Reviewerly/exHarmony huggingface dataset:
On Directed Densest Subgraph Discovery

Given a directed graph G , the directed densest subgraph (DDS) problem refers to the finding of a subgraph from G , whose density is the highest among all the subgraphs of G . The DDS problem is fundamental to a wide range of applications, such as fraud detection, community mining, and graph compression. However, existing DDS solutions suffer from efficiency and scalability problems: on a 3,000-edge graph, it takes three days for one of the best exact algorithms to complete. In this article, we develop an efficient and scalable DDS solution. We introduce the notion of [ x , y ]-core, which is a dense subgraph for G , and show that the densest subgraph can be accurately located through the [ x , y ]-core with theoretical guarantees. Based on the [ x , y ]-core, we develop exact and approximation algorithms. We further study the problems of maintaining the DDS over dynamic directed graphs and finding the weighted DDS on weighted directed graphs, and we develop efficient non-trivial algorithms to solve these two problems by extending our DDS algorithms. We have performed an extensive evaluation of our approaches on 15 real large datasets. The results show that our proposed solutions are up to six orders of magnitude faster than the state-of-the-art.
Quadrotor Path Following and Reactive Obstacle Avoidance with Deep Reinforcement Learning

Abstract A deep reinforcement learning approach for solving the quadrotor path following and obstacle avoidance problem is proposed in this paper. The problem is solved with two agents: one for the path following task and another one for the obstacle avoidance task. A novel structure is proposed, where the action computed by the obstacle avoidance agent becomes the state of the path following agent. Compared to traditional deep reinforcement learning approaches, the proposed method allows to interpret the training process outcomes, is faster and can be safely trained on the real quadrotor. Both agents implement the Deep Deterministic Policy Gradient algorithm. The path following agent was developed in a previous work. The obstacle avoidance agent uses the information provided by a low-cost LIDAR to detect obstacles around the vehicle. Since LIDAR has a narrow field-of-view, an approach for providing the agent with a memory of the previously seen obstacles is developed. A detailed description of the process of defining the state vector, the reward function and the action of this agent is given. The agents are programmed in python/tensorflow and are trained and tested in the RotorS/gazebo platform. Simulations results prove the validity of the proposed approach.
Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement

Object rearrangement has recently emerged as a key competency in robot manipulation, with practical solutions generally involving object detection, recognition, grasping and high-level planning. Goal-images describing a desired scene configuration are a promising and increasingly used mode of instruction. A key outstanding challenge is the accurate inference of matches between objects in front of a robot, and those seen in a provided goal image, where recent works have struggled in the absence of object-specific training data. In this work, we explore the deterioration of existing methods' ability to infer matches between objects as the visual shift between observed and goal scenes increases. We find that a fundamental limitation of the current setting is that source and target images must contain the same instance of every object, which restricts practical deployment. We present a novel approach to object matching that uses a large pre-trained vision-language model to match objects in a cross-instance setting by leveraging semantics together with visual features as a more robust, and much more general, measure of similarity. We demonstrate that this provides considerably improved matching performance in cross-instance settings, and can be used to guide multi-object rearrangement with a robot manipulator from an image that shares no object instances with the robot's scene. Our code is available at https://github.com/applied-ai-lab/object_matching.
Breast Cancer Detection and Classification Using Deep CNN Techniques

Breast cancer is a commonly diagnosed disease in women. Early detection, a personalized treatment approach, and better understanding are necessary for cancer patients to survive. In this work, a deep learning network and traditional convolution network were both employed with the Digital Database for Screening Mammography (DDSM) dataset. Breast cancer images were subjected to background removal followed by Wiener filtering and a contrast limited histogram equalization (CLAHE) filter for image restoration. Wavelet packet decomposition (WPD) using the Daubechies wavelet level 3 (db3) was employed to improve the smoothness of the images. For breast cancer recognition, these preprocessed images were first fed to deep convolution neural networks, namely GoogleNet and AlexNet for Adam. Root mean square propagation (RMSprop) and stochastic gradient descent with momentum (SGDM) optimizers were used for different learning rates, such as 0.01, 0.001, and 0.0001. As medical imaging necessitates the presence of discriminative features for classification, the pretrained GoogleNet architectures extract the complicated features from the image and increase the recognition rate. In the latter part of this study, particle swarm optimization-based multi-layer perceptron (PSO-MLP) and ant colony optimization-based multi-layer perceptron (ACO-MLP) were employed for breast cancer recognition using statistical features, such as skewness, kurtosis, variance, entropy, contrast, correlation, energy, homogeneity, and mean, which were extracted from the preprocessed image. The performance of GoogleNet was compared with AlexNet, PSO-MLP, and ACO-MLP in terms of accuracy, loss rate, and runtime and was found to achieve an accuracy of 99% with a lower loss rate of 0.1547 and the lowest run time of 4.14 minutes.
Performance Evaluation and Analysis of Drone-Based Vehicle Detection Techniques From Deep Learning Perspective

From smart cities development perspective, road vehicle detection exploiting drone-based aerial imagery is a crucial part of traffic surveillance and monitoring systems where effective results are of utmost demand. A recent boom in the field of deep learning (DL) has provided remarkable development in the problem of vehicle detection. Aerial views pose more complexity with respect to the ground view but the rapid advancement in the field of DL, the volume of data, and hardware configuration has facilitated the realization of these intelligent detection systems effectively. In this article, a detailed performance evaluation of some of the main state-of-the-art DL-based object detection techniques has been carried out along with an experimental analysis of vehicle detection using the RetinaNet framework on the VisDrone-benchmark data set. The performance of the RetinaNet framework has been validated together with the results provided by the VisDrone team. Further experiments are then conducted to investigate the impact of various parameters. Finally, the selection of suitable models that can be practically implemented is also discussed based both on a qualitative and quantitative analysis.
Challenges of the market for initial coin offerings

This article analyzes the main problems and the solutions adopted in the market for Initial Coin Offerings (ICO), to anticipate the future of this market and determine implications for issuers, investors and regulators. ICOs represent an alternative and innovative financing solution that has experienced spectacular growth and notoriety in recent years. ICOs rely on Blockchain protocols and the ICO market is, therefore, characterized as decentralized, disintermediated and unregulated. Our results show that although the ICO market is innovative, it already displays many of the problems of traditional financial markets, and that these problems were at the genesis of the last financial crisis. Our analysis of the problems and solutions adopted shows a tension between what the Blockchain technology offers, and the problems associated with the financing of innovation. Considering the problems and solutions adopted, we no longer expect the ICO market to be characterized as disintermediated, unregulated or even decentralized in the near future. Furthermore, it is a real possibility that ICOs may end up being a progressor model eventually replaced by similar but more specialized financing models, some of which may already exist. With respect to the particular solutions of the ICO market, while some represent the realization of the potential of Blockchain, others such as forks have important Governance implications with the potential to create as many problems as the ones they address.
Recommending Learning Objects Through Attentive Heterogeneous Graph Convolution and Operation-Aware Neural Network

Massive Open Online Courses (MOOCs) have received unprecedented attention, in which learners can obtain a large number of learning objects anytime and anywhere. However, the increasing information overload on MOOCs inhibits the appropriate choice of learning objects by learners, leading to a low efficiency and high dropout rates in the learning process of this human-computer interaction scenario. E-learning recommendation systems have been studied to present learning objects directly to learners, thereby relieving such problem. However, in MOOC platforms, recommendation network structures which can selectively extract implicit feature such as heterogeneous learning preference and knowledge organization of learning objects are still not comprehensively studied. To this end, we propose a learning object recommendation model based on heterogeneous learning behavior and knowledge graph. To generate a unified representation of each entity and relation, we first propose an Attentive Composition based Graph Convolutional Network (ACGCN). By introducing an attention mechanism, information is amplified when updating the representation of the heterogeneous graph, which eliminates the impact of noise and improves the robustness of the model. Then, a Dense Feature based Operation-Aware Network (DFOAN) is utilized to capture implicit and complex learners’ interactive behaviors, and to further provide a recommendation. Experimental results using two real-world datasets revealed that our proposed model has the best precision, recall, F1, and accuracy scores compared to those of several existing models.
Single-qubit universal classifier implemented on an ion-trap quantum device

Quantum computers can provide solutions to classically intractable problems under specific and adequate conditions. However, current devices have only limited computational resources, and an effort is made to develop useful quantum algorithms under these circumstances. This work experimentally demonstrates that a single-qubit device can host a universal classifier. The quantum processor used in this work is based on ion traps, providing highly accurate control on small systems. The algorithm chosen is the re-uploading scheme, which can address general learning tasks. Ion traps suit the needs of accurate control required by re-uploading. In the experiment here presented, a set of non-trivial classification tasks are successfully carried. The training procedure is performed in two steps combining simulation and experiment. Final results are benchmarked against exact simulations of the same method and also classical algorithms, showing a competitive performance of the ion-trap quantum classifier. This work constitutes the first experimental implementation of a classification algorithm based on the re-uploading scheme.
Driving behavior explanation with multi-level fusion

In this era of active development of autonomous vehicles, it becomes crucial to provide driving systems with the capacity to explain their decisions. In this work, we focus on generating high-level driving explanations as the vehicle drives. We present BEEF, for BEhavior Explanation with Fusion, a deep architecture which explains the behavior of a trajectory prediction model. Supervised by annotations of human driving decisions justifications, BEEF learns to fuse features from multiple levels. Leveraging recent advances in the multi-modal fusion literature, BEEF is carefully designed to model the correlations between high-level decisions features and mid-level perceptual features. The flexibility and efficiency of our approach are validated with extensive experiments on the HDD and BDD-X datasets.
Evaluating the advantage of adaptive strategies for quantum channel distinguishability

Recently, the resource theory of asymmetric distinguishability for quantum strategies was introduced by [Wang et al., Phys. Rev. Research 1, 033169 (2019)]. The fundamental objects in the resource theory are pairs of quantum strategies, which are generalizations of quantum channels that provide a framework to describe an arbitrary quantum interaction. In the present paper, we provide semi-definite program characterizations of the one-shot operational quantities in this resource theory. We then apply these semi-definite programs to study the advantage conferred by adaptive strategies in discrimination and distinguishability distillation of generalized amplitude damping channels. We find that there are significant gaps between what can be accomplished with an adaptive strategy versus a non-adaptive strategy.
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising

In this paper, we propose a regularization technique for noisy-image super-resolution and image denoising.Total variation (TV) regularization is adopted in many image processing applications to preserve the local smoothness.However, TV prior is prone to oversmoothness, staircasing effect, and contrast losses.Nonlocal TV (NLTV) mitigates the contrast losses by adaptively weighting the smoothness based on the similarity measure of image patches.Although it suppresses the noise effectively in the flat regions, it might leave residual noise surrounding the edges especially when the image is not oversmoothed.To address this problem, we propose the bilateral spectrum weighted total variation (BSWTV).Specially, we apply a locally adaptive shrink coefficient to the image gradients and employ the eigenvalues of the covariance matrix of the weighted image gradients to effectively refine the weighting map and suppress the residual noise.In conjunction with the data fidelity term derived from a mixed Poisson-Gaussian noise model, the objective function is decomposed and solved by the alternating direction method of multipliers (ADMM) algorithm.In order to remove outliers and facilitate the convergence stability, the weighting map is smoothed by a Gaussian filter with an iteratively decreased kernel width and updated in a momentum-based manner in each ADMM iteration.We benchmark our method with the state-of-the-art approaches on the public real-world datasets for super-resolution and image denoising.Experiments show that the proposed method obtains outstanding performance for superresolution and achieves promising results for denoising on realworld images.
Breast Cancer Prediction on BreakHis Dataset Using Deep CNN and Transfer Learning Model

Breast cancer (BC) is a type of disease where cells grow uncontrollably and attack on normal cells. Death rate caused by BC can be reduced by early recognition of the disease. In the recent years, convolution neural network and its various architectures that support transfer learning showed significant improvement in the classification of malignant and non-malignant tumors. In this study, we have performed performance analysis on deep CNN and four popular CNN-based architectures: VGG16, VGG19, MobileNet, and ResNet 50 on publically available BreakHis dataset for breast cancer classification on histopathological images. Among classifiers, VGG16 has best performed with highest 94.67% accuracy, 92.60% precision, 85.21% f1-score, and 80.52% recall value.
Shadow tomography based on informationally complete positive operator-valued measure

Recently introduced shadow tomography protocols use ``classical shadows'' of quantum states to predict many target functions of an unknown quantum state. Unlike full quantum state tomography, shadow tomography does not insist on accurate recovery of the density matrix for high rank mixed states. Yet, such a protocol makes multiple accurate predictions with high confidence, based on a moderate number of quantum measurements. One particular influential algorithm, proposed by Huang et al. [Huang, Kueng, and Preskill, Nat. Phys. 16, 1050 (2020)], requires additional circuits for performing certain random unitary transformations. In this paper, we avoid these transformations but employ an arbitrary informationally complete positive operator-valued measure and show that such a procedure can compute $k$-bit correlation functions for quantum states reliably. We also show that, for this application, we do not need the median of means procedure of Huang et al. Finally, we discuss the contrast between the computation of correlation functions and fidelity of reconstruction of low rank density matrices.
Learning to Fool the Speaker Recognition

Due to the widespread deployment of fingerprint/face/speaker recognition systems, the risk in these systems, especially the adversarial attack, has drawn increasing attention in recent years. Previous researches mainly studied the adversarial attack to the vision-based systems, such as fingerprint and face recognition. While the attack for speech-based systems has not been well studied yet, although it has been widely used in our daily life. In this article, we attempt to fool the state-of-the-art speaker recognition model and present speaker recognition attacker , a lightweight multi-layer convolutional neural network to fool the well-trained state-of-the-art speaker recognition model by adding imperceptible perturbations onto the raw speech waveform. We find that the speaker recognition system is vulnerable to the adversarial attack, and achieve a high success rate on both the non-targeted attack and targeted attack. Besides, we present an effective method by leveraging a pretrained phoneme recognition model to optimize the speaker recognition attacker to obtain a tradeoff between the attack success rate and the perceptual quality. Experimental results on the TIMIT and LibriSpeech datasets demonstrate the effectiveness and efficiency of our proposed model. And the experiments for frequency analysis indicate that high-frequency attack is more effective than low-frequency attack, which is different from the conclusion drawn in previous image-based works. Additionally, the ablation study gives more insights into our model.
VisDrone-MOT2021: The Vision Meets Drone Multiple Object Tracking Challenge Results

Vision Meets Drone: Multiple Object Tracking (VisDrone-MOT2021) challenge – the forth annual activity organized by the VisDrone team – focuses on benchmarking UAV MOT algorithms in realistic challenging environments. It is held in conjunction with ICCV 2021. VisDrone-MOT2021 contains 96 video sequences in total, including 56 sequences (~24K frames) for training, 7 sequences (∼3K frames) for validation and 33 sequences (~13K frames) for testing. Bounding-box annotations for novel object categories are provided every frame and temporally consistent instance IDs are also given. Additionally, occlusion ratio and truncation ratio are provided as extra useful annotations. The results of eight state-of-the-art MOT algorithms are reported and discussed. We hope that our VisDrone-MOT2021 challenge will facilitate future research and applications in the field of UAV vision. The website of our challenge can be found at http://www.aiskyeye.com/.
A Collateral Sensor Data Sharing Framework for Decentralized Healthcare Systems

Healthcare is one of the largest domains across the globe both in terms of employment and income generation. It is continuously evolving and exploiting new technological dimensions to incorporate innovations for providing universal health coverage. Sharing of data collected by various sensors deployed at the patient’s end is a growing solicitude in healthcare as the privacy of the personal healthcare data is of paramount importance. In this paper, a blockchain based healthcare framework is proposed to address the problems related to the sensor-based patient vital body parameters collection, monitoring, secured data storage and sharing among different stakeholders. This framework uses Internet of Medical Things (IoMT) devices interfaced with MAX30205 (human body temperature), and a blood pressure including heart-rate measuring device to collect the patient vital parameters. A Markov state chain is modeled to monitor the patient medical states through various phases during the treatment and monitoring process. Application layer protocols MQTT, CoAP, and AMQP are evaluated for latency and packet loss during data transfers. Ethereum permissioned blockchain is used to deploy the proposed model. Smart contracts provide access control only to authorized users. A comparative analysis is provided in the end to highlight the merits of the proposed model over the existing similar methods. Due to the parallelism and use of blockchain, the system reports 80% improvement in terms of nodes and transaction scalability compared to the existing systems.
Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling

Adaptive learning technology solutions often use a learner model to trace learning and make pedagogical decisions. The present research introduces a formalized methodology for specifying learner models, Logistic Knowledge Tracing (LKT), that consolidates many extant learner modeling methods. The strength of LKT is the specification of a symbolic notation system for alternative logistic regression models that is powerful enough to specify many extant models in the literature and many new models. To demonstrate the generality of LKT, we fit 12 models, some variants of well-known models and some newly devised, to 6 learning technology datasets. The results indicated that no single learner model was best in all cases, further justifying a broad approach that considers multiple learner model features and the learning context. The models presented here avoid student-level fixed parameters to increase generalizability. We also introduce features to stand in for these intercepts. We argue that to be maximally applicable, a learner model needs to adapt to student differences, rather than needing to be pre-parameterized with the level of each student's ability.