Reviewerly/exHarmony huggingface dataset:
KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph

Entity synonyms discovery is crucial for entity-leveraging applications. However, existing studies suffer from several critical issues: (1) the input mentions may be out-of-vocabulary (OOV) and may come from a different semantic space of the entities; (2) the connection between mentions and entities may be hidden and cannot be established by surface matching; and (3) some entities rarely appear due to the long-tail effect. To tackle these challenges, we facilitate knowledge graphs and propose a novel entity synonyms discovery framework, named KGSynNet. Specifically, we pre-train subword embeddings for mentions and entities using a large-scale domain-specific corpus while learning the knowledge embeddings of entities via a joint TransC-TransE model. More importantly, to obtain a comprehensive representation of entities, we employ a specifically designed fusion gate to adaptively absorb the entities’ knowledge information into their semantic features. We conduct extensive experiments to demonstrate the effectiveness of our KGSynNet in leveraging the knowledge graph. The experimental results show that the KGSynNet improves the state-of-the-art methods by 14.7% in terms of hits@3 in the offline evaluation and outperforms the BERT model by 8.3% in the positive feedback rate of an online A/B test on the entity linking module of a question answering system.
Underwater image enhancement with image colorfulness measure

Due to the absorption and scattering effects of the water, underwater images tend to suffer from many severe problems, such as low contrast, grayed out colors and blurring content. To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model. Two parts constitute the overall model. The first one is a non-parameter layer for the preliminary color correction, then the second part is consisted of parametric layers for a self-adaptive refinement, namely the channel-wise linear shift. For better details, contrast and colorfulness, this enhancement network is jointly optimized by the pixel-level and characteristic-level training criteria. Through extensive experiments on natural underwater scenes, we show that the proposed method can get high quality enhancement results.
Privacy Preserving Time-Series Forecasting of User Health Data Streams

Privacy preservation plays a vital role in health care applications as the requirements for privacy preservation are very strict in this domain. With the rapid increase in the amount, quality and detail of health data being gathered with smart devices, new mechanisms are required that can cope with the challenges of large scale and real-time processing requirements. Federated learning (FL) is one of the conventional approaches that facilitate the training of AI models without access to the raw data. However, recent studies have shown that FL alone does not guarantee sufficient privacy. Differential privacy (DP) is a well-known approach for privacy guarantees, however, because of the noise addition, DP needs to make a trade-off between privacy and accuracy. In this work, we design and implement an end-to-end pipeline using DP and FL for the first time in the context of health data streams. We propose a clustering mechanism to leverage the similarities between users to improve the prediction accuracy as well as significantly reduce the model training time. Depending on the dataset and features, our predictions are no more than 0.025% far off the ground-truth value with respect to the range of value. Moreover, our clustering mechanism brings a significant reduction in the training time, with up to 49% reduction in prediction accuracy error in the best case, as compared to training a single model on the entire dataset. Our proposed privacy preserving mechanism at best introduces a decrease of ≈ 2% in the prediction accuracy of the trained models. Furthermore, our proposed clustering mechanism reduces the prediction error even in highly noisy settings by as much as 38% as compared to using a single federated private model.
Coflow Scheduling in Data Centers: Routing and Bandwidth Allocation

In distributed computing frameworks like MapReduce, Spark, and Dyrad, a coflow is a set of flows transferring data between two stages of a job. The job cannot start its next stage unless all flows in the coflow finish. To improve the execution performance of such a job, it is crucial to reduce the completion time of a coflow, as it can contribute more than 50 percent of the job completion time. While several coflow schedulers have been proposed, we observe that routing, as a factor greatly impacting the Coflow Completion Time (CCT), has not been well considered. In this article, we focus on the coflow scheduling problem and jointly consider routing and bandwidth allocation. We begin by providing an analytical solution to the problem of optimal bandwidth allocation with pre-determined routes. In the following, we formulate the problem of scheduling a single coflow as a Non-linear Mixed Integer Programming problem and present its relaxed convex optimization problem. We further propose two algorithms, CoRBA and its simplified version: CoRBA-fast that solve the single coflow scheduling problem with a joint consideration of routing and bandwidth allocation. Lastly, to address multiple coflows in online scheduling, we propose an online scheduler named OnCoRBA. By comparing with the start-of-the-art algorithms and schedulers via simulations, we demonstrate that CoRBA and CoRBA-fast reduce the CCT by 30-400 percent and the OnCoRBA scheduler reduces the average online CCT by 20-230 percent. In addition, CoRBA-fast can be hundreds times faster than CoRBA with around 8 percent performance degradation compared to CoRBA, which makes the use of CoRBA-fast very appropriate in practice.
Reconciliation of anti-money laundering instruments and European data protection requirements in permissionless blockchain spaces

Abstract This article is an attempt to reconcile the requirements of the EU General Data Protection Regulation (GDPR) and anti-money laundering and combat terrorist financing (AML/CFT) instruments used in permissionless ecosystems based on distributed ledger technology (DLT). Usually, analysis is focused only on one of these regulations. Covering by this research the interplay between both regulations reveals their incoherencies in relation to permissionless DLT. The GDPR requirements force permissionless blockchain communities to use anonymization or, at the very least, strong pseudonymization technologies to ensure compliance of data processing with the GDPR. At the same time, instruments of global AML/CFT policy that are presently being implemented in many countries following the recommendations of the Financial Action Task Force, counteract the anonymity-enhanced technologies built into blockchain protocols. Solutions suggested in this article aim to induce the shaping of permissionless DLT-based networks in ways that at the same time would secure the protection of personal data according to the GDPR rules, while also addressing the money laundering and terrorist financing risks created by transactions in anonymous blockchain spaces or those with strong pseudonyms. Searching for new policy instruments is necessary to ensure that governments do not combat the development of all privacy-blockchains so as to enable a high level of privacy protection and GDPR-compliant data processing. This article indicates two AML/CFT tools which may be helpful for shaping privacy-blockchains that can enable the feasibility of such tools. The first tool is exceptional government access to transactional data written on non-transparent ledgers, obfuscated by advanced anonymization cryptography. The tool should be optional for networks as long as another effective AML/CFT measures are accessible for the intermediaries or for the government in relation to a given network. If these other measures are not available and the network does not grant exceptional access, the regulations should allow governments to combat the development of those networks. Effective tools in that scope should target the value of privacy-cryptocurrency, not its users. Such tools could include, as a tool of last resort, state attacks which would undermine the trust of the community in a specific network.
DGT: A contribution-aware differential gradient transmission mechanism for distributed machine learning

Distributed machine learning is a mainstream system to learn insights for analytics and intelligence services of many fronts (e.g., health, streaming and business) from their massive operational data. In such a system, multiple workers train over subsets of data and collaboratively derive a global prediction/inference model by iteratively synchronizing their local learning results, e.g., the model gradients, which in turn generates heavy and bursty traffic and results in high communication overhead in cluster networks. Such communication overhead has became the main bottleneck that limits the efficiency of training machine learning models distributedly. In this paper, our key observation is that local gradients learned by workers may have different contributions to global model convergence and executing differential transmission for different gradients can reduce the communication overhead and improve training efficiency. However, existing gradient transmission mechanisms treat all gradients the same, which may lead to long training time. Motivated by our observations, we propose Differential Gradient Transmission (DGT), a contribution-aware differential gradient transmission mechanism for efficient distributed learning, which transfers gradients with different transmission quality according to their contributions. In addition to designing a general architecture of DGT, we have proposed a novel algorithm and a novel protocol to facilitate fast model training. Experiments on a cluster with 6 GTX 1080TI GPUs and 1Gbps network show that DGT decreases the model training time by 19.4% on GoogleNet, 34.4% on AlexNet and 36.5% on VGG-11 compared to default gradient transmission on MXNET. Its acceleration is better than the other two related transmission solutions. Besides, DGT works well with different datasets (Fashion-MNIST, Cifar10), different data distributions (IID, non-IID) and different training algorithms (BSP, FedAVG).
An Improved Particle Swarm Optimization Algorithm for Bayesian Network Structure Learning via Local Information Constraint

At present, in the application of Bayesian network (BN) structure learning algorithm for structure learning, the network scale increases with the increase of number of nodes, resulting in a large scale of structure search space, which is difficult to calculate, and the existing learning algorithms are inefficient, making BN structure learning difficulty increase. To solve this problem, a BN structure optimization method based on local information is proposed. Firstly, it proposes to construct an initial network framework with local information and uses the Max-Min Parents and Children (MMPC) algorithm to construct an undirected network framework to reduce the search space. Then the particle swarm optimization (PSO) algorithm is used to strengthen the algorithm’s optimization ability by constructing a new position and velocity update rule and improve the efficiency of the algorithm. Experimental results show that under the same sample data set, the algorithm can obtain a more accurate BN structure while converging quickly, which verifies the correctness and effectiveness of the algorithm.
A lightweight virtualization model to enable edge computing in deeply embedded systems

Abstract Edge computing paradigm enables moving Internet of Things (IoT) applications from the Cloud to the edge of the network. Modern software engineering approaches are adhering to microservices to enable the deployment of such applications on edge devices. Microservices consist of the disaggregation of an application into smaller pieces that operate independently. Recent works have explored microservices packaged into containers and advocate that containers result in a reduced footprint and avoid the unwanted overhead caused by traditional virtualization. However, containers cannot be used in many deeply embedded systems (DES) due to an underlying operating system's (OSs) requirement. DES are edge devices with minimal resources regarding storage, memory, and processing power. Thus, they cannot afford large and sophisticated OSs. This article presents the Hellfire hypervisor, a lightweight virtualization implementation that enables separation and improves security in IoT applications on DES. Our proposal simplifies the traditional hypervisor approach and reaches devices where the existing techniques fail. The results show that the proposed model has a small footprint of 23 KB while keeping a low average virtualization overhead of 0.62% for multiple virtual machines execution.
Kernel fuzzy C- means clustering with teaching learning based optimization algorithm (TLBO-KFCM)

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM).
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors

Sensor-based human activity recognition (HAR) is now a research hotspot in multiple application areas. With the rise of smart wearable devices equipped with inertial measurement units (IMUs), researchers begin to utilize IMU data for HAR. By employing machine learning algorithms, early IMU-based research for HAR can achieve accurate classification results on traditional classical HAR datasets, containing only simple and repetitive daily activities. However, these datasets rarely display a rich diversity of information in real-scene. In this paper, we propose a novel method based on deep learning for complex HAR in the real-scene. Specially, in the off-line training stage, the AMASS dataset, containing abundant human poses and virtual IMU data, is innovatively adopted for enhancing the variety and diversity. Moreover, a deep convolutional neural network with an unsupervised penalty is proposed to automatically extract the features of AMASS and improve the robustness. In the on-line testing stage, by leveraging advantages of the transfer learning, we obtain the final result by fine-tuning the partial neural network (optimizing the parameters in the fully-connected layers) using the real IMU data. The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91.15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method.
Augmented Reality Application for Handheld Devices

The Pavia University History Museum, which houses historic items mainly connected to the physics and medicine fields, has focused in the past years on new ways to involve its public and to attract new audiences. Among different approaches, digital technologies have proven important to both external and internal communication. Lately, an Augmented Reality application has been made available to visitors, offering in one tool multimedia material of a historical-scientific nature: stories, 3D animations, images and user-generated video storytelling (developed mainly by University students, one of our least present demographics before the App, and younger students, who typically participate in the annual co-creative project). The App was designed to be as non-intrusive and discreet as possible, to preserve the historic ambiance of the museum, to unite social and educational aspects, to register user behaviour and to make the museum experience more vibrant and active and therefore captivating.
A Novel Neural Model based Framework for Detection of GAN Generated Fake Images

With the advancement in Generative Adversarial Networks (GAN), it has become easier than ever to generate fake images. These images are more realistic and non-discernible by untrained eyes and can be used to propagate fake information on the Internet. In this paper, we propose a novel method to detect GAN generated fake images by using a combination of frequency spectrum of image and deep learning. We apply Discrete Fourier Transform to each of 3 color channels of the image to obtain its frequency spectrum which shows if the image has been upsampled, a common trend in most GANs, and then train a Capsule Network model with it. Conducting experiments on a dataset of almost 1000 images based on Unconditional data modeling (StyleGan2 - ADA) gave results indicating that the model is promising with accuracy over 99% when trained on the state-of-the-art GAN model. In theory, our model should give decent results when trained with one dataset and tested on another.
Person Reidentification Based on Pose-Invariant Feature and B-KNN Reranking

Person reidentification, as one of the most important areas in the video surveillance field, is a crucial task in computer vision. It has attracted more and more attention in both academic research and industry due to its extremely high application value. However, the recognition accuracy of person reidentification is subjected to factors, such as illumination, pose, occlusion, and viewpoint. To alleviate the effect of such factors, the multiscale Retinex with color restoration (MSRSC) algorithm is adopted to preprocess the original images so that the color information can be restored and the illumination condition can be improved. To obtain pose-invariant features (PIFs), the convolution pose machine that can generate the body joint points of pedestrians is applied to divide the body into seven parts, the pose transform network is then used to align the body parts, and finally, the PIFs can be obtained from a designed pseudo-Siamese network by using the original and aligned images as the training samples. To further improve recognition accuracy, a reranking method based on bidirectional k-nearest neighbors (KNN) is presented to optimize the ranking list. Experimentally, the proposed method is implemented on three data sets: viewpoint invariant pedestrian recognition (VIPeR), CUHK03, and Market1501. The results demonstrate our method outperforms the other methods, as a result of both the representation of a more discriminative feature descriptor and the introduction of a reranking method.
A Streaming Machine Learning Framework for Online Aggression Detection on Twitter

The rise of online aggression on social media is evolving into a major point of concern. Several machine and deep learning approaches have been proposed recently for detecting various types of aggressive behavior. However, social media are fast paced, generating an increasing amount of content, while aggressive behavior evolves over time. In this work, we introduce the first, practical, real-time framework for detecting aggression on Twitter via embracing the streaming machine learning paradigm. Our method adapts its ML classifiers in an incremental fashion as it receives new annotated examples and is able to achieve the same (or even higher) performance as batch-based ML models, with over 90% accuracy, precision, and recall. At the same time, our experimental analysis on real Twitter data reveals how our framework can easily scale to accommodate the entire Twitter Firehose (of 778 million tweets per day) with only 3 commodity machines. Finally, we show that our framework is general enough to detect other related behaviors such as sarcasm, racism, and sexism in real time.
A Robust Interval Type-2 Fuzzy Logic Controller for Variable Speed Wind Turbines Based on a Doubly Fed Induction Generator

This paper presents an implementation of a new robust control strategy based on an interval type-2 fuzzy logic controller (IT2-FLC) applied to the wind energy conversion system (WECS). The wind generator used was a variable speed wind turbine based on a doubly fed induction generator (DFIG). Fuzzy logic concepts have been applied with great success in many applications worldwide. So far, the vast majority of systems have used type-1 fuzzy logic controllers. However, T1-FLC cannot handle the high level of uncertainty in systems (complex and non-linear systems). The amount of uncertainty in a system could be reduced by using type-2 fuzzy logic since it offers better capabilities to handle linguistic uncertainties by modeling vagueness and unreliability of information. A new concept based on an interval type-2 fuzzy logic controller (IT-2 FLC) was developed because of its uncertainty management capabilities. Both these control strategies were designed and their performances compared for the purpose of showing the control most efficient in terms of reference tracking and robustness. We made a comparison between the performance of the type-1 fuzzy logic controller (T1-FLC) and interval type-2 fuzzy logic controller (IT2-FLC). The simulation results clearly manifest the height robustness of the interval type-2 fuzzy logic controller in comparison to the T1-FLC in terms of rise time, settling time, and overshoot value. The simulations were realized by MATLAB/Simulink software.
Application of optical and radar satellite images for mapping tectonic lineaments in kerdous inlier of the Anti-Atlas belt, Morocco

Lineament mapping is an important step for lithological and hydrothermal alterations mapping. It is an efficient research task and a part of structural investigation and mineral ore deposits identification. The availability of optical as well as radar remote sensing data, such as Landsat 8 OLI, Terra ASTER and ALOS PALSAR data, allows lineaments mapping at regional and national scale. The accuracy of the obtained results depends strongly on the spatial and spectral resolution of the data. The aim of this study is to compare Landsat 8 OLI, Terra ASTER, and radar ALOS PALSAR satellite data for automatic and manual lineaments extraction. The module Line of PCI Geomatica software was applied on PC1 OLI, PC3 ASTER and HH and HV polarization images to automatically extract geological lineaments. Moreover, the manual extraction was achieved using the RGB color composite of the directional filtered images N - S (0°), NE - SW (45°) and E − W (90°) of the OLI panchromatic band 8. The obtained lineaments from automatic and manual extraction were compared to the faults and photo-geological lineaments digitized from the existing geological map of the study area. The extracted lineaments from PC1 OLI and ALOS PALSAR polarizations images showed the best correlation with faults and photo-geological lineaments. The results indicate that the lineaments extracted from HH and HV polarizations of ALOS PALSAR radar data and PC1 OLI were more efficient for structural lineament mapping.