Reviewerly/exHarmony huggingface dataset:
Constraint-based clustering by fast search and find of density peaks

Clustering by fast search and find of density peaks (CFDP) algorithm first proposed on Science is based on assumptions that the cluster center has the highest density among its neighbors and keeps a distance from other cluster centers. In CFDP algorithm, a local density metric and a minimal distance vector are first calculated for constructing a decision graph to select cluster centers. However, CFDP's performance is quite sensitive to parameter selection and relies on other prior knowledge. To solve the problem, this paper proposed a new clustering algorithm named constraint-based clustering by fast search and find of density peaks (CCFDP). In the proposed algorithm, several potential cluster centers are automatically formed and the structural information from constraints could be made full use of. CCFDP adopts a new method to obtain the density metric and the decision graph. After that, the decision graph is analyzed from different perspectives to help complete the final clustering. CCFDP is a semi-supervised robust clustering algorithm, combining semi-supervised constraints, density clustering and hierarchical clustering. Three synthetic and seven open datasets are used for testing its performance and robustness. The final results show that CCFDP outperforms other well-known constraint-based clustering algorithms.
Machine Learning Cryptanalysis of a Quantum Random Number Generator

Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices.
On the benefits and risks of using fitness sharing for multimodal optimisation

Fitness sharing is a well-known diversity mechanism inspired by the idea that individuals in the population that are close to each other have to share their fitnesses in a similar way to how species in nature occupying the same ecological environment have to share resources. Thus, by derating the fitness of close individuals one hopes to encourage the population to spread out more. Previous runtime analyses of fitness sharing studied a variant where selection was based on populations instead of individuals. We study the conventional fitness sharing mechanism based on individuals and use runtime analysis to highlight its benefits and dangers on the well-known bimodal test problem TwoMax, where diversity is crucial for finding both optima. In contrast to population-based sharing, a (2+1) evolutionary algorithm (EA) with conventional fitness sharing does not guarantee to find both optima in polynomial time even when problem specific knowledge is used to estimate the distance between individuals; however, a (μ+1) EA with μ≥3 always succeeds in expected polynomial time. We further show theoretically and empirically that large offspring populations in (μ+λ) EA s can be detrimental as creating too many offspring in one particular area of the search space can make all individuals in this area go extinct. We conclude the paper with an empirical study indicating that similar conclusions may be drawn when using the genotypic distance that has to be relied upon when no problem specific knowledge is available.
RedSync: Reducing synchronization bandwidth for distributed deep learning training system

Data parallelism has become a dominant method to scale Deep Neural Network (DNN) training across multiple nodes. Since synchronizing a large number of gradients of the local model can be a bottleneck for large-scale distributed training, compressing communication data has gained widespread attention recently. Among several recent proposed compression algorithms, Residual Gradient Compression (RGC) is one of the most successful approaches---it can significantly compress the transmitting message size (0.1\% of the gradient size) of each node and still achieve correct accuracy and the same convergence speed. However, the literature on compressing deep networks focuses almost exclusively on achieving good theoretical compression rate, while the efficiency of RGC in real distributed implementation has been less investigated. In this paper, we develop an RGC-based system that is able to reduce the end-to-end training time on real-world multi-GPU systems. Our proposed design called RedSync, which introduces a set of optimizations to reduce communication bandwidth requirement while introducing limited overhead. We evaluate the performance of RedSync on two different multiple GPU platforms, including 128 GPUs of a supercomputer and an 8-GPU server. Our test cases include image classification tasks on Cifar10 and ImageNet, and language modeling tasks on Penn Treebank and Wiki2 datasets. For DNNs featured with high communication to computation ratio, which have long been considered with poor scalability, RedSync brings significant performance improvements.
Efficient distribution of requests in federated cloud computing environments utilizing statistical multiplexing

One of the main questions in cloud computing environments is how to efficiently distribute user requests or Virtual Machines (VMs) based on their resource needs over time. This question is also an important one when dealing with a cloud federation environment where rational cloud service providers are collaborating together by sharing customer requests. By considering intrinsic aspects of the cloud computing model one can propose request distribution methods that play on the strengths of this computing paradigm. In this paper we look at statistical multiplexing and server consolidation as such a strength and examine the use of the coefficient of variation and other related statistical metrics as objective functions which can be used in deciding on the request distribution mechanism. The complexity of using these objective functions is analyzed and heuristic methods which enable efficient request partitioning in a feasible time are presented & compared.
High-order graph matching based on ant colony optimization

High-order graph matching utilizes the high-order relations to establish the correspondences between two sets of features. It has been formulated as an optimization problem, and this problem is an NP-hard combinatorial optimization problem. This paper presents a high-order graph matching method based on ant colony optimization. A problem-specific pheromone initialization method is introduced, meanwhile local and global pheromone updating rules are redefined. The proposed algorithm is evaluated on synthetic datasets, CMU House dataset, and real-world datasets. Experimental results demonstrate that our algorithm achieves the promising performance compared with three state-of-the-art methods.
Image captioning by incorporating affective concepts learned from both visual and textual components

Automatically generating a natural sentence describing the content of an image has been extensively researched in artificial intelligence recently, and it bridges the gap between computer vision and natural language processing communities. Most of existing captioning frameworks rely heavily on the visual content, while rarely being aware of the sentimental information. In this paper, we introduce the affective concepts to enhance the emotion expressibility of text descriptions. We achieve this goal by composing appropriate emotional concepts to sentences, which is calculated from large-scale visual and textual repositories by learning both content and linguistic modules. We extract visual and textual representations respectively, followed by combining the latent codes of the two components into a low-dimensional subspace. After that, we decode the combined latent representations and finally generate the affective image captions. We evaluate our method on the SentiCap dataset, which was established with sentimental adjective noun pairs, and evaluate the emotional descriptions with several qualitative and human inception metrics. The experimental results demonstrate the capability of our method for analyzing the latent emotion of an image and providing the affective description which caters to human cognition.
Dynamic emotion modelling and anomaly detection in conversation based on emotional transition tensor

Conversational data in social media contain a great deal of useful information, and conversation anomaly detection is an important research direction in the field of sentiment analysis. Each user has his or her own specific emotional characteristic, and by studying the distribution and sampling the users’ emotional transitions, we can simulate specific emotional transitions in the conversations. Anomaly detection in conversation data refers to detecting users’ abnormal opinions and sentiment patterns as well as special temporal aspects of such patterns. This paper proposes a hybrid model that combines the convolutional neural network long short-term memory (CNN-LSTM) with a Markov chain Monte Carlo (MCMC) method to identify users’ emotions, sample users’ emotional transition and detect anomalies according to the transition tensor. The emotional transition sampling is implemented by improving the MCMC algorithm and the anomalies are detected by calculating the similarity between the normal transition tensor and the current transition tensor of the user. The experiment was carried on four corpora, and the results show that emotions can be well sampled to conform to user’s characteristics and anomaly can be detected by the proposed method. The model proposed can be used in intelligent conversation systems, such as simulating the emotional transition and detecting the abnormal emotions.
DPC-LG: Density peaks clustering based on logistic distribution and gravitation

The Density Peaks Clustering (DPC) algorithm, published in Science, is a novel density-based clustering approach. Gravitation-based Density Peaks Clustering (GDPC) algorithm, inherited the advantages of DPC, is an improved algorithm. GDPC is able to detect outliers and to find the number of clusters correctly. However, it still has some problems in: (1) processing some data sets of varying densities; (2) processing some data sets of irregular shapes. An improved density clustering algorithm, named as DPC-LG, is proposed to overcome some weakness of GDPC. It can be seen from experimental results that the DPC-LG algorithm is more feasible and effective, compared with AP, DPC and GDPC.
Total variation with overlapping group sparsity for deblurring images under Cauchy noise

The methods based on the total variation are effective for image deblurring and denoising, which can preserve edges and details of images. However, these methods usually produce some staircase effects. In order to alleviate the staircase effects, we propose a new convex model based on the total variation with overlapping group sparsity for recovering blurred images corrupted by Cauchy noise. Moreover, we develop an algorithm under the framework of the alternating direction method with multipliers, and use the majorization minimization to solve subproblems of the proposed algorithm. Numerical results illustrate that the proposed method outperforms other methods both in visual effects and quantitative measures, such as the peak signal-to-noise ratio and the structural similarity index.
Improved fireworks algorithm with information exchange for function optimization

The fireworks algorithm, which is inspired by the explosion of fireworks, is a new swarm-based meta-heuristic algorithm for global optimization. This work proposes an improved fireworks optimization algorithm (IFWA) based on the enhanced fireworks algorithm (EFWA). Three aspects of improvement are presented after an analysis of the drawbacks of EFWA. These improvements are a new explosion scheme, GS-Gaussian explosion operator, and deep information exchange strategy. The proposed IFWA is tested on 23 benchmark function optimization problems and a real engineering problem, namely, optimal controller design for automotive active suspension. Optimization results prove that IFWA has competitive advantage compared with EFWA and other popular meta-heuristic algorithms and demonstrates the potential to solve real problems effectively.
A Deep Evaluator for Image Retargeting Quality by Geometrical and Contextual Interaction

An image is compressed or stretched during the multidevice displaying, which will have a very big impact on perception quality. In order to solve this problem, a variety of image retargeting methods have been proposed for the retargeting process. However, how to evaluate the results of different image retargeting is a very critical issue. In various application systems, the subjective evaluation method cannot be applied on a large scale. So we put this problem in the accurate objective-quality evaluation. Currently, most of the image retargeting quality assessment algorithms use simple regression methods as the last step to obtain the evaluation result, which are not corresponding with the perception simulation in the human vision system (HVS). In this paper, a deep quality evaluator for image retargeting based on the segmented stacked AutoEnCoder (SAE) is proposed. Through the help of regularization, the designed deep learning framework can solve the overfitting problem. The main contributions in this framework are to simulate the perception of retargeted images in HVS. Especially, it trains two separated SAE models based on geometrical shape and content matching. Then, the weighting schemes can be used to combine the obtained scores from two models. Experimental results in three well-known databases show that our method can achieve better performance than traditional methods in evaluating different image retargeting results.
Boosting Positive and Unlabeled Learning for Anomaly Detection With Multi-Features

One of the key challenges of machine learning-based anomaly detection relies on the difficulty of obtaining anomaly data for training, which is usually rare, diversely distributed, and difficult to collect. To address this challenge, we formulate anomaly detection as a Positive and Unlabeled (PU) learning problem where only labeled positive (normal) data and unlabeled (normal and anomaly) data are required for learning an anomaly detector. As a semi-supervised learning method, it does not require providing labeled anomaly data for the training, thus it is easily deployed to various applications. As the unlabeled data can be extremely unbalanced, we introduce a novel PU learning method, which can tackle the situation where an unlabeled data set is mostly composed of positive instances. We start by using a linear model to extract the most reliable negative instances followed by a self-learning process to add reliable negative and positive instances with different speeds based on the estimated positive class prior. Furthermore, when feedback is available, we adopt boosting in the self-learning process to advantageously exploit the instability characteristic of PU learning. The classifiers in the self-learning process are weighted combined based on the estimated error rate to build the final classifier. Extensive experiments on six real datasets and one synthetic dataset show that our methods have better results under different conditions compared to existing methods.
Vision-Based Guidance and Switching-Based Sliding Mode Controller for a Mobile Robot in the Cyber Physical Framework

This paper proposes a vision-based guidance strategy for safe navigation of a nonholonomic mobile robot in unknown indoor environments. The proposed switching-based sliding mode control (SMC) law makes the robot follow the desired trajectory as given by the guidance law. The guidance strategy uses the centroid of the depth map of an obstacle as obtained from the Red Green Blue -Depth (RGB-D) sensor to generate the desired angular velocity. The fuzzy rule-based guidance is developed to generate the desired linear velocity command. The analysis of guidance strategy is done for an infinite length obstacle. The proposed SMC is shown to be asymptotically stable using Krasovskii Method. The finite time convergence of robot navigation has been shown using Poincare Map method. The stability of the proposed SMC under burst losses has also been established. Experiments on the Pioneer P3-DX robot in different obstacle scenarios show that the robot safely navigates in presence of communication channel burst losses.
A study on rotation invariance in differential evolution

Epistasis is the correlation between the variables of a function and is a challenge often posed by real-world optimisation problems. Synthetic benchmark problems simulate a highly epistatic problem by performing a so-called problem's rotation. Mutation in Differential Evolution (DE) is inherently rotational invariant since it simultaneously perturbs all the variables. On the other hand, crossover, albeit fundamental for achieving a good performance, retains some of the variables, thus being inadequate to tackle highly epistatic problems. This article proposes an extensive study on rotational invariant crossovers in DE. We propose an analysis of the literature, a taxonomy of the proposed method and an experimental setup where each problem is addressed in both its non-rotated and rotated version. Our experimental study includes 280 problems over five different levels of dimensionality and nine algorithms. Numerical results show that 1) for a fixed quota of transferred design variables, the exponential crossover displays a better performance, on both rotated and non-rotated problems, in high dimensions while the binomial crossover seems to be preferable in low dimensions; 2) the rotational invariant mutation DE/current-to-rand is not competitive with standard DE implementations throughout the entire set of experiments we have presented; 3) DE crossovers that perform a change of coordinates to distribute the moves over the components of the offspring offer high-performance results on some problems. However, on average the standard DE/rand/1/exp appears to achieve the best performance on both rotated and non-rotated testbeds.
Sparsity-based time-frequency representation of FM signals with burst missing samples

In this paper, we present an effective time-frequency (TF) analysis of non-stationary frequency modulated (FM) signals in the presence of burst missing data samples. The key concept of the proposed work lies in the reliable sparse recovery of non-parametric FM signals in the joint-variable domains. Specifically, by utilizing the one-dimensional Fourier relationship between the instantaneous auto-correlation function (IAF) and the TF representation (TFR), the proposed approach iteratively recovers missing samples in the IAF domain through sparse reconstruction using, e.g., the orthogonal matching pursuit (OMP) method, while maintaining the TF-domain sparsity. The proposed method, referred to as missing data iterative sparse reconstruction (MI-SR), achieves reliable TFR recovery from the observed data with a high proportion of burst missing samples. This is in contrast to the existing sparse TFR recovery methods which work well only for random missing data samples. In particular, when applied in conjunction with signal-adaptive TF kernels, the proposed method achieves effective suppression of both cross-terms and artifacts due to burst missing samples. The superiority of the proposed technique is verified through analytical results and numerical examples.
Higher-Dimensional Quantum Hypergraph-Product Codes with Finite Rates

We describe a family of quantum error-correcting codes which generalize both the quantum hypergraph-product (QHP) codes by Tillich and Z\'emor, and all families of toric codes on $m$-dimensional hypercubic lattices. Similar to the latter, our codes form $m$-complexes ${\cal K}_m$, with $m\ge2$. These are defined recursively, with ${\cal K}_m$ obtained as a tensor product of a complex ${\cal K}_{m-1}$ with a $1$-complex parameterized by a binary matrix. Parameters of the constructed codes are given explicitly in terms of those of binary codes associated with the matrices used in the construction.
Publicly verifiable database scheme with efficient keyword search

The primitive of verifiable database (VDB) enables a resource-limited client to securely outsource a large and dynamic database on an untrusted server. Meanwhile, any misbehavior that attempts to tamper with the database can be detected undoubtedly. However, it seems that all existing VDB constructions only satisfy the basic query and update operations for a certain index performed by the client. In this paper, we first attempt to address the challenge of keyword-based search on VDB scheme. Specifically, we propose a concrete VDB construction supporting efficient keyword search based on the enhanced vector commitment, where each position of vector commitment is tied to a distinct keyword. Furthermore, we show how to extend the basic construction to support conjunctive keyword search. Security and efficiency analysis demonstrate that the proposed VDB schemes can achieve the desired security goals with high efficiency.
Exploiting CloudSim in a multiformalism modeling approach for cloud based systems

Cloud based architectures present many challenges, ranging from poorly dimensioned hardware to lengthy wait times and resource balancing issues. Most of these problems can be mitigated by using proper performance evaluation techniques, depending on the accuracy of the model abstracting the real system. The adoption of a formalism over another to describe the considered infrastructure plays a crucial role in achieving this goal. In this sense, multiformalism proves itself to be a powerful modeling approach describing each system component according to the most suitable representation. This paper presents a novel modeling method oriented to the prediction of cloud architectures performance, suitable for joining the advantages of high level modeling abstractions and of the detail of a specialized simulator. Generalized Stochastic Petri Nets are used to describe the workload and the behavior of users and applications, while Cloudsim, a well known cloud infrastructure simulator, is adopted for the cloud part. A case study of a simplified Edge Computing application is presented to demonstrate the effectiveness of the proposed approach.