Reviewerly/exHarmony huggingface dataset:
Techniques For Face Verification: Literature Review

The aims of this research are to conduct a survey on face verification techniques and to discuss the methods that they used and which one was the optimal and for what it is the best. We searched for articles and review papers related to face verification techniques. Ten papers were selected based on the year of publication and the techniques used. The techniques were studied and a summary report of the techniques used is created. We also suggest merging two techniques that may increase accuracy and effectiveness of verification. A summary report of various techniques by researchers is created. An approach by merging two techniques included in the summary report is suggested to enhance verification. The presence of variable illumination, pretense, facial look, growing old and other unsystematic reasons regularly cause identification errors to occur. Moreover increase in level of controlling and executing of the face verification methods is not only difficult even though it is a critical sense of function. Face verification has a huge contribution in different types of media purposes, like security access control, monitoring and control, retrieval of images, etc. They are used in many domains such as banks, airplanes, police areas and others. Several methods have proved to be the best to show a potential move towards the development of performance achieved since more than prompt and verification.
Phase-extraction algorithm for a single-shot spatial-carrier orthogonal fringe pattern with least squares method

In traditional phase measurement deflectometry (PMD), a number of sinusoidal fringe patterns are displayed on the screen in two orthogonal directions, which is time-consuming and not suitable for dynamic measurements. A phase-extraction algorithm based on the spatial-carrier phase-shifting technology for a single-shot spatial-carrier orthogonal fringe pattern is proposed. The phase increment of each pixel in two orthogonal directions is obtained by the least squares method and then the amount of spatial phase shift of all pixels relative to the probe pixel in the rectangular neighborhood centered on the probe pixel can be obtained. The number of fringe patterns required for the PMD is reduced to one by displaying a spatial-carrier orthogonal fringe pattern. Finally, the feasibility of the algorithm is verified by simulation and experiment.
Effect of recurrent infomax on the information processing capability of input-driven recurrent neural networks

Reservoir computing is a framework for exploiting the inherent transient dynamics of recurrent neural networks (RNNs) as a computational resource. On the basis of this framework, much research has been conducted to evaluate the relationship between the dynamics of RNNs and the RNNs’ information processing capability. In this study, we present a detailed analysis of the information processing capability of an RNN optimized by recurrent infomax (RI), an unsupervised learning method that maximizes the mutual information of RNNs by adjusting the connection weights of the network. The results indicate that RI leads to the emergence of a delay-line structure and that the network optimized by the RI possesses a superior short-term memory, which is the ability to store the temporal information of the input stream in its transient dynamics.
A 2020 perspective on “Online guest profiling and hotel recommendation”: Reliability, Scalability, Traceability and Transparency

Tourism crowdsourcing platforms accumulate and use large volumes of feedback data on tourism-related services to provide personalized recommendations with high impact on future tourist behavior. Typically, these recommendation engines build individual tourist profiles and suggest hotels, restaurants, attractions or routes based on the shared ratings, reviews, photos, videos or likes. Due to the dynamic nature of this scenario, where the crowd produces a continuous stream of events, we have been exploring stream-based recommendation methods, using stochastic gradient descent (SGD), to incrementally update the prediction models and post-filters to reduce the search space and improve the recommendation accuracy. In this context, we offer an update and comment on our previous article (Veloso et al., 2019a) by providing a recent literature review and identifying the challenges laying ahead concerning the online recommendation of tourism resources supported by crowdsourced data.
Software Design Patterns Recommendation : A Systematic Literature Review

Design patterns are essential for development. It provides reusability, modularization, quality and consistency between design and implementation. Expert knowledge required for the selection of correct design patterns. Moreover, the recommendation of design patterns is an important task for software development. Many tools and techniques were proposed to recommend design patterns. The main objective of this research is to perform a systematic literature review based on Kitchenham guidelines. We have analyzed the studies with different perspectives: Study objective, validation, recommendation technique for design pattern and limitation of the study. We have considered the research publication from 2010 to 2019 and examined them on four electronic databases. A total of 22 studies are identified from 2010-2019 and classified them on predefined classification criteria. Overall research findings concluded that anti-pattern detection and selection and fuzzy technique are most widely used but still there is a research gap that exists from the recent 2-years. It was observed that the traditional and most practiced recommendation techniques are not used in software design patterns recommendation.
Object detection in optical remote sensing images by integrating object-to-object relationships

In recent years, deep-learning-based methods for remote sensing image interpretation have undergone rapid development, due to the increasing amount of image data and the advanced techniques of machine learning. The abundant spatial and contextual information within the images is helpful to improve the interpretation performance. However, the contextual information is ignored by most of the current deep-learning-based methods. In this letter, we explore the contextual information by taking advantage of the object-to-object relationship. Then, the feature representation of the individual objects can be enhanced. To be specific, we first build a knowledge database which reveals the relationship between different categories and generate a region-to-region graph that indicates the relationship between different regions of interest (RoIs). For each RoI, the features of its related regions are then combined with the original region features, and the fused features are finally used for object detection. The experiments conducted on a public ten-class object detection dataset demonstrate the validity of the proposed method.
Research on association rules of course grades based on parallel FP-Growth algorithm

With the rapid growth of massive data in all walks of life, massive data faces enormous challenges such as storage capacity and computing power. In Chinese universities, traditional data analysis of student course cannot meet the growing demand for increasing data size and real-time computation of big data. In this paper, a parallel FP-Growth algorithm based on split is proposed. The established FP-Tree is split into blocks, and the split FP-Trees are equally divided into different nodes. The monitoring point is set up to monitor the operation of other nodes, dynamically migrate tasks and maintain load balancing. The experiment proves that each node has good load balancing with the given support degree, and the improved algorithm has better running performance than the classic FP-Growth algorithm in parallel processing. Finally, the parallel FP-Growth algorithm based on split is implemented on Hadoop to mine association rules between course grades. The mining process includes data preprocessing, mining results and analysis. The association rules between course grades provide suggestions for the way students learn and the way teachers teach.
Fusing Vector Space Models for Domain-Specific Applications

We address the problem of tuning word embeddings for specific use cases and domains. We propose a new method that automatically combines multiple domain-specific embeddings, selected from a wide range of pre-trained domain-specific embeddings, to improve their combined expressive power. Our approach relies on two key components: 1) a ranking function, based on a new embedding similarity measure, that selects the most relevant embeddings to use given a domain and 2) a dimensionality reduction method that combines the selected embeddings to produce a more compact and efficient encoding that preserves the expressiveness. We empirically show that our method produces effective domain-specific embeddings that consistently improve the performance of state-of-the-art machine learning algorithms on multiple tasks, compared to generic embeddings trained on large text corpora.
Asymptotic Meta Learning for Cross Validation of Models for Financial Data

Meta learning is an advanced field of artificial intelligence where automatic learning algorithms are applied to acquire learning experience for a set of learning algorithms to improve learning performance. One of popular meta learning methodologies is based on cross validation, especially for selection processes among different machine learning models. However, the challenge is that it is very time-consuming to do cross validation among models in large data sets, especially in financial big data with high noise. This article proposes two asymptotic meta learning algorithms (AML-Lin and AML-Xiang), which are ordinal optimization algorithms for meta learning based on cross validation. The numerical experiments and real-world cases are conducted to illustrate its efficiency in cross validation of models in different scenarios, especially for financial data. The method proposed in this article has significant improvement by comparing with those ones in existing algorithms OCBA and IAML (e.g., see the work done by Chen et al. and Lin et al.),8 ,9 and it is new in dealing with financial data.
Recurrent Interval Type-2 Fuzzy Wavelet Neural Network with Stable Learning Algorithm: Application to Model-Based Predictive Control

Fuzzy neural networks, with suitable learning strategy, have been demonstrated as an effective tool for online data modeling. However, it is a challenging task to construct a model to ensure its quality and stability for non-stationary dynamic systems with some uncertainties. To solve this problem, this paper presents a novel identification model based on recurrent interval type-2 fuzzy wavelet neural network (RIT2FWNN) with new learning algorithm. The model benefits from both advantages of recurrent and wavelet neural networks such as use of temporal data and fast convergence properties. The proposed antecedent and consequent parameters update rules are derived using sliding-mode-control-theory. To evaluate the proposed fuzzy model, it is utilized to design a nonlinear model-based predictive controller and is applied for the synchronization of fractional-order time-delay chaotic systems. Using Lyapunov stability analysis, it is shown that all update rules of the parameters are uniformly ultimately bounded. The adaptation laws obtained in this method are very simple and have closed forms. Some stability conditions are derived to prove learning dynamics and asymptotic stability of the network by using an appropriate Lyapunov function. The efficacy and performance of the proposed method is verified by simulation examples.
A Secure IoT-Fog-Cloud Framework Using Blockchain Based on DAT for Mobile IoT

Internet of Things (IoT) devices are becoming important and familiar products in our everyday lives. Yet, many of these IoT devices have a multitude of vulnerabilities that create a security risk, despite being products that bring a lot of convenience to our lives. Blockchain (BC) technology is gaining popularity these days due to the importance of data security. However, this BC technology is not suitable for IoT devices, because it requires a lot of computing power also IoT devices uses a limited amount of memory and has a lower performing CPU for cost reasons. Also, IoT devices collect a large amount of data and transmit that data every hour and every day to a cloud system to analyze or process the collected data. For this reason, network latency is an important factor in a cloud computing system. In this paper, we propose a secure and distributed 10T-Fog-Cloud Framework using BC to compensate for the security weaknesses of IoT nodes, the delay aware tree construction (DATC) algorithm that considers the service delay for fog-cloud computing, and the mobility of mobile IoT nodes for BC to deal with the triangular routing problem. To verify this proposed framework, we performed security analysis and experiments for the effectiveness of the algorithm through simulation and confirmed that the overall service delay was reduced by choosing the minimum delay path by the DATC algorithm.
High Capacity Reversible Data Hiding for VQ-Compressed Images Based on Difference Transformation and Mapping Technique

In this paper, two reversible data hiding (RDH) methods for vector quantization (VQ)-compressed images are presented.In the first method, the difference-index table instead of the standard-index table is designed, and all difference indices are then classified into two groups, i.e., the tobe-mapped group and the candidate group, according to the occurrence frequencies of them.Afterwards, the mapping table between those two groups is adaptively constructed and used to serve for data embedding.Furthermore, another joint method is conducted by combining the standard-index based method and the difference-index based method, resulting in a better embedding performance.The experimental results confirm that both proposed methods, especially the joint method, are significantly superior to the most recent schemes in terms of the embedding capacity, bitrate and embedding rate.
How to Construct CSIDH on Edwards Curves

CSIDH is an isogeny-based key exchange protocol proposed by Castryck, Lange, Martindale, Panny, and Renes in 2018. CSIDH is based on the ideal class group action on $$\mathbb {F}_p$$-isomorphism classes of Montgomery curves. In order to calculate the class group action, we need to take points defined over $$\mathbb {F}_{p^2}$$. The original CSIDH algorithm requires a calculation over $$\mathbb {F}_p$$ by representing points as x-coordinate over Montgomery curves. Meyer and Reith proposed a faster CSIDH algorithm in 2018 which calculates isogenies on Edwards curves by using a birational map between a Montgomery curve and an Edwards curve. There is a special coordinate on Edwards curves (the w-coordinate) to calculate group operations and isogenies. If we try to calculate the class group action on Edwards curves by using the w-coordinate in a similar way on Montgomery curves, we have to consider points defined over $$\mathbb {F}_{p^4}$$. Therefore, it is not a trivial task to calculate the class group action on Edwards curves with w-coordinates over only $$\mathbb {F}_p$$. In this paper, we prove a number of theorems on the properties of Edwards curves. By using these theorems, we extend the CSIDH algorithm to that on Edwards curves with w-coordinates over $$\mathbb {F}_p$$. This algorithm is as fast as (or a little bit faster than) the algorithm proposed by Meyer and Reith.
Content-based Node2Vec for representation of papers in the scientific literature

Lower-dimensional representation of scientific text has attracted much attention among researchers due to its impact on many data mining and recommendation tasks. This paper studies two main research streams in scientific literature representation. First, both local and distributed representation viewpoints are reviewed and their advantages and disadvantages in lower dimensional representation are discussed. The paper then proposes a novel hybrid distributed technique for text representation. Using scientific articles as the major source of textual information, both the article’s content and citation network are used to build a distributed and universal lower dimensional representation. The superiority of the new technique to the traditional methods is then justified in predicting the existence of links in large citation graphs.
Quantum Broadcast and Multicast Schemes Based on Partially Entangled Channel

In the study of quantum information transmission based on entanglement, the decoherence induced by surrounding environment has always been one of major factors affecting the effect of quantum state transfer. Therefore, the research on information transmission in partially entangled channels of quantum network is of academic value and general application. Meanwhile, as multi-participating information transmission modes, broadcast and multicast communications are two important applications in quantum information transmission and quantum computation. In this paper, we propose the quantum broadcast and multicast schemes based on partially entangled states. The schemes allow the different recipients to obtain the arbitrary single-qubit states synchronously. We verify the feasibility of the schemes and obtain the success probability of information transmission under the partially entangled channels.
Replaceability Based Web Service Selection Approach

Web services are important components of modern day software. Many services are composed on the fly to offer the required business functionality. Web services run in very dynamic environment and are prone to failure or run time change in service quality. On the occurrence of failure or unavailability of any component service, replacement by an equivalent service is required. In this paper, a web service selection approach by taking into cognizance the replaceability of service as the basis of selection is used. At the time of selection, the replaceability of a web service is determined using improved PROMETHEE method. The task of obtaining replacement service at run time is very expensive and challenging. The experimental results depict the improvements achieved in the results of selection. The experiments are conducted by taking QoS dataset of real world web services.
Binary Firefly Algorithm for Feature Selection in Classification

The large dimension of datasets influences on the classification performances and computational time. For that, feature selection is among process that avoid of those problems by selecting the relevance and eliminate the redundancy features.In this paper, we propose a new algorithm for feature selection FAFS (Firefly Algorithm for Feature Selection) which is based on the firefly algorithm. FAFS uses two objectives, which are Accuracy Rate and Reduction Rate. We propose a new formula to calculate the distance r and attractive A in Firefly algorithm.The experimental results show the capability of the proposed algorithm with three classifiers (KNN, NB, and LDA) and their outperformance against PSO-FS (Particle Swarm Optimization for feature selection).
FACE–LIGHT: Fast AES–CTR Mode Encryption for Low-End Microcontrollers

In this paper, we revisited the previous Fast AES–CTR mode Encryption (FACE) method for high-end processors and tailored the method to the microcontrollers, namely FACE–LIGHT. We targeted the 32-bit counter mode of operation for AES in constant timing. This optimized technique pre-computes the 2 Add-RoundKey, 2 Sub-Bytes, 2 Shift-Rows and 1 Mix-Columns operations. The FACE–LIGHT is implemented on the representative low-end microcontrollers (e.g. 8-bit AVR). The execution timing of AES–CTR algorithm for 128-bit and 256-bit security levels achieved the 2,218 and 3,184 clock cycles, respectively. This is faster than previous works by 22 % for 128-bit security level. The FACE–LIGHT can be used to extend the FACE to round 3. The AES is also implemented to be secure against the CPA (Correlation Power Analysis).
Delivering Scalable Deep Learning to Research with Bridges-AI

Artificial intelligence (AI), particularly deep learning, is enabling tremendous advances and is itself of great research interest. To address these research requirements, the Pittsburgh Supercomputing Center (PSC) expanded its Bridges supercomputer with Bridges-AI, providing the world’s most powerful AI servers to the U.S. national research community and their international collaborators. We describe the motivation and architecture of Bridges-AI and its integration with Bridges, which adds to Bridges’ capabilities for scalable, converged high-performance computing (HPC), AI, and Big Data. We then describe the software environment of Bridges-AI, particularly the introduction of containers for deep learning frameworks, machine learning, and graph analytics, and PSC’s approach to container deployment. We close with a discussion of the range of research challenges that Bridges-AI is enabling breakthroughs, highlighting development of AI-driven methods to identify immune responses with automated tumor detection in breast cancer.
Ontological emergence scheme in self-organized and emerging systems

This paper presents the concept of "Ontological Emergence", a process that seeks to adapt an ontology to the changes and new components in a self-organized and emergent system, through the application of a set of rules that allows the emergence of a new conceptualization (emerging concepts). The Ontological Emergence provides the structuration of the information and knowledge that could be generated in the system, creating conceptual models that can adequately represent the new behavior that is emerging. It arises from the need to represent ontologically a conceptualization of a reality that is dynamic, which cannot be pre-defined or pre-determined, in order to generate emerging knowledge models that follows the scalability and the evolution of it. In that sense, in this paper is proposed an "Ontological Emergence Scheme" based on a set of processes of registration, monitoring, analysis and adaptation of the various conceptual models that interact in the system, as well as on some processing rules in regard to requirements and information of the context, in order to allow the ontological emergence. In this proposal scheme, the Meta-ontologies guide the ontological emergence process through the definition of general categories, to facilitate the integration of concepts from different ontologies or data sources. Finally, the paper presents some case studies, showing its utility in self-organized and emergent systems.
A Data Storage and Sharing Scheme for Cyber-Physical-Social Systems

Cyber-Physical-Social System (CPSS) provides users secure and high-quality mobile service applications to share and exchange data in the cyberspace and physical world. With the explosive growth of data, it is necessary to introduce cloud storage service, which allows devices frequently resort to the cloud for data storage and sharing, into CPSS. In this paper, we propose a data storage and sharing scheme for CPSS with the help of cloud storage service. Since data integrity assurance is an inevitable problem in cloud storage, we first design a secure and efficient data storage scheme based on the technology of public auditing and bilinear map, which also ensures the security of the verification. In order to meet the real-time and reliability requirements of the CPSS, the rewards of timeliness incentive and effectiveness incentive are considered in the scheme. Secondly, based on the proposed storage scheme and ElGamal encryption, we propose a lightweight access model for users to access the final data processed by cloud server. We formally prove the security of the proposed scheme, and conduct performance evaluation to validate its high efficiency. The experimental results show that the proposed scheme has lower overheads in communication and access as compared to the technique CDS.