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A novel controllable strategy to construct a spatiotemporal dynamical hydrogel based on an M1-to-M2 immune environment for the treatment of cutaneous wounds.

Edge Computing has emerged as a new computing paradigm dedicated for mobile applications for performance enhancement and energy efficiency purposes. Specifically, it benefits today's interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes which is in close proximity. Meanwhile, Field Programmable Gate Array (FPGA) is well known for its excellence in accelerating compute-intensive tasks such as deep learning algorithms in a high performance and energy efficiency manner due to its hardware-customizable nature. In this paper, we make the first attempt to leverage and combine the advantages of these two, and proposed a new network-assisted computing model, namely FPGA-based edge computing. As a case study, we choose three computer vision (CV)-based mobile interactive applications, and implement their backend computation engines on FPGA. By deploying such application-customized accelerator modules for computation offloading at the network edge, we experimentally demonstrate that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU-based edge/cloud offloading approach.

Serverless computing is a popular cloud computing paradigm that frees developers from server management. Function-as-a-Service (FaaS) is the most popular implementation of serverless computing, representing applications as event-driven and stateless functions. However, existing studies report that functions of FaaS applications severely suffer from cold-start latency. In this paper, we propose an approach namely FaaSLight to accelerating the cold start for FaaS applications through application-level optimization. We first conduct a measurement study to investigate the possible root cause of the cold start problem of FaaS. The result shows that application code loading latency is a significant overhead. Therefore, loading only indispensable code from FaaS applications can be an adequate solution. Based on this insight, we identify code related to application functionalities by constructing the function-level call graph, and separate other code (i.e., optional code) from FaaS applications. The separated optional code can be loaded on demand to avoid the inaccurate identification of indispensable code causing application failure. In particular, a key principle guiding the design of FaaSLight is inherently general, i.e., platform - and language-agnostic . In practice, FaaSLight can be effectively applied to FaaS applications developed in different programming languages (Python and JavaScript), and can be seamlessly deployed on popular serverless platforms such as AWS Lambda and Google Cloud Functions, without having to modify the underlying OSes or hypervisors, nor introducing any additional manual engineering efforts to developers. The evaluation results on real-world FaaS applications show that FaaSLight can significantly reduce the code loading latency (up to 78.95%, 28.78% on average), thereby reducing the cold-start latency. As a result, the total response latency of functions can be decreased by up to 42.05% (19.21% on average). Compared with the state-of-the-art, FaaSLight achieves a 21.25 × improvement in reducing the average total response latency.

Background Toona is a critical genus in the Meliaceae, and the plants of this group are an asset for both restorative and restorative purposes, the most flexible of which are Toona sinensis and Toona ciliata. To concentrate on the advancement of mitochondrial(Mt) genome variety in T.sinensis and T.ciliata, the Mt genomes of the two species were sequenced in high throughput independently, after de novo assembly and annotation to construct a Mt genome map for comparison in genome structure. Find their repetitive sequences and analyze them in comparison with the chloroplast genome, along with Maximum-likelihood(ML) phylogenetic analysis with 16 other relatives. Results (1) T. sinensis and T.ciliata are both circular structures with lengths of 683482 bp and 68300 bp, respectively. They share a high degree of similarity in encoding genes and have AT preferences. All of them have the largest Phe concentration and are the most frequently used codons. (2) Both of their Mt genome are highly preserved in terms of structural and functional genes, while the main variability is reflected in the length of tRNA, the number of genes, and the value of RSCU. (3) T. siniensis and T. ciliata were detected to have 94 and 87 SSRs, respectively, of which mononucleotides accounted for the absolute proportion. Besides, the vast majority of their SSRs were found to be poly-A or poly-T. (4)10 and 11 migrating fragments were identified in the comparison with the chloroplast genome, respectively. (5) In the ML evolutionary tree, T.sinensis and T.ciliata clustered individually into a small branch with 100% support, reflecting two species of Toona are very similarly related to each other. Conclusions This research provides a basis for the exploitation of T.sinensis and T.ciliata in terms of medicinal, edible, and timber resources to avoid confusion; at the same time, it can explore the evolutionary relationship between the Toona and related species, which does not only have an important practical value, but also provides a theoretical basis for future hybrid breeding of forest trees, molecular markers, and evolutionary aspects of plants, which has great scientific significance.

Osteoarthritis (OA) is a common orthopedic degenerative disease, leading to high disability in activities of daily living. There remains an urgent need to identify the underlying mechanisms and identify new therapeutic targets in OA diagnosis and treatment. Circular RNAs (circRNAs) play a role in the development of multiple diseases. Many studies have reported that circRNAs regulate microRNAs (miRNAs) through an endogenous competitive mechanism. However, it remains unclear if an interplay between circRNAs, miRNAs, and target genes plays a deeper regulatory role in OA. Four datasets were downloaded from the GEO database, and differentially expressed circRNAs (DECs), differentially expressed miRNAs (DEMs), and differentially expressed genes (DEGs) were identified. Functional annotation and pathway enrichment analysis of DEGs and DECs were carried out to determine the main associated mechanism in OA. A protein–protein network (PPI) was constructed to analyze the function of, and to screen out, hub DEGs in OA. Based on the artificial intelligence prediction of protein crystal structures of two hub DEGs, TOP2A and PLK1, digitoxin and oxytetracycline were found to have the strongest affinity, respectively, with molecular docking. Subsequently, overlapping DEMs and miRNAs targeted by DECs obtained target DEMs (DETMs). Intersection of DEGs and genes targeted by DEMs obtained target DEGs (DETGs). Thus, a circRNA–miRNA–mRNA regulatory network was constructed from 16 circRNAs, 32 miRNAs, and 97 mRNAs. Three hub DECs have the largest number of regulated miRNAs and were verified through in vitro experiments. In addition, the expression level of 16 DECs was validated by RT-PCR. In conclusion, we constructed a circRNA–miRNA–mRNA regulatory network in OA and three new hub DECs, hsa_circ_0027914, hsa_circ_0101125, and hsa_circ_0102564, were identified as novel biomarkers for OA.

Neovascularization is crucial for peripheral nerve regeneration and long-term functional restoration. Previous studies have emphasized strategies that enhance axonal repair over vascularization. Here, we describe the development and application of an in situ prevascularization strategy that uses 3D porous nerve guidance conduits (NGCs) to achieve angiogenesis-mediated neural regeneration. The optimal porosity of the NGC is a critical feature for achieving neovascularization and nerve growth patency. Hollow silk fibroin/poly(l-lactic acid-co-e-caprolactone) NGCs with 3D sponge-like walls were fabricated using electrospinning and freeze-drying. In vitro results showed that 3D porous NGC favored cell biocompatibility had neuroregeneration potential and, most importantly, had angiogenic activity. Results from our mechanistic studies suggest that activation of HIF-1α signaling might be associated with this process. We also tested in situ prevascularized 3D porous NGCs in vivo by transplanting them into a 10 mm rat sciatic nerve defect model with the aim of regenerating the severed nerve. The prevascularized 3D porous NGCs greatly enhanced intraneural angiogenesis, resulting in demonstrable neurogenesis. Eight weeks after transplantation, the performance of the prevascularized 3D NGCs was similar to that of traditional autografts in terms of improved anatomical structure, morphology, and neural function. In conclusion, combining a reasonably fabricated 3D-pore conduit structure with in situ prevascularization promoted functional nerve regeneration, suggesting an alternative strategy for achieving functional recovery after peripheral nerve trauma.

Hydrogel-based chondrocyte implantation presents a promising tissue engineering strategy for cartilage repair. However, the widely used elastic hydrogels usually restrict cell volume expansion and induce the dedifferentiation of encapsulated chondrocytes. To address this limitation, a photoannealed granular hydrogel (GH) composed of hyaluronic acid, polyethylene glycol, and gelatin was formulated for cartilage regeneration in this study. The unannealed GH prepared by Diels-Alder cross-linked microgels could be mixed with chondrocytes and delivered to cartilage defects by injection, after which light was introduced to anneal the scaffold, leading to the formation of a stable and microporous chondrocyte deploying scaffold. The in vitro studies showed that GH could promote the volume expansion and morphology recovery of chondrocytes and significantly improve their chondrogenic phenotype compared to the nongranular hydrogel (nGH) with similar compositions. Further in vivo studies of subcutaneous culture and the rat full-thickness cartilage defect model proved that chondrocyte loaded GH could significantly stimulate hyaline cartilage matrix deposition and connection, therefore facilitating hyaline-like cartilage regeneration. Finally, the mechanistic study revealed that GH might improve chondrogenic phenotype via activating the AMP-activated protein kinase/glycolysis axis. This study proves the great feasibility of GHs as in situ chondrocyte deploying scaffolds for cartilage regeneration and brings new insights in designing hydrogel scaffold for cartilage tissue engineering.

BackgroundInfected bone nonunion is the toughest problem in fracture-related infection, leading to high disability and recurrence. The aim of this study was to evaluate the effectiveness of the Masquelet technique with radical debridement and alternative fixation in the management of infected bone nonunion.Patients and MethodsA retrospective study of prospectively collected data in two trauma centers was performed from 2016 to 2020. Patients diagnosed as infected bone nonunion were included in this study. The initial implant was removed and all patients received a two-stage Masquelet procedure with radical debridement and alternative fixation. The disappearance of inflammatory manifestations and regression of infection indicators (such as interleukin-6 (IL-6), C-reactive protein, white blood cell count) to the normal range were regarded as radical debridement. The alternative fixation depended on local soft tissue conditions. Results were evaluated according to clinical and radiographic assessment and patient satisfaction.ResultsA total of 23 patients were included in our study. Six of them received internal fixation, while the other 17 received external fixation. Of the 23 cases, 21 were successfully reconstructed without infection recurrence, except 2 reinfected cases. Mean full weight bearing time was 6.6 months follow-up post last surgery. Out of the 23, 20 cases had satisfactory functional outcomes without additional bone or soft tissue comorbidities. Discrepancies in leg length and joint stiffness were observed in three cases and marked as unsatisfied results.ConclusionsInfected bone nonunion can be successfully managed using the Masquelet technique under radical debridement combined with an alternative fixation method.

A new vision of harnessing the ocean of video data captured by cameras in an efficient, secure, and manageable way. Following the hardware trends in reality, we advocate to treat cameras as the first-class computing and storage platform, and expose their services to third parties. To this end, the cameras should move from hard-coded to software-defined, from dumb data source to autonomous data processor, from isolated to orchestrated, and from one-shot to human-in-the-loop.

The past decades witnessed the fast and wide deployment of Internet. The Internet has bred the ubiquitous computing environment that is spanning the cloud, edge, mobile devices, and IoT. Software running over such a ubiquitous computing environment environment is eating the world. A recently emerging trend of Internet-based software systems is “ resource adaptive ,” i.e., software systems should be robust and intelligent enough to the changes of heterogeneous resources, both physical and logical, provided by their running environment. To keep pace of such a trend, we argue that some considerations should be taken into account for the future operating system design and implementation. From the structural perspective, rather than the “monolithic OS” that manages the aggregated resources on the single machine, the OS should be dynamically composed over the distributed resources and flexibly adapt to the resource and environment changes. Meanwhile, the OS should leverage advanced machine/deep learning techniques to derive configurations and policies and automatically learn to tune itself and schedule resources. This article envisions our recent thinking of the new OS abstraction, namely, ServiceOS , for future resource-adaptive intelligent software systems. The idea of ServiceOS is inspired by the delivery model of “ Software-as-a-Service ” that is supported by the Service-Oriented Architecture (SOA). The key principle of ServiceOS is based on resource disaggregation, resource provisioning as a service, and learning-based resource scheduling and allocation. The major goal of this article is not providing an immediately deployable OS. Instead, we aim to summarize the challenges and potentially promising opportunities and try to provide some practical implications for researchers and practitioners.

Sentiment and emotion detection from textual communication records of developers have various application scenarios in software engineering (SE). However, commonly used off-the-shelf sentiment/emotion detection tools cannot obtain reliable results in SE tasks and misunderstanding of technical knowledge is demonstrated to be the main reason. Then researchers start to create labeled SE-related datasets manually and customize SE-specific methods. However, the scarce labeled data can cover only very limited lexicon and expressions. In this article, we employ emojis as an instrument to address this problem. Different from manual labels that are provided by annotators, emojis are self-reported labels provided by the authors themselves to intentionally convey affective states and thus are suitable indications of sentiment and emotion in texts. Since emojis have been widely adopted in online communication, a large amount of emoji-labeled texts can be easily accessed to help tackle the scarcity of the manually labeled data. Specifically, we leverage Tweets and GitHub posts containing emojis to learn representations of SE-related texts through emoji prediction. By predicting emojis containing in each text, texts that tend to surround the same emoji are represented with similar vectors, which transfers the sentiment knowledge contained in emoji usage to the representations of texts. Then we leverage the sentiment-aware representations as well as manually labeled data to learn the final sentiment/emotion classifier via transfer learning. Compared to existing approaches, our approach can achieve significant improvement on representative benchmark datasets, with an average increase of 0.036 and 0.049 in macro-F1 in sentiment and emotion detection, respectively. Further investigations reveal that the large-scale Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource but try to transform knowledge from the open domain through ubiquitous signals such as emojis. Finally, we present the open challenges of sentiment and emotion detection in SE through a qualitative analysis of texts misclassified by our approach.

Android ecosystem is heavily fragmented. The numerous combinations of different device models and operating system versions make it impossible for Android app developers to exhaustively test their apps, and thus various compatibility issues arise. Unfortunately, little is known on the characteristics of such fragmentation-induced compatibility issues. No mature tools exist to help developers quickly diagnose and fix these issues. To bridge the gap, we conducted an empirical study on 220 real-world compatibility issues collected from five popular open-source Android apps. We further interviewed Android practitioners and conducted an online survey to gain insights from real practices. Via the studies, we characterized compatibility issues, investigated common practices to handle compatibility issues, and disclosed that these issues exhibit common patterns. With these findings, we propose a technique, FicFinder , to automatically detect compatibility issues in Android apps. FicFinder performs static code analysis based on a model that captures Android APIs as well as their associated context by which compatibility issues can be triggered. FicFinder reports actionable debugging information to developers when it detects potential issues. We evaluated FicFinder with 53 large-scale open-source Android apps. The results show that FicFinder can precisely detect compatibility issues in these apps and uncover previously-unknown issues.

Smartphone applications (a.k.a., apps) have become indispensable in our everyday life and work. In practice, accomplishing a task on smartphones may require the user to navigate among various apps. Unlike Web pages that are inherently interconnected through hyperlinks, apps are usually isolated building blocks, and the lack of direct links between apps has compromised the efficiency of task completion and user experience. In this article, we present the first in-depth empirical study of page-level access behaviors of smartphone users based on a comprehensive dataset collected through an extensive user study. We propose a model to distinguish informational pages and transitional pages , based on which we can extract page-level inter-app navigation. Surprisingly, the transitional pages account for quite substantial time cost and manual actions when navigating from the current informational page to the desirable informational page. We reveal that developing “ tunnels ” between “isolated” apps under specific usage scenarios has a huge potential to reduce the cost of navigation. Our analysis provides some practical implications on how to improve app-navigation experience from both the operating system’s perspective and the developer’s perspective.

Due to their on-body and ubiquitous nature, wearables can generate a wide range of unique sensor data creating countless opportunities for deep learning tasks. We propose DeepWear, a deep learning (DL) framework for wearable devices to improve the performance and reduce the energy footprint. DeepWear strategically offloads DL tasks from a wearable device to its paired handheld device through local network connectivity such as Bluetooth. Compared to the remote-cloud-based offloading, DeepWear requires no Internet connectivity, consumes less energy, and is robust to privacy breach. DeepWear provides various novel techniques such as context-aware offloading, strategic model partition, and pipelining support to efficiently utilize the processing capacity from nearby paired handhelds. Deployed as a user-space library, DeepWear offers developer-friendly APIs that are as simple as those in traditional DL libraries such as TensorFlow. We have implemented DeepWear on the Android OS and evaluated it on COTS smartphones and smartwatches with real DL models. DeepWear brings up to 5.08X and 23.0X execution speedup, as well as 53.5 and 85.5 percent energy saving compared to wearable-only and handheld-only strategies, respectively.

In dynamic, open, and service-oriented computing environments, e.g., e-commerce and crowdsourcing, service consumers must choose one of the services or items to complete their tasks. Due to the scale and dynamic characteristics of these environments, service consumers may have little or no experience with the available services. To this end, reputation systems are proposed and have played a crucial role in the success of online service-oriented transactions. In this paper, we study the current reputation systems used in commercial environments. In these rating-based reputation systems, we found they are not only resilient to the changes (time lag) but also vulnerable to unfair ratings. To address the problems in parallel, we propose an adaptive reputation model (ARM). ARM can dynamically adjust its model parameters to adapt the latest changes in a service. To tackle time lag, the proposed model generalizes the fixed sliding window, used in current commercial platforms, into a dynamic sliding window mechanism. Thus, the model can completely mitigate the influence of obsolete ratings. To detect unfair ratings, our model implements a statistical strategy based on hypothesis testing after transforming the ratings in the linear window into residuals. Experiments not only validate the effectiveness of the proposed model but also show that it outperforms the existing reputation system by 45% on average based on five test cases. The results also show that the proposed model can asymptotically converge to the underlying reputation value as ratings begin to accumulate. Note to Practitioners —The reputation models adopted by current commercial platforms, such as Amazon, eBay, and Taobao, not only suffer heavily from unfair rating but also resilient to the changes in services. To address the problems simultaneously, we design and implement a hybrid model that continuously monitors received ratings and aggregates the reputation value in a self-adaptive way. Our model first fits received fair ratings using the Bayesian linear regression approach and captures the distribution of fair ratings; it then filters out unfair ratings leveraging hypothesis testing. Finally, to sensitively respond the dynamic service changes, the dynamic sliding window algorithm in our model shifts the rating collection window into a new one and discards outdated ratings, reputation value is aggregated in the new window to mitigate the influence of obsolete ratings. Extensive experiments are conducted on widely used scenarios to demonstrate the efficiency and the effectiveness of our proposed model.

The purpose of this study was to evaluate the clinical outcomes of the endoscopic-assisted locking block modified Krackow technique with a V-Y flap. The hypothesis was that the minimally invasive technique can reduce wound complications and facilitate early recovery.In total, 29 men with chronic Achilles tendon rupture who underwent either minimally invasive technique (n = 13) or open repair (n = 16) at our department between 2013 and 2019 were retrospectively analyzed. The rate of complications, time to return to moderate-intensity exercise, American Orthopedic Foot and Ankle Society ankle hindfoot score, Achilles tendon Total Rupture Score, heel-rise repetitions in 1 min, heel-rise height, and bilateral calf circumference at 6 months, 1 year, and 2 years postoperatively were recorded.All incisions healed primarily in the minimally invasive technique group; however, three patients in the open repair group experienced wound complications. The time to return to moderate-intensity exercise, American Orthopedic Foot and Ankle Society score, Achilles tendon Total Rupture Score, heel-rise repetition ratio, and heel-rise height ratio at 6 months postoperatively in the minimally invasive technique group were significantly better than those in the open repair group. However, it was not significantly different between both groups at 2 years postoperatively.Endoscopy allowed scar tissue and adhesions to be removed, allowing the tendon ends to be mobilized out of the small proximal and distal incisions. Minimally invasive technique may result in a lower wound complication incidence and provide better early functional recovery and return to moderate-intensity exercise time than the conventional open procedure in treating chronic Achilles tendon ruptures.Wuxi Ninth People's Hospital Medical Ethical Committee, LW2021026.III.

This paper proposes Mandheling, the first system that enables highly resource-efficient on-device training by orchestrating the mixed-precision training with on-chip Digital Signal Processing (DSP) offloading. Mandheling fully explores the advantages of DSP in integer-based numerical calculation by four novel techniques: (1) a CPU-DSP co-scheduling scheme to mitigate the overhead from DSP-unfriendly operators; (2) a self-adaptive rescaling algorithm to reduce the overhead of dynamic rescaling in backward propagation; (3) a batch-splitting algorithm to improve the DSP cache efficiency; (4) a DSP-compute subgraph reusing mechanism to eliminate the preparation overhead on DSP. We have fully implemented Mandheling and demonstrate its effectiveness through extensive experiments. The results show that, compared to the state-of-the-art DNN engines from TFLite and MNN, Mandheling reduces the per-batch training time by 5.5$\times$ and the energy consumption by 8.9$\times$ on average. In end-to-end training tasks, Mandheling reduces up to 10.7$\times$ convergence time and 13.1$\times$ energy consumption, with only 1.9%-2.7% accuracy loss compared to the FP32 precision setting.

Cobalt (Co) ions, which can mimic hypoxia to promote angiogenesis, exhibit great potential for bone repair. However, a key point for the use of Co ions is that their release profile should be controllable and, more importantly, suitable for the bone regeneration process. Here, 2-ethylimidazole (eIm) was introduced into zeolitic imidazolate framework-67 (ZIF-67) to slow down Co-ion release and fabricate eIm-doped ZIF-67 (eIm/ZIF-67), which was combined into gelatin methacrylate (GelMA) to obtain an in situ photo-cross-linking nanocomposite hydrogel as a tunable Co-ion controlled release system. A tunable and controlled release of Co ions from the nanocomposite hydrogel was achieved by variation of linker composition, and GelMA with 75% eIm/ZIF-67 (with 75% eIm in the precursor solutions) could maintain a 21-day sustained release of Co ions, which is matched with early-stage angiogenesis during the bone formation process. Our in vitro study also showed that the GelMA@eIm/ZIF-67 hydrogel could reduce cytotoxicity and effectively promote the angiogenic activity of human umbilical vein endothelial cells (HUVECs) and osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). Moreover, an in vivo rat calvarial defect model demonstrated that the GelMA@eIm/ZIF-67 hydrogel exhibited remarkably enhanced bone formation and neovascularization abilities and had good biocompatibility as shown in organ histopathological examinations. Therefore, this novel nanocomposite hydrogel has strong therapeutic potential as a desirable Co-ion controlled release system and a powerful proangiogenic/osteogenic agent for the treatment of bone defects.

Tendon injury is one of the most serious orthopedic diseases often leading to disability of patients. Major shortages of tendon healing are due to its multiple comorbidities, uncertainty of therapeutic efficacy and insufficient of angiogenesis. With a deeper understanding of angiogenic mechanism of tendon healing, we investigated an innovative microneedle patch loaded with platelet derived growth factor (PDGF) to achieve a constant systemic administration of PDGF to enhance topical tendon healing. Rat achilles tendon injury model was performed as in vivo animal models. Histological staining showed an enhancement of tendon healing quality, especially angiogenesis. Biomechanical studies demonstrated an increase of tendon stiffness, maximum load and maximum stress with treatment of PDGF-loaded microneedles. Furthermore, MAPK/p38/Cyclin D1 pathway and angiogenesis were found to play an important role in tendon healing process by using a biological high throughput RNA-sequence method and bioinformatic analysis. The high throughput RNA-seq tendon healing results were confirmed by histochemical staining and western blot. These results suggest the novel therapeutic potential of PDGF-loaded microneedle patch in tendon surgery.

Mobile malware detection has attracted massive research effort in our community. A reliable and up-to-date malware dataset is critical to evaluate the effectiveness of malware detection approaches. Essentially, the malware ground truth should be manually verified by security experts, and their malicious behaviors should be carefully labelled. Although there are several widely-used malware benchmarks in our community (e.g., MalGenome, Drebin, Piggybacking and AMD, etc.), these benchmarks face several limitations including out-of-date, size, coverage, and reliability issues, etc. In this paper, we first make efforts to create MalRadar, a growing and up-to-date Android malware dataset using the most reliable way, i.e., by collecting malware based on the analysis reports of security experts. We have crawled all the mobile security related reports released by ten leading security companies, and used an automated approach to extract and label the useful ones describing new Android malware and containing Indicators of Compromise (IoC) information. We have successfully compiled MalRadar, a dataset that contains 4,534 unique Android malware samples (including both apks and metadata) released from 2014 to April 2021 by the time of this paper, all of which were manually verified by security experts with detailed behavior analysis. Then we characterize the MalRadar dataset from malware distribution channels, app installation methods, malware activation, malicious behaviors and anti-analysis techniques. We further investigate the malware evolution over the last decade. At last, we measure the effectiveness of commercial anti-virus engines and malware detection techniques on detecting malware in MalRadar. Our dataset can be served as the representative Android malware benchmark in the new era, and our observations can positively contribute to the community and boost a series of research studies on mobile security.

Microservice systems are often deployed in complex cloud-based environments and may involve a large number of service instances being dynamically created and destroyed. It is thus essential to ensure observability to understand these microservice systems’ behaviors and troubleshoot their problems. As an important means to achieve the observability, distributed tracing and analysis is known to be challenging. While many companies have started implementing distributed tracing and analysis for microservice systems, it is not clear whether existing approaches fulfill the required observability. In this article, we present our industrial survey on microservice tracing and analysis through interviewing developers and operation engineers of microservice systems from ten companies. Our survey results offer a number of findings. For example, large microservice systems commonly adopt a tracing and analysis pipeline, and the implementations of the pipeline in different companies reflect different tradeoffs among a variety of concerns. Visualization and statistic-based metrics are the most common means for trace analysis, while more advanced analysis techniques such as machine learning and data mining are seldom used. Microservice tracing and analysis is a new big data problem for software engineering, and its practices breed new challenges and opportunities.

As we have entered the Internet-of-Things (IoT) era, further blessed with rapid advances in several key technological areas including DevOps, AI/ML, 5G/6G/, neurocomputing, to name a few, it is imperative we think big and aim high. This new venture will require professionals in both software engineering and services computing to collaborate with an unprecedented intensity, and jointly develop the new interdisciplinary field hereby named Software Services Engineering (SSE). In SSE, the ever-deepening system dynamics emerging from both environments and humans in varying contexts are imposing steep challenges to both researchers and practitioners. Humans, both developers and the vast number of end users, are embedded ever closer to IoT environments, and are being afforded ample opportunities to continuously inject inputs during system development and after deployment. In fact, humans are increasingly playing the roles of both sensor and actuator. Traditional requirements engineering researchers are being lured more than ever into exploiting the IoT environments where human users are deeply embedded, to gather contextual information that inevitably introduces lots of ambiguity and uncertainty. Provisioning of highly adaptable and scalable microservices would be key to timely meeting ever-changing human desires and ever-evolving system requirements in the nimblest manner. As such, an ultra-agile and field-programmable development methodology and environment will be imperative to achieving such ultrafine grained microservices provisioning. Such ultra-agility and ultrafine granularity requirements imposed to the services industry obligate company executives to expect extreme manageability assurance to become the centroid of system operations and administration. The ultimate goal in pursuit of such a noble dream will be to provide genuinely individualized and trustworthy service, possibly enabled by AI, but it should be both explainable and ethical. Facing such grand challenges, this declaration samples a subset of burning issues in SSE through observations in seven themes, only meant to be starting points for the SSE community to further investigate. Through our declarations we also call for heightened attention to an assorted array of existing, barely emerging or non-existent services computing and software engineering methods for a concerted effort to research and explore.

We propose a cloud-native data analytics engine for processing data stored among geographically distributed cloud regions with reduced cost. A job is split into subtasks and placed across regions based on factors including prices of compute resources and data transmission. We present its architecture which leverages existing cloud infrastructures and discuss major challenges of its system design. Preliminary experiments show that the cost is reduced by 15.1% for a decision support query on a four-region public cloud setup.

Big data has become valuable property for enterprises and enabled various intelligent applications. Today, it is common to host data in big data platforms (e.g., Spark), where developers can submit scripts to process the original and intermediate data tables. Meanwhile, it is highly desirable to manage the data to comply with various privacy requirements. To enable flexible and automated privacy policy enforcement, we propose TaintStream, a fine-grained taint tracking framework for Spark-like big data platforms. TaintStream works by automatically injecting taint tracking logic into the data processing scripts, and the injected scripts are dynamically translated to maintain a taint tag for each cell during execution. The dynamic translation rules are carefully designed to guarantee non-interference in the original data operation. By defining different semantics of taint tags, TaintStream can enable various data management applications such as access control, data retention, and user data erasure. Our experiments on a self-crafted benchmarksuite show that TaintStream is able to achieve accurate cell-level taint tracking with a precision of 93.0% and less than 15% overhead. We also demonstrate the usefulness of TaintStream through several real-world use cases of privacy policy enforcement.

Serverless computing is an emerging paradigm for cloud computing, gaining traction in a wide range of applications such as video processing and machine learning. This new paradigm allows developers to focus on the development of the logic of serverless computing based applications (abbreviated as serverless-based applications) in the granularity of function, thereby freeing developers from tedious and error-prone infrastructure management. Meanwhile, it also introduces new challenges on the design, implementation, and deployment of serverless-based applications, and current serverless computing platforms are far away from satisfactory. However, to the best of our knowledge, these challenges have not been well studied. To fill this knowledge gap, this paper presents the first comprehensive study on understanding the challenges in developing serverless-based applications from the developers’ perspective. We mine and analyze 22,731 relevant questions from Stack Overflow (a popular Q&A website for developers), and show the increasing popularity trend and the high difficulty level of serverless computing for developers. Through manual inspection of 619 sampled questions, we construct a taxonomy of challenges that developers encounter, and report a series of findings and actionable implications. Stakeholders including application developers, researchers, and cloud providers can leverage these findings and implications to better understand and further explore the serverless computing paradigm.

Background Osteogenesis is tightly coupled with angiogenesis during bone repair and regeneration. However, the underlying mechanisms linking these processes remain largely undefined. The present study aimed to test the hypothesis that epidermal growth factor-like domain-containing protein 6 (EGFL6), an angiogenic factor, also functions in bone marrow mesenchymal stem cells (BMSCs), playing a key role in the interaction between osteogenesis and angiogenesis. Methods We evaluated how EGFL6 affects angiogenic activity of human umbilical cord vein endothelial cells (HUVECs) via proliferation, transwell migration, wound healing, and tube-formation assays. Alkaline phosphatase (ALP) and Alizarin Red S (AR-S) were used to assay the osteogenic potential of BMSCs. qRT-PCR, western blotting, and immunocytochemistry were used to evaluate angio- and osteo-specific markers and pathway-related genes and proteins. In order to determine how EGFL6 affects angiogenesis and osteogenesis in vivo, EGFL6 was injected into fracture gaps in a rat tibia distraction osteogenesis (DO) model. Radiography, histology, and histomorphometry were used to quantitatively evaluate angiogenesis and osteogenesis. Results EGFL6 stimulated both angiogenesis and osteogenic differentiation through Wnt/β-catenin signaling in vitro. Administration of EGFL6 in the rat DO model promoted CD31hiEMCNhi type H-positive capillary formation associated with enhanced bone formation. Type H vessels were the referred subtype involved during DO stimulated by EGFL6. Conclusion EGFL6 enhanced the osteogenic differentiation potential of BMSCs and accelerated bone regeneration by stimulating angiogenesis. Thus, increasing EGFL6 secretion appeared to underpin the therapeutic benefit by promoting angiogenesis-coupled bone formation. These results imply that boosting local concentrations of EGFL6 may represent a new strategy for the treatment of compromised fracture healing and bone defect restoration.

In enterprise organizations, the Bring-Your-Own-Device (BYOD) requirement has become prevalent as employees use their own mobile devices to process the workflow-oriented tasks. Consequently, it calls for approaches that can quickly develop and integrate mobile user interactions into existing business processes, and adapt to various contexts. However, designing, developing, and deploying adaptive and mobile-oriented user interfaces for existing process engines are non-trivial, and require significant systematic efforts. To address this issue, we present a novel middleware-based approach, called MUIT, to developing and deploying the Mobility, User Interactions and Tasks into WS-BPEL engines. MUIT provides a Domain-Specific Language (DSL) that provides some intuitive facilities to support the declarative development of adaptive, mobile-oriented, and Web-based user interfaces in WS-BPEL. The DSL can significantly reduce developers' manual efforts of developing user interactions by preventing arbitrarily mixed code, and its runtime supports satisfactory user experiences. Additionally, MUIT can be seamlessly integrated into WS-BPEL without intrusions of existing process instances. We implement a proof-of-concept prototype by integrating MUIT into the commodity WS-BPEL-based Apusic Platform, and evaluate the performance and usability of MUIT platform.

With the increasing popularity of blockchain technologies in recent years, blockchain-based decentralized applications (DApps for short in this paper) have been rapidly developed and widely adopted in many areas, being a hot topic in both academia and industry. Despite of the importance of DApps, we still have quite little understanding of DApps along with its ecosystem. To bridge the knowledge gap, this paper presents the first comprehensive empirical study of blockchain-based DApps to date, based on an extensive dataset of 995 Ethereum DApps and 29,846,075 transaction logs over them. We make a descriptive analysis of the popularity of DApps, summarize the patterns of how DApps use smart contracts to access the underlying blockchain, and explore the worth-addressing issues of deploying and operating DApps. Based on the findings, we propose some implications for DApp users to select proper DApps, for DApp developers to improve the efficiency of DApps, and for blockchain vendors to enhance the support of DApps.

Mobile Web applications (a.k.a., Web apps) stand for an important trend for next-generation Internet-based software. Currently popular mobile Web apps need to be adapted to various and ever-changing contexts and personalized user requirements. Based on our over-decade research experiences and practice on the Internetware paradigm, this position article describes an Internetware-oriented approach to designing, developing, and deploying situational mobile Web apps, by synthesizing the resources and services of mobile and cloud. Guided by a novel Service-Model-View-Controller (SMVC) software model, a mobile Web app is organized into a well-defined structure that facilitates adaptation including online/offline data access, computation offloading, user interface optimization, hybrid composition, etc. We provide efficient runtime support spanning mobile and cloud to make mobile Web apps more flexibly adaptive. The proof-of-concept evaluation demonstrates that our approach can benefit end-users with optimized user experience of mobile Web apps.

Mobile users spend an extensive amount of time on typing. A more efficient text input instrument brings a significant enhancement of user experience. Deep learning techniques have been recently applied to suggesting the next words of input, but to achieve more accurate predictions, these models should be customized for individual users. Personalization is often at the expense of privacy concerns. Existing solutions require users to upload the historical logs of their input text to the cloud so that a deep learning predictor can be trained. In this work, we propose a novel approach, called DeepType, to personalize text input with better privacy. The basic idea is intuitive: training deep learning predictors on the device instead of on the cloud, so that the model makes personalized and private data never leaves the device to externals. With DeepType, a global model is first trained on the cloud using massive public corpora, and our personalization is done by incrementally customizing the global model with data on individual devices. We further propose a set of techniques that effectively reduce the computation cost of training deep learning models on mobile devices at the cost of negligible accuracy loss. Experiments using real-world text input from millions of users demonstrate that DeepType significantly improves the input efficiency for individual users, and its incurred computation and energy costs are within the performance and battery restrictions of typical COTS mobile devices.

Sequential recommendation techniques are considered to be a promising way of providing better user experience in mobile commerce by learning sequential interests within user historical interaction behaviors. However, the recently increasing focus on privacy concerns, such as the General Data Protection Regulation (GDPR), can significantly affect the deployment of state-of-the-art sequential recommendation techniques, because user behavior data are no longer allowed to be arbitrarily used without the user’s explicit permission. To address the issue, this paper proposes DeepRec, an on-device deep learning framework of mining interaction behaviors for sequential recommendation without sending any raw data or intermediate results out of the device, preserving user privacy maximally. DeepRec constructs a global model using data collected before GDPR and fine-tunes a personal model continuously on individual mobile devices using data collected after GDPR. DeepRec employs the model pruning and embedding sparsity techniques to reduce the computation and network overhead, making the model training process practical on computation-constraint mobile devices. Evaluation results show that DeepRec can achieve comparable recommendation accuracy to existing centralized recommendation approaches with small computation overhead and up to 10x reduction in network overhead.

To improve app quality and nip the potential threats in the bud, modern app markets have released strict guidelines along with app vetting process before app publishing. However, there has been growing evidence showing the ineffectiveness of app vetting, making potentially harmful and policy-violation apps sneak into the market from time to time. Therefore, app removal is a common practice, and market maintainers have to remove undesired apps from the market periodically in a reactive manner. Although a number of reports and news media have mentioned removed apps, our research community still lacks the comprehensive understanding of the landscape of this kind of apps. To fill the void, in this paper, we present a large-scale and longitudinal study of removed apps in iOS app store. We first make great efforts to record daily snapshot of iOS app store continuously in a span of 1.5 years. By comparing each two consecutive snapshots, we have collected the information of over 1 million removed apps with their accurate removed date. This comprehensive dataset enables us to characterize the overall landscape of removed apps. We observe that, although most of the removed apps are low-quality apps (e.g., outdated and abandoned), a number of the removed apps are quite popular. We further investigate the practical reasons leading to the removal of such popular apps, and observe several interesting reasons, including ranking fraud, fake description, and content issues, etc. More importantly, most of these mis-behaviors can be reflected on app meta information including app description, app review, and ASO keywords. It motivates us to design an automated approach to flagging the removed apps. Experiment result suggests that, even without accessing to the bytecode of mobile apps, we can identify the removed apps with good performance (F1=83%). Furthermore, we are able to flag the removed apps in advance as long as their inappropriate behaviors appear in their metadata. We believe our approach can work as a whistle blower that pinpoints policy-violation behaviors timely, which will be quite effective in improving the app maintenance process.

Mobile gambling app, as a new type of online gambling service emerging in the mobile era, has become one of the most popular and lucrative underground businesses in the mobile app ecosystem. Since its born, mobile gambling app has received strict regulations from both government authorities and app markets. However, to the best of our knowledge, mobile gambling apps have not been investigated by our research community. In this paper, we take the first step to fill the void. Specifically, we first perform a 5-month dataset collection process to harvest illegal gambling apps in China, where mobile gambling apps are outlawed. We have collected 3,366 unique gambling apps with 5,344 different versions. We then characterize the gambling apps from various perspectives including app distribution channels, network infrastructure, malicious behaviors, abused third-party and payment services. Our work has revealed a number of covert distribution channels, the unique characteristics of gambling apps, and the abused fourth-party payment services. At last, we further propose a “guilt-by-association” expansion method to identify new suspicious gambling services, which help us further identify over 140K suspicious gambling domains and over 57K gambling app candidates. Our study demonstrates the urgency for detecting and regulating illegal gambling apps.

Background: Osteogenesis is tightly coupled with angiogenesis during bone repair and regeneration. However, the underlying mechanisms linking these processes remain largely undefined. The present study aimed to test the hypothesis that epidermal growth factor-like domain-containing protein 6 (EGFL6), an angiogenic factor, also functions in bone marrow mesenchymal stem cells (BMSCs) and plays a key role in the interaction between osteogenesis angiogenesis.Methods: We evaluated how EGFL6 affects angiogenic activity of human umbilical cord vein endothelial cells (HUVECs) via proliferation, transwell migration, wound healing, and tube-formation assays. Alkaline phosphatase (ALP) and Alizarin Red S (AR-S) were used to assay the osteogenic potential of BMSCs. qRT-PCR, western blotting, and immunocytochemistry were used to evaluate angio- and osteo-specific markers and pathway-related genes and proteins. In order to determine how EGFL6 affects angiogenesis and osteogenesis in vivo, EGFL6 was injected into fracture gaps in a rat tibia distraction osteogenesis (DO) model. Radiography, histology, and histomorphometry were used to quantitatively evaluate angiogenesis and osteogenesis. Results: EGFL6 stimulated both angiogenesis and osteogenic differentiation through Wnt/β-catenin signaling in vitro. Administration of EGFL6 in the rat DO model promoted CD31hiEMCNhi type H-positive capillary formation associated with enhanced bone formation. Type H vessels were the referred subtype involved during DO stimulated by EGFL6.Conclusion: EGFL6 enhanced osteogenic differentiation potential of BMSCs and accelerated bone regeneration by stimulating angiogenesis, thus exerting therapeutic benefit by increasing EGFL6 secretion to promote angiogenesis-coupled bone formation. These results imply that boosting local concentrations of EGFL6 may represent a new strategy for the treatment of compromised fracture healing and bone defect restoration.