Reviewerly/exHarmony huggingface dataset:
Analysis of Cloud Security Framework Using Blockchain Technology

Cloud computing is an evolving term these days. It is a model which provide services that is delivered and consumed on demand, anywhere at any time through any accessible network with the computing devices using cloud computing technologies. Cloud is beneficial as it provides ondemand, managed and customized resource, at the same time the biggest challenge of cloud computing is to overcome by the security risk to the cloud system especially the user group. Cloud security is a major privacy challenge which any cloud service provider and user facing these days. This paper presents the issues related to cloud security framework. Most of the time, some innovations come to our life in a way that we could not have imagined, block chain is one of such technology. In this paper we have proposed an architecture and framework of cloud security and its concerning areas and based on that we had visualize the prime concern areas in terms of cloud security, and how integration of blockchain with cloud computing created a big impact on cloud security features.
Text-to-Speech with Model Compression on Edge Devices

The application of voice services has become more common in daily life, including traffic navigation, voice assistants, audio books and so on. However, considering the cost and variability, it is difficult to fully utilize real voice recordings in different scenarios. In practice, speech synthesis technology is usually used to mimic human voices; On the other hand, with the development of computer equipment, the computing power of edge devices has also gradually improved, which enables light deep-learning network inference. Currently, many deep learning technologies have been ported to edge devices to create different applications, such as face recognition, speech recognition, and photo retouching. Therefore, if the speech synthesis network is ported to edge devices, with the advent of the fifth generation mobile communication generation (5G), it would be able to provide more innovative basis for voice services. In this research, the speech synthesis network Tacotron2 [1] + CBHG [2] will be ported to edge device and aims to optimize the model inference time and amount of parameters. The model optimization would be based on the compression of deep learning network, quantization, structured pruning and low-rank matrix approximation techniques to allow the speech synthesis network working effectively on edge devices. On the other hand, we get over the difference in library support between TensorFlow 1.5 and TensorFlow Lite. After the compression of the model, the inference speed of the Tacotron2 speech synthesis network on edge device is increased by 1.91 times, while the model size is reduced by 86% respectively.
Alternative Paths Planner (APP) for Provably Fixed-time Manipulation Planning in Semi-structured Environments

In many applications, including logistics and manufacturing, robot manipulators operate in semi-structured environments alongside humans or other robots. These environments are largely static, but they may contain some movable obstacles that the robot must avoid. Manipulation tasks in these applications are often highly repetitive, but require fast and reliable motion planning capabilities, often under strict time constraints. Existing preprocessing-based approaches are beneficial when the environments are highly-structured, but their performance degrades in the presence of movable obstacles, since these are not modelled a priori. We propose a novel preprocessing-based method called Alternative Paths Planner (APP) that provides provably fixed-time planning guarantees in semi-structured environments. APP plans a set of alternative paths offline such that, for any configuration of the movable obstacles, at least one of the paths from this set is collision-free. During online execution, a collision-free path can be looked up efficiently within a few microseconds. We evaluate APP on a 7 DoF robot arm in semi-structured domains of varying complexity and demonstrate that APP is several orders of magnitude faster than state-of-the-art motion planners for each domain. We further validate this approach with real-time experiments on a robotic manipulator.
Tora: A Trusted Blockchain Oracle Based on a Decentralized TEE Network

Smart Contracts cannot get external data directly due to the closure and determinacy of blockchain itself, limiting the extensibility of blockchain applications. Oracle is proposed to serve as a data feed to offer authenticated and deterministic external data to smart contracts. However, existing centralized oracles are efficient but vulnerable to targeted attacks and suffering from a single point of failure, while existing decentralized oracles are inefficient. The paper presents a trusted blockchain oracle based on a decentralized Trusted Execution Environment (TEE) network called Tora, which embraces both efficiency and availability. The key of Tora is a hybrid consensus mechanism with the confidential available group selection based on Proof-of-Availability (PoA). We have implemented a prototype of Tora on Ethereum with Intel Software Guard eXtensions (SGX) and evaluated it with a data-fetch use case on different measurements, including gas costs, off-chain execution time, group selection results and throughput. The results show that Tora provides considerable efficiency and scalability.
DUKweb, diachronic word representations from the UK Web Archive corpus

Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications. Diachronic word embeddings (time-sensitive vector representations of words that preserve their meaning) have become the standard resource for this task. However, given the significant computational resources needed for their generation, very few resources exist that make diachronic word embeddings available to the scientific community. In this paper we present DUKweb, a set of large-scale resources designed for the diachronic analysis of contemporary English. DUKweb was created from the JISC UK Web Domain Dataset (1996-2013), a very large archive which collects resources from the Internet Archive that were hosted on domains ending in '.uk'. DUKweb consists of a series word co-occurrence matrices and two types of word embeddings for each year in the JISC UK Web Domain dataset. We show the reuse potential of DUKweb and its quality standards via a case study on word meaning change detection.
Algorithms are not <scp>bias‐free</scp> : Four mini‐cases

Algorithms are everywhere. As we move closer to a fully automated world, algorithms need to be investigated for possible biases and treated as imperfect systems. This case study discusses how cognitive biases infiltrate and influence algorithmic biases in everyday technologies. Mental shortcuts do not just create biases in our minds, but also create biases in algorithms through development and implementation processes. Two key questions are examined in this case study: (1) Where are algorithmic biases? (2) How do algorithmic biases occur? To further discuss these two questions, four mini-cases are used to highlight different examples of algorithmic biases. The four mini-cases cover the topics of crime identification, suspect determination, teacher evaluation, and school classification.
Aesthetic Photo Collage With Deep Reinforcement Learning

Photo collage aims to automatically arrange multiple photos on a given canvas with high aesthetic quality. Existing methods are based mainly on handcrafted feature optimization, which cannot adequately capture high-level human aesthetic senses. Deep learning provides a promising way, but owing to the complexity of collage and lack of training data, a solution has yet to be found. In this paper, we propose a novel pipeline for automatic generation of aspect ratio specified collage and the reinforcement learning technique is introduced in non-content-preserving collage. Inspired by manual collages, we model the collage generation as a sequential decision process to adjust spatial positions, orientation angles, placement order and the global layout. To instruct the agent to improve both the overall layout and local details, the reward function is specially designed for collage, considering subjective and objective factors. To overcome the lack of training data, we pretrain our deep aesthetic network on a large scale image aesthetic dataset (CPC) for general aesthetic feature extraction and propose an attention fusion module for structural collage feature representation. We test our model against competing methods on movie and image datasets and our results outperform others in several quality evaluations. Further user studies are also conducted to demonstrate the effectiveness.
Pure Gauss sums and skew Hadamard difference sets

Chowla (1962), McEliece (1974), Evans (1977, 1981) and Aoki (1997, 2004, 2012) studied Gauss sums, some positive integral powers of which are in the field of rational numbers. Such Gauss sums are called pure. In particular, Aoki (2004) gave a necessary and sufficient condition for a Gauss sum to be pure in terms of Dirichlet characters modulo the order of the multiplicative character involved. In this paper, we study pure Gauss sums with odd extension degree f and classify them for f=5,7,9,11,13,17,19,23 based on Aoki's theorem. Furthermore, we characterize a special subclass of pure Gauss sums in view of an application for skew Hadamard difference sets. Based on the characterization, we give a new construction of skew Hadamard difference sets from cyclotomic classes of finite fields.
Object Detection in Optical Remote Sensing Images Based on Positive Sample Reweighting and Feature Decoupling

The object detection head of Faster R-CNN shared by classification and localization tasks can cause greater harm to training process due to the different features required by the tasks. Besides, Faster R-CNN assigns the same weight to positive proposals, which is unfair to the positive proposal with better regression performance. Aiming at these issues, this paper proposes a novel detection framework, which consists of two important parts, i.e., a Positive Sample Reweighting (PSR) module and a Feature Decoupling (FD) module. Specifically, PSR re-weights each positive proposal according to the positional relationship between each positive proposal and its ground truth. FD employs two parallel branches to extract the weights to decouple features for classification and localization tasks. Experimental results on the DIOR and DOTA datasets show the effectiveness of our proposed method.
Spatio-Spectral Feature Fusion for Low-Light Image Enhancement

Low-light image enhancement aims to improve an image's visual quality, which is essential for many downstream computer vision and multimedia tasks. Existing spatial-domain low-light enhancement methods barely focus on the regions containing object boundaries, which take the most informative characteristics. However, solely focusing on enhancing high-frequency details not only causes over-sharpening of an image but also leads to color distortion. In this paper, we propose a novel spatio-spectral feature fusion network (S2F2N), that involves a frequency-feature representation branch (FRB) and a spatial-feature representation branch (SRB) to learn the domain-specific representation individually. Moreover, a spatial-channel mixed attention block (MAB) is introduced to learn the joint representation of spatio-spectral features for final image relighting. Extensive experiments on several benchmark datasets demonstrate that our method can produce high fidelity results for low-light images.
Secure Email - A Usability Study

Several end-to-end encryption technologies for emails such as PGP and S/MIME exist since decades. However, end-to-end encryption is barely applied. To understand why users hesitate to secure their email communication and which usability issues they face with PGP, S/MIME as well as with pEp (Pretty Easy Privacy), a fairly new technology, we conducted an online survey and user testing. We found that more than 60% of e-mail users are unaware of the existence of such encryption technologies and never tried to use one. We observed that above all, users are overwhelmed with the management of public keys and struggle with the setup of encryption technology in their mail software. Even though users struggle to put email encryption into practice, we experienced roughly the same number of users being aware of the importance of email encryption. Particularly, we found that users are very concerned about identity theft, as 78% want to make sure that no other person is able to write email in their name.
Prerequisite Learning with Pre-trained Language and Graph Embedding Models

Prerequisite learning is to automatically identify prerequisite relations between concepts. This paper proposes a new prerequisite learning approach based on pre-trained language model and graph embedding model. In our approach, pre-trained language model BERT is fine-tuned to encode latent features from concept descriptions; graph embedding model Node2Vec is first pre-trained on citation graph of concepts, and then is fine-tuned for generating discriminative features for prerequisite learning. Two models are jointly optimized to get latent features containing both textual and structural information of concepts. Experiments on manually annotated datasets show that our proposed approach achieves better results than the state-of-the-art prerequisite learning approaches.
Imbalance Data Classification Based on Belief Function Theory

Imbalance data is an important research for the classification and there are multiple techniques to deal with this problem. Each strategy has its particular advantage for solving imbalance data. To improve the classification performance, these strategies are combined in decision level via an appropriate way for taking fully advantages of the complementary information among different methods. Thus a new method is proposed as Evidence Redistributive Combination (ERC) for imbalance data. For query pattern, the classifier output produced by different techniques (i.e., undersampling, oversampling, hybridsampling) may have different reliabilities. So a cautious quality evaluation rule is created to estimate the credibility of each classification result based on the close neighborhoods. Then the revised classification results from different strategies are combined by Dempster's rule to reduce the ignorant information and to generate the final classification result. Multiple experiments are used to test the performance of the new ERC method, and it shows that ERC can efficiently improve the classification performance with respect to other related methods.
Rule-Based Chatbot Integration into Software Engineering Course

A Chatbot refers to software that can perform services based on commands or answers given by a user during an online conversation. The Chatbots are applied in different areas ranging from pure entertainment solutions to learning support systems. In this article, we investigate Chatbot systems as a way to support learners during educational video sessions. We propose a rule-based Chatbot system that uses speech recognition and synthesized answers for increased immersiveness and for supporting the learners with different sensual perceptions. We introduce the design methodology we followed while creating a Chatbot system, explain the Chatbot behavior from learner and teacher perspectives and describe the system structure. The system was validated using the System Usability Scales (SUS) methodology. It shows that the current implementation has an average usability score; therefore, it needs improvement. On the other hand, the survey shows a positive learners attitude towards the proposed system.
Simple and Efficient Unpaired Real-world Super-Resolution using Image Statistics

Learning super-resolution (SR) network without the paired low resolution (LR) and high resolution (HR) image is difficult because direct supervision through the corresponding HR counterpart is unavailable. Recently, many real-world SR researches take advantage of the unpaired image-to-image translation technique. That is, they used two or more generative adversarial networks (GANs), each of which translates images from one domain to another domain, e.g., translates images from the HR domain to the LR domain. However, it is not easy to stably learn such a translation with GANs using unpaired data. In this study, we present a simple and efficient method of training of real-world SR network. To stably train the network, we use statistics of an image patch, such as means and variances. Our real-world SR framework consists of two GANs, one for translating HR images to LR images (degradation task) and the other for translating LR to HR (SR task). We argue that the unpaired image translation using GANs can be learned efficiently with our proposed data sampling strategy, namely, variance matching. We test our method on the NTIRE 2020 real-world SR dataset. Our method outperforms the current state-of-the-art method in terms of the SSIM metric as well as produces comparable results on the LPIPS metric.
Improved Deep Clustering of Mastcam Images Using Metric Learning

In this work, we present a novel clustering method that jointly learns a triplet model and the cluster assignments of the resulting representations of image patches from data acquired by the mast cameras on the MSL Curiosity rover. Deep clustering using metric learning (DCML) iteratively clusters the features using standard K - means clustering algorithm and uses the subsequent assignments as pseudo-labels to train a triplet network with online triplet mining method. The resulting model performs better than our baseline model [1] according to visual inspection of the cluster quality and simple clustering performance measures.
Kyoto Sightseeing Map 2.0 for User-Experience Oriented Tourism

We present Kyoto sightseeing map 2.0, a web-based application, for user-experience oriented tourism through discovering and exploring sightseeing resources from User Generated Content (UGC). It focuses on adapting the massive content analysis of information from UGC, to give an additional source of information from user experience to travelers in their search information process. It decreases and bridges the information gap of sightseeing resources, especially Point of Interest (POIs), caused by the map provided by government or tourism firms from the perspective of publicity and marketing. On the one hand, Kyoto sightseeing map 2.0 offers the aesthetics quality results of photos taken in tourist spots over time in Kyoto based on the UGC by aesthetics quality assessment (AQA) with Multi-level Spatially-Pooled (MLSP) to tourists. On the other hand, the user can also use two sets of POI photos generated by the user data displayed on the map as a reference. Our application, for user-experience oriented tourism, help them make well-informed decisions of their trip based on UGC.
Real time SAR Ship Detection using novel SarNeDe method

Many research works using deep learning techniques for automatic ship detection from SAR images have good detection accuracy. But the main problem in these methods is false detection, mostly due to speckle presence. Therefore, we propose a new deep learning model with a novel SarNeDe preprocessing stage to address this problem. We are introducing a deep learning architecture to detect and localize ships in the SAR image. First, generate a three-channel SarNeDe image in the preprocessing step. Then, this SarNeDe image is used to train the model to predict the ship's position in the SAR image. We experimented on the public SAR ship detection dataset (SSDD) and Dataset of Ship Detection for Deep Learning under Complex Backgrounds (SDCD) to validate the proposed method's feasibility. We used python 3.5 for coding with the Keras framework in the NVIDIA Tesla K80 GPU hardware platform. The experimental results indicated that our proposed method's ship detection accuracy has increased with reduced false detection percentage.
Nested Causality Extraction on Traffic Accident Texts as Question Answering

As an important type of relationship, causality plays a vital role in relation reasoning. Therefore, causality extraction from natural language texts is a crucial task, especially in the field of traffic. For example, we can quickly discover the causes of traffic accidents and the correlation between the cause events. Existing methods utilize machine learning models to extract simple causality, however, there is nested causality in the traffic accident sentences, which is important for us to reason the cause and effect. In order to extract the causality successfully, we simplify the complex nested causality structure to the pairwise causality structure. On this basis, we propose a method that contains two steps. First, we extract the cause events from the input sentence, second, combine the extracted cause with the incomplete question template to obtain a complete question sentence, then we adopt the way of question answering tasks to extract the effect events. Experiments on the traffic accident dataset show the effectiveness of our model. However, we observe that due to the small training set, there is still room for improvement in the extraction accuracy of nested causality.
Temporal Multiresolution Graph Learning

Estimating time-varying graphs, i.e., a set of graphs in which one graph represents the relationship among nodes in a certain time slot, from observed data is a crucial problem in signal processing, machine learning, and data mining. Although many existing methods only estimate graphs with a single temporal resolution, the actual graphs often demonstrate different relationships in different temporal resolutions. In this study, we propose an approach for time-varying graph learning by leveraging a multiresolution property. The proposed method assumes that time-varying graphs can be decomposed by a linear combination of graphs localized at different temporal resolutions. We formulate a convex optimization problem for temporal multiresolution graph learning. In experiments using synthetic and real data, the proposed method demonstrates the promising objective performances for synthetic data, and obtains reasonable temporal multiresolution graphs from real data.
Parallax Estimation for Push-Frame Satellite Imagery: Application to Super-Resolution and 3D Surface Modeling from Skysat Products

Recent constellations of satellites, including the Skysat constellation, are able to acquire burst of images. This new acquisition mode allows for modern image restoration techniques, including multi-frame super-resolution. As the satellite moves during the acquisition of the burst, elevation changes in the scene translate into noticeable parallax. This parallax hinders the results of the restoration. To cope with this issue, we propose a novel parallax estimation method. The method is composed of a linear <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Plane}+\text{Parallax}$</tex> decomposition of the apparent motion and a multi-frame optical flow algorithm that exploits all frames simultaneously. Using SkySat L1A images, we show that the estimated per-pixel displacements are important for applying multi-frame super-resolution on scenes containing elevation changes and that can also be used to estimate a coarse 3D surface model.