Reviewerly/exHarmony huggingface dataset:
Disaster Risk Management Through the DesignSafe Cyberinfrastructure

Abstract DesignSafe addresses the challenges of supporting integrative data-driven research in natural hazards engineering. It is an end-to-end data management, communications, and analysis platform where users collect, generate, analyze, curate, and publish large data sets from a variety of sources, including experiments, simulations, field research, and post-disaster reconnaissance. DesignSafe achieves key objectives through: (1) integration with high performance and cloud-computing resources to support the computational needs of the regional risk assessment community; (2) the possibility to curate and publish diverse data structures emphasizing relationships and understandability; and (3) facilitation of real time communications during natural hazards events and disasters for data and information sharing. The resultant services and tools shorten data cycles for resiliency evaluation, risk modeling validation, and forensic studies. This article illustrates salient features of the cyberinfrastructure. It summarizes its design principles, architecture, and functionalities. The focus is on case studies to show the impact of DesignSafe on the disaster risk community. The Next Generation Liquefaction project collects and standardizes case histories of earthquake-induced soil liquefaction into a relational database—DesignSafe—to permit users to interact with the data. Researchers can correlate in DesignSafe building dynamic characteristics based on data from building sensors, with observed damage based on ground motion measurements. Reconnaissance groups upload, curate, and publish wind, seismic, and coastal damage data they gather during field reconnaissance missions, so these datasets are available shortly after a disaster. As a part of the education and community outreach efforts of DesignSafe, training materials and collaboration space are also offered to the disaster risk management community.
Fashion inspiration and technology: virtual reality in an experimental apparel design classroom

This study aimed to understand how apparel design students perceived virtual reality (VR) technology as a source of inspiration for garment design. Students interacted with VR technology as a part of an experimental apparel design project in an upper-level university course. Open-ended questionnaires, documentation, and reflections provided data for the study. A majority of the students indicated VR technology was beneficial as inspiration, and students said they would recommend VR use in future classes. After participating in this study, students expressed interest in exploring other interactive apps in future coursework that could allow students to draft patterns and design in VR. Student comments about interactions with the technology were useful and will inform future curriculum development. Little research has been published using VR in university apparel design classrooms, and this study serves as a starting point to gauge future interest in VR as a part of the apparel design process.
Application-Oriented Block Generation for Consortium Blockchain-Based IoT Systems With Dynamic Device Management

Due to its salient features, such as immutability and auditability, blockchain is becoming more integrated into the Internet of Things (IoT) for enhancing security and developing a decentralized IoT framework. However, different IoT applications require different transaction processing performance, which brings challenges to the convergence of blockchains in IoT. Moreover, the membership of a distributed IoT system may fluctuate when an IoT device joins or leaves the system. The dynamic nature of IoT systems also introduces new challenges for device management. Accordingly, we propose an application-oriented block generation (AOBG) scheme for blockchain-enabled IoT with dynamic device management and conditional traceability. Specifically, we first construct a framework for a consortium blockchain-based IoT system, including structures for application-oriented transactions and blocks, and consensus mechanism. We present different miners, respectively, for processing urgent and ordinary transactions adaptively with applications. Then, an AOBG protocol is proposed for this framework based on group signature. The group signature is used to achieve anonymity, traceability, and nonframeability. Combining time-bound keys in group signature with node accounts in blockchain, the proposed scheme can realize efficient transaction verification, dynamic device management, conditional traceability with data security, and privacy preservation. Extensive experiments demonstrate high efficiency of the proposed scheme.
Grouped Multi-Scale Network for Real-World Image Denoising

Deep learning-based methods have surpassed the traditional methods in image denoising due to the prior knowledge accumulated on the large dataset. Because of the difference between additive Gaussian white noise (AWGN) and real noise, researchers have recently paid more attention to real-world image denoising. Based on the characteristics of real-world image denoising, we revise the method of noise synthesis and design a novel network to make full use of the multi-scale context. Our proposed approach dramatically surpasses all the contemporary approaches on the sRGB track of the DND benchmark [1] with PSNR over 40 dB and SSIM over 0.96.
Discovering Maximal Periodic-Frequent Patterns in Very Large Temporal Databases

Periodic-frequent pattern mining (PFPM) is an important data mining model having many real-world applications. However, the successful industrial application of this model has been hindered by the problem of combinatorial explosion of patterns, that is the generation of too many redundant patterns, most of which may be useless to the user. To address this problem, this paper proposes a novel model of maximal periodic- frequent pattern that may exist in a temporal database. A new pattern-growth algorithm, called Maximum Periodic-Frequent Pattern-growth (maxPFP-growth), has also been introduced to efficiently find all desired patterns in the data. Experimental results demonstrate that maxPFP-growth is not only memory and runtime efficient, but also highly scalable as well. The usefulness of our model has also been demonstrated with a case study on traffic congestion analytics.
Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?

Abstract While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated. In this work, we introduce a framework to quantify the value of explanations via the accuracy gains that they confer on a student model trained to simulate a teacher model. Crucially, the explanations are available to the student during training, but are not available at test time. Compared with prior proposals, our approach is less easily gamed, enabling principled, automatic, model-agnostic evaluation of attributions. Using our framework, we compare numerous attribution methods for text classification and question answering, and observe quantitative differences that are consistent (to a moderate to high degree) across different student model architectures and learning strategies.1
Blockchain-Enabled Integrated Market Platform for Contract Production

Contract production and transaction are very common and important since it can greatly reduce future uncertainty and is well-organized by buyers, sellers, and trusted third parties (TTPs). However, current commodity trading systems (such as spot, futures, and forward contract) are still traditional centralized structures, which means that there may always be concerned about the single point of failure and data corruption. Besides, even though there already exists fragmentary decentralized applications (DApps) for the marketplace, order management, resale, delivery, financing, and insurance, they are not integrated for operating contract production and transaction comprehensively. In this work, a blockchain-enabled integrated marketing platform (BeIMP) is proposed for contract production and transactions. BeIMP is a consortium blockchain (CsBc) framework that enables better safety, efficiency, and interoperability among stakeholders. BeIMP can directly match producers and consumers and reduce the problem of intermediaries' improper market operation. BeIMP has a three-tier risk diversification mechanism. First, both parties can know the quantity and price according to the contract specification (CSpec) in advance to avoid future uncertainty. Second, the buyers can resell the established order if they need, thereby diversifying the risk. Third, the concept of insurance is introduced to reduce force majeure and other systemic risks. BeIMP can also help producers get the loan from the financial institution (FI) if they need fund for production. We implement and demonstrate the prototype in contract farming (CF) first and discuss its generalizability to other scenarios. Experiment results show that the smart contract (SC) function is stable enough and the proof of authority (PoA) has the advantage in throughput and can give users a better experience.
Anti-bandit Neural Architecture Search for Model Defense

Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks. In this paper, we defend against adversarial attacks using neural architecture search (NAS) which is based on a comprehensive search of denoising blocks, weight-free operations, Gabor filters and convolutions. The resulting anti-bandit NAS (ABanditNAS) incorporates a new operation evaluation measure and search process based on the lower and upper confidence bounds (LCB and UCB). Unlike the conventional bandit algorithm using UCB for evaluation only, we use UCB to abandon arms for search efficiency and LCB for a fair competition between arms. Extensive experiments demonstrate that ABanditNAS is about twice as fast as the state-of-the-art NAS method, while achieving an $$8.73\%$$ improvement over prior arts on CIFAR-10 under PGD-7.
Achieving Secure Search over Encrypted Data for e-Commerce

The advances of Internet technology has resulted in the rapid and pervasive development of e-commerce, which has not only changed the production and operation mode of many enterprises, but also affected the economic development mode of the whole society. This trend has incurred a strong need to store and process large amounts of sensitive data. The traditional data storage and search solutions cannot meet such requirements. To tackle this problem, in this article, we proposed Consortium Blockchain-based Distributed Secure Search (CBDSS) Scheme over encrypted data in e-Commerce environment. By integrating the blockchain and searchable encryption model, sensitive data can be effectively protected. The consortium blockchain can ensure that only authorized nodes can join the system. To fairly assign nodes for the search tasks, we developed an endorsement strategy in which two agent roles are set up to divide and match the search tasks with the virtual resources according to the load capacity of each node. The security analysis and experiments are conducted to evaluate the performance of our proposed scheme. The evaluation results have proved the reliability and security of our scheme over existing methods.
A Combined Framework of InterPlanetary File System and Blockchain to Securely Manage Electronic Medical Records

Blockchain has become a popular research area since its introduction, as the benefit has been seen in various industries. It could greatly benefit the healthcare sector as it offers anonymity, immutability, and decentralization of data. Electronic Medical Record (EMR) systems face crucial problems regarding data security, accessibility, and management. A great deal of security threats relating to patient privacy involves unauthorized access to medical records, misuse of patient’s disease reports, and so on. To address these issues, we have proposed a blockchain combined with the InterPlanetary File System solution framework for EMR in the healthcare industry. The aim is to implement the blockchain for EMR and provide access rules for various users of it. The proposed framework, while protecting patient privacy, allows convenient access by approved authorities such as healthcare providers to medical data.
Application of Blockchain Technology in Dynamic Resource Management of Next Generation Networks

With the advent of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies, the networking infrastructures are becoming increasingly agile in their attempts to offer the quality of services needed by the users, maximizing the efficiency of infrastructure utilization. This in essence mandates the statistical multiplexing of demands across the infrastructures of different Network Providers (NPs), which would allow them to cope with the increasing demand, upgrading their infrastructures at a slower pace. However, to enjoy the benefits of statistical multiplexing, a trusted authority to govern it would be required. At the same time, blockchain technology aspires to offer a solid advantage in such untrusted environments, enabling the development of decentralized solutions that ensure the integrity and immutability of the information stored in the digital ledger. To this end, in this paper, we propose a blockchain-based solution that allows NPs to trade their (processing and networking) resources. We implemented the solution in a test-bed deployed on the cloud and we present the gathered performance results, showing that a blockchain-based solution is feasible and appropriate. We also discuss further improvements and challenges.
LeakyPick: IoT Audio Spy Detector

Manufacturers of smart home Internet of Things (IoT) devices are increasingly adding voice assistant and audio monitoring features to a wide range of devices including smart speakers, televisions, thermostats, security systems, and doorbells. Consequently, many of these devices are equipped with microphones, raising significant privacy concerns: users may not always be aware of when audio recordings are sent to the cloud, or who may gain access to the recordings. In this paper, we present the LeakyPick architecture that enables the detection of the smart home devices that stream recorded audio to the Internet in response to observing a sound. Our proof-of-concept is a LeakyPick device that is placed in a user’s smart home and periodically “probes” other devices in its environment and monitors the subsequent network traffic for statistical patterns that indicate audio transmission. Our prototype is built on a Raspberry Pi for less than USD $40 and has a measurement accuracy of 94% in detecting audio transmissions for a collection of 8 devices with voice assistant capabilities. Furthermore, we used LeakyPick to identify 89 words that an Amazon Echo Dot misinterprets as its wake-word, resulting in unexpected audio transmission. LeakyPick provides a cost effective approach to help regular consumers monitor their homes for sound-triggered devices that unexpectedly transmit audio to the cloud.
IoT as a Health Guide Tool

Abstract Now-a-days, food is very important in order to feed to the live people or animals. It is required to know the statistics about the food in terms of proteins, fat, and factors that increase immunity power. Hence, the proposed methodology called food IoT is developed that will scan the food and displays the statistics that guide details to the customer or client. The details describe about calories that are helpful, the details that will increase fat, the details like food is poisoned, and from the provided statistics, any one could judge the food is useful to feed. This proposed application guarantees the good health because health depends on feeding of food. This is more proactive because it guides which items to be added in case there is less kind of positive minerals and less proteins identified. Hence, a sensor to be required that will display the statistics about the food and is more helpful to the people in order to know category of factors involved in the food. The timed out and mineral less food is identified and could come to be known in this proposed approach. Also, this proposed approach helpful in terms of alerting as a guide based on past experience and reports future predictions about the health as like future weather predictions.
Voice gender recognition under unconstrained environments using self-attention

Voice Gender Recognition is a non-trivial task that is extensively studied in the literature, however, when the voice gets surrounded by noises and unconstrained environments, the task becomes more challenging. This paper presents two Self-Attention-based models to deliver an end-to-end voice gender recognition system under unconstrained environments. The first model consists of a stack of six self-attention layers and a dense layer. The second model adds a set of convolution layers and six inception-residual blocks to the first model before the self-attention layers. These models depend on Mel-frequency cepstral coefficients (MFCC) as a representation of the audio data, and Logistic Regression for classification. The experiments were done under unconstrained environments such as background noise and different languages, accents, ages and emotional states of the speakers. The results demonstrate that the proposed models were able to achieve an accuracy of 95.11%, 96.23%, respectively. These models achieved superior performance in all criteria and are believed to be state-of-the-art for Voice Gender Recognition under unconstrained environments.
Exploring participant engagement during an astrophysics virtual reality experience at a science festival

Virtual reality applications turn abstract concepts into experienceable phenomena and present exciting opportunities to transform science education and public outreach practices. While research has started to look into the affordances of virtual reality (VR) in the formal science education context, the potential of these technologies to enhance public engagement with science is largely unexplored. To improve the way that VR may be used in informal learning and public outreach contexts, the purpose of our study was to undertake evidence-based investigations that shed light onto the relationship between VR and public engagement. Aiming to identify and develop the benefits of VR technologies, we propose a conceptual framework for engagement with VR at a science festival that comprises four aspects of participant activity: immersion, facilitation, collaboration, and visualisation. This framework guided the research design of our exploratory case study of one VR tour at a science festival. Data included visitor surveys, video recordings, VR screen captures, and focus group interviews with outreach and science professionals. Our findings reveal important ways that VR supports visitor engagement at a science festival. More generally, these findings and our framework contribute to the ongoing efforts of engaging the public with science in more diverse informal learning contexts.
BioRel: towards large-scale biomedical relation extraction

Abstract Background Although biomedical publications and literature are growing rapidly, there still lacks structured knowledge that can be easily processed by computer programs. In order to extract such knowledge from plain text and transform them into structural form, the relation extraction problem becomes an important issue. Datasets play a critical role in the development of relation extraction methods. However, existing relation extraction datasets in biomedical domain are mainly human-annotated, whose scales are usually limited due to their labor-intensive and time-consuming nature. Results We construct BioRel, a large-scale dataset for biomedical relation extraction problem, by using Unified Medical Language System as knowledge base and Medline as corpus. We first identify mentions of entities in sentences of Medline and link them to Unified Medical Language System with Metamap. Then, we assign each sentence a relation label by using distant supervision. Finally, we adapt the state-of-the-art deep learning and statistical machine learning methods as baseline models and conduct comprehensive experiments on the BioRel dataset. Conclusions Based on the extensive experimental results, we have shown that BioRel is a suitable large-scale datasets for biomedical relation extraction, which provides both reasonable baseline performance and many remaining challenges for both deep learning and statistical methods.
Locality-Constrained Discriminative Matrix Regression for Robust Face Identification

Regression-based methods have been widely applied in face identification, which attempts to approximately represent a query sample as a linear combination of all training samples. Recently, a matrix regression model based on nuclear norm has been proposed and shown strong robustness to structural noises. However, it may ignore two important issues: the label information and local relationship of data. In this article, a novel robust representation method called locality-constrained discriminative matrix regression (LDMR) is proposed, which takes label information and locality structure into account. Instead of focusing on the representation coefficients, LDMR directly imposes constraints on representation components by fully considering the label information, which has a closer connection to identification process. The locality structure characterized by subspace distances is used to learn class weights, and the correct class is forced to make more contribution to representation. Furthermore, the class weights are also incorporated into a competitive constraint on the representation components, which reduces the pairwise correlations between different classes and enhances the competitive relationships among all classes. An iterative optimization algorithm is presented to solve LDMR. Experiments on several benchmark data sets demonstrate that LDMR outperforms some state-of-the-art regression-based methods.
A spatial filtering inspired three-way clustering approach with application to outlier detection

Three-way clustering provides an effective framework for clustering of data in the presence of uncertain, imprecise and incomplete data. In this article, we used ideas inspired from two commonly used spatial filters from image processing called minimum and maximum filters to construct a three-way clustering approach named RE3WC and explore its application in outlier detection. A three-way cluster is based on a pair of sets known as the core and support of a cluster. Given the results of a hard clustering algorithm in the form of hard clusters, RE3WC uses reduction and elevation operations to shrink and enlarge a hard cluster into the core and support of a three-way cluster. The two sets are then used to obtain the three regions of a three-way cluster namely, inside, outside and partial regions. Experimental results on CHAMELEON and other similar datasets indicate that the RE3WC can detect an additional 2.5% to 4.6% of objects as outliers that went undetected with clustering algorithms that detect outliers. The RE3WC results in more compact and precise clusters when applied on top of clustering algorithms that only provide partitioning of the data. Finally, RE3WC produces comparable results to some of the notable approaches such as LOF, LoOP, ABOD and IF.