Reviewerly/exHarmony huggingface dataset:
Learning-Driven Hybrid Scaling for Multi-Type Services in Cloud

In order to keep track of the fast changing requirements of container based services in clouds, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources with the variable service workloads. However, the latest auto-scaling approaches lack the comprehensive consideration of variable workloads and hybrid auto-scaling for multi-type services. Firstly, the historical data based proactive approaches are widely used to handle complex and variable workloads in advance. The decision-making accuracy of proactive approaches depends on the prediction algorithm, which is affected by the anomalies, missing values and errors in the historical workload data, and the unexpected workload cannot be handled. Secondly, the trigger based reactive approaches are seriously affected by workload fluctuation which causes the frequent invalid scaling of service resources. Besides, due to the existence of scaling time, there are different degrees of delay in the completion of scaling actions. Thirdly, the latest approaches also ignore the different scaling time of hybrid scaling for multi-type services including stateful services and stateless services. Especially, when the stateful services are scaled horizontally, the neglected long scaling time causes the untimely supply and withdraw of resources. Consequently, all three issues above can lead to the degradation of Quality of Services (QoS) and the inefficient utilization of resources. This paper proposes a new hybrid auto-scaling approach for multi-type services to resolve the impact of service scaling time on decision making. We combine the proactive scaling strategy with the reactive anomaly detection and correction mechanism. On the basis of the predicted results obtained by a deep network structure improved ensemble prediction model, the auto-scaling decisions are made by a Deep Reinforcement Learning (DRL) model with heterogeneous action space integrating horizontal and vertical scaling actions, according to the scaling time of different types of services. Meanwhile, with the anomaly detection and correction mechanism, the workload fluctuation and unexpected workload can be detected and handled. We evaluate our approach against three different proactive and reactive auto-scaling approaches in the cloud environment, and the experimental results show the proposed approach can achieve the better scaling behavior in terms of elasticity performance metrics.
Dictionary Learning of Symmetric Positive Definite Data Based on Riemannian Manifold Tangent Spaces and Local Homeomorphism

Symmetric Positive Definite (SPD) data are increasingly prevalent in dictionary learning recently. SPD data are the typical non-Euclidean data and cannot constitute a Euclidean space. Therefore, many dictionary learning algorithms cannot be directly adopted on SPD data. Reproducing Kernel Hilbert Spaces (RKHS) is now commonly used to deal with this problem. However, RKHS is an infinite-dimensional Hilbert space, rather than a Euclidean space, resulting in the inability of the dictionary learning to be directly used on SPD data. In this paper, we propose a novel dictionary learning algorithm for SPD data, which is based on the Riemannian Manifold Tangent Space (RMTS). Since RMTS is based on a finite-dimensional Hilbert space, i.e., Euclidean space, most machine learning algorithms developed on Euclidean space can be directly applied to RMTS. The introduction of RMTS provides a better mathematical platform for machine learning of non-Euclidean data. The proposed RMTS method first selects a point on a Riemannian manifold as an anchor point. It transforms the other points on the Riemannian manifold into tangent vectors of the geodesics between these points and the anchor point, with the tangent vectors at the anchor point. We set the length of the tangent vector equal to the length of the geodesic. As a result, such tangent vectors have an explicit geometric meaning, such as direction information, while the RKHS method may cause some geometric meaning to be lost in the original data during the mapping process. In addition, the proposed algorithm adds a regular term of local homomorphism between SPD data and its RMTS dictionary coding coefficients, so that the similarity of SPD data and its RMTS dictionary coding coefficients are as close as possible. Experimental results on four public datasets demonstrate that the proposed algorithm significantly outperforms five state-of-the-art algorithms. This work opens a new pathway towards SPD data dictionary learning methods.
Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging

In recent years, large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks.But, in the unsupervised POS tagging task, works utilizing PLMs are few and fail to achieve stateof-the-art (SOTA) performance.The recent SOTA performance is yielded by a Guassian HMM variant proposed by He et al. (2018).However, as a generative model, HMM makes very strong independence assumptions, making it very challenging to incorporate contexualized word representations from PLMs.In this work, we for the first time propose a neural conditional random field autoencoder (CRF-AE) model for unsupervised POS tagging.The discriminative encoder of CRF-AE can straightforwardly incorporate PLM word representations.Moreover, inspired by featurerich HMM, we reintroduce hand-crafted features into the decoder of CRF-AE.Finally, experiments clearly show that our model outperforms previous state-of-the-art models by a large margin on Penn Treebank and multilingual Universal Dependencies treebank v2.0.
Abstract Meaning Representation Parsing for the Brazilian Portuguese Language

Abstract Meaning Representation (AMR) is a semantic formalism that has been widely adopted in the area for semantic parsing. We present in this paper our contribution to the task for Portuguese. We investigated semantic parsing methods of different paradigms, producing state of the art results for this language. We also introduced the first AMR-annotated corpus for Portuguese, a novel and better semantic parsing evaluation measure, and a new AMR-based alignment method.
Forensic Analysis of Telegram Messenger on iOS and Android Smartphones Case Study

Mobile digital forensics science is developing on a daily basis, and every day there is a new tool introduced and a new challenge as well. This chapter is to get hands-on with all available forensics tools, in order to identify the benefits of using each tool, practice them to get results faster, and be able to assess in each situation which is the most suitable tool to use in order to get accurate results in a relatively short time. In this chapter, we used two images for the evidence in order to assess the ability of different tools to extract the results and be able to build a strong case when it comes to Telegram Messenger investigations on iOS devices.
A HYBRID SENTIMENT ANALYSIS APPROACH USING BLACK WIDOW OPTIMIZATION BASED FEATURE SELECTION

This paper proposes a novel hybrid framework with BWO based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used PSO and GAbased feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analysed using performance metrices such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.
Peer review declaration

Type of peer review: Double-blind a. All papers submitted were checked their similarity index using Turnitin. b. Papers have similarity index more than 25% were rejected. c. After that, the format of the papers was checked. Papers that did not follow the IOP template will be sent back to the authors for revision. d. All papers passed the similarity index and have already follow the IOP template will be sent to the reviewers. e. Reviewers reviewed the papers and give comments to the papers. f. Scientific committee decided if the papers: accepted, accepted with major revision, accepted with minor revision, or rejected. g. Author revised their papers and send back their revision Conference submission management system: All papers from the conference submitted to SAC-SAFSE@konferensi.id Number of submissions received: 94 Submission were received Number of submissions sent for review: 91 Submission was sent for review, and the rest was rejected because out of scope, low-quality article and high similarity Number of submissions accepted: 69 Paper accepted based on IOP EES Scope and quality Acceptance Rate (Number of Submissions Accepted / Number of; Submissions Received X 100): (69/94) × 100 = 72.13 % Average number of reviews per paper: 2 Times Total number of reviewers involved: 15 Reviewer Involved + 6 Editors Any additional info on review process: Every manuscript submitted to SAC-SAFSE is independently reviewed by at least two reviewers in the form of “double-blind review”. Decision for publication, amendment, or rejection is based upon their reports/recommendation. In certain cases, the editor may submit an article for review to another, third reviewer before deciding, if necessary. Contact person for queries: Robbi Rahim, PhD Sekolah Tinggi Ilmu Manajemen Sukma Email: robbirahim@ieee.org Whatsapp +62 8126326393
Face Recognition Based Attendance System

Face recognition-based attendance systems will likely replace traditional attendance systems in classrooms in the near future; they may even replace biometric attendance systems. The goal of our project is to create a new attendance system based on cv2. Face recognition technology is used by Facebook, which tags the names of faces when you submit photographs that have already been tagged by you. The algorithm recognizes and encodes the unique traits of the faces in the database into a pattern image. Python modules are then used to find the person's name using a machine learning approach called a classifier. The steps of the operation are image capture, facial features, face recognition, and an attendance system.
Proposing Causal Sequence of Death by Neural Machine Translation in Public Health Informatics.

Each year there are nearly 57 million deaths worldwide, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical for public health, especially during the COVID-19 pandemic, as institutions and government agencies rely on death reports to formulate responses to communicable diseases. Unfortunately, determining the causes of death is challenging even for experienced physicians. The novel coronavirus and its variants may further complicate the task, as physicians and experts are still investigating COVID-related complications. To assist physicians in accurately reporting causes of death, an advanced Artificial Intelligence (AI) approach is presented to determine a chronically ordered sequence of conditions that lead to death (named as the causal sequence of death), based on decedent's last hospital discharge record. The key design is to learn the causal relationship among clinical codes and to identify death-related conditions. There exist three challenges: different clinical coding systems, medical domain knowledge constraint, and data interoperability. First, we apply neural machine translation models with various attention mechanisms to generate sequences of causes of death. We use the BLEU (BiLingual Evaluation Understudy) score with three accuracy metrics to evaluate the quality of generated sequences. Second, we incorporate expert-verified medical domain knowledge as constraints when generating the causal sequences of death. Lastly, we develop a Fast Healthcare Interoperability Resources (FHIR) interface that demonstrates the usability of this work in clinical practice. Our results match the state-of-art reporting and can assist physicians and experts in public health crisis such as the COVID-19 pandemic.
Sentiment Analysis of Online Reviews for Educational Institutions

Sentiment Analysis is a technique that is performed on the text to work out whether the user’s intentions towards a specific topic, product, etc. are positive, negative, or neutral. The projected technique discusses this assertion, and describes the theoretical and experimental work done to indicate its validity. The proposed work focuses on educational institutes’ review data, taken from schools, colleges etc. The data set is collected by a survey shared among students of schools and colleges asking about their view on their institutes. The goal of this work is to investigate about the experience of students about their educational institutes. The present work uses the support vector machine rule that could be a supervised machine learning rule which may be used for each classification or regression challenges.
Speech to Indian Sign Language Translator

Communication plays a critical role for people and is regarded as a skill in life. Having this important aspect of life and surroundings in mind, we present our project article, which focuses primarily on supporting patients with pain or silent speech. Our research work leads to improved contact with the deaf and the mute. Each sign language uses sign patterns visually conveyed to express the true meaning. The combination of hand gestures and/or motions of arm and body is called Sign Language and the Dictionary. It is the combination of hands and facial expressions. Our program project is able to understand signals in sign language. These symbols may be used to interact with hearing aids. Our article suggests a program that allows common people to interact effectively with others that are hard to understand. In this case, we are implementing the Indian Sign Language (ISL) method by using a microphone and a camera. Translation of the voice into Indian sign language system by the ISL translation system is possible. The ISL translation framework uses a microphone to get pictures (from ordinary people) or continuous video clips, which the application interprets. Deaf people always miss out the fun that a normal person does, may it be communication, playing computer games, attending seminars or video conferences, etc. Communication is the most important difficulty they face with normal people and also every normal person does not know the sign language. The aim of our project is to develop a communication system for the deaf people. It converts the audio message into the sign language. This system takes audio as input, converts this audio recording message into text and displays the relevant Indian Sign Language images or GIFs which are predefined. By using this system, the communication between normal and deaf people gets easier.
Keywords Clustering for the Interview Texts Based on Kmeans Algorithm

We generally use the extraction of keywords to study the factors affecting the growth of excellent teachers. However, an automatic research algorithm is needed to let us understand more clearly when there are many keywords and more complex which aspect has greater influence on teachers. Therefore, this paper proposes an unsupervised learning clustering method based on keyword extraction. First, segments the text and use the Word2Vec tool to train the word vector of the segmentation results; secondly, uses the TF-IDF algorithm to extract the keywords of the text, and extracts five from each document; finally, uses the extracted keywords as the clustering sample, through the Kmeans algorithm obtains the clustering result and manually marks the clustering results to evaluate the clustering effect.
Challenges in Recognition of Kashmiri Script

Optical Character Recognition (OCR) has been receiving significant interest since the inception of applications that involve processing of camera-captured or scanned documents. Even though lot of research had been performed on recognition of Indian scripts like Bengali, Devanagari, and Urdu. Many algorithms and systems have been developed for recognizing such languages. Nevertheless, no algorithm or system has been developed for recognizing Kashmiri script. This paper presents many of the complexities and open issues that are peculiar to the Kashmiri language and Nastaliq style of writing from character recognition point of view.
Effect of Encryption Delay on FTP and VoIP Traffic Based on TCP/UDP

With the rapid increase of E-commerce applications usage, the growth of data flow over the Internet is increasing these days. The negation of unnecessary data delays with data security is essential and ultimate requirement of organizations in terms of file transfer protocol (FTP) and voice over IP (VoIP) traffic in any network. This requirement is impossible to accomplish without the selection of the right protocols for data transmission. This paper provides brief modeling for two well-known transport layer protocols TCP and UDP. The core purpose of this research is to find out how TCP and UDP behave in the case of FTP and VoIP data flow over an IP cloud network (public network) with or without encryption delay. This paper estimates the performance by simulating the IP cloud network with the different scenarios for FTP and voice traffic with different parameters. For this voice and FTP Ethernet delays, FTP and voice packet sent and FTP traffic download/upload response time is simulated and tested on OPNET.
Cat Breeds Classification Using Compound Model Scaling Convolutional Neural Networks.

Cats are one of the most popular animals in the world.Many cat breeds in the world are only about 1%.Therefore, most are dominated by mixed cats or domestic cats.Nevertheless, there are so many different types of cat breeds in the world that it is sometimes difficult to identify them.Therefore, we need a system that can recognize and classify the types of cat breeds automatically.In this study, we used one of the deep learning methods that can recognize and classify an object, a Convolutional Neural Networks (CNN).The EfficientNet-B0 architecture was used as a model to extract image features automatically.The collection of nine different cat breeds containing 2700 images was used as a working dataset fed into the EfficientNet-B0 architecture.Based on the experiments, the system succeeds in classify cat breeds images, and the best model has achieved classification accuracy of 95%.
Project-based learning in schools in the USA, France and Russia

The article deals with the implementation of project-based learning in schools in the USA, France and Russia The article describes the key characteristics of the term “project”, the importance of project activities for students and the degree of involvement of teachers in the activities of educational project groups. The comparison makes it possible to identify problems of the implementation of project training in Russia. Based on the experience of foreign countries, the options for overcoming the barriers are presented.
Progressive 3-Layered Block Architecture for Image Classification

Convolutional Neural Networks (CNNs) have been used to handle a wide range of computer vision problems, including image classification and object detection.Image classification refers to automatically classifying a huge number of images and various techniques have been developed for accomplishing this goal.The focus of this article is to enhance image classification accuracy implemented on CNN models by using the concept of transfer learning and progressive resizing with split and train strategy.Furthermore, the Parametric Rectified Linear Unit (PReLU) activation function, which generalizes the standard traditional rectified unit, has also been applied on dense layers of the model.PReLU enhances model fitting with almost little significant computational cost and low over-fitting hazard.A "Progressive 3-Layered Block Architecture" model is proposed in this paper which considers the fine-tuning of hyperparameters and optimizers of the Deep network to achieve state-of-the-art accuracy on benchmark datasets with fewer parameters.
A Reference Model Based on Agile Values, Principles and Aspects of Scrum, XP and Kanban to Foster the Adoption of Agility in Industry

Currently, there is a broad range of software development agile approaches mainly based on the values and principles defined in the agile manifesto (AM); however, in many cases, their implementation is carried out informally and without being aligned with the values and principles stated. In practice, practitioners and consultants may lose sight of the AM recommendations, which could jeopardize the companies’ agility; therefore, applying an agile approach does not make a company agile. In this article, we present a reference model called AgilityRef, which allows practitioners to support the understanding and implementation of the values and principles of the agile manifesto in the software development processes of a company, this, through twenty-two aspects defined from relations established between principles and values described in the AM and processes elements described in Scrum, XP and Kanban. The evaluation of AgilityRef was carried out through a focus group where its completeness, understandability and suitability were evaluated. Our findings suggest good enough acceptance by professionals and consultants who evaluated the proposal. The proposed reference model seems to allow professionals and companies to improve the understanding and implementation in practice of the concept of agility in their companies’ software development processes, thus, minimizing the subjectivity and error of their process adoption, implementation, evaluation and articulation with the principles and values of the agile manifesto.
Variant of Niederreiter-Based Signature Scheme for Blockchain Technology

The well-known Niederreiter encryption scheme is an improved version of the classical McEliece encryption scheme in terms of key size and encryption efficiency, but the conversion of the scheme to a usable signature scheme is a very challenging process. Moreover, the current blockchain architecture still relies on hard problems that cannot resist quantum algorithms attacks. In this paper, we construct a Niederreiter-based signature scheme with a comprehensive security analysis that is suitable to be used together with the blockchain technology and resistance to Shor’s algorithm for better underlying performance and security of the blockchain architecture.
Comparison of Algorithms for Style Transfer of Images Using Texture Synthesis

Style transfer is a process of transferring a style from one image to an other image, obtaining a new image which is an artistic mixture of the two. Recent work on this problem involved adopting convolutional neural-networks (CNN). This ignited a renewed interest in this field, due to the very impressive results obtained. Gaty’s approach utilizes this method. There exists an alternative path toward handling the style transfer task via generalization of texture synthesis algorithms. Kwatra’s approach utilizes this method. We shall implement style transfer using Kwatra’s and Gaty’s approach and compare their performance. Based on the results obtained, after using 2 metrics, we shall find out which works the best.