Reviewerly/exHarmony huggingface dataset:
A Survey on FP-Tree Based Incremental Frequent Pattern Mining

Several methods for efficient mining of frequent patterns (FP) can be found in literature. But most of the approaches assume that the whole dataset to be considered can be stored on the computers on hand main memory and the dataset is static in nature. Practically, none of the transactional datasets are static. The datasets get updated due to inclusion of new transactions or exclusion of obsolete transactions as the time advances or the user may required to generate the frequent patterns for a new threshold value for the updated database. This may generate new frequent patterns or refinement of existing patterns and it becomes practically infeasible if the process starts from scratch. Many methods have been found in literature tried to deal with the issues of incremental frequent pattern mining (FPM) but most of the algorithms are main memory dependent. Therefore in this paper, we are going to discuss some of the algorithms with their pros and cons to see whether the main memory limitation of the existing techniques can be mitigated so that it can be efficiently used in incremental scenario.
Consistent Multi-robot Object Matching via QuickMatch

In this work, we present a novel solution and experimental verification for the multi-image object matching problem. We first review the QuickMatch algorithm for multi-image feature matching and then show how it applies to an object matching test. The presented experiment looks to match features across a large number of images and features more often and accurately than standard techniques. This experiment demonstrates the advantages of rapid multi-image matching, not only for improving system performance, but also for use in new applications, such as object discovery and localization.
Machine Learning in Short Video APP User Activity Prediction

In order to improve the accuracy and reduce the cost of forecasting, this paper uses machine learning related technology to solve this problem in the user activity prediction model of short video industry. Continuous use of short video APP by active users is a sufficient and necessary condition for its success. The prediction of user activity has a direct guiding effect on the subsequent user loss warning. Based on the analysis of the impact on user activity, this paper extracts the characteristics according to registration log, startup log, shooting log and behavior log, and proposes a prediction algorithm based on model fusion for user activity. Based on the experimental data, the results show that the predicted AUC value reached 0.9514.
Hardware/Software-Codesign for Hand Gestures Recognition using a Convolutional Neural Network

Hand Gesture (HG) recognition has been an interesting and challenging scientific problem in computer vision and machine learning. However, achieving high performance and high accuracy is a challenging task in gesture recognition. The intensive computation, the complex backgrounds, conditions of lights and positions are the most remarkable challenges regarding performance and accuracy. Those two aspects are taken into consideration in this paper. It proposes a convolutional neural network (CNN) model that can recognize three static HGs using a simpler architecture and fewer weights than previously proposed networks. The proposed CNN is completely implemented and several optimizations are performed to reduce runtime for frames with 28x28 pixels. The total runtime is 42 ms. Moreover, one of the layers is implemented on hardware(HW) level on ARM-FPGA SoC and it is faster than the software(SW) by 39.8x speedup. The total run time is 23.75 ms in C++/ VHDL. A comparison of the proposed model is made between our accelerated implementation and Caffe, which achieves a speedup of 1.17. The proposed model has an accuracy of 94%.
A Parallel Strategies for 2-D Lifting Wavelet Transform Using GPU

Lifting Wavelet Transform has a huge number of applications in image-processing techniques, it has been applied in field like image compression, fusion, de-noising, et al, but for large scale image, the algorithm is too slow to meet the real-time requirements. To solve this problem, this work proposed a parallel strategy based on GPU for 2-D lifting wavelet transform. Through the performance test and analysis, the proposed lifting scheme achieve considerable speedups compared with CPU version for the same image size.
Exit-Less Hypercall: Asynchronous System Calls in Virtualized Processes

Many projects of virtualized processes are emerging for less overhead than traditional virtual machines and more isolation than containers. A virtualized process uses hardware virtualization to provide a process abstraction. The virtualized processes are deemed as inefficient compared against native processes using system calls since hypercalls they use cause high-overhead context switches. However, current performance of system calls is severely damaged by Kernel Page Table Isolation (KPTI) while hypercalls are unaffected. Unexpectedly, that gives hopes for virtualized processes to reach competitive performance against native processes. In this paper, we propose and implement Exit-Less Hypercall, a new style of execution framework in virtualized processes by introducing asynchronity, new thread models and adaptive migration. We evaluate the prototype and make a detailed analysis on the impacts of context switches from the native and virtualized processes with KPTI. Moreover, the experiments also show that Exit-Less Hypercall achieves a good performance improvement of up to 121% on virtualized processes using legacy hypercalls and even outperforms native processes using legacy system calls with KPTI by 81%.
Analysis of the effect number input and hidden layer variations on the addition kohonen algorithm to backpropagation method

Abstract The process of recognizing data patterns using the method of adding kohonen to back propagation is very influential on the amount of input data and the number of hidden layers. The results of the testing with the addition of information on the back propagation algorithm have better epoch results in testing using input data 8 has an epoch value that is better than testing using the number of input data 3,4,5,6,7. The test results using layer 5 hidden have epoch 15 value which is better than the hidden layer 3, 4,6,7,8.
Data Replication Strategy Using Electre-I Method In Cloud System

Data management used in a cloud environment is always challenging. Data replication is a well-known data management technique that has been commonly adopted by cloud systems as an effective solution to achieve good performance in terms of response time, load balancing and most importantly, high data availability and reliability. To maximize the benefit of data replication, replicas placement and replacement in the system is critical. Several replica placement strategies have been proposed in the literature; some of them were based on criteria such as: mean service time, failure probability, load variance, latency, storage usage and other. For each criterion is assigned a weight by the user which reflects the importance of each criterion. We propose in this paper an original approach based on a multi-criteria decision analysis model called ELECTRE-I (ELimination and Choice Expressing REality). This modeling allows us to improve the selection of the candidate site for replica placement, which have a good impact on the simulation results, and improve performances.
Maximum Bayes Boundary-Ness Training For Pattern Classification

The ultimate goal of pattern classifier parameter training is to achieve its optimal status (value) that produces Bayes error or a corresponding Bayes boundary. To realize this goal without unrealistically long training repetitions and strict parameter assumptions, the Bayes Boundary-ness-based Selection (BBS) method was recently proposed and its effectiveness was clearly demonstrated. However, the BBS method remains cumbersome because it consists of two stages: the first generates many candidate sets of trained parameters by carefully controlling the training hyperparameters so that those candidate sets can include the optimal target parameter set; the second stage selects an optimal set from candidate sets. To resolve the BBS method's burden, we propose a new one-stage training method that directly optimizes a given classifier parameter set by maximizing its Bayes boundary-ness or increasing its accuracy during Bayes error estimation. We experimentally evaluate our proposed method in terms of its accuracy of Bayes error estimation over four synthetic or real-life datasets. Our experimental results clearly show that it successfully overcomes the drawbacks of the preceding BBS method and directly creates optimal classifier parameter status without generating too many candidate parameter sets.
Group system: An efficient dynamic memory management scheme for real-time systems

While dynamic memory management (DMM) has been widely used as one of the basic elements of computer systems, it has been rarely used in real-time systems. DMM in real-time systems must exhibit fast execution, high memory efficiency, and predictable execution time. However, it is difficult to design a DMM scheme that simultaneously satisfies all of these requirements, and therefore DMM has not been widely used in real-time systems. This study proposes a new DMM scheme, named group system, that simultaneously satisfies the three requirements of real-time systems. The new DMM scheme is an extension of buddy systems and provides significantly improved memory efficiency compared to buddy systems while maintaining the advantage of the predictable execution time of buddy systems. Group system shows a slightly better worst-case execution time (WCET) than buddy systems that are known to exhibit excellent execution time, and shows memory efficiency that is close to that of the best fit scheme that is known to have excellent memory efficiency.
Improved Real-time Pedestrian Detection Method

With the development of deep learning, algorithm research in object detection has also made significant progress. At present, there are many excellent object detectors, such as Faster R-CNN, SSD, etc. YOLO is one of the most outstanding detectors and has many practical applications in industry. Based on this, we study how to apply it to real-time pedestrian detection, and improve the detection performance. Based on the YOLO algorithm, we mainly carry out the following work: 1). Apply parameterized ReLU as main activation function of network; The classification subnet is separated from the regression subnet, and the parameters are no longer shared between the two subnetworks to improve the network performance; 2). Design loss function to reduce the effect of foreground-background class imbalance caused by anchors, and the classification is improved by hard sample mining. The weight coefficient is designed to improve the localization of the network for small objects. 3). Design the parameters of the anchors to optimize the localization of pedestrian objects.
Data Security Using Extended ECS (Encryption, Compression, Steganography)

In today’s scenario where everything is internet based, data security is on high demand. Data is considered as a crucial asset and is required to be protected. Data Security includes data confidentiality, integrity, authenticity and much more. Despite of several approaches to data security such as encryption, compression, steganography, data is still prone to potential security threats. The major reason identified is the shortcomings of these individual techniques when certain factors are taken into account. Cryptography ensures security but it comes at cost in terms of time and space. Compression techniques encrypts data and saves disk space but these compressed files can easily be uncompressed by attackers. Steganography a technique which hides data instead of encrypting it also has its merits and demerits. In this paper, approaches for data security (Encryption, Compression, Steganography) and their potential advantages and disadvantages are studied in detail. A comparison of these approaches has been generated successfully and a hybrid approach is proposed named as Extended ECS (Encryption, Compression, and Steganography) by taking selected sub approaches only with aims to overcome the flaws of existing approaches keeping high data security and low complexity as aims in mind.
Contextual Cueing in Virtual (Reality) Environments

Search in repeated contexts can lead to increased search efficiency due to contextual cueing. Contextual cueing has mainly been investigated in two-dimensional displays. However, contextual cueing is affected by depth information as well as by the “realism” of the search environment. To investigate these aspects further, we present a guide to design contextual cueing experiments in virtual three-dimensional environments. Specifically, we provide a general introduction to the Unity gaming engine and scripting in C#. We will focus on experimental workflows, but also cover topics like timing precision, how to process and handle participants’ input, or how to create visual assets and manipulate aspects like color. Ultimately, we will turn the entire project into a virtual reality experiment.
Analytical Approaches for Dynamic Scheduling in Cloud Environments

Brief Biography: Seyyed Ahmad Javadi is a researcher in the Computer Laboratory (CompAcctSys group1) at the University of Cambridge, His primary research interests include cloud computing and the Internet of Things (IoT). His current research involves addressing audit logging challenges in IoT environments, online social platforms, and cloud computing environments.
Solving Travelling Salesman Problem: A Hybrid Optimization Algorithm

There are numerous selection, crossover, and mutation methods suggested in the literature when it comes to genetic algorithms. However, behavior of each different method changes when used in combination with other methods. In this paper, a brief and clear explanation of many popular selection, crossover and mutation techniques has been presented and the combination of various optimization methods using Genetic Algorithm has been implemented to generate a hybrid algorithm as a solution to the well-known NP-hard Travelling Salesman Problem (TSP). In this study, 10 different hybrid algorithms are implemented and experimented. Each of these algorithms are formed combining two different selection methods, 3 different crossover methods and 2 different mutation methods. Each of the ten different algorithms have been implemented and their performance have been tested with two different datasets to understand which algorithm outperforms the others. Performance of the combination of various methods have been presented and the findings illustrated that combination of specific crossover, selection and mutation methods outperform in terms of the ultimate optimal result. The results have been compared with the algorithms in the literature that combines Roulette Wheel Selection (RWS), and Stochastic Universal Selection (SUS); each implemented in combination with Partially Mapped Crossover, Cycle Crossover, and Ordered Crossover. Each combination has been tried on various population sizes, mutation and crossover rates. It is found that combining specific selection, mutation and crossover methods can outperform the methods suggested in the literature in equal circumstances-when the same population size, generation size, mutation and crossover rates are used.
Implementation with R language

This chapter introduces implementations with R for analyzing spatial data. After the brief introduction in Section 7.1, Section 7.2 explains housing price data in Lucas county (Ohio, USA), which we use as an example. Section 7.3 explains handling and plotting of spatial data in R by using a fairly new package, “sf.” Sections 7.4–7.7 explain some of useful R functions for analyzing spatial big data, which we introduced in Chapters 3–6Chapter 3Chapter 4Chapter 5Chapter 6. This may be one of the original contributions of this book.
Multi-user available information providing system for tourism by pointing gesture

This research proposes a tourism information provision system that can be used by multiple users. The system allows that visitors intuitively obtain information of an exhibited object by a pointing gesture. To realize multi-user operation, this paper proposes an authentication method using directional speakers and smartphones. The system is highly robust because it does not malfunction when people cross.
Swap Minimization in Nearest Neighbour Quantum Circuits: An ILP Formulation

Quantum computing (QC) represents a great challenge for both academia and private companies and it is currently pursuing the development of quantum algorithms and physical realizations of quantum computers. Quantum algorithms exploit the concept of quantum bit (qubit). They are implemented by designing circuits which consider an ideal quantum computer where no interaction restriction between qubits is imposed. However, physical realizations of quantum computers are subject to several technological constraints and adjacency between interacting qubits is one of the most common one. To this end, additional gates, referred to as swap, can be added to a quantum circuit to make it nearest neighbour compliant. These additional gates have a cost in terms of reliability of the quantum system, hence their number should be minimized. In this paper we first give some hints about this cutting edge topic. Then we provide a review of the literature solving approaches for the swap minimization problem in quantum circuits and propose an integer linear programming formulation for it. We conclude with some preliminary results on small test instances and future work perspectives.
An Adaptive Modular-Based Compression Scheme for Address Data in the Blockchain System

Blockchain has broad development potential and application prospects. At present, the huge storage volume on each node becomes one of the main bottlenecks that restrict the expansibility of the blockchain system, so optimization for storage mechanism becomes an important issue. In this paper, a modular-based compression scheme is proposed to dramatically reduce the storage volume of the account addresses on each node in the blockchain system. The scheme could achieve optimal compression ratio for the different number of accounts by adaptively select the bit-width of the module for each account. Theoretical analysis and simulation experiments are performed to show the effectiveness of the proposed mechanism.
Efficient Algorithms for Global Outlier Removal in Large-scale Structure-from-Motion

This work addresses the outlier removal problem in large-scale global structure-from-motion. In such applications, outlier removal is very useful to mitigate the deterioration caused by mismatches in the feature point matching step. Unlike existing outlier removal methods, we exploit the structure in multiview geometry problems to propose a dimension reduced formulation, based on which two efficient methods have been developed. The first method considers a convex relaxed ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> minimization and is solved by a single linear programming (LP). The second method approximately solves the ideal ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> minimization by an iteratively reweighted method. The dimension reduction results in a significant speedup of the new algorithms. Further, the iteratively reweighted method can significantly reduce the possibility of removing true inliers. Results show that, compared with state-of-the-art algorithms (e.g., the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> method), the proposed algorithms are more than three times faster and meanwhile produce better consensus sets. Matlab code for reproducing the results is available at https://github.com/FWen/OUTLR.git.
Page Locked GPGPU Rotational Visual Secret Sharing

Visual Secret Sharing (VSS) schemes address the security concerns of digital images. Rotational VSS schemes are unique in that the shares stacked with the specific rotated shares reveal the secret images. However, these schemes iteratively process huge pixels in sequence. We propose a GPGPU based rotational (2, 3) VSS scheme which leverages concurrency inherent in the traditional rotational schemes. The proposed scheme also makes use of contemporary hardware resources efficiently. The reduction in the memory band-width demand and increased data transfer speed further accelerates the speedup of this scheme. A significant improvement in performance with an efficient resource utilization is observed.
A Compendium of Cloud Forensics

The cloud computing environment is one of the most promising technologies in the development of computing resources. The cloud service providers provide almost every resource for computing to their users through the internet. With all its advantages, cloud computing has major security issues. Especially in the case of public clouds, anyone can misuse the services for performing unlawful activities. The traditional approaches used for cyber forensics and network forensics are not adequate for the cloud environment because of many technical constraints. There is a need for setting up effective countermeasures that can help an investigator to identify and track unlawful activities happened in a cloud environment. Cloud forensics is an emerging area of research where the researchers aim to provide effective digital forensic techniques that help in the investigation of digital crimes in a cloud environment. The cloud environment helps to extract data even from devices that are not physically accessible. However, cloud forensics is not as easy as it seems; a lot of the success depends on the cloud service model implemented in the context. Getting the support of cloud service providers in accessing the potential sources of evidence necessary for investigation is also a major concern. Another critical aspect of cloud forensics is dealing with legal issues. This chapter discusses the basic concepts of cloud forensics, its challenges, and future directions.
MPCC

Comparing business KPIs with other market participants through benchmarking is a means for companies to optimize costs. Those collaborative optimizations require data of all participating actors that might include business secrets and, therefore, must be kept private in many cases. This demonstrates the demand for privacy-preserving Big Data analytics. Over the last decades, a variety of mechanisms and protocols that enable privacy-preserving collaborative computations have been presented such as trusted third party (TTP) approaches or secure multi-party computation (MPC). However, existing solutions for privacy-preserving benchmarking often only compute a fixed set of arithmetic functions and usually follow a decentralized communication scheme among all peers. We will instead investigate a generic secure MPC system that (1) can compute more general functions, (2) discuss benefits of a service-provider oriented centralized setup and (3) determine the class of arithmetic functions that the selected secure computation mechanism can compute feasibly in practice.