is derived upon order statistics of the similarity matrix or spatial convolution of the similarity matrix with the spatial kernels. The LSS algorithm facilitates simultaneous use of spectral-spatial information for the edge detection by considering the spatial pattern of similar spectra within a spatial window. The proposed edge-detection method is tested on benchmark HSIs as well as the image obtained from Airborne-Visible-and-Infra-RedImaging-Spectrometer-Next-Generation (AVIRIS-NG). Robustness of the LSS method against multivariate Gaussian noise and low spatial resolution scenarios were also verified with the benchmark HSIs. Figure-of-merit, false-alarm-count and miss-count were applied to evaluate the performance of edge detection methods. Results showed that Fractional distance measure and Euclidean distance measure were able to detect the edges in HSIs more precisely as compared to other spectral similarity measures. The proposed method can be applied to radiance and reflectance data (whole spectrum) and it has shown good performance on principal component images as well. In addition, the proposed algorithm outperforms the traditional multichannel edge detectors in terms of both fastness, accuracy and the robustness. The experimental results also confirm that LSS can be applied as a pre-processing approach to reduce the errors in clustering as well as classification outputs.