Reviewerly/exHarmony huggingface dataset:
Optimized realization of Bayesian networks in reduced normal form using latent variable model

Abstract Bayesian networks in their Factor Graph Reduced Normal Form are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. Moreover, an online version of the classic batch learning algorithm is also analysed, showing very similar results in an unsupervised context but with much better performance; which may be essential if multi-level structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood algorithms. The results obtained are discussed with particular reference to a Latent Variable Model structure.
Load balanced transaction scheduling in on-demand computing using cuckoo search-ant colony optimization

Load balanced transaction scheduling in on-demand computing system is known to be NP-hard problem. In order to solve this problem, this paper introduces a hybrid approach named cuckoo search-ant colony optimization. The approach dynamically generates an optimal schedule by clustering the on-demand computing resources considering their load and completes the transaction execution within their deadlines. The approach also balances the load of the system before scheduling the transactions. For clustering the resources we use cuckoo search method. We use ant colony optimization for selecting the appropriate and optimal resources. We evaluate the performance of the proposed algorithm with six existing algorithms.
Self-Supervised Learning from Web Data for Multimodal Retrieval

Self-supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human-annotated data. Web and social media platforms provide a virtually unlimited amount of this multimodal data. In this work we propose to exploit this free available data to learn a multimodal image and text embedding, aiming to leverage the semantic knowledge learned in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the proposed pipeline can learn from images with associated text without supervision and analyze the semantic structure of the learned joint image and text embedding space. We perform a thorough analysis and performance comparison of five different state-of-the-art text embeddings in three different benchmarks. We show that the embeddings learned with web and social media data have competitive performances over supervised methods in the text-based image retrieval task, and we clearly outperform the state of the art in the MIRFlickr dataset when training in the target data. Further, we demonstrate how semantic multimodal image retrieval can be performed using the learned embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed of Instagram images and their associated texts, which can be used for fair comparison of image–text embeddings.
Evolving Cryptographic Approach for Enhancing Security of Resource Constrained Mobile Device Outsourced Data in Cloud Computing

Mobile cloud computing in the increasing popularity among users of mobile device enables to store their data in cloud. Security is the major concern when the sensitive information is stored and transferred across the internet. It is essential to make sure that the data is secured and protected. In this paper, we discussed the problem of privacy of data with reducing the resources usage. Moreover, mobile cloud computing has limitations in resources such as power energy, processor, Memory and storage. Cryptography ensures the confidentiality, authentication, availability, and integrity of the data. This is done through cryptographic algorithms such as Data Encryption Standard (DES), Blowfish and Advanced Encryption Standard (AES). The experimental results evaluated and compared the performance of the encryption algorithms. The performance metrics used are encryption and decryption time, CPU and Memory utilization. Evaluation results showed a significant improvement in reducing the resources, amongst all the techniques, choosing a suitable encryption algorithm based on different parameters that are best fit to the future user requirements is considered.
A Survey of Privacy Preserving Utility Mining

High-utility pattern mining has emerged as an important research topic in data mining. It aims at discovering patterns having a high utility (e.g. profit or weight) in transaction or sequence databases. HUPM can be applied in various fields such as market basket analysis, website clickstream analysis, stock market analysis, retail and bioinformatics. In the era of information technology, it has become easy to locate and access information. A greater access to information has many benefits. However, it may also lead to privacy threats if datasets containing sensitive and important information are shared and made public. Therefore, privacy preservation has become a critical challenge for data mining. This chapter provides an up-to-date survey on privacy preserving utility mining (PPUM). The main purpose is to provide a general overview of recent techniques and algorithms for PPUM. The chapter focuses on research on both privacy preserving high-utility itemset mining and privacy preserving high-utility sequential pattern mining. Key concepts and terminology are introduced and discussed. Moreover, latest solutions for PPUM are compared. Finally, challenges and opportunities related to PPUM are discussed.
Combining Pose-Invariant Kinematic Features and Object Context Features for RGB-D Action Recognition

Action recognition using RGB-D cameras is a popular research topic.Recognising actions in a pose-invariant manner is very challenging due to view changes, posture changes and huge intra-class variations.This study aims to propose a novel pose-invariant action recognition framework based on kinematic features and object context features.Using RGB, depth and skeletal joints, the proposed framework extracts a novel set of pose-invariant motion kinematic features based on 3D scene flow and captures the motion of body parts with respect to the body.The obtained features are converted to a human body centric space that allows partial viewinvariant recognition of actions.The proposed pose-invariant kinematic features are extracted for both foreground (RGB and depth) and skeleton joints and separate classifiers are trained.Bordacount based classifier decision fusion is employed to obtain an action recognition result.For capturing object context features, a convolutional neural network (CNN) classifier is proposed to identify the involved objects.The proposed context features also include temporal information on object interaction and help in obtaining a final action recognition.The proposed framework works even with non-upright human postures and allows simultaneous action recognition for multiple people, which are topics that remain comparatively unresearched.The performance and robustness of the proposed pose-invariant action recognition framework are tested on several benchmark datasets.We also show that the proposed method works in real-time.
Query Expansion for Microblog Retrieval Focusing on an Ensemble of Features

In microblog search, vocabulary mismatch is a persisting problem due to the brevity of tweets and frequent use of unconventional abbreviations. One way of alleviating this problem is to reformulate the query via query expansion. However, finding good expansion terms for a given query is a challenging task. In this paper, we present a query expansion framework, where supervised learning is adopted for selecting expansion terms. Upon retrieving tweets by our proposed topic modeling based query expansion, we utilize the pseudo-relevance feedback and a new temporal relatedness approach to select the candidate tweets. Next, we devise several new features to select the temporally and semantically relevant expansion terms by leveraging the temporal, word embedding, and sentiment association of candidate term and query. Moreover, we also utilize the lexical and twitter specific features to quantify the term relatedness. After supervised feature selection using regularized regression, we estimate the feature importance by applying random forest. Then, we make use of a learning-to-rank (L2R) framework to rank the candidate expansion terms. Results of extensive experiments on TREC Microblog 2011 and 2012 test collections over the Tweets2011 corpus show that our proposed method outperforms the baseline and competitive query expansion methods.
Forecasting photovoltaics/concentrated photovoltaics at national level – Portugal experience

The solar power installed capacity all over the world is growing and this renewable energy technology is playing an important role to build a clean energy future. The solar forecast is a necessary tool for the transmission system operator in order to maintain the electricity network safety and reliability. This study aims to report the experience of R&D Nester in the solar forecast at national level in Portugal, forecasting for all solar plants connected to the very high voltage (400, 220 and 150 kV) and high voltage (60 kV) networks, providing this information to the Portuguese transmission system operator, REN. A novel approach to calculate cloud index from images from a sky camera is presented.
Phase-engineered bosonic quantum codes

Continuous-variable systems protected by bosonic quantum error-correcting codes have emerged as a promising platform for quantum information processing. To date, design of codewords has centered on optimizing the occupation of basis states in the error-relevant basis. Here, we propose utilizing the phase degree of freedom in basis state probability amplitudes to devise codes that feature destructive interference, and thus reduced overlap, between error codewords. To showcase, we first consider the correction of excitation loss using single-mode codes with Fock-space parity structure and show that, with a tailored "two-level" recovery, altering the signs of probability amplitudes can significantly suppress decoherence. We then study the joint channel of excitation loss and Kerr effect, and show the critical role of nontrivial phase for optimal quantum codes for such intricate yet important channels. The principle is extended to improve bosonic codes defined in other bases and multi-qubit codes, showing its wide applicability in quantum error correction.
On perfect powers that are sums of cubes of a seven term arithmetic progression

We prove that the equation (x−3r)3+(x−2r)3+(x−r)3+x3+(x+r)3+(x+2r)3+(x+3r)3=yp only has solutions which satisfy xy=0 for 1≤r≤106 and p≥5 prime. This article complements the work on the equations (x−r)3+x3+(x+r)3=yp in [2] and (x−2r)3+(x−r)3+x3+(x+r)3+(x+2r)3=yp in [1]. The methodology in this paper makes use of the Primitive Divisor Theorem due to Bilu, Hanrot and Voutier for a complete resolution of the Diophantine equation.
Inclusion between the frontier language of a non-deterministic recursive program scheme and the Dyck language is undecidable

In 1970's, Nivat studied recursive program schemes (a.k.a order-1 higher-order recursion schemes in modern terminology), first-order tree grammars for generating possibly infinite trees. We consider the inclusion problem between the frontier language of a non-deterministic recursive program scheme (equivalently, an order-2 word language or indexed language) and the Dyck language, and prove that it is undecidable by a reduction from the undecidability of Hilbert's 10th problem. Essentially the same result has recently been proved by Uezato and Minamide, but our proof is arguably more direct, demonstrating the expressive power of higher-order grammars.
Pixelwise Phase Unwrapping Based on Ordered Periods Phase Shift

The existing phase-shift methods are effective in achieving high-speed, high-precision, high-resolution, real-time shape measurement of moving objects; however, a phase-unwrapping method that can handle the motion of target objects in a real environment and is robust against global illumination as well is yet to be established. Accordingly, a robust and highly accurate method for determining the absolute phase, using a minimum of three steps, is proposed in this study. In this proposed method, an order structure that rearranges the projection pattern for each period of the sine wave is introduced, so that solving the phase unwrapping problem comes down to calculating the pattern order. Using simulation experiments, it has been confirmed that the proposed method can be used in high-speed, high-precision, high-resolution, three-dimensional shape measurements even in situations with high-speed moving objects and presence of global illumination. In this study, an experimental measurement system was configured with a high-speed camera and projector, and real-time measurements were performed with a processing time of 1.05 ms and a throughput of 500 fps.
An efficient code-based threshold ring signature scheme

Abstract Code based cryptography is a powerful and a promising alternative to the number theoretic cryptography as it is supposed to resist to the quantum computer. The migration to the post quantum hypothetical world requires the design of all the most important cryptographic primitives such as signature schemes. For this reason, we propose in this paper a ring signature and a threshold ring signature schemes that are based on coding theory assumptions. In the case of threshold ring signature scheme, we have a subset of users belonging to a group of users called a ring who collaborate in order to produce a signature on behalf of ring members while maintaining the anonymity of the identity of the real signers. In this paper, we use the AGS identification scheme, which is a coding theory zero-knowledge authentication protocol, to produce a ring signature and a threshold ring signature. By comparing our schemes with the best one in performance so far on 80 bits security level, we find an average 86% reduction of the signature size and also a reduction on the public key length at equivalent security level. The reduction is achieved for different values of the number of signers and ring’s size.
Reinforcement Learning and Deep Reinforcement Learning

In order to better understand state-of-the-art reinforcement learning agent, deep Q-network, a brief review of reinforcement learning and Q-learning are first described. Then recent advances of deep Q-network are presented, and double deep Q-network and dueling deep Q-network that go beyond deep Q-network are also given.
Counting points on diagonal equations over Galois rings GR(p2,p2)

Let R = G R ( p 2 , p 2 r ) be a Galois ring and N ( a 1 x 1 k 1 + ⋯ + a n x n k n = b ) denote the number of solutions of diagonal equations a 1 x 1 k 1 + ⋯ + a n x n k n = b in R , where a 1 , ⋯ , a n ∈ R \ { 0 } , x 1 , ⋯ , x n , b ∈ R . In this paper, we will express N by Jacobi sums in the finite field F p r . In particular, the precise number of solutions can be obtained when k 1 = k 2 = ⋯ = k n = 2 , but in general some estimates can be satisfied.
On Cross-Domain Transfer in Venue Recommendation

Venue recommendation strategies are built upon Collaborative Filtering techniques that rely on Matrix Factorisation (MF), to model users' preferences. Various cross-domain strategies have been proposed to enhance the effectiveness of MF-based models on a target domain, by transferring knowledge from a source domain. Such cross-domain recommendation strategies often require user overlap, that is common users on the different domains. However, in practice, common users across different domains may not be available. To tackle this problem, recently, several cross-domains strategies without users' overlaps have been introduced. In this paper, we investigate the performance of state-of-the-art cross-domain recommendation that do not require overlap of users for the venue recommendation task on three large Location-based Social Networks (LBSN) datasets. Moreover, in the context of cross-domain recommendation we extend a state-of-the-art sequential-based deep learning model to boost the recommendation accuracy. Our experimental results demonstrate that state-of-the-art cross-domain recommendation does not clearly contribute to the improvements of venue recommendation systems, and, further we validate this result on the latest sequential deep learning-based venue recommendation approach. Finally, for reproduction purposes we make our implementations publicly available.
Evaluation of structural similarity quality measures for wrapped recovered phase maps in optical metrology

We propose and evaluate structural similarity quality measures for wrapped recovered phase maps in the range of [0, 2π). The proposed measures adapt the mathematical description of a structural similarity index employed in intensity images by using circular statistics, which are suitable for analyzing the wrapped phase images obtained in the application of optical metrology techniques. The developed framework avoids 2D unwrapping pre-processing techniques and obtains a quality measure that properly evaluates visual distortions that occur in the proximities of phase discontinuities that do not correspond to significant phase errors. Numerical experiments based on the assessment of simulated wrapped images indicate the suitability of the proposed quality measure when a reference phase map is introduced.
Parallel-in-time optical simulation of history states

We present an experimental optical implementation of a parallel-in-time discrete model of quantum evolution, based on the entanglement between the quantum system and a finite dimensional quantum clock. The setup is based on a programmable spatial light modulator which entangles the polarization and transverse spatial degrees of freedom of a single photon. It enables the simulation of a qubit history state containing the whole evolution of the system, capturing its main features in a simple and configurable scheme. We experimentally determine the associated system-time entanglement, which is a measure of distinguishable quantum evolution, and also the time average of observables, which in the present realization can be obtained through one single measurement.
Bayesian Exploration for Approximate Dynamic Programming

Approximate dynamic programming has been applied to solve large-scale resource allocation problems in many domains, including transportation, energy, and healthcare. The main algorithmic challenge in these applications is to accurately estimate the value of a resource in a certain state (for example, a battery that is half full, or a truck en route to a certain destination). This “value function” must be learned as part of the decision-making process, giving rise to the “exploration/exploitation” challenge: we may, and often should, choose to experiment with seemingly suboptimal actions because they have high potential to be better than we believe. In “Bayesian Exploration for Approximate Dynamic Programming,” Ilya O. Ryzhov, Martijn R. K. Mes, Warren B. Powell and Gerald van den Berg present a principled framework for modeling uncertainty about the value function and measuring the potential of a state to improve a decision-making policy. This method is scalable and performs well in experiments.
Robust decentralised state estimation for formation flying spacecraft

In this study, a robust decentralised state estimation algorithm named decentralised Huber-based cubature filtering (DHCF) for formation flying spacecraft is proposed. In this algorithm, a decentralised architecture is designed, in which each deputy estimates its relative state with respect to the chief based on its own exteroceptive sensor measurement. The relative motion equation considering the non-spherical influence of the earth is derived. The relative measurements’ information contain not only the line-of-sight between the deputy and the chief, but also the ranges among the deputies, which improves the redundancy of the relative navigation task. A set of cubature points are selected using spherical-radial rule to map the probability distribution, which is more accurate than the linearisation of the extended Kalman filter (EKF). The non-Gaussian noise in formation is approximated by Gaussian mixture models. To deal with the non-Gaussian noise, the Huber technique is used in measurement update of each deputy and the extra measurement uncertainty caused by linearisation is compensated. Simulation results indicate that the proposed DHCF provides better performance in state estimation accuracy and robustness when compared to decentralised EKF and decentralised cubature Kalman filtering in the presence of non-Gaussian measurement noise.
HyVE: Hybrid Vertex-Edge Memory Hierarchy for Energy-Efficient Graph Processing

High energy consumption of conventional memory modules (e.g., DRAMs) hinders the further improvement of large-scale graph processing's energy efficiency. The emerging resistive random-access memory (ReRAM) has shown great potential in providing an energy-efficient memory module. However, the performance of ReRAMs suffers from data access patterns with poor locality and large amounts of written data, which are common in graph processing. In this paper, we propose HyVE, a Hybrid Vertex-Edge memory hierarchy for energy-efficient graph processing. In HyVE, we avoid random access and data written to ReRAM modules. HyVE can reduce memory energy consumption by 86.17 percent compared with conventional memory systems. We have also proposed data sharing and bank-level power-gating schemes, which improve the energy efficiency by 1.60x and 1.53x. By analyzing the graph processing model on ReRAMs, we show that ReRAMs are good for read-intensive operations in graph processing (e.g., reading edges), while ReRAM crossbars are not suitable for processing edges because of heavy writing overheads. Our evaluations show that the optimized design achieves two orders of magnitude and 5.90x energy efficiency improvement compared with the CPU-based and conventional memory hierarchy based designs, respectively. Moreover, HyVE achieves 2.83x energy reduction compared with the previous ReRAM-based graph processing architecture.
Assessment of electronic system for e-patent application and economic growth prediction

Information system development is a complex endeavor, one which is fraught with peril if one do not follow a proven method. The Rational Unified Process (RUP) is one such method which could help to organize the development process of any information system. The RUP takes an evolutionary approach to development which has been shown to be far more effective than the traditional approach which is based on simple procedure. Unified Modeling Language (UML) is a RUP graphical language for visualizing, specifying, constructing, and documenting the artifacts of a information system. UML offers a standard way to write a system's blueprints which is crucial for any information system development. The main goal of the study is to develop electronic patent (e-patent) system by RUP approach and UML standard tools. Afterwards artificial neural networks are applied to predict potential economic growth (GDP) based on science factors including patents. Extreme learning machine (ELM) is used for training process of the networks. Extreme learning machine (ELM) is a class of single-hidden layer feedforward neural network (SLFN). In theory, ELM algorithm tends to provide good generalization performance at extremely fast learning speed. Experimental results show that ELM has very high correlation with current economic dispatch.