Reviewerly/exHarmony huggingface dataset:
Standing Posture Rectification System

It has become increasingly common for modern people having poor standing postures. With a long time of poor standing posture, it causes some symptoms such as difficulty breathing, chest tightness, indigestion, palpitations, etc. Therefore, a standing posture recognition system has been developed to rectify the improper standing postures of the user by alert him/her. In this project, the specifications and advantages of data acquisition are discussed. A correct and incorrect standing posture might look similar to each other with human eyes. Therefore, a posture recognition algorithm dedicated to recognizing and differentiating between a proper and improper standing posture has been developed. Research and study have been done at work related to the posture recognition system done by other authors. Besides, the methods and solutions of research used are compared and tabulated. In this project, Kinect was proposed as data acquisition for utilizing the skeleton information. However, the software used is MATLAB, and it used to train and implement the posture recognition algorithm. A comparison and research of machine learning algorithm are performed between SVM and ANN algorithm. With the result, it shows that the SVM with RBF kernel has the highest accuracy compared with others, where the accuracy is 97.9335%. Although the accuracy of SVM with the linear kernel is approximately same with RBF kernel, the input space of training and testing sets are not linearly separable. Thus, the linear kernel is speculated that the recognition system is biased. Besides, although the accuracy of the artificial neural network exceeds expectations, its accuracy is relatively moderate compared to other classifiers. Upon detecting an improper standing posture, the system will display ‘bad posture’ in computer to alert the user to rectify his/her standing posture. The purpose of this project is to focus on the posture recognition algorithm, the system is successful and accurate posture recognition. Since the purpose of this project is to demonstrate the idea, other parts will not be studied, especially the hardware. Three objectives were achieved in this project.
Preserving Confidentiality in Data Analytics-as-a-Service

The enhancements in computation technologies in the last decades enabled businesses to analyze the data that is collected through their systems which helps to improve their services. However, performing data analytics remains a challenging task for small- and medium-scale companies due to the lack of in-house experience and computational resources. Data Analytics-as-a-Service (DAaaS) paradigm provides such companies outsourced data analytics, where a company that is specialized in data analytics serves its knowledge and computational resources to the other companies, which need data analytics for their businesses. A major challenge in DAaaS is preserving the privacy of the outsourced data, which might contain sensitive customer or employee information or the intellectual property of the outsourcing company. Leakage of sensitive information has several consequences both for outsourcing and service provider companies as legal obligations, loss of reputation, and financial loss. Therefore, a well functioning outsourced analytics service should achieve several data protection measures such as confidentiality, integrity, and availability. In this thesis, we focus on the preservation of confidentiality in data analytics-as-a-service applications. We select three analytics applications that are becoming popular in outsourced data analytics, which are process analytics, machine learning, and marketing analytics. Despite there exist several other techniques that are commonly used in outsourced data analytics, we decide to focus on the algorithms of process analytics, machine learning, and marketing analytics since the privacy concerns in these analytics have not been investigated thoroughly. In confidential data analytics-as-a-service, our goal is to achieve confidentiality by protecting input/output privacy and maintaining the correctness and efficiency of analytics computations. To protect the privacy of data we use two secure computation techniques, which are homomorphic encryption and secure multiparty computation. To assure correctness, we propose several hybrid protocol designs that minimize the loss of accuracy in computations. For the efficiency of our protocols, we use several optimization techniques that reduce the computation and communication costs of private data analytics. Our protocols show promising results for confidential data analytics in the outsourced setting.
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Causality-aware counterfactual confounding adjustment for feature representations learned by deep models: with an application to image classification tasks

Causal modeling has been recognized as a potential solution to many challenging problems in machine learning (ML). Here, we propose a counterfactual approach to remove/reduce the influence of confounders from the predictions generated a deep neural network (DNN). Rather than attempting to prevent DNNs from directly learning the confounding signal, we propose a counterfactual approach to remove confounding from the feature representations learned by DNNs in anticausal prediction tasks. By training an accurate DNN using softmax activation at the classification layer, and then adopting the representation learned by the last layer prior to the output layer as our features, we have that, by construction, the learned features will fit well a logistic regression model, and will be linearly associated with the labels. Then, in order to generate classifiers that are free from the influence of the observed confounders we: (i) use linear models to regress each learned feature on the labels and on the confounders and estimate the respective regression coefficients and model residuals; (ii) generate new counterfactual features by adding back to the estimated residuals to a linear predictor which no longer includes the confounder variables; and (iii) train and evaluate a logistic classifier using the counterfactual features as inputs. We validate the proposed methodology using colored versions of the MNIST and fashion-MNIST datasets, and show how the approach can effectively combat confounding and improve generalization in the context of dataset shift. Comparison against a variation of the SMOTE \cite{chawla2002} approach showed that the causality-aware approach compared favorably against SMOTE balancing in our experiments. Finally, we also describe how to use conditional independence tests to evaluate if the counterfactual approach has effectively removed the confounder signals from the predictions.
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Research Guides: Data Visualization Resources: Home

A guide to resources from TUL and elsewhere to help users create interesting and informative data visualizations.
Profit maximization and time minimization admission control and resource scheduling for cloud-based big data analytics-as-a-service platforms

© Springer Nature Switzerland AG 2019. Big data analytics typically requires large amounts of resources to process ever-increasing data volumes. This can be time consuming and result in considerable expenses. Analytics-as-a-Service (AaaS) platforms provide a way to tackle expensive resource costs and lengthy data processing times by leveraging automatic resource management with a pay-per-use service delivery model. This paper explores optimization of resource management algorithms for AaaS platforms to automatically and elastically provision cloud resources to execute queries with Service Level Agreement (SLA) guarantees. We present admission control and cloud resource scheduling algorithms that serve multiple objectives including profit maximization for AaaS platform providers and query time minimization for users. Moreover, to enable queries that require timely responses and/or have constrained budgets, we apply data sampling-based admission control and resource scheduling where accuracy can be traded-off for reduced costs and quicker responses when necessary. We conduct extensive experimental evaluations for the algorithm performances compared to state-of-the-art algorithms. Experiment results show that our proposed algorithms perform significantly better in increasing query admission rates, consuming less resources and hence reducing costs, and ultimately provide a more flexible resource management solution for fast, cost-effective, and reliable big data processing.
Identifying Collaborative Conversations using Latent Discourse Behaviors

In this work, we study collaborative online conversations. Such conversations are rich in content, constructive and motivated by a shared goal. Automatically identifying such conversations requires modeling complex discourse behaviors, which characterize the flow of information, sentiment and community structure within discussions. To help capture these behaviors, we define a hybrid relational model in which relevant discourse behaviors are formulated as discrete latent variables and scored using neural networks. These variables provide the information needed for predicting the overall collaborative characterization of the entire conversational thread. We show that adding inductive bias in the form of latent variables results in performance improvement, while providing a natural way to explain the decision.
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GLOBAL CITIZENSHIP AMID TRANSITIONAL PERIODS AND INCREASED IMPORTANCE OF STEM AND SCIENCE DIPLOMACY

The author reflected on government funded cross-national/multi-institutional comparative research, which focused on issues of citizenship in global contexts, the multiple uses of STEM, and individual and group rights via geopolitical alliances.The promotion of diversity and citizenship competencies demonstrates an international commitment to strengthen the bonds between nations through the education of citizens in critical fields (e.g.STEM) and further integration into the global system of scientific innovation that connects global partners and promotes what is best for societal peace and prosperity.National and global citizenship are examined in these contexts.In this manuscript I will: 1) discuss contemporary debates for and against global citizenship that are driving many of today"s national policies around the globe (i.e.US immigration policy, Brexit); 2) explicate the expansion of ideas regarding national issues in a global context and rising belief in international collaboration to deal with those issues (global citizenship); 3) elucidate our methodological approaches; 4) examine how the STEM pipeline is affected by anti-global citizenship policies; and 5) synthesize the ways that STEM promotes collaboration in dealing with global issues (i.e.Science Diplomacy, COVID-19 response).Educating economically successful engaged global citizens; expanding access to higher education; and impacting science diplomacy, international relations, and the evolution of the academy into engines of social and economic development are all issues that have been addressed through the promotion and expansion of international STEM collaborations.The conceptual literature review and analysis elucidates perspectives that show that engagements bring gifted scientists and students together from across the globe to work on issues of international significance.Hence the essence of cosmopolitanism, global citizenship education, and the global university engagement movement are portrayed.By linking global citizenship education with that of STEM education in comprehensive holistic manners, the preparation of students will promote the understanding of informed global perspectives uninhibited by border that include various populaces and viewpoints.The ideas/policies of promoting informed global perspectives through civic engagement and citizenship are an embrace of sociocultural and globally inclusive paradigms that portray beneficial results.By working collectively with various current and future higher education, government, and STEM professionals physical and cultural barriers can also be dismantled.
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm

We provide an end-to-end differentially private spectral algorithm for learning LDA, based on matrix/tensor decompositions, and establish theoretical guarantees on utility/consistency of the estimated model parameters. The spectral algorithm consists of multiple algorithmic steps, named as {edges}, to which noise could be injected to obtain differential privacy. We identify \emph{subsets of edges}, named as {configurations}, such that adding noise to all edges in such a subset guarantees differential privacy of the end-to-end spectral algorithm. We characterize the sensitivity of the edges with respect to the input and thus estimate the amount of noise to be added to each edge for any required privacy level. We then characterize the utility loss for each configuration as a function of injected noise. Overall, by combining the sensitivity and utility characterization, we obtain an end-to-end differentially private spectral algorithm for LDA and identify the corresponding configuration that outperforms others in any specific regime. We are the first to achieve utility guarantees under the required level of differential privacy for learning in LDA. Overall our method systematically outperforms differentially private variational inference.
LibGuides: 2019-2020 Reading Challenge Nominees: 2020 Award Nominees

Students in grades 3-8 can earn a free book at the last book fair by meeting the challenge: 3rd Grade - 6 books 4th Grade - 7 books 5th Grade - 8 books 6-8th Grade - 10 books (and other conditions ;))
Development of Readiness of Future Preschool Teachers to Innivative Activity

One of the important components of professional competence of teachers today is their involvement in innovation. In pedagogical science innovative activity is defined as purposeful pedagogical activity based on understanding of own pedagogical experience by means of comparison and studying, change and development of educational process for the purpose of achievement of higher results, receiving new knowledge, introduction of qualitatively other pedagogical practice. The relevance of our article is determined by the society's order for a creative teacher with a high level of readiness for innovative pedagogical activity, the need to develop a holistic system of improving readiness for innovative activity. The system-forming factor of readiness of the teacher for innovative activity is the need for transformation, improvement of pedagogical activity through innovative technologies of education.
PELATIHAN ENGLISH READING SKILL FOR CONSTRUCTION AND PROPERTY BUSINESS BAGI KELAS X JURUSAN BISNIS KONSTRUKSI DAN PROPERTI DI SEKOLAH MENENGAH KEJURUAN NEGERI JAWA TENGAH, SEMARANG

In the competitive world of construction and property business, the skill to speak and to read a foreign language is important in this modern era. Through these articles made based on community service activities specifically aimed at students of Central Java Vocational School, Semarang. Dedication in the form of training in English Reading Skills for construction and property business. The aim is to train students of SMK Central Java , Semarang in reading and understanding the context of sentences construction and property business in English. The training is carried out by equipping students with material or theory followed by practice in understanding English texts. In this training, students can properly and correctly recognize the context of English reading. It is expected that later students in the contruction and property business majors at SMK Central Java , Semarang can compete in the conctruction and property business world by mastering the language of international communication namely English. Keyword: English learning, Learning Reading skill and Comprehension, construction and property business.
A Study of the Vocabulary Base for Utilization of Hangeul Writing Systems by Sediq People in Taiwan

The first purpose of this study was to collect about 1,000 basic vocabularies of Sediq language to lay the foundation for use of the Hangeul writing systems by Sediq people and the second purpose was to verify the adaptability of the Hangeul writing systems in detail at the level of vocabularies to solidify the foundation for use of the Hangeul writing systems. The resultant achievement of such purposes is well summarized in 【Appendix】(1∼4) titled 〈Details of verification of the vocabulary foundation for use of the Hangeul writing systems by Sediq people〉.BR In summary, appendix (1) describes the process through which 1,291 basic vocabularies exceeding the original goal 1,000 vocabularies were collected, the vocabularies were divided into simple words (1,019 words) and compound words (272 words) to correct the Roman alphabet writing word forms to indicate actual pronunciations, and Hangeul writing was verified based on the results. Appendix (2) shows 137 vocabularies borrowed from Japanese separately gathered and written in Hangeul. Appendix (3) shows 446 person name vocabularies, and appendix (4) shows the results of empirical Hangeul writing of 40 place name vocabularies.BR Another outcome of this study is that it diagnosed problems in the Roman alphabet writing system, which is the basis of the establishment of the Hangeul writing system, and found out methods to correct the problems. After resolving the leading tasks as such, Hangeul writing was verified with numerals, personal pronouns, loanwords, and person name / place name vocabularies, and as a result, it was proved that there was no problem in the adaptability of the Hangeul writing.BR In addition, several new facts were found. First, matters that must be revised or supplemented were found in the writing system. It was found that three new consonants not in the list of consonant phonemes were being used in loanwords and person name vocabularies. They are initial consonants /j/ and /z/, and a final consonant /w/. Second, it was found that semi-consonant syllables included [we] and [wi]. The above-mentioned three consonants and two semi-consonant syllables were reflected on the ‘2019 Hangeul writing system’.BR In addition, the discovery of semi-homonyms(9 pairs), homographs(12 pairs), heteromorphic synonyms(28 pairs) can be also regarded as one of the outcomes. In particular, among the nine pairs of semi-homonyms, examples of conflicts between /l/ and /r/, or between /x/ and /h/ could not be found. This proved that the shortcoming of the ‘2017 Hangeul writing system’ that wrote each of these two pairs identically is not much problematic in reality. If these small outcomes can be one of conerstones for establishment of Hangeulology(Study of Hangeul writing system) later, it will be the Grace of God.
Learning From Strategic Agents: Accuracy, Improvement, and Causality

In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to game the decision rule by changing their features so as to receive better decisions. Whereas the strategic classification literature has previously assumed that agents' outcomes are not causally affected by their features (and thus that strategic agents' goal is deceiving the decision-maker), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. As our main contribution, we provide efficient algorithms for learning decision rules that optimize three distinct decision-maker objectives in a realizable linear setting: accurately predicting agents' post-gaming outcomes (prediction risk minimization), incentivizing agents to improve these outcomes (agent outcome maximization), and estimating the coefficients of the true underlying model (parameter estimation). Our algorithms circumvent a hardness result of Miller et al. (2020) by allowing the decision maker to test a sequence of decision rules and observe agents' responses, in effect performing causal interventions through the decision rules.
Collection and Analysis of Dialogues Provided by Two Speakers Acting as One

Tsunehiro Arimoto, Ryuichiro Higashinaka, Kou Tanaka, Takahito Kawanishi, Hiroaki Sugiyama, Hiroshi Sawada, Hiroshi Ishiguro. Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2020.
Soft Kernel-based Ranking on a Statistical Manifold

Classical ad-hoc retrieval models based on exact matches suffer from the issue of soft matching in text. Besides query expansion approaches, many existing neural IR approaches exploiting word embedding representation alleviate this issue to some extent. We observe that word embedding vectors are usually normalized in practice to retain cosine similarities that are used to construct the query-document interaction matrix for most neural ranking models. These vectors are in fact mapped to the surface of a high-dimensional hypersphere. Existing work in kernel-based ranking do not consider kernel to be a distribution on a certain geometry if the variable of a kernel is a geometric quantity. We propose a kernel-based neural ranking model based on a statistical manifold. We consider the interaction as geodesic on a manifold. We propose a smoothed kernel pooling scheme at different similarity levels based on Riemann normal distribution. Extensive experiments are conducted on the recent benchmark dataset with the state-of-the-art kernel-based neural ranking model, which demonstrate significant improvements brought by our model.
Research Guides: Streaming Video Guide: How to Find

This guide was created to provide information about streaming videos here at the Marquette University Raynor Memorial Libraries
Strategies for de-escalation, distraction and diversion

One of the greatest skills one will acquire throughout the teaching career will be the use of techniques that help one to calm situations one sees in front when behaviour starts to become an issue. The de-escalation techniques are used to defuse situations, caused by a variety of reasons, some of which one may well be unaware of. Or it may be the better course of action simply because to confront the issue head on will in fact either make the matter worse or disrupt the lesson and spoil relationships. It means that one needs to be using the emotional intelligence, as the trigger that starts the incident will also be triggering the emotions as well. The technique of deflecting the thoughts by asking random questions is of course a great way to take the minds off the current situation. The younger the person the easier deflection works; the older they become then conversation and engagement works better.
LibGuides: Passwords, Two-Factor Authentication, and Security Questions: Randomly Generating Passwords

This guide goes into more detailed looks at passwords, password managers, and two-factor authentication. Tips and tools for randomly creating passwords.