Reviewerly/exHarmony huggingface dataset:
ALIC

In this paper, we reconsider the problem of low computational efficiency in traditional superpixel segmentation methods based on clustering. We propose a superpixel segmentation method based on autonomous attachment named ALIC, which use a simpler and more efficient distance measurement method to accelerate the algorithm. Simultaneously, the allocation of pixel autonomous attached fully considers the natural continuity between pixels, and each pixel can share label with its neighbors, which allows us to obtain better boundary performance. In the experiment, our method only achieves convergence in five iterations. On the basis of obtaining more sensitive boundaries, our algorithm improves the operation speed. (On a simple CPU core, it only takes about 0.1s to segment a 481×321 image into 400 homogenic superpixels).
Cascade Morphological n-Gram Can Improve Chinese Words Representation Learning

This paper considers the problem of learning Chinese word embeddings. Chinese characters are composed of components, and components can be further divided into strokes. While fine-grained semantic meanings in components and strokes of words are often ignored. We propose Component based cascade n-gram(CBC n-gram) models and Stroke based cascade n-gram(SBC n-gram) models to improve word representations by capturing morphological information at different granularity levels. We keep a distinct vector for each word, and the average of fine-grained feature vectors is coupled with the word vector for enriching the word representations. We evaluate our word representations on word similarity, word analogy using datasets wordsim-240, wordsim-296, Chinese word analogy dataset respectively. The results prove the effects of these two kinds of fine-grained features on word representations. Comparisons with recently state-of-the-art Chinese word representation models demonstrate the superior performances of our models.
Application of Metaheuristic Algorithms for Determining the Structure of a Convolutional Neural Network with a Small Dataset

Inadequately labeled data can limit the accuracy of classification in image recognition tasks. Several methods have been proposed in the past to alleviate this limitation, such as transfer learning and data augmentation. However, the classification accuracy of the convolutional neural network (CNN) largely depends on its structural parameters, which are known as hyper-parameters. Therefore, in this paper, we introduce another method for minimizing the misclassification rate in a given small dataset by determining the hyper-parameters. The harmony search (HS) algorithm, improved harmony search (IHS) algorithm, self-adaptive global best harmony search (SGHS) algorithm, and novel global harmony search (NGHS) algorithm are applied for determining the optimal hyper-parameters. Additionally, we also compared the estimation performances of these four HS algorithms. It was finally observed that the HS and the IHS algorithms greatly outperform the other two algorithms.
Image Representation and Analysis by Continuous Chebyshev Polynomials

This paper proposes a spline-like Chebyshev polynomial image representation obtained by block-based compressed sampling (CS) for image analysis and processing. Due to orthogonality and close relation with the discrete cosine transform, Chebyshev polynomials have near-zero redundancy measure and possess great compression properties. Consequently, they fit perfectly into CS paradigm. We propose a spline-like model, that equalizes a desired number of derivatives on each block boundary, to avoid blocking artifacts. This can be seen as an additional constraint in the standard CS optimization problem. Still, the proposed model provides a system of equations that is underdetermined for achieving sparse reconstruction using the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> optimization. The proposed framework offers polynomial representation of the acquired image and allows further analysis and processing conducted on Chebyshev polynomial coefficients without the need of converting them into samples. In this paper, we demonstrate the efficiency of the proposed model by implementing an analytic Chebyshev polynomial gradient operator that can be used instead of discrete gradient approximations in edge detection methods. The proposed operator is compared to Sobel operator and we prove its potential by implementing it into an edge detection algorithm.
Design and Analysis of Sustainable and Seasonal Profit Scaling Model in Cloud Environment

Cloud is a widely used platform for intensive computing, bulk storage, and networking. In the world of cloud computing, scaling is a preferred tool for resource management and performance determination. Scaling is generally of two types: horizontal and vertical. The horizontal scale connects users’ agreement with the hardware and software entities and is implemented physically as per the requirement and demand of the datacenter for its further expansion. Vertical scaling can essentially resize server without any change in code and can increase the capacity of existing hardware or software by adding resources. The present study aims at describing two approaches for scaling, one is a predator-prey method and second is genetic algorithm (GA) along with differential evolution (DE). The predator-prey method is a mathematical model used to implement vertical scaling of task for optimal resource provisioning and genetic algorithm (GA) along with differential evolution(DE) based metaheuristic approach that is used for resource scaling. In this respect, the predator-prey model introduces two algorithms, namely, sustainable and seasonal scaling algorithm (SSSA) and maximum profit scaling algorithm (MPSA). The SSSA tries to find the approximation of resource scaling and the mechanism for maximizing sustainable as well as seasonal scaling. On the other hand, the MPSA calculates the optimal cost per reservation and maximum sustainable profit. The experimental results reflect that the proposed logistic scaling-based predator-prey method (SSSA-MPSA) provides a comparable result with GA-DE algorithm in terms of execution time, average completion time, and cost of expenses incurred by the datacenter.
Clifford-group-restricted eavesdroppers in quantum key distribution

Quantum key distribution (QKD) promises provably secure cryptography, even to attacks from an all-powerful adversary. However, with quantum computing development lagging behind QKD, the assumption that there exists an adversary equipped with a universal fault-tolerant quantum computer is unrealistic for at least the near-future. Here, we explore the effect of restricting the eavesdropper's computational capabilities on the security of QKD and find that improved secret key rates are possible. Specifically, we show that for a large class of discrete variable protocols higher key rates are possible if the eavesdropper is restricted to a unitary operation from the Clifford group. Further, we consider Clifford-random channels consisting of mixtures of Clifford gates. We numerically calculate a secret-key-rate lower bound for BB84 with this restriction and show that, in contrast to the case of a single restricted unitary attack, the mixture of Clifford-based unitary attacks does not result in an improved key rate.
Hardware Implementation of Karnik-Mendel Algorithm for Interval Type-2 Fuzzy Sets and Systems

The trend to accelerate the learning process in neural and fuzzy systems has led to the design of hardware implementations of different types of algorithms. In this paper we explore type-2 fuzzy logic systems acceleration, which can be applied to fuzzy logic control methods, signal processing, etc. Due to the three dimensional membership functions in the input of the system, different algorithms for the output processing stage have been developed. In order to have a fast response in type-2 fuzzy logic systems, in this paper we explore the Karnik-Mendel algorithms (KM), which are used to calculate the centroid at the output processing stage of the interval type-2 fuzzy system, through the application of iterative procedures. Because of the computation complexity of the iterative process, we propose a Hardware implementation of the KM algorithm using a High Level Synthesis tool, making possible to explore different types of implementation in order to obtain a significant reduction in computation time, and a reduction in hardware resources.
Semisupervised learning with adversarial training among joint distributions

Joint distribution matching based on generative adversarial nets (GANs) is an effective method to alleviate the insufficient diversity of labeled samples in semisupervised learning. In fact, in addition to the existing samples, generated samples and corresponding predictive labels must be taken into account to further increase the diversity of labeled samples and the controllability of generated samples. However, current works have not considered this. Therefore, a semisupervised learning model with adversarial training among joint distributions is proposed. The model consists of a generator, a classifier, and three discriminators incorporated with four joint distributions of samples and labels. The theoretical research indicates that, when the model reaches equilibrium, the classifier happens to be the inference network of the generator. Hence, the controllability of the generator and the generalization ability of the classifier are mutually improved. In semisupervised classification experiments, our model achieved state-of-the-art error rates of 0.59%, 16.45%, and 4.86% on MNIST, CIFAR10, and SVHN datasets, respectively. When only 20 labels are available on MNIST dataset, the error rate dropped from the current best of 4% to 1.09%, which indicates that the model is extremely robust to the number of labels. Meanwhile, the model also shows competitiveness in semisupervised generation.
Training Set Expansion Using Word Embeddings for Korean Medical Information Extraction

Entity recognition is an essential part of a task-oriented dialogue system and is considered as a sequence labeling task. However, constructing a training set in a new domain is extremely expensive and time-consuming. In this work, we propose a simple framework to exploit neural word embeddings in a semi-supervised manner to annotate medical named entities in Korean. The target domain is the automatic medical diagnosis, where disease name, symptom, and body part are defined as the entity types. Different aspects of the word embeddings such as embedding dimension, window size, models are examined to investigate their effects on the final performance. An online medical QA data has been used for the experiments. With a limit number of pre-annotated words, our framework could successfully expand the training set.
Robustness Analysis on Natural Language Processing Based AI Q&amp;A Robots

Recently, the natural language processing (NLP) based intelligent question and answering (Q&A) robots have been used in a wide range of applications, such as smart assistant, smart customer service, government business. However, the robustness and security issues of these NLP based artificial intelligence (AI) Q&A robots have not been studied yet. In this paper, we analyze the robustness problems in current Q&A robots, which include four aspects: (1) semantic slot settings are incomplete; (2) sensitive words are not filtered efficiently and completely; (3) Q&A robots return the search results directly; (4) unsatisfactory matching algorithms and inappropriate matching threshold settings. Then, we design and implement two types of evaluation tests, bad language and user's typos, to evaluate the robustness of several state-of-the-art Q&A robots. Experiment results show that these common inputs (bad language and user's typos) can successfully make these Q&A robots malfunction, denial of service, or speaking dirty words. Besides, we also propose possible countermeasures to enhance the robustness of these Q&A robots. To the best of the authors' knowledge, this is the first work on analyzing the robustness and security problems of intelligent Q&A robots. This work can hopefully help provide guidelines to design robust and secure Q&A robots.
Elucidating the complexity of metallogenic elements based on multifractal detrending moving average analysis

Metallogenic element distributions generally present highly irregular spatial patterns and scale-dependent variations. In the present study, the multifractal detrending moving average (MFDMA) model is applied to describe the singularity spectrum of different metallogenic element distributions and study the heterogeneity of element concentrations in the Shangzhuang deposit, Jiaodong Province, China. The results show that six metallogenic elements exhibit multifractal scaling. The multifractal spectra of Au and Cu are left-deviated, while Ag, Pb, Zn, and Hg are close to symmetric. Hence, the shape of the multifractal spectrum may give directions to study the ore-forming potential. Notably, Au and Cu are unevenly distributed with stronger multifractal characteristics than other elements with significant difference, revealing that the distributions of orebodies are significantly inhomogeneous and that inflation and contraction phenomena are prominent. In contrast, Ag is a weak multifractal and nearly a single fractal, suggesting barely mineralized zones. When the sources of multifractality are quantified by two factors, long-range correlations, and broad fat-tail distributions, we find that the multifractal structure of Ag is because of the fat-tail probability density function, and the multifractal structure of the other elements is due to both factors.
Thinking the Incorporation of LOD in Semantic Cubes as a Strategic Decision

With the advent of Linked Open Data (LOD) initiatives, organizations have seen the opportunity of augmenting their internal data cube systems with these external data. While IT actors manage technical issues of internal and external sources, a new actor has emerged "the Chief Data Officer" (CDO), the role of which is to align and prioritize data activities with key organizational priorities and goals. Existing literature managing the incorporation of LOD in internal Data cubes mainly focus on technical aspects of the LOD source and ignores the CDO role in this strategy. In this paper, we claim that technical actions should be conducted by the managerial level, which is reflected through the goals of the organization data cube and their related Key Performance Indicators (KPIs). For doing this, we first propose a metamodel aligning the three models: the data-flow model, the goal model and the KPI model. Then, we propose a process for specifying KPIs into Sparql language, the standard language for querying LOD sources. Experiments are conducted to measure the impact of the decision of integrating external LOD sources at KPI/goal level and on the technical data level. A case tool dedicated to the CDO is implemented to conduct the proposed approach.
General remainder theorem and factor theorem for polynomials over non-commutative coefficient rings

We propose some generalizations of the classical Division Algorithm for polynomials over coefficient rings (possibly non-commutative). These results provide a generalization of the Remainder Theorem that allows calculating the remainder without using the long division method, even if the divisor has degree greater than one. As a consequence we obtain an extension of the classical Factor Theorem that provides a general divisibility criterion for polynomials. Finally, we will refer to some applications of these results for evaluating and dividing on skew polynomial rings. The arguments can be used in basic algebra courses and are suitable for building classroom/homework activities.
Cost-Aware Fine-Grained Recognition for IoTs Based on Sequential Fixations

We consider the problem of fine-grained classification on an edge camera device that has limited power. The edge device must sparingly interact with the cloud to minimize communication bits to conserve power, and the cloud upon receiving the edge inputs returns a classification label. To deal with fine-grained classification, we adopt the perspective of sequential fixation with a foveated field-of-view to model cloud-edge interactions. We propose a novel deep reinforcement learning-based foveation model, DRIFT, that sequentially generates and recognizes mixed-acuity images. Training of DRIFT requires only image-level category labels and encourages fixations to contain task-relevant information, while maintaining data efficiency. Specifically, we train a foveation actor network with a novel Deep Deterministic Policy Gradient by Conditioned Critic and Coaching(DDPGC3) algorithm. In addition, we propose to shape the reward to provide informative feedback after each fixation to better guide RL training. We demonstrate the effectiveness of DRIFT on this task by evaluating on five fine-grained classification benchmark datasets, and show that the proposed approach achieves state-of-the-art performance with over 3X reduction in transmitted pixels.
Encryption in MicroSD Card Reader or USB Data Storage Devices

A portable memory stick or USB data storage devices/microsd card readers have been widely popular these days and so is the urge in prevention of exposure of the confidentiality of those information stored in these peripheral devices. Due to easy portability and accessibility, these USB oriented data storage devices have been quite a popular these days; not only is it very much flexible and feasible in commercial purposes but also for personalized uses. Here in this paper, an unique idea have been proposed for securing microSD card readers/USB data storage devices with the help of symmetric cryptographic data encryption techniques, an unique user authentication technique is introduced along with a self-destructing protocol for the USB device.
An ECC with Probable Secure and Efficient Approach on Noncommutative Cryptography

An Elliptic Curve Cryptography (ECC) is used on the Noncommutative Cryptographic (NCC) principles. The security and strengths of the manuscript are resilient on these two cryptographic assumptions. The claims on the Noncommutative cryptographic scheme on monomials generated elements is considered be based on hidden subgroup or subfield problems that strengthen this manuscript, where original assumptions are hidden and its equivalents semiring takes part in the computation process. In relation to the same, the research gap is well designed on Dihedral orders of 6 and 8, but our contributions are in security- and length-based attacks enhancement over Dihedral order 12, reported in work done. We modeled the said strategies and represent the ideal security concerns for applications.
A Test System for Vehicular Voice Cloud Service

Some test systems for voice cloud services have been developed in recent years. However, the automobile manufacturers, communications equipment merchants and network operator still lacks methods and tools to evaluate the vehicular voice cloud services from the perspective of the end user experience. Considering the user behavior and user experience, a light weight vehicular voice cloud evaluation system is designed in this paper. The system is able to send voice information to voice cloud server according to user habit, and record the user experience indicators, such as accurate, voice quality, service delay, server computation capacity, and so on. The study shows that the vehicle voice cloud evaluation system can avoid complex communication and language processing, evaluate the performance of the service from view of end user.
Determining Minimum Hash Width for Hash Chains

Cryptographic hash functions are used in authentication, and repeated application in hash chains is used in communication protocols. In embedded devices, the width of hash values and the associated effort to evaluate the hash function is crucial, and hence the hash values should be as short as possible but should still be sufficient to guarantee the required level of security. We present a new proof for a known result by Flajolet and Odlyzko (Euro-crypt 1989), using only elementary combinatoric and probabilistic arguments. Using this result, we derive a bound on the expected number of hash values still reachable after a given number of steps in the hash chain, so that given any two of the three parameters hash chain length, width of the hash value, and security level, the remaining parameter can be computed. Furthermore, we illustrate how to "refresh" a hash chain to increase the number of reachable hash values if the initial seed is long enough. Based on this, we present a scheme that allows reduced width of hash values, and thus reduced energy consumption in the device, for a hash chain of similar length and similar security level. We illustrate our findings with experiments.
ObliComm: Towards Building an Efficient Oblivious Communication System

Anonymous Communication (AC) hides traffic patterns and protects message metadata from being leaked during message transmission. Many practical AC systems have been proposed aiming to reduce communication latency and support a large number of users. However, how to design AC systems which possess strong security property and at the same time achieve optimal performance (i.e., the lowest latency or highest horizontal scalability) has been a challenging problem. In this paper, we propose an ObliComm framework, which consists of six modular AC subroutines. We also present a strong security definition for AC, named oblivious communication, encompassing confidentiality, unobservability, and a new requirement sending-and-receiving operation hiding. The AC subroutines in ObliComm allow for modular construction of oblivious communication systems in different network topologies. All constructed systems satisfy oblivious communication definition and can be provably secure in the universal composability (UC) framework. Additionally, we model the relationship between the network topology and communication measurements by queuing theory, which enables the system's efficiency can be optimized and estimated by quantitative analysis and calculation. Through theoretical analyses and empirical experiments, we demonstrate the efficiency of our scheme and soundness of the queuing model.