Reviewerly/exHarmony huggingface dataset:
A Context Driven Indoor Localization Framework for Assisted Living in Smart Homes

The proposed Context Driven Indoor Localization Framework aims to implement a standard for indoor localization to address the multiple needs in different indoor environments with a specific focus to contribute towards Ambient Assisted Living (AAL) in the Future of Smart Homes for healthy aging of the rapidly increasing elderly population. This framework has multiple functionalities. First, it presents the methodology to analyze any given IoT-based environment, in terms of spatial information, to classify it into specific zones or functional areas based on the activities performed by users with different environment parameters in those respective zones. Second, it discusses the approach to analyze the macro and micro level user interactions with context parameters for any user in the given IoT environment. Finally, it possesses the methodology to track user interactions, analyze and access the Big Data associated with these interactions and map the user to a specific zone or area in the given IoT-based environment using a learning model. To evaluate the efficacy of this framework, it has been implemented on a dataset related to performing different activities in IoT-based settings. The results presented and discussed uphold the relevance and potential for real-time implementation of this framework for addressing multiple needs associated with aging in smart homes as well as for various other applications of indoor localization in different environments and contexts.
Audio Feature Analysis for Precise Vocalic Segments Classification in English

An approach to identifying the most meaningful Mel-Frequency Cepstral Coefficients representing selected allophones and vocalic segments for their classification is presented in the paper. For this purpose, experiments were carried out using algorithms such as Principal Component Analysis, Feature Importance, and Recursive Parameter Elimination. The data used were recordings made within the ALOFON corpus containing audio signal recorded employing 7 speakers who spoke English at the native or near-native speaker level withing a Standard Southern British English variety accent. The recordings were analyzed by specialists from the field of phonology in order to extract vocalic segments and selected allophones. Then parameterization was made using Mel Frequency Cepstral Coefficients, Delta MFCC, and Delta Delta MFCC. In the next stage, feature vectors were passed to the input of individual algorithms utilized to reduce the size of the vector by previously mentioned algorithms. The vectors prepared in this way have been used for classifying allophones and vocalic segments employing simple Artificial Neural Network (ANN) and Support Vector Machine (SVM). The classification results using both classifiers and methods applied for reducing the number of parameters were presented. The results of the reduction are also shown explicitly, by indicating parameters proven to be significant and those rejected by particular algorithms. Factors influencing the obtained results were discussed. Difficulties associated with obtaining the data set, its labeling, and research on allophones were also analyzed.
An All-terrain Vehicle for Post-disaster Search and Rescue

This project mainly explores an all-terrain vehicle that can assist disaster relief after a disaster, which has good passability and certain demolition ability. This all-terrain vehicle is equipped with a variety of sensors, which can effectively search and locate the location where the victim may be buried, providing useful information for subsequent search and rescue. The terrain vehicle will adopt a triangular track structure to improve its passability; In addition, the machine is equipped with a main demolition device so that it can expand the gap that exists after the disaster and therefore increase the machine's capacity in small gaps; Equipped with a variety of sensors, the terrain vehicle can effectively perform path planning, search for survivors or locate areas where it may be, and return position and vital sign data. With the help of these data, search and rescue team members will conduct a more effective search of possible areas, thereby improving search and rescue efficiency.
Research on Integration and Sharing System of Network Movie and TV Data Resources under New Media Environment

In order to improve the sharing ability of network video data resources under the new media environment, the integration and sharing system optimization design of the network film and television data resources are carried out. In this paper, a new media integration method based on resource load balancing and scheduling is proposed. First, the collection and storage structure of network video data is analyzed, and the feature of network video data information flow is extracted. In order to realize the optimal clustering and scheduling of network film and television data resources, the attributes of network film and television are processed by template matching and data clustering, and then the software development and design of resource integration and sharing system are carried out. The hue database of the resource integration and sharing system adopts MySQL. The background server is built and the interface is designed under the framework of B/S. In the embedded Linux kernel environment, the integration of network video data resources and the software development of sharing system are conducted. The system test results show that the method is used to integrate and share the network video data resources, and the utilization rate of the resources is high. The clustering of network film and television resources data is better, the performance of resource scheduling is improved, and the effect of resource sharing is better.
The Implementation of Blended Learning Strategy Using Instagram and Edmodo to Improve Tenth Grade Students’ Ability in Writing Recount Texts at SMAN 10 Malang.

ABSTRACT This research was intended to improve thent grade students’ ability in writing recount text by implementing blended learning strategy using Instagram and Edmodo at SMAN 10 Malang. The result of the study indicated that the implementation of Blended Learning Strategy using Instagram and Edmodo successfully improve the students’ quality of writing. This was reflected in the achievement of the criteria of success. Twenty three out of 28 students or 85% of the students achieved the minimum mastery standard, which is 78. In terms of the students’ involvement in the teaching and learning activities, the average score for students’ involvement in four meetings was 92%. Therefore, the implementation of the method and the strategy has proven to be effective in improving students’ ability in writing recount texts. Keywords : blended learning, Instagram, Edmodo, recount texts, course management system, teaching writing. 1 1 1 1 MicrosoftInternetExplorer4 0 2 DocumentNotSpecified 7.8 磅 Normal 0
A Transfer Learning Method for Deep Networks with Small Sample Sizes

Abstract Transfer learning is that a machine learning model learns knowledge from more than one domain, and it is applied to the context of small sample size. Some of approaches concentrate on the correlation determination among all domains while some pay more attention on knowledge transfer among all domains. In this paper, on the basic of SVM with hinge loss, a new regularized transfer learning deep network with a specific regularization is proposed, in which a deep network learns high level representation with respect to the given samples. And a part of parameters in SVM are shared such that the similarity of data distribution can be well captured. Besides, a modified regularized SVM is exploited such that the gradient based method is feasible, which yields a parallel implementation of the proposed method. After that, in the experiment part, the comparison of our approach with state-of-the-art approaches manifests the competitive performance and the feasibility in classification.
Leapfrog and optimal kinodynamic motion planning

The motion planning for a mobile, autonomous system is solved using the Leapfrog algorithm from optimal control. Numerical optimal control has some advantages for motion planning. Differential constraints can be included in the problem formulation, and it is relatively simple to change the performance index and the nonlinear system model. The proposed algorithm finds a collision-free path for a cost functional under nonlinear differential constraints. Numerical case studies are done to show the effectiveness and efficiency of the Leapfrog algorithm and are compared with the kinodynamic-RRT* algorithm, in which optimal control is also used, but employed in a piecewise manner, between randomly-selected nodes. Path cost and execution time are used for performance comparison. The simulation results show that the Leapfrog method produces less jagged and shorter paths with smaller path cost and lower execution time compared to kinodynamic-RRT*, indicating suitability of the Leapfrog algorithm for motion planning in mobile, autonomous systems.
Public relation knowledge about disaster management in the emergency response phase as an effort for disaster risk reduction

Abstract According to Sutopo, Head of the BNPD Data, Information and Public Relations Centre, the earthquake disaster that occurred in Palu, there were 1,948 fatalities, 843 missing, and it is estimated that many victims were buried in the estimated figures until they reached thousands (BCC Indonesia, 2018). The losses suffered are also estimated to be very large. Preparedness is one part of the disaster management process and in the current developing concept of disaster, the importance of preparedness is one of the important elements of proactive disaster risk reduction before a disaster occurs. (LIPI-UNESCO, 2006). The object of this research is the people of Palu who live in Temporary Resettlement in several locations, namely Petobo, Balaroa, Silae, Lero, and Kampung Baru. This study uses economic modelling methods to determine the relationship between variables and as many as 150 people are sampled in the collection of data needed. The variables of this study are age, education, occupation, and aspects contained in the disaster risk reduction index (Carreno et al., 2004). The conclusion is that age is very influential on emergency response planning and implementation of warning systems as well as community preparedness and training, on the other hand education and employment have no effect at all in the DRR index. Therefore, there needs to be an evaluation from both the local and central government, the community, and academics to think about ways to reduce disaster risk by increasing public knowledge of disasters
An Advanced Q-Learning Model for Multi-agent Negotiation in Real-Time Bidding

This work develops a reinforcement learning method for multi-agent negotiation. While existing works have developed various learning methods for multi-agent negotiation, they have primarily focus on the Temporal-Difference (TD) algorithm (action-value methods) in general and overlooked the unique properties of parameterized policy. As such, these methods can be suboptimal for multi-agent negotiation. In this paper, we study the problem of multi-agent negotiation in real-time bidding scenario. We propose a new method named EQL, short for Extended Q-learning, which iteratively assigns the state transition probability and finally converges to a unique optimum effectively. By performing linear approximation of the off-policy critic purposefully, we integrate Expected Policy Gradients (EPG) into basic Q-learning. Importantly, we then propose a novel negotiation framework by accounting for both the EQL and edge computing between mobile devices and cloud servers to handle the data preprocessing and transmission simultaneously to reduce the load of cloud servers. We conduct extensive experiments on two real datasets. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our EQL method.
WordNet and Wiki Based Approach for Finding Polysemy Tags in a Tag Set

The tagging process involves use of labels to provide content with further information using a set of keywords (tags). A tag is a keyword or phrase used to provide metadata about a resource. Resource can be image, web page, video, audio etc., which are available on Web. In this research article the issue of polysemy (a word or phrase with multiple meanings) in tagging is analyzed because it creates a lot of confusion and is cause of ambiguity, redundancy and incorrect search results. In particular if a user looks at URLs the user can't guess about what sort of resource it refers. By looking at tag set associated with a resource a user can guess about the resource, but the guess can be ambiguous if tag set contains polysemy tags. A novel solution is proposed for the detection of polysemy tags in folk tag set. The capabilities of Word Net, Wikipedia and visual tag dictionary are exploited. Experiments are performed on tag sets that is taken from Delicious. This approach can detect many common types of polysemy that appears in folksonomies like contrastive and complementary polysemy.
Convergence Analysis of the Learning Algorithms for Multi-Valued Neurons

In this paper, based on the idea of gradient descent, the learning algorithms are derived for the multi-valued neurons (MVNs) which assure the minimum of the estimation errors. In addition, finite convergence of the proposed learning algorithms is discussed. As an extension of the MVNs, it is finally proved that such kind of learning algorithms can also be effectively applied to universal binary neurons and multi-valued neurons with periodic activation factions, and the relating algorithms converge in finite steps.
A Statistical Model for Morphology Inspired by the Amis Language

We introduce a statistical model for analysing the morphology of natural languages based on their affixes. The model was inspired by the analysis of Amis, an Austronesian language with a rich morphology. As words contain a root and potential affixes, we associate three vectors with each word: one for the root, one for the prefixes, and one for the suffixes. The morphology captures semantic notions and we show how to approximately predict some of them, for example the type of simple sentences using prefixes and suffixes only. We then define a Sentence vector s associated with each sentence, built from the prefixes and suffixes of the sentence and show how to approximately predict a derivation tree in a grammar.
An Android-based application for convenient visitation at windhoek correctional facility

A global outbreak of Coronavirus affected countries' economy, people's lives and businesses around the world due to lockdown restrictions imposed on everyday activities. Like elsewhere, logistics and visiting hours to correctional facilities in Namibia is no exception. Namibian correctional facilities are far located in remote areas, hence, visitation to these facilities tends to be cumbersome, time-consuming, costly and at times disappointing due to the lack of accurate and timely information. Also, the rate of imprisonment quadrupled due to unfortunate acts during the lockdown. In response to this challenges, authors of this paper developed an android-based application to support social distancing, help Namibians in locating their relatives and friends in Namibian rehabilitation centers and reduce recidivism while improving the morale and safety of inmates and workers amid the pandemic. This study adopted a mixed research methodology with a case study and an experimental as a research design. An Agile software development model has been used for the development of an application. Primary data were collected through open-ended questionnaires, direct observations in the prison and group interviews. The study has been also chiefly informed by related literature reviews, as secondary data. A simple random sampling technique has been used to select the respondents from a target population of thirty participants. The study concluded that the application developed was intelligent, innovative, helpful, easy and convenient to use. Mobile technology can drastically change how we can communicate. It has been recommended that the Ministry of Home Affairs, Safety and Security consider funding and fully implement this project in all thirteen correctional facilities across the country. As part of future work, a similar version of iOS is to be developed and possibly implement a remote and/or onsite Inmate video visitation.
Multi-category Classification Problem Oriented Subsampling-Based Active Learning Method

Abstract Traditional active learning methods have achieved gratifying results in the classification tasks of less categories such as binary classification, the application research of active learning in the field of big data problems still faces enormous challenges. Since many active learning query strategies need to perform matrix inversion, the amount of calculation increases exponentially with the increase of the scale of the problem, it is difficult to apply these active learning methods in large scale multi-category data classification task. In order to solve this problem, this paper designed a subsampling-based active learning model, and integrate unsupervised clustering algorithm with traditional active learning method, then conducted experiments on Binary Alphadigits and OMNIGLOT data sets. This paper compares the performance of five traditional active learning algorithms using this subsampling method, namely random sampling, uncertainty sampling, query-by-committee, density weighting and learning-based active learning. Through comparative experiments, the feasibility of active learning based on subsampling for solving the multi-category classification problem is verified, and it is found that the subsampling-based method can break the limitations of traditional active learning methods that cannot deal with large-scale data classification.
Evolution of Convolutional Neural Networks for Lymphoma Classification

There are more than 60 subtypes of Lymphoma. This diversity usually requires a specialised pathologist for diagnosis. We aimed to investigate the effectiveness of Artificial Neural Networks (ANNs) and Deep Learning at Lymphoma classification. We also sought to determine whether Evolutionary Algorithms (EAs) could optimise accuracy. Tensorflow and Keras were used for network construction, and we developed a novel framework to evolve their weights. The best network was a Convolutional Neural Network (CNN); its tenfold cross-validation test accuracy after training and weight evolution was 95.64%. The best single run test accuracy was 98.41%. This suggests that ANNs can classify Lymphoma biopsies at a test accuracy higher than the average human pathologist. The EA consistently improved accuracy, demonstrating that they are a useful technique to optimise ANNs for Lymphoma classification.
Moulinog: A Generator of Random Student Assignments Written in Prolog

We introduce, describe and discuss the potentialities of Moulinog, a tool created during the COVID-19 lockdown, designed to generate individual questionnaires for the remote evaluation of large classrooms. Starting with a list of students and a series of predicates constituting a pool of parametric questions along with rules for their parametrization, Mouling generates a list of individual questionnaires, together with a shell script allowing an easy emailing of the (password-protected) questionnaires to the students. The tool’s use in practice is illustrated on a particular course case for which it has proven to be both useful and time-saving.
Entropy Weight Method and Fuzzy Network-Based Task Completion Rate Prediction Model

This research focuses on prediction model based on entropy weight method (EWM) combined with Fuzzy Network (FN), which is an newly concept in the field of fuzzy logic. EWM and AHP are used to choose the input indicators. FN is used for rule base. And then the FN model is used as a kind of standard fuzzy system (SFS) to predict the task completion rate of a semi-physical virtual training system. The average relative error (ARE) of FN based model is 4.129% and is better than the SFS-based model which has ARE 6.231%.
Knowledge components detection in User-Generated Content

There is knowledge in user generated content that can be extracted and mined to be reused. Our work is focusing on knowledge extraction from user-generated content present in online communities. In this article, we propose an approach to extract elements of knowledge from user-generated content using ATM (Automatic terms recognition). The obtained results show the effectiveness of the process in extracting useful solutions to problems discussed by the online community members.
Prototype Relaxation With Robust Principal Component Analysis for Zero Shot Learning

Zero Shot Learning (ZSL) has been attracting increasing attention due to its powerful ability of recognizing objects of unseen classes. As one type of ZSL methods, the low rank based strategy has achieved remarkable success. However, traditional low rank based methods are often based on the assumption that a variety of visual features from a same class can be projected to a single attribute by ignoring the background information and other noisy interference in visual features. This assumption is unreasonable and often leads to bad performance when there is big variance within a class. In this paper, a novel method called Prototype Relaxation with Robust Principal Component Analysis (RPCA) is proposed to relax this assumption by adding a sparse noise constraint. In addition, to avoid the confusion between similar classes, an orthogonal constraint is employed to disperse all the class prototypes, including both seen and unseen classes, in latent space. Furthermore, to alleviate the domain shift problem, vectors from latent space are exploited to reconstruct visual features and semantic attributes respectively. Besides, the hubness problem is also mitigated by applying the max probability model in all three spaces. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method.
Artificial Intelligence System for Breast Cancer Screening Based on Malignancy-Associated Changes in Buccal Epithelium

The aim of the paper is to demonstrate the novel highly accurate AI system for screening for breast cancer based in investigation of the fractal dimension of chromatin in Feulgen-stained nuclei of buccal epithelium. The AI system consists of three models: morphological pre-processing of images, feature extraction and classification. This system implement the traditional approach to AI, based on machine learning that provides high performance on low-end computers, understanding the nature of the features and easy interpretation. Materials and Methods: The study was conducted on samples from 130 patients: 68 patients with breast cancer, 33 patients with fibroadenomatosis and 29 healthy individuals. Every sample contains in average 52 nuclei of buccal epithelium. The input data set for the study consists of 20,256 images of interphase nuclei of buccal epithelium (6752 nuclei were scanned without filter, through a yellow filter and through a violet filter). Each image consists of three channels: red, green, blue, as well as gray halftones. Minkovski fractal dimension was determined by box-counting method. Results: The accuracy of proposed screening test for breast cancer is 99.28%. Conclusion: Fractal dimension of feulgen-stained nuclei of buccal epithelium is a highly informative feature for the screening of breast cancer and traditional AI systems for breast cancer screening based on machine learning are competitive in comparison with analogous AI systems based of deep learning.