Using the Internet to Create Positive Social Changes: Case Studies in China

In recent years, companies have been increasingly under pressure to deliver programs that can create both business value and social value. Building on the positive social change framework developed by Stephan et al., this paper uses two case studies (Gongyi Baobei and Jutudi) of the Alibaba Group, a leading Internet company in China, to investigate how companies can use the Internet to bring about positive social changes (PSC) to target groups. Our focus is placed on the nature of projects, i.e., surface-level and deep-level PSC projects. Our decision to use different case studies from the same company is based on the assumption that the enabling effects of internal organizational practices should be similar. To be more specific, we want to study the link between PSC projects and the company’s existing businesses, the role of the Internet in raising customers’ awareness and participation in the programs, and the change mechanism designed and implemented to bring positive social changes to customers. Data were collected through interviews and literature review. Our research provides empirical evidence to show a deep-level PSC project (i.e., Jutudi) can be very different from a surface-level PSC project (i.e., Gongyi Baobei) in terms of the reliance on existing business operations and the design of change mechanisms. Our research limitations and direction for future research will also be discussed

Adaptive CPU Resource Allocation for Emulator in Kernel-based Virtual Machine

The technologies of heterogeneous multi-core architectures, co-location, and virtualization can be used to reduce server power consumption and improve system utilization, which are three important technologies for data centers. This article explores the scheduling strategy of Emulator threads within virtual machine processes in a scenario of co-location of multiple virtual machines on heterogeneous multi-core architectures. In this co-location scenario, the scheduling strategy for Emulator threads significantly affects the performance of virtual machines. This article focuses on this thread for the first time in the relevant field. This article found that the scheduling latency metric can well indicate the running status of the vCPU threads and Emulator threads in the virtualization environment, and applied this metric to the design of the scheduling strategy. This article designed an Emulator thread scheduler based on heuristic rules, which, in coordination with the host operating system's scheduler, dynamically adjusts the scheduling scope of Emulator threads to improve the overall performance of virtual machines. The article found that in real application scenarios, the scheduler effectively improved the performance of applications within virtual machines, with a maximum performance improvement of 40.7%

DiLogics: Creating Web Automation Programs With Diverse Logics

Knowledge workers frequently encounter repetitive web data entry tasks, like updating records or placing orders. Web automation increases productivity, but translating tasks to web actions accurately and extending to new specifications is challenging. Existing tools can automate tasks that perform the same logical trace of UI actions (e.g., input text in each field in order), but do not support tasks requiring different executions based on varied input conditions. We present DiLogics, a programming-by-demonstration system that utilizes NLP to assist users in creating web automation programs that handle diverse specifications. DiLogics first semantically segments input data to structured task steps. By recording user demonstrations for each step, DiLogics generalizes the web macros to novel but semantically similar task requirements. Our evaluation showed that non-experts can effectively use DiLogics to create automation programs that fulfill diverse input instructions. DiLogics provides an efficient, intuitive, and expressive method for developing web automation programs satisfying diverse specifications

Virtual carbon and water flows embodied in global fashion trade - a case study of denim products

The environmental impacts of the fashion industry have been aroused wide concerns. The globalization and fragmentation of the textile and fashion system have led to the uneven distribution of environmental consequences. As denim is the fabric of jeans that is representative of fashion, this study assessed virtual carbon and water flows embodied in the global denim-product trade, and footprints of denim production were quantified by life-cycle assessment and water footprint assessment. Results indicated that virtual carbon embodied in the global denim trade increased obviously from 14.8 Mt CO2e in 2001 to 16.0 Mt CO2e in 2018, and the virtual water consumption dropped from 5.6 billion m3 to 4.7 billion m3 from 2001 to 2018. The denim fabric production and cotton fibre production respectively contribute the most of the carbon emissions and water consumption. Polyester blended denim has 5% larger carbon footprint and 72% lower water footprint than cotton denim, and contributes to increasing embodied carbon emissions (from 4% in 2001 to 43% in 2018). Increasing the utilization of polyester blended denim would save water but face more pressures on carbon emission reduction. In the past two decades, virtual carbon and water flows embodied in the global denim trade are relocating, main jean consumers (i.e., the USA, EU-15, and Japan) withdraw the denim manufacturing supply chain and developing countries (i.e., China, India, and Pakistan) with higher carbon and water footprint undertake main global denim production, facing increasing climate-related risks and water crisis. The South-South cooperation helps share successful experiences, save production cost, and lessen resource consumption and environmental emissions. The production and consumption of denim should be shifted to circular and sustainable ways and new business models are required. The analysis framework can provide the basis for exploring environmental flows of product-level trade, and results can offer a basis for environmental policies and control strategies of the fashion industry, and as well as the sustainable production and consumption of garment

Nesting Forward Automatic Differentiation for Memory-Efficient Deep Neural Network Training

An activation function is an element-wise mathematical function and plays a crucial role in deep neural networks (DNN). Many novel and sophisticated activation functions have been proposed to improve the DNN accuracy but also consume massive memory in the training process with back-propagation. In this study, we propose the nested forward automatic differentiation (Forward-AD), specifically for the element-wise activation function for memory-efficient DNN training. We deploy nested Forward-AD in two widely-used deep learning frameworks, TensorFlow and PyTorch, which support the static and dynamic computation graph, respectively. Our evaluation shows that nested Forward-AD reduces the memory footprint by up to 1.97x than the baseline model and outperforms the recomputation by 20% under the same memory reduction ratio.Comment: 8 pages, ICCD 202

LSCD : A Low-Storage Clone Detection Protocol for Cyber-Physical Systems

Cyber-physical systems (CPSs) have recently become an important research field not only because of their important and varied application scenarios, including transportation systems, smart homes, surveillance systems, and wearable devices but also because the fundamental infrastructure has yet to be well addressed. Wireless sensor networks (WSNs), as a type of supporting infrastructure, play an irreplaceable role in CPS design. Specifically, secure communication in WSNs is vital because information transferred in the networks can be easily stolen or replaced. Therefore, this paper presents a novel distributed low-storage clone detection protocol (LSCD) for WSNs. We first design a detection route along the perpendicular direction of a witness path with witness nodes deployed in a ring path. This ensures that the detection route must encounter the witness path because the distance between any two detection routes must be smaller than the witness path length. In the LSCD protocol, clone detection is processed in a nonhotspot region where a large amount of energy remains, which can improve energy efficiency as well as network lifetime. Extensive simulations demonstrate that the lifetime, storage requirements, and detection probability of our protocol are substantially improved over competing solutions from the literature