Reviewerly/exHarmony huggingface dataset:
Data Curation through Catalogs: A Repository-Independent Model for Data Discovery

Institutional data repositories are the acknowledged gold standard for data curation platforms in academic libraries. But not every institution can sustain a repository, and not every dataset can be archived due to legal, ethical, or authorial constraints. Data catalogs—metadata-only indices of research data that provide detailed access instructions and conditions for use—are one potential solution, and may be especially suitable for "challenging" datasets. This article presents the strengths of data catalogs for increasing the discoverability and accessibility of research data. The authors argue that data catalogs are a viable alternative or complement to data repositories, and provide examples from their institutions' experiences to show how their data catalogs address specific curatorial requirements. The article also reports on the development of a community of practice for data catalogs and data discovery initiatives.
Multitask Regularization for Image Aesthetic Evaluation

Convolutional neural networks are data hungry and in cases when annotation is costly or difficult, additional information from other sets may be welcomed. In this paper, to improve the performance on the main task, we introduce a secondary one, over unlabeled data to provide better structuring. The solution falls in the theme of multiple task learning and unlabeled data is integrated next to the main regression task by classification via pseudo-labeling. The method is showed to improve the baseline performance for image aesthetic assessment on the AADB benchmark.
Deploying Your Azure Functions Using a CI/CD Pipeline with Azure DevOps

While a software application is being developed and deployed, you use multiple processes before the final application is completed and deployed. This often includes phases such as requirements gathering, system design, development, testing, and deployment irrespective of the SDLC models followed. These processes used to be quite time-consuming, with the development and operations teams not being in sync. With the demanding business scenarios of current times, you need a shorter time to market to release newer features of your products to serve your end customers.
Assessing the Seismicity of Shillong Plateau Using 'b value'

The Shillong Plateau is a detached North-eastern portion of the Indian plate and is part of the Eastern Himalayan Syntaxial zone.It is one of the most seismically active plateaus in the world and falls under zone V of the Indian seismic hazard zonation map, the highest seismic hazard zone.During the period of study (2013)(2014)(2015)(2016)(2017) numerous local earthquakes of varied magnitude were recorded by the installed broadband seismic stations.The present study is focused on the calculation of the 'b value' using the Gutenberg-Richter relation, which is the rate of fall in the frequency of earthquake occurrences with their increasing magnitude.The 'b-value' is estimated by the maximum likelihood method at five different layers of the lithosphere considering the geoelectric crustal configuration up to the depth of 50 km.The calculated 'b values' at different layers are estimated to be 0.78 (0 < depth ≤ 10), 0.83 (10 < depth ≤ 20), 0.74 (20 < depth ≤ 30), 0.66 (30 < depth ≤ 40), 0.71 (40 < depth ≤ 50), with an average of 0.77 (0 < depth ≤ 50).The result indicates that the overall b value of the Shillong Plateau has a moderate value and the variation of the 'b value' at a different range of depth layers is due to the converging varying stresses due to strength, stress orientations, and physical properties of the layers.
Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue

Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used since a Whole Slide Image (WSI) is huge. However, this approach loses the global information needed to distinguish between classes. In this paper, we utilized the Distance from the Boundary of tissue (DfB), which is global information that can be extracted from the original image. We experimentally applied our method to the three-class classification of cervical cancer, and found that it improved the total performance compared with the conventional method.
Discussion of the AR Design Principles for Mobile Augmented Reality Games

Mobile Augmented Reality (MAR) is a unique creation in the game playing field between digital and real physical environment surroundings the players.Designing mobile augmented reality games is a challenging mission and observation is needed every perspective in creating an enjoyable and exciting player experience.This paper will discuss existing research to review the design principles of mobile augmented reality for games.The discussion on the current AR design principles in MAR would assist in identifying potential elements of AR design principles as design guidelines for AR for designers and educators respectively.
Using Nintendo Switch Development Environment to Teach Computer Game Programming and Virtual Reality

His research interest lies in two fields.The first is research focusing on best and effective ways to teaching and learning in STEM K-16.He is currently researching on best practices in learning Dynamics, a sophomore engineering core course.The second research focus of Dr. Mativo is energy harvesting
SSH: A Self-Supervised Framework for Image Harmonization

Image harmonization aims to improve the quality of image compositing by matching the "appearance" (\eg, color tone, brightness and contrast) between foreground and background images. However, collecting large-scale annotated datasets for this task requires complex professional retouching. Instead, we propose a novel Self-Supervised Harmonization framework (SSH) that can be trained using just "free" natural images without being edited. We reformulate the image harmonization problem from a representation fusion perspective, which separately processes the foreground and background examples, to address the background occlusion issue. This framework design allows for a dual data augmentation method, where diverse [foreground, background, pseudo GT] triplets can be generated by cropping an image with perturbations using 3D color lookup tables (LUTs). In addition, we build a real-world harmonization dataset as carefully created by expert users, for evaluation and benchmarking purposes. Our results show that the proposed self-supervised method outperforms previous state-of-the-art methods in terms of reference metrics, visual quality, and subject user study. Code and dataset are available at \url{https://github.com/VITA-Group/SSHarmonization}.
Study on Campus Planning from the Perspective of Environmental Behavior&amp;lt;br/&amp;gt;—Taking Southwest University of Science and Technology in Mianyang, Sichuan Province as an Example

University campus environment is the most one important factors influencing students' daily activities, this article from the perspective of environmental behavior, respectively based on the part of the campus space environment, student traffic flow, and the campus guide identifies three aspects, analyzes the existing problems in the campus space environment and some improvement measures, hoping to design current campus environment design and construction to provide the reference.
Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences

We show that density models describing multiple observables with (i) hard boundaries and (ii) dependence on external parameters may be created using an auto-regressive Gaussian mixture model. The model is designed to capture how observable spectra are deformed by hypothesis variations, and is made more expressive by projecting data onto a configurable latent space. It may be used as a statistical model for scientific discovery in interpreting experimental observations, for example when constraining the parameters of a physical model or tuning simulation parameters according to calibration data. The model may also be sampled for use within a Monte Carlo simulation chain, or used to estimate likelihood ratios for event classification. The method is demonstrated on simulated high-energy particle physics data considering the anomalous electroweak production of a $Z$ boson in association with a dijet system at the Large Hadron Collider, and the accuracy of inference is tested using a realistic toy example. The developed methods are domain agnostic; they may be used within any field to perform simulation or inference where a dataset consisting of many real-valued observables has conditional dependence on external parameters.
ACM-CR: A Manually Annotated Test Collection for Citation Recommendation

Citation recommendation is intended to assist researchers in the process of searching for relevant papers to cite by recommending appropriate citations for a given input text. Existing test collections for this task are noisy and unreliable since they are built automatically from parsed PDF papers. In this paper, we present our ongoing effort at creating a publicly available, manually annotated test collection for citation recommendation. We also conduct a series of experiments to evaluate the effectiveness of content-based baseline models on the test collection, providing results for future work to improve upon. Our test collection and code to replicate experiments are available at https://github.com/boudinfl/acm-cr
When Bots and Users Meet: Automated Manipulation and the New Culture of Online Suspicion

This article examines the growing public awareness of deceptive online campaigns where automated networks of social media bots are deployed to manipulate public opinion, disrupt debates, and stoke intercommunal strife. Drawing on a grounded thematic analysis of discussion threads that appeared on Israeli politicians’ Facebook pages during the lead-up to Israel’s national elections in April 2019, I argue that users’ frequent allusions and reactions to the presence of suspected manipulative agents allow these users to negotiate, challenge, and raise competing claims regarding whether certain prominent voices on social media are reflective of actual public opinion. As such, this article contributes to the emerging body of literature about online manipulation, which in recent years has focused mostly on examining the nature and scope of deceptive bot campaigns around the globe and on devising new techniques for detecting and countering the activities of fake social media accounts. The present investigation, in contrast, seeks to shift attention away from bots themselves and toward their intended targets: ordinary users and their discourse. In so doing, it aims to contribute to and expand the study of how automated manipulation is shaping contemporary social media environments by asking: What do ordinary users have to say about the deployment of political bots? How does users’ growing awareness of deceptive bot campaigns inform their interpretations of their own online experiences? And how do users leverage claims about bot activity in online exchanges with others to advance their political agendas?
Single-Qubit Cross Platform Comparison of Quantum Computing Hardware

As a variety of quantum computing models and platforms become available, methods for assessing and comparing the performance of these devices are of increasing interest and importance. Despite being built of the same fundamental computational unit, radically different approaches have emerged for characterizing the performance of qubits in gate-based and quantum annealing computers, limiting and complicating consistent cross-platform comparisons. To fill this gap, this work proposes a single-qubit protocol (Q-RBPN) for measuring some basic performance characteristics of individual qubits in both models of quantum computation. The proposed protocol scales to large quantum computers with thousands of qubits and provides insights into the distribution of qubit properties within a particular hardware device and across families of devices. The efficacy of the Q-RBPN protocol is demonstrated through the analysis of more than 300 gate-based qubits spanning eighteen machines and 2000 annealing-based qubits from one machine, revealing some unexpected differences in qubit performance. Overall, the proposed Q-RBPN protocol provides a new platform-agnostic tool for assessing the performance of a wide range of emerging quantum computing devices.
Game Based Pedagogy System for Assessment using Features Like OCR and Speech-To-Text Recognition

Game-based learning is an approach to learning where gaming concepts are implemented in learning tasks designed by teachers. Here, learning activities boost a student’s commitment and inspiration to learn. Components of game-based learning include quizzes, point systems, leader boards, and classroom response systems. With the advancements in the field of education, game-based learning has proven that it is a utilitarian option in improving a student’s assessment where paper-based evaluation is not possible. Various Game-Based Learning Systems have been built previously and, in this paper, we have compared 11 existing models based on various factors like cost, quiz customization options, mobile support, etc. We have also pointed out the benefits and limitations of these systems. None of these systems give a complete set of all the features without being exorbitantly costly. So, we aspire to assemble a credible teaching project where all aspects would be included in a single application for the complete assessment of every student. The system has several key features like OCR, Speech to Text recognition, anti-cheating tools, and feedback after the quiz. For executing OCR, we have used OpenCV because of its extensive admiration and for Speech-to-Text Recognition (STR), we have adopted the Speech Recognition library because it is quite adaptable. This system aims at reducing the workload of teachers by creating a test by scanning the questions with the help of OCR or by using STR so the teacher need not type the question and also by assessing students based on their scores.
Incorporating Reading in Writing Classes and Its Effects on ESL Learners’ Writing

This study investigated how incorporating reading activities in writing classes can help secondary school students develop their writing skills.The study was conducted using samples selected conveniently which involved 30 Form Four and Five students from one of the local secondary schools in Kapit, Sarawak.This study examined specifically on the effects of engaging reading activities in writing classes on their writing performance.This qualitative study examined how incorporating reading activities into writing classes can influence students' writing results.Data was collected from a semi-structured interview and a pre-and post-test experimental research based on writing tests that were graded using the Common European Framework Reference (CEFR) writing marking scheme.The result was analysed using descriptive statistic to find the mean and standard deviation of both pre and post-test.Findings showed that incorporating reading activities into writing tasks during English language class increased the respondents' writing output significantly.The results of this study are hoped to assist English as Second Language (ESL) students and teachers in recognising the importance of reading activities in writing classes as an effort to improve writing performance.
Improved K-means algorithm based on relative mass and force range radius of the data field

Among all the clustering problems, K-means clustering is probably the most well-known one. To address the problem that the traditional K-means algorithm selects the clustering centroids randomly and leads to unstable clustering results and easy to fall into local optimal solutions, we introduce the data field into number field space by borrowing the idea of inter-particle interaction in the physical field, study the gravitational relationship between data points and influence factor in the data field, and propose the relative mass algorithm based on the data field, which is referred to as RM algorithm in the following. In the data field, the points with larger masses are calculated and selected as the initial clustering centroids by the RM algorithm, and the K-means algorithm is improved based on the relative masses of data points and the force range radius based on the data field, respectively, and then the effectiveness of the improved algorithm is verified on several publicly available datasets, and finally the improved K-means algorithm based on the force range radius of the data field is tested in a distributed cluster. The experimental results show that (1) compared with the traditional K-means algorithm, both improved K-means algorithms reduce the number of iterations and the clustering time, and the clustering effect is better. (2) The data size increased from 10, 000 to 100, 000, the number of nodes increased from 1 to 5, the speedup ratio of parallel computing increased to 1.91, and the computational efficiency improved by 33.03%.
Copyright Throughout a Creative AI Pipeline

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Detecting OODs as datapoints with High Uncertainty

Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution inputs (OODs). This limitation is one of the key challenges in the adoption of DNNs in high-assurance systems such as autonomous driving, air traffic management, and medical diagnosis. This challenge has received significant attention recently, and several techniques have been developed to detect inputs where the model's prediction cannot be trusted. These techniques detect OODs as datapoints with either high epistemic uncertainty or high aleatoric uncertainty. We demonstrate the difference in the detection ability of these techniques and propose an ensemble approach for detection of OODs as datapoints with high uncertainty (epistemic or aleatoric). We perform experiments on vision datasets with multiple DNN architectures, achieving state-of-the-art results in most cases.
Term interrelations and trends in software engineering

The Software Engineering (SE) community is prolific, making it challenging for experts to keep up with the flood of new papers and for neophytes to enter the field. Therefore, we posit that the community may benefit from a tool extracting terms and their interrelations from the SE community's text corpus and showing terms' trends. In this paper, we build a prototyping tool using the word embedding technique. We train the embeddings on the SE Body of Knowledge handbook and 15,233 research papers' titles and abstracts. We also create test cases necessary for validation of the training of the embeddings. We provide representative examples showing that the embeddings may aid in summarizing terms and uncovering trends in the knowledge base.
Limitations of Local Quantum Algorithms on Random Max-k-XOR and Beyond

Motivated by a question of Farhi et al. [arXiv:1910.08187, 2019], we study the limitations of the Quantum Approximate Optimization Algorithm (QAOA) and show that there exists $\epsilon > 0$, such that $\epsilon\log(n)$ depth QAOA cannot arbitrarily-well approximate boolean constraint satisfaction problems as long as the problem satisfies a combinatorial property from statistical physics called the coupled overlap-gap property (OGP) [Chen et al., Annals of Probability, 47(3), 2019]. We show that the random \kxors{} problem has this property when $k\geq4$ is even by extending the corresponding result for diluted $k$-spin glasses. As a consequence of our techniques, we confirm a conjecture of Brandao et al. [arXiv:1812.04170, 2018] asserting that the landscape independence of QAOA extends to logarithmic depth -- in other words, for every fixed choice of QAOA angle parameters, the algorithm at logarithmic depth performs almost equally well on almost all instances. Our results provide a new way to study the power and limit of QAOA through statistical physics methods and combinatorial properties.
A Review of Application of Cloud Computing In Education

It is evident that ICT has changed our lives and restructured the nature of daily doings. Where education plays an important role in who we are and what we can realize in our time, Learning has intensely transformed over recent years when globalization has brought different opportunities to learn with the Internet. In Today’s era where the Computer becomes one of the pillars of the learning system, the education system is facing challenges respective to resource availability, cost control and storage handling. Cloud Computing is introduced as an alternative solution to overcome these challenges.
EQUAL: Improving the Fidelity of Quantum Annealers by Injecting Controlled Perturbations

Abstract Quantum Annealers (QAs) are single-instruction quantum machines that can only sample from the ground state of an energy function, called Hamiltonian. To execute a program, the problem is cast to a Hamiltonian, embedded on the hardware, and a single quantum machine instruction (QMI) is run. Noise and imperfections in hardware result in sub-optimal solutions on QAs even if the QMI is run for thousands of trials. Owing to the limited programmability of QAs, users execute the same QMI for all trials. This subjects all trials to a similar noise profile throughout the execution, resulting in a systematic bias. We observe that systematic bias leads to sub-optimal solutions and cannot be alleviated by executing more trials or using existing error-mitigation schemes. To address this challenge, we propose EQUAL (Ensemble QUantum AnneaLing). EQUAL generates an ensemble of QMIs by adding controlled perturbations to the program QMI. When executed on the QA, the ensemble of QMIs steers the program away from encountering the same bias during all trials and thus, improves the quality of solutions. Our evaluations using the D-Wave 2000Q machine show that EQUAL bridges the difference between the baseline and the ideal by an average of 14% (and up to 26%), without requiring any additional trials. EQUAL can be combined with existing error mitigation schemes to bridge further the difference between the baseline and ideal by an average of 55% (and up to 68%).