Reviewerly/exHarmony huggingface dataset:
A Novel Vehicle Blockchain Model Based on Hyperledger Fabric for Vehicle Supply Chain Management

Counterfeit vehicle parts bring about huge economic losses to the legitimate parts industry and serious injury to users. It is difficult to identify counterfeits since very few participants have end-to-end visibility of products across their supply chain. This paper aims to explore the potential of blockchain in solving complex supply chain processes. We provide a blockchain-based vehicle supply chain management to replace rigid supply chains, resulting in more efficient and secure service for all participants. To verify the feasibility of proposed management, we simulated vehicle supply chain in Hyperledger Fabric. We use Hyperledger Caliper to conduct the performance of proposed system in terms of transactions per second and transaction latency. The simulation results show the proposed model could be used for vehicle supply chain in a real environment.
Denoising Higher-Order Moments for Blind Digital Modulation Identification in Multiple-Antenna Systems

This letter proposes a new technique that substantially improves blind digital modulation identification (DMI) algorithms that are based on higher-order statistics (HOS). The proposed technique takes advantage of noise power estimation to make an offset on higher-order moments (HOM), thus getting an estimate of noise-free HOM. When tested for multiple-antenna systems, the proposed method outperforms other DMI algorithms, in terms of identification accuracy, that are based only on cumulants or do not consider HOM denoising, even for a receiver with impairments. The improvement is achieved with the same order of complexity of the common HOS-based DMI algorithms in the same context.
A Meta-Analysis Examining the Effect of E-Book Use on Literacy Outcomes for Students in Grades K–12

To better understand the impact of using e-books on students’ reading outcomes, we conducted a meta-analysis of 14 studies with students in grades K–12 published between 2007 and 2018. Findings revealed an average effect size across all studies and reading outcomes of 0.9 that did not differ significantly from zero. There was also no statistically significant difference between e-book and non-e-book conditions on measures of reading comprehension. While these findings present preliminary evidence that e-books may be a viable alternative to traditional print books for supporting students’ reading comprehension development, reasons for interpreting these findings with caution are presented.
Computer Technology in Art (Venice Biennale 2019)

Computer technology has penetrated all spheres of human life and firmly settled in fine art, music, and theater. Research that is presented in this article is aimed at analysis of the digital and multimedia technology used in creations by the video and media artists shown at the 2019 Biennale. The main objective stated by the authors is to trace the inner trends in visual arts developed due to the new possibilities both substantially and formally. Though the tendency of art digitalization was traced long enough the Venice Biennale has not yet been studied from this point of view while its importance is hard to overestimate. This research is based upon analysis of more than 200 artistic works with the use of a multidisciplinary approach to investigate computer technology in art as a complicated social phenomenon in the sociology of culture and communication. Authors suggest a classifying of multimedia technology used in the context of meaning and according to growing complexity: background/comments, space widening, attaching other meanings to an art object, immersion of the spectator into the video flow. Thus authors underline an influence of technological progress on the artistic thought leading to the creation of the total environment surrounding spectators and involving them into a certain kind of a new reality.
Resilient method in determining the best architectural model for predicting open unemployment in Indonesia

Abstract The resilient method is a method of Artificial Neural Networks which is often used to make predictions, especially in times series data (continuous). This method is able to make predictions by learning from data that has been done before by first forming the right network architecture model. Therefore, this study will discuss the best network architectural models to make predictions using the resilient method. The data used in this study is open unemployment data according to the highest education completed in Indonesia in 2005 to 2018 based on the semester, which was sourced from the National Labor Force Survey (Sakernas) obtained from the website of the Indonesian Central Bureau of Statistics. Based on this data, a network architecture model will be formed that is used with the Bayesian Regulation method, including 12-6-2, 12-12-2, 12-18-2, 12-24-2, 12-12-12-2, 12-12-18-2, 12-18-18-2 and 12-18-24-2. From these 8 models after training and testing, the results show that the best architectural model is 12-18-2 (12 is the input layer, 18 is the number of hidden neurons and 2 is the output layer). The accuracy of the architectural model for semester 1 and semester 2 is 75% with an MSE value of 0,0022135087 and 0,0044974696.
Persian Sentiment Lexicon Expansion Using Unsupervised Learning Methods

Sentiment analysis, is a subfield of natural language processing that aims at opinion mining to analyze thoughts, orientation and, evaluation of users within some texts. The solution to this problem includes two main steps: extracting aspects and determining users' positive or negative sentiments with respect to the aspects. Two main challenges of sentiment analysis in the Persian language are lack of comprehensive tagged data sets and use of colloquial language in texts. In this paper we propose, a system to specify and extract sentiment words using unsupervised methods in the Persian language that also support colloquial words. Additionally, we also proposed and implemented a state-of-art technique to expand Persian sentiment lexicon. Our proposed method utilized neural network (Word2Vec model) with the help of rule-based methods. F1 measure for sentiment words extraction in our proposed method is 0.58.
Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning

Learning-based methods have been used to program robotic tasks in recent years. However, extensive training is usually required not only for the initial task learning but also for generalizing the learned model to the same task but in different environments. In this paper, we propose a novel Deep Reinforcement Learning algorithm for efficient task generalization and environment adaptation in the robotic task learning problem. The proposed method is able to efficiently generalize the previously learned task by model fusion to solve the environment adaptation problem. The proposed Deep Model Fusion (DMF) method reuses and combines the previously trained model to improve the learning efficiency and results. Besides, we also introduce a Multi-objective Guided Reward (MGR) shaping technique to further improve training efficiency. The proposed method was benchmarked with previous methods in various environments to validate its effectiveness.
Unseen Salient Object Discovery for Monocular Robot Vision

A key challenge in robotics is the capability to perceive unseen objects, which can improve a robot's ability to learn from and adapt to its surroundings. One approach is to employ unsupervised, salient object discovery methods, which has shown promise in the computer vision literature. However, most state-of-the-art methods are unsuitable for robotics because they are limited to processing whole video segments before discovering objects, which can constrain real-time perception. To address these gaps, we introduce Unsupervised Foraging of Objects (UFO), a novel, unsupervised, salient object discovery method designed for monocular robot vision. We designed UFO with a parallel discover-prediction paradigm, permitting it to discover arbitrary, salient objects on a frame-by-frame basis, which can help robots to engage in scalable object learning. We compared UFO to the two fastest and most accurate methods for unsupervised salient object discovery (Fast Segmentation and Saliency-Aware Geodesic), and show that UFO 6.5 times faster, achieving state-of-the-art precision, recall, and accuracy. Furthermore our evaluation suggests that UFO is robust to real-world perception challenges encountered by robots, including moving cameras and moving objects, motion blur, and occlusion. It is our goal that this work will be used with other robot perception methods, to design robots that can learn novel object concepts, leading to improved autonomy.
Illustrate Your Story

Human perception is known to be predominantly visual. As modern web infrastructure promoted the storage of media, the web-data paradigm shifted from text-only documents to those containing text and images. A multitude of blog posts, news articles, and social media posts exist on the Internet today as examples of multimodal stories. The manual alignment of images and text in a story is time-consuming and labor intensive. We present a web application for automatically selecting relevant images from an album and placing them in suitable contexts within a body of text. The application solves a global optimization problem that maximizes the coherence of text paragraphs and image descriptors, and allows for exploring the underlying image descriptors and similarity metrics. Experiments show that our method can align images with texts with high semantic fit, and to user satisfaction.
Crafting adversarial example with adaptive root mean square gradient on deep neural networks

Deep Neural Networks have achieved remarkable success in computer vision, natural language, and audio tasks. It shows excellent ability in dealing with specific tasks with surpassing efficiency and accuracy. However, researches indicated that deep neural models are extremely vulnerable to crafted adversarial perturbation. In image classification domain, crafted images with adversarial perturbation can fool deep neural models into misclassifying. Specific researches revealed that adversarial examples crafted by attack methods show substantial pixel modification strength, which causes lower similarity between the clean and corresponding sample and makes the change in crafted samples visible. To address the issues mentioned above, we propose an adversarial attack method, which generates adversarial perturbation based on adaptive root mean square gradient strategy. In our proposed approach, we formulate adversarial perturbation based on an adaptive gradient at root mean square level during crafting adversarial sample. Due to the adaptive strategy, the proposed method searches the decision boundary between the original and the adversarial classes in latent space directly by searching the extremum of loss. It helps to generate adversarial samples with higher image quality and better transferability on fooling multiple deep neural models. We evaluate several state-of-the-art attack methods with proposed methods. Experimental results show that our approaches outperform modern techniques in crafting adversarial sample with slight pixel modification, and excellent efficiency in fooling classifiers in both no-targeted and targeted attack strategies.
Modelling Quality of Experience for Online Video Advertisement Insertion

The impact of online video advertisement has an evolving and undeniable influence on the success of online video streaming. A successful online video advertisement campaign deployment necessitates: “targeting appropriate marketing audience, determining optimum intervals to insert advertisement, associating the production quality of the content while considering advertisement conceptual features, matching the relevance of advertisement context to the content theme, calculating the applicable number of ads for stitching into the content, and correlating the ratio of advertisement length to total active watch duration”. This paper proposes a novel model for inserting advertisement into online video that considers content and commercial specific properties while optimizing Quality of Experience (QoE) by estimating suitable duration for advertisement, number of splits and content relation. The proposed model has been evaluated in a controlled on-line video test environment so that the success rate of this platform has been compared with the advertisement insertion strategies of technology frontrunners YouTube and Vimeo. In terms of medium and long length online videos, advertisements located within the content provides a better QoE compared to the ones that are located at the beginning of the video. For short length online videos, the general expectation of the audience tends to see the content immediately and any advertisement insertion related delay results in a corresponding customer behavior where 25% tend to quit after 3 seconds and another 25% after 5 seconds.
An Overview of Trolling Behavior in Online Spaces and Gaming Context

Trolling is frequently observed in online platforms of communication, although, it remains as an obscure phenomenon. This is due to it having limited empirical research and lack of consensus about the definitions amongst scholars. This study aims to bring an insight into trolling behavior by presenting an overview of previous research on toxic behaviors that are considered as trolling in online communities and in the context of online gaming. Additionally, a questionnaire that is used to observe how trolling is perceived was conducted to 83 participants and the findings are discussed.
Evaluating an Interactive Tool for Teaching Design Patterns

Design patterns are tried and tested solutions that are used to tackle common object-oriented programming (OOP) problems. However, many computer science students struggle to learn and apply these patterns in large projects. Part of the challenge in learning design patterns is that the syntactic complexity of OOP languages can distract students from connecting the abstract design concepts to the code they are writing. A further challenge is that existing resources for teaching design patterns are often static and do not illustrate the dynamic nature of OOP. A review of the literature reveals a need for a greater focus on visual metaphors to teach design patterns, while still allowing students to interact with code. This paper describes a novel educational tool that teaches design patterns through a combination of real-world metaphors and scaffolded programming exercises. We evaluate our tool using a pre- and post-survey design and find that use of our tool leads to higher levels of confidence in applying design patterns in practice.
Security system design for cloud computing by using the combination of AES256 and MD5 algorithm

Abstract Data is a collection of information that is combined into one and has a very important meaning. In the study, the object to be secured is the password data, the encryption method Advanced Encryption Standard (AES) with a key length of 256 bits, before the data is encrypted with method of AES, the first password will be encrypted using MD5, and the second one will be encrypted again using the AES256 method. Based on trial data conducted through two sites about the complexity of passwords, it can be concluded that the original data (before encryption) no 1 to 5 increased to 32 bytes after being encrypted by the MD5 method, and its size increased again to 88 bytes after being encrypted by the AES256 method. Data can be obtained by value original data AES256 is 9, 96 times larger than its original size, would be but the value of the complexity of her also increased in line with the increase in the number of characters or the byte size of the data password above, thereby increasing the level of difficulty by the party that will hack the login data in the cloud.
Scalable Low-Latency Persistent Neural Machine Translation on CPU Server with Multiple FPGAs

We present a CPU server with multiple FPGAs that is purely software-programmable by a unified framework to enable flexible implementation of modern real-life complex AI that scales to large model size (100M+ parameters), while delivering real-time inference latency (~ms). Using multiple FPGAs, we scale by keeping a large model persistent in on-chip memories across FPGAs to avoid costly off-chip accesses. We study systems with 1 to 8 FPGAs for different devices: Intel® Arria® 10, Stratix® 10, and a research Stratix 10 with an AI chiplet. We present the first multi-FPGA evaluation of a complex NMT with bi-directional LSTMs, attention, and beam search. Our system scales well. Going from 1 to 8 FPGAs allows hosting ~8× larger model with only ~2× latency increase. A batch-1 inference for a 100M-parameter NMT on 8 Stratix 10 FPGAs takes only ~10 ms. This system offers 110× better latency than the only prior NMT work on FPGAs, which uses a high-end FPGA and stores the model off-chip.
RF-ARP: RFID-Based Activity Recognition and Prediction in Smart Home

Smart Home is generally considered to be the final solution for human living problem, especially for health care of the elderly and disabled, power saving, etc. Human activity recognition in smart home is the key to achieve home automation, which enables smart services automatically run according to human mind. Recent researches have made several progresses in this field, however most of them can only recognize default activities which is probably not needed by smart home services. In addition, low scalability makes such researches infeasible out of laboratory. In this work, we unwrap this issue and propose a novel framework to not only recognize human activity, but also predict it. The framework contains three stages: recognition after the activity; recognition in progress and activity prediction in advance. With the help of RFID tags, the hardware cost of our framework is low enough to popularize. And the experiment result shows that our framework can realize good performance in activity recognition and prediction with high scalability.
The Exchange Network: An Architecture for the Negotiation of Non-Repudiable Token Exchanges

Many use cases coming out of initiatives such as Industry 4.0 and Ubiquitous Computing require that systems be able to cooperate by negotiating about and agreeing on the exchange of arbitrary values. While solutions able to facilitate such negotiation exist, they tend to either be domain-specific or lack mechanisms for non-repudiation, which make them unfit for the heterogeneity and scale of many compelling applications. In this paper, we present the Exchange Network, a general-purpose and implementation-independent architecture for digital negotiation and non-repudiable exchanges of tokens, which are symbolic representations of arbitrary values. We consider the implications of implementing the architecture in three different ways, using a common database, a blockchain, and our own Signature Chain data structure, which we also describe. We demonstrate the feasibility of the architecture by outlining our own implementation of it and also describe a supply-chain scenario inspired by one transportation process at Volvo Trucks.
A Text-Context-Aware CNN Network for Multi-oriented and Multi-language Scene Text Detection

The existing deep learning based state-of-theart scene text detection methods treat scene texts a type of general objects, or segment text regions directly. The latter category achieves remarkable detection results on arbitrary orientation and large aspect ratios of scene texts based on instance segmentation algorithms. However, due to the lack of context information with consideration of scene text unique characteristics, directly applying instance segmentation to text detection task is prone to result in low accuracy, especially producing false positive detection results. To ease this problem, we propose a novel text-context-aware scene text detection CNN structure, which appropriately encodes channel and spatial attention information to construct context-aware and discriminative feature map for multi-oriented and multi-language text detection tasks. With high representation ability of text context-aware feature map, the proposed instance segmentation based method can not only robustly detect multi-oriented and multi-language text from natural scene images, but also produce better text detection results by greatly reducing false positives. Experiments on ICDAR2015 and ICDAR2017-MLT datasets show that the proposed method has achieved superior performances in precision, recall and F-measure than most of the existing studies.
Automatic Spatial Template Generation for Realistic 3D Modeling of Large-Scale Indoor Spaces

This paper proposes a realistic indoor modeling framework for large-scale indoor spaces. The proposed framework reduces the geometric complexity of an indoor model to efficiently represent large-scale environments for image-based rendering (IBR) approaches. For this purpose, the proposed framework removes geometrically excluded objects (GEOs) in point cloud and images, which represent the primary factors in high geometric complexity. In particular, GEOs are coherently removed from all images using a global geometry model. Then, the remaining holes are inpainted using globally consistent guidelines, to achieve accurate image blending in IBR approaches. The experimental results verify that the proposed GEO removal framework provides efficient point clouds and images for realistic indoor modeling in large-scale indoor spaces.
No‐reference video quality assessment method based on spatio‐temporal features using the ELM algorithm

This work presents an application of the extreme learning machine (ELM) algorithm based on a single-hidden layer feedforward neural network for no-reference video quality assessment. The present research introduces an augmented version of ELM through simple stop criteria, which proved the effectiveness of the video quality assessment method. The authors present empirical studies using LIVE video data base show that the proposed method delivers accuracy (Pearson's correlation coefficient) and monotonicity (Spearman's correlation coefficient) with subjective scores against no-reference, Joint Photographic Experts Group No-Reference, metric and full-reference metrics, for instance, peak signal-to-noise ratio, structural similarity (SSIM) and multi-scale-SSIM indexes, and the proposed method is suitable for quality monitoring of video transmission and reception system.
Path-following algorithms comparison using Software-in-the-Loop simulations for UAVs

Unmanned Aerial Vehicles (UAVs) are aircraft that can be autonomously operated and doesn't need a pilot aboard. Consequently, the autopilot is one of the main parts of an UAV, being responsible for stabilising the aircraft during the flight, executing navigation tasks and sensing the environment. Several path-following simulations for loiter paths are described in the literature, but most of them only consider the kinematics model of an aircraft. Therefore, this paper compares path-following algorithms for loiter paths in a more realistic scenario, using Software-in-the-Loop simulations and considering the dynamic model of the aircraft in the flight simulator X-Plane. The algorithms compared are: Carrot-Chasing, Pure Pursuit and Line-of-Sight (PLOS) and Non-Linear Guidance Law (NLGL). Lastly, a new algorithm (NLGL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sub> ) is proposed with a modification from the original algorithm and its results show smaller errors and less effort than all path-following algorithms.