Reviewerly/exHarmony huggingface dataset:
Topology-Preserving Augmentation for CNN-Based Segmentation of Congenital Heart Defects from 3D Paediatric CMR

Patient-specific 3D printing of congenital heart anatomy demands an accurate segmentation of the thin tissue interfaces which characterise these diagnoses. Even when a label set has a high spatial overlap with the ground truth, inaccurate delineation of these interfaces can result in topological errors. These compromise the clinical utility of such models due to the anomalous appearance of defects. CNNs have achieved state-of-the-art performance in segmentation tasks. Whilst data augmentation has often played an important role, we show that conventional image resampling schemes used therein can introduce topological changes in the ground truth labelling of augmented samples. We present a novel pipeline to correct for these changes, using a fast-marching algorithm to enforce the topology of the ground truth labels within their augmented representations. In so doing, we invoke the idea of cardiac contiguous topology to describe an arbitrary combination of congenital heart defects and develop an associated, clinically meaningful metric to measure the topological correctness of segmentations. In a series of five-fold cross-validations, we demonstrate the performance gain produced by this pipeline and the relevance of topological considerations to the segmentation of congenital heart defects. We speculate as to the applicability of this approach to any segmentation task involving morphologically complex targets.
Mining Rules Incrementally over Large Knowledge Bases

Multiple web-scale Knowledge Bases, e.g., Freebase, YAGO, NELL, have been constructed using semi-supervised or unsupervised information extraction techniques and many of them, despite their large sizes, are continuously growing. Much research effort has been put into mining inference rules from knowledge bases. To address the task of rule mining over evolving web-scale knowledge bases, we propose a parallel incremental rule mining framework. Our approach is able to efficiently mine rules based on the relational model and apply updates to large knowledge bases; we propose an alternative metric that reduces computation complexity without compromising quality; we apply multiple optimization techniques that reduce runtime by more than 2 orders of magnitude. Experiments show that our approach efficiently scales to web-scale knowledge bases and saves over 90% time compared to the state-of-the-art batch rule mining system. We also apply our optimization techniques to the batch rule mining algorithm, reducing runtime by more than half compared to the state-of-the-art. To the best of our knowledge, our incremental rule mining system is the first that handles updates to web-scale knowledge bases.
Deep Learning Based Drone Detection and Classification

As commercial drones have been widely used, concerns for collision accidents with people and invading secured properties are emerging. The detection of drone is a challenging problem. The deep learning based object detection techniques for detecting drones have been applied, but limited to the specific cases such as detection of drones from bird and/or background. We have tried not only detection of drones, but classification of different drones with an end-to-end model. YOLOv2 is used as an object detection model. In order to supplement insufficient data by shooting drones, data augmentation from collected images is executed. Also transfer learning from ImageNet for YOLOv2 darknet framework is performed. The experimental results for drone detection with average IoU and recall are compared and analysed.
A Study on the Design of Testable CAM using MTA Code

Recently, due to the depletion of fossil fuel resources and the regulation of CO2 emissions, attention and demand for solar power generation are increasing. The solar power generation has begun to be applied to small-scale power generation, and recently, largescale power plants have been built and sold to utility companies or power demand has been supplied to cities. In addition, facilities for microgrid for energy self-reliance through renewable energy such as solar power, wind power, and tidal power are being constructed and studied in places where electricity supply is difficult, such as island or inland countryside. The solar power generation is advantageous in that it has no pollution, is easy to maintain, and has a long life. However, the power generation depends on the weather, the installation site is limited, and the initial investment cost and the power generation cost are high. Particularly, since it depends on the weather condition, the generation amount is very intermittent and it is difficult to adjust the power generation amount, and it is difficult to establish a power generation plan in advance. Therefore, high accuracy solar power generation forecasting is essential to reduce the uncertainty of solar power generation and to improve the economical efficiency of solar power generation. Existing solar power generation forecasting has been conducted to predict power generation using Extreme Learning Machine (ELM), Support Vector Regression (SVR), and neural network. However, in this paper, we forecast the solar power generation using the meteorological satellite data from 2011 to 2017 and forecasting method using the CNN (Convolutional Neural Network) which is one of the deep running algorithms. And the feasibility of the proposed method was verified using the meteorological satellite data in 2018. For the forecasting program, we programmed using Python, which is specialized for deep running.
Text Generation from Triple via Generative Adversarial Nets

Text generation plays an influential role in NLP (Natural Language Processing), but this task is still challenging. In this paper, we focus on generating text from a triple (entity, relation, entity), and we propose a new sequence to sequence model via GAN (Generative Adversarial Networks) rather than MLE (Maximum Likelihood Estimate) to avoid exposure bias. In this model, the generator is a Transformer and the discriminator is a Transformer based binary classifier, both of which use encoder-decoder structure. With regard to generator, the input sequence of encoder is a triple, then the decoder generates sentence in sequence. The input of discriminator consists of a triple and its corresponding sentence, and the output denotes the probability of being real sample. In this experiment, we use different metrics including Bleu score, Rouge-L and Perplexity to evaluate similarity, sufficiency and fluency of the text generated by three models on test set. The experimental results prove our model has achieved the best performance.
Study on Watching System for Door-to-door Sales Detection and Risk Determination

In recent years, malicious visit sales are increasing in Japan, targeting elderly people living alone and dementia patients. Since it is difficult for them to deal with problems with their own power, it is necessary for surrounding people to support damage deterrence. So, we aim to realize a watching system that anyone can use if there is only a smart speaker. In this study, we propose a method to detect door-to-door sales from conversation information of elderly people and visitors with the aim of realizing a system to prevent elderly consumers' troubles. In the evaluation experiment, we evaluate how accurately door-to-door sales can be detected out of the conversation dataset. In addition, we try to determine the risk degree by learning the door-to-door sales sample data given the risk label.
Application of Weighted K-Means Decision Cluster Classifier in the Recognition of Infectious Expressions of Primary School Students Reading

In this paper, a new classification algorithm for infectious expressions of reading is proposed. This algorithm called weighted K-means decision cluster classifier (WKDCC) is based on the establishment of decision tree model, but four improvements are proposed: (1) using anchor partition instead of heuristic information such as information gain to search (2) feature weighting; (3) using k-means clustering center instead of centroid as anchor point; (4) asymmetric partition. WKDCC is used for recognize infectious expressions of students reading. The results show that WKDCC performed better in accuracy than decision tree, and its time complexity is lower than that of the classical decision tree algorithm. WKDCC is particularly suitable for large data with many samples such as audio data.
A commentary on “Learning error-correcting graph matching with a multiclass neural network”, Pattern Recognition Letters, 2018

We analyze the learning graph-matching algorithm presented in M. Martineau, R. Raveaux, D. Conte, G.Venturini, Learning error-correcting graph matching with a multiclass neural network, Pattern Recognition Letters (2018). Authors propose a new definition of the graph edit distance and also a learning algorithm that deduces some weights on this new graph edit distance. In this commentary, we first show that this new definition of the graph edit distance cannot be considered a distance and then, we discuss how this fact influences on the application of their learning methodology.
Patient identification using a smart pill-bottle

In this work, we investigate the identification of persons taking medication using a sensor-equipped pill-bottle. The bottle embeds inertial sensors in both the cap and body, making the added hardware un-obtrusive, low-cost, and wireless. Our system uses inertial data to build a patient discrimination model using classification techniques. We evaluated the system using 16 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one patient among 16 subjects with 94 % accuracy. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91 %..
<i>NActSeer</i>

Nowadays social network platforms like Twitter, Facebook, Weibo have created a new landscape to communicate with our friends and the world at large. In this landscape our social activities, purchase decisions, check-ins etc. become available immediately to our friends/followers and thus encouraging them to involve in the same activity. This gives rise to the question, given a user and her friends' previous actions, can we predict what is she going to do next? This problem can serve as a good indicator enabling policy research, targeted advertising, assortment planning etc. To capture such sequential mechanism two broad classes of methods have been proposed in the past. First one is the Markov Chain (MC), which assumes user's next action can be predicted based on her most recently taken actions while the second type of approach i.e. Recurrent Neural Network (RNN) tries to model both long and short term preferences of a user. However, none of the two classes of models contain any integrated mechanism to capture the preferences of neighbor's actions. To fill this gap, we propose a social network augmented neural network model named NActSeer which takes the neighbors' actions into account in addition to the user's history. To achieve this NActSeer maintains a dynamic user embedding based on the activities within a time window. It then learns a feature representation for each user which is augmented by her neighbors. Empirical studies on four real-world datasets show that NActSeer is able to outperform several classical and state-of-the-art models proposed for similar problems and achieves up to 71% performance boost.
Algorithm for detecting and determining the coordinates of a point object

A simple algorithm for detecting and calculating the coordinates of point objects on a digital image is presented, in which the problem of the dependence of the results on the intrapixel position of the object&#x2019;s projection in the frame is solved using an adaptive selection filter. Detection is carried out via multilevel threshold segmentation of the source frame and its filtered copy.
Research on a mixed prediction method to vehicle integrated navigation systems

Aiming to improve the positioning accuracy of vehicle integrated navigation system (strapdown inertial navigation system/Global Positioning System) when Global Positioning System signal is blocked, a mixed prediction method combined with radial basis function neural network, time series analysis, and unscented Kalman filter algorithms is proposed. The method is composed by dual modes of radial basis function neural network training and prediction. When Global Positioning System works properly, radial basis function neural network and time series analysis are trained by the error between Global Positioning System and strapdown inertial navigation system. Furthermore, the predicted values of both radial basis function neural network and time series analysis are applied to unscented Kalman filter measurement updates during Global Positioning System outages. The performance of this method is verified by computer simulation. The simulation results indicated that the proposed method can provide higher positioning precision than unscented Kalman filter, especially when Global Positioning System signal temporary outages occur.
Particle Gibbs Methods in Stochastic Volatility Models

The Stochastic Volatility (SV) model and the Multivariate Stochastic Volatility (MSV) model are powerful tools for modeling the volatility of stock price data. A lot of research has been done in the past 40 years. One popular method is to represent the SV as a hidden Markov model and employ the Bayesian inference through the Markov Chain Monte Carlo (MCMC) method. Sampling the hidden states in the SV model from the full conditional distribution is the key part in MCMC. Several particle methods had been developed to approximate the hidden states, such as the Sequential Monte Carlo (SMC) and Particle MCMC. However, these methods suffer from the path degeneracy problem. The Particle Gibbs with Ancestor Sampling (PGAS) was introduced to deal with the path degeneracy in 2014 and it made the MCMC algorithm efficient. However, we believe that the efficiency of MCMC still can be improved by choosing a suitable prior distribution for the parameters in the SV model. In this thesis, we explore the potential reason, which leads to the low efficiency problem, and propose a new method to deal with the SV model through the PGAS algorithm. In our proposed method, we employ the bivariate normal distribution as prior distribution for the parameters in the state equation. The negative correlation between the two parameters can be explained by setting a negative correlation coefficient in the prior distribution. Consequently, the efficiency of the algorithm is improved significantly after sampling parameters out jointly through the Random Walk Metropolis Hastings (RWMH). However, sometimes it is difficult to find a good proposal distribution for RWMH in practice. Thus, we apply the adaptive MCMC to our proposed method to further improve the algorithm. Moreover, we extend our proposed method to the MSV model and get a high efficiency algorithm. We provide theoretical details of particle method to justify the validity of the proposed methods. Numerical experiments, including simulation studies and applications to the S&P 500 index data and some banks' stock data, are presented to demonstrate the good performances of the proposed methods.
Multi-source Fault Tolerant Integrated Navigation for a Certain UAV Based on INS/BD/RP/TAN

In order to make up for the shortcomings of a single navigation system, an INS/BD/RP/TAN multi-source fault-tolerant integrated navigation system based on federated filter is constructed in this paper. Based on the analysis of the error characteristics of each navigation subsystem, the state equations and measurement equations corresponding to each subsystem are established, and an adaptive federated filtering algorithm based on optimization information distribution factor is proposed. At the same time, an improved residual χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> fault detection algorithm is proposed for the faults in the integrated navigation system, which solves the insufficient sensitivity problem of the residual χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> detection algorithm for slope fault detection effectively. Finally, the simulation analysis shows that the fault-tolerant integrated navigation system designed in this paper can not only provide high-precision navigation information for the UAV's navigation system for a long time under no-fault conditions, but also isolate the faulty system timely when the fault occurs, so that the entire integrated navigation system can still provide reliable navigation information.
An Indistinguishably Secure Function Encryption Scheme

In this work, we first design a function encryption scheme by using key encapsulation. We combine public key encryption with symmetric encryption to implement the idea of key encapsulation. In the key encapsulation, we use a key to turn a message (plaintext) into a ciphertext by symmetric encryption, and then we use public key encryption to turn this key into another ciphertext. In the design of function encryption scheme, we use the public key encryption system, symmetric encryption system, noninteractive proof system, indistinguishable obfuscator, and commitment scheme. Finally, we prove the indistinguishable security of our function encryption scheme.
Error Analysis on Grammar in Writing News Item Text Made by The First Year Students of MA Syekh Subakir Nglegok Blitar

This study was aimed at finding out the errors of news item text made by the tenth grade students of MA Syekh Subakir Nglegok Blitar. This study was conducted by using qualitative approach. Sukardi (2003:86) stated in qualitative research, the analysis was done to arrange and group the data in terms of facts, words, sentences, arguments or reasons. Specifically, the design of this study was qualitative research. The subjects of the study covered the tenth grade students of MA Syekh Subakir Nglegok. The researcher took the tenth grades because the researcher used news item text in his research. After getting the compositions, the researcher made three steps first; Rresearcher read each of the 10 writing carefully. Then, the researcher analyzed the data by using the analysis tools. Second the researcher continued to the next step of analysis. It was classification of the data; it was classified into error types and gave a clear description of the study. Afterwards, the identified errors were rewritten in separate list. The last the errors was classified based on the surface strategy taxonomy. The errors were counted each type. In this study, the instrument used was the result of writing test. It was about news item text. It was taken from their own teacher. After the data was collected, the data processing procedure was carried out. The reasearcher identified the errors. Then the researcher described by reconstructed sentences with the original one, the correct grammar and classify them based on the categories of errors analysis. Getting the data from the authentic results, it can be answered the statement problem and taken some conclusions that, mostly, the English learners still have difficulties in apprehending English grammar as a foreign language.
Interconnections Between Teachers’ Attitude And Inclusive Education

The study focuses on teachers attitudes as a key element in ensuring optimal teaching and learning process. The topicality of the study is justified by the shortcomings in the reforms of the national education system related to the implementation of inclusive education, where one of the most important factors is the insufficient competence of teachers. The purpose is to study the teachers subjective self-assessment of attitudes towards the implementation of the inclusive education in the context of a three-dimensional model, which includes the affective, behavioural and cognitive indicators of attitude. The research questions are: (1) How do attitudinal components affect teachers involvement in inclusive education? and (2) What components of attitudes need to be updated in teacher education? A quantitative research methodology, based on correlational research design, is used to address the topic and the survey Attitudes towards Teaching All Students (ATTAS-mm) was employed. For statistical analysis a method of descriptive statistics, Kruskal-Wallis test, and Mann-Whitney test was applied. The study involves teachers with different backgrounds, professional qualifications and involvement in inclusive education. The results confirm that the affective and behavioural dimensions of the attitudes reveal a prevailing positive attitude and the readiness to create an accepting learning environment, whereas the cognitive dimension confirms the negative attitude.Improvement of the content of teacher study programs is necessary to prevent the development of such negative attitudes.
Covariant Uniformly Continuous Quantum Markov Semigroups

In this paper we analyze the structure of decoherence-free subalgebra N(T) of a uniformly continuous covariant semigroup with respect to a representation π of a compact group G on h. In particular, we obtain that, when π is irreducible, N(T) is isomorphic to (ℬ(k) ⊗ 1m)d for suitable Hilbert spaces k and m, and an integer d related to the connected components of G. We extend this result when π is reducible and N(T) is atomic by the decomposition of h due to the Peter–Weyl theorem.
Quantum versus randomized communication complexity, with efficient players

We study a new type of separation between quantum and classical communication complexity which is obtained using quantum protocols where all parties are efficient, in the sense that they can be implemented by small quantum circuits with oracle access to their inputs. More precisely, we give an explicit partial Boolean function that can be computed in the quantum-simultaneous-with-entanglement model of communication, however, every interactive randomized protocol is of exponentially larger cost. Furthermore, all the parties in the quantum protocol can be implemented by quantum circuits of small size with blackbox access to the inputs. Our result qualitatively matches the strongest known separation between quantum and classical communication complexity and is obtained using a quantum protocol where all parties are efficient.
Hardware-Based Cyber Threats: Attack Vectors and Defence Techniques

There are certain vulnerabilities associated with computing hardware that attackers can exploit to launch destructive attacks which often go undetected by the existing hardware and software countermeasures. Side channel attacks (SCAs) and Rowhammer attacks (RHAs), the consequences of hardware vulnerabilities, pose significant security and privacy threats to self-contained computing components and their end-users respectively. Such attacks compromise the security of computational environments, even those with advanced protection mechanisms such as virtualisation, sandboxes or robust encryptions. In light of these security threats against modern computing hardware, we perform an analysis overview of the modi operandi of SCAs and RHAs in hardware implementation and techniques that can be used to extract sensitive data such as secret keys. We then propose various countermeasures to safeguard against these attacks.