Reviewerly/exHarmony huggingface dataset:
Theoretical and Experimental Results for Planning with Learned Binarized Neural Network Transition Models

We study planning problems where the transition function is described by a learned binarized neural network (BNN). Theoretically, we show that feasible planning with a learned BNN model is NP-complete, and present two new constraint programming models of this task as a mathematical optimization problem. Experimentally, we run solvers for constraint programming, weighted partial maximum satisfiability, 0–1 integer programming, and pseudo-Boolean optimization, and observe that the pseudo-Boolean solver outperforms previous approaches by one to two orders of magnitude. We also investigate symmetry handling for planning problems with learned BNNs over long horizons. While the results here are less clear-cut, we see that exploiting symmetries can sometimes reduce the running time of the pseudo-Boolean solver by up to three orders of magnitude.
Mining Graph-based Features in Multi-objective Framework for Microblog Summarization

Nowadays, micro-blogging sites are getting popular due to the involvement of a large number of users. In the case of natural disasters, a significant amount of relevant information (giving crucial information) are present amongst the tweets. Therefore, there is a need to develop a system that summarizes relevant tweets by extracting informative tweets. In the current paper, we have proposed an unsupervised approach for summarizing the relevant tweets namely, MOOTweetSumm+, which automatically selects the informative tweets. Several tweet-scoring measures: (a) anti-redundancy measuring the dissimilarity between tweets; (b) similarity with outputs provided by LexRank (a graph-based method measuring tweet importance based on the concept of eigen-vector centrality in a graph); (c) BM25 based ranking function; (d) tf-idf based ranking function; (e) length of the tweet; (f) re-tweet count, are simultaneously optimized utilizing a binary differential evolution algorithm. Further, two different versions of the LexRank, utilizing syntactic and semantic similarity, have also been explored. For evaluation, four different disaster-event related datasets are used, and performance is measured in terms of ROUGE scores. An ablation study is also performed to determine which set of measures is best suited for different datasets. From the results obtained, it is clearly evident that our approach improves by 13.2% and 5.8% in terms of ROUGE-2 and ROUGE-L scores, over the existing approaches, respectively.
Filter Pruning Based on Connection Sensitivity

For the goal of reducing the remarkable redundancy in deep convolutional neural networks (CNNs), we propose an efficient framework to compress and accelerate CNN models. This work focus on pruning at filter level, mainly removing those less important filters. Firstly, we measure the importance of the filter by introducing a saliency criterion based on its corresponding connection sensitivity. In addition, we apply an algorithm, which transform a vanilla CNN module, to provide a quantitative ranking. Next, we prune the redundancy by discarding unimportant filters. Finally, we fine-tune the network to improve its accuracy. We verify the effectiveness of our method with VGGNet and ResNet on multiple datasets, such as CIFAR-10 and ImageNet ILSVRC-12. For instance, we achieve more than 50% FLOPs reduction on ResNet-56 with virtually the same accuracy as the reference network.
Harnessing Particle Swarm optimization Through Relativistic Velocity

In the last century, Albert Einstein's perceptions of the world afforded a revolution in the understanding of the universe. In his theory of general relativity, he describes the space-time continuum, a concept capable of explaining several phenomena, ranging from gravity to black holes and supernovas. Further, it also provides a set of formulations to generalize classical physics concepts to accommodate the relativistic notions. Meanwhile, several mathematicians have been working on optimization tools aiming to solve complex problems associated with a large number of variables. Nowadays, despite the computational power, many daily tasks still pose a challenge and are becoming more prohibitives, mostly due to the massive amount of data to be processed. Therefore, efficient optimization techniques are more desirable than ever. In this context, metaheuristic optimization has arisen, i.e., stochastic nature-inspired methods capable of finding sub-optimal solutions for complex problems with a reasonable computational effort. However, such approaches still suffer from some drawbacks related to low convergence and getting stuck on local optima, among others. Therefore, in this paper, we introduce relativistic concepts into the well-known meta-heuristic optimization technique Particle Swarm optimization (PSO). The experimental results evince the robustness of the proposed approach compared to the standard PSO as well as three other variations for five benchmarking functions.
A fuzzy and spline based dynamic histogram equalization for contrast enhancement of brain images

Abstract In general, medical images acquired at insufficient lighting conditions are suffering from low contrast issues which are inadequate for image analysis steps. One standard solution to improve the image contrast is to change its intensity distribution with the help of an image histogram. The histogram‐based contrast enhancement methods treat the images as regions rather than objects which would be more useful for applications like brain image enhancement. In this paper, a Fuzzy and Spline based Histogram Equalization (FSDHE) is proposed to perform contrast enhancement with medical images. The proposed FSDHE method partitions the image into connected components, and the type of components are identified with a fuzzy membership function. The dynamic histogram equalization is applied to each component individually. The equalized sub‐histograms are combined to drive the global histogram, which is inconsistent as dynamic histogram equalization treated the intensity range for each connected component differently. Hence, a spline‐based histogram smoothing is proposed here in this research work. The equalized intensity mapping is received as control points for the polynomial curve, and a smooth intensity transformation is interpolated as a spline curve. The proposed FSDHE model is analyzed with MRI‐brain image dataset of 3064 images, which consists of both benign and malignant cases. The contrast enhancement performance of the proposed FSDHE method is quantified by various measures like Absolute Mean Brightness Error (AMBE), Peak Signal to Noise Ratio (PSNR), Contrast (C), Weber Contrast (WC), Entropy, Hausdorff Distance (HD) and Texture Preservation (TP) measures. The performance of the FSDHE method is compared against with other histogram equalization methods, and the results indicate that the FSDHE method achieves better quality measures of 3.1401, 30.5499, 21.5486, 0.7779, 4.0252, 0.2777, and 0.7836 for the seven‐performance metrics.
Weakly supervised video object segmentation initialized with referring expression

With the aid of one manually annotated frame, One-Shot Video Object Segmentation (OSVOS) uses a CNN architecture to tackle the problem of semi-supervised video object segmentation (VOS). However, annotating a pixel-level segmentation mask is expensive and time-consuming. To alleviate the problem, we explore a language interactive way of initializing semi-supervised VOS and run the semi-supervised methods into a weakly supervised mode. Our contributions are two folds: (i) we propose a variant of OSVOS initialized with referring expressions (REVOS), which locates a target object by maximizing the matching score between all the candidates and the referring expression; (ii) segmentation performance of semi-supervised VOS methods varies dramatically when selecting different frames for annotation. We present a strategy of the best annotation frame selection by using image similarity measurement. Meanwhile, we first to propose a multiple frame annotation selection strategy for initialization of semi-supervised VOS with more than one annotated frames. Finally we evaluate our method on DAVIS-2016 dataset, and experimental results show that REVOS achieves similar performance (79.94% measured by average IoU) compared with OSVOS (80.1%). Although current REVOS implementation is specific to the method of one-shot video object segmentation, it can be more widely applicable to other semi-supervised VOS methods.
An Improved Defect Detection Method for Substation Equipment

Substation equipment inspection is the fundamental of equipment management and security. However, the substation equipment inspection is facing the prominent contradiction between the surge in workload and the relative shortage of personnel. Therefore, this paper proposes an improved image-based method for detecting defects in substation equipment. This method is based on Faster R-CNN algorithm and proposes a series of improvement strategies according to the actual application scenarios, including inter-class sampling, center-guided NMS, and category-adaptive threshold. Experimental results demonstrate that this method can solve the problems of uneven sample distribution, redundant bounding-boxes, missed or false detections, etc., and improve the accuracy and practical effect of defect detection. In addition, this paper tests on a lightweight network framework, which proves the engineering applicability of this method.
Integrating Regulatory Technology (RegTech) into the digital transformation of a bank Treasury

The volume and complexity of financial regulations have increased significantly since the 2008 financial crisis. This has put increasing pressure on banks to monitor and report a range of intricate exposures to comply with new prudential requirements. Regulatory Technology (RegTech) is an emerging technology trend leveraging information technology and digital innovations that can greatly assist with a bank’s regulatory management process. A consideration is that these technologies can sometimes be expensive and inefficient if deployed on an ad hoc and stand-alone basis. An option is to incorporate RegTech into the digital transformation strategy of a management function such as Treasury. Integrated adoption would mean the digital platform can be deployed to support both strategic management activities and enhanced regulatory processes within Treasury, thereby ensuring commercial and prudential objectives are aligned. RegTech can provide an invaluable tool, in a business-as-usual environment, as well as in real-life stress events, such as the recent coronavirus outbreak. This paper explores the potential of RegTech and the merit of incorporating it into a Smart Treasury department.
On degeneracy problem of NFSRs via semi-tensor product

The degeneracy problem of nonlinear feedback shift registers (NFSRs) is investigated in this paper. A new method for finding sub-families included in a family of NFSR sequences is proposed. First, the algebraic form of Fibonacci NFSR is proposed by viewing the NFSR as a Boolean network. Some properties of the algebraic expression are provided. Then, the observability of NFSRs is defined and the observability matrix is constructed, based on some explicit forms two algorithms for obtaining the observability matrix are obtained. A way to find sub-families of the given NFSR is derived and some sufficient and necessary conditions are obtained. Finally, an example is presented to depict the theoretical results.
Systematic reliability analysis of the Dynamic Positioning (DP) control system for a deepwater drilling rig

The system-level reliability analysis of DPS-3 Dynamic Positioning (DP) control system is proposed, which include both a qualitative failure modes and effects analysis (FMEA) and a quantitative analysis. The FMEA results show that the single point failure of main components of DP control system will not have any global effect on automatic positioning, however, the components involved with high Risk Priority Number (RPNs) are advised to include in the quantitative analysis. Markov model method is used to quantitatively analyze the reliability of DPS-3 control system. To simplify the process of state transition, paralleled Markov models are built to model the main and back-up control systems respectively, the reliability of the whole DP control system would be obtained with the failure rates of main and back-up subsystems. The results show that the proposed method can treat the complex architecture of the control system in an efficient manner.
Maximum Value Matters: Finding Hot Topics in Scholarly Fields

Finding hot topics in scholarly fields can help researchers to keep up with the latest concepts, trends, and inventions in their field of interest. Due to the rarity of complete large-scale scholarly data, earlier studies target this problem based on manual topic extraction from a limited number of domains, with their focus solely on a single feature such as coauthorship, citation relations, and etc. Given the compromised effectiveness of such predictions, in this paper we use a real scholarly dataset from Microsoft Academic Graph [1] , which provides more than 12000 topics in the field of Computer Science (CS), including 1200 venues, 14.4 million authors, 30 million papers, and their citation relations over the period of 1950 till now. Aiming to find the topics that will trend in CS area, we innovatively formalize a hot topic prediction problem where, with joint consideration of both inter-, and intra-topical influence, 17 different scientific features are extracted for comprehensive description of topic status. By leveraging all those 17 features, we observe good accuracy of topic scale forecasting after 5, and 10 years with $R^2$ values of 0.9893, and 0.9646, respectively. Interestingly, our prediction suggests that the maximum value matters in finding hot topics in scholarly fields, primarily from three aspects: (1) the maximum value of each factor, such as authors' maximum h-index, and largest citation number, provides three times the amount of information than the average value in prediction; (2) the mutual influence between the most correlated topics serve as the most telling factor in long-term topic trend prediction, interpreting that those currently exhibiting the maximum growth rates will drive the correlated topics to be hot in the future; (3) we predict in the next 5 years the top 100 fastest growing (maximum growth rate) topics that will potentially get the major attention in CS area. All our findings are further demonstrated through an online visualization system.
Self-Organizing Map for Multi-view Text Clustering

Text document clustering represents a key task in machine learning, which partitions a specific documents’ collection into clusters of related documents. To this end, a pre-processing step is carried to represent text in a structured form. However, text depicts several aspects, which a single representation cannot capture. Therefore, multi-view clustering present an efficient solution to exploit and integrate the information captured from different representations or views. However, the existing methods are limited to represent views using terms frequencies based representations which lead to losing valuable information and fails to capture the semantic aspect of text. To deal with these issues, we propose a new method for multi-view text clustering that exploits different representations of text. The proposed method explores the use of Self-Organizing Map to the problem of unsupervised clustering of texts by taking into account simultaneously several views, that are obtained from textual data. Experiments are performed to demonstrate the improvement of clustering results compared to the existing methods.
Multi-feature recognition of English text based on machine learning

At present, the recognition method based on character segmentation is not effective in recognizing English text, and the traditional methods are based on the structural features and statistical characteristics of strokes. In order to improve the recognition effect of in English text, from the perspective of machine learning, this study introduces multi-features to improve the lack of information caused by the small Chinese data set. Moreover, this study disassembles the character recognition problem into a text matching problem of question and answer, and the textual entailment problem of answer and standard answer and continues training on the data set of short text score. The final result has a certain improvement, which proves the usability of the mechanism designed in this paper. In order to study the performance of the model proposed in this paper, the model proposed in this paper and the neural network recognition model are compared in terms of recognition accuracy and recognition speed. The research results show that the algorithm proposed in this paper has a certain effect.
Pragmatics, humour and the internet

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Investigating the affective part of subjective well-being (SWB) by means of sentiment analysis

The use of multiple measures for the operationalization of subjective well-being (SWB) is highly recommended, as each approach brings exposure to different errors. To supplement the approaches proposed in the literature, the paper at hand outlines a complemental text mining technique – sentiment detection – which presents an efficient solution for big data analyses. Empirical findings from its application to 466 semi-structured life satisfaction (LS) interviews are contrasted with happiness and satisfaction ratings derived from the interviewees themselves as well as from external raters. In addition, detailed insights are presented into the use of emotive language in LS interviews and into relations between base emotions for the following affective features: anger, joy, sadness, disgust, surprise, trust, fear, and anticipation.
Chicken swarm optimization algorithm based on quantum behavior and its convergence analysis

Aiming at the defects of chicken swarm optimization algorithm, such as easy to fall into local optimal, premature convergence and slow convergence, a chicken swarm optimization algorithm based on quantum behavior is proposed in this paper. A quantized potential well model is established based on the individual information of chicken swarm. According to the existing individual extremum and global extremum obtained by the original updating formula, Monte Carlo random sampling is adopted to complete the updating of individual extremum, and the search is conducted at a parallel Angle near individual extremum and global extremum, which improves the local search performance of the algorithm. At the same time, the convergence of quantum-behavior chicken swarm optimization algorithm is discussed in this paper, and QCSO is proved to be a globally convergent optimization algorithm. The optimization capability of QCSO is tested by using basic test function, and the results show that the optimization performance of this algorithm is greatly improved compared with the original algorithm.
Scalable Hash From Triplet Loss Feature Aggregation For Video De-duplication

The producing, sharing and consuming life cycle of video content creates massive amount of duplicates in video segments due to variable bit rate representation and fragmentation in the playbacks. The inefficiency of this duplicates to storage and communication motivate researchers in both academia and industry to come up with computationally efficient video deduplication solutions for storage and CDN providers. Moreover, the increasing demands of high resolution and quality aggravate the status of heavy burden of cluster storage side and restricted bandwidth resources. Hence, video de-duplication in storage and transmission is becoming an important feature for video cloud storage and Content Delivery Network (CDN) service providers. Despite of the necessity of optimizing the multimedia data de-duplication approach, it is a challenging task because we should match as many as possible duplicated videos under not removing videos by mistake. The current video de-duplication schemes mostly relies on the URL based solution, which is not able to deal with non-cacheable content like video, which the same piece of content may have totally different URL identification and fragmentation and different quality representations further complicate the problem. In this paper, we propose a novel content based video segmentation identification scheme that is invariant to the underlying codec and operational bit rates, it computes robust features from a triplet loss deep learning network that captures the invariance of the same content under different coding tools and strategy, while a scalable hashing solution is developed based on Fisher Vector aggregation of the convolutional features from the Triplet loss network. Our simulation results demonstrate the great improvement in terms of large scale video repository de-duplication compared with state-of-the-art methods.
Pragmatic Evaluation of the Impact of Dimensionality Reduction in the Performance of Clustering Algorithms

With the huge volume of data available as input, modern-day statistical analysis leverages clustering techniques to limit the volume of data to be processed. These input data mainly sourced from social media channels and typically have high dimensions due to the diverse features it represents. This is normally referred to as the curse of dimensionality as it makes the clustering process highly computational intensive and less efficient. Dimensionality reduction techniques are proposed as a solution to address this issue. This paper covers an empirical analysis done on the impact of applying dimensionality reduction during the data transformation phase of the clustering process. We measured the impacts in terms of clustering quality and clustering performance for three most common clustering algorithms k-means clustering, clustering large applications (CLARA), and agglomerative hierarchical clustering (AGNES). The clustering quality is compared by using four internal evaluation criteria, namely Silhouette index, Dunn index, Calinski-Harabasz index, and Davies-Bouldin index, and average execution time is verified as a measure of clustering performance.
Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection

Pedestrian detection is an important task in many intelligent systems, particularly driver assistance systems. Recent studies on pedestrian detection in infrared (IR) imagery have employed data-driven approaches. However, two problems in deep learning-based detection are the implicit performance and time-consuming training. In this paper, a novel channel expansion technique based on feature fusion is proposed to enhance the IR imagery and accelerate the training process. Besides, a novel background suppression method is proposed to stimulate the attention principle of human vision and shrink the region of detection. A precise fusion algorithm is designed to combine the information from different visual saliency maps in order to reduce the effect of truncation and miss detection. Four different experiments are performed from various perspectives in order to gauge the efficiency of our approach. The experimental results show that the Mean Average Precisions (mAPs) of four different datasets have been increased by 5.22% on average. The results prove that background suppression and suitable feature expansion will accelerate the training process and enhance the performance of IR image-based deep learning models.