Reviewerly/exHarmony huggingface dataset:
Evidential classification of incomplete instance based on K-nearest centroid neighbor

Classification of incomplete instance is a challenging problem due to the missing features generally cause uncertainty in the classification result. A new evidential classification method of incomplete instance based on adaptive imputation thanks to the framework of evidence theory. Specifically, the missing values of different incomplete instances in test set are adaptively estimated based on Shannon entropy and K-nearest centroid neighbors (KNCNs) technology. The single or multiple edited instances (with estimations) then are classified by the chosen classifier to get single or multiple classification results for the instances with different discounting (weighting) factors, and a new adaptive global fusion method finally is proposed to unify the different discounted results. The proposed method can well capture the imprecision degree of classification by submitting the instances that are difficult to be classified into a specific class to associate the meta-class and effectively reduce the classification error rates. The effectiveness and robustness of the proposed method has been tested through four experiments with artificial and real datasets.
Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors

The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-K POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.
Question Answering for the Curated Web: Tasks and Methods in QA over Knowledge Bases and Text Collections

Question answering (QA) systems on the Web try to provide crisp answers to information needs posed in natural language, replacing the traditional ranked list of documents. QA, posing a multitude of re
A systemic framework for addressing cybersecurity in construction

Today, the built environment is designed, built, and managed using digital technology, making it increasingly exposed to cyber security risks. Cybersecurity is a general topic, and the construction sector has been borrowing general solutions and frameworks. However, the construction industry is specific and needs a specialized framework that would assist in understanding and managing cybersecurity. We have studied general cybersecurity frameworks, cybersecurity standards, research literature, and first principles of systems theory and process engineering. Drawing from that, we developed an original framework that identifies three kinds of wrongful activities: stealing, lying, and harming. It identifies four elements that can be affected by wrongful activities: information asset, material asset, person, and system. It defines cybersecurity as the absence of the three wrongs across the four kinds of elements. The framework is construction-specific, and as such, a useful tool for senior management to understand security problems and organize security processes. It can lead to better standardization and also helps the researchers to structure future work on the topic. The latter should be concentrated in areas where construction was found to be different: the dynamic and overlapping process and organizational boundaries in the design stage, the exposed shared design information, and the vulnerability of control information of the built environment, particularly in critical infrastructures.
Multi-Swarm Co-Evolution Based Hybrid Intelligent Optimization for Bi-Objective Multi-Workflow Scheduling in the Cloud

Many scientific applications can be well modelled as large-scale workflows. Cloud computing has become a suitable platform for hosting and executing them. Workflow scheduling has gained much attention in recent years. However, since cloud service providers must offer services for multiple users with various QoS demands, scheduling multiple applications with different QoS requirements is highly challenging. This work proposes a Multi-swarm Co-evolution-based Hybrid Intelligent Optimization (MCHO) algorithm for multiple-workflow scheduling to minimize total makespan and cost while meeting the deadline constraint of each workflow. First, we design a multi-swarm co-evolutionary mechanism where three swarms are adopted to sufficiently search for various elite solutions. Second, to improve global search and convergence performance, we embed local and global guiding information into the updating process of a Particle Swarm Optimizer, and develop a swarm cooperation technique. Third, we propose a Genetic Algorithm-based elite enhancement strategy to exploit more non-dominated individuals, and apply the Metropolis Acceptance rule of Simulated Annealing to update the local guiding solution for each swarm so as to prevent it from being stuck into a local optimum at an early stage. Extensive experimental results demonstrate that MCHO outperforms the state-of-art scheduling algorithms with better distributed non-dominated solutions.
A New General Type-2 Fuzzy Predictive Scheme for PID Tuning

The proportional-integral-derivative controller is widely used in various industrial applications. But, in many noisy problems the strong methods are needed to optimize the proportional-integral-derivative parameters. In this paper, a novel method is introduced for adjusting the proportional-integral-derivative parameters through the model predictive control and generalized type-2 fuzzy-logic systems. The rules of suggested fuzzy system are online adjusted and the parameters of proportional-integral-derivative are tuned based on the fuzzy model such that a cost function to be minimized. The designed controller is applied on continuous stirred tank reactor and the performance is compared with other traditional approaches. The main advantages are that the accuracy is improved by online modeling and optimization and a predictive scheme is added to the conventional proportional-integral-derivative controller.
Labeled PSI from Homomorphic Encryption with Reduced Computation and Communication

It is known that fully homomorphic encryption (FHE) can be used to build efficient (labeled) Private Set Intersection protocols in the unbalanced setting, where one of the sets is much larger than the other~(Chen et al. (CCS'17, CCS'18)). In this paper we demonstrate multiple algorithmic improvements upon these works. In particular, our protocol has an asymptotically better computation cost, requiring only O(√|X| ) homomorphic multiplications, and communication complexity sublinear in the larger set size|X|. We demonstrate that our protocol is significantly better than that of Chen et al. (CCS'18) for many practical parameters, especially in terms of online communication cost. For example, when intersecting $228 and 2048 item sets, our protocol reduces the online computation time by more than 71% and communication by more than 63%. When intersecting 224 and 4096 item sets, our protocol reduces the online computation time by 27% and communication by 63%. Our comparison to other state-of-the-art unbalanced PSI protocols shows that our protocol has the best total communication complexity when |X| ≥ 224. For labeled PSI our protocol also outperforms Chen et al. (CCS'18). When intersecting 220 and 256 item sets, with the larger set having associated 288-byte labels, our protocol reduces the online computation time by more than 67% and communication by 34%. Finally, we demonstrate a modification that results in nearly constant communication cost in the larger set size |X|, but impractically high computation complexity on today's CPUs. For example, to intersect a 210-item set with sets of size 222, 224, or 226, our proof-of-concept implementation requires only 0.76 MB of online communication, which is more than a 24-fold improvement over Chen et al. (CCS'18).
Transformer-based approach for joint handwriting and named entity recognition in historical document

The extraction of relevant information carried out by named entities in handwriting documents is still a challenging task. Unlike traditional information extraction approaches that usually face text transcription and named entity recognition as separate subsequent tasks, we propose in this paper an end-to-end transformer-based approach to jointly perform these two tasks. The proposed approach operates at the paragraph level, which brings two main benefits. First, it allows the model to avoid unrecoverable early errors due to line segmentation. Second, it allows the model to exploit larger bi-dimensional context information to identify the semantic categories, reaching a higher final prediction accuracy. We also explore different training scenarios to show their effect on the performance and we demonstrate that a two-stage learning strategy can make the model reach a higher final prediction accuracy. As far as we know, this work presents the first approach that adopts the transformer networks for named entity recognition in handwritten documents. We achieve the new state-of-the-art performance in the ICDAR 2017 Information Extraction competition using the Esposalles database, for the complete task, even though the proposed technique does not use any dictionaries, language modeling, or post-processing.
Remote VR Studies: A Framework for Running Virtual Reality Studies Remotely Via Participant-Owned HMDs

We investigate opportunities and challenges of running virtual reality (VR) studies remotely. Today, many consumers own head-mounted displays (HMDs), allowing them to participate in scientific studies from their homes using their own equipment. Researchers can benefit from this approach by being able to recruit study populations normally out of their reach, and to conduct research at times when it is difficult to get people into the lab (cf. the COVID pandemic). In an initial online survey ( N = 227), we assessed HMD owners’ demographics, their VR setups and their attitudes toward remote participation. We then identified different approaches to running remote studies and conducted two case studies for an in-depth understanding. We synthesize our findings into a framework for remote VR studies, discuss strengths and weaknesses of the different approaches, and derive best practices. Our work is valuable for Human-Computer Interaction (HCI) researchers conducting VR studies outside labs.
Deep Adversarial Imitation Reinforcement Learning for QoS-Aware Cloud Job Scheduling

Although cloud computing is one of the promising technologies for online business services, how to schedule real-time cloud jobs with high quality of service (QoS) is still challenging current techniques. With the advancing of machine learning, deep reinforcement learning (DRL) has demonstrated its outstanding capability in dispatching time-sensitive tasks. However, the reinforced rewards in DRL are typically unavailable until the completion of the scheduling for all the jobs. Considering the fact that the trajectory of jobs in cloud is always long, current DRL-based solutions will meet challenges in finding the trajectories with high rewards, and thus would have issues such that the finally trained scheduling policy is suboptimal. To improve the problem, in this article, we propose a more advanced approach called a deep adversarial imitation reinforcement learning (AIRL) framework for scheduling time-sensitive cloud jobs. Specifically, we focus on scheduling user requests in a way to maximize job successful rate along with a significant reduction on job response time. We present the detailed design of our method, and our experimental results demonstrate that AIRL can generally outperform the existing cloud job scheduling approaches, including the DRL-based method, in the presence of different real-time workload and computing resource configurations.
HDR-NeRF: High Dynamic Range Neural Radiance Fields

We present High Dynamic Range Neural Radiance Fields (HDR-NeRF) to recover an HDR radiance field from a set of low dynamic range (LDR) views with different exposures. Using the HDR-NeRF, we are able to generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the simplified physical imaging process, which dictates that the radiance of a scene point transforms to a pixel value in the LDR image with two implicit functions: a radiance field and a tone mapper. The radiance field encodes the scene radiance (values vary from 0 to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$+\infty$</tex> ), which outputs the density and radiance of a ray by giving corresponding ray origin and ray direction. The tone mapper models the mapping process that a ray hitting on the camera sensor becomes a pixel value. The color of the ray is predicted by feeding the radiance and the corresponding exposure time into the tone mapper. We use the classic volume rendering technique to project the output radiance, colors and densities into HDR and LDR images, while only the input LDR images are used as the supervision. We collect a new forward-facing HDR dataset to evaluate the proposed method. Experimental results on synthetic and real-world scenes validate that our method can not only accurately control the exposures of synthesized views but also render views with a high dynamic range.
Promises and challenges of Blockchain in education

Abstract Blockchain is arguably the next technology-mediated socioeconomic mega trend after the ongoing era of Net Neutrality and Big Data. This theoretical paper explores blockchain technology and its impacts on education. It is argued that we cannot take for granted that the network neutrality, popularized accessibility of the Internet and its influence on education will remain as we know it today. Blockchain promises, among others, a greater control over financing and investing in education, implementing instructional projects, a certification/accreditation system and learning. Education blockchain with its distributed ledgers would set novel standards of crypto-learning and crypto-administration that are acceptable across organizations and nations, enhancing thus the objectivity, validity and control of information without being compromised by socio-economic instabilities. The slow rate of adoption of blockchain technology in education reflects the rate in the fields of finance and management but, at the same time, it poses a few critical challenges such as lacking tangible incentives for technology maintenance or ‘blockchain mining’ (inward sustainability) coupled with a rather feeble orientation to collective development of education (outward sustainability).
A decentralized structure to reduce and resolve construction disputes in a hybrid blockchain network

Disputes routinely arise in construction projects, and they have major cost and schedule impacts. Therefore, they need to be either prevented effectively by proper contract and payment execution or resolved efficiently with enhanced resolution processes regarding cost, duration, effort, and transparency. However, current practices are inadequate for both effectiveness and efficiency since they mainly depend on centralized authority. This paper is designed in two folds; first, a generic blockchain-based online dispute resolution platform is tested with two litigation cases collected in 1999 and 2009 to show the need for a construction-focused system. Then, a new construction-specific framework, Decentralized Construction Enabling Transparent Resolution (DCENTR), is proposed to address the unique nature of construction projects. Ultimately, it is shown that dispute likelihood can be minimized through reliable contract and payment execution, and if occurred, construction disputes can be resolved with higher transparency and dramatic savings in effort, time, and cost.
A New Explainable Deep Learning Framework for Cyber Threat Discovery in Industrial IoT Networks

Industrial Internet of Things (IIoT) and Industry 4.0 empower interrelation among manufacturing processes, industrial machines, and utility services. The time-critical data collected from heterogeneous sensing devices are usually communicated to processing points for analysis and aggregation as the basis of IIoT. The IIoTs’ service quality typically depends on data integrity and accuracy, which could be exploited by injecting malicious events, such as false data injection and data poisoning attacks. Thus, effective anomaly recognition and explanation are critical for ensuring quality services and empowering security administrators to interpret the causal reasoning of prediction decisions and underlying data evidence. This study proposes an autoencoder-based detection framework using convolutional and recurrent networks to discover cyber threats in IIoT networks and explain the model. A two-step sliding window (SW) is applied to learn the latent representations of data features better. Malicious points from the raw time series are transformed into fixed-length series through the first-step SW. Every series is converted into continuous-time-reliant subseries via another smaller SW to learn latent representations of malicious events. Fully connected networks use the extracted temporal and spatial features for the classification and explanation of attack events. The empirical results revealed that this framework effectively extracts features that include contexts of malicious patterns. This demonstrated that the proposed framework is robust in detecting malicious events using multiple evaluation metrics and outperforming the contemporary state-of-the-art methods, indicating its suitability as an operative application method in real-world IIoT-based networks.
Graph neural networks with global noise filtering for session-based recommendation

Session-based recommendation leverages anonymous sessions to predict which item a user is most likely to click on next. While previous approaches capture items-transition patterns within current session and neighbor sessions, they do not accurately filter out noise within session or widen the range of feasible data in a more reasonable way. In a current session, the user may accidentally click on an unrelated item, resulting in the fact that, the users’ primary intents from neighbor sessions, may mismatch the current session. Thereby, we propose a new framework, dubbed Graph Neural Networks with Global Noise Filtering for Session-based Recommendation (GNN-GNF), aiming to filter noisy data and exploit items-transition patterns in a more comprehensive and reasonable manner. In simple terms, GNN-GNF contains two parts: data preprocessing and model learning. In data preprocesing, an item-level filter module is used to obtain the main intent of user and a session-level filter module is designed to filter the sessions unrelated to the target session intent by means of edge matching. In model learning, we consider both local-level interest obtained by an aggregation of the items representing the main intent of user within a session, and global-level interest deduced from a global graph. We take two kinds of neighbor aggregations, summation and interactive aggregation, respectively, to iteratively derive the representation of the central node in the global graph. Finally, GNN-GNF concatenates the local and global preference to characterize the current session, towards better recommendation prediction. Experiments on two datasets demonstrate that GNN-GNF can achieve competitive results. The source code is available at: https://github.com/Fenglixia/GNF.
Index Networks

We show that existing upsampling operators in convolutional networks can be unified using the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can often recover boundary details considerably better than other upsampling operators such as bilinear interpolation. By viewing the indices as a function of the feature map, we introduce the concept of 'learning to index', and present a novel index-guided encoder-decoder framework where indices are learned adaptively from data and are used to guide downsampling and upsampling stages, without extra training supervision. At the core of this framework is a new learnable module, termed Index Network (IndexNet), which dynamically generates indices conditioned on the feature map. IndexNet can be used as a plug-in, applicable to almost all convolutional networks that have coupled downsampling and upsampling stages, enabling the networks to dynamically capture variations of local patterns. In particular, we instantiate and investigate five families of IndexNet. We highlight their superiority in delivering spatial information over other upsampling operators with experiments on synthetic data, and demonstrate their effectiveness on four dense prediction tasks, including image matting, image denoising, semantic segmentation, and monocular depth estimation. Code and models are available at https://git.io/IndexNet.
Blockchain technology design in accounting: Game changer to tackle fraud or technological fairy tale?

Purpose This paper aims to design, build and evaluate a blockchain platform in the accounting domain, taking an ecosystem perspective. To achieve this aim, the research provides evidence for developing a decentralised architecture rooted on blockchain technology, designing a proof of concept and modelling an accounting blockchain-based system. Design/methodology/approach Moving from the analysis of previous literature and leveraging on the design science approach, this paper provides a framework grounded on the main pillars of blockchain and accounting functions, identifying technical and non-technical issues that must be addressed embrace blockchain technology's full potential. Findings We propose and discuss a conceptual framework for a blockchain-based accounting context, moving from the identification of a typical accounting scenario. The framework is organised around three scalable levels: the first level is a technological infrastructure based on a distributed database with peer-to-peer storage; second, in the intermediate level, increasing control levels are assured through permissions and validation and third, in the higher level, the system provides the integration of business and security applications. The deployment of this system relies on a private network of nodes that validates transactions. Practical implications The proposed conceptual framework about blockchain development in accounting allows closing the knowledge gap between blockchain developers and accounting experts by suggesting technological and strategic issues for practitioners. Originality/value We provide practical guidelines to design and adopt blockchain in the accounting domain.