Reviewerly/exHarmony huggingface dataset:
An Advanced Machine Learning Framework for Cybersecurity

The world is turning out to be progressively digitalized raising security concerns and the urgent requirement for strong and propelled security innovations and procedures to battle the expanding complex nature of digital assaults. This paper examines how AI is being utilized in digital security in both resistance and offense exercises, remembering exchanges for digital attacks focused on AI models. Digital security is the assortment of approaches, systems, advancements, and procedures that work together to ensure the confidentiality, trustworthiness, and accessibility of processing assets, systems, programming projects, and information from attacks. Machine learning-based examination for cybersecurity is the following rising pattern in digital security, planned for mining security information to reveal progressed focused on digital threats and limiting the operational overheads of keeping up static relationship rules. In this paper, we are mainly focusing on the detection and diagnosis of various cyber threats based on machine learning.
Research Guides: Identity Theft: Locating videos

Guide for resources on Identity Theft which will provide an overview of Stetson library resources on the topic.
LibGuides: COVID-19 Educational Resources for home use: Connecting to the World

These are curated resources for the School Librarian to utilize as they see fit. Site went live on March 23, 2020.
Visuospatial working memory task-performance strategies and their representational modalities: a neurophenomenological study.

Traditionally, researchers in cognitive science consider intersubject variability (i.e.,differences among individuals performing the same psychological task) to be noise in the data.Contemporary approaches, however, are calling for intersubject variability to be understood asits own source of data that is to be understood and not merely removed. Such approachesprimarily rely on discovering different cognitive strategies and cognitive styles withinintersubject variability. In the present master’s thesis, the possibility that at least a part ofintersubject variability is related to differences in experience during task performance isaddressed. In order to test this possibility, a neurophenomenological study has been performed.The co-researchers who performed a change-detection task were prompted at a random momentto report on their experience. This report took the form of a detailed empirical phenomenologicalinterview. The gathered qualitative data has been analysed in accordance with the constructivistgrounded theory approach. It has been discovered that not only do individuals use differentstrategies to solve the same working memory task, but also that the task-performance isaccompanied by a wealth of parallel and background feelings. It has been conjectured that thereis a possibility that holistic descriptions of attitudes and dispositions towards the task –accessible on the level of experience as background feelings – are a phenomenon that should beexplored in future neuroimaging studies of working memory.
CCS Library: Video Resources: Circulating Videos

Information on physical and streaming movies, videos, and films, as well as links to Open Access streaming sites
Reader: Speech Synthesizer and Speech Recognizer

The text-to-speech and speech-to-text functionalities have become an integral part of our lives in this digital era. This paper proposes a system that provides a way to generate audio from text and listen to the user’s microphone and convert speech-to-text. This system shall be implemented with various user-friendly features. The target audience of this system is people with disabilities like dyslexia, reading challenges, or visual impairment which results in difficulty in reading or writing. This system can aid them and help them use technology. Its simplicity and ease of use make it stand apart from such existing systems.
Kick-Off Meeting of the Working Group Sabotage, Intrusions, Attacks took place on 15 November 2017 – UIC Communications

The Working Group (WG) Sabotage, Intrusions, Attacks is the new topic of the UIC Security Platform WG previously dedicated to metal thefts, according to a decision of the steering committee of…
Grey Wolf Optimizer - Modifications and Applications

In this chapter, we give a brief description of the grey wolf optimization algorithm (GWO) and its modifications. Then we test it on five engineering optimization problems. We modify the standard GWO by invoking for chaotic maps. The modified GWO algorithm is called Chaotic Grey Wolf Optimization (CGWO) algorithm. Finally, we solve five engineering optimization problems by CGWO algorithm. Also, we verify the efficiency of the proposed algorithm by comparing it with the standard GWO.
LibGuides: Fluid Power: Periodicals & Article Databases

Research resources, tips, and support from SCC librarians Pint and e-periodicals, recommended article databases, & periodicals free online
Generating Automobile Images Dynamically from Text Description

Synthesis of a realistic image from matching visual descriptions provided in the textual format is a challenge that has attracted attention in the recent research community in the field of artificial intelligence. Generation of the image from given text input is a problem, where given a text input, an image which matches text description must be generated. However, a relatively new class of convolutional neural networks referred to as generative adversarial networks (GANs) has provided compelling results in understanding textual features and generating high-resolution images. In this work, the main aim is to generate an automobile image from the given text input using generative adversarial networks and manipulate automobile colour using text-adaptive discriminator. This work involves creating a detailed text description of each image of a car to train the GAN model to produce images.
Students’ Perceptions of Code Switching Used By English Teachers (A Descriptive Study on the Second Semester Students of English Education Department of The State Islamic University Sultan Maulana Hasanuddin Banten).

NIDAUL HAYATI. 152301777. 2019. Students’ Perceptions of Code Switching Used By English Teachers (A Descriptive Study on the Second Semester Students of English Education Department of The State Islamic University Sultan Maulana Hasanuddin Banten).Code switching is a common linguistic phenomenon that exists in bilingual societies like in Indonesia. It also happened in English education. Therefore, this research aims to investigate the functions of teachers’ code switching in the classroom and the students’ perceptions of code switching used by English teachers. This research was conducted through descriptive qualitative research method. The researcher decided to choose two English teachers and 50 students on second semester as respondents. In collecting the data, the researcher used observation, questionnaire and interview. The findings from the data analysis indicated that the functions of code switching used by English teachers were found for three functions: topic switch, affective function, and repetitive function. In the process of code switching, the teachers practiced Indonesian and English language in order to help the students in understanding materials. The students’ perceptions of code switching used by English teachers in this study found that there were 25.3% answered strongly agree on teachers’ code switching in classroom, 59.3% answered agree, 14% answered disagree and 1.4% from the total response of the respondents answered strongly disagree on that practiced. In addition, based on the interpretation result score was 78%. It can be said that most students have positive perception on code switching used by English teachers. They agreed if teaching learning process in bilingual, the students support the teacher using code switching in the teaching learning process. Keyword: Code Switching, English Teachers, Students’ Perceptions
Adoption of Blockchain for Sustainable Supply Chains: An Identification of Benefits, Challenges, and Risks

Supply chain management (SCM) has become more challenging than ever, considering the effect of globalization on organizations as operating through transnational business networks. Conventional supply chains have been facing struggles such as lack of advanced technologies and traceability in operation, which then leads to operational inefficiencies. Blockchain (BC) technology offers a set of features that supply chains can leverage, including transparency, data reliability, security, auditability, and automation. Existing pressures from governments, communities, and consumers as drivers of reaching sustainability goals direct us to further evaluate the use of BC as a facilitator for sustainable supply chain management (SSCM). However, there are also caveats associated with the adoption of BC technology. This thesis aims to evaluate the capabilities of BC technology in the context of SSCM. The authors construct a framework consisting of mainstream BC features, the three pillars of SSCM, together with challenges and risks. The findings of this study reflect that BC is promising to benefit the sustainability aspect of SCM through increasing transparency, security and integrity of data, and automation capability by smart executions. Nonetheless, there are also issues to reckon with, such as the increasing need for awareness and technical competence, organizational collaboration, and effective change management. (Less)
Expanding Input Vocabulary Using Index Finger on and Above Back of Smartphone

This paper presents a method to expand input vocabulary for smartphones. To this end, our method uses index finger gestures on and above the back of a smartphone. The input vocabulary of our method is in accordance with index finger gestures such as touching and bending/stretching. To enable the detection of these gestures, we built a system composed of the smartphone and ring-like device. In pilot studies using our system, the touching of the back of a smartphone was detected with an accuracy of 94.3% on average.
Certain and Consistent Domain Adaptation

Unsupervised domain adaptation algorithms seek to transfer knowledge from labeled source datasets in order to predict the labels for target datasets in the presence of domain-shift. In this paper we propose the Certain and Consistent Domain Adaptation (CCDA) model for unsupervised domain adaptation. The CCDA aligns the source and target domains using adversarial training and reduces the domain adaptation problem to a semi supervised learning (SSL) problem. We estimate the target labels using consistency regularization and entropy minimization on the domain-aligned target samples whose predictions are consistent across multiple stochastic perturbations. We evaluate the CCDA on benchmark datasets and demonstrate that it outperforms competitive baselines from domain adaptation literature.
Parallelization Of RSA Algorithm Using OpenMP

In the field of Cryptography, public key algorithms satisfy all the four requirements of security: Confidentiality, Integrity, Non -repudiation, Authentication as compared to the conventional Crypto system. . RSA and RSA-based digital signature algorithms are some of the most popularly used public-key infrastructure cryptographic algorithms with their roots in the modular arithmetic. Public key Cryptography is widely known to be slower than symmetric key alternatives for massive computations of modular arithmetic. The modular arithmetic and modular exponentiation makes RSA computationally expensive when compared to symmetric algorithms. Therefore, to make a more efficient, secure and faster implementation of RSA Algorithms is publicly concerned. With the development of the OpenMP , more computing problems are solved by using the CPU(Central Processing Unit) based implementation.
Applications of Hierarchical Bayesian Cognitive Modeling

Author(s): Kupitz, Colin Nicholas | Advisor(s): Vandekerckhove, Joachim | Abstract: The hierarchical Bayesian approach to cognitive modeling often provides a quality of inference that cannot be matched with other analytical methods. In addition, the general approach is quite flexible, and can be utilized to great effect in many analytical settings. I illustrate these qualities in two applications of hierarchical Bayesian cognitive models. In the first application, I revisit a transfer-of-training study. First, I discuss a hierarchical cognitive model that describes the transfer-of-training data. I then illustrate how that hierarchical cognitive model can be further extended in order to create a cognitive latent variable model. Critically, this cognitive latent variable model directly models the latent effects of training and transfer on the cognitive parameters that drive participant behavior. I then provide an in depth analysis to illustrate how this cognitive latent variable model provides a quality of inference that far surpasses more standard analytical approaches. In the second application, I perform a cognitive meta-analysis on the spatial congruency bias literature. To do this, I extend the hierarchical Bayesian cognitive model into an integrative data analysis, creating a Model-based Integrative Data AnalysiS (MIDAS). Using this model, I create a model that is capable of simultaneously estimating cognitive effects at the individual, within-experiment, and between-experiment levels, which is what allows us to estimate cognitive effects in a meta-analytical setting.
Video Saliency Detection using Deep Learning

A deep learning model for video saliency detection is proposed and trained. The neural network architecture combines recent innovations in the field: A twostream approach merges two separate input ...
Research Guides: Online Library Resources: Library Account (Circulation) and Tech Help

Get in touch with library staff regarding circulation issues such as checkouts, overdue fees, library holds, and tech help.
Emotion Scanning of the World’s Best Colleges Using Real-Time Tweets

With the advent of technology and the popularity of social media, people have started sharing their points of view with the masses. Views of the people matter a lot to analyze the effect of dissemination of information in a large network such as Twitter. Emotion analysis of the tweets helps to determine the polarity and inclination of the vast majority toward a specific topic, issue, or entity. During elections, film promotions, brand endorsements/advertisements, and in many other areas, the applications of such research can be easily observed these days. The paper proposes performing the emotion scanning of people’s opinions on the three top colleges and universities of the world according to various surveys and indices (Harvard, MIT, and Stanford) using real-time Twitter data. Also, a comparison has been drawn between the accuracies of a few machine learning techniques used, for instance, K-nearest neighbor (KNN), support vector machines (SVM), and Naïve Bayes.
Implementation of Integer Factorization Algorithm with Pisano Period

The problem of factorization of large integers into the prime factors has always been of mathematical interest for centuries. In this paper, starting with a historical overview of integer factorization algorithms, the study is extended to some recent developments in the prime factorization with Pisano period. To reduce the computational complexity of Fibonacci number modulo operation, the fast Fibonacci modulo algorithm has been used. To find the Pisano periods of large integers, a stochastic algorithm is adopted. The Pisano period factorization method has been proved slightly better than the recently developed algorithms such as quadratic sieve method and the elliptic curve method. This paper ideates new insights in the area of integer factorization problems.
Computer Aided Diagnosis of Breast Nodule Detection and Mammogram Enhancement Using Fuzzy Sets

Breast cancer is widely used in many medical fields. The tumor pandemic drastically influences the health and well-being of the global population. In this proposed methodology, Fuzzy K-Nearest Neighbor Equality approach is performed. The extracted image of Breast cancer cannot be directly used for diagnosis. The captured image contains disturbances like noise, blurred image etc. To get a high-quality image from extracted panoramic. This approach is performed. To the extracted images, partitioning is performed to split up the images into samples. It helps for better recognition and classification. It shows the infected region of Breast cancer accurately. Before performing partitioning, the extracted image has to be preprocessed to clear out the disturbances in the image. After partitioning, feature extraction is performed by using GLCM and finally, classification is performed between the trained and test set data to produce a highly accurate image. It is done by using Fuzzy K-Nearest Neighbor Equality algorithm classifier. This processed image helps the dentist for good prediction. The objective of this work to improve the effectiveness and efficiency of the classification process. In this way to reduce the FP (false positive) rate. The proposed methodology accurately separating the benign and malignant region accuracy.
화법 교실에서의 지식 교수·학습에 대한 근거 이론적 접근 - ‘대화의 원리’에 대한 예비 국어교사의 문제 인식을 중심으로 -

This study applied a paradigm model based on the methodology of grounded theory of Strauss and Corbin (1990), analyzed the pre-service Korean language arts teachers'' problem recognition on the principles of conversation in an educational context, and suggested the teaching and learning direction of speech knowledge.Educational implications for teaching and learning speech knowledge derived from 48 participants'' data are as follows: first, speech knowledge should be identified in conjunction with practical activities; second, the concept of speech should be perceived in a balanced manner, including the addressee''s judgment and comprehension (i.e., listening), away from what was defined as merely the addresser''s regulatory compliance and expression (i.e., speaking); and third, conditional knowledge should be developed to manage prepositional and procedural knowledge and help learners'' metacognition.This study is meaningful in that it considers both the internal and external factors in a balanced manner and approaches speech knowledge as a socio-scientific issue.
Social Spider Optimization Modifications and Applications

In this chapter, we give a brief description of the social spider optimization (SSO) algorithm and its modifications. Then we test the modified SSO algorithm on one of the real-life applications, namely, economic load dispatch problem. The modified SSO algorithm is called Chaotic Social Spider Optimization algorithm (CSSO). Finally, we apply the CSSO algorithm for solving the economic load dispatch problem. Also, we compare the CSSO algorithm with the standard SSO algorithm to verify its efficiency.
VR in Workplace Meetings: Learning from Social VR in “The Wild”

ABSTRACT Recent research on commercial and artistic social VR points to emerging forms of social augmentation that could have lessons for remote collaboration in the workplace. HCI has long demonstrated opportunities for supporting meetings through various forms of technological mediation. Likewise, research on VR has explored social augmentation as a form of perceptual manipulation. However, emerging design approaches and social practices in the social VR sector point to ways that XR could support new kinds of communication affordances in remote meeting contexts. We introduce the notion of creating ‘social affordances’, conceived of at the interpersonal level of analysis of the social experience itself, to shape workplace meeting social dynamics and engagement. We present examples from existing social VR, and outline a research agenda aimed at developing workplace-focused social affordances in VR. We conclude with a set of research questions that we aim to address with this work. Keywords virtual reality, social VR, meetings, social affordances, social augmentation Joshua McVeigh-Schultz San Francisco State University jmcvs@sfsu.edu Katherine Isbister University of California Santa Cruz katherine.isbister@ucsc.edu New Future of Work 2020, August 3–5, 2020 © 2020 Copyright held by the owner/author(s).
A Lightweight Effective Randomized Caesar Cipher Algorithm for Security of Data

In any kind of organization in the present scenario, raw data or meaningful data needs to be shared among different personnel; therefore, the chances of frauds or treachery are more that creates vulnerability in the working environment. Therefore, the protection of organizations’ confidential and sensitive data among different levels of employees against these kinds of theft or illegal activity that violates the company security policy is prerequisite. ABE pronounced as attribute-based encryption was introduced. ABE is a parameter that plays a vital role in providing access control in a fine-grained manner for outsourcing data in a data sharing system. Moreover, CP-ABE was introduced that defines an access policy in order to cover all the attributes within the system. In this scheme, for encryption and decryption purposes, the user’s private key is accompanied to the group of attributes. but due to its lack of efficiency and several other paradigms, it was proved ineffective. Hence, in order to overcome the existing issues, we have implemented a new cryptographic algorithm which is competent enough to encrypt and decrypt any type of file that contains any kind of data written in the range of ASCII values. We named this algorithm as randomized Caesar cipher algorithm.
Real-time mapping of periodic dance movements to control tempo in electronic dance music

Dancing in beat to the music of one's favorite DJ leads oftentimes to a powerful and euphoric  experience. In this study we investigate the effect of putting a dancer in control of music playback t ...
Research Guides: Creative Commons Licenses: Using Creative Commons to Declare Public Domain

This guide provides information on licensing your materials using Creative Commons Licenses and using items that are so licensed.
Writing for an Academic Journal - Beyond A Published Research Record

Writing can come in many forms. A handwritten diary, for example, can have sentimental value to its owner especially after being kept for so many years. Writing a book, on the other hand, brings a certain kind of gratification to the author, where he can reach out to threaders and be able to tell stories or ways of doing things (Smith, 2013). industry practitioners writing scholarly articles can provide different perspectives to those found in university; which in turn, can help nurture the next batch of practitioners. Producing scholarly articles to be published in the relevant journals will also make a person stronger in knowledge terms and this will certainly benefit the company as we usher in the 4th Industrial Revolution (IR 4.0).