Reviewerly/exHarmony huggingface dataset:
Modeling Fluency and Faithfulness for Diverse Neural Machine Translation

Neural machine translation models usually adopt the teacher forcing strategy for training which requires the predicted sequence matches ground truth word by word and forces the probability of each prediction to approach a 0-1 distribution. However, the strategy casts all the portion of the distribution to the ground truth word and ignores other words in the target vocabulary even when the ground truth word cannot dominate the distribution. To address the problem of teacher forcing, we propose a method to introduce an evaluation module to guide the distribution of the prediction. The evaluation module accesses each prediction from the perspectives of fluency and faithfulness to encourage the model to generate the word which has a fluent connection with its past and future translation and meanwhile tends to form a translation equivalent in meaning to the source. The experiments on multiple translation tasks show that our method can achieve significant improvements over strong baselines.
A Hierarchical Control Design Framework for Fuzzy Mechanical Systems With High-Order Uncertainty Bound

Control design and performance enhancement for uncertain mechanical systems are pursued in this article. Uncertainty in a physical system is often inevitable in practice, which is best characterized by its possible bound. Mechanical systems with uncertain nonlinearity are considered. Furthermore, even the knowledge of the coefficients in the bound is unknown, which can only be described by its fuzzy association to a set. In controlling the system, there is a hierarchical performance requirement. The first level is deterministic, including uniform boundedness and uniform ultimate boundedness. This is the part the system must meet regardless of the actual value of the uncertainty. The second level is optimality, in terms of minimizing a fuzzy-theoretic performance index. We propose a novel control design with a tunable design parameter. The control guarantees the first-level performance when the design parameter falls in a range. We, then, take the advantage of this range flexibility to address the second-level requirement. The optimal choice of the design parameter can be made by solving an optimization problem. This problem is completely solved. Both the analytic (i.e., closed form) expressions of the design parameter and the resulting minimum cost are given. As a result, we accomplish a two-level control design task.
Low-Frequency Noise Suppression in Desert Seismic Data Based on an Improved Weighted Nuclear Norm Minimization Algorithm

Noise suppression is a crucial step before seismic data analysis. The noise in desert areas has the characteristics of nonstationary, non-Gaussian, and low-frequency, which makes some traditional methods cannot suppress the noise well. The weighted nuclear norm minimization (WNNM), one of the most effective methods to suppress noise in all low-rank matrix approximation methods, assigns different weights to different singular values. The good denoising performance of the WNNM is based on the accuracy of block matching, and the theoretical basis of block matching is the nonlocal self-similarity of clean signals. However, the noise will destroy the nonlocal self-similarity and greatly affect the accuracy of block matching. In this article, to reduce the influence of noise on block matching, we use a bandpass filter to process each noisy block before block-matching and use the similarity between the filtered blocks to represent the similarity between the original noisy blocks, then stack the most similar noisy blocks to construct a matrix for further denoising. Experimental results demonstrate the efficiency of the proposed method for low-frequency noise suppression.
Adapting big data standards, maturity models to smart grid distributed generation: critical review

Big data standards and capability maturity models (CMMs) help developers build applications with reduced coupling and increased breadth of deployment. In smart grids, stakeholders currently work with data management techniques that are unique and customised to their own goals, thereby posing challenges for grid-wide integration and deployment. Although big data standards and CMMs exist for other domains, no work in the literature considers adapting them to smart grids, which will benefit from both. Further, existing smart grid standards and CMMs do not fully account for big data challenges. This study bridges the gap by analysing the role of big data in smart grids, and explores if and how big data standards and CMMs can be adapted specifically to ten distributed generation (DG) use-cases that use big data. In doing so, this work provides a useful starting point for researchers and industry members developing standards and CMM assessments for smart grid DG.
3D map-guided single indoor image localization refinement

Image localization is an important supplement to GPS-based methods, especially in indoor scenes. Traditional methods depending on image retrieval or structure from motion (SfM) techniques either suffer from low accuracy or even fail to work due to the texture-less or repetitive indoor surfaces. With the development of range sensors, 3D colourless maps are easily constructed in indoor scenes. How to utilize such a 3D colourless map to improve single image localization performance is a timely but unsolved research problem. In this paper, we present a new approach to addressing this problem by inferring the 3D geometry from a single image with an initial 6DOF pose estimated by a neural network based method. In contrast to previous methods that rely multiple overlapping images or videos to generate sparse point clouds, our new approach can produce dense point cloud from only a single image. We achieve this through estimating the depth map of the input image and performing geometry matching in the 3D space. We have developed a novel depth estimation method by utilizing both the 3D map and RGB images where we use the RGB image to estimate a dense depth map and use the 3D map to guide the depth estimation. We will show that our new method significantly outperforms current RGB image based depth estimation methods for both indoor and outdoor datasets. We also show that utilizing the depth map predicted by the new method for single indoor image localization can improve both position and orientation localization accuracy over state-of-the-art methods.
Payload location for JPEG image steganography based on co-frequency sub-image filtering

In digital steganography, due to difficulties estimating the JPEG cover image, it is still very hard to accurately locate the hidden message embedded in a JPEG image. Therefore, this study proposes a payload location method for a category of pseudo-random scrambled JPEG image steganography. In order to estimate the quantized discrete cosine transform coefficients in the cover JPEG image, a cover JPEG image estimation method is proposed based on co-frequency sub-image filtering. The proposed payload location method defines a general residual, uses the estimated cover JPEG image to compute the residuals, and then employs the mean residuals of multiple stego images embedded along the same path to distinguish the stego positions. The proposed cover JPEG image estimation method constructs 64 co-frequency sub-images, and then filters the sub-image to estimate the cover JPEG image. Finally, using these methods, payload location algorithms are designed for two common JPEG image steganography algorithms: JSteg and F5. Experimental results show that the proposed location algorithms can effectively locate the stego positions in both JSteg and F5 steganography when the investigator possesses multiple stego images embedded along the same path. In addition, the location results can also be used to recover the steganography key to extract the embedded secret messages.
An IoT Based Mobile Augmented Reality Application for Energy Visualization in Buildings Environments

Augmented reality (AR) improves how we acquire, understand, and display information without distracting us from the real world. These technologies can be used in different applications and industries as they can incorporate domain-specific visualizations on a real-world screen. Mobile augmented reality (MAR) essentially consists of superimposing virtual elements over real objects on the screen, to give added value and enrich the interaction with reality. In numerous plants, it is being used for maintenance and repair tasks, as well as training. The Internet of Things (IoT) is increasingly pervading every aspect of our lives, including the power infrastructure of our buildings. IoT-enabled devices offer many connectivity options for helping supervise all-important energy assets. Aggregating data to cloud-based platforms enables operations teams to have on-time information access to make fast decisions and have a fast response regarding energy use, while maintenance teams keep on top of the appliance power quality and reliability needed by using MAR. This paper presents a novel approximation for visualizing appliance-related power quality to enhance awareness about the consumed electricity. A combined solution of MAR with IoT technologies is employed. Engineered solutions’ hands-free way to get data about surrounding appliances reduces the complexity, saves energy, and speeds up the operations. An innovative way to measure things at the right time leads to a competitive advantage.
Detecting Fake Images on Social Media using Machine Learning

In this technological era, social media has a major role in people's daily life.Most people share text, images, and videos on social media frequently (e.g.Twitter, Snapchat, Facebook, and Instagram).Images are one of the most common types of media share among users on social media.So, there is a need for monitoring of images contained in social media.It has become easy for individuals and small groups to fabricate these images and disseminate them widely in a very short time, which threatens the credibility of the news and public confidence in the means of social communication.This research attempted to propose an approach to extracting image content, classify it and verify the authenticity of digital images and uncover manipulation.Instagram is one of the most important websites and mobile image sharing applications on social media.This allows users to take photos, add digital photographic filters and upload pictures.There are many unwanted contents in Instagram's posts such as threats and forged images, which may cause problems to society and national security.This research aims to build a model that can be used to classify Instagram content (images) to detect any threats and forged images.The model was built using deep algorithms learning which is Convolutional Neural Network (CNN), Alexnet network and transfer learning using Alexnet.The results showed that the proposed Alexnet network offers more accurate detection of fake images compared to the other techniques with 97%.The results of this research will be helpful in monitoring and tracking in the shared images in social media for unusual content and forged images detection and to protect social media from electronic attacks and threats.
Motion Guided Siamese Trackers for Visual Tracking

Siamese trackers learn the appearance model of the target in the first frame and then exploit the model to locate the target in the subsequent frames. Meanwhile, the appearance model remains unchanged in the subsequent frames. Due to the powerful feature extraction capability of the deep convolutional neural networks, Siamese trackers achieve advanced performance. However, due to the non-update of the appearance model and the changing appearance of the target, the problem of tracking drift occurs frequently, especially in the background clutters scenarios. In order to tackle this issue, we propose a motion model and a discriminative model. Firstly, the motion model of the target is constructed to determine whether the tracking drift occurs or not since the position of the target predicted by the motion model is smooth in timing but the position of the target predicted by the Siamese tracker may be not smooth. In this case, the temporal information is utilized to supplement the Siamese tracker which only employs the spatial information. Secondly, the discriminative model is learned to determine the final position of the target when the tracking drift happens. Finally, a flexible model update strategy of the discriminative model is presented. In order to demonstrate the generality of the proposed method, we apply it for two famous Siamese trackers, SiamFC and SiamRPN_DW. Extensive experiments on OTB2013, OTB2015, VOT2016, VOT2019 and GOT-10k benchmarks demonstrate that the proposed trackers outperform the baseline trackers and achieve the state-of-the-art performance, especially in the background clutters scenarios. To the best of our knowledge, we are the first time to propose motion guided Siamese trackers. Moreover, We can release our code to encourage more researches in this direction.
Representation Learning of Knowledge Graphs With Entity Attributes

Most of the existing knowledge representation learning methods project the entities and relations represented by symbols in the knowledge graph into the low-dimensional vector space from the perspective of the structure and semantics of triples, and express the complex relations between entities and relations with dense low-dimensional vectors. However, triples in the knowledge graph not only contain relation triples, but also contain a large number of attribute triples. Existing knowledge representation methods often confuse these two kinds of triples and pay little attention to the semantic information contained in attributes and attribute values. In this paper, a novel representation learning method which makes use of the attribute information of entities is proposed. Specifically, deep convolutional neural network model is used to encode attribute information of entities, and both attribute information and triple structure information are utilized to learn knowledge representation, and then generate attribute-based representation of entities. The knowledge graph completion task was used to evaluate this method, and the experimental results on open data sets FB15K and FB24k showed that the attribute-embodied knowledge representation learning model outperforms the other baselines.
DiffNet: A Learning to Compare Deep Network for Product Recognition

The paper focuses on the identification of different objects in a pair of images taken from the same environment, which is challenging and has wide application.We propose a single deep convolutional neural network termed as DiffNet to solve this problem.DiffNet takes a pair of images as the input and directly regresses the bounding boxes of different objects.To train DiffNet, we only need to label the different objects, rather than all objects in input images, which significantly reduces human labeling efforts.Experiments are performed on an image dataset collected from unmanned containers.DiffNet obtains a very high product detection accuracy of 95.56% mAP at the speed of 143 fps measured on an NVIDIA TITAN Xp GPU.
Induction with Generalization in Superposition Reasoning

We describe an extension of automating induction in superposition-based reasoning by strengthening inductive properties and generalizing terms over which induction should be applied. We implemented our approach in the first-order theorem prover Vampire and evaluated our work against state-of-the-art reasoners automating induction. We demonstrate the strength of our technique by showing that many interesting mathematical properties of natural numbers and lists can be proved automatically using this extension.
Computer-aided diagnosis and decision-making system for medical data analysis: A case study on prostate MR images

Prostate cancer is the most common cancer in males and a major cause of cancer-related death. Magnetic resonance (MR) imaging is recently emerging as a powerful tool for prostate cancer diagnosis. To clinically diagnose prostate cancer, doctors need to segment the prostate area in the MR image. However, manual segmentation is time consuming and influenced by the physician’s experience. Computer-aided diagnosis and decision-making systems have shown great effectiveness in assisting doctors for this purpose. At the same time, deep learning based on Generative Adversarial Networks can be applied to the segmentation of prostate MR images. In this paper, we propose a new computer-aided diagnosis and decision-making system based on a deep learning model to automatically segment the prostate region from prostate MR images. Additionally, receptive field block (RFB) was integrated into the model to enhance the discriminability and robustness of the extracted multi-scale features. We also introduced dense upsampling convolution instead of the traditional bilinear interpolation to capture and recover fine-detailed information. Adversarial training was used to train the model, and the segmentation results were experimentally tested. The results showed that adversarial training and RFB are indeed effective, and the proposed method is superior to other methods on various evaluation metrics.
An Ontology-Based Framework for Publishing and Exploiting Linked Open Data: A Use Case on Water Resources Management

Nowadays, the increasing demand of water for electricity production, agricultural and industrial uses are directly affecting the reduction of available quality water for human consumption in the world. Efficient and sustainable maintenance of water reservoirs and supply networks implies a holistic strategy that takes into account, as much as possible, information from the stages of water usage. Next,-generation decision-making software tools, for supporting water management, require the integration of multiple and heterogeneous data sources of different knowledge domains. In this regard, Linked Data and Semantic Web technologies enable harmonization of different data sources, as well as the efficient querying for feeding upper-level Business Intelligence processes. This work investigates the design, implementation and usage of a semantic approach driven by ontology to capture, store, integrate and exploit real-world data concerning water supply networks management. As a main contribution, the proposal helps with obtaining semantically enriched linked data, enhancing the analysis of water network performance. For validation purposes, in the use case, a series of data sources from different measures have been considered, in the scope of an actual water management system of the Mediterranean region of Valencia (Spain), throughout several years of activity. The obtained experience shows the benefits of using the proposed approach to identify possible correlations between the measures such as the supplied water, the water leaks or the population.
Fall Detection with Supervised Machine Learning using Wearable Sensors

Unintentional falls can cause severe injuries to a person, and even death, especially when no immediate assistance is provided. The aim of Fall Detection Systems (FDSs) is to detect the occurrence of a fall and to automatically and promptly request the necessary assistance. This work proposes a FDS based on wearable sensors - i.e., accelerometers and gyroscopes - and Machine Learning (ML), for sensor signal processing and detection. The process extracts a number of features on portions of the signal and classifies them as falls or regular daily activities. The classifier is a Support Vector Machine (SVM) that is trained using a manually labelled dataset, where human activities are distinguished between falls and regular activities. The method is assessed on the publicly available SisFall dataset, with extended annotation, and compared with the results obtained in the literature for the same dataset; the proposed method largely outperforms the original analysis technique proposed for the SisFall dataset, with an F1 score higher than 97% and a recall higher than 99.7%.
SecCL: Securing Collaborative Learning Systems via Trusted Bulletin Boards

Massive and diverse data is crucial to train a general deep learning model, while the data collection for model training is difficult, especially training on sensitive data (e.g., medical data and face imaging). The emerging collaborative learning addresses this issue well by allowing participants to train a global model by uploading a subset of parameter changes, instead of the entire training data, to a centralized server. However, this privacy-preserving method can effectively enable privacy protection only when the involving entities are trusted (i.e., they honestly follow the protocol). Otherwise, the method may still leak private data. In this article, we propose a secure collaborative learning system named SecCL, which leverages a trusted bulletin board built on blockchain to enable strong privacy protection in collaborative learning by ensuring authentic and correct message interaction during the training process. Also, we develop a novel smart contract for SecCL so that participants can achieve consensus to restrain malicious behaviors. Therefore, SecCL ensures that the server cannot deceive participants and that participants behave well during the training process. We implement a prototype to evaluate its performance, and the promising experimental results demonstrate that SecCL can throttle malicious behaviors of participants and parameter servers while ensuring the accuracy of the global model.
Secure Image Encryption Scheme Based on DNA and New Multi Chaotic Map

Abstract Encryption of medical image is very important subject especially in wireless body area networks (WBAN) applications where the devices have many limitations in memory size, energy and computation capabilities. In this paper an image encryption scheme is introduced to be suitable for this limitations. The scheme consists of two phases. Firstly, the patient information (as name, age, ..etc) is hidden in the patient medical image (as x-ray, MRI, ..etc) using Least Signification Bit technique to reduce the capacity of data encrypted and make this information visually unavailable to unauthorized personnel. Secondly, the patient’s medical image is encrypted using DNA coding rules and a new multi chaotic map system. DNA coding improves computational speed and provides large capacity for data transmission while the new multi chaotic map consists by merging Henon, Sin and Ten map (HST) to produces pseudo random sequences with more chaotic characteristics. Results show that this scheme has a good peak signal to noise ratio, low correlation, huge key space, key-dependent pixel value replacement and can resist statistical and differential attacks.