Reviewerly/exHarmony huggingface dataset:
기능적 전기 자극이 뇌졸중 환자의 균형과 보행에 미치는 영향

Purpose : This study was conducted to evaluate the effects of FES with abdominal muscle contraction before virtual reality training on balance and gait ability in patients of stroke patients.Methods : The subjects were 30 stroke patients who satisfied the selection criteria. They were randomly assigned to a group receiving functional electrical stimulation with a virtual reality training program (the experiment group, n=15) and placebo functional electrical stimulation with a virtual reality training program (the control group, n=15). The program consisted of three 30-min sessions per week for six weeks. The timed up and go test (TUG), the BT4, the G-WALK were used to estimate subjects’ balance, gait before and after the program. For the experiment group, the functional electrical stimulation was applied to the external oblique and the rectus abdominis, For the control group, the same program and the placebo functional electrical stimulation were applied.Results : There were significant improvements in the subscales of the balance and gait ability test of those who participated in the functional electrical stimulation, while the control group showed no significant changes.Conclusion : Therefore, functional electrical stimulation with virtual reality effectively improved the balance and gait ability in patients with chronic stroke.
Using Panoramic Videos for Multi-person Localization and Tracking in a 3D Panoramic Coordinate

3D panoramic multi-person localization and tracking are prominent in many applications, however, conventional methods using LiDAR equipment could be economically expensive and also computationally inefficient due to the processing of point cloud data. In this work, we propose an effective and efficient approach at a low cost. First, we obtain panoramic videos with four normal cameras. Then, we transform human locations from a 2D panoramic image coordinate to a 3D panoramic camera coordinate using camera geometry and human bio-metric property (i.e., height). Finally, we generate 3D tracklets by associating human appearance and 3D trajectory. We verify the effectiveness of our method on three datasets including a new one built by us, in terms of 3D single-view multi-person localization, 3D single-view multi-person tracking, and 3D panoramic multi-person localization and tracking. Our code and dataset are available at \url{https://github.com/fandulu/MPLT}.
Analysis of Lower Bounds for Simple Policy Iteration.

Policy iteration is a family of algorithms that are used to find an optimal policy for a given Markov Decision Problem (MDP). Simple Policy iteration (SPI) is a type of policy iteration where the strategy is to change the policy at exactly one improvable state at every step. Melekopoglou and Condon [1990] showed an exponential lower bound on the number of iterations taken by SPI for a 2 action MDP. The results have not been generalized to $k-$action MDP since. In this paper, we revisit the algorithm and the analysis done by Melekopoglou and Condon. We generalize the previous result and prove a novel exponential lower bound on the number of iterations taken by policy iteration for $N-$state, $k-$action MDPs. We construct a family of MDPs and give an index-based switching rule that yields a strong lower bound of $\mathcal{O}\big((3+k)2^{N/2-3}\big)$.
Robustness Evaluation of the Butterfly Optimization Algorithm on a Control System

In this paper, the Butterfly Optimization Algorithm (BOA) proposed by [1] is adopted to optimize the parameters of a designed Lead-Lad Controller so as to obtain a stabilized control system. Numerical analysis was carried out for BOA on the control problem and the results are compared to those obtained from the well known Genetic Algorithm (GA) and Differential Evolution (DE) Algorithm. BOA performs better in terms of eigenvalue analysis but similar to GA and DE in terms of optimizing the minimum damping coefficient for the control system
Quantum Dots CdSe/ZnS as a Source Array of Entangled States

A quantum dot is a quantum system in classical space with unique characteristics, as a result of a large quantum limitation.The experimental results of this chapter substantiate the ability of quantum dots to play a key role in purely quantum processes, for example, teleportation of quantum objects, and the generation of macroscopic quantum gravity force and, of course, are a qubit in quantum computing.A quantum dot has the ability to capture (capture) a photo-induced charge carrier by a surface defect of its crystal structure and, thereby, create a second stable long-lived quantum state, which is a necessary requirement for a qubit.This ability puts a quantum dot out of competition with respect to many other quantum objects, like qubits, in terms of the simplicity and cheapness of their continuous generation in standard laboratory conditions.Quantum dots have received wide recognition because of their unique exciton luminescence characteristics; this chapter substantiates a fundamentally new area to use quantum dots in the development and study of both fundamental and applied physics.
Selkirk College Library: Business: Books

A subject guide to help locate print and electronic resources for Business Books relevant to the subject.
Sparsely Grouped Input Variables for Neural Networks

In genomic analysis, biomarker discovery, image recognition, and other systems involving machine learning, input variables can often be organized into different groups by their source or semantic category. Eliminating some groups of variables can expedite the process of data acquisition and avoid over-fitting. Researchers have used the group lasso to ensure group sparsity in linear models and have extended it to create compact neural networks in meta-learning. Different from previous studies, we use multi-layer non-linear neural networks to find sparse groups for input variables. We propose a new loss function to regularize parameters for grouped input variables, design a new optimization algorithm for this loss function, and test these methods in three real-world settings. We achieve group sparsity for three datasets, maintaining satisfying results while excluding one nucleotide position from an RNA splicing experiment, excluding 89.9% of stimuli from an eye-tracking experiment, and excluding 60% of image rows from an experiment on the MNIST dataset.
Partial Uploads in HTTP

This document specifies a new media type intended for use in PATCHpayloads that allows a resource to be uploaded in several segments,instead of a single large request.
Research Guides: E-Resources Survival Guide: Home

Information to help you get the most out of our e-resources during the period of online learning and assessment.
Working with first-order proofs and provers

Verifying software correctness has always been an important and complicated task. Recently, formal proofs of critical properties of algorithms and even implementations are becoming practical. Currently, the most powerful automated proof search tools use first-order logic while popular interactive proof assistants use higher-order logic. We present our work-in-progress set of tools that aim to eventually provide a usable first-order logic computer-assisted proof environment.
Open Source Cloud Computing Simulators – A Study on Issues and Challenges

Cloud computing is a technology to retrieve the Information Technology (IT) services from the internet with the help of tools of web based and web application which resources are retrieved from the Internet through web-based tools and applications. The various cloud computing advancements has been correlated in the virtualization. The cloud-based Platform as a service required to maintain the total lifecycle of building and it produce the web-based (cloud) applications with no cost and without any complexity of retail and managing the fundamental hardware, software, provisioning, and hosting. This paper has focused on cloud computing basics and various types of cloud and services. It was also explore about the features of cloud and deployment. The study of investigation among many several cloud computing investigate security and privacy issues
Extending a Knowledge-Based System with Learning Capacity.

Informal caregivers often complain about missing knowledge. A knowledge-based personalized educational system is developed, which provides caregiving relatives with the information needed. Yet, evaluation against domain experts indicated, that parts of the knowledge-base are incorrect. To overcome these problems the system can be extended by a learning capacity and then be trained further utilizing feedback from real informal caregivers. To extend the existing system an artificial neural network was trained to represent a large part of the knowledge-based approach. This paper describes the found artificial neural network's structure and the training process. The found neural network structure is not deep but very wide. The training terminated after 374.700 epochs with a mean squared error of 7.731 ∗ 10-8 for the end validation set. The neural network represents the parts of the knowledge-based approach and can now be retrained with user feedback, which will be collected during a system test in April and May 2019.
The English Microcurriculum and its influence on the development of communicative competences

The objective of the present investigation was to make a diagnosis in order to determine if the Microcurriculum influences in the development of communicative competences in English language learning in students of Ecotourism Engineering School, Natural Resources Faculty, at Escuela Superior Politecnica de Chimborazo, in Riobamba. This research was framed in the constructivist theory because teacher and students were protagonists of building their own knowledge according to their needs and context that they live. The investigation was of field, documentary and bibliographic because it was investigated in the place of the facts through the collection of information and with the association of the variables of study, which allowed determining how the microcurricular design of English influences on the development of the communicative competences of the students. The results obtained were tabulated, analyzed, interpreted and subjected to the Chi square statistical test; it allowed testing the hypotheses and obtaining as a result that the Microcurriculum influences the development of the communicative competences, since the contents do not have enough practical activities to facilitate the best development of skills. Thus, it has been proposed to redesign the microcurricular planning and implement the use of manuals with practical activities aimed at developing the basic skills of the English language: a text with activities for students and a guide text with their answers for the teacher.
ANALISA METODE SHA384 UNTUK MENDETEKSI ORISINALITAS CITRA DIGITAL

Cryptography is used to maintain the security of content or information that is personal from anyone except those who have the authority or secret key to open the information that has been encoded. Along with the development of technology and computers now, the increase in computer crime has also increased, especially in image manipulation. Many methods are used and carried out by people to manipulate images that have a detrimental effect on others. The originality of a digital image is the authenticity of the image in terms of colors, shapes, objects and information without the slightest change from the other party. Nowadays many digital images circulating on the internet have been manipulated and even images have been used for material fraud in the competition, so we need a method that can detect the image is authentic or fake like a diploma.In this study, the authors used the SHA-384 method to detect the originality of digital images, using this method an image that is still doubtful of its authenticity can be found out that the image is authentic or fake.Keywords: Originality, Citra, SHA-384
CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning

Joint extraction of entities and relations has received significant attention due to its potential of providing higher performance for both tasks. Among existing methods, CopyRE is effective and novel, which uses a sequence-to-sequence framework and copy mechanism to directly generate the relation triplets. However, it suffers from two fatal problems. The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction. It also cannot predict multi-token entities (e.g. \textit{Steven Jobs}). To address these problems, we give a detailed analysis of the reasons behind the inaccurate entity extraction problem, and then propose a simple but extremely effective model structure to solve this problem. In addition, we propose a multi-task learning framework equipped with copy mechanism, called CopyMTL, to allow the model to predict multi-token entities. Experiments reveal the problems of CopyRE and show that our model achieves significant improvement over the current state-of-the-art method by 9% in NYT and 16% in WebNLG (F1 score). Our code is available at https://github.com/WindChimeRan/CopyMTL
An Empirical Investigation into Personal Characteristics' Effect on Choosing Complaint Channels

For companies, complaints are a valuable customer reaction to dissatisfaction. They enable the company to respond to customer issues to prevent them from changing supplier or spreading negative word-of-mouth communication. Previous research identified various influencing factors of complaint behaviour. However, it has been scarcely considered which aspects influence the selection of the complaint channel (e. g. telephone, social media). Therefore, a 1x2 experimental study (n = 244) was conducted. Results reveal effects of personal characteristics (aggressiveness, argumentativeness and social anxiety) on complaint channel choice. A moderating effect of failure severity was also partially found. From a managerial perspective, it is recommended to provide various complaint options. This would allow the disappointed consumer to choose an adequate complaint channel depending on his or her personality.
KINERJA MENGAJAR DOSEN DAN PEMANFAATAN SARANA PRASARANA PEMBELAJARAN DALAM MENINGKATKAN EFEKTIVITAS MATA KULIAH PRODUKTIF

Effectiveness of Productive Subject Learning is considered very important, especially for the NHI Bandung College of Tourism in realizing educational goals and producing graduates who have skills and are competent in the field of tourism.The research method used is a survey method with a quantitative approach.The sample is part of a population of 139 students.The results of this study indicate 1) the effectiveness of learning productive subjects in the very high category 2) lecturer teaching performance in the high category, 3) the utilization of learning infrastructure in the very high category, then jointly teaching lecturer performance and utilization of influential learning infrastructure positive for the effectiveness of learning productive subjects by 43.7%.
The impact of time on hashtag reuse in twitter: A cognitive-inspired hashtag recommendation approach

In our work [KPL17], we study temporal usage patterns of Twitter hashtags, and we use the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R [An04] to model how a person reuses her own, individual hashtags as well as hashtags from her social network. The BLL equation accounts for the time-dependent decay of item exposure in human memory. According to BLL, the usefulness of a piece of information (e.g., a hashtag) is defined by how frequently and how recently it was used in the past, following a time-dependent decay that is best modeled with a power-law distribution. We used the BLL equation in our previous work to recommend tags in social bookmarking systems [KL16]. Here [KPL17], we adopt the BLL equation to model temporal reuse patterns of individual (i.e., reusing own hashtags) and social hashtags (i.e., reusing hashtags, which has been previously used by a followee) and to build a cognitive-inspired hashtag recommendation algorithm. We demonstrate the efficacy of our approach in two empirical social networks crawled from Twitter, i.e., CompSci and Random (for details about the datasets, see [KPL17]). Our results show that our approach can outperform current state-of-the-art hashtag recommendation approaches.
Project Management for the Alma Migration and Beyond

Moving to the Alma library services platform (LSP) from an integrated library system isn’t just a one-time project, but an opportunity for a library or consortium to improve its project management competency. The presenter will provide an overview, from the project management perspective, of the ILS-to-Alma migration process and will describe how learning and experience gained during the migration period can support later LSP work (such as extending Alma functionality and performing systems integrations via APIs). Finally, the presenter will provide information on tools for improving personal and organizational project management, including engagement with the Project Management Institute (PMI).
Constrained R-CNN: A general image manipulation detection model

Recently, deep learning-based models have exhibited remarkable performance for image manipulation detection. However, most of them suffer from poor universality of handcrafted or predetermined features. Meanwhile, they only focus on manipulation localization and overlook manipulation classification. To address these issues, we propose a coarse-to-fine architecture named Constrained R-CNN for complete and accurate image forensics. First, the learnable manipulation feature extractor learns a unified feature representation directly from data. Second, the attention region proposal network effectively discriminates manipulated regions for the next manipulation classification and coarse localization. Then, the skip structure fuses low-level and high-level information to refine the global manipulation features. Finally, the coarse localization information guides the model to further learn the finer local features and segment out the tampered region. Experimental results show that our model achieves state-of-the-art performance. Especially, the F1 score is increased by 28.4%, 73.2%, 13.3% on the NIST16, COVERAGE, and Columbia dataset.
Multiframe-based Adaptive Despeckling Algorithm for Ultrasound B-mode Imaging with Superior Edge and Texture

Removing speckle noise from medical ultrasound images while preserving image features without introducing artifact and distortion is a major challenge in ultrasound image restoration. In this paper, we propose a multiframe-based adaptive despeckling (MADS) algorithm to reconstruct a high-resolution B-mode image from raw radio-frequency (RF) data that is based on a multiple input single output (MISO) model. As a prior step to despeckling, the speckle pattern in each frame is estimated using a novel multiframe-based adaptive approach for ultrasonic speckle noise estimation (MSNE) based on a single input multiple output (SIMO) modeling of consecutive deconvolved ultrasound image frames. The elegance of the proposed despeckling algorithm is that it addresses the despeckling problem by completely following the signal generation model unlike conventional ad-hoc smoothening or filtering based approaches, and therefore, it is likely to maximally preserve the image features. As deconvolution is a necessary pre-processing step to despeckling, we describe here a 2-D extension of the SIMO model-based 1-D deconvolution method. Finally, a complete framework for the generation of high-resolution ultrasound B-mode image has been also established in this paper. The results show 8.55-15.91 dB, 8.24-14.94 dB improvement in terms of SNR and PSNR, respectively, for simulation data and 2.22-3.17, 13.24-32.85 improvement in terms of NIQE and BRISQUE, respectively, for in-vivo data compared to the traditional despeckling algorithms. Visual comparison shows superior texture, resolution, details of B-mode images offered by our method compared to those by a commercial scanner, and hence, it may significantly improve the diagnostic quality of ultrasound images.
A Data Driven Approach to Learning The Hamiltonian Matrix in Quantum Mechanics

We present a new machine learning technique which calculates a real-valued, time independent, finite dimensional Hamiltonian matrix from only experimental data. A novel cost function is given along with a proof that the cost function has the theoretically correct Hamiltonian as a global minimum. We present results based on data simulated on a classical computer and results based on simulations of quantum systems on IBM's ibmqx2 quantum computer. We conclude with a discussion on the limitations of this data driven framework, as well as several possible extensions of this work. We also note that algorithm presented in this article not only serves as an example of using domain knowledge to design a machine learning framework, but also as an example of using domain knowledge to improve the speed of such algorithm.
Transaction Confirmation Time Prediction in Ethereum Blockchain Using Machine Learning

Blockchain offers a decentralized, immutable, transparent system of records. It offers a peer-to-peer network of nodes with no centralised governing entity making it unhackable and therefore, more secure than the traditional paper-based or centralised system of records like banks etc. While there are certain advantages to the paper-based recording approach, it does not work well with digital relationships where the data is in constant flux. Unlike traditional channels, governed by centralized entities, blockchain offers its users a certain level of anonymity by providing capabilities to interact without disclosing their personal identities and allows them to build trust without a third-party governing entity. Due to the aforementioned characteristics of blockchain, more and more users around the globe are inclined towards making a digital transaction via blockchain than via rudimentary channels. Therefore, there is a dire need for us to gain insight on how these transactions are processed by the blockchain and how much time it may take for a peer to confirm a transaction and add it to the blockchain network. This paper presents a novel approach that would allow one to estimate the time, in block time or otherwise, it would take for a mining node to accept and confirm a transaction to a block using machine learning. The paper also aims to compare the predictive accuracy of two machine learning regression models- Random Forest Regressor and Multilayer Perceptron against previously proposed statistical regression model under a set evaluation criterion. The objective is to determine whether machine learning offers a more accurate predictive model than conventional statistical models. The proposed model results in improved accuracy in prediction.