Reviewerly/exHarmony huggingface dataset:
Non-Hermitian Engineered Low Power Thermo-Optic Silicon Phase Shifter

We present a fast response, energy-efficient thermo-optic silicon phase shifter based on Non-Hermitian engineering. A 500 kHz bandwidth and a 1.72 mW -phase-shift (Pπ) power consumption are demonstrated at 1550 nm wavelength.
Magnitude of Semicircle Tiles in Fourier-space - A Handcrafted Feature Descriptor for Word Recognition using Embedded Prototype Subspace Classifiers

The purpose of this paper is to in detail describe and analyse a Fourier based handcrafted descriptor for word recognition. Especially, it is discussed how the Variability in the results can be analysed and visualised. This efficiency of the descriptor is evaluated for the use with embedded prototype subspace classifiers for handwritten word recognition. Nonetheless, it can be used with any classifier for any purpose. An hierarchical composition of discrete semicircles in the Fourier-space is proposed and it will will be show how this compares to Gabor filters, which can be used to extract edges in an image. In comparison to Histogram of Oriented Gradients, the proposed feature descriptor performs better in this scenario. Compression using PCA turns out to be able to increase both the F1-score as well as decreasing the Variability.
Packet Representation Learning for Traffic Classification

With the surging development of information technology, to provide a high quality of network services, there are increasing demands and challenges for network analysis. As all data on the Internet are encapsulated and transferred by network packets, packets are widely used for various network traffic analysis tasks, from application identification to intrusion detection. Considering the choice of features and how to represent them can greatly affect the performance of downstream tasks, it is critical to learn high-quality packet representations. In addition, existing packet-level works ignore packet representations but focus on trying to get good performance with independent analysis of different classification tasks. In the real world, although a packet may have different class labels for different tasks, the packet representation learned from one task can also help understand its complex packet patterns in other tasks, while existing works omit to leverage them.
Türkçe Öğretmeni Adaylarının Dijital Ortamda Yazmaya İlişkin Tutumlarının Teknoloji Entegrasyonuna Yönelik İnançları ile İlişkisinin İncelenmesi

Different applications in language teaching have emerged based on developing and changing technology. It is impossible to think of writing skills training independently of technology today. If teachers have positive attitudes toward digital writing and technology use, they will be able to direct students properly. For this reason, this study examined the relationship between Turkish teacher candidates' digital writing attitudes and their beliefs about technology integration. The attitude scale for digital writing developed by Susar-Kırmızı, Kapıkıran and Akkaya (2021), and also technology adaptations application scale that was developed by Uluay, Çalışkaner Nibat and Arıkan (2018) were used to collect the data. 146 Turkish teacher candidates studying at Sakarya University were the study group of the research. For the results, the digital writing attitudes of the Turkish teacher candidates are at a moderate level while their beliefs about technology integration are at a high level. Moreover, it is seen that Turkish teacher candidates' digital writing attitudes and their beliefs about technology integration were positively and moderately related.
Identifying Radical Activity on Social Digital Platform: A Machine Learning Approach

The social media in digital form over internet is getting popularity in recent years. This digital platform is being used by many to share their thought or opinion. Though these social media had given results too many good causes, there are some users are present on these platforms for radical activities. In this paper, the tweets form the digital social platform twitter is taken for analysis based on radical keywords. The data is collected in form of tweets are analyzed using different machine learning algorithms and a comparative analysis is done. The proposed work concludes the best machine learning algorithms for analysis of such data and the new words came in light for the collected dataset. The deep learning model are also implemented and tested for sentimental analysis.&#x0D; HIGHLIGHTS&#x0D; &#x0D; Radical keywords-based message collection from social platforms&#x0D; Application of various machine learning and deep learning algorithms trained using collected datasets&#x0D; Discovery of new temporal words&#x0D; Identification of radical messages floating on social platforms&#x0D; Identifying the best performing machine learning and deep learning algorithm for radical message analysis&#x0D; &#x0D; GRAPHICAL ABSTRACT
Mode Switching for Secure Edge Devices

Many devices in various domains operate in different modes. We have suggested to use mode switching for security purposes to make systems more resilient when vulnerabilities are known or when attacks are performed. We will demonstrate the usefulness of mode switching in the context of industrial edge devices. These devices are used in the industry to connect industrial machines like cyber-physical systems to the Internet and/or the vendor’s network to allow condition monitoring and big data analytics. The connection to the Internet poses security threats to edge devices and, thus, to the machines they connect to. In this paper (i) we suggest a multi-modal architecture for edge devices; (ii) we present an application scenario; and (iii) we show first reflections on how mode switching can reduce attack surfaces and, thus, increase resilience.
Separable High Capacity Reversible Data Hiding Algorithm for Encrypted Images

This paper presents a separable Reversible Data Hiding Algorithm for Encrypted Images (RDHEI) that consists of three phases. The encryption phase in the algorithm circularly shifts the columns in the image by a random amount, blocks the image into equal and regular blocks and maps them to irregular blocks generated based on Hilbert filling curve, and finally complements a random subset of the blocks. The embedding phase is essentially an adapted version of the modification of the prediction errors algorithm that is applied to each block in the encrypted image independently. In the decryption phase, and since the algorithm is separable, the user can extract the data only, decrypt the image only, or can perform both actions depending on the type of keys he has. When compared to a very similar and recent algorithm, performance evaluation proved the ability of the proposed algorithm in increasing the embedding capacity with reasonable quality of the directly decrypted image. In terms of the security, the analytical and quantitative assessment showed the superiority of the proposed algorithm in protecting the encrypted image.
The Effect of Appearance of Virtual Agents in Human-Agent Negotiation

Artificial Intelligence (AI) changed our world in various ways. People start to interact with a variety of intelligent systems frequently. As the interaction between human and AI systems increases day by day, the factors influencing their communication have become more and more important, especially in the field of human-agent negotiation. In this study, our aim is to investigate the effect of knowing your negotiation partner (i.e., opponent) with limited knowledge, particularly the effect of familiarity with the opponent during human-agent negotiation so that we can design more effective negotiation systems. As far as we are aware, this is the first study investigating this research question in human-agent negotiation settings. Accordingly, we present a human-agent negotiation framework and conduct a user experiment in which participants negotiate with an avatar whose appearance and voice are a replica of a celebrity of their choice and with an avatar whose appearance and voice are not familiar. The results of the within-subject design experiment show that human participants tend to be more collaborative when their opponent is a celebrity avatar towards whom they have a positive feeling rather than a non-celebrity avatar.
Structured light-based dynamic 3D measurement system for cold-formed steel hollow sections

In the cold-formed steel hollow section manufacturing process, one of the key stages is forming an inspection to check whether it meets the requirement. In this paper, a dynamic 3D measurement system based on low-cost structured light was presented, which can be used to measure the dimensions of a cold-formed steel hollow section in real-time. The system is mainly composed of four sets line-structured light inspection device. A temporary coordinate is proposed to assist in solving multi the calibration of structured light plane. An optimisation method of L-M algorithm is proposed to solve the problem of stitching accuracy of point cloud contour. A point cloud simplification processing method is proposed for solving main parameters. The side length measurement error of the system is within ±0.05 mm, and the outer arc radius measurement error is within ±0.45 mm. All parallelisable tasks can achieve an acquisition rate of up to 25 fps.
Diversifying behaviors for learning in asymmetric multiagent systems

To achieve coordination in multiagent systems such as air traffic control or search and rescue, agents must not only evolve their policies, but also adapt to the behaviors of other agents. However, extending coevolutionary algorithms to complex domains is difficult because agents evolve in the dynamic environment created by the changing policies of other agents. This problem is exacerbated when the teams consist of diverse asymmetric agents (agents with different capabilities and objectives), making it difficult for agents to evolve complementary policies. Quality-Diversity methods solve part of the problem by allowing agents to discover not just optimal, but diverse behaviors, but are computationally intractable in multiagent settings. This paper introduces a multiagent learning framework to allow asymmetric agents to specialize and explore diverse behaviors needed for coordination in a shared environment. The key insight of this work is that a hierarchical decomposition of diversity search, fitness optimization, and team composition modeling allows the fitness on the team-wide objective to direct the diversity search in a dynamic environment. Experimental results in multiagent environments with temporal and spatial coupling requirements demonstrate the diversity of acquired agent synergies in response to a changing environment and team compositions.
Augmented Reality VS Virtual Reality

Augmented reality (AR) which is defined as that “the real time use of information in the form of graphics, text, audio, or other virtual enhancements integrated with real world objects”. Augmented reality involves auditory, overlaying visual or other sensory information onto the world in order to enhance one’s experiment.AR works on to by adding the digital contents onto a live camera feed, making that digital content looks as if it is part of the physical world around as. The applications which are used in AR is Healthcare, Education, Retail ,Military, Industrial and manufacturing, Engineering, Marketing and advertising etc. Virtual reality (VR) which is defined as that a computer generation simulation in which a person can interact with in an artificial 3-D environment using electronic devices such as special goggles with a screen or gloves fitted with sensors. The Virtual Reality Modelling Language (VRML) allows the creator to specify images and the rules for their display and interactions using textual language statements. The applications which are used in VR is Military, Sports, Fashion, Medical training, Education etc.
Aproximaciones a las políticas educativas en la escena global de la postpandemia COVID-19

The COVID 19 pandemic had an unusual multidimensional impact on humanity as a whole, creating or recreating new or renewed forms of relationship between people, communities and the state, as an articulating entity of social relations and spaces of human development. In this context, the general use of ICTs in teaching-learning processes at all levels of the educational system is common, to the detriment of the person-to-person encounter affected by social distancing. The objective of the article is to describe a useful conceptual framework to redefine educational policies in accordance with the new realities that characterize post-pandemic scenarios in Ecuador. Methodologically it is a documentary, analytical and prospective research. The authors conclude that investment in education is considered urgent since its material and symbolic spaces mean for the majority of young people the most legitimate opportunity to train, achieve better opportunities and ascend socially, a situation that reduces the dynamics of social conflict.
A Survey-Based Study to Understand Various Aspects of Kanban

Kanban is a Japanese coined system for workflow through cards, and the method is used in Kanban software for a real-time flow of work with optimum transparency implementing Agile and DevOps; it helps to surface the message good or bad about scope and complexity of any project. The key highlight about Kanban is it motivates the team members at the bottom level by empowering each member with an opportunity to self-commitment which in turn sets the right expectation with the stakeholders. In this paper, the survey has been done worldwide considering professional industrialists, and based on those critical conclusions have been drawn. The survey was conducted in the software industry across the globe who is extensively working on Kanban. The statistical and analytical approach has concluded that time is the best factor for Kanban, and it plays a vital role in achieving the delivery date and minimization of cycle time. A comparative study has been performed for running factors like time, scope, cost, risk, quality, and team. Quality of teamwork and time aspects after the implementation of Kanban have been explored. Jira has proven to be the best and widely used tool in terms of productivity, effectiveness, communication, and metrics. A study on reported benefits and challenges of Kanban has been identified, and the nature of the project best suited for Kanban has been investigated. Lack of experience with Kanban board and progress tracking is considered as the biggest challenges in implementing Kanban.
Research and design of CRT-based homomorphic ciphertext database system

The cloud’s storage and query of private information have the cryptographic scholar due to the proliferation of cloud computing. In the traditional query mode, the private information stored in the cloud is at risk of being leaked. In order to solve this problem, a cloud ciphertext database system based on homomorphic encryption is a valid workaround. This paper presents a new cloud ciphertext database system model, which is based on the existing ciphertext database mode research and homomorphic properties. This paper also implements a ciphertext database system based on a CRT-based additive homomorphic scheme according to the model. Through theoretical analysis, the model is CPA-level safe and correct. The experimental results show that users can correctly query and download the data in the ciphertext database on the untrusted cloud server through the model, and it has efficiency advantages.
Human Activities Recognition and Monitoring System Using Machine Learning Techniques

Human activity recognition is the wide range of field of research and challenging task to identify the actions of the human in period of time based on received signal strength data in wireless sensor network. It is important to monitor activity of a person for numerous reasons. Recently, Machine Learning approach shows capable of classifying the actions of the human by automatically using the raw sensor data. In this work, the dataset consists of received signal strength of seven activities using three sensor nodes that are trained by using supervised machine learning algorithms to recognize the actions and random activities are monitored to identify the strange action of the person using unsupervised machine learning. The proposed machine learning based human activity recognition model are evaluated and predict the seven human activities by achieving 90% of accuracy. The model is later improved to recognize the random actions of the human.
Construction of Curriculum Ideological and Political Collaborative Education Mechanism Based on Edge Computing and Neural Network Algorithm

The ideological and political collaborative education mechanism is an important course teaching method that uses all courses as a carrier to cultivate students' all-round development in morality, intelligence, physique, and beauty. The purpose of this paper is to conduct a better research on the construction of the ideological and political collaborative education mechanism by building models based on edge computing and neural network algorithm. This paper first gave a general introduction to edge computing and neural network algorithm and then analyzed the current situation of ideological and political courses in a certain school. Then, edge computing and neural network algorithm were introduced into the analysis of an important course teaching method that used all courses as a carrier to cultivate students’ comprehensive development in morality, intelligence, physique, and beauty. The BP neural network model was established. Through analysis and comparison, the experimental results showed that 56.47% of the students believed that the impact of personal morality on the future development of college students was the first in the relationship between “virtue” and “talent.” More than half of the students believed that the “virtue” of building morality and cultivating people was mainly civic morality, and about 30% of the students thought that the main value was loving the party and patriotism, which meant that most students believed that the main value of building morality and cultivating people was to establish morality.
Prevalence of Different Types of Malaria in Shendi, Sudan

Introduction: This cross-sectional descriptive study aims to detect the incidence of different types of Malaria in Shendi, Sudan, during the period from November 2019 to February 2020. This study was conducted in Shendi, Sudan, which lies 150 km from the national capital of the Republic of Sudan, Khartoum. Methods: One hundred samples were taken, and the study was conducted in several hospitals in Shendi (Shendi teaching hospital, Almak Nimer hospital, and many laboratories in Shendi). This study examined different types of malaria parasite among the population of Shendi, taking into account the age, gender, residence. A rapid immunochromatographic test and blood film were used to diagnose the incidence of Plasmodium falciparum compared to the other types of Plasmodium. Results: On blood film test, 98% of already diagnosed cases of malaria by microscope were positive, and only 2% were negative; furthermore, 96 % of malaria cases were caused by P. falciparum, and only 2 % were caused by Plasmodium vivax. Conclusion: The present study concluded that the incidence of malaria was high in the study area. More than two-thirds of patients diagnosed with malaria in Shendi in 2019-2020 were young aged 15-30 years, and more than half of the cases were female. Blood film examination is the gold standard for malaria diagnosis. P. falciparum was responsible for 96% of cases, and the other species were much less common.
Review of Different Methods of Abnormal Mass Detection in Digital Mammograms

Various images from massive image databases extract inherent, implanted information or different examples explicitly found in the images. These images may help the community in initial self-screening breast cancer, and primary health care can introduce this method to the community. This study aimed to review the different methods of abnormal mass detection in digital mammograms. One of best methods for the detection of breast malignancy and discovery at a nascent stage is digital mammography. Some of the mammograms with excellent images have a high intensity of resolution that enables preparing images with high computations. The fact that medical images are so common on computers is one of the main things that helps radiologists make diagnoses. Image preprocessing highlights the portion after extraction and arrangement in computerized mammograms. Moreover, the future scope of examination for paving could be the way for a top invention in computer-aided diagnosis (CAD) for mammograms in the coming years. This also distinguished CAD that helped identify strategies for mass widely covered in the study work. However, the identification methods for structural deviation in mammograms are complicated in real-life scenarios. These methods will benefit the public health program if they can be introduced to primary health care's public health screening system. The decision should be made as to which type of technology fits the level of the primary health care system.
Image Analysis of Children's Play: For 5 year old children who have experienced the COVID-19 pandemic

Objectives The purpose of this study is to analyze the image of play of children attending early childhood education institutions under various restrictions during the COVID-19 pandemic. Through this, it is a study to understand the image of children's play and to support appropriate play for children.&#x0D; Methods The research method targeted 159 children aged 5 and a half at a total of six institutions in Seoul, Gyeonggi, Chungnam, and Jeonbuk. A 5-year-old infant drew a picture of play and had individual interviews. Data analysis was analyzed by play relationships, play spaces, play types, and play expressions.&#x0D; Results As a result of the study, first, in the play of children, the subjects of play relationships of children were mainly recognized as peers of early childhood education institutions, and solo play was also very high. Second, the play space for children was mainly found to be the indoor environment of early childhood education institutions and homes. Third, the type of play for children was mainly physical play, and the play for games, smartphones, and media devices was also high. Fourth, the play expression of children was mainly expressed positively, and the picture without masks and features was high.&#x0D; Conclusions The play images perceived by children experiencing limited educational environments such as pandemics, unpredictable changes are expected to be used as basic data for the operation of early childhood curriculum that can be supported by children for their play in future situations.