Reviewerly/exHarmony huggingface dataset:
Dynamic Object-Aware Monocular Visual Odometry With Local And Global Information Aggregation

In this paper, we present a deep learning-based approach to monocular visual odometry. We propose a LCGR(Local Convolution and Global RNN) module which utilizes several independent 3D convolution layers to filter noise from features extracted by FlowNet, as well as to model local information, and a Bi-ConvLSTM layer to model time series and capture global information. In addition, our network jointly predicts optical flow as an auxiliary task by measuring photometric consistency in a self-supervised way to help the encoder for better motion feature extraction. In order to alleviate the effects of non-Lambertian surfaces and dynamical objects in the scene, a confidence mask layer is estimated and epipolar constraint is added to the training process. Experiment results indicate competitive performance of the proposed framework to the state-of-art methods.
An Approach to Embedding a Style Transfer Model into a Mobile APP

The prevalence of photo processing apps suggests the demands of picture editing. As an implementation of the convolutional neural network, style transfer has been deep investigated and there are supported materials to realize it on PC platform. However, few approaches are mentioned to deploy a style transfer model on the mobile and meet the requirements of mobile users. The traditional style transfer model takes hours to proceed, therefore, based on a Perceptual Losses algorithm [1], we created a feedforward neural network for each style and the proceeding time was reduced to a few seconds. The training data were generated from a pre-trained convolutional neural network model, VGG-19. The algorithm took thousandth time and generated similar output as the original. Furthermore, we optimized the model and deployed the model with TensorFlow Mobile library. We froze the model and adopted a bitmap to scale the inputs to 720×720 and reverted back to the original resolution. The reverting process may create some blur but it can be regarded as a feature of art. The generated images have reliable quality and the waiting time is independent of the content and pattern of input images. The main factor that influences the proceeding time is the input resolution. The average waiting time of our model on the mobile phone, HUAWEI P20 Pro, is less than 2 seconds for 720p images and around 2.8 seconds for 1080p images, which are ten times slower than that on the PC GPU, Tesla T40. The performance difference depends on the architecture of the model.
Guidewire Segmentation in 4D Ultrasound Sequences Using Recurrent Fully Convolutional Networks

Accurate, real-time segmentation of thin, deformable, and moving objects in noisy medical ultrasound images remains a highly challenging task. This paper addresses the problem of segmenting guidewires and other thin, flexible devices from 4D ultrasound image sequences acquired during minimally-invasive surgical interventions. We propose a deep learning method based on a recurrent fully convolutional network architecture whose design captures temporal information from dense 4D (3D+time) image sequences. The network uses convolutional gated recurrent units interposed between the halves of a VNet-like model such that the skip-connections embedded in the encoder-decoder are preserved. Testing on realistic phantom tissues, ex vivo and human cadaver specimens, and live animal models of peripheral vascular and cardiovascular disease, we show that temporal encoding improves segmentation accuracy compared to standard single-frame model predictions in a way that is not simply associated to an increase in model size. Additionally, we demonstrate that our approach may be combined with traditional techniques such as active splines to further enhance stability over time.
Modelling Nonlinear Optics in Epsilon-Near-Zero Oxides Through Carrier Kinetics

Epsilon-near-zero materials such as the transparent conducting oxides have arisen as promising nonlinear materials due to enhanced light-matter interactions. However, loss plays a key role. Here we discuss these advances and develop phenomenological models to guide efforts, focusing on the role of loss.
A Simulation-based Online Evolutionary Algorithm for Combat in StarCraft II

Real-time strategy (RTS) games have become one of the hotspots in the field of artificial intelligence research due to the large search space, long-term planning, and real-time constraint. In view of the fact that most of the current research on solving the RTS combat problem uses offline training methods, and these methods may need a lot of training time, this work uses an evolutionary algorithm (EA) to directly optimize the units' action sequences online. In order to solve the problems of repetitive actions caused by the neutral area and the size correlation caused by cooperation between units, three search operators are proposed. It is shown that this method can effectively solve the combat problem in RTS games.
Challenges and Issues in the Existing Methodology for Dynamic Data Capturing of Ontology

As we know, the World Wide Web is growing day by day and the volume of the unstructured data is increasing. So it becomes difficult for the search engine to provide the contextual result to the user query. Thus to solve this problem, we have to design the ontology using which the machine can interpret the hyperlink as well as context of the user. To provide the context and grammar, we have to integrate the ontology with the search engine. But ontology has some or the other limitations of dynamic capturing of the data. Hence in this paper, we describe the challenges and the issues in the existing methodology for the dynamic data capturing of the ontology.
Negative Label Guided Discriminative Canonical Correlation Analysis for Semi-Supervised and Semi-Paired Learning

Semi-supervised learning is a popular trend for learning based methods in recent years, as it fully exploits both the labeled and unlabeled samples in a dataset. This paper sets itself apart from most existing semi-supervised learning algorithms, which only use the exact labels of data already known. We take the negative label as side information to guide the process of semi-supervised learning. Two types of supervision information are regarded as negative label; the first type indicates that a sample definitely does not belong to a specific category, and the second indicates that two samples come from different views, and cannot have a one to one correspondence. By reasonably assuming that nearby points should have similar class indicators, the data labels are propagated under the negative label and the geometric structure revealed by both labeled and unlabeled points. Specifically, we predict one to one pair information by utilizing the neighbor information of samples, under the guidance of the negative pair label. Extensive experiments on several datasets demonstrate the effectiveness of our proposed method.
Research on Advertising Click-Through Rate Prediction Model Based on Ensemble Learning

The advertisement logs accumulated in the Internet have problems such as sparse data, large feature quantity, and extremely uneven distribution of positive and negative samples, which made it difficult to obtain interesting features and to improve precision for single prediction models. In response to these problems, this paper proposes a CTR prediction model based on GBDT-Stacking. GBDT-Stacking model uses the GBDT to automatically extract and transform features suitable for prediction and uses Stacking model to predict CTR of user, which improves the performance of baseline effectively. The experimental results in the real advertising dataset show that the GBDT-Stacking model of this paper uses increased by at least 4% compared to single model in AUC value.
Logic, Probability and Action: A Situation Calculus Perspective

The unification of logic and probability is a long-standing concern in AI, and more generally, in the philosophy of science. In essence, logic provides an easy way to specify properties that must hold in every possible world, and probability allows us to further quantify the weight and ratio of the worlds that must satisfy a property. To that end, numerous developments have been undertaken, culminating in proposals such as probabilistic relational models. While this progress has been notable, a general-purpose first-order knowledge representation language to reason about probabilities and dynamics, including in continuous settings, is still to emerge. In this paper, we survey recent results pertaining to the integration of logic, probability and actions in the situation calculus, which is arguably one of the oldest and most well-known formalisms. We then explore reduction theorems and programming interfaces for the language. These results are motivated in the context of cognitive robotics (as envisioned by Reiter and his colleagues) for the sake of concreteness. Overall, the advantage of proving results for such a general language is that it becomes possible to adapt them to any special-purpose fragment, including but not limited to popular probabilistic relational models.
Influence of regional climate characteristics on short-term photovoltaic power prediction based on fuzzy c-means clustering and Neural Network

As a kind of energy from the sun, solar energy is recognized as one of the most competitive renewable energy sources due to its inexhaustible and pollution-free nature. IN recent years, photovoltaic grid capacity is gradually increasing. However, the output of photovoltaic power station is affected by various meteorological factors to different degrees, which makes the output of photovoltaic power station show obvious periodicity and uncertainty. Therefore, photovoltaic power prediction is of great significance for the safety and economic operation of photovoltaic power stations. In order to improve the accuracy of the prediction, a climate impact model suitable for the power prediction of photovoltaic power stations was established for short-term power prediction objects. In this paper, strong meteorological factors are selected as input factors of fuzzy c-means clustering (FCM) algorithm. The original photovoltaic output data were clustered according to the weather type, and they were grouped into three differed <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sub> not weather categories: sunny day, cloudy day and rainy day. The influence of different weather conditions on photovoltaic prediction was analyzed based on the algorithm of convolutional neural network to study the effects of different climates on predictions. It provides the data basis for the establishment of short-term prediction model of photovoltaic output.
An Adaptive Weighted Deep Survival Forest

An adaptive weighted deep survival forest model for survival analysis is proposed. It can be regarded as an extension of the adaptive weighted deep forest. First, it is based on the deep forest being an ensemble-based model including a set of the random forests organized in a special form of levels of a forest cascade similarly to layers in neural networks. Second, it is based on introducing a special scheme of assigning the weights to instances in the deep forest, which allows us to adapt the random survival forests at every level to training data. One of the main ideas underlying the proposed model is to introduce and to apply a marginal concordance index (MC-index) as a measure of an instance performance and to compute the weights as functions of the MC-index. Numerical examples with real data illustrate the proposed adaptive model.
Self-destruction of data in cloud using asymmetric key with key generator

Individuals support the incredible intensity of distributed computing, however can't completely believe the cloud suppliers to have protection delicate information, because of the nonappearance of client to-cloud controllability. To guarantee privacy, information proprietors re-appropriate scrambled information rather than plaintexts. To impart the scrambled documents to different clients, Cipher text-Policy Attribute-based Encryption (CP-ABE) can be used to direct fine-grained and proprietor driven access control. This is accomplished by keeping key position framework and capacity hubs in two unique ways. Over an unreliable channel, an open key is produced alongside the comparing private key and give to number of clients independently. The Key gave is free of different keys for every clients.
Quality Control Analysis of music Education Network Online Courses in 5G Era

The traditional online course quality control method of music education has poor performance in the control of students' course learning and cannot effectively improve students' autonomous learning ability. Based on this, the quality control analysis of music education network online course in 5G Era is proposed. On the basis of monitoring the construction of teaching resources, students' ability estimation and evaluation quality estimation are calculated to realize normalization and ranking processing, so as to establish an autonomous learning platform for students. The comparison experiment between the method in this paper and the traditional control method shows that compared with the traditional method, the quality control method of music education network online course in 5G Era studied in this paper is more practical and has a prominent performance, which is more suitable for practical application.
Robust Nighttime Vehicle Detection Based on Foveal Classifiers

Visual vehicle surveillance has become an important research field due to its wide range of traffic applications. This task becomes more relevant in nighttime because accidents considerably increase. Typically, this problem is addressed by segmenting the bright image regions produced by vehicle lights, assuming they are well defined. But, often there are only flashes that occupy large image regions, invalidating the previous strategy. Thus, a real-time vehicle detection algorithm for nighttime that addresses the previous challenge is presented. First, the whole image is characterized by only one descriptor. Then, a grid of foveal classifiers that share the same previous image descriptor (unlike the traditional sliding window scheme) estimates the vehicle positions. Every classifier is trained to detect vehicles in specific image regions by analyzing the complex light patterns in the night. Furthermore, a new nighttime database has been also created to assess the effectiveness of the proposed method.
A Rating Bias Formulation based on Fuzzy Set for Recommendation

In recommender systems, the user uncertain preference results in unexpected ratings. Previous approaches (e.g., BiasMF) only adjust the rating value based on the bias vector, ignoring the uncertainty of rating. This paper makes an initial attempt in integrating the influence of user uncertain degree and user rating bias into the matrix factorization framework, simultaneously. An approach based on fuzzy set, called fuZzy Matrix Factorization (ZMF), is proposed. Specifically, a fuzzy set of like is defined for each user, and the membership function is utilized to measure the degree of an item belonging to the fuzzy set. Then, the user uncertain preference matrix is obtained, which could explain and represent the user bias and uncertainty effectively. Furthermore, to enhance the computational impact on sparse matrix, the uncertain preference is formulated as a side-information for fusion. Besides, the proposed approach could be extended to others due to independency on additional data sources. Experimental results on three datasets show that ZMF produces an effective improvement.
The Unscented Transform as a Tool to Assess Circuit Variability for Emergent Technologies

The unscented transform (UT) is proposed as an alternative to the Monte Carlo (MC) method for assessing performance variability of electronic circuits based on emergent technologies. We show that the discrete UT approximation of a continuous probability distribution of random variables can be solved by Gaussian quadrature. As an application example, we compare UT and MC yield predictions for CNTFET current-mode-logic ring oscillators. We discuss the exponential nature of our first implementation and point to the use of sparse grids and symmetry analysis as means to optimize the technique.
$$ P_{\alpha ,\beta } $$-Privacy: A Composable Formulation of Privacy Guarantees for Data Publishing Based on Permutation

Methods for Privacy-Preserving Data Publishing (PPDP) have been recently shown to be equivalent to essentially performing some permutations of the original data. This insight, called the permutation paradigm, establishes a common ground upon which any method can be evaluated ex-post, but can also be viewed as a general ex-ante method in itself, where data are anonymized with the injection of suitable permutation matrices. It remains to develop around this paradigm a formal privacy model based on permutation. Such model should be sufficiently intuitive to allow non-experts to understand what it really entails for privacy to permute, in the same way that the privacy principles lying behind k-anonymity and differential privacy can be grasp by most. Moreover, similarly to differential privacy this model should ideally exhibit simple composition properties, which are highly handy in practice. Based on these requirements, this paper proposes a new privacy model for PPDP called \( P_{\alpha ,\beta } \)-privacy. Using for benchmark a one-time pad, an absolutely secure encryption method, this model conveys a reasonably intuitive meaning of the privacy guarantees brought by permutation, can be used ex-ante or ex-post, and exhibits simple composition properties. We illustrate the application of this new model using an empirical example.
Decision Tree-Based Transdisciplinary Systems Modelling for Cognitive Status in Neurological Diseases

This paper addresses the concept of an up-to-date transdisciplinary system modelling based on decision tree within the framework of systems theory. Systems theory constructs effective models for the analysis of complex systems since this comprehensive theory is capable of providing links between the problems and dynamics of systems. Particularly, for the complex and challenging environments, the solutions to the problems can be managed more effectively based on a systems approach. Neurological diseases concern the brain which has a complex structure and dynamics. Being equipped with the accurate medical knowledge plays a critical role in tackling these neurological problems. The interconnected relationships require a carefully-characterized transdisciplinary approach integrating systems conduct and mathematical modelling. Effective solutions lie in cognitive status, namely awareness and a satisfactory level of health knowledge. Within this framework, this study aims at revealing the lack of required general and medical health knowledge on neurological diseases (Alzheimer’s, dementia, Parkinson’s, stroke, epilepsy and migraine) among individuals. For this purpose, an online survey was conducted on 381 respondents, through which awareness on medical knowledge and general health knowledge were assessed for each disease. The following approaches (methods) were applied: firstly, rule-based decision tree algorithm was applied since its structure enables the interpretation of the data and works effectively with feature computations. Subsequently, statistical analyses were performed. The decision tree analyses and statistical analyses reveal parallel results with one another, which demonstrate that when compared with the knowledge of elder people, the knowledge of young population is limited in general and medical health knowledge. Compared with previous works, no related work exists in the literature where a transdisciplinary approach with these proposed methods are used. The experimental results demonstrate the significant difference between medical knowledge and general health knowledge among individuals depending on different attributes. The study attempts to reveal a new approach for dealing with diseases, developing positive attitudes besides establishing effective long-term behavioural patterns and strategies based on required knowledge and mindfulness.
Formalization and analysis of quantitative attributes of distributed systems

While there is not much discussion on the importance of formally describing and analyzing quantitative requirements in the process of software construction; in the paradigm of API-based software systems, it could be vital. Quantitative attributes can be thought of as attributes determining the Quality of Service - QoS provided by a software component published as a service. In this sense, they play a determinant role in classifying software artifacts according to specific needs stated as requirements.
Adaptive Convolutionally Enchanced Bi-Directional Lstm Networks For Choreographic Modeling

In this paper, we present a deep learning scheme for classification of choreographic primitives from RGB images. The proposed framework combines the representational power of feature maps, extracted by Convolutional Neural Networks, with the long-term dependency modeling capabilities of Long Short-Term Memory recurrent neural networks. In addition, it uses AutoRegressive and Moving Average (ARMA) filter into the convolutionally enriched LSTM filter to face dance dynamic characteristics. Finally, an adaptive weight updating strategy is introduced for improving classification modeling performance The framework is used for the recognition of dance primitives (basic dance postures) and is experimentally validated with real-world sequences of traditional Greek folk dances.
Search Algorithms with Randomized Variable Neighborhoods for Solving Series of Clustering Problems

In this paper, we propose new algorithms for solving the classical problem of cluster analysis, k-Means, which uses the variable neighborhoods search in randomized neighborhoods formed by running the greedy agglomerative procedures. A comparison with known algorithms including the algorithms with greedy heuristics is given, and the advantage is confirmed experimentally.
Virtual Reality as an Alternative Therapy for Acrophobics

The paper presents our research progress in the development of virtual reality as an alternative therapy for acrophobics. The grand purpose of our research is to combine technology and psychology together to develop more efficient therapeutic techniques. The research methods used in this paper are experiment and questionnaire. The anxiety level of the participants is decreasing in each exposure; thus, the VR-based exposure can be considered effective to be used as acrophobic alternative therapy.
Point Set Attention Network For Semantic Segmentation

Self-attention mechanism which aggregates features by capturing long-range relation between each pixel and its context, has been widely used in semantic segmentation task. However, only considering the similarity between pixels is easily disturbed by noisy pixels. In this paper, we propose a Point Set Attention Network (PSANet) for improving self-attention mechanism by correcting the noisy pixels. Specifically, we emphasize to contribute mutual improvement between pixels of the same class. For the pixel at a certain position, we consider current pixel and pixels in its neighborhood as a point set and generate a context-aware mask for selecting pixels belong to the same class as current pixel. Then the selected pixels are aggregated for a center pixel, with which current pixel is updated. In this way, pixels belong to the same class may have similar feature representation, thus promoting intra-class mutual improvement. Next, we compute the relation between each updated pixel and its context features, which often represent all pixels in the feature map. Finally, we aggregate context features on each pixel according to their relations with the pixel. We conduct extensive experiments to validate the effectiveness of our network and achieve outstanding performance on Cityscapes and PASCAL Context datasets.