Reviewerly/exHarmony huggingface dataset:
Uneven success: automatic speech recognition and ethnicity-related dialects

Addressing racial bias in automatic speech recognition is an area of concern in fields associated with human-computer interaction. Research to date suggests that sociolinguistic variation, namely systematic sources of sociophonetic variation, has yet to be extensively exploited in acoustic model architectures. This paper reports a study that evaluates the performance of one ASR system for a multi-ethnic sample of speakers from the American Pacific Northwest (including Native American, African American, European American and ChicanX speakers). Using a sociophonetic approach to characterizing vocalic and consonantal variation, we ask which dialect features appear to be most challenging for our ASR system. We also ask which error types are particular to the four ethnic dialects sampled. Recordings of both conversational and read speech were coded for a common set of 18 sociophonetic variables with distinct phonetic profiles. Automatic transcription was achieved using CLOx, a custom-built ASR system created for sociolinguistic analysis. Normalized error frequency rates were compared across ethnic samples to evaluate CLOx performance. Nf error rates demonstrate clear differential performance in the ASR system, pointing to racial bias in system output. Specific predictions are made regarding approaches that might be taken to leverage sociophonetic knowledge to improve social dialect-recognition accuracy in ASR systems.
Using Virtual Reality to Improve the Quality of Life of Older Adults with Cognitive Impairments and their Family Members who Live at a Distance

This study examines whether using virtual reality (VR) with older adults with mild cognitive impairment (MCI) or mild to moderate dementia with a family member who lives at a distance can improve the quality of life of the older adult and the family member. Twenty-one older adults in a senior living community and a family member (who participated in the VR with the older adult from a distance) engaged in a baseline telephone call, followed by three weekly VR sessions. The VR was associated with improvements in older adults' affect and stress, relationship with their family member, and overall quality of life, compared to baseline. Family members' negative affect, depressive symptoms, and caregiver burden also decreased and their mental health improved after using the VR, compared to baseline. Using the VR, however, did not change their relationship with the older adult. In addition, older adults and family members who experienced the VR sessions as more socially engaging reported better psychological and relational well-being, with older adults also experiencing greater improvements in overall quality of life. Finally, preliminary results suggest that older adults with dementia and their family members might benefit even more from using the VR than older adults with MCI and their family members.
RanNet: Learning Residual-Attention Structure in CNNs for Automatic Modulation Classification

With the rapid emergence of advanced technologies for wireless communications, automatic modulation classification (AMC) has been deployed in the physical layer to blindly identify the modulation fashion of an incoming signal at the receiver and consequently improve the efficiency of spectrum utilization and management. Although recent works on AMC have adopted deep learning with convolutional neural networks (CNNs) to deal with large-confusing signal data, they have shown to be vulnerable to channel deterioration by primitive architectures. In this letter, we design a high-performance CNN architecture, namely Residual-attention Convolutional Network (RanNet), that mainly involves multiple advanced processing blocks to learn intrinsic features of combined waveform data (including in-phase, quadrature, amplitude, and phase components). Each block incorporates attention connection and skip connection in a sophisticated-designed structure to strengthen relevant features and weaken irrelevant features while preventing the network from vanishing gradient. Simulation results on the RadioML 2018.01A dataset show that RanNet is robust to different channel impairments and outperforms state-of-the-art deep networks in terms of accuracy while having a reasonable complexity.
On the Faults Found in REST APIs by Automated Test Generation

RESTful web services are often used for building a wide variety of enterprise applications. The diversity and increased number of applications using RESTful APIs means that increasing amounts of resources are spent developing and testing these systems. Automation in test data generation provides a useful way of generating test data in a fast and efficient manner. However, automated test generation often results in large test suites that are hard to evaluate and investigate manually. This article proposes a taxonomy of the faults we have found using search-based software testing techniques applied on RESTful APIs. The taxonomy is a first step in understanding, analyzing, and ultimately fixing software faults in web services and enterprise applications. We propose to apply a density-based clustering algorithm to the test cases evolved during the search to allow a better separation between different groups of faults. This is needed to enable engineers to highlight and focus on the most serious faults. Tests were automatically generated for a set of eight case studies, seven open-source and one industrial. The test cases generated during the search are clustered based on the reported last executed line and based on the error messages returned, when such error messages were available. The tests were manually evaluated to determine their root causes and to obtain additional information. The article presents a taxonomy of the faults found based on the manual analysis of 415 faults in the eight case studies and proposes a method to support the classification using clustering of the resulting test cases.
Disruption by design? Classification framework for the archetypes of disruptive business models

The current scholarly debates highlight the role of business models for understanding the dynamics inherent to disruptive innovations. Additionally, research on business models argues for similarities between business models, as they are shared by multiple competitors and across industries. Despite the recent advancements in both debate streams, a consolidation of the research on the underlying similarities of disruptive business models is lacking. Such a consolidation would allow learning from previous waves of disruption, ultimately informing theory and practice to cope with the increasing pace and impact of disruptive innovations. Consequently, this study systematically identifies 122 disruptive business models that have been discussed in the literature during 2006–2019. Based on qualitative content analysis, we then develop a classification framework and propose five archetypes of disruptive business models: (1) matchmakers, (2) standardizers, (3) service providers, (4) open collaborators, and (5) performance reducers. We make a twofold contribution to the discussions around the hitherto tentatively specified business models underlying disruptive innovations, thus emphasizing the contingency of the phenomenon. First, we discuss how the identified archetypes are inducing disruptive dynamics. Second, we elaborate on how the archetypes are constituting paths towards disruption.
A new oversampling method and improved radial basis function classifier for customer consumption behavior prediction

In practical applications, imbalanced data has brought great challenges to classification problems. In this paper, we propose two new methods: (1) a new oversampling method, named Tomek-CASUWO, to address the issue of class imbalance; (2) a new classifier, named ILS-RBFNN, to increase the accuracy of prediction of customer consumption behavior. Since current ASUWO algorithm is easily affected by noise samples and fuzzy class boundaries, we propose Tomek-CASUWO to address these problems: (i) the Tomek links algorithm is used to filter noise samples; (ii) CASUWO is used to avoid overlapping class boundaries; (iii) Tomek-CASUWO is used to synthesize new samples. Also, we propose a new classifier based on RBFNN, named ILS-RBFNN, to improve the prediction accuracy: (i) the hybrid kernel is developed by combining Gaussian and Polynomial; (ii) an Immune Algorithm (IA) is used to optimize the centers of RBFNN; (iii) Least-Squares (LS) is used to calculate the biases and weights. Wine-consumer behavior data is used to compare our Tomek-CASUWO with other oversampling methods. We compare ILS-RBFNN with several well-known kernel functions and parameter update methods. The experimental results show that Tomek-CASUWO can significantly improve the prediction accuracy of a classifier, and ILS-RBFNN outperforms other classification methods. We also conduct experiments on the extended real-world dataset. Finally, the robustness and applicability of ILS-RBFNN are verified on the eleven UCI datasets. All results show that the proposed two methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of ILS-RBFNN for predicting customer behavior.
A blockchain and smart contract-based data provenance collection and storing in cloud environment

Data uploading needs security and privacy in the cloud. But there are some problems like centralized provenance data (PD) collection, storage, lack of security, integrity, and more time consumption. There are methods like Rabin, Knapsack, McEliece, Elagamal, and Rivest–Shamir–Adleman for the generation of keys but it increases the encryption and decryption time and less security. Therefore, the blockchain and smart contract-based data provenance (BSCDP) Architecture is proposed for providing secure storage in the cloud environment. Initially, the fingerprint biometrics and physically uncloneable functions (PUF) have been used in verification process. The combination of PUF and fingerprint biometrics is used for secure data transmission. To protect privacy and strengthen the security, the fuzzy extractor is employed. Secondly, we use an elliptic-curve key based cyclic shift transposition cryptography algorithm for enhancing the security when sharing the key. Thirdly, we introduce the blockchain and the interplanetary file system (IPFS) for PD collection, hash computation, and storing with reduced computational overhead (CO). The integrity of data is maintained by using blockchain based secure hashing algorithm-3. By arranging fuzzy based smart contracts (FSC), the data user (DU) tracks their data. FSC is employed for tracking the history of data. The data collected is directly stored in IPFS and the DU gets a hash from IPFS to retrieve the data in the future. Finally, the data verification is done by the provenance auditor. When comparing our proposed BSCDP method with existing methods, the proposed BSCDP method achieves high security in the cloud environment for 2 K users in terms of evidence insertion time (30 ms), verification time (30 ms), response time (40 ms), total change rate (5%), CO (5.8 Kb), encryption (50 ms) and decryption time (52 ms).
State-of-the-art solutions of blockchain technology for data dissemination in smart cities: A comprehensive review

The concept of a smart city has been introduced to address the shortfalls of urbanization. The aid of technologies in smart cities is envisioned to be the key initiative to mitigate these challenges. However, the technologies exposure imposes challenging issues for the traditional centralized data dissemination schemes in smart cities. Consequently, smart city is undergoing a transition to a decentralized system through the implementation of blockchain technology. This paper presents an extensive survey of blockchain applicability for data dissemination in smart cities. We focus on the components of smart: transportation, healthcare, education, energy and building in smart cities and analyze state-of-the-art work on the integration of these components with blockchain. We discuss the advantages and shortcomings of existing literature and evaluate the performance efficiency and the extent of security and privacy achieved. The analysis leads to our new design of a secure smart city data dissemination framework using blockchain technology. To the best of our knowledge, this is the first comprehensive abstraction in the literature that can be utilized for the designation of future blockchain-smart cities data dissemination schemes with the inclusion of participating entities. Finally, we present open research issues and challenges and discuss future research directions.
Chaotic and Paillier secure image data sharing based on blockchain and cloud security

Internet of Things (IoT) is a materializing technology that has considerable technical, social, and economic importance. The present day predictions for the influence of IoT are highly monumental. This is because data of any form is found to be predominant to the IoT paradigm, therefore serving different applications like smart home, smart city, healthcare, smart grid, smart farms, manufacturing, and so on. Hence, several heterogeneous sensors are said in existence to estimate different types of parameters and found to be useful for different users. In this paper, a secured image sharing method called a Blockchain Chaotic and Paillier Map-based Authentication (BC-PMA) to provide image data integrity and security by deploying a cloud computing environment is proposed. The BC-PMA method is split into three sections. First, registration of corresponding classes of images in the cloud server via blockchain is presented by employing the Cat Map Registration model. The second step involves the authentication process carried out by employing Paillier Map-based Authentication where validation is ensured via smart contracts. Finally, upon successful authentication, unique and separate classes of images are provided with the respective, therefore ensuring secured classes of image sharing between users in the cloud environment. The results have been analyzed against traditional blockchain-based methods and validated with enhanced simulated results, that good authentication accuracy, minimizing the false positive rate, block generation time, and size considerably. Experimental Result shows that the Authentication Accuracy of the proposed BC-PMA method is improved by 23%, and minimizes the False Positive Rate and Computational Cost by 32% and 22% when compared to the Existing Method.
The differential effects of a governmental debunking campaign concerning COVID-19 vaccination misinformation

Throughout the ongoing COVID-19 pandemic debunking misinformation has been one of the most employed strategies used to address vaccine hesitancy. We investigated whether - and for whom - debunking is effective or even counterproductive in decreasing misinformation belief and vaccination hesitancy.We conducted a randomized controlled trial (N = 588) utilizing a real-world debunking campaign from the German Ministry of Health. We considered the condition (debunking vs. control) as between-subjects factor, assessed misinformation belief (pretest vs. posttest) as a repeated-measures factor and vaccination intention as a dependent variable. Preregistered subgroup analyses were conducted for different levels of a priori misinformation belief and general vaccination confidence.The analyses revealed differential effects on misinformation belief and vaccination intention in participants with low, medium, and high a priori belief: A debunking effect on misinformation belief (dRM = -0.80) was only found in participants with a medium a priori belief and did not extend to these participants' vaccination intentions. Among participants with a high a priori misinformation belief, explorative analysis revealed a small unintended backfiring effect on vaccination intentions (ηp2 = 0.03).Our findings suggest that debunking is an effective communication strategy to address moderate levels of misinformation beliefs, but it does not constitute a one-fits-all strategy to reduce vaccination hesitancy among the general public. Although countering misinformation should certainly be an integral part of public health communication, additional initiatives, which address individual concerns with targeted and authentic communication, should be taken to enhance the impact on hesitant populations and avoid backfiring effects.
Blockchain in Civil Engineering, Architecture and Construction Industry: State of the Art, Evolution, Challenges and Opportunities

Blockchain is a technology that allows the recording of information in a way that it is difficult or practically impossible to alter, hack, or cheat. It is a new, promising technology, considered by many as a general-purpose technology (GPT). GPTs are technologies that have the potential to affect an entire economy, impacting economic growth and transforming both everyday life and the ways in which we conduct business. We present a bibliometric analysis of the relevant literature, followed by a discussion about monetary mediums and the evolution of bitcoin, as the first digital medium managing to solve the “double-spending” problem and the first successful implementation of blockchain technology. The computational operations involved in blockchain are presented, together with the cryptographic technologies associated with it, its unique characteristics, and the advantages it offers as a technology. A comprehensive literature review is provided, of the current state of the art in blockchain in the fields of civil engineering, architecture and the construction industry. Six important application areas are identified, and the relevant literature is investigated. Namely, building information modelling and computer aided design, contract management and smart contracts, construction project management, smart buildings and smart cities, construction supply chain management, and real estate. Finally, we discuss the future applications, the challenges and the opportunities that blockchain technology brings to these fields.
LiCaNet: Further Enhancement of Joint Perception and Motion Prediction Based on Multi-Modal Fusion

The safety and reliability of autonomous driving pivots on the accuracy of perception and motion prediction pipelines, which reckons primarily on the sensors deployed onboard. Slight confusion in perception and motion prediction can result in catastrophic consequences due to misinterpretation in later pipelines. Therefore, researchers have recently devoted considerable effort towards enhancing perception and motion prediction models. However, targeting pixel-wise joint perception and motion prediction using different sensor modalities are often ignored. In this paper, we push performance even further by leveraging a multi-modal fusion network. We propose a novel LIDAR Camera Network (LiCaNet) that achieves accurate pixel-wise joint perception and motion prediction in real-time. LiCaNet expands on our earlier fusion network by incorporating a camera image into the fusion of LIDAR sourced sequential bird’s-eye view (BEV) and range view (RV) images. We present a comprehensive evaluation using nuScenes dataset to validate the outstanding performance of LiCaNet compared to the state-of-the-art. Experiments reveal that utilizing a camera sensor results in a substantial gain in perception and motion prediction. Moreover, most of the improvements achieved fall within the camera range, with the highest registered for small and distant objects, confirming the significance of incorporating a camera sensor into a fusion network.
A differential evolution with adaptive neighborhood mutation and local search for multi-modal optimization

In this paper, an adaptive neighborhood mutation based memetic differential evolution is proposed for multimodal optimization. In the proposed method, an adaptive neighborhood mutation (ANM) strategy is devised to allow the individuals to conduct a diverse search at the early stage of evolution and then gradually switch to an intensive search at the later stage of evolution. Further, the ANM is devised such that encouraging promising individuals for exploitation while unpromising individuals for exploration during evolution to appropriate search the multimodal space. In addition, an adaptive Gaussian based local search strategy, which considers the difference between the offspring and their paired individuals with successful replacements, is developed to appropriately improve promising individuals during evolution. The proposed method has been extensively accessed on a suite of twenty multimodal benchmark functions and the performance of which is compared with sixteen related multimodal algorithms. The results clearly demonstrate the superiority of proposed method as well as the merit of devised strategies.
AGROVOC: The linked data concept hub for food and agriculture

Newly acquired, aggregated and shared data are essential for innovation in food and agriculture to improve the discoverability of research. Since the early 1980′s, the Food and Agriculture Organization of the United Nations (FAO) has coordinated AGROVOC, a valuable tool for data to be classified homogeneously, facilitating interoperability and reuse. AGROVOC is a multilingual and controlled vocabulary designed to cover concepts and terminology under FAO's areas of interest. It is the largest Linked Open Data set about agriculture available for public use and its highest impact is through facilitating the access and visibility of data across domains and languages. This chapter has the aim of describing the current status of one of the most popular thesaurus in all FAO's areas of interest, and how it has become the Linked Data Concept Hub for food and agriculture, through new procedures put in place.
Kubernetes-Oriented Microservice Placement With Dynamic Resource Allocation

Microservices and Kubernetes are widely used in the development and operations of cloud-native applications. By providing automated placement and scaling, Kubernetes has become the main tool for managing microservices. However, existing work and Kubernetes fail to consider the dynamic competition and availability of microservices as well as the problem of shared dependency libraries among multiple microservice instances. To this end, this article proposes an integer nonlinear microservice placement model for Kubernetes with the goal of cost minimization. Specifically, we calculate the number of instances based on microservice availability and construct a model in which the total resource demand of multiple microservice instances exceeds the appropriate proportion of node resources when dynamic resource competition exists and the size of the shared dependency library is less than the node storage capacity. Finally, this article solves the microservice placement model using an improved genetic algorithm. The experimental results demonstrate that higher throughput is obtained with the same costs and that the same throughput is obtained with lower costs.
Robust graph learning with graph convolutional network

Graph convolutional network (GCN) is a powerful tool to process the graph data and has achieved satisfactory performance in the task of node classification. In general, GCN uses a fixed graph to guide the graph convolutional operation. However, the fixed graph from the original feature space may contain noises or outliers, which may degrade the effectiveness of GCN. To address this issue, in this paper, we propose a robust graph learning convolutional network (RGLCN). Specifically, we design a robust graph learning model based on the sparse constraint and strong connectivity constraint to achieve the smoothness of the graph learning. In addition, we introduce graph learning model into GCN to explore the representative information, aiming to learning a high-quality graph for the downstream task. Experiments on citation network datasets show that the proposed RGLCN outperforms the existing comparison methods with respect to the task of node classification.