Reviewerly/exHarmony huggingface dataset:
Impact of Internet of Things Technology on Healthcare Monitoring System for Afghanistan

Health is very important in human’s life. Many people described health in many ways. Like, wellness is more than being free from illness. Even there are some sayings for it as well. like, health is wealth, etc. Similarly, in today’s world where the life has gone full of stress, people are facing many physical, physiological and psychological problems, they cannot make enough time to visit doctors again and again for treatment. Sometimes there is a very serious situation to look after the patient very quickly. Therefore, an efficient settlement is required to make a solution for this problem. There is a method called (Healthcare Monitoring System based on Internet of Things Technology) which is very simple, generic and understandable for all. So Internet of Things technology can also be used in healthcare monitoring. It can collect a big amount of data about an illness, which would take many years if it’s taken manually. Tues, our initial aim in this research is to make the healthcare monitoring system smart and put the internet of things technology into use for healthcare. By doing that major advantages will come included: regular monitoring of patient, cost reduction, cutting down unnecessary hospital visits, better quality, error reduction, safety improvement, machine to machine connection etc.
Early Detection of Algae ‘Blooms’ Yields Healthcare Savings

The use of NASA satellite data by environmental managers tracking harmful algal bloom (HAB) outbreaks along lakefronts and coasts can result in earlier detections that yield significant savings on healthcare, lost work hours and other economic costs.
Tailor: Targeting heavy tails in flow cytometry data with fast, interpretable mixture modeling

Abstract Automated clustering workflows are increasingly used for the analysis of high parameter flow cytometry data. This trend calls for algorithms which are able to quickly process tens of millions of data points, to compare results across subjects or time points, and to provide easily actionable interpretations of the results. To this end, we created Tailor, a model‐based clustering algorithm specialized for flow cytometry data. Our approach leverages a phenotype‐aware binning scheme to provide a coarse model of the data, which is then refined using a multivariate Gaussian mixture model. We benchmark Tailor using a simulation study and two flow cytometry data sets, and show that the results are robust to moderate departures from normality and inter‐sample variation. Moreover, Tailor provides automated, non‐overlapping annotations of its clusters, which facilitates interpretation of results and downstream analysis. Tailor is released as an R package, and the source code is publicly available at www.github.com/matei-ionita/Tailor .
Data Analysis on Biopsies of Breast Cancer Tumors Data Using Data Science

Data Science is a blend of various tools, Algorithms and Machine Learning Principles to extract knowledge from the structured and unstructured data. It is an important scientific field which uses statistics, computing science and intelligence Science to uncover the insights and trends in the data. Data Science is playing a crucial role in medical field. In this study, biopsy of breast cancer patient’s data is considered to uncover the insights of the data that can help to improve the breast cancer diagnosis accuracy. The biopsy data is preprocessed to manage the missing values and the data is visualized through various graphs to demonstrate the data distribution. Supervised learning classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Feed-forward Neural Network (FNN) are used to classify the data. The performance of each classifier is compared and the best classifier that can classify the biopsy data efficiently is analyzed.
A 9.52 ms Latency, and Low-power Streaming Depth-estimation Processor with Shifter-based Pipelined Architecture for Smart Mobile Devices

The 3D hand gesture interface (HGI) for virtual reality and mixed reality on smart mobile devices is strongly dependent upon the robust depth-estimation with low latency and power consumption. However, the conventional depth-estimation hardware such as active depth sensors and stereo matching accelerators cannot realize the always-on and natural 3D HGI on mobile platform due to their large power consumption from active depth sensors and computations as well as the massive external memory bandwidth, respectively. To resolve the limit, we propose a depth-estimation processor that realizes the always-on and natural 3D HGI with algorithm and hardware co-optimization. The processor features: 1) shifter-based adaptive support weight aggregation that replaces complex floating-point operations with integer operations to reduce power and bandwidth by 92.2% and 69.1%; 2) line streaming 7-stage pipeline architecture with aggregation pipeline reordering optimization to realize 94% utilization and 43.9% memory reduction; and 3) shifting register-based pipeline buffer optimization to reduce 29.8% area. The proposed depth-estimation processor realizes a real-time 3D HGI with 9.52 ms of latency under QVGA stereo inputs. It achieves external memory bandwidth reduction to 18.93 MB/s with 15.56 mW power and 2.8 mm² area, which are 4.1x and 6.9x more efficient than state-of-the-arts [9, 10], respectively.
Fantasy for Cello, 1st Mvt.

Author(s): Craven, John | Abstract: This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.
How Flipped Learning Reinforce Project-Based Directing Practicum Class

Most of the university''s arts and practical education are offline, taking a face-to-face approach between professors and students. However, as the educational environment is rapidly changing due to the 4th Industrial Revolution and Covid19 Pandemic, various teaching methods are being developed accordingly, across all disciplines. Now, art education will also need to devise new teaching methods that can overcome the limitations of the existing teaching methods. This study aims to examine the learning effectiveness and educational meaning of the directing practicum class, which is applied with the new teaching method, by designing and operating the online teaching method in the directing practicum class, which had been conducted offline in the theater department. In previous directing classes, the project-based learning method had been applied and got many positive responses from students, but 3-hour session was too short to make the students'' satisfaction level to reach its peak. To compensate the lack of time, the flip-learning was incorporated in designing this new teaching method and on-line materials were given to students before each class. As for research method, first, on- and off-line methods were combined, second, the weekly class plans were designed accordingly, and then each class was operated and evaluated to find out how students'' satisfaction progressed. The new teaching method is named “Project-based flip-learning”, which examines both purpose and principle of project-based learning method and flip-learning. Each week, a newly derived project-based flip-learning was applied in the directing practicum class. Thus, the results of the application of the new teaching method were analyzed by the researchers'' self-assessment and the survey of the learners'' satisfaction in education to analyze the learning effects and educational implications of art practice classes. Through research result, self-assessment, and class satisfaction survey, it showed generally positive effects such as the active and self-led transformation of learners'' attitude, the facilitation of students'' interaction, and the improvement of learning effectiveness. On the other hand, the inconvenience of having to learn in advance through online video lectures was negative. Consequently, the design and operation of the classes by applying project-based flip-runs to the directing practicum class was deemed to require further expansion of the application of these teaching methods as the educational effects improve, although there are inconveniences in online classes.
AUTOMATED HINDI TEXT SUMMARIZATION USING TF-IDF AND TEXTRANK ALGORITHM -

AbstractText Summarization is a compressing technique of the original text to form a summary which willprovide the same meaning and information as provided by the original text. Summarizer helps insaving time and increasing efficiency. Summarization can be done using any of the twoapproaches: abstractive text summarization and extractive text summarization. Abstractivesummarization refers to recreate the whole document in a few words or lines, which may includenew words as well while extractive summarization refers to extract the important words or linesfrom the original document. A lot of work has been done in major languages like English, Chinese,etc. whereas less work has been done on Hindi. In this paper, a comparative study is done for textsummarization using Term Frequency  Inverse Document Frequency and TextRank algorithms,for the Hindi language on a single document. The idea of using the extractive approach is toprovide a summary by selecting the high ranking sentences from the input document. In TermFrequency  Inverse Document Frequency, a numerical value is assigned to each sentence andTextRank is a graph-based approach, which uses graphs for ranking the sentences.
LibGuides: Property & Conveyancing : Subject Guide

Welcome! I'm your librarian - Stephen Dunne. Contact me at stephen.dunne@tafesa.edu.au for help. And come back here often to check what's new!
Data Mining Model for Student Internship Placement Using Modified Case Based Reasoning

This paper attempts to introduce an implementation model for student internship placement involving Case-Based Reasoning, a machine learning technique as a data mining tool to select qualified students from a pool of applicants. Using a three-tier architecture, the system provides an avenue for higher education institutions, organizations, and students using web and mobile applications. The test case has shown the applicability of the CBR Retrieval Process in calculating the local and global similarities of students’ attributes against the criteria set by the organization providing internship programs.
An Analysis of Writing Activities in Japanese Textbooks in High School based on the revised curriculum in 2015

This paper analyzed the written activities presented in the Japanese textbooks used in high school under the revised 2015 curriculum and unveiled how they were organized in the textbooks. Our results showed that they consisted of limited writing exercises (91.68%), induced writing exercises (8.10%), and free writing exercises (0.22%). In other words, they were mostly comprised of imited writing exercises. Even in terms of the transitional connection between Japanese I and Japanese II, while Japanese I expands the amount of written exercises gradually from letters to words and sentences, Japanese II consists of limited writing exercises only with some exceptions. Furthermore, results of the survey on the commonality of writing exercises between the two textbooks showed that commonalities of other types of exercises rather than of the limited writing exercises were extremely low. In addition, our analysis of the reflection rate of teaching and learning methods, i.e. how far the method actually used exercises as presented in the Japanese language curriculum, showed a huge difference in the use of writing exercises between the Japanese textbooks by more than 66.66% for Japanese I and 45.00% for Japanese II. Through this analysis, we were able to identify the problem of the lack of transitional connection between Japanese I and Japanese II, which was caused by the fact that in the current textbook, writing exercises are biased to the limited type. We also suggested that because of the low commonality between the textbooks, the guided writing should be encouraged to cover the areas that have insufficient contents, as well as to engage students in various exercises. In sum, this paper discussed the problems of the revised 2015 curriculum with focus on the writing exercises in Japanese language textbooks in high school. Based on the findings of this study, in the future, we will explore the effective way of organizing the contents of the exercises so that the textbooks maintain the continuity and remain systematic.
Pedagogical basis of professional oriented technologies in education system

The current system of reproduction of pedagogical personnel objectively requires a review and clarification of its content, structure, organization, principles of relationship with educational practice and innovative processes in education. This is directly related to the design of new professionally-oriented educational technologies that ensure the achievement of high quality and guaranteed results of professional training and the formation of the personality of a teacher.The relevance of the study is also determined by the desire to prepare the future teacher, not only as a specialist in a particular subject area, but also as a person with the necessary competencies for organizing a developing educational environment: In the new socio-economic conditions, the requirements for professional competence, flexibility, and creativity have increased future specialists. The success of the activities of educational institutions depends largely on the professional and personal qualities of the teacher, his moral and intellectual potential, as well as the creative search for solving pedagogical problems. The implementation of modernization processes * of education requires updating the personal and professional potential of teachers in the development of new teaching technologies
The influencer impact : En kvalitativ studie om hur influencers påverkar följarens köpbeslut

This study provides information about customers opinions and attitudes towards influencers and influencer marketing and what driving forces persuades them to a purchase through this marketing form. ...
STANDING WHEELCHAIR WITH VOICE RECOGNITION SYSTEM

We propose to produce a helping hand in the area of Rehabilitation to aid differently abled and paralyzed people in mobility by providing them with a STANDING WHEELCHAIR WITH VOICE RECOGNITION MODULE. This wheelchair promises to fulfill the need of independency of the patient as it allows them to command and control it with their voice. This is a powered wheelchair with speech recognition system. Speech recognition enables communication with machines and tools in this modern era. The module consists of a motor merged with the wheels. The commands are programmed and stored in the microcontroller which reads the given command and moves the wheelchair in the desired direction and also changes the position of the chair. It has two positions namely the sitting and standing position which is achieved by means of mechanical support systems.As a base of this project we have to design a model of the wheelchair which consists of the electronic and mechanical part. The electronic part consists of the voice recognition system (BLUETOOTH & AWR MOBILE APP) the motor and the mechanical part is the one which aids the chair to change into different positions. The mechanical part of the wheelchair consists mostly of rods and Programmable logic circuit which are needed to bring the chair to a standing position. The Bluetooth and AWR mobile app which takes voice as input for further voice recognition process. The voice recognition system has been completed. Recognize the voice pattern and store it for future purposes. The wheelchair overcomes the people who all are not able to get to a hospital quickly, Increase self-esteem, Increase social-interaction, overall quality of life (QoL).The objective of this Project is to design an automatic standing wheelchair. This wheelchair helps those who are elder, weak or patients in rehabilitation. Decrease possibility of secondary diseases. Main scope of this Project is to design a wheelchair that can reform automatically and support the costumer standing up. This kind of wheelchair will use less electrical power. Compared to traditional electrical wheelchair, it can save energy and work stable as well.We realize that few people have the means to pay for an expensive electrical model that can climb curbs and steps, while others may find those models too bulky or cumbersome for the home or workplace. By adding some new components to the standard wheelchair, we aim to provide an inexpensive alternative that increases mobility without sacrificing maneuverability. The final goal of this project is to help those who have to rely on a wheelchair to get around. Our design will provide more freedom, more independence, and more mobility at a cheaper cost than anything on the market today. We hope to give the disabled more options for their transportation needs.
Natural Language Processing in Data Analytics

This chapter focuses on how cutting-edge artificial intelligence (AI) technology that is centered around machine learning can help advance data analytics. It discusses the applications of a subfield of AI, natural language processing (NLP), in the domain of text analytics. Predictive analytics utilizes statistical modeling and machine learning techniques to make predictions about future outcomes based on the patterns found in the existing data. The development of advanced analytics has greatly benefited from the advancement of applications of machine learning algorithms which is an intersection of statistics and computer science. The applications of NLP today are incredibly diverse. It spreads in various fields such as machine translation, question answering, information extraction, natural language generation, writing assistance, video scripting, text categorization, sentiment analysis (SA), speech technologies, hate speech detection and fake news detection. SA focuses on the extraction of people’s sentiments, opinions, emotions, and attitudes toward entities such as services, products, organizations, individuals, issues, events, topics, and their attributes.
A novel learning and prediction Bayesian hierarchical clustering-Dirichlet mixture model for effective data mining

Decision making and business support is an important process in data mining and this can be achieved by means of pattern classification and extraction. Since the huge volume of data needs starving knowledge to process and organisation faces many issues in solving those issues. Clustering is an effective technology available to analyse and convert the datasets into meaningful patterns. Clustering in data mining uses various attributes to compute large dataset and meet out the real time issues. The proposed model uses Bayesian hierarchical clustering model with Dirichlet model to resolve the issues in large dataset analysis. Experimental results prove that proposed model experience better clustering efficiency than conventional complete link agglomerative clustering by achieving 92% of clustering accuracy.
Statistical Analysis and Evaluation of the 6DOF-utilization of a Handheld Augmented Reality Museum Application

This study explored the relatively new field of public mobile handheld AR and how the touchscreen-based input of smartphones affects the way users aged 10-12 interact with the 6DOF (6 degrees of fr ...
A $2^{n/2}$-Time Algorithm for $\sqrt{n}$-SVP and $\sqrt{n}$-Hermite SVP, and an Improved Time-Approximation Tradeoff for (H)SVP.

We show a $2^{n/2+o(n)}$-time algorithm that finds a (non-zero) vector in a lattice $\mathcal{L} \subset \mathbb{R}^n$ with norm at most $\tilde{O}(\sqrt{n})\cdot \min\{\lambda_1(\mathcal{L}), \det(\mathcal{L})^{1/n}\}$, where $\lambda_1(\mathcal{L})$ is the length of a shortest non-zero lattice vector and $\det(\mathcal{L})$ is the lattice determinant. Minkowski showed that $\lambda_1(\mathcal{L}) \leq \sqrt{n} \det(\mathcal{L})^{1/n}$ and that there exist lattices with $\lambda_1(\mathcal{L}) \geq \Omega(\sqrt{n}) \cdot \det(\mathcal{L})^{1/n}$, so that our algorithm finds vectors that are as short as possible relative to the determinant (up to a polylogarithmic factor). The main technical contribution behind this result is new analysis of (a simpler variant of) an algorithm from arXiv:1412.7994, which was only previously known to solve less useful problems. To achieve this, we rely crucially on the ``reverse Minkowski theorem'' (conjectured by Dadush arXiv:1606.06913 and proven by arXiv:1611.05979), which can be thought of as a partial converse to the fact that $\lambda_1(\mathcal{L}) \leq \sqrt{n} \det(\mathcal{L})^{1/n}$. Previously, the fastest known algorithm for finding such a vector was the $2^{.802n + o(n)}$-time algorithm due to [Liu, Wang, Xu, and Zheng, 2011], which actually found a non-zero lattice vector with length $O(1) \cdot \lambda_1(\mathcal{L})$. Though we do not show how to find lattice vectors with this length in time $2^{n/2+o(n)}$, we do show that our algorithm suffices for the most important application of such algorithms: basis reduction. In particular, we show a modified version of Gama and Nguyen's slide-reduction algorithm [Gama and Nguyen, STOC 2008], which can be combined with the algorithm above to improve the time-length tradeoff for shortest-vector algorithms in nearly all regimes, including the regimes relevant to cryptography.
DETECTING THE ABANDONED BOREWELL USING IMAGE PROCESSING

The main goal of our project is to detect the abandoned borewells and report it to the corporation.Detection of uncovered borewells are carried out by processing a picture taken from a drone, the process is done byimage processing techniques. The captured is ispreprocessed and then it is segmented based on differentmethods.We can obtain better result,for the fastest computing time segmentation is based on K-Means clusteringmethod and for specificity the segmentation is based on edge detection method.
Proyectos formativos: una estrategia para promover el trabajo colaborativo en la educación superior

The objective of the communication is to analyze the characteristics of the training projects and their relationship with the development of collaborative work in Higher Education Institutions (HEI). As a study methodology, a documentary investigation was carried out, through the review and obtaining of information in various bibliographical sources. The results indicate that training projects are a strategy aimed at training people who, in a collaborative way and with the support of new technologies, are able to mobilize their knowledge in solving their most pressing context problems, so they differ from traditional educational practices, in which knowledge are transmitted by the teacher in an academic manner. In conclusion, it is necessary to continue carrying out theoretical and empirical studies that enable the educational change required by the society of our time.
Research Guides @ Fordham: Python: Getting Started

A collection of resources aimed at helping users get started and feel comfortable with using the Python programming language Resources for setting up Python on your computer
The Blockcard Protocol: It's the Proof-of-Thought That Counts

We identify a major security flaw in modern gift transaction protocol that allows for malicious entities to send questionable metadata to insecure1 recipients. To address these weaknesses we introduce the Blockcard protocol, a novel variant of Blockchain technology that uses an asymmetric proof-of-work CPU cost function over payload metadata to provide a cryptographically secure and efficient method of verifying that gift-givers thought enough about the recipients payload or lack thereof for it to count. This has the advantage of making it computationally infeasible and socially awkward for adversarial gift-givers to double-spend, spoof, or precompute their celebratory thoughts.