Reviewerly/exHarmony huggingface dataset:
Application of Big Data Analytics in Power System under Single Transmission Line Outage Condition

The contingencies on the system are carried based on the static line voltage stability indices for single transmission line outage occurred in the system. Under the static conditions, the deployment of information will be large in size with respect to the data. This data has to be communicated properly to the planning power engineer. In this regard, the data given to the power engineer has to be monitored properly with the removal of the bad data. So, big data analysis provides a new paradigm for removal of bad data in the system. In this paper, the contingencies are ranked based on the line voltage stability index. The ranking has been carried for single transmission line outage and for different variations in the system load. The data obtained for all the contingencies has been clustered using simple k-mean clustering approach using WEKA software. The proposed algorithms were carried on IEEE 30 Bus system and MATLAB environment is used for generating the data.
Introduction: From Latent Classes to Cognitive Diagnostic Models

This chapter provides historical and structural context for models and approaches presented in this volume, by presenting an overview of important predecessors of diagnostic classification models which we will refer to as DCM in this volume, or alternatively cognitive diagnostic models (CDMs). The chapter covers general notation and concepts central to latent class analysis, followed by an introduction of mastery models, ranging from deterministic to probabilistic forms. The ensuing sections cover knowledge state and rule space approaches, which can be viewed as deterministic skill-profile models. The chapter closes with a section on the multiple classification latent class model and the deterministic input noisy and (DINA) model.
Special issue – Advanced Methods for Evolutionary Many Objective Optimization

Association-rule mining (ARM) or frequent itemset mining (FIM) is the most fundamental task in knowledge discovery, which is used to find the occurrence frequency of the item/sets in transactional database. The other factors such as weight, interestingness or unit profit of the items are not considered whether in ARM or FIM. To reveal more information, high-utility itemset mining (HUIM) was designed to consider both quantity and unit profit of items to discover the high-utility itemsets (HUIs). Several algorithms for FIM or HUIM were extensively studied but fewer works concern both frequency and utility together to provide better solutions in decision-making. In the past, the SKYMINE algorithm was designed to find the skyline frequent-utility patterns (SFUPs). A SFUP is a non-dominated pattern, in which each solution dominates the others by considering the aspects of frequency and utility. The SKYMINE algorithm needs, however, amounts of computation to level-wisely discover the SFUPs. In this paper, an efficient utility-list structure is used instead of the UP-tree structure used in SKYMINE to mine the SFUPs. Two algorithms are respectively designed by using the depth-first search (called SKYFUP-D) and breath-first search (SKYFUP-B) to mine the SFUPs. An efficient structure is also designed to record the maximal utility of the potential itemsets, thus reducing the computations for finding the SFUPs in the search space. Extensive experiments are conducted on several real-world and simulated datasets and the results indicate that the designed two algorithms have better performance than that of the state-of-the-art SKYMINE algorithm in terms of runtime, memory usage, search space size and the scalability.
Breast Tumor Segmentation using Image Segmentation Algorithms

Breast tumor is one of the major reason of death among women worldwide. The main objective of this paper is to compare the accuracy of three different algorithms used to detect breast tumors. Comprehending the nature of data set in breast images is essential for evolving a model that fits well into the data. The performance of a traditional k-means algorithm is compared with a much more sophisticated saliency map and colour spacing algorithm. The three algorithms have been applied on a breast tumor MRI scanned image, and the results clearly showed the best performance is yielded.
Monotonicity properties for two-action partially observable Markov decision processes on partially ordered spaces

This paper investigates monotonicity properties of optimal policies for two-action partially observable Markov decision processes when the underlying (core) state and observation spaces are partially ordered. Motivated by the desirable properties of the monotone likelihood ratio order in imperfect information settings, namely the preservation of belief ordering under conditioning on any new information, we propose a new stochastic order (a generalization of the monotone likelihood ratio order) that is appropriate for when the underlying space is partially ordered. The generalization is non-trivial, requiring one to impose additional conditions on the elements of the beliefs corresponding to incomparable pairs of states. The stricter conditions in the proposed stochastic order reflect a conservation of structure in the problem – the loss of structure from relaxing the total ordering of the state space to a partial order requires stronger conditions with respect to the ordering of beliefs. In addition to the proposed stochastic order, we introduce a class of matrices, termed generalized totally positive of order 2, that are sufficient for preserving the order. Our main result is a set of sufficient conditions that ensures existence of an optimal policy that is monotone on the belief space with respect to the proposed stochastic order.
A Study of Security Modeling Techniques for Smart Systems

The term “smart” has been used in many ways for describing systems and infrastructure such as smart city, smart home, smart grid, smart meter, etc. These systems may lie in the domain of critical security systems where security can be estimated in terms of confidentiality, integrity and some cases may involve availability for protection against the theft or damage of system resources as well as disruption of the system services. Although, in spite of, being a hot topic to enhance the quality of life, there is no concrete definition of what smart system is and what should be the characteristics of it. Thus, there is a need to identify what these systems actually are and how they can be designed securely. This work firstly attempts to describe attributes related to the smartness to define smart systems. Furthermore, we propose a secure smart system development life cycle, where the security is weaved at all the development phase of smart systems according to principles, guidelines, attack patterns, risk, vulnerability, exploits, and defined rules. Finally, the comparative study is performed for evaluation of traditional security modeling techniques for early assessment of threats and risks in smart systems.
A study on the use of sequence-to-sequence neural networks for automatic translation of brazilian portuguese to LIBRAS

The World Health Organization estimates that approximately 466 million people have some level of hearing loss. This significant portion of the world population faces several challenges in accessing information. The main problem is that the languages in which the deaf community can perceive and produce in a natural way are sign languages (SL). An alternative to dealing with this would be the translation of the content from an oral language to SL. However, when it comes to accessing online content, it is necessary to consider translating SL not only for audio or video content, but also for more complex text on websites. This is already a difficult task by itself for the volume involved, and it also addresses some additional challenges, related to the high cost of human interpreter service and the great dynamism of Internet content. In this context, one of the most promising approaches to such scenarios is the use of machine translation applications from oral to sign language. This work evaluates the use of neural network models usually used in natural language processing for the production of LIBRAS glosses from texts in Portuguese. Using a 2k factorial experiment design, we evaluated the impact of several aspects such as database size, types of models and training parameters in the quality of automatic translation obtained. The results of the experiments were very promising and point to an initial superiority of the LightConv model in most of the evaluated scenarios.
Evaluating alternative interfaces based on puff, electromyography and dwell time for mouse clicking

Computer systems are approaching human behavior in the sense of performing similar tasks, such as listening, understanding, thinking, and speaking. Although the forms of interaction with these systems have also followed the path of technology evolution, computer mice and keyboards do continue to play the leading role as a bridge between human and computer. The design of these devices forces the user to use their hands and this represents a major problem that has been already recognized in the literature with several proposed solutions. Nevertheless, in spite of the various methods found, it is difficult to find papers that make comparisons between them. Therefore, this work proposes an evaluation, using quantitative and qualitative analyses, of three alternative methods for mouse click: dwell time, mouth-puffing and electromyography. As result, both analyses showed that the interactions based on mouth-puffing and electromyography performed better than the dwell time method.
A new event detection method for noisy hydrophone data

In this paper, a new method for detecting events in noisy hydrophone data is developed. The method takes an image processing approach to the 1D hydrophone data by first converting it into a log-frequency spectrogram image (cepstrum). This image is then filtered by reconstructing it based on mutual information (MI) criteria of the dominant orientation map. The features of the reconstructed cepstrum are then enhanced using a combination of edge-tracking and noise smoothing. Binary feature classification on the processed cepstrum is performed using a least-squares support vector machine (LS-SVM). Compared to other methods, the proposed image-based event detection method exploits both the scale and the orientation information. The method showed that the detection performance in terms of binary classification sensitivity with more than 55% for rare events such as whale calls from noisy hydrophone recordings (a database size of over 160 h (1943 × 5 min) with a total of 39 events) from the NEPTUNE Canada project, with more than 99% specificity and 98% overall accuracy. With relatively low computational cost and high accuracy, the presented method is useful for automated long-term monitoring of a wide variety of marine mammals and human related activities from hydrophone data. The effectiveness of the proposed method is demonstrated over a large number of real noisy hydrophone recordings.
Decomposition and Formulation of System of Features of Harmful Information Based on Fuzzy Relationships

A method is considered that allows forming a system of features of harmful information that can have a destructive effect on the elements of modern socio-cyber-physical systems. The goal is to prepare the criteria and indicators for procedures of ensuring the detection reliability and the increase in the effectiveness of counteraction of this kind information. The suggested method is based on the algorithms for decomposing and formulating a system of harmful information features using fuzzy relationships. Fuzzy relations, similarity subrelations, and nodes of the graph of fuzzy relations, based on them, help to form a list of the harmful information features which are used to identify such information. This method allows one to eliminate ambiguity in the synthesis of a set of features of harmful information. The proposed approach extends the capabilities of existing content analysis algorithms. It allows to increase the accuracy and reliability of the decisions for detection and counteraction of harmful information.
Study on the Mechanical Characteristics of a Towing Docking Device for USV Self-Recovering AUVs

Two new types of towing docking device for automated recovery of an autonomous underwater vehicle (AUV) from an unmanned surface vehicle (USV) are described. One type is the form of the stable wing, and the other is the form of the captured-rod. On the basis of the current towing stable wing, two tail rudders have been added to the tail of the stable wing and the docking device's posture can be changed by adjusting the two tail rudders. For the captured-rod docking device, a V-type capture mechanism needs to be installed at the front of a AUV. Firstly, the mechanical structure of the two towing docking devices is introduced, and the mechanical analysis are analyzed. Then, for the stable wing, the steady-state numerical simulations under different working conditions are carried out, and dynamic numerical simulations are also carried out when the docking device is towed by a USV. Besides, the collision process between the AUV and the captured-rod is also simulated. Finally, the stable wing towing docking device is being implemented and tested on a USV.
Writer identification with n ‐tuple direction feature from contour

This study introduces an effective solution for text-independent writer identification by generalising contour-hinge feature, which is called n-tuple direction feature. For extracting n-tuple direction feature, the authors first obtain all contours from connected components, then n + 1 points are considered on the contour with a certain distance apart, and next, the directions of the fragments connecting two successive points are computed. The n + 1 points move on the contour and the n-dimensional histogram of directions is computed. The proposed method is evaluated on large Farsi and English databases. A correct writer identification rate of 92.2% for English handwritings from 900 persons and 97.7% for Farsi handwritings from 600 persons are achieved. Comparison between the proposed method and other studies shows the promising performance and superiority of the proposed method.
Comparison of the Different Sampling Techniques for Imbalanced Classification Problems in Machine Learning

Imbalanced class distribution is a scenario where the number of observations belonging to one class is significantly lower than those belonging to the other ones. Machine learning algorithms are often designed to improve accuracy by reducing the errors. Thus, they do not consider the class distribution proportion or the balance of classes. In this paper, firstly, we describes the various approaches for solving such class imbalance problems, using various sampling techniques. Then we weigh each technique for its pros and cons. Finally, an approach purpose is revealed in which you can create a balanced class distribution and apply ensemble learning technique designed especially for imbalanced class distribution.
Radar/ESM anti‐bias track association algorithm based on track distance vector detection

To address radar/ESM track association problem in the presence of sensor biases and different targets reported by different sensors, an anti-bias track association algorithm based on track distance vectors detection is proposed according to the statistical characteristics of Gaussian random vectors. The state estimation decomposition equation is first derived in MPC. The track distance vectors are obtained by the real state cancellation method. Second, in order to eliminate most non-homologous target tracks, the rough association is performed according to the features of the azimuthal rate and inverse-time-to-go (ITG). Finally, the track-to-track association of radar and ESM is extracted based on track distance vectors chi-square distribution. The effectiveness of the proposed algorithm are verified by Monte–Carlo simulation experiments in the presence of sensor biases, targets densities, and detection probabilities.
A Novel Non-Iterative Parameter Estimation Method for Interval Type-2 Fuzzy Neural Networks Based on a Dynamic Cost Function

Non-iterative methods for parameter estimation for interval type-2 neuro-fuzzy structure are fast to implement, when compared to online methods, and need no -or a few- parameters to be tuned. In this paper, a novel dynamic cost function, which defines a relationship between the current and past errors, is defined. The minimization of the aforementioned cost function results in a decreasing sequence of error which makes the proposed method numerically more stable when compared to least squares-based methods. It is a well-known phenomenon that a matrix inversion may cause problems if the matrix to be inverted is ill-defined i.e. its condition number is far bigger than one. The use of a dynamic relationship between the current and past error adds more degrees of freedom which makes it possible to improve the condition number of the matrix. Comprehensive simulation studies are presented for the prediction of financial data sets. The simulation results shows the superior numerical stability of the proposed method as the mean value of the condition number is smaller. This finding results in more accurate matrix inversion to be done in the two-step matrix inversion.
Exploiting the user activity-level to improve the models' accuracy in point-of-interest recommender systems

Recommender Systems (RS) have been applied in several scenarios due to their ability to satisfy the user's interest. Traditionally, they have been applied in scenarios such as entertainment and e-commerce, and nowadays, in Location Based Social Network (LBSN) to recommend points-of-interest (POIs). Despite the advances in traditional scenarios, there is an opportunity for improvements in POI domains. For this scenario, it is necessary to consider the geography influence of POIs. However, we observe the main proposals are not able to achieve satisfactory results. In this work, we open a new research perspective in POI Recommendation area, proposing a post-processing approach that can be used with any RS. Basically, we measure the activity level of users in different subareas of a city and use it to re-order the POIs retrieved by a RS. We evaluate our proposal considering six recommender systems and three datasets from Yelp achieving gains up to 15% of precision.
Book Review of <i>Life Cycle Assessment: Theory and Practice</i>, edited by Michael Z. Hauschild, Ralph K. Rosenbaum, and Stig Irving Olsen; <i>Environmental Life Cycle Assessment</i>, by Olivier Jolliet, Myriam Saadé‐Sbeih, Shanna Shaked, Alexandre Jolliet, and Pierre Crettaz; and <i>Life Cycle Assessment: Quantitative Approaches for Decisions That Matter</i>, by H. Scott Matthews, Chris T. Hendrickson, and Deanna H. Matthews

The author declares no conflict of interest.
3D Face Reconstruction with Texture Details from a Single Image Based on GAN

Human face is the most important content to distinguish and remember individuals from different characters. The rich information contained in it allows people to perform information transmission and identity authentication through face images. Compared with 2D plane faces, the face model in 3D space contains more complex and rich biological information. Therefore, 3D face reconstruction technology has long been one of the hotspots in the field of computer vision. In this paper, we propose a single-image 3D face reconstruction algorithm. In order to improve the accuracy and speed of image generation, we use an improved network framework based on GAN and a joint loss function, and finally we obtain a 3d face model containing texture features through detailed filling.
Data Validation System Using QR Code and Meaningless Reversible Degradation

QR Codes are used as information channel on several cryptographic architectures due to their technical properties, such as data capacity and retrieval reliability. This paper presents a novel string data validation system using QR Codes and meaningless reversible degradation. The proposed scheme exploits reversible degradation properties, using the systematic Berlekamp Reed-Solomon error correction algorithm and the QR Code. This new mechanism encodes up to 388 characters in two information channels: a dynamic version QR Code (channel 1) and a wireless network (channel 2). A byte mode QR Code stores partial corrupted and masked data input bits. Its version size varies between 1 and 11 according the stored data quantity. The wireless channel downloads a previous generated Reed-Solomon redundancy file to correct the QR Code retrieved information and decode the secret message. The QR Code information is meaningless when scanned by a standard QR Code reader. Compared to real-time retransmission data validation systems, the proposed scheme reduces the download data (channel 2) up to 50%.
Comparative Study Of APRIORI and FP Algorithm For Decision Making

Decision Making is one of the hardest and most crucial skill and as there are many tools to support the leader of big companies to make an error free decisions, but ever since data mining has arrived in the world of business the job of such leaders has become much more crucial in order to take best decision. Apriori and FP Growth are two widely used algorithm to predict the data. These algorithm are used to do extensive research on the frequent itemsets in order to get possible results. This research contributes as an insight in selective perception on how we can prowess the apt of decision making using business analytics and data mining creating all the possible models and warnings for the leader of the company to take best fit decisions as per the given problem statement.
Transfer Learning in Underwater Operations

This paper investigates a method for reducing the reality gap that occurs when applying simulated data in training for vision-based operations in a subsea environment. The distinction in knowledge in the simulated and real domains is denoted the reality gap. The objective of the presented work is to adapt and test a method for transferring knowledge obtained in a simulated environment into the real environment. The main method in focus is the machine learning framework CycleGAN, mapping desired features in order to recreate environments. The overall goal is to enable a framework trained in a simulated environment to recognize the desired features when applied in the real world. The performance of the learning transfer is measured by the ability to recreate the different environments from new test data. The obtained results demonstrates that the CycleGAN framework is able to map features characteristic for an underwater environment presented with the unlabeled datasets. Evaluation metrics, such as Average precision (AP) or FCN-score can be used to further evaluate the results. Moreover, this requires labeled data, which provides additional development of the current datasets.