Reviewerly/exHarmony huggingface dataset:
Possibilistic Similarity Measures

The concept of similarity plays an essential role in a wide range of application fields like pattern recognition, reasoning, data, and knowledge mining. Nevertheless, the formulation of a valid and general-purpose definition of the similarity concept cannot be easily and simply expressed by a formula and remains a challenging issue. An important concern is that often similarity judgments are based on partial matching and a consideration of the whole structure of the compared objects is missing. Nevertheless, and despite these criticisms, the fundamental place that similarity holds in different theories of perception, knowledge representation, decision-making, and reasoning cannot be denied. Generally speaking, similarity allows assessing how two objects are alike, classifying patterns into different classes, inferring knowledge in order to “categorize” objects into a higher semantic level (classes, categories, etc.), helping a decision-maker to deal with a new encountered situation by comparing it with similar previously encountered ones, etc. From an engineering point of view, several similarity measures have been proposed in order to mathematically express and measure the similarity. All these measures are derived from a set of assumptions, are tied to particular applications, and are strongly related to different forms of knowledge representation and the available information.
Two-Dimensional Hybrid Authentication for ATM Transactions

Advancement of information technology leads toward a world with process automation to perform a task more efficiently and avail the services with ease. Banking sectors are not an exception and are moving from traditional manual banking system to an electronic entity. The basic functionality of a bank, out of many, is to deposit money into user accounts and retrieve as per account holder’s necessity. However, as time is precious, eventually account holders may not expect to spend too much time in the queue for depositing or retrieving their money. That is why the need for ATM comes into the picture to make the user’s life easier. However, it comes with some questionable possibilities for false attacks as well. Thus, a proper user authentication mechanism is needed to overcome these fraudulent activities. Our proposed method gives a new dimension to this authentication which is a hybrid version of an existing authentication system for the ATM transaction by using a Graphical pattern password along with current PIN code supplied from the bank. This Graphical password is a version, which has been invented by Google’s Android pattern unlock system. In our proposed mechanism, we combine both Graphical pattern and PIN and incorporated security to enhance reliable transactions. More specifically, the secret encryption key is generated from a PIN using the PRESENT algorithm. Finally, the ciphertext is created using digit stream from the Graphical pattern and secret encryption key. This hybrid process to detect intrusion will significantly enhance security. Our primary focus is to develop a robust and flexible user authentication system to avoid common authentication problems. The proposed approach needs no additional hardware and device dependency.
Multi-Stage Enhancement Approach for Image Dehazing

Over the past decades, huge efforts have been devoted for image enhancement under uncontrolled scene such as fog and haze.This work proposes Multi-stage de-hazing approach for improving the quality of hazy images.Four main stages are introduced, in our approach, to achieve an automated, efficient and robust de-hazy processing.The first two stages are utilized to diminish the blurring noise and enhance the contrast using Wiener filter and contrast scattering in the RGB color, respectively.In contrast, the last two stages are utilized for luminance and quality enhancement using luminance spreading and color correction.It is obvious from the experimental that the proposed approach significantly improves the prominence of the hazy images and outperforms the performance of conventional methods, such as multi-scale fusion and histogram equalization.In addition, it is also found that our approach exhibits low complexity compared to existing works.
Comparative Analysis for an Optimized Data-Driven System

While designing huge systems based on data access, storage, and retrieval, there are lot of problems faced by developers including ensuring portability, cross-platform support, scalable design, secure platforms, and reduced latency and redundancy. These problems need to be discussed and tackled in order to increase performance and efficiency for the benefit of the end-user. This study makes use of one such data-driven problem involving huge quantities of astronomical data to demonstrate the advantages of using umpteen efficient technologies and services in order to achieve significant improvement in results. Further sections compare and analyse the pros and cons of different technologies and protocols in use in order to derive fruitful conclusions.
Towards a Phylogenetic Measure to Quantify HIV Incidence

One of the cornerstones in combating the HIV pandemic is the ability to assess the current state and evolution of local HIV epidemics. This remains a complex problem, as many HIV infected individuals remain unaware of their infection status, leading to parts of HIV epidemics being undiagnosed and under-reported. We first present a method to learn epidemiological parameters from phylogenetic trees, using approximate Bayesian computation (ABC). The epidemiological parameters learned as a result of applying ABC are subsequently used in epidemiological models that aim to simulate a specific epidemic. Secondly, we continue by describing the development of a tree statistic, rooted in coalescent theory, which we use to relate epidemiological parameters to a phylogenetic tree, by using the simulated epidemics. We show that the presented tree statistic enables differentiation of epidemiological parameters, while only relying on phylogenetic trees, thus enabling the construction of new methods to ascertain the epidemiological state of an HIV epidemic. By using genetic data to infer epidemic sizes, we expect to enhance our understanding of the portions of the infected population in which diagnosis rates are low.
Towards Understanding ICU Treatments Using Patient Health Trajectories

Overtreatment or mistreatment of patients is a phenomenon commonly encountered in health care and especially in the Intensive Care Unit (ICU) resulting in increased morbidity and mortality. We explore the MIMIC-III intensive care unit database and conduct experiments on an interpretable feature space based on the fusion of severity subscores, commonly used to predict mortality in an ICU setting. Clustering of medication and procedure context vectors based on a semantic representation has been performed to find common and individual treatment patterns. Two-day patient health state trajectories of a cohort of congestive heart failure patients are clustered and correlated with the treatment and evaluated based on an increase or reduction of probability of mortality on the second day of stay. Experimental results show differences in treatments and outcomes and the potential for using patient health state trajectories as a starting point for further evaluation of medical treatments and interventions.
Inline multiphoton quantum state tomography

Latest advances in integrated single-photon detectors offer possibilities for gaining information inside quantum photonic circuits. We introduce a concept and provide experimental evidence for the inline tomographic mea- surement of multiphoton quantum states, while keeping the transmitted ones undisturbed. We establish that by recording photon correlations from optimally positioned detectors on top of coupled waveguides with de- tuned propagation constants, one can perform robust reconstruction of multiphoton density matrices describing the amplitude, phase, coherence and quantum entanglement. We report proof-of-principle experiments. Our method opens a pathway towards practical and fast inline state tomography for diverse applications in quantum photonics.
Reduction of energy-gap scaling by coherent catalysis in models of quantum annealing

Non-stoquastic drivers are known to improve the performance of quantum annealing by reducing first-order phase transitions into second-order ones in several mean-field-type model systems. Nevertheless, statistical-mechanical analysis shows that some target Hamiltonians still exhibit unavoidable first-order transitions even with non-stoquastic drivers, making them difficult for quantum annealing to solve. Recently, a mechanism called coherent catalysis was proposed by Durkin [Phys. Rev. A \textbf{99}, 032315 (2019)], in which he showed the existence of a particular point on the line of first-order phase transitions where the energy gap scales polynomially as expected for a second-order transition. We show by extensive numerical computations that this phenomenon is observed in a few additional mean-field-type optimization problems where non-stoquastic drivers fail to change the order of phase transition in the thermodynamic limit. This opens up the possibility of using coherent catalysis to search for exponential speedups in systems previously thought to be exponentially slow for quantum annealing to solve.
The impact of mining activities toward the behavioral change of social aspect in the village outer mining area

Abstract The existence of mining activities is aimed at improving the welfare of society, especially the mine circle society. Mining activities have been changed based on the three parameters of sustainable development, i.e. economic, social, and environmental. On the economic and social parameters, they have interactions that have a potential impact on influencing the change of mine circumference behavior which is closely related to the existence of the mine circle society itself. The methodology used in this study is the literature review based on existing studies and a brief observation around the mine. The observation technique is carried out by a random selection of respondents (random sampling) with a confidence number of 95%. The analysis results show that there are three types of behavioral changes that occur, namely natural change, planned change, and willingness to change. The factors influencing the behavioral change in the community around the mine areas, especially external factors, are education, religion, culture, environment, and socio-economic. The behavioral changes and factors affecting these changes can be used as a foundation for a mining company to develop community empowerment programs as part of good mining practices. The good mining practices will facilitate the mining company to get a social license to operate.
Comparison of CNNs and SVM for Detection of Activation in Malaria Cell Images

Malaria is a disease caused by parasites that are transmitted through the enzymes of Anophele mosquito and cause symptoms in fatal danger. Thick and thin film microscopic examination of smears taken from blood is the most reliable method for diagnosis. In the manual examination of the smears, the expertise of examiner and the quality of the smear significantly affect the accuracy of the diagnosis. Malaria's automatic diagnosis of pattern recognition and classification techniques on blood smear images is among the subjects of research. In this study, well-known Convolutional Neural Networks include InceptionV3, GoogLeNet, AlexNet, Resnet50, Vgg16 networks and six-fold cross validation was applied and performance evaluations were performed with a Machine Lerning method, Support Vector Machine. It was found that Deep Learning methods achieved at least 10.08% of accuracy difference performance compared to SVM based on the features of the input sample images. This difference has been 0.07 for F-Score, 0.06 for sensitivity.
Paraconsistent or Paracomplete?

This paper attempts a comprehensive account of the comparative merits of paracomplete and dialetheic approaches to the semantic paradoxes. It argues that aside from issues about conditionals, there can be no strong case for paracomplete approaches over dialetheic, or dialetheic over paracomplete, and indeed that in absence of conditionals, the two approaches are plausibly seen as notational variants. Graham Priest disagrees: many of his arguments favoring dialetheic solutions over paracomplete do not turn on issues about conditionals. The paper discusses his arguments on these points in some detail. On the matter of conditionals, it argues that extant paracomplete approaches are far better than extant dialetheic approaches, a fact that traces in part to dialetheists having focused too heavily on “relevant logics”. It holds out hope for better dialetheic treatments of conditionals (including of how the distinct kinds of conditionals interact), but it also suggests that this is the one area where paracomplete approaches are inevitably better.
The Measure of Regular Relations Recognition Applied to the Supervised Classification Task

The probability measure of regularities recognition in an information stream is introduced in the paper. The measure allows for the creation of machine-learning models without a supervisor. The experiment described in the paper empirically proves that the measure allows the recognition of regularities and helps to find out regular relations between the values of variables. The machine learning model finds out regular relations in data set and by the model allow reconstructing unknown values of the classification variable. The classification algorithm on the basis of the probability measure of regularities recognition is described in the paper. The measure connection with entropy is demonstrated and mutual information is used to optimise the algorithm’s performance. The accuracy of the algorithm matches the accuracy of well-known supervised machine learning algorithms and also exceeds them.
Polycultural environment: challenges and risks to social stability

The trivial thesis about the collapse of the policy of multiculturalism has not abolished the essence of the multicultural environment in the countries of the modern world. Moreover, the trend towards an increase in the number of "non-titular" ethnic groups, which is rapidly changing the stable images of culture, has received more and more pronounced development. This creates challenges, adequate management responses to which in the transitive society require flexible invariance of mechanisms and predictive assessments of the consequences of control actions. In the context of growing global political instability, the inability of existing government institutions to minimize social risks is becoming increasingly evident. In the article we emphasize that the key cultural space where the most effective models of multicultural interaction can be developed is the education system. We draw attention to the fact that the institutional function of the education system is changing - from the mission of the enlightened mind to the actor of the financial market. We believe that the ultimate success of reforms is determined not by financial capital, but by socio-cultural capital. The meaning of the reforms is the ability to build such value systems that allow to accumulate experience of social and cultural solidarity, to motivate and support the desire of people to self-development and self-improvement. As well as in the civil participation of people in state and municipal control, in adjusting the activities of traditional democratic and administrative institutions and creating new opportunities to solve the most pressing problems of our time.
Research on Collaborative Filtering Recommendation Method Based on Context and User Credibility

In the traditional collaborative filtering recommendation, similarity measurement methods only consider the user rating and the credibility of user rating is not taken into account, user’s contexts are considered inadequate in the mobile environment, and the scalability problem exists in the recommendation system. A parallel collaborative filtering model based on user context and credibility is proposed. This method firstly evaluates user rating credit degree. Secondly, the method builds the context vector of the user, calculates the context similarity between the target user and other users, and searches for similar nearest neighbors for the target user based on trust and context, and finally implements the parallel recommendation on the cloud computing Mapreduce. Experimental results show that this method achieved lower error values of MAE than the traditional recommendation method and higher recommendation accuracy, and effectively improved the performance of the recommendation system. This method could be applied in the contextual recommendation oriented the big data.
Mixed Attribute Clustering Algorithm Based on Filtering Mechanism

For the existing mixed data clustering algorithm, there are some problems such as low clustering accuracy and parameters sensitive, this paper proposes a mixed attributes data clustering algorithm (MC-FM) based on filtering mechanism. The algorithm measures the similarity between objects with improved mixed attribute similarity, and uses the local mean shift of each object, which based on KNN and mean shift, then distinguishes the core object and the non-core object according to the filtering mechanism. Finally, the non-core objects are divided into corresponding clusters to form the final cluster. The experimental results of the algorithm on the synthetic dataset and UCI dataset verify the effectiveness of the algorithm. Compared with similar algorithms, MC-FM has higher clustering accuracy.
Medical Time-series Prediction With LSTM-MDN-ATTN

We introduce the LSTM-MDN-ATTN model for predicting the medical time-series data. The LSTM-MDN-ATTN model predicts the future value of medical data by approximating the distribution of target data. Since medical data is multivariate data with various test items, attention mechanism is used to model the distribution suitable for target data. The attention layer used in this study predicts target data by focusing on the distribution that is related to the target data. The proposed LSTM-MDN-ATTN model shows better results compared to baseline models using lab test data from Asan Medical Center in Seoul.
Real-Time Multiple Pedestrians Tracking in Multi-camera System

This paper proposes a novel 3D realtime multi-view multi-target tracking framework featured by a global-to-local tracking by detection model and a 3D tracklet-to-tracklet data association scheme. In order to enable the realtime performance, the former maximizes the utilization of temporal differences between consecutive frames, resulting in a great reduction of the average frame-wise detection time. Meanwhile, the latter accurately performs multi-target data association across multiple views to calculate the 3D trajectories of tracked objects. Comprehensive experiments on PETS-09 dataset well demonstrate the outstanding performance of the proposed method in terms of efficiency and accuracy in multi-target 3d trajectory tracking tasks, compared to the state-of-the art methods.
Open Mission Systems Design Considerations for Optimal Fusion Performance

Future mission systems architectures are being designed to support open-system standards that include common interfaces in order to reduce the cost and integration time of adding or replacing sensors. While intended to spawn innovation, competition, and flexibility, if care is not taken in defining these open systems, it can lead to a significant degradation in accuracy and performance. This paper explores how these poor decisions can lead to performance limitations and identifies considerations for an open architecture that minimizes or avoids this loss of performance through small changes to the sensor requirements, key updates to common interface standards, and judicious architecture design decisions.
SPECIFICITY OF PREPARING A FUTURE MUSIC TEACHER TO DIRECT SCHOOL MUSICAL ENSEMBLES

The article is devoted to identifying pedagogical conditions promoting the efficiency of future teachers’ preparation to direct children’s music-making. The paper reveals the essence of the modern teacher-musician’s administrative activity in the integrity of its principles, forms and methods. The author identifies and characterizes three stages of students’ preparation (pre-communicative, communicative, communicative-creative ones) aimed to form the ability to stimulate children to creative self-expression in the process of music-making.
Hybridization of Local Search Optimization and Support Vector Machine Algorithms for Classification Problems Enhancement

The classification technique is defined as a task of supervised learning aims to identify the set for which a new input element is classified. SVM is a classifier, which is simple in structure, easy for training and often applied in classification problems. In the paper introduced, we improved SVM model that employs local search (LS) to enhance the accuracy of the classification. The proposed approach was experimented by eleven standard benchmark datasets from UCI repository. The results show enhancement in classification accuracy of proposed SVM model in comparison with that of the conventional SVM model. Our proposed new technique combines local search optimizer and support vector machine classifier as a reasonable solution to handle classification problems. SVM was utilized and then optimized by investing the LS optimization technique. Results of experiments drove using 10 benchmark datasets showed that the proposed LS-SVM outperformed the original SVM on all datasets. Also, when comparing the accuracy of LS-SVM to the accuracy of other approaches detailed in the literature, LS-SVM's classification accuracy was the highest on most of eleven datasets.
Structural Pyramid Network for Cascaded Optical Flow Estimation

To achieve a better balance of accuracy and computational complexity for optical flow estimation, a Structural Pyramid Network (StruPyNet) is designed to combine structural pyramid processing and feature pyramid processing. In order to efficiently distribute parameters and computations among all structural pyramid levels, the proposed StruPyNet flexibly cascades more small flow estimators at lower structural pyramid levels and less small flow estimators at higher structural pyramid levels. The more focus on low-resolution feature matching facilitates large-motion flow estimation and background flow estimation. In addition, residual flow learning, feature warping and cost volume construction are repeatedly performed by the cascaded small flow estimators, which benefits training and estimation. As compared with state-of-the-art convolutional neural network-based methods, StruPyNet achieves better performances in most cases. The model size of StruPyNet is 95.9% smaller than FlowNet2, and StruPyNet runs at 3 more frames per second than FlowNet2. Moreover, the experimental results on several benchmark datasets demonstrate the effectiveness of StruPyNet, especially StruPyNet performs quite well for large-motion estimation.
Supervised Deep Learning for Hierarchical Image Data Retrieval

The techniques of feature extraction and representation on image data have been significantly progressed in recent years due to the development of deep learning. With a large number of representative image features being extracted from ImageNet by convolutional neural networks, many object recognizing applications were successfully accomplished effectively. In this paper, two supervised image retrieval models for retrieving images with similar hierarchical concept are investigated and compared. First, image features are extracted by pre-trained VGG convolutional networks. Then, the supervised retrieval models are learned from a set of images with hierarchical concept labels. The experimental results show that the hash-based model generally is superior to classifier-based model both in F1 measure and MAP no matter what in coarse level or fine level of concept hierarchy.