Reviewerly/exHarmony huggingface dataset:
Desktop-Based Population Data Information System to Support The Sumbawa Electronic Government in Rhee District

Abstract This research aims to improve the population service system which is optimal in supporting electronic government in Sumbawa Regency. In software development using the waterfall model and desktop-based Programming with Visual Studio and SQL Server as a Database Management System. The system testing method uses the Black Box Method, a test that focuses on the functional requirements of the software. The results of the research can be used easily, quickly, and useful for processing population data so that it can provide optimal service. This system has been used at the Luk Village Office (Rhee District) in improving service to the community optimally.
Research on Software Architecture Optimization of Cloud Computing Data Center Based on Hadoop

Abstract This paper introduces the architecture optimization design of high performance cloud computing data center (HCDC), and proposes an efficient parallel algorithm based on Hadoop system and MapReduce framework. The performance of the algorithm in big data analysis and processing, and the impact of the algorithm on energy efficiency are studied. Finally, the challenges of future work in this area of research are discussed.
A Low Cost DFS-based Approach for Detection Speedup

Region of interest (ROI) is commonly used in many application areas, including medical imaging, wafer defect detection, geographical information systems and computer vision and optical character recognition. Within a ROI may lie individual points of interest (POI). Many searching algorithms are proposed to find POI. Based on how they decide their trajectories, they can be categorized to sequential search and convergent iterative search. However, they are not efficient and fast enough. In this paper, we proposed a low cost DFS-based searching algorithm to speedup finding POI when the POI are not evenly-distributed. Representative algorithms of sequential search and convergent iterative search are implemented to compare with our approach. The results reveal that our algorithm can efficiently find the POI with less cost in diverse POI distribution models.
Nonparametric prior elicitation for a binomial proportion

This paper proposes a nonparametric Bayesian approach based on a density estimation with an open unit interval (0,1) using binomial data. We propose a very efficient nonparametric Bayesian approach method to infer smooth density defined on (0,1) through the transformation of a random variable. For practical implementation, we provide the corresponding blocked Gibbs sampling procedure based on the stick-breaking representation. The greatest advantage of this method is that it does not require us to draw from the complete conditional posterior distribution using a Metropolis-Hastings transition probability because the proposed transformation leads to a pair of conjugate priors and likelihoods. The validity of the proposed method is assessed through simulated and real data analysis.
Determining Location and Detecting Changes Using a Single Training Video

This paper proposes a new approach to find a robot’s current location and to detect any changes in its path, using monocular vision. A recorded single obstacle-free training video is first obtained and saved. Then, a moving robot can use its camera to find its current location, within its path, by matching current frames with the ones from the training video. This frame-to-frame matching is performed using extracted feature points. Once a match is found, the corresponding frames are aligned (registered) using a homography that is calculated based the matched feature points. This allows to compensate for viewpoint changes between the observed and saved frames. Finally, we compare the regions of interest (ROIs) of the aligned frames, using their colour histograms. We carried out seventeen tests using this approach. The videos, for both training and testing, were recorded using off-the-shelf phone camera by walking down different paths. Four tests were performed in an outdoor environment, and 13 in an indoor environment. Our tests have shown excellent results, with an accuracy above 95% for most of them, for finding the robot’s location and for detecting obstacles in the robot’s path. Both training and testing videos used in our tests were realistic and very challenging, as they consisted of a mix of indoor and outdoor environments with cluttered backgrounds, repetitive floor textures and glare.
Optimal Quantization Methods in Problems of Visual Data Multichannel Wavelet-Compression

Telecommunication systems and multimedia technologies advance is closely related to inevitable increase of transferred media data volumes and sets higher requirements to data coding systems efficiency, including video compression algorithms. Using multichannel scheme of the discrete wavelet transform (DWT) performs a new promising approach to video coding presumed to increase compression ratio while saving restored image quality. Coefficients quantization leads to inevitable image quality loss, however, is necessary for data compression. A rigorous analysis of statistical properties of frequency subbands of decomposition coefficients, considering inherent features of wavelet coefficients, may help to develop a new quantization method that would provide maximum compression of the output data while maintaining image quality, thereby allowing more efficient use of the DWT features. Thus, the aim of this study was to find methods of optimal and quasi-optimal quantization of DWT coefficients. A few innovative quantization methods were proposed within the framework of the paper and their efficiency was investigated. Some of the proposed techniques give a stable efficiency gain compared to standard quantization schemes.
From Accuracy to Versatility: Analysing Text Classification Models Regarding Transfer Learning

While the discriminative performance and classification benchmarks capture most of the attention, the vast amount of data required to train state-of-the-art models is often overlooked. Inductive transfer learning suggests shifting a model between different data domains and hence introduces versatility. In this work, we (1) thoroughly analyse the ability of text classification models to adapt to transfer learning tasks, whether they are specifically designed for it or not. We directly compare the Transformer Model with an attention-based bi-directional LSTM and naive Logistic Regression as a baseline. (2) Exploiting semantic embeddings, we quantify the domain shift between different classes and predict the expected model transferability and performance. (3) Drawing from our extensive analysis, we experimentally modify the vocabulary size, layer freezing and learning rates, pursing the goal to improve eligibility for transfer learning. Our study reveals that simplistic models may be advantageous in easy transfer learning tasks due to faster convergence. An inter-active, online colaboratory notebook that allows reproducing all results, is available here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://colab.research.google.com/drive/18CxphAH1Ym4T5dySUIvjjl-b2gGiti4d.
Assistive Communication Application for Amyotrophic Lateral Sclerosis Patients

Eye tracking is one of the advanced techniques to enable the people with Amyotrophic lateral Sclerosis and other locked-in diseases to communicate with normal people and make their life easier. The software application is an assistive communication tool designed for the ALS Patients, especially people whose communication is limited only to eye movements. Those patients will have difficulty in communicating their basic needs. The design of this system’s Graphical User Interface is made in such a way that it can be used by physically impaired people to convey their basic needs through pictures using their eye movements. The prototype is user friendly, reliable and performs simple tasks in order the paralyzed or physically impaired person can have an easy access to it. The application is tested and the results are evaluated accordingly.
Initial Assessment of Agile Development in the Undergraduate Curricula

Agile is the most popular approach for software development nowadays, present in many companies and also in academia. Many universities have included agile in their curricula but there are no formal studies focused in the Argentinean Universities. In this study we aim to make an initial assessment of the state of Agile Education in the context of the Information Technology and Computer Science programs in Argentina. With this goal we conducted a survey in a national conference of Systems Engineering. Our results confirm that Agile is present in the curricula but in most cases it is only covered from a theoretical point of view. We also identified some situations that suggest a lack of depth in the way some practices are taught.
Primary School Teacher Vocational Education: Model Building

The subject of consideration of the author of the article is the problem of creating conditions for the formation of readiness of primary school teachers to act effectively in changing educational situations, to solve new professional tasks for the implementation of a new educational standard. The feasibility of leaving additional education in the field of informal, involving significant academic freedom, an active subjective position of the participants, their reflexive partnership, is substantiated.
The Effect of User Experience on the Quality of User Interface Design in Healthcare

Electronic health records (EHRs) improve the quality of healthcare, prevent medical errors, and decrease paperwork. As EHR end users, the viewpoint of healthcare physicians, or user experience (UX), should be considered in the design of a user interface to help reduce their complex workloads. The purpose of this study is to create two personas, one for pediatric cardiology and one for general physicians from different specialties in King Abdulaziz University Hospital (KAUH) in Jeddah, Saudi Arabia. The applicability of UX is explored as well as the ability of the personas to assist healthcare professionals and inform design decisions. The results of this study show no difference between the requirements of either persona, however there are differences in the priorities for each. In relation to design decisions, the researchers found that there are fundamental elements designers need to consider to ensure a highly efficient system that suits physicians and increases their effectiveness.
CGSPA: Comprehensive Group Similarity Preference Aggregation Algorithm for Group Itinerary Recommendation System

One of the key factors to group multi-session recommendation system is about aggregating the preference of all users. To address the specific issues, we propose the Comprehensive Group Similarity Preference Aggregation (CGSPA) algorithm for Group Itinerary Recommendation System implemented in this paper. CGSPA comprehensively considers three aspects to aggregate group preference requirement, which are (1) the average maximum similarity between the recommendation plan and group preference; (2) the difference between the similarity of each session in the recommendation plan and the corresponding preference needs of the user group; (3) the historical similarity between the recommendation plan and group preference. We conduct extensive experiments to verify the preference aggregation performance and the robustness of CGSPA algorithm. We conclude that with different group scales, CGSPA’s group average satisfaction score is higher compared to the traditional preference aggregation algorithms.
Recent PTO Guidance Charts a New Course Through the Patent Eligibility Quagmire

Biotechnology Law ReportVol. 38, No. 6 The Holman ReportRecent PTO Guidance Charts a New Course Through the Patent Eligibility QuagmireBy Christopher M. HolmanBy Christopher M. HolmanChristopher M. Holman is the Executive Editor of Biotechnology Law Report and a Professor at the University of Missouri–Kansas City School of Law.Search for more papers by this authorPublished Online:9 Dec 2019https://doi.org/10.1089/blr.2019.29152.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 38Issue 6Dec 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:By Christopher M. Holman.Recent PTO Guidance Charts a New Course Through the Patent Eligibility Quagmire.Biotechnology Law Report.Dec 2019.341-349.http://doi.org/10.1089/blr.2019.29152.cmhPublished in Volume: 38 Issue 6: December 9, 2019PDF download
Aster : An Adaptive Step Recognition Algorithm

Optimization of stochastic functions forms the basis of different machine learning tasks, and several first and second order methods have been proposed for such optimizations. In this paper, we introduce Aster, an adaptive learning rate method for stochastic optimizations. The method dynamically estimates the effective learning rate per parameter dimension over time, based on first order gradient information. The proposed method is simple to implement, appears robust to noisy and sparse gradients and different model architectures. The method is also suited for optimizing non-stationary objective functions, and models involving large number of parameters. The most remarkable feature of Aster is that it requires no manual tuning or the existence of a defined global learning rate. We allude briefly to the methods based on which Aster has been designed. We then describe the proposed method and explain the policy of its parameter updates. The update rule in Aster has intuitive significance. We evaluate our proposed method on popular machine learning tasks. Experimental results demonstrate that Aster works well in practice, and performs better than other popular optimization methods. Finally, we elucidate certain changes that can be made to the default algorithm for enhancing the convergence of Aster during the final phases of optimization.
CTCs extraction method based on frequency-domain filtering from microscope images

Circulating tumor cells (CTCs) can be used for the early detection of cancer and determination of the effectiveness of anticancer drug treatment. Sometimes CTC grows on a culture medium which has a honeycomb texture, and existing detection methods cannot achieve satisfactory results because the honeycomb textures are more salient than the CTCs edges. Thus, a CTCs extraction method for honeycomb textures from microscope images is needed.In this paper, we propose a new algorithm to remove honeycomb texture from microscope images. We confirmed that our proposed algorithm can sufficiently remove the honeycomb texture even the periodicity of the patterns are weak. Also our method achieves a high segmentation accuracy for images where the background is more salient than the CTCs edges.
Shu-Ha-Ri for RE? An Agile Approach to Requirements Engineering Practitioner Maturity

An interesting phenomenon occurred a couple of months ago when I elicited thoughts and feedback on meaningful requirements engineering (RE) metrics from a few RE practitioners and peers. Project teams across industry are often expected to put forth some sort of indicators for management indicating that their company's investment in the work of writing requirements provides some sort of payoff in the end. As I began my career in RE, I pored over a study from Raytheon1 that comfortably concluded that yes, it is far better to discover lack of alignment or shared understanding in what a team is to develop early. Defects cost less to fix when the system to be released is still in its definition phases, rather than when development hours have been expended, when potential releases are being tested, and certainly before a product is released to its customers as complete. The cost of quality in the form of better practice is lower than the cost of cleanup later.
Website-Based Personnel Information System Design in Vocational High School

Abstract Education is a means of creating high-quality human resource. Science and Technology development, in addition to affecting industrial world, also affects education realm. A website-based personnel information system simulation application used for the learning of personnel administration management automation course in supporting learning media in Vocational High Schools throughout Ex-Surakarta Residency is designed using FAST ( Framework for the Application Of System Thinking ) model with object-oriented method and UML ( Unified modeling language ) tools. The use of FAST model made the website-based personnel information system simulation system designed more structured and more organized.
Video Frame Synthesis via Plug-and-Play Deep Locally Temporal Embedding

We propose a generative framework that tackles video frame interpolation. Conventionally, optical flow methods can solve the problem, but the perceptual quality depends on the accuracy of flow estimation. Nevertheless, a merit of traditional methods is that they have a remarkable generalization ability. Recently, deep convolutional neural networks (CNNs) have achieved good performance at the price of computation. However, to deploy a CNN, it is necessary to train it with a large-scale dataset beforehand, not to mention the process of fine tuning and adaptation afterwards. Also, despite the sharp motion results, their perceptual quality does not correlate well with their pixel-to-pixel difference metric performance due to various artifacts created by erroneous warping. In this paper, we take the advantages of both conventional and deep-learning models, and tackle the problem from a different perspective. The framework, which we call deep locally temporal embedding (DeepLTE), is powered by a deep CNN and can be used instantly like conventional models. DeepLTE fits an auto-encoding CNN to several consecutive frames and embeds some constraints on the latent representations so that new frames can be generated by interpolating new latent codes. Unlike the current deep learning paradigm which requires training on large datasets, DeepLTE works in a plug-and-play and unsupervised manner, and is able to generate an arbitrary number of frames from multiple given consecutive frames. We demonstrate that, without bells and whistles, DeepLTE outperforms existing state-of-the-art models in terms of the perceptual quality.
Viaggio in Barberia di Luciano Bianciardi

Luciano Bianciardi visited Maghreb in 1968 and wrote about this experience in Viaggio in Barberia , published in 1969 by Editrice dell’Automobile. This trip offered him – a naturally sedentary writer – the opportunity to reflect theoretically on the possibilities and limits of travelling and of travel writing. By linking Viaggio in Barberia to other textual references from both Bianciardi and Enzo Melandri, this paper is going to propose a reading of the book which stresses its theoretical metaliterary force. In particular, Bianciardi’s reflections on analogy as the logical structure most frequently employed by travellers seem to open a fertile terrain for further exploration.
Single Image Super-Resolution Technique using Precision learning of Low Resolution Images

A single image super-resolution technique is used to generate a high-resolution image from a low-resolution image by combining Non-subsampled contourlet transform and example based learning method. Sets of filters are learnt at coarse and fine scale from training dataset to produce an image of higher quality. The proposed method shows improvement both quantitatively and qualitatively in generating images of higher quality. The PSNR values have improved by a mean value of 3.64 dB when compared with bicubic interpolation and a mean value of 1.42 dB when compared with a state of the art method.
MULTIPHONICS ON VIBRATING STRINGS

Abstract Multiphonics on vibrating strings have been an important element in my compositions since the early 1990s. In order to calculate the frequency components of so-called pure multiphonics (multiphonics consisting of harmonic partials of the fundamental) on vibrating strings, I developed my fraction windowing algorithm. The first section of this article details the use of multiphonics in my compositions and the second section discusses how the fraction windowing algorithm works and its relationship to the closely related mathematical concept of a continued fraction. The article also discusses the online apps I have developed as tools for composers and performers who are interested in using these methods in their own work on string multiphonics.
Towards a Model for Serendipitous Discoveries

A serendipitous experience in information browsing stands for an unexpected information encounter that induces some creativity or new ideas. In this paper we report our ongoing project on a model for measuring the serendipity of an information space in which users navigate by traversing hyperlinks to find desired information. For the model, we define the ‘serendipity’ value of an information space based on the summation of likelihoods that may induce a serendipitous discovery.
D-ALICE: Domain Adaptation-based Labeling the human In Cartoon imagE

How did Alice, who went to the Wonderland, solve the problems? In this paper, as Alice solved problems by changing the size of her body in the Wonderland, we classified the person by changing the style using the image translation technique for the cartoon image in pretrained segmentation model. In general, when you test a cartoon image on a pretrained segmentation model based on real image, the results do not appear correctly. To solve this problem, the ground truth for new images should be created and newly trained. This approach is costly and time consuming. So, we propose a method based on domain adaptation to label the human in cartoon image (D-ALICE) without training a new segmentation model by transforming images using a CycleGAN-based model that can be trained with an unpaired dataset. The quantitative and qualitative evaluation of pre and post conversion images resulted from the segmentation model trained as MIT ADE20K were conducted, and the mean-IoU was increased by more than 35%. The results of this research can be applied to other domains without newly training the deep learning model, and furthermore it can help to provide the ground truth for the data which does not have ground truth which does not have before.
Researching Mobile-Assisted English Language Learning Among Adult Distance Learners in China

Guided by the conceptual framework for next generation designs for mobile-assisted language learning (MALL) in informal setting, our study investigates how mobile devices impact the learning practices and habits amongst adult distant learners of English at a higher education institution in China. Data sources include quantitative data of 148 completed responses to an online survey and qualitative data from follow-up email interviews. The study demonstrates that students' main motivation for engaging in mobile assisted English learning is to fill in the gaps in their daily schedule in order to maximize available time, and the majority use mobile devices to support formal course learning. The skills and knowledge areas practiced with mobile devices are listening, followed by speaking and pronunciation, vocabulary, reading, and translation. Our study also reveals that teachers play a very limited role as perceived by students, while students expect more support from teachers for their out-of-class hour mobile assisted language learning. The research findings bear significant pedagogical implications in terms of integrating MALL into language learning curriculum, learner support and the need for continuous teacher training.