Reviewerly/exHarmony huggingface dataset:
A Systematic Review of Interaction in Search-Based Software Engineering

Search-Based Software Engineering (SBSE) has been successfully applied to automate a wide range of software development activities. Nevertheless, in those software engineering problems where human evaluation and preference are crucial, such insights have proved difficult to characterize in search, and solutions might not look natural when that is the expectation. In an attempt to address this, an increasing number of researchers have reported the incorporation of the 'human-in-the-loop' during search and interactive SBSE has attracted significant attention recently. However, reported results are fragmented over different development phases, and a great variety of novel interactive approaches and algorithmic techniques have emerged. To better integrate these results, we have performed a systematic literature review of interactive SBSE. From a total of 669 papers, 26 primary studies were identified. To enable their analysis, we formulated a classification scheme focused on four crucial aspects of interactive search, i.e., the problem formulation, search technique, interactive approach, and the empirical framework. Our intention is that the classification scheme affords a methodological approach for interactive SBSE. Lastly, as well as providing a detailed cross analysis, we identify and discuss some open issues and potential future trends for the research community.
Multi-Script-Oriented Text Detection and Recognition in Video/Scene/Born Digital Images

Achieving good text detection and recognition results for multi-script-oriented images is a challenging task. First, we explore bit plane slicing in order to utilize the advantage of the most significant bit information to identify text components. A new iterative nearest neighbor symmetry is then proposed based on shapes of convex and concave deficiencies of text components in bit planes to identify candidate planes. Further, we introduce a new concept called mutual nearest neighbor pair components based on gradient direction to identify representative pairs of texts in each candidate bit plane. The representative pairs are used to restore words with the help of edge image of the input one, which results in text detection results (words). Second, we propose a new idea by fixing window for character components of arbitrary oriented words based on angular relationship between sub-bands and a fused band. For each window, we extract features in contourlet wavelet domain to detect characters with the help of an SVM classifier. Further, we propose to explore HMM for recognizing characters and words of any orientation using the same feature vector. The proposed method is evaluated on standard databases such as ICDAR, YVT video, ICDAR, SVT, MSRA scene data, ICDAR born digital data, and multi-lingual data to show its superiority to the state of the art methods.
An improved block based joint reversible data hiding in encrypted images by symmetric cryptosystem

Earlier joint EIRDH (Encrypted Image Reversible Data Hiding) techniques divided cover image into equal sized blocks and embedded one secret bit per block and after decryption of stego image with same key as encryption key, secret message is extracted through data hiding key and original cover image is recovered using spatial correlation of the image. After carrying out a feasibility analysis, improved block based joint EIRDH algorithm is employed in this paper which embedded n(n=2,3,4.....)secret bits per block by dividing blocks of same size (4 × 4, 8 × 8, 16 × 16, 32 × 32.....)into n sub-blocks. Optimal visual quality and enhanced embedding rate are acquired by the proposed scheme. Test results and correlations are exhibited to outline the viability and focal points of the proposed technique with conventional ones.
Real-Time Event-Triggered Object Tracking in the Presence of Model Drift and Occlusion

In this paper, we propose a novel event-triggered tracking framework for fast and robust visual tracking in the presence of model drift and occlusion. The resulting tracker not only operates in real time, but also is resilient to tracking failures caused by factors such as fast motion and heavy occlusion. Specifically, the tracker consists of an event-triggered decision model as the core module that coordinates other functional modules, including a short-term tracker, occlusion and drift identification, target redetection, short-term tracker updating, and online discriminative learning for a detector. Each functional module is associated with a defined event that is triggered when the proposed conditions are met. The occlusion and drift identification module is intended to perform online evaluation of the short-term tracking. When a model drift event occurs, the target redetection module is activated by the event-triggered decision model to relocate the target and reinitialize the short-term tracker. The short-term tracker updating is carried out at each frame with a variable learning rate depending on the degree of occlusion. A sampling pool is constructed to store discriminative samples that are used to update the detector model. Extensive experiments on large benchmark datasets demonstrate that the proposed tracking algorithm can effectively detect model drift and restore tracking, and more importantly, it outperforms the state-of-the-art approaches in terms of accuracy, efficiency, and robustness.
A New Robust Approach for Reversible Database Watermarking with Distortion Control

Nowadays information is crucial in many fields such as medicine, science and business, where databases are used effectively for information sharing. However, the databases face the risk of being pirated, stolen or misused, which may result in a lot of security threats concerning ownership rights, data tampering and privacy protection. Watermarking is utilized to enforce ownership rights on shared relational databases. Many reversible watermarking methods are proposed recently to protect rights of owners along with recovering original data. Most state-of-the-art methods modify the original data to a large extent, result in data quality degradation, and cannot achieve good balance between robustness against malicious attacks and data recovery. In this paper, we propose a robust and reversible database watermarking technique, Genetic Algorithm and Histogram Shifting Watermarking (GAHSW), for numerical relational database. The genetic algorithm is used to select the best secret key for grouping database, where the watermarking can be embedded with balanced distortion and capacity. The histogram of the prediction error is shifted to embed the watermark with good robustness. Experimental results demonstrate the effectiveness of GAHSW and show that it outperforms state-of-the-art approaches in terms of robustness against malicious attacks and preservation of data quality.
Privacy-preserving governmental data publishing: A fog-computing-based differential privacy approach

With the growing availability of public open data, the protection of citizens’ privacy has become a vital issue for governmental data publishing. However, there are a large number of operational risks in the current government cloud platforms. When the cloud platform is attacked, most existing privacy protection models for data publishing cannot resist the attacks if the attacker has prior background knowledge. Potential attackers may gain access to the published statistical data, and identify specific individual’s background information, which may cause the disclosure of citizens’ private information. To address this problem, we propose a fog-computing-based differential privacy approach for privacy-preserving data publishing in this paper. We discuss the risk of citizens’ privacy disclosure related to governmental data publishing, and present a differential privacy framework for publishing governmental statistical data based on fog computing. Based on the framework, a data publishing algorithm using a MaxDiff histogram is developed, which can be used to realize the function of preserving user privacy based on fog computing. Applying the differential method, Laplace noises are added to the original data set, which prevents citizens’ privacy from disclosure even if attackers get strong background knowledge. According to the maximum frequency difference, the adjacent data bins are grouped, then the differential privacy histogram with minimum average error can be constructed. We evaluate the proposed approach by computational experiments based on the real data set of Philippine families’ income and expenditures provided by Kaggle. It shows that the proposed data publishing approach can not only effectively protect citizens’ privacy, but also reduce the query sensitivity and improve the utility of the data published.
Limitations of the Lipschitz Constant as a Defense Against Adversarial Examples

Several recent papers have discussed utilizing Lipschitz constants to limit the susceptibility of neural networks to adversarial examples. We analyze recently proposed methods for computing the Lipschitz constant. We show that the Lipschitz constant may indeed enable adversarially robust neural networks. However, the methods currently employed for computing it suffer from theoretical and practical limitations. We argue that addressing this shortcoming is a promising direction for future research into certified adversarial defenses.
One-pass lossless data hiding and compression of remote sensing data

The information obtained by means of spectral remote sensing (i.e., the hyperspectral images) are involved in several real-life scenarios and applications. Historical research, monitoring of environmental hazards, forensics and counter-terrorism are some examples of contexts in which the hyperspectral data play an important role. In many contexts, the hyperspectral images could also play sensitive roles (e.g., in military applications, etc.) and are generally exchanged among several entities, in order to carry out different tasks on them. Therefore, it is important to guarantee their protection. A meaningful choice is the protection through data hiding techniques. In fact, by means of reversible data hiding techniques, the imaging data become a sort of information carrier and can be used for delivering other important data that can be used, for instance, to check the integrity of the original imaging data. In this paper, we introduce a one-pass framework that is able to perform the lossless data hiding and the lossless compression of the marked stream, at the same time, by exploiting the capabilities of the predictive paradigm. Substantially, in a single pass, a marked and compressed stego image is obtained, which can be exactly restored by the receiver: by decompressing and reversibly reconstructing the original unaltered image. In addition, our framework also permits to perform only the decompression (without the extraction of the hidden information). In this manner, the resulting stego (marked) hyperspectral image, could be used for several purposes, in which it is not necessary to extract the original data and an acceptable grade of degradation is tolerated. We also implement a proof-of-concept of the proposed framework to assess the effectiveness of our contribution. Finally, we report the achieved experimental results, which outperform other similar approaches.
Dynamic Composite Data Physicalization Using Wheeled Micro-Robots

This paper introduces <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic composite physicalizations</i> , a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work.
A Two-Stage Clustering Based 3D Visual Saliency Model for Dynamic Scenarios

Three-dimensional (3D) visual saliency is fundamental for vision-guided applications such as human-computer interaction in virtual reality, image quality assessment, object tracking, and event retrieval. Classical models for 3D visual saliency can draw an appropriate saliency map when the quality of the required depth maps or auxiliary cues is high enough. However, the depth map is usually impaired with artifacts (such as holes or noise) from faults in stereo matching or multipaths in range sensors. In these cases, challenges arise in those 3D visual saliency models because the core preliminary processes, such as the detection of low-level visual features, may fail. To solve this problem, we proposed a two-stage clustering-based 3D visual saliency model for human visual fixation prediction in dynamic scenarios. In this model, a two-stage clustering scheme is designed to handle the negative influence of impaired depth videos. With the help of this scheme, representative cues are selected for saliency modeling. After that, multimodal saliency maps are obtained from depth, color, and 3D motion cues. Finally, a cross-Bayesian model is designed for the pooling of multimodal saliency maps. The experimental results demonstrate that the proposed 3D saliency model based on two-stage clustering outperforms other state-of-the-art models on a variety of metrics. Furthermore, the consistency and robustness of our model are also verified.
Quantum-assisted telescope arrays

Quantum networks provide a platform for astronomical interferometers capable of imaging faint stellar objects. In a recent work [E. T. Khabiboulline et al., Phys. Rev. Lett. 123, 070504 (2019)], we presented a protocol that circumvents transmission losses with efficient use of quantum resources and modest quantum memories. Here we analyze a number of extensions to that scheme. We show that it can be operated as a truly broadband interferometer and generalized to multiple sites in the array. We also analyze how imaging based on the quantum Fourier transform provides improved signal-to-noise ratio compared to classical processing. Finally, we discuss physical realizations including photon-detection-based quantum state transfer.
Automatic construction of target-specific sentiment lexicon

Sentiment lexicon plays an important role in sentiment analysis system. In most existing sentiment lexica, each sentiment word or phrase is given a sentiment label or score. However, a sentiment word may express different sentiment orientations describing different targets. It's beneficial but challenging to incorporate knowledge of opinion targets into sentiment lexicon. In this paper we propose an automatic approach to construct a target-specific sentiment lexicon, in which each term is an opinion pair consisting of an opinion target and an opinion word. The approach solves two principle problems in construction process, namely, opinion target extraction and opinion pair sentiment classification. An unsupervised algorithm is proposed to extract opinion pairs in high quality. Both semantic feature and syntactic feature are incorporated in the algorithm, to extract opinion pairs containing correct opinion targets. A group of opinion pairs are generated and a framework is proposed to classify their sentiment polarities. Knowledge of available resources including general-purpose sentiment lexicon and thesaurus, and context knowledge including syntactic relations and sentiment information in sentences, are extracted and integrated in a unified framework to calculate sentiment scores of opinion pairs. Experimental results on product reviews datasets in different domains prove the effectiveness of our method in target-specific sentiment lexicon construction, which can improve performances of opinion target extraction and opinion pair sentiment classification. In addition, our lexicon also achieves better performance in target-level sentiment classification compared with several general-purpose sentiment lexicons.
Probabilistic Solar Irradiance Forecasting by Conditioning Joint Probability Method and Its Application to Electric Power Trading

This study presents a method for probabilistic forecasting of solar irradiance based on the joint probability distribution function (PDF) of irradiance predicted by numerical weather prediction (NWP) and irradiance observed.Multidimensional kernel density estimation was used to construct this joint PDF.The probabilistic forecast is obtained by deriving a conditional PDF given a current NWP by using the Bayes rule.The proposed method can naturally handle the nonlinear nature of the relation between observed and predicted irradiance.The method showed better statistical performance in terms of the continuous ranked probability score, reliability, and sharpness than an existing probabilistic forecasting method based on ensemble NWP.Simulation of solar power trading based on pricing of an actual electric power market also confirmed that the proposed method results in greater profit by suppressing penalties imposed due to overbidding.
Hessian matrix-based fourth-order anisotropic diffusion filter for image denoising

Image denoising is one of important tasks in image processing. Diffusion filter-based methods have been widely used for image denosing. In order to solve the staircase effect introduced by second-order diffusion filter, different fourth-order diffusion filters were proposed, which can achieve good performance on image denoising. However, fourth-order diffusion filter often suffers over-smoothness. In this paper, we propose an anisotropic fourth-order diffusion filter for astronomical image denoising, in which the eigenvalue of Hessian matrix is introduced for preserving edge information. The experimental results of denoising on several astronomical images show that the proposed method can achieve better performance than several other methods.
Short Lattice-Based One-out-of-Many Proofs and Applications to Ring Signatures

In this work, we construct a short one-out-of-many proof from (module) lattices, allowing one to prove knowledge of a secret associated with one of the public values in a set. The proof system builds on a combination of ideas from the efficient proposals in the discrete logarithm setting by Groth and Kohlweiss (EUROCRYPT '15) and Bootle et al. (ESORICS '15), can have logarithmic communication complexity in the set size and does not require a trusted setup. Our work resolves an open problem mentioned by Libert et al. (EUROCRYPT '16) of how to efficiently extend the above discrete logarithm proof techniques to the lattice setting. To achieve our result, we introduce new technical tools for design and analysis of algebraic lattice-based zero-knowledge proofs, which may be of independent interest. Using our proof system as a building block, we design a short ring signature scheme, whose security relies on "post-quantum" lattice assumptions. Even for a very large ring size such as 1 billion, our ring signature size is only 3 MB for 128-bit security level compared to 216 MB in the best existing lattice-based result by Libert et al. (EUROCRYPT '16).
Deep Learning for Image Denoising: A Survey

Since the proposalTian, Chunwei of big data analysis and GraphicXu, Yong Processing Unit (GPU), theFei, Lunke deep learning techniqueYan, Ke has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising in recent years. Moreover, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising.
Land use/land cover dynamics using landsat data in a gold mining basin-the Ankobra, Ghana

The Ankobra River basin, which forms part of the Southern-Western River System of Ghana, has become a hub for both large and small-scale mining activities. Some of the activities of these mines threatens the sustainability of its resources in the basin. A study in the basin which focuses on changes in LULC patterns in the basin over the past years is therefore paramount to understand the changes that has taken place and its potential impact on resources in the basin. This study assessed the pattern of Land use/cover changes, and the possible drivers of change, in the basin. The study used multi-spectral Landsat images of 30 m resolution for the years 1991, 2002, 2008 and 2016. The Semi-Automatic Classification Plugin (SCP) in QGIS was used for atmospheric correction, image classification using the spectral angle mapping algorithm, and post processing. The overall difference between the reference and the classified map of 2016 was 5.5% with the overall quantity, exchange and shift components as 1.5%, 3% and 1% respectively. Findings from the study show that, the Ankobra River basin has witnessed noticeable changes over the 25-year study period. Closed forest which occupied 40.4% of the total basin area in 1991 reduced drastically to 22.8% in 2016. The dominant LULC change patterns in the basin are from Closed Forest to Open Forest; Open Forest to Farmland, Settlement/Bare land and Mining Area; and the increase in surface area of Water consequently resulting from the increase in Mining Area. Mining activities, particularly illegal mining was identified to be the dominant driver of deforestation in the Ankobra Basin between the years 2008 and 2016 where mining activities in the basin sharply increased.
Couple-based particle swarm optimization for short-term hydrothermal scheduling

A novel couple-based particle swarm optimization (CPSO) is presented in this paper, and applied to solve the short-term hydrothermal scheduling (STHS) problem. In CPSO, three improvements are proposed compared to the canonical particle swarm optimization, aimed at overcoming the premature convergence problem. Dynamic particle couples, a unique sub-group structure in maintaining population diversity, is adopted as the population topology, in which every two particles compose a particle couple randomly in each iteration. Based on this topology, an intersectional learning strategy using the partner learning information of last iteration is employed in every particle couple, which can automatically reveal useful history information and reduce the overly rapid evolution speed. Meanwhile, the coefficients of each particle in a particle couple are set as distinct so that the particle movement patterns can be described and controlled more precisely. In order to demonstrate the effectiveness of our proposed CPSO, the algorithm is firstly tested with four multimodal benchmark functions, and then applied to solve an engineering multimodal problem known as STHS, in which two typical test systems with four different cases are tested, and the results are compared with those of other evolutionary methods published in the literature.