Reviewerly/exHarmony huggingface dataset:
Mobile Based Application of Mosquito Larvae Checking Reports : Malaka Sari Village Case

Jumantik officers are government officials who have the duty to check the presence of aedes aegpyti mosquito larvae in the community. The purpose of this research is to help Jumantik officers and village officials to carry out the process of checking mosquito larvae. This application is can be used to assist officers in creating reports for the inspection of mosquito larvae, helping officers to report the problematic addresses and scheduling for inspection of mosquito larvae. This application was developed using the React Native framework and Javascript programming language. The results of this study are useful applications as a substitute for reporting the presence of conventional mosquito larvae.
The Learning Algorithm of Real-Time Automata

Traditional machine learning algorithms heavily depend on training data. In order to reduce the amount of training data, active learning is proposed to find out the critical data, which place more important roles against other data. The active learning algorithm is also used to learn real-time automaton(RTA). However, a huge number of membership queries and equivalence queries are generated in the learning process. In this paper, We design a new data structure to store the information obtained by membership queries. This data structure is a kind of tree structure, and improve the efficiency of the active learning for real-time RTA because this structure can process counter-examples effectively. Some experiments are conducted, and the results show that the algorithm can significantly reduce the number of membership queries without increasing the equivalence queries numbers. From the data point of view, our algorithm reduces the number of membership queries by 50% and the execution time by 80%.
Developing the Gaois Linguistic Database of Irish-language Surnames

It is now commonplace to see surnames written in the Irish language in Ireland, yet there is no online resource for checking the standard spelling and grammar of Irish-language surnames. We propose a data structure for handling Irish-language surnames which comprises bilingual (Irish–English) clusters of surname forms. We present the first open, data-driven linguistic database of common Irish-language surnames, containing 664 surname clusters, and a method for deriving Irish-language inflected forms. Unlike other Irish surname dictionaries, our aim is not to list variants or explain origins, but rather to provide standard Irish-language surname forms via the web for use in the educational, cultural, and public spheres, as well as in the library and information sciences. The database can be queried via a web application, and the dataset is available to download under an open licence. The web application uses a comprehensive list of surname forms for query expansion. We envisage the database being applied to name authority control in Irish libraries to provide for bilingual access points.
Connecting Distributed Pockets of Energy Flexibility through Federated Computations: Limitations and Possibilities

Electric grids are traditionally operated as multientity systems with each entity managing a geographical region. Interest and demand for decarbonization and energy democratization is resulting in growing penetration of controllable energy resources. In turn, this process is increasing the number of grid entities. The paradigm shift is also fueled by increased adoption of intelligent sensors and actuators equipped with advanced processing and computing capabilities. While collaboration among power grid entities (agents) reduces energy cost and increases overall reliability, achieving effective collaboration is challenging. The main challenges stem from the heterogeneity of system agents and their collected information. Furthermore, the scale of data collection is constantly increasing and many grid entities have strict privacy requirements. Another challenge is the energy industry's common practice of keeping data in silos. Federated computation is an approach well suited to addressing these issues that are increasingly important for multi-agent energy systems. Through federated computation, agents collaboratively solve learning and optimization problems while respecting each agent's privacy and overcoming barriers of cross-device and cross-organization data isolation. In this paper, we first establish the need for federated computations to achieve energy optimization goals of the future power grid. We discuss practical challenges of performing multi-agent data processing in general. Then we address challenges that arise specifically for orchestrating operation of connected distributed energy resources (DERs) in the Internet of Things (IoT). We conclude this paper by presenting a novel federated computation framework that addresses some of these issues, and we share examples of two initial field test setups in research demonstrations and commercial building applications with Grid Fruit, LLC.
Explicitly Privacy-Aware Space Usage Analysis

Surveillance in private and public spaces provides observers with information that can enhance protection and efficiency but usually infringes upon the privacy of the individuals and groups. These informational privacy risks are centered on users' perceived and design-induced threats. They cannot be removed completely but can be minimized using suitable anonymization techniques. To minimize the users' informational privacy threats, we designed a privacy-aware surveillance system that gives the users leverage over the anonymization filters, to physically adjust the opaqueness of the camera lens used in the prototype according to their privacy requirements. We implement our prototype in the context of office space surveillance, where the proposed solution considers privacy requirements in such environments to improve users' trust in the surveillance system and reduce their privacy concerns.
Tackling Counterfeit Medicine Through an Automated System Integrated with QR Code

Adulteration and the introduction of counterfeit medicine in the pharmaceutical market is an increasing problem in our country. Official reports from WHO states that nearly 35% of the fake drugs in the world are being imported from India making up to a 4000 crore drug mafia market. The main reason is human intervention at every stage of the stringent process in the current system. This induces inevitable human error which is taken advantage of. The absence of an automated system is the main and the biggest drawback fof the currently implemented system. The paper proposes an automated system that provides a unique recognition mechanism by generating machine-readable code for each entity. This generation of the machine-readable code is backed up by encryption and hashing and hence cannot be replicated by a third party.
Can Judges Trust the I-Vectors Scores?: A Comparative Study of Voices Comparison in the Forensic Domain

Phonetic forensics is now widely used. Indeed, in some cases, the voice can be the only potential proof for investigations. In order to evaluate the performance of Forensic Voice Comparison domain (FVC), we will study two factors limiting the robustness of verification task. One depends on the type of transmission channel (telephone, microphone, etc.) and the other is related to the physiological difference between the speakers’ voices. Our work consist in adapting an open source platform for Automatic Speaker Recognition (ASR), “ALIZE”, for use in the forensic domain to estimate and represent the voice as an exhibit. For this, we will study and compare the two models GMM-UBM and i-vectors to evaluate performance.
Recursion Polynomials of Unfolded Sequences

Watermarking digital media is one of the important challenges for information hiding. Not only the watermark must be resistant to noise and against attempts of modification, legitimate users should not be aware that it is embedded in the media. One of the techniques for watermarking is using an special variant of spread-spectrum technique, called frequency hopping. It requires ensembles of periodic binary sequences with low off-peak autocorrelation and cross-correlation. Unfortunately, they are quite rare and difficult to find. The small Kasami, Kamaletdinov, and Extended Rational Cycle constructions are versatile, because they can also be converted into Costas-like arrays for frequency hopping. We study the implementation of such ensembles using linear feedback shift registers. This permits an efficient generation of sequences and arrays in real time in FPGAs. Such an implementation requires minimal memory usage and permits dynamic updating of sequences or arrays.The aim of our work was to broaden current knowledge of sets of sequences with low correlation studying their implementation using linear feedback shift registers. A remarkable feature of these families is their similarities in terms of implementation and it may open new way to characterize sequences with low correlation, making it easier to generate them. It also validates a conjecture made by Moreno and Tirkel about arrays constructed using the method of composition.
An Empirical Study to Investigate Data Sampling Techniques for Improving Code-Smell Prediction Using Imbalanced Data

A code smell refers to a surface indication that usually indicates a deeper problem within a system. Usually it is associated with an easily traceable issue that often indicates a deeper inherent problem in the code. It has been observed that codes containing code smells are more susceptible to a higher probability of change during the software development process. Refactoring the code at an early stage during the development process saves a lot of time and prevents any kind of hassles at later stages. This paper aims at finding eight different types of code smells using feature engineering and sampling techniques with the purpose of handling imbalanced data. Three naive Bayes classifier are used to find code smells over 629 different packages. The results of this research indicate that the Gaussian Naive Bayes classifier performed the best out of all three classifiers in all samples of data. The results also indicate that the original data was the best data to use in which all three classifiers performed better than other two data sets.
Vietnamese Sign Language Recognition using Dynamic Object Extraction and Deep Learning

Sign languages were invented to help deaf-mute people can communicate with each other and with ordinary people. This paper proposes a novel method to recognize the VSL (Vietnamese Sign Language) based on the combination of Gauss distribution and correlation coefficient to extract the single dynamic object in the video, GoogLeNet (CNN model) and BiL-STM (Bidirectional Long Short-Term Memory) to classify video sequences. The used dataset includes 2700 samples corresponding to 27 signs in VSL. Our experimental results reach an accuracy of 99.38% with the validation set and 98.15% with the test set.
Image-Based Lifelogging: User Emotion Perspective

Lifelog is a digital record of an individual’s daily life. It collects, records, and archives a large amount of unstructured data; therefore, techniques are required to organize and summarize those data for easy retrieval. Lifelogging has been utilized for diverse applications including healthcare, self-tracking, and entertainment, among others. With regard to the image-based lifelogging, even though most users prefer to present photos with facial expressions that allow us to infer their emotions, there have been few studies on lifelogging techniques that focus upon users’ emotions. In this paper, we develop a system that extracts users’ own photos from their smartphones and configures their lifelogs with a focus on their emotions. We design an emotion classifier based on convolutional neural networks (CNN) to predict the users’ emotions. To train the model, we create a new dataset by collecting facial images from the CelebFaces Attributes (CelebA) dataset and labeling their facial emotion expressions, and by integrating parts of the Radboud Faces Database (RaFD). Our dataset consists of 4,715 high-resolution images. We propose Representative Emotional Data Extraction Scheme (REDES) to select representative photos based on inferring users’ emotions from their facial expressions. In addition, we develop a system that allows users to easily configure diaries for a special day and summaize their lifelogs. Our experimental results show that our method is able to effectively incorporate emotions into lifelog, allowing an enriched experience.
Research on cloud computing high-density data center infrastructure and environment matching technology

The current rapid development of cloud computing and networks has put forward new requirements for the construction of new infrastructure such as data centers. This paper compares traditional data centers and high-density data centers, proposes a three-tier infrastructure for high-density data centers, and analyzes the data center environment. To solve the obvious problem of software and hardware heterogeneity in high-density data centers, this paper uses virtualization technology to pool resources in high-density data centers and introduces SOA architecture to manage software and hardware resources hierarchically. Finally, the data center infrastructure and environment matching technology are studied.
Graceful Performance Degradation in Apache Storm

The concept of stream data processing is becoming challenging in most business sectors where try to improve their operational efficiency by deriving valuable information from unstructured, yet, contentiously generated high volume raw data in an expected time spans. A modern streamlined data processing platform is required to execute analytical pipelines over a continues flow of data-items that might arrive in a high rate. In most cases, the platform is also expected to dynamically adapt to dynamic characteristics of the incoming traffic rates and the ever-changing condition of underlying computational resources while fulfill the tight latency constraints imposed by the end-users. Apache Storm has emerged as an important open source technology for performing stream processing with very tight latency constraints over a cluster of computing nodes. To increase the overall resource utilization, however, the service provider might be tempted to use a consolidation strategy to pack as many applications as possible in a (cloud-centric) cluster with limited number of working nodes. However, collocated applications can negatively compete with each other, for obtaining the resource capacity in a shared platform that, in turn, the result may lead to a severe performance degradation among all running applications.The main objective of this work is to develop an elastic solution in a modern stream processing ecosystem, for addressing the shared resource contention problem among collocated applications. We propose a mechanism, based on design principles of Model Predictive Control theory, for coping with the extreme conditions in which the collocated analytical applications have different quality of service (QoS) levels while the shared-resource interference is considered as a key performance limiting parameter. Experimental results confirm that the proposed controller can successfully enhance the \(p\)-99 latency of high priority applications by 67%, compared to the default round robin resource allocation strategy in Storm, during the high traffic load, while maintaining the requested quality of service levels.
Privacy Preservation in Mobile-Based Learning Systems: Current Trends, Methodologies, Challenges, Opportunities and Future Direction

The adoption of mobile technologies in education are evolving like in the business and health sectors. The design of user-centric platform to enable individuals participate in the activities of learning and teaching is currently area of research. The Learning Management Systems (LMS) area assists learners and academic activities but, it continues to fall short of desired impact due to huge demands of the application. More importantly, the mobile applications offer enormous convenience not without the possibility of eavesdropping and maliciously exploiting data about users. The original structure of mobile learning requires that data and processing heads have centralized entity, which is not possible in wireless application arrangements due to communication overhead of transmitting raw data to central learning processor. This led to the use of distributed mobile learning structure, which preserve privacy of learners. This study discusses the challenges, current trends, methodology, opportunities and future direction of privacy preservation in mobile-based learning systems. The study highlighted the use of learners' private data and behavioral activities by LMS especially in understanding the needs of learners as well as improvement of their experiences. But, it raises concerns about the risks of learners' privacy on LMS due the mining processes of learners, which were not considered in existing related studies in literature.
Multiple Smile Detection Using Histogram of Oriented Gradient and Support Vector Machine Methods

Abstract The face or the front of the head consists of the eyes, nose and mouth. Each face has its uniqueness from the many human faces. The face is used to show happy and sad expressions and feelings. Smiling also includes self-expression from others. The analysis of facial expressions plays a key role in analyzing human emotions and behavior. Smile detection is a specialized task in facial expression analysis with a variety of potential applications such as photo selection, user experience analysis, smile payments, and patient monitoring. Conventional approaches often extract low-level face descriptors and smile detection based on strong binary classifiers. In this paper, we propose an effective Histogram of Oriented method supporting vector engines for smile detection. The experimental results show that our proposed network outperforms the current state of the method. The test results using the Histogram of Oriented Gradient and Support Vector Machine method for smile detection were 87% for a precision value of 88% and a recall value of 83% and accuracy. In the future, we want to exploit some of the latest effective designs. we will try to update our mouth model so we can support a bigger head turn and face size scale.
Who's that knocking at the door? The problem of credential abuse

At the heart of IT security is a simple concept – proving you are who you say you are. In this context, ‘you’ might be a human logging into a network or service, a device interacting with an application programming interface (API), one network talking to another or any number of other scenarios. And the proof could be a certificate, an SSH key, a token of some form or our old favourite – and inevitably the artefact we'll be talking most about here – the old, fragile and yet seemingly unkillable password. At the heart of IT security is a simple concept – proving that you are who you say you are. But the ways we have of doing that, through credentials of some form, are flawed. Credential abuse comes in many forms. The question we need to ask is, where does it sit in terms of an organisation's attack surface and security priorities? In the first of a two-part feature, Steve Mansfield-Devine surveys a number of industry experts to get their views on what forms of credential abuse are being encountered and the threat these pose to enterprises.
Basics of Neural Networks

In this chapter, we derive neural networks from the viewpoint of physical models. A neural network is a nonlinear function that maps an input to an output, and giving the network is equivalent to giving a function called an error function in the case of supervised learning. By considering the output as dynamical degrees of freedom and the input as an external field, various neural networks and their deepened versions are born from simple Hamiltonians. Training (learning) is a procedure for reducing the value of the error function, and we will learn the specific method of backpropagation using the bra-ket notation popular in quantum mechanics. And we will look at how the “universal approximation theorem” works, which is why neural networks can express connections between various types of data.
Mapping the Spread of Covid-19 in Asia Using Data Mining X-Means Algorithms

Abstract Until now, Covid-19 is a phenomenal problem faced by almost all over the world, especially countries on the Asian continent. Not only causing casualties, but this virus also affects the wheels of the country’s economy. The purpose of this paper is to view and map the spread of the Covid-19 virus in Asia based on Total Cases, Total Deaths, Total Cured and Active Cases from 49 Countries. The research data in this paper were obtained from the Worldometer website sourced from WHO, CDC, NHC and others. In this proposed paper, the algorithm used is X-Means Clustering with the help of Rapidminer. The results of this proposed paper are in the form of grouping or mapping the spread of the Covid-19 virus in Asia which is divided into 4 zones, including the Red Zone (the number of active cases of Covid -19 and the death rate is very high) which consists of 1 country, the Orange Zone (number of cases active covid -19 and the mortality rate is quite high) consisting of 1 countries, the Yellow Zone (active case rate of Covid -19 and moderate mortality) consisting of 39 countries, and the Blue Zone (active case rate of covid -19 and low mortality rate) which consists of 8 countries.
Efficiency Evaluation of Mixed Mathematics Teaching in Basic Mathematics Curriculum Under the Background of Internet Plus

In the background of Internet plus, when assessing the efficiency of the mixed teaching classroom in the basic mathematics course, the data samples are affected by isolated noise points, resulting in the excessive error of the final classroom teaching efficiency evaluation. In view of this deficiency, we study a method of classroom teaching efficiency evaluation of the mixed mathematics teaching in the background of Internet plus. From different perspectives, the paper determines the efficiency evaluation index, delimits the different stages of the mixed teaching classroom, calculates the risk of classroom efficiency under the Internet + ”mode, searches the evaluation value of the index by using the distance weighted proximity regression method, controls the external interference in the data set, and constructs the classroom efficiency evaluation function. We will prepare 20 classes of mixed teaching data sets and build an Internet plus experimental environment, using two traditional classroom teaching efficiency evaluation methods and the classroom efficiency evaluation method designed to carry out the experiment. The results show that the evaluation error produced by the classroom efficiency evaluation method is the smallest.