Reviewerly/exHarmony huggingface dataset:
AutoSrh: An Embedding Dimensionality Search Framework for Tabular Data Prediction

Prediction over tabular data is often a crucial task in many real-life applications. Recent advances in deep learning give rise to various deep models for tabular data prediction. A common and essential step in these models is to vectorize raw input features in tabular data into dense embeddings. Choosing a suitable dimension for each feature is challenging yet necessary to improve model&#x2019;s performance and reduce memory cost of model parameters. Existing solutions to embedding dimensionality search always choose dimensions from a restricted candidate set. This restriction improves the search efficiency but would produce suboptimal embedding dimensions that hurt model&#x2019;s predictive performance. In this paper, we develop AutoSrh, a flexible embedding dimensionality search framework that can select varying dimensions for different features through differentiable optimization. The key idea of AutoSrh&#x00A0;is to relax the search space to be continuous and optimize the selection of embedding dimensions via gradient descent. After optimization, AutoSrh&#x00A0;performs embedding pruning to derive the mixed embedding dimensions and retrains the model to further improve the performance. Extensive experiments on five real-world tabular datasets demonstrate that AutoSrh&#x00A0;can achieve better predictive performance than the existing approaches with 1.1<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>1.6x lower training time cost and reserve model&#x2019;s predictive performance while reducing 50<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>95&#x0025; embedding parameters.
MANTA: Multi-Lane Capsule Network Assisted Traffic Classification for 5G Network Slicing

As network slicing is an enabling technology for the fifth-generation (5G) networks, it comes with complex challenges to ensure that resource management is consistent with slice tenant activities to provide better performance and cost-effective services to different tenants tailored to their needs. To this end, traffic classification is fundamental for the provisioning of the resources in a network by analyzing the network traffic to anticipate future requests. However, the massive increase of heterogeneous traffic features challenges dynamic network slices traffic classification. Previous literature have explored statistical and machine learning techniques but are constrained by feature engineering and computational costs. In this letter, we propose the multi-lane CapsNet assisted network traffic classification (MANTA), a framework based on multi-lane Capsule Networks (CapsNet) deep learning technique, to identify and classify heterogeneous traffic flows in 5G network slicing. Furthermore, we conduct a comparative analysis of the model with previous literature using deep learning techniques. The experimental results exhibit improved performance with high accuracy of 97.3975%, compared with other classifiers from previous literature.
How does varying the number of personas affect user perceptions and behavior? Challenging the ‘small personas’ hypothesis!

A C TStudies in human-computer interaction recommend creating fewer than ten personas, based on stakeholders' limitations to cognitively process and use personas.However, no existing studies offer empirical support for having fewer rather than more personas.Investigating this matter, thirty-seven participants interacted with five and fifteen personas using an interactive persona system, choosing one persona to design for.Our study results from eye-tracking and survey data suggest that when using interactive persona systems, the number of personas can be increased from the conventionally suggested 'less than ten', without significant negative effects on user perceptions or task performance, and with the positive effects of increasing engagement with the personas, having a more diverse representation of the end-user population, as well as users accessing personas from more varied demographic groups for a design task.Using the interactive persona system, users adjusted their information processing style by spending less time on each persona when presented with fifteen personas, while still absorbing a similar amount of information than with five personas, implying that more efficient information processing strategies are applied with more personas.The results highlight the importance of designing interactive persona systems to support users' browsing of more personas.
DHSA: efficient doubly homomorphic secure aggregation for cross-silo federated learning

Secure aggregation is widely used in horizontal federated learning (FL), to prevent the leakage of training data when model updates from data owners are aggregated. Secure aggregation protocols based on homomorphic encryption (HE) have been utilized in industrial cross-silo FL systems, one of the settings involved with privacy-sensitive organizations such as financial or medical, presenting more stringent requirements on privacy security. However, existing HE-based solutions have limitations in efficiency and security guarantees against colluding adversaries without a Trust Third Party. This paper proposes an efficient Doubly Homomorphic Secure Aggregation (DHSA) scheme for cross-silo FL, which utilizes multi-key homomorphic encryption (MKHE) and seed homomorphic pseudorandom generator (SHPRG) as cryptographic primitives. The application of MKHE provides strong security guarantees against up to $$N-2$$ participates colluding with the aggregator, with no TTP required. To mitigate the large computation and communication cost of MKHE, we leverage the homomorphic property of SHPRG to replace the majority of MKHE computation by computationally friendly mask generation from SHPRG, while preserving the security. Overall, the resulting scheme satisfies the stringent security requirements of typical cross-silo FL scenarios, at the same time providing high computation and communication efficiency for practical usage. We experimentally demonstrate that our scheme brings a speedup to 20 $$\times $$ over the state-of-the-art HE-based secure aggregation and reduces the traffic volume to approximately 1.5 $$\times $$ inflation over the plain learning setting.
A Deep Graph Network with Multiple Similarity for User Clustering in Human–Computer Interaction

User counterparts, such as user attributes in social networks or user interests, are the keys to more natural Human–Computer Interaction (HCI) . In addition, users’ attributes and social structures help us understand the complex interactions in HCI. Most previous studies have been based on supervised learning to improve the performance of HCI. However, in the real world, owing to signal malfunctions in user devices, large amounts of abnormal information, unlabeled data, and unsupervised approaches (e.g., the clustering method) based on mining user attributes are particularly crucial. This paper focuses on improving the clustering performance of users’ attributes in HCI and proposes a deep graph embedding network with feature and structure similarity (called DGENFS ) to cluster users’ attributes in HCI applications based on feature and structure similarity. The DGENFS model consists of a Feature Graph Autoencoder (FGA) module, a Structure Graph Attention Network (SGAT) module, and a Dual Self-supervision (DSS) module. First, we design an attributed graph clustering method to divide users into clusters by making full use of their attributes. To take full advantage of the information of human feature space, a k-neighbor graph is generated as a feature graph based on the similarity between human features. Then, the FGA and SGAT modules are utilized to extract the representations of human features and topological space, respectively. Next, an attention mechanism is further developed to learn the importance weights of different representations to effectively integrate human features and social structures. Finally, to learn cluster-friendly features, the DSS module unifies and integrates the features learned from the FGA and SGAT modules. DSS explores the high-confidence cluster assignment as a soft label to guide the optimization of the entire network. Extensive experiments are conducted on five real-world data sets on user attribute clustering. The experimental results demonstrate that the proposed DGENFS model achieves the most advanced performance compared with nine competitive baselines.
Towards Generating Realistic Trace for Simulating Functions-as-a-Service

Serverless computing is a step forward to provide a cloud environment that responds to user requests by mainly focusing on managing infrastructure, resources and configurations. Despite the widespread use of cloud simulators, they are still mainly focused on supporting more traditional Infrastructure-as-a-Service scenarios and this reduces their applicability in the serverless and function as a service domains. Moreover, workload traces typically employed by IaaS simulators to represent user behaviour, are not well adoptable for serverless model. More realistic and serverless-like traces are essential to simulate and predict the behaviour of functions in serverless systems. Therefore, this paper focuses on generating realistic traces for simulating serverless computing platforms. The generated traces produced by our approach are based on the Azure Functions dataset and they are readily applicable in an already available versatile and high performance simulator (DISSECT-CF). We validated the generated approach using the coefficient of determination ( $$R^2$$ ), which shows very good values for the average and percentiles of the execution time and memory. To demonstrate the benefits of the generated traces we introduced a rudimentary model for serverless systems to DISSECT-CF. Our evaluation shows that our workloads are realistic and closely follow the behaviour of Azure’s function as a service component.
Toward an Agile Product Management: What Do Product Managers Do in Agile Companies?

Abstract The product manager (PM) role is well established in leading technological companies, such as Google, Amazon, Microsoft, and Facebook. PMs are responsible for integrating technical, design, and business perspectives when developing software products and product portfolios. In agile methods (e.g., Scrum), similar responsibilities are linked to the Product Owner (PO) role. In contrast, in large-scale agile, one can find both Product Owners and product managers who sometimes compete. Despite the widespread adoption of the product manager role, the attention toward it in the agile academic community has been surprisingly limited. In this multiple case study, we analyzed 17 interviews with 11 product managers from four agile companies. We found that the PMs facilitated continuous product experimentation and innovation, supported the product teams, and engaged in additional activities to achieve optimal product development. Our summary of the product management activities can guide product managers working in agile companies.
Teaching research data management courses in higher learning institutions in Tanzania

Purpose The study investigated teaching research data management (RDM) courses in higher learning institutions (HLIs) in Tanzania to enable postgraduate students to work with their research data. Design/methodology/approach The study triangulated research methods. Postgraduate students were investigated using survey questionnaires to learn about their needs and perceptions of the teaching RDM courses in HLIs. Key informants (academicians, information and communication technologists and library staff) were also investigated using in-depth interviews to explore their experiences and knowledge of teaching RDM courses. SPSS statistical software was used for analysing quantitative data; qualitative data were analysed thematically. Findings A total of 70 questionnaires were distributed to postgraduate students with a returning rate of 44 (69%). On the other hand, 12 key informants were interviewed. A low level of RDM literacy was revealed among 38 (86%) respondents. Most respondents 40 (91%) reported the need for HLIs to start teaching RDM courses. A lack of skills and knowledge in teaching RDM courses was revealed among key informants. The competency-based, adaptive and constructive teaching techniques were selected for teaching RDM courses, whereas intensive training and online tutorials were revealed as teaching formats. Research limitations/implications This study focused on teaching RDM courses in HLIs. The survey questionnaires were distributed to all 2nd year postgraduate students, however, the findings cannot be generalised to all postgraduate students due to the response rate obtained. The findings obtained from key informants can also not be used as a basis for generalization across HLIs. Practical implications This study concluded that postgraduate students need to be well equipped with skills and knowledge on RDM and its related concepts; teaching RDM courses should be regarded as a continuous programme for benefit of students, researchers and the community at large. Social implications Appropriate teaching of RDM courses among students not only ensures that students meet the funders’ and publishers’ requirements, but also encourages students to store and share their research among researchers worldwide; thus increasing collaboration and visibility of the datasets and data owners through data citations and acknowledgements. Originality/value This is a comprehensive study that provides findings for HLIs to teach RDM courses in HLIs, especially for postgraduate students. The findings revealed the need for teaching RDM courses in HLIs. The study provides the basis for further RDM research in HLIs and research institutions.
The Postdigital Settler Spectacle: An Educators’ Dérive to Unveil a ‘New Colonizer’ During Covid-19

This chapter is meant to transcend settler space to unveil what we call the Postdigital Settler Spectacle, a mesmerizing and manipulative façade used by settler capitalist society through mass media and digital technologies, which systemically hides the truths of social inequity in our daily lives. We aim to contribute to the discourse of the postdigital age within the scope of ecopedagogies and our (post)pandemic realities, using Indigenous and allied perspectives. Through a ‘decolonized lens’ this chapter examines the lived realities of two educators who identify and attempt to move beyond the veil of the Settler Spectacle by using the theory of the dérive, drifting, or ‘mythographical’ walking during Covid-19. We examine our truths during the pandemic including the meanings of land, our individual/ collective mourning, and relationship-building, primarily in the ‘rust belt’ cities of Buffalo and Syracuse, New York, and other settler locations of the United States, situated on Indigenous Haudenosaunee lands. Through use of storytelling, we reimagine a way back home, back to the land, towards a journey of restorative healing during our individual and collective (post)pandemic realities within this postdigital age.
Composting The Anti-Human University

As the web of life is under assault from the temporal and spatial logics of capitalist reproduction and the law of value, ‘how do we live?’, has become the imperative pedagogical question for intellectual workers. Inside the capitalist University, answering this question has been foreclosed upon by the demands for valorisation, commodification, efficiencies, value-for-money, impact and so on. From inside the capitalist University, there is no way to engage meaningfully with crises of social reproduction, amplified as planetary boundaries are overwhelmed, and as communities and individuals suffer ecological and environmental dislocation. Instead, the capitalist University represents an enclosed pedagogical space that is deeply methodological and pathological. This chapter sets up whether the ways of knowing the world ossified inside an abstract universe of value, and predicated upon the generation of surpluses and processes of exploitation, expropriation and extraction, might be resisted through a fuller, human knowing of the world. This is a renewed pedagogical imperative for-life rather than for-value, and a refusal of the common sense of the University-as-is. It is a yearning for alternative ways of knowing, doing and being in the world, and a demand for a liveable way of life predicated upon the pedagogical imperative of asking, ‘how shall we live?’
Biopolitics, Postdigital Temporality and the New Chronic: Pedagogical Praxis Within, Against, and Beyond the Meantime

Building on Giorgio Agamben’s concerns about the transformations taking place in the name of health and biosecurity, this chapter characterizes an emerging matrix of power, and more specifically the logic of temporality that accompanies it. This logic—the meantime—is an ideological and temporal formation of late capitalism that offers the future as nothing other than an extension of the present. The author considers the significance of Agamben's concept of the state of exception for our current moment, and supplements his insights with decolonial perspectives in order to better understand how the meantime devastates the political imagination and fashions subjective figures that are both tethered to and severed from the present. Finally, the chapter concludes by considering what it might involve to exit the meantime. Specifically, the author focuses on exit as a crucial component of pedagogical praxis within, against, and beyond the meantime, and calls for attention to the rhythms of exit, and thus also the rhythms of exopedagogy and exo-exopedagogy.
Pan-African Socialism and Postdigital Considerations

In this chapter I take Ford and Sasaki’s work on postdigital listening and anti-colonialism and consider the ways in which it can expand our understanding of decolonial movements in Africa, and simultaneously open possibilities for current struggles. After briefly situating this work within the emerging literature on postdigital ecopedagogies, I provide some general comments on pan-Africanism and its international orientation. I then turn to a discussion of Kwame Nkrumah as one of the key architects of twentieth century pan-Africanism. Amilcar Cabral and the national liberation struggle of Guinea-Bissau is then explored. Cabral’s influence on the work of world-renowned revolutionary educator Paulo Freire is explored as a globally-significant contribution.
Secure and Efficient Text Encryption Using Elliptic Curve Cryptography

As data is being generated and shared in very large amounts, there is a need to secure the data during transmission, processing, or even storing. Elliptic Curve Cryptography has been in wide use in recent times to secure the data transmission as it provides higher level of security with significantly smaller sized keys than other methods of encryption and decryption. In this paper, we propose a simple but efficient way to protect data with the use of ECC. The proposed techniques are compared with some other similar techniques.
MINING ONLINE REVIEWS TO SUPPORT CUSTOMERS’ DECISION-MAKING PROCESS IN E-COMMERCE PLATFORMS: A NARRATIVE LITERATURE REVIEW

By dint of the massive daily production of user-generated content (textual reviews) in E-commerce platforms, the need to automatically process it and extract different types of knowledge from it becomes a necessity. In this work, an attempt has been made to summarize some studies that aim to propose systems, which automatically mine textual reviews expressed in natural languages for the purpose of supporting customers’ decision-making process in E-commerce (buying, renting, and booking). The given review is the first work of this type and it includes 44 studies (30 aspect/feature-based summarizers and 14 reputation systems) published from 2004 to 2021. First, it investigates aspect and feature-based summarizers that aim to help customers in making an informed decision toward online entities (products, movies, hotels, services …). Second, it introduces reputation generation systems that seek to provide valuable information about online items. Finally, it provides recommendations for future research directions and open problems.
Prototype of Interactive Multimedia Learning About Natural Resources and Energy to Elementary School Students

This prototype is a part of development interactive multimedia learning which is aimed toward optimizing the implementation of both distance and face to face learning about natural resources and energy subjects in elementary school. The product of the development is interactive multimedia which may be used for science learning, especially for online and independent learning. This development is produced for 2 months in October-November 2021. The product is advanced on students&#x0027; needs for a meaningful learning experience. This study uses APPED R&#x0026;D model which still needs to be practically tested by users. The device used to create and compute this multimedia is Adobe Flash CS6.
Leveraging Blockchain Technology in Supply Chain Sustainability: A Provenance Perspective

Technological advancements in the past few decades have played a critical role in enhancing supply chain management from varied dimensions. While advancements in information technology have significantly improved various facets of the supply chain, supply chain visibility still lags in tracking and tracing capabilities. The disruptive and emergent blockchain technology (BCT) presents enormous potential to address issues of supply chain sustainability. This paper seeks to evaluate the proximity of BCT to resolve provenance issues throughout the supply chain. The research considers the immaturity of BCT alongside limited success stories in supply chain management. It uses a content analysis methodology to examine recent literature about using BCT in supply chain sustainability and provenance. A conceptual model for using BCT to create an enabled, visible, and sustainable supply chain is provided to frame the investigation. Findings reveal that the benefits of BCT have mostly been conceptually demonstrated in the sustainable supply chain literature, but with few practical applications. Moreover, technically driven challenges including scalability and control are still prevalent. It reaffirms the fact that participants within a blockchain enabled supply chain network must be at the same level of knowledge regarding the technology and its functionality. The study also indicates evidence of a growing research interest in the niche, which will eventually bring about solutions to address ongoing challenges faced by BCT in its integration into supply chain management. These findings contribute to the literature about improving the security and utility of supply chain sustainability and provenance by using BCT.