Reviewerly/exHarmony huggingface dataset:
Blind Identification of SFBC-OFDM Signals Based on the Central Limit Theorem

Previous approaches for blind identification of space-frequency block codes (SFBC) do not perform well for short observation periods due to their inefficient utilization of frequency-domain redundancy. This paper proposes a hypothesis test (HT)-based algorithm and a support vector machine (SVM)-based algorithm for SFBC signals identification over frequency-selective fading channels to exploit two-dimensional space-frequency domain redundancy. Based on the central limit theorem, space-domain redundancy is exploited to construct the cross-correlation function of the estimator and frequency-domain redundancy is incorporated in the construction of the statistics. The difference between the two proposed algorithms is that the HT-based algorithm constructs a chi-square statistic and employs an HT to make the decision, while the SVM-based algorithm constructs a non-central chi-square statistic with unknown mean as a strongly-distinguishable statistical feature and uses an SVM to make the decision. Both algorithms do not require knowledge of the channel coefficients, modulation type or noise power, and the SVM-based algorithm does not require timing synchronization. Simulation results verify the superior performance of the proposed algorithms for short observation periods with comparable computational complexity to conventional algorithms, as well as their acceptable identification performance in the presence of transmission impairments.
An experimental quantum Bernoulli factory

There has been a concerted effort to identify problems computable with quantum technology which are intractable with classical technology or require far fewer resources to compute. Recently, randomness processing in a Bernoulli factory has been identified as one such task. Here, we report two quantum photonic implementations of a Bernoulli factory, one utilising quantum coherence and single-qubit measurements and the other which uses quantum coherence and entangling measurements of two qubits. We show that the former consumes three orders of magnitude fewer resources than the best known classical method, while entanglement offers a further five-fold reduction. These concepts may provide a means for quantum enhanced-performance in the simulation of stochastic processes and sampling tasks.
SEMKC: Secure and Efficient Computation over Outsourced Data Encrypted under Multiple Keys

Cloud computing is a popular paradigm to facilitate massive data computation and storage. The issue is challenging as data owners outsource their data after encryption to preserve privacy. In this work, we propose two efficient techniques namely SEMKC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> and SEMKC* to compute remotely over encrypted data. SEMKC* can be used for execution which has more number of multiplications and fewer additions, while SEMKC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> would be used for the process with more number of additions and fewer multiplications. Both the schemes facilitate to compute over the data from multiple owners encrypted with different keys, without leaking the privacy. Further, the scheme is proved to be secure on “Real world-Ideal world” paradigm. The performance comparison has been made with existing schemes and observed that our scheme requires less computation and communication in comparison to the other popular existing schemes.
Minor Probability Events’ Detection in Big Data: An Integrated Approach With Bayes Detection and MIM

In some common scenarios, rare events tend to be more important and worth careful detection. In this case, minor probability event detection rises up as an important issue in big data analytics. With the existence of empirical prior probability distributions of events, one common method to minimize the average detection error is Bayes detection. However, in extreme cases where the prior probability is small, the Bayes method may result in high miss detection rate. To overcome this problem, we proposed a modified detection algorithm by integrating the Bayes method with the message importance measure. Simulation results show that the algorithm significantly reduces the miss detection rate of extremely rare events, which may be helpful to dig rarely occurring phenomenons in big data.
MOBA-Slice: A Time Slice Based Evaluation Framework of Relative Advantage Between Teams in MOBA Games

Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games is large. It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result. In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games. MOBA-Slice is a quantitative evaluation method based on learning, similar to the value network of AlphaGo. It establishes a foundation for further MOBA related research including AI development. In MOBA-Slice, with an analysis of the deciding factors of MOBA game results, we design a neural network model to fit our discounted evaluation function. Then we apply MOBA-Slice to Defense of the Ancients 2 (DotA2), a typical and popular MOBA game. Experiments on a large number of match replays show that our model works well on arbitrary matches. MOBA-Slice not only has an accuracy 3.7% higher than DotA Plus Assistant (A subscription service provided by DotA2) at result prediction, but also supports the prediction of the remaining time of a game, and then realizes the evaluation of relative advantage between teams.
BES: Differentially private event aggregation for large-scale IoT-based systems

The emergence of Internet of Things (IoT) offers many advantages, but it also raises significant challenges with respect to efficient and distributed processing of large data and also privacy concerns related to large data disclosure. We investigate the above problems from a system-perspective and study how differential privacy can be used to complement other privacy-enhancing technologies to allow for controlled large data disclosure. We present a streaming-based framework, Bes, where we leverage the often distributed nature of typical IoT systems for efficient computation of differentially private aggregates. We also propose methods to limit the noise that is commonly introduced for differential privacy in real-world applications, by bounding the outliers based on (differentially private) parameters of the actual system at hand or data from other similar systems. We also provide a thorough evaluation based on a fully implemented Bes prototype using real-world data from of a concrete IoT system, namely an Advanced Metering Infrastructure (AMI). We show how a large number of events can be aggregated in a private fashion with low processing latency, even when the processing is made by a single-board device, with similar capabilities to the devices deployed in AMIs. Moreover, by implementing a de-pseudonymization attack known from the literature, we also show the strong complementary protection offered by Bes’ differentially private aggregation, compared to other privacy-enhancing technologies.
Some results on vanishing moments of wavelet packets, convolution and cross-correlation of wavelets

A formula for calculating moments for wavelet packets is derived and a sufficient condition for moments of wavelet packets to be vanishing is obtained. Also, the convolution and cross-correlation theorems for Hilbert transform of wavelets are proved. Finally, using MRA of L2(R), some results on the vanishing moments of the scaling functions, wavelets and their convolution in two dimension are given.
3-D Granulometry Using Image Processing

Image-based methods for estimating the particle size distribution (granulometry) usually analyze two-dimensional (2-D) samples of particles disposed on a conveyor belt. Such approaches have to deal with occlusions and cannot evaluate the thickness of each particle. Three-dimensional (3-D) vision systems can reduce the acquisition constraints and speed up the quality control process. This paper proposes a novel 3-D vision system for analyzing the granulometry of falling particles. The system is designed to work in real time and to compute a partial 3-D reconstruction of the particle from a single pair of two-view images, which is then enhanced by using a neural-based technique. The validation of the proposed approach has been performed by considering three application scenarios for which the system achieved satisfactory accuracy and robustness.
Service deployment and scheduling for improving performance of composite cloud services

Cloud computing and services have greatly changed the way how people develop and use software, and also raised a lot of new research issues. In this paper, we investigate two such important issues, service deployment and service request scheduling, for composite cloud services in dynamic cloud environments. We present load-aware service deployment approaches for dynamic workload and a service request scheduling method based on task ranking mechanisms. The proposed approaches can effectively improve the execution performance of composite cloud services in dynamic cloud environments. A series of simulation experiments have been conducted to evaluate the proposed approaches. The experimental results show that our approaches outperform previous methods significantly in terms of average service response time.
A Grayscale Image Hiding Encode Scheme for Secure Transmission

Background: To transmit the secret data are in a secure manner or to prevent the intruder/ third party activities while transmitting secret data’s through the public networks are challenging task now. In order to deal with these situations, this paper presents an encryption/encoding technique of MSI (Modified Steganography for Image) for secret data before transmitting over the network. Methods: The MSI technique is classified into two phases, one is stegano image creation by encode process and another one is reconstructing the secret data from the stegano image by decode process. In encode process, the grayscale cover and secret images are considering as an input. In addition, segregation, 8-bit binary conversion, substitution and decimal conversion processes are doing a vital role in hiding the secret image into a cover image. The MSI encoded stegno image is almost equal to the cover image and it's not an easy to identify by the Human Visual Attack (HVA). Results &amp; Conclusion: To evaluate the proposed MSI encoding process the time, signal to noise ratio, complexity and strength are considering as the parameters. In result, the MSI encode technique is providing good reconstruction image quality, strong against the pixel attack and HVA, less execution time than the conventional schemes.
A unified multilingual handwriting recognition system using multigrams sub-lexical units

We address the design of a unified multilingual system for handwriting recognition. Most of multi- lingual systems rests on specialized models that are trained on a single language and one of them is selected at test time. While some recognition systems are based on a unified optical model, dealing with a unified language model remains a major issue, as traditional language models are generally trained on corpora composed of large word lexicons per language. Here, we bring a solution by con- sidering language models based on sub-lexical units, called multigrams. Dealing with multigrams strongly reduces the lexicon size and thus decreases the language model complexity. This makes pos- sible the design of an end-to-end unified multilingual recognition system where both a single optical model and a single language model are trained on all the languages. We discuss the impact of the language unification on each model and show that our system reaches state-of-the-art methods perfor- mance with a strong reduction of the complexity.
Investigation of the traveling thief problem

The Traveling Thief Problem (TTP) is a relatively new benchmark problem created to study problems which consist of interdependent subproblems. In this paper we investigate what the fitness landscape characteristics are of some smaller instances of the TTP with commonly used local search operators in the context of metaheuristics that uses local search. The local search operators include: 2-opt, Insertion, Bitflip and Exchange and metaheuristics include: multi-start local search, iterated local search and genetic local search. Fitness landscape analysis shows among other things that TTP instances contain a lot of local optima but their distance to the global optimum is correlated with its fitness. Local optima networks with respect to an iterated local search reveals that TTP has a multi-funnel structure. Other experiments show that a steady state genetic algorithm with edge assembly crossover outperforms multi-start local search, iterated local search and genetic algorithms with different tour crossovers. At last we performed a comparative study using the genetic algorithm with edge assembly crossover on relatively larger instances of the commonly used benchmark suite. As a result we found new best solutions to almost all studied instances.
Evolving large-scale data stream analytics based on scalable PANFIS

The main challenge in large-scale data stream analytics lies in the ability of machine learning to generate large-scale data knowledge in reasonable timeframe without suffering from a loss of accuracy. Many distributed machine learning frameworks have recently been built to speed up the large-scale data learning process. However, most distributed machine learning used in these frameworks still uses an offline algorithm model which cannot cope with the data stream problems. In fact, large-scale data are mostly generated by the non-stationary data stream where its pattern evolves over time. To address this problem, we propose a novel Evolving Large-scale Data Stream Analytics framework based on a Scalable Parsimonious Network based on Fuzzy Inference System (Scalable PANFIS), where the PANFIS evolving algorithm is distributed over the worker nodes in the cloud to learn large-scale data stream. Scalable PANFIS framework incorporates the active learning (AL) strategy and two model fusion methods. The AL accelerates the distributed learning process to generate an initial evolving large-scale data stream model (initial model), whereas the two model fusion methods aggregate an initial model to generate the final model. The final model represents the update of current large-scale data knowledge which can be used to infer future data. Extensive experiments on this framework are validated by measuring the accuracy and running time of four combinations of Scalable PANFIS and other Spark-based built in algorithms. The results indicate that Scalable PANFIS with AL improves the training time to be almost two times faster than Scalable PANFIS without AL. The results also show both rule merging and the voting mechanisms yield similar accuracy in general among Scalable PANFIS algorithms and they are generally better than Spark-based algorithms. In terms of running time, the Scalable PANFIS training time outperforms all Spark-based algorithms when classifying a multi-class label dataset.
Uniform Color Space-Based High Dynamic Range Video Compression

Recently, there has been a significant progress in the research and development of the high dynamic range (HDR) video technology and the state-of-the-art video pipelines are able to offer a higher bit depth support to capture, store, encode, and display HDR video content. In this paper, we introduce a novel HDR video compression algorithm, which uses a perceptually uniform color opponent space, a novel perceptual transfer function to encode the dynamic range of the scene, and a novel error minimization scheme for accurate chroma reproduction. The proposed algorithm was objectively and subjectively evaluated against four state-of-the-art algorithms. The objective evaluation was conducted across a set of 39 HDR video sequences, using the latest x265 10-bit video codec along with several perceptual and structural quality assessment metrics at 11 different quality levels. Furthermore, a rating-based subjective evaluation (n=40) was conducted with six sequences at two different output bitrates. Results suggest that the proposed algorithm exhibits the lowest coding error amongst the five algorithms evaluated. Additionally, the rate-distortion characteristics suggest that the proposed algorithm outperforms the existing state-of-the-art at bitrates ≥ 0.4 bits/pixel.
Computational Modeling of Phonological Learning

Recent advances in computational modeling have led to significant discoveries about the representation and acquisition of phonological knowledge and the limits on language learning and variation. These discoveries are the result of applying computational learning models to increasingly rich and complex natural language data while making increasingly realistic assumptions about the learning task. This article reviews the recent developments in computational modeling that have made connections between fully explicit theories of learning, naturally occurring corpus data, and the richness of psycholinguistic and typological data possible. These advances fall into two broad research areas: ( a) the development of models capable of learning the quantitative, noisy, and inconsistent patterns that are characteristic of naturalistic data and ( b) the development of models with the capacity to learn hidden phonological structure from unlabeled data. After reviewing these advances, the article summarizes some of the most significant consequent discoveries.
Culling for Extreme-Scale Segmentation Volumes: A Hybrid Deterministic and Probabilistic Approach

With the rapid increase in raw volume data sizes, such as terabyte-sized microscopy volumes, the corresponding segmentation label volumes have become extremely large as well. We focus on integer label data, whose efficient representation in memory, as well as fast random data access, pose an even greater challenge than the raw image data. Often, it is crucial to be able to rapidly identify which segments are located where, whether for empty space skipping for fast rendering, or for spatial proximity queries. We refer to this process as culling. In order to enable efficient culling of millions of labeled segments, we present a novel hybrid approach that combines deterministic and probabilistic representations of label data in a data-adaptive hierarchical data structure that we call the label list tree. In each node, we adaptively encode label data using either a probabilistic constant-time access representation for fast conservative culling, or a deterministic logarithmic-time access representation for exact queries. We choose the best data structures for representing the labels of each spatial region while building the label list tree. At run time, we further employ a novel query-adaptive culling strategy. While filtering a query down the tree, we prune it successively, and in each node adaptively select the representation that is best suited for evaluating the pruned query, depending on its size. We show an analysis of the efficiency of our approach with several large data sets from connectomics, including a brain scan with more than 13 million labeled segments, and compare our method to conventional culling approaches. Our approach achieves significant reductions in storage size as well as faster query times.
Mittag-Leffler-Gaussian distribution: Theory and application to real data

In this paper, we introduce the class of Mittag-Leffler-Gaussian distribution which is more flexible than the Gaussian distribution. Some properties of this class are investigated. Then, for two real data sets, the model is examined using the Akaike, Bayesian and small sample Akaike information criteria.
Fog-enabled Event Processing Based on IoT Resource Models

Complex Event Processing (CEP) systems extract interest situations from event flows based on event detection patterns. However, local event processing for distributed Internet of Things (IoT) has not been discussed yet. Besides, it is complex or impossible to discover such patterns in some applications of IoT. In this article, we design a complex event service to process event flows based on IoT resource models, which does not depend on existing patterns, and deals with both discrete events and continuous variables. To improve the CEP performance, local IoT resources are used for local event processing, and a lazy exchange method is designed to realize the collaborated event processing between network edges and a data center. Our evaluation shows that our solution is feasible and effective.
Comprehensive link sharing avoidance and switch aggregation for software-defined data center networks

Abstract An effective way to reduce network energy consumption of data center networks (DCNs) is to activate network elements as few as possible, complete transmission in as short a time as possible, and set unnecessary network elements to sleep mode. At present, most existing energy saving works considered the network energy saving from the dimension of time or power separately. However, in fact these two dimensions can interact with each other, i.e., reducing the network delay may lead to the increase of network energy consumption, and vice versa. In this paper, two dimensions of time and power are comprehensively studied in the Minimum Network Energy Consumption (MNEC) problem. First of all, we formulate the MNEC problem by considering both time and power, and prove that it is a NP-hard problem. Furthermore, we propose a heuristic Integrated Time and Power (ITP) algorithm, which combines the link sharing avoidance algorithm to reduce the network delay from the dimension of time as well as the switch aggregation algorithm to reduce the energy consumption from the power dimension. Finally, the performance of ITP algorithm is evaluated under different network topology, network size, traffic size and flow number under the network environment based on Mininet and Ryu controller. Experimental results show that the ITP algorithm outperforms the existing network energy saving algorithm in terms of energy consumption.
Bayesian denoising hashing for robust image retrieval

Learning to hash is one of the most popular techniques in image retrieval, but few work investigates its robustness to noise corrupted images in which the unknown pattern of noise would heavily deteriorate the performance. To deal with this issue, we present in this paper a Bayesian denoising hashing algorithm whose output can be regarded a denoised version of the input hash code. We show that our method essentially seeks to reconstruct a new but more robust hash code by preserving the original input information while imposing extra constraints so as to correct the corrupted bits. We optimized this model in variational Bayes framework which has a closed-form update in each iteration that is more efficient than numerical optimization. Furthermore, our method can be added at the top of any original hashing layer, serving as a post-processing denoising layer with no change to previous training procedure. Experiments on three popular datasets demonstrate that the proposed method yields robust and meaningful hash code, which significantly improves the performance of state-of-the-art hash learning methods on challenging tasks such as large-scale natural image retrieval and retrieval with corrupted images.