Reviewerly/exHarmony huggingface dataset:
Profil Gangguan Fungsi Kognitif Pasien HIV/AIDS di Klinik VCT RSUP Sanglah

HIV had been being a global problem. HIV complication in central nervous system is cognitive disorder. Early detection in this problem take important role to do early management. The objective of this study is to knew the profile of cognitive function disorders of patients of HIV/AIDS treated in Sanglah General Hospital Denpasar. This was descriptive study using cross-sectional design. This study was conducted to 60 patients HIV/AIDS in VCT clinic of Sanglah General Hospital since July 2016 until September 2016. The cognitive function was assessed using MoCA INA and IHDS. This study shows 31 patients (51,7%) with cognitive disorder with the most domain impairment is delayed memory 34 patients (56,7%). Â Prevalence of cognitive function disorders in this study was 51,7%Â Â Â
LibGuides: 12 Month Children's Programming: Resources

Created by Jessica Jean Walker for INFO 260A: Children's Programming and Services (Fall 2019), Instructor Wrenn-Estes
Supervised Domain Adaptation: Were we doing Graph Embedding all along?

The performance of machine learning models tends to suffer when the distributions of the training and test data differ. Domain Adaptation is the process of closing the distribution gap between datasets. In this paper, we show that existing Domain Adaptation methods can be formulated as Graph Embedding methods in which the domain labels of samples coming from the source and target domains are incorporated into the structure of the intrinsic and penalty graphs used for the embedding. To this end, we define the underlying intrinsic and penalty graphs for three state-of-the-art supervised domain adaptation methods. In addition, we propose the Domain Adaptation via Graph Embedding (DAGE) method as a general solution for supervised Domain Adaptation, that can be combined with various graph structures for encoding pair-wise relationships between source and target domain data. Moreover, we highlight some generalisation and reproducibility issues related to the experimental setup commonly used to evaluate the performance of Domain Adaptation methods. We propose a new evaluation setup for more accurately assessing and comparing different supervised DA methods, and report experiments on the standard benchmark datasets Office31 and MNIST-USPS.
Text to Complicated Image Synthesis with Segmentation Information Guidance

In this paper, we propose a novel method called Segmentation Information Guidance (SIG). In this method, additional segmentation information is added to guide the process of text to complicated image synthesis. We demonstrate the effectiveness of SIG model on Microsoft Common Objects in Common (MSCOCO) dataset. It proves that the image results generated by directly using the segmentation image are more authentic and coherent than that without background.
Implementing the software defined management framework

Software Defined Infrastructure (SDI) has become a relevant topic for computing and communication industry. Despite this huge technological movement, Network and Systems Management has been disregarded as one of the main themes in this ecosystem, and Software Defined Infrastructure has been managed by semi-software-defined management solutions. In order to reduce this gap, this paper presents SDMan, a Software Defined Management framework. The SDMan's proof of concept uses the OpenStack cloud platform and aims to demonstrate the feasibility of the proposed solution.
LibGuides: Online Resources for Teaching and Learning : Home

This guide was built using resources from the College of Education Faculty and Education Librarian at William Paterson University of New Jersey. The resources will be continually updated. Many of the resources were generated to address the need for qual
BU Library: BU Canvas Support & Resources: Home

We've moved to Canvas! This space offers essential updates and important information to help you learn to use it effectively. Check back for more updates.
Content Based Search Engine for E-Books

The learning strategy of today's generation is changing from subject oriented learning to topic-oriented learning. Due to ease of access to online content, students and teachers prefer E-books. However, referring multiple E-books for a particular topic is difficult and the content obtained is so large that it is virtually impossible to differentiate which content is relevant. Also, it is widely recognized in the digital Information Retrieval community that keyword-based search engine does not work well when the user's needs are not well-defined. Hence, a search engine is required to explore the content of E-books. This paper proposes a `Content Based Search Engine for E-Books' where E-books are indexed and the search results are clustered according to frequent phrases. The clustering of search results allows the search engine to understand the semantics behind user's query. In comparison with other existing search engines, this paper focuses on developing an improved search engine to explore the content of E-books. The results show that a particular topic can be searched across multiple E-books and can be clustered based on its content.
BU Library: BU Canvas Support & Resources: The Course Shell

We've moved to Canvas! This space offers essential updates and important information to help you learn to use it effectively. Check back for more updates. The instructor and student modules, the Week 1 survey, and the checklist.
Review on continuous speech recognition system

Automatic Speech Recognition System is based on the voice as the research area as a cross-disciplinary. Speech Recognition is the high-tech that allows the machine to turn the speech signal into the text through the process of identification and understanding and also make the function of natural voice communication. It has a very close relationship with acoustics, phonetics, linguistics, information theory, pattern recognition theory and neurobiology disciplinary. Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech application area, especially speech recognition step. However, in the past few years, many research has developed on the deep learning approach for speech related application areas such as speech emotional recognition system, speaker recognition and motor sound classification system. Nowadays, this development of deep learning approach has yielded the better results when compared to the others various applications including speech. Deep learning algorithm have been mostly used to further enhance the capabilities of computers so that it understands what humans can do, which includes speech recognition. . Deep Learning classifier is used in many research areas of speech recognition system and speaker recognition system to improve the accuracy of the system. The extracted features and converted feature images are used as the input of the various Deep learning classifiers to get the higher accuracy for speech recognition system. This paper provides the various result based on the different analysis of different speech recognition process when deep learning become a new popular area of machine learning for speech applications. As the experimental results of the system, the various recognition results of different deep learning classifiers in the recognition step of the speech recognition system.
The Effect of Using Short Storieson Secondary School Students’ Critical Reading

Critical reading skills are very important in both academic and everyday lives. These skills enable individuals to detect bias, prejudice, misleading opinion, and illogicalconclusions, in any oral or written discourse. English education, therefore, should contribute to the development of students’ critical reading skills. This study aimed atinvestigating whether or not short stories use effects students’ critical reading skills. It focused on comparing between short stories and non-literary texts in developing critical reading. The participants were sixty-fourteenth-grade English as a Foreign Language (EFL) students dividing into two groups: an experimental group and a control group.Short stories were used to teach the experimental group, whereas non-literary texts were used to teach the control group. To achieve the goal, data were collected using a critical reading test. The findings revealed that the post-test mean score of the experimental class is 75.30 and the post-test mean score of the control class is 68.14. The hypothesis test showed there is a significant effect short story use. The value of Sig. of equality variances (0.379) was higher than Sig. α (0.05). Based on the findings, it was concluded that there is a significant effect of using short stories in improving tenth-grade EFL students’ critical reading skills. This study pointed out that short stories can be an effective tool to improve critical reading.
Spatial Analysis of Malaria Cases in Palawan, Philippines

Palawan identifies malaria as one of the threats to its overall health care and development. Nearly 95 percent of malaria cases in the country in 2016 were recorded in Palawan alone – indicating that malaria cases are highly concentrated in the said province. The need to monitor and recognize geographical patterns of the disease is a great help in controlling malaria outbreak and in increasing awareness about the disease risk.  Early action and detection of malaria epidemics are important to reduce the number of deaths and illnesses caused by malaria and to reduce its impact to the socioeconomic burden among barangays in Palawan. This study investigated malaria cases among the barangays in Palawan, and its spread across neighboring barangays. Spatial analysis was performed using different neighborhood criteria - queen's and rook's contiguity weight matrices, k-nearest neighbors weights matrices (k value set to 3, 4, 5), and distance-band weight matrices (a cutoff distance of 8, 9, and 10 km). Results showed that barangays in the southernmost part of Palawan which include Bunog, Iraan, Ransang, Campong Ulay, Culasian, Candawaga, Canipaan, Punta Baja, Bulalacao, Panaligaan, Malihud, Salogon, Marangas, Samarenana, Malis, Saraza, Mainit, Culandanum, Ipilan and Pangobilian were consistently classified as high-high cluster using the different weights matrices.
A Semi-discriminative Approach for Sub-sentence Level Topic Classification on a Small Dataset

This paper aims at identifying sequences of words related to specific product components in online product reviews. A reliable baseline performance for this topic classification problem is given by a Max Entropy classifier which assumes independence over subsequent topics. However, the reviews exhibit an inherent structure on the document level allowing to frame the task as sequence classification problem. Since more flexible models from the class of Conditional Random Fields were not competitive because of the limited amount of training data available, we propose using a Hidden Markov Model instead and decouple the training of transition and emission probabilities. The discriminating power of the Max Entropy approach is used for the latter. Besides outperforming both standalone methods as well as more generic models such as linear-chain Conditional Random Fields, the combined classifier is able to assign topics on sub-sentence level although labeling in the training data is only available on sentence level.
Tren Topik Penelitian Jurnal Terakreditasi Peringkat Sinta 2 Bidang Ilmu Perpustakaan Dan Informasi Di Indonesia Periode 2013-2019 (Analisis Subjek Menggunakan Pendekatan Bibliometrik Co-Word)

This study aims to determine trends in research in the Sinta 2 accredited journal in the field of library and information science in Indonesia for the period 2013-2019. This research uses the bibliometric method using shared words. The number of samples to be analyzed were 400 journal articles from 4 journal editors, namely the BACA journal, the BIP journal, the Khizanah Al Hikmah Journal and JKIP journal  for the 2013-2019 period. Conducting keyword groupings using subject analysis, after grouping based on the same keywords using the Jaccard index then analyzing using subject analysis based on the hawkins taxonomy. The results of this study indicate that research trends in accreditation journals in 2 fields of library and information science in the period 2013-2019 tend to be in 3 main taxonomies including Library and Library Services (LIS), Information Professionals, and Information Science Research (ISR). The conclusion of this study is that research trends are more inclined to Libraries and Library Services (LIS), The Professional Information, and Information Science Research (ISR). In the future, all researchers are expected to study other topics so that library development and information science are evenly distributed.
A Study on Designing an Education Cooperation Model for HRD in Asia-Pacific Region: Focusing on Education Project of APEC

The purpose of this study was to propose an education cooperation model for Human Resources Development (HRD) in Asia-Pacific region in response to environmental changes in business and industry. In order to carry out the model, this study reviewed the key features of human resources in the APEC which is a symbolic regional integration entity in Asia-Pacific region, shaped critical issues related with HRD in this region and then analyzed the trends of education cooperation projects conducted by the APEC in terms of topics, implementation methods and proposing economies of the projects. In result, this study proposed a triangular education cooperation model for HRD consisting of three elements: Voluntary Partnership, Information on Needs and Support for Cooperation. These interconnected and interdependent elements were designed to encourage actors to participate in education cooperative activities with their own willingness, produce and manage research-based information required for sustainable cooperation and support communication and connectivity among actors for effective activities. Also, this study expected that this model would make a commitment to narrowing educational divides, enhancing global-standard skills development, facilitating public and private partnership and organizing the foundation of future education for cultivating creative talents in the era of innovation for APEC members.
Socio-cultural Identity of the Digital Generation in the 21st Century: Cultural and Philosophical Analysis

The article studies the problem of the worldview typical of academic and technical experts in the context of digital society. The purpose of this study is to explore the social and cultural characteristics of young generation. Within the study, key differences of the modern students determining their approach to education process were presented. The author also analyzed the generational differences in the educational process and workplace. On the one hand, ethno-cultural integration, virtual reality and IT technologies greatly influenced the specific types of thinking and system of social values of modern youth. On the other hand, the penetration of digital culture in the educational process affected the formation of professional competencies. The paper reveals the results of the digital education and its influence on modern students. The research allowed to analyze the advantages and disadvantages of international practices of the use of modern digital technologies in higher education. The conducted analysis shows that to date young experts are capable of universal skills of using digital technologies in workplace such as tools for financial modeling, business planning and decision-making process. In conclusion, generational differences and similarities are identified, and implications for employers are discussed.
Development and evaluation of a system for automatic emotion recognition from facial expressions

For a machine to have efficient human-computer interaction, relevant information about the subject is needed. As the automation of an increasing number of tasks is being developed, the need for artificial emotion recognition solutions is becoming more apparent. In this work, we focus on the solutions that can measure human emotions from a distance by using a camera. We developed one that uses detected facial expressions to classify emotions. It employs OpenFace to measure present facial action units which are in turn used as the input to a Support Vector Machine classifier. We tested our method’s performance against Noldus FaceReader by using metrics of precision, recall, accuracy, and F1 score. For testing, we used two multi-modal datasets with emotion-annotated video files: (1) Bahcesehir University Multimodal Face Database of Affective and Mental States (BAUM-1, which has two parts – BAUM-1s and BAUM-1a) and (2) Geneva Multimodal Emotion Portrayals Core Set (GEMEP); we also added a dataset of emotion-annotated images that we compiled by manual selection from the BAUM-1s database. While GEMEP and BAUM-1a databases contain acted emotional expressions, BAUM-1s is only composed of videos with spontaneous emotional expressions. We compared the classifiers’ performance while using different interpretations of probabilistic classifications, analyzed the differences based on the used input datasets, and discussed factors responsible for the measured outcomes. While FaceReader seemed to perform more consistently, our classification method achieved a better mean in F1 scores, even though the difference was not statistically significant. The pros and cons of each classifier and possible classifier upgrades are discussed. A high enough performance of such emotion-recognizing systems would enable the development of useful applications for various purposes, such as security, healthcare, administration, marketing, and many others.
Big Data Analysis for Child Well-being on Academic Research

Background/Objectives: This study is to examine the well-being of child in the world of child’s life such as development, education, and play in a phenomenological manner.Method/Statistical Analysis: We based data collected through educational and academic information sources (RISS) search as big data, and analyzed the keywords and the network analyzed by the ‘well-being’, ‘wellness” and ‘Chamsari’ keywords. The frequency, ranking, and connection of the keywords were analyzed through the process of processing the collected unstructured text into the data suitable for research and analysis.Findings: This study tried to reveal the reality of child well-being through phenomenological perspective based on the understanding of experts about child well-being through academic big data analysis. The results of the study showed that psychological themes such as activities, body, emotion, subjectivity, and psychology were highly linked with child’s well-being. Child who live well in the phenomenological attitude exhibition were distinguished from adults and they were justifying adult control and intervention by refracting them as immature beings. In addition, it points out that the uniqueness of individual existence undermines the originality of the existing child, and furthermore, it can realize the child’s disaster by expanding the unique life style of the child’s existence by the advancement of the fusion of expression through the subjective expression of the unique individual existence experienced by the child, and the dialogue based on the ethics with the other.Improvements/Applications: This study suggests that the ideological foundation of living and human public service should be prepared.
Algorithmic Music for Synthesised Strings

This output consists of a suite of 12 compositions titled “Algorithmic Music for Synthesised Strings” (AMSS) released as a 37 minute album under the Dane Law alias.The album was released on 24th April 2020 by Astral Plane recordings and available on Spotify, Apple Music and Bandcamp to stream or download. The compositions use a set of algorithms to generate musical sequences and synthesise sound. AMSS communicates research from the academic discipline of Music Computing to the broader cultural sector, reaching wider audiences in popular electronic.
Penerapan Algoritma Naive Bayes Untuk Diagnosa Penyakit Gagal Ginjal Kronis

Kidney is an organ that has a big role in filtering substances that enter the body. This can be seen in kidney function, which is excreting metabolic waste. Damage to the kidneys that cause damage to kidney function. One of the diseases of the kidney that cannot be treated is chronic kidney failure (Chronic Kidney Disease). Chronic kidney failure is difficult to know, certain methods are needed and it is not enough just by a medical examination. This study discusses the application of the Naif Bayes algorithm method for diagnosis or predict chronic kidney failure. This system is based on a dataset from Indian Chronic Kidney Disease (CKD) obtained from the UCI Machine Learning Repository website created in 2015.The results of this study get an accuracy of 91.25% for the 400 datasets tested.
Explicit Uniquely Decodable Codes for Space Bounded Channels That Achieve List-Decoding Capacity.

We consider codes for space bounded channels. This is a model for communication under noise that was introduced by Guruswami and Smith (J. ACM 2016) and lies between the Shannon (random) and Hamming (adversarial) models. In this model, a channel is a space bounded procedure that reads the codeword in one pass, and modifies at most a p fraction of the bits of the codeword. (1) Explicit uniquely decodable codes for space bounded channels: Our main result is that for every 0 ≤ p 0 and a uniquely decodable code with rate 1−H(p) for channels with space nδ. This code is explicit (meaning that encoding and decoding are in poly-time). This improves upon previous explicit codes by Guruswami and Smith, and Kopparty, Shaltiel and Silbak (FOCS 2019). Specifically, we obtain the same space and rate as earlier works, even though prior work gave only list-decodable codes (rather than uniquely decodable codes). (2) Complete characterization of the capacity of space bounded channels: Together with a result by Guruswami and Smith showing the impossibility of unique decoding for p ≥ 1/4, our techniques also give a complete characterization of the capacity R(p) of space n1−o(1) channels, specifically: For 0≤pp R(·) is not continuous at p=1/4. Our results are incomparable to recent work on casual channels (these are stronger channels that read the codeword in one pass, but there is no space restriction). The best known codes for casual channels, due to Chen, Jaggi and Langberg (STOC 2015), are shown to exist by the probabilistic method, and no explicit codes are known. A key new ingredient in our construction is a new notion of “evasiveness” of codes, which is concerned with whether a decoding algorithm rejects a word that is obtained when a channel induces few errors to a uniformly chosen (or pseudorandom) string. We use evasiveness (as well as several additional new ideas related to coding theory and pseudorandomness) to identify the “correct” message in the list obtained by previous list-decoding algorithms.
Breach of faith

Boards need to be aware that data security is crucial - a cyber attack is just a matter of when, not if. And in the event of an actual data breach, management response is critical.