Reviewerly/exHarmony huggingface dataset:
Controllable Image Super-Resolution by SOM based Data Augmentation and its Applications

In order to effectively find the appropriate combination of data augmentation (CutBlur, CutOut, etc.) to control/improve the quality of super-resolution, we propose a method to visualize the relationship of evaluation indices (PSNR, Affinity, and Diversity) and control valuables as data augmentation based on the Self-Organizing MAP (SOM). Using the proposed method, it is possible to visualize the results of a small number of trials, to grasp the characteristics of the combination of each data augmentation and their effectiveness for the quality control of super-resolution. Furthermore, in the case of super-resolution considered as a pre-processing step for various image processing (e.g., the image classification and 3D reconstruction), the proposed method can also provide an effective combination of data augmentation. To investigate the effectiveness of the proposed method, the enhanced deep super-resolution network (EDSR) model is employed by using 16 combinations of data augmentation, CutBlur, RGB, Cutout, and CutMix, and the evaluation indices, PSNR, Affinity, and Diversity are measured. In the case of the experiment using data sets, Manga109, Urban100, the quality of super-resolution images obtained by the EDSR optimized by the proposed method, is improved the PSNR by 0.08 (dB) over the original super-resolution. Furthermore, we applied the proposed super-resolution as pre-processing of the image classification and 3D reconstruction, and it is confirmed that the accuracy and quality of the image classification (CIFAR-10) and 3D-reconstruction (buckwheat real field images) based on the proposed method are improved compared with the original ones. Comparing the number of cases without super-resolution and with super-resolution based on the proposed method, the number of keypoints is approximately 4.9 times and the number of matching points is approximately 2.2 times, indicating that, from a quantitative perspective.
Leveraging Blockchain-Based Smart Contracts in the Management of Supply Chain: Evidence from Carrefour UAE

The fourth industrial revolution brought digital solutions to modernize how firms manufacture and distribute their products. A fusion of artificial intelligence advances is utilized to blur boundaries between the biological, physical, and digital worlds. Firms are redesigning their supply chains to attain high levels of operational effectiveness. Supply chain activities are crucial for the success of manufacturing firms. Blockchain is a promising technology for managing the supply chain efficiently. Blockchain is an immutable, distributed ledger to record transactions digitally. A smart contract is a coded agreement between two parties that executes an exchange automatically in the absence of trusted authorities. This study aimed to explore the prospects of utilizing blockchain and smart contracts as empowering technologies for food supply chain management. The study used a case study approach. Blockchain has the potential to play a vital role in almost every aspect of the future. Implementing blockchain in the supply chain will enhance the transparency, traceability, and flow of goods and services across the supply chain. Additionally, a blockchain-based smart contract will facilitate, validate and impose negotiation and execution agreements among multiple parties in real-time, enhancing the collaboration and quality of management. Carrefour UAE successfully joined IBM Food Trust. Carrefour's initiative came after growing customers' concerns about product provenance and ethical sourcing. The Carrefour initiative helped to resolve the problem of trust in the food supply. Improvements in the supply chain are expected to happen, including enhanced efficiency, traceability, reduced execution risk, low cost, etc.
A Distributed Method for UAV Swarm Path Planning

In Unmanned Aerial Vehicle (UAV) swarm applications there is a need for autonomous mapping and route planning. These applications include collaborative crop monitoring, aerial transportation, offshore delivery, facility monitoring, disaster relieve as well as search and rescue. Current UAV operations are heavily dependent on human operators that require each UAV to be controlled by a dedicated safety pilot as well as a separate payload operator which limits the efficient use of UAVs required in swarm applications. There is thus an imperative to mature mapping and path planning technologies to allow UAV swarm operations. This paper explores mapping and path planning strategies that allow higher levels of autonomy for a UAV swarm to operate reliably. In this work we developed novel strategies to facilitate swarm mapping, and swarm path planning. The methods developed can be easily scaled to adapt to different UAV swarm sizes. The developed strategies are implemented and tested using simulations. Results and conclusions drawn from these simulation experiments are presented.
Face Mask Detection Using Image Processing and Convolutional Neural Networks

Face masks have become a crucial part of our everyday lives as the number of COVID cases around the world has increased, and new varieties appear every few months. We must wear a mask every time we walk outside, so the installation of face mask detectors in public places has become quite significant. In this study, we use image processing to create a face mask detection system on cascading multiple architectures of convolutional neural networks (CNN) and deep neural networks (DNN), with investigate the findings using MobileNetV2, Xception and ResNet152V2 models. The proposed technique was able to obtain excellent accuracy by training the system with CNN and DNN architectures.
Utilizing Educational Robotics for Learning About Sustainability Issues

Nowadays, schools are trying to insert knowledge and learning about sustainability issues and environmental conservation in the academic context from an early age. It is also observed the use of information technology with new didactic and ludic resources that can encourage elementary and high school students to obtain knowledge about sustainability. To this end, this research presents some results using Educational Robotics, Gestalt, and information technology, which students received from a Brazilian public elementary school. In this study, after attending workshops on sustainability, the students developed robotic resources to help them with sustainability-related knowledge. The resources were tested according to their objectives: a mobile robot that recognizes the food and shows its footprint and a box with gas sensors to identify if food is in consumable condition or should be recycled. The experiment was very successful and is in a continuous application phase.
A Routing-based Strategy to Socially Approach Multiple Individuals in Cluttered Environments

The use of autonomous mobile robots is expected to continue increasing in different parts of society in the next years. In this context, when not restricted to industrial environments only, it is essential to provide such devices with the ability to behave in a so-called socially acceptable manner. In the Human-Robot Interaction (HRI) field, a fundamental problem is to approach people, i.e., try to initiate and maintain an interaction. This task involves different challenges since some implicit nonverbal social rules must be respected, especially those concerning people's personal spaces (called proxemics). Considering the presence of multiple individuals to be approached, a combinatorial optimization aspect must also be tackled, where an optimal sequence of visits must be selected. In this paper, we formulate this problem as a Generalized Travelling Salesman Problem (GTSP) and combine it with a Probabilistic Roadmap (PRM) planner in order to account for obstacles in the environment. Numerical simulations show that the methodology produces length-optimized paths that are feasible, safe, and respect the social conventions.
Implementation of the Rainbow signature scheme on SoC FPGA

Thanks to the research progress, quantum computers are slowly becoming a reality and some companies already have their working prototypes. While this is great news for some, it also means that some of the encryption algorithms used today will be rendered unsafe and obsolete. Due to this fact, NIST (US National Institute of Standards and Technology) has been running a standardization process for quantum-resistant key exchange algorithms and digital signatures. One of these is Rainbow—a signature scheme based on the fact that solving a set of random multivariate quadratic system is an NP-hard problem. This work aims to develop an AXI-connected accelerator for the Rainbow signature scheme, specifically the Ia variant. The accelerator is highly parameterizable, allowing to choose the data bus width, directly affecting the FPGA area used. It is also possible to swap components to use the design for other variants of Rainbow. This allows for a comprehensive experimental evaluation of our design. The developed accelerator provides significant speedup compared to CPU-based computation. This paper includes detailed documentation of the design as well as performance and resource utilisation evaluation.
Co-Optimizing Sensing and Deep Machine Learning in Automotive Cyber-Physical Systems

Accurate perception of the environment is critical to achieving safety and performance goals in emerging semi-autonomous vehicles. Building a perception architecture to support autonomy goals in vehicles requires solving many complex problems related to sensor selection and placement, sensor fusion, and machine leaning driven object detection. In this paper, we present a framework for co-optimizing sensing and machine learning to meet autonomy goals in emerging automotive cyber-physical systems. Experimental results that target level 2 autonomy goals for the Audi-TT and BMW-Minicooper vehicles demonstrate how our framework can intelligently traverse the massive design space to find robust, vehicle-specific perception architecture solutions.
Inference Time Reduction of Deep Neural Networks on Embedded Devices: A Case Study

From object detection to semantic segmentation, deep learning has achieved many groundbreaking results in recent years. However, due to the increasing complexity, the execution of neural networks on embedded platforms is greatly hindered. This has motivated the development of several neural network minimisation techniques, amongst which pruning has gained a lot of focus. In this work, we perform a case study on a series of methods with the goal of finding a small model that could run fast on embedded devices. First, we suggest a simple, but effective, ranking criterion for filter pruning called Mean Weight. Then, we combine this new criterion with a threshold-aware layer-sensitive filter pruning method, called T-sensitive pruning, to gain high accuracy. Further, the pruning algorithm follows a structured filter pruning approach that removes all selected filters and their dependencies from the DNN model, leading to less computations, and thus low inference time in lower-end CPUs. To validate the effectiveness of the proposed method, we perform experiments on three different datasets (with 3, 101, and 1000 classes) and two different deep neural networks (i.e., SICK-Net and MobileNet V1). We have obtained speedups of up to 13x on lower-end CPUs (Armv8) with less than 1% drop in accuracy. This satisfies the goal of transferring deep neural networks to embedded hardware while attaining a good trade-off between inference time and accuracy.
Hardware-Software Codesign of a CNN Accelerator

The explosive growth of deep learning applications based on convolutional neural network (CNN) in embedded sys-tems is spurring the development of a hardware CNN accelerator, called a neural processing unit (NPU). In this work, we present how the hardware-software codesign methodology could be applied to the design of a novel adder-type NPU. After devising a baseline datapath that enables fully-pipelined execution of layers, we define a high-level behavior model based on which a high-level compiler and a virtual prototyping system are built concurrently. Since it is easy to change the microarchitecture of an NPU by modifying the simulation models of the hardware modules, we could explore the design space of NPU microarchitecture easily. In addition, we could evaluate the effect of hardware extensions to support various types of non-convolutional operations that recent CNN models use widely. After the final datapath is determined, we design the control structure and low-level compiler and implement the NPU prototype. Implementation results on an FPGA prototype show the viability of the proposed methodology and its outcome.
Central Bank Digital Currencies: The Motivation

A growing number of central banks are considering the issuance of central bank digital currencies (CBDCs). CBDCs for the general public (“retail CBDCs”) would constitute a central bank liability and a form of digital cash. To the public, they would be an alternative to central bank issued cash and private money, such as traditional bank deposits. The evolution of payments plays a pivotal role in developing CBDCs. Given the declining role of cash in some jurisdictions, CBDCs as a new form of central bank money may contribute to safeguarding trust in the public currency and improve financial inclusion outcomes. CBDCs have the potential to encourage competition and efficiency in an otherwise oligopolistic market for payment services, increasingly dominated by BigTechs, and increase overall resilience in payment markets of the future. Upon their introduction and depending on their exact design, CBDCs may have considerable consequences for policy makers and the general public. This chapter sets out four of the main motivations for issuing CBDCs, all while acknowledging considerable divergences across jurisdictions.
Innovation and economic performance of firms in national science &amp; technology park, Islamabad, Pakistan

Purpose Science and technology parks (STP) are established to facilitate innovation and the rapid development of cutting-edge technologies. The innovation performance of tenants is the primary feature of all successful STPs globally. The purpose of this study is to investigate firms’ innovation and economic performance at the National Science and Technology Park (NSTP), Islamabad, Pakistan. Design/methodology/approach The CDM (the acronym of the three authors’ names, Crépon, Duguet and Mairesse) model following a two-stage approach was used to analyze the survey data of 105 tenants. The innovation performance of tenants was estimated through probit regression at Stage 1, and the economic performance of tenants given their innovation performance was examined at Stage II using Tobit regression and the Heckman model. Findings The findings suggest that compatibility of innovation with the existing competitive advantage of a firm increases the innovation performance of firms, whereas collaboration of firms with NUST for research and development has only a marginal effect on innovation performance. However, the tenant’s business and social networking were weak, possibly due to the short time spent on NSTP. Originality/value These STPs are expected to be hubs of technology development and transfer by fostering open innovation through internal and external collaborations. To the best of the authors’ knowledge, this is the first study to estimate the innovation performance of tenants at NSTP, the first fully integrated STP in Pakistan. Despite shortcomings, the innovation and economic performance of NSTP tenants warrant further public policy support to inculcate open innovation culture.
X-ray absorption spectroscopic study of Pd2+ on Ni site in pentlandite

Abstract Norilsk sulfide ores are one of the largest known sources of Pd on Earth. Palladium in these ores is presented in platinum-group minerals (PGM) and base metal sulfides (BMS), especially in pentlandite [(Fe,Ni)9S8]. Although several studies demonstrated high concentrations along with heterogeneous distribution of Pd in pentlandites from Norilsk, the form of Pd in pentlandite has not been established. Here, we provide the first evidence for Pd incorporation in the structure of pentlandite from Norilsk ores using X-ray absorption near edge structure (XANES) spectroscopy, synchrotron-based micro-X-ray fluorescence (μXRF), and electron backscatter diffraction (EBSD). We present the first ever measured XANES spectra of Pd in pentlandite and atokite [(Pd,Pt)3Sn] as well as in other common Pd minerals. Divalent Pd in pentlandite was detected by XANES. The Pd spectra in pentlandite show no similarities with Pd spectra in PGM, metallic Pd, PdS, PdCl2, and PdSO4 which signifies that Pd incorporates into the lattice of pentlandite. Substitution of Ni by Pd in the lattice of pentlandite is supported by negative correlations shown by μXRF and electron probe microanalysis (EPMA) and complies with the previous studies. The additional EBSD study demonstrates a resemblance in cell parameters of the Pd-rich and Pd-poor parts of the pentlandite grains and reflects that Pd incorporation into the pentlandite structure does not imply any notable structure distortion. The combination of analytical techniques used in the present study demonstrates the great potential of these methods for understanding the mechanisms of noble metal incorporation into ore minerals.
Emptiness in the study of emotions in the teaching-learning process of reading-writing during the COVID-19 pandemic

The teaching-learning process of reading and writing has great relevance in the psycho-emotional and socio-psychological development of school-age children. This is an exercise in which they develop imagination, attention and memory capacity and through this process the management of emotions and sensitivity and capacity of apprehension of reality. The crisis derived from the COVID-19 pandemic transformed reality in an unprecedented way in the recent history of humanity and the educational context was shaken by all these changes. With confinement, the teaching-learning process of reading and writing, which is designed to take place in person, had to be developed in a hybrid or online way, which was a major challenge for teachers and families and, of course, also for children who were in the process of learning. One of the aspects that was blurred in this context was the role of the teacher, which in this case is one of the most important elements, to achieve adequate learning of reading and writing. On one hand, the teacher is in charge of helping to manage the emotions derived from the learning process itself and, on the other hand, he is a key figure so that this is not only technical learning but also contributes to the child's socio-emotional development. The aim of this study was to analyze the impact of the socio-psychological changes that have occurred in the educational context since the COVID-19 pandemic on the emotions linked to the teaching process, learning of reading and writing through a systematic review of the studies carried out on the subject, in order to provide recommendations for face-to-face learning in the post-COVID-19 era. A systematic review strategy was devised and the literature search was conducted. The search was conducted using ERIC, Dialnet, Scopus, WoS, EBSCO, and Google Scholar databases. This systematic review took place during the month of May 2022. The results show that given the scarce presence of empirical studies on the subject, the objective has only been partially met. However, a systematic review of the studies carried out on the subject. For the identification of recommendations in the development of face-to-face learning in the post-covid era, it has been possible to identify some ideas of interest for future curricular designs in primary school students who are immersed in learning to read and write.
Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

Handling sentiment drifts in real time twitter data streams are a challenging task while performing sentiment classifications, because of the changes that occur in the sentiments of twitter users, with respect to time.The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time.This work proposes an adaptive learning algorithm-based framework, Twitter Sentiment Drift Analysis-Bidirectional Encoder Representations from Transformers (TSDA-BERT), which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time.The framework also works on static data by converting them to data streams using the Kafka tool.The experiments conducted on real time and simulated tweets of sports, health care and financial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model, with accuracies of 91%, 87% and 90%, respectively.Though the results have been provided only for a few topics, as a proof of concept, this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.
Performance Analysis of Hybrid RR Algorithm for Anomaly Detection in Streaming Data

Automated live video stream analytics has been extensively researched in recent times.Most of the traditional methods for video anomaly detection is supervised and use a single classifier to identify an anomaly in a frame.We propose a 3-stage ensemble-based unsupervised deep reinforcement algorithm with an underlying Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN).In the first stage, an ensemble of LSTM-RNNs are deployed to generate the anomaly score.The second stage uses the least square method for optimal anomaly score generation.The third stage adopts award-based reinforcement learning to update the model.The proposed Hybrid Ensemble RR Model was tested on standard pedestrian datasets UCSDPed1, USDPed2.The data set has 70 videos in UCSD Ped1 and 28 videos in UCSD Ped2 with a total of 18560 frames.Since a real-time stream has strict memory constraints and storage issues, a simple computing machine does not suffice in performing analytics with stream data.Hence the proposed research is designed to work on a GPU (Graphics Processing Unit), TPU (Tensor Processing Unit) supported framework.As shown in the experimental results section, recorded observations on framelevel EER (Equal Error Rate) and AUC (Area Under Curve) showed a 9% reduction in EER in UCSD Ped1, a 13% reduction in ERR in UCSD Ped2 and a 4% improvement in accuracy in both datasets.