Reviewerly/exHarmony huggingface dataset:
Inverting the 'black box' of technology: The Digital Ghost Hunt

The Digital Ghost Hunt was a 2017-2019 collaboration between The University of Sussex, King’s Digital Lab and KIT Theatre. The project was funded by AHRC in the Immersive Experience funding stream and was awarded a full grant for the pilot and an extension to build and widen audiences in the second phase. We were primarily interested in how immersive experience can be designed so as to emphasise and deepen sociality and engagement with the physical world, in contrast with the idea of ‘black box’ applications. We also wanted to explore the interactions between design and technology as means to bring people of different ages and attitudes to technology together through shared learning and problem-solving, with a nod to the popularity of escape rooms. Designing technology-led experience to enhance engagement with external worlds rather than enclosing the attention and focus of the ‘user’ or audience within display-dependent experiences raises many challenges. First and perhaps foremost the degree to which control can be exercised over the environment and the interactions of the audience, but also what type of environments and stories will lend themselves to storytelling that appeals to a range of audiences; different technological abilities, different experience of participatory experience, and different ages. We decided to focus on heritage venues, as they offer rich histories and collections but also ‘worlds’ that are, at least to some degree, contained. Our stated aims and objectives included not only the design of a replicable live immersive augmented reality experience for young audiences, but the production and publication of scripts and open-source code libraries for other producers (primarily in the third sector) to use and adapt for other heritage venues and audience cohorts.
ANALISIS EFEKTIVITAS SISTEM PEMANTAUAN PASAR KEBUTUHAN POKOK (SP2KP) TERHADAP STABILITAS HARGA BARANG KEBUTUHAN POKOK DI KOTA MAKASSAR

Herlianti Rahman. 2020. Thesis. Analysis of the Effectiveness of the Staple Needs Market Monitoring System (SP2KP) on the Price Stability of Staple Needs in Makassar City, supervised by Dr. Hj. Herminawaty Abubakar, S.E., M.Si and Rafiuddin, SE., M.Si.The purpose of this study was to analyze the effectiveness of the Staple Needs Market Monitoring System (SP2KP) on the price stability of basic necessities in the city of Makassar. The object of this research is the Officials and Employees of the Makassar City Trade Office.The results of this study indicate that the market monitoring system for poko needs carried out by the Makassar City Trade Office is effective in controlling and stabilizing the price of poko in Makassar City in controlling and stabilizing the prices of basic necessities by looking at several existing assessment indicators such as those of stakeholders who need it, early anticipation. regarding price hikes and the number of app users.
MuBeFE: Multimodal Behavioural Features Extraction Method

The paper aims to provide a method to analyse and observe the characteristics that distinguish the individual communication style such as the voice intonation, the size and slant used in handwriting and the trait, pressure and dimension used for sketching. These features are referred to as Communication Extensional Features. Observing from the Communication Extensional Features, the user&amp;rsquo;s behavioural features, such as the communicative intention, the social style and personality traits can be extracted. These behavioural features are referred to as Communication Intentional Features. For the extraction of Communication Intentional Features, a method based on Hidden Markov Models is provided in the paper. The Communication Intentional Features have been extracted at the modal and multimodal level; this represents an important novelty provided by the paper. The accuracy of the method was tested both at modal and multimodal levels. The evaluation process results indicate an accuracy of 93.3% for the Modal layer (handwriting layer) and 95.3% for the Multimodal layer.
PENGARUH VARIASI METODE MENGAJAR GURU PAI TERHADAP MOTIVASI BELAJAR SISWA KELAS VIII SEMESTER GANJIL DI SMPS YPSA INTERNATIONAL SCHOOL T.P. 2019/2020

This research aims to: (1) Knowing the variety of teaching methods PAI teacher first semester of class VIII SMPS YPSA International School lessons 2019/2020, (2) knowing the learning motivation class VIII student of SMPS YPSA International School first semester year 2019/2020, (3) the effect of variations of PAI teacher teaching methods to motivate students of class VIII SMPS YPSA International School first semester year 2019/2020. In this research using quantitative descriptive, method product moment. The population of this research is class VIII SMPS YPSA International School numbering 49. After analyzing the data collected, it was found that: (1) the variation of teaching methods PAI teacher VIII SMPS YPSA International School is very good. (2) Motivation class VIII student of VIII SMPS YPSA International School is very good. (3) There is a variety of teaching methods influence on the motivation of teachers PAI class VIII student of VIII SMPS YPSA International School by 36.3%.
Empirical Performance Evaluation of Communication Libraries for Multi-GPU based Distributed Deep Learning in a Container Environment

Recently, most cloud services use Docker container environment to provide their services.However, there are no researches to evaluate the performance of communication libraries for multi-GPU based distributed deep learning in a Docker container environment.In this paper, we propose an efficient communication architecture for multi-GPU based deep learning in a Docker container environment by evaluating the performances of various communication libraries.We compare the performances of the parameter server architecture and the Allreduce architecture, which are typical distributed deep learning architectures.Further, we analyze the performances of two separate multi-GPU resource allocation policiesallocating a single GPU to each Docker container and allocating multiple GPUs to each Docker container.We also experiment with the scalability of collective communication by increasing the number of GPUs from one to four.Through experiments, we compare OpenMPI and MPICH, which are representative open source MPI libraries, and NCCL, which is NVIDIA's collective communication library for the multi-GPU setting.In the parameter server architecture, we show that using CUDA-aware OpenMPI with multi-GPU per Docker container environment reduces communication latency by up to 75%.Also, we show that using NCCL in All-reduce architecture reduces communication latency by up to 93% compared to other libraries.
LibGuides: Agribusiness: Multimedia

The Bachelor of Applied Science degree, majoring in Agribusiness, is the only program of its kind in North America. You can earn while you learn to apply business skills to analyze, debate, and solve current agricultural industry challenges.
Unsupervised and Supervised Learning of ComplexRelation Instances Extraction in Natural Language

Relation extraction has been considered as one of the most popular topics nowadays, thanks for its common application in knowledge graph, machine reading and other artificial intelligence sub-field. However, this field has long been suffered from data hunger. Annotating large high-quality datasets for relation extraction is troublesome and time-consuming. This thesis project will main focus on efficient way of annotating text datasets for extracting complex relations between entities. Moreover, we put some efforts on compare the influence of different components in the pipeline. The main contributions of this project are the comparisons and analysis regarding the influences of components, which are in place for the majority of relation extraction models, and the clear literature review together with the summary of available datasets in the relation extraction flow.
마이크로러닝 콘텐츠의 설계 및 개발 - 서한 번역연습 코스를 중심으로 -

Learning environments are rapidly changing with increasing smartphone usage and constantly improving Internet environment. Considering the evolution of learning environments, strong need for lifelong education, and development of e-learning, the emergence of micro-learning is a very natural result.In this study, we explored the characteristics of micro-learning and studied specialized design strategies through development of an actual micro-learning content.Firstly, micro-learning is developed by reflecting Mezirow''s ''Transformative Learning Theory'', Ebbinghaus''s ''Forgetting Curve'', ''Chunking Theory'', etc. This differentiates micro-learning from e-learning, and it assists students to learn and remember more efficiently.Secondly, by developing a foreign language learning content using micro-learning platform, we verified that it is essential to select specific learners, set simple learning themes, and expand the learning experience by activating interactions. The expansion of experience is what the learners want, and ultimately, it is a shortcut to enhancing problem-solving skills.Lastly, based on these arguments, we came to the conclusion that micro-learning content has many advantages and this would be very useful as a complement to the regular curriculum.
LibGuides: Guided Pathways: School of Communications, English and World Languages

This guide will outline the ways in which the library can support Guided Pathways efforts at Moreno Valley College.
The Clustering of Provinces in Indonesia by The Economic Impact of Covid-19 using Cluster Analysis

The Covid-19 pandemic has hit Indonesia since March 2020. Several policies have been issued by the Indonesian government to reduce the level of the spread of Covid-19. This policy has an impact on various fields of life, especially the economic sector in various sectors. This study was conducted to analyze the grouping of provinces whose economies are at risk of being affected by Covid-19 based on various economic sectors, namely the unemployment rate, the percentage of poor people, the provincial minimum wage, and the occupancy rate of hotels using cluster analysis. Cluster analysis was performed using several hierarchical methods, namely Simple, Complete, Average, and Centroid Linkage and Ward. The Cophenetic correlation coefficient (rCoph) was used to determine the best method, while the number of clusters was determined based on the Dunn, Connectivity, and Silhoutte indexes. The analysis result shows that Average Linkage is the best method with two clusters. The first cluster consists of all provinces in Indonesia except Papua, whose economy is highly at risk of being affected by Covid-19, characterized by a low percentage of the poor and a low provincial minimum wage, as well as high levels of open unemployment and hotel occupancy rates. Meanwhile, the second cluster consists of the Province of Papua, which is an economic group with a low risk of being affected by Covid-19. By looking at the impact of the Covid-19 disaster, the government can make recovery efforts and generalize economic recovery policies due to Covid-19 which have an impact on the economy of almost all provinces in Indonesia.
Library Guides: Asset and Maintenance Management: Standards

This is a curated list of databases relevant to asset and maintenance management. To see all databases, please go to the Library's A-Z Databases page.
An Effective Framework Using Region Merging and Learning Machine for Shadow Detection and Removal

Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, the foreground is detected by background subtraction and the shadow is detected by combination of Mean-Shift and Region Merging Segmentation. Using Gabor method, we obtain the moving targets with texture features. According to the characteristics of shadow in HSV space and texture feature, the shadow is detected and removed to eliminate the shadow interference for the subsequent processing of moving targets.&#x0D; Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on publicly common datasets that the performance of the proposed framework is superior to representative state-of-the-art methods.
Weather Forecasting Based on Supervised Learning Using K-Nearest Neighbour Algorithm

Abstract Weather is influenced by many natural factors causing it to change frequently at any time so that it is sometimes difficult to predict. An accurate weather prediction is needed so that people and policy-makers could anticipate this problem. Many factors that influence the weather cause difficulty in classifying the weather on a particular day . Locality Sensitive Hashing (LSH) works on training data by assigning hash values to a vectors that contain values that represent factors that affect weather and perform weather classification. Furthermore, the k-Nearest Neighbor (k-NN) algorithm will calculate the predictions of the factors that affect the weather on a certain day. Based on the tests carried out, k-NN and LSH in weather prediction has Mean Square Error (MSE) 0,301. Keywords —  k-Nearest Neighbou r(k-NN), weather forecasting, Locality Sensitive Hasihing (LSH
Improving design smell detection for adoption in industry

This work identifies and analyses some barriers to improve the use of Design Smells detection in industry. First, the lack of agreement between tools and experts is established and analyzed for new information to help justify the differences in criteria used in identification. In this work we find indications that the information that describes the context of the analyzed project can help in this problem. Second, the excess of detected Design Smells is analyzed and addressed by proposing the prioritization of God Classes identified, as opposed to the all-or-nothing strategies of current tools, to help developers order its maintenance operations. This prioritization is validated according to this criterion.
LibGuides: Library Course Reserves: Help!

Library Course Reserves use Leganto. Created by Ex Libris, Leganto is an online application that supports the creation and management of course resource reading lists.
Cross-Lingual Transfer with MAML on Trees

In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related. Sharing information between unrelated tasks might hurt performance, and it is unclear how to transfer knowledge across tasks that have a hierarchical structure. Our research extends a meta-learning model, MAML, by exploiting hierarchical task relationships. Our algorithm, TreeMAML, adapts the model to each task with a few gradient steps, but the adaptation follows the hierarchical tree structure: in each step, gradients are pooled across tasks clusters and subsequent steps follow down the tree. We also implement a clustering algorithm that generates the tasks tree without previous knowledge of the task structure, allowing us to make use of implicit relationships between the tasks. We show that TreeMAML successfully trains natural language processing models for cross-lingual Natural Language Inference by taking advantage of the language phylogenetic tree. This result is useful since most languages in the world are under-resourced and the improvement on cross-lingual transfer allows the internationalization of NLP models.
What are some good forks of Chromium ...

What are some good forks of Chromium out there, that gives different/better/enhanced browsing experience from Chrome? I'm aware of Bromite and Vivaldi. What other ones
Continual Learning From Unlabeled Data Via Deep Clustering

Continual learning, a promising future learning strategy, aims to learn newtasks incrementally using less computation and memory resources instead ofretraining the model from scratch whenever new task arrives. However, existingapproaches are designed in supervised fashion assuming all data from new taskshave been annotated, which are not practical for many real-life applications.In this work, we introduce a new framework to make continual learning feasiblein unsupervised mode by using pseudo label obtained from cluster assignments toupdate model. We focus on image classification task under class-incrementalsetting and assume no class label is provided for training in each incrementallearning step. For illustration purpose, we apply k-means clustering, knowledgedistillation loss and exemplar set as our baseline solution, which achievescompetitive results even compared with supervised approaches on bothchallenging CIFAR-100 and ImageNet (ILSVRC) datasets. We also demonstrate thatthe performance of our baseline solution can be further improved byincorporating recently developed supervised continual learning techniques,showing great potential for our framework to minimize the gap betweensupervised and unsupervised continual learning.
The Extent of the Proteus Effect as a Behavioral Measure for Assessing User Experience in Virtual Reality

Assessing the user experience (UX) while being immersed in a virtual environment (VE) is crucial to obtain insights about the quality and vividness of the experience created by virtual reality (VR) systems. These valuable insights are necessary to understand a user's response to VEs and, therefore, to advance in VR research. However, a standardized and effective measure for assessing UX is still missing. Consequently, this lack of suitable measures hinders researchers to gain knowledge and understanding about the effects of VEs on users and in turn slows down the progress in VR technology. To tackle this problem, we propose a behavioral measure for assessing UX based on a phenomenon known as the Proteus effect, which describes changes in behavior and attitude due to the embodiment of avatars with stereotypical characteristics. As avatars are a crucial part of an immersive experience, the extent of behavioral changes caused by the embodiment of avatars may pose an opportunity to implicitly quantify the UX of a VE. This paper discusses an alternative behavioral measure and contributes to the debate about suitable methods for assessing UX in VR systems.
Cryptography using Multiple – Chaos

Abstract Seemingly random, chaotic dynamic systems have state variables that move about in a non periodic, bounded fashion. The sensitivity to initial conditions of chaotic signals also holds an interesting pattern. A seemingly tiny change in the values of initial condition, can greatly affect the values of output. This means that the cross-correlation of two chaotic functions of same source is very low. This pattern of chaotic functions makes their use in the field of cryptography more interesting. A more complex version of chaos is the multiple-chaos, where two or more chaotic functions are used, enhances the security of the procedure. Additionally the dynamically varying look-up table, which is being transmitted along with the encrypted message, ensures high reliability in transmitting the data. Overall, this method ensures high security and high reliability in transmitting the data.
Research Guides: Electronic Discovery (eDiscovery) (brief course guide): Home

This is a guide to research materials selected by a librarian that may be related to Prof. Custer's eDiscovery course.
Client Adaptation improves Federated Learning with Simulated Non-IID Clients

We present a federated learning approach for learning a client adaptable, robust model when data is non-identically and non-independently distributed (non-IID) across clients. By simulating heterogeneous clients, we show that adding learned client-specific conditioning improves model performance, and the approach is shown to work on balanced and imbalanced data set from both audio and image domains. The client adaptation is implemented by a conditional gated activation unit and is particularly beneficial when there are large differences between the data distribution for each client, a common scenario in federated learning.
Contact tracing in COVID-19: Justifying the need and strengthening the process

The Corona Virus Disease 2019 (COVID-19) pandemic continues to rise in terms of its geographical distribution, the number of people infected and the number of people losing their lives due to the resulting complications. Contact tracing is an effective strategy to identify, assess and manage people with a positive history of exposure to a confirmed case, so that we succeed in interrupting the transmission chain and further transmission of infection. It is essential to follow-up the contacts for a period of 2 weeks from the last date of exposure, but the successful implementation of this strategy will depend upon the support of the general population, planning of the entire process after considering the local contexts, trained and adequate number of workforce, logistics, and a supportive mechanism to collect, compile and analyze the data in real-time. In conclusion, the component of contact tracing is a crucial link in interrupting the chain of transmission of the COVID-19. It is the responsibility of the public health authorities and members of the community to work in close collaboration to identify the contacts at the earliest and quarantine them with an aim to prevent onward transmission.
Zoom fatigue: online communication platforms and technologies of the self

In a recent paper in the journal Technology, Mind and Behavior Stanford University communications professor Jeremy Bailenson discusses the psychological impact of online communication platforms. Essentially, he argues, these platforms put us under “nonverbal overload” by showering us with nonverbal communication, notably images of faces. We are constantly under scrutiny, either looked at by others or looking at ourselves. This encourages a sort of hyper-vigilance and “zoom fatigue”, he says.
LibGuides: Business Management: Books

A guide to assist library users to easily find library resources related to Business management
LibGuides: Library Curbside Pickup Guide: Interlibrary loans

Learn more about our curbside pickup, interlibrary loan and book return drop-off services How to request and return interlibrary loans using the curbside service
Research and Practice on the Training Model of Micro-Professional Talents of “Web Front-End Development” Based on School-Enterprise Co-Construction

With the popularization of Web 2.0 technology and people's higher and higher requirements for user experience, traditional Web interfaces can no longer meet people's requirements for interactive features.Web front-end development technologies are getting more and more attention, and major industries are developing Web front-ends.The demand for talents has also increased substantially.This article proposes a new teaching model for the web front-end development technology course in the teaching process, and discusses the specific teaching reform and implementation methods in the new model.