Reviewerly/exHarmony huggingface dataset:
Evaluation of Machine Learning-based Patient Outcome Prediction Using Patient-specific Difficulty and Discrimination Indices

Given the extensive use of machine learning in patient outcome prediction, and the understanding that the challenging nature of predictions in this field may considerably modify the performance of predictive models, research in this area requires some forms of context-sensitive performance metrics. The area under the receiver operating characteristic curve (AUC), precision, recall, specificity, and F1 are widely used measures of performance for patient outcome prediction. These metrics have several merits: they are easy to interpret and do not need any subjective input from the user. However, they weight all samples equally and do not adequately reflect the ability of predictive models in classifying difficult samples. In this paper, we propose the Difficulty Weight Adjustment (DWA) algorithm, a simple method that incorporates the difficulty level of samples when evaluating predictive models. Using a large dataset of 139,367 unique ICU admissions within the eICU Collaborative Research Database (eICU-CRD), we show that the classification difficulty and the discrimination ability of samples are critical aspects that need to be considered when comparing machine learning models that predict patient outcomes.
A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing

In spatiotemporal crowdsourcing applications, sensing data uploaded by participants usually contain spatiotemporal sensitive data. If application servers publish the unprocessed sensing data directly, it is easy to expose the privacy of participants. In addition, application servers usually adopt the static publishing mechanism, which is easy to produce problems such as poor timeliness and large information loss for spatiotemporal crowdsourcing applications. Therefore, this paper proposes a spatiotemporal privacy protection (STPP) method based on dynamic clustering methods to solve the privacy protection problem for crowd participants in spatiotemporal crowdsourcing systems. Firstly, the working principles of a dynamic privacy protection mechanism are introduced. Then, based on k -anonymity and l -diversity, the spatiotemporal sensitive data are anonymized. In addition, this paper designs the dynamic k -anonymity algorithm based on the previous anonymous results. Through extensive performance evaluation on real-world data, compared with existing methods, the proposed STPP algorithm could effectively solve the problem of poor timeliness and improve the privacy protection level while reducing the information loss of sensing data.
In Optimizing Vocabulary Teaching and Learning for the Third Grade Students of SMP Al Irsyad Pekalongan

This paper focuses on the use of mnemonics technique in teaching and learning English vocabulary. This study aims to know the effectiveness of using mnemonics in helping the students memorizing English vocabulary. Mnemonics is considered to be the most suitable teaching technique as it will give a deeper impression which will lead to the long term memory. Teachers should apply an interesting teaching technique so that the students will have more attention to the material given to them. Based on some language experts, the right teaching technique will give a good atmosphere and great outcome in language acquisition. This paper uses a mix method triangulation. The data were obtained from the quetionnaire, observation before, during and after the implementation of the actions, interview, and the students test scores. The data were in the forms of questionnaire, field notes, observation checklist and students test scores. Then the researcher also tested the normality data analyzed using Sapphiro-Wilk. Independent T-Test used to know wheter the distribution of the data is normal or not. Based on the research result, the application of mnemonics is believed can help students to learn vocabulary and the teacher can teach the students easily as the normality data is normal and the distribution of the data is normal too.
Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wild

Deep learning-based scene text detection can achieve preferable performance, powered with sufficient labeled training data. However, manual labeling is time consuming and laborious. At the extreme, the corresponding annotated data are unavailable. Exploiting synthetic data is a very promising solution except for domain distribution mismatches between synthetic datasets and real datasets. To address the severe domain distribution mismatch, we propose a synthetic-to-real domain adaptation method for scene text detection, which transfers knowledge from synthetic data (source domain) to real data (target domain). In this paper, a text self-training (TST) method and adversarial text instance alignment (ATA) for domain adaptive scene text detection are introduced. ATA helps the network learn domain-invariant features by training a domain classifier in an adversarial manner. TST diminishes the adverse effects of false positives~(FPs) and false negatives~(FNs) from inaccurate pseudo-labels. Two components have positive effects on improving the performance of scene text detectors when adapting from synthetic-to-real scenes. We evaluate the proposed method by transferring from SynthText, VISD to ICDAR2015, ICDAR2013. The results demonstrate the effectiveness of the proposed method with up to 10% improvement, which has important exploration significance for domain adaptive scene text detection. Code is available at https://github.com/weijiawu/SyntoReal_STD
Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for English-to-Chinese

This research presents a fine-grained human evaluation to compare the Transformer and recurrent approaches to neural machine translation (MT), on the translation direction English-to-Chinese. To this end, we develop an error taxonomy compliant with the Multidimensional Quality Metrics (MQM) framework that is customised to the relevant phenomena of this translation direction. We then conduct an error annotation using this customised error taxonomy on the output of state-of-the-art recurrent- and Transformer-based MT systems on a subset of WMT2019's news test set. The resulting annotation shows that, compared to the best recurrent system, the best Transformer system results in a 31% reduction of the total number of errors and it produced significantly less errors in 10 out of 22 error categories. We also note that two of the systems evaluated do not produce any error for a category that was relevant for this translation direction prior to the advent of NMT systems: Chinese classifiers.
AI solutions for drafting in Magic: the Gathering

Drafting in Magic: the Gathering is a sub-game of a larger trading card game, where several players progressively build decks by picking cards from a common pool. Drafting poses an interesting problem for game-playing and AI research due to its large search space, mechanical complexity, multiplayer nature, and hidden information. Despite this, drafting remains understudied in part due to a lack of high-quality, public datasets. To rectify this problem, we present a dataset of over 100,000 simulated, anonymized human drafts collected from this http URL. Additionally, we propose four diverse strategies for drafting agents, including a primitive heuristic agent, an expert-tuned complex heuristic agent, a Naive Bayes agent, and a deep neural network agent. We benchmark their ability to emulate human drafting, and show that the deep neural network agent outperforms all other agents, while Naive Bayes and expert-tuned agents outperform simple heuristics. We analyze the accuracy of AI agents across the timeline of a draft, for different cards, and in terms of approximating subtle inconsistencies of human behavior, and describe unique strengths and weaknesses for each agent. This work helps to identify next steps in the creation of humanlike drafting agents, and can serve as a set of useful benchmarks for the next generation of drafting bots.
Security cameras in taxicabs with three rows of seating

Taxicab security cameras are widely used to deter crimes against taxicab drivers in two-row-seating taxicabs. Some of these cameras have difficulty for use in three-row-seating taxicabs due to increased distance between the camera and the third-row seats. This project tested five sample taxicab security cameras with different image-sensor pixel counts to determine their utility for three-row-seating taxicabs. The cameras videotaped a normalized camera resolution test chart mounted in the third-row seat of a simulated three-row-seating taxicab in both daylight and dark (with infrared radiation) conditions. The resolution of each camera was measured and compared with the resolution threshold for customer facial identification. A dome-mounted camera with a standard-definition image-sensor is suggested as an effective camera in sustaining high camera resolution with small data file size for facial identification in the third-row seats. The image-sensors with at least 1280 × 720 pixels are suggested for windshield-mounted cameras in three-row-seating taxicabs for facial identification.
DeepBench: Open-Source Tools for A.I. in the Sky

In recent years, the intervention of Deep Learning as a promising tool for science has simultaneously driven the demand for resources that aid in understanding and developing new learning models. Deep Learning has shown promise in being able to solve classification and regression problems in Astronomy and Physics
Multi Objectives Optimisation in Robotics by using the LabVIEW Instrumentation and Pounder Theory

Multi objectives optimisation in the dynamic behaviour of robots is one of the most difficult problem to be solved. In the paper will be shown the complex matrix form of the positions, velocities, accelerations, active forces and moments in the robot's joints, the mathematical models for the inertial tensors for different form of the robot's body and the proper algorithm to establish the best case between some studied cases when were changed: the movements- simultaneously, successive or combination of them; the acceleration and deceleration time. The mathematical matrix form of the positions, velocities, accelerations, active forces and moments equations were transposed in to the virtual LabVIEW instrumentation with the goals to obtain some characteristics of the positions, velocities, accelerations, active forces and moments variation vs. time in each joints of the robot, in the studied cases. The multi-objective method to choose what will be the best solution of the movement what determine the minimum variation of the linear and angular velocities, linear and angular accelerations and active forces and moments was construct by using the pounder and neutrosophic theory and the simulation with LabVIEW instrumentation. By using proper optimization algorithm was choosing the best solution between all these studied cases. The applied method, the algorithm and the proper virtual instrumentation solve one small part of the complex problems of the optimisation in the dynamic behaviour of robots.
Multimodal brain tumor classification

Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by observing histopathological slides, radiology images should yield additional knowledge towards the efficacy of cancer diagnostics. This work investigates a deep learning method combining whole slide images and magnetic resonance images to classify tumors. In particular, our solution comprises a powerful, generic and modular architecture for whole slide image classification. Experiments are prospectively conducted on the 2020 Computational Precision Medicine challenge, in a 3-classes unbalanced classification task. We report cross-validation (resp. validation) balanced-accuracy, kappa and f1 of 0.913, 0.897 and 0.951 (resp. 0.91, 0.90 and 0.94). For research purposes, including reproducibility and direct performance comparisons, our finale submitted models are usable off-the-shelf in a Docker image available at this https URL.
DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION

The missing values in the datasets are a problem that will decrease the machine learning performance. New methods are recommended every day to overcome this problem. The methods of statistical, machine learning, evolutionary and deep learning methods are among these methods. Although deep learning is one of the popular subjects of today, there are limited studies in the missing data imputation. Several deep learning techniques have been used to handling missing data, one of them is the auto-encoder and its denoising and stacked variants. In this study, the missing value in three different real-world datasets was estimated by using denoising auto-encoder (DAE), k-nearest neighbor (kNN) and multivariate imputation by chained equations (MICE) methods. The estimation success of the methods was compared according to the root mean square error (RMSE) criterion. It was observed that the DAE method was more successful than other methods in estimating the missing values.
Introduction to the Special Issue: Select Papers Presented at the 2019 OLC Accelerate Conference and the 2020 OLC Innovate Conference

We are happy to present five articles selected from the many presented at OLC Accelerate, held November 19-22, 2019 in Orlando, Florida and OLC Innovate, moved from Chicago to a virtual conference in June 15-26, 2020. We invite the readers to consider presenting their research to OLC conferences in the future.
Exploiting Latent Codes: Interactive Fashion Product Generation, Similar Image Retrieval, and Cross-Category Recommendation using Variational Autoencoders

The rise of deep learning applications in the fashion industry has fueled advances in curating large-scale datasets to build applications for product design, image retrieval, and recommender systems. In this paper, the author proposes using Variational Autoencoder (VAE) to build an interactive fashion product application framework that allows the users to generate products with attributes according to their liking, retrieve similar styles for the same product category, and receive content-based recommendations from other categories. Fashion product images dataset containing eyewear, footwear, and bags are appropriate to illustrate that this pipeline is applicable in the booming industry of e-commerce enabling direct user interaction in specifying desired products paired with new methods for data matching, and recommendation systems by using VAE and exploiting its generated latent codes.
SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation

Capturing global contextual representations by exploiting long-range pixel-pixel dependencies has shown to improve semantic segmentation performance. However, how to do this efficiently is an open question as current approaches of utilising attention schemes or very deep models to increase the models field of view, result in complex models with large memory consumption. Inspired by recent work on graph neural networks, we propose the Self-Constructing Graph (SCG) module that learns a long-range dependency graph directly from the image and uses it to propagate contextual information efficiently to improve semantic segmentation. The module is optimised via a novel adaptive diagonal enhancement method and a variational lower bound that consists of a customized graph reconstruction term and a Kullback-Leibler divergence regularization term. When incorporated into a neural network (SCG-Net), semantic segmentation is performed in an end-to-end manner and competitive performance (mean F1-scores of 92.0% and 89.8% respectively) on the publicly available ISPRS Potsdam and Vaihingen datasets is achieved, with much fewer parameters, and at a lower computational cost compared to related pure convolutional neural network (CNN) based models.
Digital Literacy and Modern Languages: How to Make a Digital Video

In this article, you will learn how to integrate digital literacy and Modern Languages through digital videos. The first part of the article is mainly addressed to teachers and instructors and will extensively discuss pedagogical questions. It looks at how the digital video can help teach and learn the four language skills. In the second part of this article, i.e. the actual tutorial, you will learn how to make your own teacher/student-generated videos. The tutorial will explain how to combine and edit text, video and audio in a variety of ways, in order to serve different teaching and learning outcomes in Modern Languages, as well as looking at what software and applications can assist this purpose. In the final section, I will present a number of pedagogical activities using digital videos and what learning outcomes they can help achieve. <strong>Editor's Note: </strong>Adobe Spark has since been rebranded to Adobe Express: https://www.adobe.com/express/create/video
Emergency Clinical Procedure Detection With Deep Learning

Information about a patient's state is critical for hospitals to provide timely care and treatment. Prior work on improving the information flow from emergency medical services (EMS) to hospitals demonstrated the potential of using automated algorithms to detect clinical procedures. However, prior work has not made effective use of video sources that might be available during patient care. In this paper we explore the use convolutional neural networks (CNNs) on raw video data to determine how well video data alone can automatically identify clinical procedures. We apply multiple deep learning models to this problem, with significant variation in results. Our findings indicate performance improvements compared to prior work, but also indicate a need for more training data to reach clinically deployable levels of success.
Recognition of Data Matrix Codes in Images and their Applications in Production Processes

Abstract Data Matrix codes can be a significant factor in increasing productivity and efficiency in production processes. An important point in deploying Data Matrix codes is their recognition and decoding. In this paper is presented a computationally efficient algorithm for locating Data Matrix codes in the images. Image areas that may contain the Data Matrix code are to be identified firstly. To identify these areas, the thresholding, connected components labelling and examining outer bounding-box of the continuous regions is used. Subsequently, to determine the boundaries of the Data Matrix code more precisely, we work with the difference of adjacent projections around the Finder Pattern. The dimensions of the Data Matrix code are determined by analyzing the local extremes around the Timing Pattern. We verified the proposed method on a testing set of synthetic and real scene images and compared it with the results of other open-source and commercial solutions. The proposed method has achieved better results than competitive commercial solutions.
Complexity Analysis of VMs Auction-Based Scheduling in Cloud Datacenters for Grid Balancing

Auction-based mechanisms are widely used tools for price discovery and optimizing the allocation of resources under competition. However, the computational complexity of auctions is always a main factor to consider when determining the applicability of the different auction mechanisms for particular settings. In this work, we consider the complexity of our auction-based scheduling system used for providing grid balancing by scheduling the consumption of excess energy on cloud datacen-terse. We implemented our system using the IBM ILOG CPLEX optimization solver and evaluated its solution-quality vs. time-complexity under three different settings used to manage the solve-time required by the solver. Our results show that the solve-time of the proposed system increases quickly for larger problem sizes and therefore, the development of heuristic algorithms would be more practical for a wider range of scenarios.
Challenges in Patent Information Retrieval

Iwayama, Makoto Fujii, Atsushi Nanba, HidetsuguWe organized tasks on patent information retrieval during the decade from NTCIR-3 to NTCIR-8. All of the tasks were ones that reflected real needs of professional patent searchers and used large numbers of patent documents. This chapter describes the designs of the tasks, the details of the test collections, and the challenges addressed in the research field of patent information retrieval.
Post-quantum MACsec key agreement for ethernet networks

The industrial demand on MACsec in Ethernet networks is increasing substantially, in particular for 5G networks, mainly due to its efficiency paired with strong security. MKA (MACsec Key Agreement) is a companion protocol of MACsec that provides methods of authentication and cryptographic key establishment. In this paper, the MACsec and MKA protocol are analysed under a quantum attack scenario. Even though the threat of quantum computers should not be overstated, it is necessary to provide a new countermeasure that is robust against this potential, yet critical risk. Symmetric-key crypto algorithms defined in MACsec and MKA can achieve 128-bit quantum security if 256-bit keys are mandated. However, classical public-key crypto schemes are known to be vulnerable to quantum attacks so that MKA protocol needs to support post-quantum public-key crypto schemes. We implemented a McEliece-based key establishment which is the most conservative post-quantum public-key cryptosystem with a large size of key, yet feasible for MKA. For entity authentication, we implemented a XMSS hash-based signature scheme that is standardised in IETF. We verified by experiments that selected schemes fit well for a MACsec-enabled Ethernet network.
PEMANFAATAN WEBSITE MADRASAH SEBAGAI MEDIA PEMBELAJARAN E-LEARNING DI TENGAH PANDEMI COVID-19 DI MTs KEDUNGJAMBE SINGGAHAN TUBAN

AbstractÂ The world is shocked by a global pandemic, known as covid-19 (Corona Virus Diseases-19). Almost all affected countries, including Indonesia. It affects all aspects of life, including education in Indonesia.Pemerintah enforce policies to work from home(WorkFromHome),worship at home, and learn at home. So that Teaching and Learning Activities (KBM) are replaced using online systems .Â The application oflearning electronic(E-Learning) makes educators think seriously about the learning media that will be used. Various methods are used by Madrasas to facilitate these needs. One of them is making Madrasah Website as a learning tool that can be accessed by students from home.Keywords: Learning Media, E-Learning, Madrasa Website, Covid-19Â Â Â