Reviewerly/exHarmony huggingface dataset:
Name, things, places: towards a semantic, sustainable, usable integration?

In the nowadays world characterized by complexity, the digital systems for archival and bibliographic description are, especially in Italy, a faithful mirror of too many horizontal (among bodies with national functions) and vertical (among central and peripheral levels) complications. The integration of data sets conceived according to the up-to-date conceptual domain models would simplify the dynamics of representation, improve user experiences, and optimize the environmental costs of computing infrastructures. The concepts to be shared should relate to agents (individual or collective), places, and chronological data, deepening on how to address the more complex issues opened by things. This paper introduces three possible scenarios of semantic cooperation between archivists and librarians, not necessarily alternative: activating a conceptual matching through common entities, relying on a neutral semantic data infrastructure such as Wikidata, or, finally, developing a transversal core ontology. Hoping that disciplinary boundaries will not impede cooperation, two types of impediments must be considered: the organizational one (bottom-up, top-down, or a virtuous synergy between the two organizational models?) and the crucial issue of offering easy-to-use interfaces to end-users, not only constituted by software agents.
Analysis of various Pre-processing filtering methods for MRI brain tumour detection in a noisy environment

Abstract Magnetic Resonance Imaging (MRI) is playing a very important role in the detection of various abnormalities in the human body. Biomedical image processing is helping us in the proper identification of various abnormalities such as a malignant tumour in various parts of the body. In this paper different types of filtering methods applied in the pre-processing stage have been compared. When MRI images have been corrupted by Gaussian noise then various filters are applied to obtain recovered MRI images. After performing filtering operation segmentation has been applied on MRI images to obtain tumour region. The processes have been implemented with the help of MatLab 2018.
Comparison of various CNN encoders for image captioning

Abstract Image captioning is the ability of machines to study the features of an image and then give a textual description of that image. It uses computer vision to extract the features from the image and natural language processing for generating the caption. Image captioning requires both Image analysis and natural language processing. It is a very important task for further research on visual intelligence in line with human perception. In this project we will compare the different CNN pretrained models that are available like VGG16, RESNET50, Xception and INCEPTION and find out which performs better for image captioning.
Analysis on the Influencing Factors of Teacher-Student Relationship in Postgraduate Education

The relationship between teachers and students in postgraduate education directly affects the level of postgraduate education. Based on the current situation of postgraduate education, this paper analyzes the influencing factors of teacher-student relationship in postgraduate education, mainly from the macro and micro levels. That is, the training mode at the macro level, the tutor's knowledge and professional power at the micro level, the personality charm, the way to deal with the relationship between teachers and students, and the role of teachers and students.
An adaptive search optimization algorithm for improving the detection capability of software vulnerability

Deep learning-based vulnerability detection frees human experts from the tedious task of defining features and allows for better detection capabilities. The common practice is to convert program code into vector representation for neural network model training. Since the length of the vector representation varies across program code, finding the optimal vector length is critical to ensuring detection accuracy. This paper proposes an adaptive search optimization algorithm for finding the optimal vector length. It sorts all the vector lengths obtained by word2vec and takes the vector length corresponding to the point where the trend changes from slow to fast as the output. We evaluate our algorithm on three publicly available datasets against state-of-the-art algorithms. The results show that, without significantly increasing the time overhead, our algorithm can more accurately choose an appropriate vector length instead of setting a value empirically or arbitrarily. Furthermore, it shows that while a larger vector length can usually produces a higher detection accuracy, the extra time overhead incurred often does not suffice to compensate for the corresponding accuracy improvement.
WAIN: Automatic Web Application Identification and Naming Method

As the defense shifts from vulnerability-centric to threat-centric and efficient security architecture can exclusively be constructed with adequate comprehension of the threat of the critical assets. In order to classify and identify the assets, the recognition and naming of the Web applications are the fundamental approaches. At present, the traditional Web application identification methods mainly rely on rules matching, which are extracted from the Web pages by manual analysis. This low coverage and labor-consuming method, which is not suitable for this time of explosive growth in Web applications and inevitably leaves some uncommon applications unrecognized and at risk. In this paper, we propose WAIN, an automatic method for Web application identification and naming, it first clusters different types of applications in numerous samples using K-Means algorithm, and then leverages a novel TF-IDF calculation method to extract keyword. After that, LDA is applied to explain why some parts of data are similar and extract possible fingerprints. Finally, WAIN utilizes filters and a statistic means to generate possible names for clusters. When evaluating, data from 30,000 instances of eight kinds of Web applications is processed, and the generated fingerprints and names can distinguish each type of application in the dataset. We manually checked all the results and found that fingerprints and at least one name that summarizes at least one of the product names, manufacturers, and functions are successfully generated for each kind of application.
Detecting Defects in Deep Learning Systems: a Survey

Thanks to the recent breakthroughs in deep learning (DL) methods and ever-increasing computation power, nowadays DL models are being increasingly applied to all kinds of fields. They, along with other software modules, compose complex DL systems such as autonomous driving systems. Unfortunately, fatalities happen to these systems as is reported in real-life situations, e.g., traffic accidents involving autonomous driving vehicles. Further analyses show that this is because DL systems contain defects. To this end, understanding defects in DL systems is critical for preventing such accidents. To help improve the reliability of DL systems, many researchers have devoted efforts to their testing. In this paper, we investigated state-of-the-art methods through in-depth exploration. Firstly, we made a detailed analysis of deep learning system defects. We proposed a defect classification scheme, and summarized the characteristics of defects and their impacts on the system. Secondly, we systematically investigated the detection methods and tools and evaluated their capability in defect detection.
Research on Course Teaching Quality Evaluation Based on Improved BP Neural Network Algorithm

BP neural network is a model that abstractly portrays the neuronal system of the brain, however, the selection of the number of neurons in the Hide layer becomes the key to the construction of the neural network model. This paper addresses the problems of Hide layer selection in the transmission model, improves the "in-and-out method" and applies it to the neural network model, and combines the improved nonlinear Levenberg-Marquardt function to make the evaluation model operation process faster and more reasonable. Then the evaluation model was applied to the SPOC+MOOC learning evaluation of a major course at University L to assess student learning behavior. The results show that the course teaching quality evaluation model based on improved BP neural network algorithm has fast convergence speed and small error, and its evaluation results can effectively reflect the level of course teaching quality, and the analysis results have certain reference, which can provide reference basis for improving and improving online course teaching.
A Production-Oriented Evaluation System for English Culture-Based Courses

In the post covid-19 era, learning methods and learning focuses have experienced great changes: for one thing, many universities tend to provide distance learning courses online so that those quarantined students can attend classes; for another, as covid-19 makes graduates even more difficult to find jobs, many students tend to learn courses that provide practical skills, rather than culture-based courses that cultivate students' humanistic quality. Under such circumstances, this essay decides to scaffold a production-oriented system to evaluate students' performance in English culture-based courses. It is expected that this new production-oriented evaluation system can be applicable in distance learning, and can try to motivate undergraduate students in learning culture-based courses.
The Application of Human-computer Interaction in Computer-assisted Second Language Acquisition

In view of the lack of application of human-computer interaction in language learning research, this paper expounds the relationship between human-computer interaction and computer-assisted second language acquisition research. This paper introduces the relevant theoretical models and interdisciplinary research methods that are widely used in human-computer interaction research, as well as research examples of the application of human-computer interaction technology in computer-assisted second language acquisition in recent years. This paper analyzes the advantages and disadvantages of the application of human-computer interaction technology in computer-assisted second language acquisition, and how to better apply human-computer interaction technology to play a key role in computer-assisted second language acquisition research. This paper proposes possible research directions for the human-computer interaction technology in application second language acquisition in the future.
Application of Genetic Algorithm in Intelligent Student Management Information System

In order to meet the needs of talent cultivation under the new situation, the existing student management mode of colleges and universities also needs corresponding reform and innovation. The rapid development of today's computer network and software development technology also brings new opportunities for the reform of student management mode in colleges and universities. Nowadays, most colleges and universities are also gradually carrying out campus informatization projects to improve the daily work efficiency of all administrative departments of the school to meet the needs of teaching management reform. Therefore, based on network and software technology, it is of practical significance. In this paper, we study the application of genetic algorithm in intelligent student management information system under the complex environment of multi-campus and multi-discipline in the current smart campus environment, to support the reform of student management information system. The results show that the application of intelligent management system is very necessary in student information management. The college student management system needs to be further developed and improved.
Data Visualization Literacy: Multimodal skills in the Age of Internet of Things (IoT)

The global networked infrastructure, also known as the Internet of Things (IoT), has been embedded into various hardware and software to enable communication capabilities and serves as one of the significant influences that shape the education sector. IoT advances fundamental data visualization technologies as a game-changer for the coveted skills required for 21st century graduates. Despite the discernible pre-requisites to possess the skills, there is a knowledge and pedagogic gap where a large portion of the existing curriculum does not integrate the expected skills to successfully bridge students with the real-world and practical application. A non-experimental research design was employed in this study to examine the significance of data visualization literacy (DVL) for the students’ learning process. The overarching findings underscore the importance of visual learning as a preferred learning approach.
ANALYZING TYPES AND LEVELS OF STUDENTS' ENGLISH-SPEAKING ANXIETY

This research was aimed to analyze and describe the types and levels of students' English-speaking anxiety in foreign language learning activities to get broadened understanding of this phenomenon. This study was categorized as descriptive quantitative research. It was conducted at the English Education Department of Muhammadiyah University of Prof. Dr. HAMKA. The participants were the second-semester Students of the Transactional Listening and speaking class. The questionnaires that were given to the participants consisted of 33 statements and were based on FLCAS and adapted from Horwitz and Cope (1986) to find out the dominant types and levels. The author found that the dominant levels of speaking anxiety experienced by the participants were at the "Anxious" level which was 36% with none of the students who experienced the "Relaxed” and "Very Relaxed" levels. Whereas, the author found that "Fear of Negative Evaluation" with 67% as the dominant type of speaking anxiety experienced by the students.&#x0D; Keywords: Speaking Anxiety, Types of Speaking Anxiety, Levels of Speaking Anxiety, Speaking Skill
Is your access allowed or not? A Verified Tag-based Access Control Framework for the Multi-domain TEE

The challenge of requirements for the finer-grained isolated domain in Trusted Execution Environment (TEE) has been increasing, including the accuracy and security of resource management. However, the current access control mechanism for TEE cannot provide strict security assurances due to a lack of strict formal verification. In order to address the problem, in this paper, we first present the definition of multi-domain TEE, and propose a verified tag-based access control framework called REAL to provide the strict access control policy. We develop a high-level formal functional specification of REAL, and prove its correctness and security properties with 119 lemmas/theorems and ∼ 4,000 LOC of Isabelle/HOL. We also implement a page-level access control prototype called SOP-TEE and demonstrate that it correctly achieve the security objectives while merely incurring less than 0.3% overhead.
Abstractive Text Summarization For Multimodal Data

The availability of data from different sources such as newspapers, images, online articles, social media, etc is rapidly increasing. As a result, finding the most relevant article according to one’s requirement has become a time consuming and tedious task because it is not possible to read each article and then decide which particular information would be the most useful. This problem can be solved with the help of an abstractive summarizer that would generate concise, easy to understand summaries of those articles which would be an approximate representation of human written summaries. The proposed system generates abstractive text summaries of contents existing in multi-modal data formats, namely, text files, image files and video files using the Recurrent Neural Network model with encoder-decoder architecture and attention mechanism. The paper also discusses various text extraction methods, the implementation of the mentioned RNN model on a particular corpus and its results.
Human-Computer Music Co-Production Using Genetic Algorithms

In this paper, the analysis of generating appealing musical hooks is discussed. Songs generally possess a particular musical piece called the hook. It is that part of the song which helps people quickly recognise the song just by hearing it. However, creating captivating musical hooks is a very tiresome and time-consuming process. Even a few musical notes can generate vast quantities of permutations and combinations of musical pieces! The assistance of genetic algorithms can be used to ease out the process of a musician finding a catchy hook. Humans have a great perception of music, while computers can efficiently crunch many data. The main objective of this project is to combine the best of both worlds, i.e., human-computer music co-production. The user will provide their initial musical notes as input via the provided interface. Then in a cycle of the model’s output generation and the user’s relevance feedback, newer musical hooks will be created. The proposed system can be used by musicians to discover new possibilities that might have otherwise been possibly hidden.
A Comparative Analysis of Image Forgery Detection Techniques

An image is an essential medium for information sharing. Growth in technology led to the development of various applications like Adobe Photoshop, Snapseed, Gimp, and Prisma. These applications make it very easy for anyone to modify an image, giving rise to a new field of research called image forgery. Different pixel-level, block-based, camera-based, and mathematical algorithm-based techniques were developed to identify forged or modified images. Deep learning methods showed promising results as they considered more features than traditional methods. The study aims to conduct a comparative examination of image forgery methods. This paper also proposes an eight-layer CNN-based model with a relu activation function for the first seven layers and a sigmoid for the last one. Pretrained models VGG-16 and VGG-19 are used for comparison with the proposed model as they are the most widely used image classification models. In the proposed approach, the image dataset is first used to create a new dataset by applying the ELA (error level analysis) method to the original images. It is used for training the proposed model. The Casia dataset and MICC F2000 datasets were used for experimentation. This paper presents a comparative analysis of forgery methods based on the results. Results reveal that the proposed CNN model trained on ELA images outperforms other pre-trained models. All the models produced better results for MICC-F2000 than for the Casia dataset. The proposed model showed promising results on the small-sized image.
Survey on Underwater Image Enhancement using Deep Learning

Underwater image quality improvement is very much needed in underwater imagery as images obtained may contain huge amounts of noise, haziness, and color loss. Several steps are done to remove these and obtain high-resolution images. One of the main techniques used for processing is by using General Adversarial Networks(GANs) giving super resolute images as result. Basic traditional methods are the usage of filters and deep learning methods which are the most commonly used techniques. In this paper, different types of GANs are discussed and their evaluation is studied. The GANs studied include WaterGAN, Pyramid Attention Mechanism-Oriented Symmetry Generative Adversarial Network (PAMSGAN), Underwater GAN(UWGAN), SpiralGAN and General Adversarial Networks used to improve LiDAR (Light Detection and Ranging) images and real-time underwater images which in result show great results when compared to traditional methods of underwater image enhancement.
Deep Multi-scale Residual Learning for Single Image De-Hazing

Images play an important role in various applications such as computer vision, scenario analysis, object detection, advanced driver assistance system (ADAS) etc. Usually images that are captured from outdoor get degraded due to the presence of turbid media and causes the outdoor vision system to provide deficient performance. The turbid media can be rain, haze, smog, fog, snow etc. With the help of developing technologies, it has become easier for everyone to recover such kind of distorted images. In order to overcome the limitations of existing models a new variant called deep multi-scale residual network has been proposed. The multi-scale deep residual learning network along with the simplified convolutional neural network map the hazy image to its corresponding haze-free image there by providing dehazed output image. The basic building block of the multi-scale residual network, passes the output from the previous layer and is fed to next layer. So that the possible color distortion in the recovered image would be avoided. The simplified CNN designed with the help of generic agnostic model helps in enhancing the haze-free image. Qualitative and quantitative analysis is performed. A simplified architecture with less parameters is designed and the model shows an excellent performance compared to state-of-art methods.
Composite Privacy Preservation in Location Based Advertisement

In the era where online businesses are booming ranging from online shopping to ordering food, the use of location-based services has increased dramatically. To get the benefits of location-based services, users have to share some sensitive information like real-time locations. The leakage of such data could have serious consequences like identity theft and personal attacks. Certain models have been developed but also lack in achieving the right trade-off between privacy and utility. The proposed solution achieves a reasonable tradeoff between utility and privacy. To protect the user’s privacy without degrading the Quality of Service, the proposed idea uses multiple privacy preservation techniques together such as Geo-Indistinguishability, Differential Privacy, and Hashing.
Navitrolley -An AI Integrated Path Finding Robot

Airports are getting busier each day and it is important to create a smooth experience for the end user. With the advancement of technologies airports are aiming for digital connectivity creating an environment which is user friendly. This paper describes the development of a navigating robot through the airport which is usually daunting or confusing for a first-time flier or an elderly person since there are many procedures to be completed, verifications to be done. This project aims to provide this assistance with the help of an AI based system to navigate through airports easily for a first-time flier as well as the elderly. Navitrolley is designed in a user-friendly manner to guide people to correct locations just by following it. As the user provides details the trolley locates each correct location.