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The reliability of replications: a study in computational reproductions.
Get PDFThis study investigates researcher variability in computational reproduction, an activity for which it is least expected. Eighty-five independent teams attempted numerical replication of results from an original study of policy preferences and immigration. Reproduction teams were randomly grouped into a 'transparent group' receiving original study and code or 'opaque group' receiving only a method and results description and no code. The transparent group mostly verified original results (95.7% same sign and p-value cutoff), while the opaque group had less success (89.3%). Second-decimal place exact numerical reproductions were less common (76.9 and 48.1%). Qualitative investigation of the workflows revealed many causes of error, including mistakes and procedural variations. When curating mistakes, we still find that only the transparent group was reliably successful. Our findings imply a need for transparency, but also more. Institutional checks and less subjective difficulty for researchers 'doing reproduction' would help, implying a need for better training. We also urge increased awareness of complexity in the research process and in 'push button' replications
Aid targeting in post-conflict Nepal
Get PDFThis study investigates the political underpinnings of aid allocations during Nepal's post-conflict transition. Were post-conflict aid allocations sensitive to civilian support for the rebels during the war, or driven by new electoral coalitions after the end of fighting? Constructing a municipal-level dataset, we leverage the geo-location of Nepalese army barracks prior to the conflict as instrument for civilians killed by the government, which in turn proxies government support. We find that rebel support significantly increased post-war aid allocations, while voting for the rebel party after the war did not
Multi-Task Semantic Communication: A Mutual Information-Aided Semi-Supervised Approach
Get PDFIn this paper, we design an end-to-end digital semantic communication system to transmit semantic symbols that simultaneously facilitate image classification tasks and reconstruction tasks. By training a mutual information-assisted joint source-channel coding (MIJSCC) framework, the learned semantic representation can incorporate both pixel-level generative information for reconstruction and structural discriminative information for classification, which are obtained label-free via global and local mutual information estimation and maximization, as well as mean square error (MSE) minimization. Then, the high-resolution semantic representation is quantized into finite constellation symbols to satisfy the hardware constraint on discrete control in practical radio frequency systems. Considering dynamic channel conditions in practical communication systems, we further design an adaptive MIJSCC framework with attention-based semantic enhancement (A-MIJSCC), which allows for the sequential activation of varying dimensions of the semantic representation according to channel signal-to-noise ratio. Compared to existing semantic communication frameworks that are dominated by end target and labels, the MIJSCC addresses the semi-supervised learning of intermediate semantics. Simulation results show that the proposed MIJSCC supports both image classification and reconstruction via task-agnostic semantic extraction, whose performance surpasses the benchmark frameworks. It is also demonstrated that the A-MIJSCC method facilitates the adaptive semantic transmission under varying channel conditions, which effectively reduces the transmission overhead while preserving task performance
Climbing the Dark Ladder: How Status and Inclusion Aspirations, Perceived Attainment, and Behaviors Relate to the Dark Triad
Get PDFIndividual differences in the Dark Triad may partially reflect differences in interper-sonal motivational patterns such as a strong desire for status. These studies examine how desires for status and inclusion, perceived attainment of status and inclusion, and status-seeking and inclusion-seeking behavior relate to the Dark Triad (grandiose nar-cissism, Machiavellianism, and psychopathy). Two studies (N = 591) find that indi-viduals high in Dark Triad traits generally desire status, feel they have attained high status, and report behaving in status-seeking ways (once desires for inclusion, per-ceived attainment of inclusion, and inclusion-seeking behavior are controlled, respec-tively). They generally do not desire inclusion, do not feel they have attained inclusion, and do not report behaving in inclusion-seeking ways (once desires for status, per-ceived attainment of status, and status-seeking behavior are controlled, respectively). These associations are largely observed for the dimensions of the Dark Triad involving agentic extraversion and antagonism, but not for those involving impulsivity. This re-search delineates the motivational, social, and behavioral profile of the Dark Triad and its dimensions with implications for understanding the “core” of the Dark Triad
'It's about collaboration': a whole-systems approach to understanding and promoting movement in Suffolk.
Get PDFBackground Population-levels of physical activity have remained stagnant for years. Previous approaches to modify behaviour have broadly neglected the importance of whole-systems approaches. Our research aimed to (i) understand, (ii) map, (iii) identify the leverage points, and (iv) develop solutions surrounding participation in physical activity across an English rural county.
Methods A systems-consortium of partners from regional and local government, charities, providers, deliverers, advocacy groups, and health and social care, and public health engaged in our research, which consisted of two-phases. Within Phase 1, we used secondary data, insight-work, a narrative review, participatory workshops, and interviews in a pluralistic style to map the system-representing physical activity. Phase 2 began with an initial analysis using markers from social network analysis and the Action Scales Model. This analysis informed a participatory workshop, to identify leverage points, and develop solutions for change within the county.
Results The systems-map is constructed from biological, financial, and psychological individual factors, interpersonal factors, systems partners, built, natural and social environmental factors, and policy and structural factors. Our initial analysis found 13 leverage points to review within our participatory workshop. When appraised by the group, (i) local governing policies, (ii) shared policies, strategies, vision, and working relationships, (iii) shared facilities (school, sport, community, recreation), and (iv) funding were deemed most important to change. Within group discussions, participants stressed the importance and challenges associated with shared working relationships, a collective vision, and strategy, the role of funding, and management of resources. Actions to leverage change included raising awareness with partners beyond the system, sharing policies, resources, insight, evidence, and capacity, and collaborating to co-produce a collective vision and strategy.
Conclusions Our findings highlight the importance and provide insight into the early phase of a whole-systems approach to promoting physical activity. Our whole-systems approach within Suffolk needs to consider methods to (i) grow and maintain the systems-consortium, (ii) create a sustainable means to map the system and identify leverage points within it, and (iii) monitor and evaluate change
Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing
Get PDFIntroductionThe accurate and timely diagnosis of skin diseases is a critical concern, as many skin diseases exhibit similar symptoms in the early stages. Most existing automated detection/classification approaches that utilize machine learning or deep learning poses privacy issues, as they involve centralized computing and require local storage for data training.MethodsKeeping the privacy of sensitive patient data as a primary objective, in addition to ensuring accuracy and efficiency, this paper presents an algorithm that integrates Federated learning techniques into an IoT-based edge-computing environment. The purpose of the proposed technique is to protect the sensitive data by training the model locally on the edge device and transferring only the weights to the central server where the aggregation takes place. This process ensures data security at the edge level and eliminates the need for centralized storage. Furthermore, the proposed framework enhances the network’s real-time processing capabilities using IoT-integrated sensors, which in turn facilitates swift diagnoses. In addition, this paper also focuses on the design and execution of the federated framework, which includes the processing power, memory, and the number of nodes present in the network.ResultsThe accuracy and effectiveness of the proposed algorithm are demonstrated using precise parameters, such as accuracy, precision, f1-score, and recall, along with all the intricacies of the secure federated approach. The accuracy achieved by the proposed algorithm is 98.6%. As the model was trained locally, the bandwidth utilization was almost negligible.DiscussionThe proposed model can assist skin specialists in diagnosing conditions. Additionally, with federated learning, the model continuously improves as new input data accumulates, enhancing the accuracy of subsequent training rounds.</jats:sec
Exploring government-citizen interaction in public service performance assessment: trade-offs, synergies, and critical issues
Get PDFCitizens’ participation and direct initiatives are on the rise, including in assessing public service performance. Performance measurement and government-citizen interactions have been traditionally studied separately in public administration scholarship. To bridge this gap, this article integrates these two bodies of literature, proposing a typology of approaches to government-citizen interactions in public service performance assessment and highlighting their features. It also discusses the possible synergies and trade-offs emerging at the intersection between “interaction” and “assessment”. In particular, the article focuses on how relevance, reliability, and understandability shape and are shaped by the interaction between governments and citizens in both government-led and citizen-led initiatives of performance assessment. Finally, the paper puts forward a research agenda for the study of interactive forms of public service performance measurement
Marketing agility and financial performance in migrant enterprises during crises: does resilience capability matter?
Get PDFPurpose
The business literature has established that marketing agility can improve business performance; however, the relationship between the two becomes less clear in a turbulent context. There is a compelling case that resilience capability can support agile firms through such unstable and challenging times. The question of resilience is particularly relevant for migrant entrepreneurs, who have historically encountered difficulties in accessing the required resources; however, little research has been done on this topic. Utilising the literature on business resilience and crisis management, this study addresses this knowledge gap in three ways: i) by re-examining the direct relationship between marketing agility and the financial performance of migrant enterprises (MEs) in the context of the COVID-19 pandemic; ii) by exploring whether the lack of financial and human resources of migrant entrepreneurs from smaller ethnic communities may hinder their ability to develop marketing agility; and iii) by examining the mediating role of resilience capability in the relationship between marketing agility and performance.
Design/Methodology/Approach
335 Nepalese MEs in the UK participated in the survey from July to October 2021, during the COVID-19 pandemic. The survey data were analysed using structural equation modelling with Mplus.
Findings
First, the study confirms the existence of a direct positive effect of marketing agility on the financial performance of MEs. Second, while marketing agility significantly correlates with human capital, the relationship with financial capital is insignificant. Third, the study finds resilience capability to be a significant mediating factor, with the indirect effect accounting for about 10% of the total impact of marketing agility on financial performance; this suggests that the mediating effect is not inconsiderable.
Originality/Value
This study confirms that the established relationship between marketing agility and performance can also be applied to turbulence, such as the COVID-19 pandemic, consistent with the crisis management literature. It sheds further light on the importance of financial capital in developing marketing agility, aligning with bricolage theory. A lack of finance, as faced by many MEs from small ethnic communities, is not necessarily a debilitating factor; however, human capital remains crucial. Finally, consistent with the crisis management literature, the relationship between marketing agility and the performance of firms can be strengthened if the firms are resilient and have a good understanding of the nature of the turbulence.
Keywords: Marketing agility, Resilience capability, Financial performance, Migrant enterprises, COVID-1
Sparse Zero Correlation Zone Arrays for Training Design in Spatial Modulation Systems
Get PDFThis paper presents a novel training matrix design for spatial modulation (SM) systems, by introducing a new class of two-dimensional (2D) arrays called sparse zero correlation zone (SZCZ) arrays. An SZCZ array is characterized by a majority of zero entries and exhibits the zero periodic autoand cross-correlation zone properties across any two rows. With these unique properties, we show that SZCZ arrays can be effectively used as training matrices for SM systems. Additionally, direct constructions of SZCZ arrays with large ZCZ widths and controllable sparsity levels based on 2D restricted extended generalized Boolean functions (REGBFs) are proposed. Compared with existing training schemes, the proposed SZCZbased training matrices have larger ZCZ widths, thereby offering greater tolerance for delay spread in multipath channels. Simulation results demonstrate that the proposed SZCZ-based training design exhibits superior channel estimation performance over frequency-selective fading channels compared to existing alternatives