Reviewerly/exHarmony huggingface dataset:
PSO-based ANFIS for quadrotor system trajectory-tracking control

This paper presents an application of particle swarm optimization algorithm (PSO) to tune the scaling gains of Adaptive Neuro-Fuzzy Inference System (ANFIS) controller with a multi-closed loop control applied to nonlinear quadrotor system. First, PID control approach is used to obtain the training data set that is necessary to ANFIS design. Precisely, only the error and the error rate inputs in addition to output are used in training data set vector. Afterwards, an adaptive Neuro-Fuzzy inference system controller is designed. Then, to tune the scaling gains associated with ANFIS controller, PSO algorithm is used. Finally, an integral control action is added to the ANFIS controller output for controlling each state of the MIMO quadrotor system. The simulation test show that the results obtained with ANFIS-PSO controller are better, when compared to those obtained using conventional ANFIS controller and traditional PID.
A Survey on Challenges and Research Opportunities in Opinion Mining

With huge development of internet and its usage, and with the advancement in latest mobile technology, internet usage and its users are essentially increasing day by day, and as a part of this, people are using this platform to express their feeling, opinion and reviews about their activity, feeling, frustration, place, person, sports, movie, politics, trolls, gossips, etc., i.e., today whatever happening around us, we are interested in sharing them either in our social media walls, or we want to express them, as a comments or reviews in many private blogs. Today even feedbacks were asked by many search engines like, “how about sharing your recent experience?”and consider Google itself, which do ask every users opinion about the recent activity they did. It may be a place they have visited, or shop where they did transaction, or new Gmail experience, or it may be anything. So many things happening around world are reached out in internet to people and also being shared very fast. Hence, opinion mining has become, a way to extract users feeling and analyze them for later usage. These extracted opinions are very important for manufacturers or the owner of an enterprise to modify their existing service, so as to upgrade and improve their quality and services that user wish for. Opinions are useful in all most every field. Opinions are not only useful but also misleading sometimes. Usually, public opinion consisting of both negative and positive feedback which plays a vital role, but while doing analysis we may face lot of new challenges. Challenges may be while identifying user views based on good or bad, worth or not worth, real or fake, etc. In today’s time, it’s not only text, but also emoji’s, hashtags and gif videos speaks about opinions. This paper consists of all those recent challenges that are faced with some examples, which may helpful to present issues and discussed with next state of text opinion mining process.
A new fuzzy unit selection cost function optimized by relaxed gradient descent algorithm

In data-driven corpus-based text-to-speech synthesis systems, the main issue is to select the most natural-sounding sequence of acoustic units without unnatural acoustic transitions, and to minimize all acoustic mismatches at the concatenation points. Unit selection algorithms incorporating unit selection cost functions have been known to synthesize speech close to natural quality. However, these algorithms operate over large acoustic inventories with huge number of acoustic units in a broad spectrum of linguistic, prosodic and acoustic contexts, and with a huge number of concatenation possibilities. Moreover, the shape of the unit selection cost function, which evaluates the cost of concatenating two subsequent acoustic units, is modelled manually in a time-consuming and laborious iterative process, which is based on subjective evaluation. Since this process must be repeated for any new acoustic inventory, or even after changes in a given acoustic inventory, we propose instead a new fuzzy unit selection cost function. We further propose to optimize fully automatically the shape of the fuzzy unit selection cost function to the given acoustic inventory’s context by using a relaxed gradient descent algorithm, where the subjective tests are replaced by a novel objective measure needed to evaluate unit selection cost function performance. Furthermore, the proposed approach is fully interpretable and also highlights insights into which parts of the fuzzy unit selection cost function’s shape could be further improved. The experiments show that the optimized fuzzy unit selection cost function significantly outperforms the baseline fuzzy unit selection cost function. Moreover, the results prove that the unit selection optimization algorithm is capable of finding the optimal shape of the fuzzy unit selection cost function, even when optimized over a small subset of sentences.
Replay anti-spoofing countermeasure based on data augmentation with post selection

Automatic Speaker Verification (ASV) systems have been widely applied for speaker authentication for biometric security especially in e-business scenarios. However, vulnerabilities of the ASV technology have been discovered and have generated much interest to design anti-spoofing countermeasures. Serious threats can be posed by replay attacks, which are difficult to detect and easy to mount with accessible devices. In this paper, an efficient replay anti-spoofing countermeasure based on data augmentation with post selection is proposed. The auxiliary classifier generative adversarial network (AC-GAN) is adopted to generate more speech samples with diverse variants. To select generated samples of high quality and avoid the bias caused by human subjective perception, we also propose a convolutional neural network (CNN) based post-filter. By integrating data augmentation and post selection approaches, issues of over-fitting and lack of generalization can be significantly alleviated with extra informative training data. The proposed anti-spoofing countermeasure is evaluated on the ASVspoof 2017 Version 2.0 database. Experimental results measured by equal error rates (EERs) indicate a promising improvement over the development and evaluation subsets. This provides the motivation for novel audio data augmentation and also promotes the future research on generation selection in the application of speaker spoofing detection.
Augmented Reality for the Manufacturing Industry: The Case of an Assembly Assistant

This paper studies the impact of augmented reality (AR) on manual assembly operations in the manufacturing industry. A review of AR-based solutions in this field shows that assembly assistants capable to support the user in key activities (identification, handling, alignment, joining, adjustment and inspection) are particularly beneficial. Since assembly activities are generally not addressed within comprehensive solutions, an AR-based tool supporting all of them is proposed, and its effectiveness in terms of completion time and error rate is compared with the use of the corresponding paper-based instructions. Results of a user study unveil that the proposed tool generally reduces the number of errors, but the time needed to complete the assembly tends to increase. Limitations of the current solution and potential directions for future work are discussed.
A Convex Discriminant Semantic Correlation Analysis for Cross-View Recognition

Canonical correlation analysis (CCA) is a typical statistical model used to analyze the correlation components between different view representations of the same objects. When the label information is available with the data representations, CCA can be extended to its discriminative counterparts by incorporating supervision in the analysis. Although most discriminative variants of CCA have achieved improved results, nearly all of their objective functions are nonconvex, implying that optimal solutions are difficult to obtain. More important, that cross-view representations from the same sample should be consistent, that is, the cross-view semantic consistency has however not been modeled. To overcome these drawbacks, in this article, we propose a discriminant semantic correlation analysis (DSCA) model by modeling the cross-view semantic consistency for each object in the sample space rather than in the commonly used feature space. To boost the nonlinear discriminating capability of DSCA, we extend it from the Euclidean to the geodesic space by transforming the metric and incorporating both the cross-view semantic and representation correlation information and consequently obtain our final model with convex objective, namely, convex DSCA (C-DSCA). Finally, with extensive experiments and comparisons, we validate the effectiveness and superiority of the proposed method.
Complexity of abstract argumentation under a claim-centric view

Abstract argumentation frameworks have been introduced by Dung as part of an argumentation process, where arguments and conflicts are derived from a given knowledge base. It is solely this relation between arguments that is then used in order to identify acceptable sets of arguments. A final step concerns the acceptance status of particular statements by reviewing the actual contents of the acceptable arguments. Complexity analysis of abstract argumentation so far has neglected this final step and is concerned with argument names instead of their contents, i.e. their claims. As we outline in this paper, this is not only a slight deviation but can lead to different complexity results. We, therefore, give a comprehensive complexity analysis of abstract argumentation under a claim-centric view and analyse the four main decision problems under seven popular semantics. In addition, we also address the complexity of common sub-classes and introduce novel parameterisations – which exploit the nature of claims explicitly – along with fixed-parameter tractability results.
Virtual and mixed reality in the study of the geometry of the crystal lattice

Abstract The article considers the possibility of using virtual and mixed reality in studying the geometry of the crystal lattice in the framework of the course of solid state physics. The application of mixed and virtual reality technologies in the study of various branches of physics is receiving more and more attention. Virtual reality headsets were used in practical and laboratory classes, where students had to create crystal structures and carry out modeling of various processes. When studying complex spatial objects of crystallography, virtual reality technology allowed students to familiarize themselves with the object of study in more detail, understand the structure of crystals and, accordingly, complete the task in a timely manner. At the same time, the prolonged use of virtual reality headsets led to rapid fatigue of students.
On the Design of Co-operating Blockchains for IoT

Enabling blockchain technology into IoT can help to achieve a proper distributed consensus based IoT system that overcomes disadvantages of today's centralized infrastructures, such as, among others, high cloud server maintenance costs, weakness for supporting time-critical IoT applications, security and trust issues. However, meeting requirements posed by IoT in blockchain domain is not an easy endeavour. [1] proposes Hybrid-IoT, as a step towards decentralizing IoT with the help of blockchain technology. Hybrid-IoT consists of multiple PoW sub-blockchains to achieve distributed consensus among IoT devices and an inter-connector framework, to execute transactions between sub-blockchains. In this paper, we take the first step towards designing an inter-connector for multiple blockchains for IoT that is specifically tailored for the Hybrid-IoT architecture. We also provide a detailed security discussion, in order to identify threats and we provide discussion on how to cope with threats.
A New Approach to Robust Estimation of Parametric Structures

Most robust estimators require tuning the parameters of the algorithm for the particular application, a bottleneck for practical applications. The paper presents the multiple input structures with robust estimator (MISRE), where each structure, inlier or outlier, is processed independently. The same two constants are used to find the scale estimates over expansions for each structure. The inlier/outlier classification is straightforward since the data is processed and ordered with the relevant inlier structures listed first. If the inlier noises are similar, MISRE’s performance is equivalent to RANSAC-type algorithms. MISRE still returns the correct inlier estimates when inlier noises are very different, while RANSAC-type algorithms do not perform as well. MISRE’s failures are gradual when too many outliers are present, beginning with the least significant inlier structure. Examples from 2D images and 3D point clouds illustrate the estimation.
Online Multi-Object Tracking With Visual and Radar Features

Multi-object tracking (MOT) constructs multiple object trajectories by associating detections between consecutive frames while maintaining object identities.In many autonomous systems equipped with a camera and a radar, an amplitude and visual features can be measured.Therefore, our goal is to solve a MOT problem by associating detections with both features.To achieve it, we propose a unified MOT framework based on object model learning and confidence-based association.For improving discriminability between different objects, we present a method to learn several visual and amplitude object models during online tracking.By applying the learned object models for the affinity evaluation, we improve the confidence-based association further.In addition, we present a practical track management method to initialize and terminate tracks, and eliminate duplicated false tracks.We implement several MOT systems with different object model learning and association methods, and compare our system with them on challenging visual MOT datasets.We further compare our method with the recent deep appearance learning methods.These comparisons verify that our method can achieve the competitive tracking accuracy while maintaining a low MOT complexity.
Hierarchical interval type-2 fuzzy path planning based on genetic optimization

This paper focuses on the path planning of mobile robot. Fuzzy logic is employed to deal with the uncertainty in the process of path planning. The hierarchical interval type-2 fuzzy method is obtained by combining the hierarchical fuzzy and interval type-2 fuzzy method, which is used in the path pl anning of mobile robot. Hierarchical fuzzy structure can simplify complex system and get fuzzy rules more easily. For multi input system, it can also solve the problem of rule explosion. Compared with type-1 fuzzy, interval type-2 fuzzy can better deal with the uncertainty in the process of path planning. Finally, in order to get a better path, genetic algorithm is used to optimize the membership function in the fuzzy path planner. Through the simulation experiment, the proposed hierarchical type-2 fuzzy planning method can effectively solve the path planning problem. Compared with the type-1 fuzzy method, the interval type-2 fuzzy method and the hierarchical type-1 fuzzy method, the proposed method obtains better results.
Security analysis on dummy based side-channel countermeasures—Case study: AES with dummy and shuffling

Side-channel analysis is a serious type of attack that can break mathematically secure cryptographic algorithms. Many studies have designed countermeasures against side-channel analysis, such as masking and hiding schemes. Frequently, designers employ combined countermeasures that use both a first-order masking scheme and a hiding scheme to provide sufficient security and efficiency. Random insertion of dummy operations scheme, which is one of the hiding schemes, randomly changes the execution time of the operation to be attacked by inserting dummy operations. However, if the dummy operations can be distinguished from real ones, attackers could extract secret information with lower complexity than the intended attack complexity with the designer inserting the dummy operations. In this paper, we present a novel vulnerability that can enable dummy and real operations to be distinguished for various implementation methods using C language on the XMEGA128D4 microprocessor. This novel vulnerability occurs regardless of the four methods of implementation of dummy operations and the compile levels. We also present a new countermeasure against this vulnerability and demonstrate the security through practical experimentation.
Image fusion for stabilized medical video sequence using multimodal parametric registration

Medical imaging is a boon to mankind. Processing of medical images is extensively blooming with the aid of technological advancements. Medical images taken from different modalities are stabilized to remove the jittery artifacts and further processed for fusing the images to enable the physicians to visualize the combined features of CT and MRI. Alignment of both the images is performed to ensure parametric registration so as to effectively blend and overlay for proper diagnosis. To assure the quality of the images, basic optimization is implemented and the subjective visualization and objective evaluation are performed. The proposed approach, image fusion with stabilization and registration outperforms the existing techniques in terms of subjective and objective evaluation.
A Practical Universal Consortium Blockchain Paradigm for Patient Data Portability on the Cloud Utilizing Delegated Identity Management

This paper presents a blockchain system that exploits the base modular features of the Hyperledger Fabric framework to allow for a new, patient-centric, practical data portability paradigm. The system empowers patients by allowing them to choose the entities they trust in the partially decentralized model, in an attempt to minimize the drawbacks associated with the resultant partial centralization. Data is stored in the cloud, and a fingerprint of the data is stored on the blockchain, allowing for data provenance, integrity, auditability and entity accountability. A simple yet effective method of identity management delegation is detailed, which accounts for and addresses human error in private key management in a practical manner.
Image inpainting using sparse multiscale representations: Image recovery performance guarantees

Several strategies have been applied for the recovery of the missing parts in an image, with recovery performance depending significantly on the image type and the geometry of missing data. To provide a deeper understanding of such image restoration problem, King et al. recently introduced a rigorous multiscale analysis framework and proved that a shearlet based inpainting approach outperforms methods based on more conventional multiscale representations when missing data are line singularities. In this paper, we extend and improve the analysis of the inpainting problem to the more realistic and more challenging setting of images containing curvilinear singularities. We derive inpainting performance guarantees showing that exact image recovery is achieved if the size of the missing singularity is smaller than the size of the structure elements of appropriate functional representations of the image. Our proof relies critically on the microlocal and sparsity properties of the shearlet representation.
Multi-scale measurement of high-reflective surfaces by integrating near-field photometric stereo with touch trigger probe

Precision measurement of large scale surface topography is a challenging task for high-reflective parts. This paper presents a multi-sensor system to address this issue by integrating the technical merits of touch trigger probe and photometric stereo. The probe is used to extract a set of high-precision sparse points for an initial surface construction, which is then applied to drive a near-field photometric stereo for recovering dense surface normal of the surface. By fusing the sparse discrete points with the dense surface normal, high-resolution surface topography can be obtained accordingly. Experiments confirm the state-of-the-art performance of the proposed method.
New High Capacity Reversible Data Hiding Using the Second-Order Difference Shifting

Reversible data hiding (RDH) algorithm is developing rapidly for many years.Usually, the histogram shifting (HS) based RDH algorithm has two main steps: Firstly, a steep difference histogram is generated with some effective methods for difference.Secondly, by extending and shifting some differences in the histogram, bits of information can be embedded into the cover image which can be restored reversibly.In this paper, we proposed the second-order difference to obtain a steeper difference histogram.Firstly, we slide a window with size 2 × 2 through the image.For each block, we can get the two first-order differences by calculating the absolute values of the two differences for its two columns.Thus, the secondorder difference of each block, which is the absolute values of the difference of the two first-order differences, can be obtained.By extending and shifting the second-order difference, a bit may be embedded into the block finally.Experiments reveal that the proposed algorithm outperforms the previous state-of-the-art RDH methods in terms of the computational complexity, image distortion and the embedding performance.
A Fuzzy Approach for Diabetes Mellitus Type 2 Classification

This paper proposes an automatic fuzzy classification system for glycemic index, which indicates the level of Diabetes Mellitus type 2. Diabetes is a chronic disease occurred when there is deficiency in insulin production or in its action, or both, causing complications. Neuro-fuzzy systems and Decision Trees are used to obtain, respectively, the numerical parameters of the membership functions and the linguistic based rules of the fuzzy classification system. The results goal to categorize the glycemic index into 4 classes: decrease a lot, decrease, stable and increase. Real database from [1] is used and the input attributes of the system are defined. In addition, the proposed automatic fuzzy classification system is compared with an "expert" fuzzy classification system, which is totally modeled using expert knowledge. From linguistic based rules obtained from fuzzy inference process, new scenarios are simulated in order to obtain a larger data set which provides a better evaluation of the classification systems. Results are promising, since they indicate the best treatment - intervention or comparative - for each patient, assisting in the decision-making process of the health care professional.
R-FCN Based Laryngeal Lesion Detection

In recent years, with the emergence of high-computing GPUs and the introduction of new convolutional neural network models, deep learning technology has ushered in a new round of development. At the same time, in the era of rapid development of the Internet, a large amount of high-quality medical image data is generated every day, which provides a new opportunity for the deep learning to develop in the field of automatic analysis and auxiliary diagnosis of medical images. In this paper, R-FCN (region-based fully convolutional networks) are introduced into the lesion detection of the larynx, and the classification result of the lesion can be given while confirming the lesion area. In view of the existing image-based classification, there can only be one organ in the image and the background must be simple. Through the target detection method, multi-organ target can be realized and detected under complex background. The experimental results show that the target in complex scenes can be effectively identified when the training data is sufficient.
A Hyperledger-based P2P Energy Trading Scheme using Cloud Computing with Low Capabillity Devices

We propose a P2P energy trade system using Hyperledger Fabric and cloud computing. We illustrate the requirements of P2P energy trade system that can provide enhanced performance in terms of speed, flexibility and scalability.