Reviewerly/exHarmony huggingface dataset:
Technological Ecosystems in Care and Assistance: A Systematic Literature Review

Applying the concepts of technological ecosystems to the care and assistance domain is an emerging field that has gained interest during the last years, as they allow to describe the complex relationships between actors in a technologically boosted care domain. In that context, this paper presents a systematic review and mapping of the literature to identify, analyse and classify the published research carried out to provide care and assistance services under a technological ecosystems' perspective. Thirty-seven papers were identified in the literature as relevant and analysed in detail (between 2003⁻2018). The main findings show that it is indeed an emerging field, as few of the found ecosystem proposals have been developed in the real world nor have they been tested with real users. In addition, a lot of research to date reports the proposal of platform-centric architectures developed over existing platforms not specifically developed for care and services provision. Employed sensor technologies for providing services have very diverse natures depending on the intended services to be provided. However, many of these technologies do not take into account medical standards. The degree of the ecosystems' openness to adding new devices greatly depends on the approach followed, such as the type of middleware considered. Thus, there is still much work to be done in order to equate other more established ecosystems such as business or software ecosystems.
Fast and high-fidelity generation of steady-state entanglement using pulse modulation and parametric amplification

We explore an intriguing alternative for a fast and high-fidelity generation of steady-state entanglement. By exponentially enhancing the atom-cavity interaction, we obtain an exponentially-enhanced effective cooperativity of the system, which results in a high fidelity of the state generation. Meanwhile, we modulate the amplitudes of the driving fields to accelerate the population transfer to a target state, e.g., a Bell state. An exponentially-shortened stabilization time is thus predicted. Specifically, when the cooperativity of the system is $C=30$, the fidelity of the acceleration scheme reaches $98.5\%$, and the stabilization time is about 10 times shorter than that without acceleration. Moreover, we find from the numerical simulation that the acceleration scheme is robust against systematic and stochastic (amplitude-noise) errors.
Commutative Encryption and Data Hiding in HEVC Video Compression

In this paper, an efficient commutative encryption and data hiding scheme for HEVC videos is proposed. The commutative property allows ciphering a steganographic video without interfering with the embedded signal or to perform steganography on an encrypted video while still allowing perfect decryption. The syntax elements in the HEVC standard, including the sign of quantized transform coefficient (QTC), the sign of motion vector difference (MVD), and the intra-prediction mode (IPM) are encrypted, which distorts video content greatly and thus keeps perception security. Data embedding employs a specific QTC modification method, which makes the visual distortion caused by data hiding very low. Furthermore, the proposed framework will produce an HEVC format compliant bitstream and permits the hidden information to be extracted either in the encrypted domain or in the decrypted domain. Several benchmark video sequences of different resolutions and diverse contents are used for experimental evaluation.
From a Comprehensive Experimental Survey to a Cost-based Selection Strategy for Lightweight Integer Compression Algorithms

Lightweight integer compression algorithms are frequently applied in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. In recent years, the vectorization of basic techniques such as delta coding and null suppression has considerably enlarged the corpus of available algorithms. As a result, today there is a large number of algorithms to choose from, while different algorithms are tailored to different data characteristics. However, a comparative evaluation of these algorithms with different data and hardware characteristics has never been sufficiently conducted in the literature. To close this gap, we conducted an exhaustive experimental survey by evaluating several state-of-the-art lightweight integer compression algorithms as well as cascades of basic techniques. We systematically investigated the influence of data as well as hardware properties on the performance and the compression rates. The evaluated algorithms are based on publicly available implementations as well as our own vectorized reimplementations. We summarize our experimental findings leading to several new insights and to the conclusion that there is no single-best algorithm. Moreover, in this article, we also introduce and evaluate a novel cost model for the selection of a suitable lightweight integer compression algorithm for a given dataset.
A quantum walk-assisted approximate algorithm for bounded NP optimisation problems

This paper describes an application of the Quantum Approximate Optimisation Algorithm (QAOA) to efficiently find approximate solutions for computational problems contained in the polynomially bounded NP optimisation complexity class (NPO PB). We consider a generalisation of the QAOA state evolution to alternating quantum walks and solution-quality-dependent phase shifts, and use the quantum walks to integrate the problem constraints of NPO problems. We apply the recent concept of a hybrid quantum-classical variational scheme to attempt finding the highest expectation value, which contains a high-quality solution. The algorithm is applied to the problem of minimum vertex cover, showing promising results using only a fixed and low number of optimisation parameters.
HAS-QA: Hierarchical Answer Spans Model for Open-Domain Question Answering

This paper is concerned with open-domain question answering (i.e., OpenQA). Recently, some works have viewed this problem as a reading comprehension (RC) task, and directly applied successful RC models to it. However, the performances of such models are not so good as that in the RC task. In our opinion, the perspective of RC ignores three characteristics in OpenQA task: 1) many paragraphs without the answer span are included in the data collection; 2) multiple answer spans may exist within one given paragraph; 3) the end position of an answer span is dependent with the start position. In this paper, we first propose a new probabilistic formulation of OpenQA, based on a three-level hierarchical structure, i.e., the question level, the paragraph level and the answer span level. Then a Hierarchical Answer Spans Model (HASQA) is designed to capture each probability. HAS-QA has the ability to tackle the above three problems, and experiments on public OpenQA datasets show that it significantly outperforms traditional RC baselines and recent OpenQA baselines.
A GraphQL approach to Healthcare Information Exchange with HL7 FHIR

Interoperability is accepted as a fundamental necessity for the successful realization of Healthcare Information Systems. It can be achieved by utilizing consistent standards defining syntactic and semantic meaning of the information being exchanged. HL7 FHIR is one of such open standards for Health Information Exchange (HIE). While HL7 FHIR supports Representational State Transfer (REST) architecture and Service-oriented Architecture (SOA) for seamless information exchange, it inherits the inflexibility and complexity associated with the RESTful approach. GraphQL is a query language developed by Facebook that provides promising techniques to overcome these issues. In this paper, we exploit the use of GraphQL and HL7 FHIR for HIE; present an algorithm to map HL7 FHIR resources to a GraphQL schema, and created a prototype implementation of the approach and compare it with a RESTful approach. Our experimental results indicate that the combination of GraphQL and HL7 FHIR-based web APIs for HIE is performant, cost-effective, scalable and flexible to meet web and mobile clients requirements.
On Augmented Reality for the Learning of 3D-Geometric Contents: A Preliminary Exploratory Study with 6-Grade Primary Students

Nowadays, Augmented Reality (AR) is one of the emerging technologies with a greater impact in the Education field. Research has proved that AR-based activities improve the teaching and learning processes. Also, the use of this type of technology in classroom facilitates the understanding of contents from different areas as Arts, Mathematics or Science. In this work we propose an AR-based instruction in order to explore the benefits in a 6th-grade Primary course related to 3D-geometry shapes. This first experiment, designed from an exploratory approach, will shed light on new study variables to perform new implementations whose conclusions become more consistent. The results obtained allow us to envisage that AR-based proposals slightly improve the classical didactic methods.
Lower bounds for trace reconstruction

In the trace reconstruction problem, an unknown bit string ${\mathbf{x}}\in\{0,1\}^{n}$ is sent through a deletion channel where each bit is deleted independently with some probability $q\in(0,1)$, yielding a contracted string ${\widetilde{\mathbf{x}}}$. How many i.i.d. samples of ${\widetilde{\mathbf{x}}}$ are needed to reconstruct ${\mathbf{x}}$ with high probability? We prove that there exist ${\mathbf{x}},{\mathbf{y}}\in\{0,1\}^{n}$ such that at least $cn^{5/4}/\sqrt{\log n}$ traces are required to distinguish between ${\mathbf{x}}$ and ${\mathbf{y}}$ for some absolute constant $c$, improving the previous lower bound of $cn$. Furthermore, our result improves the previously known lower bound for reconstruction of random strings from $c\log^{2}n$ to $c\log^{9/4}n/\sqrt{\log\log n}$.
Designing Precise and Robust Dialogue Response Evaluators

Automatic dialogue response evaluator has been proposed as an alternative to automated metrics and human evaluation.However, existing automatic evaluators achieve only moderate correlation with human judgement and they are not robust.In this work, we propose to build a reference-free evaluator and exploit the power of semi-supervised training and pretrained (masked) language models.Experimental results demonstrate that the proposed evaluator achieves a strong correlation (> 0.6) with human judgement and generalizes robustly to diverse responses and corpora.We open-source the code and data in https://github.com/ZHAOTING/dialog-processing.
NeuInfer: Knowledge Inference on N-ary Facts

Knowledge inference on knowledge graph has attracted extensive attention, which aims to find out connotative valid facts in knowledge graph and is very helpful for improving the performance of many downstream applications.However, researchers have mainly poured attention to knowledge inference on binary facts.The studies on n-ary facts are relatively scarcer, although they are also ubiquitous in the real world.Therefore, this paper addresses knowledge inference on n-ary facts.We represent each n-ary fact as a primary triple coupled with a set of its auxiliary descriptive attribute-value pair(s).We further propose a neural network model, NeuInfer, for knowledge inference on n-ary facts.Besides handling the common task to infer an unknown element in a whole fact, NeuInfer can cope with a new type of task, flexible knowledge inference.It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s).Experimental results demonstrate the remarkable superiority of NeuInfer.
A Novel Location Privacy-Preserving Approach Based on Blockchain

Location-based services (LBS) bring convenience to people’s lives but are also accompanied with privacy leakages. To protect the privacy of LBS users, many location privacy protection algorithms were proposed. However, these algorithms often have difficulty to maintain a balance between service quality and user privacy. In this paper, we first overview the shortcomings of the existing two privacy protection architectures and privacy protection technologies, then we propose a location privacy protection method based on blockchain. Our method satisfies the principle of k-anonymity privacy protection and does not need the help of trusted third-party anonymizing servers. The combination of multiple private blockchains can disperse the user’s transaction records, which can provide users with stronger location privacy protection and will not reduce the quality of service. We also propose a reward mechanism to encourage user participation. Finally, we implement our approach in the Remix blockchain to show the efficiency, which further indicates the potential application prospect for the distributed network environment.
A robust first-arrival picking workflow using convolutional and recurrent neural networks

A deep-learning-based workflow is proposed in this paper to solve the first-arrival picking problem for near-surface velocity model building. Traditional methods, such as the short-term average/long-term average method, perform poorly when the signal-to-noise ratio is low or near-surface geologic structures are complex. This challenging task is formulated as a segmentation problem accompanied by a novel postprocessing approach to identify pickings along the segmentation boundary. The workflow includes three parts: a deep U-net for segmentation, a recurrent neural network (RNN) for picking, and a weight adaptation approach to be generalized for new data sets. In particular, we have evaluated the importance of selecting a proper loss function for training the network. Instead of taking an end-to-end approach to solve the picking problem, we emphasize the performance gain obtained by using an RNN to optimize the picking. Finally, we adopt a simple transfer learning scheme and test its robustness via a weight adaptation approach to maintain the picking performance on new data sets. Our tests on synthetic data sets reveal the advantage of our workflow compared with existing deep-learning methods that focus only on segmentation performance. Our tests on field data sets illustrate that a good postprocessing picking step is essential for correcting the segmentation errors and that the overall workflow is efficient in minimizing human interventions for the first-arrival picking task.
Exact and approximate symmetry projectors for the electronic structure problem on a quantum computer

Solving the electronic structure problem on a universal-gate quantum computer within the variational quantum eigensolver (VQE) methodology requires constraining the search procedure to a subspace defined by relevant physical symmetries. Ignoring symmetries results in convergence to the lowest eigenstate of the Fock space for the second quantized electronic Hamiltonian. Moreover, this eigenstate can be symmetry broken due to limitations of the wavefunction ansatz. To address this VQE problem, we introduce and assess methods of exact and approximate projection operators to irreducible eigen-subspaces of available physical symmetries. Feasibility of symmetry projection operators in the VQE framework is discussed, and their efficiency is compared with symmetry constraint optimization procedures. Generally, projectors introduce higher numbers of terms for VQE measurement compared to the constraint approach. On the other hand, the projection formalism improves accuracy of the variational wavefunction ansatz without introducing additional unitary transformations, which is beneficial for reducing depths of quantum circuits.
Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder

Topic models have been prevailing for many years on discovering latent semantics while modeling long documents.However, for short texts they generally suffer from data sparsity because of extremely limited word cooccurrences; thus tend to yield repetitive or trivial topics with low quality.In this paper, to address this issue, we propose a novel neural topic model in the framework of autoencoding with a new topic distribution quantization approach generating peakier distributions that are more appropriate for modeling short texts.Besides the encoding, to tackle this issue in terms of decoding, we further propose a novel negative sampling decoder learning from negative samples to avoid yielding repetitive topics.We observe that our model can highly improve short text topic modeling performance.Through extensive experiments on real-world datasets, we demonstrate our model can outperform both strong traditional and neural baselines under extreme data sparsity scenes, producing high-quality topics.
Improving biomedical named entity recognition with syntactic information

Abstract Background Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the lack of large-scale labeled training data and domain knowledge. To address the challenge, in addition to using powerful encoders (e.g., biLSTM and BioBERT), one possible method is to leverage extra knowledge that is easy to obtain. Previous studies have shown that auto-processed syntactic information can be a useful resource to improve model performance, but their approaches are limited to directly concatenating the embeddings of syntactic information to the input word embeddings. Therefore, such syntactic information is leveraged in an inflexible way, where inaccurate one may hurt model performance. Results In this paper, we propose BioKMNER , a BioNER model for biomedical texts with key-value memory networks (KVMN) to incorporate auto-processed syntactic information. We evaluate BioKMNER on six English biomedical datasets, where our method with KVMN outperforms the strong baseline method, namely, BioBERT, from the previous study on all datasets. Specifically, the F1 scores of our best performing model are 85.29% on BC2GM, 77.83% on JNLPBA, 94.22% on BC5CDR-chemical, 90.08% on NCBI-disease, 89.24% on LINNAEUS, and 76.33% on Species-800, where state-of-the-art performance is obtained on four of them (i.e., BC2GM, BC5CDR-chemical, NCBI-disease, and Species-800). Conclusion The experimental results on six English benchmark datasets demonstrate that auto-processed syntactic information can be a useful resource for BioNER and our method with KVMN can appropriately leverage such information to improve model performance.
Image scene geometry recognition using low-level features fusion at multi-layer deep CNN

The image scene geometry recognition is an important element for reconstructing the 3D scene geometry of a single image. It is useful for computer vision applications, such as 3D TV, video categorization, and robot navigation system. A 3D scene geometry with a unique depth represents a rough structure of 2D images. An approach to efficient implementation and achieving high recognition accuracy of 3D scene geometry remains a significant challenges in the computer vision domain. Existing approaches attempt to use the pre-trained deep convolutional neural networks (CNN) models as feature extractor and also explore the benefits of multi-layer features representation for small or medium-size datasets. However, these studies pay little attention to building a discriminative feature representation by exploring the benefits of low-level features fusion with multi-layer feature from a single CNN model. To address this problem, we propose a novel model of image scene geometry recognition in which the low-level handcrafted features are integrated with deep CNN multi-stage features (HF-MSF) by using the feature-fusion and score-level fusion strategies. The low-level features contain rich discriminative information of 3D scene geometry, including shape, color, and depth estimation. In feature-fusion, the multi-layer features at different stages and handcrafted features are fused at an early phase, and in score-level fusion, the handcrafted features are integrated with multi-layer feature of a single CNN model at different stages and each stage is connected with a classifier and then score-level fusion of these classifiers is performed automatically to recognize the scene geometry type. For validation and comparison purposes, two well-known deep learning architectures, namely GoogLeNet and ResNet are employed as a backbone of proposed model. Experimental results exhibited that by taking the advantages of both types of fusion, the proposed HF-MSF model has an improved recognition accuracy of 12.21% and 4.96% when compared to G-MS2F model for 12-Scene and 15-Scene image datasets, respectively. Similarly, it improves the accuracy by 3.85% when compared with the FTOTLM model for the 15-Scene dataset.
Factors Affecting Knowledge Management and Its Effect on Organizational Performance: Mediating the Role of Human Capital

This study aims to investigate and identify the factors affecting the empowerment and implementation of knowledge management in organizations as well as the impact of knowledge management on organizational performance. This study also examines the mediating role of human capital in the relationship between knowledge management and performance of Kabul Steel Plant, which is the largest steel plant in Afghanistan. The research model was developed through the literature review. The initial data were collected through a questionnaire containing 48 questions. Participants were 108 managers and administrative staff of the company. The collected data were analyzed by using the SPSS and SmartPLS software. The hypotheses regarding the impact of strategy and technology on knowledge management were rejected by using correlation analysis and t-test statistic. Finally, the findings showed the positive effects of variables of structure, culture, leadership, and trust on knowledge management in an organization. Also, knowledge management influences the organizational performance, both directly and through the mediating variable of human capital. This research encourages the managers and employees of organizations to use the available organizational resources to implement knowledge management in organizations and improve knowledge management practices and human resources that are the most valuable resources of any organization in order to remain competitive in the markets.
Self-Matching CAM: A Novel Accurate Visual Explanation of CNNs for SAR Image Interpretation

Synthetic aperture radar (SAR) image interpretation has long been an important but challenging task in SAR imaging processing. Generally, SAR image interpretation comprises complex procedures including filtering, feature extraction, image segmentation, and target recognition, which greatly reduce the efficiency of data processing. In an era of deep learning, numerous automatic target recognition methods have been proposed based on convolutional neural networks (CNNs) due to their strong capabilities for data abstraction and mining. In contrast to general methods, CNNs own an end-to-end structure where complex data preprocessing is not needed, thus the efficiency can be improved dramatically once a CNN is well trained. However, the recognition mechanism of a CNN is unclear, which hinders its application in many scenarios. In this paper, Self-Matching class activation mapping (CAM) is proposed to visualize what a CNN learns from SAR images to make a decision. Self-Matching CAM assigns a pixel-wise weight matrix to feature maps of different channels by matching them with the input SAR image. By using Self-Matching CAM, the detailed information of the target can be well preserved in an accurate visual explanation heatmap of a CNN for SAR image interpretation. Numerous experiments on a benchmark dataset (MSTAR) verify the validity of Self-Matching CAM.