Reviewerly/exHarmony huggingface dataset:
A General Framework for Matching Pattern Hiding in Deep Packet Inspection

Recently proposed proposals, such as BlindBox (SIGCOMM 2015), PrivDPI (CCS 2019) and Pine (ESORICS 2020), enable privacy-preserving deep packet inspection (DPI) on the encrypted traffic. Despite that they protect traffic privacy and/or rule privacy against enterprises and the third-party middleboxes, they might not be really satisfactory, due to the leakage of the matching pattern. The matching pattern refers to the matched token-rule pairs during the process of inspection, which can be learnt by the middleboxes, leading to the privacy leakage concerns. Our work aims to hide the matching pattern, thereby enhancing the privacy on top of the mentioned proposals. Specifically, we propose a general framework for matching pattern hiding in DPI and construct a concrete scheme by resorting to the DDH-based private set intersection cardinality technique under the proposed framework. Besides, we implement the constructed scheme, and conduct extensive evaluations which demonstrate the practical performance of the scheme.
The Challenges of Cyber Security and Impact on Latest Technologies - A Study to Prevent Cyber Attacks

In the sphere of information technology, cyber security plays a critical role. In today's world, protecting information has become one of the most difficult tasks. When we think of cyber security, the first thing that comes to mind is cybercrimes, which are on the rise at an alarming rate. Various governments and businesses are taking a variety of steps to combat cybercrime. Despite many steps, cyber security remains a major issue for many people. This paper focuses on the issues that cyber security faces in the modern era. It also covers the most up-to-date information on cyber security tactics, ethics, and trends that are transforming the face of cyber security.
Mental Maps as a Creative Tool of Marketing Analysis in Education

Marketing research in education involves new techniques to overcome the mismatch between the unconscious and emotional perception of a brand and verbal-centered survey approaches to consumer studies. The article illustrates the use of non-verbal marketing research technique in order to create a mental map describing the perception of an educational institution and giving an additional insight into students’ knowledge structures. The research included two stages in order to obtain data by means of ZMET and the mental maps’ construction. The images obtained by ZMET allow to clarify the unconscious perception of an educational organization and its brand. The images a result of ZMET study can be used to create mental maps to depict the interrelated concepts or constructions of a respondent. These deep images can be directly translated into the marketing communications and branding strategies of the educational institution. The dominant metaphor for the image of an educational institution can be identified, and the subconscious negative associations can be probed to work with. Based on ZMET, the mental maps of perception of the university, higher school, specialty, as well as a map of negative images associated with learning were constructed. Mental mapping enables the educators and marketers to aggregate data in diagrams to visualize and systematize images, thoughts and associations to provide creative and tailored marketing and communication strategy.
UNI-CAPTCHA: A Novel Robust and Dynamic User-Non-Interaction CAPTCHA Model Based on Hybrid biLSTM+Softmax

The security of web applications is protected by firewalls, intrusion detection systems or deep learning-based approaches. Although existing systems perform rules and content-based filtering, they can be bypassed with payloads and advanced bots in different scenarios. CAPTCHA is preferred to prevent bots in web applications to minimize possible risks. Although captcha applications make bot-user distinction, it can be solved with software-based systems. In addition, increasing the difficulty level of CAPTCHA schemes have bringing usage difficulties. In this study, a robust behavior-based CAPTCHA (UNI-CAPTCHA) was developed that detects user-bot without interaction with user. A web application with a landing page, login page, and register page has been developed for UNI-CAPTCHA to learn user and bot behavior. The application was tested with 16 different vulnerability tool bots and real users, creating a unique dataset containing 13 different behaviors. A risk rating was made using the k-Means++ algorithm based on characteristics of user behavior in the dataset. The trained hybrid bi-LSTM + Softmax based UNI-CAPTCHA engine intuitively performs user-bot labeling of requests from the web application simultaneously. It also determines the risk rating of user labels from 1 to 5. The threshold user value (εt) for high protection is determined in the evaluation, and results below εt, its value is evaluated as bot behavior and prevented. In UNI-Captcha, the threshold value of suspicious user behavior is adjusted according to the in-app security risk level. Analysis shows that the UNI-CAPTCHA engine shows better stability, speed, and detection than traditional bot detection and CAPTCHA applications.
Filtering algorithm in 3D reconstruction of wheel tread based on moire profilometry

The train will inevitably be damaged in the long-term running process, and there is a big security risk, so it is very necessary to inspect the train regularly. The wheel is an important part of the train, and the wear and defects of the wheel tread are directly related to the safety of the train, so the wheel tread testing is the key link of the train testing. In this paper, a three-dimensional profile reconstruction method of wheel tread based on moire profilometry (MP) is proposed. The digital grating patterns generated by computer will be projected onto the surface of the wheel tread, and the moire fringe patterns of the wheel tread can be obtained by frequency domain filtering. By processing moire fringe patterns, the wrapped phase will be acquired. Finally, the reconstruction of wheel tread profile can be realized after phase unwrapping, which provides a new method for wheel tread testing. At the same time, in the frequency domain filtering part of moire profilometry, the effects of different filtering algorithms on the reconstruction results are compared and analyzed in simulations and experiments, which proves the validity and feasibility of the measurement method in this paper.
Shift of pairwise similarities for data clustering

Several clustering methods (e.g., Normalized Cut and Ratio Cut) divide the Min Cut cost function by a cluster dependent factor (e.g., the size or the degree of the clusters), in order to yield a more balanced partitioning. We, instead, investigate adding such regularizations to the original cost function. We first consider the case where the regularization term is the sum of the squared size of the clusters, and then generalize it to adaptive regularization of the pairwise similarities. This leads to shifting (adaptively) the pairwise similarities which might make some of them negative. We then study the connection of this method to Correlation Clustering and then propose an efficient local search optimization algorithm with fast theoretical convergence rate to solve the new clustering problem. In the following, we investigate the shift of pairwise similarities on some common clustering methods, and finally, we demonstrate the superior performance of the method by extensive experiments on different datasets.
ON DEMAND: Working with the Digital Scholarship Center to Promote Data Literacy

What resources are available to Mason faculty who are interested in pursuing digital scholarship and teaching data literacy? Our video will discuss what the Digital Scholarship Center (DiSC) does and how we can facilitate your research. DiSC partners with students, faculty, and staff by providing research support to facilitate digital research and teaching across the university in all disciplines. We work with researchers in the areas of finding, accessing, and utilizing data; GIS and geospatial data; digital humanities; research data management; and software and technology. We provide individual consultations, workshops, and classroom instruction sessions. We are located on the second floor of Fenwick Library, where we have a computer lab equipped with Mac and PC computers with specialized software for digital scholarship, along with other hardware. We are a member of the Carpentries organization, and we have taught workshops on foundational coding and data science skills to Mason students and faculty. During the COVID-19 pandemic, almost all of our services continue to be available to the Mason community, albeit in a virtual format. Our video will also walk viewers through the DiSC website, and how to contact us. We are here to help and look forward to assisting GMU faculty with their digital scholarship.
Constructions of Iterative Near-MDS Matrices with the Lowest XOR Count

Compared with maximum distance separable (MDS) matrices, Near-MDS matrices which have sub-optimal branch numbers provide better trade-offs between security and efficiency when used in lightweight cryptography as a diffusion layer. In this paper, we construct some iterative Near-MDS matrices that can be used to design lightweight linear diffusion layers. Firstly, we identify the lower bound of the cost for \(4 \times 4\) iterative Near-MDS block matrices is 1 XOR gate, and the corresponding lower bound of iterations is also provided. Moreover, in order to make some trade-offs between the Near-MDS order and area, we also explore some other iterative Near-MDS matrices with lower iterations and prove the bounds of the optimal solutions. Finally, we prove the lower bound of the cost for \(5 \times 5\) iterative Near-MDS block matrices is also 1, and meanwhile, we also give some other improved results. To our knowledge, this is the first time to construct iterative Near-MDS matrices. This paper presents quite a few sparse diffusion layers that may retain very strong security guarantees in combination with well-chosen ShiftRows.
What The Future Upholds!

As the world is changing with better infrastructure, fiber, clouds, networks, data centers have become a source of providing information around the globe, providing us one of the biggest businesses, with a large amount of spending done over them. By the end of 2021, “Global Information Technology” is calculated to be spending $3.8 trillion over the companies shifting to the cloud. [1] The various networks of the software, hardware, infrastructure, databases: all are highly developed for making the IT to boom up for the incoming eras. Arthur C Clarke, once said, “any sufficiently advanced technology is indistinguishable from magic.” Everything going to the advanced level, the shared amount of data relies on fast connectivity. There is a lot of talking going on the capabilities provided by the 5G from past few years, which is finally out with its first batch of 5G handsets have been announced hitting the market soon.[2]The market opportunities of various companies are as bigger as they have ever been. Organizations such as DARPA, IBM, Shipchain, Paxful, Circle [3] are few of the companies working on providing number of applications to the technological world. Companies such as Honeywell, Microsoft, AWS, Spunk, Google are actively working in order to update the computing capabilities which we are accessing upon by providing us with quantum world. [4]Quantum computing provides us a redefine form of what the computer is all about. It gives us the computing power which is billions of times more renowned and powerful than the computing environment we are existing in today. Subdermal technologies are overpowered with new rulings neuro link. In today’s world, people working on completely virtual spaces with power assist fabrics (Digital fabrics) [5]. It provides human the evolution of added strength and gaining mobility to another level. United Nations have developed a universal translator, which in turn reduces the requirement of human interpreters and translators
The Development of Creative Thinking in Engineering Students Through Web-related Language Learning

This article is devoted to the use of special techniques for the development of creative thinking with engineering students using foreign language acquisition as a tool. The process was exacerbated with the COVID pandemic in 2020 through distance language learning. The authors described the use of “linguistic surfing” method as one of the tasks enhancing non-standard thinking while learning the professional vocabulary in the foreign language in a creative way. The results were analyzed basing on the testing interview and opinion survey comparing two groups of students: those who used the suggested educational technique at the process of online education and the students, who studied online without the above mentioned web-related “linguistic surfing” method. The analysis of the received results of the interview showed the efficiency of using of this technique especially for professional vocabulary mastering at the digital environment educational process. The opinion survey demonstrated that the percentage of the students with positive attitude to online transition was higher among those who studied online with the help of the “linguistic surfing” learning technique. The research proved the suggested technique to be effective in enhancing the creative thinking among technical students through web-related task aimed at vocabulary acquisition within the conditions of forced distance format of education.
Online Shopping Preferences Visualization System Using Web Content Mining

Recently, social media sites have been attracting people to start and operate online business. It has been gaining more attention especially during this COVID-19 pandemic, where most activities are conducted via online platform. For instance, Instagram is one of the social media platform where people share and post their pictures and videos on their account, but some people take this opportunity to promote their business and shop online via features provided by Instagram. Based on observation, there is no specific function that visualizes the information of Instagram's business accounts to help people in gaining information and making decisions during shopping process. Thus, a system that specifically visualizes Instagram's extracted data was developed using Web Content Mining technique with the assistance of hashtags as data-points and Phantombuster API. Tableau software integrated with JavaScript library is used as a visualization tool to display the users shopping activities. The result of the extracted data was visualized in the form of Bubble Chart, Bar Chart, and Multiple Bar Chart that were placed in different dashboards. The results from the obtained functional testing and users' feedbacks have indicated 64.3% of the respondents agreed that the proposed system helps users in making the right choice during online shopping. However, this system has some limitation that could be further enhanced such as updating the information automatically in real time without using any manpower and to increase the interactivity between system and user instead of using static visualization graphs.
Large-Scale Information Extraction under Privacy-Aware Constraints

In this digital age, people spend a significant portion of their lives online and this has led to an explosion of personal data from users and their activities. Typically, this data is private and nobody else, except the user, is allowed to look at it. This poses interesting and complex challenges from scalable information extraction point of view: extracting information under privacy aware constraints where there is little data to learn from but need highly accurate models to run on large amount of data across different users. Anonymization of data is typically used to convert private data into publicly accessible data. But this may not always be feasible and may require complex differential privacy guarantees in order to be safe from any potential negative consequences. Other techniques involve building models on a small amount of seen (eyes-on) data and a large amount of unseen (eyes-off) data. In this tutorial, we use emails as representative private data to explain the concepts of scalable IE under privacy-aware constraints.
Intuitionistic Fuzziness, Standard and Extended Modality

Ever since the first publication over intuitionistic fuzziness, the two standard modal operators “necessity” ( ) and “possibility” ( ) are introduced over intuitionistic fuzzy sets. In the years, these two operators are object of a lot of extensions and modifications. In the present paper, a new direction of two modifications and two extensions of the two standard modal operators is discussed. We introduce six new operators and study their properties.
A Cloud Portal for Consumer’s Needs in the Cloud Context

Facing to many Cloud providers’ offers in the Cloud Computing market, the consumer is confused in choosing the appropriate Cloud. Therefore, we propose a Cloud Portal, which helps this consumer to choose the adequate Cloud provider according to his needs. This portal is based primarily on customer needs on one side, on the other side on concepts and techniques like the Multi-criteria AnalysisMethod, the Weighted K-Nearest Neighbor Method. Using this solution avoids the consumer to lose time, money and help him to select the right Cloud. Finally, the proposed process to build the portal is illustrated by using a case study and demonstrates how it works.
Improved Word Sense Determination in Malayalam using Latent Dirichlet Allocation and Semantic Features

Recent years have witnessed phenomenal developments worldwide in the field of NLP . But developments in Indian regional languages are very few compared to them. This work is a step towards the construction of a target word sense disambiguation system in Malayalam, which is the regional language of the state of Kerala, India. Word Sense Disambiguation/Determination refers to the task of correctly identifying the sense of an ambiguous word from its context. This is considered an AI-Complete problem in the field of Natural Language Processing . For this purpose, an exclusive corpus of 1,147 contexts of target ambiguous words has been created, which to the best of our knowledge is the first attempt in Malayalam. This work describes how the performance of an unsupervised LDA-based approach towards WSD could be improved using semantic features like synonyms and co-occurrence information.
PSYCHOLOGICAL ASPECTS OF LEADERSHIP QUALITY FORMATION IN PROFESSIONAL TRAINING OF FUTURE LEADING OFFICERS

The article considers leadership as an internal state of personality, which is aimed at success, deeply motivated, leads an active life, confident in their decisions and actions. It is established that the leader provides the functions of integration and purposefulness of the team and acts on the principle of direct participation in its life. The need to take into account the peculiarities of the social environment, the specifics of professional activity and the challenges facing the specialist is substantiated in the question of formulation of leadership qualities. Thus, the use of case technology makes it possible to significantly strengthen the system of training officer leaders by integrating theoretical training with professional and practical and research activities, the use of appropriate content (leadership experience), interactive development of professionally important leadership qualities for military leaders. Purposeful formation of leadership qualities in the process of professional training of cadets of higher education institutions (higher military educational institutions) not only ensures the successful exercise of power, but also becomes the basis of socio-psychological impact on the environment and servicemen in the future. It is proved that the process of formation of leadership qualities in cadets of higher education institutions is defined as a set of pedagogical situations that contribute not only to the successful development of leadership qualities, but also to the general self-improvement of the individual. Understanding of scientific sources allows us to state that the leadership qualities of the personality of the future officer-leader are the traits that provide the ability to stand out in a particular case and make responsible decisions in significant situations; use innovative approaches to solve the problem; successfully influence followers to achieve common goals; to create a favourable sociopsychological climate in the military.
Research of Online Course Quality Evaluation System by AHP Entropy Algorithm and Weight Coefficient

In order to study the teaching quality of online Chinese courses for international students, the teaching quality evaluation indexes were constructed from two aspects: expert rating and student evaluation, by collecting the data samples of class teaching evaluation of international students from a university. In order to deeply mine the characteristic information in the data, considering the influence of subjective decision-making and objective data of evaluation indexes on teaching quality evaluation, the analytic hierarchy process (AHP) and entropy weight method are adopted to determine the weight coefficient of indexes, and the weight of each index is determined by combining the two methods. Through the analysis of the teaching quality of different courses in the online Chinese course for foreign students, the conclusion shows that the overall teaching quality score of H01 course is the highest, and that of H33 course is the lowest. It is expected that the results will provide some suggestions for curriculum teaching reform, strengthen teachers' attention to the evaluation of teaching quality, perfect the evaluation system of teaching quality in colleges and universities, optimize the evaluation mechanism of teaching quality in colleges and universities, and provide some analysis methods and research ideas for the evaluation of teaching quality in colleges and universities.
Research on image recognition technology based on machine learning in the context of big data

This essay discusses the important role of deep learning technology in optimizing the mobile visual search function of digital library. On this basis, it analyzes the necessity and feasibility of constructing mobile visual search mechanism based on deep learning technology, and expounds the application of deep neural network to construct mobile visual search. The main content of the visual search mechanism, including mobile visual big data input layer, mobile visual resource organization layer, deep neural network analysis layer, and mobile visual service interaction layer, etc., are designed to enhance the value of mobile visual search in a big data environment and promote big data The realization of personalized intelligent services of the Times Digital Library.
Toward Predicting User Waist Location From VR Headset and Controllers Through Machine Learning

Commercial VR Headsets typically include a headset and two motion controllers. From this VR setup, we have access to the user’s head and hands, but lack information about other parts of the user’s body without using additional equipment. Accurate position of other body parts such as the waist would expand the user’s interaction space. In this paper, we describe our efforts at using machine learning to predict the position and rotation of the user’s waist using only the headset and two motion controllers with an additional tracker at the waist for training.