Reviewerly/exHarmony huggingface dataset:
Nature Writing in transzendentaler Perspektive: Friedrich Hölderlin und Henry David Thoreau

Proceeding from the common roots of Friedrich Hölderlin and Henry David Thoreau in the philosophical ideas of (German) idealism, the essay explores concepts of Nature Writing in the work of both authors. Their philosophical and literary reflections show that Hölderlin critically distances himself from Johann Gottlieb Fichte’s systemic philosophy of pure and subjective idealism as Thoreau critically dissociates his concept from Ralph Waldo Emerson’s transcendental idealism. Based on Lawrence Buell’s criteria defining Nature Writing, Thoreau’s Walden Pond and Hölderlin’s hymn “The Rhine” are examined as literary examples which share the common aim of acknowledging nature in the sense of its own agency and power of understanding as opposed to the ‚Ich‘ or to the ‚spirit‘. Special attention is paid to four areas: first, to Hölderlin’s and Thoreau’s redefinition of the quality ‚pure‘ which had predominantly been denoted by the term ‚understanding‘ in Kantian philosophy for natural phenomena e.g. as water; next to a receptive orientation with respect to man and nature as well as the overlapping of this polarisation by a concept of processuality; and finally to the discussion of the ‚unconditioned‘ as the reason for existence.
A Parking Trajectory Planning Method based on Geometric Constraints

Planning a safe trajectory is a crucial procedure for autonomous parking. In existing approaches, parking trajectories are usually designed without considering the evolution from one configuration to the next configuration. Hence, these trajectories are probably unfeasible in some scenarios. To address this problem, a guaranteed collision-free trajectory planning method is proposed in this paper. A dynamic optimization problem is built, which includes vehicle kinematics, collision avoidance and physical constraints. To describe collision avoidance constraints, the concepts of virtual protection frame (VPF) and magnification parameter are introduced. It is highlighted that a novel collision detection criterion is raised from the view of geometry. In the parallel parking scenario, it is illustrated that the trajectory generated by the proposed method is more feasible than that generated by the conventional method.
Philosophy and Pedagogy: Aspects of Interaction

The purpose of the study is to show possible aspects of interaction between philosophy and pedagogy. Using historical material as an example, the article demonstrates that philosophy at various stages of its development provided a foundation for pedagogy. The researcher ascertains worldview and methodological role of philosophy for pedagogy in modern conditions. The study is novel in that it takes a comprehensive, interdisciplinary approach to solving the issue of interaction between philosophy and pedagogy. As a result, it was proved that interaction between philosophy and pedagogy is realised through their integration, the result of which is emergence of philosophy of education, a new academic discipline and field of knowledge. Due to its interdisciplinary nature, there occurs fusion of contradictions between philosophy and pedagogy.
A Survival Game Analysis to Personal Identity Protection Strategies

Throughout the years, authentication processes of individuals' identities have become essential parts of our modern daily life. These authentication processes also introduced the heavy use of Personally Identifiable Information (PII) in various applications. On the other hand, the continuous increase of using identifiable information -the unauthorized use of such PII-has created rich business opportunities for identity protection service providers. These services usually consist of a monitoring system that continuously searches through the Internet for incidents that supposedly indicates identity theft activities. However, these solutions are largely based on case studies and a quantified method is missing among different identity protection services. This research offers a tool that provides quantitative analysis among different identity protection services. By bringing together previous work in the field, namely the UT Center for Identity (CID) Identity Ecosystem (a Bayesian network mathematical representation of a person's identity), real world identity theft data, stochastic game theory, and Markov decision processes, we generate and evaluate the best strategy for defending against the theft of personal identity information. One of the research problems that this paper addresses is the computation complexity of quantitatively evaluating identity protection strategies with real world data. In a real world database like Identity Threat Assessment and Prediction (ITAP) project which the UT CID Identity Ecosystem is built on, the number of PII attributes in use are normally in the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . We propose a reinforcement learning algorithm for solving the optimal strategy to protect the user's identity against a malicious and efficient attacker. We aim to understand how initial individual PII exposure evolves into crucial PII breaches over time in terms of the dynamic integrity of the Identity Ecosystem. Real world identity protection strategies are then translated into the system and we evaluate/simulate their effectiveness against a malicious attacker for quantitative comparison in our experiment. We present the survival analysis to these strategies and calculate the survival gap between these strategies against our active protection strategy as our experiment result. This study is aimed to understand the evolutionary process of identity under attack which may inspire a new direction for future identity protection strategies.
Improvement and simulation verification of a novel Unscented Kalman Filter based on FPGA

Unscented Kalman filter (UKF) has been widely used in engineering practice to solve the problem of system state change in nonlinear system. However, the traditional structure of UKF in high-dimensional system will appear filtering numerical instability or filtering divergence, low robustness, the filtering precision can not meet the requirements and so on. This design aims at this series of problems, introduce q differentiation theory and Huber estimation to improve the measurement and update mode of UKF, and take advantage of the advantages of FPGA technology, such as strong reconfiguration, good expansibility, less hardware resources. Using maneuvering target tracking CA model to verify the novel UKF, and using Verilog HDL language to design and implement each module. Modelsim and Matlab are used to carry out simulation and verification for the improved UKF application example. The experimental results show that the novel UKF filtering accuracy and robustness are improved and meet the design requirements
Shared Mobile-Cloud Deep Learning Model Inference

As AI applications for mobile devices become more prevalent, there is an increasing need for faster inference on mobile. Inference is the process of taking input data (e.g. images, audio), processing it through a deep learning model, and retrieving a result (e.g. a description of the image/audio contents).Currently, models are either run completely on the mobile device or completely on the cloud. However, running inference only on the cloud costs network bandwidth introduces latency, and requires the input data to be fully transferred to the cloud, creating privacy concerns.We demonstrate an alternative approach: shared mobile-cloud inference. Partial inference is performed on the mobile device to reduce the size of the input data. The results of partial inference are then transmitted to the server for further inference. This strategy can improve latency, reduce bandwidth usage, and provide privacy protection because the input data never leaves the mobile.
Parallelism strategies for the tuneable golden-claw finding problem

In this paper we study a strategy for adapting the “Tiny Claw” Grover-based attack of Biasse and Pring (A framework for reducing the overhead of the quantum oracle for use with Grover's algorithm with applications to cryptanalysis of SIKE, J. Math. Cryptol. 15 (2019), pp. 143–156) for attacking SIKE and abstract it under a realistic model of classical memory-access costs. Our results allow us to retain the almost quadratic reduction in the overheads involved with the implementing the quantum oracle in this cost model and demonstrate how the cost of the parallel version of this attack scales in a manner superior to that of a naive use of Grover’s algorithm. In order to investigate the utility of the Tiny Claw approach, we perform a quantum resource estimation of the classical and quantum resources required to attack various SIKE instances with Tiny Claw when when we are limited to ≤296 hardware, finding interesting price-points.
A Layer-Wise Ensemble Technique for Binary Neural Network

Binary neural networks (BNNs) have drawn much attention because of the most promising techniques to meet the desired memory footprint and inference speed requirements. However, they still suffer from the severe intrinsic instability of the error convergence, resulting in increase in prediction error and its standard deviation, which is mostly caused by the inherently poor representation with only two possible values of [Formula: see text]1 and [Formula: see text]1. In this work, we have proposed a cost-aware layer-wise ensemble method to address the above issue without incurring any excessive costs, which is characterized by (1) layer-wise bagging and (2) cost-aware layer selection for the bagging. One of the experimental results has shown that the proposed method reduces the error and its standard deviation by 15% and 54% on CIFAR-10, respectively, compared to the BNN serving as a baseline. This paper demonstrated and discussed such error reduction and stability performance with high versatility based on the comparison results under the various cases of combinations of the network base model with the proposed and the state-of-the-art prior techniques while changing the network sizes and datasets of CIFAR-10, SVHN, and MNIST for the evaluation.
Metric-Type Identification for Multilevel Header Numerical Tables in Scientific Papers

Numerical tables are widely used to present experimental results in scientific papers. For table understanding, a metric-type is essential to discriminate numbers in the tables. Herein, we introduce a new information extraction task, i.e., metric-type identification from multilevel header numerical tables, and provide a dataset extracted from scientific papers comprising header tables, captions, and metric-types. We propose joint-learning neural classification and generation schemes featuring pointer-generator-based and pretrained-based models. Our results show that the joint models can manage both in-header and out-of-header metric-type identification problems. Furthermore, transfer learning using fine-tuned pretrained-based models successfully improves the performance. The domain-specific of BERT-based model, SciBERT, achieves the best performance. Results achieved by a fine-tuned T5-based model are comparable to those obtained using our BERT-based model under a multitask setting.
Anaffective Aware Pseudo Association Method to Connect Disjoint Users Across Multiple Datasets – An Enhanced Validation Method Fortext-based Emotion Aware Recommender

We derive a method to enhance the evaluation for a text-based Emotion Aware Recommender that we have developed. However, we did not implement a suitable way to assess the top-N recommendations subjectively. In this study, we introduce an emotion-aware Pseudo Association Method to interconnect disjointed users across different datasets so data files can be combined to form a more extensive data file. Users with the same user IDs found in separate data files in the same dataset are often the same users. However, users with the same user ID may not be the same user across different datasets. We advocate an emotion aware Pseudo Association Method to associate users across different datasets. The approach interconnects users with different user IDs across different datasets through the most similar users' emotion vectors (UVECs). We found the method improved the evaluation process of assessing the top-N recommendations objectively.
WITHDRAWN: Demonstrating impact of noise over classifier performance in a privacy-preserving data mining application

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Defining the normal range for screening panel of neutrophil function tests in healthy adult Indian participants

Background: Neutrophils, the most abundant white blood cells are known to play a major role in the fundamental physiological response to tackle with microbial infections via different mechanisms. A series of assays have been developed that mimic neutrophil functions both in vivo and in vitro to look at specific deficiencies of neutrophil function. There is need to develop method to have defined normal range for these tests to differentiate between health and disease. We aim to define normal range for panel of screening assays in healthy adult Indian participants.Materials and Methods: The study included 30 male and 30 female adult healthy participants. A volume of 10 ml of blood was collected from each participant. Four different assays that included chemotaxis, phagocytosis, intracellular killing (candidacidal assay), and nitroblue tetrazolium (NBT) test were performed. For defined range was established for all the assays. Simultaneously controls were set up for all the tests.Results: For NBT assay, the mean values for control and test were 13.58 and 58.90 respectively. The mean values for the control and test for phagocytosis assay were 1.77 and 4.49. Similarly, the values for candidacidal assay were 11.67% and 34.98. Finally, the values for chemotaxis test were 2.13 cm for Formyl-Methionyl-Leucyl-Phenylalanine, 0.54 cm for participant's serum and 0.48 cm for the medium.Conclusion: Based on the data generated, the values produced in the present investigation should be considered as normal range for adult population between the age group of 25–50 years for the protocols followed in the study.
Modelling and Factorizing Large-Scale Knowledge Graph (DBPedia) for Fine-Grained Entity Type Inference

Recent years have witnessed a rapid growth of knowledge graphs (KGs) such as Freebase, DBpedia, or YAGO. These KGs store billions of facts about real-world entities (e.g. people, places, and things) in the form of triples. KGs are playing an increasingly important role in enhancing the intelligence of Web and enterprise search and in supporting information integration and retrieval. Linked Open Data (LOD) cloud interlinks KGs and other data sources using the W3C Resource Description Framework (RDF) and makes accessible on web querying. DBpedia, a large-scale KG extracted from Wikipedia has become one of the central interlinking hubs in the LOD cloud. Despite these impressive advances, there are still major limitations regarding coverage with missing information, such as type, properties, and relations. Defining fine-grained types of entities in KG allows Web search queries with a well-defined result sets. Our aim is to automatically identify entities to be semantically interpretable by having fine-grained types in DBpedia. This paper embeddings entire DBpedia, and applies a new approach based on a tensor model for fine-grained entity type inference. We demonstrate the benefits of our task in the context of fine-grained entity type inference applying on DBpedia, and by producing a large number of resources in different fine-grained entity types for connecting them to DBpedia type classes.
RECURSIVE AXIOMATISATIONS FROM SEPARATION PROPERTIES

Abstract We define a fragment of monadic infinitary second-order logic corresponding to an abstract separation property. We use this to define the concept of a separation subclass . We use model theoretic techniques and games to show that separation subclasses whose axiomatisations are recursively enumerable in our second-order fragment can also be recursively axiomatised in their original first-order language. We pin down the expressive power of this formalism with respect to first-order logic, and investigate some questions relating to decidability and computational complexity. As applications of these results, by showing that certain classes can be straightforwardly defined as separation subclasses, we obtain first-order axiomatisability results for these classes. In particular we apply this technique to graph colourings and a class of partial algebras arising from separation logic.
Content based Video Retrieval using Improved Gray Level Co-Occurrence Matrix with Region-based Pre Convoluted Neural Network–RPCNN

With the improvement of sight and sound information types and transfer of accessible data capacity, there is a gigantic interest of video retrieval framework, as clients move from text based retrieval frameworks to content based retrieval framework. Choice of removing highlights assumes a significant part in the content based video retrieval paying some consideration to video credits. These highlights are planned for choosing, ordering and positioning as per their likely enthusiasm towards the client. This paper proposes an improved technique for feature extraction and classification using Improved GLCM (I-GLCM) with Region based pre convoluted neural network (R-PCNN). The simulation result shows the optimized accuracy, F-1 score, precision and recall as the parameters.
Legal Issues Regarding Protection of Genetic Information

This paper examines both international and national legislation, regulating specific issues about genetic information. Particularly the Anglo-American system, especially the USA, according to the authorsâ€™ opinion, is leading in this category.
COMPOUND PROPOSITIONAL LAW FOR LOGICAL EQUIVALENCE, TAUTOLOGY AND CONTRADICTION

This paper presents a Compound Propositional Law for Logical Equivalence, Tautology and Contradiction. The proposed Law is developed with the help of negation, disjunction, conjunction, exclusive or, conditional statement and bi-conditional statement. The idea of research is taken from de-Morgan law. This proposed law is important and useful for Logical Equivalence, Tautology and Contradiction for the research purpose because these are the rare cases in the field of research. This article aims to help readers understand the compound proposition and proposition equivalence in conducting research. This article discusses propositions that are relevant for proposition equivalence. Six main compound propositions are distinguished and an overview is given in the article. Hence, it is observed from the result and discussion that the compound proposition law is a good achievement in discrete structure for the logical Equivalence, Tautology and Contradiction purpose.
UAS-based hyperspectral and magnetic mineral exploration targeting Ni-PGE mineralization on Northern Disko Island, West Greenland.

&amp;lt;p&amp;gt;Geologic mapping in arctic regions faces demanding challenges, from accessibility to weather, light and infrastructure conditions. Field expeditions need to cover substantial area, and mostly are supported by satellite and airborne data. While named methods offer large-scaled insights, they often lack the required resolution for precise ground investigations. The rise of unmanned aerial systems (UAS) as new state-of-the-art platform in geoscience provides the means needed to close that scale gap.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Fieldwork within the frame of the EIT project MULSEDRO focused on the Paleocene flood basalt province of Disko Island (West Greenland). On the example of the Qullissat area, we demonstrate how UAS can bring new insights into strategies for magmatic Ni-PGE exploration in the area. Mineralization is associated to basalt sills of the Asuk Member, emplaced locally in coal-bearing cretaceous sandstones. &amp;amp;#160;We conducted photogrammetric outcrop modelling, interpretation of orthoimagery, multi- and hyperspectral based lithological classification and analysis of magnetic data. While magnetics give the location, orientation and subsurface extension of the basaltic sills, spectral imaging, in particular with focus on the iron absorption feature, reveals mineral proxies due to sulphide weathering. A total of 216 line-km for magnetics and 18.5 km&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; of multi- and hyperspectral data was covered.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;First results show that integration of drone-borne spectroscopic and magnetic data highlights potential local mineralization. Based on our results, possible indications for mineralization are linear features in the first vertical derivative of the magnetic data and specific iron absorptions in the spectral data. Resulting maps are validated using handheld spectroscopy, ground magnetics, susceptibility measurements, combined with geochemistry and mineralogy of rock samples examined in the laboratory. Conclusively, the study solidifies UAS as highly valuable tool for exploration.&amp;lt;/p&amp;gt;
Quantum Information Theory

The present chapter introduces the mathematical modeling of quantum communication systems in the spirit of Shannon’s conception of information theory. Regarding the choice of topics treated as well as the methodological approach pursued, one can draw analogies with the material covered in standard classical information theory modules, which are intended for master’s as well as bachelor’s students of electrical engineering and communication sciences, among other majors. After covering the rather fundamental communication protocols, we move on to more advanced models in which communication involving different types of quantum channels can be performed. Topics covered in the chapter include, quantum teleportation and dense coding, quantum hypothesis testing, source coding for memoryless quantum sources and coding theorems for classical message transmission over quantum and classical quantum memoryless channels. We conclude the chapter with selected advanced topics such as information-theoretic security for quantum channels, optimal protocols for generation of classical and quantum resources and finally, communication under more realistic models such as the compound and arbitrarily varying channels.