Reviewerly/exHarmony huggingface dataset:
Improving the Performance of Heterogeneous Data Centers through Redundancy

We analyze the performance of redundancy in a multi-type job and multi-type server system. We assume the job dispatcher is unaware of the servers' capacities, and we set out to study under which circumstances redundancy improves the performance. With redundancy an arriving job dispatches redundant copies to all its compatible servers, and departs as soon as one of its copies completes service. As a benchmark comparison, we take the non-redundant system in which a job arrival is routed to only one randomly selected compatible server. Service times are generally distributed and all copies of a job are identical, i.e., have the same service requirement. In our first main result, we characterize the sufficient and necessary stability conditions of the redundancy system. This condition coincides with that of a system where each job type only dispatches copies into its least-loaded servers, and those copies need to be fully served. In our second result, we compare the stability regions of the system under redundancy to that of no redundancy. We show that if the server's capacities are sufficiently heterogeneous, the stability region under redundancy can be much larger than that without redundancy. We apply the general solution to particular classes of systems, including redundancy-d and nested models, to derive simple conditions on the degree of heterogeneity required for redundancy to improve the stability. As such, our result is the first in showing that redundancy can improve the stability and hence performance of a system when copies are non-i.i.d..
A Spatially Constrained Probabilistic Model for Robust Image Segmentation

In general, the hidden Markov random field (HMRF) represents the class label distribution of an image in probabilistic model based segmentation. The class label distributions provided by existing HMRF models consider either the number of neighboring pixels with similar class labels or the spatial distance of neighboring pixels with dissimilar class labels. Also, this spatial information is only considered for estimation of class labels of the image pixels, while its contribution in parameter estimation is completely ignored. This, in turn, deteriorates the parameter estimation, resulting in sub-optimal segmentation performance. Moreover, the existing models assign equal weightage to the spatial information for class label estimation of all pixels throughout the image, which, create significant misclassification for the pixels in boundary region of image classes. In this regard, the paper develops a new clique potential function and a new class label distribution, incorporating the information of image class parameters. Unlike existing HMRF model based segmentation techniques, the proposed framework introduces a new scaling parameter that adaptively measures the contribution of spatial information for class label estimation of image pixels. The importance of the proposed framework is depicted by modifying the HMRF based segmentation methods. The advantage of proposed class label distribution is also demonstrated irrespective of the underlying intensity distributions. The comparative performance of the proposed and existing class label distributions in HMRF model is demonstrated both qualitatively and quantitatively for brain MR image segmentation, HEp-2 cell delineation, natural image and object segmentation.
Two-qubit quantum gate and entanglement protected by circulant symmetry

We propose a method for the realization of the two-qubit quantum Fourier transform (QFT) using a Hamiltonian which possesses the circulant symmetry. Importantly, the eigenvectors of the circulant matrices are the Fourier modes and do not depend on the magnitude of the Hamiltonian elements as long as the circulant symmetry is preserved. The QFT implementation relies on the adiabatic transition from each of the spin product states to the respective quantum Fourier superposition states. We show that in ion traps one can obtain a Hamiltonian with the circulant symmetry by tuning the spin-spin interaction between the trapped ions. We present numerical results which demonstrate that very high fidelity can be obtained with realistic experimental resources. We also describe how the gate can be accelerated by using a "shortcut-to-adiabaticity" field.
Efficient detection of inhomogeneous magnetic fields from a single spin with Dicke states

The efficient detection of a single spin is a significant goal of improving the sensitivity of quantum magnetic-field sensors. Recent results show that a specific type of entanglement such as Greenberger-Horne-Zeilinger (GHZ) states can be used as a resource to improve the performance of single spin detection. However, scalable generation of the GHZ states is experimentally difficult to realize. It is desirable to use a practical entangled state that can be easily generated. In this paper, we propose the efficient detection of a single spin with Dicke states. We show a way to prepare and measure Dicke states via a global control. Moreover, we investigate how dephasing due to unwanted coupling with the environment affects the performance of our proposal, and show that single spin detection with Dicke states with dephasing has a significant advantage over the classical strategy with separable states. Our results are important toward realizing entanglement enhanced single spin detection.
A joint framework for underwater sequence images stitching based on deep neural network convolutional neural network

Panoramic stitching technology provides an effective solution for expanding visual detection range of the autonomous underwater vehicle. However, absorption and scattering of light in the water seriously deteriorate the underwater imaging in terms of distance and quality, especially the scattering sharply decreases the underwater image contrast and results in serious blur. This reduces the number of matching feature points between the underwater images to be stitched, while fewer matched points generated make image registration and stitching difficult. To solve the problem, a joint framework is established, which firstly involves a convolutional neural network-like algorithm composed of a symmetric convolution and deconvolution framework for underwater image enhancement. Then, it proposes an improved convolutional neural network-random sample consensus method based on VGGNet-16 framework to generate more correct matching feature points for image registration. The fusion method based on Laplacian pyramid is applied to eliminate artificial stitching traces and correct the position of stitching seam. Experimental results indicate that the proposed framework can restore the color and detail information of underwater images and generate more effective and sufficient matching feature points for underwater sequence images stitching.
All-Pass Parametric Image Registration

Image registration is a required step in many practical applications that involve the acquisition of multiple related images. In this paper, we propose a methodology to deal with both the geometric and intensity transformations in the image registration problem. The main idea is to modify an accurate and fast elastic registration algorithm (Local All-Pass-LAP) so that it returns a parametric displacement field, and to estimate the intensity changes by fitting another parametric expression. Although we demonstrate the methodology using a low-order parametric model, our approach is highly flexible and easily allows substantially richer parametrisations, while requiring only limited extra computation cost. In addition, we propose two novel quantitative criteria to evaluate the accuracy of the alignment of two images ("salience correlation") and the number of degrees of freedom ("parsimony") of a displacement field, respectively. Experimental results on both synthetic and real images demonstrate the high accuracy and computational efficiency of our methodology. Furthermore, we demonstrate that the resulting displacement fields are more parsimonious than the ones obtained in other state-of-the-art image registration approaches.
Quantum direct communication protocols using discrete-time quantum walk

The unique features of quantum walk, such as the possibility of the walker to be in superposition of the position space and get entangled with the position space, provide inherent advantages that can be captured to design highly secure quantum communication protocols. Here we propose two quantum direct communication protocols, a quantum secure direct communication protocol and a controlled quantum dialogue (CQD) protocol using discrete-time quantum walk on a cycle. The proposed protocols are unconditionally secure against various attacks such as the intercept-resend attack, the denial of service attack, and the man-in-the-middle attack. Additionally, the proposed CQD protocol is shown to be unconditionally secure against an untrusted service provider and both the protocols are shown more secure against the intercept resend attack as compared to the qubit-based LM05/DL04 protocol.
What lies beneath: transparency in online service supply chains

ABSTRACT There is a noticeable trend towards the increased centralisation of Internet-based services. Though much focus is on the dominance of organisations such as Facebook, Google and Netflix, popular consumer-facing services, there has been considerably less discussion regarding the organisations providing the infrastructure that supports online services. This bears consideration, given that many online services rely on a range of platforms and services operated by third-parties. As such, this paper explores issues of consolidation as regards the systems supply chains that underpin and drive online services. Specifically, we note that while there are trends towards the increased centralisation and dominance in the provision of supporting technical infrastructure, the nature of these technical supply chains are relatively hidden. We explore the broader societal implications of this with regards to power and resilience, emphasising the lack of means, legal or technical, for uncovering the nature of the supply chains on which online services rely. Given society's ever-growing reliance on data-driven technology, we argue that more can be done to increase levels of transparency over the supply arrangements of technical infrastructure. This is a necessary precursor to determining what interventions, if any, may be required to deal with issues of consolidation in online infrastructure.
Multi-Layer Mini-Sudoku Based High-Capacity Data Hiding Method

The main purpose of data hiding is to hide secret data imperceptibly into multimedia, ensuring secure data transmission. Existing magic matrix-based data hiding schemes cannot achieve satisfactory visual quality for a given low payload because they adopt a single-layer embedding (i.e., the fixed-sized reference matrix (RM) embedding), rather than adaptive multi-layer embedding (i.e., variable-sized RM embedding). To this end, we propose an adaptive data hiding scheme by constructing multi-layer RM using mini-Sudoku. Our scheme can adjust the number of layers (e.g., one, two, or more) of the RM to obtain the desired payload (i.e., low, moderate, or large payloads). In addition, our method also has the capability of obtaining the optimal number of layers of RM achieving the highest visual quality for a given payload. For each pixel pair, our scheme selects the optimal pair having the smallest Euclidean distance with this pair as its stego pair, resulting in a large decrease in distortion. Experimental results show that our proposed scheme can obtain various payloads, and more importantly, it can achieve better or comparable rate-distortion performance than several existing magic matrix-based data hiding schemes.
Radial Basis Function Network: Its Robustness and Ability to Mitigate Adversarial Examples

This work is a continuation of an ongoing effort to increase the robustness of the deep neural network, and thus mitigate possible adversarial examples. In our previous work, the emphasis was placed on denoising the input dataset by adding colored noise before processing. In that work, the evaluation made with the empirical robustness score, resulted in a 1% improvement on average for individual noise and a 3.74% improvement on average for ensemble noise. The aim of this paper is to demonstrate the effective robustness of a well-designed radial basis function neural network in tackling adversarial examples. With the empirical robustness as a metric, the results show a 72.5% increase with Fast Gradient Sign Method (FGSM) attack on the MNIST dataset in comparison to a simple deep network and a 6.4 % increase with FGSM on the CIFAR10 dataset.
Teaching Design and Practice of Intensive Reading Course Based on BOPPPS

BOPPPS, based on constructivism and communicative approach, is famous for effective teaching. It is a closed-loop teaching activity model that emphasizes students’ participation and feedback in the process of classroom teaching organization. Taking the course of Intensive English as an example, the paper designs teaching plan based on BOPPPS, formulates teaching tasks and implementation approaches in line with BOPPPS, implements the teaching plan, and reflects the teaching effect through teaching practice. Taking features of Intensive Reading course into consideration, the paper claims that BOPPPS is an effective teaching model, which not only stimulates students’ self-directed learning, but also save teachers and students’ energy to solve puzzles within the limited credit periods. Hopefully, BOPPPS might be gradually and widely applied to the English teaching.
Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification

This data manuscript presents a set of signals collected from the Llaima volcano located at the western edge of the Andes in Araucania Region, Chile. The signals were recorded from the LAV station between 2010 and 2016. After individually processing and analyzing every signal, specialists from the Observatorio Vulcanológico de los Andes Sur (OVDAS) classified them into four class according to their event source: i) Volcano-Tectonic (VT); ii) Long Period (LP); iii) Tremor (TR), and iv) Tectonic (TC). The dataset is composed of 3592 signals separated by class and filtered to select the segment that contains the most representative part of the seismic event. This dataset is important to support researchers interested in studying seismic signals from active volcanoes and developing new methods to model time-dependent data. In this sense, we have published the manuscript “In-Depth Comparison of Deep Artificial Neural Network Architectures on Seismic Events Classification” [1] analyzing such signals with different Deep Neural Networks (DNN). The main contribution of such manuscript is a new DNN architecture called SeismicNet, which provided classification results among the best in the literature without demanding explicit signal pre-processing steps. Therefore, the reader is referred to such manuscript for the interpretation of the data.
The grammar of interactive explanatory model analysis

The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One cannot sufficiently explain a black-box machine learning model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, leading to wrong or simplistic reasoning. This problem is known as the Rashomon effect and refers to diverse, even contradictory, interpretations of the same phenomenon. Surprisingly, most methods developed for explainable and responsible machine learning focus on a single-aspect of the model behavior. In contrast, we showcase the problem of explainability as an interactive and sequential analysis of a model. This paper proposes how different Explanatory Model Analysis (EMA) methods complement each other and discusses why it is essential to juxtapose them. The introduced process of Interactive EMA (IEMA) derives from the algorithmic side of explainable machine learning and aims to embrace ideas developed in cognitive sciences. We formalize the grammar of IEMA to describe potential human-model dialogues. It is implemented in a widely used human-centered open-source software framework that adopts interactivity, customizability and automation as its main traits. We conduct a user study to evaluate the usefulness of IEMA, which indicates that an interactive sequential analysis of a model increases the performance and confidence of human decision making.
A Scalable Feature Selection and Opinion Miner Using Whale Optimization Algorithm

Due to the fast-growing volume of text document and reviews in recent years, current analyzing techniques are not competent enough to meet the users’ needs. Using feature selection techniques not only support to understand data better but also lead to higher speed and also accuracy. In this article, the Whale Optimization algorithm is considered and applied to the search for the optimum subset of features. As known, F-measure is a metric based on precision and recall that is very popular in comparing classifiers. For the evaluation and comparison of the experimental results, PART, random tree, random forest, and RBF network classification algorithms have been applied to the different number of features. Experimental results show that the random forest has the best accuracy on 500 features.
Multi-View Collaborative Network Embedding

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, then they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this article, we propose M ulti-view coll A borative N etwork E mbedding (MANE), a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration—while diversity enables views to maintain their individual semantics, collaboration enables views to work together. However, we also discover a novel form of second-order collaboration that has not been explored previously, and further unify it into our framework to attain superior node representations. Furthermore, as each view often has varying importance w.r.t. different nodes, we propose MANE <?TeX $^{+}$?> , an attention -based extension of MANE, to model node-wise view importance. Finally, we conduct comprehensive experiments on three public, real-world multi-view networks, and the results demonstrate that our models consistently outperform state-of-the-art approaches.
Detection of spam and threads identification in E-mail spam corpus using content based text analytics method

In the internet world Era, Email user valuable data’s are continuously increasing in spite of the evolution of mobile applications, social networks and so on. In that, Email is an essential mechanism to communicate people around the world for sharing useful financial and personal information within family, friends or even for official reasons. Hacking is intensifying nowadays due to the growth of unnecessary spam messages. The existing work handled the prevention of hacking process incompetently and to prevent unnecessary spam mail receive from spammers. In this research work, focus the robust spam filter techniques to reduce the incoming spam emails. Internet users facing numerous type of spam threads. To avoid unnecessary spam email problems in existing algorithm, the proposed algorithm classifies different type of spam threads that check the spam corpus data base using spam keywords by content text analytics method and classify the spam according to its categories. In the proposed method, extract the frequent spam threads and provides better solutions in terms of handling the ethical hacking from spammers.
Semi-Automatic Classification and Duplicate Detection From Human Loss News Corpus

Automatic news repository collection systems involve a news crawler that extracts news from different news portals, subsequently, these news need to be processed to figure out the category of a news article e.g. sports, politics, showbiz etc. In this process there are two main challenges first one is to place a news article under the right category of news, while the second one is to detect a duplicate news, i.e. when the news are being extracted from multiple sources, it is highly probable to get the same news from many different portals, resulting into duplicate news; failing to which may result into inconsistent statistics obtained after pre-processing the news text. This problem becomes more pertinent when we deal with human loss news involving crime, accident etc. related news articles. As the system may count the same news many times resulting into misleading statistics. In order to address these problems, this research presents the following contributions. Firstly, a news corpus comprising of human loss news of different categories has been developed by gathering data from different sources of well-known and authentic news websites. The corpus also includes a number of duplicate news. Secondly, a comparison of different classification approaches has been conducted to empirically find out the best suitable text classifier for the categorization of different sub-categories of human loss news. Lastly, methods have been proposed and compared to detect duplicate news from the corpus by involving different pre-processing techniques and widely used similarity measures, cosine similarity, and Jaccard's coefficient. The results show that conventional text classifiers are still relevant and perform well in text classification tasks as MNB has given 89.5% accurate results. While, Jaccard coefficient exhibits much better results than Cosine similarity for duplicate news detection with different pre-processing variations with an average accuracy of 83.16%.
Recognizing Automatic Link Establishment Behaviors of a Short-Wave Radio Station by an Improved Unidimensional DenseNet

It is difficult to recognize Automatic Link Establishment (ALE) behaviors of a short-wave radio station, if we do not acquire the radio station's communication protocol standard. A method is proposed to recognize different ALE behaviors by using an improved unidimensional DenseNet. In this work, we directly recognize ALE signals in physical layer without the radio station's communication protocol standard. Hence, we can avoid difficulties in demodulation, decryption and so on. Actually, the original DenseNet is used extensively in the field of computer vision, so the original DenseNet is firstly adapted for the unidimensional input. And then, two parallel dense blocks are used in our improved unidimensional DenseNet, which could improve the capability of network to extract ALE signals' deep features. The experimental results show that the proposed method is able to recognize different ALE behaviors of a short-wave radio station. And improved DenseNet has better recognition performance than simple DenseNet. The simple DenseNet only contains one dense block. Finally, the results of comparison experiments also show that some classic networks have worse performance in ALE behaviors recognition, such as LeNet-5, ResNet-34, and DenseNet-121.