Reviewerly/exHarmony huggingface dataset:
Learning Hierarchical Acquisition Functions for Bayesian Optimization

Learning control policies in robotic tasks requires a large number of interactions due to small learning rates, bounds on the updates or unknown constraints. In contrast humans can infer protective and safe solutions after a single failure or unexpected observation. In order to reach similar performance, we developed a hierarchical Bayesian optimization algorithm that replicates the cognitive inference and memorization process for avoiding failures in motor control tasks. A Gaussian Process implements the modeling and the sampling of the acquisition function. This enables rapid learning with large learning rates while a mental replay phase ensures that policy regions that led to failures are inhibited during the sampling process. The features of the hierarchical Bayesian optimization method are evaluated in a simulated and physiological humanoid postural balancing task. The method out- performs standard optimization techniques, such as Bayesian Optimization, in the number of interactions to solve the task, in the computational demands and in the frequency of observed failures. Further, we show that our method performs similar to humans for learning the postural balancing task by comparing our simulation results with real human data.
A Multi-Oriented Scene Text Detection Method Based on Location-Sensitive Segmentation

In recent years, regression-based scene text detection methods have achieved great success. However, because the network has a limited receptive field, the predicted bounding boxes cannot enclose the entire text instance when dealing with the long text instance. In this paper, we propose a multi-oriented scene text detection method based on location-sensitive segmentation. The main idea is that we divide the whole text instance detection into three sub-text instances (left part, middle part, and right part) detection. To form the final detection bounding box, we get three candidate bounding boxes from three sub-text instances and then merge them by getting the minimum rectangular area. Finally, the pixel-level score maps are used to filter false positives. Experiments on ICDAR2015 and MSRA-TD500 demonstrate that the proposed method achieves great performance. For ICDAR2015 Dataset, the method achieves an F-measure of 0.822 and a precision rate of 0.876.
On the Applicability of Users' Operation-action Characteristics for the Continuous Authentication in IIoT Scenarios

Industrial Internet of Things (IIoT) systems are facing an urgent demand for continuously protecting sensitive information during the manufacturing processes. This paper studies the problem of user authentication across various scenarios in IIoT systems, based on operation features. Specifically, utilizing the routine operations (e.g., walking, scanning, and touchscreen), a user authentication approach is proposed to continuously and implicitly validate the identity of current user, and the availability and reliability of operation features are investigated. A systematic authentication method is then designed to evaluate the distinctiveness and accuracy of operation characteristics. With the help of support vector machine (SVM), a two-class classifier is also developed to discriminate between legitimate users and impostors. Finally, we construct extensive experiments to verify the discriminability and reliability of the proposed authentication approach.
E-Tool: Online Emotion Mining Tool

Emotion recognition is the process of identifying human emotion. People vary widely in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively blossoming research area. Many different sources of information, such as speech, text and visual can be used to analyze emotions. Nowadays, writings may take many forms like social media posts, blogs, news pieces, research papers etc., and the content of these writings can be useful resource for text mining to discover and reveal various aspects, including emotions. Extracting emotions behind these writings is a very complicated task. To tackle this problem, researchers from assorted fields are trying to find an efficient way to more accurately detect human emotions from various sources, including text and speech. In this sense, different word-based and sentence-based techniques, machine learning, natural language processing methods, etc., have been used to achieve better accuracy. Analyzing emotions can be helpful in many different domains. One such domain is detecting emotions from the research papers written by different writers. The emotion recognition online tool, help users understand the emotional state of the writer and make a better choice in choosing his/her domain. This study covers, detecting of different emotions from the titles of research papers found at Google scholar using online emotion recognition tool.
Comparative analysis between theoretical and simulatory learning methods by data science methodology approach

Abstract Data Science is the most trending interdisciplinary science that integrates the steps of data collection, preprocessing data, transforming data, storing data, data visualization, and hence extracting the insights from the data to serve stakeholder’s purposes. Python is commonly used and, along with being a versatile and open-source language, is a favorite tool in Data science studies. The vast libraries are being used to manipulate data and are very simple for even a beginner data scientist to understand. In the present work, we intend to apply the data science methodology to decision making and predictive analysis using the python programming language. We consider the problem of selecting the better mode of study concerning some of the impractical phenomena from physics for the exact understanding of the process. Data collection has been from an educational institute and the comparison has been made between theoretical learning and simulatory learning for selected topics from the vast fields like mechanics, thermodynamics, fluid dynamics, and radioactivity. The steps of data science methodology are germinated to achieve the insights into the data procured and the results are wangled concerning the teaching methodology that could be employed. In the present work, we undertake a comparative study between the theoretical and simulatory modes of teaching by exploring the modes individually through evaluating the responses imparted by a class of high school students. The analysis reported the more inclination of the student’s responses towards the simulatory methods when compared to the theoretical method of learning.
Bar Bets and Generating Functions: The Distribution of the Separation of Two Distinct Card Ranks

What is the probability two distinct ranks (say, a king and a five) will appear within a card of one another in a shuffled deck? An old bar bet is predicated on it being nearly guaranteed. By modeling the outcome using a finite automaton, we construct the probability generating function for card rank separation. We then derive the corresponding probability mass function, mean, and variance. We conclude with an unimpressive modification to make the scam a virtual lock.
Relative worth of Factors Affecting Acquisition in Libraries of Higher Educational Institutions

New e-resources are believed to affect preferences of readers and hence acquisition policy of libraries in general and preference for resources in particular need to be tuned accordingly. The paper reports a study undertaken to determine priorities of factors and their components in library acquisition. The literature review has been done by searching in Emerald, Taylor and Francis and Web of Science using PRISMA protocol to understand the past research in this direction. A closedended structured questionnaire designed for the purpose with factors and their components of acquisition, pilot-tested and used for collection of primary data from library professionals of selected institutions of higher education governed and funded by Government of India. The data so collected was analyzed using SPSS 23 to determine and prioritize the factors and their components based on mean scores. The study concluded that policy makers need to focus more on the high ranked factors and their components of acquisition for cost-efficiency, cost-effectiveness and better service to the intended users.
Proposing Gesture Recognition Algorithm Using Two-Stream Convolutional Network and LSTM

Identifying gestures and actions of human through video will help provide important and valuable information for interaction with surrounding devices as well as health care and entertainment activities. In recent years, many machine learning models have been proposed to identify human actions. In the paper, we propose a system to identify gestures of action of videos based on a two stream-convolution network (ConvNet) model. Besides, we also propose the method for recognizing human hand gestures in accordance with the purpose of the indoor environment. Our main contribution is to improve the two-stream ConvNet model based on [1]. The simulation results show that the proposal model has improved 20 percent of the processing speed and resources comparing with the existing models (VGG16, InceptionV3, Mobilenet V1, and Densenet201).
Efficacy of Model Fine-Tuning for Personalized Dynamic Gesture Recognition

Dynamic hand gestures are usually unique to individual users in terms of style, speed, and magnitude of the gestures’ performance. A gesture recognition model trained with data from a group of users may not generalize well for unseen users and its performance is likely to be different for different users. To address these issues, this paper investigates the approach of fine-tuning a global model using user-specific data locally for personalizing dynamic hand gesture recognition. Using comprehensive experiments with state-of-the-art convolutional neural network architectures for video recognition, we evaluate the impact of four different choices on personalization performance - fine-tuning the earlier vs the later layers of the network, number of user-specific training samples, batch size, and learning rate. The user-specific data is collected from 11 users performing 7 gesture classes. Our findings show that with proper selection of fine-tuning strategy and hyperparameters, improved model performance can be achieved on personalized models for all users by only fine-tuning a small portion of the network weights and using very few labeled user-specific training samples.
Scaling Up Generalized Kernel Methods

Kernel methods have achieved tremendous success in the past two decades. In the current big data era, data collection has grown tremendously. However, existing kernel methods are not scalable enough both at the training and predicting steps. To address this challenge, in this paper, we first introduce a general sparse kernel learning formulation based on the random feature approximation, where the loss functions are possibly non-convex. In order to reduce the scale of random features required in experiment, we also use that formulation based on the orthogonal random feature approximation. Then we propose a new asynchronous parallel doubly stochastic algorithm for large scale sparse kernel learning (AsyDSSKL). To the best our knowledge, AsyDSSKL is the first algorithm with the techniques of asynchronous parallel computation and doubly stochastic optimization. We also provide a comprehensive convergence guarantee to AsyDSSKL. Importantly, the experimental results on various large-scale real-world datasets show that, our AsyDSSKL method has the significant superiority on the computational efficiency at the training and predicting steps over the existing kernel methods.
The efficiency analysis of the turbo air refrigerators for producing solid carbon dioxide from the boiler combustion products flow

Abstract The paper considers the efficiency analysis of the turbo air refrigerators for producing solid carbon dioxide from the boiler combustion products flow. The process of obtaining solid carbon dioxide in the turbo air refrigerating machine was modelled. A numerical study of the process and degree of solid carbon dioxide freezing in the turbo air refrigerating machine was performed. The range of the boiler power characteristics, where the most effective solid carbon dioxide freezing is possible within the technical specifications of the turbo air refrigerating machines, was determined.
Towards obstacle Identification by Markovian-Evolutionary Segmentation

When a robot navigates autonomously through an environment, it is necessary that it can accurately identify the obstacles with which the robot can collide. To specifically estimate the time of contact with the obstacle, the segmentation of the obstacle must be as accurate and rapid as possible. This paper presents a work in progress where we show a proposal to segment obstacles using probabilistic Hidden Markov Chains with which the search for the best position of control points is optimized by means of an evolutionary algorithm. Although it is a work in progress, the use of a Genetic Algorithm allows not to explore all possible contour configurations, while the Hidden Markov Chain allows a balance between a good positioning of the control points on the object and a good distribution of the points on the contour.
SIRN, IA and Their Conjunction (SIRNIA)

Unlike natural complex systems, cities are hybrid complex systems, implying two selforganization processes at two different scales: At the scale of the city, the urban agents, by means of their interaction, give rise to the city and its OP, that then enslaves the behavior of it’s urban agents in a top-down manner. At the local scale, each individual urban agent extracts information from the city, processes this incoming information by means of previously learned and memorized information, and on the basis of this interaction acts and reacts on the city. The processes of SIRN (synergetic inter-representation networks), IA (information adaptation) and their conjunction (SIRNIA) were specifically designed to capture this process. In this chapter we introduce and elaborate the notion of SIRNIA and their role in the dynamics of cities.
Finding and Segmenting Mathematical Equations in Students’ Online Handwritten Assignments

In this paper, we tackle the problem of finding individual equations in students' handwritten assignments. Mathematical equations are the most abundant type of content in solutions to free-response problems in various academic subjects, particularly engineering and science. Finding the individual equations within a solution is an important step toward automated grading. We formulate this task as stroke classification and grouping problems. In the stroke classification problem, we classify each stroke as an equation or non-equation stroke. Stroke grouping is the task of identifying the equation strokes that belong to each equation. In this work, we present a GRU-CRF model that considers both intrinsic properties of the strokes and features computed from pairs of chronologically consecutive strokes. The method simultaneously classifies and groups strokes by computing labels for them. A chaining process is used to combine consecutively drawn equation strokes into equation groups. A binary classifier is then used to combine equation segments comprising single equations that were not written contiguously. We report the performance of our approach on a dataset of 1,060 pages written by 132 students that consists of 9,949 equations. Our approach can find 54% of the equations perfectly and finds an additional 16% with only a few mistakes.
Functional Neural Network for Decision Processing, a Racing Network of Programmable Neurons with Fuzzy Logic Where the Target Operating Model Relies on the Network Itself

In this paper, we are introducing a novel model of artificial intelligence, the functional neural network for modeling of human decision-making processes. This neural network is composed of multiple artificial neurons racing in the network. Each of these neurons has a similar structure programmed independently by the users and composed of an intention wheel, a motor core and a sensory core representing the user itself and racing at a specific velocity. The mathematics of the neuron's formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education and medicine including the opportunity to design an intelligent learning agent with application in business operations supervision. We believe that this functional neural network has a promising potential to transform the way we can compute decision-making and lead to a new generation of neuromorphic chips for seamless human-machine interactions.
Universal Elements of Quantum Circuits

We will need some definitions from Boolean algebra for further description. Recall the main definitions [, ]. A Boolean variable p can take the values 0 or 1 (and only them), \(p\in \mathbb {B}\equiv \{0,1\}\). Boolean algebra is a set \(\mathbb {B}\) with operations of disjunction (\(\vee \)), conjunction (\(\wedge \)), and negation (\(\overline{\phantom {a}}\)) defined on it. A Boolean expression is constructed from Boolean variables using the operations \(\vee \), \(\wedge \), \(\overline{\phantom {a}}\), and brackets. The expression of the form \(x_1\wedge x_2\) is sometimes written as \( x_1\, { \& }\, x_2\) or simply \(x_1 x_2\). The collection \(\mathbf {x}=(x_1,x_2,\dots ,x_n)\), where \(x_i\in \{0,1\}\), \(1\leqslant i\leqslant n\), is called the Boolean tuple (vector). The elements of the tuple are components, or coordinates. The function \(f:\mathbb {B}^n\rightarrow \mathbb {B}\), where \(f(x_1,\dots ,x_n)\) is a Boolean expression, is called the Boolean function.
Priority Based Modeling and Comparative Study of Google Cloud Resources between 2011 and 2019

The cloud resource allocation for jobs must be further optimized and prioritised due to ever increasing demand for cloud computing resources to handle big data. In this research, we have examined the relationship between resource allocation, usages, and priority of tasks to reveal the influence of priority in resource allocation and resource usages. The analysis and modeling of this paper have used the Google cloud public dataset of 2011 and 2019. After processing and cleaning of one month data of Google cloud, we have revealed, the tasks are classified in 12 priorities in the 2011 cluster model whereas 500 priorities in the 2019 cluster model. However, both models have grouped these priorities into five groups. Therefore, we have modeled resource allocation versus usages based on five main priority groups using XGBoost (Extreme Gradient Boosting) and correlation coefficient. The comparative study on the developed models shows, the priority grouping of 2019 has better evenly distribution of resources for jobs but less efficient in most of the priority groups for resource allocation. Based on the performance parameters of the developed models, the resource allocation works more efficiently for most of the 2011 priority groups except `other'. These findings are useful for researchers to develop a balanced priority-based resource allocation-usages model to further optimise resources to reduce the management cost of cloud clusters.
Geoenvironmental and health risks associated with toxic metals in goldmine site of Iperindo, southwestern Nigeria

Abstract Mineral exploitation enhances the nation’s economic growth and development but poses serious threats to the environment thereby leading to ecological imbalances and public health risks. This study evaluates heavy metal pollution in an abandoned gold mining site of Iperindo, Southwest Nigeria and associated environmental and health risks were investigated. Soil samples collected at the site were subjected to a total digestion technique and analyzed for metals (As, Pb, Cd, Cr, Cu, Zn, Co, Ni, and Fe) using AAS. Only SS15 had a baseline pollution level while all other sampling points had significant heavy metal pollution. Generally, the analysis showed that 33.3% and 66.7% of the sites are included in the unpolluted and moderately polluted classes respectively for As, Cr, Cu, Co, and Ni. For Cd and Zn, 13.3% of the sites were in unpolluted class while it was 40.0% and 66.7% for Pb and Fe respectively. By implication, the highest ecological risk was at SS3 while metal that posed the highest ecological risk was Cd. HQ estimated for all metals via ingestion and dermal pathways for both adults and children were &gt; 1 but &lt; 1 via inhalation pathways in both age groups. However, the values were greater in children than adults. This suggested that oral and dermal exposures to As, Pb, Cd, Cr, Cu, Zn, Co, Ni and Fe in the soil of the study area would elicit non-carcinogenic adverse health hazard. This result shows the critical need to carry out remediation of the study site despite the abandonment of mining operations to protect possible health hazards, particularly to children.
Evaluating the effectiveness of information security based on the calculation of information entropy

Abstract Recently, the emergence and increasingly widespread of wireless networks has generated considerable interest in the information-theoretical approach to ensuring secure communications. The basic principle of information-theoretic security requires a combination of cryptographic methods with channel coding techniques that use the randomness of communication channels to ensure that messages sent cannot be intercepted or decrypted by a third party maliciously eavesdropping on the wireless medium. This paper discusses the information-entropy method for assessing security. We show that information-theoretical security means that any algorithm has a negligible probability of violating the security property. This is the same as unconditional security: it does not rely on any computational assumptions and is not limited to probabilistic violators.
Noise-regression GAN for Image Inpainting and Multiple Generation

Image inpainting is a challengeing task for machine, recent years the emergency of deep learning and generative adversarial networks(GAN) shows that such task can besolved perfectly, however most approaches tend to generate only one plausible result for each input. in this paper, we propose a new model which can not only generate plausible image but also get multiple generation it seems that our method give creativity and imagination to machine. the model is based on GAN and convolutional neural network(CNN) by add noise to the input and build the mapping between ground truth and noise, our model eventually learns how to generate multiple plausible images, experiment on CelebA(a large dataset that contains 202599 images of celebrities) proves our model generates high quality results.
Deploying and Accessing Databases

When finished developing, it is time to deploy your database into the target workflow. There are three primary choices for deployment: in a folder directory, on an iOS device, or on a host server. Each option has different benefits, and the choice affects who can access the file, how, and from where. Each product in the FileMaker family has different capabilities for deployment and access. In this chapter, we explore these options, introducing specific topics related to deploying and accessing a solution.