Reviewerly/exHarmony huggingface dataset:
Promoting Radical Innovation Through Performance-Based Rewards: The Mediating Role of Knowledge Acquisition and Innovative Work Behavior

The purpose of this paper is to study the effect of performance-based rewards on radical innovation. In doing so, it explores the role of knowledge acquisition and innovative work behavior (IWB) as possible mechanisms that underlie the relationship between performance-based rewards and radical innovation. The subsequent effect of IWB on promoting radical innovation is explored. The study employs data from 235 unit managers in manufacturing and technology firms in Jordan. Structural equation modeling was used to test for reliability, validity, and the hypothesized relationships. The results indicate that performance-based rewards positively affect knowledge acquisition. In addition, knowledge acquisition mediates the link between performance-based rewards and IWB. IWB is also significant and positive in promoting radical innovation. This paper is one of the first to examine how radical innovation can be promoted through IWB. It offers insights into and contributions to the literature on the role of performance-based rewards in driving radical innovation through the mediating effect of knowledge acquisition and the subsequent impact of IWB on radical innovation.
Radar waveform recognition based on a two‐stream convolutional network and software defined radio

As one of the key technologies of electromagnetic spectrum operations, radar waveform recognition is an important basis for judging the threat degree of enemy’s weapons. At present, it is difficult to recognize the waveform at low signal-to-noise ratio (SNR), and the theoretical simulation is used in most research studies, which leads to the problem that the engineering practicality of the algorithm is poor. In this paper, a two-stream convolutional network radar waveform recognition (TCNRWR) algorithm that fuses the original time–frequency image and the continuous frames autocorrelation time–frequency image is proposed, and an experimental platform is built by using software defined radio technology. Compared with traditional theoretical simulations, this paper focusses on the influence of spatial background interference and signal incompleteness on the recognition of intra-pulse signals, and proposes an automatic removal method of invalid signal in the time–frequency domain. In order to further improve the recognition rate at low SNR, a deeper feature of short-time autocorrelation flow is excavated, and a two-stream time–frequency images fusion convolutional network (TTIFCNet) is constructed. The network effectively fuses the pre-classification results of the two streams through Softmax layer and different weights. Finally, the hardware platform HackRF is used to realize the communication and processing of signals in real space, which proves the effectiveness of the TCNRWR algorithm.
Noun-based attention mechanism for Fine-grained Named Entity Recognition

Fine-grained Named Entity Recognition is a challenging Natural Language Processing problem as it requires classifying entity mentions into hundreds of types that can span across several domains and be organized in several hierarchy levels. This task can be divided into two subtasks: Fine-grained Named Entity Detection and Fine-grained Named Entity Typing. In this work, we propose solutions for both of these subtasks. For the former, we propose a system that uses a stack of Byte-Pair Encoded vectors in combination with Flair embeddings, followed by a BILSTM-CRF network, which allowed us to improve the current state of the art for the 1k-WFB-g dataset. In the second subtask, attention mechanisms have become a common component in most of the current architectures, where the patterns captured by these mechanisms are generic, so in theory, they could attend to any word in the text indistinctly, regardless of its syntactic type, often causing inexplicable errors. To overcome this limitation we propose an attention mechanism based specifically on the use of elements of the noun syntactic type. We have compared our results to those obtained with a generic attention mechanism, where our method presented better results.
SymSpell4Burmese: Symmetric Delete Spelling Correction Algorithm (SymSpell) for Burmese Spelling Checking

Spell checker is a crucial language tool of natural language processing (NLP) and becomes important due to the increase of text-based communication at work, information retrieval, fraud detection, search engines, social media and research areas. In this paper, automatic spelling checking for Burmese is studied by applying Symmetric Delete Spelling Correction Algorithm (SymSpell). We experimented by using an open source SymSpell python library and applied our developing Burmese spelling training corpus together with four frequency dictionaries on ten error types. For the error detection phase, the N-gram language model is used to check our developing spelling training corpus against a dictionary. For the correction phrase, SymSpell is applied to propose candidate corrections within a specified maximum edit distance from the misspelled word. After generating candidates, the best correction in the given context is automatically chosen according to the highest frequency with a minimum edit distance. We investigated the performance of each error type and studied the importance of the dictionary depending on the average term length and maximum edit distance for Burmese spell checker based on SymSpell. Moreover, we observed that syllable level segmentation with a maximum edit distance of 3 gives faster and higher quality results compared with word level segmentation results.
How a 4-Day Work Week and Remote Work Affect Agile Software Development Teams

Context: Agile software development (ASD) sets social aspects like communication and collaboration in focus. Thus, one may assume that the specific work organization of companies impacts the work of ASD teams. A major change in work organization is the switch to a 4-day work week, which some companies investigated in experiments. Also, recent studies show that ASD teams are affected by the switch to remote work since the Covid 19 pandemic outbreak in 2020. Objective: Our study presents empirical findings on the effects on ASD teams operating remote in a 4-day work week organization. Method: We performed a qualitative single case study and conducted seven semi-structured interviews, observed 14 agile practices and screened eight project documents and protocols of agile practices. Results: We found, that the teams adapted the agile method in use due to the change to a 4-day work week environment and the switch to remote work. The productivity of the two ASD teams did not decrease. Although the stress level of the ASD team member increased due to the 4-day work week, we found that the job satisfaction of the individual ASD team members is affected positively. Finally, we point to affects on social facets of the ASD teams. Conclusion: The research community benefits from our results as the current state of research dealing with the effects of a 4-day work week on ASD teams is limited. Also, our findings provide several practical implications for ASD teams working remote in a 4-day work week.
Speech Emotion Recognition for Power Customer Service

Improving user experience is one of the most important goals of power marketing. Power customer service is the first-line system to contact customers in power marketing, and customer emotion is an important indicator to describe customer satisfaction. Therefore, it is a very valuable task for power marketing to analyze the user's feelings from the speech data of power customer service. However, the speech emotion recognition based on power customer service is vulnerable to the interference of emotion independent factors, such as speaker differences, environmental noise, voice quality and so on. And the data has unbalanced sample classes. In this paper, we calculate the spectrogram of speech and its first-order and second-order difference, stack the three as the input of neural network to reduce the influence of emotion independent factors; we use CNN and LSTM to extract speech data features, and add attention mechanism to make the model focus on the time-frequency region related to emotion. For the problem of unbalanced sample classes, we add Focal Loss to reduce the weight of samples which are easy to classify in loss. The results of experiment show that the recognition accuracy of our model is 92.60% of weighted accuracy and 92.02% of unweighted accuracy, which is significantly improved compared with the traditional DNN-ELM method.
Improved Artificial Bee Colony Algorithm with Adaptive Parameter for Numerical Optimization

The problem that ABC (Artificial Bee Colony) algorithm is good at exploration but poor at exploitation for the numerical optimization is investigated in this paper. PA-ABC (Parameter Adaptive ABC) algorithm is proposed, which adopts different search equations with different search abilities for the employed bee and the onlooker bee. Firstly, the best-so-far solution is introduced into each search equation to enhance exploitation; secondly, the employed bee uses two random solutions to search, so as to keep high ability of exploration; thirdly, the onlooker bee searches around a random solution to keep population diversity; most importantly, adaptive parameter computed by fitness function is introduced in the search equation of the onlooker bee, which makes the search step adjust according to the search process. So the search equation of the employed bee has balanced abilities of exploration and exploitation, while the search equation of the onlooker bee can make the search focus transfer from exploration to exploitation adaptively. The experiment results on benchmark functions show that the search performance of PA-ABC is higher than or at least comparable to basic ABC and typical improved ABCs. In addition, compared to the performance of the state-of-the-art ABC variants under their original parameter configuration, PA-ABC is verified to have similar performance to them.
Does Dataset Complexity Matters for Model Explainers?

Strategies based on Explainable Artificial Intelligence - XAI have emerged in computing to promote a better understanding of predictions made by black box models. Most XAI measures used today explain these types of models, generating attribute rankings aimed at explaining the model, that is, the analysis of Attribute Importance of Model. There is no consensus on which XAI measure generates an overall explainability rank. For this reason, several proposals for tools have emerged (Ciu, Dalex, Eli5, Lofo, Shap and Skater). An experimental benchmark of explainable AI techniques capable of producing global explainability ranks based on tabular data related to different problems and ensemble models are presented herein. Seeking to answer questions such as "Are the explanations generated by the different measures the same, similar or different?" and "How does data complexity play along model explainability?" The results from the construction of 82 computational models and 592 ranks shed some light on the other side of the problem of explainability: dataset complexity!
Learnable Linear Phase FIR Filter Designs Using a Generative Adversarial Network

This paper proposes a new design technique to make trainable / learnable linear phase FIR (Finite Impulse Response) filters using a GAN (generative adversarial network). Instead of random noise for conventional GANs, the new FIR filter design uses an ideal LPF in time domain (i.e., sinc function) as an input to the GAN. With the ideal LPF in time, a learnable window function is trained by the GAN to make a close frequency response to a desired filter. The GAN includes also a symmetric extension to satisfy a linear phase constraint. The proposed design for learnable linear phase FIR filters is accomplished by two competing objects, generator and discriminator, in the GAN. It will be shown in this paper that the new technique can be applied to design various FIR filters such as LPFs (low pass filters), HPFs (high pass filters), BPFs (band pass filters), and BSFs (band stop filters) for any given arbitrary cutoff frequencies. Obtained window by training the GAN shows periodic deep valleys/ hills in a similar window shape for traditional FIR filter designs. Comparing with conventional approximate solutions to design such FIR filters, the proposed technique leads to a data-driven learnable filter design. The proposed filter design can be embedded to other DNNs requiring learnable filters and be jointly trained/ adjusted with other DNN parameters given by diverse loss or objective functions depending on applications.
Investigating a neural all pass warp in modern TTS applications

We present a neural implementation of the all pass warp (APW) previously used for vocal tract length normalisation. This includes an efficient back-propagation, which can easily be integrated in modern neural network frameworks. The APW offers a low-dimensional control to alter the spectrum, which by design generalises over different speakers. We investigate the APW in two tasks required for future dialogue or translation agents, and provide a fairly thorough literature review for both: (1) Zero-shot speaker adaptation to allow keeping the source speaker identity with very small amounts of data. Experiments show increased speaker similarity and prove that the APW increases the generalisability of a multi-speaker model. (2) Emotional speech synthesis to translate or produce affective cues. To the best of our knowledge this is the first attempt on emotional speech synthesis with an APW. While the APW is not able to increase expressiveness or audio quality, our analysis shows that the warping correlates with the level of valence in the emotion. This work should enable future research on emotion translation during machine translation.
A Complete Privacy-Preserving Credit Score System Using Blockchain and Zero Knowledge Proof

As an essential means of privacy protection technology, zero-knowledge proof has gradually been applied into various fields with the development of blockchain technology, such as the Internet of Vehicles and Bitcoin. Personal comprehensive credit score as a measure to promote social governance is closely related to personal privacy. Although there are currently credit score calculation systems for various application scenarios, these systems almost ignore user privacy protection, which leads to user information leakage or abuse. The combination of zero-knowledge proof and personal credit score calculation has been studied by a number of researchers at the present stage. However, all data are provided by users directly in the current schemes, which did not consider the data authenticity under the situation that users provided false data. In this paper, we utilize zero-knowledge proof to design a novel privacy protection scheme for personal credit score calculation, taking into account the authenticity verification of multi-dimensional user data. In addition, our scheme also proposes the concept of a universal verification platform based on blockchain for personal credit scores. This platform has more substantial applicability and versatility for any qualified institution that requires querying and verifying user’s credit scores. At the end of the paper, we conducted a security analysis and performance evaluation for the overall scheme.
Semantic segmentation for self-driving cars using deep learning

Autonomous driving is a promising field in today's industry. Many aspects need to be considered when designing a self-driving system. Understanding a situation is an important factor for any self-driving system. An accurate real-time processing of image signal to produce pixel-wise classified images, also known as semantic segmentation, is vital for situation understanding and then for the adoption of this new technology. Such efficiency in terms of processing time and accuracy could not be reached before the recent improvements in deep learning (DL) techniques due to the complicated relationship between pixels in each frame of the received camera signal. Many methods have been proposed to perform semantic segmentation efficiently. Convolutional neural networks (CNNs) with autoencoders (AEs), also known as convolutional AEs, have been proved to be one of the most effective models to perform semantic segmentation. However, a lot of improvements can be made on current models. In this paper, we analyze the different models for semantic segmentation for self-driving cars using DL architectures of CNNs and AEs and state-of-the art techniques such as feature pyramid networks and bottleneck residual blocks.
GDPR-Aware Revocable P2P File-Sharing System Over Consortium Blockchain

With the rise of blockchain technology, the peer-to-peer (P2P) network system has once again caught people's attention to equipping a blockchain with a big storage capacity. In the traditional P2P file-sharing network systems, such as InterPlanetary File System (IPFS), data stored in the other nodes cannot be revoked by the owner and can only be removed by other nodes themselves. To comply with the criteria of the European Union's General Data Protection Regulation, it is important to ensure that personal data can be completely removed by their owners. To improve the privacy and security of the P2P file-sharing system, we propose a revocable and monitorable P2P file-sharing system over a consortium blockchain to achieve revocation of files in the decentralized environment. By using a trusted execution environment, such as Intel Software Guard Extensions (SGX), the proposed scheme can verify the integrity of the executables of the P2P file-sharing system and generate a file authentication code for each IPFS node to make sure that the system is synchronized correctly. This scheme elaborately integrates the autonomous smart contracts and Intel SGX hardware to obtain the monitorable merit. The experimental results suggest that enhancing the security and privacy take modest computing costs into consideration. To the best of our knowledge, this scheme is the first attempt to achieve the P2P file-sharing system with securely revocable functions.
Exploring Group Discussion with Conversational Agents Using Epistemic Network Analysis

Conversational agents—dialogue systems that provide learning support to students in real time—have shown promise in facilitating science discussion. To enact conversation norms, the appearances, personalities, or tones of the agents often resemble personas that students are familiar with, such as peers or mentors. This study uses epistemic network analysis (ENA) to explore how students interacted with two personas agent (a peer and an expert) in collaborative settings. Data came from chat logs of three student groups with low, mixed, and high ability. The groups interacted with both prototypes. The chat logs received qualitative codes for discussion types, including claim-making, reasoning, building on prior ideas, and responsiveness to the agents. ENA visualized the differences in discussion between groups and between the agent conditions. Overall, the higher-ability groups engaged in more claim-making in tandem with building on prior ideas when interacting with the peer agent, compared to the expert agent. Meanwhile, the low-ability group showed more syntheses of previous ideas when responding to the expert agent. Findings illuminate the design space for adapting agent personas to group settings to facilitate productive exchange.
JointCloud Cross-chain Verification Model of Decentralized Identifiers

When multiple entities communicate or collaborate in JointCloud, identities are the very prior basis to build trust with each other. Decentralized identifier (DID) can provide a trusted identity with blockchain technology and a complete method of identity verification based on verifiable credentials, which solves problems of conventional centralized identity. However, current DIDs can only conduct verification within a single blockchain, which limits the interoperability of DIDs on different blockchains. Network isolation hinders the verification of DIDs on different blockchains and thus there is a need to break the barrier between blockchains. In this paper, we propose a model to conduct cross-chain verification of DIDs. We build a system of credit evaluation to describe the credibility of DIDs in a unified way and deploy smart contracts to implement cross-chain verification of DIDs. Experimental results verifies the feasibility of the model, which realizes cross-chain verification of DIDs in the networks of blockchain.
Hash-Chain Fog/Edge: A Mode-Based Hash-Chain for Secured Mutual Authentication Protocol Using Zero-Knowledge Proofs in Fog/Edge

Authentication is essential for the prevention of various types of attacks in fog/edge computing. Therefore, a novel mode-based hash chain for secure mutual authentication is necessary to address the Internet of Things (IoT) devices' vulnerability, as there have been several years of growing concerns regarding their security. Therefore, a novel model is designed that is stronger and effective against any kind of unauthorized attack, as IoT devices' vulnerability is on the rise due to the mass production of IoT devices (embedded processors, camera, sensors, etc.), which ignore the basic security requirements (passwords, secure communication), making them vulnerable and easily accessible. Furthermore, crackable passwords indicate that the security measures taken are insufficient. As per the recent studies, several applications regarding its requirements are the IoT distributed denial of service attack (IDDOS), micro-cloud, secure university, Secure Industry 4.0, secure government, secure country, etc. The problem statement is formulated as the "design and implementation of dynamically interconnecting fog servers and edge devices using the mode-based hash chain for secure mutual authentication protocol", which is stated to be an NP-complete problem. The hash-chain fog/edge implementation using timestamps, mode-based hash chaining, the zero-knowledge proof property, a distributed database/blockchain, and cryptography techniques can be utilized to establish the connection of smart devices in large numbers securely. The hash-chain fog/edge uses blockchain for identity management only, which is used to store the public keys in distributed ledger form, and all these keys are immutable. In addition, it has no overhead and is highly secure as it performs fewer calculations and requires minimum infrastructure. Therefore, we designed the hash-chain fog/edge (HCFE) protocol, which provides a novel mutual authentication scheme for effective session key agreement (using ZKP properties) with secure protocol communications. The experiment outcomes proved that the hash-chain fog/edge is more efficient at interconnecting various devices and competed favorably in the benchmark comparison.