Reviewerly/exHarmony huggingface dataset:
A Gesture-based Recognition System for Augmented Reality

With the geometrical improvement in Information Technology, current conventional input devices are becoming increasingly obsolete and lacking. Experts in Human Computer Interaction (HCI) are convinced that input devices remain the bottleneck of information acquisition specifically in when using Augmented Reality (AR) technology. Current input mechanisms are unable to compete with this trend towards naturalness and expressivity which allows users to perform natural gestures or operations and convert them as input. Hence, a more natural and intuitive input device is imperative, specifically gestural inputs that have been widely perceived by HCI experts as the next big input device. To address this gap, this project is set to develop a prototype of hand gesture recognition system based on computer vision in modeling basic human-computer interactions. The main motivation in this work is a technology that requires no outfitting of additional equipment whatsoever by the users. The gesture-based had recognition system was implemented using the Rapid Application Development (RAD) methodology and was evaluated in terms of its usability and performance through five levels of testing, which are unit testing, integration testing, system testing, recognition accuracy testing, and user acceptance testing. The test results of unit, integration, system testing as well as user acceptance testing produced favorable results. In conclusion, current conventional input devices will continue to bottleneck this advancement in technology; therefore, a better alternative input technique should be looked into, in particularly, gesture-based input technique which offers user a more natural and intuitive control.
On the Prediction of Possibly Forgotten Shopping Basket Items

With the easy availability of shopping transaction data, it is now possible to use historical information to enhance the shopping experience of shoppers. In particular, we focus on the issue of forgotten items. We have all had the experience of shopping at a supermarket and either forgetting to buy certain items or not realizing that certain items are needed. In this paper, we focus on predicting such items during a shopper’s visit and providing hints to the shopper that they may in fact need certain items that were not purchased. Note that, by providing hints for forgotten items, the shopper benefits by avoiding a return trip to the supermarket and the supermarket benefits because the shopper purchases additional items. We provide examples to illustrate the utility of the proposed approach.
Derive Summary Using Latent Factors

Understanding a big document by reading it, or finding intended information from a diverse and vast collection of information from online sources is the most time consuming and most tedious task in today era of information explosion. The solution to this problem is to find a summary of the document that explains the important aspects of the document in brief. Trivial approaches to obtain a summary of the document are based on Clustering, Text Ranking and Lexical Chains generate summary but fail to address duplicates problem. The proposed method makes use of latent factors of weighted algorithms and finds topics. And the topics frame the base for building summary.
Decentralized Cloud Scheduling via Smart Contracts. Operational constraints and costs

The applications of Blockchain and Smart Contracts cover all aspects of digital information processing, varying from Asset Management platforms to Online Shops and Digital Identity Management. Several start-up companies and fellow researchers are interested in creating decentralized Clouds using the Blockchain as the mechanism for synchronization, identity management, and payment. This paper presents a conceptual platform exploiting Ethereum Smart Contracts to handle resource scheduling for Cloud Applications, investigates the operational constraints and measures the cost and latency for three scheduling strategies. In addition, it presents the constraints under which instances can be terminated asynchronously, which increases the cost of the transaction. The minimum cost for executing an application is similar to paying 12 hours for a standard Virtual Machine in today's Clouds if the Smart Contracts are deployed on the Ethereum Network, which limits the interest of users with short running applications.
Stumbling to Syntax: narrative, structure, and syntax in Steven Mackey’s Stumble to Grace

Author(s): Neufeld, Zachery Daniel | Advisor(s): Lefkowitz, David S; Krouse, Ian | Abstract: The primary focus of this dissertation is a theoretical analysis of Steven Mackey’s piano concerto, Stumble to Grace, commissioned by the Los Angeles Philharmonic, New Jersey Symphony Orchestra, and St. Louis Symphony, and premiered in 2011. Inspired by his son “learning to become human,” the concerto moves through 5 stages (movements) that progress from awkward all-thumbs playing in the first stage—which represents an immature, childlike way of playing—to complex, mature counterpoint in the fifth stage, which represents the arrival as a fully mature, sophisticated human being. The motivation for this study comes out of the dearth of analytical study of Mackey’s music, despite its originality and growing reputation. Two questions are central to this study: 1) how does Stumble to Grace interact with and comment on the history of the concerto genre? and 2) how does Mackey create a musical syntax with his personal harmonic language? The primary method for the analysis of harmonic syntax is set-theory using Pitch-Class set notation. Edward Pearsall’s method of analyzing consonance and dissonance in atonal music is used, as well as Santa’s Modulo-7 approach to post-tonal diatonic music. The study will show that Stumble to Grace, like many concertos before it, invites the listener to consider the role of virtuosity in concert music, but in a new way: the concept of virtuosity is center-stage in the narrative of the piece. The narrative generates a progressive form, in which individual stages have returns to their opening material while the piece as a whole does not have returns or recapitulations (beyond structural pitch-class sets). The results of the analysis reveal (1) a motivic, thematic, and harmonic design interconnected by important pitch-class sets, (2) phrase-level harmonic syntax that is partially controlled by those sets, and (3) a theory of the logic of consonant and dissonant sonorities within the piece. It is my hope that this study not only furthers the understanding of Mackey’s music among theorists and composers, but also that conductors and concert programmers looking for connections to programmatic works about a life’s journey discover Stumble to Grace.
PEMEROLEHAN KOSAKATA DASAR NOMINA BAHASA INDONESIA ANAK USIA 3 TAHUN (STUDI KASUS DI KELURAHAN LEBAK BANDUNG KOTA JAMBI)

This research is aimed at identifying the acquisition of basic noun vocabulary of Indonesian language on 3 years old childern in Lebak Bandung area. Thsi research is qualitative descriptive. In conducting the data, the question answer technique is used by showing pictures to 3 years old children in Kelurahan Lebak Bandung. Based on the result of the research, it can be found that 3 years old children are able to pronounce one to six vocabulary which categorize good and less good with their own words. Keywords : Acquisition, Indonesian Language Noun, 3 Years Old Children
Research on Data-driven Teaching Mode of Teachers

With the rapid development of smart campus and educational informatization, teaching activities are becoming more and more intelligent and scientific.When data science and education science are deeply integrated, teaching activities will be more precise and personalized.This study first summarizes the data types of middle school students in teaching, then explores the combination mode of student data and teacher teaching at home and abroad, and finally gives the implementation mode and scheme of data-driven teaching in teacher teaching.According to the implementation plan of data-driven teaching, teachers can use the data generated by students to achieve accurate teaching.On the one hand, it can improve the teaching efficiency of teachers.On the other hand, it can grasp the short board of students' performance and improve students' performance.
The Application of Extended Weighted Tree Similarity Algorithm for Similarity Searching

Unemployment is a term given to someone who has not have a job or looking for a job. High unemployment can also affect the economy because it can cause poverty and other social problems. To increase labour force participation, media for searching a job are needed to provide job information and recommendations that are match with job seekers profile.Extended Weighted Tree Algorithms is one of the semantic search algorithms that existing metadata consists of trees, labeled nodes, labeled and weighted branches. The similarity calculation will be done by comparing two tree structures, namely the job advertisement tree with the job search tree carried out by job applicants. The extended weighted tree similarity algorithm is string matching on the leaf node. Thus, if the leaf node is the same then it will produce a value of 1, whereas if it is different it produces a value of 0.The metadata of the Extended Weighted Tree Similarity Algorithm is arranged based on semantic information such as taxonomy, ontology, preference, synonym, homonym, and stemming. The metadata can be used to represented searching results with higher precision than full-text. This research produces an accuracy rate of 88%, precision level of 84.30% and recall of 93.40%.
Multimodal Interfaces: A Study on Speech-Hand Gesture Recognition

One common unimodal human-computer interface is via a formal and discrete behavior on physical or virtual button clicks. That method may not be convenient for some people with knowledge or physical limitations. The raise for more pervasive, subtly blending computers in daily tasks may also find the method to be unnatural and inconvenient. Combination of modalities, or multimodal interaction is suggested to have advantages over unimodal input. Synergic multiple input and output modal is more expressive and natural. Specifically, studies have suggested that a combination of speech and gesture is preferred as being more effective and natural than speech or gesture alone. There are many successful approaches in providing robust gesture-only and speech-only interfaces, however, under extreme environments, unimodal approaches have user experience issues. To address the issue, the authors seek to explore and conduct a preliminary study and evaluation of speech-hand gesture recognition using Leap Motion and Windows Speech Recognition API. A conceptual framework would be developed as a result of this study.
New composition strategies and the EMS Synthi 100

The Synthi 100 never lived up to its promise. Some of its features were terribly badly implemented, counterintuitive, and prone to failure. Three notable works made on the machine at the WDR Studio in Cologne – Rolf Gehlhaar’s “Funf Deutsche Tanze”, Laszlo Dubrovay’s “Sohaj” and Karlheinz Stockhausen’s “Sirius” – are testimony to the composers’ dedication in pursuing their intentions despite the almost crippling pushback of the technology during the realisation process.I examine some of Stockhausen’s innovative composition and realisation techniques in “Sirius” by means of practical recreations using the restored Synthi 100 at KSYME, Athens, and the original machine at the WDR Museum Studio, Cologne. The examination of techniques is informed by composition sketches and correspondence, and interviews with WDR technician Volker Muller, and intern Otto Kranzler, and the recreation of certain elements illuminates the details of the performance practice involved in making the original electronic music.In order to illustrate some finer details, I have recreated some of Stockhausen’s original Synthi patches in Max, showing the potential for employing these in contemporary practice with rather more affordable technology.The key ideas are how the design flaws of the Synthi contributed to the astonishing gap between cost and effectiveness of the instrument on the one hand, and its tantalising potential for opening up new kinds of approaches to electronic music on the other.
Lightweight Deep Embeddings Fusion Methods for Face Verification on Mobile Devices

With the emergence of deep learning technology, face verification performance on the LFW benchmark outperformed human-level accuracy. In recent, face verification technique is applied to mobile devices to authenticate owner of them. Face verification on mobile devices needs to be considered with both performance and computational cost which is trade-off. In this paper, we propose lightweight network architectures and fusion methods for efficient face verification task on mobile devices. The proposed lightweight networks achieved comparable accuracy on both LFW and YTF datasets with only about 9x fewer size of model and 2x shorter inference time than Inception-resent-v1 which is a deeper network architecture. In addition, proposed score-level fusion method shows improvement of 2.38% on VR@FAR=1e-6 on LFW BLUFR and that of 0.28% on accuracy on YTF than single lightweight network.
LibGuides: Academic Areas of Interest: Manufacturing & Construction

The Harper College Library supports the College’s Academic Areas of Interest with a wide range of resources, study spaces and services--including research consultations and 24/7 online chat.
A New Ensemble Approach to Predict Breast Cancer

The primary objective of using a variety of Data mining techniques in health care domain is to construct a useful model that can effectively interpret the data from a cluster of medical datasets. To reveal the hidden pattern in data, Data Mining techniques and algorithms rely on a wide variety of machine learning techniques. Classification along with prediction techniques play an essential role in medical decision making. This type of knowledge-based system can aid doctors in predicting the disease accurately. The main objective of this paper is to create an ensemble of classification algorithms to classify the cancer data set with higher classification accuracy when compared with the existing classification algorithms available in the literature.
Danger Detection based on Fusion Analysis of Multimodal Data from a Smartphone and Smartwatch

In recent days, technologies for detecting dangerous situations have been developed to support public safety. This paper proposes a danger detection technology based on fusion analysis of a smartphone's and smartwatch's multimodal data that include data from accelerometer and gyroscope sensors. The proposed danger detection technology enables fast and accurate danger detection by recognizing movements of an arm based on analysis of sensor data from a smartwatch and by recognizing movements of a leg based on analysis of sensor data from a smartphone. The fast and accurate danger detection of the proposed technology will help to enable fast and appropriate responses to dangers.
Optimized Latent Features for Deep Image Compression

The work presented in this paper is focused on deep image compression where a neural network architecture is used to extract the small-sized latent features which encodes the full information of the input image. A common drawback in such system is low quality of the reconstructed image. This paper aims to alleviate this drawback by augmenting the latent feature using an optimization strategy. The values in a given latent features are updated iteratively to produce an output image with lower reconstruction loss. The proposed method has been evaluated MNIST dataset where the results shows that it could provide significant gain compared to the reconstruction results which are generated from the non optimized latent features.
Coronary Artery Motion Tracking Using Landmark Guided Point Set Registration

Coronary artery lesions are highly correlated with their movement patterns. However, until now few reports have been devoted to coronary motion analysis based on Computed Tomography Angiography (CTA). In this paper, a registration method is proposed to match the 3D coronary point sets between adjacent cardiac phases, derived from CTA. The proposed method considers the alignment problem as the estimation of a mixture probability density and makes full use of the global and local structure information of the point sets. Furthermore, the landmark points in some specific positions, such as coronary artery bifurcations, are exploited as a priori knowledge to impr-ove the matching accuracy. The movement function and corres-ponding relationship between two coronary point sets are obtained by the Expected Maximum (EM) algorithm. Exper-imental results demonstrate the effectiveness of the proposed algorithm for deformed and missing coronary point sets.
A Secure Density Peaks Clustering Algorithm on Cloud Computing

Cloud computing provides users with the convenience of data outsourcing computing at risk of privacy leakage, and clustering algorithms have high computational overhead when dealing with large datasets. Aiming at the above problems, this paper presents a security density peak clustering algorithm based on grid in hybrid cloud environment. First, the client uses the homomorphic encryption method to build the encrypted objects with user datasets. Second, the client uploads the encrypted objects to the cloud servers to implement the security protocols proposed in this paper. Finally, the cloud servers return the perturbation clustering results to the client to eliminate the disturbance. In the proposed scheme, only encryption and removing perturbation are performed on the client, ensuring that the client has lower computational complexity. Security analysis and experimental results show that the scheme proposed in this paper can improve the efficiency and accuracy of clustering algorithm under the premise of protecting user privacy.
The method for hierarchical context awareness based on activity recognition

We propose a hierarchical context awareness method based on human activity recognition (HAR). More specifically, the smart phone collects sensor data including wearable devices and peripheral IoE sensors, classifies the user's simple actions based on machine learning, and transmits only the changed context information to the server to provide quick context aware service. At this time, in order to provide a service suited to the situation, the smart phone can provide connection request and connection reconfiguration with peripheral devices. The server provides high-level context awareness based on transmitted context information and various knowledge base data. At this time, knowledge base data can be newly generated based on machine learning, and context information history can be used to extract user's behavior pattern, Situation prediction, and personalized behavior analysis model.
Inquiry and Practice of Mobile Interactive Internent Classroom Construction Based on UMU

In view of the fact that the traditional classroom teaching interaction is inactive and less effective, in which the students' initiative is not fully involved, accordingly we have to construct a more vivid, robust, and effective teaching mode to meet the requirements of the huge demands of qualified higher education reform in the Internet era. In this paper, we proposed a new internet based interactive classroom teaching pattern with the platform called UMU. With the help of UMU, we can design various kinds of interaction actions which can awake the enthusiasm and eagerness of the students to make an inquiry learning effectively. For example, we can initiate a questionnaires, or organize some quiz, or take some exercises photographs accomplished by students, or distribute assignments, or launch a collaborative work, and so on. Students can response this actions with their mobile phones within the classroom or not. We also give a brief talk about how the UMU can support the engineering education accreditation. In the end, we made a performance promotion evaluation under the course “compiler principle” to show the validity of our proposed newly teaching mode.
Mobile-Based Translation System for Cebuano Language with Object Detection for Travel Assistance using Neural Machine Translation

The Philippines as an archipelagic nation has the numerous number of dialects per island that hampers local tourist communication. Cebu is one of the top tourist destinations by locals also has a vast language hurdle compared to other local dialects. Because of huge language differences of native Cebuano and Filipino speakers, travelers in Cebu often experience difficulty in expressing their needs and requests to the island locals. The solution is a mobile-based translation system with object detection for travel assistance. The system uses Neural Machine Translation to translate Filipino to Cebuano language and vice versa based on the user's keyboard input, extracted text strings from a detected image, and the detected object itself. The Filipino-Cebuano model obtained 31.1 BLEU score, while the Cebuano-Filipino model obtained 31.6 BLEU score.
Computer Application for 3D Reconstruction from Medical Images and Integration of a Haptic Tool

This article presents the processes performed to obtain a medical image in three dimensions (3D), reconstructed from a stack of 2D medical images, acquired through a computerized axial tomography (CAT). Additionally, it is described the integration of a haptic tool within the developed system for the interaction and manipulation of the 3D virtual solid, generated in the application developed in this work, by a user who operates this tool (Phantom Omni).
An Issue Driven Teaching Method for Collaborative Programming Experiment

GitHub is a prevailing open and collaborative development platform for open source software. Previous studies showed its social coding service, features and methodology have a wealth of benefits for various disciplines of education, but one of the most valuable experience for computer education is the issue driven collaborative programming practice. The paper proposed an issue driven collaborative programming experiment teaching method, which utilized the issue feature as a start point, executed all programing teaching activities with the rest social coding features provided by GitHub, and implemented an integrated performance assessment program to automatically generate experiment reports. We conducted two semesters of teaching experiments on two programming courses, Android and Java Web. The trial courses output well expected results and the proposed method played its role effectively in programming education.