Reviewerly/exHarmony huggingface dataset:
Engaging Low-Income African American Older Adults in Health Discussions through Community-based Design Workshops

Community-based approaches to participatory design, such as the design workshop, promise to engage underserved populations in collaborative dialog and provide a platform for promoting the views of communities who are not typically given a space to engage in design. Yet, we know little about how design workshops as a research site can engage underserved individuals (i.e., due to class, race, or age status) or address personal concerns (e.g., health). As a way of exploring these issues, we conducted a series of five design workshops with low-income African-American older adults to understand their health experiences. Our findings reveal three insights associated with the design workshop and the topic of health: comfort with community versus personal health; the sociocultural configuration of interaction; and empowerment in the context of systematic inequality of opportunity. We discuss the importance of understanding the situated nature of design workshops, particularly when engaging underserved groups in the topic of health, and the potential of the design workshop as a mechanism for activism.
Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks

Fine-grained classification of cervical cells into different abnormality levels is of great clinical importance but remains very challenging. Contrary to the traditional classification methods that rely on hand-crafted or engineered features, convolution neural network (CNN) can classify cervical cells based on automatically learned deep features. However, CNN in previous studies does not involve cell morphological information, and it is unknown whether morphological features can be directly modeled by CNN to classify cervical cells. This paper presents a CNN-based method that combines cell image appearance with cell morphology for classification of cervical cells in Pap smear. The training of cervical cell dataset consists of adaptively re-sampled image patches coarsely centered on the nuclei. Several CNN models (AlexNet, GoogLeNet, ResNet, and DenseNet) pre-trained on ImageNet dataset were fine-tuned on the cervical dataset for comparison. The proposed method is evaluated on the Herlev cervical dataset by five-fold cross-validation at patient-level splitting. The results show that by adding cytoplasm and nucleus masks as raw morphological information into appearance-based CNN learning, higher classification accuracies can be achieved in general. Among the four CNN models, GoogLeNet fed with both morphological and appearance information obtains the highest classification accuracies of 94.5%, 71.3%, and 64.5%, for two-class (abnormal versus normal), four-class (“The Bethesda System”), and seven-class (“World Health Organization classification system”) classification tasks, respectively. Our method demonstrates that combining cervical cell morphology with appearance information can provide improved classification performance. Although the initial results are promising, deep learning-based fine-grained cervical cell classification remains a very challenging task for a high-precision diagnosis.
Field Test of Twin-Field Quantum Key Distribution through Sending-or-Not-Sending over 428 km

Quantum key distribution endows people with information-theoretical security in communications. Twin-field quantum key distribution (TF-QKD) has attracted considerable attention because of its outstanding key rates over long distances. Recently, several demonstrations of TF-QKD have been realized. Nevertheless, those experiments are implemented in the laboratory, remaining a critical question about whether the TF-QKD is feasible in real-world circumstances. Here, by adopting the sending-or-not-sending twin-field QKD (SNS-TF-QKD) with the method of actively odd parity pairing (AOPP), we demonstrate a field-test QKD over 428~km deployed commercial fiber and two users are physically separated by about 300~km in a straight line. To this end, we explicitly measure the relevant properties of the deployed fiber and develop a carefully designed system with high stability. The secure key rate we achieved breaks the absolute key rate limit of repeater-less QKD. The result provides a new distance record for the field test of both TF-QKD and all types of fiber-based QKD systems. Our work bridges the gap of QKD between laboratory demonstrations and practical applications, and paves the way for intercity QKD network with high-speed and measurement-device-independent security.
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization

Federated learning (FL), as a type of collaborative machine learning framework, is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. Nevertheless, from a viewpoint of information theory, it is still possible for a curious server to infer private information from the shared models uploaded by MTs. To address this problem, we first make use of the concept of local differential privacy (LDP), and propose a user-level differential privacy (UDP) algorithm by adding artificial noise to the shared models before uploading them to servers. According to our analysis, the UDP framework can realize <inline-formula><tex-math notation="LaTeX">$(\epsilon _{i}, \delta _{i})$</tex-math></inline-formula> -LDP for the <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula> th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes. We then derive a theoretical convergence upper-bound for the UDP algorithm. It reveals that there exists an optimal number of communication rounds to achieve the best learning performance. More importantly, we propose a communication rounds discounting (CRD) method. Compared with the heuristic search method, the proposed CRD method can achieve a much better trade-off between the computational complexity of searching and the convergence performance. Extensive experiments indicate that our UDP algorithm using the proposed CRD method can effectively improve both the training efficiency and model quality for the given privacy protection levels.
ProUM: Projection-based utility mining on sequence data

Utility is an important concept in Economics. A variety of applications consider utility in real-life situations, which has lead to the emergence of utility-oriented mining (also called utility mining) in the recent decade. Utility mining has attracted a great amount of attention, but most of the existing studies have been developed to deal with itemset-based data. Time-ordered sequence data is more commonly seen in real-world situations, which is different from itemset-based data. Since they are time-consuming and require large amount of memory usage, current utility mining algorithms still have limitations when dealing with sequence data. In addition, the mining efficiency of utility mining on sequence data still needs to be improved, especially for long sequences or when there is a low minimum utility threshold. In this paper, we propose an efficient Projection-based Utility Mining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store the necessary information of the sequence-order and utility. ProUM can significantly improve the mining efficiency by utilizing the projection technique in generating utility-array, and it effectively reduces the memory consumption. Furthermore, a new upper bound named sequence extension utility is proposed and several pruning strategies are further applied to improve the efficiency of ProUM. By taking utility theory into account, the derived high-utility sequential patterns have more insightful and interesting information than other kinds of patterns. Experimental results showed that the proposed ProUM algorithm significantly outperformed the state-of-the-art algorithms in terms of execution time, memory usage, and scalability.
Deep reflectance fields

We present a novel technique to relight images of human faces by learning a model of facial reflectance from a database of 4D reflectance field data of several subjects in a variety of expressions and viewpoints. Using our learned model, a face can be relit in arbitrary illumination environments using only two original images recorded under spherical color gradient illumination. The output of our deep network indicates that the color gradient images contain the information needed to estimate the full 4D reflectance field, including specular reflections and high frequency details. While capturing spherical color gradient illumination still requires a special lighting setup, reduction to just two illumination conditions allows the technique to be applied to dynamic facial performance capture. We show side-by-side comparisons which demonstrate that the proposed system outperforms the state-of-the-art techniques in both realism and speed.
When Blockchain Meets Smart Grid: Secure Energy Trading in Demand Response Management

Increasing demand for electricity necessitates the use of efficient mechanisms for demand response management (DRM) in the existing smart grid (SG) system. In the Industry 4.0 era, the usage of information and communication technologies in the energy industry revolutionized the existing grid called SG, which provides a bi-directional flow of energy and data. To handle the energy demand of the consumers, DRM is crucial. It provides the active participation of consumers in the energy trading system (ETS) between consumers and service providers. The traditional energy trading system (TETS) relies on the centralized system or trusted third parties, which may act as a single point of failure. So, it is essential to equip the SG system with a secure energy trading system (SETS) to provide privacy and security to the consumer's data. In this direction, one of the emerging technology, called blockchain, can handle the issue as mentioned above, which is a chain of decentralized and distributed transaction ledger that is retained and maintained by each user. It performs peerto- peer (P2P) energy transactions among different consumers, such as individual houses, using smart contracts, and without a central control body. In a decentralized system, each consumer has its energy storage locally generated using renewable energy resources (RES). In this article, SETS, a blockchain-based decentralized ETS framework, is proposed for storing and processing the data generated from smart meters (SMs). In SETS, miner node is designated to validate the requests of energy requirements, dynamic pricing, and time of stay. Then, an energy transaction execution approach is designed for SETS. The evaluation results obtained show that SETS outperforms the TETS in terms of computation time and communication costs.
Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements

Combining data content with visual embellishments, infographics can effectively deliver messages in an engaging and memorable manner. Various authoring tools have been proposed to facilitate the creation of infographics. However, creating a professional infographic with these authoring tools is still not an easy task, requiring much time and design expertise. Therefore, these tools are generally not attractive to casual users, who are either unwilling to take time to learn the tools or lacking in proper design expertise to create a professional infographic. In this paper, we explore an alternative approach: to automatically generate infographics from natural language statements. We first conducted a preliminary study to explore the design space of infographics. Based on the preliminary study, we built a proof-of-concept system that automatically converts statements about simple proportion-related statistics to a set of infographics with pre-designed styles. Finally, we demonstrated the usability and usefulness of the system through sample results, exhibits, and expert reviews.
Smart Contract-Based Agricultural Food Supply Chain Traceability

The complexity of a supply chain makes product safety or quality issues extremely difficult to track, especially for the basic agricultural food supply chains of people's daily diets. The existing agricultural food supply chains present several major problems, such as numerous participants, inconvenient communication caused by long supply chain cycles, data distrust between participants and the centralized system. The emergence of blockchain technology effectively solves the pain-point problem existing in the traceability system of agricultural food supply chains. This paper proposes a framework based on the consortium and smart contracts to track and trace the workflow of agricultural food supply chains, implement traceability and shareability of supply chains, and break down the information islands between enterprises as much as possible to eliminate the need for the central institutions and agencies and improve the integrity of the transaction records, reliability and security. At the same time, farmers record details of the environment and crop growth data in the InterPlanetary File System (IPFS) and store file IPFS hashes in smart contracts, which not only increases data security but also alleviates the blockchain storage explosion problem. This framework has been applied in Shanwei Lvfengyuan Modern Agricultural Development Co., Ltd. Although there are still many defects, the framework has successfully realized functions such as disintermediation and tracing of agricultural product information through QR codes. Thus, the framework proposed in this paper is of great significance and reference value for enterprises to ensure product quality and safety traceability.
STEAM in education: a bibliometric analysis of performance and co-words in Web of Science

Emerging methodologies that apply and integrate science, technology, engineering, art, and math (STEAM) in education have appeared in recent years as a pedagogical alternative providing more holistic and attractive education.The research methodology used in this work is of a bibliometric nature. Specifically, an academic performance analysis and a co-word analysis has been carried out. The term STEAM was analyzed in the Web of Science (WoS) database. The WoS programs Analyze Results, Creation Citation Report, and SciMAT were used. A total of 1116 manuscripts were analyzed.The results show that studies in the field education of STEAM began in 2006 and have continued uninterruptedly up to the present day, although interest generated in the scientific community has been irregular.It can be concluded that STEAM studies have not had an established and robust line of research over time, although it can be observed that the trends in this aspect are focused on the scientific branch of education. In addition, the topics of study on STEAM include points related to gender differences, the influence of STEAM on people of different races, the skills developed by students, and training teachers to implement teaching and learning processes with STEAM.
The promise of spintronics for unconventional computing

Novel computational paradigms may provide the blueprint to help solving the time and energy limitations that we face with our modern computers, and provide solutions to complex problems more efficiently (with reduced time, power consumption and/or less device footprint) than is currently possible with standard approaches. Spintronics offers a promising basis for the development of efficient devices and unconventional operations for at least three main reasons: (i) the low-power requirements of spin-based devices, i.e., requiring no standby power for operation and the possibility to write information with small dynamic energy dissipation, (ii) the strong nonlinearity, time nonlocality, and/or stochasticity that spintronic devices can exhibit, and (iii) their compatibility with CMOS logic manufacturing processes. At the same time, the high endurance and speed of spintronic devices means that they can be rewritten or reconfigured frequently over the lifetime of a circuit, a feature that is essential in many emerging computing concepts. In this perspective, we will discuss how spintronics may aid in the realization of efficient devices, primarily focusing on magnetic tunnel junctions. We then provide a perspective on how these devices can impact the development of three unconventional computing paradigms, namely, reservoir computing, probabilistic computing and memcomputing. These paradigms may be used to address some limitations of modern computers, providing a realistic path to intelligent hybrid CMOS-spintronic systems.
Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

Feature selection is a process to reduce the dimension of a dataset by removing redundant features, and to use the optimal subset of features for machine learning or data mining algorithms. This helps to minimize the time requirement to train a learning algorithm as well as to lessen the storage requirement by ignoring the less-informative features. Feature selection can be considered as a combinatorial optimization problem. In this paper, the authors have presented a new feature selection algorithm called Mayfly-Harmony Search (MA-HS) based on two meta-heuristics namely Mayfly Algorithm and Harmony Search. Mayfly Algorithm has not hitherto been used for feature selection problems to the best of the author's knowledge. An S-shaped transfer function is incorporated for converting it into a binary version of Mayfly Algorithm. When different candidate solutions obtained from various regions of the search space using Mayfly Algorithm are taken into the harmony memory and processed by Harmony Search, a superior solution can be ensured. This is the primary reason for proposing a hybrid of Mayfly Algorithm and Harmony Search. Thus, combining harmony search with Mayfly Algorithm leads to an increased exploitation of the search space and an overall improvement in the performance of Mayfly-Harmony Search (MA-HS) algorithm. The proposed algorithm has been applied on 18 UCI datasets and compared with 12 other state-of-the-art meta-heuristic FS methods. Experiments have also been performed on three high-dimensional microarray datasets. The results obtained support the superior performance of the algorithm compared to the other methods. The source code of the proposed algorithm can be found using the link as follows: https://github.com/trin07/MA-HS.
WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition

In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. Firstly, we collect 4M name list and download 260M faces from the Internet. Then, a Cleaning Automatically utilizing Self-Training (CAST) pipeline is devised to purify the tremendous WebFace260M, which is efficient and scalable. To the best of our knowledge, the cleaned WebFace42M is the largest public face recognition training set and we expect to close the data gap between academia and industry. Referring to practical scenarios, Face Recognition Under Inference Time conStraint (FRUITS) protocol and a test set are constructed to comprehensively evaluate face matchers.Equipped with this benchmark, we delve into million-scale face recognition problems. A distributed framework is developed to train face recognition models efficiently without tampering with the performance. Empowered by Web-Face42M, we reduce relative 40% failure rate on the challenging IJB-C set, and rank the 3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior performance compared with public training set. Furthermore, comprehensive baselines are established on our rich-attribute test set under FRUITS-100ms/500ms/1000ms protocol, including MobileNet, EfficientNet, AttentionNet, ResNet, SENet, ResNeXt and RegNet families. Benchmark website is https://www.face-benchmark.org.
A systematic review of blockchain scalability: Issues, solutions, analysis and future research

Blockchain is an inspiring emerging technology that takes much attention from various researchers and companies. The technology offers various benefits such as data security, autonomy, immutability, transparency, and auditability. Hence, blockchain is getting large adoptions for various applications besides cryptocurrencies. Despite these benefits, scalability is a big challenge to blockchain impeding its mainstream adoption. This paper gives a systematic review of blockchain scalability. We follow a systematic process to investigate the research trend on blockchain scalability and review its state of the art. We review the various proposed solutions and methods for blockchain scalability. We also review the performance analysis of blockchain systems. We assess the proposed scalability solutions, deduce future research directions on the blockchain scalability, and finally discuss the blockchain adoption. We hope this paper will serve as a guide for learning and research on blockchain scalability.
Automated language essay scoring systems: a literature review

Background Writing composition is a significant factor for measuring test-takers’ ability in any language exam. However, the assessment (scoring) of these writing compositions or essays is a very challenging process in terms of reliability and time. The need for objective and quick scores has raised the need for a computer system that can automatically grade essay questions targeting specific prompts. Automated Essay Scoring (AES) systems are used to overcome the challenges of scoring writing tasks by using Natural Language Processing (NLP) and machine learning techniques. The purpose of this paper is to review the literature for the AES systems used for grading the essay questions. Methodology We have reviewed the existing literature using Google Scholar, EBSCO and ERIC to search for the terms “AES”, “Automated Essay Scoring”, “Automated Essay Grading”, or “Automatic Essay” for essays written in English language. Two categories have been identified: handcrafted features and automatically featured AES systems. The systems of the former category are closely bonded to the quality of the designed features. On the other hand, the systems of the latter category are based on the automatic learning of the features and relations between an essay and its score without any handcrafted features. We reviewed the systems of the two categories in terms of system primary focus, technique(s) used in the system, the need for training data, instructional application (feedback system), and the correlation between e-scores and human scores. The paper includes three main sections. First, we present a structured literature review of the available Handcrafted Features AES systems. Second, we present a structured literature review of the available Automatic Featuring AES systems. Finally, we draw a set of discussions and conclusions. Results AES models have been found to utilize a broad range of manually-tuned shallow and deep linguistic features. AES systems have many strengths in reducing labor-intensive marking activities, ensuring a consistent application of scoring criteria, and ensuring the objectivity of scoring. Although many techniques have been implemented to improve the AES systems, three primary challenges have been identified. The challenges are lacking of the sense of the rater as a person, the potential that the systems can be deceived into giving a lower or higher score to an essay than it deserves, and the limited ability to assess the creativity of the ideas and propositions and evaluate their practicality. Many techniques have only been used to address the first two challenges.