Reviewerly/exHarmony huggingface dataset:
High-Performance Smart Contracts Concurrent Execution for Permissioned Blockchain Using SGX

Since there are no security concerns such as Sybil attacks, selfish mining, etc., the higher the system throughput, the better for the permissioned blockchain. And with the emergence of consensus algorithms, the throughput rates of permissioned blockchain can be up to thousands of transactions per second. The existing serial execution method for smart contracts becomes a new bottleneck for the system. Due to the lack of mutual trust between nodes, for a batch of smart contracts contained in a block, the traditional two-phase smart contract concurrency approach can only achieve concurrency within a single node, but not the parallel execution of contracts between nodes. In this paper, we propose a new two-phase framework based on trusted hardware Intel SGX, which can avoid the re-execution of all smart contracts on all nodes and improve parallelism between nodes. And consistency between nodes is achieved directly with state replication, rather than by re-executing transactions. We design a pre-execution mechanism for smart contracts in untrusted memory to batch fetch all the state data that a smart contract needs to access to reduce frequent enclave transitions during smart contract execution. Besides, we propose a method that generates a compact read-write set and a data structure named Merkle Forest which can generate the compact Merkle multiproofs for the initial data in untrusted memory in parallel and can quickly verify the correctness of the data passed in the enclave. Finally, we integrate all the techniques proposed in this paper into an open-source system BFT-SMaRt to evaluate our approach in a distributed setting. Experimental results show the efficiency of the proposed methods.
Towards cost-effective and elastic cloud database deployment via memory disaggregation

It is challenging for cloud-native relational databases to meet the ever-increasing needs of scaling compute and memory resources independently and elastically. The recent emergence of memory disaggregation architecture, relying on high-speed RDMA network, offers opportunities to build cost-effective and elastic cloud-native databases. There exist proposals to let unmodified applications run transparently on disaggregated systems. However, running relational database kernel atop such proposals experiences notable performance degradation and time-consuming failure recovery, offsetting the benefits of disaggregation.
MR-contrast-aware image-to-image translations with generative adversarial networks

A magnetic resonance imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through multiple acquisition parameters that influence image contrast, signal-to-noise ratio, acquisition time, and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As MR sequence acquisition is time consuming and acquired images may be corrupted due to motion, a method to synthesize MR images with adjustable contrast properties is required.Therefore, we trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition time and echo time. Our approach is motivated by style transfer networks, whereas the "style" for an image is explicitly given in our case, as it is determined by the MR acquisition parameters our network is conditioned on.This enables us to synthesize MR images with adjustable image contrast. We evaluated our approach on the fastMRI dataset, a large set of publicly available MR knee images, and show that our method outperforms a benchmark pix2pix approach in the translation of non-fat-saturated MR images to fat-saturated images. Our approach yields a peak signal-to-noise ratio and structural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model significantly.Our model is the first that enables fine-tuned contrast synthesis, which can be used to synthesize missing MR-contrasts or as a data augmentation technique for AI training in MRI. It can also be used as basis for other image-to-image translation tasks within medical imaging, e.g., to enhance intermodality translation (MRI → CT) or 7 T image synthesis from 3 T MR images.
Actionable Analytics: Stop Telling Me What It Is; Please Tell Me What To Do

The success of software projects depends on complex decision making (e.g., which tasks should a developer do first, who should perform this task, is the software of high quality, is a software system reliable and resilient enough to deploy, etc.). Bad decisions cost money (and reputation) so we need better tools for making better decisions. This article discusses the "why" and "how" of explainable and actionable software analytics. For the task of reducing the risk of software defects, we show initial results from a successful case study that offers more actionable software analytics. Also, we present an interactive tutorial on the subject of Explainable AI tools for SE in our Software Analytics Cookbook (https://xai4se.github.io/book/), and we discuss some open questions that need to be addressed.
Why Are Digital Health Care Systems Still Poorly Designed, and Why Is Health Care Practice Not Asking for More? Three Paths Toward a Sustainable Digital Work Environment

Knowledge of how to design digital systems that are ergonomically sound, high in usability, and optimized for the user, context, and task has existed for some time. Despite this, there are still too many examples of new digital health care systems that are poorly designed and that could negatively affect both the work environment of health care staff and patient safety. This could be because of a gap between the theoretical knowledge of design and ergonomics and the practical implementation of this knowledge in procuring and developing digital health care systems. Furthermore, discussions of digitalization are often at a general level and risk neglecting the nature of direct interaction with the digital system. This is problematic since it is at this detailed level that work environment and patient safety issues materialize in practice. In this paper, we illustrate such issues with two scenarios concerned with contemporary electronic health care records, based on field studies in two health care settings. We argue that current methods and tools for designing and evaluating digital systems in health care must cater both to the holistic level and to the details of interaction and ergonomics. It must also be acknowledged that health care professionals are neither designers nor engineers, so expectations of them during the development of digital systems must be realistic. We suggest three paths toward a more sustainable digital work environment in health care: (1) better tools for evaluating the digital work environment in the field; (2) generic formulations of qualitative requirements related to usability and for adaptation to the user, context, and task, to be used in procurement; and (3) the introduction of digital ergonomics as an embracing concept capturing several of the ergonomic challenges (including physical, cognitive, and organizational aspects) involved in implementing and using digital systems.
FAIEr: Fidelity and Adequacy Ensured Image Caption Evaluation

Image caption evaluation is a crucial task, which involves the semantic perception and matching of image and text. Good evaluation metrics aim to be fair, comprehensive, and consistent with human judge intentions. When humans evaluate a caption, they usually consider multiple aspects, such as whether it is related to the target image without distortion, how much image gist it conveys, as well as how fluent and beautiful the language and wording is. The above three different evaluation orientations can be summarized as fidelity, adequacy, and fluency. The former two rely on the image content, while fluency is purely related to linguistics and more subjective. Inspired by human judges, we propose a learning-based metric named FAIEr to ensure evaluating the fidelity and adequacy of the captions. Since image captioning involves two different modalities, we employ the scene graph as a bridge between them to represent both images and captions. FAIEr mainly regards the visual scene graph as the criterion to measure the fidelity. Then for evaluating the adequacy of the candidate caption, it high-lights the image gist on the visual scene graph under the guidance of the reference captions. Comprehensive experimental results show that FAIEr has high consistency with human judgment as well as high stability, low reference dependency, and the capability of reference-free evaluation.
Unnoticeable synthetic face replacement for image privacy protection

A rapidly growing amount of personal sensitive information is being released to the public due to unprotected sharing of face images and videos on social networks. Although some pioneering face de-identification techniques, such as face blurring, have been proposed, there is still a long way towards providing full protection of one’s facial privacy. In this paper, we propose a novel end-to-end privacy protection approach to seamlessly replace a face in an image with a synthesized face that looks as natural as normal photos yet pertaining very different look from the original face. The synthesized face images will prevent potential attackers from de-identifying the users. Specifically, our approach relies on generative adversarial network and considers both the foreground and background constrains with respect to the input face image to achieve the following two goals: Firstly, to make synthesized images perceptually unaltered, we design a new generative model to effectively fuse a synthesized face with the original background. Secondly, to ensure a synthesized face to be much different from the original face, we define multiple losses to distinguish the synthesized face from the original face. The experimental results on public datasets have validated the effectiveness of our approach compared with the-state-of-the-art.
Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes

Appropriate training data is a requirement for building good machine-learned models. In this paper, we study the notion of coverage for ordinal and continuous-valued attributes, by formalizing the intuition that the learned model can accurately predict only at data points for which there are "enough" similar data points in the training data set.
Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things

This article introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps: 1) data preparation; 2) object detection; and 3) hyperparameter optimization. Inspired by deep learning and evolutionary computation (EC) techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyperparameters optimization model based on EC that can be used to tune parameters of our deep learning framework. In the validation of the framework’s usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO data sets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.
A Robust Model for Handwritten Digit Recognition using Machine and Deep Learning Technique

In the era of research, pattern recognition is one of the most famous and widely used area in the field of research work. There are various types of patterns are available for the researches like: audio, video, handwritten digit images and handwritten characters images etc. In this paper, we concentrate in the field of handwritten digit recognition for classification of patterns. We have used famous handwritten digit datasets named as MNIST, which is collection of 70000 images. Many of machine learning and deep learning techniques have been already used by the researches for handwritten digit recognition like Support Vector Machine (SVM), RFC, K-nearest Neighbor (K-NN), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) etc. In this research work, we have suggested CNN as deep learning technique on keras for MNIST handwritten digit recognition and compare the performance of CNN with SVM and KNN. The proposed CNN based on keras model used to classify handwritten digit images with RMSprop optimizer for optimizing the model. The main contribution of this research work is to increase the convolutional layer with pooling and dropout and also tuned the model using filter, kernel size and number of neurons. The proposed CNN model achieves 99.06% of training accuracy and 98.80% of testing accuracy with epoch 10. Experiment results reveals that proposed CNN is more effective compare to other techniques.
gMatch: Knowledge base question answering via semantic matching

Effectiveness is essential for knowledge base question answering (KBQA) to determine whether the query can return the correct answers. Existing works for KBQA mainly focus on converting input questions into corresponding logic formats, such as SPARQL queries. However, since these works are largely decoupled from the knowledge base, the converted query may be ineffective. In this paper, we propose a novel semantic matching-based approach to model the query intention of the input question by extracting the subgraph of the knowledge base. The generation of the SPARQL query is reduced to semantic matching in the knowledge base to solve the ineffectiveness of the query. Firstly, a semantic query graph is proposed to model the reliable query intention of the input question. The SPARQL query graph could be extracted by matching the semantic query graph in the knowledge base. Secondly, an embedding-based method is developed to represent different forms of questions and queries in a common space. It is easy to detect semantic loss between the question and the converted query with the common representation. Finally, a data-driven semantic completion technique is presented to reduce the semantic loss by expanding the incomplete SPARQL query in the knowledge base. The experiments evaluated on benchmark datasets show that the proposed approach significantly outperforms state-of-the-art methods in efficiency and effectiveness. The code is available at https://github.com/offerhub-ht/gMatch.
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems

Database management systems (DBMSs) are notoriously difficult to deploy and administer. The goal of a self-driving DBMS is to remove these impediments by managing itself automatically. However, a critical problem in achieving full autonomy is how to predict the DBMS's runtime behavior and resource consumption. These predictions guide a self-driving DBMS's decision-making components to tune and optimize all aspects of the system. We present the ModelBot2 end-to-end framework for constructing and maintaining prediction models using machine learning (ML) in self-driving DBMSs. Our approach decomposes a DBMS's architecture into fine-grained operating units that make it easier to estimate the system's behavior for configurations that it has never seen before. ModelBot2 then provides an offline execution environment to exercise the system to produce the training data used to train its models. We integrated ModelBot2 in an in-memory DBMS and measured its ability to predict its performance for OLTP and OLAP workloads running in dynamic environments. We also compare ModelBot2 against state-of-the-art ML models and show that our models are up to 25x more accurate in multiple scenarios.
A Computationally Virtual Histological Staining Method to Ovarian Cancer Tissue by Deep Generative Adversarial Networks

Histological analysis to tissue samples is elemental for diagnosing the risk and severity of ovarian cancer. The commonly used Hematoxylin and Eosin (H&E) staining method involves complex steps and strict requirements, which would seriously impact the research of histological analysis of the ovarian cancer. Virtual histological staining by the Generative Adversarial Network (GAN) provides a feasible way for these problems, yet it is still a challenge of using deep learning technology since the amounts of data available are quite limited for training. Based on the idea of GAN, we propose a weakly supervised learning method to generate autofluorescence images of unstained ovarian tissue sections corresponding to H&E staining sections of ovarian tissue. Using the above method, we constructed the supervision conditions for the virtual staining process, which makes the image quality synthesized in the subsequent virtual staining stage more perfect. Through the doctors' evaluation of our results, the accuracy of ovarian cancer unstained fluorescence image generated by our method reached 93%. At the same time, we evaluated the image quality of the generated images, where the FID reached 175.969, the IS score reached 1.311, and the MS reached 0.717. Based on the image-to-image translation method, we use the data set constructed in the previous step to implement a virtual staining method that is accurate to tissue cells. The accuracy of staining through the doctor's assessment reached 97%. At the same time, the accuracy of visual evaluation based on deep learning reached 95%.
An Efficient Approach for Cross-Silo Federated Learning to Rank

Traditional learning-to-rank (LTR) models are usually trained in a centralized approach based upon a large amount of data. However, with the increasing awareness of data privacy, it is harder to collect data from multiple owners as before, and the resultant data isolation problem makes the performance of learned LTR models severely compromised. Inspired by the recent progress in federated learning, we propose a novel framework named Cross-Silo Federated Learning-to-Rank (CS-F-LTR), where the efficiency issue becomes the major bottleneck. To deal with the challenge, we first devise a privacy-preserving cross-party term frequency querying scheme based on sketching algorithms and differential privacy. To further improve the overall efficiency, we propose a new structure named reverse top-K sketch (RTK-Sketch) which significantly accelerates the feature generation process while holding theoretical guarantees on accuracy loss. Extensive experiments conducted on public datasets verify the effectiveness and efficiency of the proposed approach.
Education 4.0: AI Empowers Smart Blended Learning Process with Biofeedback

This paper describes the teaching model for Education 4.0, that uses elements from different disciplines such as electronic technology, Artificial Intelligence (AI), health, multimodal learning analytics, education, etc. Smart blended learning process for Education 4.0, implements in each phase AI methods such as user modeling, adaptivity, machine learning, chatbots, and semantic text recognition tools to increase learning success and to reduce the dropout rate in examinations. On the one hand, multimodal data, from online course and wearables, enables further statements about the concrete learning situation, and on the other hand, it increases the students' subjective experience of emotional states, their stress perception, well-being or energy level. This paper reports on experiments where the combination of AI, Internet of Things (IoT) and Biofeedback in a smart blended learning process attempt to consider the characteristics of learners better.
Hybrid Message Passing with Performance-Driven Structures for Facial Action Unit Detection

Message passing neural network has been an effective method to represent dependencies among nodes by propagating messages. However, most of message passing algorithms focus on one structure and messages are estimated by one single approach. For real-world data, like facial action units (AUs), the dependencies may vary in terms of different expressions and individuals. In this paper, we propose a novel hybrid message passing neural network with performance-driven structures (HMP-PS), which combines complementary message passing methods and captures more possible structures in a Bayesian manner. Particularly, a performance-driven Monte Carlo Markov Chain sampling method is proposed for generating high performance graph structures. Besides, hybrid message passing is proposed to combine different types of messages, which provide the complementary information. The contribution of each type of message is adaptively adjusted along with different inputs. The experiments on two widely used benchmark datasets, i.e., BP4D and DISFA, validate that our proposed method can achieve the state-of-the-art performance.
EMORL: Effective multi-objective reinforcement learning method for hyperparameter optimization

Hyperparameter optimization is critical for the performance of machine learning algorithms. Significant efforts have been dedicated to improve the final accuracy of algorithm by hyperparameter tuning. However, some indicators (such as latency, cpu utilization) are also very important in the actual environment. In this paper, we propose a novel method EMORL (Effective Multi-Objective Reinforcement Learning) based on multi-objective reinforcement learning for hyperparameter optimization to solve the above limitations. Specifically, we extend hyperparameter optimization problem to the reinforcement learning framework and employ an agent to select hyperparameters sequentially, and design a scalarization function that combines accuracy and latency as a multi-objective reward to guide the policy update. To improve the efficiency of hyperparameter optimization, previously successful configuration is reused for reshaping the advantage function. In the experiment, we apply the proposed method to tune the hyperparameters of the eXtreme Gradient Boosting on 101 tasks and convolutional neural networks on 2 tasks. The experimental results demonstrate that the proposed method is better than other methods in most tasks, especially in terms of latency. In addition, we verify the various components of the proposed method through ablation experiments.
Googling for Software Development: What Developers Search For and What They Find

Developers often search for software resources on the web. In practice, instead of going directly to websites (e.g., Stack Overflow), they rely on search engines (e.g., Google). Despite this being a common activity, we are not yet aware of what developers search from the perspective of popular software development websites and what search results are returned. With this knowledge, we can understand real-world queries, developers' needs, and the query impact on the search results. In this paper, we provide an empirical study to understand what developers search on the web and what they find. We assess 1.3M queries to popular programming websites and we perform thousands of queries on Google to explore search results. We find that (i) developers' queries typically start with keywords (e.g., Python, Android, etc.), are short (3 words), tend to omit functional words, and are similar among each other; (ii) minor changes to queries do not largely affect the Google search results, however, some cosmetic changes may have a non-negligible impact; and (iii) search results are dominated by Stack Overflow, but YouTube is also a relevant source nowadays. We conclude by presenting detailed implications for researchers and developers.