Reviewerly/exHarmony huggingface dataset:
Post-Quantum Cryptography in MPSoC Environments

Multi-processors System-on-Chip (MPSoC) are a key enabling technology for different applications characterized by hyper-connectivity and multi-tenant requirements, where resources are shared and communication is ubiquitous. In such an environment, security plays a major role. To cope with these security needs, MPSoCs usually integrate cryptographic functionalities deployed as software and/or hardware solutions. Quantum computing represents a threat for the current cryptography. To overcome such a threat, Post-quantum cryptography (PQC) can be used, thus ensuring the long term security of different applications. Since 2017, NIST is running a PQC standardization process. While the focus has been the security analysis of the different PQC candidates and the software implementation, the MPSoC PQC implementation has been neglected. To this end, this work presents two contributions. First, the exploration of the multicore capabilities for developing optimized PQC implementations. As a use case, NTRU lattice-based PQC, finalist for the NIST standardization process, is discussed. Second, NTRU was deployed on an AURIX microcontroller of Infineon Technologies AG with the Real-Time Operating System PXROS-HR from HighTec EDV-Systeme GmbH. Results show that NTRU can be efficiently implemented and optimized on a multicore architecture, improving the performance up to 43% when compared to single core solutions.
Lightweight Deep Learning based Intelligent Mobile Augmented Reality

Recently augmented reality technology is developing from simply displaying virtual objects in the real world to technologies that realistically fuse with real information. This paper introduces intelligent mobile AR technology that provides object and scene information by utilizing lightweight object detection and scene segmentation techniques operable in mobile phones. We provide object detection and scene segmentation capabilities with effective model structure improvements and model compression-based deep learning models for lightweight deep-learning models.In addition, we have made intelligent demonstration contents based on ARCore to provide object and scene information as well as display simple virtual objects.
Feature Aided Extended Target Tracking For High Resolution Radar

In high-resolution radar applications, the scatter distribution of target usually evolves non-linearly with the target movement, and thus it is difficult for a Bayesian filter to achieve an accurate estimation of the target extension state on its own. To solve this problem, we propose to use measurement features to help to propagate the posterior of the target extension in this paper, resulting in a feature-aided extended target probability hypothesis density (FA-ET-PHD) filter, where the features are applied to calculate the partition weights. Since the feature-based weights do not abide by the Bayesian inferring framework, the FA-ET-PHD filter can effectively avoid the deterioration of the performance in multi-target tracking caused by the nonlinear change of the distribution of scatters. Simulation results show that the proposed method can improve the accuracy of the multi-target state estimation as well as the robustness.
COVID-19 Contact Tracing: Ghana’s Efforts in the Application of Geospatial Technology in Minimizing the Impact of the Pandemic

COVID-19 has presented unusual challenges for individuals, governments and societies across the globe. Several non-medical and non-pharmaceutical interventions have demonstrated to be critical in addressing the resultant impacts. One notable tool among these interventions is the application of technology in identifying infected persons or individuals coming into contact with those infected. Policy think-tanks have invested in geospatial technology and information systems to help resolve contact tracing inefficiencies to curtail the fast spread of the disease. This study highlights the extent of the application of geospatial technology in COVID-19 contact tracing in Ghana. Here, it was demonstrated that majority of young adults that form the greater part of Ghana’s population have access to digital devices which serve as primary catalysts in facilitating effective and efficient contact tracing. Case count of the pandemic continues to surge sharply from one month to the other since the first recorded case on March 12, 2020. A huge number of cases were recorded in the southern part of the country, as against cases recorded in the north. Mobility patterns depicted the migration of more people from regions with a high number of case count to regions with lower counts. We recommend a holistic and proactive approach to the use of smart mobile devices and applications in enhancing contact tracing. Privacy and data protection laws must be prioritized and supported by effective legislative and policy frameworks that serve as the legal basis for the management of personal information.
Solar Power Prediction Using Iterative Network Pruning Technique for Renewable-based Microgrid

This paper proposes a simple and efficient day-ahead solar power prediction method by improving our previous feedforward neural networks (FNNs) based approach, which is applied to a renewable-base microgrid (MG). The proposed prediction algorithm uses only public available weather data such as day-ahead solar irradiation. Based on weather clustering such as sunny/cloudy/rainy, the multiple FNNs are developed for different weather patterns and time zones. An iterative pruning (IP) algorithm is introduced in the previous FNNs structure to achieve efficient computation. Confidence intervals (CIs) indicating the reliability of the forecasting are displayed in the forecasting result. The values of the CIs are effectively provided for the local energy management and robust power system security in MG. The computation times are improved about 90% against the previously proposed method. At the same time, the prediction accuracy is maintained against previous methods. The rate at which the measured value appears outside the confidence interval is 10% or less.
Optimal Control in Agent-Principle Problems for the Quasi-Public Sector

The article discusses human resource management problems in the quasi-public sector (education, healthcare, military industry, etc.). It is shown that the behavior of these organizations can be described as unproductive workers' payment maximization. The formalization of this task based on the model of the external and internal labor market is proposed.
Towards Automated Semantic Grouping in Workflows for Multi-Disciplinary Analysis

When designing multidisciplinary tool work-flows in visual development environments, researchers and engineers often combine simulation tools which serve a functional purpose and helper tools that merely ensure technical compatibility by, e.g., converting between file formats. If the development environment does not offer native support for such groups of tools, maintainability of the developed workflow quickly deteriorates with an increase in complexity.We present an approach towards automatically identifying such groups of closely related tools in multidisciplinary workflows implemented in RCE by transforming the workflow into a graph and applying graph clustering algorithms to it. Further, we implement this approach and evaluate multiple clustering algorithms. Our results strongly indicate that this approach can yield groups of closely related tools in RCE workflows, but also that solutions to this problem will have to be tailor-made to each specific style of workflow design.
Predictive quality for hypoid gear in drive assembly

In rear axle drive assembly, drive bevel gear and crown gear are aligned within a narrow tolerance range and installed in the gearbox. Due to limited adaptability after installation, the ideal dimension is estimated a priori based on physical correlations and empirical correction terms. This paper follows a data-driven approach to predict the installation dimension with sufficient accuracy within a comprehensive optimization strategy in industrial application. An initial evaluation with production data logged in day-to-day operations suggests a significant improvement in accuracy compared to status quo and contributes to the dissemination of predictive analytics in industrial practice.
The Rainbow Steiner Tree Problem

Given an undirected and edge-colored graph with non-negative edge lengths, the aim of the Rainbow Steiner Tree Problem (RSTP) is to find a minimum Steiner Tree that uses at most one edge for each color. In this paper, the RSTP is introduced, a mathematical model is proposed to formally represent the problem and its theoretical properties are investigated. Since the RSTP belongs to the NP-class, two heuristic methods are designed: a Lagrangian relaxation approach and a multistart algorithm. Extensive computational experiments are carried out on a significant set of test problems to empirically evaluate the performance of the proposed approaches. The computational results show that the two approaches are both effective and efficient compared to the ILOG CPLEX solver.
Analysis of Image Steganography using Wavelet and Cosine Transforms

In this cyber era, protecting data has been a must, one of which is performed by implementing data hiding methods or steganography. It is a technique of hiding data or messages into a media cover, which can be an audio, image, or video file. This paper examines two transformed-based steganography, implementing DCT (Discrete Cosine Transform) and DWT (Discrete Wavelet Transform) to two types of cover images, general and medical. The purpose of this research is to analyze the performance of the embedding process using the low, middle, and low-middle frequency components of the DC coefficient and embed the payload bits into Low-Low (LL), Low-High (LH), High-Low (HL), High-High (HH) of the DWT bands. The experimental results show that, in general, the DCT method has better PSNR (Peak Signal to Noise Ratio) and payload capacity than DWT. Meanwhile, the combined low and middle frequency in the DCT method has a greater PSNR than other components in this transformation, which is 51.6751. Whereas in the DWT technique, LH and HL frequency components show better PSNR values than other DWT band components, which are 43.6939 and 43.5576, respectively.
Multispectral Images Deblurring via Interchannel Correlation Relationship

Image blur is very common in multispectral images, especially out-of-focus blur when the multispectral cameras are well focused on a specific channel. Therefore, it is important to recover the latent clear images from the blurred image. Many researchers have proposed a large number of methods for deblurring multispectral images. These algorithms assume that the relationship between channels is consistent. In fact, the relationship between different channels in multispectral images suffers from different blurring. Therefore it needs different processing for different channels. We propose a joint spatial and spectral filtering algorithm for multispectral images deblurring. We have introduced an adaptive Gaussian distribution as a constraint relationship between channels, which can effectively deblur the multispectral images. Extensive experiments show that our method achieves state-of-the-art results.
Links do Matter: Understanding the Drivers of Developer Interactions in Software Ecosystems

Studies of collaborating individuals engaged in collective enterprises usually focus on the individuals, rather than the links supporting their interaction. Accordingly, large scale software development ecosystems have also been examined primarily in terms of developer engagement. We posit that communication links between developers play a central role in the sustenance and effectiveness of such ecosystems. In this paper, we investigate whether and how developer attributes relate to the importance of the communication channels between them. We present a technique using 2nd order Markov models to extract features of interest of the links and apply the technique on data from a real-world project. Our statistical models - developed on records involving 900+ software developers, exchanging 20,000+ comments, across 500 units of work - offer surprising insights on factors associated with link importance, even after controlling for known effects. These results inform a deeper appreciation of the importance of links in large scale software development along with a number of practical implications.
Secure ADS-B protection scheme supporting query

Automatic dependent surveillance-broadcast (ADS- B) records provide an important basis and evidence for future route planning and accountability. However, due to the lack of effective support for the integrity and confidentiality of ADS-B, the air traffic control (ATC) system based on ADS-B faces serious security threats. Once the data is tampered with, it will cause immeasurable losses to society. The ADS-B data is arranged in chronological order, and the order-preserving encryption method allows users to directly search for ciphertexts by time. However, encryption alone does not guarantee the integrity of the data. The attacker can still destroy the integrity of the data by modifying the ciphertext. This paper proposes a secure ADS- B protection scheme that supports queries. We construct a dynamic order-preserving encryption (DOPE) scheme to achieve data confidentiality and sequential search of target data in the ciphertext. In addition, the scheme achieves fast integrity checking by calculating the unique verification label of the entire ciphertext, and supports blockless verification, which means that all data does not need to be transmitted during the audit phase. In the meanwhile, the auditor can verify the integrity of multiple ADS-B documents at once, which improves the computational efficiency of the audit. We analyze the integrity and security of the scheme and proved that DOPE is indistinguishable under an ordered chosen-plaintext attack (IND-OCPA). Furthermore, we conclude through performance analysis that the communication overhead is constant and computation overhead is logarithmic level. The proposed scheme is applicable to all data arranged in order, such as hospital records arranged by date and so on. At the same time, ADS-B can be used for urban vehicle monitoring and is a basic means to realize smart transportation.
Grains of Sound: Visual and Sonic Textures in<i>Sand or Peter and the Wolf</i>

There is a tendency in animation studies to discuss sound in the language of images, stressing sound’s alignment with visual cues (as in mickey mousing and leitmotifs). But sounds do not only mimic images: they add textures and emotions that change what we see. This article explores grain (texture) and timbre (tone color produced by specific instruments and techniques) as qualities shared by visual and sonic material. To do so, the author closely reads Sand or Peter and the Wolf (1969), where Caroline Leaf’s haptic sand animation is matched by Michael Riesman’s electroacoustic score. Leaf painstakingly molds animals by scraping away individual sand grains, and Riesman sculpts sonic textures with tiny adjustments to knobs and touch-sensitive pads on the Buchla modular synthesizer. Their collective improvisation with sands and sounds reveals new ways to think about artists’ material practices and the friction and interplay between images and sounds. They encourage spectators to perceive the animals as not merely plasmatic, or Sergei Eisenstein’s notion of contour-bending character animation. Instead, Leaf and Riesman deploy what the author calls ‘granular modulation’, expressing sand and animals with sensuous materiality. In Leaf’s and Riesman’s improvisations, grainy textures are the seeds of understanding how sound and vision become symbiotic – and encounter friction – in animation.
Implementation of CNN based Demosaicking on FPGA

This paper proposes a hardware-friendly Convolutional Neural Network (CNN) based demosaicking scheme. For hardware implementation, we used the KD (Knowledge Distillation) technique for CNN, and the parameters can be reduced by about 97.96% compared to the teacher network. The student network compressed through KD is quantized to 16-bit integer for hardware implementation. The proposed system is implemented on the Xilinx Virtex-7 FPGA. It achieved high PSNR performance 39.47dB.
Research on Online Interaction and Consumer Impulsive Buying Behavior in Live Shopping Platform Based on the Structural Equation Model

Affected by the COVID-19 epidemic, most people are used to shopping online. Online e-commerce live broadcast has won the favor of merchants and has become an important means of marketing. Firstly, theoretical framework of impulse shopping is constructed based on the analyzed literature on online interaction, presence and impulse shopping. Secondly, the hypotheses about the relationship among online interaction, presence, flow experience and impulse shopping are put forward. Finally, the structural equation model is used to analyze the influence path of online interaction on people's impulse shopping behavior surrounded bye-commerce live broadcast. The results show that synchrony is positively correlated with both spatial presence and flow experience, and responsiveness is positively correlated with spatial presence. In addition, fun is positively correlated with both social presence and flow experience. Interpersonal relationship is positively related with both social presence and flow experience. What's more, presence has both direct and indirect effects on impulse buying. The flow experience plays a mediating role in the indirect effects.
A Wireless Fully-Passive Acquisition of Biopotentials

Biopotential signals contain essential information for assessing the functionality of organs and diagnosing diseases. We present a flexible sensor, capable of measuring biopotentials, in real time, in a wireless and fully-passive manner. The flexible sensor collects and transmits biopotentials to an external reader without wire, battery, or harvesting/regulating element. The sensor is fabricated on a 90 μm-thick polyimide substrate with a footprint of 18 × 15 × 0.5 mm3. The wireless fully-passive acquisition of biopotentials is enabled by the RF (Radio Frequency) microwave backscattering effect where the biopotentials are modulated by an array of varactors with incoming RF carrier that is backscattered to the external reader. The flexile sensor is verified and validated by emulated signal and electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG), respectively. A deep learning algorithm analyzes the signal quality of wirelessly acquired data, along with the data from commercially available wired sensor counterparts. Wired and wireless data shows <3% discrepancy in deep learning testing accuracy for ECG and EMG up to the wireless distance of 240 mm. Wireless acquisition of EOG further demonstrates accurate tracking of horizontal eye movement with deep learning training and testing accuracy reaching up to 93.6% and 92.2%, respectively, indicating successful detection of biopotentials signal as low as 250 μVPP. These findings support that the real-time wireless fully-passive acquisition of on-body biopotentials is indeed feasible and may find various uses for future clinical research.
Research on Commodity Recommendation System Based on Deep Learning

Abstract The traditional matrix factorization model cannot effectively extract the features of users and items, but the feature information can be extracted well based on the deep learning model. At present, the mainstream recommendation algorithms based on deep learning only make recommendation prediction in the form of the product of neural network output or item features and user features, and cannot fully mine the relationship between users and items. Based on this, this paper proposes a recommendation algorithm based on the combination of text convolution neural network and singular value decomposition (Bias SVD) with biased terms. The text convolution neural network (Text CNN) is used to fully extract the feature information of users and items, and then the singular value decomposition method is used to make recommendations to deeply understand the document context information and further improve the accuracy of recommendation. The algorithm is widely evaluated and analyzed on two real data sets of MovieLens, and the accuracy of recommendation is obviously better than that of ConvMF algorithm and mainstream deep learning recommendation algorithm.
Facilitating 3D Geometry Learning with Augmented Reality in Authentic Contexts

In this study, we developed 3D-UG with augmented reality (AR) to facilitate students while learning 3D geometry in authentic contexts. 3D-UG provided exploring activity with real-time measuring the authentic 3D objects in surrounding with AR. Students learn 3D geometry, measure 3D objects and calculate the volume or surface of the authentic 3D objects in their home during pandemic Covid-19 for one month. Participants were forty fifth-grade elementary schools’ students that divide into two groups, twenty students in the experimental group (EG) used our purpose 3D-UG and twenty students in the control group (CG) used ruler. The results found that students in EG have improved geometry ability and outperformed compared with CG. Students in EG has a better understanding of geometry concept than CG. In addition, students in EG made fewer mistakes in their formula input and calculation process. It is because students carefully complete the solution and 3D-UG has clear steps to accomplish the solution. Therefore, our 3D-UG with AR could facilitate students to learn geometry learning in their home during Covid-19, exploring 3D objects their surroundings, and experienced 3D object measurement with augmented reality to enhance their geometry ability.
Privacy-preserving inpainting for outsourced image

In this article, a framework of privacy-preserving inpainting for outsourced image and an encrypted-image inpainting scheme are proposed. Different with conventional image inpainting in plaintext domain, there are two entities, that is, content owner and image restorer, in our framework. Content owner first encrypts his or her damaged image for privacy protection and outsources the encrypted, damaged image to image restorer, who may be a cloud server with powerful computation capability. Image restorer performs inpainting in encrypted domain and sends the inpainted and encrypted image back to content owner or authorized receiver, who can acquire final inpainted result in plaintext domain through decryption. In our encrypted-image inpainting scheme, with the assist of Johnson–Lindenstrauss transform that can preserve Euclidean distance between two vectors before and after encryption, the best-matching block with the smallest distance to current block can be found and utilized for patch filling in Paillier-encrypted image. To eliminate mosaic effect after decryption, weighted mean filtering in encrypted domain is conducted with Paillier homomorphic properties. Experimental results show that our privacy-preserving inpainting framework can be effectively applied in secure cloud computing, and the proposed encrypted-image inpainting scheme achieves comparable visual quality of inpainted results with some typical inpainting schemes in plaintext domain.