Reviewerly/exHarmony huggingface dataset:
A Technique to Analyze a Cassandra Tombstone for Identification of Operations

High scalability, flexible storage of data, linear performance, and many other strong features made Cassandra database a popular database. The Cassandra is one of the column-based NoSQL databases. Cassandra performs faster write operations with an additional overhead of tombstone. Whenever logical delete operation is performed, it will result in a tombstone. This tombstone value is useful to update the other replica about the deletion. The major problem with tombstone is that it is not only generated with delete operation but there are other factors also which generate this tombstone. This paper is an effort to identify the key of deleted data and other reasons for tombstone generation. The technique is proposed for the same. This paper is the first attempt to work on Cassandra’s tombstone.
Feature Extraction for ContentBased Image Retrieval Using a PreTrained Deep Convolutional Neural Network

This thesis examines the performance of features, extracted from a pre-trained deep convolutional neural network, for content-based image retrieval in images of news articles. The industry constant ...
Implementation and Evaluation of Ate Pairings on Elliptic Curves with Embedding Degree 10 Applied Type-II All-One Polynomial Extension Field of Degree 5

Pairing-based cryptography has received much attention as an encryption method for realizing advanced cryptography such as ID-based encryption and searchable encryption. In a pairing-friendly elliptic curve of embedding degree 10, calculation cost of multiplication and squaring on extension fields of degree 5 are related to efficiency of pairing. Therefore, it is necessary to select an efficient multiplication and squaring algorithms on the extension field of degree 5. To apply these algorithms, the authors implement a pairing on an elliptic curve of embedding degree 10 with type-II all-one polynomial extension field (AOPF) of degree of 5 and optimal extension field (OEF) of degree of 5. As a result, the computational complexity of pairing with the expanded AOPF of degree 5 are almost 7.4% efficient than that of the expanded OEF of degree 5.
Batch norm with entropic regularization turns deterministic autoencoders into generative models

The variational autoencoder is a well defined deep generative model that utilizes an encoder-decoder framework where an encoding neural network outputs a non-deterministic code for reconstructing an input. The encoder achieves this by sampling from a distribution for every input, instead of outputting a deterministic code per input. The great advantage of this process is that it allows the use of the network as a generative model for sampling from the data distribution beyond provided samples for training. We show in this work that utilizing batch normalization as a source for non-determinism suffices to turn deterministic autoencoders into generative models on par with variational ones, so long as we add a suitable entropic regularization to the training objective.
Exhaustive hard triplet mining loss for Person Re-Identification

Person reidentification (Re-ID) is an important task in computer vision and has many applications in videobased surveillance. Recently, the triplet loss has been popular in the deep learning framework for person Re-ID. It is particularly important to note that the selection of hard triplets has significant influence on the performance of the learned deep model. However, the existing triplet losses only focus on some specific forms of hard triplets, thus leading to weaker generalization capability. To address this issue, we propose a novel variant of the triplet loss, named exhaustive hard triplet mining loss (EHTM), which is able to deal with various forms of hard triplets in a comprehensive manner. Moreover, the proposed loss comprises a term to facilitate distinguishing different identities by directly narrowing intraclass distances and indirectly enlarging interclass distances. We also provide an effective training strategy to further enhance model performance. Extensive experiments on several benchmark datasets show that our method outperforms state-of-the-art approaches by a large margin.
Elephant Flow Detection for Flow Table Reduction under Realistic Traffic Distributions

The majority of Internet traffic is caused by a relatively small number of flows (so-called elephant flows). This phenomenon can be exploited to facilitate traffic engineering: resource-costly individual flow forwarding entries can be created only for elephants, while serving mice over shortest paths. Although this idea already appeared as a part of proposed TE systems, it was not examined by itself. It remains unknown what extent of flow table occupancy and operations number reduction can be achieved, how to select thresholds or sampling rates to cover the desired fraction of traffic or how to detect elephants with low computational and memory overhead. In this paper, we use reproducible traffic models obtained from 30-day-long campus/residential trace covering 4 billion flows to answer these questions. The most important finding is that simple packet sampling performs surprisingly well on realistic traffic, reducing the number of flow entries by a factor up to 400 with the aim to cover 80% of the traffic. Its superb performance and negligible overhead questions the need for more sophisticated algorithms. We also provide an open-source software package allowing the replication of our experiments or the performing of similar evaluations for other algorithms or flow distributions.
LibGuides: History Unfolded Newspaper Sprint 2021: Overview of Project

Explains how to participate in the History Unfolded Newspaper Sprint at Snow College. This page provides an overview of the project.
Inout Secure DB: Maximizing Security for Data INside and OUTside the Database

As cloud services are becoming an alternative for IT infrastructures in many organizations, guarantees ofdata privacy become a priority. This article presents a secure database system that takes privacy as a design principle.The proposed system offers improved privacy guarantees for data in primary and secondary memory as well as for datathat is served to users as results of SQL queries. Data in working memory is protected using Intel’s SGX platform fortrusted execution, while data in secondary memory uses network coding for secure storage. SGX provides hardware-based processing privacy offering protection for a wide range of sophisticated attacks. Network coding provides interand intra-cloud privacy for stored data (by means of a storage provided by Chocolate Cloud). For privacy of data servedto the outside world, we propose a flexible role-based access control mechanism that anonymizes data at query-time.We have implemented a modular, multi-service architecture that is well suited to the advantages and limitations of theSGX platform. We present the architecture of the system, its components and performance evaluation.
Research on the Characteristics of “Humanization” in Physical Education of College Students in Hebei Province

Through the research of this subject, this paper discusses the education idea of “health first” and “people-oriented” in current physical education, and studies the common and individual characteristics of the research on the characteristics of “people-oriented” in physical education of college students in Hebei Province, so as to promote the development of physical education in Hebei Province towards the direction that is more conducive to the cultivation of healthy and confident talents in the new era.
Concurrent Quantum State Transfer and Random Channel Sniffing

Harnessing the interplay between polarization and temporal degrees of freedom, the quantum polarization state of a single photon can be transferred with high fidelity through a polarization-drifting channel whose stochastic parameters are concurrently estimated.
Developing Visual Collaborative Tools

Collaboration has been increasingly requiredto address the current challenges faced byorganizations. With digitalization, thesechallenges are more and more complex but havecommon characteristics: they concern theorganization as a whole, involve different andheterogeneous stakeholders, and evolve during theorganization’s lifetime. Moreover, they are at theheart of a paradox: they are of paramountimportance for companies, but they are verydifficult to grasp. Although practitioners havedeveloped very different definitions andperspectives, each challenge needs to becollectively addressed as the result of discussionand inquiry from different perspectives. Thesechallenges are, for instance, developinginnovative solutions to face rapidly changingenvironments, digitalizing processes, developingbusiness ecosystems, defining projects orinitiatives, fostering creativity, or designing andevaluating a new business model.Recently a “new” generation of tools hasappeared. These tools are commonly called“canvas” as they were initially inspired by theBusiness Model Canvas. In fact, we designate thisfamily of tools as visual inquiry tools or visualcollaborative tools. These tools have commonfeatures that allow diverse stakeholders that face ajoint problem to address the aforementionedchallenges:• First, developing a shared language andunderstanding of the problem they are trying tosolve.• Second, assisting diverse groups in exploringand/or brainstorming on a given problemthanks to their support for structuring andbounding the problem.• Third, supporting a less linear and morecreative and innovative process mainly relyingon design techniques as they allow a socialdesign process, which has been proven usefulto increase engagement within projects.Given the increasing amount and use of suchvisual inquiry tools, it seems crucial toaccumulate knowledge on how to develop andevaluate them. Research is needed both into thedesign processes of such tools and/or theirmodelling, as well as their ontological and/orcognitive foundations. For the second year, thishas been the main motivation to organize thismini-track at HICSS-53, as we believe that the ISdiscipline is well-suited to contribute to the designof such visual collaborative tools as it has a longtradition in design science research, modellingand UX.
Ontological Engineering For Source Code Generation

Source Code Generation (SCG) is the sub-domain of the Automatic Programming (AP) that helps programmers to program using high-level abstraction. Recently, many researchers investigated many techniques to access SCG. The problem is to use the appropriate technique to generate the source code due to its purposes and the inputs. This paper introduces a review and an analysis related SCG techniques. Moreover, comparisons are presented for: techniques mapping, Natural Language Processing (NLP), knowledgebase, ontology, Specification Configuration Template (SCT) model and deep learning.
Balancing Technology with Cybersecurity Needs in Kansas

Kansas Chief Information Technology Officer Lee Allen explains how, in addition to implementing agency-level security, he gives his CISO the autonomy he needs to protect the enterprise at large.
Generalized Canonical Correlation Based Bagging Ensembled Relevance Vector Machine Classifier for Software Quality Analysis

Early detection of defects helps to save cost and efforts. Few research works have been designed in existing works for analyzing the quality of software program using various machine learning techniques. However, the classification performance of existing work was lower which reduces the defect detection accuracy. In order to overcome the above existing issues, the Generalized Canonical Correlation Analysis based Bagging Ensembled Relevance Vector Machine Classification (GCCA-BERVMC) model is proposed. The GCCA-BERVMC model considers the number of source code lines from software program dataset as input. After that, the generalized Canonical Correlation Analysis (gCCA) algorithm designed in GCCA-BERVMC model selects the relevant features (i.e., code metrics) in order to improve the quality of software program. By applying Bagging Ensembled Relevance Vector Machine Classification (BERVMC) algorithm, the classification of program files is performed in GCCA-BERVMC model. The boosting algorithm creates ‘n’ number of weak learners to classify the input source code lines as normal or defected by analyzing the source codes and chosen metrics. After that, the weak learner’s results are combined into strong classifier by using the majority votes. This helps for GCCA-BERVMC model to enhance the accuracy of defect prediction for software quality analysis with a lower amount of time. Experimental evaluation of GCCA-BERVMC model is conducted using metrics such as defect detection accuracy, false positive rate, and time complexity with respect to various software code sizes. The experimental result shows that the GCCA-BERVMC model is able to increase the defect detection accuracy and also minimizes the amount of time required for software quality analysis when compared to state-of-the-art works.
Library: Access Services: Overview

The Access Services Department is responsible for checkout of library materials and equipment and for Interlibrary Loan.
Fractional Fourier transform method of chirped fringe pattern analysis for profilometry

A method is proposed to obtain the 3D profile of an object or the shape of a wavefront. The analysis of linearly chirped cosenoidal fringes is performed in the fractional Fourier domain.
Stable Learning via Self-supervised Invariant Risk Minimization

Empirical Risk Minimization based methods are based on the consistency hypothesis that all data samples are generated i.i.d. However, this hypothesis cannot hold in many real-world applications. Consequently, simply minimizing training loss can lead the model into recklessly absorbing all statistical correlations in the training dataset. It is why a well-trained model may perform unstably in different testing environments. Hence, learning a stable predictor that can simultaneously performs well in all testing environments is important for machine learning tasks. In this work, we study this problem from the perspective of Invariant Risk Minimization. Specifically, we propose a novel Self-supervised Invariant Risk Minimization method based on the fact that the real causality connections between features are consistent no matter how the environment changes. First, we propose a self-supervised invariant representation learning objective function, which aims to learn a stable representation of the consistent causality. Based on that, we further propose a stable predictor training algorithm. This algorithm aims to improve the predictor's stability using the invariant representation learned by using our proposed objective function. We conduct extensive experiments on both synthetic and real-world datasets to show that our proposal outperforms previous state-of-the-art stable learning methods. The code will be released later.
LibGuides: Research Using UB Special Collections & Archives: IDIS 101: First Year Seminar

This guide will explain to students new to research with primary sources how to locate, access, and use primary sources at University of Baltimore.
Variational Optimization of Informational Privacy.

The datasets containing sensitive information can’t be publicly shared as a privacy-risk posed by several types of attacks exists. The data perturbation approach uses a random noise adding mechanism to preserve privacy, however, results in distortion of useful data. There remains the challenge of studying and optimizing privacy-utility tradeoff especially in the case when statistical distributions of data are unknown. This study introduces a novel information theoretic framework for studying privacy-utility tradeoff suitable for multivariate data and for the cases with unknown statistical distributions. We consider an information theoretic approach of quantifying privacy-leakage by the mutual information between sensitive data and released data. At the core of privacy-preserving framework lies a variational Bayesian fuzzy model approximating the uncertain mapping between released noise added data and private data such that the model is employed for variational approximation of informational privacy. The suggested privacy-preserving framework consists of three components: 1) Optimal Noise Adding Mechanism; 2) Modeling of Uncertain Mapping Between Released Noise Added Data and Private Data; and 3) Variational Approximation of Information Privacy.
Abstractive Summarization of Spoken and Written Instructions with BERT.

Summarization of speech is a difficult problem due to the spontaneity of the flow, disfluencies, and other issues that are not usually encountered in written texts. Our work presents the first application of the BERTSum model to conversational language. We generate abstractive summaries of narrated instructional videos across a wide variety of topics, from gardening and cooking to software configuration and sports. In order to enrich the vocabulary, we use transfer learning and pretrain the model on a few large cross-domain datasets in both written and spoken English. We also do preprocessing of transcripts to restore sentence segmentation and punctuation in the output of an ASR system. The results are evaluated with ROUGE and Content-F1 scoring for the How2 and WikiHow datasets. We engage human judges to score a set of summaries randomly selected from a dataset curated from HowTo100M and YouTube. Based on blind evaluation, we achieve a level of textual fluency and utility close to that of summaries written by human content creators. The model beats current SOTA when applied to WikiHow articles that vary widely in style and topic, while showing no performance regression on the canonical CNN/DailyMail dataset. Due to the high generalizability of the model across different styles and domains, it has great potential to improve accessibility and discoverability of internet content. We envision this integrated as a feature in intelligent virtual assistants, enabling them to summarize both written and spoken instructional content upon request.
Musikla: Language for Generating Musical Events.

In this paper, we'll discuss a simple approach to integrating musical events, such as notes or chords, into a programming language. This means treating music sequences as a first class citizen. It will be possible to save those sequences into variables or play them right away, pass them into functions or apply operators on them (like transposing or repeating the sequence). Furthermore, instead of just allowing static sequences to be generated, we'll integrate a music keyboard system that easily allows the user to bind keys (or other kinds of events) to expressions. Finally, it is important to provide the user with multiple and extensible ways of outputing their music, such as synthesizing it into a file or directly into the speakers, or writing a MIDI or music sheet file. We'll structure this paper first with an analysis of the problem and its particular requirements. Then we will discuss the solution we developed to meet those requirements. Finally we'll analyze the result and discuss possible alternative routes we could've taken.
Using Attention-based Sequence-to-Sequence Neural Networks for Transcription of Historical Cipher Documents

Encrypted historical manuscripts (also called ciphers), containing encoded information, provides a useful resource for giving new insight into our history. Transcribing these manuscripts from image ...
Quality Assurance Standards of E-Learning and the Basis for Their Application in Preparation of Language Study Plans and the Science of Holy Qur’an

Ensuring the quality of E-Learning in the field of higher education requires in the first place aninstitutional vision accompanied by commitment, leadership, and proper planning, while ensuring that this isavailable to the partners. The E-Learning policy should conform to the overall vision of the institution andthe tasks and services they provide. Leaders and managers must be able to explain the reasons for choosingE-Learning as an appropriate learning strategy for the students who serve them. Online educationcomponents should include the quality components that were identified in the first part of this post.Additionally, organizations need to comply with the regulations governing online learning and ensure thatthey are reflected in policies and practices.This research also seeks to establish the rules and foundations ofspecialized education in the field of teaching Arabic to non-native speakers, and link them to Quran. Thereis no doubt that all scientific institutions aim to ensure the quality of the application of their academicstandards that set them to achieve what they want in the field of raising the level of targeted scientificseekers. Therefore, this paper raises a fundamental research problem, namely, ways to ensure scientificquality. This paper referred to a number of questions, including: What are the standards? What does qualitymean in the preparation of language teaching plans and Quranic sciences? And how to find academiccontent that combines between Arabic and Quranic sciences? What possibilities are there to ensure thatlearners are improved through quality standards? This paper discusses the concept of educational quality andits role in achieving the objectives of the educational process, addressing the role of scientific standards inthe field of the preparation of study plans, indicating the possibilities that must be available to help in the Solid State Technologysuccess of the implementation of study plans prepared to teach Arabic and Quranic sciences, taking thedescriptive analytical approach. Thus, this paper added a new dimension to the studies that preceded it inthis area, where it is to indicate the criteria required to achieve the objectives set by linking the teaching ofArabic language with the science of the Quran. The paper recommends further studies on ensuring thatquality standards are applied in the educational process.
SOCIAL MEDIA AND THE RIGHTS OF THE INDIVIDUAL: THE NIGERIAN PERSPECTIVE

The main focus of this paper is to examine to what extent the right to freedom of expression and opinion on the social media is protected in Nigeria. While applauding Nigeria's impressive record, the paper points out that the manifestation of hate speech cannot be ruled out in a climate of freedom. The solution is not in restraining the rights and freedoms of the citizenry but in safeguarding and championing them. This will ultimately make for a better governed society.
Committees: Proceedings of the International Conference of Computational Methods in Sciences and Engineering 2019 (ICCMSE-2019)

First Page
Relaxation Strategy for the Landweber Method in Light Field Reconstruction

An optimized relaxation strategy for the Landweber iterative method is proposed to reconstruct the light field based on the sparseness of the projection matrix. Experiments show its effectiveness using the BRISQUE index.