bluuebunny/crossref_metadata_2025 huggingface dataset:
Crup en dos pacientes pediátricos con COVID-19
Authors: Salguero-Lobos Dulce , Orozco-Vizquerra Ana
Published: December 2022

La patogenia de la coexistencia de Croup y Covid-19 es aún desconocida.  No existen datos ni suficientes casos para correlacionar dichas patologías. En el Hospital Roosevelt, Guatemala, de Enero a Marzo 2022, se atendieron 2 pacientes pediátricos Covid-19 positivos que presentaron signos clínicos y radiológicos de Crup. Presentamos estos casos con el fin de agregar información al tema.
Grundinformation Dogmatik
Authors: Leonhardt Rochus
Published: December 2022

Das Buch vermittelt einen Einblick in die Hauptinhalte und -probleme der christlichen Dogmatik sowie maßgebliche dogmen- und theologiegeschichtliche Zusammenhänge. Die quellennah ausgearbeitete Darstellung ist didaktisch aufbereitet: Definitionen, Arbeitsaufgaben, Übersichten, Hinweise auf vertiefende
Literatur, Tipps zur Arbeit mit dem Internet, Glossar etc. – völlig neu bearbeitet.
Projektarbeit erfolgreich gestalten
Authors: Traub Silke
Published: December 2022

"Projektarbeit erfolgreich gestalten" konkretisiert selbstgesteuerte Kleingruppenprojektarbeit.
Ausgehend vom individualisierten und kooperativen Lernen stellt Silke Traub dar, wie schrittweise selbstgesteuerte Kleingruppenarbeit entwickelt und ein erfolgreicher Projektunterricht erreicht wird. Auf Grundlage selbstgesteuerten Lernens und historischer Projektmodelle wird das Sandwich-Prinzip als Rahmen für individualisiertes und kooperatives Lernen begründet. Die Umsetzung erfolgt über die PROGRESS-Methode zur Entwicklung von Lernstrategien für individualisiertes und kooperatives Lernen.
Arbeitsaufgaben unterstützen die inhaltliche Auseinandersetzung.
Das Buch richtet sich an Studierende und Lehrende aller Schularten, die sich mit selbstgesteuertem Lernen und Projektarbeit befassen, aber auch an Lehrende in der Erwachsenenbildung.
Geschichte Japans
Authors: Zöllner Reinhard
Published: December 2022

Vom Ende der Frühen Neuzeit bis in die Gegenwart: Dieses Buch bietet eine klar strukturierte Darstellung der Geschichte Japans. Die Leser:innen erhalten dabei nicht nur einen Überblick über die Ereignisgeschichte des Landes, sondern gewinnen auch einen Einblick in die japanische Mentalität sowie die Wechselwirkungen zwischen Politik, Wirtschaft, Sozialordnung, Kultur und Medien, in deren Spannungsfeld sich das moderne Japan konstituierte.
Reinhard Zöllner liefert auf der Basis aktueller methodischer Zugänge eine anschauliche und reich bebilderte Einführung in die Geschichte des ostasiatischen Inselreichs während der letzten 200 Jahre.
Deutschdidaktik
Authors: Hochstadt Christiane , Krafft Andreas , Olsen Ralph
Published: December 2022

Dieser Band liefert eine Übersicht über wesentliche deutschdidaktische Konzeptionen und präsentiert sprach-, literatur- und mediendidaktische Ansätze. Dabei orientiert er sich an den Kompetenzbereichen der KMK-Bildungsstandards. Jede Konzeption wird überblickshaft dargestellt, problematisiert sowie durch Aufgaben und kommentierte Literaturhinweise ergänzt. Das Buch bietet eine unersetzliche Grundlage, um Deutschunterricht fundiert zu planen und zu reflektieren.
Für die 3. Auflage wurden einige Kapitel neu erstellt; daneben wurden alle bedeutsamen deutschdidaktischen Erkenntnisse der letzten Jahre eingearbeitet und das Thema Inklusion stärker in den Vordergrund gerückt.
„Wer eine gut verständliche und fachlich fundierte Einführung in Konzeptionen der Deutschdidaktik sucht, lese den Band von Hochstadt, Krafft und Olsen.“
Prof. em. Dr. Dr. h. c. Kaspar H. Spinner, Universität Augsburg
Digitale Methoden für qualitative Forschung
Authors: Franken Lina
Published: December 2022

Text Mining und Topic Modeling, Netzwerkanalyse, Sentiment Analyse, Visualisierungen oder digitales Annotieren: Computationelle Methoden sind auch in der qualitativen Forschung angekommen. Sie können Forschungsprozesse unterstützen und erweitern.
Der Band zeigt in verständlicher Form mit konkreten Lösungen auf, welche Verfahren für welche Analyseschritte geeignet sind. Dafür werden auch Möglichkeiten der digitalen Datenerhebung sowie Grundkenntnisse der Digital Literacy vermittelt.
In einem Glossar sind hilfreiche (open source) Software, Online-Portale und Datenrepositorien gebündelt. Übungsaufgaben ermöglichen die praktische Anwendung des Grundlagenwissens. Studierende und Forschende können auf Basis des Bandes selbstständig digitale Daten und Verfahren zielgenau verstehen, anwenden und hinterfragen.
Sozialarbeitswissenschaftliche Diagnostik
Authors: Richter-Mackenstein Joseph
Published: December 2022

Die Diagnostik ist ein zentraler Bestandteil der Tätigkeit von Fachkräften Sozialer Arbeit. Mit diesem Lehrbuch stellt Prof. Dr. Richter-Mackenstein die erste vollwertige Systematik sozialarbeitswissenschaftlicher Diagnostik zur Verfügung. Es wird in die zentralen Grundprinzipien und Methoden sozialarbeitswissenschaftlicher Diagnostik eingeführt und somit die Basis für Wissen und Können unter besonderer Berücksichtigung der Haltungsbildung rund um die sozialarbeitswissenschaftliche Diagnostik geschaffen.
Zu den Grundlagen zählen ein Überblick über das Fach und seine Systematik, das Nachzeichnen seiner historischen Entwicklung sowie die Einführung in zentrale Methoden und Ziele sowie Hintergründe sozialarbeitswissenschaftlicher Diagnostik. Lernzielformulierungen zu Beginn und Kontrollfragen sowie vertiefende Literaturhinweise am Ende jedes Kapitels runden das Basiswerk ab.
Neutestamentliche Exegese kompakt
Authors: Kaiser Ursula Ulrike
Published: December 2022

In allen theologischen Studiengängen ist die Fähigkeit zur methodengeleiteten Auslegung biblischer Texte eine zu erlangende Kernkompetenz.
Diese Einführung verbindet die traditionellen Schritte der historisch-kritischen Exegese des Neuen Testaments mit neueren Ansätzen aus der Linguistik und Literaturwissenschaft.
Das Buch motiviert die Lesenden durch eine direkte Ansprache, nimmt viele Fragen aus der Lehrpraxis auf und stellt die praktische Umsetzung in den Vordergrund.
Viele Beispiele der neutestamentlichen Exegese illustrieren das methodische Vorgehen und den damit verbundenen Erkenntnisgewinn.
Insbesondere die Voraussetzungen von Studierenden ohne Kenntnisse des Altgriechischen werden durchgehend beachtet.
Eingehend behandelt wird auch der zielführende Umgang mit digital verfügbaren Ressourcen, wie Online-Lexika, Textdatenbanken, digitalisierten Handschriften, Bibelsoftware etc.
Zehn Gebote für das philosophische Schreiben
Authors: Hübner Dietmar
Published: December 2022

Dietmar Hübner bietet Hilfe zur Selbsthilfe beim Verfassen von philosophischen Seminar- und Abschlussarbeiten.
Wesentliche Aspekte des wissenschaftlichen Schreibens – von Themenfindung und Strukturierung über Gedankenführung und Stil bis zu Quellennutzung und Zitierweise – werden kurz und prägnant vermittelt. Zahlreiche Beispiele positiver wie negativer Art machen deutlich, was eine gelungene Arbeit im Philosophie-Studium auszeichnet.
Graph Based Deep Learning for Spatial Reuse Optimization in Dense WLAN Deployments
Authors: Azeez Stephen , Henna Shagufta
Published: December 2022

The IEEE 802.11ax standard is designed to provide high-efficiency WLAN operating in dense deployment, with a focus on increasing robustness and uplink transmission.  IEEE 802.11ax aims to deliver self-configuration and self-adaptation capabilities for interference management techniques in dense deployments, to improve overall network conditions. The IEEE 802.11ax medium strives to intelligently use network allocation vectors (NAV), which block simultaneous transmission of neighboring co-existing access points from transmitting for a set period of time. Given an acceptable collision probability, this collision avoidance strategy tends to degrade network throughput and increase transmission delays since the medium is never completely exploited. In this paper, we propose affinityGNN, a graph neural network approach to actively optimize network co-existence transmission in a dense scenario. We introduce direct-affinityGNN which considers direct communications between IEEE802.11ax-compliant wireless systems and in contrast, skip-affinityGNN, which considers communication between IEEE802.11ax and Legacy devises by aggregating neural messages from direct and skip (two-hop) communicating neighbors in the dense wireless network deployment. Extensive experimental results not only show the superior performance of our proposed model over the state-of-the-art, but also demonstrate its potentially good interpretability and robustness for wireless network structure.
Computational Processes for General Intelligence
Authors: Balakrishnan Prabhakar
Published: December 2022

The ‘brain-mind-intelligence’ structure may be considered analogous to a ‘computer-operating system (OS)-application’ construct. Software development would be far more difficult without the benefit of abstractions like file and memory handles, and graphical user interfaces, implemented in operating systems. The easy access to application-level semantics, provided by the infrastructure of the OS, simplifies application development.  Abstractions can play a similar role in producing intelligent systems. The power of abstractions may be determined by their ability to unify the treatment of several types of higher concepts. Unifying various concepts in the domain of intelligence, such as problems, solutions, objects, and emotions, through abstractions will be of immense value. This paper shows how ‘higher-level perceptions’ could be used to serve this objective and construct an OS-like infrastructure. With the infrastructure in place, ordinary software objects developed and deployed using standard methods, become accessible using the semantics provided by the infrastructure. Now, after brief training exercises, the objects would be available for intelligent use. The focus, therefore, is on the design of the infrastructure that could play a mind-like role in humans.

The design results in a new, non-symbolic, non-connectionist, transmutable method of representation, essential for intelligence. The infrastructure enables the conversion of sensory inputs to transmutable representations amenable to semantic modifications, to flexibly determine actions to perform on software objects developed externally. Therefore, the OS-like infrastructure serves to bridge the rudimentary semantics of connectionist outputs to the semantics at the symbolic level. Thus, the new model plays a critical role in building intelligent systems that overcome major problems in learning and action selection, including brittleness, associated with the narrow semantics of purely symbolic and connectionist designs. 

The new model is then evaluated in terms of features, against criteria set forth independently by Newell, Sun, Vernon, and others. In addition, a new measure of intelligence introduced in this paper is used to quantitatively compare the proposed model with some others developed over the years. 
FARMYARD: A Generic GPU-based Pipeline for Feature Discovery from Massive Planetary LiDAR Data
Authors: Liang Shen , Lucas Antoine , Porder Stephen , Sainton Gregory , Fournier Alexandre , Palpanas Themis
Published: December 2022

In recent decades, with the placement of LiDAR remote sensing instruments in orbit, we now have global coverage of the bare-ground elevation on the Earth, Mars and beyond. Encoded in such planetary LiDAR data are interesting landscape features that promise to further our knowledge of planetary topography. However, discovery of such features entails 3 major challenges: 1) massive data; 2) the need for local multi-scale features; 3) sensitivity to interfering factors. To address these challenges, we propose FARMYARD, a generic pipeline for \underline{F}e\underline{a}ture Discove\underline{r}y Fro\underline{m} Planetar\underline{y} LiD\underline{AR} \underline{D}ata Data. To our knowledge, this is the first time such a pipeline has been proposed, which provides a brand new methodology for comparative studies of planetary topography. Specifically, drawing on the parallel computing power of the Graphics Processing Unit (GPU), we propose a novel pseudo-on-pass sweep (POPS) framework for fast and memory-efficient feature extraction for massive planetary LiDAR data, a two-level division scheme for local regions with support for multi-scale features, and a Domain-Shifted Partition (DSP) scheme for feature evaluation that is robust against interfering factors. To showcase the utility of our FARMYARD pipeline, we deploy it to a real-world research project, which seeks to find topographical signatures of life by discovering features that can potentially distinguish between the Earth and alien worlds with no known life activity. We also highlight the efficiency of our POPS framework with experiments on both synthetic and real data, which can be thousands of times faster than its CPU-based counterpart, including a multi-core parallel solution.
Domain Adaptation-Based Deep Learning Models for Forecasting and Diagnosis of Glaucoma Disease
Authors: Madadi Yeganeh , Abu-Serhan Hashem , Yousefi Siamak
Published: December 2022

Domain adaptation methods are designed to extract shared domain-invariant features by projecting data on a common subspace in order to align their domain distributions. However, these methods do not usually consider domain-specific features, and therefore their distributions may not be well aligned. To address this problem, we introduce a novel model that learns domain-invariant and domain-specific representations to extract both their general and specific features. We also propose progressive weighting to accurately transfer the source domain knowledge and to mitigate negative knowledge transfer from the source to the target domain and to employ low-rank coding for aligning the source and target distributions. We evaluated the model based on several real-world datasets and showed that our method significantly improved the accuracy for forecasting and diagnosis of glaucoma disease from fundus photographs.
Secure Ultra Fast Authentication Protocol for Electric Vehicle Charging
Authors: Mookherji Srijanee , Odelu Vanga , Prasath Rajendra
Published: December 2022

In recent years, Electric Vehicles (EVs) are becoming popular due to their positive effects on the environment. The EVs receive charging service from the power grids, and also act as a charge reservoir to provide services to power grids as and when required. In this communication, the users' credentials, like identity and credit/debit card details, may be exchanged while providing and receiving services. Since the users' credentials are very sensitive, we need to protect the communications against any Probabilistic Polynomial Time (PPT) adversary. Therefore, authentication plays a vital role in protecting such sensitive information. In this paper, we analyse the recently proposed authentication protocol by Wang \textit{et} al. (2022) for EV charging. It has been observed that, this protocol is vulnerable to the \emph{Man-In-the-Middle-Attack} against any PPT-adversary under the Dolev-Yao threat model. Thus, Wang \textit{et} al.'s protocol is not suitable for industrial applications. We then propose an enhanced authentication protocol that provide security against all well-known attacks under the widely accepted CK-adversary model. We present a thorough security analysis using the Real-Or-Random (ROR) model to prove the security of the proposed protocol. We also compare our protocol with the existing related protocols to demonstrate the efficiency. The results show that the proposed protocol is efficient and provides high security, and therefore, it is more suitable for industrial applications. 
Estimation of the effective irradiance and bifacial gain for PV arrays using the maximum power current
Authors: Abe Caio Felippe , Dias João Batista , Notton Gilles , Faggianelli Ghjuvan Antone , Pigelet Guillaume , Ouvrard David
Published: December 2022

Manuscript Version 2, including corrections as per IEEE reviewers' comments. 

Abstract: Bifacial photovoltaic modules are able to convert the solar radiation reaching their front and rear-sides, which means that more electricity can be produced using the same array area as monofacial modules with similar ratings. In some locations, the cost per power unit for such a technology has already become cost-competitive with conventional monofacial modules. The so-called effective irradiance and the bifacial gain are useful metrics, respectively, to assess the solar resource and the performance of bifacial arrays. To calculate the effective irradiance, studies previously published employ rear-side irradiance measurements, whereas to compute the bifacial gain, other works make use of monofacial modules with rating similar to those of the bifacial modules under analysis. In this paper, a straightforward method is presented, allowing to calculate the effective irradiance from the maximum power current, and to calculate the bifacial gain using a power scaling relation. The proposed method was experimentally tested using an outdoor platform with a dual-axis tracking system with bifacial modules. The effective irradiance calculated using the novel method presented nRMSE of 2.88 %, relative to the results obtained using the consolidated method. The bifacial gains obtained were 6.24 % and 6.69 %, respectively, using the proposed and traditional calculation methods. The procedure presented in this study might be useful for the quantification of the effective irradiance and the bifacial gain for PV installations which do not have extensive monitoring hardware. 
Affective Physiological State Analysis and Interpretable Predictive Modeling of Marksmanship in Go/NoGo VR Shooting Difficulty Task
Authors: Pratiher Sawon , Sahoo Karuna P. , Acharya Mrinal , Radhakrishnan Ananth , ALAM SAZEDUL , Kerick Scott E. , Banerjee nilanjan , Ghosh Nirmalya , patra amit
Published: December 2022

This work has been submitted to the IEEE Transactions on Affective Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Overburdening an individual’s limited cognitive resources, especially when engaged in critical operations, may result in disastrous mishaps. Regular assessments of individuals’ physiological states and associated performance become vital to improving their mission readiness in such scenarios. As a key step towards a field-ready system, this treatise discusses the experimental findings pertinent to affective physiological state modulation and predictive modeling of marksmanship during a Go/NoGo shooting task in an immersive virtual reality environment. The shooting exercise requires the participants to hit the enemy targets and spare the friendly targets. The shooting difficulty levels (SDLs) are introduced by modulating the subject-specific target exposure time. The physiological signals used for analysis comprise electrocardiogram (ECG), 64-channel electroencephalogram (EEG), and standard shooting performance scores from 31 subjects. Experimental results with ECG features encompass involuntary physiologic process regulation and the interplay between the autonomic nervous system (ANS) components varying with SDL. Similarly, EEG features highlight the variations in brain region activations with SDLs. Predictive modeling of shooting performance (enemy hit, friendly spare, overall score) and behavioral response (mean enemy reaction time) from physiological (ECG and EEG) features evince the potency of physiological sensing for marksmanship estimation in operational contexts. Moreover, interpretable Shapley value analysis of the predictive models comprehend the (positive/negative) marginal impact of the underlying physiological features on marksmanship. This multimodal physiological sensing framework may assess the alterations in psychophysiological affective states and cognitive effects for performance analysis in operational contexts.
Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions
Authors: Gong Tierui , Gavriilidis Panagiotis , Ji Ran , Huang Chongwen , Alexandropoulos George C. , Wei Li , Zhang Zhaoyang , Debbah Merouane , Poor H. Vincent , Yuen Chau
Published: December 2022

Future wireless systems are envisioned to create an endogenously holography-capable, intelligent, and programmable radio propagation environment, that will offer unprecedented capabilities for high spectral and energy efficiency, low latency, and massive connectivity. A potential and promising technology for supporting the expected extreme requirements of the sixth-generation (6G) communication systems is the concept of the holographic multiple-input multiple-output (HMIMO), which will actualize holographic radios with reasonable power consumption and fabrication cost. The HMIMO is facilitated by ultra-thin, extremely large, and nearly continuous surfaces that incorporate reconfigurable and sub-wavelength-spaced antennas and/or metamaterials. Such surfaces comprising dense electromagnetic (EM) excited elements are capable of recording and manipulating impinging fields with utmost flexibility and precision, as well as with reduced cost and power consumption, thereby shaping arbitrary-intended EM waves with high energy efficiency. The powerful EM processing capability of HMIMO opens up the possibility of wireless communications of holographic imaging level, paving the way for signal processing techniques realized in the EM-domain, possibly in conjunction with their digital-domain counterparts. However, in spite of the significant potential, the studies on HMIMO communications are still at an initial stage, its fundamental limits remain to be unveiled, and a certain number of critical technical challenges need to be addressed. In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems. We also compare the HMIMO with existing multi-antenna technologies, especially the massive MIMO, present various promising synergies of HMIMO with current and future candidate technologies, and provide an extensive list of research challenges and open directions for future HMIMO-empowered wireless applications.
Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions
Authors: Gong Tierui , Vinieratou Ioanna , Ji Ran , Huang Chongwen , Alexandropoulos George C. , Wei Li , Zhang Zhaoyang , Debbah Merouane , Poor H. Vincent , Yuen Chau
Published: December 2022

Future wireless systems are envisioned to create an endogenously holography-capable, intelligent, and programmable radio propagation environment, that will offer unprecedented capabilities for high spectral and energy efficiency, low latency, and massive connectivity. A potential and promising technology for supporting the expected extreme requirements of the sixth-generation (6G) communication systems is the concept of the holographic multiple-input multiple-output (MIMO) surface (HMIMOS), which will actualize holographic radios with reasonable power consumption and fabrication cost. An HMIMOS is an ultra-thin and nearly continuous aperture that incorporates reconfigurable and sub-wavelength-spaced antennas and/or metamaterials. Such surfaces comprising dense electromagnetic (EM) excited elements are capable of recording and manipulating impinging fields with utmost flexibility and precision, as well as with reduced cost and power consumption, thereby shaping arbitrary-intended EM waves with high energy efficiency. The powerful EM processing capability of HMIMOS opens up the possibility of wireless communications of holographic imaging level, paving the way for signal processing techniques realized in the EM domain, possibly in conjunction with their digital-domain counterparts. However, in spite of the significant potential, the studies on HMIMOS-based wireless systems are still at an initial stage, its fundamental limits remain to be unveiled, and critical technical challenges with holographic MIMO communications need to be addressed. In this survey, we present a comprehensive overview of the latest advances in the holographic MIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMOS systems. We also compare HMIMOS systems with conventional multi-antenna technologies, especially massive MIMO systems, present various promising synergies of HMIMOS with current and future candidate technologies, and provide an extensive list of research challenges and open directions for future HMIMOS-empowered wireless applications.
MSA-GCN:A multi-information selection aggregation graph convolutional network for breast tumor grading
Authors: Li Kang
Published: December 2022

This manuscript has been successfully submitted to IEEE-JBHI and is currently in the peer review stage.
Active Differential Reflection Coefficient of Differentially-Fed Antenna Array
Authors: Xu Guanghui
Published: December 2022

In this communication, a definition for the active differential reflection coefficient of the differentially-fed antenna array is proposed.  The each antenna is fed by two ports which input the equal-amplitude anti-phase signals. According to the active reflection coefficient of the conventional single-ended antenna array, the active differential reflection coefficient definition of the differential antenna array is given and analyzed. To verify the active differential reflection coefficient definition, the differential three-element microstrip patch antenna array is designed, fabricated and measured. The active differential reflection coefficient definition will give a port match design guidance for the differential antenna array. 
Multimode Multiline TRL Calibration of Vector Network Analyzers
Authors: Buesa-Zubiria Ana , Esteban Jaime
Published: December 2022

A novel vector network analyzer multimode multiline thru-reflect-line calibration is presented. 

It has the advantage over previous calibrations that it is accurate for all measured modes, and more important, accuracy can be increased by simply adding more transmission medium sections to the calibration standards set. 

The calibration is based on considering each of the k measurements of k transmission media lengths as part of a tensor that takes into account N modes.

The measurement of a WR-90 band-pass filter in multimode operation, from 20.5 GHz to 23.5 GHz, has been carried out by implementing a multimode multiline calibration kit. Measurements show excellent agreement with theory.
Plant-Cyber Tradeoffs in Frame-Based Cyber-Physical Systems
Authors: Krishna C.
Published: December 2022

A tradeoff between the Quality of Control of a controlled plant and the thermal stress suffered by the controlling cyber platform is modeled under conditions allowing a small fraction of control update overruns. 

This paper has been submitted for possible publication to an IEEE journal.
Public Trace-and-Revoke Proxy Re-encryption
Authors: Luo Fucai
Published: December 2022

Proxy re-encryption (PRE), as a promising cryptographic primitive for secure data sharing in cloud, has been widely studied for decades. PRE allows the proxies to use the re-encryption keys to convert ciphertexts computed under the delegator’s public key into ones that can be decrypted using the delegatees’ secret keys, without knowing anything about the underlying plaintext. This delegable property of decryption rights gives rise to an important issue: if some proxies reveal their re-encryption keys, or collude with some delegatees to create a pirate decoder, then anyone who gains access to the pirate decoder can decrypt all ciphertexts computed under the delegator’s public key without the delegator’s permission. Several works have provided potential solutions to this issue by designing tracing mechanisms on PRE, where proxies that reveal their re-encryption keys can be identifified by the delegator. However,  these solutions perform poorly in terms of the sizes of the public, the secret and the re-encryption keys, and support neither multi-hop nor public traceability. 

This paper advances the research of tracing mechanisms on PRE and proposes the fifirst public trace-and-revoke PRE system, where the malicious delegatees involved in the generation of a pirate decoder can be identifified by anyone who gains access to the pirate decoder, and their decryption capabilities can subsequently be revoked by the content distributor. Our construction is multi-hop, supports user revocation and public (black-box) traceability, and achieves signifificant effificiency advantages over previous constructions. Our construction is a generic transformation from inner-product functional PRE (IPFPRE) that we introduce to trace-and-revoke PRE. In addition, we instantiate our generic construction of trace-and-revoke PRE from the Learning with Errors (LWE) assumption, which was widely believed to be quantum-resistant. This is achieved by proposing the fifirst LWE-based IPFPRE scheme, which may be of independent interest.
Medical BigBERTa: An Optimized Transformer for Long Medical Document
Authors: Mugisha Chérubin , Paik Incheon
Published: December 2022

For this research, we propose a biomedical language model trained on biomedical publicly available datasets from Kaggle, Pubmed abstract baseline 2019, and MIMIC III.
Terahertz Sensing using Deep Neural Network for Material Identification
Authors: Thushan Sivalingam , Ali Samad , Mahmood Nurul Huda , Rajatheva Nandana , Latva-aho Matti
Published: December 2022

Terahertz (THz) spectrum is identified as a potential enabler for advanced sensing and positioning, where THz-Time domain spectroscopy (THz-TDS) is specified for investigating the unique material properties. The transmission THz-TDS measures the light absorption of materials. This paper proposes a novel low-complex deep neural network (DNN)-based multi-class classification architecture to sense a wide variety of materials from the transmission spectroscopy. Based on the spectroscopic measurements made across a chosen THz region of interest, DNN extracts and learns the distinctive crystal structure of materials as features. With sufficient quantities of noisy spectroscopic data and labels, we train and validate the model. In low SNR regions, the proposed DNN classification architecture achieves about 92%success rate, which is greater than those of the state-of-the-art methods.
Finite-time Nonlinear H∞ Control of Robot Manipulators with Prescribed Performance
Authors: Golestani Mehdi , Chhabra Robin , Esmailzadeh Majid
Published: December 2022

This letter addresses the problem of robust finite-time tracking control with prescribed performance for robot manipulators experiencing uncertain inertia, external disturbance, and actuator fault. We develop a control strategy that incorporates the nonlinear H∞ concept into the backstepping approach, using a novel virtual control, to guarantee practical finite-time convergence to a trajectory, whilst the closed-loop L2 gain is less than a prespecified value. We also use adaptive gains, instead of complex error transformations (common in prescribed performance controllers), to simultaneously impose constraints on the steady-state and transient response of the closed-loop, including maximum error, maximum overshoot, and minimum convergence rate his letter addresses the problem of robust finite-time tracking control with prescribed performance for robot manipulators experiencing uncertain inertia, external disturbance, and actuator fault. We develop a control strategy that incorporates the nonlinear H∞ concept into the backstepping approach, using a novel virtual control, to guarantee practical finite-time convergence to a trajectory, whilst the closed-loop L2 gain is less than a pre-specified value. We also use adaptive gains, instead of complex error transformations (common in prescribed performance controllers), to simultaneously impose constraints on the steady-state and transient response of the closed-loop, including maximum error, maximum overshoot, and minimum convergence rateT. The developed controller is not contingent on solving the Hamilton-Jacobi or Riccati equations and is free of the singularities associated with using fractional power in finite-time control. The performance and efficacy of the proposed control framework are demonstrated through simulation studies and comparisons with pertinent works.
Finite-time Nonlinear H∞ Control of Robot Manipulators with Prescribed Performance
Authors: Golestani Mehdi , Chhabra Robin , Esmailzadeh Majid
Published: December 2022

This study addresses the problem of constrained finite-time nonlinear H∞ tracking control for robot manipulators, despite the existence of actuator faults, system uncertainties, and external disturbances. Following the backstepping recursive design procedure, a constrained tracking control with a simple structure is developed to guarantee that the state errors converge to zero in finite time and the L2 gain of the closed-loop system is not greater than a prescribed value. Further, the favorable performance of the control system in steady-state and transient response, including maximum tracking error, maximum overshoot, and minimum convergence rate, are simultaneously imposed. Moreover, the developed controller is free of the singularity associated with the use of fractional power in finite-time control and it is not contingent on solving the Hamilton-Jacobi or Riccati equations. The fault tolerant capability and efficacy of the proposed control framework are demonstrated through simulation studies and comparisons with pertinent works.
AI-based Dynamic web server for real-time classification of raw genome sequences of ESKAPEE pathogens
Authors: Mishra Sarthak , Sharma Priyanka , Jabin Suraiya , Ahmad Sumaiya , Srivastava Divyanshu , Tiwari Krishna Mohan , Maurya Wanchha , Khan Arzoo , Singh Pulkit , Kaur Punit , Singh Harpreet
Published: December 2022

It's a collaborative effort of JMI, AIIMS, and ICMR, New Delhi, India.

"Dynamic Web Server for ESKAPEE Pathogen classification" is the deployment of a robust machine learning model which accepts raw clinical sequences i.e. SRA data in the fast/fq format, and classifies it into 8 classes of Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter species, E. coli (i.e. ESKAPEE) and non-ESKAPEE that consists of Human DNA, Fungi, and bacteria other than ESKAPEE.
AI-based Dynamic web server for real-time classification of raw genome sequences of ESKAPEE pathogens
Authors: Mishra Sarthak , Sharma Priyanka , Jabin Suraiya , Ahmad Sumaiya , Srivastava Divanshu , Tiwari Krishna Mohan , Khan Arzoo , Singh Pulkit , Kaur Punit , Singh Harpreet
Published: December 2022

It's a collaborative effort of JMI, AIIMS, and ICMR, New Delhi, India.

"Dynamic Web Server for ESKAPEE Pathogen classification" is the deployment of a robust machine learning model which accepts raw clinical sequences i.e. SRA data in the fast/fq format, and classifies it into 8 classes of Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter species, E. coli (i.e. ESKAPEE) and non-ESKAPEE that consists of Human DNA, Fungi, and bacteria other than ESKAPEE. 
Runtime Adaptation Framework for Fulfilling Availability and Continuity Requirements of Network Services
Authors: Azadiabad Siamak , Khendek Ferhat , Toeroe Maria
Published: December 2022

There is no data collection.
Crash vs Byzantine fault tolerance at scale: the cost of distributing trust in a (trans)national invoicing system
Authors: Eklund Peter
Published: December 2022

This paper examines the feasibility of blockchain solutions for national and transnational business-to-business and business-to-government (B2B/B2G) compliance frameworks, namely a trust-less, de-centralised, self-regulating distributed ledger. In particular, the paper examines whether blockchain platforms scale to support national and transnational e-business trading. 
A Compressive Sensing Codec Architecture for ECG Signals with Adaptive Quantization and Stream Entropy Coding
Authors: Kumar Shailesh , Prasad Surendra , Lall Brejesh
Published: December 2022

Compressive sensing (CS) is quite appealing as a low-complexity method for the compression of ECG data in resource-limited wearable devices. We propose a codec architecture comprising adaptive quantization and asymmetric numeral systems (ANS) based entropy coding of compressive measurements that can boost the compression ratio without sacrificing reconstruction performance. The quantized Gaussian entropy model for the compressive measurements is estimated directly from the data and is adapted dynamically to achieve better compression. We have tested our encoder with Block Sparse Bayesian learning as well as CS-NET sparse recovery algorithms on the MIT-BIH Arrhythmia database. Our encoder can achieve 5-25\% of additional space savings over compressive sensing.

The software code implementing this codec and all scripts for the experimental studies conducted in this work have been released as opensource software on GitHub.
Deep Learning aided SIC for Wavelet-based Massive MIMO-NOMA
Authors: Ahmad Muneeb , Shin Soo Young
Published: December 2022

In this study, a deep neural network (DNN) for a massive multiple-input-multiple-output (mMIMO) nonorthogonal multiple access (NOMA) system’s channel estimation and detection is presented. The proposed network applies deep learning (DL) to a discrete wavelet transform (DWT) based orthogonal frequency-division-multiplexing (OFDM) to reduce the noise and inter-channel interference (ICI) while maintaining compatibility with the existing networks. The transmission overhead is reduced because the presented network is trained to estimate and detect symbols with and without pilot signals.

The results of this study demonstrate that our provided DL-based successive interference cancellation (SIC) method performs better than the conventional SIC-based signal detection in the mMIMONOMA network. The suggested wavelet-based mMIMO-NOMA is also compared to the traditional fast-Fourier-transform (FFT) based mMIMO-NOMA, particularly in terms of symbol error rate (SER).
The Reference Impedance in 2x-thru Calibration, and its Estimation for High Conductor-loss Transmission Lines
Authors: Chou Chiu-Chih
Published: December 2022

The 2x-thru calibration has emerged as an attractive alternative to the classical TRL for S parameter measurement of printed circuit board and packaging devices, due to its simplicity that only one THRU standard suffices to fully characterize the test fixtures, thus eliminating the LINE measurement in TRL and also overcoming the bandwidth limit. Although various 2x-thru calibration tools are available nowadays, there are still some issues that have not been studied in detail. This paper addresses the following two fundamental questions: (i) what is the reference impedance (Zref) of the S parameters after 2x-thru calibration? (ii) what is the time-domain reflectometry (TDR) response of the THRU for high conductor-loss transmission lines, and how to estimate Zref given such TDR? For problem (i), we rigorously show that the Zref is equal to the characteristic impedance of the center transmission line in the THRU standard, and provide the important interpretation for the S parameters in terms of the traveling-wave and pseudowave concepts. For problem (ii), we show that the TDR impedance is a monotonically increasing function instead of constant, and we propose a new method to estimate the characteristic impedance by fitting the TDR with a causal impedance model. Simulation and measurement examples are provided to validate the proposed theory and techniques. In the last example, we also compare five commercial/open-source 2x-thru calibration tools; the results reveal that with the proposed method of impedance estimation, better accuracy can be achieved. 
Design Prediction with Phylogenetic Networks
Authors: He Youwei , Lee Jeong-Dong , Jeong Dawoon , Choi Sungjun , Kim Jiyong
Published: December 2022

This study proposes an effective method to predict the development of technology from an evolutionary perspective. Product evolution is the result of technological evolution and market selection. A phylogenetic network is the main method to study product evolution. The formation of the dominant design determines the trajectory of technology development. How to predict future dominant design has become a key issue in technology forecasting and new product development. This study uses machine learning methods, combined with product evolutionary theory, and constructs a Fully Connected Phylogenetic Network dataset to effectively predict the future dominant design.
Prediction of Dominant designs with Technological Product Phylogenetic Networks
Authors: He Youwei , Lee Jeong-Dong , Jeong Dawoon , Choi Sungjun , Kim Jiyong
Published: December 2022

This study proposes an effective method to predict the development of technology from an evolutionary perspective. Product evolution is the result of technological evolution and market selection. A phylogenetic network is the main method to study product evolution. The formation of the dominant design determines the trajectory of technology development. How to predict future dominant design has become a key issue in technology forecasting and new product development. This study uses machine learning methods, combined with product evolutionary theory, and constructs a Fully Connected Phylogenetic Network dataset to effectively predict the future dominant design.
On the Convergence of Fourier Representations and Schwartz Distributions
Authors: Singh Pushpendra , Singhal Amit , Fatimah Binish , Gupta Anubha , Joshi Shiv Dutt
Published: December 2022

Fourier theory is a popular tool for analyzing various signals and interpreting their spectral contents. It finds applications in a wide variety of subject areas. However, some of the popular signals, such as sinusoidal signals, Dirac delta, signum, and unit step, fail to have a convergent Fourier representation (FR) in the conventional sense. Hence, it becomes imperative to utilize the distribution theory to understand and build a suitable representation for these signals. The signal processing and communication engineering literature does not explain these concepts clearly. As a result, many of the concepts of FR for signals that do not conform to the conventional derivations remain obscure to researchers. We attempt to bridge this gap and provide a comprehensive explanation regarding the existence of FR. Further, we have proposed a new linear space of Gauss--Schwartz (GS) functions and corresponding tempered superexponential (TSE) distributions. It is shown that the Fourier transform (FT) is an isomorphism on the GS space of test functions and hence by duality is an isomorphism on TSE distributions. The space GS is smallest in the sense that its dual space, a set of TSE distributions, is the largest linear space over which FT can be defined by duality.
On the Fourier Representations and Schwartz Distributions
Authors: Singh Pushpendra , Singhal Amit , Fatimah Binish , Gupta Anubha , Joshi Shiv Dutt
Published: December 2022

Fourier theory is one of the most important tools used ubiquitously for understanding the spectral content of a signal, extracting and interpreting information from signals, and transmitting, processing, and analyzing the signals and systems. Undergraduate engineering students are exposed to these concepts, usually in their second year, to build their foundation in the areas of signal processing and communication engineering. However, the popular signal processing literature \cite{book1,book2,book3,book4} does not offer a clear explanation regarding the convergence or the existence of Fourier representations for certain well-known signals. Because of this subtle gap, it becomes hard for young students to assimilate the Fourier theory with clarity, and they are forced to be familiar with some of these concepts without understanding them. To bring clarity to the existence and the convergence of Fourier representation, including Fourier series and transform, lecture notes were published recently in IEEE Signal Processing Magazine's September 2022 issue \cite{PSDCs}. This lecture note is in the continuation with technical details added from yet another mathematical topic of distribution theory that connects delta Dirac functions with the Fourier theory. 

The distribution theory by Schwartz in 1945 is one of the great revolutions in mathematical function analysis. It is considered as a completion of differential calculus, similar to how the revolutionary measure theory or Lebesgue integration theory proposed in 1903, is considered as a completion of integral calculus. Both these theories unlocked new paradigms of mathematical development. 

Although distribution theory is a powerful tool for understanding Fourier theory, it is ignored in engineering textbooks. In this lecture note, we utilize the concepts of this theory to show how some signals that fail to exhibit FT in the conventional sense can have FT in the distributional sense.
Generative Adversarial Networks as an Accommodative Memory for Cognitive Waveform Synthesis
Authors: Esmaeili Najafabadi Hamid , Leung Henry , Moo Peter W.
Published: December 2022

 This paper presents a practical example where generative adversarial networks (GANs) can be employed as an accommodative memory unit (AMU). An array of such units can memorize/learn any algorithm's results. This kind of memory can accommodate their response to new unseen scenarios by traversing the GAN's latent space and finding the best answer. Accordingly, accommodative memory (AM) can be viewed as a generalization of look-up tables (LUT), in which writing and reading operations are equivalent to training and inference of an AMU or traversing its latent space.

 We explore cognitive radar waveform synthesis to showcase a practical application of the proposed AM concept. In this regard, a Wasserstein GAN (WGAN) is trained as an AMU for a particular ambiguity function (AF) shaping scenario. 

 Here, retrieving information for the most frequent scenarios, called input basis scenarios (IBSs), involves only the inference of the generator. For more complicated input scenarios, the memory accommodates the input by traversing the latent space using ADAM optimization. Compared to redesigning the AF, the AM can remember or accommodate new scenarios several orders of magnitude faster at the expense of more memory hardware.

 As an auxiliary result, we also demonstrate that traditional algorithms can be defeated in terms of suppression level by penalizing the loss function according to desired AF.
Simple Link-Budget Estimation Formulas for Channels Including Anomalous Reflectors
Authors: Kosulnikov Sergei , Francisco Cuesta Soto , Wang Xuchen , Tretyakov Sergei
Published: December 2022

Reconfigurable intelligent surfaces (RIS) are a promising tool for optimization of propagation channels for advanced  wireless communication systems. These tools are especially relevant for high-frequency (millimeter-band) links where directive antennas are used. RIS panels act as high-gain passive repeaters, whose reflected waves interfere with the waves reflected from the illuminated spots at  supporting walls, creating a complex field pattern in the far zone. In this work, we develop a simple link-budget model for non-line-of-sight channels via reflections from a finite size metasurface designed as a perfect anomalous reflector. The developed model takes into account diffraction at  the RIS panel edges as well as interference with reflections from supporting structures. We take into account realistic losses and confirm the validity of results by numerical simulations.