Reviewerly/exHarmony huggingface dataset:
A Remote-Sensing Image Encryption Scheme Using DNA Bases Probability and Two-Dimensional Logistic Map

Remote-sensing images contain lots of visual information about transportation systems, geomorphic conditions, and communal facilities. The secure storage and transmission of remote-sensing images are of great significance. This paper proposes a novel image encryption scheme-based DNA bases probability and applies it to remote-sensing images for data protection. First, the plain-image is randomly encoded in DNA rules and its result participates in DNA addition with DNA mask generated by 2-D logistic map. Second, the cryptosystem executes 2-D logistic map again with DNA bases probability against differential attacks. Third, the pixel-level rearrangement and the DNA base-level rearrangement are, respectively, implemented in order of chaos sequences for permutation and diffusion. The cipher-image has a uniform distribution, low coefficients, and ideal entropy. The cryptosystem has acceptable encryption speed ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$speed=\textrm {0.651308Mbit/s}$ </tex-math></inline-formula> ) and high sensitivity to the plain-image and the security key. The experimental results show that the proposed algorithm can resist various existing attack schemes against remote-sensing images.
Synthesis of Dynamic Masks for Infinite-Step Opacity

We investigate the problem of synthesizing dynamic masks that preserve the infinite-step opacity in the context of discrete-event systems. Dynamic mask is an information acquisition mechanism that controls the observability of the system's events dynamically online, e.g., by turning sensors on/off. A system equipped with a dynamic mask is said to be infinite-step opaque if an outside intruder that can access all acquired information can never infer that the system was at some secret state for any specific previous instant. Existing works on the dynamic mask synthesis problem can only preserve the current-state opacity. However, synthesizing dynamic masks for the infinite-step opacity, which is stronger than the current-state opacity, is much more challenging. The main reason is that the delayed information is involved in this problem and whether or not a current secret can be revealed depends on sensing decisions to be synthesized in the future. In this paper, a new type of information state is proposed to capture all the delayed information in the infinite-step opacity synthesis problem. An effective algorithm is then presented to solve the synthesis problem, which extends existing dynamic mask synthesis techniques from the current-state opacity to infinite-step opacity. Additionally, an information-state-reduction-based approach is proposed to further mitigate the computational complexity of the synthesis procedure. Finally, we discuss how to generalize our results to a class properties with delayed information including infinite-step K-anonymity and infinite-step indistinguishability.
Value-aware Recommendation based on Reinforcement Profit Maximization

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-k recommendation lists in terms of precision, recall, MAP, etc. However, an important expectation for commercial recommendation systems is to improve the final revenue/profit of the system. Traditional recommendation targets such as rating prediction and top-k recommendation are not directly related to this goal.
Co-designing a digital platform with boundary objects: bringing together heterogeneous users in healthcare

Healthcare is increasingly permeated with digital platforms supporting cooperative care involving both caregivers (i.e. nurses and physicians) and also patients. New mobile technologies allow for patients to continuously monitor and document their symptoms and gather data that can increase self-care and support the nurse's decision-making process. A design process of such platforms calls for new design approaches involving heterogeneous conditions and goals. Our research is conducted at a clinic that supports cancer patients in their struggles with treatment induced illnesses. The methodological approach is design ethnography that draws from two years of following a design process that resulted in a digital platform to support the care provided by the clinic. The aim of the paper is to analyze how the boundary objects are engaged in the design phases, both concerning what type of boundary objects as well as how they play a role in the different stages of design and we show how boundary objects in design can be used as a mediator for different users' needs and conditions. The research question that this paper explores is: what type of boundary objects can be used, and how are those boundary objects engaged in different design phases during healthcare platform design? We show how different boundary objects come into play during different design phases, from rich narratives, to conceptual formulations and finally into concrete prototypes of the platform. We argue that using boundary objects actively as design tools can inform and forward the design of healthcare platforms and that the approach can guide future design processes, where co-designing with boundary objects can be especially useful as a design approach when doing design with heterogenous user groups in complex settings, such as healthcare settings.
Sketching and Ideation Activities for Situated Visualization Design

We report on findings from seven design workshops that used ideation and sketching activities to prototype new situated visualizations - representations of data that are displayed in proximity to the physical referents (such as people, objects, and locations) to which the data is related. Designing situated visualizations requires a fine-grained understanding of the context in which the visualizations are placed, as well as an exploration of different options for placement and form factors, which existing methods for visualization design do not account for. Focusing on small displays as a target platform, we reflect on our experiences of using a diverse range of sketching activities, materials, and prompts. Based on these observations, we identify challenges and opportunities for sketching and ideating situated visualizations. We also outline the space of design activities for situated visualization and highlight promising methods for both designers and researchers.
Personalized Recommendation of Social Images by Constructing a User Interest Tree With Deep Features and Tag Trees

In view of the great diversity and complexity of social images, it is of great significance to improve the performance of personalized recommendation by learning a user interest from large-scale social images. Deep learning, as the latest research in the field of artificial intelligence, provides a new personalized recommendation solution of social images for learning a user's interest. Moreover, social image sharing websites (such as Flickr) allow users to tag uploaded images with tags. As an important image semantic cue, effective tags not only represent the latent image information but also show personalized user interest. Therefore, a personalized recommendation method of social image is proposed by constructing a user-interest tree with deep features and tag trees in this paper. The main contributions of our paper are as follows: first, to efficiently make use of tags, a tag tree of social images is created by the re-ranked tags; second, for compactly representing the image content, deep features are learned by training the AlexNet network; third, a user-interest tree is constructed with deep features and tag trees that include the user-interest tree of social images and the user-interest tree of tags, respectively, and finally, a personalized recommendation system of social images is built based on a user-interest tree. Experiments on the NUS-WIDE dataset have shown that our method outperforms state-of-the-art methods in terms of both precision and recall of personalized recommendations.
Robust manifold broad learning system for large-scale noisy chaotic time series prediction: A perturbation perspective

Noises and outliers commonly exist in dynamical systems because of sensor disturbations or extreme dynamics. Thus, the robustness and generalization capacity are of vital importance for system modeling. In this paper, the robust manifold broad learning system(RM-BLS) is proposed for system modeling and large-scale noisy chaotic time series prediction. Manifold embedding is utilized for chaotic system evolution discovery. The manifold representation is randomly corrupted by perturbations while the features not related to low-dimensional manifold embedding are discarded by feature selection. It leads to a robust learning paradigm and achieves better generalization performance. We also develop an efficient solution for Stiefel manifold optimization, in which the orthogonal constraints are maintained by Cayley transformation and curvilinear search algorithm. Furthermore, we discuss the common thoughts between random perturbation approximation and other mainstream regularization methods. We also prove the equivalence between perturbations to manifold embedding and Tikhonov regularization. Simulation results on large-scale noisy chaotic time series prediction illustrates the robustness and generalization performance of our method.
Uncle-Block Attack: Blockchain Mining Threat Beyond Block Withholding for Rational and Uncooperative Miners

Blockchain-based cryptocurrency replaces centralized institutions with a distributed network of Internet-based miners to generate currency and process financial transactions. Such blockchain systems reach consensus using proof of work (PoW), and the miners participating in PoW join mining pools to reduce the variance for more stable reward income. Prior literature in blockchain security/game theory identified practical attacks in block withholding attack (BWH) and the state of the art fork-after-withholding (FAW), which have the rational and uncooperative attacker compromise a victim pool and pose as a PoW contributor by submitting shares but withholding the blocks. We advance such threat strategy (creating greater reward advantage to the attackers at the expense of the other miners in the victim pool) and introduce the uncle-block attack (UBA) which exploits uncle blocks for block withholding. We analyze UBA's incentive compatibility and identify and model the critical systems- and environmental- parameters which determine the attack's impacts. Our analyses and simulations results show that a rational attacker is always incentivized to launch the UBA attack strategy (over FAW or protocol compliance) and that UBA is effective even in the unfavorable networking environment (in contrast, in such case, FAW is reduced to the suboptimal BWH attack and does not make use of the withheld block).
String correction using the Damerau-Levenshtein distance

In the string correction problem, we are to transform one string into another using a set of prescribed edit operations. In string correction using the Damerau-Levenshtein (DL) distance, the permissible edit operations are: substitution, insertion, deletion and transposition. Several algorithms for string correction using the DL distance have been proposed. The fastest and most space efficient of these algorithms is due to Lowrance and Wagner. It computes the DL distance between strings of length m and n, respectively, in O(mn) time and O(mn) space. In this paper, we focus on the development of algorithms whose asymptotic space complexity is less and whose actual runtime and energy consumption are less than those of the algorithm of Lowrance and Wagner.We develop space- and cache-efficient algorithms to compute the Damerau-Levenshtein (DL) distance between two strings as well as to find a sequence of edit operations of length equal to the DL distance. Our algorithms require O(s min{m,n}+m+n) space, where s is the size of the alphabet and m and n are, respectively, the lengths of the two strings. Previously known algorithms require O(mn) space. The space- and cache-efficient algorithms of this paper are demonstrated, experimentally, to be superior to earlier algorithms for the DL distance problem on time, space, and enery metrics using three different computational platforms.Our benchmarking shows that, our algorithms are able to handle much larger sequences than earlier algorithms due to the reduction in space requirements. On a single core, we are able to compute the DL distance and an optimal edit sequence faster than known algorithms by as much as 73.1% and 63.5%, respectively. Further, we reduce energy consumption by as much as 68.5%. Multicore versions of our algorithms achieve a speedup of 23.2 on 24 cores.
Model Predictive Control Design for Dynamical Systems Learned by Echo State Networks

The objective of this letter is to analyze the stability properties, the training procedure and the use in predictive control schemes of echo state networks (ESNs), a specific class of recurrent neural networks. First, a sufficient condition guaranteeing incremental input-to-state stability (δ ISS) of ESNs is derived. Then, an automatic procedure to optimally tune the parameters of the ESN in the training phase is presented, which allows to enforce δ ISS. Finally, the application of the ESN as a model of the plant for predictive control purposes is studied. In particular, an asymptotically convergent observer is designed, and a model predictive controller with guaranteed stabilizing properties is devised for the solution to regulation problems. Simulation results on a nonlinear process for pH neutralization confirm the effectiveness of the proposed control scheme.
Evolutionary Planning of Multi-UAV Search for Missing Tourists

In recent years, there have been increasing reports of missing tourists around the world. The use of unmanned aerial vehicles (UAVs) can significantly improve the performance of search and rescue operations. However, planning the search paths of UAVs can be a highly complex optimization problem, and one of the most challenging tasks in the problem formulation is the estimation of target location probability distribution over time. This paper presents a problem of scheduling multiple UAVs to search for missing tourists and proposes a method for estimating tourist location probabilities which change with topographic features, weather conditions, and time. To solve the problem efficiently, we propose a hybrid evolutionary algorithm which consists of the main algorithm and a sub-algorithm. The main algorithm uses specific migration and mutation operators to evolve a population of main solutions, and the sub-algorithm combines a problem-specific heuristic and tabu search method to optimize each UAV path. The experimental results on a wide variety of test instances (including five real-world instances) demonstrate the performance advantages of the proposed method.
Interactive visualization of big data in the field of accounting

Purpose Big Data introduces high amounts and new forms of structured, unstructured and semi-structured data into the field of accounting and this requires alternative data management and reporting methods. Generating insights from these new data sources highlight the need for different and interactive forms of visualization in the field of visual analytics. Nonetheless, a considerable gap between the recommendations in research and the current usage in practice is evident. In order to understand and overcome this gap, a detailed analysis of the status quo as well as the identification of potential barriers for adoption is vital. The paper aims to discuss this issue. Design/methodology/approach A survey with 145 business accountants from Austrian companies from a wide array of business sectors and all hierarchy levels has been conducted. The survey is targeted toward the purpose of this study: identifying barriers, clustered as human-related and technological-related, as well as investigating current practice with respect to interactive visualization use for Big Data. Findings The lack of knowledge and experience regarding new visualization types and interaction techniques and the sole focus on Microsoft Excel as a visualization tool can be identified as the main barriers, while the use of multiple data sources and the gradual implementation of further software tools determine the first drivers of adoption. Research limitations/implications Due to the data collection with a standardized survey, there was no possibility of dealing with participants individually, which could lead to a misinterpretation of the given answers. Further, the sample population is Austrian, which might cause issues in terms of generalizing results to other geographical or cultural heritages. Practical implications The study shows that those knowledgeable and familiar with interactive Big Data visualizations indicate high perceived ease of use. It is, therefore, necessary to offer sufficient training as well as user-centered visualizations and technological support to further increase usage within the accounting profession. Originality/value A lot of research has been dedicated to the introduction of novel forms of interactive visualizations. However, little focus has been laid on the impact of these new tools for Big Data from a practitioner’s perspective and their needs.
Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification

For Zero-Shot Learning (ZSL), the Nearest Neighbor (NN) search is generally conducted for classification, which may cause unacceptable computational complexity for large-scale datasets. To compress zero-shot classes by the trained quantizer for efficient search, it tends to induce large quantization error because distributions between seen and unseen classes are different. However, as semantic attributes of classes are available in ZSL, both seen and unseen classes have the same distribution for one specific property, e.g., animals have or not have spots. Based on this intuition, a Product Quantization Zero-Shot Learning (PQZSL) method is proposed to learn embeddings as well as quantizers to compress visual features into compact codes for Approximate NN (ANN) search. Particularly, visual features are projected into an orthogonal semantic space, and then the Product Quantization (PQ) is utilized to quantize individual properties. Experimental results on five benchmark datasets demonstrate that unseen classes are represented by the Cartesian product of quantized properties with little quantization error. As classes in orthogonal common space are more discriminative, the classification based on PQZSL achieves state-of-the-art performance in Generalized Zero-Shot Learning (GZSL) task, meanwhile, the speed of ANN search is 10-100 times higher than traditional NN search.
Energy Equilibria in Proof-of-Work Mining

The Bitcoin protocol induces miners, through monetary rewards, to expend energy in order to add blocks to the chain. We show that, when energy costs are substantial and taken into account, counterintuitive and unintended strategic behavior results: In a simple bounded-horizon setting with two identical miners there is a unique pure symmetric equilibrium in which both miners first "slow down" in order to decrease the crypto complexity and then take advantage of this decrease. If miners have different energy efficiencies and are restricted to choose the same hash rate for many epochs, there is a unique pure equilibrium in which miners either participate at low levels that depend in intricate ways on all the other miners' efficiencies, or choose to abstain from mining if their efficiency is too low. In the general setting in which miners can adapt their hash rates over time, we show that, unless the number of miners is very small, the only possible pure equilibria are rather chaotic, with miners quitting and starting again periodically --- or there is no pure equilibrium at all. We discuss the implications of these results for the stability of proof-of-work protocols.
Towards Explaining the Effects of Data Preprocessing on Machine Learning

Ensuring the explainability of machine learning models is an active research topic, naturally associated with notions of algorithmic transparency and fairness. While most approaches focus on the problem of making the model itself explainable, we note that many of the decisions that affect the model's predictive behaviour are made during data preprocessing, and are encoded as specific data transformation steps as part of pre-learning pipelines. Our research explores metrics to quantify the effect of some of these steps. In this initial work we define a simple metric, which we call volatility, to measure the effect of including/excluding a specific step on predictions made by the resulting model. Using training set rebalancing as a concrete example, we report on early experiments on measuring volatility in two public benchmark datasets, Students' Academic Performance and German Credit, with the ultimate goal of identifying predictors for volatility that are independent of the dataset and of the specific preprocessing step.
An empirical study of sentiments in code reviews

Modern code reviews are supported by tools to enhance developers' interactions allowing contributors to submit their opinions for each committed change in form of comments. Although the comments are aimed at discussing potential technical issues, the text might enclose harmful sentiments that could erode the benefits of suggested changes. In this paper, we study empirically the impact of sentiment embodied within developers' comments on the time and outcome of the code review process. Based on historical data of four long-lived Open Source Software (OSS) projects from a code review system we investigate whether perceived sentiments have any impact on the interval time of code changes acceptance. We found that (1) contributors frequently express positive and negative sentiments during code review activities; (2) the expressed sentiments differ among the contributors depending on their position within the social network of the reviewers (e.g., core vs peripheral contributors); (3) the sentiments expressed by contributors tend to be neutral as they progress from the status of newcomer in an OSS project to the status of core team contributors; (4) the reviews with negative comments on average took more time to complete than the reviews with positive/neutral comments, and (5) the reviews with controversial comments took significantly longer time in one project. Through this work, we provide evidences that text-based sentiments have an impact on the duration of the code review process as well as the acceptance or rejection of the suggested changes.
A feasible-ratio control technique for constrained optimization

In constrained optimization problems (COPs), a crucial issue is that most constraint-handling evolutionary algorithms (EAs) approach the optimum either mainly from feasible regions or mainly from infeasible regions. This may result in bias in search of feasible and infeasible solutions. To address this issue, we propose a feasible-ratio control technique which controls the ratio of feasible solutions in the population. By using the control technique, an EA can maintain the search balance from feasible and infeasible regions. Based on this technique, we propose a constraint-handling EA, named FRC-CEA. It consists of two-stage optimization. In the first stage, an enhanced dynamic multi-objective evolutionary algorithm (DCMOEA) with the feasible-ratio control technique is adopted to handle constraints. In the second stage, a commonly used differential evolution (DE) is used to speed up the convergence. The performance of the proposed method is evaluated and compared with six state-of-the-art constraint-handling algorithms on two sets of benchmark test suites. Experimental results suggest that the proposed method outperforms or is highly competitive against the compared algorithms on most test problems.