Reviewerly/exHarmony huggingface dataset:
Optimization of Fuzzy C-Means Clustering Algorithm with Combination of Minkowski and Chebyshev Distance Using Principal Component Analysis

Optimization is used to find the maximum or minimum of a function. In this research, optimization is applied to the objective function of the FCM algorithm. FCM is an effective algorithm for grouping data, but it is often trapped in local optimum solutions. Therefore, the similarity measure in the clustering process using FCM is very important. This study uses a new method, which combines the Minkowski distance with the Chebyshev distance which is used as a measure of similarity in the clustering process on FCM. The amount of data that is quite large and complex becomes one of the difficulties in providing analysis of multivariate data. To overcome this, one of the techniques used is dimensional reduction using Principal Component Analysis (PCA). PCA is an algorithm of the dimensional reduction method based on the main components obtained from linear combinations, which can help stabilize cluster analysis measurements. The method used in this research is dimensional reduction using PCA, clustering using FCM with a combination of Minkowski and Chebyshev distances (FCMMC), and clustering evaluation using the Davies Bouldin Index (DBI). The purpose of this research is to minimize the objective function of FCM using new distances, namely, the combination of Minkowski and Chebyshev distances through the assistance of dimensional reduction by PCA. The results showed that the cluster accuracy of the combined application of the PCA and FCMMC algorithms was 1.6468. Besides, the minimum value of the combined objective function of the two methods is also obtained, namely, 0.0373 which is located in the 15th iteration, where this value is the smallest value of the 100 maximum iterations set.
Dense RepPoints: Representing Visual Objects with Dense Point Sets

We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level. Techniques are proposed to efficiently process these dense points, maintaining near-constant complexity with increasing point numbers. Dense RepPoints is shown to represent and learn object segments well, with the use of a novel distance transform sampling method combined with set-to-set supervision. The distance transform sampling combines the strengths of contour and grid representations, leading to performance that surpasses counterparts based on contours or grids. Code is available at https://github.com/justimyhxu/Dense-RepPoints .
A robust fixed-interval smoother for nonlinear systems with non-stationary heavy-tailed state and measurement noises

We propose a robust fixed-interval smoother for nonlinear systems with non-stationary heavy-tailed state and measurement noises, in which the state and measurement noises are modelled as Gaussian-Student’s t mixture distributions. The variational Bayesian technique is utilized to deduce the smoother approximately. The standard cubature Kalman smoother (CKS) and the robust Gaussian approximate smoother (RGAS) with fixed scale matrices and dof parameters are two particular cases of the proposed smoother. Numerical simulation and target tracking example show the merits of the proposed smoother.
Deep Spatial-Angular Regularization for Compressive Light Field Reconstruction over Coded Apertures

Coded aperture is a promising approach for capturing the 4-D light field (LF), in which the 4-D data are compressively modulated into 2-D coded measurements that are further decoded by reconstruction algorithms. The bottleneck lies in the reconstruction algorithms, resulting in rather limited reconstruction quality. To tackle this challenge, we propose a novel learning-based framework for the reconstruction of high-quality LFs from acquisitions via learned coded apertures. The proposed method incorporates the measurement observation into the deep learning framework elegantly to avoid relying entirely on data-driven priors for LF reconstruction. Specifically, we first formulate the compressive LF reconstruction as an inverse problem with an implicit regularization term. Then, we construct the regularization term with an efficient deep spatial-angular convolutional sub-network to comprehensively explore the signal distribution free from the limited representation ability and inefficiency of deterministic mathematical modeling. Experimental results show that the reconstructed LFs not only achieve much higher PSNR/SSIM but also preserve the LF parallax structure better, compared with state-of-the-art methods on both real and synthetic LF benchmarks. In addition, experiments show that our method is efficient and robust to noise, which is an essential advantage for a real camera system. The code is publicly available at https://github.com/angmt2008/LFCA .
A Survey on Point-of-Interest Recommendation in Location-based Social Networks

The popularization of Location-based social networks (LBSNs) in last years has provided a lot of improvements in several Recommender Systems to the task of points-of-interest (POI) recommendation. In this paper, we provide an updated view of the POI recommendation, identifying relevant efforts, results, contributions, and limitations. Through a systematic mapping, we selected 73 relevant papers published in the last three years (2017, 2018, and 2019) in the main vehicles of the area (e.g., RecSys, VLDB, SIGIR, WWW, TKDE, etc.). As major limitations, first, we identified that these works prioritize accuracy over other quality dimensions, despite the consensus in the RS community that accuracy is not enough to assess the practical effectiveness of RSs. Further, we found a low intersection of metrics and datasets used in these works, along with a large number of metrics used in a few distinct studies. These observations show restrictions for reproducibility and straightforward comparison of results in the area. Finally, we highlight as a promising future work the in-depth exploitation of textual data, since just a few of the evaluated papers marginally use this rich data source.
Stereotyping in the digital age: Male language is “ingenious”, female language is “beautiful” – and popular

The huge power for social influence of digital media may come with the risk of intensifying common societal biases, such as gender and age stereotypes. Speaker's gender and age also behaviorally manifest in language use, and language may be a powerful tool to shape impact. The present study took the example of TED, a highly successful knowledge dissemination platform, to study online influence. Our goal was to investigate how gender- and age-linked language styles-beyond chronological age and identified gender-link to talk impact and whether this reflects gender and age stereotypes. In a pre-registered study, we collected transcripts of TED Talks along with their impact measures, i.e., views and ratios of positive and negative talk ratings, from the TED website. We scored TED Speakers' (N = 1,095) language with gender- and age-morphed language metrics to obtain measures of female versus male, and younger versus more senior language styles. Contrary to our expectations and to the literature on gender stereotypes, more female language was linked to higher impact in terms of quantity, i.e., more talk views, and this was particularly the case among talks with a lot of views. Regarding quality of impact, language signatures of gender and age predicted different types of positive and negative ratings above and beyond main effects of speaker's gender and age. The differences in ratings seem to reflect common stereotype contents of warmth (e.g., "beautiful" for female, "courageous" for female and senior language) versus competence (e.g., "ingenious", "informative" for male language). The results shed light on how verbal behavior may contribute to stereotypical evaluations. They also illuminate how, within new digital social contexts, female language might be uniquely rewarded and, thereby, an underappreciated but highly effective tool for social influence. WC = 286 (max. 300 words).
RETRACTED: A Fast 2-D Otsu lung tissue image segmentation algorithm based on improved PSO

In order to reduce the time of lung tissue image segmentation, we proposed a fast 2-D Otsu lung tissue image segmentation algorithm based on improved PSO. Firstly, in the 2-D Otsu algorithm, the search scope of 2-D gray threshold is limited in a long and narrow region, which is the neighbourhood of the diagonal from the third region to the first region of the 2-D gray histogram, and the search scope and the computation are reduced, operation speed is improved. Secondly, In the PSO algorithm, the position of the particles is adjusted during iterating based on the principle of symmetric disposition, so as to avoid PSO falling into local optimal solution and improves the accuracy of threshold searching. Finally, a lung CT image with 1280×960 resolutions is segmented by our algorithm and other traditional algorithms, and a comparison is given. The segmentation threshold of our method is 85, the difference is less than 5 comparing with that of other traditional algorithms, and it shows that our method has almost the same searching precision as the traditional algorithms. The time cost is only 162ms, which is far less than the traditional algorithms, and it shows that our method improve the segmentation speed. It can be concluded that our method can not only satisfy the requirement of segmentation precision, but also meet the requirement of operation speed.
The transformation of national patents for high-technology exports: Moderating effects of national cultures

National culture as an informal institution influences the national absorptive capacity (NAC) measured in patents per capita in the upstream and entrepreneurial activity in the downstream. Does national culture moderate the link between NAC and high-technology exports? We explored this question in a comprehensive study design. Based on a panel of 101 countries over 21 years, first, we assessed the direct effect of NAC on high-technology exports in the baseline hypothesis. Then, we analysed the moderating role of national culture between the country’s NAC and high-technology exports. The results show several insights from the comparison between the literature and our evidence. (i) We proposed that power distance negatively moderates patent and high-tech exports; the results show a positive correlation of the interaction. (ii) We proposed that individualist culture positively moderates patent and high-technology exports; the results show a positive correlation of the interaction. (iii) We proposed that high masculinity has no moderating effects; the results show a positive interaction effect. (iv) We proposed that uncertainty avoidance negatively moderates the link between NAC and high-technology exports; the results show negative and significant moderation without the introduction of long-term orientation; however, it is non-significant after the introduction of long-term orientation. Last, (v) we proposed that long-term orientation positively moderates the link between NAC and high-technology exports; the results are positive and significant without uncertainty avoidance, and they are non-significant with uncertainty avoidance in the analysis. Uncertainty avoidance and long-term orientation neutralise each other’s effects. Based on these insights, we contribute to the literature in three ways. First, we address the issue of high-technology exports in international business at the national level and link it to the vertical transformation of national science in patents per capita. Second, we contribute to the institutional theory encompassing informal norms (culture) and formal rules (policy) in the flow of NAC to commercial performance. Third, we make several policy-related suggestions.
Content-adaptive image encryption with partial unwinding decomposition

This study designs a novel image encryption cryptosystem through the two-dimensional partial unwinding decomposition (2D-PUD). It consists of three stages. Firstly, a stream sequence (first part of the security key) is generated by pseudo-random number. Secondly, the plain image is decomposed into three parts by 2D-PUD: one 2D decomposition component, two 1D decomposition components, and the average intensity value of the image. Finally, the 2D decomposition component is shuffled by a generalized Arnold transform where the average intensity value is selected as second part of the security key. The diffusion scheme is subsequently applied to the scrambled image via exclusive OR operations with the randomized 1D decomposition components (third part of the security key) along rows and columns to obtain the cipher image. Due to the adaptive attribute of 2D-PUD, the generated 1D decomposition components are completely distinct for different images. In addition, we can also make them significantly different by tuning the decomposition times for the same image. Thus, the proposed algorithm is an image-content-adaptive encryption scenario that can effectively resist cryptographic attacks. Simulation results demonstrate that our proposed method has excellent encryption performance and can resist against various typical attacks, including brute force, statistical, entropy, and differential attacks.
A Maturity Model for Secure Software Design: A Multivocal Study

Security is one of the most important software quality attributes. Software security is about designing and developing secure software that does not allow the integrity, confidentiality, and availability of its code, data, or service to be compromised. Organizations tend to consider security as an afterthought, and they continue to suffer from security risks. Developing secure software requires taking security into consideration in all phases of the Software Development Life Cycle (SDLC). Several approaches have been developed to improve software quality, such as Capability Maturity Model Integration (CMMI). However, software security issues have not been addressed in a proper manner and incorporating security practices into the SDLC remains a challenge. The objective of this paper is to develop a framework to improve the process of designing secure products in software development organizations. To achieve this objective, a Multivocal Literature Review (MLR) was conducted to identify the relevant studies in both the formal and grey literature. A total of 38 primary studies were identified, and available evidence was synthesized into 8 knowledge areas and 65 best practices to build a Secure Software Design Maturity Model (SSDMM). The framework was developed based on the structure of CMMI v2.0 and evaluated through case studies in real-world environments. The case study results indicate that SSDMM is useful in measuring the maturity level of an organization for the secure design phase of SDLC. SSDMM will assist organizations in evaluating and improving their software design security practices. It will also provide a foundation for researchers to develop new software security approaches.
Closing the Gap of Simulation to Reality in Electromagnetic Imaging of Brain Strokes via Deep Neural Networks

Bringing deep learning techniques to electromagnetic imaging is of interest considering its great success in various fields. Deep neural nets however are known for being data hungry machines, and in many practical cases, such as electromagnetic medical imaging, there is not enough to feed them. Scarcity of data necessitates reliance on simulations to generate a sufficiently large dataset for deep learning to perform any complicated task. Simulations however, can not perfectly represent real environments and therefore, any neural net trained on simulation data will invariably fail when evaluated on real data. This work customizes a deep domain adaptation technique for matching distributions of complex-valued electromagnetic data. We demonstrate the advantage of using complex-valued models over regular ones. An operational neural network trained on simulation data and adapted to practical data to perform brain injury localization is presented.
A New Trend of Quantum Image Representations

Quantum image processing will be an important issue in the age of quantum computers.Quantum image processing generally includes quantum image representations, quantum image processing algorithms, and quantum image measurement.Among them, quantum image representation is a very important part of quantum image processing.Therefore, it is of great significance for quantum image processing to study the representation of quantum images.First, we elaborate on the main quantum image representations and analyze, compare and summarize them.This paper focuses on the definition of these quantum image representations the preparation of quantum images and the number of quantum bits required as well as the computational complexity.Then, this paper analyzes, compares and summarizes the similarities and differences between these quantum image representations.Finally, the challenges and future development in the field of quantum image processing are summarized and forecasted.This work will help researchers to understand the advances related to quantum image representations in the field of quantum image processing and is therefore of some reference value.
Immersive 3D sketching tools: Implications for visual thinking and communication

Paper-based (PB) sketching involves the challenge of representing three-dimensional (3D) shapes on two-dimensional (2D) surfaces. The recent generation of virtual reality (VR) sketching tools offer a way to overcome this challenge. These immersive 3D sketching environments permit the rapid construction of freehand stroke-based 3D models in 3D space while replicating the immediate experience of PB sketching. To explore the potential advantages of VR sketching in visual thinking and visual communication, we conducted investigations with sixteen architectural students engaged in PB and VR sketching tasks. We observed their visualization behavior during VR sketching and their behavior in transitioning between PB and VR sketching. The participants experiences of the two media were also recorded in semi-structured interviews and questionnaires. Our observations show that immersive 3D sketching is a unique form of visual representation that facilitates the rapid and flexible creation of large and detailed (but inaccurate) 3D computer models. It is a multimodal medium that supports visual thinking and communication behaviors associated with PB sketching, CAD modeling, physical model-making and gesturing, all within the same space. This unique combination enables users to engage in visual thinking and visual communication activities in ways that cannot presently be achieved with any other single representation technique.
A profile-free non-parametric approach towards generation of synthetic hourly global solar irradiation data from daily totals

Solar radiation is an essential input in the design and operation of many engineering systems. However, access to high-resolution data (hourly or sub-hourly) is usually limited, especially in developing countries, either due to its unavailability or expensive costs. A novel data-driven approach is proposed to predict the hourly global irradiation profiles from the cheaper and more likely available records of daily global irradiation. The proposed approach is based on a prior categorization of hourly observations using the K-means clustering algorithm, followed by non-parametric function approximation using the multi-layered perceptron artificial neural network. This approach is applied to measured data (130,000 data points) at six locations in the North African Sahara, and the developed models are benchmarked against all existing parametric models in the literature. The artificial neural network-based models outperformed all existing models, with maximum and minimum coefficients of determination of 0.960 and 0.930, respectively. The non-parametric models also captured the true asymmetric profiles of hourly irradiation with enhanced distributions of the residuals. Hence, the suggested models can be used to generate synthetic hourly data for multiple applications, most notably for building energy simulations and scheduling the operation of power generation systems.
Improving Query Efficiency of Black-Box Adversarial Attack

Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting). As plenty of machine learning models have been deployed via online services that only provide query outputs from inaccessible models (e.g., Google Cloud Vision API2), black-box adversarial attacks (inaccessible target model) are of critical security concerns in practice rather than white-box ones. However, existing query-based black-box adversarial attacks often require excessive model queries to maintain a high attack success rate. Therefore, in order to improve query efficiency, we explore the distribution of adversarial examples around benign inputs with the help of image structure information characterized by a Neural Process, and propose a Neural Process based black-box adversarial attack (NP-Attack) in this paper. Extensive experiments show that NP-Attack could greatly decrease the query counts under the black-box setting. Code is available at https://github.com/Sandy-Zeng/NPAttack .
Multi-label feature selection with constraint regression and adaptive spectral graph

Like single-label learning, multi-label learning also suffers from the curse of dimensionality. Due to the existence of high-dimensional data, feature selection as a preprocessing tool always plays a key position in the multi-label learning. Although a variety of multi-label feature selection methods have been developed, they neglect to consider the redundancy of features, thereby degrading learning performance. To address this problem, we present a novel multi-label feature selection approach with uncorrelated regression and adaptive spectral graph. Specifically, we first construct a manifold framework with uncorrelated regression model to hunt for uncorrelated yet discriminative features, which also utilize the low-dimensional representation based on feature space to fit the label distribution. Then, a spectral graph term based on information entropy is incorporated into the manifold framework, so as to ensure the local geometric structure of data in subsequent learning process. Following the above principles, we design an objective function to achieve multi-label feature selection, and propose a detailed optimization method. Comprehensive experiments are conducted on multiple public multi-label data sets, the results show that our method is superior to other compared methods.
Prime-Aware Adaptive Distillation

Knowledge distillation (KD) aims to improve the performance of a student network by mimicing the knowledge from a powerful teacher network. Existing methods focus on studying what knowledge should be transferred and treat all samples equally during training. This paper introduces the adaptive sample weighting to KD. We discover that previous effective hard mining methods are not appropriate for distillation. Furthermore, we propose Prime-Aware Adaptive Distillation (PAD) by the incorporation of uncertainty learning. PAD perceives the prime samples in distillation and then emphasizes their effect adaptively. PAD is fundamentally different from and would refine existing methods with the innovative view of unequal training. For this reason, PAD is versatile and has been applied in various tasks including classification, metric learning, and object detection. With ten teacher-student combinations on six datasets, PAD promotes the performance of existing distillation methods and outperforms recent state-of-the-art methods.