An Empirical Study of Cohesion and Coupling: Balancing Optimisation and Disruption

Search based software engineering has been extensively applied to the problem of finding improved modular structures that maximise cohesion and minimise coupling. However, there has, hitherto, been no longitudinal study of developers’ implementations, over a series of sequential releases. Moreover, results validating whether developers respect the fitness functions are scarce, and the potentially disruptive effect of search-based remodularisation is usually overlooked. We present an empirical study of 233 sequential releases of 10 different systems; the largest empirical study reported in the literature so far, and the first longitudinal study. Our results provide evidence that developers do, indeed, respect the fitness functions used to optimise cohesion/coupling (they are statistically significantly better than arbitrary choices with p << 0.01), yet they also leave considerable room for further improvement (cohesion/coupling can be improved by 25% on average). However, we also report that optimising the structure is highly disruptive (on average more than 57% of the structure must change), while our results reveal that developers tend to avoid such disruption. Therefore, we introduce and evaluate a multi-objective evolutionary approach that minimises disruption while maximising cohesion/coupling improvement. This allows developers to balance reticence to disrupt existing modular structure, against their competing need to improve cohesion and coupling. The multi-objective approach is able to find modular structures that improve the cohesion of developers’ implementations by 22.52%, while causing an acceptably low level of disruption (within that already tolerated by developers)

Involving local communities in natural resource management : xilingol biosphere reserve

This study is to assess natural resource management practices in Xilingol Biosphere Reserve (XBR) located in northern China in their institutional contexts. Institutions including legislation, regulations and administrative structures, responsibilities associated with land and resource rights, decision making powers and processes, and community participation, and the interactions between these institutions have exerted great impacts on how natural resources are used and managed in XBR. Local people have experienced great socioeconomic losses accompanied with the establishment of XBR. However, they have not been provided with adequate compensation for their lost benefits. This gave rise to the conflict between local people and XBR. The findings indicate that partnerships prove to be an inevitable trend for improving the reserve management and facilitating the resolution of a series of issues facing XBR

Energy Bandgap Engineering of Transmission-Mode AlGaAs/GaAs Photocathode

Aiming to enhance the photoemission capability in the waveband region of interest, a graded bandgap structure was applied to the conventional transmission-mode AlGaAs/GaAs photocathodes based on energy bandgap engineering, wherein the composition in AlxGa1−xAs window layer and the doping concentration in GaAs active layer were gradual. According to Spicer’s three-step model, a photoemission theoretical model applicable to the novel transmission-mode AlxGa1−xAs/GaAs photocathodes was deduced so as to guide the cathode structural design. Then the cathode material was grown by the metalorganic chemical vapor deposition technique, and the epitaxial cathode material quality was evaluated by the means of scanning electron microscope, electrochemical capacitance-voltage, X-ray diffraction and spectrophotometry. Through a series of specific processes, the cathode material was made into the multilayered module, possessing a glass/Si3N4/AlxGa1−xAs/GaAs structure. After the surface treatment including heat cleaning and Cs▬O activation for the cathode module, the image intensifier tube comprising the activated cathode module, microchannel plate, and phosphor screen was fabricated by indium sealing. The spectral response test results confirm the validity of the novel structure for the enhancement of blue-green photoresponse

Increasing Fairness in Compromise on Accuracy via Weighted Vote with Learning Guarantees

As the bias issue is being taken more and more seriously in widely applied machine learning systems, the decrease in accuracy in most cases deeply disturbs researchers when increasing fairness. To address this problem, we present a novel analysis of the expected fairness quality via weighted vote, suitable for both binary and multi-class classification. The analysis takes the correction of biased predictions by ensemble members into account and provides learning bounds that are amenable to efficient minimisation. We further propose a pruning method based on this analysis and the concepts of domination and Pareto optimality, which is able to increase fairness under a prerequisite of little or even no accuracy decline. The experimental results indicate that the proposed learning bounds are faithful and that the proposed pruning method can indeed increase ensemble fairness without much accuracy degradation.Comment: 18 pages, 15 figures, and 6 table