Reviewerly/exHarmony huggingface dataset:
Efficient Group Fault Tolerance for Multi-tier Services in Cloud Environments

Fault tolerance is the key technology to achieve high availability for non-stop and long-lasting services, which is usually carried out by the virtualization technology in the era of cloud computing. However, most virtualization-based fault tolerance methods only focus on the resilience of a single server (Individual FT), which can cause great performance degradation for the services that have heavy communication among multiple nodes. One of the solutions is the Group Fault Tolerance (Group FT) technique, which synchronizes a group of VMs within single fault tolerance states to avoid the latency accumulation problem. In this paper, we present an efficient implementation of Group FT, as well as the methods to enhance the performance of Group FT's failover process. Experiments show that Group FT can reduce 88% system latency of the Individual FT when running the OLTP workload in SysBench. Similar results are also shown for a more complicated synthetic multi-tier architecture. Moreover, the performance of the failover process for Group FT is also optimized so that it is comparable to the performance of Individual FT.
IC Random Number Generator Exploiting Two Simultaneous Metastable Events of Tetrahedral Oscillators

A low-cost random number generator (RNG) based on a tetrahedral ring oscillator, which can be utilized in cryptographic systems, is presented. The proposed design exploits two metastable events in the oscillator caused by turning on and off three additional inverters in the classical tetrahedral oscillator, which improves entropy. The design is implemented on FPGA as a proof of concept, but the ASIC implementation will be carried out in the future. A method has been devised to implement this circuit on FPGA. Techniques to increase randomness have been used, such as adjusting the sampling signal considering 1 and 0 balance of the output. The proposed design is compared with the classical tetrahedral oscillator based RNG, and it is observed to be better. The RNG is tested with the National Institute of Standard and Technology (NIST) test suite to prove randomness. The design occupies an area of 0.0036mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with a power consumption of 200mW and a bit rate of 10Mb/s. The number of oscillators has been dropped by 60% compared to the classical method.
Conducting Natural Language Inference with Word-Pair-Dependency and Local Context

This article proposes to conduct natural language inference with novel Enhanced-Relation-Head-Dependent triplets (RHD triplets) , which are constructed via enhancing each word in the RHD triplet with its associated local context. Most previous approaches based on deep neural network (DNN) for this task either perform token alignment without considering syntactic dependency among words, or directly use tree- LSTM to generate passage representation with irrelevant information. To improve token alignment and inference judgment with word-pair-dependency, the RHD triplet structure is first proposed. To avoid incorporating irrelevant information, this proposed approach performs comparison directly on each triplet-pair of the given passage-pair (instead of comparing each triplet in a passage with the content merged from the whole opposite passage). Furthermore, to take local context into consideration while conducting token alignment and inference judgment, we also enhance the words of the triplets with their associated local context to improve the performance. Experimental results show that the proposed approach is better than most previous approaches that adopt tree structures, and its performance is comparable to other state-of-the-art approaches (however, our approach is more human comprehensible).
OPTCOMNET: Optimized Neural Networks for Low-Complexity Channel Estimation

The use of machine learning methods to tackle challenging physical layer signal processing tasks has attracted significant attention. In this work, we focus on the use of neural networks (NNs) to perform pilot-assisted channel estimation in an OFDM system in order to avoid the challenging task of estimating the channel covariance matrix. In particular, we perform a systematic design-space exploration of NN configurations, quantization, and pruning in order to improve feedforward NN architectures that are typically used in the literature for the channel estimation task. We show that choosing an appropriate NN architecture is crucial to reduce the complexity of NN-assisted channel estimation methods. Moreover, we demonstrate that, similarly to other applications and domains, careful quantization and pruning can lead to significant complexity reduction with a negligible performance degradation. Finally, we show that using a solution with multiple distinct NNs trained for different signal-to-noise ratios interestingly leads to lower overall computational complexity and storage requirements, while achieving a better performance with respect to using a single NN trained for the entire SNR range.
Fast t-SNE algorithm with forest of balanced LSH trees and hybrid computation of repulsive forces

An acceleration of the well-known t-Stochastic Neighbor Embedding (t-SNE) (Hinton and Roweis, 2003; Maaten and Hinton, 2008) algorithm, probably the best (nonlinear) dimensionality reduction and visualization method, is proposed in this article. By using a specially-tuned forest of balanced trees constructed via locality sensitive hashing is improved significantly upon the results presented in Maaten (2014), achieving a complexity significantly closer to true O(nlogn), and vastly improving behavior for huge numbers of instances and attributes. Such acceleration removes the necessity to use PCA to reduce dimensionality before the start of t-SNE. Additionally, a fast hybrid method for repulsive forces computation (a part of the t-SNE algorithm), which is currently the fastest method known, is proposed. A parallelized version of our algorithm, characterized by a very good speedup factor, is proposed.
Unimodular polynomial matrices over finite fields

We consider some combinatorial problems on matrix polynomials over finite fields. Using results from control theory, we give a proof of a result of Lieb, Jordan and Helmke on the number of linear unimodular matrix polynomials over a finite field. As an application of our results, we give a new proof of a theorem of Chen and Tseng which answers a question of Niederreiter on splitting subspaces. We use our results to affirmatively resolve a conjecture on the probability that a matrix polynomial is unimodular.
Modeling of Rakugo Speech and Its Limitations: Toward Speech Synthesis That Entertains Audiences

We have been investigating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rakugo</i> speech synthesis as a challenging example of speech synthesis that entertains audiences. Rakugo is a traditional Japanese form of verbal entertainment similar to a combination of one-person stand-up comedy and comic storytelling and is popular even today. In rakugo, a performer plays multiple characters, and conversations or dialogues between the characters make the story progress. To investigate how close the quality of synthesized rakugo speech can approach that of professionals’ speech, we modeled rakugo speech using Tacotron 2, a state-of-the-art speech synthesis system that can produce speech that sounds as natural as human speech albeit under limited conditions, and an enhanced version of it with self-attention to better consider long-term dependencies. We also used global style tokens and manually labeled context features to enrich speaking styles. Through a listening test, we measured not only naturalness but also distinguishability of characters, understandability of the content, and the degree of entertainment. Although we found that the speech synthesis models could not yet reach the professional level, the results of the listening test provided interesting insights: 1) we should not focus only on the naturalness of synthesized speech but also the distinguishability of characters and the understandability of the content to further entertain audiences; 2) the fundamental frequency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{o}$ </tex-math></inline-formula> ) expressions of synthesized speech are poorer than those of human speech, and more entertaining speech should have richer <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{o}$ </tex-math></inline-formula> expression. Although there is room for improvement, we believe this is an important stepping stone toward achieving entertaining speech synthesis at the professional level.
Iterative Optimal Control Syntheses for Nonlinear Systems in Constrained Environments

This paper presents computational solutions for nonlinear optimal control problems with general state-space constraints that may not be representable in terms of analytical expressions. The presented approaches hinge on the iterative nature of an underlying computational nonlinear optimal control methodology by which the non-analytical constraint description can be incorporated as quadratic constraints within each iteration. The functionality and efficiency of the proposed methods are discussed from a computational point of view and illustrated on a standard parallel parking problem.
Vision-Based Autonomous Driving: A Model Learning Approach

We present an integrated approach for perception and control for an autonomous vehicle and demonstrate this approach in a high-fidelity urban driving simulator. Our approach first builds a model for the environment, then trains a policy exploiting the learned model to identify the action to take at each time-step. To build a model for the environment, we leverage several deep learning algorithms. To that end, first we train a variational autoencoder to encode the input image into an abstract latent representation. We then utilize a recurrent neural network to predict the latent representation of the next frame and handle temporal information. Finally, we utilize an evolutionary-based reinforcement learning algorithm to train a controller based on these latent representations to identify the action to take. We evaluate our approach in CARLA, a high-fidelity urban driving simulator, and conduct an extensive generalization study. Our results demonstrate that our approach outperforms several previously reported approaches in terms of the percentage of successfully completed episodes for a lane keeping task.
An Algebraic Closed-Form Solution for Bearings-Only Maneuvering Target Motion Analysis From a Nonmaneuvering Platform

In bearings-only target motion analysis (TMA), the observer is often required to outmaneuver the target. However, under specific conditions, an observer moving with constant velocity is sufficient to compute the state of a target that changes its course. A maximum likelihood estimator (MLE) has been developed for this problem in the literature. Unfortunately, the MLE is not only computationally expensive but also prone to divergence problems when poorly initialised. To overcome these shortcomings, this paper proposes a novel quadratically constrained weighted instrumental variable (QC-WIV) estimator. Being a closed-form algorithm, the proposed QC-WIV is inherently more stable than the MLE while at the same time being computationally much more efficient. Moreover, it is shown analytically to be asymptotically unbiased. Numerical simulation studies are presented to corroborate the performance advantage of the proposed QC-WIV, where it is observed to be asymptotically efficient as well. In addition, the QC-WIV performance is on par with the MLE in small noise scenarios for both cases of known and unknown maneuver time. More importantly, the QC-WIV is observed to produce stable estimation performance in large noise levels at which the MLE suffers from divergence.
The Dirichlet Mechanism for Differential Privacy on the Unit Simplex

As members of a network share more information with Each other and network providers, sensitive data leakage raises privacy concerns. To address this need for a class of problems, we introduce a novel mechanism that privatizes vectors belonging to the unit simplex. Such vectors can be seen in many applications, such as privatizing a decision-making policy in a Markov decision process. We use differential privacy as the underlying mathematical framework for these developments. The introduced mechanism is a probabilistic mapping that maps a vector within the unit simplex to the same domain according to a Dirichlet distribution. We find the mechanism well-suited for inputs within the unit simplex because it always returns a privatized output that is also in the unit simplex. Therefore, no further projection back onto the unit simplex is required. We verify the privacy guarantees of the mechanism for two cases, namely, identity queries and average queries. In the former case, we derive expressions for the differential privacy level of privatizing a single vector within the unit simplex. In the latter case, we study the mechanism for privatizing the average of a collection of vectors, Each of which is in the unit simplex. We establish a trade-off between the strength of privacy and the variance of the mechanism output, and we introduce a parameter to balance the trade-off between them. Numerical results illustrate these developments.
A Neural Network-based Sentiment Analysis Scheme for Tang Poetry

Poetry is a very popular literary form. Currently, its sentiment analysis is one of the hottest research trends. However, there are few relevant studies focusing on the sentiment analysis of ancient Chinese poetry, especially for Tang Poetry. In this paper, we propose a deep learning-based method to solve the above problem. Specifically, we combine Convolutional Neural Network and Gate Recurrent Unit to better extract the characteristics of Tang poetry. In addition, considering the special structural characteristics of Tang poetry, a multi-channel processing model is used to reshape the feature vector of sentences. Finally, in order to verify the rationality and superiority of the proposed methods, we construct a dataset by labeling more than 2500 representative Tang poems. The experimental results prove that our scheme has a higher accuracy rate, up to 64%, compared against three other competing methods.
Bridge the Trustworthiness Gap amongst Multiple Domains: A Practical Blockchain-based Approach

In isolated network domains, global trustworthiness (e.g., consistent network view) is critical to the multiple-domain business partners who aim to perform the trusted corporations depending on each isolated network view. However, to achieve such global trustworthiness across distributed network domains is a challenge. This is because when multiple-domain partners are required to exchange their local domain views with each other, it is difficult to ensure the data trustworthiness among them. In addition, the isolated domain view in each partner is prone to be destroyed by malicious falsification attacks. To this end, we propose a blockchain-based approach that can ensure the trustworthiness among multiple-party domains. In this paper, we mainly present the design and implementation of the proposed trustworthiness-protection system. A cloud-based prototype and a local testbed are developed based on Ethereum. Finally, experimental results demonstrate the effectiveness of the proposed prototype and testbed.
A Region Generation based Model for Occluded Face Detection

Face detection is a necessary first-step in face recognition systems and other applications, such as intelligent interaction, identity verification, and mobile social networks. However, there is a lack of considering face occlusion in existing detection methods, resulting in the difficulty using these methods in practical scenarios. In this paper, we propose a novel detection model named FSG-FD with attention mechanism, and aim to detect face with occlusion. Experiments on several datasets (including occlusion and mutilscale features of faces) show that our proposed algorithm achieves 9.4% improvement than faster RCNN on wider dataset and 85.1% average precision on self-labeled monitoring dataset.
Diagonal rotor Hopfield neural networks

A complex-valued Hopfield neural network (CHNN) is a multistate Hopfield model and has been applied to the storage of image data. It has the weak noise tolerance due to the inherent property of rotational invariance. A hyperbolic-valued Hopfield neural network (HHNN) resolves rotational invariance and improves the noise tolerance. A rotor Hopfield neural network (RHNN) is an extension of CHNN and the weights are represented by matrices. It provides excellent noise tolerance by resolving the rotational invariance. However, an RHNN needs double weight parameters of a CHNN, unlike an HHNN. In this work, we propose a diagonal RHNN (DRHNN), which restricts the weights of RHNN to diagonal matrices and reduces the number of weight parameters. The number of weight parameters in a DRHNN, an HHNN, and a CHNN is same. In addition, a DRHNN resolves the rotational invariance and provides excellent noise tolerance like an RHNN.
Online Voting System using Homomorphic Encryption

An important part of a democratically fair society are elections. The conventional democratic framework becomes hard for the individuals who can’t visit the polling stall for casting their votes. With quick advancement of data innovation, online Internet voting framework is all the more intriguing to use for the nations where vote casting cooperation is low and it is too helpful for people living far off from their homes. Security is a major worry for Internet voting system. The proposed framework guarantees security prerequisites for example, validation, precision, secrecy, receipt-freeness, unwavering quality, obviousness and reasonableness in the political race.
Medical Named Entity Recognition Based on Overlapping Neural Networks

Named entity recognition (NER) of medical text is a basic task in electronic medical text processing. In recent years, chinese named entity recognition system, especially in the medical field, has problems with insufficient semantic information and poor coding ability. Aiming at the existing problems, we put forward a overlapping neural network for medical named entity recognition. Compared with the mainstream methods for sequence tagging task in recent years, our proposed method can learn context feature automatically and handle encoding process better. The comparative experiments are carried out on medical NER data set, and the experimental results show that our proposed overlapping neural network model can obtain better performance than the state-of-the-art models.
Operational Safety Control for Unmanned Aerial Vehicles Using Modular Barrier Functions

Keep-in operational envelopes are essential to maintain the safety of autonomous systems such as unmanned aerial vehicles (UAVs). System constraints, including actuator saturation, dramatically affect the maneuverability of the system inside the operational envelope. Moreover, sate-of-the-art safety control depends heavily on the specifications of the operational envelope. Thus, this paper presents a modular technique to transform safety envelopes into low and high barriers along position and velocity axes. The proposed safety envelope guarantees safety and asymptotic stability simultaneously. The closed-form solution of the safety rule is obtained in the form of allowable high and low control limits, which are calculated adaptively. Thus, the design scalability is improved, and control tuning effort is minimized. Furthermore, it is shown that the proposed safety design seamlessly integrates with an existing motion control algorithm with minimum modification. Numerical simulations are conducted to verify the effectiveness of the proposed algorithm on a quadrotor drone.
Medical Privacy-preserving Service Recommendation

With the rapid development of the mobile Internet and the increasing popularity of smart terminals, various mobile social applications are emerging. Medical data has become a valuable data asset and is being continuously explored and utilized, which has greatly promoted the improvement of the medical service level. However, publishing and using user data makes the user vulnerable to reasoning attacks. Due to the special nature of the medical field, medical data not only carries the health status of patients and medical process information but also involves individual sensitive information of a large number of patients. Allowing users to fully enjoy the advantages brought by social networks while ensuring security is an important issue that needs to be solved urgently in the era of big data. In this paper, we first provide an overview of social network data privacy risks and various types of attacks. Aiming at the privacy leakage of weighted social networks, we propose a privacy protection recommendation algorithm based on differential privacy. The algorithm utilizes the change of edge weight grouping, which greatly reduces the amount of calculation and satisfies the user's rapid response. It minimizes a privacy leak of user private data under data availability while supporting personalized rankings. Compared to the most advanced methods, this method protects users from reasoning attacks and reduces the distortion of ranking results caused by data confusion to ensure the accuracy of recommendations. Experiments on real-world datasets show that our framework can achieve more effective and lasting protection for user-sensitive data.
Adapting to User Interest Drifts for Recommendations in Scratch

Scratch is a popular programming platform with plenty of learning resources. However, it is quite difficult for users to find suitable resources. In this paper, in order to provide the resources which the users require, we propose a Scratch Recommendation Framework Adaptive to User Interest Drifts (SRFA-UID). First, a user interest drifts model is designed, which adopts the time decay factor and the weights of operation behaviors to track users' dynamic interest. Then, on the basis of users' current and historical interest, we calculate their combined user similarity. Next, we present a novel two-hop-algorithm to update users' friend community. Considering the preferences of the whole friend community, the Computational Thinking (CT) skills of users and the impact factors of items, we put forward a recommendation function to obtain items that are related to users. For an item, the function can calculate its F value to determine if we can recommend it to the users. Experimental results show that SRFA-UID performs better than other state-of-the-art methods in the Scratch dataset.