Reviewerly/exHarmony huggingface dataset:
Optimal 1-NN prototypes for pathological geometries

Using prototype methods to reduce the size of training datasets can drastically reduce the computational cost of classification with instance-based learning algorithms like the k-Nearest Neighbour classifier. The number and distribution of prototypes required for the classifier to match its original performance is intimately related to the geometry of the training data. As a result, it is often difficult to find the optimal prototypes for a given dataset, and heuristic algorithms are used instead. However, we consider a particularly challenging setting where commonly used heuristic algorithms fail to find suitable prototypes and show that the optimal number of prototypes can instead be found analytically. We also propose an algorithm for finding nearly-optimal prototypes in this setting, and use it to empirically validate the theoretical results. Finally, we show that a parametric prototype generation method that normally cannot solve this pathological setting can actually find optimal prototypes when combined with the results of our theoretical analysis.
Body coherence in curved-space virtual reality games

Virtual-reality simulations of curved space are most effective and most fun when presented as a game (for example, curved-space billiards), so the user not only has something to see in the curved space, but also has something fun to do there. However, such simulations encounter a geometrical problem: they must track the player's hands as well as her head, and in curved space the effects of holonomy would quickly lead to violations of body coherence. That is, what the player sees with her eyes would disagree with what she feels with her hands. This article presents a solution to the body coherence problem, as well as several other questions that arise in interactive VR simulations in curved space (radians vs. meters, visualization of the projection transformation, native-inhabitant view vs. tourist view, and mental models of curved space).
Research on Image Deblurring Processing Technology Based on Genetic Algorithm

Abstract Image deblurring, which is an important branch of digital image processing, is one of the difficulties in digital image processing. The main goal of image deblurring is to improve the quality of the given image, and to reconstruct or restore the original image using the relevant prior knowledge. Genetic algorithm (GA) is a global optimization search algorithm, which can quickly and effectively calculate complex multidimensional data area and indirectly. Now,genetic algorithm is slowly showing its excellent performance in the field of image processing. Therefore, based on the fuzzy features of the image, this paper uses the image enhancement algorithm to realize the image deblurring by capturing the gray value of the pixels in the image, and the improved genetic algorithm is used to better select the image threshold. The experimental results show that the new algorithm obtains higher definition than the traditional image deblurring method, and the improved genetic operator algorithm improves the degree of noise reduction.
An Algorithm to Belief Revision and to Verify Consistency of a Knowledge Base

The belief revision process involves several problems considered hard. One of the crucial problems is how to represent to the knowledge base K to consider, as well as how to represent and to add new information , which may even be contradictory to the knowledge base. In this work, both the knowledge base and the new information are in conjunctive normal form. Each clause of a conjunctive normal form is encoded by a string consisting of: 0, 1, *, representing the falsifying assignments of the clause. To use the falsifying assignments of the clauses allows to perform efficiently different logical operators among conjunctive forms. Our belief revision process (K * ) between conjunctive forms is based on solving first the propositional inference, i.e. K = . Based on to count falsifying assignments represented by tertiary chains, an algorithmic proposal is made that allows to determine in a practical way, when (K E ( K * )) is inconsistent. Finally, the time-complexity analysis of our algorithmic proposal is carried out.
Blockhain impact on economic security

The aim of this paper is to review impact of the one of the most modern cutting edge technologies‘, namely Blockchain on Economic Security. First of all article describes concept - technological background of Blockchain technology nature, emphasizing main features that have most significant and even disruptive impact on separate industries and even whole Economy including Economic Security. Secondly contemporary challenges of this technology are provided. Thirdly multifaced concept of Economic security is explained, followed by importance of this phenomenon specifically within modern mega trends such as Globalization and Information Society. Blockhain impact has been researched through three main industries/functions – eCommerce, Payments and Logistics. Finally conclusions of the paper suggest to use Blockhain with another Industry 4.0 technologies (such as Big Data and Internet of Thing) to reach maximum possible synergy.
A dynamic Bayesian network model for predicting organ failure associations without predefining outcomes

Critical care medicine has been a field for Bayesian networks (BNs) application for investigating relationships among failing organs. Criticisms have been raised on using mortality as the only outcome to determine the treatment efficacy. We aimed to develop a dynamic BN model for detecting interrelationships among failing organs and their progression, not predefining outcomes and omitting hierarchization of organ interactions. We collected data from 850 critically ill patients from the national database used in many intensive care units. We considered as nodes the organ failure assessed by a score as recorded daily. We tested several possible DBNs and used the best bootstrapping results for calculating the strength of arcs and directions. The network structure was learned using a hill climbing method. The parameters of the local distributions were fitted with a maximum of the likelihood algorithm. The network that best satisfied the accuracy requirements included 15 nodes, corresponding to 5 variables measured at three times: ICU admission, second and seventh day of ICU stay. From our findings some organ associations had probabilities higher than 50% to arise at ICU admittance or in the following days persisting over time. Our study provided a network model predicting organ failure associations and their evolution over time. This approach has the potential advantage of detecting and comparing the effects of treatments on organ function.
Gaza Wallet: A Simple and Efficient Blockchain Application

Blockchain technology is a core technology in modern deployed payment applications. There has been widespread progress regarding the application of Blockchain in the financial field. Using the blockchain technology in the financial field, resulted in the creation and establishment of multiple applications of Blockchain Wallets based on Blockchain technology. For Gaza Strip residents who suffer from the siege and closure of crossings because of the occupation, there are many difficulties in performing financial transactions and banking deals. As a contribution, to facilitate financial deals in Gaza strip we created the Gaza Wallet under the Blockchain technology. Gaza wallet deals with its currency called Gaza Coin (GC). It facilitates the conduct of trade and financial operations with less cost and without any intermediate third-party. Gaza Wallet has two applications, one is a web-based and the other is a mobile application using the QR Code technology. Individuals can access Gaza wallet through mobile or web application for payments, to store cryptocurrencies, or to manage their balances.
Practical quantum tokens without quantum memories and experimental tests

Unforgeable quantum money tokens were the first invention of quantum information science, but remain technologically challenging as they require quantum memories and/or long distance quantum communication. More recently, virtual 'S-money' tokens were introduced. These are generated by quantum cryptography, do not require quantum memories or long distance quantum communication, and yet in principle guarantee many of the security advantages of quantum money. Here, we describe implementations of S-money schemes with off-the-shelf quantum key distribution technology, and analyse security in the presence of noise, losses, and experimental imperfection. Our schemes satisfy near instant validation without cross-checking. We show that, given standard assumptions in mistrustful quantum cryptographic implementations, unforgeability and user privacy could be guaranteed with attainable refinements of our off-the-shelf setup. We discuss the possibilities for unconditionally secure (assumption-free) implementations.
A Clinically Practical and Interpretable Deep Model for ICU Mortality Prediction with External Validation.

Deep learning models are increasingly studied in the field of critical care. However, due to the lack of external validation and interpretability, it is difficult to generalize deep learning models in critical care senarios. Few works have validated the performance of the deep learning models with external datasets. To address this, we propose a clinically practical and interpretable deep model for intensive care unit (ICU) mortality prediction with external validation. We use the newly published dataset Philips eICU to train a recurrent neural network model with two-level attention mechanism, and use the MIMIC III dataset as the external validation set to verify the model performance. This model achieves a high accuracy (AUC = 0.855 on the external validation set) and have good interpretability. Based on this model, we develop a system to support clinical decision-making in ICUs.
Improved deep learning model text classification

Deep learning technology develops rapidly. Convolutional Neural Network (CNN), as a key technology in deep learning, has been favored and concerned by many scholars and widely applied in information retrieval, classification, data management, mining and other fields. In order to fully obtain the local features and key words of text, a TCNN-DAM model is proposed based on the study of TCNN model, which aims at maximizing the representation of text features, improving the text classification effect, and promoting the model to better classify in sogou news corpus. Tests show that the improved model has outstanding classification effects, which can effectively improve the accuracy, precision, recall and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> value of classification.
Toward Efficient Bayesian Approaches to Inference in Hierarchical Hidden Markov Models for Inferring Animal Behavior

The study of animal behavioral states inferred through hidden Markov models and similar state switching models has seen a significant increase in popularity in recent years. The ability to account for varying levels of behavioral scale has become possible through hierarchical hidden Markov models, but additional levels lead to higher complexity and increased correlation between model components. Maximum likelihood approaches to inference using the EM algorithm and direct optimization of likelihoods are more frequently used, with Bayesian approaches being less favored due to computational demands. Given these demands, it is vital that efficient estimation algorithms are developed when Bayesian methods are preferred. We study the use of various approaches to improve convergence times and mixing in Markov chain Monte Carlo methods applied to hierarchical hidden Markov models, including parallel tempering as an inference facilitation mechanism. The method shows promise for analysing complex stochastic models with high levels of correlation between components, but our results show that it requires careful tuning in order to maximize that potential.
Adaptive Boosting for Domain Adaptation: Toward Robust Predictions in Scene Segmentation

Domain adaptation is to transfer the shared knowledge learned from the source domain to a new environment, i.e., target domain. One common practice is to train the model on both labeled source-domain data and unlabeled target-domain data. Yet the learned models are usually biased due to the strong supervision of the source domain. Most researchers adopt the early-stopping strategy to prevent over-fitting, but when to stop training remains a challenging problem since the lack of the target-domain validation set. In this paper, we propose one efficient bootstrapping method, called Adaboost Student, explicitly learning complementary models during training and liberating users from empirical early stopping. Adaboost Student combines the deep model learning with the conventional training strategy, i.e., adaptive boosting, and enables interactions between learned models and the data sampler. We adopt one adaptive data sampler to progressively facilitate learning on hard samples and aggregate "weak" models to prevent over-fitting. Extensive experiments show that (1) Without the need to worry about the stopping time, AdaBoost Student provides one robust solution by efficient complementary model learning during training. (2) AdaBoost Student is orthogonal to most domain adaptation methods, which can be combined with existing approaches to further improve the state-of-the-art performance. We have achieved competitive results on three widely-used scene segmentation domain adaptation benchmarks.
IMPLEMENTATION OF SMART TECHNOLOGY, ARTIFICIAL INTELLIGENCE, ROBOTICS, AND ALGORITHMS (STARA): A THREAT OR OPPORTUNITY FOR WORKERS 'FUTURE

This qualitative phenomenological study aims to examine andcompare the perceptions of workers in the banking sector regarding theimplementation of STARA in their work. This study was conducted throughin-depth interviews with informants about perceptions regarding optimizing STARA implementation in the banking sector, in the form of bankingdigitalization in the current Industrial Revolution 4.0 era. The informantsin this study were bank employees such as the teller, customer service, andback-office personnel. Sampling was done using non-random samplingmethod with purposive sampling technique which amounted to 12 informants, consisting of four tellers, four customer service officers, and fourback-office personnel. The results of the study showed that the majority ofbank employees in frontliner positions, namely tellers, felt that their position could be replaced by the implementation of STARA. However, someinformants in the frontliner position, namely customer service, stated thatthey did not feel threatened, because they believed there was a certaincomfort when customers were served by humans rather than by machines.Meanwhile, bank employees in the back-office position have not felt anythreat due to the implementation of STARA because they feel that the analysis process cannot be fully carried out properly by a system and still requires humans. In addition, employees in the back-office position statedthat the implementation of STARA makes it easy for them to completetheir work and can be used as an opportunity to achieve work performance.The results of this study also show that overall employees have not felt adecrease in commitment to the organization, obstacles in career advancement, desire to move, cynicism, or depression when working with the implementation of STARA, which is predicted to take their jobs.
Transferability of a Convolutional Neural Network (CNN) to Measure Traffic Density

Whereas detecting individual vehicles in a video image using a convolutional neural network (CNN) prevails for traffic surveillance, CNNs also have been successfully adapted to counting vehicles via a regression method, which conveys the advantages of simplifying the model structure, and inference time can be reduced in the field. This model also demands much less human effort to tag images with labels. The number of vehicles in an image becomes the label, rather than bounding boxes drawn around every single vehicle. Nonetheless, the labeling task takes considerable time whenever a CNN model is trained and tested for a new road segment. There are two ways to alleviate the human effort involved in using this method. A previous study used a pseudo label pre-training method, and another study employed an image synthesis method to solve the problem. Besides these two methods, we investigated the model transferability to reduce the labeling effort. Using a CNN that was fully trained on images of a road segment, we devised a robust way to utilize the trained model for another site by transforming the model output with a simple quadratic equation. The utility of the proposed method was confirmed at the expense of a minute amount of deterioration in accuracy.
BoostNet: A Boosted Convolutional Neural Network for Image Blind Denoising

Deep convolutional neural networks and generative adversarial networks currently attracted the attention of researchers because it is more effective than conventional representation-based methods. However, they have been facing two serious problems in the trade-off between noise removal, artifacts, and preserving low-contrast features and high-frequency details. In particular, deep convolutional neural networks might fail to remove strong noise in regions with higher noise levels while completely erasing low-contrast features and high-frequency details. By contrast, compared with conventional deep convolutional neural networks, generative adversarial networks might be better in balancing between erasing different types of noise and recovering texture details. However, they often generate fake details and unexpected artifacts in the image owing to the instability of their discriminator during training. In this study, we explored an innovative strategy for handling the serious problems of image denoising. With this strategy, we propose a novel boosting generative adversarial network (BoostNet) that not only combines all advantages of a generative adversarial sub-network and a deep convolutional neural network, it also successfully avoids the serious problems caused by the corruption and instability of training. BoostNet is developed by integrating a stand-alone deep convolutional neural network and a robust generative adversarial network into an ensemble network, which can effectively boost the denoising performance. We conducted several experiments using challenging datasets of additive white Gaussian noise and real-world noisy images. The experimental results show that our proposed method is superior to other state-of-the-art denoisers in terms of quantitative metrics and visual quality. Our source codes and datasets for BoostNet are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZeroZero19/BoostNet.git</uri> .
Visiting Bijective Digitized Reflections and Rotations Using Geometric Algebra

Geometric algebra has become popularly used in applications dealing with geometry. This framework allows us to reformulate and redefine problems involving geometric transformations in a more intuitive and general way. In this paper, we focus on 2D bijective digitized reflections and rotations. After defining the digitization through geometric algebra, we characterize the set of bijective digitized reflections in the plane. We derive new bijective digitized rotations as compositions of bijective digitized reflections since any rotation is represented as the composition of two reflections. We also compare them with those obtained through geometric transformations by computing their distributions.
Parts of Speech Tagging and Extractive Summarization Techniques for Kannada Documents

Summaries of documents were initially done manually, but today, huge data is available on the Internet and everywhere, where manual summarization is failing miserably. A massive amount of data is being put on the Internet every single day. The Internet is being inflated, since the last few years. The increasing availability of information online has created the problem of information overload which has also increased the demand for automatic text summarization. Instead of reading a huge document with lot of theory and examples, the reader would always prefer to read the summarized document which has all the important details. Our proposed work of Kannada text summarization is meant for the same purpose. The reader is provided with most important and relevant information from the original input text document.
Blockchain and its Role in the Internet of Things (IoT)

Blockchain (BC) in the Internet of Things (IoT) is a novel technology that acts with decentralized, distributed, public and real-time ledger to store transactions among IoT nodes. A blockchain is a series of blocks, each block is linked to its previous blocks. Every block has the cryptographic hash code, previous block hash, and its data. The transactions in BC are the basic units that are used to transfer data between IoT nodes. The IoT nodes are different kind of physical but smart devices with embedded sensors, actuators, programs and able to communicate with other IoT nodes. The role of BC in IoT is to provide a procedure to process secured records of data through IoT nodes. BC is a secured technology that can be used publicly and openly. IoT requires this kind of technology to allow secure communication among IoT nodes in heterogeneous environment. The transactions in BC could be traced and explored through anyone who are authenticated to communicate within the IoT. The BC in IoT may help to improve the communication security. In this paper, I explored this approach, its opportunities and challenges.
The Eight Class of Service Model - An Improvement over the Five Classes of Service

Due to the massive growth in traffic data generation on networks, the existing Five Class of Service (5CoS) model is not adequate to guarantee the required Quality of Service (QoS). In this work, we introduce the 8CoS model that consists of eight traffic classes to improve the QoS. The 8CoS model is intended to accommodate as many classes of traffic with unique CoS requirements as possible. Both the integrated and differentiated methods are used in experiments. The differentiated method makes sure that the QoS of the network is based on the service of each class. Functions such as classifiers, policers, queues, and schedulers are considered to evaluate the proposed 8CoS. Simulation results suggest that the throughput due to the proposed 8CoS model increases about 99% when compared with that of a 5CoS model. Also, the delay and packet loss due to the 8CoS model decrease by 40% and 25%, respectively, when compared with those of the 5CoS model. Therefore, the proposed 8CoS model shows promise to replace the existing 5CoS model.