Reviewerly/exHarmony huggingface dataset:
Malicious Domain Detection Using Memory Augmented Deep Autoencoder

An Advanced Persistent Threat (APT) is a sophisticated assault that obtains personal information by staying inside infected systems for an extended period of time. When APT assaults occur in a dynamic and sophisticated infrastructure like the cloud, traditional techniques of detection are difficult. The research offers an autoencoder-based deep learning methodology for recognizing the probable phases in the APT lifecycle to address the limitations of previous approaches. Because APTs change their attacking methods, current intrusion detection techniques are unable to detect those attacks. Because present systems have low generalization capability and attack samples are gathered occasionally as compared to regular attacks. To circumvent these issues, in this regard, APT detection using an autoencoder has been extensively researched, and semi-supervised learning has been employed. This approach is based on the premise that reconstruction mistakes will be large for non-training data; however, an autoencoder is commonly over-generalized, and this assumption is readily translatable. To tackle the autoencoder over-generalization problem, we presented a detection approach for Command control communication detection in an APT-based memory augmented deep autoencoder. The suggested model is trained to rebuild the input of an abnormal sample that is comparable to a normal sample, overcoming the generalization problem for such abnormal samples.
Vehicle Object Detection Using Deep Learning-Based Anchor-Free Detector

Detection of road objects is crucial for autonomous vehicles, and it needs constant improvement. There are certain challenges in this area of computer vision such as extreme weather conditions, blurred images. These challenges limit the object detection performance. In this paper, we analyze and enhance the approach called CenterNet, which is an anchor-free detector. Dataset used in this paper comprises of real-time data, which is grouped in different ways to accommodate the range of real-world scenarios. The experimental results indicate that the suggested method can greatly increase object identification performance in a variety of traffic situations.
Efficient Pseudo-Random Number Generator Using Number-Theoretic Transform

In the finite field of integers, the number theoretic transform (NTT) is a specific variant of the discrete fourier transform (DFT). NTT is the essential method that permits efficient computing. Therefore, NTT could be used in the construction of lattice-based pseudo-random number generator. To construct the lattice-based pseudo-random number generator (PRNG), higher degree of polynomial multiplication is required. Therefore, NTT could be used for fast multiplication as it performs point-wise multiplication. In this paper, we would examine the algorithmic properties and performance of NTT for fast multiplication by implementing pseudo-random number generator using NTT.
Transfer Learning for Arabic Question-Answering

Intent classification aims at determining the customers’ future actions based on the produced text or the used language. This process differs from other issues of topic classification that study matter and subjective text classification like sentiment and emotion classification dealing with the present state of affairs. This paper focuses on the problem of the Arabic question-answering by applying several machine learning (ML) methods. The latter are compared on an open-sourced academic dataset. However, small-scale human-labeled training data, which reduces the generalization capacity, represents the major weakness of Arabic Question-Answering dataset. It reduces generalization capability, particularly when analyzing rare words. In the last decades, a novel language representation model, called Arabic bidirectional encoder representations from transformers (AraBERT), has been introduced to make easier the pre-training of deep bidirectional representations on large-scale unlabeled corpora. Subsequently, novel methods have been suggested and applied in various natural language processing tasks after simple fine-tuning. Experiments show that the support vector machines (SVMs) models outperform the other models, are more efficient, and can be integrated into a basic prototype responder.
Sentiment Analysis on Social Media Data: A Survey

Machines are continually being harnessed in the present era of advanced technology to deliver accurate translations of what people communicate on social media. Humankind is today engulfed in the notion of how humans think and what they think. The decisions made as a result are mostly dependent on the masses’ drift on social media platforms. This paper presents a multidimensional look at how sentiment analysis rose to prominence as a result of the unexpected expansion of content on the web. This manuscript also discusses the process of acquiring data from social media over time, as well as the detection of similarities based on similar choices made by people on social media. The approaches for communalizing user information are also discussed. The data in various forms have been examined and presented. Aside from that, the techniques for reckoning sentiments have been researched, classified, and compared, along with certain limitations, in the hopes of paving the way for future research.
Smart Computer Monitoring System Using Neural Networks

In order to solve the problem that the recognition effect of traditional monitoring and recognition algorithms cannot meet the needs, a computer monitoring system based on Internet of things and neural network algorithm is proposed. Firstly, the basic functions and development status of the computer monitoring system are analyzed. Then, based on the study of the three-tier architecture and key technologies of the Internet of things, and based on the structure and characteristics of the computer monitoring system, the three-tier architecture of the computer monitoring system based on the Internet of things (sensing layer, network layer, and application layer) is put forward. Finally, according to the demand analysis of the real intelligent monitoring system, the overall framework of the server is designed, and the intrusion detection algorithm and wandering detection algorithm of the human behavior recognition algorithm based on the Internet of things and neural network are applied to the identification server of the intelligent monitoring system. The results show that the system can support more than 16 channels of real-time recognition through accelerated optimization. Compared with traditional intrusion detection, neural network algorithm can distinguish whether the intrusion subject is human body, and has better recognition effect and greater practical value.
A Soft Computing Based Approach for Pixel Labelling on 2D Images Using Fine Tuned R-CNN

Nowadays Artificial Intelligence (AI) is an integral component of the core business model of several organisations and a crucial strategic component of many industries’ strategies such as—healthcare, finance, retail education etc. on a global scale. Machine Learning (ML) is a technology to achieve AI. Machine learning methods utilize a variety of statistical, A vast, unstructured, and complicated dataset may be analysed using probabilistic and optimization approaches to learn from the past and find relevant trends. Automated oriented text categorization, network intervention detection, spam email filtering, credit card deceit detection, consumer purchase behaviour detection, and manufacturing process optimization are just a few of the many uses for these algorithms, image categorization, object recognition and disease modelling. Allocating a different class oriented label to every pixel of a particular image is certainly one of the significant steps in constructing complex automated systems like driver-less vehicles/drones, human-friendly oriented robotic machines, robot-assisted medical procedure, and savvy military systems. Pixel labeling’s computational purpose is to give each individual pixel in an imagery a class label. It is critical long-range (pixel) label dependencies in a model must be captured pictures to provide strong visual coherence and high class accuracy. This may be accomplished in a feed-forward architecture by considering a suitably big input to the context patch surrounding each individual pixel to be labelled. We present a strategy in this chapter that consists of an ideally finetuned recurrent convolutional neural network that mainly allows us to incorporate a broad input context without restricting the model’s capacity. Our solution does not rely on any such segmentation techniques or task-specific characteristics, in contrast to the majority of the conventional methods. The system is trained on raw pixels from beginning to end, and it models complicated spatial dependencies with low inference costs. The system detects and corrects its own faults as the context dimension grows using the built-in repetition. On the Stanford Background Dataset, our method achieves best-in-class results, while being considerably much faster at test time. The methodology presented in this chapter has an empirical scope for societal improvement, modernization and progress.
Bone Cancer Detection Using Deep Learning

There have been many registered cases of bone cancer or tumor across the world with fatal cases involving in deaths too due to bone cancer. Thus, detecting them at the early stages becomes necessary which can save many lives. In today’s world, tumor is detected using MRI scans, and the process involved is costly and tedious. In the era of machine learning and artificial intelligence, detecting the tumors is still a tedious process, and it is very costly for setting up to train networks. There are various segmentation techniques, and convolutional neural networks can be used using X-ray images to detect the tumors at the early stages. If the tumor is detected and classified at the correct time, then without any hesitance, further complication can be prevented with right medications given at the required time. In this paper, we presented a modified architecture using convolutional neural network and inception modules to classify the stages of the bone cancers. The proposed model is trained on a dataset provided by ‘Stanford ML’ group which is available publicly for research and achieved accuracy of 92.68% on the testing dataset.
Development of a Cognitive Question Answering System to Learn Concepts for Placement Assistance

Cognitive assistants help humans and enhance their capability to solve a large range of complex tasks. A cognitive assistant has developed a pedagogical assistant. This work aims to improve learning capabilities and helps to identify learning preferences. A cognitive assistant can hold conversations with users in natural language to help the user to solve a complex problem. The proposed system has been implemented to assist as a personal agent for students to learn Python programming language. The steps are user capability level identification, construction of assertion graph, QA analyzer, question analyzer, and primary search analysis, hypothesis generation, evidence identification and evidence scorers, final evidence identification user answer validation, and resource generation. The cognitive assistant facilitates natural interactions with the students, and it applies human reasoning skills to judge the student’s ability and train them further. Normal conversational is employed in question-answering systems. This increases users’ satisfaction and easily engages them with the system. A social dialogue and question-answering system has achieved significantly higher learning gains than a non-interactive online course. The result of the system is evaluated using the confidence-weighted score and expert judgments.
Classification of Cervical Cells Using Deep Learning Feature Extraction

A practical approach is required for segmenting cervical cancer pictures. This study shows a novel approach to cervical cancer picture segmentation. An HSI color model is created by first converting an RGB image to an HSI color model. For the saturation and intensity components, thresholding is used to generate binary images. A new mask is created by combining the binary images. Using the linked component idea, the nucleus and cytoplasm are appropriately segregated. This technique delivers higher accuracy than the edge and region-based segmentation when segmenting the nucleus. A suggested framework for determining the cancer stage as normal or abnormal is based on nucleus size and other factors. The experimental findings and performance assessment revealed that the suggested system delivers excellent outcomes. The performance of the proposed system is compared with that of the current system using the above parameters, and it is demonstrated that the image is segmented successfully with excellent features. The execution is done and implemented in MATLAB.
Using Deep Learning to Perform Payload Classification

Today, one of the most critical matters for a network administrator to heavily focus on their network is to manage the large volumes of traffic that are generated, otherwise there will be congestion over the pipe and eventual data loss. Most common reasons given for poor performance could stem from too many packets at a given router interface and an equalizing traffic condition. Network operators are sometimes required to provide Quality of Service for different applications which benefits customer’s network experience. Processing packet traffic is usually done in two ways; engineering approach with manual efforts and self-learning approach by leveraging on deep learning technology. The focus of this paper is on the process of designing deep learning models for network classification and yielding a packet unit and flow unit training data set using network traffic preprocessing. In order to accomplish this task, the authors attempt to detect network traffic and assign it to either unit from flow or packet classifier levels with good accuracy. They experiment with deep learning models including CNN, RNN, LSTM, and ResNet. The detailed flow-level detection procedure is introduced as well on how packet-level and flow-level detection procedure should work with F1-score comparisons. It is noticed that the F1-score across LSTM is higher than the total F1-score RNN and CNN + RNN. It indicates that LSTM alone can provide similar or better performance without using complex models in the flow unit learning model.
Mathematical Models for the Ouroboros Protocol Based on Attacks Over Blockchain Systems

The decentralized ledger technology has garnered traction worldwide due to the high degree of security that it entails. The secure functioning of blockchains through this technology has rendered results so promising that it drew the attention of the world’s most revered scientists. In this study, the researchers offer an analysis of diverse consensus protocols and adversarial models. We mainly considered classical Bitcoin-like proof-of-work protocol and the Greedy Heaviest-Observed Sub-Tree (GHOST) protocol for the study. The objective of this initiative is to present a comparative analysis between the proof-of-work protocol that underlies Bitcoin and the novel proof-of-stake algorithm that was introduced by Ouroboros.
Virtual Machine Migration Framework with Configuration Change Management

Research on ways to reduce the cost of managing and relocating VMs is clearly needed in light of the current increase in data center utilization and virtual machine maintenance costs. Because energy is a major component of virtual machine administration, the most effective migration and management solutions must use the least amount of energy feasible. Researchers have extremely limited options for improving the energy efficiency of virtual machines since it is totally reliant on the number of apps running on the computer. There has been a lot of research done to find ways to reduce energy consumption as a second parameter. This research investigates the issue of energy consumption during virtual machine migration and provides a novel virtual machine migration approach that reduces energy consumption. In two upgrades, VM selection and VM migration, the novel technique has been shown to reduce energy consumption by over 47%.
ORGANISATION OF LEVEL-BASED DIFFERENTIATED INSTRUCTION FOR HETEROGENEOUS GROUPS IN MOODLE

Nowadays adaptation of the learning process to students’ needs is one of most frequently addressed topics in pedagogical research. Different aspects of this topic are studied both by Ukrainian and foreign scientists. The paper deals with one of the questions in the field - knowledge level diversity within an academic group in higher education institutions. The difference in knowledge level influences not only the ability to achieve learning outcomes but also the motivation to study. Thus, providing students with possibilities to study at their own level and learning paths might increase their interest in the discipline and improve their results. Differentiated instruction is considered as a way to meet the needs of multi-level groups. Moodle is addressed as a means of level based differentiated instruction as many higher education institutions use learning management systems to support the learning process. The results of the teachers survey at Borys Grinchenko Kyiv University are used as a background for differentiated e-learning course design planning. The main requirements to a differentiated e-learning course design are defined: opportunities to define students’ levels and knowledge gaps, easy navigation for students in the diversity of tasks and materials, ability for teachers to combine various levels tasks in the same activities, suitability for flexible groupings, moderate time consumption for design and maintenance of the e-learning course. The model of e-learning course design for level based differentiated instruction is offered which includes testing, adapting learning materials to the students’ needs, groups and grouping settings, formative assessment, summative assessment and feedback. Utilisation of Moodle activities and resources for implementation of all the model components is described. The role of groups and groupings in the implementation of differentiated instruction is underlined and their usage in Moodle is described.
Penerapan Quality Function Deployment (QFD) Pada Desain Smart Aquaculture Untuk Sektor Tambak Udang Vaname Berbasis IOT

In entering the era of industry 4.0. where the internet world has the main role as a link to every device to collect and analyze data. Internet Of Things (IOT) technology can optimize previous technologies. In the fishing industry, Indonesia has the potential for fishery land in 2020 of 1,201,275 ha. The large area of water area in Indonesia has potential for aquaculture such as vannamei shrimp. Water quality such as pH, turbidity level and shrimp feeding schedule in ponds are closely related to shrimp health conditions. The existence of good water quality management can maintain water quality in order to increase pond productivity. Technology and information integration through paired IOT sensors will transmit data collected in the field to cloud computing via internet-based radio frequency networks through virtualization. This study aims to design a realtime IOT-based shrimp pond monitoring system. The Quality Function Deployment (QFD) method is a method that aims to plan and develop products according to needs and desires of consumers. This method begins with making a matrix of consumer needs and desires for the cultivation of Vaname Shrimp ponds. From this data a House of Quality matrix will be created which contains any technical interests needed. For the evaluation results of the questionnaire based on the analysis of the answers given by the respondents, the questionnaire that has been made can be used as a reference so that the proposed system design can be realized to overcome Vaname Shrimp ponds in order to improve the quality and quantity of vannamei shrimp harvest, cultivate effectively and efficiently, and facilitating the supervision of pond managers on pond water quality, feeding and feed availability.
The Metaverse in Medical Education and Clinical Practice

Emerging technologies are boosting the generation of computer-based academic training procedures, which are of great interest in many different fields of the health sciences. Moreover, they allow to create artificial environments that are very close to the real ones, increasing the motivation of the students who use them. The visualization of real images in virtual learning environments is a very efficient way to better understand, study and analyze content knowledge in different areas of the health sciences. In this work we present different immersive virtual and augmented environments, using different models of stereoscopic vision glasses, which constitute a metaverse where content knowledge can be shared for enhanced medical training.
Testing and Improvements of KoopaML: A Platform to Ease the Development of Machine Learning Pipelines in the Medical Domain

Machine Learning (ML) applications in complex domains, such as the medical domain, can be highly beneficial, but also hazardous if some concepts are overlooked. In this context, however, health professionals denote expertise in their domain, but they often lack skills in terms of ML. In this sense, to leverage ML applications in the medical domain, it is important to combine both domain expertise and ML-related skills. In previous works, we tackled this challenge in the health context through a visual platform (KoopaML) that enables lay users to build ML pipelines. The present work describes the challenges derived from the first version of the platform and the prototypes for the new features designed to address them. The prototypes have been validated by two experts, obtaining highly valuable feedback.
Students’ Acceptance and Efficiency of Virtual Reality in French Language Learning at Higher Education

Learning a new language is a challenge for those who live in places where this language is not spoken. For this reason, promoting any kind of interaction in the language is the main challenge for foreign language teachers. Therefore, the application of Virtual Reality (VR) such as the app Mondly helps students to experiment with real situations in simulated environments. Those allow the practice and improve communication skills. The objective of this research is to know the perception of students from a university in the south of Ecuador about the ease of use and usefulness of technologies in their daily life (pre-test), and Mondly in their French language learning (post-test). 30 students of A1.1 level participated in the pre-test and 17 in the practices with the app and the post-test. Then, the efficiency of the app and use of 3D glasses were analysed thanks to the statistic test of Mann-Whitney U. The general perception about VR use is that it makes a positive contribution to French learning. In terms of acceptance, students noted that thanks to immersion in a 3D virtual world, it is possible to learn a foreign language without physical boundaries and thanks to concrete communication situations.