Reviewerly/exHarmony huggingface dataset:
Rancang Bangun Score Board Digital pada Olahraga Bola Voli

Abstract - The scoreboard is a tool that can provide values ​​or numbers in a match, to show the results of a match in the field of volleyball. Scoreboards that still use conventional technology are very inefficient in changing scores or scores. Therefore, a digital scoreboard was created that uses an android application that is sent via a bluetooth connection which is displayed on the screen. The digital scoreboard displays the score with an LED screen display that can make it easier for the audience to see clearly. Design and build a digital score board in volleyball using Arduino Uno which is connected to several components, by connecting the P10 Panel to the Arduino Uno, then Bluetooth is connected to the Arduino Uno, with the main brain using the Atmega328P-PU IC programmed using the Arduino IDE. The digital score board design has an average device transmission speed of 16 bits per second while the transmission efficiency is 7.69%. In the trial of data retrieval, the transmission speed at a distance of 9 meters, the device could not send a signal due to the disconnection from the smartphone and the scoreboard. This is due to the distance factor that affects the process of sending data by bluetooth and writing coding in programming languages ​​at a distance of 9 meters.Keywords — score board, android, blueooth Abstrak—Papan score board merupakan sebuah alat yang dapat memberikan nilai atau angka di sebuah pertandingan, untuk menunjukan hasil sebuah pertandingan di bidang olahraga bola voli. Papan score yang masih mengunkan teknologi konvesional sangatlah kurang efisien dalam merubah score ataupun nilai. Maka dari itu diciptakanlah papan score board digital yang mengunakan aplikasi android yang dikirimkan melalui koneksi bluetooth yang ditampilkan di layar. Papan score board digital menampilkan score dengan tampilan layar LED yang dapat memudahkan penonton melihat dengan jelas. Rancang bangun score board digital pada olahraga bola voli menggunakan arduino uno yang disambungkan dengan beberapa komponen, dengan cara mengkoneksikan Panel P10 disambungkan ke Arduino Uno, kemudian bluetooth disambungkan dengan Arduino Uno, dengan otak utama menggunakan IC Atmega328P-PU yang diprogram menggunakan Arduino IDE. Rancang bangun score board digital memiliki kecepatan transmisi alat rata rata 16 bit per second sedangkan untuk efisiensi transmisi sebesar 7,69%.  Pada uji coba pengambilan data kecepatan transmisi pada jarak 9 meter alat tidak bisa mengirimkan sinyal dikarenakan terputusnya koneksi dari smartphone dan papan score board. Hal ini dikarenakan faktor jarak yang berpengaruh dalam proses pengiriman data oleh bluetooth dan penulisan koding dalam bahasa pemrograman pada jarak 9 meter.Kata Kunci—score board; android; bluetooth.
Detection of Thyroid Nodule Disease by using Deep Convolution Neural Network

Nowadays thyroid disease is more common in nature. In India, one out of ten people gets easily affected by this disease. The endocrine gland present in the neck stimulates two hormones namely triiodothyronine and thyroxine that regularize the metabolism in our body. It is a very essential hormone for the digestion of food by which it conserves energy for fulfilling our daily activities. It causes diseases like hypothyroidism, hyperthyroidism, thyroid nodule, and thyroiditis. Detection of these diseases helps to gradually minimize their potential risks sometimes it protects our life with timely treatment. The traditional method of identifying these diseases is highly dependent upon a doctor's skill and experience. The development of radiological imaging and Magnetic Resonance Imaging (MRI) techniques facilitates the detection process even then it requires an expert's knowledge. The evolution of deep learning techniques and its progress towards computer vision, image processing, and natural language processing initiates us to build an effective computer-assisted diagnostic system that serves as a screening tool for doctor’s analysis. Our proposed diagnostic system employs a deep convolution neural network for detecting the thyroid nodule disease and its associated class types viz. benign and malignant[13]. We have experimented with our proposed system using open-access image data of  99 cases available in an online repository. The proposed diagnostic system gives a remarkable performance with an accuracy of 75.73%, a precision of 72.73%, recall 100%, and f1score 84.21%.
Development of Financial Accounting Subject Learning Modules Using Learning Management System (LMS) for Students of Class XI Accounting at SMKN 1 Boyolangu

In accounting learning activities in schools, students are still minimal in using modules because there are no modules that are in accordance with student competence. In financial accounting lessons students are required to be able to understand the material and problems that exist for it needs a financial accounting module that can attract students to be active in working on problems in the form of case studies so as to improve the competence of accounting students. The purpose of this research is to design an e-learning-based financial accounting learning module using a learning management system for class XI students at SMKN 1 Boyolangu, know the validity of LMS-based financial accounting learning modules and find out the feasibility level of financial accounting learning modules using class XI learning management system at SMKN 1 Boyolangu. This type of research is Research and Development. 4D development model Thiagarajan, Semmel and Semmel, for the stage of deployment in the scope of smkn 1 boyolangu for accounting majors. The results of the study showed the development process produced the final product in the form of financial accounting modules with materials in odd semesters. Expert feasibility analysis of materials with an average score of 3.94, media expert eligibility of 3.95, eligibility by accounting practitioners 3.95 and student assessment results of modules 3.95. It can be concluded that the modules developed are suitable for use in learning activities.
Simple Mathematics : Interactive Mobile-Based Application Using Augmented Reality Targeting DHH Children

The idea of the Simple Mathematics Mobile Application using Augmented Reality targeting Children with Hearing Disability is to help children with hearing disability to learn Mathematics in a more visually interactive approach. As we know, Mathematics is an essential subject; therefore, Mathematics is a compulsory subject taught in schools. This is no exception to Special Education schools in Malaysia. However, the method of teaching children with this disability is different from normal children. The different method of teaching children with hearing disability requires a lot of patience as each child have different types of hearing disability. Teachers teaching these children will need special materials that can help the children learnand understand the subject. With the advancement of technology, interactive education is available everywhere. Interactive education integrates computer technology in the classroom that gives a more engaging and fun learning experience for children. Simple Mathematics Mobile Application using Augmented Reality targeting Children with Hearing Disability offers 2 learning languages which are English and Malay Language.
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations

We consider the task of representation learning for unsupervised segmentation of 3D voxel-grid biomedical images. We show that models that capture implicit hierarchical relationships between subvolumes are better suited for this task. To that end, we consider encoder-decoder architectures with a hyperbolic latent space, to explicitly capture hierarchical relationships present in subvolumes of the data. We propose utilizing a 3D hyperbolic variational autoencoder with a novel gyroplane convolutional layer to map from the embedding space back to 3D images. To capture these relationships, we introduce an essential self-supervised loss -- in addition to the standard VAE loss -- which infers approximate hierarchies and encourages implicitly related subvolumes to be mapped closer in the embedding space. We present experiments on both synthetic data and biomedical data to validate our hypothesis.
Bounds all around: training energy-based models with bidirectional bounds

Energy-based models (EBMs) provide an elegant framework for density estimation, but they are notoriously difficult to train. Recent work has established links to generative adversarial networks, where the EBM is trained through a minimax game with a variational value function. We propose a bidirectional bound on the EBM log-likelihood, such that we maximize a lower bound and minimize an upper bound when solving the minimax game. We link one bound to a gradient penalty that stabilizes training, thereby providing grounding for best engineering practice. To evaluate the bounds we develop a new and efficient estimator of the Jacobi-determinant of the EBM generator. We demonstrate that these developments significantly stabilize training and yield high-quality density estimation and sample generation.
Design and Implementation of Radar Signal Processing System Based on GNU Radio

When the single-threaded operating mode of the existing radar simulation system is used to process a large amount of data or execute multiple tasks?it will increase the burden of the CPU?result in slow system operation and data loss?and affect the effectiveness of the simulation test.To solve the problems?a radar signal system is proposed based on the GNU Radio software platform by using its multi-threading technology and modular ideas?and the method of transmitting memory addresses is designed and used to establish the radar signal system?and improve the resource utilization of the computer.System operation in multi-threaded mode can not only process a large amount of data?but also increase the speed of data processing.The final test data shows the effectiveness of the radar signal processing system?which achieves the expected design goals?and meets the actual application requirements.
Can Language Models be Biomedical Knowledge Bases

Pre-trained language models (LMs) have become ubiquitous in solving various natural language processing (NLP) tasks. There has been increasing interest in what knowledge these LMs contain and how we can extract that knowledge, treating LMs as knowledge bases (KBs). While there has been much work on probing LMs in the general domain, there has been little attention to whether these powerful LMs can be used as domain-specific KBs. To this end, we create the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs. We find that biomedical LMs with recently proposed probing methods can achieve up to 18.51% Acc@5 on retrieving biomedical knowledge. Although this seems promising given the task difficulty, our detailed analyses reveal that most predictions are highly correlated with prompt templates without any subjects, hence producing similar results on each relation and hindering their capabilities to be used as domain-specific KBs. We hope that BioLAMA can serve as a challenging benchmark for biomedical factual probing.
Variational Quantum Algorithms: for Optimizing Probe States

As quantum computers are developing, they are beginning to become useful for practical applications, for example in the field of quantum metrology. In this work, a variational quantum algorithm is used to find an optimal probe state for measuring parameters in a noisy environment. This is achieved by optimizing a cost on a quantum computer, based on the Fisher information of the parameters to be estimated. These parameters are then estimated using maximum likelihood estimators. In a simulation, a probe state was found that performed better than the best possible state for noiseless measurements, although this could not be reproduced on an actual quantum computer.
LibGuides: Developmental Education: How to Search the Library Catalog

This guide provides resources that can be used to supplement the various courses and disciplines covered by developmental education courses at HCC.
Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality

Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword tokenization process of language models as it provides multiple benefits. However, this process is solely based on pre-training data statistics, making it hard for the tokenizer to handle infrequent spellings. On the other hand, though robust to misspellings, pure character-level models often lead to unreasonably long sequences and make it harder for the model to learn meaningful words. To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT. Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table. The module is robust to character-level alterations such as misspellings, word inflection, casing, and punctuation. We integrate it further with BERT through pre-training while keeping BERT transformer parameters fixed–and thus, providing a practical method. Finally, we show that incorporating our module to mBERT significantly improves the performance on the social media linguistic code-switching evaluation (LinCE) benchmark.
Development of Earthquake Early Warning Dissemination System for Northern India

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Development of Earthquake Early Warning Dissemination System for Northern IndiaAuthorsGovindRathoreiDKamal.iDRavi S.JakkaMukat LalSharmaAshokKumarSee all authors Govind RathoreiDCorresponding Author• Submitting AuthorIndian Institute of Technology RoorkeeiDhttps://orcid.org/0000-0001-6885-1600view email addressThe email was not providedcopy email addressKamal .iDIndian Institute of Technology RoorkeeiDhttps://orcid.org/0000-0003-0856-759Xview email addressThe email was not providedcopy email addressRavi S. JakkaIndian Institute of Technology Roorkeeview email addressThe email was not providedcopy email addressMukat Lal SharmaIndian Institute of Technology Roorkeeview email addressThe email was not providedcopy email addressAshok KumarIndian Institute of Technology Roorkeeview email addressThe email was not providedcopy email address
Towards a Theoretical Framework of Out-of-Distribution Generalization

Generalization to out-of-distribution (OOD) data, or domain generalization, is one of the central problems in modern machine learning. Recently, there is a surge of attempts to propose algorithms for OOD that mainly build upon the idea of extracting invariant features. Although intuitively reasonable, theoretical understanding of what kind of invariance can guarantee OOD generalization is still limited, and generalization to arbitrary out-of-distribution is clearly impossible. In this work, we take the first step towards rigorous and quantitative definitions of 1) what is OOD; and 2) what does it mean by saying an OOD problem is learnable. We also introduce a new concept of expansion function, which characterizes to what extent the variance is amplified in the test domains over the training domains, and therefore give a quantitative meaning of invariant features. Based on these, we prove OOD generalization error bounds. It turns out that OOD generalization largely depends on the expansion function. As recently pointed out by Gulrajani and Lopez-Paz (2020), any OOD learning algorithm without a model selection module is incomplete. Our theory naturally induces a model selection criterion. Extensive experiments on benchmark OOD datasets demonstrate that our model selection criterion has a significant advantage over baselines.
Going Deeper with Point Networks

In this work we introduce Lean Point Networks (LPNs) to train deeper and more accurate point processing networks by relying on three novel point processing blocks that improve memory consumption, inference time, and accuracy: a convolution-type block for point sets that blends neighborhood information in a memory-efficient manner; a crosslink block that efficiently shares information across low- and high-resolution processing branches; and a multi-resolution point cloud processing block for faster diffusion of information. By combining these blocks, we design wider and deeper point-based architectures. We report systematic accuracy and memory consumption improvements on multiple publicly available segmentation tasks by using our generic modules as drop-in replacements for the blocks of multiple architectures (PointNet++, DGCNN, SpiderNet, PointCNN).
Role of Information Systems in Customer Relationship Management

Applications of Information System (IS) have been instrumental in achieving Customer Relationship Management (CRM). Decision Support Systems (DSS) is an IS application that aids in decision making processes for many business concepts and CRM is one of them. CRM depends on firm’s tasks for developing and retaining customers while achieving their satisfaction and enhancing sense of belongingness for their products and services. Profit maximization, process of customer value and building strategic values for the firm are the three empirical benefits of CRM that are achieved through analytical, operational, and direction capabilities respectively. This research focus on applications of DSS models for CRM at analytical, operational and directional level (AOD). Three online retail firms of KSA are referred as data source for the analysis of CRM by DSS models. The research determined how a customer-oriented firm uses DSS to attain CRM and profitability at three levels namely; Analytical, Operational and Strategic.
LibGuides: Research Data Management: Writing your DMP

Courtesy of Florida Institute of Technology's Evans Library Resources to help with writing DMPs
Determining Priority of Ten Usability Heuristic Using Consistent Fuzzy Preference Relations

One of the most commonly used usability checking methods is heuristic evaluation. This method has ten usability principles called usability heuristics by involving experts as evaluators. In conducting heuristic evaluations, evaluation results have been found with the same severity ratings of several heuristic principles but have not been discussed by previous studies. Therefore, the purpose of this study is to calculate the level of importance/priority of the ten principles/criteria of usability heuristics. The method used in this study is consistent fuzzy preference relationship which is included in multi-criteria decision making. The results of this study obtained the importance/priority of the ten most commonly used usability heuristics. The order from highest priority to lowest priority is as follows: (1)visibility of system status, (2)user control and freedom, (3)consistency and standards, (4)compatibility between the system and the real world, (5)error prevention, (6)recognition rather than recall, (7)flexibility and efficiency of use, (8)help and documentation, (9)aesthetic and minimalist design, (10)helping users identify, diagnose and recover from errors.
Evaluation rules! On the use of grammars and rule-based systems for NLG evaluation

NLG researchers often use uncontrolled corpora to train and evaluate their systems, using textual similarity metrics, such as BLEU. This position paper argues in favour of two alternative evaluation strategies, using grammars or rule-based systems. These strategies are particularly useful to identify the strengths and weaknesses of different systems. We contrast our proposals with the (extended) WebNLG dataset, which is revealed to have a skewed distribution of predicates. We predict that this distribution affects the quality of the predictions for systems trained on this data. However, this hypothesis can only be thoroughly tested (without any confounds) once we are able to systematically manipulate the skewness of the data, using a rule-based approach.
Pengembangan Penyandian Dalam Aplikasi Pada Kriptografi Dengan Menggunakan Metode RC6 Berbasis Web

Cryptography is the art of encoding to keep messages from being easy to read, or commonly called secret messages. There are two main concepts, namely encryption, and decryption. Encryption is the process by which the information sent is converted into a form that is not recognized as the initial information by using a certain algorithm. Decryption is the opposite of encryption which is changing back the disguised form into initial information. In data encoding, namely document encoding with the RC6 method, it is carried out to secure the document into a file that cannot be read by others so that the data can be said to be safe, the data will be changed in content and format so that only those who have the key can return the data into data. readable start. In the design in building the document data security encoding application, supporting applications are used, namely sublime text and the xampp database so that the application built is web-based.
Sketchable Interaction: Core Interaction Concepts (2019-06-24)

Sketchable Interaction: Core Interaction Concepts (2019-06-24) tagged with: sketchable_interaction Sketchable Interaction: Core Interaction Concepts Sketching Mechanic Description Purpose Functionality Sketching Mechanic TL;DR Collision Mechanic
Handwritten Digit Classification using CNN

Since people's writing styles vary in form and orientation, handwritten digit identification is a difficult job. Two steps are essential for the production of effective handwritten digit recognition. The extraction of discriminating features from handwritten images is the first step, and the classification of new digit images is the second. Convolutional Neural Networks (CNN) are at the heart of spectacular developments in deep learning that combine Artificial Neural Networks (ANN) and cutting-edge deep learning techniques. Pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document interpretation, scene recognition, and handwritten digit recognition are only a few of the applications. Our project's aim is to improve detection accuracy by expanding the model depth. This is accomplished by increasing the number of layers and the number of filters. The Modified National Institute of Standards and Technology (MNIST) datasets were used for this performance assessment using CNN.
Privacy & National Security: A balancing act?

The tussle between law enforcement agencies (LEAs) and technology companies over the power to access communications, either in real time or post facto is not new. However with the advent of accessible technologies and the internet, the issue of balancing privacy and surveillance for national security concerns has entered the mainstream conversation. This paper aims to elucidate the contemporary debates around the ‘balancing act’ between national security surveillance (both pre and post facto) and the civil liberties of people, and traces the development of privacy as it is understand now. This paper analyses the legal development of a right to privacy in India and USA, and finally aims to find framing for a good privacy legislation.