Reviewerly/exHarmony huggingface dataset:
Learning to Recognize Actions on Objects in Egocentric Video With Attention Dictionaries

We present EgoACO, a deep neural architecture for video action recognition that learns to pool action-context-object descriptors from frame level features by leveraging the verb-noun structure of action labels in egocentric video datasets. The core component of EgoACO is class activation pooling (CAP), a differentiable pooling operation that combines ideas from bilinear pooling for fine-grained recognition and from feature learning for discriminative localization. CAP uses self-attention with a dictionary of learnable weights to pool from the most relevant feature regions. Through CAP, EgoACO learns to decode object and scene context descriptors from video frame features. For temporal modeling in EgoACO, we design a recurrent version of class activation pooling termed Long Short-Term Attention (LSTA). LSTA extends convolutional gated LSTM with built-in spatial attention and a re-designed output gate. Action, object and context descriptors are fused by a multi-head prediction that accounts for the inter-dependencies between noun-verb-action structured labels in egocentric video datasets. EgoACO features built-in visual explanations, helping learning and interpretation. Results on the two largest egocentric action recognition datasets currently available, EPIC-KITCHENS and EGTEA, show that by explicitly decoding action-context-object descriptors, EgoACO achieves state-of-the-art recognition performance.
Mining Emotions on Plutchik's Wheel

Tweets embed rich information about users' moods, emotions and feelings. Mining for these latent emotions can offer clues about users' affective state on a broad range of topics ranging from their mental health to political opinions. This paper proposes a supervised machine learning approach to detect emotions from tweets. The approach is built around a Crowdflower data set of 40,000 tweets labeled with 13 distinct emotions. These 13 labels were mapped to emotions guided by the Plutchik's wheel, and are further organized into pairs of polar opposites leading to four binary classification problems: Love vs. Hate, Joy vs. Sadness, Trust vs. Disgust, and Anticipation vs. Surprise. For each classification problem, five supervised machine learning models were trained on a combination of linguistic and metadata features extracted from the tweets. The performance of these models is evaluated using sensitivity, specificity, accuracy and AUC. These results suggest that Random Forest and Support Vector Machine classifiers show close to highest accuracy in distinguishing between pair-wise emotions. Although the emotions in each pair are polar opposites on the Plutchik's wheel, their classification performance differs widely; distinguishing between Love vs. Hate and Anticipation vs Surprise show the highest (87%) and lowest (77%) accuracy respectively. Feature importance splits the discriminating power 60% - 40% over linguistic and metadata features. Our results thus suggest that every pair of polar opposite emotions are not equally differentiable, and using both linguistic and metadata features leads to better accuracy over exclusively using text-based or sentiment features.
Detection of Sleeping Cells in Self-Organizing Cellular Networks: An Adversarial Auto-Encoder Method

Automatic fault management is one of important components in self-organizing networks to mitigate and recover from failures of problematic cells. As a special case of cell outage, sleeping cell, which provides degraded service for subscribers without triggering alarms, brings challenges to fault detection. Machine learning provides effective tools for such a detection task. However, traditional algorithms likely constitute biased classifiers when there are imbalance input measurements. Also, misclassification is unavoidable and existing schemes are insensitive to the different costs generated by different misclassifications. To address these issues, we propose a novel method to learn from multi-class imbalance measurements, through combining a modified adversarial autoencoder (AAE) and cost sensitive support vector machine (SVM). Specifically, the proposed method uses AAE to generate more data for minority classes and the loss function of AAE is modified to promote the stability of model training. Then, the cost sensitive SVM is utilized to classify synthetic balanced samples, assigning different costs for varying classification results. Experiment results, evaluated by effective metrics, reveal that the proposed algorithm can improve the detection performance of imbalance sleeping cell data and show superior results compared with state-of-the-art schemes.
Global path planning of mobile robot based on improved JPS+ algorithm

Jump point search algorithm (JPS algorithm) is widely used in mobile robot path planning. In view of the long path search time and poor security of JPS algorithm in path planning, this paper proposes a Bidirectional JPS+ algorithm. This method adopts a bidirectional alternate orthogonal search mechanism to ensure that bidirectional searches can meet and improve the safety of the mobile robot during the movement. The map is preprocessed to speed up the search speed, and the performance is more obvious in large maps. In order to verify the effectiveness of the algorithm, the algorithm is compared with the JPS algorithm and the A* algorithm. The simulation results show that the algorithm speeds up the search speed, improves the safety, and the path planning is reasonable, which meets the path planning requirements.
Can Attention Masks Improve Adversarial Robustness?

Deep Neural Networks (DNNs) are known to be susceptible to adversarial examples. Adversarial examples are maliciously crafted inputs that are designed to fool a model, but appear normal to human beings. Recent work has shown that pixel discretization can be used to make classifiers for MNIST highly robust to adversarial examples. However, pixel discretization fails to provide significant protection on more complex datasets. In this paper, we take the first step towards reconciling these contrary findings. Focusing on the observation that discrete pixelization in MNIST makes the background completely black and foreground completely white, we hypothesize that the important property for increasing robustness is the elimination of image background using attention masks before classifying an object. To examine this hypothesis, we create foreground attention masks for two different datasets, GTSRB and MS-COCO. Our initial results suggest that using attention mask leads to improved robustness. On the adversarially trained classifiers, we see an adversarial robustness increase of over 20% on MS-COCO.
Brain Tumor Segmentation from 3D MRI Slices Using Cascading Convolutional Neural Network

Brain tumor accounts for 80–90% of all primary CNS tumors. Brain cancer is tenth leading cause of death. Automatic detection and classification can help in early diagnosis and deliver efficient treatment. Recent developments in medical imaging modalities such as MRI provide insightful image of our brain. Due to large amount data and variability of data, diagnosing in faster and proper manner is not humanly possible. In this paper, we focus on segmentation of brain tumor of 3D MRI images using 3D CNN in cascading format. Whole tumor is extracted from the images through the first CNN model, then the output is fed to the next CNN model to extract the core tumor and finally, it is fed to the last CNN of cascading network to segment the enhanced tumor core. To improve the brain tumor segmentation, three neural networks belonging to the class of convoluted neural networks (CNNs) were connected having 20 interconnected kernel slices with four downsampling slices to trade off simplicity with feature extraction. The proposed method was evaluated by considering Brats 2015 3D dataset consisting of 274 MRI images with their ground truth having different four modalities. The activation function used was Relu and the results obtained were calculated over DICE coefficient (F-measure) which was found to be 0.78 for core tumor in flair modalities.
Comparison of Generic Similarity Measures in E-learning Content Recommender System in Cold-Start Condition

Recommender systems in the e-learning domain assist learners in finding relevant learning materials based on their preferences and goals. One of the main components of such a recommender system is a similarity measurement unit, used to determine the set of learners having the same behavior. Several similarity functions have been proposed in the e-learning domain, with different performances in terms of accuracy and quality of recommendations. Most of these similarity methods do not perform satisfactorily in the presence of cold-start users. In this paper, we present a comparative study of 4 generic similarity measures (Pearson Correlation Similarity, Cosine Vector Similarity, Euclidean Distance Similarity, Jaccard Similarity Correlation) that are widely used in e-learning recommender systems. The evaluation metrics Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to evaluate the performance of the recommender system with the 4 similarity measures. The results indicate better recommendation performance when using Cosine Vector Similarity in cold-start condition.
Enhancing Trust Management via Blockchain in Social Internet of Things

Social Internet of Things (SIoT) integrates the social concept into the Internet of Things (IoT) for enhancing the efficiency of services. Due to the increasing scale of the network and the intellectualization of IoT objects, the development of SIoT has also been accompanied by the issue of trust management which is crucial to the security and reliability of the system. The definition and evaluation of the trustworthiness of objects are vital to build cooperation among objects. In this paper, we employ the blockchain technology to address the trust management issue in SIoT. Blockchain will be adopted as an underlying support technology for trust-related data storage and retrieval. Moreover, we present a flexible and adaptive trust computation method to enhance the reliability of objects’ trustworthiness evaluation. The simulated experiment results demonstrate that the proposed blockchain-based method enhances the effectiveness and security of trust management in SIoT.
Weakly Supervised Multi-Object Tracking and Segmentation

We introduce the problem of weakly supervised Multi-Object Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation. To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively.
Node classification using kernel propagation in graph neural networks

In this work, we introduce a kernel propagation method that enables graph neural networks (GNNs) to leverage higher-order network structural information without increasing the complexity of the networks. Recent studies have introduced GNNs that include higher-order neighborhood features containing global network information by propagating node features using a higher-order feature propagation rule. Though these GNNs have shown to improve node classification performance, they fail to include local connectivity information. Alternatively, GNNs also concatenate increasing orders of adjacency matrix in deeper layers in order to include higher-order structural information. In addition to global network information, GNNs also make use of node features which are network and node dependent features that serve to distinguish structurally isomorphic sub-structures within graphs. However, such node features may not always be available or depending on the network, may lead to deteriorating classification performance. Hence, to resolve these limitations, we propose a kernel propagation method that introduces a pre-processing step for GNNs to leverage higher-order structural features. The higher-order structural features are computed using a weighted random walk matrix which is node independent while using the first-order spectral propagation rule which explicitly considers local connectivity. Through our benchmark experiments, we find that the computed higher-order structural features are capable of replacing node dependent features while performing node classification task with performance on par with the state of the art approaches. Further, we also find that including both node features and higher-order structural features increases the performance of GNNs on large scale benchmark networks considered in this study. Our results show that considering local and global structural information as input to GNNs lead to an improvement in node classification performance in the absence/presence of node features without loss of performance.
An Improved Kubernetes Scheduling Algorithm for Deep Learning Platform

Most existing deep learning platforms only focus on helping users to start task training quickly, but they tend to ignore the application scenario of multi-team collaboration using one resource pool. In this paper, we propose an improved scheduling algorithm oriented to a multi-tenant model, in which team users are modeled as virtual clusters and cluster load will be monitored regularly. We apply the optimized Kubernetes scheduling algorithm to the Docker-based deep learning platform, our method can ensure the load balance and meet the needs of users.
Computer Vision based Accident Detection for Autonomous Vehicles

Numerous Deep Learning and sensor-based models have been developed to detect potential accidents with an autonomous vehicle. However, a self-driving car needs to be able to detect accidents between other vehicles in its path and take appropriate actions such as to slow down or stop and inform the concerned authorities. In this paper, we propose a novel support system for self-driving cars that detects vehicular accidents through a dashboard camera. The system leverages the Mask R-CNN framework for vehicle detection and a centroid tracking algorithm to track the detected vehicle. Additionally, the framework calculates various parameters such as speed, acceleration, and trajectory to determine whether an accident has occurred between any of the tracked vehicles. The framework has been tested on a custom dataset of dashcam footage and achieves a high accident detection rate while maintaining a low false alarm rate.
Emerging Technologies in a Modern Competitive Scenario

Users' personal, highly sensitive data such as photos and voice recordings are kept indefinitely by the companies that collect it. Users can neither delete nor restrict the purposes for which it is used. Learning how to machine learning that protects privacy, we can make a huge difference in solving many social issues like curing disease, etc. Deep neural networks are susceptible to various inference attacks as they remember information about their training data. In this chapter, the authors introduce differential privacy, which ensures that different kinds of statistical analysis don't compromise privacy and federated learning, training a machine learning model on a data to which we do not have access to.
Experimental determination of deformation homogeneity and shear states using the digital image correlation method

One of the great challenges in the pure shear and simple shear tests is to maintain the deformation state and deformation homogeneity of a specimen under large deformations. For this reason, the aim of this work is to investigate the state of shear deformation and degree of deformation homogeneity of three different shear specimens. A simple and easily replicable methodology was proposed to estimate the degree of deformation homogeneity of specimens, while a time-independent kinematic vorticity parameter was employed to identify pure shear and simple shear deformation states. All data were obtained from full-field displacements that were extracted with the two-dimensional digital image correlation (2D-DIC) method. Specimens of polytetrafluoroethylene (PTFE) were tested using the modified slotted shear test and the V-notched rail shear test, which are used to induce a simple shear state. The planar tensile test was used to produce a pure shear state in a thin sheet of rubber-like material. For small deformations, both slotted and V-notched shear tests induced a good degree of deformation homogeneity along the shear path between the notches of specimens, mainly in the central region. However, the V-notched specimens buckled at large deformation, producing a decrease in the degree of deformation homogeneity. In this case, the results were qualitatively assessed due to the errors associated with out-of-plane motions. High degree of deformation homogeneity was also observed in pure shear specimens. All tests demonstrated to be effective for inducing a state of pure shear or simple shear. Results show that the two approaches can be used as supplementary tools for determining the state of shear deformation and degree of deformation homogeneity.
Research on Multi-Agent Task Optimization and Scheduling Based on Improved Ant Colony Algorithm

Abstract As a key problem of cloud computing, the performance of task scheduling strategy may seriously affect the efficiency and service quality of the system. With the purpose of achieving balanced task scheduling with optimal completion time, ant colony optimization (ACO) algorithm is adopted as task scheduling strategy in this paper. Considering the problem of premature convergence to local optimal solution of ACO, an improved Max-Min Ant System (MMAS) is introduced to find the global optimal solution. Based on the study of pheromone’s updating mechanism of MMAS, MMAS is applied as task scheduling strategy with reasonable mapping from the objective of shortest path to shortest completion time. The task scheduling strategy based on MMAS has been simulated from views of tasks’ number, size and load balance, and the results shows that MMAS task scheduling strategy is with a better performance on completion time and load balance than ACO based strategy.?
Make it real: Simulation of 3D molecules using Augmented Reality in chemical bonding topic

Abstract The chemical bonding is one of the crucial topics in chemistry as it becomes a basic concept to comprehend other topics. Unfortunately, it is difficult for students to understand because it is at the submicroscopic level. This study proposes a way to comprehend that topic by developing a simulation of 3D molecules using Augmented Reality (AR). The data was collected by questionnaire. This study was used ADDIE procedure to develop the product. From the results of this study, it can be concluded that the Augmented Reality learning media has a very good quality and is worthy of being used as a supporting media in chemical learning activities in the chemical bond topic.
Identity Authentication Based on Domestic Commercial Cryptography with Blockchain in the Heterogeneous Alliance Network

Aiming at ensure the security and self-control of heterogeneous alliance network, this paper proposes a novel structure of identity authentication based on domestic commercial cryptography with blockchain in the heterogeneous alliance network. The domestic commercial cryptography, such as SM2, SM3, SM4, SM9 and ZUC, is adopted to solve the encryption, decryption, signature and verification of blockchain, whose key steps of data layer are solved by using domestic commercial cryptographic algorithms. In addition, it is the distributed way to produce the public key and private key for the security of the keys. Therefore, the cross domain identity authentication in the heterogeneous alliance network can be executed safely and effectively.
German Speech Recognition System using DeepSpeech

Speech recognition focus on the translation of speech from an audio format to a text. Popular models are available for the English language as open source in the domain of voice/speech recognition; however, German language open models and training schemes are rather rare. An end-to-end real-time German speech-to-text system based on multiple German language datasets is worthy of more attention and further investigation. In this paper, we combined multiple German datasets on the market and optimizes the Deep-speech for training a real-time German speech-to-text model. A GUI is also proposed for functionality demonstration. Our model performs considerably well compared to other state-of-the-art since we utilized noisy data to replicate real-life scenarios. We released our fully trained German model along with its parameter configurations to promote the diversification of the open-source model for the German language.
Online Spatio-Temporal Action Detection in Long-Distance Imaging Affected by the Atmosphere

Current state-of-the-art approaches for spatio-temporal action detection deal with stable videos and quite sterilized environments, as seen in the UCF-101 benchmark.In addition, the objects of interest are typically relatively close to the camera, and therefore fairly clear and easily distinguished.This study presents an approach method for online human action detection in long-distance imaging affected by atmospheric distortions.We created a unique dataset of typical actions in long-range imaging.Various CNN frameworks were examined for the initial moving object detection phase, including 2D, 3D, one stream, and two-stream (RGB frames and optical flow).The basic object detection methods examined within these frameworks include the YOLOv3 and an extension of the inflated 3D ConvNet with a Feature-Fused Single Shot Multibox Detector (FFSSD) to improve small object detection.To cope with the harmful effect of the spatio-temporal random movements induced by atmospheric effects on motion estimation, we first fit the optical flow stream characteristics to a temporally noisy turbulent environment.A significant improvement of the action detection quality under such noisy conditions was obtained by constructing an online tracking algorithm that incrementally constructs and labels the objects' tracks from the network's frame-level detections.Experimental results show that our approach outperforms the state-of-the-art on our dataset in terms of the mAP measure.
Sentifiers: Interpreting Vague Intent Modifiers in Visual Analysis using Word Co-occurrence and Sentiment Analysis

Natural language interaction with data visualization tools often involves the use of vague subjective modifiers in utterances such as "show me the sectors that are performing" and "where is a good neighborhood to buy a house?." Interpreting these modifiers is often difficult for these tools because their meanings lack clear semantics and are in part defined by context and personal user preferences. This paper presents a system called Sentifiers that makes a first step in better understanding these vague predicates. The algorithm employs word co-occurrence and sentiment analysis to determine which data attributes and filters ranges to associate with the vague predicates. The provenance results from the algorithm are exposed to the user as interactive text that can be repaired and refined. We conduct a qualitative evaluation of the Sentifiers that indicates the usefulness of the interface as well as opportunities for better supporting subjective utterances in visual analysis tasks through natural language.