Reviewerly/exHarmony huggingface dataset:
Poster: Fragmentation Attacks on DNS over TCP

The research and operational community believe that TCP provides protection against IP fragmentation based attacks and recommend that servers avoid sending responses over UDP and use TCP instead. In this work we show for the first time that IP fragmentation attacks may also apply to communication over TCP. We perform a study of the nameservers in the 100K-top Alexa domains and find that 454 domains are vulnerable to IP fragmentation attacks. Of these domains, we find 366 additional domains that are vulnerable only to IP fragmentation attacks on communication with TCP. We also find that the servers vulnerable to TCP fragmentation can be forced to fragment packets to much smaller sizes (of less than 292 bytes) than servers vulnerable to UDP fragmentation (not below 548 bytes). This makes the impact of the attacks against servers vulnerable to fragmentation of TCP segments much more detrimental. Our study not only shows that the recommendation to use TCP and avoid UDP is risky but it also shows that the attack surface due to fragmentation is larger than was previously believed. We evaluate known IP fragmentation-based DNS cache poisoning attacks against DNS responses over TCP.
Path Planning in Unknown Environment Based on Reinforcement Learning

In order to solve the path planning problem of robot in unknown environment, this paper proposes a path planning algorithm based on direction detection reinforcement learning combined with virtual sub target optimization. Firstly,the direction-detection method is proposed to replace the grid method in the discrete state, which reduces the dimension. Secondly, a virtual sub-target optimization algorithm embedded in reinforcement learning process is proposed to optimize nodes in the continuous iteration process. Finally, simulation experiments show that the convergence speed of the proposed algorithm is 98.2% higher than that of traditional reinforcement learning, and the path is more stable and smooth.
A Cluster-Based Method for Action Segmentation Using Spatio-Temporal and Positional Encoded Embeddings

A crucial task to overall video understanding is the recognition and localisation in time of different actions or events that are present along the scenes. To address this problem, action segmentation must be achieved. Action segmentation consists of temporally segmenting a video by labeling each frame with a specific action. In this work, we propose a novel action segmentation method that requires no prior video analysis and no annotated data. Our method involves extracting spatio-temporal features from videos in samples of 0.5s using a pre-trained deep network. Data is then transformed using a positional encoder and finally a clustering algorithm is applied with the use of a silhouette score to find the optimal number of clusters where each cluster presumably corresponds to a different single and distinguishable action. In experiments, we show that our method produces competitive results on Breakfast and Inria Instructional Videos dataset benchmarks.
A Post Quantum Signature Scheme for Secure User Certification System

Enhancement of technology requires better systems for user certifications, which are resistant to quantum attacks. A nominative signature is a better candidate for the user certification system. Most of the existing nominative signature schemes are based on bilinear pairing. The desire to expand the hardness assumption beyond factoring or discrete logarithm problem on which digital signatures can rely on. Lattice-based assumptions have seen a certain rush in recent years, as they are quantum resistant. In this article, we proposed the quantum secure user certification schemes signature scheme. The security of proposed signature scheme relies on Shortest-Integer problem, which can withstand the quantum attacks. Moreover, we prove that the proposed scheme satisfies unforgeability and other security aspects (invisibility, non-repudiation and impersonation) under the standard security model.
Analysis of E-learner’s Opinion Using Automated Sentiment Analysis in E-learning and Comparison with Naive Bayes Classification, Random Forest and K-Nearest Neighbour Algorithms

E-learning becomes an online learning network that is more efficient and popular. Recognition of the views of the Commission’s e-learning users is also significant. E-learning is commonly referred to as instructional efforts transmitted to communicate information through computers in a non-traditional school atmosphere. Buyers cannot ask if their e-courses are relaxed. Sentiment mapping allows users using e-learning portals to be identified quickly. Romantic research uses natural information using language text analysis and computational linguistics to understand and extract contextual information from source materials. The goal is to determine the speaker’s attitude or the writer to the subject of the article’s general qualitative polarity. Automated identification of emotion allows recognizing user feelings on the webpages navigated by people. A topic or region in which they're interested for the e-learning method will use this information to understand emotional triggers of the individual and can be used to analyse the behaviour of the consumer every time. Feelings of the student against a course will serve reviews on online e-learning portals. In the proposed e-learning method, automatic emotion analysis allows evaluating the pages viewed on the customer’s e-portal. This allows the maker and instructor to realize where the learner works in particular fields for some physical labour or other feedback from the customer. Here, for the Bayesian predictive classification’s automatic emotion analysis, Naive Bayes classification, random forest and K-nearest neighbour algorithms are used here.
Classes of Functions

In this chapter, we briefly present well-known classes of functions that will play an important role in our considerations. Of course, the list of such classes goes far beyond those discussed in this chapter. The latter classes will be overviewed (briefly) just because they are very general and commonly used. More specific classes will appear later in our story. In other words, if the main subject of the book is claimed to be prerequisites in harmonic analysis applicable to certain problems, this chapter is a kind of preliminaries to those prerequisites. Maybe the section about functions of bounded variation is special and it is therefore presented in more detail. After all, this topic has a special place in the author’s preference scale. So to say, all spaces are equal, but some spaces are more equal than others, at least, in the eyes of the author.
Multimodal Retrieval with Contrastive Pretraining

In this paper, we present multimodal data retrieval aided with contrastive pretraining. Our approach is to pretrain a contrastive network to assist in multimodal retrieval tasks. We work with multimodal data, which has image and caption (text) pairs. We present a dual encoder deep neural network with the image and text encoder to encode multimodal data (images and text) to represent vectors. These representation vectors are used for similarity-based retrieval. Image encoder is a 2D convolutional network, and text encoder is a recurrent neural network (Long-Short Term Memory). MS-COCO 2014 dataset has both images and captions, and it is used for multimodal training with triplet loss. We used a convolutional Siamese network to compute the similarities between images before the dual encoder training (contrastive pretraining). The advantage is that Siamese networks can aid the retrieval, and we seek to show if Siamese networks can be used in practice. Finally, we investigated the performance of Siamese assisted retrieval with BLEU score metric. We conclude that Siamese can help with image-to-text retrieval tasks.
Object retrieval system based on feature matching technology

As the underlying visual technology, image matching technology plays an extremely important role in many fields. In these many fields, in order to get better results, most of them currently use deep learning as the mainstream algorithm, but deep learning algorithms have high requirements for the performance of the device, so it is not enough for mobile terminal to support its carrying deep learning tasks. This thesis attempts to build a suitable application scenario for them using image matching technology which is more suitable than deep learning. The main content of this thesis is to focus on the most widely used SIFT algorithm in the classic feature-based matching technology, and based on the SIFT algorithm, combined with the knowledge of homography to construct an object retrieval system for small mobile terminal. The system can retrieve the object in a picture containing the object in the system. The system uses the classic SIFT algorithm as the mainstream algorithm to meet the needs of these small mobile terminal. At the same time, the system also has a very high retrieval success rate.
The Commercial Feasibility Analysis on Pet Online Diagnosis and Treatment Platform

In order to solve the problem of pet diagnosis and treatment, the concept of pet online diagnosis and treatment platform was conceived in recent years. By using tools such as Business Model Canvas and Porter's Five Forces Model, this paper analyzes the commercial feasibility of online pet diagnosis and treatment platform, hoping to provide some enlightenment for the related types of start-ups’ development. It is obvious that the increase of the urban population and a large number of pet owners provide a rich reserve of potential customers for the pet online diagnosis and treatment platform. In conclusion, the core value of the platform is that it can break through the limitations of space at any time and provide dedicated medical services. This also determines that the main user group of the platform is people who do not have enough time to take care of pets. Although the homogenization of products is serious and the entry threshold is low, the competition is not fierce on the whole, so it is the main task for such enterprises to seize market share as soon as possible. In order to attract more users, the platform also needs to add other features, such as social or shopping.
DIVERSITY AES IN MIXCOLUMNS STEP WITH 8X8 CIRCULANT MATRIX

In AES MixColumns operation, the branch number of circulant matrix is raised from 5 to 9 with 8´8 circulant matrices that can be enhancing the diffusion power. An efficient method to compute the circulant matrices in AES MixColumns transformation for speeding encryption is presented. Utilizing 8´8 involutory matrix multiplication is required 64 multiplications and 56 additions in in AES Mix-Columns transformation. We proposed the method with diversity 8´8 circulant matrices is only needed 19 multiplications and 57 additions. It is not only to encryption operations but also to decryption operations. Therefore, 8´8 circlant matrix operation with AES key sizes of 128bits, 192bits, and 256 bits are above 29.1%, 29.3%, and 29.8% faster than using 4´4 involutory matrix operation (16 multiplications, 12 additions), respectively. 8´8 circulant matrix encryption/decryption speed is above 78% faster than 8´8 involutory matrix operation. Ultimately, the proposed method for evaluating matrix multiplication can be made regular, simple and suitable for software implementations on embedded systems.
Media Platform: Software Testing Prologue

Media Platform is an evolving industry with footprints in many domains. There are many application types which use Media platforms. Accurate testing ensures superior quality experiences and on time delivery to the end users. With a large suite of best practices that spans the entire software development lifecycle [1] [2] [3] [4], this technical note possesses a library of referenceable test cases that significantly reduce test cases and hence development time.The major challenge of media platforms is delivering quality content over web & mobile application, meeting customer centric content, enabling software systems and network infrastructure.
GloFlow: Whole Slide Image Stitching from Video Using Optical Flow and Global Image Alignment

The application of deep learning to pathology assumes the existence of digital whole slide images of pathology slides. However, slide digitization is bottlenecked by the high cost of precise motor stages in slide scanners that are needed for position information used for slide stitching. We propose GloFlow, a two-stage method for creating a whole slide image using optical flow-based image registration with global alignment using a computationally tractable graph-pruning approach. In the first stage, we train an optical flow predictor to predict pairwise translations between successive video frames to approximate a stitch. In the second stage, this approximate stitch is used to create a neighborhood graph to produce a corrected stitch. On datasets of simulated video scans of pathology slides, we find that our method outperforms known approaches to slide-stitching, and stitches images resembling those produced by slide scanners. Our method allows for creation of whole slide images using widely-available low cost microscopes.
The influence of social factors on the development of the motivational sphere of student youth

The article is devoted to the analysis and empirical study of the most promising developmental tasks of the modern educational environment for the development of new approaches to the organization and psychological support of the development of a person's productive activity. These tasks clearly demonstrate the relationship between the motivational sphere of the individual and the social environment, the effectiveness of the influence of social factors on the development of the individual, the importance of studying their hierarchization according to the degree of influence. The relevance of the research lies in the fact that the study of the possibility of a targeted impact through social factors, norms, requirements on the motivational-need sphere of a developing personality will allow to determine to a large extent, the direction of its development.&#x0D; Our research is based on the assumption that the analysis of the motivational profile will allow us to understand how student youth comes to the realization of the need to coordinate the goals, requirements, wishes of the external environment with their own motivation. The aim of the study was to study the influence of the individual's objective attitudes on the structure of professional activity motives. To solve the set tasks, the following methods were used: the motivational profile of S. Ritchie and P. Martin, the test "20 statements". The results obtained by the method of S. Ritchie and P. Martin confirmed our hypothetical assumption, the respondents predominantly chose the objective components of the motivational structure, which were substantively related to the social expectations of the subjects (from society to themselves). The results obtained confirmed the prospects of studying the influence of social factors on the development of the motivational-need-related sphere of the individual. The analysis of social factors makes it possible to study and model the conditions that contribute to the rethinking of the actual situation by the individual and the creation of new meanings of activity that provide for a wider non-utilitarian social context that goes beyond the current situation.
Ethical Considerations of Using VR in Mental Health Training

Mental health students, who are still undergoing training, might find it challenging to visualise and fully understand what their patients experience. For this reason, the authors created a virtual reality simulator which mimics the symptoms of a person suffering from schizophrenia at a virtual workplace. The simulation is managed by an artificial intelligence system which asks the user to attempt simple tasks, while simultaneously facing both visual and auditory hallucinations. The AI also adapts the storyline and character behaviour dynamically to increase the immersiveness of the experience. A pilot study was carried out, and the initial results were very encouraging. In fact, the absolute majority of the users stated that the simulation has helped increase their understanding of schizophrenia. In this chapter, the authors evaluate this experiment but from a different perspective. They focus mainly on the use of emerging technologies such as AI and VR and discuss the ethical considerations of their use within the field of mental health.
Formation of practical skills of students using the a la prime method

The article studies the impact of using the a la prime method in plein air practice on the development of students' creative abilities. There were used a complementary and mutually verifying methods such as: analysis of theoretical literature on pedagogy and psychology, programs on painting, plein-air practice; survey and diagnostic methods; theoretical analysis; quantitative and qualitative data processing; pedagogical experiment; pedagogical observation of students' level of creativity and quality of sketches in watercolors in the audience and in plein-air practice. The a la prime method contributes to the development of creativity and has many advantages in the process of working with watercolors than other methods and has a positive influence on the development of creativity and on the ability to draw without preliminary sketches of students. The abilities to see the future drawing, reflect it, and make decisions about how to draw quickly are very important for future artists. A la prime technique contributes to the comprehensive development of these necessary abilities.
Research on Malicious Traffic Detection Methods Based on Deep Learning

The number of malicious traffic in the real network is growing rapidly, such as common DoS attacks, web attacks, DDoS attacks, and so on. These attacks may cause huge economic losses to enterprises, countries, and individuals. Traditional machine learning methods are hardly meeting the accuracy and real-time requirements of malicious traffic identification technology in the face of huge and multi-dimensional online data. Large-scale data can provides training data for deep learning, and deep learning can directly obtain advanced features from the data. Therefore, the malicious traffic identification technology based on a convolutional neural network (CNN) is adopted. The outliers and useless features in the data set are removed by preprocessing, and then the model is obtained by multiple training and optimization of the convolutional neural network. The experimental results show that CNN performs well in the malicious traffic detection tasks, and the metric F1 values reach 95%, which is better than the deep learning models such as RNN, and CRNN.
SCALPEL: Exploring the Limits of Tag-enforced Compartmentalization

We present Secure Compartments Automatically Learned and Protected by Execution using Lightweight metadata (SCALPEL), a tool for automatically deriving compartmentalization policies and lowering them to a tagged architecture for hardware-accelerated enforcement. SCALPEL allows a designer to explore high-quality points in the privilege-reduction vs. performance overhead tradeoff space using analysis tools and a detailed knowledge of the target architecture to make best use of the available hardware. SCALPEL automatically implements hundreds of compartmentalization strategies across the privilege-performance tradeoff space, all without manual tagging or code restructuring. SCALPEL uses two novel optimizations for achieving highly performant policies: the first is an algorithm for packing policies into working sets of rules for favorable rule cache characteristics, and the second is a rule prefetching system that allows it to exploit the highly predictable nature of compartmentalization rules. To create policies, SCALPEL introduces a quantitative privilege metric (the Overprivilege Ratio) that is used to drive its algorithmic compartment generation. We implement SCALPEL on a FreeRTOS stack and target a tag-extended RISC-V core. Our results show that SCALPEL-created policies can reduce overprivilege by orders of magnitude with hundreds of logical compartments while imposing low overheads (&lt;5%).
Automatic Aspectual Classification of Chinese Sentences

The generalization and ability to predict unseen data is a criterion to evaluate a theory. This chapter will provide a set of experiments on the automatic classification of sentence event types. The classification has different levels. The first is a coarse-grained level of sentence types, namely speech act, modality and events. In the second level, the classification is done on the finer-grained categories, including discriminating different modalities, different speech acts and different event types. Finally, the experimental results will be discussed.
An agile-stage-gate model for medical startups: A case study of a medical product startup in Thailand

The agile manifesto and the agile-stage-gate model has been introduced as a product development process, widely used among many large well-established firms, and showed positive effects on those firms. However, less attention has been paid to whether or not this way of thinking and methodological processes can be fully applied to specific settings like medical startups. Some challenges such as implementation, resource allocation, working procedures, and organizational transformation could be different in shapes and forms and in intensity vis-à-vis medical startups. The study aims to cope with this deficiency by, firstly, reviewing previous empirical studies that presented process models, challenges, and positive effects of the agile manifesto and the agile-stage-gate model on well-established firms. Secondly, to investigate the agile application in startup settings, the researchers conducted a longitudinal case study by drawing on a successful medical product startup in Thailand that has engaged in the actual implementation of the agile manifesto and agile-stage-gate model in its product development. Thirdly, the study compared the results of implementing the agile in large firms and the medical startup case in this study to investigate similarities and differences in terms of challenges and effects. The study showed both correlations in the same and contrasting directions of the previous studies and the case study’s results. The findings of this study can be created and categorized into four aspects of applying the agile-stage-gate in a startup, including balance transformation, work process, person-job fit, and team collaboration. Additionally, the study revealed the relationship among these four aspects, which together form an agile model for medical startups that balance between the long-term objectives and short-term goals, the adaptability and predictability, and the flexibility and rigidity of the set objectives.
Towards Understanding Users’ Engagement and Enjoyment in Immersive Virtual Reality-Based Exercises

Exergaming has shown promising results for promoting user's wellbeing in terms of physical and cognitive benefits. Although non-immersive virtual reality-based games have proven to be useful for user's physical wellbeing, there is limited study in immersive virtual reality (iVR) for exergaming particularly in user's engagement and enjoyment. Furthermore, there is limited study in comparing user's engagement and enjoyment between iVR exercises and conventional exercises without using digital games. Hence, in this exploratory study, user's engagement and enjoyment in doing iVR exercises were explored through a survey study. The preliminary findings from a two-week survey study show that users were more engaged in iVR than conventional exercises, and a similar pattern can be found in user's enjoyment. The findings suggest that iVR exercises are potentially useful and enjoyable for users to engage in physical exercises, while they may be an alternative to conventional exercises. These preliminary findings have created opportunities for future research in iVR-based exercises for users.