Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Trust Model for Air Traffic Management Network

The Air Traffic Management (ATM) system is a network-centric intelligent information system that faces severe and complex challenges in the process of aviation information interaction. From the perspective of data security transmission in ATM networks, this paper proposes a blockchain-based trust model BlockTrust, combined with domestic cryptographic algorithms to establish a trust chain in the ATM air-space-ground integrated network. This paper uses such distributed ledger technology to design an architecture for ensuring the security of ATM information, ensuring the secure transmission and reliable sharing of aviation business data. In this paper, we will also take the submission and update process of flight plans as an example to discuss the possible benefits and risks of applying blockchain in this field.
Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

This paper addresses the problem of data vectors modeling, classification and recognition using infinite mixture models, which have been shown to be an effective alternative to finite mixtures in terms of selecting the optimal number of clusters. In this work, we propose a novel approach for localized features modelling using an infinite mixture model based on multivariate generalized Normal distributions (inMGNM). The statistical mixture is learned via a nonparametric MCMC-based Bayesian approach in order to avoid the crucial problem of model over-fitting and to allow uncertainty in the number of mixture components. Robust descriptors are derived from encoding features with the Fisher vector method, which considers higher order statistics. These descriptors are combined with a linear support vector machine classifier in order to achieve higher accuracy. The efficiency and merits of the proposed nonparametric Bayesian learning approach, while comparing it to other different methods, are demonstrated via two challenging applications, namely texture classification and human activity categorization.
Automatic Extraction of Code Dependency in Virtual Reality Software

Virtual Reality (VR) is an emerging technique with various applications in education, navigation, remote communication, and gaming. In virtual reality software, VR objects, which are graphics units linking with script files, play an important role and they often need to be updated together with correlated source code in the script files. However, traditional code dependency analysis tools detect only direct source code dependencies and can hardly detect code dependency across VR objects. In this paper, we propose a novel technique, VRDepend, to extract code-asset dependency within VR software projects. In particular, we extract (1) the direct association between VR objects and script files, (2) the compositional relations between VR objects, (3) event triggering relations between scripts and VR objects. We evaluated VRDepend based on script dependency extracted from file co-revisions. Our evaluation results show that VRDepend can cover 64% script file dependencies in historical commits of our subject VR software projects.
Microservice Anomaly Detection Based on Tracing Data Using Semi-supervised Learning

The Container-based platform of microservices has been continuously applied and developed in recent years. However, with the rapid expansion of services, how to ensure their stability and maintenance their quality has gradually become a research hotspot. Massive call chain logs and performance monitoring data of services, make it more possible to conduct in-depth research on operation and efficient maintenance. Among the expanded dataset, the tracing data can not only reflect the time-consuming of calls between services, but also express the call relationships between services. As a result, the location of the root cause can be more accurate. In this work, we trained features and obtained abnormal thresholds using semi-supervised learning, which based on the tracing and performance monitoring indicators data of a real microservice application system. Then, we detected microservice failures using dynamic sliding window, and located root causes by sorting algorithm based on call tracking. In order to evaluate the feasibility of the model, we used the public data of the Artificial intelligence for IT operations Challenge (AIOps Challenge 2020) to practice. The experimental results proved that the model has a good performance on the time-consuming curve with certain jitter frequency. Furthermore, the accuracy of anomaly detection has reached 99%, and the accuracy of root cause location has reached 98.5%.
LQF: Linear Quadratic Fine-Tuning

Classifiers that are linear in their parameters, and trained by optimizing a convex loss function, have predictable behavior with respect to changes in the training data, initial conditions, and optimization. Such desirable properties are absent in deep neural networks (DNNs), typically trained by non-linear fine-tuning of a pre-trained model. Previous attempts to linearize DNNs have led to interesting theoretical insights, but have not impacted the practice due to the substantial performance gap compared to standard non-linear optimization. We present the first method for linearizing a pre-trained model that achieves comparable performance to non-linear fine-tuning on most of real-world image classification tasks tested, thus enjoying the interpretability of linear models without incurring punishing losses in performance. LQF consists of simple modifications to the architecture, loss function and optimization typically used for classification: Leaky-ReLU instead of ReLU, mean squared loss instead of cross-entropy, and pre-conditioning using Kronecker factorization. None of these changes in isolation is sufficient to approach the performance of non-linear fine-tuning. When used in combination, they allow us to reach comparable performance, and even superior in the low-data regime, while enjoying the simplicity, robustness and interpretability of linear-quadratic optimization.
OF-VO: Efficient Navigation Among Pedestrians Using Commodity Sensors

We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our system uses commodity visual sensors, including a mono-camera and a 2D Lidar, to explicitly predict the velocities and positions of surrounding obstacles through optical flow estimation, object detection, and sensor fusion. A key aspect of our work is coupling the perception (OF: optical flow) and planning (VO) components for reliable navigation. Overall, our OF-VO algorithm using learning-based perception and model-based planning methods offers better performance than prior algorithms in terms of navigation time and success rate of collision avoidance. Our method also provides bounds on the probabilistic collision avoidance algorithm. We highlight the realtime performance of OF-VO on a Turtlebot navigating among pedestrians in both simulated and real-world scenes. A demo video is available at https://gamma.umd.edu/ofvo/
Numerical Analysis of 2-D Positioned, Indoor, Fuzzy-Logic, Autonomous Navigation System Based on Chromaticity and Frequency-Component Analysis of LED Light

Topics concerning autonomous navigation, especially those related to positioning systems, have recently attracted increased research attention. The commonly available global positioning system (GPS) is unable to determine the positions of vehicles in GPS-shaded regions. To address this concern, this paper presents a fuzzy-logic system capable of determining the position of a moving robot in a GPS-shaded indoor environment by analyzing the chromaticity and frequency-component ratio of LED lights installed under the ceiling. The proposed system’s performance was analyzed by performing a MATLAB simulation of an indoor environment with obstacles. During the simulation, the mobile robot utilized a fuzzy autonomous navigation system with behavioral rules to approach targets successfully in a variety of indoor environments without colliding with obstacles. The robot utilized the x and y coordinates of the fuzzy positioning system. The results obtained in this study confirm the suitability of the proposed method for use in applications involving autonomous navigation of vehicles in areas with poor GPS-signal reception, such as in tunnels.
Development of Granular Fuzzy Relation Equations Based on a Subset of Data

Developing and optimizing fuzzy relation equations are of great relevance in system modeling, which involves analysis of numerous fuzzy rules. As each rule varies with respect to its level of influence, it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance. In this study, we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules. The subset of rules is selected by maximizing their performance of the obtained solutions. The originality of this study is conducted in the following ways. Starting with developing granular fuzzy relation equations, an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules (the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations), which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency. Then, the particle swarm optimization (PSO) is implemented to solve a multi-objective optimization problem, in which not only an optimal subset of rules is selected but also a parameter ε for specifying a level of information granularity is determined. A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance. A visible improvement of particle swarm optimization (about 78.56% of the encoding mechanism of particle swarm optimization, or 90.42% of particle swarm optimization with an exploration operator) is gained over the method conducted without using the particle swarm optimization algorithm.
Smooth multifocal wavefronts with a prescribed mean curvature for visual optics applications

Multifocal lenses comprising progressive power surfaces are commonly used in contact and intraocular lens designs. Given a visual performance metric, a wavefront engineering approach to design such lenses is based on searching for the optimal wavefront at the exit pupil of the eye. Multifocal wavefronts distribute the energy along the different foci thanks to having a varying mean curvature. Therefore, a fundamental step in the wavefront engineering approach is to generate the wavefront from a prescribed mean curvature function. Conventionally, such a thing is done by superimposing spherical wavefront patches and maybe adding a certain component of spherical aberration to each spherical patch in order to increase the depth-of-field associated with each focus. However, such a procedure does not lead to smooth wavefront solutions and also restricts the type of available multifocal wavefronts. We derive a new, to the best of our knowledge, mathematical method to uniquely construct multifocal wavefronts from mean curvature functions (depending on radial and angular coordinates) under certain numerically justified approximations and restrictions. Additionally, our procedure leads to a particular family of wavefronts (line-umbilical multifocal wavefronts) described by 2 conditions: (1) to be smooth multiplicative separable functions in the radial and angular coordinates; (2) to be umbilical along a specific segment connecting the circle center with its edge. We provide several examples of multifocal wavefronts belonging to this family, including a smooth variant of the so-called light sword element.
Perceptual Friendly Variable Rate Image Compression

In this paper, we study high fidelity variable rate compression framework. Both conventional and learned codecs in prior works are optimized for objective quality commonly measured by PSNR or SSIM, leaving perceptual quality optimization underexplored. Besides, to circumvent the need of training separate models under different rate conditions, we design a novel coding framework to support variable rate compression. Aside from the variable rate functionality, we propose an adaptive bit allocation unit to strengthen rate-distortion optimization across different rates. Extensive experimental results demonstrate that our proposed approach achieves better subjective quality than methods optimized by the objective metrics such as MSE, and MS-SSIM on CLIC 2021 validation dataset.
Efficient Real-Time Object Detection based on Convolutional Neural Network

Visual objects recognition based on deep learning with the CNN (Convolutional Neural Network) have been extensive developments in computer image algorithms and improvements in computer hardware. In this work, a simplified CNN structure with a simple hardware system based on the Raspberry Pi with a compatible camera is developed. It can take pictures or videos in real time to recognize the existence of a human among ten predefined classes. The proposed model is implemented with less memory and less processing power while handling large amounts of data with Pascal VOC and Microsoft COCO datasets. The detection algorithm can quickly distinguish the object in 0.82 seconds and higher. The accuracy reaches 98%.
Using CNNs to Identify the Origin of Finger Vein Sample Images

We study the finger vein (FV) sensor model identification task using a deep learning approach. So far, for this biometric modality, only correlation-based PRNU and texture descriptor-based methods have been applied. We employ five prominent CNN architectures covering a wide range of CNN family models, including VGG16, ResNet, and the Xception model. In addition, a novel architecture termed FV2021 is proposed in this work, which excels by its compactness and a low number of parameters to be trained. Original samples, as well as the region of interest data from eight publicly accessible FV datasets, are used in experimentation. An excellent sensor identification AUC-ROC score of 1.0 for patches of uncropped samples and 0.9997 for ROI samples have been achieved. The comparison with former methods shows that the CNN-based approach is superior and improved the results.
Challenges in the Development of a Global Software User Interface by Multicultural Teams: an Industrial Experience

The process of creating a global system to support the maintenance of devices is a great challenge, due to the amount of aspects involved, such as performance, security, communication, and user experience. This tends to become even more complex when the development team is multicultural and located in several countries. Therefore, this paper presents and discusses the development trajectory, as well as the challenges and overcome during the process of creating RETAS (Runtime Evaluation and Trend Analysis Service), that is an interactive platform for the execution and visualization of automated tests on smartphones. We discuss the challenges of design, implementation, and project organization of industry point of view. Besides, we show aspects of validation of the user experience and usability in the global application context. The discussions demonstrate a learning roadmap for global maintenance applications context.
Evaluating the Stability of Digital Ink Chinese Characters from CFL Beginners Based on Center of Gravity Guided by Calligraphy

Beginners to Chinese as a foreign language (CFL) lack the awareness of Chinese orthography, treat Chinese characters as polyline shapes, and often suffer from unstable characters written. It is widely accepted that computing the center of gravity of Chinese character (CGCC) is an effective way to evaluate the writing performance. This paper proposes a CGCC computation approach by analyzing the Chinese character writing behaviors of CFL beginners combined with calligraphy theory to support Chinese character stability evaluation of CFL beginners. A CGCC database is also constructed with character stability information such as CGCC coordinates and computation approaches. The stability of Chinese characters from CFL beginners is quantitatively evaluated by comparing the deviation of center of gravity (CG) and the aspect ratio of handwriting characters with that of templates. Experiments with digital ink character data from CFL beginners have shown that the proposed approach is effective.
Engage!: Co-designing Search Engine Result Pages to Foster Interactions

In this paper, we take a step towards understanding how to design search engine results pages (SERP) that encourage children's engagement as they seek for online resources. For this, we conducted a participatory design session to enable us to elicit children's preferences and determine what children (ages 6–12) find lacking in more traditional SERP. We learned that children want more dynamic means of navigating results and additional ways to interact with results via icons. We use these findings to inform the design of a new SERP interface, which we denoted CHIRP. To gauge the type of engagement that a SERP incorporating interactive elements–CHIRP–can foster among children, we conducted a user study at a public school. Analysis of children's interactions with CHIRP, in addition to responses to a post-task survey, reveals that adding additional interaction points results in a SERP interface that children prefer, but one that does not necessarily change engagement levels through clicks or time spent on SERP.
A Dual-Attention Autoencoder Network for Efficient Recommendation System

Accelerated development of mobile networks and applications leads to the exponential expansion of resources, which causes problems such as trek and overload of information. One of the practical approaches to ease these problems is recommendation systems (RSs) that can provide individualized service. Video recommendation is one of the most critical recommendation services. However, achieving satisfactory recommendation service on the sparse data is difficult for video recommendation service. Moreover, the cold start problem further exacerbates the research challenge. Recent state-of-the-art works attempted to solve this problem by utilizing the user and item information from some other perspective. However, the significance of user and item information changes under different applications. This paper proposes an autoencoder model to improve recommendation efficiency by utilizing attribute information and implementing the proposed algorithm for video recommendation. In the proposed model, we first extract the user features and the video features by combining the user attribute and the video category information simultaneously. Then, we integrate the attention mechanism into the extracted features to generate the vital features. Finally, we incorporate the user and item potential factor to generate the probability matrix and defines the user-item rating matrix using the factorized probability matrix. Experimental results on two shared datasets demonstrates that the proposed model can effectively ameliorate video recommendation quality compared with the state-of-the-art methods.
Developing Data-Conscious Deep Learning Models for Product Classification

In online commerce systems that trade in many products, it is important to classify the products accurately according to the product description. As may be expected, the recent advances in deep learning technologies have been applied to automatic product classification. The efficiency of a deep learning model depends on the training data and the appropriateness of the learning model for the data domain. This is also applicable to deep learning models for automatic product classification. In this study, we propose deep learning models that are conscious of input data comprising text-based product information. Our approaches exploit two well-known deep learning models and integrate them with the processes of input data selection, transformation, and filtering. We demonstrate the practicality of these models through experiments using actual product information data. The experimental results show that the models that systematically consider the input data may differ in accuracy by approximately 30% from those that do not. This study indicates that input data should be sufficiently considered in the development of deep learning models for product classification.
Training needs of medical librarians in Nigeria: A survey

Health library and information professionals need continuing professional development to update their skills and knowledge. There is very limited evidence on the training needs of medical librarians in Nigeria.To identify the training needs of medical librarians in Nigeria, principally the content topics required, preferred formats of training, and barriers to training.An online questionnaire was distributed to 94 registered members of the Medical Library Association of Nigeria.The majority of the 64 medical librarian respondents wanted training in information systems and technology usage. The respondents also indicated that they prefer participating in seminar/workshops and being involved in face-to-face courses. Very few of the respondents have attended any specialist training aimed at their work in the medical field. The foremost challenge identified is the high cost of training.This study confirmed that the librarianship degree acquired from library schools only serves as a foundation for medical librarians, and it does not provide the specific subject training required to work successfully as medical librarians.There is a gap in knowledge base of medical librarians in Nigeria that needs to be filled by specific on-the-job training and courses which are affordable.
Immersive Technology in the Public School Classroom: When a Class Meets

Recent research suggests that students feel disconnected in distance educational environments. There is an immediate and sustained need for innovative approaches that provide personalized and interactive instruction, digital equity, and scale-up remote teaching. Shared, collaborative virtual spaces, such as Mozilla Hubs, can provide new learning modalities for educators and students, especially during the pandemic. We describe our experience with using Mozilla Hubs in formal and informal educational settings. We conducted several user studies to collect qualitative feedback from the participants. These studies include an educator workshop, a girl scout meeting, and a class gathering. The findings provide an insight how practical, useful, and entertaining such virtual spaces are to the participants.
Adaptive Novelty Detection over Contextual Data Streams at the Edge using One-class Classification

Online novelty detection is an emerging task in Edge Computing trying to identify novel concepts in contextual data streams which should be incorporated into predictive analytics and inferential models locally executed on edge computing nodes. We introduce an unsupervised adaptive mechanism for online novelty detection over multi-variate data streams at the network edge based on the One-class Support Vector Machine; an instance of One-class Classification paradigm. Due to the proposed adjustable periodic model retraining, our mechanism timely and effectively recognises novelties and resource-efficiently adapts to data streams. Our experimental evaluation and comparative assessment showcase the effectiveness and efficiency of the proposed mechanism over real data-streams in identifying novelty conditioned on the necessary model retraining epochs.