Reviewerly/exHarmony huggingface dataset:
En-HACN: Enhancing Hybrid Architecture With Fast Attention and Capsule Network for End-to-end Speech Recognition

Automatic speech recognition (ASR) is a fundamental technology in the field of artificial intelligence. End-to-end (E2E) ASR is favored for its state-of-the-art performance. However, E2E speech recognition still faces speech spatial information loss and text local information loss, which results in the increase of deletion and substitution errors during inference. To overcome this challenge, we propose a novel Enhancing Hybrid Architecture with Fast Attention and Capsule Network (termed En-HACN), which can model the position relationships between different acoustic unit features to improve the discriminability of speech features while providing the text local information during inference. Firstly, a new CNN-Capsule Network (CNN-Caps) module is proposed to capture the spatial information in the spectrogram through the capsule output and dynamic routing mechanism. Then, we design a novel hybrid structure of LocalGRU Augmented Decoder (LA-decoder) that generates text hidden representations to obtain text local information of the target sequences. Finally, we introduce fast attention instead of self-attention in En-HACN, which improves the generalization ability and efficiency of the model in long utterances. Experiments on corpora Aishell-1 and Librispeech demonstrate that our En-HACN has achieved the state-of-the-art compared with existing works. Besides, experiments on the long utterances dataset based on Aishell-1-long show that our model has a high generalization ability and efficiency.
Energy Minimization for Wireless-Powered Federated Learning Network With NOMA

Limited energy resources remain a challenge for wireless devices in federated learning (FL) networks. To tackle this issue, a joint resource allocation problem is formulated to minimize the total energy consumption for wireless-powered FL networks with non-orthogonal multiple access. A two-dimensional search algorithm (TDSA) is first proposed to obtain the optimal solution for this challenging problem. To lower the computational complexity, the bisection algorithm (BA) is further employed by exploiting the inherent structure of the formulated problem. Numerical results verify the equal performance between the BA and TDSA, and the superiority of the proposed scheme over the benchmark schemes in energy minimization.
Visual Description of Digital IT Consulting Services Using DITCOS-DN: Proposal and Evaluation of a Graphical Editor

The digital transformation of the IT consulting domain recently gained momentum due to the Covid-19 pandemic. However, the range of IT consulting services that are fully digital is still very limited. Plus, there are no standardized and established methods for describing digital IT consulting services, nor there is any suitable tooling for digital IT consulting service provisioning. The present work aims to reduce this gap by contributing to establishing a well-defined approach to formally describing digital IT consulting services that could possibly be a candidate for standardization. Building upon (i) the ontology DITCOS-O, which provides the semantic basis for our approach, and (ii) the YAML-based description notation DITCOS-DN, which we leverage to describe digital IT consulting service models, we propose a graphical, web-based editor (called DITCOS-ModEd) to simplify service model maintenance. Following a design science based research process, we developed a prototype and empirically evaluated its applicability with the help of IT consultants. This first evaluation allowed us to identify some limitations and to plan specific improvements, both to the underlying artifacts DITCOS-O and DITCOS-DN, as well as to DITCOS-ModEd itself.
The AdaBoost Approach Tuned by SNS Metaheuristics for Fraud Detection

Recent advances in online payment technologies drastically increased the number of online credit card transactions, which had been additionally fueled by the recent COVID-19 pandemic. Consequently, the number of frauds related to credit cards had also increased drastically, affecting users, merchants, issuing companies, and bank institutions worldwide. One of the crucial tasks is to implement a mechanism that can assure both credit card security and integrity for each online transaction. This paper proposes an adaptive boosting algorithm, that was subjected to the optimization process by the social network search algorithm. The proposed hybrid approach has been validated on the imbalanced synthetic credit card fraud detection benchmark dataset, and acquired outcomes were compared to other cutting-edge machine learning models. The evaluation was performed by utilizing the standard performance indicators—accuracy, recall, precision, Matthews correlation coefficient, and area under the curve. The experimental findings have shown that the proposed SNS-based AdaBoost approach obtained superior results, clearly outperforming all other machine learning models included in the analysis.
An Analytical Appraisal on Recent Trends and Challenges in Secret Sharing Schemes

Secret sharing is one of the most widely studied research areas in cryptography and is developed as a method to safeguard cryptographic keys. Modern technology has substituted almost all forms of communication and information processing. Security in electronic media has been a topic of challenging issues. This made the advancement of cryptographic algorithms using secret sharing schemes. Secret sharing provides an efficient solution by protecting vital information being lost, modified or destroyed. The concept of secret sharing is to split the secret into pieces called shares or shadows, which are allocated to users so that the collective shares of permitted subsets of users allow reconstruction of the original secret. This paper reviewed and analyzed the recent works done by researchers on various schemes and techniques used in secret sharing. This also proposed a security enhanced method of threshold scheme in order to overcome the existing challenges of cheating detection and cheater identification.
Coulomb’s Law: Augmented Reality Simulation

Coulomb’s law is a typical representative of the inverse-square laws that appear in various learning contexts in physics. In the usual classroom setting, most students do not have the opportunity to experimentally discover the interdependence of variables described by this law. Recently, digital technologies have become more prevalent at all levels of education. Teachers and researchers are successfully using computer-based simulations to improve physics education. Augmented reality is one of the emerging digital technologies that have the potential to improve students’ understanding of a wide range of educational topics. Nevertheless, it is advisable that students and teachers are familiar with the most efficient use of selected digital tools before using augmented reality-enriched physics lectures. In this paper, we present our augmented reality simulation called CoulombsLawAR, which could be useful for teaching Coulomb’s law.
Aerial Shooting Manipulator for Distant Grasping

In this letter, the first prototype of an aerial shooting manipulator (ASM) powered by air pressure is proposed for distant grasping. The design of the ASM is described in detail, which is combined by a multirotor and a shooting manipulator including a pulse force generator, a retracting device and an end-effector. Compared to other types of aerial manipulators, the ASM can grasp the target in motion despite obstacles around the target and promote efficiency. To achieve a better control performance under parameter uncertainties, the adaptive sliding mode controller is designed for trajectory tracking of the aerial platform. In addition, a shooting point selection and trajectory modification strategy is proposed to ensure shooting the end-effector on the target. The trajectory is modified based on the original task waypoints to make flying distance as shortest as possible. The validity and effectiveness of the proposed method is demonstrated by both simulation results and real experiments in different situations.
Research data management needs assessment for social sciences graduate students: A mixed methods study

The complexity and privacy issues inherent in social science research data makes research data management (RDM) an essential skill for future researchers. Data management training has not fully addressed the needs of graduate students in the social sciences. To address this gap, this study used a mixed methods design to investigate the RDM awareness, preparation, confidence, and challenges of social science graduate students. A survey measuring RDM preparedness and training needs was completed by 98 graduate students in a school of education at a research university in the southern United States. Then, interviews exploring data awareness, knowledge of RDM, and challenges related to RDM were conducted with 10 randomly selected graduate students. All participants had low confidence in using RDM, but United States citizens had higher confidence than international graduate students. Most participants were not aware of on-campus RDM services, and were not familiar with data repositories or data sharing. Training needs identified for social science graduate students included support with data documentation and organization when collaborating, using naming procedures to track versions, data analysis using open access software, and data preservation and security. These findings are significant in highlighting the topics to cover in RDM training for social science graduate students. Additionally, RDM confidence and preparation differ between populations so being aware of the backgrounds of students taking the training will be essential for designing student-centered instruction.
LG-MLFormer: local and global MLP for image captioning

Self-attention-based image captioning model exists visual features’ spatial information loss problem, introducing relative position encoding can solve the problem to some extent. However, it will bring additional parameters and greater computational complexity. To solve the above problem, we propose a novel local–global MLFormer (LG-MLFormer) with specifically designed encoder module Local–global multi-layer perceptron (LG-MLP). The LG-MLP can capture the latent correlations between different images and its linear stacking calculation mode can reduce computational complexity. It consists of two independent local MLP (LM) modules and a cross-domain global MLP (CDGM) module. The LM specially designs the mapping dimension between linear layers to realize the self-compensation of visual features’ spatial information without introducing relative position encoding. The CDGM module aggregates cross-domain potential correlations between grid-based features and region-based features to realize the complementary advantages of these global and local semantic associations. Experiments on the Karpathy test split and the online test server reveal that our approach provides superior or comparable performance to the state-of-the-art (SOTA). Trained models and code for reproducing the experiments are publicly available at: https://github.com/wxx1921/LGMLFormer-local-and-global-mlp-for-image-captioning .
Enhancing Privacy in Federated Learning with Local Differential Privacy for Email Classification

With federated learning, information among different clients can be accessed to train a central model that aims for an optimal use of data while keeping the clients' data local and private. But since its emergence in 2017, several threats such as gradient attacks or model poisoning attacks against federated learning have been identified. Therefore, federated learning cannot be considered as stand alone privacy preserving machine learning technique. Thus, we analyse how and where local differential privacy can compensate for the drawbacks of federated learning while keeping its advantage of combining data from different sources. In this work, we analyse the different communication channels and entities in the federated learning architecture that may be attacked or try to reveal data from other entities. Thereby, we evaluate where local differential privacy is helpful. Finally, for our spam and ham email classification model with local differential privacy, we find that setting a local threshold of F1-Score on the clients' level can reduce the consumption of privacy budget over several rounds, and decrease the training time. Moreover, we find that for the central model a significantly higher F1-Score than those set on the local level for the clients can be achieved.
Study on students' satisfaction with using Chatbot on Facebook's messenger platform to learn Chinese vocabulary

In the context of the ever-evolving scientific and technological revolution and the strong globalization trend, proficiency in foreign languages ​​becomes extremely important because this is the bridge between countries in all areas of life. Currently, using some technological applications in learning foreign languages is becoming a new trend in the digital edge. A chat box product (Chatbot) integrated into Facebook's Messenger platform helps students majoring in Chinese language review vocabulary in Boya Chinese textbooks at elementary and intermediate levels, including three main functions: learning vocabulary, checking an online dictionary and reviewing vocabulary. This research is conducted in order to assess students' satisfaction with the functions of Chatbot with the participation of 101 students majoring in the Chinese language at Hanoi University of Industry. The result indicates that Chatbot stimulates students' interest with user-friendly configuration to improve students' academic performance. The higher Chinese proficiency learners have, the more stringent requirements they have for study methods and the capacity to provide vocabulary from Chatbot. This application's preface significantly impacts learners' emotions during their learning progress.
External Knowledge Flows and Small and Medium-Sized Enterprises’ Innovation Capabilities Enhancement: An Empirical Investigation

This study aimed to identify sources of external knowledge in small- and medium-sized enterprises (SMEs). Additionally, it aimed to determine how external knowledge flow affects the different types of SMEs’ innovation capabilities (product, process, organizational, and marketing innovation capability). A quantitative approach was adopted to achieve the objectives. A questionnaire survey was used to collect study data from 83 random SME managers/owners in Saudi Arabia. The data were analyzed using regression analysis and descriptive statistics. The findings of the study showed that customers were the main source of external knowledge in SMEs. The results of the regression analysis also demonstrated that external knowledge flow has a significant effect on SMEs’ innovation capabilities. External knowledge explains the changes in SMEs’ products and their process innovation capability. Additionally, external knowledge flow was a significant predictor of SMEs’ organizational innovation capability. The findings of the study provide policymakers and managers with many suggestions for developing innovations in SMEs. Additionally, this study provides a basis for researchers to conduct more future studies related to innovation and knowledge flow in SMEs.
Semantic Segmentation of Remote Sensing Imagery Based on Multiscale Deformable CNN and DenseCRF

The semantic segmentation of remote sensing images is a significant research direction in digital image processing. The complex background environment, irregular size and shape of objects, and similar appearance of different categories of remote sensing images have brought great challenges to remote sensing image segmentation tasks. Traditional convolutional-neural-network-based models often ignore spatial information in the feature extraction stage and pay less attention to global context information. However, spatial context information is important in complex remote sensing images, which means that the segmentation effect of traditional models needs to be improved. In addition, neural networks with a superior segmentation performance often suffer from the problem of high computational resource consumption. To address the above issues, this paper proposes a combination model of a modified multiscale deformable convolutional neural network (mmsDCNN) and dense conditional random field (DenseCRF). Firstly, we designed a lightweight multiscale deformable convolutional network (mmsDCNN) with a large receptive field to generate a preliminary prediction probability map at each pixel. The output of the mmsDCNN model is a coarse segmentation result map, which has the same size as the input image. In addition, the preliminary segmentation result map contains rich multiscale features. Then, the multi-level DenseCRF model based on the superpixel level and the pixel level is proposed, which can make full use of the context information of the image at different levels and further optimize the rough segmentation result of mmsDCNN. To be specific, we converted the pixel-level preliminary probability map into a superpixel-level predicted probability map according to the simple linear iterative clustering (SILC) algorithm and defined the potential function of the DenseCRF model based on this. Furthermore, we added the pixel-level potential function constraint term to the superpixel-based Gaussian potential function to obtain a combined Gaussian potential function, which enabled our model to consider the features of various scales and prevent poor superpixel segmentation results from affecting the final result. To restore the contour of the object more clearly, we utilized the Sketch token edge detection algorithm to extract the edge contour features of the image and fused them into the potential function of the DenseCRF model. Finally, extensive experiments on the Potsdam and Vaihingen datasets demonstrated that the proposed model exhibited significant advantages compared to the current state-of-the-art models.
Development of a Modular Real-time Shared-control System for a Smart Wheelchair

In this paper, we propose a modular navigation system that can be mounted on a regular powered wheelchair to assist disabled children and the elderly with autonomous mobility and shared-control features. The lack of independent mobility drastically affects an individual's mental and physical health making them feel less self-reliant, especially children with Cerebral Palsy and limited cognitive skills. To address this problem, we propose a comparatively inexpensive and modular system that uses a stereo camera to perform tasks such as path planning, obstacle avoidance, and collision detection in environments with narrow corridors. We avoid any major changes to the hardware of the wheelchair for an easy installation by replacing wheel encoders with a stereo camera for visual odometry. An open source software package, the Real-Time Appearance Based Mapping package, running on top of the Robot Operating System (ROS) allows us to perform visual SLAM that allows mapping and localizing itself in the environment. The path planning is performed by the move base package provided by ROS, which quickly and efficiently computes the path trajectory for the wheelchair. In this work, we present the design and development of the system along with its significant functionalities. Further, we report experimental results from a Gazebo simulation and real-world scenarios to prove the effectiveness of our proposed system with a compact form factor and a single stereo camera.
Memory-Efficient Domain Incremental Learning for Internet of Things

In Internet of Things (IoT) scenarios such as smart homes, autonomous vehicles, and wearable devices, data pattern changes over time due to changing environments and user requirements, known as domain shifts. When encountering domain shifts, deep neural network models in IoT suffers from performance degradation and need to retrain from scratch to adapt to domain shifts incrementally. Therefore, incremental learning is needed to adapt a model to domain shifts without retraining. Existing methods using the parameter isolation technique perform well in incremental learning of new domains without performance degradation. However, they cannot be directly adopted in IoT applications as they store masks and require users to label the task to indicate task-specific parameters during inference, which is memory inefficient and cumbersome. In this paper, we propose a memory-efficient method for IoT to incrementally adapt to domain shifts in a fixed neural network, named E-DomainIL. Our method freezes learned parameters and allows reusing them later in training to avoid interference between different domains. E-DomainIL does not require task labels or storing masks as it uses all parameters during inference. We use data-driven pruning to adjust the parameter ratio according to the dataset, thus maintaining the balance between accuracy and parameter efficiency. Experimental results on image classification benchmarks demonstrate our method's efficiency and accuracy.
Vulnerabilities and Threat Management in Relational Database Management Systems

Databases are at the heart of modern applications and any threats to them can seriously endanger the safety and functionality of applications relying on the services offered by a DBMS. It is therefore pertinent to identify key risks to the secure operation of a database system. This paper identifies the key risks, namely, SQL injection, weak audit trails, access management issues and issues with encryption. A malicious actor can get help from any of these issues. It can compromise integrity, availability and confidentiality of the data present in database systems. The paper also identifies various means and ways to defend against these issues and remedy them. This paper then proceeds to identify from the literature, the potential solutions to these ameliorate the threat from these vulnerabilities. It proposes the usage of encryption to protect the data from being breached and leveraging encrypted databases such as CryptoDB. Better access control norms are suggested to prevent unauthorized access, modification and deletion of the data. The paper also recommends ways to prevent SQL injection attacks through techniques such as prepared statements.
Embracing Permissioned Blockchain for a Secure and Reliable Land Registration System

Digitization of Land Registry System has been in the initial phases of implementation in various countries all over the world. Especially in India, the existing land recording system is vulnerable and prone to manipulation. The key issues associated with the land registration and management are first lack of transparency in recordkeeping (among government official involved as well as the related private individual), second time consuming (verify or access the information takes time), the slow processing brought on by the numerous officials involved in the process, and the lack of required data availability on their end (such as inconsistent, incomplete, or out-of-date data). In this paper, authors tried to embrace the features of blockchain technology to tackle these issues without hampering or eliminating the mandatory government official's roles. The permissioned blockchain, Hyperledger Fabric is used to implement the proposed system. The roles like and Sub-registrar offices are regarded as government officials and are required to conform to the land title registration policy. The Hyperledger chain codes are used to enforced the requirement and provide a hassle-free land registration and management process.