Reviewerly/exHarmony huggingface dataset:
The Rise of Chatbots in Political Campaigns: The Effects of Conversational Agents on Voting Intention

This study investigates the role and effectiveness of political chatbots’ anthropomorphism in influencing voting intentions. Our findings reveal that participants show higher voting intention when they were exposed to the political chatbot with higher (vs. lower) levels of anthropomorphic visual cues. This research also investigated two moderators underlying this relationship: message type (factual vs. emotional message) and message interactivity (high vs. low interactivity). We found that compared to the less anthropomorphized political chatbot, participants’ voting intention was enhanced when the highly anthropomorphic chatbot delivered emotional (vs. factual) messages. In terms of message interactivity, the participants exhibited higher voting intentions when the political chatbot had lower levels of anthropomorphism and higher (lower) message interactivity. Theoretical and practical implications of these findings are discussed.
Analysis of digital textbooks

This study describes the current status of digital textbooks (DTBs) for Primary Education in Spain. Thirty digital teaching materials were analyzed. The method applied was content analysis. An analysis guide aimed at unraveling the technological, pedagogical and functional characteristics of this type of materials was designed, validated and applied. We found simple digitization of printed textbooks enriched with some multimedia resources perpetuating the deficiencies of these resources. The DTBs currently present in the Spanish publishing market are a response of this sector to the existing demand due to the evolution of the digital educational market. The novelty of this study lies in providing the first guide that analyzes these materials considering all their defining characteristics.
The loopholes of algorithmic public services: an “intelligent” accountability research agenda

Purpose Governments are increasingly turning to artificial intelligence (AI) algorithmic systems to increase efficiency and effectiveness of public service delivery. While the diffusion of AI offers several desirable benefits, caution and attention should be posed to the accountability of AI algorithm decision-making systems in the public sector. The purpose of this paper is to establish the main challenges that an AI algorithm might bring about to public service accountability. In doing so, the paper also delineates future avenues of investigation for scholars. Design/methodology/approach This paper builds on previous literature and anecdotal cases of AI applications in public services, drawing on streams of literature from accounting, public administration and information technology ethics. Findings Based on previous literature, the paper highlights the accountability gaps that AI can bring about and the possible countermeasures. The introduction of AI algorithms in public services modifies the chain of responsibility. This distributed responsibility requires an accountability governance, together with technical solutions, to meet multiple accountabilities and close the accountability gaps. The paper also delineates a research agenda for accounting scholars to make accountability more “intelligent”. Originality/value The findings of the paper shed new light and perspective on how public service accountability in AI should be considered and addressed. The results developed in this paper will stimulate scholars to explore, also from an interdisciplinary perspective, the issues public service organizations are facing to make AI algorithms accountable.
Reputation-based service provisioning for vehicular fog computing

The startling rise in smart vehicles stimulates the rapid development of new paradigms such as Social Internet of Vehicle (SIoV) and Vehicular Fog Computing (VFC). Trustworthiness has been regarded as a dominating issue in all the have-to-be-addressed issues in SIoV, and many reputation-based countermeasures have been adopted to solve the trustiness-related issues in IoV. However, little attention has been paid to the reputation of vehicles when they provision computational resources in VFC, which is worthy of further investigation since some fog vehicles pursue more revenues or fewer costs at the expense of delivering poor-quality computing services. Such selfish behaviors should be discouraged. In this paper, we put forward a reputation-based service provisioning scheme, and a reputation management scheme consisting of the decentralized reputation updating and global reputation synchronization in VFC, aiming to prevent the fog vehicles from delivering low-quality computing services by maximizing the accumulated reputation of all the serving fog vehicles in the optimization period. An online approach is adopted to handle the requests in a slot-by-slot way. The simulation results show its effectiveness and advantages when compared to other existing approaches.
Lightweight prohibited item detection method based on YOLOV4 for x-ray security inspection

In the area of public safety and crime prevention, some research based on deep learning has achieved success in the detection of prohibited items for x-ray security inspection. However, the number of parameters and computational consumption of most object detection methods based on deep learning are huge, which makes the hardware requirements of these methods extremely high and limits their applications. In this paper, a lightweight prohibited item detection method based on YOLOV4 is proposed for x-ray security inspection. First, the MobilenetV3 is used to replace the backbone network of YOLOV4, and the depthwise separable convolution is used to optimize the neck and head of YOLOV4 to reduce the number of parameters and computational consumption. Second, an adaptive spatial-and-channel attention block is designed to optimize the neck of YOLOV4 in order to improve the feature extraction capability of our method and maintain the detection accuracy. Third, the focal loss is utilized to avoid the class imbalance problem during the training process. Finally, the method is evaluated on our real x-ray pseudocolor image dataset with YOLOV4 and YOLOV4-tiny. For the overall performance, the mean average precision of our method is 4.98% higher than YOLOV4-tiny and 0.07% lower than YOLOV4. The number of parameters and computational consumption of our method are slightly higher than YOLOV4-tiny and much lower than YOLOV4.
Airborne geophysics and remote sensing of an Nimas-Khadra area, southern Arabian shield: New insights into structural framework and mineral occurrences

The Shwas-Tayyah and Khadra belts belonging to the Asir terrane include potential mineralization zones, hosted in intensely deformed Precambrian basement rocks. Analysis of geophysical aeromagnetic data and satellite images, constrained by field data, highlights the deep hierarchy and structuring of the An Nimas-Khadra area in its regional litho-structural setting and geodynamic evolution. The N-S and NNE-SSW major trends, with other E-W, NE-SW and NW-SE minor directions, were identified. The Tarj, Ibran, and Junaynah close to N-S oriented master shear zones, delimit and cross the Al Lith-Bidah, Shwas-Tayyah and Khadra belts; they characterize the Asir composite terrane. Structural and kinematic models were highlighted, showing the control of intense deformations on the basement structures. The deep-seated faults are linked to wide tectonic movements induced by regional E-W to ENE-WSW contractional orogenic episodes. The tectonics induced genesis of the accreted terrane rocks with differentiated belts and shear zones. The resulted structures seem to be in accordance with the Riedel model that includes conjugate movements of right-lateral strike slip faults and right-oblique reverse faults. Correlation of geophysical and geochemical analyses is a crucial technique used for better assessment and valorization of mineral resources. The spatial association between the interpreted lineament patterns and mineralization rates testifies the tectonic deformations-mineral deposits relationship. The AHP-based MCDM statistical support model highlights the mining interest areas. The MCDM allows further spatial statistical evaluation and reveals new prospective metal occurrences in the Shwas-Tayyah and Khadra belts. N-S and NNE-SSW lineament networks show the highest mineralization rates, suggesting their priority for more detailed prospections. The metallic mineral deposits have to be related to prominent steeply dipping and brittle-ductile shear zones.
Implementation of a symmetric chaos-based cryptosystem for image security in real time

This paper puts forward a real-time architecture of a chaos-based encryption system dedicated to perform image encryption and decryption. An evolutionary Pseudo Random Number Generator (PRNG) was designed and implemented to generate sensitive pseudo-random numbers based on the Lorenz chaotic system. We propose a new image security approach based on XORing image pixels with the generated key sequence. To increase complexity R’ cycles of encryption could be done. The proposed design was implemented, and validated using a platform Xilinx Zynq FPGA. In addition, the applied synthesis shows that the hardware design provides high frequency and throughput performance.
Arabic speech recognition based on a CNN-BLSTM combination

Despite advances in speech recognition technology, Arabic speech recognition remains largely unsolved due to its many difficulties and challenges. The performance of the best existing recognizers is much lower than those developed in English. Deep Neural Networks (DNNs) have shown excellent performance in acoustic modeling for speech recognition. In this work, a new discontinuous Arabic speech recognition model is proposed. It associates a deep convolutional neural network (CNN) architecture with a long-term bi-directional memory (BLSTM). The optimal network structure and training strategy for the model are examined. The Arabic Speech Corpus of Isolated Words (ASDS) and the Spoken Arabic Digits (SAD) database were used for all experiments. The results demonstrate the strength and benefits of the CNN-BLSTM method, which provides the best detection accuracy.
Blockchain for WSN and IoT Applications

This paper reviews the important application areas of blockchain in Wireless Sensor Networks (WSN) and Internet of Things (IoT) applications. We identify areas where current IoT processes can be enhanced through the use of this new technology with emphasis on the issues addressed in Wireless Body Sensor Networks. This use case is discussed in detail, identifying a suitable blockchain architecture for an industry that is averse to risk, gathering security challenges in this architecture, and providing existing blockchain solutions for these challenges.
Hardware implementation of a strong pseudo-random numbers generator with an application to image encryption

This paper suggests a high-performance FPGA implementation of a strong pseudo-random numbers generator for several applications mainly in the security domain. An improved PRNG utilizing the Lorenz chaotic map is propounded. Then, using the Xilinx System Generator software of Xilinx society an FPGA architecture of the DPRNG is developed. Next, the hardware DPRNG is implemented on an FPGA-Zynq resulting in low resource utilization, a good frequency of 194.714 MHz, and high throughput of 25702 Mbps. The NIST 800-22 SP test suite result demonstrates that the suggested hardware DPRNG allows for generating high-quality random numbers. It provides a strong key with a high space. In addition, an application of the hardware DPRNG for image encryption is presented. The experimental results show better results when compared to other recent works.
Design, Modeling and Control of a Two Flight Mode Capable Single Wing Rotorcraft With Mid-Air Transition Ability

Monocopters are nature-inspired, single-wing, rotating aerial vehicles that fly by spinning their entire body. Meanwhile, bicopters are twin propeller-based aerial vehicles that control their attitude by changing the direction of thrust from the motors using two servos. In this paper, we present a novel single-wing aerial vehicle, which can fly in both the monocopter and bicopter modes. To enhance its maneuverability while still being in the air, the platform can perform a mid-air transition from one mode to another. To achieve this, we fused the attributes of monocopter and bicopter, while allowing the monocopter to maintain its natural shape for flight. Considering forces and torques experienced by both modes of flight, the dynamics are described, and a cascaded control strategy is developed. A novel approach is proposed to control the angular velocity of the monocopter. An innovative blending and transition method of controllers for both modes is developed to allow transition between the two modes. We constructed a prototype to demonstrate the flight of the aerial vehicle in both modes. The results verify the proposed concept for the design of the aerial vehicle, along with the control strategy implemented for the control over the states during the flight modes as well as the transition between the two modes.
Radar-Based Gesture Recognition Using a Variational Autoencoder With Deep Statistical Metric Learning

Radar-based gesture recognition systems are a promising concept for new human–machine interfaces. However, the systems are sensitive to user-dependent gesture patterns, sensor noise characteristics, background environments, and unknown gestures or background motions. To overcome such challenges, we propose a variational autoencoder architecture-based deep metric learning, which is optimized using a novel loss function combining a statistical distance triplet loss and the center loss. By learning the statistical distance between distributions, the proposed solution, compared with triplet loss, is able to better learn the nonlinear characteristics in the data, is less sensitivity to training strategy, and is capable of creating close knit embedding clusters. Moreover, it is experimentally demonstrated that the proposed gesture recognition system using radar spectrograms has improved classification accuracy and random gesture rejection accuracy compared with the state-of-the-art deep metric learning approaches. Furthermore, we proof the real-world capability of the system by implementing a real-time demonstrator, which allows playing “Tetris” with hand gestures.
A Systematic Review on Visualizations for Self-Generated Health Data for Daily Activities

Due to the development of sensing technology people can easily track their health in various ways, and the interest in personal healthcare data is increasing. Individuals are interested in controlling their wellness, which requires self-awareness and an understanding of various health conditions. Self-generated health data are easily accessed through mobile devices, and data visualization is commonly used in applications. A systematic literature review was conducted to better understand the role of visualizations and learn how to develop effective ones. Thirteen papers were analyzed for types of data, characteristics of visualizations, and effectiveness for healthcare management. The papers were selected because they represented research on personal health data and visualization in a non-clinical setting, and included health data tracked in everyday life. This paper suggests six levels for categorizing the efficacy of visualizations that take into account cognitive and physical changes in users. Recommendations for future work on conducting evaluations are also identified. This work provides a foundation for personal healthcare data as more applications are developed for mobile and wearable devices.
Online multi-object tracking with δ-GLMB filter based on occlusion and identity switch handling

In this paper, we propose an online multi-object tracking (MOT) method based on the delta Generalized Labeled Multi-Bernoulli (δ-GLMB) filter framework to address occlusion and miss-detection issues and recover identity switch (ID switch). Along with the principal δ-GLMB filter that performs multi-object tracking, we propose a one-step δ-GLMB filter to handle occlusion and miss-detection. The one-step δ-GLMB filter is non-iterative and only requires current measurements. The filter is based on a proposed measurement-to-reappeared track association method and addresses MOT issues by incorporating all occluded and miss-detected objects. We introduce a novel similarity metric to apply in the measurement-to-reappeared track association process to define the weight of hypothesized reappeared tracks. To ensure the track consistency, we also extend the principal δ-GLMB filter to efficiently recover switched IDs using the cardinality density, size, and visual features of the hypothesized tracks. In addition, we perform an ablation study to demonstrate the contribution of the main parts of the proposed method. We evaluate the proposed method on well-known and publicly available test datasets focused on pedestrian tracking. Note that our proposed method is online and not based on the learning paradigm. So it does not use any additional source of information such as private detections and pre-trained networks. Despite that, we achieved a reliable performance in multiple persons tracking at complex scenes by applying occlusion/miss-detection and ID switch handlers. Experimental results show that the proposed tracker performs better or at least at the same level of the state-of-the-art online and offline MOT methods.
An Image-Based Deep Learning Approach with Improved DETR for Power Line Insulator Defect Detection

Insulators are basic parts of high-voltage transmission, and detecting faults of insulators is a critical task. Most state-of-the-art methods contain two or more stages, including insulator detection and defect locating. Some also involve hand-designed components to improve the performance due to the complicated and misleading background of the wild. To automatically detect faults in UAV-captured insulator images, this paper presents a method that introduces DETR into insulator defect detection. With the self-attention mechanism in Transformer, the model can naturally exploit its advantage in focusing on the target area. However, training DETR requires large data sets and long training schedules to establish spatial relations in sparse locations, which makes it generally not feasible to train in small data sets. To explore the possibility of training a well-performing model with a data set that minimizes the cost of collecting insulator images, transfer learning techniques were applied to this process. To compensate for the disadvantage of DETR in detecting small objects at more precise scales, an improved loss was transplanted to this model. The results show that our proposed method can detect defects directly from UAV images without the need to locate the insulator first, while providing competitive performance with a lower cost of collecting training samples.
School organisers’ expression on the expansion of the access and application of digital technologies in educational systems

Purpose This paper is aimed at describing and analysing what school organisers express when they network to expand the access and application of digital technologies in educational systems. Digital technologies develop rapidly in society, creating challenges and opportunities in people’s lives. Schools have an important task: to prepare young people for a future permeated by digital technology. Design/methodology/approach Participant observations and semi-structured interviews were used to describe and analyse how school organisers describe digitalisation work in the educational system. Findings The findings show that school organisers describe digital competence and providing support to schools as being important for expanding the access and application of digital technologies in the educational system. They also talk about equality connected to digitalisation work, changes in organisations, the importance of a holistic perspective, and a gold thread. They furthermore explain that networks, a sharing culture, good relations, and good communication enable digitalisation work. In addition, they point out that the lack of time, attitudes towards digital technologies, the lack of digital competence, and resistance to change are some of the constraints for digital technologies in the educational system. Practical implications School organisers need to implement digitalisation policies, which can be problematic because they work strategically to support operational activities. Originality/value This paper contributes knowledge about school organisers’ work to expand the access and application of digital technologies in the educational system.
Exploring multi-programming-language commits and their impacts on software quality: An empirical study on Apache projects

Modern software systems (e.g., Apache Spark) are usually written in multiple programming languages (PLs). There is little understanding on the phenomenon of multi-programming-language commits (MPLCs), which involve modified source files written in multiple PLs. This work aims to explore MPLCs and their impacts on development difficulty and software quality. We performed an empirical study on eighteen non-trivial Apache projects with 197,566 commits. : (1) the most commonly used PL combination consists of all the four PLs, i.e., C/C++, Java, JavaScript, and Python; (2) 9% of the commits from all the projects are MPLCs, and the proportion of MPLCs in 83% of the projects goes to a relatively stable level; (3) more than 90% of the MPLCs from all the projects involve source files in two PLs; (4) the change complexity of MPLCs is significantly higher than that of non-MPLCs; (5) issues fixed in MPLCs take significantly longer to be resolved than issues fixed in non-MPLCs in 89% of the projects; (6) MPLCs do not show significant effects on issue reopen; (7) source files undergoing MPLCs tend to be more bug-prone; and (8) MPLCs introduce more bugs than non-MPLCs. MPLCs are related to increased development difficulty and decreased software quality.
Semantic guided knowledge graph for large-scale zero-shot learning

Zero-shot learning has received growing attention, which aims to improve generalization to unseen concepts. The key challenge in zero-shot tasks is to precisely model the relationship between seen and unseen classes. Most existing zero-shot learning methods capture inter-class relationships via a shared embedding space, leading to inadequate use of relationships and poor performance. Recently, knowledge graph-based methods have emerged as a new trend of zero-shot learning. These methods use a knowledge graph to accurately model the inter-class relationships. However, the currently dominant method for zero-shot learning directly extracts the fixed connection from off-the-shelf WordNet, which will inherit the inherent noise in WordNet. In this paper, we propose a novel method that adopts class-level semantic information as a guidance to construct a new semantic guided knowledge graph (SG-KG), which can correct the errors in the existing knowledge graph and accurately model the inter-class relationships. Specifically, our method includes two main steps: noise suppression and semantic enhancement. Noise suppression is used to eliminate noise edges in the knowledge graph, and semantic enhancement is used to connect two classes with strong relations. To promote high efficient information propagation among classes, we develop a novel multi-granularity fusion network (MGFN) that integrates discriminative information from multiple GCN branches. Extensive experiments on the large-scale ImageNet-21K dataset and AWA2 dataset demonstrate that our method consistently surpasses existing methods and achieves a new state-of-the-art result.