Reviewerly/exHarmony huggingface dataset:
Minimax functions on Galton–Watson trees

Abstract We consider the behaviour of minimax recursions defined on random trees. Such recursions give the value of a general class of two-player combinatorial games. We examine in particular the case where the tree is given by a Galton–Watson branching process, truncated at some depth 2 n , and the terminal values of the level 2 n nodes are drawn independently from some common distribution. The case of a regular tree was previously considered by Pearl, who showed that as n → ∞ the value of the game converges to a constant, and by Ali Khan, Devroye and Neininger, who obtained a distributional limit under a suitable rescaling. For a general offspring distribution, there is a surprisingly rich variety of behaviour: the (unrescaled) value of the game may converge to a constant, or to a discrete limit with several atoms, or to a continuous distribution. We also give distributional limits under suitable rescalings in various cases. We also address questions of endogeny . Suppose the game is played on a tree with many levels, so that the terminal values are far from the root. To be confident of playing a good first move, do we need to see the whole tree and its terminal values, or can we play close to optimally by inspecting just the first few levels of the tree? The answers again depend in an interesting way on the offspring distribution. We also mention several open questions.
Inferring User Preferences Using Reviews for Rating Prediction

Nowadays, E-commerce websites have been developed and are very popular among online users. Users deal with the problems to choose the right items that meet with their needs. Recommender systems try to suggest the right items to the user by applying different recommendation approaches. Collaborative Filtering recommendation (CF) approach makes recommendations to users using the user-item matrix which has the ratings on each item given by users. Data sparsity problems may occur when a user-item matrix is built based on the ratings of users (one to five stars). User reviews on the products contain more information and opinions than user ratings. This paper proposes the rating prediction approach that infers the user preferences from textual reviews of hotels by performing sentiment analysis. The preference scores obtained from the sentiment analysis are integrated into the rating prediction process which applies two approaches named memory-based CF and model-based CF. The performance of the proposed system is tested on the Myanmar hotel reviews which are crawled from TripAdvisor site and hotel reviews which are downloaded from the Kaggle site. The resulted rating prediction accuracy of two approaches on two data sets is compared by using Root Mean Square Error (RMSE).
Bricolage in a hybrid digital lutherie context

Interaction design research typically differentiates processes involving hardware and software tools as being led by tinkering and play, versus engineering and conceptualisation. Increasingly however, embedded maker tools and platforms require hybridisation of these processes. In the domain of digital musical instrument (DMI) design, we were motivated to explore the tensions of such a hybrid process. We designed a workshop where groups of DMI designers were given the same partly-finished instrument consisting of four microphones exciting four vibrating string models. Their task was to refine this simple instrument to their liking for one hour using Pure Data software. All groups sought to use the microphone signals to control the instrument's behaviour in rich and complex ways, but found even apparently simple mappings difficult to realise within the time constraint. We describe the difficulties they encountered and discuss emergent issues with tinkering in and with software. We conclude with further questions and suggestions for designers and technologists regarding embedded DMI design processes and tools.
Formal Intent-Based Trajectory Description Languages for Quadrotor Aircraft

This paper describes a formal language for describing intent of quadrotor aircraft. The aim of this language is to support the prediction of quadrotor trajectories modeling guidance instructions in a compact and univocal manner, enabling to efficiently share trajectory information by several actors. A trajectory computation engine, translating from sentences of this language into a common trajectory is also described. Its use is demonstrated for a complex flight and in real-flight operations.
Survey on Security Issues in Mobile Cloud Computing and Preventive Measures

Mobile Cloud Computing (MCC) is a recent technology used by various people worldwide. In 2015, more than 240 million users used mobile cloud computing which earns a profit of $5.2 billion for service providers. MCC is a combination of mobile computing and cloud computing that presents various challenges like network access, elasticity, management, availability, security, privacy etc. Here, the security issues involved in both mobile computing and cloud computing, such as data security, virtualisation security, partitioning security, mobile cloud application security and mobile device security are considered extremely important. This paper presents a detailed study of security issues in mobile cloud computing and enumerates their preventive measures.
Commercial Banks' Digital Paradigm and Customers Responses in the UAE

One of the central factors influencing the banks in the UAE is the changing needs and perceptions of the customers towards digital banking services. With this significance, the researcher made an attempt to study commercial banks' digital paradigm and customer's responses. Based on this, the volume of business and the customer's top 11 banks were identified for the study. For collecting data from the respondents pretested scheduled questionnaire was used. In order to finalize the number of samples the researcher calculated standard deviation and found the sample size was 350. Collected data was analyzed by the researcher to know the impact of digital banking services and the customer responses. The researcher has done an inferential analysis followed by descriptive analysis. This analyst recognized factors, which were embraced amid the examination included; privacy, reliability, technology, convenience, security, and satisfaction. Among every one of the elements, innovation and accommodation reflected negative connection thinking about negative effect by and large consumer loyalty.
An Additive Cellular Automata Based Random Key Generation for One-Time Pad Encryption Using Luhn Method

This article attempts an additive cellular automata based random key generation using Luhn method which satisfies avalanche, confusion, and diffusion properties for public clouds. It is found that by combining additive cellular automata and Luhn mechanism, a robust master key in the public cloud could be generated. The simulation results show that the proposed scheme has large key space and prevents the traditional attacks in addition to improves execution time for accessing cloud data in the cloud server database. The mathematical analysis confirmed that the proposed scheme result provides high security and integrity in the cloud.
Differential Compression for Mobile Edge Computing in Internet of Vehicles

Internet of Vehicle (IoV) is a promising Internet of Thing (IoT) application, where roadside unit (RSU) plays an important role to transmit traffic information to cloud server through internet. However, there exists redundancy among the data collected with a data collection period by a single vehicle, which may cause network congestion and waste the storage of cloud. In this paper, we propose to equip each RSU with a mobile edge computing (MEC) server, and differential compress data at edge node, for saving the transmission time and storage space. First, we present a new cost model for COPY/ADD class, then a metric to measure a differential compression algorithm is given. we propose a Maximal Length of COPYs (MLOC) algorithm that can construct a good delta encoding based on our cost model and evaluation. Theoretical analysis proves that the proposed MLOC algorithm constructs a delta encoding with minimal amount of COPYs on the premise of maximal total length of data segments copied. Numerical results show that MLOC algorithm can get better performance in constructing a good delta encoding between two data compared with Simple Greedy algorithm and Hash Suffix Array Delta (Hsadelta) algorithm.
Compression and Transfer of Images in Wireless Sensor Networks Using the Transformation of Residue Number System

The paper proposes a new approach to compression and data transmission in Internet of Things environment, based on the transformation of the Residue Number System and the method of arithmetic coding. The transformation of the Residue Number System is used to divide the data into parts. As a result, the received remainders are compressing in parallel used the arithmetic coding. Then they are transmitting through parallel routes, if the last ones are present. The proposed approach improves the processing speed through the parallel work and provides a compression ratio of depending on the type of data.
The Comparison of Pooling Functions in Convolutional Neural Network for Sentiment Analysis Task

Convolutional Neural Network (CNN) has gained considerable attention in many Natural Language Processing applications including sentiment analysis task. A typical CNN model usually is made up of several convolutional and pooling layers. In this paper, our aim is to acquire detailed understanding into different type of pooling function by directly differentiate them on a same architecture layers for sentiment analysis tasks. These insights should prove useful for future development of pooling function in CNN models for sentiment analysis task.
Automatic Summarization of Technical Documents in the Oil and Gas Industry

We address extractive summarization of technical documents in the oil and gas industry, a major and urgent task due to the large volume of critical reports in that industry. We examine five distinct state-of-the-art extractive algorithms; to assess performance, a new open dataset was created using the open access Journal of Petroleum Exploration and Production Technology (JPEPT). Abstracts for papers in this journal were used as ground truths for summarization. Algorithms were refined to work with these documents in the best possible way. Our most effective algorithm achieved a state-of-the-art ROUGE-2 score of 0.123, taking 83 minutes to summarize the entire JPEPT dataset.
Comparison of Different Image Segmentation Techniques on MRI Image

Image processing techniques have been important and widely used for image analysis due to the advancement of computer learning. Analyzing of processed image is done to obtain any quantitative information or data from the processed images. Hence, segmentation is a part of image processing that has various types of techniques and algorithms to be used. Generally, the main purpose of the segmentation is to improve or potentially change the image digitally so that any useful information is easier to analyze. However, the effectiveness of segmentation algorithms evaluation has become the priority to get results through the process. Therefore, this study is used to make comparisons of different image segmentation techniques based on MRI images. Different clustering algorithms are tested on MRI images to identify white matter hyperintensities (WMH) region on human brain. The accuracy of the identification assessment is compared between several MRI image segmentation techniques. The best performance analysis is suitable to be implemented on any computer-aided tool for medical monitoring or analyzing purpose.
Construction of Scenic Spot Knowledge Graph Based on Ontology

The scenic spot knowledge graph is a kind of domain knowledge graph. This paper analyzes the construction method of domain knowledge graph, designs and implements an ontology-based scenic spot knowledge graph. According to the purpose of ontology, reusable ontology is looked for, concepts and classifications are designed, the attributes of classes are defined, and scenic spot ontology is constructed. Then, data sources are collected, and scenic spot knowledge is extracted based on ontology. A string-based and structure-based entity alignment method is designed, which aligns entity from two levels of string and entity attributes, respectively. Finally, the scenic spot knowledge graph is stored in Neo4j graph database. Experiments show that the method in this paper is feasible.
Quantifying conditional probability tables in Bayesian networks: Bayesian regression for scenario-based encoding of elicited expert assessments on feral pig habitat

Bayesian networks are now widespread for modelling uncertain knowledge. They graph probabilistic relationships, which are quantified using conditional probability tables (CPTs). When empirical data are unavailable, experts may specify CPTs. Here we propose novel methodology for quantifying CPTs: a Bayesian statistical approach to both elicitation and encoding of expert-specified probabilities, in a way that acknowledges their uncertainty. We illustrate this new approach using a case study describing habitat most at risk from feral pigs. For complicated CPTs, it is difficult to elicit all scenarios (CPT entries). Like the CPT Calculator software program, we ask about a few scenarios (e.g. under a one-factor-at-a-time design) to reduce the experts' workload. Unlike CPT Calculator, we adopt a global rather than local regression to 'fill out' CPT entries. Unlike other methods for scenario-based elicitation for regression, we capture uncertainty about each probability in a sequence that explicitly controls biases and enhances interpretation. Furthermore, to utilize all elicited information, we introduce Bayesian rather than Classical generalised linear modelling (GLM). For large CPTs (e.g. >3 levels per parent) we show Bayesian GLM supports richer inference, particularly on interactions, even with few scenarios, providing more information regarding accuracy of encoding.
Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines

Background: Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. Objective: The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. Methods: The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Results: Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Conclusion: Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.
Low-Resource Language Identification From Speech Using Transfer Learning

Identification of low-resource data is a traditionally difficult machine learning problem, since the sparsity of available resources prevents classifiers from being adequately trained. An effective way to address the inevitable data sparsity in certain applications, such as in low-resource speech language identification, is transfer learning, which uses the knowledge learned from tasks with large labeled data in settings of limited data. Motivated by the fact that various languages share common phonetic and phonotactic characteristics, we explore transfer learning systems that employ various neural network architectures. We leverage readily available large datasets for creating robust instantiations of language identification models using feed-forward neural networks. These are further fine-tuned on the low-resource data from a target domain to improve the system performance. We apply the proposed approach to the automatic identification of African languages, which comprises a challenging task due to the low-resource data from such languages. We conduct our experiments using two publicly available datasets: the VoxForge corpus which contains 7 Indo-European languages as source data, and the Lwazi corpus which includes 11 African languages as target data. Our results indicate the effectiveness of transfer learning for the identification of low-resource languages from speech signals.
Nested Distributed Gradient Methods with Stochastic Computation Errors

In this work, we consider the problem of a network of agents collectively minimizing a sum of convex functions. The agents in our setting can only access their local objective functions and exchange information with their immediate neighbors. Motivated by applications where computation is imperfect, including, but not limited to, empirical risk minimization (ERM) and online learning, we assume that only noisy estimates of the local gradients are available. To tackle this problem, we adapt a class of Nested Distributed Gradient methods (NEAR-DGD) to the stochastic gradient setting. These methods have minimal storage requirements, are communication aware and perform well in settings where gradient computation is costly, while communication is relatively inexpensive. We investigate the convergence properties of our method under standard assumptions for stochastic gradients, i.e. unbiasedness and bounded variance. Our analysis indicates that our method converges to a neighborhood of the optimal solution with a linear rate for local strongly convex functions and appropriate constant steplengths. We also show that distributed optimization with stochastic gradients achieves a noise reduction effect similar to mini-batching, which scales favorably with network size. Finally, we present numerical results to demonstrate the effectiveness of our method.
A coarse-grained Markov chain is a hidden Markov model

An aggregated or 'coarse-grained' Markov chain (in discrete time and on a finite state-space) is a special case of a hidden Markov model, a type of model well known in the statistics literature and extensively applied in a wide variety of time-series or similar analyses. Such a coarse-grained Markov chain does not in general satisfy the Markov property, but recent work appearing in the physics and applied mathematics literature discusses relevant Markov-chain approximations, both at aggregated level and at microstate level, and establishes bounds on the approximation error. This note demonstrates that, for some purposes at least, such approximations are unnecessary; exact properties of hidden Markov models are available and can be used. In particular, the likelihood of the coarse-grained process can be computed routinely, and such a computation makes available various probabilities of interest associated with the process. Furthermore, this likelihood can be maximized numerically if there are unknown parameters and one has data from which one wishes to estimate them. This question of parameter estimation appears not to be addressed in the relevant literature.
Efficient Resource Provisioning Through Workload Prediction in the Cloud System

The resource provisioning in the virtualized environment is the significant phase for application deployment. However, the dynamic handling of virtual resources for inconsistent workload remains an exigent dilemma for cloud service providers. To handle vast requests optimally on cloud infrastructure, autonomic management of cloud resources is the crucial aspect. However, the issue that arises from the provisioning of resources is that if demand is low, excess of resources are available which leads to over-provisioning and if demand is high, the resources may not be enough, which leads to under-provisioning and poor QoS (Quality of Services). As in the case of cloud environment, resources are provided on demand, efficient resource provisioning needs a proactive approach which can predict the future load before provision of resources to reduce the under-/over-provisioning problem. A prediction-based efficient resource provisioning methodology can solve under-/over-provisioning problem remarkably by estimating the future resource demands of deployed applications. The issue can be overcome by deploying and provisioning of resources efficiently based on the demands without loss of QoS using a predictive approach.
Needs analisys pedagogy project management of technology and vocational educational with the approach of project base learning in higher education

Abstract This research aims to analyze the needs of the pendagogi on the project management courses in building engineering education State University of Padang. This needs analysis look deeply the importance of project management courses in building engineering education from the aspect of the curriculum, pedagogy and learning materials based on current conditions, standard processes and priorities in learning. This type of descriptive study with survey method. With the spread of data gathering techniques questionnaires and interviews. Sample student research educator and lecturer of building engineering. Data analysis techniques was kuantitaf and interviews. Based on the results of the analysis it can be concluded that (1) there is a gap between the needs of the students and professors against the learning model of project management related methods, approaches, learning materials, assessment and evaluation of student learning (2) requires the development of a learning model that supports and meningkatakan knowledge, skills and attitudes.