Reviewerly/exHarmony huggingface dataset:
Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers

This article describes an optimization methodology based on a model of competitiveness between different metaheuristic methods. The main contribution is a strategy to dynamically find the algorithm that obtains the best result based on the competitiveness of methods to solve a specific problem using different performance metrics depending on the problem. The algorithms used in the preliminary tests are: the firefly algorithm (FA), which is inspired by blinking fireflies; wind-driven optimization (WDO), which is inspired by the movement of the wind in the atmosphere, and in which the positions and velocities of the wind packages are updated; and finally, drone squadron optimization (DSO)—the inspiration for this method is new and interesting—based on artifacts, where drones have a command center that sends information to individual drones and updates their software to optimize the objective function. The proposed model helps discover the best method to solve a specific problem, and also reduces the time that it takes to search for methods before finding the one that obtains the most satisfactory results. The main idea is that with this competitiveness approach, methods are tested at the same time until the best one to solve the problem in question is found. As preliminary tests of the model, the optimization of the benchmark mathematical functions and membership functions of a fuzzy controller of an autonomous mobile robot was used.
An improved infrared image processing method based on adaptive threshold denoising

This paper combines the image adaptive threshold denoising algorithm and performs double threshold mapping processing to the infrared image, which effectively reduces the influence of these phenomena to the infrared image and improves the quality of the image. In this paper, the infrared image denoising technology is studied, and an infrared image denoising method based on the wavelet coefficient threshold processing is proposed. This method is based on the noise distribution characteristics of infrared images, the multiplicative noise in the infrared image is transformed into an additive noise, and the wavelet transform coefficient of the transformed infrared image is processed to denoise the image. On this basis, the advantages and disadvantages of the soft and hard threshold functions are deeply analyzed, and an adaptive threshold function with adjustable parameter is constructed. At the same time, in order to suppress the Gibbs visual distortion caused by the absence of translation invariance of the orthogonal wavelet transform, the two-input wavelet transform with translation invariance is introduced, and a double threshold mapping infrared image processing method based on the adaptive threshold denoising algorithm based on the two-input wavelet transform is formed. Simulation results show that the method proposed in this paper has a better suppression of noise, maintains the integrity of image details, and improves the image quality to a certain extent.
Fraud Detection in Dynamic Interaction Network

Fraud detection from massive user behaviors is often regarded as trying to find a needle in a haystack. In this paper, we suggest abnormal behavioral patterns can be better revealed if both sequential and interaction behaviors of users can be modeled simultaneously, which however has rarely been addressed in prior work. Along this line, we propose a COllective Sequence and INteraction (COSIN) model, in which the behavioral sequences and interactions between source and target users in a dynamic interaction network are modeled uniformly in a probabilistic graphical model. More specifically, the sequential schema is modeled with a hierarchical Hidden Markov Model, and meanwhile it is shifted to the interaction schema to generate the interaction counts through Poisson factorization. A hybrid Gibbs-Variational algorithm is then proposed for efficient parameter estimation of the COSIN model. We conduct extensive experiments on both synthetic and real-world telecom datasets in different scales, and the results show that the proposed model outperforms some competitive baseline methods and is scalable. A case is further presented to show the precious explainability of the model.
Exploiting deep convolutional neural networks for a neural-based learning classifier system

Classification is a key factor in accuracy, simplicity, and expressiveness, and it is difficult to optimize all of these factors at the same time. The learning classifier system (LCS) is a suitable technique for addressing an adaptive classification problem. It is a combination of fast approximation and evolutionary optimization techniques. A neural-based learning classifier system (N-LCS) includes an architecture for maintaining expressiveness by incorporating neural networks into a supervised classifier system, which is also an LCS specializing in classification studies. In recent years, studies using deep artificial neural networks have been actively conducted. In particular, deep convolutional neural networks (CNN) provide a powerful representation in an extremely fundamental method and demonstrates the high performance in various domains. In this paper, we exploit various deep CNN architectures in convolutional neural-based learning classifier systems (CN-LCS) combining the CNN and LCS to explore the possibility of a CN-LCS. By using various CNNs as an action of a classifier in an N-LCS, better classification accuracy can be obtained and classifier can be optimized. Experimental results show that our models achieve the higher performance than N-LCS for database intrusion detection as well as two other datasets, and extract effective features from deep representation by projecting data samples learned by several deep CNN models into the feature space.
Streaming, Multi-Screens and YouTube

Internet use and online services underpin everyday life, and the resultant energy demand is almost entirely hidden, yet significant and growing: it is anticipated to reach 21% of global electricity demand by 2030 and to eclipse half the greenhouse gas emissions of transportation by 2040. Driving this growth, real-time video streaming ('watching') is estimated at around 50% of all peak data traffic. Using a mixed-methods analysis of the use of 66 devices (e.g. smart TVs, tablets) across 20 participants in 9 households, we reveal the online activity of domestic watching and provide a detailed exploration of video-on-demand activities. We identify new ways in which watching is transitioning in more rather than less data demanding directions; and explore the role HCI may play in reducing this growing data demand. We further highlight implications for key HCI and societal stakeholders (policy makers, service providers, network engineers) to tackle this important issue.
Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images

The availability of massive amounts of data in histopathological whole-slide images (WSIs) has enabled the application of deep learning models and especially convolutional neural networks (CNNs), which have shown a high potential for improvement in cancer diagnosis. However, storage and transmission of large amounts of data such as gigapixel histopathological WSIs are challenging. Exploiting lossy compression algorithms for medical images is controversial but, as long as the clinical diagnosis is not affected, is acceptable. We study the impact of JPEG 2000 compression on our proposed CNN-based algorithm, which has produced performance comparable to that of pathologists and which was ranked second place in the CAMELYON17 challenge. Detecting tumor metastases in hematoxylin and eosin-stained tissue sections of breast lymph nodes is evaluated and compared with the pathologists' diagnoses in three different experimental setups. Our experiments show that the CNN model is robust against compression ratios up to 24:1 when it is trained on uncompressed high-quality images. We demonstrate that a model trained on lower quality images-i.e., lossy compressed images-depicts a classification performance that is significantly improved for the corresponding compression ratio. Moreover, it is also observed that the model performs equally well on all higher-quality images. These properties will help to design cloud-based computer-aided diagnosis (CAD) systems, e.g., telemedicine that employ deep CNN models that are more robust to image quality variations due to compression required to address data storage and transmission constraints. However, the results presented are specific to the CAD system and application described, and further work is needed to examine whether they generalize to other systems and applications.
Leveraging Knowledge Graph Embeddings for Natural Language Question Answering

A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to translate natural language questions into graph-structured queries. During the translation, a vital process is to map entity/relation phrases of natural language questions to the vertices/edges of underlying knowledge graphs which can be used to construct target graph-structured queries. However, due to linguistic flexibility and ambiguity of natural language, the mapping process is challenging and has been a bottleneck of KG-QA models. In this paper, we propose a novel framework, called KemQA, which stands on recent advances in relation phrase dictionaries and knowledge graph embedding techniques to address the mapping problem and construct graph-structured queries of natural language questions. Extensive experiments were conducted on question answering benchmark datasets. The results demonstrate that our framework outperforms state-of-the-art baseline models in terms of effectiveness and efficiency.
Sentiment Classification Using N-Gram Inverse Document Frequency and Automated Machine Learning

We propose a sentiment classification method with a general machine learning framework.For feature representation, n-gram IDF is used to extract software-engineeringrelated, dataset-specific, positive, neutral, and negative n-gram expressions.For classifiers, an automated machine learning tool is used.In the comparison using publicly available datasets, our method achieved the highest F1 values in positive and negative sentences on all datasets.
A Sequence-Based Machine Comprehension Modeling Using LSTM and GRU

Machine comprehension deals with the idea of teaching machines the ability to read a passage and provide the correct answer to a question asked from it. Creation of machines with the ability to understand natural language is the prime aim of natural language processing. A machine comprehension task is an extension of question answering technique which provides the machines an ability to answer questions. This task revolutionizes the way in which humans interact with machines and retrieve information from them. Recent works in the field of natural language processing reveal the dominance of deep learning technique in handling complex tasks which suggest the use of neural network models for solving machine comprehension tasks. This paper discusses the performance of code-mixed Hindi data for handling machine comprehension using long short-term memory network and gated recurrent unit. A comparative analysis on the basis of accuracy is performed between the two sequence models to determine the best-suited model for handling this task.
The Things We Talk About When We Talk About Browsing: An Empirical Typology of Library Browsing Behavior

Libraries increasingly offer much of their collection online, rendering it invisible or unavailable to readers who, for reasons of information experience, prefer to browse the shelves. Although the evidence that shelf browsing is an important part of information behavior is increasing, information browsing as a behavior is somewhat of a black box (in contrast to web browsing, which is relatively well understood). It seems likely from early work that browsing is not, in fact, a monolithic behavior, but rather a set of behaviors and goals. The typologies presented in these works, however, are of a too high level to offer much insight into what support is needed for successful online browsing. In contrast, a recent spate of speculative browsing technologies meet some browsing needs, but offer little theoretical understanding of how systems support browsing. The major contribution of this article is a new typology of library browsing behavior based on recent observations of browsing behavior in libraries. The secondary contribution is an understanding of the interface features that would support these types of information browsers in an online environment.
Affective video content analysis based on multimodal data fusion in heterogeneous networks

In heterogeneous networks, different modalities are coexisting. For example, video sources with certain lengths usually have abundant time-varying audiovisual data. From the users’ perspective, different video segments will trigger different kinds of emotions. In order to better interact with users in heterogeneous networks and improve their user experiences, affective video content analysis to predict users’ emotions is essential. Academically, users’ emotions can be evaluated by arousal and valence values, and fear degree, which provides an approach to quantize the prediction accuracy of the reaction of the audience and users towards videos. In this paper, we propose the multimodal data fusion method for integrating the visual and audio data in order to perform the affective video content analysis. Specifically, to align the visual and audio data, the temporal attention filters are proposed to obtain the time-span features of the entire video segments. Then, by using the two-branch network structure, matched visual and audio features are integrated in the common space. At last, the fused audiovisual feature is employed for the regression and classification subtasks in order to measure the emotional responses of users. Simulation results show that the proposed method can accurately predict the subjective feelings of users towards the video contents, which provides a way to predict users’ preferences and recommend videos according to their own demand.
Pictorial System Usability Scale (P-SUS)

We have developed a pictorial multi-item scale, called P-SUS (Pictorial System Usability Scale), which aims to measure the perceived usability of mobile devices. The scale is based on the established verbal usability questionnaire SUS (System Usability Scale). A user-centred design process was employed to develop and refine its 10 pictorial items. The scale was tested in a first validation study (N=60) using student participants. Psychometric properties (convergent validity, criterion-related validity, sensitivity, and reliability), as well as the motivation to fill in the scale were assessed. The results indicated satisfactory convergent validity for about two-thirds of the items. Furthermore, strong correlations were obtained for the sum scores between verbal and pictorial SUS, and the pictorial scale was perceived as more motivating than the verbal questionnaire. The P-SUS represents a first attempt to provide a pictorial usability scale for the evaluation of (mobile) devices.
Person Reidentification via Unsupervised Cross-View Metric Learning

Person reidentification (Re-ID) aims to match observations of individuals across multiple nonoverlapping camera views. Recently, metric learning-based methods have played important roles in addressing this task. However, metrics are mostly learned in supervised manners, of which the performance relies heavily on the quantity and quality of manual annotations. Meanwhile, metric learning-based algorithms generally project person features into a common subspace, in which the extracted features are shared by all views. However, it may result in information loss since these algorithms neglect the view-specific features. Besides, they assume person samples of different views are taken from the same distribution. Conversely, these samples are more likely to obey different distributions due to view condition changes. To this end, this paper proposes an unsupervised cross-view metric learning method based on the properties of data distributions. Specifically, person samples in each view are taken from a mixture of two distributions: one models common prosperities among camera views and the other focuses on view-specific properties. Based on this, we introduce a shared mapping to explore the shared features. Meanwhile, we construct view-specific mappings to extract and project view-related features into a common subspace. As a result, samples in the transformed subspace follow the same distribution and are equipped with comprehensive representations. In this paper, these mappings are learned in an unsupervised manner by clustering samples in the projected space. Experimental results on five cross-view datasets validate the effectiveness of the proposed method.
Multi-manifold clustering: A graph-constrained deep nonparametric method

For multi-manifold clustering, it is still a challenging problem on how to learn the cluster number automatically from data. This paper presents a novel nonparametric Bayesian model to cluster the multi-manifold data and estimate the number of submanifolds simultaneously. Our model firstly assumes that every submanifold is a probability distribution defined in the manifold space. Then, we approximate the manifold distribution with a deep neural network. To maintain the data similarity among data, we regularize the data generation process with a modified k-nearest neighbor graph. Though the posterior inference is hard, our model leads to a very efficient deterministic optimization algorithm, which incorporates the mean field variational inference with the Graph regularized Variational Auto-Encoder (Graph-VAE). By applying the Graph-VAE, our model exhibits another advantage of realistic image generation which overcomes the conventional clustering methods. Furthermore, we expand our proposed manifold algorithm with the Dirichlet Process Mixture (DPM) to model the real datasets, in which the manifold data and non-manifold data are coexisting. Experiments on synthetic data verify our theoretical analysis. Clustering results on motion segmentation, coil20 and 3D pedestrian show that our approach can significantly improve the clustering accuracy. The handwritten database experiment demonstrates the image generation capability.
Nature Inspired Meta-heuristic Algorithms for Deep Learning: Recent Progress and Novel Perspective

Deep learning is presently attracting extra ordinary attention from both the industry and the academia. The application of deep learning in computer vision has recently gain popularity. The optimization of deep learning models through nature inspired algorithms is a subject of debate in computer science. The application areas of the hybrid of natured inspired algorithms and deep learning architecture includes: machine vision and learning, image processing, data science, autonomous vehicles, medical image analysis, biometrics, etc. In this paper, we present recent progress on the application of nature inspired algorithms in deep learning. The survey pointed out recent development issues, strengths, weaknesses and prospects for future research. A new taxonomy is created based on natured inspired algorithms for deep learning. The trend of the publications in this domain is depicted; it shows the research area is growing but slowly. The deep learning architectures not exploit by the nature inspired algorithms for optimization are unveiled. We believed that the survey can facilitate synergy between the nature inspired algorithms and deep learning research communities. As such, massive attention can be expected in a near future.
Digital forensic analysis of encrypted database files in instant messaging applications on Windows operating systems: Case study with KakaoTalk, NateOn and QQ messenger

Instant messaging applications store users' personal data (e.g., user profile, chat messages, photos and video clips). Because those data typically include privacy sensitive information, most instant messaging applications are trying to protect the stored data in an encrypted form so that the authorized messaging application itself can only access the data. In this paper, we analyzed the locations and file formats of personal data files in three instant messaging applications (KakaoTalk, NateOn, and QQ) which are the most popularly used in China and South Korea. We particularly examined the encryption and decryption procedures for internal databases in those messaging applications through reverse-engineering. Our analysis results demonstrate how the database files of those instant messaging applications are stored and encrypted. Moreover, in the cases of KakaoTalk and NateOn applications, we found that their encrypted database files can successfully be recovered without requiring user password. We also found that QQ messenger stores the encryption key for the database files into an external server. This implementation may raise another privacy concern because users' personal data can be freely accessed by the service provider without user consent.
VST-Net: Variance-stabilizing transformation inspired network for Poisson denoising

Poisson noise occurs in various applications including medical imaging and night vision. There exist many traditional Poisson denoising algorithms, particularly, one class of impressive algorithm is based on variance-stabilizing transformation (VST). Inspired by the traditional VST scheme, in this paper we propose a novel Poisson denoising model based on convolutional neural network, called variance-stabilizing transform network (VST-Net). VST-Net inherits the structures and strengths of the traditional VST scheme via optimizing the nonlinear transformation by means of network design and supervised learning. The whole VST-Net network contains three sub-networks. The first and third sub-networks simulate the forward and inverse Anscombe transforms, respectively. Meanwhile, the second sub-network is devoted to playing the role of approximate Gaussian denoising. Joint learning strategy and two-stage progressive learning strategy are exploited to investigate the rationality and strength of the VST scheme. Experimental results verify the great potential of the VST-driven network. Code is available at: https://github.com/yqx7150/VST-Net.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math> -logarithmic negativity

The logarithmic negativity of a bipartite quantum state is a widely employed entanglement measure in quantum information theory, due to the fact that it is easy to compute and serves as an upper bound on distillable entanglement. More recently, the $\kappa$-entanglement of a bipartite state was shown to be the first entanglement measure that is both easily computable and has a precise information-theoretic meaning, being equal to the exact entanglement cost of a bipartite quantum state when the free operations are those that completely preserve the positivity of the partial transpose [Wang and Wilde, Phys. Rev. Lett. 125(4):040502, July 2020]. In this paper, we provide a non-trivial link between these two entanglement measures, by showing that they are the extremes of an ordered family of $\alpha$-logarithmic negativity entanglement measures, each of which is identified by a parameter $\alpha\in[ 1,\infty] $. In this family, the original logarithmic negativity is recovered as the smallest with $\alpha=1$, and the $\kappa$-entanglement is recovered as the largest with $\alpha=\infty$. We prove that the $\alpha $-logarithmic negativity satisfies the following properties: entanglement monotone, normalization, faithfulness, and subadditivity. We also prove that it is neither convex nor monogamous. Finally, we define the $\alpha$-logarithmic negativity of a quantum channel as a generalization of the notion for quantum states, and we show how to generalize many of the concepts to arbitrary resource theories.