Reviewerly/exHarmony huggingface dataset:
How types of prior knowledge and task properties impact the category-based induction: diverging evidence from the P2, N400, and LPC effects

Category-based induction task was combined with ERP to unravel whether prior knowledge and property interact when inferring on genes or diseases. Larger P2 amplitudes for near taxonomic/causal distances relative to far ones, as well as larger LPC for taxonomic relation relative to thematic relation, are found in both gene and disease tasks. However, smaller N400 is found for taxonomic relation in gene task and thematic relation in disease task, respectively, and larger LPC at 700-850 ms for near taxonomic distance in the gene task and near causal distance in the disease task. These results suggested that the category-based inductive reasoning is context-sensitive, and there may be four stages of category-based inductive reasoning: the early automatic comparison of features/relations (P2), features/relations generalization process (N400), the extraction of common relationship/rule (LPC at 550-700 ms), the inference generation (LPC at 700-850 ms).
Safety-Aware Hardening of 3D Object Detection Neural Network Systems

We study how state-of-the-art neural networks for 3D object detection using a single-stage pipeline can be made safety aware. We start with the safety specification (reflecting the capability of other components) that partitions the 3D input space by criticality, where the critical area employs a separate criterion on robustness under perturbation, quality of bounding boxes, and the tolerance over false negatives demonstrated on the training set. In the architecture design, we consider symbolic error propagation to allow feature-level perturbation. Subsequently, we introduce a specialized loss function reflecting (1) the safety specification, (2) the use of single-stage detection architecture, and finally, (3) the characterization of robustness under perturbation. We also replace the commonly seen non-max-suppression post-processing algorithm by a safety-aware non-max-inclusion algorithm, in order to maintain the safety claim created by the neural network. The concept is detailed by extending the state-of-the-art PIXOR detector which creates object bounding boxes in bird’s eye view with inputs from point clouds.
Distillation of lossy hyperentangled states

Hyperentanglement has a higher information density than conventional single-degree-of-freedom entanglement, which has attracted much attention due to its fascinating applications in quantum communication. However, since the inevitable interactions between quantum entangled systems and the environment will drive hyperentangled systems into less hyperentangled states or even mixed hyperentangled states, the efficiency and security of quantum communication will be greatly depressed. The currently existing distillation protocols are not universal, i.e., they only work for the lossy hyperentangled states in specific systems. In this paper, based on local positive-operator-valued measures (POVMs), we present a distillation protocol for lossy hyperentangled photonic Bell and Greenberger-Horne-Zeilinger (GHZ) states. The intrinsic property of our protocol is twofold, i.e., the POVM nature of the protocol guarantees that the protocol is a universal one, and the distillation operation on only one of the two degrees of freedom (DOFs) can enhance the fidelity of the system in both DOFs. Furthermore, in the implementation level, our hyperentanglement distillation protocol (HEDP) has other two merits: no auxiliary local entanglement resources and sophisticated single-photon detectors are required, and only one copy of the lossy state will be operated in each distillation round, which show that our HEDPs are relatively simple and feasible.
CORAL: COde RepresentAtion learning with weakly-supervised transformers for analyzing data analysis

Abstract Large scale analysis of source code, and in particular scientific source code, holds the promise of better understanding the data science process, identifying analytical best practices, and providing insights to the builders of scientific toolkits. However, large corpora have remained unanalyzed in depth, as descriptive labels are absent and require expert domain knowledge to generate. We propose a novel weakly supervised transformer-based architecture for computing joint representations of code from both abstract syntax trees and surrounding natural language comments. We then evaluate the model on a new classification task for labeling computational notebook cells as stages in the data analysis process from data import to wrangling, exploration, modeling, and evaluation. We show that our model, leveraging only easily-available weak supervision, achieves a 38% increase in accuracy over expert-supplied heuristics and outperforms a suite of baselines. Our model enables us to examine a set of 118,000 Jupyter Notebooks to uncover common data analysis patterns. Focusing on notebooks with relationships to academic articles, we conduct the largest study of scientific code to date and find that notebooks which devote an higher fraction of code to the typically labor-intensive process of wrangling data in expectation exhibit decreased citation counts for corresponding papers. We also show significant differences between academic and non-academic notebooks, including that academic notebooks devote substantially more code to wrangling and exploring data, and less on modeling.
Priority Combinatorial Double Auction Based Resource Allocation in the Cloud

With the development of large-scale data centers, how to reduce the energy consumption of data centers has becoming one of the most important issues in the cloud computing. To provide the better solution to resource allocation across data centers, one resource allocation scheme based on the Priority Combinatorial Double Auction (PCDA) is proposed. In the PCDA, the auction process of cloud resources includes three phases: cloud user brokers quote, cloud resource providers bidding, and the resource administrate negotiation the bidding. Through the priority of users and the urgency of tasks, PCDA would estimate the SLA (Service Level Agreement) violations for each task during the auction process, and compute the profit of cloud providers. At the same time, multiple transactions are allowed per round. Cloud resources would be allocated reasonably based on the user levels. Simulation results demonstrate that the proposed PCDA can guarantee the bidding success rate. Compared with the traditional combinational double auction (CDA) - one auction, one transaction, the proposed PCDA can reduce the energy consumption by 35.00% during the auction period, and increase the profit of cloud providers by about 38.84%.
Boundary-aware High-resolution Network with region enhancement for salient object detection

Existing deep learning based salient object detection methods have achieved gratifying progress, however they still suffer from the coarse boundaries and incompleteness of salient objects. To alleviate these issues, this paper presents a novel method that supplements the low-resolution high-level semantic information to high-resolution low-level information with boundary optimization and region enhancement. Based on the parallel architecture, we design a Boundary-aware High-resolution Network (BHNet). First, BHNet maintains high resolution to extract features of the image at the first pathway, and the resolution of the other four pathways are lower, which are used to provide more semantic information for the first pathway. Second, in order to better integrate multi-level semantic information into the first pathway and improve the ability of the model to perceive salient objects, several Multi-path Channel Weight Modules (MCWMs) and Region Enhancement Modules (REMs) are further designed for corresponding blocks. Finally, we also propose a boundary loss function to guide the network to learn more detailed boundary information, which leads to accurate predictions with clear boundaries. Exhaustive evaluations on 6 popular datasets illustrate that the proposed method outperforms the state-of-the-art approaches due to its superior performance, nice generalization and powerful learning ability.
Toward Arbitrary-Shaped Text Spotting Based on End-to-End

At present, text spotting in natural scenes has become one of the research hotspots. Among them, curvilinear text and long text are the main difficulties of text spotting in natural scenes. To better solve these two types of problems, we propose a novel end-to-end text spotting model. The model includes three parts: shared convolution module, text detector module and text recognizer module. For the problem of long text, we adopt the corner attention mechanism to extract the features of long text more effectively. For the problem of curve text, we feed the rectification feature map into the SA-BiLSTM decoder to recognize the curve text more effectively. More importantly, the joint optimization strategy realizes the mutual promotion function of the text detection task and the text recognition task. Experimental results on TotalText, ICDAR2015, ICDAR2013, CTW1500, COCO-Text and MLT datasets prove that our method achieves excellent performance and robustness in text spotting tasks based on end-to-end natural scenes.
Burstable Instances for Clouds: Performance Modeling, Equilibrium Analysis, and Revenue Maximization

Leading cloud providers recently introduced a new instance type named burstable instances to better match the time-varying workloads of tenants and further reduce their costs. In the research community, however, little has been done to understand burstable instances from a theoretical perspective. This paper presents the first unified framework to model, analyze, and optimize the operation of burstable instances. Specifically, we model the resource provisioning of burstable instances, identify key performance metrics, and derive the analytical performance given the resource provisioning decisions. We then characterize the equilibrium behind tenants' responses to the prices offered for different burstable instance service classes, taking into account the impact of tenants' actions on the performance achieved by each service class. In addition, we investigate how a cloud provider can leverage knowledge of this equilibrium to find the prices that maximize its total revenue. Finally, we validate our framework on real traces and demonstrate its usage to price burstable offerings in a public cloud.
Measures in Visualization Space

Measurement is an integral part of modern science, providing the fundamental means for evaluation, comparison, and prediction. In the context of visualization, several different types of measures have been proposed, ranging from approaches that evaluate particular aspects of visualization techniques, their perceptual characteristics, and even economic factors. Furthermore, there are approaches that attempt to provide means for measuring general properties of the visualization process as a whole. Measures can be quantitative or qualitative, and one of the primary goals is to provide objective means for reasoning about visualizations and their effectiveness. As such, they play a central role in the development of scientific theories for visualization. In this chapter, we provide an overview of the current state of the art, survey and classify different types of visualization measures, characterize their strengths and drawbacks, and provide an outline of open challenges for future research.
A unified model for recommendation with selective neighborhood modeling

Neighborhood-based recommenders are a major class of Collaborative Filtering models. The intuition is to exploit neighbors with similar preferences for bridging unseen user-item pairs and alleviating data sparseness, in other words, learn the sub-graph representation of each user in a user graph. Many existing works propose neural attention networks to aggregate neighbors and place higher weights on the specific subsets of users for recommendation. However, the neighborhood information is not necessarily always informative, and the noises in the neighborhood can negatively affect the model performance. To address this issue, we propose a novel neighborhood-based recommender, where a hybrid gated network is designed to automatically separate similar neighbors from dissimilar (noisy) ones, and aggregate those similar neighbors to comprise neighborhood representations. The confidence in the neighborhood is also addressed by putting higher weights on the neighborhood representations if we are confident with the neighborhood information, and vice versa. In addition, a user-neighbor component is proposed to explicitly regularize user-neighbor proximity in latent space. These two components are combined into a unified model to complement each other for the recommendation task. Extensive experiments on three public datasets demonstrate that the proposed model consistently outperforms the state-of-the-art neighborhood-based recommenders. Furthermore, we study different variants of the proposed model to justify the underlying intuition of the proposed hybrid gated network and user-neighbor modeling components.
Solar Energy Forecasting With Fuzzy Time Series Using High-Order Fuzzy Cognitive Maps

Various studies indicate that Fuzzy Time Series (FTS) methods can obtain high accuracy in a variety of forecasting applciations. However, weighted FTS methods tend to show superiority in contrast to weightless ones. This study exploits the use of Fuzzy Cognitive Map (FCM) technique to generate the rules in the knowledge base for the FTS forecasting method. The proposed hybrid method, named HFCM-FTS, combines High Order Fuzzy Cognitive Maps (HFCM) and High Order Fuzzy Time Series (HOFTS), where the weight matrices associated with the state transitions are learned via the genetic algorithm from the data. The objective of FCM is to find the weight matrices that model the causal relations among the concepts defined in the Universe of Discourse. As a case study, we consider solar energy forecasting with public data for Brazilian solar stations from the year 2012 to 2015. The proposed HFCM-FTS is compared with HOFTS, Weighted High Order FTS (WHOFTS), and Probabilistic Weighted FTS (PWFTS) methods. The experiments also cover the influence of three modeling elements on the accuracy of the presented model including the number of concepts, activation function, and bias. The results show that the HFCM-FTS is able to achieve the best results with a low number of concepts.
Synthesized A* Multi-robot Path Planning in an Indoor Smart Lab Using Distributed Cloud Computing

Finding the shortest path for an autonomous robot in static environments has been studied for many years and many algorithms exist to solve that problem. While path finding in the static setting is very useful, it is very limiting in real world scenarios due to collisions with dynamic elements in an environment. As a result, many static path planning algorithms have been extended to cover dynamic settings, in which there are more than one moving objects in the environment. In this research, we propose a new implementation of multi agent path finding setting through A* that emphasizes on the path finding through a centralized meta-planner that operates on the base of Bag of Tasks (BoT), running on the distributed computing platforms on the cloud or fog infrastructures and avoiding dynamic obstacles during the planning. We also propose a model to offer a “Multi-Agent A* path planning as-a-Service” to abstract the details of the algorithm to make it more accessible.
A Comprehensive Study of Deep Neural Networks for Unsupervised Deep Learning

Deep learning methods aims at learning meaningful representations in the field of machine learning (ML). Unsupervised deep learning architectures has grown at a fast pace owing to their ability to learn intricate problems. Availability of large amount of labelled and unlabeled data with highly efficient computational resources makes deep learning models more practicable for different applications. Recently, deep neural networks (DNNs) have become an extremely effective and widespread research area in the field of machine learning. The significant aim of deep learning is to learn the primary structure of input data and also to investigate the nonlinear mapping between the inputs and outputs. The main emphasis of this chapter is on unsupervised deep learning. We first study difficulties with neural networks while training with backpropagation-algorithms. Later, different structures, namely, restricted Boltzmann machines (RBMs), Deep Belief Networks (DBNs), nonlinear autoencoders, deep Boltzmann machines are covered. Lastly, sustainable real applications in agricultural domain with deep learning are described.
Towards Partial Supervision for Generic Object Counting in Natural Scenes

Generic object counting in natural scenes is a challenging computer vision problem. Existing approaches either rely on instance-level supervision or absolute count information to train a generic object counter. We introduce a partially supervised setting that significantly reduces the supervision level required for generic object counting. We propose two novel frameworks, named lower-count (LC) and reduced lower-count (RLC), to enable object counting under this setting. Our frameworks are built on a novel dual-branch architecture that has an image classification and a density branch. Our LC framework reduces the annotation cost due to multiple instances in an image by using only lower-count supervision for all object categories. Our RLC framework further reduces the annotation cost arising from large numbers of object categories in a dataset by only using lower-count supervision for a subset of categories and class-labels for the remaining ones. The RLC framework extends our dual-branch LC framework with a novel weight modulation layer and a category-independent density map prediction. Experiments are performed on COCO, Visual Genome and PASCAL 2007 datasets. Our frameworks perform on par with state-of-the-art approaches using higher levels of supervision. Additionally, we demonstrate the applicability of our LC supervised density map for image-level supervised instance segmentation.
Stroboscopic Hamiltonian engineering in the low-frequency regime with a one-dimensional quantum processor

Current quantum devices execute specific tasks that are hard for classical computers and have the potential to solve problems such as quantum simulation of material science and chemistry, even without error correction. For practical applications it is highly desirable to reconfigure the connectivity of the device, which for superconducting quantum processors is determined at fabrication. In addition, we require a careful design of control lines and couplings to resonators for measurements. Therefore, it is a cumbersome and slow undertaking to fabricate a new device for each problem we want to solve. Here we periodically drive a one-dimensional chain to engineer effective Hamiltonians that simulate arbitrary connectivities. We demonstrate the capability of our method by engineering driving sequences to simulate star, all-to-all, and ring connectivities. We also simulate a minimal example of the 3-SAT problem including three-body interactions, which are difficult to realize experimentally. Our results open a new paradigm to perform quantum simulation in near term quantum devices by enabling us to stroboscopically simulate arbitrary Hamiltonians with a single device and optimized driving sequences
NPRportrait 1.0: A three-level benchmark for non-photorealistic rendering of portraits

Abstract Recently, there has been an upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer (NST). However, the state of performance evaluation in this field is poor, especially compared to the norms in the computer vision and machine learning communities. Unfortunately, the task of evaluating image stylisation is thus far not well defined, since it involves subjective, perceptual, and aesthetic aspects. To make progress towards a solution, this paper proposes a new structured, three-level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its construction, and its consistency was validated by user studies. Moreover, a new methodology has been developed for evaluating portrait stylisation algorithms, which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the faces. We perform evaluation for a wide variety of image stylisation methods (both portrait-specific and general purpose, and also both traditional NPR approaches and NST) using the new benchmark dataset.
An Optimized Coverage Robot SLAM Algorithm Based on Improved Particle Filter for WSN Nodes

In order to realize the positioning and creation of the environment of mobile robots, this article proposes an optimized coverage robot SLAM algorithm based on an improved particle filter for WSN nodes. The algorithm overcomes the disadvantages of the standard particle filter SLAM algorithm in the simultaneous positioning of robot poses and creation of environmental maps. By constructing the sensor node to cover the high coverage of the SLAM positioning information node of the robot, the algorithm can search for the ideal result under the existing information, and the local optimization is performed to obtain the ideal result in another local state. Thus, the global accurate robot SLAM information is finally obtained. Simulation experiments show that the influence of the time delay parameter for simultaneous positioning of the robot SLAM is almost zero at different speeds, which shows the superior positioning stability of the new algorithm.
Intrusion Detection and Classification Using Decision Tree-Based Feature Selection Classifiers

Feature selection method applied on an intrusion dataset is used to classify the intrusion data as normal or intrusive. Based on the performance evaluation using various feature selection algorithms and the behavior of attributes, we can distinguish the features which plays an important role for detecting intrusions. The dataset has 41 features, out of which some features play significant role in detecting the intrusions, and others do not contribute in the detection process. We have applied different feature selection techniques to extract the predominant feature that are actually effective in detecting intrusions.
Quantum Conditional Probability and Measurement Induced Disturbance of a Quantum Channel

In classical information theory the conditional probability uniquely defines a classical channel. Introducing a quantum analogue of conditional probability we analyze the mutual information between input and output states of a given quantum channel when the compound state is fully quantum-quantum and classical-quantum. We introduce an analogue of the quantum discord of the channel which measures the distortion of the channel from being classical-quantum. Finally, we generalize our analysis using the mutual information defined via the family of sandwiched Rényi divergencies.