Reviewerly/exHarmony huggingface dataset:
Quality Matters: Understanding the Impact of Incomplete Data on Visualization Recommendation

Incomplete data is a crucial challenge to data exploration, analytics, and visualization recommendation. Incomplete data would distort the analysis and reduce the benefits of any data-driven approach leading to poor and misleading recommendations. Several data imputation methods have been introduced to handle the incomplete data challenge. However, it is well-known that those methods cannot fully solve the incomplete data problem, but they are rather a mitigating solution that allows for improving the quality of the results provided by the different analytics operating on incomplete data. Hence, in the absence of a robust and accurate solution for the incomplete data problem, it is important to study the impact of incomplete data on different visual analytics, and how those visual analytics are affected by the incomplete data problem. In this paper, we conduct a study to observe the interplay between incomplete data and recommended visual analytics, under a combination of different conditions including: (1) the distribution of incomplete data, (2) the adopted data imputation methods, (3) the types of insights revealed by recommended visualizations, and (4) the quality measures used for assessing the goodness of recommendations.
DC-MMD-GAN: A New Maximum Mean Discrepancy Generative Adversarial Network Using Divide and Conquer

Generative adversarial networks (GANs) have a revolutionary influence on sample generation. Maximum mean discrepancy GANs (MMD-GANs) own competitive performance when compared with other GANs. However, the loss function of MMD-GANs is an empirical estimate of maximum mean discrepancy (MMD) and not precise in measuring the distance between sample distributions, which inhibits MMD-GANs training. We propose an efficient divide-and-conquer model, called DC-MMD-GANs, which constrains the loss function of MMD to tight bound on the deviation between empirical estimate and expected value of MMD and accelerates the training process. DC-MMD-GANs contain a division step and conquer step. In the division step, we learn the embedding of training images based on auto-encoder, and partition the training images into adaptive subsets through k-means clustering based on the embedding. In the conquer step, sub-models are fed with subsets separately and trained synchronously. The loss function values of all sub-models are integrated to compute a new weight-sum loss function. The new loss function with tight deviation bound provides more precise gradients for improving performance. Experimental results show that with a fixed number of iterations, DC-MMD-GANs can converge faster, and achieve better performance compared with the standard MMD-GANs on celebA and CIFAR-10 datasets.
Robust integrated covariance intersection fusion Kalman estimators for networked systems with random measurement delays, multiplicative noises, and uncertain noise variances

Summary In this article, the robust distributed fusion Kalman filtering problems are addressed for the networked mixed uncertain multisensor systems with random one‐step measurement delays, multiplicative noises, and uncertain noise variances. A new augmented state approach with fictitious measurement noises modeled by the first‐order moving average models is presented, by which the original system is transformed into a standard uncertain system only with uncertain‐variance fictitious white noises. Based on the minimax robust estimation principle and Kalman filtering theory, a universal integrated covariance intersection (ICI) fusion approach is presented in the sense that first of all the robust local estimators and their conservative error variances and crosscovariances are presented, and then integrating the local estimation information yields ICI fusers. An extended Lyapunov equation approach with two kinds of Lyapunov equations is presented in order to prove the robustness and to compute fictitious noise statistics. Applying these approaches, the minimax robust local, ICI, and fast ICI fused Kalman estimators (predictor, filter, and smoother) are presented, such that for all admissible uncertainties, their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. Their robustness, accuracy relations, and convergence are also proved. The proposed ICI fusers improve the robust accuracies and overcome the drawbacks of the original covariance intersection fusers, such that the robust local estimators and their conservative variances are assumed to be known, and their conservative crosscovariances are ignored. Two simulation examples applied to the offshore platform system verify their correctness, effectiveness, and applicability.
A Reputation Incentive Mechanism of Crowd Sensing System Based on Blockchain

Crowd Sensing (CS) is a kind of crowdsourcing that utilizes the built-in sensors and applications in intelligent devices, and has recently become a promising solution for distributed sensing. Crowd Sensing perception is a new data acquisition mode combining crowdsourcing idea and mobile device perception ability. CS refers to the formation of interactive and participatory perception network through people’s existing mobile devices, and the release of perception tasks to individuals or groups in the network to complete, so as to help professionals or the public to collect data, analyze information and share knowledge. This paper proposes a kind of CS perception system based on blockchain network, which processes the data of CS perception, and protects the privacy and information security of CS perception data. The reputation incentive mechanism is applied to the CS data analysis, which improves the data quality effectively. Experiments show that the mechanism proposed in this paper has an effective effect on improving quality of data.
Understanding barriers to medical instruction access for older adults

Recalling medical instructions provided during a doctor's visit can be difficult due to access barriers, primarily for older adults who visit doctors multiple times per year and rely on their memory to act on doctor's recommendations. There are several interventions that aid patients in recalling information after doctors' visits; however, some have been proven ineffective, and those that are effective can present additional challenges for older adults. In this paper, we explore the challenges that older adults with chronic illnesses face when collecting and recalling medical instructions from multiple doctors' visits and discuss implications for AI-assisted tools to enable older adults better access medical instructions. We interviewed 12 older adults to understand their strategies for gathering and recalling information, the challenges they face, and their opinions about automatic transcription of their conversations with doctors to help them recall information after a visit. We found that participants face accessibility challenges such as hearing information and recalling medical instructions that require additional time or follow-up with the doctor. Therefore, patients saw potential value for a tool that automatically transcribes and helps with recall of medical instructions, but desired additional features to summarize, categorize, and highlight critical information from the conversations with their doctors.
Performance-Agnostic Fusion of Probabilistic Classifier Outputs

We propose a method for combining probabilistic outputs of classifiers to make a single consensus class prediction when no further information about the individual classifiers is available, beyond that they have been trained for the same task. The lack of relevant prior information rules out typical applications of Bayesian or Dempster-Shafer methods, and the default approach here would be methods based on the principle of indifference, such as the sum or product rule, which essentially weight all classifiers equally. In contrast, our approach considers the diversity between the outputs of the various classifiers, iteratively updating predictions based on their correspondence with other predictions until the predictions converge to a consensus decision. The intuition behind this approach is that classifiers trained for the same task should typically exhibit regularities in their outputs on a new task; the predictions of classifiers which differ significantly from those of others are thus given less credence using our approach. The approach implicitly assumes a symmetric loss function, in that the relative cost of various prediction errors are not taken into account. Performance of the model is demonstrated on different benchmark datasets. Our proposed method works well in situations where accuracy is the performance metric; however, it does not output calibrated probabilities, so it is not suitable in situations where such probabilities are required for further processing.
Vision models fine-tuned by cinema professionals for High Dynamic Range imaging in movies

Abstract Many challenges that deal with processing of HDR material remain very much open for the film industry, whose extremely demanding quality standards are not met by existing automatic methods. Therefore, when dealing with HDR content, substantial work by very skilled technicians has to be carried out at every step of the movie production chain. Based on recent findings and models from vision science, we propose in this work effective tone mapping and inverse tone mapping algorithms for production, post-production and exhibition. These methods are automatic and real-time, and they have been both fine-tuned and validated by cinema professionals, with psychophysical tests demonstrating that the proposed algorithms outperform both the academic and industrial state-of-the-art. We believe these methods bring the field closer to having fully automated solutions for important challenges for the cinema industry that are currently solved manually or sub-optimally. Another contribution of our research is to highlight the limitations of existing image quality metrics when applied to the tone mapping problem, as none of them, including two state-of-the-art deep learning metrics for image perception, are able to predict the preferences of the observers.
Control of constrained high dimensional nonlinear liquid level processes using a novel neural network based Rapidly exploring Random Tree algorithm

The control of constrained nonlinear liquid level systems is a problem of fundamental importance in pharmaceutical, chemical, food-processing, oil refining and natural liquid gas separation industries. This paper proposes a novel control strategy for the control of such constrained high-dimensional interacting liquid level systems. The nonlinear liquid level regulation problem is formulated as a path planning problem in high-dimensional state space where constraint satisfaction is viewed as obstacle avoidance. An approximate control policy to steer the system to the goal state while satisfying numerous level and flow-rate constraints is computed using the famous RRT path planning algorithm which can efficiently explore non-convex spaces. To further improve performance a neural network was trained to generalize the approximate control policy computed by the RRT to unexplored states and provide smooth control. The generalized control policy learnt by the neural network is then used to achieve large changes in state and bring the system close to the goal state after which computationally cheap linear control is used to keep the system close to the goal state. The effectiveness of the proposed ANN-RRT control approach is demonstrated by applying it to the control of constrained high dimensional 5, 10, 20, 30 and 50 interacting tank systems. Experimental results for a highly interacting quadruple tank system indicate that the ANN-RRT algorithm significantly outperforms alternate approaches like PID, Fuzzy control, MPC, IMC and SMC from recent literature.
Mode Switching from a Security Perspective: First Findings of a Systematic Literature Review

With increased interoperability of cyber-physical systems (CPSs), security becomes increasingly critical for many of these systems. We know mode switching from domains like aviation and automotive, and we imagine to use this mechanism for the development of resilient systems that continue to function correctly even if under malicious attack. If vulnerabilities are detected or even known, modes can be switched to reduce the attack surface and to minimize attackers' range of activity. We propose to engineer CPSs with multi-modal software architectures to overcome the interval between the time when zero-day vulnerabilities become known and the time when corresponding updates become available. Thus, affected companies, operators and people will be able to protect themselves and their customers without having to wait for security updates. This paper presents first findings of a systematic literature review (SLR) on mode switching from a security perspective.
Performing Tasks Can Improve Program Comprehension Mental Model of Novice Developers

Program comprehension is challenging for many novice developers. Literature indicates that program comprehension is greatly influenced by the specific purpose of reading a program, i.e., the task. However, the task has often been used in research as a measure for program comprehension. Our study takes an inverse approach to investigate the effect of using the task as a facilitator to improve novice developers program comprehension. To measure the effect, our previously published program comprehension mental model of novice developers was utilized. In a sense, the study provides an empirical evaluation of our proposed model in terms of its ability to capture the novice developer's mental model properly. The comprehensive experiment involved one hundred and seventy-eight (178) novice developers from three (3) universities and investigated the effect of six (6) tasks with difficulties ranked according to the cognitive categories of Revised Bloom Taxonomy. The results of the experiment confirmed that performing the tasks can improve program comprehension of novice developers. It demonstrated that different tasks improved different abstraction levels of the mental model and further indicated that higher cognitive category tasks improve program comprehension mental model at higher abstraction levels. The results also showed that the mental model we have proposed earlier is able to capture what novice developers know in response to the tasks they perform. The general implication of the study is that the tasks can be an effective tool for computing educators to incorporate program comprehension in programming courses, whereby these tasks need to be introduced in stages in the teaching of programming; starting initially from the lower cognitive categories' tasks such as Recall and culminating at the higher cognitive categories' tasks such as Modification by first taking into consideration the novices' programming levels.
Ambiguous requirements: A semi-automated approach to identify and clarify ambiguity in large-scale projects

In large-scale projects, the client defines a set of product requirements, which the provider is then expected to deliver within the agreed time, cost and scope. If a client sets ambiguous requirements for the project, this may result in the receipt of an unsatisfactory product. Therefore, reducing ambiguity in product requirements by the client is a critical success factor. Despite its significance and regular occurrence, the requirement ambiguity problem has not yet received a methodological solution that can fit large-scale projects, which commonly include a great number of requirements. This paper proposes a semi-automated approach, which combines natural language processing (NLP) to identify ambiguous terms and statements and a soft computing technique to specify these terms using fuzzy set theory. This work contributes to the current literature on requirement specification by highlighting a line of research which paves the way to leverage the applications of advanced tools to allow the clarification of ambiguous requirements in large-scale projects.
Object-oriented software metrics in software code vulnerability analysis

Development of quality object-oriented software contains security as an integral aspect of that process. During that process, a ceaseless burden on the developers was posed in order to maximize the development and at the same time to reduce the expense and time invested in security. In this paper, the authors analyzed metrics for object-oriented software in order to evaluate and identify the relation between metric value and security of the software. Identification of these relations was achieved by study of software vulnerabilities with code level metrics. By using OWASP classification of vulnerabilities and experimental results, we proved that there was relation between metric values and possible security issues in software. For experimental code analysis, we have developed special software called SOFTMET.
Adaptive Regularization-Incorporated Latent Factor Analysis

The valuable knowledge contained in High-dimensional and Sparse (HiDS) matrices can be efficiently extracted by a latent factor (LF) model. Regularization techniques are widely incorporated into an LF model to avoid overfitting. The regularization coefficient is very crucial to the prediction accuracy of models. However, its tuning process is time-consuming and boring. This study aims at making the regularization coefficient of a regularized LF model self-adaptive. To do so, an adaptive particle swarm optimization (APSO) algorithm is introduced into a regularized LF model to automatically select the optimal regularization coefficient. Then, to enhance the global search capability of particles, we further propose an APSO and particle swarm optimization (PSO)-incorporated (AP) algorithm, thereby achieving an AP-based LF (APLF) model. Experimental results on four HiDS matrices generated by real applications demonstrate that an APLF model can achieve an automatic selection of regularization coefficient, and is superior to a regularized LF model in terms of prediction accuracy and computational efficiency.
Target Tracking Analysis for Stone Soup

The International Society of Information Fusion (ISIF) Stone Soup project seeks to bring together advances in target tracking through an open-source repository of software libraries. Additionally, the ISIF uncertainty reasoning working group provides an open-source ontology. This paper seeks to demonstrate the correspondence between the open source tracking repository and the Uncertainty Representation and Reasoning Evaluation Framework (URREF) ontology. For example, many target tracking challenge problems propose a scenario for data fusion techniques to solve, from which various performance metrics are considered for evaluation. The Stone Soup framework has the MetricGenerator class and the URREF has the accuracy class. The example presented in the paper utilizes the cubature Kalman filter to determine the impact of corrupted measurements on the track accuracy as an instance of the Stone Soup and URREF metrics.
Plenty of Phish in the Sea: Analyzing Potential Pre-attack Surfaces

Advanced Persistent Threats (APTs) are one of the main challenges in modern computer security. They are planned and performed by well-funded, highly-trained and often state-based actors. The first step of such an attack is the reconnaissance of the target. In this phase, the adversary tries to gather as much intelligence on the victim as possible to prepare further actions. An essential part of this initial data collection phase is the identification of possible gateways to intrude the target. In this paper, we aim to analyze the data that threat actors can use to plan their attacks. To do so, we analyze in a first step 93 APT reports and find that most (80%) of them begin by sending phishing emails to their victims. Based on this analysis, we measure the extent of data openly available of 30 entities to understand if and how much data they leak that can potentially be used by an adversary to craft sophisticated spear phishing emails. We then use this data to quantify how many employees are potential targets for such attacks. We show that 83% of the analyzed entities leak several attributes of uses, which can all be used to craft sophisticated phishing emails.
Statically Checking REST API Consumers

Consumption of REST services has become a popular means of invoking code provided by third parties, particularly in web applications. Nowadays programmers of web applications can choose TypeScript over JavaScript to benefit from static type checking that enables validating calls to local functions or to those provided by libraries. Errors in calls to REST services, however, can only be found at runtime. In this paper, we present SRS, a language that extends the support of static analysis to calls to REST services, with the ability to statically find common errors such as missing or invalid data in REST calls and misuse of the results from such calls. SRS features a syntax similar to JavaScript and is equipped with a rich collection of types and primitives to natively support REST calls that are statically validated against specifications of the corresponding APIs written in the HeadREST language.
Learning perception prediction and English hierarchical model based on neural network algorithm

The traditional English examination and the current examination system have been unable to meet the needs of the education industry for English examinations. In view of this, based on the neural network algorithm, this study proposes a hierarchical network management model from the user’s perspective. Based on the in-depth study of the neural network, this study combined with the network performance characteristics of large data volume, complex data to propose a new BP neural network algorithm. By dynamically changing the momentum factor and learning rate, the algorithm has greatly improved the accuracy and stability of the error. In addition, this study proposes a user perception prediction model, and the model is continuously trained on the model based on the improved BP neural network algorithm and the monitored network performance. In order to study the performance of the research model, a control experiment is designed to analyze the performance of the model. The research results show that the intelligent model and algorithm proposed in this paper are completely feasible and effective.
Neglected Cybersecurity Risks in the Public Internet Hosting Service Providers

The provision of cybersecurity is of basic importance for every effective information system It is possible to provide most rich information services, but only one neglected cybersecurity risk may compromise the system and all services it provides Therefore, meeting the cybersecurity requirements is a prerequisite for the safety and security of IT infrastructures, digital resources, and the protection of private data In that respect, the themes of cryptography and sufficiently robust random number generation are of particular interest This article looks for the golden ratio between the provision of mass services and the efforts to meet cybersecurity requirements It suggests a method and discusses the possibilities to increase the cryptographic protection in information systems