Reviewerly/exHarmony huggingface dataset:
Intramode-correlation–enhanced simultaneous multiparameter-estimation precision

The multiparameter estimation in quantum metrology is significant for a large number of applications for quantum information tasks. Nevertheless, how mode correlations affect the multiparameter-estimation precision with and without photon-loss cases remains ambiguous. For this reason, here, we present the relationship between the quantum Cram\'er-Rao bound and the mode correlations in the framework of the simultaneous two-phase estimation for a three-mode Mach-Zehnder interferometer induced by the tritter which is composed of three cascaded beam splitters and phase shifters. By choosing different input resources, including a two-mode squeezed vacuum state $\ensuremath{\bigotimes}$ a coherent state (scheme I) and a twin squeezed vacuum state $\ensuremath{\bigotimes}$ a coherent state (scheme II), the results show that compared with scheme II, scheme I shows a better estimation performance even in the presence of photon losses. The reason may be that the intramode correlation of the probe state for scheme I is stronger than that for scheme II, while the intermode correlation of the probe state for the former is weaker than that for the latter. This allow us to open vital perspectives on the essence of enhanced multiparameter-estimation precision.
Individuality and Fairness in Public Health Surveillance Technology: A Survey of User Perceptions in Contact Tracing Apps

Machine learning algorithms are playing an increasingly important role in public health measures, accelerated by the Covid-19 pandemic. It is therefore vital that machine learning algorithms are applied in ways that are generally considered fair. However, the question of how to define fairness in a public health context is still an open one. In this study, we investigated people’s attitudes towards two ways of defining fairness in the context of Covid-19 contact tracing apps. In the first, ‘high-individuality’ approach, the likelihood of an algorithm asking a person to self-isolate would depend on the person’s individual characteristics, such as their risk of spreading the virus through regular contacts. In the second ‘low individuality’ approach, these individual characteristics would not be used to come to a decision. For each approach, participants rated its fairness, overall quality, and their privacy concerns, and answered questions about basic psychological need satisfaction. Participants rated the high-individuality approach as fairer and better overall compared to the low-individuality approach, despite having greater privacy concerns. Further, we found a strong correlation between the participants’ fairness perceptions and their overall impression of the tracking tool. Together, these findings suggest that people prefer individualised approaches in some contexts and perceive them as fairer. However, policy makers should consider the privacy trade-off of employing such measures.
Onomasiological modeling in the information field

Abstract The article investigates onomasiological modeling in the information field. It briefly describes the information field as an integral model of reality. It is shown that there are more relations in the information field than in reality. This ensures the integration of models. Onomasiological modeling is based on the transfer of linguistic methods to computer science. The article introduces the notion of “onomasiological information modeling”. The peculiarity of onomasiological information modeling is that it allows to realize simultaneously three kinds of modeling: semantic, systematic, and descriptive. To realize this possibility, onomasiological information modeling must include three groups of information units: semantic, systemic, and descriptive.
DESIGN OF ADIABATIC CIRCUITS WITH REVERSIBLE LOGIC BASED FULL ADDER AND MULTIPLIER IN CURRENT-MODE LOGIC CIRCUITS FOR EFFICIENT POWER DISSIPATION

The widely used reversible full adder circuits are actualized in preceding research that optimizes design as well as speed of circuits.Theselogic gates that are reversible such as Peres, TSG, Toffoli, Feynman, and Fredkin are predominantly deployed in designing the reversible circuits. Nevertheless, its results are not promising in the context of dissipating static power. Power dissipation has turned out to be crucial issue in designing VLSI circuits. To overcome this problem, the energy recovery principle is introduced, and it is known as Adiabatic Logic. The famous adiabatic, low powered circuits work on reversible logic to store power and revert the same. Multiple Adiabatic Techniques (AT)is employed for dissipating the power in efficient manner. These ATs reduce power dissipation especially in VLSI circuits by performs both charging as well as discharging. The proposed research implements the Reversible Logic (RL) in Full Adder (FA) of Adiabatic Logic Reversible MOS Current-Mode Logic (MCML) FA generally termed Adiabatic Logic Reversible MCML Full Adder (FA) as well as on the Adiabatic Logic Reversible MCML multiplier. This is aimed to produce high speed circuits at reduced power. Reliable performance augmented with speedy operation is prominently exhibited in MCML than CMOS logic family. Coverage area along with improved power consumption may help in deployment of reversible logic in FA pertaining to MCML. Minimal quantity garbage output when given constant inputs is employed in reversible FA. The detailed experimental analysis portrays that proposed circuit can attain improved performance than existing RL like Feynman gate, Peres gate, TSG, Reversible MCML with Full Adder in the context of mean power, static power and current, and coverage area.
An expressive and modular layer activation mechanism for Context-Oriented Programming

There is a trend in the software industry towards building systems that dynamically adapt their behavior in response to their surrounding environment, given the proliferation of various technological devices, such as notebooks, smartphones, and wearables, capable of capturing their execution context. Context-oriented Programming (COP) allows developers to use layer abstractions to adapt software behavior to the context. A layer is associated with a context and can be dynamically activated in direct response to gathered information from its surrounding execution environment. However, most existing layer activation mechanisms have been tailored specifically to address a particular concern; implying that developers need to tweak layer definitions in contortive ways or create new specialized activation mechanisms altogether if their specific needs are not supported. Complementing ideas to expressively declare activation mechanism models with interfaces that define conditionals of activation mechanisms modularly, this paper proposes an Expressive and Modular Activation mechanism, named EMA. To propose EMA, we analyze existing activation mechanisms in COP regarding activation features and scope strategies. After, we propose the design of EMA and validate it with a case study discussion. Using a concrete JavaScript implementation of EMA, named EMAjs, we can implement two Web applications: a smartphone application as an example to illustrate EMAjs in action, and an application of home automation to discuss and compare our proposal. Our proposed mechanism allows developers to instantiate different activation scope strategies and interfaces to decouple the declaration of activation mechanism conditionals from the base code.
Neurodynamics-driven supervised feature selection

Feature selection is an important dimensionality reduction technique in machine learning, pattern recognition, image processing, and data mining. Most existing feature selection methods are greedy in nature thus are prone to sub-optimality. Though some feature selection methods based on global optimization of unsupervised redundancy may potentiate performance improvements, they may or may not be relevant to classification as the information on pairwise features with class labels is missing. In this paper, based on a supervised similarity measure, a biconvex optimization problem is formulated for holistic feature section with a quadratically weighted objective function subject to linear equality and nonnegativity constraints. In addition, an iteratively reweighted convex quadratic program is reformulated. A two-timescale duplex neurodynamic system is applied to solve the formulated biconvex optimization problem and a projection neural network is customized to solve the iteratively reweighted convex optimization problem. Experimental results of the proposed neurodynamics-based supervised feature selection are elaborated in comparison with several existing feature selection methods based on twenty benchmark datasets to substantiate the efficacy and superiority of the neurodynamics-based method for selecting informative features in classification.
Faster service with less resource: A resource efficient blockchain framework for edge computing

By integrating edge computing with blockchain technology, traceable and immutable services can be provided to address the distrust issue between edge devices. However, the contradiction between the computing and storage consumption of blockchain deployment and the constrained resources of edge devices is the greatest obstacle standing in the way. Reducing blockchain demand for computing power and storage becomes the main challenge to ameliorate the current dilemma. To address the above challenge, this article proposes a resource-efficient blockchain framework for edge computing. By analyzing the necessity of resources in the consensus, we divide the resource bottleneck of edge devices into the unnecessary computing resource and necessary storage resource to provide different resource reduction approaches. For unnecessary computing resource, a novel cloud–edge collaboration consensus protocol called the Proof-of-Ticket (PoT) has been proposed to completely offload the computing consumption in the execution of consensus protocol from edge devices to the cloud. For necessary storage resource, we propose a framework division mechanism based on the closure of service scenarios called the edge–terminal consensus zone (ETCZ). The storage requirement for edge devices working as full nodes in each ETCZ has significant decline. The extensive experiments are tested to demonstrate the effectiveness of our framework. Experiments show that the individual computing cost of edge devices has been completely offload to the cloud, and the minimum storage cost for a full node is reduced into 1K of the traditional blockchain in a K-ETCZ framework.
PASE: An autonomous sequential framework for the state estimation of dynamical systems

Kalman filter (KF) and its variants are commonly used in estimating states of dynamical systems. For accurate estimation of states, researchers have developed intelligent KFs by combining them with machine learning algorithms. The inherent variations in the vehicle dynamics and uncertainties in the environment require the development of an autonomous sequential framework for continuous estimation. Further, the need for real-time estimation of states in certain applications requires low computer memory usage and low computational cost while implementing autonomous-structured learning algorithms. In this paper, a parsimonious autonomous sequential estimator (PASE) is proposed, which combines the KF-based estimator and autonomous-structured recurrent parsimonious learning machine (rPALM) in a sequential manner. The rPALM overcomes the dependency on target variables while point-to-hyperplane distance calculation. The performance of PASE has been evaluated extensively by comparing it with various batch-learning algorithms and single-pass learning-based intelligent estimators. The results clearly indicate that PASE provides better estimation accuracy with a compact architecture for both linear and nonlinear dynamical systems. Finally, the performance of PASE has been evaluated with experimental data for the state estimation of an unmanned ground vehicle while the training of the learning machine is performed with the simulated data. The estimation accuracy in such a scenario is justifying its appropriateness in real-world applications.
tension: A Python package for FORCE learning

First-Order, Reduced and Controlled Error (FORCE) learning and its variants are widely used to train chaotic recurrent neural networks (RNNs), and outperform gradient methods on certain tasks. However, there is currently no standard software framework for FORCE learning. We present tension, an object-oriented, open-source Python package that implements a TensorFlow / Keras API for FORCE. We show how rate networks, spiking networks, and networks constrained by biological data can all be trained using a shared, easily extensible high-level API. With the same resources, our implementation outperforms a conventional RNN in loss and published FORCE implementations in runtime. Our work here makes FORCE training chaotic RNNs accessible and simple to iterate, and facilitates modeling of how behaviors of interest emerge from neural dynamics.
Predicting ICU Length of Stay for Patients with Diabetes Using Machine Learning Techniques

Diabetes is a prevalent chronic disease that can result in serious damages to various organ systems gradually. Patients with diabetes in the intensive care unit (ICU) have poor health outcomes and require more intensive care with higher healthcare costs. To facilitate resource management of hospitals and to improve health outcomes of patients with diabetes, accurately estimating the length of stay at an early stage of ICU admissions is necessary. This study is aimed to predict the length of stay for patients with diabetes by applying machine learning techniques on clinical data available during the first 8 hours of ICU admissions. Two prediction tasks, the number of days in ICU and whether an ICU stay is long or short distinguished by the threshold 10 days, were explored. The neural network model achieved the best performance in predicting the number of days in ICU with a R2 value 0.3969 and a mean absolute error 1.94 days. The gradient boosting model is the best one to classify long and short ICU stays with an accuracy 0.8214. The results demonstrate that these two models are promising to estimate the length of stay at an early stage of ICU admissions for patients with diabetes.
DDoS Attack Detection via Privacy-aware Federated Learning and Collaborative Mitigation in Multi-domain Cyber Infrastructures

Interconnected cyber infrastructures, accessible via the Internet, are a common target of DDoS attacks intending to downgrade their operations and services. Collaborative protection mechanisms are prime candidates to defend against massive attacks but, although collaborations were instrumental in the Internet success story, this is largely not extended to multi-domain cyber security. Notably, collaborative DDoS detection is hindered by data privacy legislations, while mitigation is limited to operations of stand-alone rigid firewalls. Motivated by these shortcomings, we propose a Federated Learning schema for collaborative privacy-aware DDoS detection. Coordination is orchestrated by a third trusted party that aggregates machine learning models proposed by collaborators based on their private attack and benign traces, without exchanging sensitive data. Attacks detected via the privacy-aware federated model are subsequently mitigated by efficient and scalable firewalls, implemented within the eXpress Data Path (XDP) data plane programmability framework. Our approach was evaluated using production traffic traces in terms of packet classification accuracy and packet processing performance. We conclude that our proposed Federated Learning framework enabled collaborators to accurately classify benign and attack packets, thereby improving individual domain accuracy. Furthermore, our data plane programmable firewalls promptly mitigated large-scale attacks in emulated federated cyber infrastructures.
Adapting Kubernetes controllers to the edge: on-demand control planes using Wasm and WASI

Kubernetes' high resource requirements hamper its adoption in constrained environments such as the edge and fog.Its extensible control plane is a significant contributor to this, consisting of long-lived processes called "controllers" that constantly listen for state changes and use resources even when they are not needed.This paper presents a WebAssembly-based framework for running lightweight controllers on-demand, only when they are needed.This framework extends the WebAssembly System Interface (WASI), in order to run Kubernetes controllers as lightweight Wasm modules.The framework runs these Wasm controllers in a modified version of Wasmtime, the reference WebAssembly (Wasm) runtime, that swaps idle controllers to disk and activates them when needed.A thorough evaluation shows this framework achieves a 64% memory reduction compared to traditional container-based controller frameworks.
UAV Target Tracking Method Based on Deep Reinforcement Learning

This study proposes a UAV target tracking method using reinforcement learning algorithm combined with Gate Recurrent Unit (GRU) to promote UAV target tracking and visual navigation in complex environment. Firstly, an algorithm Twins Delayed Deep Deterministic policy gradient algorithm (TD3) using deep reinforcement learning and the GRU gated loop unit are introduced. The unit is then added to the neural network to process continuous time data, and the algorithm TD3 is adopted to train the model so that it can drive the UAV to make autonomous flight decisions and accomplish target tracking. The proposed method is verified on the AirSim simulation platform.
Development and Testing of a Core System for Red and Blue Scenario in Cyber Security Incidents

In the context of continuous digitalization in all business areas, cyber-attacks became a major challenge. The data security component is a priority in the development of this changing environment. There have been many types of cyber-attacks such as Distributed Denial-of-Service, Malware Attack, Password Attack, or SQL Injection Attack that have affected different countries around the world. These security vulnerabilities led to data exfiltration or unavailability of certain critical services for long periods of time, while causing significant material damage. In order to properly manage the problems that may occur, it is necessary for any entity offering services on the Internet to have a cyber security component. Moreover, for a better coordination in the response to cyber incidents, it is necessary to train the teams providing these services. The development of capabilities can be achieved in a controlled environment, where different types of real-life attacks are simulated. A classic training method is considered to be a Red vs. Blue competition. In this paper, a different strategy is proposed, in which the two types are combined in a Red and Blue scenario. To develop it, a central system is needed to generate the information to be captured, to validate its accuracy and to monitor the availability of services. Also, through this, it was possible to observe the behaviour of the players and how they improve their knowledge. Compared to the Red vs. Blue version, the proposed one in which the teams are combined offers more advantages for understanding how a hacker acts and for reducing the response time in case of a cyber security incident.
Information-theoretic secure rational secret sharing in asynchronous networks for untrusted cloud environments

Abstract Today, cloud storage services increased the popular for data storage in the cloud and retrieve from any location without any time limitations. One of the most important demands required in cloud is secured data transmission in un-trusted cloud applications. Particularly, secure and efficient multiparty communications in Untrusted Cloud Environments (UCE) attract widespread attentions. The equipment used in UCE have the particularity of being heterogeneous and UCE communication environment are asynchronous networks in which multiple users cannot transmit their messages simultaneously. How to ensure secure communication between these heterogeneous intelligent devices is a major challenge for multiparty communication applied in UCE. In such an asynchronous environment, the asynchronous transmission can cause security problems in cryptographic functions. Therefore, how to implement rational secret sharing (RSS) in an asynchronous model of the UCE networks has become a burning research topic. The RSS refers to finding a solution composed of strategies to encourage players in the secret reconstruction to act honestly even players are rational to act for their own interest. If each player plays the game for the best response to the best response of other players, the game is in Nash equilibrium. The objective of an RSS is to achieve the Nash equilibrium state corresponding to the global optima. In this paper, we propose an information-theoretic secure RSS in asynchronous model for UCE. Our design uses Petersen’s VSS to allow every player to divide his share into multiple pieces for other players. Then, shares can be revealed asynchronously. If any player acts maliciously, his share can be recovered by other players. This feature can encourage players to act honestly since any malicious action (i.e., either revealing a fake share or refusing to release one) is useless. Our scheme is practically valuable for secure group-oriented applications in UCE.
Information and Communication Technology and Knowledge Sharing: a Literary Referential Study

This study examines "information and communication technology and knowledge sharing" articles. It discusses the topic's literary relevance. Researchers examined research, studies, and Arab and global trends. The study read books. Originally, scientific databases tracked the expansion of ICT in Arab and foreign intellectual output. The first computer, the IBM 7001, was released in the mid-1970s, igniting the expansion of information and communication technology (ALTAIR). Then followed a study on ICT's impact on businesses. It was detected 7.5 million times between 1999 and 2020. 21 studies centered on the Arab world. Information sharing via research and monitoring was also explored in the study. Wilson investigated knowledge sharing to improve organizational performance in 1983. From 1980 to 2020, the number of citations for knowledge sharing in research titles in scientific articles continuously increased, reaching over a thousand. The notion of information technology and knowledge sharing developed in 1990, when Hendersk said that knowledge sharing needed information and communication technology. Robert Yinks et al. examined IT &amp; K in 1991 AD. This study addresses ICT and knowledge sharing. From 1991 to 2020, the digital index of "information and communication technology and knowledge sharing" reveals a lot of international research on the issue. From 2005 to 2017, several studies on the same topic were presented at conferences, workshops, and scholarly forums. These studies emphasized IT and knowledge transfer. An international study on public organizations exists. The Internet, intranet, extranet, and other ICT have been studied for their influence on knowledge exchange. Other studies linked ICT to corporate culture, performance, job happiness, organizational justice, communication, and other management necessities. Another study examined how ICT may promote knowledge sharing. Some studies looked at social media's impact on knowledge sharing.