Reviewerly/exHarmony huggingface dataset:
Combining containers and virtual machines to enhance isolation and extend functionality on cloud computing

Virtualization technology is the underlying element of cloud computing. Traditionally, cloud computing has employed virtual machines to distribute available resources and provide isolated environments among users. Multiple virtual machines, with their own operating system and services, can be deployed and run simultaneously on the same physical machine on the cloud infrastructure. Recently, a more lightweight virtualization technology is being rapidly adopted and it is based on containers. A key difference between virtual machines and containers, is that containers share the same underlying operating system. In this work, we studied the combination of these two virtualization technologies by running containers on top of virtual machines. This synergy aims to enhance containers’ main drawback, which is isolation and, among others, to simplify the system management and upgrade, and to introduce the new functionalities of containerized applications to virtual machines. The benefits of this method have been recognized by big cloud companies, which have been employing this approach for years. With this paper, we aimed to present the advantages of running containers on virtual machines and to explore how various virtualization techniques and configurations affect the performance of this method. Although, in bibliography there are a few papers that used this method partially to conduct experiments, there is not any research which considers this method from our integral aspect of view. In this study, we ran Docker containers and evaluated their performances, not only on KVM and XEN virtual machines, but also we ran Linux containers on Windows Server. We adopted an empirical approach, to quantify the performance overhead introduced by the additional virtualization layer of virtual machines, by executing various benchmarks and deploying real world applications as use cases. Furthermore, we presented for first time, how isolation is applied on virtual machines and containers, we evaluated different operating systems optimized to host containers, mechanisms of storing persistent data and, last but not least, we experimentally quantified the power and energy consumption overhead of containers running on virtual machines.
CP-ABSE: A Ciphertext-Policy Attribute-Based Searchable Encryption Scheme

Searchable encryption provides an effective mechanism that achieves secure search over encrypted data. A popular application model of searchable encryption is that a data owner stores encrypted data to a server and the server can effectively perform keyword-based search over encrypted data according to a query trapdoor submitted by a data user, where the owner’s data and the user’s queries are kept secret in the server. Recently, many searchable encryptions have been proposed to achieve better security and performance, provide secure data updatable feature ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamics</i> ), and search results verifiable capability ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">verifiability</i> ). However, most of the existing works endow the data user an unlimited search capacities and do not consider a data user’s search permissions. In practical application, granting search privileges for data users is a very important measure to enforce data access control. In this paper, we propose an attribute-based searchable encryption scheme by leveraging the ciphertext-policy attribute-based encryption technique. Our scheme allows the data owner to conduct a fine-grained search authorization for a data user. The main idea is that a data owner encrypts an index keyword under a specified access policy, if and only if, a data user’s attributes satisfy the access policy, the data user can perform search over the encrypted index keyword. We provide the detailed correctness analyses, performance analyses, and security proofs for our scheme. The extensive experiments demonstrate that our proposed scheme outperforms the similar work CP-ABKS proposed by Zheng on many aspects.
An ensemble learning approach for XSS attack detection with domain knowledge and threat intelligence

Cross-site scripting (XSS) attack is one of the most dangerous attacks for web security. Traditional XSS detection methods mainly focus on the vulnerability itself, relying on static analysis and dynamic analysis, which appear weak in defending the flood of various kinds of payloads. In this paper, the XSS attack detection method is proposed based on an ensemble learning approach which utilizes a set of Bayesian networks, and each Bayesian network is built with both domain knowledge and threat intelligence. Besides, an analysis method is proposed to further explain the results, which sorts nodes in the Bayesian network according to their influences on the output node. The results are explainable to the end users. To validate the proposed method, experiments are performed on a real-world dataset about the XSS attack. The results show the priority of the proposed method, especially when the number of attacks increases. Moreover, the node sorting results could help the security team to cope with the attack in time.
Deep Universal Generative Adversarial Compression Artifact Removal

Image compression is a need that arises in many circumstances. Unfortunately, whenever a lossy compression algorithm is used, artifacts will manifest. Image artifacts, caused by compression tend to eliminate higher frequency details and, in certain cases, may add noise or small image structures. There are two main drawbacks of this phenomenon. First, images appear much less pleasant to the human eye. Second, computer vision algorithms, such as object detectors, may be hindered and their performance reduced. Removing such artifacts means recovering the original image from a perturbed version of it. This means that one ideally should invert the compression process through a complicated nonlinear image transformation. We propose an image transformation approach based on a feedforward fully convolutional residual network model. We show that this model can be optimized either traditionally, directly optimizing an image similarity loss (SSIM), or using a generative adversarial approach (GAN). Our GAN is able to produce images with more photorealistic details than SSIM-based networks. We describe a novel training procedure based on subpatches and devise a novel testing protocol to evaluate restored images quantitatively. We show that our approach can be used as a preprocessing step for different computer vision tasks in case images are degraded by compression to a point that state-of-the art algorithms fail. In this case, our GAN-based approach obtains better performance than MSE or SSIM trained networks. Different from previously proposed approaches, we are able to remove artifacts generated at any QF by inferring the image quality directly from data.
Learning binary codes with neural collaborative filtering for efficient recommendation systems

The fast-growing e-commerce scenario brings new challenges to traditional collaborative filtering because the huge amount of users and items requires large storage and efficient recommendation systems. Hence, hashing for collaborative filtering has attracted increasing attention as binary codes can significantly reduce the storage requirement and make similarity calculations efficient. In this paper, we investigate the novel problem of deep collaborative hashing codes on user–item ratings. We propose a new deep learning framework for it, which adopts neural networks to better learn both user and item representations and make these close to binary codes such that the quantization loss is minimized. In addition, we extend the proposed framework for out-of-sample cases, i.e., dealing with new users, new items, and new ratings. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed framework.
Self-adapting control parameters in particle swarm optimization

Particle Swarm Optimization (PSO) is a powerful nature-inspired metaheuristic optimization method. Compared to other methods, PSO can determine the optimal solution in fewer evaluations and generally performs more efficiently and effectively. However, researches show that the PSO method suffers from premature convergence and a dependence on the initial control settings. Due to these shortcomings, the application of PSO may lead to failure in obtaining the global optimal solution. In this work, modifications were performed on the original PSO algorithm to adapt the control parameters to the circumstances of the particles at a specific moment. The proposed method is known as the Unique Adaptive Particle Swarm Optimization (UAPSO). In the developed approach, constraints were handled by forcing the particles to learn from their feasible solutions only. Therefore, the constraint handling technique worked in accord with the adapting scheme to ensure that the particles were adapting to the environment by directing itself to the feasible regions. The performance of UAPSO was verified by a comparative study involving eight benchmark constrained optimization problems and a real-world design problem. The numerical results showed the superiority of UAPSO compared to the selected state-of-the-art metaheuristic methods and PSO variants, its ability in avoiding premature convergence and its consistency and efficiency.
Building Redactable Consortium Blockchain for Industrial Internet-of-Things

Applying consortium blockchain as a trust layer for heterogeneous industrial Internet-of-Things devices is cost-effective. However, with an increase in computing power, some powerful attacks (e.g., the 51% attack) are inevitable and will cause severe consequences. Recent studies also confirm that anonymity and immutability of blockchain have been abused to facilitate black market trades, etc. To operate controllable blockchain for IIoT devices, it is necessary to rewrite blockchain history back to a normal state once the chain is breached. Ateniese et al. proposed redactable blockchain by using chameleon hash (CH) to replace traditional hash function, it allows blockchain history to be written when needed (EuroS&P 2017). However, we cannot apply this idea directly to IIoT without solving the following problems: (1) achieve a decentralized design of CH; (2) update the signatures accordingly to authenticate the redacted contents; (3) satisfy the low-computing need of the individual IIoT device. In this paper, we overcome the above issues by proposing the first threshold chameleon hash (TCH) and accountable-and-sanitizable chameleon signature (ASCS) schemes. Based on them, we build a redactable consortium blockchain which is efficient for IIoT devices to operate. It allows a group of authorized sensors to write and rewrite blockchain without causing any hard forks. Basically, TCH is the first TCH and ASCS is a public-key signature supporting file-level and block-level modifications of signatures without impairing authentications. Additionally, ASCS achieves accountability to avoid abuse of redaction. While security analysis validates our proposals, the simulation results show that redaction is acceptably efficient if it is executed at a small scale or if we adopt a coarse-grained redaction while sacrificing some securities.
Adversarial Learning for Constrained Image Splicing Detection and Localization Based on Atrous Convolution

Constrained image splicing detection and localization (CISDL), which investigates two input suspected images and identifies whether one image has suspected regions pasted from the other, is a newly proposed challenging task for image forensics. In this paper, we propose a novel adversarial learning framework to learn a deep matching network for CISDL. Our framework mainly consists of three building blocks. First, a deep matching network based on atrous convolution (DMAC) aims to generate two high-quality candidate masks, which indicate suspected regions of the two input images. In DMAC, atrous convolution is adopted to extract features with rich spatial information, a correlation layer based on a skip architecture is proposed to capture hierarchical features, and atrous spatial pyramid pooling is constructed to localize tampered regions at multiple scales. Second, a detection network is designed to rectify inconsistencies between the two corresponding candidate masks. Finally, a discriminative network drives the DMAC network to produce masks that are hard to distinguish from ground-truth ones. The detection network and the discriminative network collaboratively supervise the training of DMAC in an adversarial way. Besides, a sliding window-based matching strategy is investigated for high-resolution images matching. Extensive experiments, conducted on five groups of datasets, demonstrate the effectiveness of the proposed framework and the superior performance of DMAC.
Demand forecasting in pharmaceutical supply chains: A case study

Demand forecasting plays a critical role in logistics and supply chain management. In the paper, state-of-art methods and key challenges in demand forecasting for the pharmaceutical industry are discussed. An integrated procedure for in-market product demand forecasting and purchase order generation in the pharmaceutical supply chain is described. A case study for supply of pharmaceutical products from a wholesaler to a distribution company located in an emerging market is presented. Alternative forecasting scenarios for thebaseline demand calculations using the SMA model, multiple linear regressions and symbolic regression with genetic programming are experimentally investigated, and their practical implicationsare discussed.
Automatic Modulation Classification Using Contrastive Fully Convolutional Network

Automatic modulation classification (AMC) aims at identifying the modulation format of the received signal. In this letter, we propose a novel grid constellation matrix (GCM)-based AMC method using a contrastive fully convolutional network (CFCN). We use GCMs as the input of the network, which are extracted from the received signals using low-complexity preprocessing. Moreover, a loss function with contrastive loss is designed to train the CFCN, which boosts the discrepancies among different modulations and obtains discriminative representations. Extensive simulations demonstrate that CFCN performs superior classification performance and better robustness to model mismatches with low training time comparing with other recent methods.
Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking

Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for a non-stationary base is presented, using multiple 3D LiDARs for perception. The merged LiDAR data is treated with an efficient MODT framework, considering the limitations of the vehicle-embedded computing environment. The ground classification is obtained through a grid-based method while considering a non-planar ground. Furthermore, unlike prior works, 3D grid-based clustering technique is developed to detect objects under elevated structures. The centroid measurements obtained from the object detection are tracked using Interactive Multiple Model-Unscented Kalman Filter-Joint Probabilistic Data Association Filter (IMM-UKF-JPDAF). IMM captures different motion patterns, UKF handles the nonlinearities of motion models, and JPDAF associates the measurements in the presence of clutter. The proposed algorithm is implemented on two slightly dissimilar platforms, giving real-time performance on embedded computers. The performance evaluation metrics by MOT16 and ground truths provided by KITTI Datasets are used for evaluations and comparison with the state-of-the-art. The experimentation on platforms and comparisons with state-of-the-art techniques suggest that the proposed framework is a feasible solution for MODT tasks.
Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Networks

This chapter proposes an efficient hybrid training technique (ALOMLP) based on the Ant Lion Optimizer (ALO) to be utilized in dealing with Multi-Layer Perceptrons (MLPs) neural networks. ALO is a well-regarded swarm-based meta-heuristic inspired by the intelligent hunting tricks of antlions in nature. In this chapter, the theoretical backgrounds of ALO are explained in details first. Then, a comprehensive literature review is provided based on recent well-established works from 2015 to 2018. In addition, a convenient encoding scheme is presented and the objective formula is defined, mathematically. The proposed training model based on ALO algorithm is substantiated on sixteen standard datasets. The efficiency of ALO is compared with differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and population-based incremental learning (PBIL) in terms of best, worst, average, and median accuracies. Furthermore, the convergence propensities are monitored and analyzed for all competitors. The experiments show that the ALOMLP outperforms GA, PBIL, DE, and PSO in classifying the majority of datasets and provides improved accuracy results and convergence rates.
Threshold ECDSA from ECDSA Assumptions: The Multiparty Case

Cryptocurrency applications have spurred a resurgence of interest in the computation of ECDSA signatures using threshold protocols---that is, protocols in which the signing key is secret-shared among n parties, of which any subset of size t must interact in order to compute a signature. Among the resulting works to date, that of Doerner et al. requires the most natural assumptions while also achieving the best practical signing speed. It is, however, limited to the setting in which the threshold is two. We propose an extension of their scheme to arbitrary thresholds, and prove it secure against a malicious adversary corrupting up to one party less than the threshold under only the Computational Diffie-Hellman assumption in the Random Oracle model, an assumption strictly weaker than those under which ECDSA is proven. Whereas the best current schemes for threshold-two ECDSA signing use a Diffie-Hellman Key Exchange to calculate each signature's nonce, a direct adaptation of this technique to a larger threshold t would incur a round count linear in t; thus we abandon it in favor of a new mechanism that yields a protocol requiring log(t)+6 rounds in total. We design a new consistency check, similar in spirit to that of Doerner et al., but suitable for an arbitrary number of participants, and we optimize the underlying two-party multiplication protocol on which our scheme is based, reducing its concrete communication and computation costs. We implement our scheme and evaluate it among groups of up to 256 of co-located and 128 geographically-distributed parties, and among small groups of embedded devices. We find that in the LAN setting, our scheme outperforms all prior works by orders of magnitude, and that it is efficient enough for use even on smartphones or hardware tokens. In the WAN setting we find that, despite its logarithmic round count, our protocol outperforms the best constant-round protocols in realistic scenarios.
Predicting pipeline leakage in petrochemical system through GAN and LSTM

In petrochemical system, how to predict the leakage of pipeline is one of the most critical problems due to its huge side effects. Prior studies are mostly based on statistical analysis and they focus on the healthiest state of the system. However, these approaches ignore the trend of changes among data and lead to low accuracies. In this paper, we conduct research on characteristic of petrochemical data and get four core findings. We find that the data are (1) noisy, (2) high dimension, (3) imbalanced, (4) temporal correlated. According to these findings, we propose a novel neural network based classification model using GAN and LSTM to predict the leakage of pipeline. Specifically, we firstly apply a sliding window and a key feature selecting method in data preparation stage to address the noise and high dimension problems. We then propose a GAN based data enhancement approach to synthesize fault data to address the imbalance problem. After that, we propose an LSTM based classifier approach to learn the temporal correlation of data and classify the state of pipeline to predict leakage. We conduct extensive experiments and they show that our approach achieves 2× F1 score (0.8166) and 3× AUC (0.8940) compared to the existing methods. Our proposed neural network based approach is more suitable for fault prediction of pipeline leakage in petrochemical system.
Evaluating the Combination of Visual Communication Cues for HMD-based Mixed Reality Remote Collaboration

Many researchers have studied various visual communication cues (e.g. pointer, sketching, and hand gesture) in Mixed Reality remote collaboration systems for real-world tasks. However, the effect of combining them has not been so well explored. We studied the effect of these cues in four combinations: hand only, hand + pointer, hand + sketch, and hand + pointer + sketch, with three problem tasks: Lego, Tangram, and Origami. The study results showed that the participants completed the task significantly faster and felt a significantly higher level of usability when the sketch cue is added to the hand gesture cue, but not with adding the pointer cue. Participants also preferred the combinations including hand and sketch cues over the other combinations. However, using additional cues (pointer or sketch) increased the perceived mental effort and did not improve the feeling of co-presence. We discuss the implications of these results and future research directions.
Metaphoria

Creative writing, from poetry to journalism, is at the crux of human ingenuity and social interaction. Existing creative writing support tools produce entire passages or fully formed sentences, but these approaches fail to adapt to the writer's own ideas and intentions. Instead we posit to build tools that generate ideas coherent with the writer's context and encourage writers to produce divergent outcomes. To explore this, we focus on supporting metaphor creation. We present Metaphoria, an interactive system that generates metaphorical connections based on an input word from the writer. Our studies show that Metaphoria provides more coherent suggestions than existing systems, and supports the expression of writers' unique intentions. We discuss the complex issue of ownership in human-machine collaboration and how to build adaptive creativity support tools in other domains.
Encoding Pose Features to Images With Data Augmentation for 3-D Action Recognition

Recently, numerous methods have been introduced for three-dimensional (3-D) action recognition using handcrafted feature descriptors coupled traditional classifiers. However, they cannot learn high-level features of a whole skeleton sequence exhaustively. In this paper, a novel encoding technique - namely, pose feature to image (PoF2I), is introduced to transform the pose features of joint-joint distance and orientation to color pixels. By concatenating the features of all skeleton frames in a sequence, a color image is generated to depict spatial joint correlations and temporal pose dynamics of an action appearance. The strategy of end-to-end fine-tuning a pretrained deep convolutional neural network, which completely capture multiple high-level features at multiscale action representation, is implemented for learning recognition models. We further propose an efficient data augmentation mechanism for informative enrichment and overfitting prevention. The experimental results on six challenging 3-D action recognition datasets demonstrate that the proposed method outperforms state-of-the-art approaches.