Reviewerly/exHarmony huggingface dataset:
Feasibility of digital contact tracing in low-income settings – pilot trial for a location-based DCT app

Data about the effectiveness of digital contact tracing are based on studies conducted in countries with predominantly high- or middle-income settings. Up to now, little research is done to identify specific problems for the implementation of such technique in low-income countries.A Bluetooth-assisted GPS location-based digital contact tracing (DCT) app was tested by 141 participants during 14 days in a hospital in Monrovia, Liberia in February 2020. The DCT app was compared to a paper-based reference system. Hits between participants and 10 designated infected participants were recorded simultaneously by both methods. Additional data about GPS and Bluetooth adherence were gathered and surveys to estimate battery consumption and app adherence were conducted. DCT apps accuracy was evaluated in different settings.GPS coordinates from 101/141 (71.6%) participants were received. The number of hours recorded by the participants during the study period, true Hours Recorded (tHR), was 496.3 h (1.1% of maximum Hours recordable) during the study period. With the paper-based method 1075 hits and with the DCT app five hits of designated infected participants with other participants have been listed. Differences between true and maximum recording times were due to failed permission settings (45%), data transmission issues (11.3%), of the participants 10.1% switched off GPS and 32.5% experienced other technical or compliance problems. In buildings, use of Bluetooth increased the accuracy of the DCT app (GPS + BT 22.9 m ± 21.6 SD vs. GPS 60.9 m ± 34.7 SD; p = 0.004). GPS accuracy in public transportation was 10.3 m ± 10.05 SD with a significant (p = 0.007) correlation between precision and phone brand. GPS resolution outdoors was 10.4 m ± 4.2 SD.In our study several limitations of the DCT together with the impairment of GPS accuracy in urban settings impede the solely use of a DCT app. It could be feasible as a supplement to traditional manual contact tracing. DKRS, DRKS00029327 . Registered 20 June 2020 - Retrospectively registered.
A General Framework for Remote Command and Control of Robots in Space

Future space missions are expected to be increasingly uncrewed and robots are expected to perform station keeping duties. The state of the art in robotics makes full autonomous operation at rigorous space safety standards extremely challenging. At the same time, the communication delay between robots in space and operators on earth makes traditional teleoperation painfully slow. We have developed a hardware-agnostic framework for remotely operating robots that bridges the gap between full autonomy and teleoperation. The framework provides parametrized, reusable building blocks (called behaviors) for performing robot operations such as opening doors, pressing buttons, and picking objects. When using these behaviors, an operator provides a few inputs and approves an automatically computed plan before it is executed. These behaviors can be combined into much more complex objectives that can encode fallback behaviors if a particular behavior fails or conditional behaviors that depend on user or sensor inputs.
Real-time Multiple Objects Following Using a UAV

Multiple objects following is a challenge in the area of computer vision, which is continuously gaining popularity for civilian and military purposes. However, there currently exists no baseline to apply real-time multiple objects following a system in Unmanned Aerial System (UAV). Therefore, this paper proposes a real-time following system aiming to keep all the aerial and ground objects in sight of a monocular camera, which is fixed on a UAV. This system applies tracking-by-detection architecture, where YOLO V5s, a model based on Convolutional Neural Network (CNN), is utilized for Object Detection whereas the SORT algorithm is used for Object Tracking. Furthermore, a visual servo controller is designed to control the UAV to complete the tracking tasks autonomously. Finally, three experiments are carried out in a simulator, where the proposed system is proved to be capable of handling multiple objects following problems within a given condition.
Confidentiality and Data Integrity in Consortium Blockchain Applications for Model Based Systems Engineering

Blockchain is a decentralized ledger technology with many unique desirable properties such as immutability, security, trustworthiness, traceability, decentralized authority, fault tolerance, and smart contracts (SC). It has the potential of serving as the foundational technology for building effective data repositories for demanding applications. In consortium Model Based Systems Engineering (MBSE) projects, a single Authoritative Source of Truth (AST) repository of digitalized models is essential for a team of collaborators with semi-trust between each other. Two major challenges in building blockchain-based solutions for MBSE are confidentiality and data integrity. In confidentially, strong, nuanced security rules must be specified and assured to preserve privacy and intellectual property between participating organizations. Unlike cryptocurrency and non-fungible assets, MBSE models have rich internal data structures, relationships, and constraints that need to be captured and assured. Based on the design and implementation of a minimum viable product's blockchain prototyping project on a consortium aerospace MBSE application using Hyperledger’s Fabric, a permissioned blockchain platform, this paper addresses how these two challenges are resolved. We use eXtensible Access Control Markup Language (XACML) with Abbreviated Language for Authorization (ALFA) as its front-end to specify security rules in Attribute Based Access Control (ABAC). These ABAC rules are then implemented in SC. A large subset of ABAC attributes is stored as metadata in the blockchain. We designed JSON Schema to capture selected data constraints and stored these metadata as blockchain assets. Specialized SC are developed to assure the constraints are satisfied before the assets are changed in the blockchain. The approaches are based on using JSON and JSON schema as the cornerstone of the conceptual data model, capturing metadata of different asset types, and storing metadata in the blockchain.
Mapping Application Ontologies as a Gateway into OBDA Systems in the Internet of Production

The aim of the Industry 4.0 research initiative is to enable globally networked, flexible and adaptable production. This results in the vision of the Cluster of Excellence Internet of Production (IoP): Creation of a new level of cross-domain collaboration between all production process participants, so that semantically adequate and context-aware data is available along the value chain. The main challenges hereby are the large amount and heterogeneity of generated production data. Both challenges can be handled by combining the advantages of existing object relational databases (ORDBMS) for storing production data and ontologies for data management and integration within the ontology based data access (OBDA) approach. This paper provides a concept of mapping an application ontology as a gateway into an OBDA system for management of production data. For that, transformation rules of an ORDBMS into an application ontology are defined and implemented. The proposed system can enable a first step into the vision of a global Internet of Production, consisting of different levels of semantically enriched data. The aim of this paper is thereby to highlight potentials of semantic data modeling for a global sustainable production network.
Generalized deblocking filter for AVM

The AV1 deblocking filter did not sufficiently attenuate visual quality artifacts when used in the AOMedia video model (AVM), especially at lower bitrates. A generalized deblocking filter described in this paper uses one equation for any filter length. The filter brings PSNR-YUV BD-rate $-\mathbf{0. 1 7 \%},\ -0.90\%,-1.07\%$, and $-0.92\%$ on All Intra, Random Access, LowDelay, and Adaptive Streaming configurations in the AOMedia common test conditions. Improvement of visual quality is observed on a number of sequences, while the computational complexity is close to that of the AVM deblocking.
Learning-Based Conditional Image Coder Using Color Separation

Recently, image compression codecs based on Neural Networks (NN) outperformed the state-of-art classic ones such as BPG, an image format based on HEVC intra. However, the typical NN codec has high complexity, and it has limited options for parallel data processing. In this work, we propose a conditional separation principle that aims to improve parallelization and lower the computational requirements of an NN codec. We present a Conditional Color Separation (CCS) codec which follows this principle. The color components of an image are split into primary and non-primary ones. The processing of each component is done separately, by jointly trained networks. Our approach allows parallel processing of each component, flexibility to select different channel numbers, and an overall complexity reduction. The CCS codec uses over 40% less memory, has 2x faster encoding and 22% faster decoding speed, with only 4% BD-rate loss in RGB PSNR compared to our baseline model over BPG.
Perceptual impact of the loss function on deep-learning image coding performance

Nowadays, deep-learning image coding solutions have shown similar or better compression efficiency than conventional solutions based on hand-crafted transforms and spatial prediction techniques. These deep-learning codecs require a large training set of images and a training methodology to obtain a suitable model (set of parameters) for efficient compression. The training is performed with an optimization algorithm which provides a way to minimize the loss function. Therefore, the loss function plays a key role in the overall performance and includes a differentiable quality metric that attempts to mimic human perception. The main objective of this paper is to study the perceptual impact of several image quality metrics that can be used in the loss function of the training process, through a crowdsourcing subjective image quality assessment study. From this study, it is possible to conclude that the choice of the quality metric is critical for the perceptual performance of the deep-learning codec and that can vary depending on the image content.
Deep video coding with gradient-descent optimized motion compensation and Lanczos filtering

Variational autoencoders have shown promising results for still image compression and have gained a lot of consideration in this field. Recently, noteworthy attempts were made to extend such end-to-end methods to the setting of video compression. Here, low-latency scenarios have been commonly investigated. In this paper, it is shown that the compression efficiency in this setting is improved by applying tools that are typically used in block-based hybrid coding such as rate-distortion optimized encoding of the features and advanced interpolation filters for computing samples at fractional positions. Additionally, a separate motion estimation network is trained to further increase the compression efficiency. Experimental results show that the rate-distortion performance benefits from including the aforementioned tools.
Guided classification for Arabic Characters handwritten Recognition

Arabic text recognition is a difficult task due to the cursive nature of the Arabic writing system, the different forms of Arabic characters in words, the large number of ligatures, and many other challenges. Deep Learning models have made significant progress in many fields, especially in the field of Optical Characters Recognition (OCR). This article presents a model capable of recognizing handwritten Arabic characters based on deep learning. The proposed model uses Convolutional Neural Networks (CNNs) to divide the 28 Arabic characters into subclasses to improve the classification phase in OCR. The model was tested on the Handwritten Arabic Characters Database (HACDB) dataset and it gave 98% of recognition rates.
GPGPU-based High Throughput Image Pre-processing Towards Large-Scale Optical Character Recognition

Studies have shown that pre-processing digital images through scaling, rotation and blurring type of operations allow optical character recognition (OCR) to focus on the key features in the image and result in improving recognition accuracy. We leverage the open-source Tesseract OCR and show that its accuracy can be improved through a pre-processing flow that includes thresholding, rotation, rescaling, erosion, dilation, and noise removal steps based on a dataset that is formed of 560 phone screen images. However, the serial CPU-based implementation of this flow introduces a latency of 48.32 ms per image on average. Even though time scale is low in the context of a single image, this latency poses as a barrier when processing millions of images with OCR. To address this, we parallelize the entire pre-processing flow on the Nvidia P100 GPU, implement a streaming based execution, and reduce the latency to 0.846 ms. This streaming-enabled implementation enables setting up a GPU based OCR engine to process large scale workloads.
Modeling User-Centric Threats in Smart City: A Hybrid Threat Modeling Method

The ever-changing technological trends form the basis of smart cities. The integration of blockchain in a smart city architecture can solve the increasing economic and social problems that negatively impact human life. On one hand, the smart cities are effectively improving the living standards of the citizens. On the other hand, the data gathering, processing and storing requirements of such technologies raise various security and privacy issues. Therefore, we aim to investigate the threats related to the privacy and security of users in a blockchain-based smart city. We use a hybrid threat modeling technique to investigate user-centric threats in a smart city architecture. For the threat analysis, we leverage NetObjex as our smart city use case. Our threat assessment unveils six different types of threats that are critical to the safety and well-being of residents in a smart city.
Resource Allocation and Scheduling of Linear Workflow Applications with Ageing Priorities and Transient Failures

In distributed environments, applications are usually complex and computationally demanding, having a linear workflow (LW) structure. Additionally, such LW jobs may also have different priorities for processing. This entails the danger of long delays for low priority jobs. Furthermore, transient software failures may occur during the execution of the workload. Consequently, resource allocation, scheduling and fault tolerance are three crucial aspects that should be efficiently and effectively addressed in such environments, in order to achieve good system performance. To this end, in this paper we investigate the resource allocation and scheduling of LW jobs that arrive dynamically in an environment of distributed resources. We consider that the LW jobs have different priorities and that transient software failures may occur during their execution. A novel scheduling technique is proposed, which takes into account the ageing priorities of the LW jobs, as well as the resulting scheduling overhead. We examine the performance of three routing strategies in this framework, under various load cases and different failure probabilities, taking also into account their implementation complexity. The simulation results reveal how each routing strategy is affected in each of the examined scenarios.
A BERT-based Deep Learning Approach for Reputation Analysis in Social Media

Social media has become an essential part of the modern lifestyle, with its usage being highly prevalent. This has resulted in unprecedented amounts of data generated from users in social media, such as users' attitudes, opinions, interests, purchases, and activities across various aspects of their lives. Therefore, in a world of social media, where its power has shifted to users, actions taken by companies and public figures are subject to constantly being under scrutiny by influential global audiences. As a result, reputation management in social media has become essential as companies and public figures need to maintain their reputation to preserve their reputational capital. However, domain experts still face the challenge of lacking appropriate solutions to automate reliable online reputation analysis. To tackle this challenge, we proposed a novel reputation analysis approach based on the popular language model BERT (Bidirectional Encoder Representations from Transformers). The proposed approach was evaluated on the reputational polarity task using RepLab 2013 dataset. Compared to previous works, we achieved 5.8% improvement in accuracy, 26.9% improvement in balanced accuracy, and 21.8% improvement in terms of F-score.
A New Algorithm for Energy Efficient Task Scheduling Towards Optimal Green Cloud Computing

These days the continuous use of the cloud data centers has been increased, which augmented the consumption of energy as a whole heap. The main concerns of green cloud computing are reducing and managing large amount of energy uses. In this paper, we propose a new algorithm for energy efficient task scheduling in green cloud operation. The proposed algorithm uses the Dynamic Voltage and Frequency Scaling (DVFS) and DVFS enabled Efficient energy Workflow Task Scheduling (DEWTS) in calculating the order of tasks and assigns it to the processor nodes. The algorithm helps in utilizing the idle slots of CPU without violating the constraints in the whole process. Slack time is optimized by merging inefficient processors, and the deadline is computed based on the finish time of the task heterogeneously. The containerization is used with Kubernetes as a package instead of Virtual Machines (VMs), which has advantages over the Docker Swarm. The initial result shows that the novel hybrid scheduling algorithm reduces approximately fifty percent of energy consumption balancing the scheduling task in parallel applications, but not in serial tasks.
An optimized feature extraction algorithm based on SuperPoint

Feature extraction and matching of remote sensing images is becoming increasingly important with a wide range of applications. It matches and superimposes images obtained from the same scene at different times, different sensors, and different angles, and maps the optimal to the target image. CNN-based algorithms have shown superior expressiveness compared to traditional methods in almost all fields with image. This paper optimises the network based on SuperPoint by replacing convolution with a depth-separable convolution which has smaller number of parameters, and replacing the conv block with a spindle-type Inverted Residuals block composed of dimension expansion, depth-separable convolution and Dimension reduction. The network depth is fine-tuned to ensure accuracy. The model is trained on the RSSCN7 remote sensing dataset. Compared with other traditional algorithms in a cross-sectional manner with the combination of SuperGlue, the optimized algorithm shows the superior comprehensive performance.
Exploration on Teaching Reform of Railway Container Transport Under the Background of Intelligent Logistics

With the emergence of cloud computing, big data, intelligent hardware and other new information technologies and devices, the logistics enterprises need many professional talents. The vocational colleges should conform to the development of the times and cultivate skilled talents for the society. The traditional teaching mode is not conducive to the development of teachers and the progress of students. In order to improve the quality of classroom, the reform and exploration are carried out in the form, content and evaluation mechanism of curriculum. The practical and applied characteristics of the railway container transportation course determine the necessity of the teaching reform. Based on the perspective of intelligent logistics, this paper explores the reform measures of curriculum teaching, which has certain reference value for the teaching. The positive role of teachers, students and assistants in the curriculum has been brought into full play, and the higher order of the curriculum construction has been promoted. Practical results show that the curriculum reform has achieved good results, fostered students’ practical ability, and effectively improved their vocational skills and innovative ability.
Special Issue “Selected Papers from CD-MAKE 2020 and ARES 2020”

In the current era of rapid technological advancement, machine learning (ML) is quickly becoming a dominant force in the development of smart environments [...]
Fractional-Ant Lion Optimization Algorithm for Privacy Protection in Cloud with MapReduce Framework

Cloud computing environment offers huge computation power and enormous storage capacity. The cloud framework enables users to organize the data-intensive applications without the investment of substructure and it acts as a key role in the digital era, as it simultaneously handles the data from the number of users. Besides the useful characteristic features, providing security to data that is stored in cloud platform offers a great challenge in cloud storage. Using the proposed F-ALO technique, a privacy protection model is developed to encrypt the data before sharing it in the cloud. To achieve effective data protection, the proposed F-ALO algorithm was developed by combining the Fractional Calculus (FC) and Ant Lion Optimizer (ALO) algorithms. By incorporating the privacy protection and data sharing phases, employing the MapReduce framework's mappers and reducers, the shared data is safeguarded. Using the proposed algorithm based on the factors, such as markedness and sensitivity index, the optimal coefficient is calculated. The proposed F-ALO algorithm achieved higher performance with measures, like sensitivity index, markedness, accuracy, Database Difference Ratio (DBDR), computational overhead, time complexity and space complexity with the values of 0.8422, 0.9366, 0.9339, 0.8192, 0.7357, 0.4678, and 0.3679 using the Hungarian database.
Cystoscopic depth estimation using gated adversarial domain adaptation

Monocular depth estimation from camera images is very important for surrounding scene evaluation in many technical fields from automotive to medicine. However, traditional triangulation methods using stereo cameras or multiple views with the assumption of a rigid environment are not applicable for endoscopic domains. Particularly in cystoscopies it is not possible to produce ground truth depth information to directly train machine learning algorithms for using a monocular image directly for depth prediction. This work considers first creating a synthetic cystoscopic environment for initial encoding of depth information from synthetically rendered images. Next, the task of predicting pixel-wise depth values for real images is constrained to a domain adaption between the synthetic and real image domains. This adaptation is done through added gated residual blocks in order to simplify the network task and maintain training stability during adversarial training. Training is done on an internally collected cystoscopy dataset from human patients. The results after training demonstrate the ability to predict reasonable depth estimations from actual cystoscopic videos and added stability from using gated residual blocks is shown to prevent mode collapse during adversarial training.