Reviewerly/exHarmony huggingface dataset:
Genetic intuitionistic weighted fuzzy k-modes algorithm for categorical data

Data clustering with categorical attributes has been widely used in many real-world applications. Most of the existing clustering algorithms proposed for the categorical data face two major drawbacks of termination at a local optimal solution and considering all attributes equally. Thus, this study proposes a novel clustering method, named genetic intuitionistic weighted fuzzy k-modes (GIWFKM) algorithm, based on the conventional fuzzy k-modes and genetic algorithm (GA). The proposed algorithm firstly introduces the intuitionistic weighted fuzzy k-modes (IWFKM) algorithm which employs the intuitionistic fuzzy set in the clustering process and the new similarity measure for categorical data based on frequency probability-based distance metric. Then, the GIWFKM algorithm, which integrates the IWFKM algorithm and GA, is proposed to employ the global optimal solution. Moreover, the GIWFKM algorithm performs the unsupervised feature selection based on the correlation coefficient to remove some redundant features which can both improve the clustering performance and reduce the computational time. To evaluate the clustering result, a series of experiments in different categorical datasets are conducted to compare the performance of the proposed algorithms with that of other benchmark algorithms including fuzzy k-modes, weighted fuzzy k-modes, genetic fuzzy k-modes, space structure-based clustering, and many-objective fuzzy centroids clustering algorithms. The experimental results conducted on the datasets collected from UCI machine learning repository exhibit that the GIWFKM algorithm outperforms the other benchmark algorithms in terms of Adjusted Rank Index (ARI) and clustering accuracy (CA).
Meta-cognitive Recurrent Recursive Kernel OS-ELM for concept drift handling

In this paper, a Meta-cognitive Recurrent Recursive Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (meta-RRKOS-ELM-DDM) is proposed. It combines the strengths of Recurrent Kernel Online Sequential Extreme Learning Machine with a new modified Drift Detector Mechanism (DDM) and Approximate Linear Dependency Kernel Filter (ALD) in solving concept drift problems and reducing complex computations in the learning. The recursive kernel method successfully replaces the normal kernel method in Recurrent Kernel Online Sequential Extreme Learning Machine with DDM (RKOS-ELM-DDM) and generates a fixed reservoir with optimized information in enhancing the forecasting performance. Meta-cognitive learning strategy decides when the incoming data needs to be updated, retrained, or discarded during learning and automatically finding ALD threshold that reduces the learning time of prediction model. In the experiment, six synthetic and three real-world time series datasets are used to evaluate the ability of recursive kernel method, the performance of concept drift detectors, and meta-cognitive learning strategy in time series prediction. Experimental results indicate the meta-RRKOS-ELM with DDM has superior prediction ability in the different predicting horizons as compared with other algorithms.
Peer Prediction-Based Trustworthiness Evaluation and Trustworthy Service Rating in Social Networks

With the development of online applications based on social networks, many different approaches have emerged to evaluate the service that these applications provide. Reports made by end users regarding the consumer's experience or opinion are commonly used to rate the quality of different online services. Therefore, ensuring the authenticity of the users' reports, and the detection of malicious users' dishonest reports, have both become important issues to achieve accuracy in the rating of such services. In this paper, we propose and evaluate a private-prior peer prediction-based trustworthy service rating system, which requires users to report their prior and posterior beliefs regarding whether their peers will report a high-quality opinion of the service. The reports are made to a data processing center which evaluates the users' trustworthiness by applying a strictly proper scoring rule, and removes reports received from users whose trustworthiness rating is low. This peer prediction method is compatible with incentives to motivate users to report honestly. In addition, an unreliability index is proposed to identify malicious users, and malfunctioning or unreliable users who have a high error rate in making judgments about quality. Thus, reports with high unreliability values will also be excluded from the service rating system. By combining trustworthiness and unreliability, malicious users face the dilemma that they cannot receive both a high trustworthiness and low unreliability rating simultaneously when their reports are false. Simulation results indicate that the proposed peer prediction-based trustworthy service rating can identify malicious and unreliable behaviors effectively and motivate users to report truthfully, and that a relatively high service rating accuracy is achieved by the proposed system.
Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm

Functional Data Analysis (FDA) is devoted to the study of data which are functions. Support Vector Machine (SVM) is a benchmark tool for classification, in particular, of functional data. SVM is frequently used with a kernel (e.g.: Gaussian) which involves a scalar bandwidth parameter. In this paper, we propose to use kernels with functional bandwidths. In this way, accuracy may be improved, and the time intervals critical for classification are identified. Tuning the functional parameters of the new kernel is a challenging task expressed as a continuous optimization problem, solved by means of a heuristic. Our experiments with benchmark data sets show the advantages of using functional parameters and the effectiveness of our approach.
Self-Dual Near MDS Codes from Elliptic Curves

In recent years, self-dual MDS codes have attracted a lot of attention due to theoretical interest and practical importance. Similar to self-dual MDS codes, self-dual near MDS (NMDS for short) codes have nice structures as well. From both theoretical and practical points of view, it is natural to study self-dual NMDS codes. Although there has been lots of work on NMDS codes in literature, little is known for self-dual NMDS codes. It seems more challenging to construct self-dual NMDS codes than self-dual MDS codes. The only work on construction of self-dual NMDS codes shows existence of q-ary self-dual NMDS codes of length q - 1 for odd prime power q or length up to 16 for some small primes q with q ≤ 197. In this paper, we make use of properties of elliptic curves to construct selfdual NMDS codes. It turns out that, as long as 2|q and n is even with 4 ≤ n ≤ q + 12√qJ - 2, one can construct a self-dual NMDS code of length n over Fq. Furthermore, for odd prime power q, there exists a self-dual NMDS code of length n over Fq if q ≥ 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+3</sup> x (n + 3) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Handling interpretability issues in ANFIS using rule base simplification and constrained learning

Adaptive neuro-fuzzy inference system (ANFIS) is a well-known neuro-fuzzy model for approximating highly complex non-linear systems. ANFIS uses precise fuzzy modelling concept that aims at accuracy of a fuzzy model being designed than on interpretability. But interpretability of a fuzzy system is an equally important aspect of fuzzy modelling as accuracy. So far the research based on ANFIS has been mostly application based due to which the various issues related to the interpretability of ANFIS have not been dealt with. The rule base of ANFIS is typically obtained using data driven clustering algorithms. But this process introduces redundancy in the system in terms of similar fuzzy sets and redundant fuzzy rules which unnecessarily increases system complexity. This in turn reduces both the interpretability as well as generalization capability of ANFIS. Additionally in case of ANFIS, unconstrained gradient descent based learning algorithms are used to fine-tune the membership function parameters which usually result in a rule base with inconsistent, excessively overlapping and indistinguishable membership functions for input variables and thus the interpretability of the final optimized system is not guaranteed. This paper is based on addressing the issue of rule base redundancy in ANFIS to reduce complexity and enforcing constraints during learning phase to ensure interpretability of the final optimized system. Rule base redundancy is removed using similarity analysis based rule base simplification in which similar fuzzy sets are merged and subsequently resulting fuzzy rules with equal premises are combined. Hybrid learning technique which is an efficient parameter tuning method for ANFIS is constrained to prevent inconsistency, excessive overlapping and inclusion of membership functions so that the final fuzzy partitions of inputs stay interpretable. The empirical analysis of the impact of rule base simplification and constrained learning on ANFIS is done by application to two well-known benchmark problems and a real world stock price prediction problem. The introduction of rule base simplification and constrained learning in ANFIS modeling has shown better results in terms of obtaining a desired accuracy-interpretability tradeoff than conventional ANFIS.
Blind image quality assessment with semantic information

Abstract No-reference (NR) image quality assessment (IQA) aims to evaluate the quality of an image without reference image, which is greatly desired in the automatic visual signal processing system. Distortions degrade the visual contents and affect the semantics acquisition during the process of human perception. Although the existing methods evaluate the quality of images based on the structure, texture, or statistical characteristics, and deliver high quality prediction accuracy, they do not take the spatial semantics into account. From the perspective of human perception, distortions decrease the structural semantics that represent the structural information, and disturb the spatial semantics that describe the contents of images. Therefore, we attempt to measure the image quality by its degradation of semantics in an image. To extract the semantics of an image, a semantic network is proposed. The network contains convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) that correspond to structural semantics and spatial semantics, respectively. CNN can be regarded as a coarse imitation of human visual mechanism to obtain the structural information, and LSTM can express the contents of an image. Then, by measuring the degradations of different semantics on images, a novel NR IQA is introduced. The proposed approach is evaluated on the databases of LIVE, CSIQ, TID2013, and LIVE multiply distorted database as well as LIVE in the wild image quality challenge database, and the results show superior performance to other state-of-the-art NR IQA methods. Furthermore, we explore the generalization capability of the proposed approach, and the experimental results indicate the proposed approach has a high robustness.
Influence of the Method of Colors on Levels of Electromagnetic Emissions From Video Standards

Every electronic device that processes information becomes a source of spurious emissions. These emissions can be used in the process of electromagnetic eavesdropping, resulting in the possibility of obtaining information in a non-invasive manner. The graphic tracks of information systems are particularly sensitive sources of such emissions. This paper analyzes the levels of electromagnetic interference from two graphic standards: video grafics array (VGA) and digital video interface (DVI). The DVI standard, due to the assumptions made by the Digital Display Working Group, was to replace the analog VGA standard and increase the electromagnetic safety of transmitted graphic data. However, the reality and ongoing research did not confirm this. The DVI digital standard, as a source of spurious emissions, must be protected in the same way as the VGA standard. This paper analyzes the impact of the color method on levels of electromagnetic interference for the two considered graphic standards. The color method (the right color for the background and text) is one of the solutions that can counteract the process of electromagnetic eavesdropping. The proper selection of colors can limit the level of electromagnetic interference but only in the case of the VGA standard (change in the amplitude of the voltage of the electrical signal). The DVI standard, due to a different way of color coding (the value of voltage amplitude of the electrical signal is constant; the shape of the signal changes), does not show any relation between the choice of background colors and text or the level of generated electromagnetic interference. Very often, this interference has the characteristics of information being processed and becomes valuable in the process of electromagnetic infiltration. That is why, the analog VGA standard is still widely used in information systems, where the most important aspect is the protection of information against electromagnetic penetration.
Hierarchical Integration of Rich Features for Video-Based Person Re-Identification

Person re-identification (ReID) aims to associate the identity of pedestrians captured by cameras across non-overlapped areas. Video-based ReID plays an important role in intelligent video surveillance systems and has attracted growing attention in recent years. In this paper, we propose an end-to-end video-based ReID framework based on the convolutional neural network (CNN) for efficient spatio-temporal modeling and enhanced similarity measuring. Specifically, we build our descriptor of sequences by basic mathematical calculations on the semantic mid-level image features, which avoids the time consuming computations and the loss of spatial correlations. We further hierarchically extract image features from multiple intermediate CNN stages to build multi-level sequence descriptors. For a descriptor at one stage, we design an effective auxiliary pairwise loss which is jointly optimized with a triplet loss. To integrate hierarchical representation, we propose an intuitive yet effective summation-based similarity integration scheme to match identities more accurately. Furthermore, we extend our framework by a multi-model ensemble strategy, which effectively assembles three popular CNN models to represent walking sequences more comprehensively and improve the performance. Extensive experiments on three video-based ReID datasets show that the proposed framework outperforms the state-of-the-art methods.
Modeling and Forecasting of Time-Aware Dynamic QoS Attributes for Cloud Services

Currently, statistical time-series methods have primarily been employed to predict time-aware dynamic quality of service (QoS) attributes for Web services. In this paper, we propose the application of genetic programming (GP) for such predictions. Our experimental results indicate that the GP-based approach is more accurate than the other approaches presented for comparison. However, for the efficient management of such attributes for cloud services, including their modeling and forecasting, the current research is insufficient because a set of research questions remains unanswered. In this paper, we first clearly define these research questions and then design and perform a set of empirical experiments to address the questions. Finally, the experimental results are exhaustively discussed to answer the studied research questions. The empirical study and analysis presented in this paper could be informative for the management (modeling and forecasting) of the time-aware dynamic QoS attributes of cloud services. For example, we verify that machine-learning approaches are generally superior to the widely used statistical time-series methods in terms of both modeling accuracy and forecasting accuracy. Furthermore, after considering a variety of situations and cases, the GP-based approach is still the best option for the studied problem. In addition, except for the technical approaches, this paper also exhaustively studies the influence of the properties of the cloud dynamic QoS attributes, including their size and time granularity.
Discovering spatio-temporal action tubes

In this paper, we address the challenging problem of spatial and temporal action detection in videos. We first develop an effective approach to localize frame-level action regions through integrating static and kinematic information by the early- and late-fusion detection scheme. With the intention of exploring important temporal connections among the detected action regions, we propose a tracking-by-point-matching algorithm to stitch the discrete action regions into a continuous spatio-temporal action tube. Recurrent 3D convolutional neural network is used to predict action categories and determine temporal boundaries of the generated tubes. We then introduce an action footprint map to refine the candidate tubes based on the action-specific spatial characteristics preserved in the convolutional layers of R3DCNN. In the extensive experiments, our method achieves superior detection results on the three public benchmark datasets: UCFSports, J-HMDB and UCF101.
A Novel and Safe Two-Stage Screening Method for Support Vector Machine

To make support vector machine (SVM) applicable to large-scale data sets, safe screening rules are developed recently. The main idea is to reduce the scale of SVM by safely discarding the redundant training samples. Among existing safe screening rules, the dual screening method with variational inequalities (DVI) and the dynamic screening rule (DSR) based on duality gap are two representative strategies. DVI is efficient, while its safety may be affected by inaccurate solving algorithms. DSR is guaranteed to be safe; however, accurate feasible solutions are required for good efficiency. Based on the above-mentioned studies, in this paper, a two-stage screening (TSS) rule, which fully exploits the advantages of the above-mentioned two approaches and improves their shortcomings, is proposed. First, DVI is applied prior to training for sample screening. It reduces the scale of SVM and, meanwhile, produces a better initial feasible solution for DSR. Then, by embedding DSR into the solving algorithm, the solver becomes more accurate, and the safety of DVI can be strengthened. In the end, for safety guarantee, a postchecking step is added to search the wrongly identified samples and retrain them. To theoretically analyze the safety of DVI, an upper bound of the deviation in DVI is estimated, and a Safe-DVI is given based on it. To ensure the efficiency of TSS, the superiority of DVI over initial DSR is verified. In addition, kernel version of TSS is also given for nonlinear SVM. Numerical experiments on synthetic data sets and 12 real-world data sets verify the efficiency and safety of this TSS.
Temporally local recurrent radial basis function network for modeling and adaptive control of nonlinear systems

In this paper, a novel temporally local recurrent radial basis function network for modeling and adaptive control of nonlinear systems is proposed. The proposed structure consists of recurrent hidden neurons having weighted self-feedback loops and a weighted linear feed-through from the input layer directly to the output layer neuron(s). The dynamic back-propagation algorithm is developed and used for updating the parameters of the proposed structure. To improve the performance of learning algorithm, discrete Lyapunov stability method is used to develop an adaptive learning rate scheme. This scheme ensures the faster convergence of the parameters and maintains the stability of the system. A total of 5 complex nonlinear systems are used to test and compare the performance of the proposed network with other neural network structures. The disturbance rejection tests are also carried out to check whether the proposed scheme is able to handle the external disturbance/noise signals effects or not. The obtained results show the efficacy of the proposed method.
IR Feature Embedded BOF Indexing Method for Near-Duplicate Video Retrieval

Due to the explosive increase in online videos, near-duplicate video retrieval (NDVR) has attracted much researcher attention. NDVR has very wide applications, such as copyright protection, online video monitoring, and automatic video tagging. Local features serve as elementary building blocks in many NDVR algorithms, and most of them exploit the local volume information using a bag of features (BOF) representation. However, such representation ignores potentially valuable information about the global distribution of interest points. Moreover, the discriminative power of the local descriptors is significantly reduced by the quantizer in BOF. Our motivation is that if we use the global features to classify the same or similar keyframes into the same class, it will be very useful in improving the performance of NDVR. In this paper, we present an improved radon transform (IR) feature which captures the detailed global geometrical distribution of interest points. It is calculated by using the 2D discrete Radon transform, and then applying a principal component analysis. Such IR feature is not only invariant to the geometry transformations but also robust to the noises. In addition, we propose a fusion strategy to combine the BOF representation with the global IR feature for further improving the recognition accuracy. Convincing experimental results on several publicly available datasets demonstrate that our proposed approach outperforms the state-of-the-art approaches in NDVR.
Syntax-Directed Hybrid Attention Network for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis is a fine-grained task in sentiment analysis that aims at detecting sentiment polarity towards a specific target in a sentence.Previous studies focus on using global attention mechanism that attends to all words in the context to model the interaction between target and sentence.However, global attention suffers from assigning high-attention score to irrelevant sentiment words in the cases where sentence contains noisy words or multiple targets.To address this problem, we propose a novel syntax-directed hybrid attention network (SHAN).In SHAN, a global attention is employed to capture coarse information about the target, and a syntax-directed local attention is used to take a look at words syntactically close to the target.An information gate is then utilized to synthesize the information from local and global attention results and adaptively generate a less-noisy and more sentiment-oriented representation.The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of the proposed method.
Control laws to avoid collision with three dimensional obstacles using sensors

Collision avoidance in 3D environments is important to the problem of planning safe trajectories for an autonomous vehicle. Existing literature on collision avoidance assumed that obstacle shapes are known a priori and modeled obstacles as spheres or bounding boxes. However, in 3D environments, an obstacle shape is unknown to the autonomous vehicle, and the vehicle detects an obstacle boundary using 3D sensors, such as 3D sonar. In this paper, we introduce control laws for collision avoidance, considering scenarios where a vehicle detects arbitrarily shaped and non-convex obstacles using sensors. Moreover, our control laws are designed considering motion constraints, such as the maximum turn rate and the maximum speed rate of the vehicle. The effectiveness of our control laws is verified using MATLAB simulations.