Reviewerly/exHarmony huggingface dataset:
Novel Searchable Attribute-Based Encryption for the Internet of Things

As the product of the third information technology revolution, the Internet of Things (IoT) has greatly altered our way of lifetime. Cloud storage has gradually become the best choice for data processing due to its scalability and flexibility. However, the cloud is not a completely trusted entity, such as tampering with user data or leaking personal privacy. Therefore, cloud storage usually adopts attribute-based encryption schemes to accomplish data confidentiality and fine-grained access control. However, applying the ABE scheme to the Internet of Things still faces many challenges, such as dynamic user revocation, data sharing, and excessive computational burden. In this paper, we propose a novel searchable attribute encryption system that replaces the traditional key generation center with consortium blockchain to generate and manage partial keys. In addition, our scheme can perform predecryption operations in the cloud, and users only need to spend a small amount of computational cost to achieve decryption operations. Security analysis proves that our scheme achieves security under both the chosen keyword attack and the chosen plaintext attack. Compared with other schemes, this scheme is more economical in terms of computing and storage.
Motion planning of unmanned aerial vehicles in dynamic 3D space: a potential force approach

Abstract This research focuses on a collision-free real-time motion planning system for unmanned aerial vehicles (UAVs) in complex three-dimensional (3D) dynamic environments based on generalized potential force functions. The UAV must survive in such a complex heterogeneous environment while tracking a dynamic target and avoiding multiple stationary or dynamic obstacles, especially at low hover flying conditions. The system framework consists of two parts. The first part is the target tracking part employing a generalized extended attractive potential force into 3D space. In contrast, the second part is the obstacle avoidance part employing a generalized extended repulsive potential force into 3D space. These forces depend on the relative position and relative velocity between the UAV and respective obstacles. As a result, the UAV is attracted to a moving or stationary target and repulsed away from moving or static obstacles simultaneously in 3D space. Accordingly, it changes its altitude and projected planner position concurrently. A real-time implementation for the system is conducted in the SPACE laboratory to perform motion planning in 3D space. The system performance is validated in real-time experiments using three platforms: two parrot bebop drones and one turtlebot robot. The pose information of the vehicles is estimated using six Vicon cameras during real-time flights. The demonstrated results show the motion planning system’s effectiveness. Also, we propose a successful mathematical solution of the local minima problem associated with the potential field method in both stationary and dynamic environments.
Model-based reasoning using answer set programming

Abstract Diagnosis, i.e., the detection and identification of faults, provides the basis for bringing systems back to normal operation in case of a fault. Diagnosis is a very important task of our daily live, assuring safe and reliable behavior of systems. The automation of diagnosis has been a successful research topic for several decades. However, there are limitations due to complexity issues and lack of expressiveness of the underlying reasoning mechanisms. More recently logic reasoning like answer set programming has gained a lot of attention and practical use. In this paper, we tackle the question whether answer set programming can be used for automating diagnosis, focusing on industrial applications. We discuss a formalization of the diagnosis problem based on answer set programming, introduce a general framework for modeling systems, and present experimental results of an answer set programming based diagnosis algorithm. Past limitations like not being able to deal with numerical operations for modeling can be solved to some extent. The experimental results indicate that answer set programming is efficient enough for being used in diagnosis applications, providing that the underlying system is of moderate size. For digital circuits having less than 500 components, diagnosis time has been less than one second even for computing triple fault diagnoses.
An Interactive Web Application Helps Students Explore Water Balance Concepts

The concept of a water balance is a foundational topic in hydrology classrooms. While understanding and applying this concept is crucial to the introduction of more advanced topics, students often struggle to develop a thorough understanding of the relationships between components, assumptions, and limitations of a water balance. To aid students in developing a working understanding of a water balance, we developed a web application that runs a one dimensional Thornthwaite-type water balance at any of thousands of NOAA climate stations across the continental United States using the local soil-water storage capacity at the station location. Within the app, students can manipulate the soil-water storage capacity, latitude, temperature, and precipitation to better understand how it works and explore scenarios of land use, extreme weather, and climate change. The application is free and will run on any device that can open an internet browser window (laptops, chromebooks, smartphones, etc). Here we present the details of the model, functionality of the application, and link to several ready-made classroom activities. Finally, results from student surveys in two hydrology classrooms show that students may learn water balance concepts more effectively than traditional methods such as spreadsheet computations.
Argument strength in probabilistic argumentation based on defeasible rules

It is common for people to remark that a particular argument is a strong (or weak) argument. Having a handle on the relative strengths of arguments can help in deciding on which arguments to consider, which arguments to regard as acceptable, and on which arguments to present to others in a discussion. In computational models of argument, there is a need for a deeper understanding of argument strength. It is a multidimensional problem, and in this paper, we focus on one aspect of argument strength for deductive argumentation based on a defeasible logic. We assume a probability distribution over models of the language and consider how there are various ways to calculate argument strength based on the probabilistic necessity and sufficiency of the premises for the claim, the probabilistic sufficiency of competing premises the claim, and the probabilistic necessity of the premises for competing claims. We provide axioms for characterizing probability-based measures of argument strength, and we investigate four specific probability-based measures.
Designing Possibilistic Information Fusion—The Importance of Associativity, Consistency, and Redundancy

One of the main challenges in designing information fusion systems is to decide on the structure and order in which information is aggregated. The key criteria by which topologies are constructed include the associativity of fusion rules as well as the consistency and redundancy of information sources. Fusion topologies regarding these criteria are flexible in design, produce maximal specific information, and are robust against unreliable or defective sources. In this article, an automated data-driven design approach for possibilistic information fusion topologies is detailed that explicitly considers associativity, consistency, and redundancy. The proposed design is intended to handle epistemic uncertainty—that is, to result in robust topologies even in the case of lacking training data. The fusion design approach is evaluated on selected publicly available real-world datasets obtained from technical systems. Epistemic uncertainty is simulated by withholding parts of the training data. It is shown that, in this context, consistency as the sole design criterion results in topologies that are not robust. Including a redundancy metric leads to an improved robustness in the case of epistemic uncertainty.
Permissionlessness

Permissionlessness" is a term often used in association with public blockchains.In this glossary entry, we explore the origins, evolution, and coexisting uses and meanings of the term "permissionless" to contextualise it.We argue that a technosocial system is deemed permissionless if it is possible to participate in the use, development, and governance of that system or infrastructure without requiring permission from an authority, by adhering to publicly stated procedures.This term is much more broadly applicable then just blockchain systems although it is relevant to decentralized systems.It can be conceptualised as a technical attribute, an ideology, and a cultural value, and links to the access, control, governance, entry and exit of an open information system.
Cell Nuclei Segmentation in Cryonuseg dataset using Nested Unet with EfficientNet Encoder

Cell Nuclei Segmentation is one of the important stages in clinical research. Many applications in medical treatment, drug discoveries, and disease diagnosis can be benefited from cell nuclei segmentation results. Although it plays a significant role, the task of cell nuclei segmentation seems tedious and time-consuming because of the large number of cells in one histopathology image and the manual nature of the task. The segmentation task of nuclei cells can be automated by computer programs which help to save lots of time and work for histopathologists and also produce stable results, preventing mistakes made by humans. Recently, with the emergence of deep learning methods, many segmentation tasks were done by neural nets with superb output and outperforming many traditional methods. In this paper, we run experiments with CryonuSeg dataset using Nested Unet, a variant of the Unet neural net for medical and biomedical image segmentation and combine the model with EfficientNet as the encoder of the network. The modified network results surpassed many other state-of-the-art techniques on medical image segmentation tasks with the Dice score = 0.929, AJI=0.604, and PQ=0.503.
Large - Scale LEO Satellite Constellation to Ground QKD links: Feasibility Analysis

As Quantum Key Distribution (QKD) seems to turn towards satellite communication infrastructure to interconnect distant nodes aiming to a global quantum secured network, the number of assisting satellites is expected to increase. In this work, a feasibility analysis of a large-scale Low Earth Orbit (LEO) satellite constellation supporting LEO to ground QKD links is presented. A software tool to simulate the physical properties of a large-scale satellite constellation and a link budget calculator tailored for LEO satellite to ground QKD links have been developed. A large-scale LEO satellite constellation with 100 satellites orbiting at 550km altitude with 53deg inclination angle in ten different orbital planes (10 satellites per orbital plane) has been simulated and subsatellite points for 1 year have been obtained. Assuming, an optical ground station network consisting of nine different optical ground stations located in observatories across Europe, the performance of this large-scale constellation is evaluated in terms of availability and Secure Key Rate (SKR) employing the prepare and measure Decoy-State BB84 QKD protocol, during nighttime, under different atmospheric conditions including cloud coverage and turbulence effects among others, while two different wavelengths (i.e., 800nm and 1550nm) are examined. For each Optical Ground Station (OGS) location, Gbits of distilled keys per year per ground station and key rates up to Kbps are reported.
HOW DOES ENTREPRENEURIAL LEADERSHIP PROMOTE INNOVATIVE WORK BEHAVIOUR? THROUGH MEDIATING ROLE OF KNOWLEDGE SHARING AND MODERATING ROLE OF PERSON-JOB FIT

The purpose of this study is to clarify the effects of entrepreneurial leadership (EL) on innovative work behaviour (IWB). It also heightens the perception of the pathways to improve innovative work behaviour by evaluating the mediating role of knowledge sharing (KS) and of person–job fit. The findings indicate that KS mediates the effects of EL on innovative work behaviour. In addition, the influences of KS on aspects of innovative work behaviour are different and depend on the extent of person–job fit. This paper highlights the important role of person–job fit in enhancing the effects of KS activities and innovative work behaviour. The study provides evidence that EL is the main driving force of KS and innovative work behaviour performance. Thus, organisations should prioritise implementing leadership development programs to stimulate KS activities and foster the firm’s innovative work behaviour. This study conducts a questionnaire survey and applies Structural Equation Modelling (SEM) to test hypotheses in the proposed research model based on data collected from 355 participants in Jordanian firms Securities Commission list of approximately 235 enterprises. Originality/value. The study significantly fills the gaps in the literature that emphasises how KS mediates the influence of EL on innovative work behaviour. By exploring the moderator of a person–job fit, this paper significantly contributes to advancing the deeper insight of the specific conditions to strengthen the relationship between KS and innovative work behaviour.
A new picking algorithm based on the variance piecewise constant models

Abstract In this paper, we propose a novel picking algorithm for the automatic P- and S-waves onset time determination. Our algorithm is based on the variance piecewise constant models of the earthquake waveforms. The effectiveness and robustness of our picking algorithm are tested both on synthetic seismograms and real data. We simulate seismic events with different magnitudes (between 2 and 5) recorded at different epicentral distances (between 10 and 250 km). For the application to real data, we analyse waveforms from the seismic sequence of L’Aquila (Italy), in 2009. The obtained results are compared with those obtained by the application of the classic STA/LTA picking algorithm. Although the two algorithms lead to similar results in the simulated scenarios, the proposed algorithm results in greater flexibility and automation capacity, as shown in the real data analysis. Indeed, our proposed algorithm does not require testing and optimization phases, resulting potentially very useful in earthquakes routine analysis for novel seismic networks or in regions whose earthquakes characteristics are unknown.
Measuring Embedded Human-Like Biases in Face Recognition Models

Recent works in machine learning have focused on understanding and mitigating bias in data and algorithms.Because the pre-trained models are trained on large real-world data, they are known to learn implicit biases in a way that humans unconsciously constructed for a long time.However, there has been little discussion about social biases with pre-trained face recognition models.Thus, this study investigates the robustness of the models against racial, gender, age, and an intersectional bias.We also present the racial bias with a different ethnicity other than white and black: Asian.In detail, we introduce the Face Embedding Association Test (FEAT) to measure the social biases in image vectors of faces with different race, gender, and age.It measures social bias in the face recognition models under the hypothesis that a specific group is more likely to be associated with a particular attribute in a biased manner.The presence of these biases within DeepFace, DeepID, VGGFace, FaceNet, OpenFace, and ArcFace critically mitigate the fairness in our society.
An empirical study of emoji use in software development communication

Similar to social media platforms , people use emojis in software development related communication to enrich the context and convey additional emotion. With the increasing emoji use in software development-related communication, it has become important to understand why software developers are using emojis and their impact. Gaining a deeper understanding is essential because the intention of emoji usage might be affected by the demographics and experience of developers; also, frequency and the distribution of emoji usage might change depending on the activity, stage of the development, and nature of the conversation, etc. We present a large-scale empirical study on the intention of emoji usage conducted on 2,712 Open Source Software (OSS) projects. We build a machine learning model to automate classifying the intentions behind emoji usage in 39,980 posts. We also surveyed 60 open-source software developers from 17 countries to understand developers’ perceptions of why and when emojis are used. Our results show that we can classify the intention of emoji usage with high accuracy (AUC of 0.97). In addition, the results indicate that developers use emoji for varying intentions, and emoji usage intention changes throughout a conversation. Our study opens a new avenue in Software Engineering research related to automatically identifying the intention of the emoji use that can help improve the communication efficiency and help project maintainers monitor and ensure the quality of communication. Another thread of future research could look into what intentions of emoji usage or what kind of emojis are more likely to attract users and how that is associated with emoji usage diffusion in different levels (threads, projects, etc.)
Review of the application of intelligent speech technology in education

Abstract Education is the main application field of intelligent speech technology, including automatic speech recognition and speech assessment. In this paper, we introduce some popular application cases of intelligent speech technology in education, such as intelligent micro-lessons, online distance learning and spoken English learning and testing. With the help of intelligent speech technology, people can acquire knowledge faster, learn languages more efficiently, and even communicate by using different languages. Moreover, we propose three kinds of challenging problems in the application of intelligent speech technology, including the recognition of domain-related terms, the recognition of code-switching and the fine-grained mispronunciation diagnosis. Viable state-of-art solutions to these problems are also discussed in detail. Meanwhile, we also discuss what language teachers and researchers can do in solving the above technical problems.
Adaptive Real-Time Object Detection for Autonomous Driving Systems

Accurate and reliable detection is one of the main tasks of Autonomous Driving Systems (ADS). While detecting the obstacles on the road during various environmental circumstances add to the reliability of ADS, it results in more intensive computations and more complicated systems. The stringent real-time requirements of ADS, resource constraints, and energy efficiency considerations add to the design complications. This work presents an adaptive system that detects pedestrians and vehicles in different lighting conditions on the road. We take a hardware-software co-design approach on Zynq UltraScale+ MPSoC and develop a dynamically reconfigurable ADS that employs hardware accelerators for pedestrian and vehicle detection and adapts its detection method to the environment lighting conditions. The results show that the system maintains real-time performance and achieves adaptability with minimal resource overhead.
Contrast Enhancement in Mammograms Using Convolution Neural Networks for Edge Computing Systems

A good contrast is significant for analysis of medical images, and if the images have poor contrast, then some methods of contrast enhancement can be of much benefit. In this paper, a convolution neural network-based transfer learning approach is utilized for contrast enhancement of mammographic images. The experiments are conducted on ISP and MIAS datasets, where ISP dataset is used for training and MIAS dataset is used for testing (contrast enhancement). Experimental comparison of the proposed technique is done with the most popular direct and indirect contrast enhancement techniques such as CLAHE, BBHE, RMSHE, and contrast stretching. A qualitative comparison is done using mean square error (MSE), signal to noise ratio (SNR), and peak signal to noise ratio (PSNR). It is observed that the proposed technique outperforms the other techniques HE, RMSHE, CLAHE, BBHE, and contrast stretching.
Witnessing localization of a quantum state via quantum speed limits in a driven avoided-level crossing system

In this work, we investigate the witnessing of the localization of quantum states through quantum speed limits (QSLs) in a two-level driven avoided-level crossing system. As the characteristic natures of the localized quantum states, the QSL presents the periodic oscillations and coherence. The coherence partition of QSL is much bigger than the population partition of QSL. Our study gives us the possibilities to manipulate dynamics of quantum states locally by employing the coherent destruction of tunneling, which is significant in quantum information process. In addition, we analyze the effects of the rotating-wave approximation and the generalized Van Vleck approach on QSL and show that they wipe out the quantum coherence.
Star-Convex Constrained Optimization for Visibility Planning with Application to Aerial Inspection

The visible capability is critical in many robot applications, such as inspection and surveillance, etc. Without the assurance of the visibility to targets, some tasks end up not being complete or even failing. In this paper, we propose a visibility guaranteed planner by star-convex constrained optimization. The visible space is modeled as star convex polytope (SCP) by nature and is generated by finding the visible points directly on point cloud. By exploiting the properties of the SCP, the visibility constraint is formulated for trajectory optimization. The trajectory is confined in the safe and visible flight corridor which consists of convex polytopes and SCPs. We further make a relaxation to the visibility constraints and transform the constrained trajectory optimization problem into an unconstrained one that can be reliably and efficiently solved. To validate the capability of the proposed planner, we present the practical application in site inspection. The experimental results show that the method is efficient, scalable, and visibility guaranteed, presenting the prospect of application to various other applications in the future.
Design of Groundwater Level Monitoring Networks for Maximum Data Acquisition at Minimum Travel Cost

Groundwater monitoring networks represent the main source of information about water levels and water quality within aquifers. In this paper, a method is proposed for the optimal design of monitoring networks to obtain groundwater-level data of high spatial relevance at a low cost. It uses the estimate error variance reduction obtained with the static Kalman filter as optimization criteria, while simultaneously evaluating the optimal routes to follow through the traveling salesman problem. It was tested for a network of 49 wells in the Calera aquifer in Zacatecas, Mexico. The study area was divided into three zones, and one working day (8 h) was taken to visit each one, with an average speed of 40 km/h and a sampling time of 0.5 h. An optimal network of 26 wells was obtained with the proposal, while 21 wells should be monitored if the optimal routing is neglected. The average standard error using 49 wells of the original network was 35.01 m, an error of 38.35 m was obtained for 21 wells (without optimal routing) and 38.36 m with the 26 wells selected using the proposal. However, the latter produce estimates closer to those obtained with the 49 wells. Following the proposal, more field data can be acquired, reducing costs.