Reviewerly/exHarmony huggingface dataset:
Integrated IoT and Block chain for secured access and managing education data

The emergence of IoTtechnology has brought a revolutionary change in the world, transforming every sector smarter like smart education, smart industry, smart health through their data driven techniques. Smart education is one of the most impacted areas of this technology.Smart education represents a systemwhere traditional processes of the education system are efficiently and effectively carried out by smart modules consisting of inter connected intelligent or smart devices. It includes smart classrooms to smart campuses which use Internet of Things (IoT) Technology. IoT devices can collect real time sensor data to process and analyze. However, due to its centralized structure which is considered as a single point of failure, the privacy and security issue exists. Attackers have access to educational data that they are not authorized to see, edit, or even destroy.Most of the architectural drawbacks of IoT can be solved by a technology called Block chain which has the capabilities of data encryption, immutability, auditability, transparency and operational resilience.In this paper we propose a combination of block chain and IoT technologies to store, access and manage education data in a secured way.
Digital Currencies and Monetary Policy in the New Era

The centuries-old financial structure and the modern financial system are built on physical currencies. After the global financial crisis of 2008-2009 and the COVID-19 pandemic of 2020, along with the introduction of all-new cryptocurrencies and stable coins, even many central banks have voiced an interest in the introduction of digital currencies. Hence, the pros and cons of the new central bank digital currencies (CBDC) are the newest widely discussed topic. This paper focuses on the ways a new instrument, central bank digital currency (CBDC), is expected to improve the modern payment systems, increase the efficacy of monetary policy and ensure financial stability in the new era. The study concludes that although CBDCs might boost monetary policy effectiveness and financial stability, it is vital to reevaluate and thoroughly analyse the geopolitical, geoeconomic, and geostrategic implications and effects of having a CBDC.
Editable Indoor Lighting Estimation

We present a method for estimating lighting from a single perspective image of an indoor scene. Previous methods for predicting indoor illumination usually focus on either simple, parametric lighting that lack realism, or on richer representations that are difficult or even impossible to understand or modify after prediction. We propose a pipeline that estimates a parametric light that is easy to edit and allows renderings with strong shadows, alongside with a non-parametric texture with high-frequency information necessary for realistic rendering of specular objects. Once estimated, the predictions obtained with our model are interpretable and can easily be modified by an artist/user with a few mouse clicks. Quantitative and qualitative results show that our approach makes indoor lighting estimation easier to handle by a casual user, while still producing competitive results.
Long-Tailed Class Incremental Learning

In class incremental learning (CIL) a model must learn new classes in a sequential manner without forgetting old ones. However, conventional CIL methods consider a balanced distribution for each new task, which ignores the prevalence of long-tailed distributions in the real world. In this work we propose two long-tailed CIL scenarios, which we term ordered and shuffled LT-CIL. Ordered LT-CIL considers the scenario where we learn from head classes collected with more samples than tail classes which have few. Shuffled LT-CIL, on the other hand, assumes a completely random long-tailed distribution for each task. We systematically evaluate existing methods in both LT-CIL scenarios and demonstrate very different behaviors compared to conventional CIL scenarios. Additionally, we propose a two-stage learning baseline with a learnable weight scaling layer for reducing the bias caused by long-tailed distribution in LT-CIL and which in turn also improves the performance of conventional CIL due to the limited exemplars. Our results demonstrate the superior performance (up to 6.44 points in average incremental accuracy) of our approach on CIFAR-100 and ImageNet-Subset. The code is available at https://github.com/xialeiliu/Long-Tailed-CIL .
Combining Multiagent Reinforcement Learning and Search Method for Drone Delivery on a Non-grid Graph

With the high development of online commerce, drone delivery has shown a potential to reduce logistical costs. Multiple drone delivery can be formulated as a multiagent path finding (MAPF) problem which is used to identify a group of collision-free paths for multiple agents. However, most prior work on MAPF has studied on grid graphs, which is not proper for drone delivery problem. We study here a non-grid MAPF problem for drone delivery. Some algorithms for solving grid MAPF can also be applied to this new problem, which can be categorized into two types: search-based methods and dynamic programming methods. However, the challenges created by non-grid features, such as a large state/action space, impede the application of either of these two methods. We therefore propose a novel approach that combines a search method and a dynamic programming method which can accelerate the learning process. The experimental results show our proposed method to be more effective than some existing algorithms.
UIKit and Storyboards

For many years, UIKit was the only way to implement user interfaces in iOS apps, and storyboards mechanism was the main tool to make layouts. Recently, SwiftUI was introduced, which is a logical part of the transition from Objective-C to native Swift-only code. But UIKit still plays an important role in iOS development.
Obstacle Detection and Path Planning for Intelligent Overhead Cranes Using Dual 3D LiDARs in a Brewing Environment

This paper focuses on the autonomous obstacle avoidance problem of overhead cranes in the brewing environment, such that the overhead crane has better environmental perception and obstacle avoidance capabilities. Firstly, a horizontal installation layout scheme of dual LiDARs is designed, which can expands the obstacle perception range. Secondly, in order to solve the problems of under-segmentation and over-segmentation, a radially bounded nearest neighbor clustering algorithm based on partitioned multi-segment dynamic thresholds is proposed, which improves the effect of clustering detection. Thirdly, we propose a path planning method adapt to the kinematic model of the crane which combines a globe fixed path and a dynamic window algorithm (DWA), so that the crane can run to the target station autonomously and avoid obstacles based on the real-time detected 3D obstacle information. Finally, the feasibility and effectiveness of the scheme are verified by comprehensive experiments of crane operation in a real brewing environment.
Dirichlet Prior Networks for Continual Learning

Deep Neural Networks (DNNs) suffer from the long known phenomenon of catastrophic forgetting when trained on a sequence of tasks without appropriate counter measures. Overcoming this is of great interest as it would enable DNNs to accumulate knowledge over a potentially long sequence of tasks without forgetting. In this paper, we study the commonly used method of rehearsal for mitigating catastrophic forgetting and show that it can cause an unwanted distribution shift that negatively affects performance. Building on recently introduced Dirichlet Prior Networks (DPNs), we propose a novel method that incorporates prior knowledge of known distribution shifts into its predictions in order to reduce their negative influence. The proposed method is evaluated on commonly used benchmark datasets and compared to related methods.
New technologies in personalisation of STEM and STEAM education - international context

This article focuses on new technologies in personalisation of STEM and STEAM education in contemporary education, as viewed by experts from different countries: Austria, Bulgaria, Poland, Russia and Ukraine. The article aims to review research literature, and provide opinions, views and reflections presented by scientists and experts from several European universities. The research review includes the theoretical background of the discussed topic, a review of national and international research and literature, identification and definition of key concepts, examples of practical achievements, and a description of contemporary trends of STEM and STEAM education and adaptive learning as well as microlearning - effective methods of e-learning. In the end a number of conclusions are drawn.
Volterra series based nonlinear system identification methods and modelling capabilities

The current study discusses Volterra series based nonlinear system models such as the Taylor series, time-delay neural network (TDNN) and nonlinear autoregressive model (NARX). The study aims to construct a truncated second-order Volterra model that can be used to identify nonlinear systems and compare their performance to that of the TDNN using benchmark cases. The feasibility of the feedback and feedforward networks is evaluated using a dataset of cortical responses evoked by wrist joint manipulation. It is observed that the TDNN is a mathematical model with more customisable parameters and requires less computation time than the Volterra system with particle swarm optimisation (PSO). Also, open-loop connections with fewer a-prior system assumptions, such as the Volterra model, can estimate 42% of wrist dynamics and closed-loop connections like the NARX model can estimate 93% of complex nonlinear dynamics.
Network-aware Prefetching Method for Short-Form Video Streaming

Recent years have witnessed the rising of short-form video platforms such as TikTok. Apart from conventional videos, short-form videos are much shorter and users frequently change the content to watch. Thus, it is crucial to have an effective streaming method for this new type of video. In this paper, we propose a resource-efficient prefetching method for short-form video streaming. Taking into account network throughput conditions and user viewing behaviors, the proposed method dynamically adapts the amount of prefetched video data. Experiment results show that our method can reduce the data waste by 37 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim$</tex> 52% compared to other existing methods.
Recurrence Quantification Analysis for PPG/ECG-Based Subject Authentication

Nowadays, the internet is used by billions of users around the world for many different purposes, some of which require user identity authentication. To face the challenge of reliable authentication, researchers have proposed various techniques, ranging from face recognition to fingerprints. The aim of this study is to develop a PPG/ECG-based (Photoplethysmography/Electrocardiogram) user identity authentication approach using features extracted by recurrence quantification analysis (RQA). The PPG/ECG signal is segmented into frames and thirteen features are obtained using RQA. These features are then fed into four classifiers for user identity authentication purposes: linear discriminant analysis, support vector machine, naïve Bayes, and random forest. The best result obtained using this methodology is an average accuracy of 99.48% achieved using a random forest classifier with frame length of three seconds.
The Advantages and Challenges of Distance Learning

This study sought to describe the advantages and challenges of studying at a distance and has a general objective: to report the advantages and challenges of studying at a distance. It adopts as a type of research the quantity-qualitative approach. The methodological procedure took place in the first place to make a theoretical foundation to search in books, websites, magazines, etc., 7 closed and two open, and finally, the results sought to analyze the questions in order to seek the advantages and challenges of distance learning. The data revealed to us as an advantage, the opportunity to study at a time chosen by the academic, be it during the day or any time of the night, and the challenges are the difficulty of answers when there is a question at the time of study that often the time is not conventional. In this study, it showed that studying at a distance gives the possibility for some academics to study who live more than 100km away from the pole and also an opportunity for others who are in the day-to-day, working to support the home.
Silhouette Coefficient Regularization Based Adversarial Domain Adaptation Network for Fault Diagnosis of Rotating Machinery

Despite the fact that machine learning approaches have been proven to be useful in the diagnosis of rotating machinery, there is a key assumption that all the data are distributed in a same manner. However, given the varying working conditions, this assumption is difficult to maintain in real-world settings, and it will result in unsupervised and domain shift issues, increasing the difficulty of fault diagnosis. Aimed at these issues, the adversarial domain adaptation method has provided a way to solve these problems caused by changeable working parameters. Due to the good embeddedness with deep learning network, and applicability of unsupervised learning, the adversarial domain adaptation has been widely concerned. Most of the adversarial domain adaptation based methods focus on how to adapt the source and target domain data to improve the fault diagnosis performance under variable working conditions. However, these methods only focus on the adaptation of inter-domain data, and ignore the separation of inter-class data, thereby limiting the improvement of fault diagnosis performance. In this study, we employ the silhouette coefficient to regularize the adversarial domain adaptation network in order to extract domain-invariant features while maintaining inter-class data separability. To enhance fault diagnostic performance, the proposed method has the ability to obtain a better classification boundary while achieving the alignment of the cross-domain in an unsupervised way. The experiments are designed via the CWRU dataset. The results suggest that the proposed method is effective to improve the adversarial domain adaptation-based framework in fault diagnosis.
A gray wolf optimization algorithm using position mutation strategy

In order to overcome the imbalance between global and local searching process, and the over-reliance on three head wolves in the population, an improved gray wolf optimization algorithm using the position mutation strategy is proposed in this work. Firstly, a nonlinear control strategy is presented to increase the global searching capability in the early iterations and increase the convergence rate in the late iterations. Secondly, a new position updating formula is proposed to enhance the communication among individuals in the population, which avoids the wolves falling into the local optimum. Finally, a position mutation strategy is advanced to improve the convergence rate by selecting different formulas through mutation operation when the wolf population decides the position updating formula. Nine benchmark functions are chosen to verify the superiority of the proposed algorithm.
Regularizing Vector Embedding in Bottom-Up Human Pose Estimation

The embedding-based method such as Associative Embedding is popular in bottom-up human pose estimation. Methods under this framework group candidate keypoints according to the predicted identity embeddings. However, the identity embeddings of different instances are likely to be linearly inseparable in some complex scenes, such as crowded scene or when the number of instances in the image is large. To reduce the impact of this phenomenon on keypoint grouping, we try to learn a sparse multidimensional embedding for each keypoint. We observe that the different dimensions of embeddings are highly linearly correlated. To address this issue, we impose an additional constraint on the embeddings during training phase. Based on the fact that the scales of instances usually have significant variations, we utilize the scales of instances to regularize the embeddings, which effectively reduces the linear correlation of embeddings and makes embeddings being sparse. We evaluate our model on CrowdPose Test and COCO Test-dev. Compared to vanilla Associative Embedding, our method has an impressive superiority in keypoint grouping, especially in crowded scenes with a large number of instances. Furthermore, our method achieves state-of-the-art results on CrowdPose Test (74.5 AP) and COCO Test-dev (72.8 AP), outperforming other bottom-up methods. Our code and pretrained models are available at https://github.com/CR320/CoupledEmbedding .
Large-pose facial makeup transfer based on generative adversarial network combined face alignment and face parsing

Facial makeup transfer is a special form of image style transfer. For the reference makeup image with large-pose, improving the quality of the image generated after makeup transfer is still a challenging problem worthy of discussion. In this paper, a large-pose makeup transfer algorithm based on generative adversarial network (GAN) is proposed. First, a face alignment module (FAM) is introduced to locate the key points, such as the eyes, mouth and skin. Secondly, a face parsing module (FPM) and face parsing losses are designed to analyze the source image and extract the face features. Then, the makeup style code is extracted from the reference image and the makeup transfer is completed through integrating facial features and makeup style code. Finally, a large-pose makeup transfer (LPMT) dataset is collected and constructed. Experiments are carried out on the traditional makeup transfer (MT) dataset and the new LPMT dataset. The results show that the image quality generated by the proposed method is better than that of the latest method for large-pose makeup transfer.
Trust-No-Pixel: A Remarkably Simple Defense against Adversarial Attacks Based on Massive Inpainting

Deep Learning systems, able to achieve significant breakthroughs in many fields, including computer vision and speech recognition, are not inherently secure. Adversarial attacks on computer vision models can craft slightly perturbed inputs that exploit the models' multi-dimensional boundary shape to dramatically reduce their performance without compromising the perception that human beings have of such input. In this work, we present Trust-No-Pixel, a novel plug-and-play strategy to harden neural network image classifiers from adversarial attacks, based on a massive inpainting strategy. The inpainting technique of our defense performs a total erase of the input image and its reconstruction from scratch. Our experiments show Trust-No-Pixel improved accuracy against the more challenging type of such attacks, namely the white box adversarial attacks. Moreover, an exhaustive comparison of our technique against state-of-the-art approaches taken from academic literature confirmed the solid defense performances of Trust-No-Pixel under a wide variety of scenarios, including different attacks and attacked network architectures.
End-to-end speaker diarization of Tibetan based on BLSTM

At present, the research on speaker diarization mainly focuses on English. However, there are few studies on speaker diarization of Tibetan. Therefore, this paper takes Tibetan speech as the research goal, and proposes a new method for end-to-end speaker diarization of Tibetan based on BLSTM. The method uses an end-to-end architecture, and the model uses a single neural network, which can directly output the label classification results of Tibetan speakers. Experimental results show that our method can solve the speech overlap problem of Tibetan speakers, and the proposed method is evaluated on the simulated mixed speech of Tibetan. The DER of this method is 15.33%.
Instance-Wise Contrastive Learning for Multi-object Tracking

Multi-Object Tracking (MOT) is an important yet challenging problem in the field of computer vision. We observed that it is difficult for a single motion model to maintain the consistency of ID in complex scenes such as camera shake and pedestrian relative motion. Therefore, we propose an Instance-wise Contrastive Learning (ICL) method to jointly perform detection and embedding in a unified network. Specifically, to deal with the instance matching problem in the dynamic clutter situations, an instance-wise contrastive loss is introduced to make all the same instances to be near together, whereas all negatives are separated by a specified distance. Consequently, the semantic embedding space can be learned to not only help detect the moving objects but also contribute to the instance matching process. Furthermore, we adopt contextual information to perform the moving objects association along sequence frames. Comprehensive evaluations on three public tracking datasets (i.e., MOT16, MOT17, and MOT20) well demonstrate the superiority of our ICLTracker over other state-of-the-arts for the multi-object tracking task.
Association Models for Relating Problems with Semiologic Data in Intensive Medicine

In Intensive Medicine, the large amount of data that medical professionals are subject to can be overwhelming, leading to the use of techniques and treatments that may not be the most effective in treating patients. Should there be a need to cross planning registries made by doctors and nurses with patients' problems, the situation becomes unmagenable. To support health professionals' decision-making process, and consequently allow health professionals to make informed and timely decisions, by promoting proactive actions, the current study approaches the establishment of a correlation between medical problems and medication and therapies, using association rule mining algorithms, so that physicians can have the correct and timely information regarding patients and consequently, the most appropriate treatments for them in every situation. The main objective is for doctors and nurses to be able to look through problems and have them associated with the most frequently used and reliable therapies and medication, in order to assist patients with the highest healthcare quality. The results of this work corroborate that in order to improve the care provided to Intensive Care Units patients, it is essential to implement intelligent systems that can support hospital staff and assist to provide healthcare more efficiently.
Sustainment and management of software process improvement initiatives

Software development firms have come up as major drivers of economic growth, radical innovation and employment opportunities. During the coronavirus (COVID-19) crisis, technology and project-driven software services continued to play a critical role helping to shift work from offices to home. This paper examines the initiatives of process improvement in software firms and compares the existing models CMMI and ISO. The paper also seeks and suggests the need of a resilient model for improving the software development process based on a survey of 12 software development firms. The recommendations made for effective software process improvement with data in these firms are also discussed in this paper.