Reviewerly/exHarmony huggingface dataset:
Improving Domain Generalization using Style Regularization

We study the problem of improving domain generalization on deep networks by reducing the bias towards texture learned by these models when pre-trained in large color image datasets like ImageNet. To do so, we present a style regularization to enforce more shape-biased learning. Also, we propose an experimental setup using synthetically created test sets using state-of the-art style transfer methods. We report our experiments on stylized versions of CIFAR-10 and STL-10 datasets. In our preliminary results presented here, we show that our style regularization improves performance on new domains but not as significantly as with style augmentation.
Surgical Magnification Glasses with Augmented Reality

Background: During cardiac surgery, surgeons need to assimilate a plethora of data and communicate with the team about it, while performing complex procedures. Although modern operating rooms are equipped with multiple monitors displaying standard parameters like vital signs, other information such as heart-lung-machine procedural data are not visible for the surgeon. When needed, it requires intense communication efforts, often leading to distraction and interruption of the procedure. To bridge the gap between information needs, communication, and display possibilities we develop a novel surgical magnifying glass with augmented reality (AR) functionality. With this glass it is possible to visualize surgeon-authored data in a non-disturbing way.
Counting king permutations on the cylinder

The name comes from the way one can see these permutations as describing locations of n kings on a chessboard of order n × n in such a way that • Each row and each column contains exactly one king.• No two kings are attacking each other.• A king can move off a certain row and reappear at the beginning of that row.We present some results regarding the distribution of the cylindrical king permutations, including some interesting recursions.We also show that asymptotically, almost all the 'king' permutations are cylindrical king permutations, where a 'king' is a permutation that satisfies the first two of the three conditions above.In the second part of this article, we investigate the poset of the cylindrical king permutations with respect to containment and its structure.We examine those cylindrical king permutations whose downset is as large as possible in the upper ranks.We use a modification of Manhattan distance of the plot of a permutation and some of its applications to the cylindrical context to find a criterion for such a permutation to be k-prolific.One of our main results is that for every two cylindrical king permutations, π ∈ S k and σ ∈ S m such that σ covers π in the sub-poset of cylindrical king permutations one has m -k ≤ 4.
Estimation of Centroid Position of Silicon Particles Based on GAN and Visual Tracking

In this paper, we used generative adversarial network (GAN) and visual tracking to detect the growth of silicon particles. To solve the problem of fewer data sets, we use the Wasserstein-GAN (WGAN) model to expand the data set. From the loss functions of the generator and discriminator, the quality of the data generated by the model is high. The position of the center of mass of the silica particles during the melting process was determined by the extracted tracking mark results, and the motion trajectory of the center of mass of the silica was given.
On a Segment Partition for Entropy Estimation

Let $${{Q}_{n}}$$ be a partition of the interval $$[0,1]$$ constructed by the rule $$\begin{array}{*{20}{c}} {{{Q}_{1}} = \{ 0,{{q}^{2}},q,1\} .} \\ {Q_{{n + 1}}^{'} = q{{Q}_{n}} \cap {{q}^{2}}{{Q}_{n}},Q_{{n + 1}}^{{''}} = {{q}^{2}} + q{{Q}_{n}} \cap q{{Q}_{n}},Q_{{n + 1}}^{{'''}} = {{q}^{2}} + q{{Q}_{n}} \cap q + {{q}^{2}}{{Q}_{n}},} \\ {{{Q}_{{n + 1}}} = Q_{{n + 1}}^{'} \cup Q_{{n + 1}}^{{''}} \cup Q_{{n + 1}}^{{'''}},} \end{array}$$ where $${{q}^{2}} + q = 1$$ . We introduce the sequence of numbers $$d = 1,2,1,0,1,2,1,0,1,0,1,2,1,0,1,2,1, \ldots $$ by setting $$\begin{array}{*{20}{c}} {{{d}_{1}} = 1,{{d}_{2}} = 2,{{d}_{4}} = 0;} \\ {d[2{{F}_{{2n}}} + 1:2{{F}_{{2n + 1}}} + 1] = d[1:2{{F}_{{2n - 1}}} + 1];} \\ {\quad n = 0,1,2, \ldots ;} \\ {d[2{{F}_{{2n + 1}}} + 2:2{{F}_{{2n + 1}}} + 2{{F}_{{2n - 2}}}] = d[2{{F}_{{2n - 1}}} + 2:2{{F}_{{2n}}}];} \\ {d[2{{F}_{{2n + 1}}} + 2{{F}_{{2n - 2}}} + 1:2{{F}_{{2n + 1}}} + 2{{F}_{{2n - 1}}} + 1] = d[1:2{{F}_{{2n - 3}}} + 1];} \\ {d[2{{F}_{{2n + 1}}} + 2{{F}_{{2n - 1}}} + 2:2{{F}_{{2n + 2}}}] = d[2{{F}_{{2n - 1}}} + 2:2{{F}_{{2n}}}];} \\ {\quad n = 1,2,3, \ldots ;} \end{array}$$ where $${{F}_{n}}$$ are the Fibonacci numbers ( $${{F}_{{ - 1}}} = 0,{{F}_{0}} = {{F}_{1}} = 1$$ ). The main result of this paper is the following: $$Q_{n}^{'} = 1 - Q_{n}^{{'''}} = \left\{ {\sum\limits_{i = 1}^k \,{{q}^{{n + {{d}_{i}}}}},k = 0,1, \ldots ,{{m}_{n}}} \right\},$$ $$Q_{n}^{{''}} = 1 - Q_{n}^{{''}} = \left\{ {{{q}^{2}} + \sum\limits_{i = {{m}_{n}}}^k \,{{q}^{{n + {{d}_{i}}}}},k = {{m}_{n}} - 1,{{m}_{n}}, \ldots ,{{m}_{{n + 1}}}} \right\},$$ where $${{m}_{{2n}}} = 2{{F}_{{2n - 2}}},{{m}_{{2n + 1}}} = 2{{F}_{{2n - 1}}} + 1$$ .
Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

Wireless Visual Sensor Networks (WVSN) play an essential role in tracking moving objects. WVSN's key drawbacks are storage, power, and bandwidth. Background subtraction is used in the early stages of target tracking to extract moving targets from video images. Many standard methods of subtracting backgrounds are no longer suitable for embedded devices because they use complex statistical models to manage small changes in lighting. This paper introduces a system based on the Partial Discrete Cosine Transform (PDCT), reducing the vast dimensions of processed data while retaining most of the important information, thereby reducing processing and transmission energy. It also uses a dual-mode single Gaussian model (SGM) for accurate detection of moving objects. The proposed system's performance is to be assessed using the standard CDnet 2014 benchmark dataset in terms of detection accuracy and time complexity. Furthermore, the suggested method is compared to previous WVSN background subtraction methods. Simulation results show that the proposed method consistently has 15% better accuracy and is up to 3 times faster than the state-of-the-art object detection methods for WVSN. Finally, we showed the practicality of the suggested method by simulating it in a sensor network environment using the Contiki OS Cooja Simulator and implementing it in a real testbed using Cortex M3 open nodes of IOT-LAB.
民粹國家領導人及其線上防疫傳播之研究：以新冠疫情下菲律賓總統杜特帝的臉書為例

&lt;p&gt;本文旨在探討新冠疫情（COVID-19）全球大流行下，民粹主義國家領導人是如何利用社群媒體來傳播訊息？透過分析菲律賓總統杜特帝的社群媒體使用，研究發現他的防疫民粹主義傳播風格有四，包括：（1）訴求人民的傳播方式有所改變；（2）打擊菁英和反建制的特色消失；（3）唯一的共同敵人是COVID-19；（4）寄望中國疫苗。結論進一步指出，杜特帝高度表揚自己的防疫政策、規劃和官員，以及透過違背民主價值觀的方式進行表述，恐形成一種防疫民粹威權主義。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;This article aims to explore how populist national leaders use social media to communicate with people during the COVID-19 pandemic. By examining Philippine President Duterte’s use of social media, the researcher identified four populist communication styles in his pandemic prevention and control messages: (1) The way to &amp;quot;appeal to the people&amp;quot; has changed; (2) The anti-elite and antiestablishment messages have disappeared; (3) COVID-19 is defined as the only common enemy to people; (4) A hope is constructed for people that the solution to the pandemic has to rely on the aid of Chinese vaccines. Findings of the research indicate that Duterte’s online populist messages were full of self-praising of his own pandemic control policy, his plans, and his officials. However, since Duterte’s messages were still expressed in the styles that violated the values of democracy, it is afraid that a kind of populist authoritarianism was formed in the name of pandemic prevention and control.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Optimizing Federated Edge Learning on Non-IID Data via Neural Architecture Search

To exploit the vast amount of distributed data across edge devices, Federated Learning (FL) has been proposed to learn a shared model by performing distributed training locally on participating devices and aggregating the local models into a global one. The existing FL algorithms suffer from accuracy loss due to that data samples across all devices are usually not independent and identically distributed (non-i.i.d.). Besides, devices might lose connection during the training process in wireless edge computing. Thus, we advocate one-shot Neural Architecture Search technique as a basis to propose a solution which can deal with non-i.i.d. problem and is robust to the intermittent connection issue. We adopt a large network as the global model which includes all the candidate network architectures. The non-i.i.d. problem is alleviated by two steps: (1) identify and train the candidate networks which are potentially high performance and trained with less bias using a heuristic sampling scheme; (2) search for the final model with the highest accuracy rate from the candidate networks. Experimental results show that the model trained by our proposed method is robust to non-i.i.d. problem and can achieve 84&#x0025; reduced communication overhead compared with the baselines.
Guiding legacy systems for evolution. PmatE: a case study of maintenance and engineering

Even though software change is inevitable, accurate maintenance can extend software lifespan in a subtle way when both budget and time constraints get in the way of software replacement. In the University of Aveiro, the project PmatE – a quiz web platform created to encourage students to like Math – emerged in the early 1990’s and stacked several applications over the decades without major planning, cleaning or upgrade. This resulted in a huge-sized framework that was crucial to be always available and online and had high operational cost, leading to an increasing amount of technical debt. After 3 decades, the project was studied, refactored and refurbished, leading to a stable consistent framework ready for evolution and software spinouts. This work shows how to manage and engineer solutions to maintain a legacy system and evolve it even when tied up to heavy constraints.
On the Shoulders of Giants [From the Editor’s Desk]

As we begin our journey into 2022, we can all hope for a year closer to “normalcy” following a bumpy ride through the global pandemic. As I begin my fourth year serving as your editor, I look back feeling both proud and thankful for the many experts who have helped me in this endeavor. Every article in this issue was composed by a dedicated IEEE Industry Applications Society (IAS) volunteer leader who willingly offered writing skills and technical expertise. One expert, in particular, is Geri Krolin-Taylor, IEEE senior managing editor for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Industry Applications Magazine</i> . Geri and her team are behind-the-scenes players responsible for producing the magazine you are reading today. They are nearly invisible (listed by title and name at the lower right-hand corner of page 2), unsung heroes and certainly indispensable.
Autonomous Road Crossing Surveillance System

Accidents related with vulnerable pedestrians around crosswalks are continued so that proactive safety support system is required. Pedestrian detection from frames captured by a camera is a significant and yet challenging task. An autonomous road crossing surveillance system would be ideal for tracking pedestrians who want to cross the road and assist them. A practical solution for aiding pedestrians regularly, a road crossing surveillance with real-time Pedestrian Detection. Since the background subtraction from videos and images is still a persistent problem and difficult to accomplish. A Haar Cascade Classifier with the full-body detection algorithm is used to detect people in real-time captured by a camera. Keywords: Pedestrian crossing, surveillance, road crossing surveillance, surveillance system, autonomous, Haar Cascade Classifier.
ANN-based Age Detection System in Nigeria

The Nigerian electoral system has witnessed a barrage of underage voting from time immemorial and the apex electoral body of the country has almost been incapacitated in providing a lasting solution to this menace of underage voter registration and voting.This act is commonly attributed to the northern part of the country where electoral officials were always under threat by politicians to allow children that are not up to eighteen (18) years of age to be registered as eligible voters.This has necessitated the proposal of an age detection system that will detect the age of an individual that wants to be registered for voting using iris biometric technology that employs Artificial Neural networks (ANN).
Experiencia de un Curso Internacional de Desarrollo Profesional Docente usando el aprendizaje híbrido/ Experience of an International Course on Teacher Professional Development using hybrid learning

This paper analyses a one-week international Teacher Professional Development (TPD) course about integrating robots in early childhood education.The TPD took place during the COVID-19 pandemic and used a hybrid learning approach, employing simultaneously two physical learning spaces in Tallinn (Estonia) and Universidad Rey Juan Carlos (Spain), and a video conferencing virtual space for online learners from Hong-Kong, Czechoslovakia, Greece, Germany, Spain and Estonia.We used focus group interviews to collect the participant evaluation and opinions about the hybrid learning approach and about the impact of this approach to learning outcomes.So far, hybrid learning was an uncommon teaching mode because there were no reasons to use it widely.Interest towards using this approach has changed with the COVID-19 conditions, and it is our belief that hybrid learning environments will become much more frequent from now on.This paper presents some thoughts and guidelines for applying it to different teaching environments.
Multi-Stage Threat Modeling and Security Monitoring in 5GCN

This chapter presents a framework for modeling threats for the purpose of identifying security-monitoring requirements in the fifth generation core network (5GCN) that includes sensor placement and detection methods through threat decomposition and threat modeling using graphs. It presents the related work in 5GCN threat assessments and modeling of multi-stage attack scenarios. With threat-based risk assessments, the first step of the process involves the identification of threats, which helps to define the requirements for protecting the network and detection. The chapter demonstrates a method of modeling multi-stage attack threat scenarios using graphs applicable to the 5GCN. It describes how threat graphs can be used to provide decision support on security monitoring through sensor placement and how this can be used for the detection of multi-stage attack threat scenarios. Finally, the chapter outlines the open research challenges and future work.
DIAGNOSIS OF THE RESULT OF A SEWING ENTERPRISE PRODUCTION ACTIVITY

In the scientific article, the process of diagnosing the production activity result of a sewing enterprise is investigated. An assessment of the state of production and marketing activities of a sewing enterprise based on the dynamics of the volume of marketable and sold products. The factor analysis of the efficiency of resources using (number of human resources, cost of materials and fixed assets) on an enterprise of sewing branch is carried out. Econometric modeling and forecasting of the production activity results, concerning the change of the basic components of resource potential of a sewing enterprise, are applied. Several econometric and one trend models, which describe the change of the volume of marketable products on a sewing factory, have been built. In addition to linear dependences, the power and multiplicative production function of changing the production activity result is formed. All constructed models have a high value of the coefficient of determination, which indicates a significant share of the influence of selected factors on the performance indicator. At the same time, they are adequate, so they can be used for forecasting the volume of marketable products. The results of modeling and further forecasting proved that the best models are the three-factor model, power model, two-factor model built on the impact of staff and cost of material resources, and one-factor relationship between product volume and cost of materials. These models have a sufficiently high value of the coefficient of determination, the lowest value of the standard error, and the adequacy of these models according to Fisher's criterion and the reliability of their parameters according to Student's criterion.
GDLL: A Scalable and Share Nothing Architecture Based Distributed Graph Neural Networks Framework

Deep learning has recently been shown to be effective in uncovering hidden patterns in non-Euclidean space, where data is represented as graphs with complex object relationships and interdependencies. Because of the implicit data dependence in the big graphs with millions of nodes and billions of edges, it is hard for industrial communities to exploit these methods to address real-world challenges at scale. The skewness property of big graphs, distributed file system performance penalty on small k-hop neighborhood subgraphs, and varying size of subgraph makes Graph Neural Networks (GNNs) training further challenging in a distributed environment using parameter servers. To address such issues, we propose a scalable, layered, fault-tolerance, and in-memory distributed computing-based graph neural network framework called Graph Distributed Learning Library (GDLL). The base layer utilizes an optimized distributed file system and a scalable graph data store to reduce the performance penalty. The second layer provides distributed graph processing using in-memory graph programming models while optimizing and hiding the underlying complexity of information complete subgraph computation. In the third layer, GNN modules are deployed on top of the first two layers for efficient distributed training using parameter servers. Finally, we evaluate and compare GDLL with the state-of-the-art solutions and outperform it significantly in terms of efficiency while maintaining similar GNN convergence.
Perception, Visual Inference, and Exploratory Visualization

In addition to announcing the Best Paper and Best Associate Editor awards, we thank the outgoing Editorial Board members for their service and introduce their replacements. Finally, we outline the articles published in this New Year's issue.
Counterfeit Currency Detection using Resource Efficient Neural Networks

One of the leading causes of economic instability is the large-scale counterfeiting of the paper currency notes. Several media reports bring to light the alarming cases and the humungous scales of currency counterfeiting and how this issue has become very serious now. A report on how the Government is coping with these threats with new and stricter rules however counterfeiters adapt to the new rules in an alarmingly fast pace. Criminals continue to find a loophole in the system despite such strict security features. There have been impressive discoveries in the field of counterfeit currency, and this coupled with new age digital technology, counterfeiting is being fought well. However, it is impossible to track all counterfeit notes and impossible to have them checked at a short amount of time. Existing systems involve filing a case with the police, sending the documents for verification and waiting for the results to come. This method is based on Deep Learning, which has seen tremendous success in image classification tasks in recent times. This technique can help both people and machine in identifying a fake currency note in real time through an image of the same. Traditional Deep Learning algorithms require tremendous amount of compute power and storage and hence it is an expensive and elaborate process. The main goal is to make a faster and simpler mechanism to detect a counterfeit note that can be implemented in any random place like an ATM dispenser or an android application. The success of this application will greatly help the quick identification of the threat and help law enforcement in finding the source of the threat faster.
ANALISIS KEBUTUHAN HANDOUT DIGITAL PEMBELAJARAN SD BERBASIS TRI N UNTUK MEMBEKALI KETERAMPILAN MENGAJAR MAHASISWA CALON GURU

This study aims to analyze the need for digital handouts for teaching elementary school based on Tri N to provide teaching skills for prospective teacher students. This type of research is qualitative research. This study intends to reveal a needs analysis for digital handouts for elementary learning based on Tri N to equip teaching skills for prospective teacher students. The research subjects were undergraduate students of PGSD FKIP UST in semester 4 of the 2020/2021 academic year. The data collection technique in this research used participant observation, interviews, and documentation. Data analysis in this study used interactive model data analysis. The analysis consists of three flows of activities, namely: data reduction, data presentation, and concluding/verification. The results of this study, begin with a needs analysis by identifying the material from the RPS for the Lesson Planning course. In the RPS, materials were found in the form of curriculum analysis, program Prosem, RPP, and attachments (teaching materials, LKPD, learning media, and learning evaluation). In the RPS, the concept of the Tri N teaching has been found, but the teaching materials used are still using printed handouts and PPT. From the analysis of the material, researchers can develop innovative teaching materials, namely digital handouts, so that the material presented can be easily understood and interactive for students.
Warship and Mission: The Plan for Joseon of J. B. Cécile, D. Jancigny, N. Libois in 1842 and Its Reversed Execution, the Entry and Martyrdom of Kim Daegeon and Choi Yangeop

The “Warship” and the “Bible” were closely connected as symbols of “Western Powers over the East” in the mid-19th century. From the present perspective, the combination of the warships and the mission seems mismatched and wrong, but formerly often praised as “the Devine Providence.” The “Joseon Plan” of the French Admiral J.B. Cécille and the special envoy D. Jancigny, and the favorable response of the priest N. Libois to the plan, encouraged the boarding of J. A. Maistre, Kim Daegoeon and Choi Yangeop on French warship in 1842. However, in the middle of 1842, Cécile and Jancigny withdrew their trip plan to Joseon. This decision was related to French policy of active intervention in China and Vietnam after the signing of Nanjing Treaty. The complete entry of Kim Daegeon, Choi Yangeop, and J. A. Maistre to Joseon took ten year, and none of their entry was made by warship. The belated appearance of Cécille’s warship on the Korean west coast and its premature evacuation in 1846, on the contrary, hastened Kim Daegeon’s death. The basic position of Kim Daegeon and the French priests was to obtain religious freedom in a peaceful way. Although they advocated the usage of warships or the armed demonstration, they did not intend to wage war.