Reviewerly/exHarmony huggingface dataset:
Quantum algorithm for multiple signal classification

The direction of arrival (DOA) estimation in array signal processing is an important research area. The effectiveness of the direction of arrival greatly determines the performance of multi-input multi-output (MIMO) antennas. The multiple signal classification (MUSIC) algorithm, which is the most canonical and widely used subspace-based method for direction angle estimation, has a moderate estimation performance of DOA. In this article, we present a quantum algorithm for performing MUSIC. Compared with the best-known classical algorithm, quantum MUSIC can show a polynomial speedup. In our scheme, we first efficiently implement a non-Hermitian chain product subroutine for the matrix ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{R}}_{ss}\ensuremath{\in}{R}^{N\ifmmode\times\else\texttimes\fi{}N}$ with $L$ snapshots, where the method is different from that of Cong and Duan [I. Cong and L. Duan, New J. Phys. 18, 073011 (2016)]. In this subroutine, we perform amplitude transduction [Y. R. Sanders, G. H. Low, A. Scherer, and D. W. Berry, Phys. Rev. Lett. 122, 020502 (2019)] by testing an inequality to neglect the calculation of the approximate arcsine function. Second, a quantum labeling operation based on the principal eigenvalues is proposed for preparing the coherent state of the principal eigenvectors. Finally, we generalize our results to some quantum algorithms with quantum principal component analysis subroutines and some existing quantum algorithms where the covariance matrices occur in the non-Hermitian chain product form.
The Poincaré-Bendixson theorem in Isabelle/HOL

The Poincaré-Bendixson theorem is a classical result in the study of (continuous) dynamical systems. Colloquially, it restricts the possible behaviors of planar dynamical systems: such systems cannot be chaotic. In practice, it is a useful tool for proving the existence of (limiting) periodic behavior in planar systems. The theorem is an interesting and challenging benchmark for formalized mathematics because proofs in the literature rely on geometric sketches and only hint at symmetric cases. It also requires a substantial background of mathematical theories, e.g., the Jordan curve theorem, real analysis, ordinary differential equations, and limiting (long-term) behavior of dynamical systems.
A Systematical Review Study to Investigate the Use of Biometric Security Techniques in Automatic Teller Machines: Insight from the Past 15 Years

In a Technological financial world where everything is progressing very fast and the amount of financial transactions through the automatic teller machines (ATM) on a daily basis is increasing. In parallel to these developments, fraudulent attacks and identity theft targeting to access the ATM and steal money or use bank accounts. Those attacks are always possible through the weak security points in the ATMs. The main aim of this study is to investigate and categorize the different approaches taken to overcome those weak security points by using biometric data along with some traditional methods. Accordingly, a systematical mapping was conducted by focusing on research studies published in the years 2004 to 2019 and addressing to the ATMs and biometric security technologies. After an intense investigation, 23 different systems were investigated using different single and multi-biometric models which make use of 8 different biometric technologies and 5 different traditional technologies as key components. As a conclusion from this research that researchers are leaning towards using biometric data in enhancing the security of the ATMs especially in the past 4 years more than they did before.
A Novel Fishing Finder Technique Based on VMS Data in Indonesia

Indonesian waters are very wide and have very high fisheries potential. This clearly benefits the Indonesian fishing industry. Tuna is a primary trading commodity in this industry, but catching this fish is not easy. In addition, the movement of tuna follows the pattern of changes in Earth's temperature, so determining its position clearly requires adequate equipment. The original purpose of Vessel Monitoring System (VMS) is for enforcement and control of vessel sailing. In this paper, we use VMS to find the position of tuna to reduce fishing costs for Indonesia tuna fishermen such as fuel and labor costs. This study does three things. Firstly, conduct tracking modeling of the movements of each type of fishing vessel in Indonesian waters, and use cross-reference with weather data and earth conditions, then filtering the data for fishing tuna vessel only. Secondly, develop visualization techniques resulting from algorithms by programming Python Dash. Finally, develop fish position prediction techniques using the Machine Learning ensemble by Support Vector Machine technology. It concludes that system can predict the location of tuna correctly 97.6%.
A Sub-Linear Scalable MapReduce-based Apriori Algorithm

Association Rule Mining is one of the most popular data analytic algorithms where the well-known Apriori algorithm is its core. Like most machine learning and data mining algorithms, Apriori algorithm is designed for in-memory data. One natural way to cope with this limitation and emerging Bigdata challenges is to adapt the algorithm to parallel and distributed computing infrastructures, particularly a widely used Map-Reduce model. Much research has developed a variety of MapReduce-based Apriori algorithms. However, most have focused on either improving performance over that of the original Apriori algorithm or mechanisms MapReduce infrastructure. This paper presents yet another MapReduce-based Apriori algorithm. Unlike most traditional MapReduce-based Apriori that mimics incremental level-wise computation of the original Apriori, our MapReduce-based algorithm opportunistically exploits the map's keys for non-level-wise parallelism to fully benefit of parallel processing to gain efficiency. The paper describes our proposed approach and shows an empirical evaluation of its performance compared with that of the representative traditional MapReduce-based algorithm. The results show significant improvement with an average reduction of the execution time of about 70%, over 50,000-200,000 transactions on one to three machines, with 10% of support threshold. In fact, the execution time of the proposed MapReduce-based Apriori algorithm is empirically shown to scale sub-linearly (better than linear) in the number of transactions.
Ionospheric science in the age of big data

Ionospheric science is in the middle of a data revolution, and poised to take advantage of modern data science methods that are gaining popularity for scientific applications. This chapter explores the challenges in ionospheric science that can benefit from data science approaches, outlines suitable machine-learning methods, discusses the ways to use these methods to advance scientific inquiry, and the infrastructure needed to facilitate these new endeavors.
Simultaneously Advising via Differential Privacy in Cloud Servers Environment

Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers’ capabilities and make optimal decisions on selecting the best available servers for users’ tasks. We consider the process that users learn servers’ capabilities as a multi-agent Reinforcement learning process. The learning speed and efficiency in Reinforcement learning can be improved by transferring the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by a requirement during experience transfer, which all learning agents in a Reinforcement learning environment must have the completely same available choices, also called actions. To address the above limit, this paper proposes a novel differential privacy agent advising approach in Reinforcement learning. Our proposed approach can significantly improve the conventional advising frameworks’ application when agents’ choices are not the completely same. The approach can also speed up the Reinforcement learning by the increase of possibility of experience transfer among agents with different available choices.
Iterative self-correction for secured images using turbo codes and soft input decryption

An algorithm for improved error correction of the data protected using standard security mechanisms, like Message Authentication Codes, is introduced in this paper. Images are taken as example data, and the correction of images using the additionally available authentication data is investigated. P arts of the protected image, the so called “landmarks”, are considered as the Region of Interest and protected by their respective authentication tags. An iterative process is used as a basis for the learning ability of the algorithm: in every iteration, parts of the decoder’s Trellis path are learned by the algorithm, thereby improving the channel decoding results. In this way, the knowledge gained in the previous iterations of the algorithm is used in the current iteration for further improvement of decoding results. Additionally, iterative processes used for error corrections are supported by authentication tags, which are used as a measure of correction success. Simulation results for images are presented to show the effectiveness of the proposed algorithm. A security analysis of the proposed algorithm is also given in the paper.
Hand Pose Ensemble Learning Based on Grouping Features of Hand Point Sets

In this paper, we mainly consider using 3D point sets as input to deal with the task of 3D hand pose estimation. We make some improvements to PointNet++ structure, including proposing adaptive pooling which introduces the self-attention mechanism to make the network could select features itself, and putting forward an ensemble strategy to fully utilize hand features. These improvements can enhance the expressive ability of features and make full use of the information contained in features. In addition, we propose a data augmentation method for point net, which directly transforms the original point cloud data without the aid of simulation models. Experiments results on three hand pose datasets demonstrate that our method can achieve comparable performance with state-of-the-arts.
Automatic kernel code synthesis and verification

Formal verification of an OS kernel is widely considered a major challenge; however, traditional interactive theorem provers used in OS kernel verification require manually written proofs and come with a non-trivial cost. In this paper, we propose a formal verification framework to build a verifiably correct OS kernel, named iv6, with a high degree of proof automation and a low burden of proof. Using this framework, programmers only need to write the specification as required, and it can be translated into the corresponding implementation of C code automatically. The verification framework can guarantee that the behaviour of the implementation code adheres to its specifications. Iv6 introduces four key ideas to achieve proof automation: its interfaces and corresponding specifications are designed to be finite to avoid unbounded loops or recursion; it separates kernel and user address spaces using a kernel page table isolation approach to simplify reasoning about virtual memory; it partitions the modules of a kernel state machine according to a state transition function to improve verification performance; and to avoid modelling complicated C semantics, it performs verification at the LLVM intermediate representation level. A total of 48 system calls and some high-level system properties in iv6 have been verified using this method. Experience shows that this framework can reduce the impact of human error on kernel development and make the verification of iv6 more efficient and straightforward.
An Improvement of Spatial Fuzzy C-means Clustering Method for Noisy Medical Image Analysis

Medical image Segmentation plays a major role in MRI images processing; it’s performed before the analysis and decision-making stages in several medical processes. Many investigators have developed several Fuzzy C-means methods. In this work, a reliable automatic segmentation algorithm based on the spatial FCM clustering is developed to minimize the effect of noise and intensity inhomogeneities. This approach combines two properties of the Spatial FCM using neighbor’s statistical characteristics and pillar k-means. The proposed system has been implemented with simulink. Experimental results on brain MRI images show the improvement and increase the segmentation accuracy.The Current research work is compared with some wellknown existed methods to show the effectiveness that contributes to the development of tools needed in computer aided diagnosis systems aiming to assist specialists in making diagnosis decisions.
An Information Theoretic Approach to Reveal the Formation of Shared Representations

Modality-invariant categorical representations, i.e., shared representation, is thought to play a key role in learning to categorize multi-modal information. We have investigated how a bimodal autoencoder can form a shared representation in an unsupervised manner with multi-modal data. We explored whether altering the depth of the network and mixing the multi-modal inputs at the input layer affect the development of the shared representations. Based on the activation of units in the hidden layers, we classified them into four different types: visual cells, auditory cells, inconsistent visual and auditory cells, and consistent visual and auditory cells. Our results show that the number and quality of the last type (i.e., shared representation) significantly differ depending on the depth of the network and are enhanced when the network receives mixed inputs as opposed to separate inputs for each modality, as occurs in typical two-stage frameworks. In the present work, we present a way to utilize information theory to understand the abstract representations formed in the hidden layers of the network. We believe that such an information theoretic approach could potentially provide insights into the development of more efficient and cost-effective ways to train neural networks using qualitative measures of the representations that cannot be captured by analyzing only the final outputs of the networks.
Affective Computing and Motivation in Educational Contexts: Data Pre-processing and Ensemble Learning

Affective computing involves the research area focusing on the implementation of systems and mechanisms that are capable of identifying, understanding and elaborating human affections. It is a field that spans computer science, psychology and cognitive science. Nowadays, affective computing is considered to play a vital role in the field of e-learning since knowledge acquisition can be greatly affected by the changing emotional states of learners. The machines should understand the emotional states of students and adapt their behavior to them, thus providing a tailored response to these emotions. Affective computing systems identify the user’s emotional state and react accordingly. In view of the above, this chapter includes a short presentation of the concepts of affective computing tailored to social networking-based learning and learners’ affective states. Special mention is made of students’ frustration as an emotional state that can either influence the students’ learning rates or dropout rates and motivation strategies to overcome problems emerging from negative emotions. This chapter also presents well-known motivational theories as well as pre-processing techniques and ensemble classifiers for sentiment analysis through social networks. Motivation theories concern the support of students to achieve a goal or a certain performance level, leading to goal-directed behaviors. Pre-processing techniques deal with the necessary information to preprocess the reviews in order to find sentiment and make analysis whether it is positive or negative. Finally, Sentiment analysis refers to the use of expert methods (such as natural language processing, text analysis, computational linguistics) to systematically identify, extract, quantify, and study affective states and subjective information.
Design Different: Pen and Paper for Laser Cutting

Interdisciplinary teams and studies need new approaches to design prototypes using tools indistinguishable from the ones they are used to. We utilize a digital pen and physical paper, as the everyday life tools, to build a smart interface for laser-cutters, giving non-technical experienced people the possibility to rapidly, seamlessly, and collaboratively fabricate creative prototypes.
Nipple Detection in Mammogram Using a New Convolutional Neural Network Architecture

Mammogram is an X-ray image of the breast. It plays an important role in the breast cancer early diagnosis. In recent years, computer aided detection (CAD) is used for breast cancer detection. Multi-view of mammograms are needed to achieve high accuracy of automatic detection. Since nipple is the only landmark on mammogram of different views (mediolateral oblique (MLO) and craniocaudal (CC) views), nipple detection becomes the first important step of many CAD systems. Researchers have developed different models to detect nipple in recent 20 years. Grey scale, geometric feature and breast edge's gradient are used to find the nipple on the mammogram. For most methods, MLO and CC views need to be tested separately, and obvious and subtle types of nipples also need different methods to detect. In this paper, a model with deep learning is designed to locate nipples on mammogram of both MLO and CC views. Both obvious and subtle types are used for experiment. Four convolutional neural network blocks are used to attain candidate blocks. Normalization layers are added to the proposed model in order to improve the domain adaptation. Based on the intersection of candidates, the model computes the final block of nipple. In this experiment, train set and test set are randomly attained from Digital Database for Screening Mammography (DDSM). Our proposed method achieved an overall nipple detection accuracy of 98.00 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , which outperformed three comparative methods.
Disruption Management of Multi-objective Flexible Job-Shop Scheduling Problem

Aiming at the problem that the initial scheduling scheme needs to be changed due to the disturbance event in the production process, based on the prospect theory, a disruption management model considering the degree of disturbance perception of the three actors of the customer, the enterprise manager and the workshop worker is proposed. An improved multi-objectives method is proposed to solve the dynamic Job-shop scheduling problem based on disruption management. A quantum genetic algorithm for adaptively adjusting the rotation angle is proposed. The randomness and stability tendency of the cloud model are used to adaptively adjust the rotation angle. Numerical experiments show that the algorithm has good performance for solving disruption management problems.
MLFFNet: Multi-level Feature Fusion Net for Underwater Image Enhancement

Underwater image enhancement has been attracting much attention recently since many underwater vision tasks are relying on the acquisition of clear underwater images. Inspired by the success of deep convolutional neural networks (CNNs) for many high-level vision tasks, in this paper, we propose a multi-scale feature fusion based neural network for underwater image enhancement. First, multi-scale features, including the local features and global features, are extracted. Then, we propose to fuse the global feature with local feature at each scale dynamically. Considering the global features encodes the high-level semantic information and local features holds the structure details at different scales, this fusion strategy is beneficial to underwater image enhancement. Extensive experiments are conducted and the comparisons are made among the state of the art methods as well, where we show great improvements.
Separation of factors important for the adoption of business process management practices in labor-intensive activities

The adoption of business process management practices is subject to assessment based on various influencing factors. Within the work, the factors that appear in the literature were extracted in order to form a list of comprehensive factors that help in assessing the pace and manner of BPM practice adoption in labor-intensive activities. Using the fuzzy Delphi method, a list of factors was drawn up based on expert responses, and considering the labor-intensive character as a specificity within this method, a list was extracted and ranked. By defuzzification of triangular fuzzy numbers, a solution is obtained, based on which the top ranked factor is the human resource management.
Landsat's past paves the way for data democratization in earth science

The Landsat Program has been observing and collecting Earth system data from space since 1972. Over this time, advances in spatial, spectral, and radiometric resolution have allowed the Landsat satellites to evolve into powerful global Earth observation imaging systems. The longevity of the Landsat Program has produced a massive catalogue of imagery data. The sheer magnitude of this collection necessitates the use of scalable techniques to extract value from decades of remote sensing data. With the introduction of commercial cloud computing, it is now possible to cheaply provision resources for processing and storing enormous amounts of such data. Machine learning–based computer vision is advancing at a blistering pace and has shown great promise when applied to satellite imagery. The combination of cloud computing and machine learning offers the ability to analyze the decades of imagery collected by the Landsat Program at a cost never before possible. The focus of this chapter is to discuss the Landsat Program progression, cloud computing, machine learning, and data policy and how all these components contribute to greater understanding on the state of our planet Earth.
An Improved Fuzzy Neural Network for Reinforcement Learning

Reinforcement learning (RL) plays an important role in artificial intelligent (AI) realization. It is a trial-and-error machine learning algorithm for agent's adaptive behavior acquisition in unknown environments. In this paper, an improved fuzzy neural network (iFNN) is proposed with shortcut connection concept for RL. iFNN is based on a self-organizing fuzzy neural network (SOFNN) which is a data-driven fuzzy inference system for RL, and iFNN changes the structure of SOFNN by adding the input vector to units of the middle layer, i.e., fuzzy rules. Furthermore, iFNN is also adopted into multi-layered fuzzy neural network (MLFNN) which is a variation of SOFNN with a deep structure. Goal-navigated exploration experiment results showed the effectiveness of the proposed iFNN.