Reviewerly/exHarmony huggingface dataset:
A linear passive route to transmission asymmetry with meta-gratings

Transmission symmetry is considered an inherent property of passive linear systems. It is believed that violating transmission symmetry would mean violating reciprocity which would require some active intervention, e.g., via an external magnetic field, or with time modulation or "non-linear self-biasing" of the system's optical components. Recent works though have shown that judiciously designed planar asymmetries at a linear passive structure's interfaces can cause polarization rotation and lead to transmission symmetry breaking without breaking reciprocity. Here, we present a linear and passive class of metagratings which preserve the polarization of the input beam but break transmission symmetry while still obeying reciprocity [1]. We analyze the underpinning mechanism and explain why it does not violate reciprocity. Breaking transmission symmetry with these simple systems can open-up several capabilities in integrated photonics from infrared to optical frequencies. [1] S. Foteinopoulou, Breaking Transmission Symmetry Without Breaking Reciprocity in Linear All-Dielectric Polarization-Preserving Metagratings, Phys. Rev. Applied 17, 024064 (2022); https://doi.org/10.1103/PhysRevApplied.17.024064
Research on lossless restoration method of digital media image based on regularisation method

In order to overcome the problems of high mean square error (MSE) and low peak signal-to-noise ratio (PSNR) in existing image lossless restoration methods, a new digital media image lossless restoration method based on regularisation method is proposed. The image is segmented by two-dimensional histogram and distance measure. According to the above processing results, a degradation model of digital media image is constructed. On the basis of the degradation model, a joint variation regularised image restoration model is constructed by regularisation method, and the pre-processed digital media image is restored losslessly. The experimental results show that compared with the traditional methods, the MSE of the proposed method is significantly reduced, the minimum MSE is only 0.17, the PSNR is high and the improved signal-to-noise ratio is significantly improved.
Listen and tell me who the user is talking to: Automatic detection of the interlocutor’s type during a conversation

In the well-known Turing test, humans have to judge whether they write to another human or a chatbot. In this article, we propose a reversed Turing test adapted to live conversations: based on the speech of the human, we have developed a model that automatically detects whether she/he speaks to an artificial agent or a human. We propose in this work a prediction methodology combining a step of specific features extraction from behaviour and a specific deep learning model based on recurrent neural networks. The prediction results show that our approach, and more particularly the considered features, improves significantly the predictions compared to the traditional approach in the field of automatic speech recognition systems, which is based on spectral features, such as Mel-frequency Cepstral Coefficients (MFCCs). Our approach allows evaluating automatically the type of conversational agent, human or artificial agent, solely based on the speech of the human interlocutor. Most importantly, this model provides a novel and very promising approach to weigh the importance of the behaviour cues used to make correctly recognize the nature of the interlocutor, in other words, what aspects of the human behaviour adapts to the nature of its interlocutor.
Using image symmetries to uniquely align aspheric mirrors to a focus and axis

The Point Source Microscope (PSM) is used to find five aberrations related to the symmetries of the autostigmatic image viewed when aligning aspheric mirrors to a point along an axis. These five aberrations exactly match in number the five degrees of mechanical freedom required to align the mirror to an axis and thus provide an exact solution to a unique focus and alignment to an axis. We show how the PSM is used to capture and analyze a set of images as the PSM is moved through focus using the symmetry properties of the image.
Garment image style transfer based on deep learning

Before neural networks, image style transfer procedures had a common idea: analyze images with a certain style, build a mathematical or statistical model for the style, and then change the image to be transferred, so that it better fits the established model. In this paper, k-means and semantic closed natural matting algorithm is combined for image segmentation, the style and content in the image are extracted based on neural network, and the resulting image is synthesized by image reconstruction technology to realize the migration of national costume styles. Due to the serious artifacts of the output image, an improved image style transfer algorithm is adopted to constrain the transformation from the input image to the output image in the local affine transformation of the color space, and this constraint is expressed as a completely differentiable parameter term, image distortion is suppressed effectively. In the process of real photo style transfer, there is also space inconsistency. Smoothing is done to ensure that the space style is consistent after style processing, it greatly speeds up the operation speed. On NVIDIA GTX1080TI graphics card, algorithm is tested with 256×256 resolution images. It includes three indicators of average running time, memory usage and the number of styles generated by a single model, which are 0.06 s, 136.06 MB and 1 respectively. These indicators can reflect the efficiency and flexibility of the algorithm.
Cyber-Archaeology and the Study of Ancient Edom and Israel

One of the most important developments in the archaeology of the southern Levant in recent decades has been the rise in digital techniques applied to data acquisition, curation, analysis, and dissemination. This cyber-archaeology has already revolutionized archaeological field recording. However, other parts of the data lifecycle require attention from cyber-archaeologists. The long-term curation of digital data is a pressing responsibility that requires careful thought and consideration. Similarly, sharing data with the interested public and stakeholding communities is another ethical obligation where digital advances can play a major role. Finally, paired with appropriate theoretical grounding (and critique), analysis of born-digital data can unlock new perspectives on and ways of thinking about the past. This holds true especially in the exploration of three-dimensional datasets. In short, cyber-archaeology has enormous potential for changing the way we approach the archaeological record but requires care and consideration in all aspects of the data journey, from acquisition to dissemination.
Formal analysis of timeliness in the RaSTA protocol

Formal reasoning about the correctness of safetycritical system properties is crucial since such systems may impact their environment when malfunctioning.The Rail Safe Transport Application (RaSTA) Protocol is a protocol for systems used in railway applications such as signaling.It claims to provide highly available and timely communication based on the application's demands.We investigate timeliness, i.e., the property that application data do not become obsolete.We analyze the protocol's specification and provide assumptions necessary to resolve imprecisions.Under the specified error model, we find that the deadlines proposed bound until messages are considered timely is too restrictive, disabling RaSTA's mechanisms to recover from lost messages in time.We formalize the specification of timeliness to provide a counterexample for the proposed bound and create an improved bound that does not lead to violated deadlines under the same assumptions and error model.
Heuristic Risk Treatment for ISO/SAE 21434 Development Projects

Due to new technologies for connectivity, automotive systems shift from a closed to an open system approach.Therefore, automotive systems have a rising demand for security, letting security be an upcoming field in research and practice.Also, the newly published process standard ISO/SAE 21434 demands adjustments in the development process to address cybersecurity.The unique characteristics of automotive systems leave many approaches from other system types inapplicable.This work concentrates on the risk treatment step in the cybersecurity development process.Due to the vast amount of differing terminology, we see the need to define a flexible taxonomy adaptable to several system types and used in systems with normative references.We use this taxonomy to develop a heuristic approach for risk treatment based on a distinct terminology for security requirements.The presented method is extendable to include several trade-off points.
Customized Line and Scatter Plots

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
Combination of a 2D-RCA model and ANNs for texture image segmentation

In this paper, a region growing technique is used to achieve image segmentation by merging some starting points or internal small areas if they are homogeneous according to a measurement of a local region property. A 2D random coefficients autoregressive model (2D RCA) is fitted. First, an estimation procedure using a generalised method of moments (GMM) technique is proposed to extract some local region properties. For this, a gradient-based neural network (GNN) is used to estimate the 2D RCA model parameters from a given texture. The cost function of the proposed GNN is based on a strong matching of the statistical moments of the corresponding 2D-RCA model and the sample moments of population image data. Experimental results demonstrate the effectiveness and the relevance of the proposed method.
The Rising Importance of Soft Skills for IT-Students: Working Online in Agile Globally Distributed Teams During the Pandemic

During the current pandemic, globally distributed software development teams have had to adapt their communication strategies. IT-students will need to learn new soft skills during their university studies. This work presents results from qualitative interviews of IT professionals during the current pandemic. Specific problems in adapting to remote, online work were identified. Although technical hurdles
Mine Remote Sensing Dataset Construction for Multi-level Tasks

This chapter mainly covers the constructions of mine remote sensing datasets for multi-level tasks, i.e., mine target detection, mine scene classification, and mine land cover classification based on semantic segmentation methods. The multi-level interpretation tasks were based on “pixel, target, and scene primitives”, focusing on problems related to the mine geo-environment, with a close focus on land use and land cover issues within a mining area. For each dataset, the remote sensing data source, construction method, classification scheme, the partition of training, validation, and test sets, and visual presentation were described in detail.
SOVERIGNITY OF HUMAN RESOURCE MANAGEMENT ON ORGANIZATION EFFECTIVENESS AND DEVELOPMENT

Human Resource Management is a critical function in both the corporate and public sectors. The process addresses several operational objectives, including recruitment, providing proper training to the employee, selecting the employee, assessing the employee, motivating and maintaining a proper relationship with the employee, and ensuring the welfare and health of the employees in the organisation through laws enacted by the relevant state and country. Human resource management is the process through which an organisation makes use of its limited skilled staff. The fundamental goal is to make the most use of the organization's existing human resources. One of the primary goals of human resource management in the firm is to offer employees with a good working environment. The actions that promote the employee's dignity and growth in the organisation are referred to as the quality of work life. It enables employees to collaborate inside the organisation and the activities, adjustments, and improvements required for goal achievement, improving quality of life, and increasing organisational effectiveness. The purpose of this paper is to discuss the effects of human resource management on organisational performance and development. In recent times, organisations have focused not just on employees for work-related aspects, but also on an employee's overall well-being. Human resource management is comprised of four major functions they are Human resource acquisition, development, maintenance, and motivation. These are the fundamental elements or critical stages that assist an organization in achieving its objectives. It is a truth that the employee's life cannot be compartmentalised and that any disruption in the employee's personal life will disrupt the professional life as well. As a result, human resource management works for the overall growth of the employee by minimising stress and providing a healthy work environment.
‘SOUL’ Coaching Model for Spiritual Maturity A Composition of the Program Lee, So

The purpose of this study is to compose the <SOUL Coaching Model Program> for spiritual material of Christians, and then to review the program’s suitability. As a study(1) before performing(2) the empirical study, total three research questions were set up as achieve the purpose of the study. First, this study examined how the SOUL-COACH Coaching Model for spiritual maturity was composed, and then reviewed the model’s suitability. As a result, the second validation that was modified/complemented based on the first validation, was analyzed through a 7-point Likert-type scale. Shown as 6.33/7. which an average level of 90%, suitability of the <SOUL Coaching Model Program> was recognized. Second, this study examined how the ‘SOUL’ Coaching Model Program based on the SOUL-COACH Coaching Model was composed by selecting six items such as The Lord's Prayer, the Apostles' Creed, the Ten Commandments, the Beatitudes, the Book of Love in 1 Corinthians, and the 9 Fruits of the Holy Spirit, which are considered as the basics for spiritual maturity of Christians. Third, this study examined how the coach curriculum for the dissemination of the SOUL Coaching Program was composed. The satisfaction with education for the composition of the coach training course was 5.50/7. which showed a satisfaction average of 78.6 %. The effectiveness of executing the <SOUL Coaching Model Program> developed by this study will be analyzed in a follow-up empirical study.
Ontology Modeling

Knowledge modeling is an important task that constitutes the process of building knowledge-intensive systems. Ontology modeling allows for the use of appropriate formalisms for representing relevant domain knowledge using ontological structures to promote machine interoperability and communication. Heterogeneous knowledge and legacy systems benefit from interoperability derivable from ontology models. However, this interoperability can be limited by the choice of ontology formalism used for modeling. In addition, this could also impact the inference-making process of the resulting ontology. It is therefore important to understand the wide range of ontology modeling languages available, and their pros and cons. Ontology engineering methodology applied to the process of ontology modeling is another important concept needed for accurately representing domain knowledge. There are various tools and paradigms employed by knowledge engineers to modeling domain knowledge. This chapter presents a systematic overview of semantic web languages and formalism for ontology representation. We also examine different paradigms applied to the engineering process of ontology modeling and provide a comparative analysis of these approaches which supports a comprehensive and accurate domain knowledge representation. In addition, a review of available ontology modeling tools and editors is considered with emphasis on Protégé editor. We emphasize the need for an appropriate selection of ontology formalism, engineering methodology, and the use of editing tools during the engineering process of domain ontology to leverage the semantic web and its ecosystem of languages, tools, reasoners, and inference making capability.
A Machine-Learning Pipeline for Semantic-Aware and Contexts-Rich Video Description Method

Video description (VD) methods use machine learning to automatically generate sentences to describe video contents. Global-description based VD (gVD) methods generates global description to provide the big picture of video scenes but they lack finer grain entities information. Meanwhile, modern entity-based VD (eVD) use deep learning to train ML models like object model (YOLOv3), human activity model (CNN), location tracking (DeepSORT) to resolve individual entity that made up the complete sentences. However, existing eVD are limited in the types of supported entities; thus, resulting in eVD generating sentences that contexts-deprived and incomplete to clearly describe video scenes. In addition, the entities resolved by eVD are isolated since they are inferred from different ML models; resulting in sentences that are not semantically cohesive; contextually and grammatically. In this paper, a two-stages ML pipeline (teVD) is proposed for a holistic and semantic-aware VD sentence generation. Firstly, a ML pipeline is designed to aggregate several high performing ML models for resolving fine grain entities to improve the accuracy of resolved entities. Second, the components in the entities set are ‘stitched’ together using an entity trimming method to (1) remove shadow entities and (2) to re-arrange entities based on linguistic rules to generate video descriptions that are context-aware and less ambiguous. The experimental results showed that teVD successfully improved the quality of generated sentences in short videos; achieving BLEU score of 48.01 and METEOR score of 32.80 on MSVD dataset.
The Recent Trends and Essential factors of Blockchain Technology with The Internet of Things: A Literature Review

Blockchain technology is developing very rapidly, especially those using smart contracts. Some of the research conducted has combined several technologies such as IoT with Blockchain (Blockchain and IoT/BIoT). BIoT creates new features and benefits that go beyond just internetconnected sensors. Many research articles have been conducted. However, it very difficult to track the trend of BIoT research development recently. This research is the initial stage of all the steps in the research being carried out to describe the latest trend developments of research on the BIoT. Apart from that, this research will explain the trend development of BIoT, advantages and problems of BIoT that are currently found. This research was facilitated by the VosViewer application, which shows first-hand the development of BIoT. The benefits of research directly or indirectly will accelerate the process of scientifically developing BIoT and using BIoT at the industry in the future. Keywords— Blockchain Technology, Internet of Things, BIoT, Publish and Perish, Bibliography study.
The Insurtech Revolution in Insurance Industry: Emerging Trends, Challenges and Opportunities

The insurance sector is undergoing a global upswing in technology improvements to its operations and products, especially in those areas where the FinTech environment has expanded significantly. From the perspective of InsurTech, India is noteworthy because of the scale of its uninsured population. The term "InsurTech" refers to technical advancements developed and applied to increase the effectiveness of the insurance sector. In the upcoming years, InsurTech as a sector is guaranteed to gain the attention of insurance industry pioneers. The primary goal of this study is to discuss the Indian insurance industry's adoption of InsurTech. It explains the development of the insurance technology sector in India as well as the current state of insurance technology. The study used a descriptive form of research approach because the paper attempted to detail many features of the phenomena in this setting. InsurTech development, Indian InsurTech structure, InsurTech start-up in India, InsurTech trends in India, and InsurTech issues are all examined in this research study.
Exploring the Applicability of Topic Modeling in SARS-CoV-2 Literature and Impact on Agriculture

For the last two years, countries around the globe have been suff ering and severely aff ected by the Covid-19 pandemic due to the novel coronavirus. Researchers from various disciplines are conducting research and publishing number of articles related to this virus and its eff ects. Furthermore, articles related to Covid-19 are being continuously published in the form of research papers, popular articles, blogs, surveys, short stories etc. These possess useful information and this information can be processed to infer important knowledge by applying text mining techniques. The Latent Dirichlet Allocation (LDA) technique provides an effi cient way to analyse unclassifi ed text into useful sets of terms, called topics. LDA can group terms with similar semantic meaning into topics called "themes". A theme is a group of terms that frequently appear together. The objective of the present study is to explore the applicability of topic modeling in identifying the hidden themes or topics by using published research articles related to Covid-19 and agriculture through Google scholar. After pre-processing of titles and abstracts, two approaches namely LDA with Bag of Words (LDAB) and LDA with Term Frequency-Inverse Document Frequency (LDAT) were applied to fi nd the hidden themes. There are thirteen and seven topics are identifi ed by applying LDAB and LDAT respectively. These identifi ed topics comprised with diff erent set of words or features will play an important role in developing the information retrieval system for specifi c search related to agricultural production, supply chain mechanism in agriculture, health and agri-tourism.