Reviewerly/exHarmony huggingface dataset:
Semantic-Based Road Segmentation for High-Definition Map Construction

The development of autonomous driving technology proposes higher requirements of the fidelity of the high-definition maps (HD maps). The construction of HD map based on orthophotos generated from panoramic images is the state-of-the-art approach. However, in this process, the dynamic obstacles and shadows on the road captured by the panoramic camera has significant impact on reducing the map quality. Moreover, the GNSS signal may inevitably unavailable or have jitter error, leading to the unsatisfactory of the orthophoto mosaic. Therefore, an approach is proposed to tackle these problems of HD map construction. The semantic segmentation of the panoramic images is firstly implemented to extract the dynamic obstacles such as vehicles and the road segments. Then the shadows on the road segments are removed through GAN networks to generate clean orthophotos. Afterwards, the clean orthophotos are used for feature extraction and image registration based on the road segmentation to provide finer pose estimations. Finally, the GNSS data, odometer data, and estimated poses are combined to optimize the vehicle pose for orthophoto mosaic. The experimental results illustrate that the proposed approach can improve the HD map construction accuracy under the congested environment.
An Ensemble of Deep Semantic Representation for Medical X-ray Image Classification

An efficient medical image classification system has gained high interest in the scientific community. This paper presents a classification algorithm that aims to gain a high accuracy rate by addressing some of the typical challenges involved in classification of large medical datasets. In this paper, the convolutional neural networks (CNNs) are employed together with probabilistic latent semantic analysis (PLSA) which are capable of mining hidden semantics of images. This high-level semantic representation of the images is then fed into a discriminative support vector machine (SVM) to build a classification model. An ensemble of machine learning models is also employed to utilize the capability of classification models created from different sets of data. The evaluation is based on a medical image dataset consisting of 11,000 X-ray images from 116 distinct categories. The classification accuracy rate obtained by the proposed classification model is 94.5 %. The results show that the proposed classification model outperformed the methods in the literature evaluated on the same benchmark dataset.
Accelerating Fully Homomorphic Encryption Through Microarchitecture-Aware Analysis and Optimization

Homomorphic Encryption (HE) [11] draws significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Among numerous FHE schemes [2]–[4], [8], [9], HE for Arithmetic of Approximate Numbers (HEAAN [3]), which is also known as CKKS (Cheon-Kim-Kim-Song), is rapidly gaining popularity [10] as it supports computation on real numbers. A critical shortcoming of HE is the high computational complexity of ciphertext arithmetic, especially, HE multiplication (HE Mul). For example, the execution time for computation on encrypted data (ciphertext) increases from 100s to 10,000s of times compared to that on native, unen-crypted messages. However, a large body of HE acceleration studies, including ones exploiting GPUs and FPGAs, lack a rigorous analysis of computational complexity and data access patterns of HE Mul with large parameter sets on CPUs, the most popular computing platform.
Assessing Malaysian University English Test (MUET) Essay on Language and Semantic Features Using Intelligent Essay Grader (IEG)

Automated Essay Scoring (AES) refers to the Artificial Intelligence (AI) application with the “intelligence” in assessing and scoring essays. There are several well-known commercial AES adopted by western countries, as well as many research works conducted in investigating automated essay scoring. However, most of the products and research works are not related to the Malaysian English test context. The AES products tend to score essays based on the scoring rubrics of a particular English text context (e.g., TOEFL, GMAT) by employing their proprietary scoring algorithm that is not accessible by the users. In Malaysia, the research and development of AES are scarce. This paper intends to formulate a Malaysia-based AES, namely Intelligent Essay Grader (IEG), for the Malaysian English test environment by using our collection of two Malaysian University English Test (MUET) essay dataset. We proposed the essay scoring rubric based on its language and semantic features. We analyzed the correlation of the proposed language and semantic features with the essay grade using the Pearson Correlation Coefficient. Furthermore, we constructed an essay scoring model to predict the essay grades. In our result, we found that the language featured such as vocabulary count and advanced part of speech were highly correlated with the essay grades, and the language features showed a greater influence on essay grades than the semantic features. From our prediction model, we observed that the model yielded better accuracy results based on the selected high-correlated essay features, followed by the language features.
The aesthetics of animated sound: François Bayle, Bernard Parmegiani and the Service de la recherche de l’ORTF

Founded by Pierre Schaeffer in 1960, the Service de la recherche at Radiodiffusion-Télévision Française sought to incubate technical and aesthetic research in television and radio, supporting the development of novel animation techniques, pedagogical films for television and experimental short films. As such, the Service served as a fertile meeting point for composers and filmmakers, playing a significant role in the early careers of a number of well-known French composers of electroacoustic music. The early work of both François Bayle and Bernard Parmegiani principally consisted of music and sound for the moving image – and in particular for experimental animated shorts by filmmakers including Robert Lapoujade and Piotr Kamler – created with the support of the Service de la recherche. In attending to the particular configurations of sound and image worked out in these collaborations, the idea of ‘animation’ emerges as a recurring concern in the electroacoustic music of the period, underwriting both a general approach to recorded sound and, I argue, particular formal and technical developments in the aesthetics of French electroacoustic music in the 1960s and beyond.
Convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images

Abstract The installation of dense seismometer arrays in Japan approximately 20 years ago has led to the discovery of deep low-frequency tremors, which are oscillations clearly different from ordinary earthquakes. As such tremors may be related to large earthquakes, it is an important issue in seismology to investigate tremors that occurred before establishing dense seismometer arrays. We use deep learning aiming to detect evidence of tremors from past seismic data of more than 50 years ago, when seismic waveforms were printed on paper. First, we construct a convolutional neural network (CNN) based on the ResNet architecture to extract tremors from seismic waveform images. Experiments applying the CNN to synthetic images generated according to seismograph paper records show that the trained model can correctly determine the presence of tremors in the seismic waveforms. In addition, the gradient-weighted class activation mapping clearly indicates the tremor location on each image. Thus, the proposed CNN has a strong potential for detecting tremors on numerous paper records, which can enable to deepen the understanding of the relations between tremors and earthquakes.
Predicting Bit Error Rate from Meta Information using Random Forests and Partial Data Augmentation

With the increasing power of machine learning-based reasoning, the use of meta information (e.g., digital signal modulation parameters, channel conditions, etc.) to predict the performance of various signal processing techniques has become feasible. One such problem of practical interest is choosing a proper interference mitigation method based on the meta information of the received signal. Since heuristic table-based methods suffer from limited prediction capability for unseen cases, we propose a recommendation system based on the use of Random Forests (RF). Specifically, RF are used to predict the Bit-Error-Rate (BER) of all mitigation approaches so as to determine the approach with the best performance. In this work we show that RF can predict BER with high accuracy, and the Importance Factor (IF) demonstrates the input attributes which matter most. Furthermore, the IF can help to design a data augmentation of partial features. These BER prediction results can also benefit other functions such as adaptive modulation, channel sensing, beam selection, etc.
Markov Chains and Hitting Times for Error Accumulation in Quantum Circuits

We study a classical model for the accumulation of errors in multi-qubit quantum computations. By modeling the error process in a quantum computation using two coupled Markov chains, we are able to capture a weak form of time-dependency between errors in the past and future. By subsequently using techniques from the field of discrete probability theory, we calculate the probability that error quantities such as the fidelity and trace distance exceed a threshold analytically. The formulae cover fairly generic error distributions, cover multi-qubit scenarios, and are applicable to the randomized benchmarking protocol. To combat the numerical challenge that may occur when evaluating our expressions, we additionally provide an analytical bound on the error probabilities that is of lower numerical complexity. Besides this, we study a model describing continuous errors accumulating in a single qubit. Finally, taking inspiration from the field of operations research, we illustrate how our expressions can be used to decide how many gates one can apply before too many errors accumulate with high probability, and how one can lower the rate of error accumulation in existing circuits through simulated annealing.
Classification of Blockchain based Voting: Challenges and Solutions

Voting is a significant procedure for people to settle on their own leader for the government. The issue of preventing fraud and establishing democracy has always been a major challenge in all countries. In the Digital age, the conventional paper votes have been replaced by the benefits of low costs, high performance and little errors. The simple truth that electronic voting processes have been preserved in the central database causes issues: voting data isn't accessible and clear enough, and they are easy to hack and to falsify, the users experience a risk of leakage and the voting outcomes can not be checked by voters. Blockchain is the infrastructure that will help us reduce the identity uncertainty and provide accountability, transparency in voting systems. Many researchers have proposed several solutions inheriting the diverse features of the Blockchain. Understanding the features and their importance in Blockchain Voting is an important topic. Therefore, this paper focuses on a systematic review by several authors of different forms of blockchain-based voting to identify challenges and solutions of blockchain for using in voting and help researchers to make a proper voting framework in the perspective of their country.
Kannada ImageNet: A Dataset for Image Classification in Kannada

Most of the ongoing research for image classification happens in English and there is very little research about classification of images in regional Indian languages like Kannada. There are several uses and advantages of classifying images natively in Kannada. A diverse dataset of 425,723 images which is accurately labelled in Kannada is obtained through this study, which adds to the contribution this paper makes to image classification literature, that is verified and corrected by a human judge. It consists of 1,083 unique classes which was constructed by a combination of randomly selecting classes from ImageNet, a database commonly used for Image classification in English as well as 193 manually thought out classes. The classes were translated first using an online translation service and later corrected by a human judge to achieve a translation accuracy of 97.15% on the entire dataset.
Cluster-based fine-to-coarse superpixel segmentation

As an image preprocessing technology, superpixel segmentation has become an important tool in the field of computer vision. How to obtain a more accurate, faster, and easier-to-apply superpixel segmentation algorithm is a problem faced by researchers. In this paper, a cluster-based fine-to-coarse superpixel segmentation (FCSS) algorithm is proposed. By introducing color thresholds and depth thresholds with practical physical meanings as algorithm parameters, high-quality segmentation with fewer superpixels is achieved. It not only reduces the complexity of the upper application, but also provides an easy to understand interface. Superpixel segmentation methods often cannot achieve high-quality segmentation through a set of parameters. Experimental results show that FCSS can achieve finer segmentation by setting different parameters, and the segmentation results are superior to other algorithms. When the number of superpixels is 100, the segmentation performance of FCSS is better than that of existing state-of-the-art methods.
Multi-Viewpoint Patterns and Occlusions Handling Using Hybrid Features for Vehicle Tracking

A novel vehicle tracking algorithm robust to multi- viewpoint pattern and occlusion is proposed. To improve accuracy, after object detection, the overlapping parts of bounding boxes are removed before block matching. It is helpful for reducing the interference from nearby vehicles or objects. To perform block matching well, in addition to partial similarity and position correlation, many advanced features, including saliency features and several new global features, are adopted. These features can retrieve important information from vehicles and are helpful for tracking. Experiments show that the proposed algorithm achieves favorable performance against state-of-the-art vehicle tracking methods, including rule-based and learning-based methods.
RNN Training along Locally Optimal Trajectories via Frank-Wolfe Algorithm

We propose a novel and efficient training method for RNNs by iteratively seeking a local minima on the loss surface within a small region, and leverage this directional vector for the update, in an outer-loop. We propose to utilize the Frank-Wolfe (FW) algorithm in this context. Although, FW implicitly involves normalized gradients, which can lead to a slow convergence rate, we develop a novel RNN training method that, surprisingly, even with the additional cost, the overall training cost is empirically observed to be lower than backpropagation. Our method leads to a new Frank-Wolfe method, that is in essence an SGD algorithm with a restart scheme. We prove that under certain conditions our algorithm has a sublinear convergence rate of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1/ϵ) for ϵ error. We then conduct empirical experiments on several benchmark datasets including those that exhibit long-term dependencies, and show significant performance improvement. We also experiment with deep RNN architectures and show efficient training performance. Finally, we demonstrate that our training method is robust to noisy data.
Technology of goal setting as a method of formation of creative lifestyle of the future teacher

The article is devoted to the problem of whole laying as a factor in the formation of the creative lifestyle of the future teacher. Scientific approaches to the study of the phenomenon of whole laying, and its impact on the development of future teachers, the formation of life meanings and style, personality development and the formation of professional self-awareness are considered. Lifestyle is the most general characteristic of the life of a given social group or individual, which highlights specific to this group or individual activities or life values. The time perspective of the future is a projection of the motivational sphere of a person and represents to some extent the hopes, plans, projects, aspirations realized by him, which are connected with the near and future. The formation of these plans takes place throughout childhood almost spontaneously, mainly as an internalization of parents' values, their expectations of a particular child and through the development of motivation. The time perspective of the future formed in this way acquires its own motivating force, which influences the further development of the child's personality. A life plan is not only a prediction of a possible future, but also a certain self-attribution, a definition of the tasks that a person sets for himself and which he must fulfill. From this point of view, the life plan is the most important means of self-education: a person creates the desired image of himself, the ideal self-model. Lifestyle is the most general characteristic of the life activity of a given social group or individual, in which specific for this group or individual activities or life values are highlighted [1, p. 41]. Since the specific features of the activity may be different, the classification of lifestyle may be different. It cannot be assumed that a person always lives life in only one style. There is a mixture of styles, and the transition from one to another. This is due to qualitative changes that occur with a person: the transition of age, social status, change of values. Thus, the development of their own lifestyle for the future teacher is one of the main creative tasks on the path of self-improvement.
Interior design of nusantara beauty and health library

Abstract This research aimed to design a library that could arouse the interest of the community. Indonesia was known for its variety of cultures, traditional beauty, and health treatment using natural ingredients. The Indonesian beauty and health industry’s growth had been very rapid these days but still less appreciated by lots of local people. Therefore, Nusantara Beauty and Health Library that provided the information about natural and local cosmetics could be a new and exciting thing that had high potential to attract people where cosmetics had become a necessity and a lifestyle. This created a new impression of the library, where most of the libraries in Jakarta usually seemed rigid and boring. The research method was in the form of collecting data through reference books, magazines, articles, and journals related to Indonesian libraries and cosmetics Nusantara Beauty and Health. Besides, observation, survey, interview, and design process were done to complete this research. It is hoped that this library will be the place to exchange information about the world of Indonesian natural beauty and health with exciting and educational concepts that are different from other libraries in Jakarta.
A Light-Weight Stereo Matching Network with Color Guidance Refinement

Deep-learning-based stereo matching methods have achieved significant improvement over traditional methods and obtained great successes in recent years. However, how to trade off accuracy and speed and predict accurate disparity in real time has been a long-standing problem in the stereo matching area. We present an end-to-end light-weight convolutional neural network (CNN) to quickly estimate accurate disparity maps. Our proposed model is based on AnyNet, a real-time network which generates disparity in stages to achieve anytime prediction. Hourglass architecture with dilated convolutional layers is exploited to extract richer features of input stereo images. We also introduce residual connections in 2D CNN and 3D CNN to avoid information loss. Besides, we adopt a color guidance refinement to improve disparity performance. Depthwise separable convolution is used to replace standard convolution in color guidance refinement to sharply decrease the number of parameters and computational complexity. We refer to our proposed model as Light-Weight Stereo Network (LWSN). LWSN is trained and evaluated on three well-known stereo datasets. Experiments indicate that our model is effective and efficient.
Artificial Intelligence-Based Joint Movement Estimation Method for Football Players in Sports Training

Football is a product in the process of human socialization; it can strengthen the body and enhance the ability of teamwork. The introduction of artificial intelligence into football training is an inevitable trend; this trend must be bound to intensify, but how to apply artificial intelligence to solve the problem of the joint movement estimation method for football players in sports training is still the main difficulty now. The basic principle of football training action pattern recognition is to determine the type of football player’s action by processing and analyzing the movement information obtained by the sensor. Due to the complex movements towards football players and the changeable external environment, there are still many problems with action recognition. Focusing on the detailed classification of different sports modes, this article conducts research on the recognition of the joint movement estimation method for football players in sports training. This paper uses the recognition algorithm based on the multilayer decision tree recognizer to identify the joint movement; the experiment shows that the method used in this paper accurately identified joint movement for football players in sports training.
Deep Convolutional Long Short-Term Memory Network based video abnormal behavior prediction

A deep convolutional long short-term memory network (DC-LSTM) based video abnormal behavior prediction method has been proposed. The DC-LSTM deepens the single-layer convolution of the convolutional LSTM, which can be applied to suppress structural information ambiguity in predicting multiple frames. Mean square error is combined with structural similarity to perform loss reconstruction for correlative calculating between predicted and input frames. An adaptive dynamic double-threshold strategy based on the loss reconstruction is adopted to determine whether the video is abnormal. Comparisons with some state-of-the-arts have highlighted the superior performance of the proposal.
Few-Shot Font Generation with Deep Metric Learning

Designing fonts for languages with a large number of characters, such as Japanese and Chinese, is an extremely labor-intensive and time-consuming task. In this study, we addressed the problem of automatically generating Japanese typographic fonts from only a few font samples, where the synthesized glyphs are expected to have coherent characteristics, such as skeletons, contours, and serifs. Existing methods often fail to generate fine glyph images when the number of style reference glyphs is extremely limited. Herein, we proposed a simple but powerful framework for extracting better style features. This framework introduces deep metric learning to style encoders. We performed experiments using black-and-white and shape-distinctive font datasets and demonstrated the effectiveness of the proposed framework.
Kolmogorov-Smirnov APF Test for Inhomogeneous Poisson Processes with Shift Parameter

In this article, we study the Kolmogorov-Smirnov type goodness-of-fit test for the inhomogeneous Poisson process with the unknown translation parameter as multidimensional parameter.The basic hypothesis and the alternative are composite and carry to the intensity measure of inhomogeneous Poisson process and the intensity function is regular.For this model of shift parameter, we propose test which is asymptotically partially distribution free and consistent.We show that under null hypothesis the limit distribution of this statistic does not depend on unknown parameter.