Reviewerly/exHarmony huggingface dataset:
Model Predictive Visual Servoing of Fully-Actuated Underwater Vehicles With a Sliding Mode Disturbance Observer

This paper presents a sliding-mode observer-based model predictive control (SMO-MPC) strategy for image-based visual servoing (IBVS) of fully-actuated underwater vehicles subject to field of view and actuator constraints and model uncertainties.In the proposed SMO-MPC controller, the visual system model and the approximate underwater vehicle model are used to predict the future trajectories from the current states driven by input candidates over a certain horizon.With the consideration of system uncertainties, including external disturbances and unknown dynamic parameters, a sliding-mode observer is designed to estimate the modeling mismatch, which is feedforward to the dynamic model in MPC.The actual control signals are generated at each step by minimizing a cost function of predicted trajectories under system constraints.The effectiveness of the proposed SMO-MPC IBVS controller is verified by comparative simulations using a fully-actuated underwater vehicle with different control configurations.
Twitter sentiment analysis using fuzzy integral classifier fusion

A thorough analysis of people’s sentiment about a business, an event or an individual is necessary for business development, event analysis and popularity assessment. Social networks are rich sources of obtaining user opinions about people, events and products. Sentiment analysis conducted using multiple user comments and messages on microblogs is an interesting field of data mining and natural language processing (NLP). Different techniques and algorithms have recently been developed for conducting sentiment analysis on Twitter. Different proposed classification and pure NLP-based methods have different behaviours in predicting sentiment orientation. In this study, we combined the results of the classic classifiers and NLP-based methods to propose a new approach for Twitter sentiment analysis. The proposed method uses a fuzzy measure for determining the importance of each classifier to make the final decision. Fuzzy measures are used with the Choquet fuzzy integral for fusing the classifier outputs in order to generate the final label. Our experiments with different Twitter sentiment datasets show that fuzzy integral-based classifier fusion improves the average accuracy of sentiment classification.
Smart System Based on DOA &amp; IoT for Products Monitoring &amp; Anti-Counterfeiting

Counterfeit products is a Global Problem affecting a Wide Range of Industries. There are huge losses due to counterfeits every year in the world. At present, there are many anti-counterfeiting technologies such as RFID tags, NFC tags, barcode, phosphor physical uncloneable function (PUF), laser security mark, and digital watermarking. In this paper, is proposed an anti-counterfeiting smart system based on digital object architecture (DOA) for products identification to protect them from counterfeiting. The main idea behind using DOA with IoT technologies gateways is to provide standard access to heterogeneous information and services about products such as identification, description, search and retrieval, security, integrity, trust and typing of data. This system gives effective technical references for enterprises to monitor whole life cycle of products with internet of things. At the end of this paper, we gave an example of using this method is to monitor the production of halal products.
Adaptive rational fractal interpolation function for image super-resolution via local fractal analysis

Image super-resolution aims to generate high-resolution image based on the given low-resolution image and to recover the details of the image. The common approaches include reconstruction-based methods and interpolation-based methods. However, these existing methods show difficulty in processing the regions of an image with complicated texture. To tackle such problems, fractal geometry is applied on image super-resolution, which demonstrates its advantages when describing the complicated details in an image. The common fractal-based method regards the whole image as a single fractal set. That is, it does not distinguish the complexity difference of texture across all regions of an image regardless of smooth regions or texture rich regions. Due to such strong presumption, it causes artificial errors while recovering smooth area and texture blurring at the regions with rich texture. In this paper, the proposed method produces rational fractal interpolation model with various setting at different regions to adapt to the local texture complexity. In order to facilitate such mechanism, the proposed method is able to segment the image region according to its complexity which is determined by its local fractal dimension. Thus, the image super-resolution process is cast to an optimization problem where local fractal dimension in each region is further optimized until the optimization convergence is reached. During the optimization (i.e. super-resolution), the overall image complexity (determined by local fractal dimension) is maintained. Compared with state-of-the-art method, the proposed method shows promising performance according to qualitative evaluation and quantitative evaluation.
Differentially-Private Deep Learning from an optimization Perspective

With the amount of user data crowdsourced for data mining dramatically increasing, there is an urgent need to protect the privacy of individuals. Differential privacy mechanisms are conventionally adopted to add noise to the user data, so that an adversary is not able to gain any additional knowledge about individuals participating in the crowdsourcing, by inferring from the learned model. However, such protection is usually achieved with significantly degraded learning results. We have observed that the fundamental cause of this problem is that the relationship between model utility and data privacy is not accurately characterized, leading to privacy constraints that are overly strict. In this paper, we address this problem from an optimization perspective, and formulate the problem as one that minimizes the accuracy loss given a set of privacy constraints. We use sensitivity to describe the impact of perturbation noise to the model utility, and propose a new optimized additive noise mechanism that improves overall learning accuracy while conforming to individual privacy constraints. As a highlight of our privacy mechanism, it is highly robust in the high privacy regime (when ∈ → 0), and against any changes in the model structure and experimental settings.
Observability analysis and autonomous navigation for two satellites with relative position measurements

The autonomous navigation of multi-satellite systems using only on-board measurements is a fundamental task in many space missions. The main purpose of this study is to establish new methods for the autonomous orbit determination of a dual satellite system using relative position measurements only. A novel concept, k-order local weak observability, is proposed. Based on unperturbed Kepler orbit dynamics and J2-perturbed orbit dynamics respectively, sufficient and necessary conditions of k-order local weak observability are mathematically proved for the autonomous navigation system. Observability-based modified EKF, modified UKF, and modified SDREF are presented and used as navigation algorithms. Simulation results illustrate the performance of the developed navigation algorithms in comparison with the traditional EKF, UKF, and SDREF.
On the Pitfalls and Vulnerabilities of Schedule Randomization Against Schedule-Based Attacks

Schedule randomization is one of the recently introduced security defenses against schedule-based attacks, i.e., attacks whose success depends on a particular ordering between the execution window of an attacker and a victim task within the system. It falls into the category of information hiding (as opposed to deterministic isolation-based defenses) and is designed to reduce the attacker's ability to infer the future schedule. This paper aims to investigate the limitations and vulnerabilities of schedule randomization-based defenses in real-time systems. We first provide definitions, categorization, and examples of schedule-based attacks, and then discuss the challenges of employing schedule randomization in real-time systems. Further, we provide a preliminary security test to determine whether a certain timing relation between the attacker and victim tasks will never happen in systems scheduled by a fixed-priority scheduling algorithm. Finally, we compare fixed-priority scheduling against schedule-randomization techniques in terms of the success rate of various schedule-based attacks for both synthetic and real-world applications. Our results show that, in many cases, schedule randomization either has no security benefits or can even increase the success rate of the attacker depending on the priority relation between the attacker and victim tasks.
Early Action Prediction With Generative Adversarial Networks

Action Prediction is aimed to determine what action is occurring in a video as early as possible, which is crucial to many online applications, such as predicting a traffic accident before it happens and detecting malicious actions in the monitoring system. In this work, we address this problem by developing an end-to-end architecture that improves the discriminability of features of partially observed videos by assimilating them to features from complete videos. For this purpose, the generative adversarial network is introduced for tackling action prediction problem, which improves the recognition accuracy of partially observed videos though narrowing the feature difference of partially observed videos from complete ones. Specifically, its generator comprises of two networks: a CNN for feature extraction and an LSTM for estimating residual error between features of the partially observed videos and complete ones, and then the features from CNN adds the residual error from LSTM, which is regarded as the enhanced feature to fool a competing discriminator. Meanwhile, the generator is trained with an additional perceptual objective, which forces the enhanced features of partially observed videos are discriminative enough for action prediction. Extensive experimental results on UCF101, BIT and UT-Interaction datasets demonstrate that our approach outperforms the state-of-the-art methods, especially for videos that less than 50% portion of frames is observed.
A recommender system based on personal constraints for smart tourism city

In a smart tourism ecosystem, travel communication websites play a critical role in choosing destinations and hotels. This research suggests a travel recommender system for automating word-of-mouth (WOM) effects and providing personalized travel-planning services to tourists. Collaborative filtering (CF)-based recommender systems have been extensively employed for personalization services in diverse areas; the basic principle of CF is WOM communication. This research proposes a travel recommender system that helps a tourist build his/her personalized travel plan based on CF and constraint satisfaction filtering. Constraint satisfaction filtering is adopted to profile a tourist’s needs and circumstances. For this purpose, this research modifies the existing constraint satisfaction method to an approximate constraint satisfaction filtering method that incorporates indifference intervals into constraints. We build a prototype system and a benchmark system to evaluate the effectiveness, usability, and novelty of the proposed travel recommender system. The experimental results demonstrate a methodology for performing personalized tourist’s travel planning and automating WOM communication outperforms the benchmark system.
When is it not Craft?

Craft has emerged as an important reference point for HCI. To avoid a misrepresenting, all-encompassing application of craft to interaction design, this position paper first discerns craft from HCI. It develops material engagement and mediation as differentiating factors to reposition craft in relation to tangible interaction design. The aim is to clarify craft's relation to interaction design and to open up new opportunities and questions that follow from this repositioning.
Information security burnout: Identification of sources and mitigating factors from security demands and resources

This study examines how information security burnout can develop from complying with organisational security demands, and whether security burnout can be reduced by engaging organisational and personal resources. The Job Demands-Resources model was extended to the IT security context, to develop and empirically test a security burnout model, using a sample of 443 participants in Vietnam. The results demonstrate that security task overload and difficult access to security requirements increased security burnout while dealing with challenging security requirements reduced burnout. Neither organisational resources nor user self-efficacy were effective in reducing burnout. Moreover, simple security tasks did not guarantee a burnout-free experience for users. The findings emphasise the significance of providing resources and designing security tasks as challenging and rewarding experiences, rather than simply reducing user involvement as a source of decreasing cyber security risks. The research establishes a theoretical basis for further studying the phenomenon of security burnout and its role in user security management.
High-Performance SAR Image Matching Using Improved SIFT Framework Based on Rolling Guidance Filter and ROEWA-Powered Feature

Severe speckle noise and complex local deformation in synthetic aperture radar (SAR) images degrade the performance of common scale-invariant feature transform (SIFT) based SAR image matching algorithms. To address this issue, this paper presents a new SIFT framework based algorithm by designing a novel nonlinear multiscale space construction strategy and a new local feature detector and descriptor. In the proposed algorithm, the multiscale images are generated by embedding a rolling guidance filter into the procedure of scale space construction, which can achieve better results in edge-preserving despeckling than other strategies. The improved nonlinear multiscale space construction strategy lays a solid foundation for high-quantity feature point detection with a low false ratio. For feature detection, a new feature detector, Harris-Laplace technique combined with the ratio of exponential weighted average, Harris-Laplace ratio of exponential weighted average (ROEWA), is proposed to effectively suppress the false feature points in SAR images. Moreover, a gradient location and orientation histogram powered by ROEWA is designed to obtain feature descriptors, which is robust to local deformations. Finally, the K-nearest neighbor approach is used to speed up the search for initial matching; the spatially consistent random sample consensus is adopted to remove false match points (outliers). The experimental results using simulated and real SAR images demonstrate that the proposed algorithm can achieve much better performance in terms of the matching accuracy and inlier ratio than other methods.
A Machine Learning Approach to Comment Toxicity Classification

Nowadays, derogatoryChakrabarty, Navoneel comments are often made by one another, not only in offline environment but also immensely in online environments like social networking websites and online communities. So, an Identification combined with Prevention System in all social networking websites and applications, including all the communities, existing in the digital world is a necessity. In such a system, the Identification Block should identify any negative online behavior and should signal the Prevention Block to take action accordingly. This study aims to analyze any piece of text and detect different types of toxicity like obscenity, threats, insults and identity-based hatred. The labeled Wikipedia Comment Dataset prepared by Jigsaw is used for the purpose. A 6-headed Machine Learning tf–idf Model has been made and trained separately, yielding a Mean Validation Accuracy of 98.08% and Absolute Validation Accuracy of 91.64%. Such an Automated System should be deployed for enhancing the healthy online conversation.
Alteration and mineralization styles of the orogenic disseminated Zhenyuan gold deposit, southeastern Tibet: Contrast with carlin gold deposit

Orogenic disseminated and Carlin gold deposits share much similarity in alteration and mineralization. The disseminated orogenic Zhenyuan Au deposit along the Ailaoshan shear zone, southeastern Tibet, was selected to clarify their difference. The alteration and mineralization from the different lithologies, including meta-quartz sandstone, carbonaceous slate, meta-(ultra)mafic rock, quartz porphyry and lamprophyre were researched. According to the mineral assemblage and replacement relationship in all types of host rocks, two reactions show general control on gold deposition: (1) replacement of earlier magnetite by pyrite and carbonaceous material; (2) alteration of biotite and phlogopite phenocrysts in quartz porphyry and lamprophyre into dolomite/ankerite and sericite. Despite the lamprophyre is volumetrically minor and much less fractured than other host rocks, it contains a large portion of Au reserve, indicating that the chemically active lithology has played a more important role in gold precipitation compared to structure. LA-ICP-MS analysis shows that Au mainly occurs as invisible gold in fine-grained pyrite disseminated in the host rocks, with Au content reaching to 258.95 ppm. The diagenetic core of pyrite in meta-quartz sandstone enriched in Co, Ni, Mo, Ag and Hg is wrapped by hydrothermal pyrite enriched in Cu, As, Sb, Au, Tl, Pb and Bi. Different host rock lithology has much impact on the alteration and mineralization features. Carbonate and sericite in altered lamprophyre show they have higher Mg than those developed in other of host rocks denoting that the carbonate and sericite incorporated Mg from phlogopite phenocrysts in the primary lamprophyre during alteration. The ore fluid activated the diagenetic pyrite in meta-quartz sandstone leading the hydrothermal pyrite enriched in Cu, Mo, Ag, Sb, Te, Hg, Tl, Pb and Bi, but the hydrothermal pyrite in meta-(ultra)mafic rock is enriched in Co and Ni as the meta-(ultra)mafic rock host rock contain high content of Co and Ni. However, Au and As shear similar range in both types of host rocks indicating that these two elements most likely come from the deep source fluid rather than the host rocks. It was shown in the disseminated orogenic gold deposit that similar hydrothermal alteration with mineral assemblage of carbonate (mainly dolomite and ankerite), sericite, pyrite and arsenopyrite develops in all types of host rocks. This is different from the Nevada Carlin type, in which alteration is mainly dissolution and silicification of carbonate host rock. On the other hand, Au mainly occur as invisible gold in both disseminated orogenic and Carlin gold deposits.
Are Refactorings to Blame? An Empirical Study of Refactorings in Merge Conflicts

With the rise of distributed software development, branching has become a popular approach that facilitates collaboration between software developers. One of the biggest challenges that developers face when using multiple development branches is dealing with merge conflicts. Conflicts occur when inconsistent changes happen to the code. Resolving these conflicts can be a cumbersome task as it requires prior knowledge about the changes in each of the development branches. A type of change that could potentially lead to complex conflicts is code refactoring. Previous studies have proposed techniques for facilitating conflict resolution in the presence of refactorings. However, the magnitude of the impact that refactorings have on merge conflicts has never been empirically evaluated. In this paper, we perform an empirical study on almost 3,000 well-engineered open-source Java software repositories and investigate the relation between merge conflicts and 15 popular refactoring types. Our results show that refactoring operations are involved in 22% of merge conflicts, which is remarkable taking into account that we investigated a relatively small subset of all possible refactoring types. Furthermore, certain refactoring types, such as EXTRACT METHOD, tend to be more problematic for merge conflicts. Our results also suggest that conflicts that involve refactored code are usually more complex, compared to conflicts with no refactoring changes.
Ground Feature Oriented Path Planning for Unmanned Aerial Vehicle Mapping

Unmanned aerial vehicles (UAVs) are being used to take roles that were previously performed by traditional manned aircraft, such as remote sensing and photogrammetry. The standard path planning for UAV mapping is mainly executed by adopting the “lawnmower” mode. However, some situations that have sparse or repetitive features are problematic to map with this technique, given that orthoimage stitching relies heavily on the number and quality of image tie points. Traditional path planning can result in some unregistered images due to a lack of tie points. This paper proposes a ground feature oriented path-planning method for UAV mapping. The method first estimates the distribution of the ground feature points from a lower-resolution image. Then, image footprints are selected by applying a three-step optimization. The flight path for the UAV is then generated by solving the “grouped traveling salesman” problem. This approach ensures the georegistration of images during orthoimage stitching while maximizing the orthoimage coverage. Two cases, including a simulation and a real-world case, together with standard path-planning modes with different overlaps, are selected to evaluate the proposed method. The results show that the proposed method covers the same area with the smallest number of images. The model excludes problematic areas from the scanning path to generate a more efficient processing dataset.
Data stream classification using active learned neural networks

Due to variety of modern real-life tasks, where analyzed data is often not a static set, the data stream mining gained a substantial focus of machine learning community. Main property of such systems is the large amount of data arriving in a sequential manner, which creates an endless stream of objects. Taking into consideration the limited resources as memory and computational power, it is widely accepted that each instance can be processed up once and it is not remembered, making reevaluation impossible. In the following work, we will focus on the data stream classification task where parameters of a classification model may vary over time, so the model should be able to adapt to the changes. It requires a forgetting mechanism, ensuring that outdated samples will not impact a model. The most popular approaches base on so-called windowing, requiring storage of a batch of objects and when new examples arrive, the least relevant ones are forgotten. Objects in a new window are used to retrain the model, which is cumbersome especially for online learners and contradicts the principle of processing each object at most once. Therefore, this work employs inbuilt forgetting mechanism of neural networks. Additionally, to reduce a need of expensive (sometimes even impossible) object labeling, we are focusing on active learning, which asks for labels only for interesting examples, crucial for appropriate model upgrading. Characteristics of proposed methods were evaluated on the basis of the computer experiments, performed over diverse pool of data streams. Their results confirmed the convenience of proposed strategy.