Reviewerly/exHarmony huggingface dataset:
Towards Next Generation Technical Documentation in Augmented Reality Using a Context-Aware Information Manager

Technical documentation is evolving from static contents presented on paper or via digital publishing to real-time on-demand contents displayed via virtual and augmented reality (AR) devices. However, how best to provide personalized and context-relevant presentation of technical information is still an open field of research. In particular, the systems described in the literature can manage a limited number of modalities to convey technical information, and do not consider the ‘people’ factor. Then, in this work, we present a Context-Aware Technical Information Management (CATIM) system, that dynamically manages (1) what information as well as (2) how information is presented in an augmented reality interface. The system was successfully implemented, and we made a first evaluation in the real industrial scenario of the maintenance of a hydraulic valve. We also measured the time performance of the system, and results revealed that CATIM performs fast enough to support interactive AR.
Algorithmic handwriting analysis of the Samaria inscriptions illuminates bureaucratic apparatus in biblical Israel

Past excavations in Samaria, capital of biblical Israel, yielded a corpus of Hebrew ink on clay inscriptions (ostraca) that documents wine and oil shipments to the palace from surrounding localities. Many questions regarding these early 8th century BCE texts, in particular the location of their composition, have been debated. Authorship in countryside villages or estates would attest to widespread literacy in a relatively early phase of ancient Israel's history. Here we report an algorithmic investigation of 31 of the inscriptions. Our study establishes that they were most likely written by two scribes who recorded the shipments in Samaria. We achieved our results through a method comprised of image processing and newly developed statistical learning techniques. These outcomes contrast with our previous results, which indicated widespread literacy in the kingdom of Judah a century and half to two centuries later, ca. 600 BCE.
Research on Cold-Start Problem of Collaborative Filtering Algorithm

Collaborative Filtering is the most successfully and widely used recommendation algorithm nowadays. However, it faces severe challenges of the cold-start problem. This paper proposes a hybrid algorithm by using the users' attributes and the back propagation neural network (BPNN) to solve the new user problem in cold-start. Firstly, users' attributes are collected and analyzed to calculate the component similarity respectively. Secondly, take these similarities as the input neurons of the BPNN and set the initial values of weight and biases parameters, through the self-adjusting and self-adaptive features of the BPNN, optimize the rating prediction model to get a higher recommendation accuracy of the algorithm. Finally, take the output as the predicted similarity of the new user, so that the algorithm can recommend for the new user. After a series of experiments carried out on a public data set -- MovieLens(ml-1m), the experimental results show that the proposed method can effectively accomplish novel user recommendation for collaborative filtering cold-start and reduce the performance loss.
FPGA-based Power Efficient Face Detection for Mobile Robots

Autonomous mobile robots need perception module to understand nearby environments, avoid obstacles and, most importantly, interact with the humans in the dynamic environments. In order to perform human robot interaction for mobile robots, face detection is an inevitable and important function. In recent years, deep learning has emerged to be the most accurate approach for face detection problems. However, deep learning models have very high computational density, therefore cannot be executed in real-time on the embedded low-end processors that most of the robot systems equip with. Switching to high-end CPU or GPU generally means much higher cost and power consumption. Meanwhile, FPGA is well suited for robot application because it provides high computing power with low cost and high power efficiency. In this paper we design an FPGA based system for the acceleration of MTCNN, which is one of the most accurate face detection neural networks. We evaluate the system using a Zynq 706 board and compare the performance with embedded CPU and popular GPU edge computing platform Jetson TX2. Our FPGA system has 40× lower latency than Jetson TX2 with 2.5 × higher power efficiency, making it a promising candidate for egde computing on mobile robot applications.
Adversarial training for fast arbitrary style transfer

We adversarially train a fast feed-forward generator network for arbitrary style transfer. The trained generator can output stylized images given unseen content and style image pairs as input during inference time. Our generator network is an encoder–decoder network that stylized the content by shifting the statistics of deep features, which has a novel mask module that can automatically and spatially decide the stylization level. We introduce an interface to manually and spatially control the stylization levels, and combine multiple styles in the generator. Our training objective is composed of traditional perceptual loss for content, traditional statistical loss for style, and adversarial loss that learns the intrinsic property of image styles from large-scale multi-domain artistic images. We use a conditional discriminator based on the coarse category of styles to tackle the challenging adversarial training for arbitrary style transfer, as both the input and output of our generator are diverse multi-domain images. We observe the mask module can stabilize adversarial training and help avoiding mode collapse in practice. As a side effect, our trained discriminator can be applied to rank and select representative stylized images, and the stylized images can significantly improve the efficiency of optimization-based style transfer. We qualitatively and quantitatively evaluate the proposed method, and compare with recent style transfer methods. We perform systematic study on arbitrary style transfer on multi-domain style images, and release our code and model at https://github.com/nightldj/behance_release.
Analysis of Noisy Evolutionary Optimization When Sampling Fails

In noisy evolutionary optimization, sampling is a common strategy to deal with noise. By the sampling strategy, the fitness of a solution is evaluated multiple times (called sample size) independently, and its true fitness is then approximated by the average of these evaluations. Most previous studies on sampling are empirical, and the few theoretical studies mainly showed the effectiveness of sampling with a sufficiently large sample size. In this paper, we theoretically examine what strategies can work when sampling with any fixed sample size fails. By constructing a family of artificial noisy examples, we prove that sampling is always ineffective, while using parent or offspring populations can be helpful on some examples. We also construct an artificial noisy example to show that when using neither sampling nor populations is effective, a tailored adaptive sampling (i.e., sampling with an adaptive sample size) strategy can work. These findings may enhance our understanding of sampling to some extent, but future work is required to validate them in natural situations.
An Efficient 3D Pedestrian Detector with Calibrated RGB Camera and 3D LiDAR

Pedestrian detection plays an important role in the environmental perception and autonomous navigation for robotics, which provides critical information for the safe operation in complex environments. In this paper, a 3D pedestrian detector with calibrated LiDAR and RGB Camera is proposed, which takes full advantage of the precise range information of 3D LiDAR scanner and semantic information acquired from RGB image. The proposed approach integrates the segmented object clusters of point cloud and 2D bounding boxes generated by a visual object detector. The point cloud is segmented by a three-step segmentation approach, which can segment point cloud with both high precision and high efficiency. By fusion multi-sensor information in the image domain, the proposed approach provides 3D information of pedestrians in both LiDAR and camera coordinate systems. Experiments was conducted to evaluate the performance of the proposed approach.
Rabin Karp And Winnowing Algorithm For Statistics Of Text Document Plagiarism Detection

The Karp-Rabin algorithm was created by Michael O. Rabin and Richard M. Karp in 1987 who used the hashing function to find patterns in text strings and the Winnowing Algorithm is a method used to detect common subsequence in two or more texts compared. Two texts are known to have similar words/sentences if fingerprint is found in the document, this fingerprint will be used as the basis of comparison between texts, this algorithm will look for fingerprints (similarity in two texts) by changing the n-gram of a text into a numerical value called a hash value, the technique for finding that value is Hashing This algorithm is widely used in determining the close resemblance in a biology sequence. Plagiarism is an action that must be avoided, but there are still many people who know and understand plagiarism. Apart from preventing, detecting plagiarism is an attempt to reduce plagiarism. The problem of plagiarism that is often found among students is plagiarism in text documents. This study aims to build a plagiarism detection system in text documents using the Rabin-Karp algorithm in a computerised manner. This plagiarism detection system is aiding the action of plagiarism with the similarity of sequences of the two documents compared. The system that is compared is a basic process that can be further developed to build better detection applications for plagiarism.
Learning Structural Similarity with Evolutionary-GAN: A New Face De-identification Method

With the explosive growth of image sources, the development of face recognition technology and the emphasis on privacy, face deidentification has become increasingly important. The purpose of face de-identification is to hide the identity of individuals in a video or image while still retaining certain facial attributes. The mainstream methods of face de-identification are mostly based on the k-same framework, which generates anonymized faces that are not diverse and have poor visual quality. This paper presents a face de-identification method that uses an improved evolutionary generative adversarial network to synthesize faces to de-identificate, using the structural similarity index and the distance between the original face and the de-identificated face for generator selection, choosing the optimal generator to enter the next round of evolution. We have proved the feasibility of the proposed method through extensive experiments, indicating that our alteration method is indeed effective.
What Criterion Can We Get From Precise Entanglement Witnesses?

Entanglement detection is one of the important topics in quantum information theory. Wootters formula can be seen as a high level criterion for entanglement detection or entanglement quantification. However, it is restricted to two-qubit system, and its generalization is also limited. Based on a thorough study of a three-qubit Greenberger-Horne-Zeilinger (GHZ) state mixed with a W state and white noise, we find a connection between Wootters formula and the matched entanglement witness. Further study shows that for a four-qubit GHZ state mixed with a Dicke state and white noise, there is a new kind of Wootters formula.
Two cluster validity indices for the LAMDA clustering method

The learning algorithm and multivariable data analysis (LAMDA) is an algorithm to group quantitative and qualitative data, applying self-learning and/or directed learning. Usually, LAMDA automatically generates classes by assigning the best data partition to a class. To evaluate the data partitions generated by LAMDA, the internal evaluation is used to find the optimal number of clusters. For the LAMDA algorithm, the cluster validity (CV) is the most popular index which is based on inter-class contrast (ICC). However, other indices have not been defined for LAMDA and a comparative analysis is required to evaluate its performance. In this paper, two metrics called cluster validity index based on granulation error and the ratio of the distance (CVGED) and cluster validity index based on the ratios of covariance and distance (CVCOD) are proposed. Such indices are compared with the CV and ICC indices for two experiments: using a databases repository and selected open data and experimental laboratory data. According to the main results, CVGED and CVCOD have a better performance in compactness, separation, and coefficient of variation than ICC and CV for most of the selected repository databases but the accuracy is limited for the four indices. Nevertheless, CVCOD improves the quality of data partition when the open data and experimental laboratory data are used.
Blockchain Compliance With Federal Cryptographic Information-Processing Standards

Under current Federal Information Security Management Act of 2002 (FISMA) requirements, all new federal IT programs and modernization efforts using blockchain must meet National Institute of Standards and Technology (NIST) cryptographic standards. This article evaluates four major blockchain technologies-Ethereum, Hyperledger Fabric, R3's Corda, and Multichain-to determine their level of NIST compliance.
Detecting and Characterizing Arab Spammers Campaigns in Twitter

Social media platforms play a significant role in today’s society as billions of users use them to share or seek information on a regular basis. The content in social media has a significant impact that influences individuals’ opinions and decision, which can range from buying a small product to voting for a political campaign. This role has been fueling the interest in utilizing social media content for research and commercial purposes. The significant role of social media also attracted other individuals who intentionally abuse or misuse these platforms by producing and managing a tremendous amount of fake accounts to perform various malicious activities. Their activities are often center on sharing unwanted, misleading, or harmful content to either send unwanted ads, manipulate public opinion, or spread harmful malware. This act would eventually reduce the quality of social media content as a mean of information source used for various purposes. Therefore, this research proposes a machine-learning based model that aims to detect malicious users and groups on Twitter. We have, in particular, focus on malicious Arab accounts as it has not been sufficiently researched. The proposed model adopts a semi-supervised technique that labels Twitter accounts based on their behavior and profile information into spam or genuine account. To evaluate this technique, we collected a dataset through a Twitter API by targeting active Arab users. We manually labeled approximately 500 accounts as the ground truth, and then, we developed a classification model with a set of redefined features with the aim of identifying individual and groups of spam accounts. We have also evaluated the performance of the proposed model, and the results show that our model achieves 0.89 F measure: 0.89 and 0.91 Accuracy.
Implementation of Text Mining Model to Emotions Detection on Social Media Comments Using Particle Swarm Optimization and Naive Bayes Classifier

Emotion is defined as human beings' psychological state in which it is also used to establish a relationship and convey messages between the author and the readers. The lifestyle that has been heavily influenced by social media makes the delivery of information and ideas becoming unrestricted. Meanwhile, information and ideas always involve the authors' emotions, such as anger, fear, joy, or sadness. This research applied a text mining model to detect through the classification process. The dataset used was the best-worst scaling technique that has been classified by previous researchers. The challenge of this dataset is to classify sentences or texts into four emotional categories, namely anger, fear, joy, and sadness. This research employed Naïve Bayes classifiers as the method that was optimized by particle swarm optimization. From several previous types of research, this method was able to generate high accuracy. By applying it to the emotional dataset that has different characters from most datasets, this method was able to provide a quite good classification performance.
From Creating Spaces for Civic Discourse to Creating Resources for Action

In this paper, we investigate the role of technology to address the concerns of a civil society group carrying out community-level consultation on the allocation of £1 million of community funds. We explore issues of devolved decision-making through the evaluation of a sociodigital system designed to foster deliberative virtues. We describe the ways in which this group used our system in their consultation practices. Our findings highlight how they adopted our technology to privilege specific forms of expression, ascertain issues in their community, make use of and make sense of community data, and create resources for action within their existing practices. Based on related fieldwork we discuss the impacts of structuring and configuring tools for 'talk-based' consultation in order to turn attention to the potential pitfalls and prospects for designing civic technologies that create resources for action for civil society.
Liver Backscatter and the Hepatic Vasculature’s Autocorrelation Function

Ultrasound imaging of the liver is an everyday, worldwide clinical tool. The echoes are produced by inhomogeneities within the interrogated tissue, but what are the mathematical properties of these scatterers? In theory, the spatial correlation function and the backscatter coefficient are linked by a Fourier transform relationship, however direct measures of these are relatively rare. Under the hypothesis that the fractal branching vasculature and fluid channels are the predominant source of scattering in normal tissues, we compare theory and experimental measures of the autocorrelation function, the frequency dependence of scattering, and fractal dimension estimates from high contrast 3D micro-CT data sets of rat livers. The results demonstrate a fractal dimension of approximately 2.2 with corresponding power law estimates of autocorrelation and ultrasound scattering. These results support a general framework for the analysis of ultrasound scattering from soft tissues.
Advanced Pedestrian Dataset Augmentation for Autonomous Driving

Having the ability of generating people images in arbitrary, yet admissible, pose is a crucial prerequisite for Autonomous Driving applications. Firstly, because the existing datasets are quite limited in the human pose variation and appearance. Secondly, because the strict safety requirements call for the ability of validation on rare situations. Generating realistically looking people images is very challenging problem due to various transformations of individual body parts [2,6] self occlusions etc. We propose a novel approach for person image generation. Our approach allows generating people images in a required pose, indicated by specific pose keypoints and deals with occlusions. We build on top of the recent prevailing success of Generative Adversarial Networks [10]. Our contributions comprise of the networks architecture, as well as the novel loss terms specifically designed to generate visually appealing pedestrians fitting the surrounding environment well.
SC-RBAC: A Smart Contract based RBAC Model for DApps

Blockchain technology with its non-centralized, transparent, trustful, traceable and tamper-resistant features draws more and more attention both in commercial and scientific area. Smart contracts and DApps (Decentralized Applications) are programs naturally running automatically on blockchain. Access control is a principle that regulates the access to critical resources. RBAC (Role based Access Control) is one of access control mechanisms and it involves three parts: user, role and permission, with their relations, corresponding to real business. However, traditional implementation of RBAC relies on centralized server which is in danger of being modified, invaded or a single point of failure. The paper proposes a decentralized and smart contract based RBAC model named SC-RBAC for DApps. It is developed by Ethereum's Solidity and offers a strong compatibility with different DApps. The features of SC-RBAC associated with flexible interfaces, traceability and security enrich the community of DApps. The results of two experiments are discussed to evaluate the overheads of SC-RBAC model.
Environment Influences on Uncertainty of Object Detection for Automated Driving Systems

Possible causes for malfunctioning behaviour in the Safety Of The Intended Functionality (SOTIF) are closely related to object detection algorithm for the functionality under development. In order to analyse uncertainty of object detection for Automated Driving Systems (ADS), we provide a new approach to systematically analyse the influence of environmental factors on object detection with Average Precision (AP). We generate test cases depending on real-world changes driving environment like dark, sunset, rain, snow, etc. This paper uses a Recurrent Rolling Convolution (RRC) model which is an outstanding Deep Neural Network (DNN), it can improve the accuracy of vehicle while realizing the real-time object detection. In particular, by using AP, this paper evaluated our model under different environment and found thousands of erroneous detection many of which may lead to potentially safety risk in the top-performing DNNs. The method proposed in this paper can help to improve system design, verification and validation work for the SOTIF.
Privacy-Preserving Media Sharing with Scalable Access Control and Secure Deduplication in Mobile Cloud Computing

Benefiting from cloud computing and mobile devices, a huge number of media contents, such as videos are shared in mobile networks. Although scalable video coding can be utilized to provide flexible adaptation, the cloud poses a serious threat to media privacy. In this paper, we propose a privacy-preserving multi-dimensional media sharing scheme named SMACD in mobile cloud computing. First, each media layer is encrypted with an access policy based on attribute-based encryption, which guarantees media confidentiality as well as fine-grained access control. Then, we present a multi-level access policy construction with secret sharing scheme. It ensures that the mobile consumers who obtain a media layer at a higher access level must satisfy the access trees of its child layers at the lower access level, which is compatible with the characteristics of multi-dimensional media and also reduces the complexity of access policies. Moreover, we introduce decentralized key servers to achieve both intra-server and inter-server deduplication by associating different access policies into the same encrypted media. Finally, we conduct experimental evaluation on mobile device and cloud platform with real-world datasets. The results indicate that SMACD protects media privacy against cloud media center and unauthorized parties, while incurring less computational and storage cost.