Reviewerly/exHarmony huggingface dataset:
A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition

Brain storm optimization (BSO) algorithm is a simple and effective evolutionary algorithm. Some multiobjective brain storm optimization algorithms have low search efficiency. This paper combines the decomposition technology and multiobjective brain storm optimization algorithm (MBSO/D) to improve the search efficiency. Given weight vectors transform a multiobjective optimization problem into a series of subproblems. The decomposition technology determines the neighboring clusters of each cluster. Solutions of adjacent clusters generate new solutions to update population. An adaptive selection strategy is used to balance exploration and exploitation. Besides, MBSO/D compares with three efficient state-of-the-art algorithms, e.g., NSGAII and MOEA/D, on twenty-two test problems. The experimental results show that MBSO/D is more efficient than compared algorithms and can improve the search efficiency for most test problems.
Multipopulation-based multi-level parallel enhanced Jaya algorithms

To solve optimization problems, in the field of engineering optimization, an optimal value of a specific function must be found, in a limited time, within a constrained or unconstrained domain. Metaheuristic methods are useful for a wide range of scientific and engineering applications, which accelerate being able to achieve optimal or near-optimal solutions. The metaheuristic method called Jaya has generated growing interest because of its simplicity and efficiency. We present Jaya-based parallel algorithms to efficiently exploit cluster computing platforms (heterogeneous memory platforms). We propose a multi-level parallel algorithm, in which, to exploit distributed-memory architectures (or multiprocessors), the outermost layer of the Jaya algorithm is parallelized. Moreover, in internal layers, we exploit shared-memory architectures (or multicores) by adding two more levels of parallelization. This two-level internal parallel algorithm is based on both a multipopulation structure and an improved heuristic search path relative to the search path of the sequential algorithm. The multi-level parallel algorithm obtains average efficiency values of 84% using up to 120 and 135 processes, and slightly accelerates the convergence with respect to the sequential Jaya algorithm.
Context-aware energy optimization for perpetual IoT-based safe communities

The IoT revolution has provided a promising opportunity to build powerful perpetual awareness systems. Perpetual awareness systems are sensing systems characterized by continuous monitoring and ubiquitous sensing; they are essential to many safety and mission-critical applications, e.g. assisted living, healthcare and public safety. In this paper, we present SAFER, a perpetual heterogeneous IoT system; deployed in homes to detect critical events (injury, hazardous-environment) that must trigger immediate action and response. A key challenge here is the energy consumption associated with perpetual operations. We propose a novel energy-aware perpetual home IoT system where battery-operated and wall-powered IoT devices co-execute to ensure safety of occupants. We use a semantic approach that utilizes context of extracted activities of daily living (ADLs) from device data to drive energy optimized sensor activations. To validate our approach, we developed an elderly fall detection system using multi-personal and in-situ sensing devices derived from real world deployments in SCALE project which has been deployed in Montgomery county, MD. Using initial measurements to drive larger simulations, we show that proposed Cost-Function-Gradient algorithm can achieve greater than 4X reductions in energy dissipation and doubling system of battery powered devices lifetime without loss of sensing accuracy.
Hyperovals and bent functions

We consider Niho bent functions (they are equivalent to bent functions which are linear on the elements of a Desarguesian spread). Niho bent functions are in one-to-one correspondence with line ovals in an affine plane. Furthermore, Niho bent functions are in one-to-one correspondence with ovals (in a projective plane) with nucleus at a designated point. We introduce a new analog of o-polynomials for description of hyperovals and present a criteria for existence of such polynomials. These connections allow us to present new simple descriptions of the Adelaide and Subiaco hyperovals and their automorphism groups. There are notions of duality for bent functions and for projective planes, we show that these notions are consistent for Niho bent functions.
Swarm intelligence clustering ensemble based point of interest recommendation for social cyber-physical systems

Cyber-physical Social Network (CPSN) becomes an essential component of daily life. In recent years, CPSN has dragged millions of users to convey their social opinions. It is highly desirable to mine influential features from such diverse data to make a prediction on users' Point of Interest (POI). Notably, social ties of the user in a specific location have a tendency to share similar opinions. Thus with the appearance of social links, the location based recommendations become popular to acquire reliable POI recommendations. Collaborative Filtering Recommender System (CFRS) able to discover reliable POI recommendations for the target user based on Location-based CPSN. To enhance the performance of CFRS, a clustering ensemble model is proposed in this article. Four different swarm intelligent based cluster optimization algorithms were utilized to generate finite clusters. The experiment is conducted on two real-time social network dataset to exhibit the performance of the proposed CE-CFRS. The result shows that the clustering ensemble model outperforms a single clustering model in terms of assessment metrics.
Multi-modal Spectral Image Super-Resolution

Recent advances have shown the great power of deep convolutional neural networks (CNN) to learn the relationship between low and high-resolution image patches. However, these methods only take a single-scale image as input and require large amount of data to train without the risk of overfitting. In this paper, we tackle the problem of multi-modal spectral image super-resolution while constraining ourselves to a small dataset. We propose the use of different modalities to improve the performance of neural networks on the spectral super-resolution problem. First, we use multiple downscaled versions of the same image to infer a better high-resolution image for training, we refer to these inputs as a multi-scale modality. Furthermore, color images are usually taken at a higher resolution than spectral images, so we make use of color images as another modality to improve the super-resolution network. By combining both modalities, we build a pipeline that learns to super-resolve using multi-scale spectral inputs guided by a color image. Finally, we validate our method and show that it is economic in terms of parameters and computation time, while still producing state-of-the-art results (Code at https://github.com/IVRL/Multi-Modal-Spectral-Image-Super-Resolution ).
A Session-Based Customer Preference Learning Method by Using the Gated Recurrent Units With Attention Function

In this paper, we investigate an attention function combined with the gated recurrent units (GRUs), named GRUA, to raise the accuracy of the customer preference prediction. The attention function extracts the important product features by using the time-bias parameter and the term frequency-inverse document frequency parameter for recommending products to a customer in the ongoing session. We show that the attention function with the GRUs can learn the customer's intention in the ongoing session more precisely than the existing session-based recommendation (SBR) methods. The experimental results show that the GRUA outperforms two SBR methods: the stacked denoising autoencoders with collaborative filtering (SDAE/CF) and the GRUs with collaborative filtering (GRU/CF) based on the precision and recall evaluation metrics. The data from three publicly available datasets, the Amazon Product Review dataset, the Xing dataset, and the Yoo-Choose Click dataset, are used to evaluate the performance of the GRUA with the SDAE/CF and the GRU/CF. This paper shows that adopting the attention function into the GRUs can dramatically increase the accuracy of the product recommendation in the SBR.
Big Data Analytics for Intelligent Internet of Things

The Internet of Things (IoT) is going to be the next technological revolution. According to the Internet, the revenue generated from IoT products and services are going to be approximately 300 billion in 2020. Simultaneously, with the massive amount of data that the IoT will generate, its impact will be reflected across the entire Big data universe that will coerce the organizations to upgrade current tools and technology to evolve to accommodate this additional data volume and take advantage of the insights. IoT and Big data basically are two sides of the same coin according to some experts. It is a challenging task to manage and extract insights from IoT data. Therefore, a proper analytics platform/infrastructure to analyse the IoT data is a vital aspect for any organization when it is also true that not all IoT data is important.
SemTra: A semi-supervised approach to traffic flow labeling with minimal human effort

Network traffic classification is an essential component for service differentiation, network design and management and security systems. The limitations of traditional port-based and payload methods have been addressed by recent promising studies which rely on the analysis of the statistics of traffic flows and the use of machine learning techniques. However, due to the high cost of manual labeling, it is hard to obtain sufficient, reliable and up-to-date labeled data for effective IP traffic classification. This paper proposes a novel semi-supervised approach, called SemTra, which automatically alleviates the shortage of labeled flows for machine learning by exploiting the advantages of both supervised and unsupervised models. In particular, SemTra involves the following: (i) generating multi-view representations of the original data based on dimensionality reduction methods to have strong discrimination ability, (ii) incorporating the generated representations into the ensemble clustering model to provide a combined clustering output with better quality and stability, (iii) adapting the concept of self-training to iteratively utilize the few labeled data along with unlabeled within local and global viewpoints; and (iiii) obtaining the final class decision by combining the decisions of mapping strategy of clusters, the local self-training and global self-training approaches. Extensive experiments were carried out to compare the effectiveness of SemTra over representative semi-supervised methods using sixteen network traffic datasets.
A parameterized model to select discriminating features on keystroke dynamics authentication on smartphones

Nowadays, smartphones work not only as personal devices, but also as distributed IoT edge devices uploading information to a cloud. Their secure authentications become more crucial as information from them can spread wider. Keystroke dynamics is one of prominent candidates for authentications factors. Combined with PIN/pattern authentications, keystroke dynamics provide a user-friendly multi-factor authentication for smartphones and other IoT devices equipped with keypads and touch screens. There have been many studies and researches on keystroke dynamics authentication with various features and machine-learning classification methods. However, most of researches extract the same features for the entire user and the features used to learn and authenticate the user's keystroke dynamics pattern. Since the same feature is used for all users, it may include features that express the users' keystroke dynamics well and those that do not. The authentication performance may be deteriorated because only the discriminative feature capable of expressing the keystroke dynamics pattern of the user is not selected. In this paper, we propose a parameterized model that can select the most discriminating features for each user. The proposed technique can select feature types that better represent the user's keystroke dynamics pattern using only the normal user's collected samples. In addition, performance evaluation in previous studies focuses on average EER(equal error rate) for all users. EER is the value at the midpoint between the FAR(false acceptance rate) and FRR(false rejection rate), FAR is the measure of security, and FRR is the measure of usability. The lower the FAR, the higher the authentication strength of keystroke dynamics. Therefore, the performance evaluation is based on the FAR. Experimental results show that the FRR of the proposed scheme is improved by at least 10.791% from the maximum of 31.221% compared with the other schemes.
SIFT Detector Boosted by Adaptive Contrast Threshold to Improve Matching Robustness of Remote Sensing Panchromatic Images

Although scale invariant feature transform (SIFT) based algorithms have a wide range of applications in remote sensing panchromatic image matching, but minor changes in the contrast threshold can bring about drastic changes in the image matching quality. In order to effectively improve the matching quality of the SIFT detector, this paper proposes an adaptive contrast threshold-SIFT (ACT-SIFT) procedure. The ACT-SIFT method set forth herein harmoniously calculates two sought after contrast thresholds for the target and the reference images separately an objective achieved through minimizing certain proposed criterion. As an introductory step, the entropy in the keypoints (EiK) is defined as a control or balancing parameter being estimated in terms of the entropy of the image and the number of the keypoints. To that end, the candidate keypoints are first extracted from the scale space of SIFT method by applying the initial contrast threshold for both images. Then, the contrast threshold values are modified iteratively for both images to reach the needed values by minimizing the relative difference between the EiK of the target and the reference images. Next, in agreement with the threshold values obtained, the keypoints are extracted with the help of the SIFT keypoint detector. The correct matching pairs are also created using the matches acquired through the SIFT descriptor. The results obtained by applying our proposed approach promise boosted matching pairs in remote sensing image correspondences, for it extracts the keypoints in a robust manner by simultaneously checking the information content of the reference and the target images.
Vagueness in Implicature: The Case of Modified Adjectives

We show that the interpretation of sentences like John is not very Adj depends on whether Adj is vague. We argue that this follows from a constraint on the interaction between vagueness and conversational implicature, a domain that has not been studied extensively. The constraint states that implicatures are not drawn if they lead to “borderline contradictions” (see Ripley 2011; Alxatib & Pelletier 2011; a.o.), a natural extension of the idea that implicatures should not contradict assertions (Hackl 2006; Fox 2007; a.o.). Experiment 1 establishes that not very Adj gives rise to the implicature Adj for the non-vague absolute adjective late, but not for the vague relative adjective tall (in the terminology of Kennedy & McNally 2005a). Experiment 2 generalizes this result to three relative adjectives in the positive form (tall, hot, fast), against those same adjectives in their (non-vague) comparative forms (taller/hotter/faster than the average X). We also constructed quantitative meaning representations for complex predicates of the form Adj |$ \wedge \neg $|very Adj, using fuzzy logic to model the contribution of boolean connectives and our experimental data to represent the meanings of adjectives. The results of these analyses suggest that strengthening not very Adj with Adj leads to a more contradictory interpretation when Adj is vague than when it is not, as expected on our theory. While our results apply directly to only a specific set of lexical items, we hypothesize that they reflect a more general pattern among gradable predicates. This motivates more systematic investigation into the role that vagueness can play in the derivation of conversational implicatures.
Dual-biprism-based digital image correlation for defect detection of pipelines

A dual-biprism-based stereo camera system, with an imaging model derived from geometrical optics analysis, is proposed for detection of defects on the external and internal surfaces of pipelines. The proposed system, which comprises two biprisms, a lens, a single camera, and common light sources, overcomes the difference in the camera specifications and synchronization mechanism of conventional two-camera systems. Moreover, different fields of views can be obtained by adjusting the angles of the two prisms and the distance between the dual-biprism and the camera. Measurements can be made even for regions in narrow and small areas using a coaxial illumination and speckle-based three-dimensional digital image correlation method. After a typical experiment performed to verify the efficacy of the proposed system, three different types of defects were accurately detected: (1) a local indentation on the external pipe wall; (2) a weld defect in the pipeline that was measured, and the shape and location retrieved after image processing; and (3) a crack on the internal surface of the pipe determined based on strain calculation. These results validate the proposed system as an efficient and convenient tool for defect detection.
Implementation of Robo-Advisor Services for Different Risk Attitude Investment Decisions Using Machine Learning Techniques

In this paper we have researched how to use machine learning in the financial industry on the example of robo-advisor; defined the basic functionality of robo-advisor, an implementation of robo-advisor based on analysis of the most popular financial services, such as Betterment, FutureAdvisor, Motif Investing, Schwab Intelligent and Wealthfront. We have also compared their functionality, formulated a list of critical features and described our own high-level architecture design of a general robo-advisor tool for private investors. Our goal is to build three application modules for a single robo-advisor which combines its architecture and modern financial instruments – cryptocurrencies for the first time. The first module is a Long short-term memory (LSTM) neural network, which forecasts cryptocurrencies prices daily. As a result of simulation experiment through the application using real data from open sources, we have found that the combination of criterion can explain 61% of cryptocurrencies prices variation. The second module uses robo-advising approach to build an investment plan for novice cryptocurrencies investors with different risk attitude investment decisions. The third module is ETL (Extract-Transform-Load) for a statistics dataset and neural networks models. Results of the investigation show that investing in cryptocurrencies can give 23.7% per year for risk-averse, 31.8% per year for risk-seeking investors and 16.5% annually for investors of hybrid type.
Facial Expression Recognition Using Graph Signal Processing on HOG

The Histogram of Oriented Gradient (HOG) has been found to be an effective method for the face detection as well as the facial expression recognition. However, due to the large feature length of the facial expression, there is a challenge to decrease the size of the feature vector. A novel method is proposed using the graph signal processing (GSP) with the HOG to reduce the length of the feature vector and to increase the accuracy for recognizing the facial expression. The proposed method is demonstrated on CK+ and JAFFE database and the experimental performance is compared with the results of other methods using the same databases. The better recognition rate with the significant reduction in the feature length in the experimental results shows the effectiveness of the proposed algorithm.
Finite-key analysis of practical decoy-state measurement-device-independent quantum key distribution with unstable sources

Measurement-device-independent quantum key distribution (MDI-QKD) is immune to detector side channel attacks, which is a notorious security loophole problem in QKD. Importantly, both finite-key effects and intensity fluctuations of the practical photon sources are nonnegligible in a full key rate analysis of practical QKD. In this paper, for the case with finite-key effects and without intensity fluctuations, we present a way to estimate the phase error rate for MDI-QKD and compare performances of the parameter estimation based on Hoeffding’s inequality and the Chernoff bound. By using Azuma’s inequality, we also present the finite-key analysis with composable security against general attacks for a biased decoy-state MDI-QKD protocol with intensity fluctuations. Our simulation results show that the effect of our phase error rate estimation is almost the same as the results in Phys. Rev. A91, 022313 (2015)PLRAAN1050-294710.1103/PhysRevA.91.022313, and the parameter estimation using the Chernoff bound is tighter than that using Hoeffding’s inequality. In addition, the influence of intensity fluctuations is more obvious when the data size of total transmitting signals is small. Our results highlight that it is important to keep the stability of the intensity modulator and estimate accurately the emitted pulse’s intensity for practical implementations.
Twin Bounded Weighted Relaxed Support Vector Machines

Data distribution has an important role in classification.The problem of imbalanced data has occurred when the distribution of one class, which usually attends more interest, is negligible compared with other class.Furthermore, by the existence of outliers and noise, the classification of these data confronts more challenges.Despite these challenges, doing fast classification with good performance is desired.One of the successful classifier methods for dealing with imbalanced data and outliers is weighted relaxed support vector machines (WRSVMs).In this paper, the improved twin version of this classifier, which is called twin-bounded weighted relaxed support vector machines, is introduced to confront the mentioned challenges; besides, it performs in a significant fast manner and it is more accurate in most cases.This method benefits from the fast classification manner of twin-bounded support vector machines and outlier robustness capability of WRSVM in the imbalanced problems.The experimentally, the proposed method is compared with the WRSVM and other standard SVM-based methods on the public benchmark datasets.The results confirm the efficiency of the proposed method.