Reviewerly/exHarmony huggingface dataset:
Outlier Detection for Monitoring Data Using Stacked Autoencoder

Monitoring data contain the important status information of the monitored object, and are the basis for following data mining and analysis.However, the monitoring data usually suffer the pollution of the outliers, leading to negative effect on the subsequent data processing.To address the problem, this paper proposed an outlier detection method based on stacked autoencoder (SAE).SAE has a powerful capability of feature extraction and greatly preserves the original information of the data.The trained SAE by normal data can learn the characteristics of normal data.When a set of data with outliers are inputted to the trained network, there are larger reconstruction errors at the outliers between the original input data and the reconstructed data obtained by using the encoding parameters and the decoding parameter mapping, which provides a basis for locating outliers.Meanwhile, this paper introduced the Grubbs criterion and the PauTa criterion to identify the reconstruction errors corresponding to the outliers based on the traditional threshold method.The method can quickly isolate the abnormal data from the normal data according to the reconstruction error and the identification criterion.The effectiveness and superiority of the proposed method have been validated by experiment on real data and comparisons with traditional outlier detection algorithms.
A Survey of Explainable AI Terminology

The field of Explainable Artificial Intelligence attempts to solve the problem of algorithmic opacity.Many terms and notions have been introduced recently to define Explainable AI, however, these terms seem to be used interchangeably, which is leading to confusion in this rapidly expanding field.As a solution to overcome this problem, we present an analysis of the existing research literature and examine how key terms, such as transparency, intelligibility, interpretability, and explainability are referred to and in what context.This paper, thus, moves towards a standard terminology for Explainable AI.
Secure Data Sharing and Search for Cloud-Edge-Collaborative Storage

Cloud-edge-collaborative storage (CECS) is a promising framework to process data of the internet of things (IoT). It allows edge servers to process IoT data in real-time and stores them on a cloud server. Hence, it can rapidly respond to the requests of IoT devices, provide a massive volume of cloud storage for IoT data, and conveniently share IoT data with users. However, due to the vulnerability of edge and cloud servers, CECS suffers from the risk of data leakage. Existing secure CECS schemes are secure only if all edge servers are trusted. In other words, if any edge server is compromised, all cloud data (generated by IoT devices) will be leaked. Additionally, it is costly to request expected data from the cloud, which is linear with respect to the number of edge servers. To address the above problems, we propose a new secure data search and sharing scheme for CECS. Our scheme improves the existing secure CECS scheme in the following two ways. First, it enables users to generate a public-and-private key pair and manage private keys by themselves. In contrast, the existing solution requires edge servers to manage users' private keys. Second, it uses searchable public-key encryption to achieve more secure, efficient, and flexible data searching. In terms of security, our scheme ensures the confidentiality of cloud data and secure data sharing and searching and avoids a single point of breakthrough. In terms of performance, the experimental results show that our scheme significantly reduces users' computing costs by delegating most of the cryptographic operations to edge servers. Especially, our scheme reduces the computing and communication overhead for generating a search trapdoor compared with the existing secure CECS scheme.
Storytelling from an Image Stream Using Scene Graphs

Visual storytelling aims at generating a story from an image stream. Most existing methods tend to represent images directly with the extracted high-level features, which is not intuitive and difficult to interpret. We argue that translating each image into a graph-based semantic representation, i.e., scene graph, which explicitly encodes the objects and relationships detected within image, would benefit representing and describing images. To this end, we propose a novel graph-based architecture for visual storytelling by modeling the two-level relationships on scene graphs. In particular, on the within-image level, we employ a Graph Convolution Network (GCN) to enrich local fine-grained region representations of objects on scene graphs. To further model the interaction among images, on the cross-images level, a Temporal Convolution Network (TCN) is utilized to refine the region representations along the temporal dimension. Then the relation-aware representations are fed into the Gated Recurrent Unit (GRU) with attention mechanism for story generation. Experiments are conducted on the public visual storytelling dataset. Automatic and human evaluation results indicate that our method achieves state-of-the-art.
COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment

Entity alignment is a fundamental and vital task in Knowledge Graph (KG) construction and fusion. Previous works mainly focus on capturing the structural semantics of entities by learning the entity embeddings on the relational triples and pre-aligned "seed entities". Some works also seek to incorporate the attribute information to assist refining the entity embeddings. However, there are still many problems not considered, which dramatically limits the utilization of attribute information in the entity alignment. Different KGs may have lots of different attribute types, and even the same attribute may have diverse data structures and value granularities. Most importantly, attributes may have various "contributions" to the entity alignment. To solve these problems, we propose COTSAE that combines the structure and attribute information of entities by co-training two embedding learning components, respectively. We also propose a joint attention method in our model to learn the attentions of attribute types and values cooperatively. We verified our COTSAE on several datasets from real-world KGs, and the results showed that it is significantly better than the latest entity alignment methods. The structure and attribute information can complement each other and both contribute to performance improvement.
MOSS: End-to-End Dialog System Framework with Modular Supervision

A major bottleneck in training end-to-end task-oriented dialog system is the lack of data. To utilize limited training data more efficiently, we propose Modular Supervision Network (MOSS), an encoder-decoder training framework that could incorporate supervision from various intermediate dialog system modules including natural language understanding, dialog state tracking, dialog policy learning and natural language generation. With only 60% of the training data, MOSS-all (i.e., MOSS with supervision from all four dialog modules) outperforms state-of-the-art models on CamRest676. Moreover, introducing modular supervision has even bigger benefits when the dialog task has a more complex dialog state and action space. With only 40% of the training data, MOSS-all outperforms the state-of-the-art model on a complex laptop network trouble shooting dataset, LaptopNetwork, that we introduced. LaptopNetwork consists of conversations between real customers and customer service agents in Chinese. Moreover, MOSS framework can accommodate dialogs that have supervision from different dialog modules at both framework level and model level. Therefore, MOSS is extremely flexible to update in real-world deployment.
Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics

The shear volumes of data generated from earth observation and remote sensing technologies continue to make major impact; leaping key geospatial applications into the dual data and compute-intensive era.As a consequence, this rapid advancement poses new computational and data processing challenges.We implement a novel remote sensing data flow (RESFlow) for advancing machine learning to compute with massive amounts of remotely sensed imagery.The core contribution is partitioning massive amounts of data into homogeneous distributions for fitting simple models.RESFlow takes advantage of Apache Spark and the availability of modern computing hardware to harness the acceleration of deep learning inference on expansive remote sensing imagery.The framework incorporates a strategy to optimize resource utilization across multiple executors assigned to a single worker.We showcase its deployment in both computationally and data-intensive workloads for pixel-level labeling tasks.The pipeline invokes deep learning inference at three stages; during deep feature extraction, deep metric mapping, and deep semantic segmentation.The tasks impose compute-intensive and GPU resource sharing challenges motivating for a parallelized pipeline for all execution steps.To address the problem of hardware resource contention, our containerized workflow further incorporates a novel GPU checkout routine and the ticketing system across multiple workers.The workflow is demonstrated with NVIDIA DGX accelerated platforms and offers appreciable compute speed-ups for deep learning inference on pixel labeling workloads; processing 21 028 TB of imagery data and delivering output maps at area rate of 5.245 sq.km/s, amounting to 453 168 sq.km/day-reducing a 28 day workload to 21 h.
Message Passing Attention Networks for Document Understanding

Graph neural networks have recently emerged as a very effective framework for processing graph-structured data. These models have achieved state-of-the-art performance in many tasks. Most graph neural networks can be described in terms of message passing, vertex update, and readout functions. In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD). We also propose several hierarchical variants of MPAD. Experiments conducted on 10 standard text classification datasets show that our architectures are competitive with the state-of-the-art. Ablation studies reveal further insights about the impact of the different components on performance. Code is publicly available at: https://github.com/giannisnik/mpad.
Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm

Mobile robots are becoming more and more widely used in industry and life, so the navigation of robots in dynamic environments has become an urgent problem to be solved. Dynamic path planning has, therefore, received more attention. This paper proposes a real-time dynamic path planning method for mobile robots that can avoid both static and dynamic obstacles. The proposed intelligent optimization method can not only get a better path but also has outstanding advantages in planning time. The algorithm used in the proposed method is a hybrid algorithm based on the beetle antennae search (BAS) algorithm and the artificial potential field (APF) algorithm, termed the BAS-APF method. By establishing a potential field, the convergence speed is accelerated, and the defect that the APF is easily trapped in the local minimum value is also avoided. At the same time, by setting a security scope to make the path closer to the available path in the real environment, the effectiveness and superiority of the proposed method are verified through simulative results.
Optimizing batched winograd convolution on GPUs

In this paper, we present an optimized implementation for single-precision Winograd convolution on NVIDIA Volta and Turing GPUs. Compared with the state-of-the-art Winograd convolution in cuDNN 7.6.1, our implementation achieves up to 2.13X speedup on Volta V100 and up to 2.65X speedup on Turing RTX2070. On both Volta and Turing GPUs, our implementation achieves up to 93% of device peak.
A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition

In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper.
Soft Error Resilience of Deep Residual Networks for Object Recognition

Convolutional Neural Networks (CNNs) have truly gained attention in object recognition and object classification in particular. When being implemented on Graphics Processing Units (GPUs), deeper networks are more accurate than shallow ones. Residual Networks (ResNets) are one of the deepest CNN architectures used in various fields including safety-critical ones. GPUs have proven to be the major accelerator for CNN models. However, modern GPUs are prone to radiation-induced soft errors, which is a serious issue in safety-compliant systems. In this work, we analyze and propose an approach to address the reliability of ResNet on GPUs. We firstly analyze three popular ResNet models, explicitly, ResNet-50, ResNet-101, and ResNet-152 through NVIDIA’s fault injector, SASSIFI. We perform an in-depth analysis of the model from the perspective of layer and kernel vulnerability. Then, we experimentally show the vulnerability of ResNet models and identify the most vulnerable portions. Finally, we validate our solution, which is a selective-hardening technique, through hardening the worth-hardening kernels to avoid unnecessary overheads. Our strategy is demonstrated to mask up to 93.38% of the injected errors with performance overhead less than 5.35%. Furthermore, the percentage of the errors causing misclassifications can be reduced from 4.2% to 0.104%, thereby significantly improving the model’s reliability.
Monetization of Services Provided by Public Fog Nodes Using Blockchain and Smart Contracts

Public fog nodes can be deployed in public places closer to the edge where many personal and commercial devices (e.g., a sensor, an application, or a device) can connect to.These public fog nodes can provide real-time and localized services for networking, computing, storage and content delivery to the connected devices.The monetization and payment of such services is typically manual, centralized, and lacks the necessary trust.The providers of the public fog nodes typically offer fixed pricing models for their services, and the customers manually select and pay for the used services, with little or no transparency and trust in the provided service in terms of the used time, network bandwidth, and quality of service (QoS).This paper presents a novel scheme to enable blockchain-based monetization and automated payment in cryptocurrency for services provided by public fog nodes.The proposed scheme is decentralized, trustworthy, automated, and with certain guarantees for QoS, customer satisfaction, and dispute resolutions through a reputation system.The proposed solution uses the Ethereum blockchain and its native smart contract features to govern the interactions between devices and fog nodes.The proposed solution is implemented, tested and evaluated to show correct behavior and functionality.We also provide cost and security analysis and show that our solution is resilient against major security attacks.Our smart contract is made publicly available on Github 1 .
Mixed Reality Enhanced User Interactive Path Planning for Omnidirectional Mobile Robot

This paper proposes a novel control system for the path planning of an omnidirectional mobile robot based on mixed reality. Most research on mobile robots is carried out in a completely real environment or a completely virtual environment. However, a real environment containing virtual objects has important actual applications. The proposed system can control the movement of the mobile robot in the real environment, as well as the interaction between the mobile robot’s motion and virtual objects which can be added to a real environment. First, an interactive interface is presented in the mixed reality device HoloLens. The interface can display the map, path, control command, and other information related to the mobile robot, and it can add virtual objects to the real map to realize a real-time interaction between the mobile robot and the virtual objects. Then, the original path planning algorithm, vector field histogram* (VFH*), is modified in the aspects of the threshold, candidate direction selection, and cost function, to make it more suitable for the scene with virtual objects, reduce the number of calculations required, and improve the security. Experimental results demonstrated that this proposed method can generate the motion path of the mobile robot according to the specific requirements of the operator, and achieve a good obstacle avoidance performance.
Robot Path Planning Based on Genetic Algorithm Fused with Continuous Bezier Optimization

In this study, a new method of smooth path planning is proposed based on Bezier curves and is applied to solve the problem of redundant nodes and peak inflection points in the path planning process of traditional algorithms. First, genetic operations are used to obtain the control points of the Bezier curve. Second, a shorter path is selected by an optimization criterion that the length of the Bezier curve is determined by the control points. Finally, a safe distance and adaptive penalty factor are introduced into the fitness function to ensure the safety of the walking process of the robot. Numerous experiments are implemented in two different environments and compared with the existing methods. It is proved that the proposed method is more effective to generate a shorter, smoother, and safer path compared with traditional approaches.
Real Network Traffic Collection and Deep Learning for Mobile App Identification

The proliferation of mobile devices over recent years has led to a dramatic increase in mobile traffic. Demand for enabling accurate mobile app identification is coming as it is an essential step to improve a multitude of network services: accounting, security monitoring, traffic forecasting, and quality-of-service. However, traditional traffic classification techniques do not work well for mobile traffic. Besides, multiple machine learning solutions developed in this field are severely restricted by their handcrafted features as well as unreliable datasets. In this paper, we propose a framework for real network traffic collection and labeling in a scalable way. A dedicated Android traffic capture tool is developed to build datasets with perfect ground truth. Using our established dataset, we make an empirical exploration on deep learning methods for the task of mobile app identification, which can automate the feature engineering process in an end-to-end fashion. We introduce three of the most representative deep learning models and design and evaluate our dedicated classifiers, namely, a SDAE, a 1D CNN, and a bidirectional LSTM network, respectively. In comparison with two other baseline solutions, our CNN and RNN models with raw traffic inputs are capable of achieving state-of-the-art results regardless of TLS encryption. Specifically, the 1D CNN classifier obtains the best performance with an accuracy of 91.8% and macroaverage F -measure of 90.1%. To further understand the trained model, sample-specific interpretations are performed, showing how it can automatically learn important and advanced features from the uppermost bytes of an app’s raw flows.
Adaptive Component Embedding for Domain Adaptation

Domain adaptation is suitable for transferring knowledge learned from one domain to a different but related domain. Considering the substantially large domain discrepancies, learning a more generalized feature representation is crucial for domain adaptation. On account of this, we propose an adaptive component embedding (ACE) method, for domain adaptation. Specifically, ACE learns adaptive components across domains to embed data into a shared domain-invariant subspace, in which the first-order statistics is aligned and the geometric properties are preserved simultaneously. Furthermore, the second-order statistics of domain distributions is also aligned to further mitigate domain shifts. Then, the aligned feature representation is classified by optimizing the structural risk functional in the reproducing kernel Hilbert space (RKHS). Extensive experiments show that our method can work well on six domain adaptation benchmarks, which verifies the effectiveness of ACE.
Ultra-Short-Term Photovoltaic Power Prediction Based on Self-Attention Mechanism and Multi-Task Learning

Due to the volatility and randomness of the photovoltaic power generation, it is difficult for traditional models to predict it accurately. To solve the problem, we established a model based on the self-attention mechanism and multi-task learning to predict the ultra-short-term photovoltaic power generation. First, we selected the data with the optimal timing length and input the data into the Encoder-Decoder network based on the self-attention. The validity of features extracted by the encoder was checked by the decoder. Then, we added a restriction to the middle layer of the Encoder-Decoder network to prevent the autoencoder from copying the input to the output mechanically. This condition is used to predict the photovoltaic power generation, so a multi-task learning model was established. Finally, to take full advantage of the features that are efficiently expressed and allow our main task, the prediction task, to learn some unique features autonomously, we proposed a step-by-step training method and have validated the effectiveness of this view in experiments. Through experimental contrast, it is found that compared with the Encoder-Decoder network based on CNN and LSTM, the performance of the proposed method has been increased by 14.82% and 8.09% respectively. The RMSE and MAE of the Encoder-Decoder model based on the self-attention mechanism using step-by-step training are 0.071 and 0.040 respectively.