Reviewerly/exHarmony huggingface dataset:
Migration of healthcare relational database to NoSQL cloud database for healthcare analytics and management

Due to the exponential increase in the volume of data in the healthcare sector generated from various sources such as ECGs, X-rays, CT scans, ultrasound reports, lab reports, radiology tests, and clinical research in the form of Electronic Health Records and Electronic Medical Records, relational databases cannot reasonably meet the requirements, such as Web service availability and scaling. Handling more users and data means the addition of big servers, and big servers are tremendously complex and extremely expensive. So, as organizations are facing performance problems with relational databases they are currently using for existing or new applications, especially when the number of users increases, they realize the necessity for a database tier that is faster and more flexible. This is the correct time to migrate from relational database systems to a cloud-based architecture for a NoSQL (NOT ONLY SQL) cloud database. The NoSQL cloud database is being adopted in order to scale out more applications and to support more users. NoSQL cloud databases allow an increase in performance and availability of services, so it seems to be favorable to put forward timely information on data migration from Structured Query Language (SQL) databases to NoSQL cloud databases. First, we present the challenges and analysis of data migration and also present data migration techniques from the relational database management system (RDBMS) to NoSQL using NoSQL technology. Second, we discuss the technology background, applications, and challenges related to NoSQL cloud-based technology. Then we present cloud-based architectures for databases, with technology, applications, and challenges of cloud databases and show how data migration techniques are used to migrate a relational database to a NoSQL cloud database.
Learning Fused Representations for Large-Scale Multimodal Classification

Multimodal strategies combine different input sources into a joint representation that provides enhanced information from the unimodal strategy. In this article, we present a novel multimodal approach that fuses image and encoded text description to obtain an information-enriched image. This approach casts encoded text obtained from Word2Vec word embedding into visual embedding to be concatenated with the image. We employ standard convolutional neural networks to learn representations of information-enriched images. Finally, we compare our approach with the unimodal approach and their combination on three large-scale multimodal datasets. Our findings indicate that the joint representation of encoded text and image in feature space improves the multimodal classification performance aiding the interpretability.
Watermelon Ripeness Detection via Extreme Learning Machine with Kernel Principal Component Analysis Based on Acoustic Signals

Many investigations have proved that the acoustics method is intuitive and effective for determining watermelon ripeness. The objective of this work is to drive a new robust acoustics classification scheme KPCA-ELM, which is based on the kernel principal component analysis (KPCA) and extreme learning machine (ELM). Acoustic signals are sampled by a microphone from unripe, ripe and over-ripe watermelon samples, which are randomly divided into two sample sets for training and testing. A set of basic signals is first obtained via KPCA of the training sample. Thus, any given signal can be represented as a linear combination of basis signals, and the coefficients of linear combination are extracted as the features of a signal. Corresponding to the unripe, ripe and over-ripe watermelons, a three-class ELM identification model is constructed based on the training data. The scheme presented in this paper is tested with the testing sample and an accuracy of 92% is achieved. To further evaluate the scheme performance, a comparison of ELM and SVM is conducted in terms of the classification results. The results reveal that the proposed scheme can classify faster than SVM, while ELM is better than SVM in accuracy.
Sequentially adaptive Bayesian learning algorithms for inference and optimization

The sequentially adaptive Bayesian learning algorithm (SABL) builds on and ties together ideas from sequential Monte Carlo and simulated annealing. The algorithm can be used to simulate from Bayesian posterior distributions, using either data tempering or power tempering, or for optimization. A key feature of SABL is that the introduction of information is adaptive and controlled, ensuring that the algorithm performs reliably and efficiently in a wide variety of applications with off-the-shelf settings, minimizing the need for tedious tuning, tinkering, trial and error by users. The algorithm is pleasingly parallel, and a Matlab toolbox implementing the algorithm is able to make efficient use of massively parallel computing environments such as graphics processing units (GPUs) with minimal user effort. This paper describes the algorithm, provides theoretical foundations, applies the algorithm to Bayesian inference and optimization problems illustrating key properties of its operation, and briefly describes the open source software implementation.
Joint residual pyramid for joint image super-resolution

Joint image super-resolution refers to methods to enhance the resolution of an image with the guidance of a higher resolution image. It is similar to image completion, which is shown to benefit from larger receptive fields in recent deep neural network based methods. However, larger receptive fields increase the depths and parameters of the network, which may cause degradation and large memory consumption. To this end, we propose a joint residual pyramid model by introducing residual blocks and linear interpolation layers into the convolutional neural pyramid (CNP), and adopting the CNP in the joint super-resolution framework. Our model consists of three sub-networks, two for feature extraction concatenated by another for image reconstruction. Experimental results show that our model outperforms existing state-of-the-art algorithms not only on data pairs of RGB/depth images, but also on data pairs like color/saliency and color-scribbles/colorized images, without significantly sacrificing computation efficiency and memory space.
Interface design for in-home displays

In-home displays can support behavioral changes by providing users with real time information (or feedback) on their energy consumption. Previous studies have shown that the design of such devices plays an important role in effectively communicating such information to users and that preferences are culturally-depend. This paper examines information elements best suited to the Brazilian context for children, adults, and elderly. It explores the preference, ranking, and understanding in addition to the ideal in-home display for each user profile. Lastly, display prototypes suited to each user profile are also proposed based on the results of seven focus groups totaling fifty participants. Real-time consumption was one of the most important information whereas penalty was the least. All participants preferred historical comparison to normative comparison. Numerical formats were better understood than ambient formats for real-time consumption. In addition, children and adults preferred and designed ambient formats whereas elderly preferred numerical information in monetary unit. Elderly would like to keep the display simple whereas adults and children need more interactive designs. These findings contribute to more effective in-home display design particularly for the Brazilian context, where smart meter and in home-displays are not extensively adopted.
Approaches and challenges of privacy preserving search over encrypted data

More and more data owners are encouraged to outsource their data onto cloud servers for reducing infrastructure, maintenance cost and also to get ubiquitous access to their stored data. However, security is one issue that discourages data owners from adopting cloud servers for data storage. Searchable Encryption (SE) is one of the few ways of assuring privacy and confidentiality of such data by storing them in encrypted form at the cloud servers. SE enables the data owners and users to search over encrypted data through trapdoors. Most of the user information requirements are fulfilled either through Boolean or Ranked search approaches. This paper aims at understanding how the confidentiality and privacy of information can be guaranteed while processing single and multi-keyword queries over encrypted data using Boolean and Ranked search approaches. This paper presents all possible leakages that happen in SE and also specifies which privacy preserving approach to be adopted in SE schemes to prevent those leakages to help the practitioners and researchers to design and implement secure searchable encryption systems. It also highlights various application scenarios where SE could be utilized. This paper also explores the research challenges and open problems that need to be focused in future.
Towards a blind image quality evaluator using multi-scale second-order statistics

Natural image statistics have proved to be effective indicators in measuring quality degradations. Most of the current statistics-based Image Quality Assessment (IQA) metrics aim at utilizing features derived from first-order models. However, second-order statistics are also of great value in image quality prediction, which are not yet fully studied. In this paper, a Blind image Quality Evaluator based on Multi-scale Second-order Statistics (BQEMSS) is proposed. The distorted image is first transformed into an opponent color space, and then quality-aware features are extracted in multiple scales from the joint distribution of adjacent sub-band coefficients in the wavelet domain and the histogram of Gaussian derivative pattern in the spatial domain respectively. To quantify the statistical regularities between sub-band coefficients, three types of image dependencies are explored, including spatially adjacent dependency, sub-band orientation dependency and sub-band scale dependency. In the final step, features are stacked to form a feature vector and a regression module is employed to map the feature vectors into quality scores. Extensive experiments on several public image quality databases demonstrate that BQEMSS is superior over the relevant state-of-the-art general-purpose blind IQA models.
Robust Discriminative Subspace Learning for Person Reidentification

Metric learning is one of the fundamental problems in person re-identification. However, a good number of the current techniques do not generalize well in the presence of outliers. Toward this, we present a robust discriminative subspace learning technique in this letter. We learn the subspace by maximizing the ratio of between class covariance and within class covariance using L1 norm instead of the conventional L2 norm. We theoretically show the iterative approach of computing the subspace. In case of noisy data, our experimental results demonstrate an overall average improvement of more than 4.2% in Rank-1 accuracy on CUHK03, Market1501, and DukeMTMC4ReID compared to popular metric learning algorithms.
Engineering Hand-designed and Deeply-learned features for person Re-identification

Person re-identification is still a useful and challenging task in pattern recognition and computer vision. Based on a probe image, the task focuses on identifying a set of matching images from a gallery set. In this work, a novel multi-feature fusion-based person re-identification framework is proposed. The key technique is to intelligently fuze hand-crafted feature and deeply-learned feature. More specifically, hand-crafted features from both local and global region are extracted from each image. Afterward, a novel Convolutional Neural Networks (CNN) is trained based on combining the three datasets, wherein robust deep features can be obtained. Finally, the above hand-crafted feature as well as the deep features are optimally fuzed for calculating a robust re-identification result. Extensive experimental results on three state-of-the-art data sets have demonstrated the effectiveness of our method.
A standardization process for mental model analysis in socio-ecological systems

Large-scale infrastructure plays instrumental roles in defining trajectories of land use, influencing the distribution of risk, and enabling or eroding ecological and social wellbeing. While considerable attention has been paid to the outcomes and implications of such infrastructure investments, less analytical attention has been given to the decision processes of infrastructure investment intended to enhance sustainability transformations. Decision-making over infrastructure is characterized by uncertainty — in which the system models and probability functions are unknown, and the desired outcomes are highly controversial. Approaches are needed to address issues of political contention and power asymmetries, disparate values, justice and equity in large-scale infrastructure decision-making. All these elements augment uncertainty and challenge decision processes, while underscoring the role of knowledge co-production. We draw from empirical cases in Mexico and Vietnam to highlight promising directions and an emergent research agenda on addressing these challenges in infrastructural decision processes.
Path Category for Free

Abstract There are different categorical approaches to variations of transition systems and their bisimulations. One is coalgebra for a functor G, where a bisimulation is defined as a span of G-coalgebra homomorphism. Another one is in terms of path categories and open morphisms, where a bisimulation is defined as a span of open morphisms. This similarity is no coincidence: given a functor G, fulfilling certain conditions, we derive a path-category for pointed G-coalgebras and lax homomorphisms, such that the open morphisms turn out to be precisely the G-coalgebra homomorphisms. The above construction provides path-categories and trace semantics for free for different flavours of transition systems: (1) non-deterministic tree automata (2) regular nondeterministic nominal automata (RNNA), an expressive automata notion living in nominal sets (3) multisorted transition systems. This last instance relates to Lasota’s construction, which is in the converse direction.
Quantum chemistry on a quantum computer

Abstract The recently developed techniques of quantum computation show promise of being able to deal with increasingly large Hilbert spaces in atomic and molecular systems. Whereas the computational complexity of a classical computer increases roughly factorially with the dimension of the Hilbert space, the increase for a quantum computer is, in principle, just polynomial. Moreover, by virtue of quantum entanglement, 2n operations of a classical computer can be simulated by a single operation on an n-qubit quantum state. The fundamental principles underlying quantum computation are described, following the recent monograph of Fano and Blinder, Twenty-First Century Quantum Mechanics, Springer 2017. Recent applications of quantum computers to atomic and molecular systems are briefly described.
MOBIUS: Model-Oblivious Binarized Neural Networks

A privacy-preserving framework in which a computational resource provider receives encrypted data from a client and returns prediction results without decrypting the data, i.e., oblivious neural network or encrypted prediction, has been studied in machine learning that provides prediction services. In this work, we present MOBIUS (Model-Oblivious BInary neUral networkS), a new system that combines Binarized Neural Networks (BNNs) and secure computation based on secret sharing as tools for scalable and fast privacy-preserving machine learning. BNNs improve computational performance by binarizing values in training to $-1$ and $+1$, while secure computation based on secret sharing provides fast and various computations under encrypted forms via modulo operations with a short bit length. However, combining these tools is not trivial because their operations have different algebraic structures and the use of BNNs downgrades prediction accuracy in general. MOBIUS uses improved procedures of BNNs and secure computation that have compatible algebraic structures without downgrading prediction accuracy. We created an implementation of MOBIUS in C++ using the ABY library (NDSS 2015). We then conducted experiments using the MNIST dataset, and the results show that MOBIUS can return a prediction within 0.76 seconds, which is six times faster than SecureML (IEEE S\&P 2017). MOBIUS allows a client to request for encrypted prediction and allows a trainer to obliviously publish an encrypted model to a cloud provided by a computational resource provider, i.e., without revealing the original model itself to the provider.
Preprocess-then-NTT Technique and Its Applications to Kyber and NewHope

The Number Theoretic Transform (NTT) provides efficient algorithm for multiplying large degree polynomials. It is commonly used in cryptographic schemes that are based on the hardness of the Ring Learning With Errors problem (RLWE), which is a popular basis for post-quantum key exchange, encryption and digital signature. To apply NTT, modulus q should satisfy that $$q \equiv 1 \mod 2n$$ , RLWE-based schemes have to choose an oversized modulus, which leads to excessive bandwidth. In this work, we present "Preprocess-then-NTT (PtNTT)" technique which weakens the limitation of modulus q, i.e., we only require $$q \equiv 1 \mod n$$ or $$q \equiv 1 \mod n/2$$ . Based on this technique, we provide new parameter settings for Kyber and NewHope (two NIST candidates). In these new schemes, we can reduce public key size and ciphertext size at a cost of very little efficiency loss.
Parallel online event calculus learning for complex event recognition

Logic-based event recognition systems infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing, among others, direct connections to machine learning, via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system that learns event definitions in the form of Event Calculus theories, in a single pass over a data stream. We present two strategies for parallel online learning with OLED. We evaluate our proposed approaches on three datasets from the domains of activity recognition and maritime surveillance and show that they can significantly reduce training times, while they are capable of achieving super-linear speed-ups under certain circumstances.
HiWalk: Learning node embeddings from heterogeneous networks

Heterogeneous networks, such as bibliographical networks and online business networks, are ubiquitous in everyday life. Nevertheless, analyzing them for high-level semantic understanding still poses a great challenge for modern information systems. In this paper, we propose HiWalk to learn distributed vector representations of the nodes in heterogeneous networks. HiWalk is inspired by the state-of-the-art representation learning algorithms employed in the context of both homogeneous networks and heterogeneous networks, based on word embedding learning models. Different from existing methods in the literature, the purpose of HiWalk is to learn vector representations of the targeted set of nodes by leveraging the other nodes as “background knowledge”, which maximizes the structural correlations of contiguous nodes. HiWalk decomposes the adjacent probabilities of the nodes and adopts a hierarchical random walk strategy, which makes it more effective, efficient and concentrated when applied to practical large-scale heterogeneous networks. HiWalk can be widely applied in heterogeneous networks environments to analyze targeted types of nodes. We further validate the effectiveness of the proposed HiWalk through multiple tasks conducted on two real-world datasets.
Automatic Face Recognition Based on Sparse Representation and Extended Transfer Learning

Sparse representation has exhibited excellent performance in face recognition. However, this method requires some areas for improvement, especially on insufficient face samples. We aim to design a simple and efficient method to improve sparse representation to solve problems with a small sample size. This paper provides two primary contributions that are very effective in small sample face recognition. First, in order to enhance recognition robustness, we designed an intuitive and mathematically controllable transfer learning method of sparse representation by introducing labeled samples. Second, to obtain high recognition accuracy, we developed a weighted fusion scheme to integrate the sparse representation results generated from original and labeled samples. In the ORL dataset, our algorithm’s highest accuracy rate is 95%. In the FERET dataset, our highest classification accuracy rate is 95%. In the more complex LFW dataset, our highest classification accuracy rate has also reached 83.33%. This shows that our experimental results demonstrate that the proposed method can obtain sufficient performance, whereas the weighted fusion scheme can take advantage of sparse representation on the basis of original and labeled samples. This paper will be very useful for identification based on the Internet-of-Medical-Things.
Web Usability Testing With Concurrent fNIRS and Eye Tracking

The wealth management industry has been one of the prime beneficiaries of the digitization wave, with incessantly growing investment from fintech companies to develop new online platforms. The current methods of evaluating the effectiveness, usability, and ease of use of these web platforms are rudimentary and subjective, as they rely largely on traditional survey measures and may fail to uncover critical usability issues with a platform. When companies launch such web platforms, early adopter users are usually faced with an array of issues, ranging from ineffective navigation to poor layout, which may cause consumers to lose trust and eventually switch to other brands and services. There has been a long-standing need to develop more effective methods to assess web platforms. We propose and empirically validate a multimodal approach that combines traditional methods (survey and performance measures) with physiological (eye-tracking and neuroimaging) measures to enable more holistic and rigorous testing of web platforms. This approach overcomes the limitations of traditional methods and enables a more comprehensive usability assessment.