Reviewerly/exHarmony huggingface dataset:
Generalized Register Context-Free Grammars

Register context-free grammars (RCFG) is an extension of context-free grammars to handle data values in a restricted way. This paper first introduces register type as a finite representation of the register contents and shows some properties of RCFG. Next, generalized RCFG (GRCFG) is defined by permitting an arbitrary relation on data values in the guard expression of a production rule. We extend register type to GRCFG and introduce two properties of GRCFG, the simulation property and the type oracle. We then show that $$\varepsilon $$ -rule removal is possible and the emptiness and membership problems are EXPTIME solvable for GRCFG that satisfy these two properties.
Improving the Shilling Attack Detection in Recommender Systems Using an SVM Gaussian Mixture Model

With recent advances in e-commerce platforms, the information overload has grown due to increasing number of users, rapid generation of data and items in the recommender system. This tends to create serious problems in such recommender systems. The increasing features in recommender systems pose some new challenges due to poor resilience to mitigate against vulnerable attacks. In particular, the recommender systems are more prone to be attacked by shilling attacks, which creates more vulnerability. A recommender system with poor detection of attacks leads to a reduced detection rate. The performance of the recommender system is thus affected with poor detection ability. Hence, in this paper, we improve the resilience against shilling attacks using a modified Support Vector Machine (SVM) and a machine learning algorithm. The Gaussian Mixture Model is used as a machine learning algorithm to increase the detection rate and it further reduces the dimensionality of data in recommender systems. The proposed method is evaluated against several result metrics, such as the recall rate, precision rate and false positive rate between different attacks. The results of the proposed system are evaluated against probabilistic recommender approaches to demonstrate the efficacy of machine learning language in recommender systems.
Fine-grained Action Detection in Untrimmed Surveillance Videos

Spatiotemporal localization of activities in untrimmed surveillance videos is a hard task, especially given the occurrence of simultaneous activities across different temporal and spatial scales. We tackle this problem using a cascaded region proposal and detection (CRPAD) framework implementing frame-level simultaneous action detection, followed by tracking. We propose the use of a frame-level spatial detection model based on advances in object detection and a temporal linking algorithm that models the temporal dynamics of the detected activities. We show results on the VIRAT dataset through the recent Activities in Extended Video (ActEV) challenge that is part of the TrecVID competition[1, 2].
Tenant-Oriented Monitoring for Customized Security Services in the Cloud

The dramatic proliferation of cloud computing makes it an attractive target for malicious attacks. Increasing solutions resort to virtual machine introspection (VMI) to deal with security issues in the cloud environment. However, the existing works are not feasible to support tenants to customize individual security services based on their security requirements flexibly. Additionally, adoption of VMI-based security solutions makes tenants at the risk of exposing sensitive information to attackers. To alleviate the security and privacy anxieties of tenants, we present SECLOUD, a framework for monitoring VMs in the cloud for security analysis in this paper. By extending VMI techniques, SECLOUD provides remote tenants or their authorized security service providers with flexible interfaces for monitoring runtime information of guest virtual machines (VMs) in a non-intrusive manner. The proposed framework enhances effectiveness of monitoring by taking advantages of architectural symmetry of cloud environment. Moreover, we harden our framework with a privacy-preserving capacity for tenants. The flexibility and effectiveness of SECLOUD is demonstrated through a prototype implementation based on Xen hypervisor, which results in acceptable performance overhead.
The Axiological Approach in Teaching High School Students Reading Comprehension at Russian Language Lessons

The use of axiological approaches in the teaching of the Russian language is one of the main trends in Russian highschool education. In this research we applied the axiological educational approach in teaching high-school students to work with a publicistic hypertext. It is shown that such activities can contribute to the formation of students’ value system and their personal position.
Visual Content Learning in a Cognitive Vision Platform for Hazard Control (CVP-HC)

This work is part of an effort for the development of a Cognitive Vision Platform for Hazard Control (CVP-HC) for applications in industrial workplaces, adaptable to a wide range of environments. The paper focuses on hazards resulted from the nonuse of personal protective equipment (PPE). Given the results of previous analysis of supervised techniques for the problem of classification of a few PPE (boots, hard hats, and gloves extracted from frames of low resolution videos), which found the Deep Learning (DL) methods as the most suitable ones to integrate our platform, the objective of this paper is to test two DL algorithms: Single Shot Detector (SSD) and Faster Region-based Convolutional Network (Faster R-CNN). The testing uses pretrained models on a second version of our PPE dataset (containing 11 classes of objects) and evaluates which of examined algorithms is more appropriate to compose our system reasoning.
An End-to-End Noise-Weakened Person Re-Identification and Tracking With Adaptive Partial Information

Aiming to recognize persons of interest cross cameras in different locations, the technique of person re-identification (re-ID) has attracted unprecedented attention in the field of public security. However, most of the existing work ignores the influence of background noise and pedestrian's partial information on recognition accuracy. Moreover, the tracking procedure which has a great importance on the real world is often stripped out of the re-ID framework. Therefore, this paper proposes an end-to-end noise-weakened person re-ID and tracking model with adaptive partial information. First, to suppress the background noise and improve the feature discriminability, Mask R-CNN is applied to extract the foreground “pedestrians” out of the complicated background for feature supplement. Second, an adaptive pose estimation model is proposed to make an in-depth analysis of every human body part, thus boosting the robustness against the posture change and individual difference. Finally, to fuse the tracking procedure, a scope prediction scheme based on the pedestrian's moving speed is presented to replace the traditional full domain estimation approach, thus greatly reducing the computational complexity. The extensive experiments have been conducted and the results demonstrate that our method achieves 89.78% and 81.87% rank 1 accuracy on Market-1501 and DukeMTMC-reID with real-time tracking capability, which exhibits great superiority than the state-of-the-art methods.
Reconstructing 3D Face Models by Incremental Aggregation and Refinement of Depth Frames

Face recognition from two-dimensional (2D) still images and videos is quite successful even with “in the wild” conditions. Instead, less consolidated results are available for the cases in which face data come from non-conventional cameras, such as infrared or depth. In this article, we investigate this latter scenario assuming that a low-resolution depth camera is used to perform face recognition in an uncooperative context. To this end, we propose, first, to automatically select a set of frames from the depth sequence of the camera because they provide a good view of the face in terms of pose and distance. Then, we design a progressive refinement approach to reconstruct a higher-resolution model from the selected low-resolution frames. This process accounts for the anisotropic error of the existing points in the current 3D model and the points in a newly acquired frame so that the refinement step can progressively adjust the point positions in the model using a Kalman-like estimation. The quality of the reconstructed model is evaluated by considering the error between the reconstructed models and their corresponding high-resolution scans used as ground truth. In addition, we performed face recognition using the reconstructed models as probes against a gallery of reconstructed models and a gallery with high-resolution scans. The obtained results confirm the possibility to effectively use the reconstructed models for the face recognition task.
Secure cloud computing model for communication network management

To build a safety behavior evaluation system in network management, the cloud computing model is introduced in this study for parametric study. First, the development model of cloud computing management is expounded, a parametric model of safety behavior coefficient is constructed by using a cloud computing platform. Then, an optimization and update scheme is put forward from the algorithm flow and evaluation model, and an adequate evaluation model is established by effectively evaluating the state of security management. Finally, the parameter data is refined through USES data mining algorithm to ensure the reasonable calculation of the data. In the test of the parameter model, the simulation is carried out by adopting the cloud computing platform. The test results prove that the application of the parameter model can guarantee the reasonable management of safety behavior, the error in the process of safety behavior management can be reduced, and the accuracy of control can be enhanced. The parameter model is worthy of further application and extension.
Subversion in Practice: How to Efficiently Undermine Signatures

Algorithm substitution attack (ASA) on signatures can have severe consequences as the authentication services of numerous systems and applications rely on signature schemes. In this paper, we present a highly efficient ASA on the widely-used digital signature algorithm (DSA). Compared with the generic ASAs on signature schemes proposed in the literature, our attack provides fast and undetectable subversion, which can extract the user's private signing key by collecting the maximum three signatures arbitrarily. Moreover, our ASA is proven to be robust against state reset. We implemented the proposed ASA by replacing the original DSA in Libgcrypt (a popular cryptographic library used in many applications) with our subverted DSA. Our experiment shows that the user's private key can readily be recovered once the subverted DSA is used to sign messages. In our implementation, various measures have been considered to significantly reduce the possibility of detection through comparing the running time of the original DSA and the subverted one (i.e. timing analysis). To our knowledge, this is the first implementation of the ASA in practice, which shows that the ASA is a real threat rather than only theoretical speculation.
State of the art in hybrid strategies for context reasoning: A systematic literature review

Several strategies have been used to implement context reasoning, and a strategy that can be applied satisfactorily in different smart systems applications has not yet been found. Because of this, hybrid proposals for context reasoning are gaining prominence. These proposals allow the combination of two or more strategies. This work aims to identify the state of the art in the context awareness field, considering papers that use hybrid strategies for context reasoning. A Systematic Literature Review was explored, contributing to the identification of relevant works in the field, as well as the specification of criteria for its selection. In this review, we analyzed papers published between 2004 and 2018. During the process, we identified 3241 papers. After applying filtering and conditioning processes, ten papers about hybrid strategies for context reasoning were selected. We described, discussed, and compared the selected papers. The Systematic Literature Review showed that some researchers explore hybrid proposals, but these proposals do not offer flexibility regarding the reasoning strategies used. Thus, we noted that research efforts related to the topic are still necessary, mainly focusing on the development of dynamic approaches that allow the applications to choose how they want to use the different resources available.
Demand-driven Acquisition of Print Books: Applying 21st-Century Procurement Strategies to a 5th-Century Format

The Marriott Library at the University of Utah was an early champion of the demand-driven model of acquiring ebooks. Beginning in 2017, the Marriott Library applied a similar approach to the acquisition of print books. Demand-driven acquisition of physical formats is a means for stretching compressed library budgets and a technique for managing the explosion in book publishing. Demand-driven acquisition addresses publishers that refuse to sell individual electronic books to libraries and better serves disciplines like the fine arts. The Marriott Library’s experiences with demand-driven print acquisition are discussed and compared with other libraries that are testing this acquisition method.
Asymptotic Limits of Privacy in Bayesian Time Series Matching

Various modern and highly popular applications make use of user data traces in order to offer specific services, often for the purpose of improving the user's experience while using such applications. However, even when user data is privatized by employing privacy-preserving mechanisms (PPM), users' privacy may still be compromised by an external party who leverages statistical matching methods to match users' traces with their previous activities. In this paper, we obtain the theoretical bounds on user privacy for situations in which user traces are matchable to sequences of prior behavior, despite anonymization of data time series. We provide both achievability and converse results for the case where the data trace of each user consists of independent and identically distributed (i.i.d.) random samples drawn from a multinomial distribution, as well as the case that the users' data points are dependent over time and the data trace of each user is governed by a Markov chain model.
Tell the Device Password: Smart Device Wi-Fi Connection Based on Audio Waves

IoT devices are now enriching people's life. However, the security of IoT devices seldom attracts manufacturers' attention. There are already some solutions to the problem of connecting a smart device to a user's wireless network based on the 802.11 transmission such as Smart Config from TI. However, it is insecure in many situations, and it does not have a satisfactory transmission speed, which does not mean that it has a low bit rate. It usually takes a long time for the device to recognize the data it receives and decode them. In this paper, we propose a new Wi-Fi connection method based on audio waves. This method is based on MFSK (Multiple frequency-shift keying) and works well in short distance, which enables the correctness and efficiency. In addition, audio waves can hardly be eavesdropped, which provides higher security than other methods. We also put forward an encryption solution by using jamming signal, which can greatly improve the security of the transmission.
Online Data Stream Analytics for Dynamic Environments Using Self-Regularized Learning Framework

For the upcoming Internet of things era, a plethora of data will need to be processed on a real-time basis. However, most prior data streaming-related studies ignored the semantic meaning of the input data, leading to unexpected or even unacceptable contextual models. This issue may worsen given the concept-drift problem in a dynamic environment. Since streaming data do not have ground-truth labels, it is challenging to reflect upon what has been learned for continuous improvement in learning performance. To address the aforementioned issues, this study incorporates self-regulated learning from pedagogy. There are three elements in self-regulated learning: Forethought, Performance, and Reflection. Each element's corresponding module is designed and implemented in this study. The goal is to reduce the need for human intervention in online data streaming analytics, while enhancing the abilities of semantic understanding and metacognitive self-monitoring. With the aforementioned enhancements, the experimental results of our enhanced model reached a level comparable to those of prior studies that required human interventions. Furthermore, the proposed approach helps guarantee that the model does not go astray from a preset learning goal.
ASDS: Attribute‐based secure data sharing scheme for reliable cloud environment

Cloud computing is a new revolution of information technology which provides many benefits to customers such as scalability, on‐demand, 24 × 7 × 365 support, and reduce cost usage of computing resources. But storing data on these untrusted cloud servers makes secure data sharing a big challenge issue. On one hand, enforce data access policies on these untrusted storage servers provider is an effective way to ensure data security. On the other hand, confidentiality of sensitive data should be protected against them. This paper presents an attribute‐based secure data sharing (ASDS) scheme for cloud environment, which provides data access control, data confidentiality, data authentication, and flexible user revocation. In addition, the proposed scheme is able to resist collusion attack and replay attack. The analysis of security properties and the comparison performance with other data sharing schemes have demonstrated that ASDS is very suitable for cloud environment.
Optimization of the operation of the anthropomorphic manipulator in a three-dimensional working space

This article presents the use of the particle swarm method to optimize the working cycle of an anthropomorphic manipulator. The working space contained stationary obstacles which made the working cycle difficult. Three objective functions were adopted in the study, concerning trajectory minimization, reduction of electricity consumption and multi-criteria optimization (minimization of both trajectory length and electricity consumption). The effects of selected parameters on the particle swarm optimization (PSO) algorithm and the results of the examinations were analysed.
Fast estimation of prestack common reflection surface parameters

ABSTRACT We present a method for fast estimation of finite offset common reflection surface parameters. Firstly, the derivatives with respect to offset are derived from the velocity guide. Secondly, we apply structure tensors to extract the derivatives with respect to midpoint from stacked common offset sections. Finally, the mixed derivative is estimated using a one‐parametric semblance search. The proposed method is compared to the global five‐parametric semblance search and the pragmatic sequential two‐parametric semblance search on one synthetic and one real data set. The experiments show that the proposed method is more robust against noise than the pragmatic search and have comparable robustness with the global search. The proposed method smoothes parameter estimates in a local window, and the window size is set to give the best trade‐off between detail and robustness. Since the proposed method is dependent on a velocity guide, the quality of the other parameter estimates may be influenced by any inaccuracies in the guide. The main advantage of the proposed method is the computational efficiency. When compared with a gridded implementation of the semblance search, the proposed method is 10 and 400 times faster than the pragmatic and global search. Alternative search strategies significantly reduce the computational cost of the global search. However, since more than 99% of the computational cost of the proposed method comes from the semblance search to estimate the mixed derivative, it is expected that such techniques also reduce the computational cost for the proposed method.
Research on Mongolian-Chinese machine translation based on the end-to-end neural network

With the development of natural language processing and neural machine translation, the neural machine translation method of end-to-end (E2E) neural network model has gradually become the focus of research because of its high translation accuracy and strong semantics of translation. However, there are still problems such as limited vocabulary and low translation loyalty, etc. In this paper, the discriminant method and the Conditional Random Field (CRF) model were used to segment and label the stem and affixes of Mongolian in the preprocessing stage of Mongolian-Chinese bilingual corpus. Aiming at the low translation loyalty problem, a decoding model combining Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) was constructed. The target language decoding was performed by using the GRU. A global attention model was used to obtain the bilingual word alignment information in the process of bilingual word alignment processing. Finally, the quality of the translation was evaluated by Bilingual Evaluation Understudy (BLEU) values and Perplexity (PPL) values. The improved model yields a BLEU value of 25.13 and a PPL value of [Formula: see text]. The experimental results show that the E2E Mongolian-Chinese neural machine translation model was improved in terms of translation quality and semantic confusion compared with traditional statistical methods and machine translation models based on Recurrent Neural Networks (RNN).
Anomaly detecting and ranking of the cloud computing platform by multi-view learning

Anomaly detecting as an important technical in cloud computing is applied to support smooth running of the cloud platform. Traditional detecting methods based on statistic, analysis, etc. lead to the high false-alarm rate due to non-adaptive and sensitive parameters setting. We presented an online model for anomaly detecting with the machine learning theory. However, most existing methods based on machine learning linked all features from difference sub-systems into a long feature vector directly, which is difficult to both exploit the complement information among sub-systems and ignore multi-view features enhancing the classification performance. Aiming to above problems, the proposed method automatic fuses multi-view features and optimizes the discriminative model to enhance the accuracy. This model takes advantage of extreme learning machine (ELM) to improve detection efficiency. ELM is the single hidden layer neural network, which is transforming iterative solution of the output weights to solution of linear equations and avoiding the local optimal solution. Moreover, we rank anomies according to the relationship between samples and the classification boundary, and then assigning weights for ranked anomalies, retraining the classification model finally. Our method exploits the complement information among sub-systems sufficiently, and avoids the influence from the imbalance distribution, therefore, deal with various challenges from the cloud computing platform. We deploy the privately cloud platform by Openstack, verifying the proposed model and comparing results to the state-of-the-art methods with better efficiency and simplicity.