Reviewerly/exHarmony huggingface dataset:
RSSGG_CS: Remote Sensing Image Scene Graph Generation by Fusing Contextual Information and Statistical Knowledge

To semantically understand remote sensing images, it is not only necessary to detect the objects in them but also to recognize the semantic relationships between the instances. Scene graph generation aims to represent the image as a semantic structural graph, where objects and relationships between them are described as nodes and edges, respectively. Some existing methods rely only on visual features to sequentially predict the relationships between objects, ignoring contextual information and making it difficult to generate high-quality scene graphs, especially for remote sensing images. Therefore, we propose a novel model for remote sensing image scene graph generation by fusing contextual information and statistical knowledge, namely RSSGG_CS. To integrate contextual information and calculate attention among all objects, the RSSGG_CS model adopts a filter module (FiM) that is based on adjusted transformer architecture. Moreover, to reduce the blindness of the model when searching semantic space, statistical knowledge of relational predicates between objects from the training dataset and the cleaned Wikipedia text is used as supervision when training the model. Experiments show that fusing contextual information and statistical knowledge allows the model to generate more complete scene graphs of remote sensing images and facilitates the semantic understanding of remote sensing images.
Fidelity-disturbance-entropy tradeoff in quantum channels

Quantum channels are indispensable instruments for transmitting, extracting, and processing information. According to the spirit of the Bohr complementarity principle and the Heisenberg uncertainty principle, one expects that there are intrinsic tradeoff relations between state disturbance and information gain for any channel, which indeed have been widely studied and characterized from various angles. In this work, we investigate this issue from the perspective of information conservation. More specifically, we divide the information associated with a channel into three categories: transmitted information (quantified by operation fidelity), disturbance (quantified by the Hilbert-Schmidt norm), and extracted information (quantified by the increase of Tsallis-2 entropy), which are all derived naturally from a channel with direct physical motivations. We reveal basic properties of these three information-theoretic quantities and establish a triality relation between them. As applications, we apply these quantities to shed insights into the Mach-Zehnder interferometry and several prototypical channels.
Modeling Methodology for Autonomous Cyber-Physical Systems

Cyber-Physical Systems (CPS) must be robust against several sources of uncertainty. For high-assurance applications, robustness against uncertainty drives development and ownership costs, as well as size, weight and power. Hazard analysis and requirement generation are critical early-design steps that can help identify cost-effective solutions. As CPS evolve towards more autonomous systems, new sources of uncertainty arise which require complementing current methods with new ones. We present a modeling formalism to support the requirement generation process for autonomous CPS.
Explanatory Paradigms in Neural Networks: Towards relevant and contextual explanations

In this article, we present a leap-forward expansion to the study of explainability in neural networks by considering explanations as answers to abstract reasoning-based questions. With P as the prediction from a neural network, these questions are “Why <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> ?”, “What if not <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> ?”, and “Why <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> , rather than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> ?” for a given contrast prediction <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> . The answers to these questions are observed correlations, counterfactuals, and contrastive explanations, respectively. Together, these explanations constitute the abductive reasoning scheme. The term observed refers to the specific case of posthoc explainability when an explanatory technique explains the decision <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> after a trained neural network has made the decision. The primary advantage of viewing explanations through the lens of abductive reasoning-based questions is that explanations can be used as reasons while making decisions. The posthoc field of explainability, which previously justified decisions, becomes active by being involved in the decision-making process and providing limited but relevant and contextual interventions. The contributions of this article are 1) realizing explanations as reasoning paradigms, 2) providing a probabilistic definition of observed explanations and their completeness, 3) creating a taxonomy for evaluation of explanations, and 4) positioning gradient-based complete explainability’s replicability and reproducibility across multiple applications and data modalities, and 5) code repositories, which are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/olivesgatech/Explanatory-Paradigms</uri> .
Hand Gesture Recognition and Infrared Information System

The discontinuation of remote controls and physical switches in favor of gestures could improve user experience in entertainment and hospitals but can also be potentially beneficial for people with impaired cognitive function or physical disabilities. This paper investigates a method of controlling infrared capable devices using hand gestures.IR signals from any controller device are captured by a receiver and mapped to a hand gesture for reference. A camera is used to capture a video stream which is then processed for hand detection. On a positive result, the detection is stopped and a tracking algorithm is applied. This maps the position of each joint of the hand and recognizes any predefined gestures. Thereafter IR signals are broadcasted using an emitter based on the recognized gesture and signal reference.
Cybersecurity for Beginners

New possibilities and future growth tools are developing across all organizations’ sizes in an era of technical innovation.
A Survey on Electronic Voting On Blockchain

Public elections are a basic tenet of representative democracy but most current voting methods e.g., Ballot-based voting and Electronic Voting Machines are riddled with drawbacks such as a lack of trust from voters and poor voter turnouts. Blockchain voting addresses the limitations in these legacy-voting methods by serving as a secure, easy, and cost-effective alternative to these voting methods while also having various other upsides (like quicker vote tallying). This paper analyses the underlying technology of Blockchain and compares various proposed implementations available to date
Comparing concordances of language patterns and words by ESL intermediate learners: a preliminary experiment with two mobile concordancers

As a corpus-assisted method for language pedagogy, DDL (data-driven learning) may have the potential to enhance language exposure and promote active learner engagement. Concordancing, or KWIC (Key Words in Context), has been the traditional method used in DDL to retrieve numerous language examples, while the method has limitations with overreliance on individual words to search. This paper aims to propose and promote concordancing alternatively based on grammar patterns, a multi-word concept in corpus linguistics. The conceptualised method is named as PIC (Patterns in Context), an extended form of KWIC. An empirical study was conducted to investigate whether the PIC method has any advantages over the traditional KWIC method, using two custom-built Android apps. The research involved 18 pre-university intermediate learners (and six pilot study participants), who used the apps in a self-directed way for two weeks. Then the assessment of the two apps and methods was conducted based on data from automatic logs and responses from questionnaires and interviews. The results suggest that, compared to KWIC, PIC could be slightly advantageous in efficiently helping learners find the target language use, while this approach seems not strong in user engagement and perceived effectiveness. The implications for DDL are discussed, and further investigation is also planned.
ML/AI Empowered 5G and beyond Networks

The emergence of wireless communication systems heralds the development of new technologies such as virtual reality, autonomous vehicles, Internet of things, autonomous robots and unmanned aerial systems. These advanced technologies call for high transfer rates, high reliability and, extremely low latency. These requirements may be promised by the fifth generation to meet them all. The fifth generation cannot meet these requirements without the use of artificial intelligence technology, it is expected that the fifth generation networks will generate unprecedented traffic, which in turn will enable wireless research designers to access large data that helps to predict the demands of users. Many researchers have applied artificial intelligence techniques to several ways of 5G network design like cybersecurity, network management and radio resource allocation. A deep inspection of artificial intelligence technology for the fifth generation of wireless communication systems will be conducted within this paper. This paper aims to conduct survey on the use of artificial intelligence technology within the fifth-generation wireless networks via reviewing several subjects and highlighting the difficulties associated with them, in addition to mentioning some future research directions in fifth-generation wireless communications.
Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults

COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were used to quantify the energy expenditure of ageing adults. To this end, a technological platform based on Raspberry Pi 4, as an elaboration unit, was designed and implemented. It integrates an ambient sensor node, a wearable sensor node and a coordinator node that uses the information provided by the two sensor technologies in a combined manner. Ambient and wearable sensors are used for the real-time recognition of four human postures (standing, sitting, bending and lying down), walking activity and for energy expenditure quantification. An important first aim of this work was to realize a platform with a high level of user acceptability. In fact, through the use of two unobtrusive sensors and a low-cost processing unit, the solution is easily accessible and usable in the domestic environment; moreover, it is versatile since it can be used by end-users who accept being monitored by a specific sensor. Another added value of the platform is the ability to abstract from sensing technologies, as the use of human posture and walking activity for energy expenditure quantification enables the integration of a wide set of devices, provided that they can reproduce the same set of features. The obtained results showed the ability of the proposed platform to automatically quantify energy expenditure, both with each sensing technology and with the combined version. Specifically, for posture and walking activity classification, an average accuracy of 93.8% and 93.3% was obtained, respectively, with the wearable and ambient sensor, whereas an improvement of approximately 4% was reached using data fusion. Consequently, the estimated energy expenditure quantification always had a relative error of less than 3.2% for each end-user involved in the experimentation stage, classifying the high level information (postures and walking activities) with the combined version of the platform, justifying the proposed overall architecture from a hardware and software point of view.
Is Arabic text categorization a solved task?

The amount of Arabic content on the Internet is being proliferated day by day, scoring the highest growth among the top online languages during the last two decades. Arabic is the fourth most used language on the world wide web, making producing reliable data mining applications and information retrieval engines significantly challenging. Therefore, Arabic text categorization has gained the attention of different researchers from various fields. As a result, considerable research works have addressed Arabic text categorization. Some of these research works reported accuracy rates that were very close to 100%. Due to that, one can only think if Arabic text categorization is a solved task or is still under-studied? To answer this question, we screened 262 related papers based on eight former surveys and reviews, following the PRISMA-ScR guidelines. Then, we focused on top-ranked results that are over 95%. In this paper, we present the outcomes of our investigation by addressing several research questions regarding the datasets, the preprocessing techniques, the dimensionality reduction methods, and the classifiers that have been used.
Digital watermarking—A tutorial

Digital watermarking is a technique in which secret information is embedded inside a host signal, which may be an audio, video, or image signal. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Watermarking</i> is also defined as a branch of information hiding that is used to hide proprietary information in digital media. Digital watermarking may be used for authentication, integrity, or information security. In this article, we present a comprehensive survey on watermarking techniques based on their characteristics and applications, including visible, invisible, robust, fragile, and semifragile watermarking methods. The parameters that are used for the analysis of watermarking algorithms are peak signal-to-noise ratio (PSNR), mean square error (MSE), embedding capacity, bit rate, and bit error rate (BER).
An Architecture for Social Robot-assisted Subjective and Objective Health Monitoring

The population of the world is growing older, while the shortage of required health care workers is growing increasingly severe. To combat this situation and support the independence and autonomy of older adults while reducing the burden of their caregivers, we propose a health monitoring system to assess and monitor subjective and objective health data. The system includes a conversational AI integrated into a social robot, various connected health devices, and a mobile and web app to visualize all acquired data. We formulated system requirements based on expert interviews with health care workers and literature findings. In this paper, we present the resulting system architecture integrating all health monitoring components using individual microservices, along with an app designed for our target group of older adults and their caregivers. Our goal with the proposed system is to support older adults in taking charge of their own health, thus promoting healthy and positive aging.
Visual Tracking of mini-UAVs using Modified YOLOv5 and Improved DeepSORT Algorithms

Over the past few years, unmanned aerial vehicles (UAVs) or drones have been deployed across the military services. At the same time, the civilian market has seen an exponential growth of essentially smaller systems (mini-UAVs) intended for public use. Recently, the malicious use of mini-UAVs has begun to emerge among terrorists, criminals and smugglers. The probability and frequency of the attacks of these devices, which have become more technologically advanced and cheaper, are both high and their impact can be very dangerous with devastating effects. Therefore, the need for counter-measure solution is highly required. For this purpose, the aim of this work is to propose a solution to early warning detect and track rogue mini-UAVs in restricted area. To meet this challenge, an online multiple object tracking (MOT) strategy is proposed based on YOLOv5 and the DeepSORT tracker. We adapt the former to the context of detecting small objects (ie.e. drones) by modifying the architecture. We enhance the discriminative power of DeppSORT tracker based on deep feature-based appearance. We combine detection and tracking that allows us the capability to handle small object localization. The experimental results show that the proposed multi-object tracking system is able to track mini-UAVs in real time.
Recognition of Human Activity and Abnormal Behavior using Deep Neural Network

Recognizing various abnormal activities of human behavior from video is very challenging. The overall results are affected by the available data-sets. The available data-sets contain various abnormal activities, but few of them focus mainly on non-standard human behavior. In data-sets such as KTH, they focus on abnormal activities such as a sudden change in behavior or an object occurrence where it should not occur but also various changes in human interaction. The UCF-crime data-set focuses on data that are more interesting to us, such as fight, abuse, explosions or robbery etc. However, the data-set is very demanding due to the videos length, which contains a given event in just a few seconds. This may affect the overall results of the algorithm used to detect the incident. In this paper, we create a data-set dealing with abnormal activities such as robbery, fight, hijack, harassment and a normal videos. We use the created data-set when training and testing the neural network ConvLSTM (Convolutional Long Short-Term Memory). We have obtained a classification accuracy of 97.64 % on the created data-set and used architecture of neural network.
Gaussian Process Tomography at ASDEX Upgrade with Magnetic Equilibrium Information and Nonstationary Kernels

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Construction of heterogeneous medical knowledge graph from electronic health records

AbstractAbstractKnowledge graph (KG) is a knowledge organization that enables the users to quickly and accurately query the information required. It is stored in the form of triples. It finds its application in various fields of enterprises, academics, medical etc. In this paper, Medical Knowledge Graph is constructed from Electronic Health Records (EHR) that maps the relationships between the entities of patients, disease and drugs. The heterogeneous graph is constructed using different entities derived from distinct datasets and the information is extracted in the form of queries among the wide between the quantity demand and the wide variety of variables. The steps for construction of Medical Knowledge Graph are data collection, Named entity recognition, entity normalization, entity ranking, and Graph Neural Networks. The hybrid approach of Bi-directional Long Short-Term Memory Multi-Head Attention Conditional Random Field (BILSTM-MULATT-CRF) is used for Named Entity Recognition and the result of Precision 91.4%, Recall 90.15% and F1 score 90.77% is obtained.Subject Classification: Primary 92C40Keywords: Medical knowledge graphDrug interactionElectronic health records
Pupillometry as an index for cognitive processing in behavioral variant FrontoTemporal Dementia: a series of case studies

We investigated whether pupil size can variate with the intensity of cognitive processing in patients with behavioral-variant-Frontotemporal-Dementia (bvFTD). We invited five bvFTD participants and 21 controls to perform forward spans and backward spans, and, in a control condition, to count aloud. We recorded pupil activity using eye-tracking-glasses during the spans and control condition. Analysis demonstrated larger pupil sizes during backward spans than during forward spans, and larger pupil sizes during forward spans than during counting in both bvFTD and control participants. These findings demonstrate how increased cognitive load triggers increased pupil size and how this connection is maintained in bvFTD.
Analysis of O2O Teaching Assistant Mode of College English in MOOC Environment

In the past, teaching assistants always focused on the physical environment and actions, resulting in a lot of carbon emissions and material waste. This paper discusses the online and offline integration of college English teaching assistance model, which helps inspire the significance of saving energy and reduce carbon emissions in higher education environment. Our country has been putting education informationization in a very important position. In recent years, the demand of college education informatization is more urgent, which calls for the reform of college classroom teaching mode. "O2O teaching mode" is a teaching mode that integrates online and offline teaching, uses computer information network technology, makes use of network media, and relies on online MOOC platform to carry out online network learning and offline face-to-face classroom learning. "O2O teaching mode" has the characteristics of openness, interactivity, individuality, convenience, and generation, which is conducive to changing students' learning mode and teachers' teaching mode, realizing resource sharing, and improving teaching quality. English is a practical course. In the practice of modern English teaching, it is necessary to maintain and develop the advantages of traditional humanistic teaching, and to make efficient use of the media resources and network resources under the condition of modern educational technology, so as to ensure that English learners have active and sufficient practice opportunities. With the popularity of the Internet and the development of MOOC, more and more English learners begin to learn independently online. English online learning platform has become an indispensable tool for independent learning and daily English teaching. Especially when the Internet is connected with the campus network, the role of the network has changed from teaching auxiliary resources to teaching platform, making online learning become a new way of learning, realizing the interaction of English teaching in the network environment.
Formalising Linked-Data based Verifiable Credentials for Selective Disclosure

In this paper, we propose a formal definition for Linked-Data based verifiable credential to enable secure selective disclosure among one or multiple verifiable creden-tials a user has. Previous schemes considered using a single verifiable credential and could not hide the user's identifying information when performing selective disclosure. We pro-pose the first Linked-Data based verifiable credentials that can perform selective disclosure free from the restrictions the previous scheme had, and prove its property. We also discuss a novel use of combining multiple certificates issued by independent issuers to still allow users to perform selective disclosure on the set of credentials. Our scheme has been implemented as an open source Web-based application that generates a verifiable presentation for a given selection of attributes. The performance evaluation is also provided in the paper.