Reviewerly/exHarmony huggingface dataset:
Topological computing of arrangements with (co)chains

In many areas of applied geometric/numeric computational mathematics, including geo-mapping, computer vision, computer graphics, finite element analysis, medical imaging, geometric design, and solid modeling, one has to compute incidences, adjacencies and ordering of cells, generally using disparate and often incompatible data structures and algorithms. This paper introduces computational topology algorithms to discover the 2D/3D space partition induced by a collection of geometric objects of dimension 1D/2D, respectively. Methods and language are those of basic geometric and algebraic topology. Only sparse vectors and matrices are used to compute both spaces and maps, i.e., the chain complex, from dimension zero to three.
Fast Tracking-by-Detection of Bus Passengers with Siamese CNNs

Knowing the exact number of passengers among the city bus fleets allows public transport operators to optimally distribute their vehicles into the traffic. However, interpreting overcrowded scenarios, at rush hour, with day/night illumination changes can be tricky. Based on the visual trackingby-detection paradigm, we benefit from video stream information provided by cameras placed above doors to infer people trajectories and deduce the number of enterings/leavings at every bus stop. In this way a person detector estimates the location of the passengers in each image, a tracker matches detections between successive frames based on different cues such as appearance or motion, and infers trajectories over time. This paper proposes a fast and embeddable framework that performs person detection using relevant state-of-the-art CNN detectors, and couple the best one (in our applicative context) with a newly designed Siamese network for real-time tracking/data association purposes. Evaluations on our own large scale in-situ dataset are very promising in terms of performances and real-time constraint expected for on-board processing.
Joint Super-Resolution and Alignment of Tiny Faces

Super-resolution (SR) and landmark localization of tiny faces are highly correlated tasks. On the one hand, landmark localization could obtain higher accuracy with faces of high-resolution (HR). On the other hand, face SR would benefit from prior knowledge of facial attributes such as landmarks. Thus, we propose a joint alignment and SR network to simultaneously detect facial landmarks and super-resolve tiny faces. More specifically, a shared deep encoder is applied to extract features for both tasks by leveraging complementary information. To exploit the representative power of the hierarchical encoder, intermediate layers of a shared feature extraction module are fused to form efficient feature representations. The fused features are then fed to task-specific modules to detect landmarks and super-resolve face images in parallel. Extensive experiments demonstrate that the proposed model significantly outperforms the state-of-the-art in both landmark localization and SR of faces. We show a large improvement for landmark localization of tiny faces (i.e., 16*16). Furthermore, the proposed framework yields comparable results for landmark localization on low-resolution (LR) faces (i.e., 64*64) to existing methods on HR (i.e., 256*256). As for SR, the proposed method recovers sharper edges and more details from LR face images than other state-of-the-art methods, which we demonstrate qualitatively and quantitatively.
Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision

Training convolutional networks for semantic segmentation with strong (per-pixel) and weak (per-bounding-box) supervision requires a large amount of weakly labeled data. We propose two methods for selecting the most relevant data with weak supervision. The first method is designed for finding visually similar images without the need of labels and is based on modeling image representations with a Gaussian Mixture Model (GMM). As a byproduct of GMM modeling, we present useful insights on characterizing the data generating distribution. The second method aims at finding images with high object diversity and requires only the bounding box labels. Both methods are developed in the context of automated driving and experimentation is conducted on Cityscapes and Open Images datasets. We demonstrate performance gains by reducing the amount of employed weakly labeled images up to 100 times for Open Images and up to 20 times for Cityscapes.
â€œLE SOLITUDINI DEL MAREâ€: UN INEDITO TESTO PER MUSICA DI RICCARDO BACCHELLI

The author present an unpublished text of Riccardo Bacchelli, a short poem entitled â€œLe solitudini del mareâ€, written by Bacchelli during â€™60 years for the composer Bruno Bettinelli, who never set it to music. Different witnesses show revision both Bacchelliâ€™s and Bettinelliâ€™s. Textâ€™s language show interesting literary quality, also in consideration of association with music.
dpVAEs: Fixing Sample Generation for Regularized VAEs.

Unsupervised representation learning via generative modeling is a staple to many computer vision applications in the absence of labeled data. Variational Autoencoders (VAEs) are powerful generative models that learn representations useful for data generation. However, due to inherent challenges in the training objective, VAEs fail to learn useful representations amenable for downstream tasks. Regularization-based methods that attempt to improve the representation learning aspect of VAEs come at a price: poor sample generation. In this paper, we explore this representation-generation trade-off for regularized VAEs and introduce a new family of priors, namely decoupled priors, or dpVAEs, that decouple the representation space from the generation space. This decoupling enables the use of VAE regularizers on the representation space without impacting the distribution used for sample generation, and thereby reaping the representation learning benefits of the regularizations without sacrificing the sample generation. dpVAE leverages invertible networks to learn a bijective mapping from an arbitrarily complex representation distribution to a simple, tractable, generative distribution. Decoupled priors can be adapted to the state-of-the-art VAE regularizers without additional hyperparameter tuning. We showcase the use of dpVAEs with different regularizers. Experiments on MNIST, SVHN, and CelebA demonstrate, quantitatively and qualitatively, that dpVAE fixes sample generation for regularized VAEs.
Spatial-Aware GAN for Unsupervised Person Re-identification

The recent person re-identification research has achieved great success by learning from a large number of labeled person images. On the other hand, the learned models often experience significant performance drops when applied to images collected in a different environment. Unsupervised domain adaptation (UDA) has been investigated to mitigate this constraint, but most existing systems adapt images at pixel level only and ignore obvious discrepancies at spatial level. This paper presents an innovative UDA-based person re-identification network that is capable of adapting images at both spatial and pixel levels simultaneously. A novel disentangled cycle-consistency loss is designed which guides the learning of spatial-level and pixel-level adaptation in a collaborative manner. In addition, a novel multi-modal mechanism is incorporated which is capable of generating images of different geometry views and augmenting training images effectively. Extensive experiments over a number of public datasets show that the proposed UDA network achieves superior person re-identification performance as compared with the state-of-the-art.
Gradient Perturbation is Underrated for Differentially Private Convex Optimization

Gradient perturbation, widely used for differentially private optimization, injects noise at every iterative update to guarantee differential privacy. Previous work first determines the noise level that can satisfy the privacy requirement and then analyzes the utility of noisy gradient updates as in the non-private case. In contrast, we explore how privacy noise affects optimization property. We show that for differentially private convex optimization, the utility guarantee of differentially private (stochastic) gradient descent is determined by an \emph{expected curvature} rather than the minimum curvature. The \emph{expected curvature}, which represents the average curvature over the optimization path, is usually much larger than the minimum curvature. By using the \emph{expected curvature}, we show that gradient perturbation can achieve a significantly improved utility guarantee that can theoretically justify the advantage of gradient perturbation over other perturbation methods. Finally, our extensive experiments suggest that gradient perturbation with the advanced composition method indeed outperforms other perturbation approaches by a large margin, matching our theoretical findings.
Analysis and Visualization of Website Log Data from the Perspective of Big Data

Human society has entered the era of big data in which everything can be digitized, how to efficiently analyze massive data through limited resources is the common requirement of current enterprises to obtain valuable information from complex data. This paper takes the website log data of a consulting company as the research object. SOM and FCM double layer clustering model are used to analyze massive log data efficiently. At the same time, T-sne algorithm is used to reduce the dimensionality of high-dimensional data, and data visualization is realized in the form of two-dimensional graph coordinate diagram. For website maintenance staff more easily understand the network security situation, identify abnormal access and further data analysis to provide ideas.
Edge-Guided Occlusion Fading Reduction for a Light-Weighted Self-Supervised Monocular Depth Estimation

Self-supervised monocular depth estimation methods generally suffer the occlusion fading issue due to the lack of supervision by the per pixel ground truth. Although a post-processing method was proposed by Godard et. al. to reduce the occlusion fading, the compensated results have a severe halo effect. In this paper, we propose a novel Edge-Guided post-processing to reduce the occlusion fading issue for self-supervised monocular depth estimation. We further introduce Atrous Spatial Pyramid Pooling (ASPP) into the network to reduce the computational costs and improve the inference performance. The proposed ASPP-based network is lighter, faster, and better than current commonly used depth estimation networks. This light-weight network only needs 8.1 million parameters and can achieve up to 40 frames per second for $256\times512$ input in the inference stage using a single nVIDIA GTX1080 GPU. The proposed network also outperforms the current state-of-the-art on the KITTI benchmarks. The ASPP-based network and Edge-Guided post-processing produce better results either quantitatively and qualitatively than the competitors.
Diginalysis: The Man-Machine Collaboration in Music Analysis

The digital technology of the twenty-first century has put man and machine in the center stage where electronic generation, production and manipulation of the musical sound are the norm. The dynamics of the century have made time more elusive and patience more diminutive. Time and patience are vital for any form of successful exercise in music analysis. The intricacies of applying logic to resolve complex musical structures, facts, propositions, and concepts into their elements demand more than technical know-how; they demand a lot of time and patience. With the continued fleeing of time and patience, mechanical accuracy in music analysis would need a full-blown computer-driven “diginalysis.” However, inherent limitations of the computer in music analysis, such as decoding the composer’s ideologies, necessitate human-machine collaboration. An in-depth descriptive survey has shown that this effective collaboration between man and machine will collapse time and energy by providing immediate feedback, technical accuracy and dependable results.
Automatically Generating Macro Research Reports from a Piece of News

Automatically generating macro research reports from economic news is an important yet challenging task. As we all know, it requires the macro analysts to write such reports within a short period of time after the important economic news are released. This motivates our work, i.e., using AI techniques to save manual cost. The goal of the proposed system is to generate macro research reports as the draft for macro analysts. Essentially, the core challenge is the long text generation issue. To address this issue, we propose a novel deep learning technique based approach which includes two components, i.e., outline generation and macro research report generation.For the model performance evaluation, we first crawl a large news-to-report dataset and then evaluate our approach on this dataset, and the generated reports are given for the subjective evaluation.
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning

Learning powerful data embeddings has become a center piece in machine learning, especially in natural language processing and computer vision domains. The crux of these embeddings is that they are pretrained on huge corpus of data in a unsupervised fashion, sometimes aided with transfer learning. However currently in the graph learning domain, embeddings learned through existing graph neural networks (GNNs) are task dependent and thus cannot be shared across different datasets. In this paper, we present a first powerful and theoretically guaranteed graph neural network that is designed to learn task-independent graph embeddings, thereafter referred to as deep universal graph embedding (DUGNN). Our DUGNN model incorporates a novel graph neural network (as a universal graph encoder) and leverages rich Graph Kernels (as a multi-task graph decoder) for both unsupervised learning and (task-specific) adaptive supervised learning. By learning task-independent graph embeddings across diverse datasets, DUGNN also reaps the benefits of transfer learning. Through extensive experiments and ablation studies, we show that the proposed DUGNN model consistently outperforms both the existing state-of-art GNN models and Graph Kernels by an increased accuracy of 3% - 8% on graph classification benchmark datasets.
Enhanced auto associative neural network using feed forward neural network: an approach to improve performance of fault detection and analysis

Biosensors have played a significant role in many of present day's applications ranging from military applications to healthcare sectors. However, its practicality and robustness in its usage in real time scenario is still a matter of concern. Primarily issues such as prediction of sensor data, noise estimation and channel estimation and most importantly in fault detection and analysis. In this paper an enhancement is applied to the auto associative neural network (AANN) by considering the cascade feed forward propagation. The residual noise is also computed along with fault detection and analysis of the sensor data. An experimental result shows a significant reduction in the MSE as compared to conventional AANN. The regression based correlation coefficient has improved in the proposed method as compared to conventional AANN.
Word Sense Disambiguation and Semantics techniques

Of the many kinds of ambiguity in language is word sense. This problem had received the most attention in computational linguistics, and the reasons for this are clear: their resolution is seemingly essential for any practical application, and they seem to require a wide variety of methods and knowledge-sources with no pattern apparent in what any particular instance requires.Since the beginning of NLP field and the researchers trying to bypass the bottleneck of word sense disambiguation (WSD). The lack of high-performing methods for sense disambiguation may be considered the major obstacle that prevented an extensive use of natural language processing techniques in many areas of information technology, such as information classification and retrieval, query processing, advanced Web search, document warehousing, etc.This obstacle inspired many researchers to come up with new methods, approaches, and to exploit whatever knowledge resources exist to develop a solution. Previously most of the techniques used were based on a huge amount of knowledge sources, which suffers from the problems of incomplete, inadequate, and expensive resources.After that a trained techniques and the statistical approaches dominant over the field for a while were at the beginning good results obtained with limited and small domains. But with the expanding of the domains, the luck of good trained knowledge sources besides the neglecting of the semantic roles did not lay good results at the end.Now a day’s the research had been directed towards a hybrid approach that depends on the available and more reliable knowledge sources besides the semantic techniques, and training methods which consider the role of the words in its context.In this research we will take a case study of WSD. Then we will display the semantic techniques used in it and display the effect of implementing consistence concept evaluation method to produce more reliable results based on semantic similarity, besides that we will show how more conceptualization of the semantic relations can be implemented through semantic information.
Decision Support System for Determining Website-Based Fruit Quality in Pagar Alam Fruit Farmers Group

Fruit is a healthy supplement, the biggest consumer of fruit is found in big cities. Pagar Alam is an area that produces various types of fruit. Assessment of fruit quality by farmer groups, still using manual methods so that it has some weaknesses. Weakness in evaluating the quality of fruit manually causes an inaccurate assessment because the assessment system is done by estimating the quality of a sample of fruits based on the experience of quality assessors and not based on standardized aspects of assessment. For this reason, a Decision Support System (SPK) was developed to determine the quality of fruit based on a website that helps assessors of the quality of fruits in farmer groups in determining accurate fruit quality with a quick process. This system uses PHP as its programming language, the method used by RAD is an object oriented approach. The system can be used to support fruit quality assessment decisions, thus providing data on the quality of current fruit yields and can be used as a reference for farmers to improve the quality of fruit production in the next harvest.
Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines

Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large number of technical indicators to capture patterns in the cryptocurrency market. We then test various classification methods to forecast short-term future price movements based on these indicators. On both PPV and NPV metrics, our classifiers do well in identifying up and down market moves over the next 1 hour. Beyond evaluating classification accuracy, we also develop a strategy for translating 1-hour-ahead class predictions into trading decisions, along with a backtester that simulates trading in a realistic environment. We find that support vector machines yield the most profitable trading strategies, which outperform the market on average for Bitcoin, Ethereum and Litecoin over the past 22 months, since January 2018.
Cognitive Scheduling and Resource Allocation for Space to Ground Communication

This paper presents a mathematical model for a cognitive communication network applicable to satellite communications with ground stations. The model employs abstract elements to describe a communications network, allowing the approach to be applied to a wide range of real-world communications systems and problems. The model includes representation of communications paths, spacecraft capabilities, time-varying demand for data transfer, changes in visibility due to satellite motion, time-varying availability of channels, and regulatory constraints on the use of radio communication bands. These model elements permit the detailed description of the structure and constraints of a communications problem. The model establishes a formal definition for a communication schedule which assigns communications resources to specific communicators at specific times. The model also formalizes constraints on the interactions between communicators, establishing the definition of a valid schedule in which communications conflicts do not occur and the definition of a good schedule in which communications resources are used efficiently. The paper also presents a dynamic reasoning methodology which uses the model to allocate communications resources in response to changing network conditions and communications loads. Implementation of the reasoning process using Answer Set Programming is demonstrated, providing illustration of the practicality of the approach. The application of the model and methodology to an example satellite communication network is presented. Using this approach significantly improved performance with respect to static resource allocation is demonstrated.
Towards Efficient Superconducting Quantum Processor Architecture Design

More computational resources (i.e., more physical qubits and qubit connections) on a superconducting quantum processor not only improve the performance but also result in more complex chip architecture with lower yield rate. Optimizing both of them simultaneously is a difficult problem due to their intrinsic trade-off. Inspired by the application-specific design principle, this paper proposes an automatic design flow to generate simplified superconducting quantum processor architecture with negligible performance loss for different quantum programs. Our architecture-design-oriented profiling method identifies program components and patterns critical to both the performance and the yield rate. A follow-up hardware design flow decomposes the complicated design procedure into three subroutines, each of which focuses on different hardware components and cooperates with corresponding profiling results and physical constraints. Experimental results show that our design methodology could outperform IBM's general-purpose design schemes with better Pareto-optimal results.