Reviewerly/exHarmony huggingface dataset:
Malware Traffic Classification Based on GAN and BP Neural Networks

Based on the imbalance of network traffic detection samples, we propose a model based on generative adversarial network and back propagation neural networks for malware traffic classification, which is to identify malware traffic, normal traffic, and traffic types. The model is composed of generative adversarial network and back propagation neural networks. The generator of the generative adversarial network is responsible for inputting random noise and generating traffic pictures with original input characteristics. The discriminator of the generative adversarial network is responsible for identifying generated pictures and input pictures. BP neural network is used as a classifier. The pictures generated by the generator and the pictures generated by original traffic data are used as inputs of the BP neural network to identify and classify traffic. The experimental data shows that the combined model based on the generative adversarial network and BP neural network proposed in this paper performs well, and the comprehensive index F1 value has significantly improved in most situations.
Assessing Vulnerability from Its Description

This paper shows an end-to-end Artificial Intelligence (AI) system to estimate the severity level and the various Common Vulnerability Scoring System (CVSS) components from natural language descriptions without reproducing the vulnerability. This natural language processing-based approach can estimate the CVSS from only the Common Vulnerabilities and Exposures description without the need to reproduce the vulnerability environment. We present an Error Grid Analysis for the CVSS base score prediction task. Experiments on CVSS 2.0 and CVSS 3.1 show that state-of-the-art deep learning models can predict the CVSS scoring components with high accuracy. The low-cost Universal Sentence Encoder (large) model outperforms the Generative Pre-trained Transformer-3 (GPT-3) and the Support Vector Machine baseline on the majority of the classification tasks with a lower computation overhead than the GPT-3.
Support Tool Selection in Digital Forensics Training

One of the most common challenges for digital forensic investigations is the selection of suitable analysis tools in an ever-changing environment. In recent years, live digital forensic investigations are emerging throughout organizations due to Advanced Persistent Threats (APT). At the same time, the variety and availability of digital forensic tools expand rapidly. As there is no objective guideline to enable decision-support for tool selection, forensic analysts mostly rely on their experience. They apply tools they are familiar with, although, these tools might not be the most suitable ones for the analysis task at hand. We propose a concept that enables a well-considered tool selection for experts based on desired tool characteristics. The concept supports training the right tool selection to be forensically ready for future investigations and to structure cybersecurity knowledge within an organization. To evaluate our approach, we apply the concept to a use case and demonstrate its application and performance.
Hyperparameter optimization of data-driven AI models on HPC systems

Abstract In the European Center of Excellence in Exascale Computing ”Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE), researchers develop novel, scalable AI technologies towards Exascale. This work exercises High Performance Computing resources to perform large-scale hyperparameter optimization using distributed training on multiple compute nodes. This is part of RAISE’s work on data-driven use cases which leverages AI- and HPC cross-methods developed within the project. In response to the demand for parallelizable and resource efficient hyperparameter optimization methods, advanced hyperparameter search algorithms are benchmarked and compared. The evaluated algorithms, including Random Search, Hyperband and ASHA, are tested and compared in terms of both accuracy and accuracy per compute resources spent. As an example use case, a graph neural network model known as MLPF, developed for Machine Learned Particle-Flow reconstruction, acts as the base model for optimization. Results show that hyperparameter optimization significantly increased the performance of MLPF and that this would not have been possible without access to large-scale High Performance Computing resources. It is also shown that, in the case of MLPF, the ASHA algorithm in combination with Bayesian optimization gives the largest performance increase per compute resources spent out of the investigated algorithms.
Class-dependent and cross-modal memory network considering sentimental features for video-based captioning

The video-based commonsense captioning task aims to add multiple commonsense descriptions to video captions to understand video content better. This paper aims to consider the importance of cross-modal mapping. We propose a combined framework called Class-dependent and Cross-modal Memory Network considering SENtimental features (CCMN-SEN) for Video-based Captioning to enhance commonsense caption generation. Firstly, we develop class-dependent memory for recording the alignment between video features and text. It only allows cross-modal interactions and generation on cross-modal matrices that share the same labels. Then, to understand the sentiments conveyed in the videos and generate accurate captions, we add sentiment features to facilitate commonsense caption generation. Experiment results demonstrate that our proposed CCMN-SEN significantly outperforms the state-of-the-art methods. These results have practical significance for understanding video content better.
Development of creativity from the perspectives of primary education students

The importance of creativity in addressing economic and social crises has been recognized in many international policies.These policies have been increasingly calling for the strengthening of the creative capacity and development of knowledge-based societies by strengthening the role of education.Both the school and the initial education are recognized as key factors in the development and strengthening of the creative capacities of individuals that are necessary for his further education and working life.The paper was inspired by the need to contribute to the debate on creativity in education.Among other things, this paper aims to diagnose a picture showing how creativity is perceived by students and how certain classroom practices can support and enhance the creative potential of students.The results, obtained with a questionnaire constructed in a specific way, show that the schools still lack the freedom and tolerance for the work of their students, as well as the openness to giving opportunities and time out of the curriculum, to innovate, adopt and research.
Performance Investigation of Generalized Rain Pattern Absorption Attention Network for Single-Image Deraining

Rainy weather conditions are challenging issues for many computer vision applications. Rain streaks and rain patterns are two crucial environmental factors that degrade the visual appearance of high-definition images. A deep attention network-based single-image deraining algorithm is more famous for handling the image with the statistical rain pattern. However, the existing deraining network suffers from the false detection of rain patterns under heavy rain conditions and ineffective detection of directional rain streaks. In this paper, we have addressed the above issues with the following contributions. We propose a multilevel shearlet transform-based image decomposition approach to identify the rain pattern on different scales. The rain streaks in various dimensions are enhanced using a residual recurrent rain feature enhancement module. We adopt the Rain Pattern Absorption Attention Network (RaPaat-Net) to capture and eliminate the rain pattern through the four-dilation factor network. Experiments on synthetic and real-time images demonstrate that the proposed single-image attention network performs better than existing deraining approaches.
Real Time Recording and Monitoring of Wild Animal Movements

Animal attacks are one of the major threats that we face today. As the human population and resources need to grow, we humans are leaving less and less room for wildlife, which has increased animal attacks in many human-populated places worldwide. Animals are losing their homes due to globalisation and industrialisation, and they are forced to seek sanctuary and food in human-populated areas. Wildlife encroachment is harmful to humans and animals in areas with a high population and high human mobility. Through this paper, we intend to safeguard the life of wild animals as well as humans. We try to answer the problem by combining deep learning approaches from many computer vision domains, such as object detection. Here we use the YOLOv5 object detection model to ascertain the presence of wild animals in images. Once detected, the animals’ intentions are found, and different frequency of sound, depending on the species of the animal, is used to terrify them back to their habitats; simultaneously, alerts are sent to the residents of the village.
Intersection over Area: A Sensor Fusion Algorithm for Accurate Distance Estimation in Autonomous Vehicles

Perception is one of the main tasks in the functioning of autonomous vehicles. It employs sensors to carry out the detection of objects in the vicinity of the vehicle and the estimation of distance from the vehicle to the detected objects. Sensors of a singular modality have their own individual drawbacks, which can be superseded by utilising a sensor fusion approach. This work provides an approach utilising the fusion of camera and LiDAR sensors. While cameras are good at detecting objects, they fall short in their accuracy of estimating distance. Conversely, LiDARs are excellent at estimating distance to vehicles but exhibit poor object detection capabilities. A fusion of camera and LiDAR to carry out the perception task exhibits better performance in both tasks. An algorithm for distance estimation was developed and tested on a GPU and an Nvidia Jetson TX2 module and was found to be more accurate that previous work.
Analysis Comparison of FastText and Word2vec for Detecting Offensive Language

Twitter is one of the most popular platforms for sharing opinions, ideas, feelings and information. Tweets on Twitter may have language that is similar to that of a group or individual that is considered offensive. One issue brought on by offensive language is cyberbullying, which can encourage someone to ask questions online and use strong language to discuss hate. As a result, many users who interact online, including on social media, run the risk of being made fun out or harassed using abusive language that can affect the users mentally. Thus, identifying offensive language is both a necessary and useful task especially in social media platform. Offensive language can be classified to irony, sarcasm, and figurative. Currently, many research on offensive language detection simply pay attention to one of irony or sarcasm. However, offensive language may contain multi-class classification such as figurative that consist of both irony and sarcasm label. Here, we suggest categorizing tweets into four categories: irony, sarcasm, figurative or not an offensive at all (regular). Specifically, we first identify the relationship between each word using Word2vec and FastText word embedding using Continuous Bag of Words Model (CBOW) and Skip-gram architectures, and then we classify the offensive language label using CNN-BiLSTM, a combination of deep learning approaches Convolutional Neural Networks (CNN) and Bidirectional-Long Short Term Memory (Bi-LSTM) by first examining the impact of hyper-parameters on language classification. The experiment indicates using the Kaggle Dataset, CNN-BiLSTM with Word2vec with CBOW architecture outperforms CNN-BiLSTM with FastText.
Automatic Fast Fuzzy Clustering Algorithm for Nucleus Segmentation of Color Pathology Images

Due to the repeated computation of the distance between pixels and cluster centres, the effective image clustering technique fuzzy c-means (FCM) takes a long time to process. In other words, FCM’s computational time greatly depends on image size or number of pixels. Therefore, researchers utilized histogram of the image for clustering for developing time-reduced clustering approach. However, finding the histogram for color image is very challenging. Another challenge of FCM is the selecting the required number of clusters before segmentation process. Therefore, this study proposes a novel automatic color image clustering approach called fully automatic histogram based fast fuzzy clustering (FAHBFFC) to overcome the both mentioned demerits. The proposed FAHBFFC automatically find the required number of clusters for the segmentation of the color images. It also utilized the histograms of the color channels of the images and hence, the proposed approach is very fast. Finally morphological reconstruction has been applied for membership filtering of the FCM to incorporate the noise-immunity. In comparison to other state-of-the-art algorithms for pathology picture segmentation, the FAHBFFC produces results that are competitive.
The Delivery of Speaking Tests in Traditional or Online Proctored Mode: A Comparability Study

This paper investigates the comparability of test scores recorded for high-stakes English language Speaking Tests administered face-to-face in either a traditional centre-based mode (TM) or in an online proctored mode (OLP).The data comprise a large sample of test takers taking English language Speaking Tests at four CEFR (the 'Common European Framework of Reference for Languages') levels -B1 to C2via TM or OLP.The data were analysed using descriptive statistics, effect size differences and equivalence tests.While a degree of difference in scores obtained between modes was apparent at C2 level, the differences were not found to be statistically significant.The paper concludes that whether Speaking Tests are delivered in online proctored mode or in traditional face-to-face mode, test takers receive similar scores.The study confirms that mode of test delivery does not significantly affect test taker scores.
From Rational Agent to Human with Bounded Rationality

To clarify and address the errors that occur in model parameter estimations and behavior predictions, researchers may need to start with investigating the hidden gaps between rational agent and human that are ignored or covered by oversimplified model assumptions. These gaps could occur in both factual, ad hoc retrieval and whole-session interactive retrieval and involve multiple aspects of search interactions, including not only user characteristics and their search strategies but also search task features, search interfaces, as well as situational factors. In this chapter, we summarize and briefly discuss the gaps we identified between simplified rational assumptions and empirically confirmed human biases and then propose a preliminary bias-aware evaluation framework to describe the connections between different stages of search sessions and diverse types of biases. The identified gaps will serve as the basis for developing bias-aware user models, search systems, and evaluation metrics.
Future Challenges in Artificial Intelligence for Smart Healthcare

This chapter outlines the major challenges for artificial intelligence (AI) adoption in various aspects of a smart healthcare system. While AI techniques have shown promising improvement in disease diagnosis and prognosis, security remains a major challenge. AI-enabled multiomics analysis helps us to identify new biomarkers and design targeted therapy. The Electronic Health Record (EHR) is a widely adopted mechanism for managing patient information and administering treatment/medicine. Traditionally, labor intensive and expert dependent phenotyping has been used to make them useful for further exploitation as it is really challenging to produce workable analytic models for EHR data for further processing. The paradigm of explainable AI in healthcare is enormous because of its multidimensional perspectives. It involves not only the technical work but also medical, ethical, legal, and social aspects that indeed need in-depth investigation. With more powerful hardware processing, software agility, networking and communication capabilities, AI is becoming a dominant player in smart healthcare systems.
Segmenting Glandular Biopsy Images Using the Separate Merged Objects Algorithm

The analysis of the structure of histopathology images is crucial in determining whether biopsied tissue is benign or malignant. It is essential in pathology to be precise and, at the same time, to be able to provide a quick diagnosis. These imperatives inspired researchers to automate the process of segmenting and diagnosing biopsies. The main approach is to utilize semantic segmentation networks. Our research presents a post-processing algorithm that addresses one weakness of the semantic segmentation method - namely, the separation of close objects that have been mistakenly merged by the classification algorithm. If two or more objects have been merged, the object can be mis-classified as cancerous. This might require the pathologist to manually validate the biopsy. Our algorithm separates the objects by drawing a line along the points where they touch. We determine whether a line should be passed along the edges to separate the objects according to a loss function that is derived from probabilities based on semantic segmentation (of various classes of pixels) and pixel distances from the contour. This method is general and can be applied to different types of tissue biopsies with glandular objects. We tested the algorithm on colon biopsy images. The newly developed method was able to improve the detection rate on average from 76% to 86%.
Learning about learning as an essential element of high-quality education

This article highlights the issue of educational efficiency and enhancing the quality of education, referring to Sustainable Development Goal 4. It has been recognized that much attention is now being paid to implementing educational tools to improve teaching, teacher training, or the provision of appropriate infrastructure. In contrast, little space is dedicated to teaching students to use effective learning methods. The text cites research on the range of practices undertaken during learning. The final section proposes models for enhancing the quality of education by strengthening the “learning” component that takes into account specific variables that directly and indirectly affect the acquisition of knowledge and skills.
The Four-Dimensionality of Utility as a Philosophic Category

The “utility” category is vital for the philosophy of technology as well as the modern thinking and reflecting on the technological systems and devices. That is why it requires the thorough examination. In order to achieve the goal, the four utility dimensions are to be analyzed. Each dimension is to clarify a certain problematic issue. Ontologically, it may be asked as how does “utility” determine being? Gnoseologically, what is the place occupied by “utility” in the area of cognition? Axiologically, what is the place of “utility” among the value primalities? Praxeologically, how does “utility” affect the act? The methodological ground of the upcoming study is the contrastive-comparative analysis of concepts suggested by domestic and West European thinkers for their compatibility with ontological, gnoseological, axiological and praxeological dimensions of the “utility” category. For this purpose, we have to pay our attention to the ideas by Ch. S. Peirce, W. James, F. Schiller, E. Coreth, P. Engelmeyer, F. Dessauer, T. Kotarbinsky, D.I. Dubrovsky, M.S. Kagan, A.N. Ognev. The result of the present research is presented via the determination methods of the utility notion in accordance with the above-mentioned dimensions. Therefore, ontologically, “utility” acts as an effect affirming the subject in his existence, gnoseologically – as a true idea, axiologically – as a gradation of aspirations, whose sum could be represented within a phenomenon of the will, praxeologically – as a rational balance between the means and the goal, with a number of subjective qualities, which constitute the basis for maintaining their interrelation.
HinPage: Illegal and Harmful Webpage Identification Using Transductive Classification

With the growing popularity of the Internet, websites could make significant profit by hosting illegal and harmful content, such as violence, sexual, illegal gambling, drug abuse, etc. They are serious threats to a safe and secure Internet, and they are especially harmful to the underage population. Government agencies, ISPs, network administrators at various levels, and parents have been seeking for accurate and robust solutions to block such illegal and harmful webpages. Existing solutions detect inappropriate pages based on content, e.g., using keyword matching or content-based image classification. They could be easily escaped by altering the internal format of texts or images, e.g., mixing different alphabets. In this paper, we propose to utilize relatively stable features extracted from the relationships between the targeted illegal/harmful webpages to discover and identify illegal webpages. We introduce a new mechanism, namely HinPage, that utilizes such features for the robust identification of PG (pornographic and gambling) pages. HinPage models the candidate PG pages and the resources on the pages with a heterogeneous information network (HIN). A transductive classification algorithm is then applied to the HIN to identify PG pages. Through experiments on 10,033 candidate PG pages, we demonstrate that HinPage achieves an accuracy of 83.5% on PG page identification. In particular, it is able to identify illegal/harmful PG pages that cannot be recognized by SOTA commercial products.
Speaking Performance in Vocational Students in Rural Area by Using Power Teaching

Speaking is one of the crucial skills which has to be learned by non-native students to support their capability in learning a foreign language. Increasing the students’ speaking performance within a middle school is considered crucial as well as in vocational school which requires the students to have specific skills after they graduate. The aim of this study is to identify the influence on the students’ speaking performance by using Power Teaching (PT) Technique. The method of this research applied pre-experimental by involving 36 students from vocational high schools in remote areas. The finding of this research showed that one element of the speaking performance, namely pronunciation, has the highest mean compared to the other element; otherwise, the result of grammar has the lowest mean. However, overall elements have increased compared to the first test it was proved that there was a significant difference in this research. It means that there was an increasing speaking performance among the students who have been given treatments by using Power Teaching (PT).
Establishment and Practice of Teaching Evaluation System of Gerontology Introduction Course from the Perspective of Big Data

The development of Internet technology has led to profound changes in the field of education, but also brought about a new development of the subject curriculum evaluation system.In the era of big data, the curriculum assessment system can deepen the curriculum practice, effectively combine student habits, learning characteristics, off class performance, professional ethics and comprehensive quality and other evaluation indicators, and achieve the authenticity and effectiveness of student and teaching evaluation through the induction, integration and collection of massive teaching practice data.For this reason, this paper takes Introduction to Gerontology as the starting point, analyzes the main problems of curriculum evaluation in the era of big data, and puts forward strategies for establishing the curriculum teaching evaluation system of Introduction to Gerontology.