Reviewerly/exHarmony huggingface dataset:
A blockchain-based secured and trusted framework for information propagation on online social networks

The online social networks facilitate naturally for the users to share information. On these platforms, each user shares information based on his or her interests. The particular information being shared by a user may be legitimate or fake. Sometimes a misinformation, propagated by users and group can create chaos or in some cases, might leads to cases of riots. Nowadays the third party like ALT news and Cobrapost check the information authenticity, but it takes too much time to validate the news. Therefore, a robust and new system is required to check the information authenticity within the network, to stop the propagation of misinformation. In this paper, we propose a blockchain based framework for sharing the information securely at the peer level. In the blockchain model, a chain is created by combining blocks of information. Each node of network propagates the information based on its credibility to its peer nodes. The credibility of a node will vary according to the respective information. Trust is calculated between sender and receiver in two ways:(i) Local trust used for sharing information at the peer level and (ii) global trust is used for a credibility check of each user in the network. We evaluate our framework using real dataset derived from Facebook. Our approach achieves an accuracy of 83% which shows the effectiveness of our proposed framework.
Variational quantum eigensolver for dynamic correlation functions

Recent practical approaches for the use of current generation noisy quantum devices in the simulation of quantum many-body problems have been dominated by the use of a variational quantum eigensolver (VQE). These coupled quantum-classical algorithms leverage the ability to perform many repeated measurements to avoid the currently prohibitive gate depths often required for exact quantum algorithms, with the restriction of a parameterized circuit to describe the states of interest. In this work, we show how the calculation of zero-temperature dynamic correlation functions defining the linear response characteristics of quantum systems can also be recast into a modified VQE algorithm, which can be incorporated into the current variational quantum infrastructure. This allows for these important physical expectation values describing the dynamics of the system to be directly converged on the frequency axis, and they approach exactness over all frequencies as the flexibility of the parameterization increases. The frequency resolution hence does not explicitly scale with gate depth, which is approximately twice as deep as a ground state VQE. We apply the method to compute the single-particle Green's function of ab initio dihydrogen and lithium hydride molecules, and demonstrate the use of a practical active space embedding approach to extend to larger systems. While currently limited by the fidelity of two-qubit gates, whose number is increased compared to the ground state algorithm on current devices, we believe the approach shows potential for the extraction of frequency dynamics of correlated systems on near-term quantum processors.
Full Page Handwriting Recognition via Image to Sequence Extraction

We present a Neural Network based Handwritten Text Recognition (HTR) model architecture that can be trained to recognize full pages of handwritten or printed text without image segmentation. Being based on Image to Sequence architecture, it can extract text present in an image and then sequence it correctly without imposing any constraints regarding orientation, layout and size of text and non-text. Further, it can also be trained to generate auxiliary markup related to formatting, layout and content. We use character level vocabulary, thereby enabling language and terminology of any subject. The model achieves a new state-of-art in paragraph level recognition on the IAM dataset. When evaluated on scans of real world handwritten free form test answers - beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR cloud APIs. It is deployed in production as part of a commercial web application.
The impact of business process reengineering on organizational performance during the coronavirus pandemic: moderating role of strategic thinking

Purpose This paper aims to investigate the impact of business process reengineering on organizational performance in the Malaysian electronics manufacturing industry during the coronavirus pandemic. It also studied the moderating effect of strategic thinking on the relationship between these two concepts. Design/methodology/approach Data of 103 samples are obtained from a survey of the electronics manufacturing companies listed in the Federation of Malaysia Manufacturers’ directory. Data is analysed using partial least squares structural equation modelling. Findings The results show that business process reengineering dimensions, i.e. top management commitment, organizational readiness for change, information technology capabilities and people management have significant positive impacts on organizational performance; whereas organizational structure and other abovementioned dimensions were found to have a much stronger impact on organizational performance whilst strategic thinking exists in the organization. Practical implications As the studied firms have been affected by the pandemic, the results will be useful for managers of the firms in the Malaysian electronics manufacturing industry by highlighting the dimensions of business process reengineering that can have a positive and significant impact on the organizational performance and by advising them to incorporate strategic thinking. Originality/value The results extend the literature on business process reengineering dimensions, which impact organizational performance by empirically testing the relationship of those dimensions on the performance, with strategic thinking as a moderating variable in Malaysian electronics manufacturing companies. From the knowledge, studying strategic thinking as a moderator in the relationship between business process reengineering and organizational performance in electronics manufacturing companies in Malaysia is unique, especially during a global crisis, i.e. the pandemic.
Rapid and unconditional parametric reset protocol for tunable superconducting qubits

Qubit initialization is a critical task in quantum computation and communication. Extensive efforts have been made to achieve this with high speed, efficiency and scalability. However, previous approaches have either been measurement-based and required fast feedback, suffered from crosstalk or required sophisticated calibration. Here, we report a fast and high-fidelity reset scheme, avoiding the issues above without any additional chip architecture. By modulating the flux through a transmon qubit, we realize a swap between the qubit and its readout resonator that suppresses the excited state population to 0.08% $\pm$ 0.08% within 34 ns (284 ns if photon depletion of the resonator is required). Furthermore, our approach (i) can achieve effective second excited state depletion, (ii) has negligible effects on neighbouring qubits, and (iii) offers a way to entangle the qubit with an itinerant single photon, useful in quantum communication applications.
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to different timestamps.TKG reasoning aims to predict potential facts in the future given the historical KG sequences.One key of this task is to mine and understand evolutional patterns of facts from these sequences.The evolutional patterns are complex in two aspects, lengthdiversity and time-variability.Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length.Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on.Thus, we propose a new model, called Complex Evolutional Network (CEN), which uses a length-aware Convolutional Neural Network (CNN) to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy.Besides, we propose to learn the model under the online setting so that it can adapt to the changes of evolutional patterns over time.Extensive experiments demonstrate that CEN obtains substantial performance improvement under both the traditional offline and the proposed online settings.
A Calibrated Individual Semantic Based Failure Mode and Effect Analysis and Its Application in Industrial Internet Platform

This article proposes a calibrated individual semantic (CIS)-based failure mode and effect analysis (FMEA) to deal with the risk evaluation of industrial internet platforms (IIP) from four perspectives: network security, data processing capability, equipment performance, and openness. The novelty of the CIS model is based on the deviation between linguistic terms and numerical values to calibrate linguistic scales of decision-makers (DMs). Not only can it handle situations in which different DMs have different understandings of the same term, but it is also suitable for multiple attributes decision-making with uncertainty. In addition, this new FMEA framework considers the consensus-reaching process as a way to eliminate the disagreement among DMs from different departments. Finally, a comparison between the proposed and traditional method is presented to illustrate the advantages of new method.
In-memory photonic dot-product engine with electrically programmable weight banks

Abstract Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic–electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic–electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ/dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (≥87.36) that leads to an enhanced computing accuracy (standard deviation σ ≤ 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%.
A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

Limited resources and harsh deployment environments may cause raw observations collected by sensor nodes to have poor data quality and reliability, which will influence the accuracy of the analysis and decision making in wireless sensor networks (WSNs). Therefore, anomaly detection must be implemented on the data collected by nodes. Support vector data description based on spatiotemporal and attribute correlations (STASVDD) can efficiently detect outliers. A novel optimization method based on STASVDD (N-STASVDD) is put forward in this paper. The proposed method considers that outliers can independently occur in each attribute when the collected data vectors are independent and identically distributed in WSNs. The proposed method applies the concept of core-sets to reduce the computational complexity of the quadratic programming problem in STASVDD, consequently reducing the energy consumption of resources-constrained WSNs. In addition, comparing the distributed and centralized detection approach of this method, the results show that the distributed approach has better performance because it relieves the communication burden. Extensive experiments were performed on both synthetic and real WSNs datasets. Results revealed that N-STASVDD achieves low time complexity and high detection accuracy.
Gated bidirectional feature pyramid network for accurate one-shot detection

Despite recent advances in machine learning, it is still challenging to realize real-time and accurate detection in images. The recently proposed StairNet detector (Sanghyun et al. in Proceedings of winter conference on applications of computer vision (WACV), 2018), one of the strongest one-stage detectors, tackles this issue by using a SSD in conjunction with a top-down enrichment module. However, the StairNet approach misses the finer localization information which can be obtained from the lower layer and lacks a feature selection mechanism, which can lead to suboptimal features during the merging step. In this paper, we propose what is termed the gated bidirectional feature pyramid network (GBFPN), a simple and effective architecture that provides a significant improvement over the baseline model, StairNet. The overall network is composed of three parts: a bottom-up pathway, a top-down pathway, and a gating module. Given the multi-scale feature pyramid of deep convolutional network, two separate pathways introduce both finer localization cues and high-level semantics. In each pathway, the gating module dynamically re-weights the features before the combining step, transmitting only the informative features. Placing GBFPN on top of a basic one-stage detector SSD, our method shows state-of-the-art results.
Fast and Scalable VMM Live Upgrade in Large Cloud Infrastructure

High availability is the most important and challenging problem for cloud providers. However, virtual machine monitor (VMM), a crucial component of the cloud infrastructure, has to be frequently updated and restarted to add security patches and new features, undermining high availability. There are two existing live update methods to improve the cloud availability: kernel live patching and Virtual Machine (VM) live migration. However, they both have serious drawbacks that impair their usefulness in the large cloud infrastructure: kernel live patching cannot handle complex changes (e.g., changes to persistent data structures); and VM live migration may incur unacceptably long delays when migrating millions of VMs in the whole cloud, for example, to deploy urgent security patches.
Runtime Object Lifetime Profiler for Latency Sensitive Big Data Applications

Latency sensitive services such as credit-card fraud detection and website targeted advertisement rely on Big Data platforms which run on top of memory managed runtimes, such as the Java Virtual Machine (JVM). These platforms, however, suffer from unpredictable and unacceptably high pause times due to inadequate memory management decisions (e.g., allocating objects with very different lifetimes next to each other, resulting in severe memory fragmentation). This leads to frequent and long application pause times, breaking Service Level Agreements (SLAs). This problem has been previously identified, and results show that current memory management techniques are ill-suited for applications that hold in memory massive amounts of long-lived objects (which is the case for a wide spectrum of Big Data applications).
Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis Through Audio Analysis

The field of Text-to-Speech has experienced huge improvements last years benefiting from deep learning techniques. Producing realistic speech becomes possible now. As a consequence, the research on the control of the expressiveness, allowing to generate speech in different styles or manners, has attracted increasing attention lately. Systems able to control style have been developed and show impressive results. However the control parameters often consist of latent variables and remain complex to interpret. In this paper, we analyze and compare different latent spaces and obtain an interpretation of their influence on expressive speech. This will enable the possibility to build controllable speech synthesis systems with an understandable behaviour.
Reinforcement Learning Based Meta-Path Discovery in Large-Scale Heterogeneous Information Networks

Meta-paths are important tools for a wide variety of data mining and network analysis tasks in Heterogeneous Information Networks (HINs), due to their flexibility and interpretability to capture the complex semantic relation among objects. To date, most HIN analysis still relies on hand-crafting meta-paths, which requires rich domain knowledge that is extremely difficult to obtain in complex, large-scale, and schema-rich HINs. In this work, we present a novel framework, Meta-path Discovery with Reinforcement Learning (MPDRL), to identify informative meta-paths from complex and large-scale HINs. To capture different semantic information between objects, we propose a novel multi-hop reasoning strategy in a reinforcement learning framework which aims to infer the next promising relation that links a source entity to a target entity. To improve the efficiency, moreover, we develop a type context representation embedded approach to scale the RL framework to handle million-scale HINs. As multi-hop reasoning generates rich meta-paths with various length, we further perform a meta-path induction step to summarize the important meta-paths using Lowest Common Ancestor principle. Experimental results on two large-scale HINs, Yago and NELL, validate our approach and demonstrate that our algorithm not only achieves superior performance in the link prediction task, but also identifies useful meta-paths that would have been ignored by human experts.
Building an awareness-centered information security policy compliance model

Purpose The purpose of this paper is to build an awareness-centered information security policy (ISP) compliance model, asserting that awareness is the key to ISP compliance and that awareness depends upon several variables that influence successful ISP compliance. Design/methodology/approach The authors built a model with seven constructs, i.e., leadership, trusting beliefs, information security issues awareness (ISIA), ISP awareness, understanding resource vulnerability, self-efficacy (SE) and intention to comply. Seven hypotheses were stated. A sample of 285 non-management employees was used from various organizations in the USA. The authors used path modeling to analyze the data. Findings The findings indicated that IS awareness depends on effective organizational leadership and elevated employees’ trusting beliefs. The understanding of resource vulnerability (URV) and SE are influenced by IS awareness resulting from effective leadership and elevated employees’ trusting beliefs which guide employees to comply with ISP requirements. Practical implications Practical implications were aimed at organizations embracing an awareness-centered information security compliance program to secure organizations’ assets against threats by implementing various security education and training awareness programs. Originality/value This paper asserts that awareness is central to ISP compliance. Leadership and trusting beliefs variables play significant roles in the information security awareness which in turn positively affect employees’ URV and SE variables leading employees to comply with the ISP requirements.
Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor

Racquet sports can provide positive benefits for human healthcare. A reliable detection device that can effectively distinguish movement with similar sub-features is therefore needed. In this paper, a racquet sports recognition wristband system and a multilayer hybrid clustering model are proposed to achieve reliable activity recognition and perform number counting. Additionally, a Bluetooth mesh network enables communication between a phone and wristband, and sets-up the connection between multiple devices. This allows users to track their exercise through the phone and share information with other players and referees. Considering the complexity of the classification algorithm and the user-friendliness of the measurement system, the improved multi-layer hybrid clustering model applies three-level K-means clustering to optimize feature extraction and segmentation and then uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to determine the feature center of different movements. The model can identify unlabeled and noisy data without data calibration and is suitable for smartwatches to recognize multiple racquet sports. The proposed system shows better recognition results and is verified in practical experiments.
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarification questions, potential answers and user feedback in information-seeking conversations is critical for retrieving good responses. In this paper, we analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model “IART”, which refers to “Intent-Aware Ranking with Transformers”. IART is built on top of the integration of user intent modeling and language representation learning with the Transformer architecture, which relies entirely on a self-attention mechanism instead of recurrent nets [35]. It incorporates intent-aware utterance attention to derive an importance weighting scheme of utterances in conversation context with the aim of better conversation history understanding. We conduct extensive experiments with three information-seeking conversation data sets including both standard benchmarks and commercial data. Our proposed model outperforms all baseline methods with respect to a variety of metrics. We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.
Learning Over Multitask Graphs—Part I: Stability Analysis

This paper formulates a multitask optimization problem where agents in the network have individual objectives to meet, or individual parameter vectors to estimate, subject to a smoothness condition over the graph.The smoothness condition softens the transition in the tasks among adjacent nodes and allows incorporating information about the graph structure into the solution of the inference problem.A diffusion strategy is devised that responds to streaming data and employs stochastic approximations in place of actual gradient vectors, which are generally unavailable.The approach relies on minimizing a global cost consisting of the aggregate sum of individual costs regularized by a term that promotes smoothness.We show in this Part I of the work, under conditions on the step-size parameter, that the adaptive strategy induces a contraction mapping and leads to small estimation errors on the order of the small step-size.The results in the accompanying Part II will reveal explicitly the influence of the network topology and the regularization strength on the network performance and will provide insights into the design of effective multitask strategies for distributed inference over networks.