Reviewerly/exHarmony huggingface dataset:
Proximity Curves for Potential-Based Clustering

Abstract The concept of proximity curve and a new algorithm are proposed for obtaining clusters in a finite set of data points in the finite dimensional Euclidean space. Each point is endowed with a potential constructed by means of a multi-dimensional Cauchy density, contributing to an overall anisotropic potential function. Guided by the steepest descent algorithm, the data points are successively visited and removed one by one, and at each stage the overall potential is updated and the magnitude of its local gradient is calculated. The result is a finite sequence of tuples, the proximity curve , whose pattern is analysed to give rise to a deterministic clustering. The finite set of all such proximity curves in conjunction with a simulation study of their distribution results in a probabilistic clustering represented by a distribution on the set of dendrograms. A two-dimensional synthetic data set is used to illustrate the proposed potential-based clustering idea. It is shown that the results achieved are plausible since both the ‘geographic distribution’ of data points as well as the ‘topographic features’ imposed by the potential function are well reflected in the suggested clustering. Experiments using the Iris data set are conducted for validation purposes on classification and clustering benchmark data. The results are consistent with the proposed theoretical framework and data properties, and open new approaches and applications to consider data processing from different perspectives and interpret data attributes contribution to patterns.
Decision Support System for Quran Teacher Selection Using Profile Matching Method on TPQ Anbata

Abstract Al-Quran Reading Park (TPQ) Anbata is a reading park with a Quranic learning system that has an educational system of recitation values, reading fluency, Adab Values Reading reading, Attitude Values and Worship Value. The selection of the Koran teacher in the Anbata Al-Quran Reading Park (TPQ) is through a rigorous selection to guarantee the quality of its students. But sometimes these things are relatively balanced, causing new problems, namely the difficulty of determining the right quran teacher to be positioned as a teacher. Therefore a Decision Support System is needed that can provide recommendations for considering the selection of Quran teachers. In this case using the Profile Matching method. The method works by using the level of importance of a value needed to become a quran teacher. Calculations on this method produce the best value that meets the assessment criteria to be used as a recommendation for the selection of quran teachers in the Anbata Al-Quran Reading Park (TPQ).
Method for Rapid Development of Arduino-based Applications Enclosing ANN

The implementation of stand-alone applications enclosing artificial neural networks (ANNs) running on low-cost platforms tends to be prohibitive, primarily due to the demand for (very) large computational resources. This paper proposes a very effective method to solve this kind of problem. We rely on the existing approach of first configuring and training the ANN on a platform offering the necessary computing power, memory, and appropriate software tools, and then implementing the ANN model on the host platform. In our method, the code for the ANN model is automatically generated and integrated into the final application. The MATLAB environment, running on a PC, plays the roles of both a training platform and a code generator. The Arduino IDE is used for automatic code integration, while Arduino development boards provide the host for the stand-alone application. Some experimental tests confirm the proper operation of our approach. Quite a large ANN can be simulated on low-cost Arduino boards: Uno can accommodate an architecture with 315 parameters (83% utilization of 2KB SRAM) while Mega can accommodate an architecture with 1805 parameters (94% utilization of 8KB SRAM).
Fixed Point Theory for Digital k-Surfaces and Some Remarks on the Euler Characteristics of Digital Closed Surfaces

The present paper studies the fixed point property (FPP) for closed k-surfaces. We also intensively study Euler characteristics of a closed k-surface and a connected sum of closed k-surfaces. Furthermore, we explore some relationships between the FPP and Euler characteristics of closed k-surfaces. After explaining how to define the Euler characteristic of a closed k-surface more precisely, we confirm a certain consistency of the Euler characteristic of a closed k-surface and a continuous analog of it. In proceeding with this work, for a simple closed k-surface in Z 3 , say S k , we can see that both the minimal 26-adjacency neighborhood of a point x ∈ S k , denoted by M k ( x ) , and the geometric realization of it in R 3 , denoted by D k ( x ) , play important roles in both digital surface theory and fixed point theory. Moreover, we prove that the simple closed 18-surfaces M S S 18 and M S S 18 ′ do not have the almost fixed point property (AFPP). Consequently, we conclude that the triviality or the non-triviality of the Euler characteristics of simple closed k-surfaces have no relationships with the FPP in digital topology. Using this fact, we correct many errors in many papers written by L. Boxer et al.
Designing non-linear minimax and related discriminants by disjoint tangent configurations applied to RBF networks

Non-linear classification machines seldom are trained under criteria that are usual and useful for linear discriminants, such as minimax, Fisher's, and other similar criteria. The reason is the learning difficulties that transformation-trainable machines suffer when applying such criteria. However, the possibility of using non-linear machines whose transformations are pre-designed merits attention. In this contribution, we propose and study an efficient and potentially effective option: Applying Disjoint Tangent Configurations (DTC), a formulation that includes discriminants such as Fisher's, Bayes for normal distributions, Minimax Probabilistic Decision Hyperplane (MPDH), and others, to the output of a Radial Basis Function (RBF) network which has been previously designed with a moderate number of nodes to reduce the computational load, but with a high quality centroid selection algorithm, Frequency Sensitive Competitive Learning (FSCL), which allows to obtain networks with high representation capabilities. Experiments demonstrate that this approach leads to good performance results with acceptable computational efforts.
On Design and Implementation a Federated Chat Service Framework in Social Network Applications

As many organizations deploy their chatbots on social network applications to interact with their customers, a person may switch among different chatbots for different services. To reduce the switching cost, this study proposed the Federated Chat Service Framework. The framework maintains user profiles and historical behaviors. Instead of deploying chatbots, organizations follow the rules of the framework to provide chat services. Therefore, the framework can organize service requests with context information and responses to emulate the conversations between users and chat services. Consequently, the study can hopefully contribute to reducing the cost for a user to communicate with different chatbots.
Arabic Language Interface for XML Databases

Since its start in the last of sixties, Natural Language Interface for database (NLIDB) was one of the classic problems in of Natural Language Processing (NLP). The mean objective of NLIDB is to improve the interaction between human and database without needing to have skills in database languages such as Structured Query Language (SQL). In that, to extract data from databases, users need to put simple queries in their native language. Until now, many works have done in the field of NLIDB, almost of them deal with the problems of ambiguity in natural language query and the independence of database domain.
Predicting 30-Day Hospital Readmission for Diabetics Based on Spark

The hospital readmission rates have increasingly been used as an outcome measures in health services and as a quality benchmark for health systems. In the face of growing medical data rapidly, this paper proposed a distributed Random Forest model based on Spark to predict the 30 day readmission rates of diabetics. Given that the class imbalance will affect the performance of model, the paper implemented distributed Synthetic Minority Over-sampling Technique (SMOTE) algorithm, in which the positive class data was broadcast to all nodes to generate accurate k-nearest neighbor of minority samples, then the distributed Isolation Forest algorithm was developed for removing abnormal samples. Gini index of Random Forest was used to assess the importance of the features that afford assistance support for professionals. The experiments result showed that this model obtained a better classification effect than traditional stand-alone models by balancing class and removing outliers. Moreover, it can scale well with cluster size as it can dramatically decrease the training time and enhance the ability of processing massive medical data by increasing the number of nodes in the cluster.
Data-driven marketing

Email marketing is an effective channel in marketing strategies not only as a tool to increase brand visibility and brand awareness, but also as an excellent tool to help promote and sell. It will continue to be a critical channel for content marketers. Even with the advent of social media and networking platforms, email marketing still remains the most preferred channel for generating leads, informing and influencing customers.
Swift a New Programming Language for Development and Education

The development of information and communication technologies also develops programming languages ​​and development tools. Swift programming language is one of many emerging languages. The language was primarily designed for developing applications for iOS and macOS. Gradually, Swift becomes the primary language for developing iOS mobile apps. This paper describes the essential features, advantages and disadvantages of the Swift programming language and compares it with other languages. The article shows some measurement results and a comparison of Swift program speeds and some other programming languages. We find out from the tracking of students’ work and practical projects that working with Swift is often faster, brighter and safer than the previously used Objective-C language. The article describes the results of the survey among students and demonstrates the advantages of language in the field of programming teaching. Another goal of the article is to show the possibilities, advantages and limitations of Swift’s programming language in the development of mobile applications. The paper also shows the possibilities of Swift language in teaching.
Video as Educational Multimedia to Teach English Speaking

Abstract The aim of this research is to develop an educational multimedia in the form of video as one of media to teach speaking. Research and development design is used to produce an educational multimedia entitled Nginggris Narsis. Procedures of this research includes identification students and teachers need, planning and product design, product small scale tryout, validation of the expert, product revision, product large scale try out and revision, and dissemination. This video is used to teach the first grade students of Junior High School in Indonesia of which curriculum implemented is 2013 curriculum. The results show that NginggrisNarsis is an appropriate material to teach EFL learners in speaking, although there are still some weaknesses found within this video. Nginggris Narsis as educational media to teach speaking help both teachers and students in introducing English as a foreign language for Indonesian students.
MISE of wavelet estimators for regression derivatives with biased strong mixing data

Using a wavelet basis, this article considers the mean integrated squared error (MISE) of linear and non linear wavelet estimators for regression derivatives r(d)(x) based on biased strong mixing data. It turns out that the convergence rates coincide with those of Chesneau and Shirazi’s (Communication in Statistics-Theory and Methods, 2014), when d = 0 and the random sample is independent.
Multiple filterbanks for image processing: Implementation issues

Multiple filterbanks have recently been introduced as an effective tool for processing image data, in particular for the detection of directional singularities. The practical implementation of such a technique, however, encounters many intrinsic difficulties. Indeed, the fact that different, nontrivial and anisotropic scaling matrices are used at each multiresolution level can easily result in a highly inefficient way of carrying out the downsampling, upsampling and convolution operations involved in the process, and of recording and tracking their outputs. The aim of this paper is to propose a general strategy to overcome these implementation issues by properly reformulating the above operations and taking advantage of the representation of the scaling factors in terms of their associated Smith decompositions.
Simple and low-cost object detection method based on observation of effective permittivity change

The permittivity of a material is generally larger than that of air. When an object is present near an electric wire, the effective permittivity of the space around the wire becomes larger than that without the object. It follows that in the presence of an object, the velocity of the signal propagating along the wire decreases and the propagation delay time increases. This change can be easily detected by a simple electronic circuit composed of standard semiconductor devices. The principle of this object detection method can be used not only for proximity sensors but also for the detection or measurement of phenomena involving changes in permittivity.
Fuzzy Reinforcement Learning based Microservice Allocation in Cloud Computing Environments

Nowadays the Cloud Computing paradigm has become the defacto platform for deploying and managing user applications. Monolithic Cloud applications pose several challenges in terms of scalability and flexibility. Hence, Cloud applications are designed as microservices. Application scheduling and energy efficiency are key concerns in Cloud computing research. Allocating the microservice containers to the hosts in the datacenter is an NP-hard problem. There is a need for efficient allocation strategies to determine the placement of the microservice containers in Cloud datacenters to minimize Service Level Agreement violations and energy consumption. In this paper, we design a Reinforcement Learning-based Microservice Allocation (RL-MA) approach. The approach is implemented in the ContainerCloudSim simulator. The evaluation is conducted using the real-world Google cluster trace. Results indicate that the proposed method reduces both the SLA violation and energy consumption when compared to the existing policies.
Improved Bangla Language Modeling with Convolution

Language modeling is a fundamental task for building any natural language processing application or language understandable intelligent system. Language modeling becomes difficult with the increase of input sequence length. It is even more difficult for Bangla as it is more complex than many other languages and contains more character in its alphabet. Few previous works that were done on Bangla language modeling used statistical language modeling or simple neural network very recently. With the neural network, the RNN is the fundamental choice to deal with sequential data but we follow the recent development in convolutional neural network in sequential modeling. In this work, we used dilated convolution to solve the issues of long sequence and improved the performance of Bangla language modeling.
Analysis, Diagnosis and Correction of Rain Streaks

In any unique and dynamic climate, for example, rain and snow, it causes varieties in local intensities of the rainy pictures. Such changes can remarkably influence the frameworks that rely upon highlights of the picture for object recognition, tracking, object detection, segmentation etc. Accordingly, there is a requirement for quick and precise detection [1]-[3] and evacuation of rain streaks or the marks of snow. Here, we build up a strategy for diagnosis and dismissal of rain components. Rain components have a higher intensity compare to the non-rain component of the image. This property has been utilized to recognize the rain components in a picture. To decrease the false alarm cases, we use the attributes of raindrops, (for example, major and minor axis) and recognize the identified rain and non-rain component. Correction of the color is utilized to reestablish shading of rain-component with the color of the background. We exhibit that our proposed technique performs well for rain-component identification with more prominent precision compare to other state-of-the-art methods.
A Smart Contract Grammar to Protect IoT Firmware Updates using Hyperledger Fabric

Securing firmware updates for IoT devices is a challenging undertaking because of their limited hardware resources. Most of the existing solutions are based on centralized architectures that may expose a single point of failure. Blockchain technology is largely accepted as a secure, robust and distributed platform for a number of different applications. This paper proposes the use of a blockchain platform to facilitate firmware updates for IoT devices. The distributed nature of the framework helps secure the firmware update process against single points of failure. In addition, the use of smart contracts can further strengthen the process by specifying firmware update conditions, verifying firmware update legitimacy, and protecting against potential cyber-attacks. Previous work in this area is extended by introducing a grammar and compiler to assist stakeholders in generating smart contracts. To validate the approach, a web- based prototype has been implemented to directly generate smart contracts in chaincode format that can be deployed in Hyperledger Fabric, an open source and permission-based blockchain framework. The grammar and compiler were evaluated for different use cases of the firmware update operations. Preliminary results with a prototype implementation show potential in simplifying the smart contract development process and reducing the amount of work needed to generate a working chaincode.
Watermarking using SMRT on Grayscale Images and Attack analysis

Digital images are important data types with an extensive field of application and users are interested in implementing strategies to protect its content from preview or exploitation. The aim of creating an image - watermarking strategy is to fulfil the requirements of both robustness and imperceptibility. An image - watermarking scheme based on Sequency based MRT (SMRT) is proposed in this paper to accomplish this goal. In the embedding phase, the watermark is embedded in the SMRT of the grayscale cover image. The value of the scaling factor determines the embedded strength of the watermark. The scaling factor is varied from 0.01 to 1 to evaluate the performance of the methodology. Simulation results reveal that the algorithm outlined has better invisibility and robustness performance.
Combining Local and Global Descriptors Through Rotation Invariant Texture Analysis for Ulos Classification

Performing images augmentation for data multiplication, to some degree, has negative impact to classification tasks, particularly to the object that has texture patterns with specific direction (anisotropic). As Ulos data mostly are anisotropic textures, the convolution neural networks (CNNs) fail to discriminate image if the images are arbitrarily rotated. This is due to CNNs are not rotation invariant. To benefit anisotropic and isotropic (has no specific direction) textures, conducting features extraction with discrete techniques is needed. Extracting features by wavelet transform (DWT) for directional specific patterns changes the wavelet energy features significantly while the isotropic one does not. To address the issue radon transform is first being employed, as to get principal direction for anisotropic textures. The output of wavelet transform is just globally rotation invariant. On this work, we propose a new approach to obtain robust features set by combining both local and global rotation invariant, as the output from LBP-ROR and wavelet transform. Our work shows that the performance outperforms the previous research done by scholars.
Reassembly of Two-Dimensional Irregular Fragments by Improved Polygon Feature Matching

Automatic reassembly of fragments by computer is necessary when the number of fragments is large and mixed with each other for reconstruction by artificial contrast is complicated and time-consuming. In order to achieve 2D irregular fragments reassembly, a new reassembly method of 2D fragments based on contours is proposed. Firstly, the spatial adjacent fragment pairs are found by using the improved local matching method for polygon features, and the matching degree of each pair is measured. Then a new path-generating method is given based on matching angle. By generating the global path and reassembling the fragments according to the path, the multi-fragment reassembly is finally realized. Simulation results show that this method can make full use of the local matching results to obtain ideal reassembly results. And the calculation amount can be greatly reduced.
Smart Cap for Alzheimer Patients using Deep Learning

Due to medical advancements in 20th century there has been an explosive growth in the population and life expectancy. Even after such medical advancements there still exist some irreversible diseases from which modern medicine cannot help humans to recover fully. Alzheimer's is one such progressive disease that causes severe problems with thinking, memory and behaviour. The people suffering from Alzheimer's disease have a tendency to forget people they know, scheduled meetings, medicines they are supposed to take, their daily routine and other important things. In this paper, we present the prototype developed for wearable smart cap designed using deep learning model for face recognition to recognize people related to the patient. In contrast to the other approaches which rely on deep networks trained on specific number of classes making introduction of new classes difficult in the system, our approach extracts unique feature vectors from faces in the dataset and trains classifier on them so that it can easily be trained for classifying new classes. We have developed a 3D model for this smart cap with all hardware modules embedded in it. This cap is very much useful for the patients as it enables them to recognize the people nurturing them and also harmful strangers.
A Word Representation to Improve Named Entity Recognition in Low-resource Languages

Named Entity Recognition (NER) is a fundamental task in many NLP applications that seek to identify and classify expressions such as people, location, and organization names. Many NER systems have been developed, but the annotated data needed for learning is not available for low-resource languages, such as Cameroonian languages. In this paper we exploit the low frequency of named entities in text to define a new suitable word representation for named entity recognition. We build the first Ewondo (a Bantu language of Cameroon) named entities recognizer by projecting named entity tags from English using our word representation. In terms of Recall, Precision and F-score, the obtained results show the effectiveness of the proposed word representation.