Reviewerly/exHarmony huggingface dataset:
Local Binary Pattern Algorithm with Weight Threshold for Image Classification

Image classification has attracted the attention in many research field. As an efficient and fast image feature extraction operator, LBP is widely used in the Image classification. The traditional local binary pattern (LBP) algorithm only considers the relationship between the center pixel and the edge pixel in the pixel region, which often leads to the problem of partial important information bias. To solve this problem, this paper proposes an improved LBP with threshold, which can significantly optimize the processing of texture features, and also be used to address the problems of multi-type image classification. The experimental results show that the algorithm can effectively improve the accuracy of image classification.
The making and development of Baxter the Empowered Chatbot impered with Machine Intelligence

Functioning of simple A
Impact of Cloud Computing on Banking Sector, Its Security and Future Trends.

Government of India initiative of Digital India has encouraged banks and other financial organisations to adopt cloud computing. Because of which; Cloud Computing has seen a substantial amount of growth in every sector. Many government sector &amp; Private sector organisations have started using cloud computing as a preferred medium for storage and analyse data which can accessed from anywhere and at any time. Cloud computing has reduced the cost of management of physical &amp; technical Infrastructure at the same time. Banking sector has specifically seen much development after accepting cloud computing as a medium to store data, Analyse security framework &amp; maintaining privacy at the same time. With an adoption of cloud computing, banking services industry continues to be under strict regulatory and compliance framework to maintain privacy of data and security of systems. In this document we are going to learn about cloud computing and its impact on Banking sector. Along with that we are going to understand some future trends of cloud computing with respect to banking sector.
Design and Implementation of an Intelligent Wheelchair Controlled by Multifunctional Parameter

Physically challenged persons those who are suffering from different physical disabilities face many challenging problems in their day-to-day life for commutating from one place to another and even sometimes they need to have to be dependent on other people to move from one place to another. The purpose of this task is to make a Multi-Functional Wheelchair using Accelerometer ADXL335 and ESP32 as sensor to help physically disabled human beings in transferring from one region to any other simply by giving path from the hand, voice, Bluetooth. This Accelerometer ADXL335 and ESP32 primarily based totally challenge use Accelerometer ADXL335 and ESP32 for transferring backward path, ahead path, left path or proper path. We have advanced a Multi-useful wheelchair for medically disable human beings. We are working wheelchair by multi-feature 1) Bluetooth (Using Bluetooth device, ESP32), 2) Hand Gesture (Using Accelerometer ADXL335 MEMS), 3) Voice (ESP32). This makes challenge very powerful. The fundamental of this challenge is achieved retaining in thoughts to able the handicapped man or woman to transport round their domestic without any assist of different character.
The Usage of Agile Adoption Framework to Assess Scrum Process and Recommend Improvements

The Scrum framework is based on the values, principles, and practices that form the foundation for organizations to work. This case study examines an Indonesia-based technology on implementing software development using Scrum. The goal is to assess the process and recommend improvements so XYZ Company can achieve better results at delivering products and services using Scrum. Agile Adoption Framework is used as the assessment framework, evaluating 3 teams represented by its Manager, Product Owner, and Developers. Indicator assessment is done by using data collection methods such as questionnaires or observations. The assessment result shows that XYZ Company achieves Level 2, and the teams achieve Level 2, 4, and 5 respectively. Furthermore, 7 agile practices need improvements, such as evolutionary requirements, continuous delivery, risk-driven iterations, low process ceremony, agile project estimation, pair programming, and test-driven development.
Meta-X: A Technique for Reducing Communication in Geographically Distributed Computations

Computations, such as syndromic surveillance and e-commerce, are executed over the datasets collected from different geographical locations. Modern data processing systems, such as MapReduce/Hadoop or Spark, also, require to collect the data from different geographical locations to a single global location, before executing an application, and thus, result in a significant communication cost. While MapReduce/Hadoop and Spark have proven to be the most useful paradigms in the revolution of distributed computing, the federation of cloud and big-data activities is the challenge, wherein data processing should be modified to avoid (big) data migration across remote (cloud) sites. This is exactly our scope of work, where only the very essential data for obtaining the final result is transmitted, for reducing communication and processing, and for preserving data privacy as much as possible. In this work, we propose an algorithmic technique for geographically distributed computations, called Meta-X, that decreases the communication cost by allowing us to process and moves metadata to among different locations, instead of the entire datasets. We illustrate the usefulness of Meta-X in terms of MapReduce computations for different operations, such as equijoin, k-nearest-neighbors finding, and shortest path finding.
Impact of Types of Change on Software Defect Prediction

Churn metrics are commonly used in software defect prediction due to high performance, language independence and ease of extraction. The main data sources for churn metrics-based prediction models in the literature are the change logs. But changes may also be due to several reasons other than fixing defects, such as enhancement and applying new requirements. Therefore, without awareness of type of the change and effects on defect prediction, fitting the best model is too difficult. In this paper, we propose a churn type-aware defect prediction model. We observe the impacts of churn-type awareness on prediction accuracy level. In our experiments, we used a real work item and change log dataset which is produced by a software development team. In our study, we also reveal the correlations among the change types.
On the feasibility of short-lived dynamic onion services

Tor onion services utilize the Tor network to enable incoming connections on a device without disclosing its network location. Decentralized systems with extended privacy requirements like metadata-avoiding messengers typically rely on onion services. However, a long-lived onion service address can itself be abused as identifying metadata. Replacing static onion services with dynamic short-lived onion services may be a way to avoid such metadata leakage. This work evaluates the feasibility of short-lived dynamically generated onion services in decentralized systems. We show, based on a detailed timing analysis of the onion service deployment process, that dynamic onion services are already feasible for peer-to-peer communication in certain scenarios.
Implementasi Pengembangan Kemampuan Mengenal Huruf Selama Masa Belajar di Rumah Pada Anak Kelompok A2 Tk ABA Jogokaryan

This study describes the implementation of language learning in children aged 4-5 years in TK ABA JOGOKARYAN while studying at home. The assessed research was to describe the implementation of letter recognition in children aged 4-5 years at TK ABA Jogokaryan. This research is a qualitative approach and descriptive research type. The subjects of this study were the principal, class A teachers, parents of class A students, and grade A children. The object of this study was the implementation of letter recognition in children aged 4-5 years at TK ABA JOGOKARYAN in class A2 while studying at home. The data collection techniques used were interviews and documentation. The data analysis technique uses the theory of Miles and Huberman, namely data collection, data condensation, data presentation, and concluding. The validity of the research data was re-tested using source triangulation and method triangulation. The results of this study indicate: 1) Planning begins with making daily lesson plans, then sent to parents via Whatsapp group or give to parents directly when they come to school. 2) The implementation of letter recognition is played by parents by accompanying and documenting the learning activity process. 3) Evaluation is carried out through documentation reported by parents on the Whatsapp group. 4) Supporting factors include guidance from parents in letter recognition while children are studying at home, online media, which is used as a communication tool for teachers and parents, and special programs on television that help children's learning while at home. At the same time, the inhibiting factors are some families who do not make it a habit knowing letters, parents' busyness, mood swings, and a lack of enthusiasm for learning.
ChunkedHCs algorithm for authorship verification problems: Reddit case study

Cybercrime can be associated with undisclosed social media accounts deliberately used to conduct unethical or illegal activities such as cyberbullying, fraudulent transactions, human trafficking, etc. The objective of this paper is to identify whether two social media accounts belong to the same person by examining the accounts' writing, i.e. comments and posts. To that end, this preliminary study introduces a new algorithm, ChunkedHCs, specifically designed for the authorship verification task to decide whether a pair of texts are written by the same person. In the domain of machine learning and deep learning, there have been previous authorship verification approaches, which often involve complex feature selections or sophisticated pre-processing steps due to the complexity of topic heterogeneity. Such limits provide motivations to seek a simpler yet more robust approach that could offer competitive verification ability. ChunkedHCs is based on the statistical testing Higher Criticism (Donoho and Jin, 2004) and the HC-based similarity algorithm (Kipnis, 2020a & 2020b) (Kestemont et al., 2020). Using Reddit users’ data, ChunkedHCs offer a promising performance with an accuracy of 0.94 and an F1 of 0.9381 for texts between 29,000 and 30,000 characters. It is speculated that the algorithm could also be highly applicable to identify if two accounts are used by the same person for other social media platforms such as Facebook, Twitter and even dark web forums. Various avenues of further research on ChunkedHCs are also proposed.
Multi-language Handwritten Numeral Recognition with Convolutional Neural Network

Compared with handcrafted features, convolutional neural network (CNN) is a more effective model to solve the handwritten numeral recognition problem. In recent years, many different datasets have appeared, but there is a lack of a collection of multi-language handwritten numeral datasets, and the evaluation of multi-language handwritten numeral recognition for CNN is lacking. In this paper, we collect and present the biggest dataset for the multi-language handwritten numeral recognition problem ever, consisting of 15 different languages. We also contribute two baseline CNNs and evaluate them in this newly combined dataset. We found that LeNet is more effective than a more complex CNN. We also found that Devanagari and Telugu are the most difficult to distinguish when mixed with other similar languages.
Sensing, Wireless Transmission, and Smart Processing of Heart Signals

In recent years, by increasing the number of epidemics, the concept of telemedicine and its combination with the Internet of Things (IoT), has become a more prominent subject in engineering. The purpose of this study is to implement an IoT system that can sense the heart signal and detect the arrhythmia in a heartbeat by using smart signal processing and send them through the internet to a relative of the patient or doctor. In this system, Electrocardiogram (ECG) signals are received by electrodes and sent to the AD8232 chip. Then, by connecting to the ESP32 module, information through the Wi-Fi network and Message Queuing Telemetry Transport (MQTT) protocol, is sent to the broker created on Raspberry Pi. Then, using Python and through the Decision Tree algorithm, the heartbeat's performance is evaluated as a smart process. The accuracy of the algorithm in detecting arrhythmias is over 95%. Finally, with an android application and through the TCP/IP protocol, the health or arrhythmia of the heartbeat will be informed on the doctor's or one of the patient's relative's cell phone, and its real-time display will be provided. Also, the possibility of sending a command from a mobile to the broker has been provided.
Existing Practices in India: Case Studies from Different Geomining Setup

In this chapter, selected case studies representing different geomining conditions of India have been presented as the authors regard the delineation of case records as an effective way for exposure of practical aspects of various mining operations and their challenges. The case study approach opens the door for advancement of new mining technologies, like digital innovations, and increases the scope, leading to the achievement of breakthroughs. Moreover, the applicable techniques and their principles, as per the characteristics of mines and their deposits, can be better understood from this approach. The experience gained often proves very useful in tackling the day-to-day problems associated with core mining and environmental issues—be it an open mine or underground mine.
Coordinate ascent MORE with adaptive entropy control for population-based regret minimization

Model-based Relative Entropy Policy Search (MORE) is a population-based stochastic search algorithm with desirable properties such as a well defined policy search objective, i.e., it optimizes the expected return, and exact closed form information theoretic update rules. This is in contrast with existing population-based methods, that are often referred to as evolutionary strategies, such as CMA-ES. While these methods work very well in practice, the updates of the search distribution are often based on heuristics and they do not optimize the expected return of the population but instead implicitly optimize the return of elite samples, which may yield a poor expected return and unreliable or risky solutions. We show that the MORE algorithm can be improved with distinct updates based on coordinate ascent on the mean and covariance of the search distribution, which considerably improves the convergence speed while maintaining the exact closed form updates. In this way, we can match the performance of elite samples of CMA-ES while also showing a considerably improved performance of the sample average. We evaluate our new algorithm on simulated robotic tasks and compare to the state of the art CMA-ES.
Nomen est Omen - The Role of Signatures in Ascribing Email Author Identity with Transformer Neural Networks

Authorship attribution, an NLP problem where anonymous text is matched to its author, has important, cross-disciplinary applications, particularly those concerning cyber-defense. Our research examines the degree of sensitivity that attention-based models have to adversarial perturbations. We ask, what is the minimal amount of change necessary to maximally confuse a transformer model? In our investigation we examine a balanced subset of emails from the Enron email dataset, calculating the performance of our model before and after email signatures have been perturbed. Results show that the model’s performance changed significantly in the absence of a signature, indicating the importance of email signatures in email authorship detection. Furthermore, we show that these models rely on signatures for shorter emails much more than for longer emails. We also indicate that additional research is necessary to investigate stylometric features and adversarial training to further improve classification model robustness.
Using Dynamic Operational features to Identify Embedded Devices

This paper investigates the use of system memory features as the characteristics of simpler embedded devices. The ICmetrics technology is based on the hardware and software features of the systems in order to generate the unique identifiers. Usually memory features are considered very volatile as they are considered very unstable usually. But, in this case the memory features are very crucial as they tell us more about the system function. Hence, these features were used for analysis and a multivariate gaussian model was created and validated by comparing testing sample against the training data with 98% accuracy amongst 6 devices. This confirms the usefulness of the system memory features in adding a layer of security to the devices.
A Knowledge Graph Enhanced Semantic Matching Method for Plan Recommendation

In order to solve the problem of rapid matching and optimization of the plan, a semantic feature-based smart matching method of the plan is proposed, which can be used to solve the problem of the typical small sample date for recommendation of the best plan in the military field. In this method, the semantic feature of the battle plan is established to describe the combat scenario through a military knowledge graph. The semantic feature annotation of the plan is constructed based on the military knowledge map too. So the semantic features corresponding to each matching target plan object are described, which realize the explicit definition of the hidden knowledge of the combat plan. Based on knowledge enhancement technology, the similarity measurement of semantic features is calculated, realizing the intelligent semantic matching of combat plans, so as to solve the problem of low matching efficiency and accuracy based on pragmatic level features such as index or keywords, satisfy the rapid matching and precise recommendation of plans.
Using Machine Learning to Identify Methods Violating Immutability

A characteristic example of logic errors is the problem of poorly written Java methods that render immutable objects mutable by exposing member variables. These variables can then be used to modify or delete the underlying data regardless of class invariants, leading to serious problems such as data corruption or loss. In this paper, we offer a solution for identifying such functions by implementing a machine learning-based static code analyzer. We use a Recurrent Neural Network, trained on examples that were generated with a context-free grammar, to inspect and classify Java methods that return arrays of primitive or collections of non-primitive types. Every example can be classified as “exposes class”, “does not expose class”, or, if there is insufficient information, “cannot be decided”. The trained model is capable of classifying previously unseen code snippets with good accuracy.
GVUI: Graphic-Assisted Voice User Interface Based on Multi-modal Human-Machine Conversation

This paper proposes a user interface model named GVUI, which provides a design guidance for the voice assistant running on smart display. The theory of GVUI is divided into three parts. First, a multi-modal structure is built, which allows each modality to play to its strength and cooperate with each other. Second, three principles of conversational user interface are proposed, including intuitive, inclusive and stress-free, and 15 tips are provided to explain how to achieve these goals. Third, the paper describes a card-based visual reply design pattern and introduces three types of arrangement for multiple results. In the conclusion based on the practice of weather query function, it is proved that an intuitive and efficient user experience of voice can be gained via GVUI.
Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy

Massive Open Online Courses (MOOC) often face low course retention rates due to lack of adaptability. We consider the personalized recommendation of learning content units to improve the learning experience, thus increasing retention rates. We propose a deep learning-based learning path construction model for personalized learning, based on knowledge tracing and reinforcement learning. We first trace a student’s knowledge using a deep learning-based knowledge tracing model to estimate its current knowledge state. Then, we adopt a deep reinforcement learning approach and use a student simulator to train a policy for exercise recommendation. During the recommendation process, we incorporate Bloom’s taxonomy’s cognitive level to enhance the recommendation quality. We evaluate our model through a user study and verify its usefulness as a learning tool that supports effective learning.
Teaching Machines How to Speak: Identifying Sensible Sentences from Phrases with Deep Neural Network

Natural language processing is one of the most important applications of machine learning algorithms. Over the recent years, the rapid progress of deep neural networks has enabled the vast advance of natural language processing technologies empowered by machine learning algorithms, which has become the backbone of many real-life applications, such as online chatting robots, smart home controllers, machine translations, etc. All these applications share one critical need in common, which is to accept a sequence of words (such as an input sentence), identifying its sensibility, understanding its context, and answer with another sequence of words (such as an answer from a chatting robot, or a translated sentence of a different language from the original sentence). In this paper, we will explore one critical block in this chain, which is to identify sensible sentences from phrases with deep neural work.
vIIIS: A Vocational Intelligent Interactive Immersive Storytelling Framework to Support Task Performance

This paper presents a framework for developing Intelligent, Interactive, and Immersive Storytelling systems for vocational training by improving task performance. We present a systematic framework that can be used to personalize training for a worker in a factory environment. We also present a system implementation that builds upon the vIIIS framework and also describes the design decisions made throughout the system. In this paper, we focus on improving a user's episodic and working memory by employing picture sequence and object sorting tasks taken from the NIH toolbox and presenting an intelligent Augmented Reality system. A major advantage of using an intelligent and interactive system using storytelling is that the training can be curated towards each specific user, thereby enhancing the learning outcome based on individual needs.
Robust Visual Vocabulary Based On Grid Clustering

Content-based image retrieval (CBIR) is the task of finding the images in the dataset that are considered similar to an input query based on their visual content. Many methods based on visual description try to solve the CBIR problem. In particular, bag of visual words (BoVW) is one of the most algorithm used to image classification and recognition. But, even with the discriminative power of BoVW, this problem is still a challenge in computer vision. We propose in this paper an efficient CBIR approach based on bag of visual words model (BoVW). Our aim here is to improve the image representation by transforming the BoVW model to the bag of visual phrase (BoVP) based on grid clustering approach. We show experimentally that the proposed model leads to the increase of accuracy of CBIR results. We study the performance of the proposed approach on four different datasets (Corel 1 K, UKB, Holidays, MSRC v1) and two descriptors (SURF, KAZE).