Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Blurred Face Recognition System Using GLDA Features with LCDR Classification

Objective: To design an effective face recognition system invariant to all image degradation parameters.Methods: The proposed system designed an efficient image restoration based on Iterative Graph based Image Restoration technique.It provides high reconstruction rate.Gabor Linear Discriminant Analysis (GLDA) feature extraction method is used to extract features for the restored faces images and Linear Collaborative Discriminant Regression Classifier (LCDRC) is adopted.GLDA based feature extraction is the combination of both the features like Gabor and LDA which are used to obtain the maximum recognition rate.Findings: The LCDRC gives a discriminant subspace with maximum collaborative between-class reconstruction error and minimum within-class reconstruction error.Applications/Improvements: It is an improvement over the linear discriminant regression classifier (LDRC).From the experimentation results, it has been achieved a recognition rate of 99.2% even in the case of blurred face images.
A Bivariate Exponential Distribution

We introduce a new bivariate exponential distribution that is analytically tractable and easily implementable. Some of its properties are discussed. Explicit expressions of the expected value of the larger and of the smaller of a pair of correlated exponentials are also provided. An application to tandem queues offers several interesting implications. We first review three of our papers that provide background to the main result of this chapter—a new bivariate exponential distribution.
Tuning Fuzzy SPARQL Queries in a Fuzzy Logic Programming Environment

We have recently designed FSA-SPARQL, an extension of the SPARQL query language for querying fuzzy RDF datasets. Answers of FSA-SPARQL queries are usually annotated with truth degrees which are computed from fuzzy connectives and operators that act on truth degrees associated to RDF triples. While FSA-SPARQL offers a rich repertoire of fuzzy connectives and operators, it is not always easy to retrieve the user's expected answers. This is very often due to wrong formulation of queries, caused by inadequate use/combination of fuzzy connectives, operators and thresholds. For instance, a high threshold for truth degrees in some RDF datasets can lead to an empty set of answers, some strong or weak restrictive combination of fuzzy conditions might produce few or too many answers, etc. On the other hand, our research group has also developed the fuzzy logic programming language $\mathcal{F}\mathcal{A}\mathcal{S}\mathcal{I}\mathcal{L}\mathcal{L}$, which has been equipped with tuning techniques for enabling the customization of queries from test cases. In this paper, our goals are: (1) to provide a FSA-SPARQL translation to $\mathcal{F}\mathcal{A}\mathcal{S}\mathcal{I}\mathcal{L}\mathcal{L}$ and (2) apply the tuning techniques to FSA-SPARQL queries for getting more precise formulation of queries from test cases.
Quantum detection theory and optimum strategy in quantum radar system

As new concept radar combing quantum technology with radar technology, quantum radar is an extension of traditional radar theory and has the potential to break through the limit of conventional radar detection performance. We study quantum radar optimum decision strategies for various signal and background forms and compare them with traditional photon-counting strategies. Quantum optimum strategy gives better detection probability with fixed false-alarm probability, thus reducing needed signal strength to detect the target, but its validity strongly depends on exact knowledge of signal forms. We briefly discuss the influence of deviating signal amplitudes and phases, and find that in the condition that the false-alarm probability is very small quantum optimum strategy is still useful when the signal form varies in a small region.
Performance Evaluation of Deep Learning frameworks on Computer Vision problems

Deep Learning (DL) applications have skyrocketed in recent years and are being applied in various domains. There has been a tremendous surge in the development of DL frameworks to make implementation easier. In this paper, we aim to make a comparative study of GPU-accelerated deep learning software frameworks such as Torch and TenserFlow (with Keras API). We attempt to benchmark the performance of these frameworks by implementing three different neural networks, each designed for a popular Computer Vision problem (MNIST, CIFAR10, Fashion MNIST). We performed this experiment on both CPU and GPU(Nvidia GeForce GTX 960M) settings. The performance metrics used here include evaluation time, training time, and accuracy. This paper aims to act as a guide to selecting the most suitable framework for a particular problem. The special interest of the paper is to evaluate the performance lost due to the utility of an API like Keras and a comparative study of the performance over a user-defined neural network and a standard network. Our interest also lies in their performance when subjected to networks of different sizes.
Transactional editing: giving ACID to programmers

Collaboration among programmers today mostly relies on file-based version control systems. These systems typically use optimistic locking to enable parallel work, meaning that competing edits (edit conflicts) are detected and have to be resolved at update or commit time. While merging edits can partly be automated, it is an error-prone task that can introduce inconsistencies. Pessimistic locking of the files to be edited does not appear to be a popular alternative, however, and in any case is insufficient to avoid inconsistency, since it does not account for the dependence of (code in) files on others. To address these problems, we show how the notions of atomicity, consistency, and isolation known from transactional databases can be enforced in the context of collaborative programming. We do so by presenting editing as a set of primitive edit operations applied to an abstract syntax graph overlaid by a constraint graph expressing the consistency criteria mandated by the rules of well-formedness of a language, and by deriving for every sequence of primitive edit operations the write- and read-locks sufficient to: perform the edit sequence, either completely or not at all, in isolation from others; and to achieve global consistency before committing.
Research on a Blockchain-Based Medical Data Management Model

Medical data plays an important role in government regulation of resources, scientific research and precise treatment of medical staffs. Due to the different data management systems used by each hospital, it is difficult to exchange data among them, resulting in a waste of medical resources. In this paper, a medical data management model based on the blockchain is proposed, which takes advantage of the characteristics of the blockchain, such as decentralisation, tamper-proofing and realizability. A data-sharing reward mechanism was designed to maximise the benefits of both a medical data producer (MDP) and a medical data Miner (MDM) in the process of data sharing, while reducing the risk of leakage of a patient's private information. Finally, a reward mechanism was analysed through experiments, which proved the validity and reliability of the medical data management model based on blockchain.
Nonparametric Methods in Cognitively Diagnostic Assessment

Parametric estimation is the prevailing method for fitting diagnostic classification models. In the early days of cognitively diagnostic modeling, publicly available implementations of parametric estimation methods were scarce and often encountered technical difficulties in practice. In response to these difficulties, a number of researchers explored the potential of methods that do not rely on a parametric statistical model—nonparametric methods for short—as alternatives to, for example, MLE for assigning examinees to proficiency classes. Of particular interest were clustering methods because efficient implementations were readily available in the major statistical software packages. This article provides a review of nonparametric concepts and methods, as they have been developed and adopted for cognitive diagnosis: clustering methods and the Asymptotic Classification Theory of Cognitive Diagnosis (ACTCD), the Nonparametric Classification (NPC) method, and its generalization, the General NPC method. Also included in this review are two methods that employ the NPC method as a computational device: joint MLE for cognitive diagnosis and the nonparametric Q-matrix refinement and reconstruction method.
Lesion Detection by Efficiently Bridging 3D Context

Lesion detection in CT (computed tomography) scan images is an important yet challenging task due to the low contrast of soft tissues and similar appearance between lesion and the background. Exploiting 3D context information has been studied extensively to improve detection accuracy. However, previous methods either use a 3D CNN which usually requires a sliding window strategy to inference and only acts on local patches; or simply concatenate feature maps of independent 2D CNNs to obtain 3D context information, which is less effective to capture 3D knowledge. To address these issues, we design a hybrid detector to combine benefits from both of the above methods. We propose to build several light-weighted 3D CNNs as subnets to bridge 2D CNNs’ intermediate features, so that 2D CNNs are connected with each other which interchange 3D context information while feed-forwarding. Comprehensive experiments in DeepLesion dataset show that our method can combine 3D knowledge effectively and provide higher quality backbone features. Our detector surpasses the current state-of-the-art by a large margin with comparable speed and GPU memory consumption.
Adversarial Learning for Deformable Image Registration: Application to 3D Ultrasound Image Fusion

We present an adversarial learning algorithm for deep-learning-based deformable image registration (DIR) and apply to 3D liver ultrasound image fusion. We consider DIR as a parametric optimization model that aims to find displacement field of deformation. We propose an adversarial learning framework inspired by generative adversarial network (GAN) to predict the displacement field without ground-truth spatial transformation. We use convolutional neural network (CNN) and a spatial transform layer as registration network to generate the registered image. Similarity metrics of image intensity and vessel masks are used as loss function for the training. We also optimize a discrimination network to measure the divergence between the registered image and the fixed image. Feedback from the discrimination network can guide the registration network for more accurate and realistic deformation. Moreover, we incorporate an autoencoder network to extract anatomical features from vessel masks as shape regularization. Our approach is end-to-end, only requires image pair as input in registration tasks. Experiments show that the proposed method outperforms state-of-the-art deep-learning-based methods.
Educational Mobile Apps for Negotiation: Evaluation and Design Criteria for Curriculum Designers and Developers

Mobile learning (m-learning) through smartphones is gaining in popularity. Nascent Vietnamese entrepreneurs tend to generate new entrepreneurial knowledge and skills through m-learning due to its characteristics. Acquiring negotiation skills and strategies through m-learning is a practical route for nascent Vietnam entrepreneurs. This study describes and applies a set of criteria, covering both pedagogical and technical aspects, to evaluate negotiation education applications (apps) on mobile Android operating system.The results showed that no app related to negotiation education in the Vietnamese language was found on Google Play Store. In the English language, there are 6 distinct free apps appropriate to English speakers who are nascent Vietnamese entrepreneurs. Learning contents were limited to 2 goals of negotiation, which were reaching a business agreement and increasing profits. Target users were not clearly defined. Materials used were not diverse when all apps heavily included text in the learning contents. The virtual environment was not interactive since all the apps did not offer a feedback feature. The customisation, including the levels of difficulties of the educational apps was absent. The authors posit that curriculum designers and developers have opportunities to improve the negotiation training apps used on smartphones by filling the aforementioned gaps.
Actor-based incremental tree data processing for large-scale machine learning applications

A number of online machine learning techniques based on tree model have been studied in order to cope with today's requirements of quickly processing large scale data-sets. We present a design pattern for incremental tree data processing as gradually constructing on-demand tree-model on memory. Our approach adopts the actor model as making use of multi-cores and distributed computers without largely rewriting code for algorithms. The pattern basically defines a node in the tree as an actor which is the unit of asynchronous processes and each data instance flows between actor nodes as a message. We study concrete two machine learning algorithms, VFDT for decision tree's top-down growth and BIRCH for hierarchical clustering's bottom up growth. For supporting VFDT, we propose an extension mechanism of replicating root nodes so that it can address bottleneck as starting of inputs. For supporting BIRCH, we split processes of recursive construction into asynchronous steps with correcting target node by traversing extra horizontal links between sibling nodes. We carried out machine learning tasks with our implementation on top of Akka Java, and we confirmed reasonable performance for the tasks with large scale data-sets.
Sea‐surface floating small target detection based on feature compression

Owing to characteristics of sea clutter and diversity of floating small targets, it is significant to extract features to jointly detect targets. Due to the complexity of detectors in high-dimensional (HD) space, feature compression is an important procedure in the design of detector. Besides, considering that the capacities of detecting target about extracted features are varied with different datasets, feature selection is supposed to be an effective method. Here, it is found that building a feature-compression matrix can realise that mapping the feature vectors in HD space into low-dimensional space, where the matrix is built efficiently by using the results of feature selection. Whereas information about targets which is used in building feature-compression matrix is unknown, a training sample generator which can emulate the fundamental state of targets to help to build a feature-compression matrix is proposed. Finally, a one-class classifier about the feature vector which has been compressed is provided with using a new 3D convexhull learning algorithm. The experiment results on the IPIX datasets show that the proposed detector attains better detection performance than several existing detectors.
Colour image denoising by eigenvector analysis of neighbourhood colour samples

Colour image smoothing is a challenging task because it is necessary to appropriately distinguish between noise and original structures, and to smooth noise conveniently. In addition, this processing must take into account the correlation among the image colour channels. In this paper, we introduce a novel colour image denoising method where each image pixel is processed according to an eigenvector analysis of a data matrix built from the pixel neighbourhood colour values. The aim of this eigenvector analysis is threefold: (i) to manage the local correlation among the colour image channels, (ii) to distinguish between flat and edge/textured regions and (iii) to determine the amount of needed smoothing. Comparisons with classical and recent methods show that the proposed approach is competitive and able to provide significative improvements.
Method of Moving Object Detection from Mobile Vision System

The task of detecting mobile objects is important in the development of video surveillance systems, optical navigation systems, etc. Currently, in connection with processes and the organization of product development, in which the most effective means are being used, technical vision systems are increasingly introduced. The most topical direction in this area is the development associated with the visual perception of moving objects. Due to the existence of the possibility of wide application of the method in the vision systems of robots, there is considerable interest in the detection of objects moving in space. In this paper, a stationary vision system is analyzed, with an object moving relative to it entering its field of view. In this case, the task of object selection is narrowed down to analyzing the sequence of images and identifying changes. However, the situation is different in the case of a dynamic vision system, when not only the movement of the object under study takes place, but the system that conducts observation also moves relative to the environment. Since dynamic vision systems can be used in mobile surveillance systems, they are of the greatest interest for study. The article investigates the method of analyzing the two-dimensional motion of an object and suggests a method for selecting objects moving in three-dimensional space, as well as a mathematical model for describing this method. For this, it is necessary to define an additional variable characterizing the depth of the points of the scene, in contrast to the method of two-dimensional motion of the object. The proposed method allows determining the spatial coordinates of dynamic objects from the moving bases of the vision systems.
Automatic identification of irony

Sentiment analysis has been applied to large masses of data produced by social media, allowing to investigate users' opinion about products, brands and news. However, the analysis of text that contains irony remains a challenge, since irony reverses the meaning of a text. This paper aims to detect irony in Twitter posts. For this purpose a dataset was built by crawling ironic and not ironic posts. The construction of the dataset included the creation of features through Bag of words (BOW) and n-grams. The dataset was used to construct a Support-vector machine (SVM) model which was evaluated by K-fold cross-valiation method.
LocPass: A Graphical Password Method to Prevent Shoulder-Surfing

Graphical passwords are a method of authentication in computer security. Computer security is one of the disciplines of computer science. Shoulder-surfing attacks are a well-known threat to graphical passwords, although is getting commonly used especially in granting access for a secure system. Shoulder-surfing occurs when attackers skillfully capture important data/activities, such as login passwords, via direct observation or video recording methods. Many methods have been proposed to overcome the problem of shoulder-surfing attacks. After we reviewed some related works, we found out that most of the existing methods are still vulnerable to multiple observations and video-recorded shoulder-surfing attacks. Thus, we propose a new method to combat this problem. In our proposed method, we make used of two concepts to combat shoulder-surfing attacks. In the first concept, we used registered locations (something that only the users know) and 5 image directions (something that the users can see) to determine a pass-location (new knowledge). Secondly, the images used in our proposed method have higher chances to offset each other. The idea of offset could increase the password spaces of our proposed method if an attacker intended to guess the registered location used. By combining these two concepts, the pass-location produced by our proposed method in each challenge set could be varied. Therefore, it is impossible for the attackers to shoulder-surf any useful information such as the images/locations clicked by the user in each challenge set. A user study was conducted to evaluate the capabilities of the proposed method to prevent shoulder-surfing attacks. The shoulder-surfing testing results indicated that none of the participants were able to login, although they knew the underlying algorithm and they have been given sufficient time to perform a shoulder-surfing attack. Therefore, the proposed method has proven it can prevent shoulder-surfing attacks, provided the enrolment procedure is carried out in a secure manner.
Effect of Multiple Additive Misinformation Noise on Immunity of Associative Stegoprotection

Associative steganography is a special case of the historical stencil method of steganography, when a hidden message is stencilled onto a blank sheet, after which a meaningful text is formed with such an insert. The difference is that in this case the message is embedded in the noise picture. To form a message, a two-dimensional associative masking mechanism is used. On the basis of previous studies, it was shown that the use of multiple sets of masks (stego-keys) significantly increases the noise immunity of the stored and transmitted data under the action of random and purposive noise. This paper presents the results of experimental studies to assess noise immunity using the method of forming intentional additive noise with redundancy. It also gives practical recommendations for noise control.
Heuristic search via graphical structure in temporal interval-based planning for deep space exploration

Operations of conventional spacecraft used to be planned on ground and are uploaded as telecommands and executed on board at due time. However, because of difficulties in communicating with distant spacecraft, direct human control for the spacecraft is infeasible. Therefore, great hopes are placed on automated planning techniques to enhance the security of the spacecraft. By deciding a coon, as to support opportunistic science, the planner is mplex set of activities or states, an onboard planner is able to effectively arrange the daily tasks on a spacecraft. In additialso required to respond in the shortest possible time. Typically, to better characterize the spacecraft, the timeline-based knowledge representation benefits from its powerful ability to describe time and temporal behaviors, which is essential to effectively address real world problems. Compliant with the representation method, an elegant approach is devised for search guidance and solving problems efficiently in space-like contexts. Specifically, the key technique we build on is the heuristic estimate strategy based on a graphical structure defined in the model. Furthermore, a search algorithm joint with the heuristic function is proposed to avoid redundant work. By evaluating the branching nodes, this approach is able to prune irrelevant search space and make improvements in onboard planning efficiency. Our experiments exhibit an excellent performance on tested instances compared to Europa2.