Reviewerly/exHarmony huggingface dataset:
How to Put Users in Control of their Data via Federated Pair-Wise Recommendation.

Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, privacy is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations, read books, bought items) with a central server. Unfortunately, harvesting and collection is at the basis of modern, state-of-the-art approaches to recommendation. Decreased users' willingness to share personal information along with minimization/protection policies (such as the European GDPR), can result in the data dilemma affecting data-intensive applications such as recommender systems (RS). We argue that scarcity of adequate due to privacy concerns can severely impair the quality of learned models and, in the long term, result in a turnover and disloyal customers with direct consequences for lives, society, and businesses. To address these issues, we present FPL, an architecture in which users collaborate in training a central factorization model while controlling the amount of sensitive leaving their devices. The proposed approach implements pair-wise learning to rank optimization by following the Federated Learning principles conceived originally to mitigate the privacy risks of traditional machine learning. We have conducted an extensive experimental evaluation on three Foursquare datasets and have verified the effectiveness of the proposed architecture concerning accuracy and beyond-accuracy objectives. We have analyzed the impact of communication cost with the central server on the system's performance, by varying the amount of local computation and training parallelism. Finally, we have carefully examined the impact of disclosed users' information on the quality of the final model and ...
Constructing Driver Hamiltonians for Several Linear Constraints.

Recent advances in the field of adiabatic quantum computing and the closely related field of quantum annealers has centered around using more advanced and novel Hamiltonian representations to solve optimization problems. One of these advances has centered around the development of driver Hamiltonians that commute with the constraints of an optimization problem - allowing for another avenue to satisfying those constraints instead of imposing penalty terms for each of them. In particular, the approach is able to use sparser connectivity to embed several practical problems on quantum devices than other common practices. However, designing the driver Hamiltonians that successfully commute with several constraints has largely been based on strong intuition for specific problems and with no simple general algorithm to generate them for arbitrary constraints. In this work, we develop a simple and intuitive algebraic framework for reasoning about the commutation of Hamiltonians with linear constraints - one that allows us to classify the complexity of finding a driver Hamiltonian for an arbitrary set of constraints as NP-Complete.
An Exact Quantum Query Algorithm Beyond Parity using Maiorana-McFarland (MM) type Bent functions.

The Exact Quantum Query model is the least explored query model, and almostall of the functions for which non-trivial query algorithms exist are symmetricin nature. In this paper we explore the Maiorana-McFarland(MM) type Bentfunctions, defined on all even $n$ variables. The (classical) DeterministicQuery Complexity ($D(f)$) of all functions in this class is $n$. In this regardwe construct an $\frac{n}{2} + \lceil \frac{n}{8} \rceil$ query exact quantumalgorithm that is not a parity decision tree and evaluates a non-symmetricsubclass of MM type Bent functions on $n$ variables consisting of$\left(2^{\lfloor \frac{n}{4} \rfloor}!\right)\left(2^{\lceil \frac{n}{4}\rceil}!\right) 2^{2^{\lfloor \frac{n}{4} \rfloor}}$ functions. Finally we alsoextend this technique for functions defined on odd values of $n$ using the Bentconcatenation method. To the best of our knowledge, this is the first algorithmbeyond parity for a general class of non-symmetric functions.
Archer Library: Library - Research Resources: Home: Home

Created for the new faculty orientation, but can be used by anyone conducting research at the University of Regina!
LibGuides: Project Muse Libguide for RHL Undergrad Students: Home

A walkthrough Libguide dedicated to showing the undergraduate students of the Russian History & Literature department how to better optimize their searches on the Project Muse database.
Logistic and Softmax Regression for Prognosis & Diagnosis of Breast Cancer Using Multilayered Neural Networks

Presently Breast Cancer is spiking as 3rd most dangerous cancer in the world. Breast Cancer has become the wide known epidemic as it occurs in all ages of women from adult to old ones too. The malignant tumor is the main cause, which is very hard to even identify with mammography & human discretion. Thus, advancement in technological researches have become a winning factor as Machine Learning and Artificial Neural Networks are helping researchers and doctors worldwide. The valuation of prediction is being done by using algorithms like Logistic Regression and SoftMax Regression to train models with data set Wisconsin Breast Cancer Data of UCI Machine Learning Repository. To perform all of these operations we have used Google Colab (Jupyter notebook backed by Python 3.x). The proposed work results into trained neural network model which is having much better accuracy when compares to the other pre-existing models.
The Influences of Experience Elements(4Es) of Cultural Tourism Destination on Autobiographical Memory, Attitude, Loyalty, and Support for Cultural Tourism Development

In the era of experiential economy, the demand for customer experience management is increasing to provide memorable experiences for cultural tourists. The purpose of this study was to examine the influences of experience elements(4Es) of cultural tourism destination on autobiographical memory, attitude, loyalty, and support for cultural tourism development. Excluding inappropriate data from the collected survey data, 309 samples were used for empirical analysis. The final valid data were analyzed using SPSS and AMOS statistical package, and the research hypothesis was verified through structural equation modeling. The results of the study are as follows. First, education, entertainment, and aesthetic experience had a significant effect on positive memory recall. Second, escapist, entertainment, and aesthetic experience had a significant effect on vividness of memory. Third, positive memory recall and vividness of memory had a significant effect on attitude, and attitude had a significant effect on loyalty. Fourth, loyalty had a significant effect on support for cultural tourism development. In the future, it is necessary to discover and brand a memorable tourism experience and to design an optimal tourism experience service for cultural tourist destinations.
ENHANCING ANTI-BIOFOULING PERFORMANCE ON POLYVINYLIDENE FLUORIDE (PVDF) MEMBRANE INTEGRATED BY GINGER EXTRACT

Selected paper pada ICChEAS 2019 untuk dipublikasi pada Asia Pacific Journal of Chemical Engineering Journal.
LibGuides: Library Tutorials: Citation Help

Online videos and step-by-step tutorials to use the Library resources and complete research assignments.
Predictive gaze in action selection within virtual reality

The human gaze is pivotal in motion planning and control. Gaze is typically directed at visual target sites prior to physical interactions with them. This proactive gaze (PEG) behavior has been obs ...
Deep Learning for Session Aware Conversational Agents

In the last 2 years the state of NLP research has made a huge step forward. Since the release of ELMo, a new race for the leading scoreboards of all the main linguistic tasks has begun. Several models came out every 2 months achieving promising results in all the major NLP applications, from QA to text classification, passing through NER. These great research discoveries coincide with an increasing trend for voice and textual technologies in the customer care market. One of the next biggest challenge will be the handling of multi-turn conversations, a types of conversation which differs from single-turn by the presence of the concept of session. A session is a set of related QA between the user and the agent to fulfill a single user request. A conversational agent has to be aware about the session to effectively carry on the conversation and understand when the goal can be achieved. The proposed work focused on three main parts: i) the study of the state of the art deep learning techniques for NLP ii) the presentation of a model, MTSI BERT (Multi Turn Single Intent BERT), using one of such NLP milestones in a multi-turn conversation scenario iii) The study of a real case scenario. The work takes in consideration both Recurrent Neural Networks and attention based models, as well as word embedding such as Word2Vec and Glove. The proposed model, based on BERT and biLSTM, achieves promising results in conversation intent classification, knowledge base action prediction and detect of end of dialogue session, to determine the right moment to fulfill the user request. The study about the realization of PuffBot, an intelligent chatbot to support and monitor asthma suffering children, shows how this type of technique could be an important piece in the development of future chatbot.
Guides: Bush Library: Faculty Resources during COVID-19 Outbreak: Online Collections

This guide will provide information on what resources, tools and services will be available for Hamline patrons during the COVID-19 outbreak.
LibGuides at USA: Marx Library: COVID-19 FAQ: Printing, Copying, Scanning, and Faxing

Here you find frequently asked questions related to:- Returning and Renewing Materials- Checking-Out materials- Finding Materials- Research Assistance- General Library Access- Using the McCall Library- Accessing Study spaces- Reservation
LibGuides: Research Introduction: Making a Research Plan

With the challenges presented by the Fall 2020 semester and the increased dependence on the UVA Library's online resources, this guide is intended to walk you through the basics of access to our online databases as well as tips and tricks for honing your
Human-computer interaction using eye-gaze : Formation of user interface design guidelines from a cognitive science perspective

Motor and communication disabilities are common conditions that may implicate restrictions in daily life. With development of eye tracking technology, a solution referred to as eye-gaze interaction ...
Unsupervised Learning of Useful and Interpretable Representations from Image Data

This master thesis tackles the problem of unsupervised learning of useful and interpretable representations from image data using deep Convolutional Neural Networks (CNN). Recent years have seen re ...
Contextualisation of Datasets for Better Classification Models: Application to Airbus Helicopters Flight Data

For helicopters, anticipating failures is crucial. To this end, the analysis of flight data allows to develop predictive maintenance approaches, for which Airbus Helicopters (AH) has proposed several solutions, some based on machine learning using predictive models. One recurrent problem in this setting is the contextualization of the data, that is to identify the data better fitting the phenomenon being modeled. Indeed, helicopters are complex systems going through different flight phases. Experts therefore have to identify the adequate ones, in which the selected flight parameters are stable and consistent with the studied problem. In this paper, we propose a generic solution to contextualize classification data, and present an experimental study on AH flight data: the results are encouraging and allow to keep domain experts involved the process.
WPSP: A Multi-correlated Weighted Policy for VM Selection and Migration for Cloud Computing

Using virtualization, cloud environments satisfy dynamically the computational resource necessities of the user. The dynamic use of the resources determines the demand for working hosts. Through virtual machine (VM) migrations, datacenters perform load balancing to optimize the resource usage and solve saturation. In this work, a policy, named WPSP (Weighted Pearson Selection Policy), is implemented to choose which virtual machines are more suitable to be migrated. For each VM, the policy evaluates both the CPU load and the Network traffic influence on the assigned host. The corresponding Pearson correlation coefficients are calculated for each of the VMs and then weighted in order to provide a relationship between the values and the host behavior. The main goal is to clearly identify and then migrate the VMs that are responsible of the Host saturation but also considering their communications. Using the CloudSim simulator, the policy is compared with the rest of heuristic techniques in the literature, resulting in a reduction of 89% in the number of migrations, and thus reducing the use of bandwidth (5%), network saturation (20%) and over-saturated hosts (51%). Additionally, an improved VM allocation technique to reduce the distance the VMs must travel in order to be migrated is presented, obtaining an average reduction of 87% in the quantity of migrated data.
Guides: Animation Research Guide: Common Course Textbooks

Curated resources to start your research on 2D animation, 3D animation, illustration, and visual effects.
Research Guides: ADV 208: The Big Idea in Advertising (Fall 2020): Digital New York Times

An SU Libraries research starting points guide for students enrolled in ADV 208 (course sections taught by Profs Brian Sheehan and Mel White)
Everett Library: QCP 410 - Queens Culminating Experience : Finding Experts

A guide to databases and websites to assist you with your research for this senior level class. How can you find an expert for your interview?
Depth Image Inpainting method based on sparse gradient prior

Abstract This paper solves the inpainting problem of single depth images. depth images are regarded as natural images without texture. Because of the sparsity property of natural images and the textureless property of depth images, we propose a similar group-based sparse model with sparse gradient regularization. For one thing, the similar group-based sparse model can better represent the local smooth and nonlocal self-similarity. For another, the sparse gradient regularization can better represent the textureless properties. The proposed algorithm takes advantage of the properties of depth images. The experimental results show the effect of the proposed algorithm.
The effective entropy of next/previous larger/smaller value queries

We study the problem of storing the minimum number of bits required to answer next/previous larger/smaller value queries on an array A of n numbers, without storing A. We show that these queries can be answered by storing at most 3.701n bits (the time for answering queries is O(n)). Our result improves the result of Jo and Satti (2016) [12] that gives an upper bound of 4.088n bits for this problem.
Comparative Study of Virtual Machines and Containers for DevOps Developers

This paper presents a comparative study of virtual machines (VMs) and containers for DevOps developers. The study explores the benefits and drawbacks of each technology in terms of their functionality, performance, security, and resource utilization. The paper examines the underlying architecture of VMs and containers, and how they differ from each other. The study includes a series of experiments that compare the performance and resource utilization of VMs and containers in different scenarios. The experiments evaluate factors such as startup time, memory usage, disk I/O, network latency, scalability, and administrative overhead. Finally, the paper provides recommendations for DevOps developers on which technology to choose based on their specific requirements and use cases. Overall, the study aims to provide a comprehensive understanding of the strengths and limitations of VMs and containers, helping developers to make informed decisions when choosing between them.
Discovering Gated Recurrent Neural Network Architectures

Gated recurrent networks such as those composed of Long Short-Term Memory (LSTM) nodes have recently been used to improve state of the art in many sequential processing tasks such as speech recognition and machine translation. However, the basic structure of the LSTM node is essentially the same as when it was first conceived 25 years ago. Recently, evolutionary and reinforcement-learning mechanisms have been employed to create new variations of this structure. This chapter proposes a new method, evolution of a tree-based encoding of the gated memory nodes, and shows that it makes it possible to explore new variations more effectively than other methods. The method discovers nodes with multiple recurrent paths and multiple memory cells, which lead to significant improvement in the standard language modeling benchmark task. The chapter also shows how the search process can be speeded up by training an LSTM network to estimate performance of candidate structures, and by encouraging exploration of novel solutions. Thus, evolutionary design of complex neural network structures promises to improve performance of deep learning architectures beyond human ability to do so.
Assigning Rated Items to Locations in Non-List Display Layouts

One of the most common ways in which results are displayed by an information retrieval system is in the form of a list, in which the most relevant results appear in the first positions. Today's large screens, however, allow one to create more complex displays of results, especially in cases such as image retrieval, in which each unit returned is fairly compact. For these layouts the simple list model is no longer valid, since the relations between the slots in which the results are placed do not form a sequence, that is, the relation among them is no longer that of a total order. In this paper we model these layouts as partial orders and show that a “stalwart display” property (a layout in which items' relevance is unambiguously conveyed by their display position) can be obtained only in the case of lists. For the other layouts, we define two classes of representation functions: “safe” functions (which display results without adding spurious structure) and “rich” functions (which do not drop any structure from the result set), as well as an algorithm to optimally display fully ordered result sets in arbitrary display layouts.
FeynmanPAQS: a graphical interface program for photonic analog quantum computing

We present a user-friendly software for photonic analog quantum computing with a graphical user interface (GUI) that allows for convenient operation without requiring programming skills. Hamiltonians can be flexibly set by either importing the waveguide position files or manually plotting the configuration on the interactive board of the GUI. Our software provides a powerful approach to theoretical studies of two-dimensional quantum walks, quantum stochastic walks, multiparticle quantum walks, and boson sampling, which may all be feasibly implemented in the physical experimental system on photonic chips, and it will inspire a rich diversity of applications for photonic quantum computing and quantum simulation. We have improved algorithms to ensure the efficiency of permanent calculation and provided case studies on educational uses, which bring users easier access to the studies of photonic quantum simulation.
Secure Communication Using Scramble Phase Assisting WFRFT

In this paper, a scramble phase assisting weighted-type fractional Fourier transform (SPA-WFRFT) based system is proposed to guarantee the communication's security. The original transmitting signal is divided into two parts. The first part is modulated by WFRFT and subsequently makes up the constellation beguiling. The other part is used to generate the scramble phase and also to assist in the encryption of the WFRFT modulated signal dynamically. The novel constellation optimal model is built and solved through the genetic algorithm (GA) for the constellation beguiling. And the double pseudo scheme is implemented for the scramble phase generation. Theoretical analyses show that excellent security performances and high spectral efficiency can be attained. Final simulations are carried out to evaluate the performances of the SPA-WFRFT based system, and demonstrate that the proposed system can effectively degrade the unauthorized receivers' bit error rate (BER) performance while maintaining its own communication quality.
Selecting Services for Multiple Users: Let’s Be Democratic

Service selection is a challenging task, and a lot of effort has been devoted to tools that assist the user in choosing the service whose non-functional parameters better match her/his preferences. In many practical situations, the responsibility to decide which is the appropriate service is shared among multiple parties. A standard approach to this service selection problem is to discard services that are unanimously considered inappropriate and focus on the rest. However, as the involved parties may have colliding interests, only a few services may be eliminated. This work addresses this shortcoming and enables users to reach a “democratic” decision by means of a majority vote: a service is eliminated if the majority of the parties find it inappropriate. We formulate the problem using dominance relationships, and propose algorithms that return an appropriate subset of services for the parties, while being more efficient than standard techniques. Moreover, we consider the problem of defining an appropriate ranking for the non eliminated services, and formulate it as an instance of a group recommendation problem. Finally, we demonstrate the effectiveness and the efficiency of our approach through extensive experimental evaluation on real-based and synthetic datasets.
Space–Time Sampling for Network Observability

Designing sparse sampling strategies is crucial in having resilient estimation and control in networked systems as they make sampling less expensive and networks more resilient w.r.t where and when samples are collected. It is shown under what conditions taking coarse samples from a network will contain the same amount of information as a finer set of samples. Our goal is to estimate the initial condition of linear time-invariant networks using a set of noisy measurements. The observability condition is reformulated as the frame condition, where one can easily trace the location and time stamps of each sample. We compare the estimation quality of various sampling strategies using estimation measures, which depend on the spectrum of the corresponding frame operators. Using properties of the minimal polynomial of the state matrix, deterministic and randomized methods are suggested to construct observability frames. Intrinsic tradeoffs assert that collecting samples from fewer subsystems dictates taking more samples (in average) per subsystem. Three scalable algorithms are developed to generate sparse space–time sampling strategies with explicit error bounds.