Reviewerly/exHarmony huggingface dataset:
Collaborative information behaviour during epidemics: The case of Nipah outbreak in Southern India

This study primarily investigates facilitating role of ICT during collaborative Information seeking at the time of epidemics. A detailed and systematic literature review has been done to identify themes related to Collaborative Information Behavior (CIB) in the literature. These themes are further assessed in light of the preliminary data collected from the four interviews from health care officials on the Nipah Virus epidemic in Kerala in the year 2018 and a conceptual model is proposed. Our study will contribute to the literature in CIB in the context of an epidemic.
APPLYING SOCIAL NETWORK ANALYSIS TO PRODUCTION DEVELOPMENT OF LOCAL HAND-WOVEN GROUP IN THAILAND

The objectives of this research were to study social network analysis for production development of Hand-Woven Group in material procurement, production, product development, and marketing. This research was carried out by using mixed-methods methodology which focused on a sequential explanatory design. Data were collected through social network questionnaires, observation, and in-depth interview.  Quantitative data were analyzed by social network analysis program (Uci.net and Netdraw), and qualitative data were analyzed by qualitative data analysis software respectively (Atlas.ti).     Research results indicated that the structure of weaving process involved the preparation of warp and weft cotton, making Ikat cotton, dyeing, winding the warp threads, and waving process.   Social network analysis on material procurement also found that there were 2 groups involved in this activity. For the network analysis of production activity, there were 2 persons who participated in this process.  However, the research result demonstrated that there were 4 people took part in product development step. Lastly, for the marketing activity, the result of social network analysis exhibited on 2 people joined in this activity.
An Intelligent Signal Detection Method Based on DNN for MBM System

Media-based modulation (MBM) is a novel method that embedding part or all of the information in the variations of the transmission media. The traditional detection algorithms for MBM system have to estimate the channel state informations (CSIs) to recover the transmitted symbols and they are affected by the number of receiving antennas (RAs). In this paper we propose a deep neural networks (DNN) detector with a novel loss function (LF) which does not require estimation of CSIs and is independent of the number of RA. Numerical results validate that the proposed DNN detector has better bit-error-ratio (BER) performance than that of the traditional detection algorithms and the performance advantage of the proposed LF is also confirmed.
Tail Modulo Cons

OCaml function calls consume space on the system stack. Operating systems set default limits on the stack space which are much lower than the available memory. If a program runs out of stack space, they get the dreaded Stack Overflow exception -- they crash. As a result, OCaml programmers have to be careful, when they write recursive functions, to remain in the so-called _tail-recursive_ fragment, using _tail_ calls that do not consume stack space. This discipline is a source of difficulties for both beginners and experts. Beginners have to be taught recursion, and then tail-recursion. Experts disagree on the right way to write `List.map`. The direct version is beautiful but not tail-recursive, so it crashes on larger inputs. The naive tail-recursive transformation is (slightly) slower than the direct version, and experts may want to avoid that cost. Some libraries propose horrible implementations, unrolling code by hand, to compensate for this performance loss. In general, tail-recursion requires the programmer to manually perform sophisticated program transformations. In this work we propose an implementation of Tail Modulo Cons (TMC) for OCaml. TMC is a program transformation for a fragment of non-tail-recursive functions, that rewrites them in _destination-passing style_. The supported fragment is smaller than other approaches such as continuation-passing-style, but the performance of the transformed code is on par with the direct, non-tail-recursive version. Many useful functions that traverse a recursive datastructure and rebuild another recursive structure are in the TMC fragment, in particular `List.map` (and `List.filter`, `List.append`, etc.). Finally those functions can be written in a way that is beautiful, correct on all inputs, and efficient.
Implement New Connection

In Chapter 13, we implemented GeneralView and made a good start of implementing SettingsView. This chapter is all about completing the SettingsView SourceConnection property implementation, which is the most complex part of building the Execute Catalog Package Task Complex editor (thus far, at least).
Perancangan Desain User Interface Pada Website Alumni Universitas Dinamika Untuk Meningkatkan Usability Dengan Menggunakan Metode User Centered Design

In Dinamika University exists an alumni’s website at http://alumni.dinamika.ac.id. According to an interview with the PLKA (Pusat Layanan Karir dan Alumni) division and other alumnis of the Dinamika University, it is found that the Alumni website is not fulfilling the needs of its user. From there, it is deduced that the main problems faced on this project is how to design the user interface on the Dinamika University’s Alumni website for increased usability with the UCD approach. In the UCD method, early and final evaluation of the design is carried on using heuristic evaluation with a severity rating scale ranging from 0 to 4, from the early design evaluation it is revealed that the old UI has problems of Catastrophic severity, which warrants a design adjustments and repairs. The designing process utilizes wireframes, storyboards, and prototypes. The general base and data for the designing process is gained from surveys of the PLKA and the alumnis, from which this design would later be subjected to a final evaluation to find out any usability problems, which using the same rating scale, is the rounded severity scale of 0, meaning that already fulfilling the need
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations

As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions. A significant hurdle to allowing for such explanatory dialogue could be the vocabulary mismatch between the user and the AI system. This paper introduces methods for providing contrastive explanations in terms of user-specified concepts for sequential decision-making settings where the system's model of the task may be best represented as a blackbox simulator. We do this by building partial symbolic models of a local approximation of the task that can be leveraged to answer the user queries. We empirically test these methods on a popular Atari game (Montezuma's Revenge) and modified versions of Sokoban (a well-known planning benchmark) and report the results of user studies to evaluate whether people find explanations generated in this form useful.
FERRY EXPERIMENT: READING LINE AND SOUND | PHYSICAL MOVEMENT LAB

Ferry Experiment: Reading Line and Sound aims to grasp movement in different artistic elements and trace their interconnection. A sound of a ferry trip Lisbon-Berreiro is recorded as if from two differing ears of a passenger. One traces the movement of detailed noise inside while the other lowers itself to the machinery and gives an impulse of the repetitive swing of moving water. A drawing is created as a result, dismounted to its detail and used together with the sounds as a continuation searching itself in the movement of a body.
An integer programming-based algorithm for optimising the WS-BPEL scenario execution adaptation process

In this work we present an integer programming-based algorithm for adapting the execution of WS-BPEL scenarios, through the dynamic selection of the services to be invoked, according to criteria and policies set by the user. The proposed algorithm is experimentally evaluated both in terms of adaptation quality and adaptation computation overhead. The experimental results demonstrate that the proposed approach achieves to considerably improve adaptation speed, as compared to the exhaustive search algorithm which is considered as a baseline, while at the same time maintaining adaptation quality.
Natural World Distribution via Adaptive Confusion Energy Regularization

We introduce a novel and adaptive batch-wise regularization based on the proposed Batch Confusion Norm (BCN) to flexibly address the natural world distribution which usually involves fine-grained and long-tailed properties at the same time. The Fine-Grained Visual Classification (FGVC) problem is notably characterized by two intriguing properties, significant inter-class similarity and intra-class variations, which cause learning an effective FGVC classifier a challenging task. Existing techniques attempt to capture the discriminative parts by their modified attention mechanism. The long-tailed distribution of visual classification poses a great challenge for handling the class imbalance problem. Most of existing solutions usually focus on the class-balancing strategies, classifier normalization, or alleviating the negative gradient of tailed categories. Depart from the conventional approaches, we propose to tackle both problems simultaneously with the adaptive confusion concept. When inter-class similarity prevails in a batch, the BCN term can alleviate possible overfitting due to exploring image features of fine details. On the other hand, when inter-class similarity is not an issue, the class predictions from different samples would unavoidably yield a substantial BCN loss, and prompt the network learning to further reduce the cross-entropy loss. More importantly, extending the existing confusion energy-based framework to account for long-tailed scenario, BCN can learn to exert proper distribution of confusion strength over tailed and head categories to improve classification performance. While the resulting FGVC model by the BCN technique is effective, the performance can be consistently boosted by incorporating extra attention mechanism. In our experiments, we have obtained state-of-the-art results on several benchmark FGVC datasets, and also demonstrated that our approach is competitive on the popular natural world distribution dataset, iNaturalist2018.
Edge Detection using Canny Edge Detection technique

This paper presents the edge detection and level of pixels to proceed further. Edge Detection is a kind of image- segmentation process and image-segmentation is one of the pre-processing steps in Image Processing. It is used in many places such as the detection of an object lying on the line of sight, the extraction of damaged videos, enhancing the image so it can be recognized well by adjusting the light and dark areas etc. There are variety of image detection algorithms, for example, Canny Edge Detection, Prewitt Edge Detection, Sobel Edge Detection, Roberts Edge Detection, etc. but this research paper will focus on Canny Edge Detection algorithm as it overcomes the shortcomings of other very popular edge detection algorithm called as Sobel Edge Detection algorithm and how we can use the technique for precise detection of obstacles as it is a very important part for our robot system should include.
Teachers’ Strategies in Teaching Speaking for Young Learners (A Case Study at EvFiA Land School in Elementary School Serang Banten)

Masriah, 161230044, 2020, “Teachers’ Strategies in Teaching Speaking for Young Learners (A Case Study at EvFiA Land School in Elementary School Serang Banten)”. (Undergraduate Research Paper, English Education Department, Faculty of Education and Teacher Training State Islamic University of Sultan Maulana Hasanudin Banten).Adviser: Prof. Dr. H. Ilzamudin Ma’mur, M.A and Hj. Eulis Rahmawati, M.Pd.This research investigates Teachers’ Strategies in Teaching Speaking for Young Learners. It is aimed to know what strategies are used by teacher in teaching speaking for young learners and the response of young learners to the strategies used by teacher in teaching speaking at EvFiA Land School in Elementary School Serang Banten. The research method is a qualitative case study approach that uses observation and interviews.The results of this research revealed that the strategy used by the teacher. The teacher use the rules of speak English and communication strategies in teaching speaking. That is the most dominant strategy applied by the teacher. The response of young learners to the strategies used by teacher in teaching speaking, almost all of them felt happy to communicate in English and students could understand English lessons from their teacher. Students also seem to like and enjoy during the delivery of lessons using communication strategies. Students look active and enthusiastic about speaking English during the teaching and learning process. Students also obey the rules of speaking English when in class and in the school environment.Based on the result of this research, the researcher concluded that at EvFiA Land School in Elementary School Serang Banten, the teacher used effective and creative strategies in teaching speaking. The advantage of using the rules of speak English and communication strategies in teaching speaking for young learners, students have basic English skills since elementary school.
Dynamical Analysis of the EIP-1559 Ethereum Fee Market

Participation in permissionless blockchains results in competition over system resources, which needs to be controlled with fees. Ethereum's current fee mechanism is implemented via a first-price auction that results in unpredictable fees as well as other inefficiencies. EIP-1559 is a recent, improved proposal that introduces a number of innovative features such as a dynamically adaptive base fee that is burned, instead of being paid to the miners. Despite intense interest in understanding its properties, several basic questions such as whether and under what conditions does this protocol self-stabilize have remained elusive thus far. We perform a thorough analysis of the resulting fee market dynamic mechanism via a combination of tools from game theory and dynamical systems. We start by providing bounds on the step-size of the base fee update rule that suffice for global convergence to equilibrium via Lyapunov arguments. In the negative direction, we show that for larger step-sizes instability and even formally chaotic behavior are possible under a wide range of settings. We complement these qualitative results with quantitative bounds on the resulting range of base fees. We conclude our analysis with a thorough experimental case study that corroborates our theoretical findings.
한국경제의 추세 성장률 하락과 원인 (A slowdown in GDP Trend Growth in Korea)

Korean Abstract: 본고는 비관측 요인 모형을 통해 1981년부터 2019년까지 한국의 1인당 실질 GDP 추세 성장률을 추정하고 성장회계를 통해 추세 성장률 하락의 원인을 분석한다. 1인당 실질 GDP 추세 성장률은 외환위기를 극복하던 시기인 2000년 전후를 제외하고 1980년대 후반부터 2010년대 초반까지 지속적으로 하락한 후 2.0% 수준을 유지하고 있다. 신고전파 생산함수를 적용한 성장회계 접근법을 통해 추세 성장률의 하락을 분석한 결과, 외환위기 이전부터 하락하기 시작한 1990년대의 추세 성장률은 총요소생산성 요인 증가율의 둔화에 가장 큰 영향을 받는 것으로 나타났다. 또한 2000년대 이후의 추세 성장률 둔화는 총요소생산성 요인 증가율 둔화와 더불어 자본집약적 경제로 전이되는 상황에서의 부진한 투자에 기인하는 것으로 나타났다.English Abstract: Using an unobserved components model that features trend growth as a random walk, we find that GDP trend growth rates had gradually declined from the late 1980s to early 2010s in Korea. To uncover the underlying drivers of the slowdown, we use trend growth accounting. A major, common driver appears to be a significant decline in the growth rate of total factor productivity. In addition, the first gradual decline in trend growth, which started in the late 1980s and continued to 1998, is associated with a drop in working hours by the revised Labor Standard Act in 1989. Sluggish investment growth is behind the second period of the gradual slowdown, from 2001 to the early 2010s. This finding is inconsistent with the hypothesis that the slowdown in GDP trend growth can be attributed to the 1997-1998 Korean financial crisis.
GALILEO@UGA Subject Guides: Using Electronic Library Resources Remotely: Alternate Options for Access

Tips for using electronic library resources from remote locations and common solutions for access errors.
Innovation on Manual Wheelchair for Hemiplegic and Tetraplegic Patients

While almost 90% of wheelchair users are using manual pushrim wheelchair for their mobility due to the cost and less maintenance. But for individuals with hemiplegic and tetraplegic, they are facing difficulty to propel it around. To overcome this problem, many researchers used current technology to develop mechanisms by adding extra features on manual pushrim wheelchair. Mechanism that can reduce propelling forces, number of hands needed to propel and as a rehabilitation tool to speed up their recovery process. Positive result obtained from innovated wheelchair in providing assistance as discussed further below. More researchers need to focus more in this area to help disabilities live their life as a normal person.
Connecting a Chatbot with Channels

The usually last step in a bot development life cycle is to connect a chatbot to the desired channels. This process basically enables you to let users start communicating with the bot using the channels you enable the bot in. As mentioned in earlier chapters, you should already know which channels you want to target when building your bot as not all channels act the same and support the same features. For instance, Microsoft Teams is capable of handling rich attachments like Adaptive Cards, whereas the Twilio channel is used for text messages. As text messages are not capable of displaying rich attachments, you cannot use Adaptive Cards in this channel. Therefore, you would need to think about the target channels even during the build phase of your development life cycle to avoid major change requests afterward.
Visual Tracking via Spatial-Temporal Regularized Correlation Filters with Advanced State Estimation

Discriminative correlation filter (CF) based visual trackers achieves outstanding performance with the handcrafted feature in visual tracking. In this work, based on the discriminative correlation filter, we propose a new Spatial-Temporal regularized correlation filters with advanced state estimation (CFASE) to achieve more significant tracking performance. First, we propose a new method to estimate correlation filters more precisely using prediction from the previous two filters, considering the drift during the tracking process. Second, we train two correlation filters models to obtain scale estimation and object location, respectively. The separated two correlation filter models help to reduce the adverse effects of scale changes on object location. Third, our tracker introduces average peak-to-correlation energy (APCE) to evaluate the accuracy of scale estimation and object location. Experimentally, the proposed tracker (CFASE) achieves outstanding and real-time performance for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).
MSM Library: Audio Files for Scores: Home

This is a page where students and faculty and find and download files that accompany scores that have an audio component.
How Technology Affects the Brand Behaviour? A Case of Indian Retail Sector

Purpose: The purpose of the current study is to explore the impact service quality on the relationship between technology variables and brand behaviour in Indian retail by using the SO-R model as the theoretical framework.Research Methodology: The research Data was collected by using questionnaire survey from 382 professional courses students studying in reputed institutes in North India. Hierarchical regression has been used to examine the mediating role of service quality between technology and brand preference.Results: The major finding of this study is that service quality partially mediates the relationship between technology and brand preference. This study makes theoretical contribution to the literature of service quality between technology and brand preference.Implications: The study suggests useful managerial implications for the academicians/researchers and managers. Limitations of the present study and the future research directions are also discussed.Originality: The study would contribute to the sparse literature on technological innovativeness and optimism, brand behaviour further adding to the existing S-O-R model.
How Trump tweets: A comparative analysis of tweets by US politicians

This paper analyses tweets sent from Donald Trump’s Twitter account @realDonaldTrump and contextualises them by contrasting them with several genres (i.e. political and ‘average’ Twitter, blogs, expressive writing, novels, The New York Times and natural speech). Taking common claims about Donald Trump’s language as a starting point, the study focusses on commonalities and differences between his tweets and those by other US politicians. Using the sentiment analysis tool Linguistic Inquiry and Word Count (LIWC) and a principal component analysis, I examine a newly compiled 1.5-million-word corpus of tweets sent from US politicians’ accounts between 2009 and 2018 with a special focus on the question whether Trump’s Twitter voice has linguistic features commonly associated with informality, I-talk, negativity and boasting. The results reveal that all political tweets are grammatically comparatively formal and centre around the topics of achievement, money and power. Trump’s tweets stand out, however, because they are both more negative and more positive than the language in other politicians’ tweets, i.e. his Twitter voice relies far more strongly on adjectives and emotional language.