Reviewerly/exHarmony huggingface dataset:
Improving The Effectiveness of Classification Using The Data Level Approach and Feature Selection Techniques in Online Shoppers Purchasing Intention Prediction

Abstract Online shopping is a form of trading using electronic devices that allows consumers to buy goods or services from sellers via the internet. Other names for these activities are: e-web-shop, e-shop, e-shop, internet shop, web-shop, web-store, online shop, and virtual shop. An online store generates purchases of products or services at retailers or shopping centers, which are referred to as business-to-consumer (B2C) online shopping. n another process where a business buys from another business, it is called business-to-business (B2B). Nowadays online shopping has become more sophisticated with trading via mobile phones (m-commerce). Cellular phones have been optimized with an application to buy from online sites. In this study, we proposed a data level approach and feature selection techniques as a solution for the classification of imbalanced data. The imbalance class classification is one of the classic problems in the field of artificial intelligence, especially for classification in machine learning. Imbalanced data have been proven to reduce the performance of machine learning algorithms, where imbalance data means that the total data from each class is significantly different. The proposed method is evaluated using a dataset from the UCI repository and area under the curve (AUC) as the main evaluation. The results have shown that the proposed method produces good performance. (AUC¿ 0.8). Overall the second experiment outperformed and was better than the first and third experiments because the main evaluation in the unbalanced class classification is AUC. Therefore, it can be concluded that the proposed method produces optimal performance both for large scale data sets. Overall the second experiment outperformed and better than the first and third experiments, because the main evaluation in the unbalanced class classification was AUC.
Denoising Non-stationary Signals by Dynamic Multivariate Complex Wavelet Thresholding

Over the past few years, we have seen an increased need for analyzing the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method to separate and capture the signal and noise components of the series. Traditionally, wavelet thresholding methods rely on the discrete wavelet transforms (DWT), a static thresholding technique that may not capture the time series of the estimated variance in the additive noise process. We introduce a novel continuous wavelet transform (CWT) dynamically-optimized, multivariate thresholding method. Applying this method we are simultaneously able to separate and capture the signal and noise components while estimating the dynamic noise variance. Our method shows improved results when compared to well-known methods, especially for high-frequency signal rich time series, typically observed in finance. Supplementary materials for your article are available online.
Deep Group-Wise Variational Diffeomorphic Image Registration

Deep neural networks are increasingly used for pair-wise image registration. We propose to extend current learning-based image registration to allow simultaneous registration of multiple images. To achieve this, we build upon the pair-wise variational and diffeomorphic VoxelMorph approach and present a general mathematical framework that enables both registration of multiple images to their geodesic average and registration in which any of the available images can be used as a fixed image. In addition, we provide a likelihood based on normalized mutual information, a well-known image similarity metric in registration, between multiple images, and a prior that allows for explicit control over the viscous fluid energy to effectively regularize deformations. We trained and evaluated our approach using intra-patient registration of breast MRI and Thoracic 4DCT exams acquired over multiple time points. Comparison with Elastix and VoxelMorph demonstrates competitive quantitative performance of the proposed method in terms of image similarity and reference landmark distances at significantly faster registration.
Grid Based approach (GBA): a new approach based on the grid-clustering algorithm to solve a CPP type problem for air surveillance using UAVs

Air surveillance over large area using UAVs (Unmanned Aerial Vehicles) -also called drones- requires good planning. This kind of problem is classified as a CPP (Coverage Path Planing) problem which aims at finding a mission plan for the UAVs to cover the zone of interest. This type of problem is difficult because it relates to contradictory or combinatorial optimization problems. Therefor we need to find a heuristic solution. This zone is too large for one drone in a mission to reach and scan, thus it must be partitioned to small parts. This partitioning task must maximize each little part while respecting the performance constraints of the UAV. In this article we discuss a new approach called GBA (Grid Based Approach) different from the PGA (Point Gathering Approach) that we proposed in an earlier work. The GBA approach models the mission environment in a grid of points, each point is defined by its longitude, altitude and the obligation to reach that point expressed by a priority value limited between 0 and 100. Then it uses the grid-clustering algorithm to divide the whole zone in such a way to maximize the sum of the priorities of each partition (referred as gain). Finally we will compare the GBA and PGA approaches. In terms of time consumption, the GPA gives better results than PGA.
On Compositional Information Flow Aware Refinement

The concepts of information flow security and refinement are known to have had a troubled relationship ever since the seminal work of McLean. In this work we study refinements that support changes in data representation and semantics, including the addition of state variables that may induce new observational power or side channels. We propose a new epistemic approach to ignorance-preserving refinement where an abstract model is used as a specification of a system's permitted information flows, that may include the declassification of secret information. The core idea is to require that refinement steps must not induce observer knowledge that is not already available in the abstract model. Our study is set in the context of a class of shared variable multiagent models similar to interpreted systems in epistemic logic. We demonstrate the expressiveness of our framework through a series of small examples and compare our approach to existing, stricter notions of information-flow secure refinement based on bisimulations and noninterference preservation. Interestingly, noninterference preservation is not supported “out of the box” in our setting, because refinement steps may introduce new secrets that are independent of secrets already present at abstract level. To support verification, we first introduce a “cube-shaped” unwinding condition related to conditions recently studied in the context of value-dependent noninterference, kernel verification, and secure compilation. A fundamental problem with ignorance-preserving refinement, caused by the support for general data and observation refinement, is that sequential composability is lost. We propose a solution based on relational pre-and postconditions and illustrate its use together with unwinding on the oblivious RAM construction of Chung and Pass.
New RNN Activation Technique for Deeper Networks: LSTCM Cells

Long short-term memory (LSTM) has shown good performance when used with sequential data, but gradient vanishing or exploding problem can arise, especially when using deeper layers to solve complex problems. Thus, in this paper, we propose a new LSTM cell termed long short-time complex memory (LSTCM) that applies an activation function to the cell state instead of a hidden state for better convergence in deep layers. Moreover, we propose a sinusoidal function as an activation function for LSTM and the proposed LSTCM instead of a hyperbolic tangent activation function. The performance capabilities of the proposed LSTCM cell and the sinusoidal activation function are demonstrated through experiments on various natural language benchmark datasets, in this case the Penn Tree-bank, IWSLT 2015 English-Vietnamese, and WMT 2014 English-German datasets.
On 3d Face Attributes Analysis Using Deep Learning: A Preliminary Case Study on Gender and Ethnicity Recognition

Human faces provide us with not only identity, but also de-mographic attributes like gender and ethnicity. Recognizing such attributes from 2D face images has been rapidly developed due to deep learning (DL). However, it is still unknown about the effectiveness of DL in facial attributes analysis using 3D data. This paper systematically investigates the performance of DL-based 3D face gender and ethnicity recognition from three aspects: data representation, data augmentation, and comparison to the state-of-the-art. Using two typical deep networks in the literature, five representations including point clouds, depth images, normal maps, HHA (Horizontal disparity, Height over ground, and Angle between local surface normal and gravity direction) and DAE (Depth, Azimuth and Elevation angles of surface normal) maps are compared on two benchmark databases, FRGC v2 and BU3D-FE. Data augmentation by synthesizing multiview 3D faces is proven effective in cross-database evaluation, and the proposed DAE-based deep model effectively advances the state-of-the-art.
Interactive Minimal Latency Laser Graphics Pipeline

We present the design and implementation of a ”Laser Graphics Processing Unit” (LGPU) featuring a proposed re-configurable graphics pipeline capable of minimal latency interactive feedback, without the need of computer communication. This is a novel approach for creating interactive graphics where a simple program describes the interaction on a vertex. Similar in design to a geometry or fragment shader on a GPU, these programs are uploaded on initialisation and do not require input from any external micro-controller while running. The interaction shader takes input from a light sensor and updates the vertex and fragment shader, an operation that can be parallelised. Once loaded onto our prototype LGPU the pipeline can create laser graphics that react within 4 ms of interaction and can run without input from a computer. The pipeline achieves this low latency by having the interaction shader communicate with the geometry and vertex shaders that are also running on the LGPU. This enables the creation of low latency displays such as car counters, musical instrument interfaces, and non-touch projected widgets or buttons. From our testing we were able to achieve a reaction time of 4 ms and from a range of up to 15 m.
Control of Trms using Adaptive Neuro Fuzzy Inference System (ANFIS)

This paper gives approach about the designing of ANFIS controller to control a TRMS to track the desired trajectory and make the system stable. The stages of development of a two input ANFIS model were presented. It shows the designing of controller is easy and it reduces the time and memory. In MATLAB and SIMULINK environment, the controller and TRMS are simulated by using ANFIS editor GUI. The proposed controller response is compared with the PID controller. The response shows that ANFIS controller improves setpoint tracking, decrease the settling time and control effort.
Textual restoration of occluded Tibetan document pages based on side-enhanced U-Net

It is very challenging to recognize the information of occluded Tibetan document pages due to the lack of digitization and their long-term storage. Multiple pages are stuck, and textual characters are occluded with each other, which causes difficulties in restoration. Due to the large size of Tibetan documents, it is impossible to separate and repair these occluded pages by professionals. Therefore, the separation of overlapping pages and restoration of occluded pages play important roles in the digitization of Tibetan documents. We extract underlying pages by show-through scanning and eliminating the text area of top pages. In order to restore the occluded underlying pages, we present a side-enhanced U-Net (SEU-Net) that attaches side feature extraction module and side classification module to the U-Net to improve the classification of textual edges. Experiments performed on the dataset of Tibetan documents restoration patches show that SEU-Net is able to classify the textual pixels in the occluded pages accurately, and both side feature extraction module and side classification module improve performance independently.
Digital Copyright Depository System Enhanced by Blockchain

This study proposes a blockchain-based copyright depository scheme. Blockchain is used as an underlying architecture to develop the digital copyright depository system. The system is built to solve the existing problems of the copyright depository, such as ambiguous ownership, low cost of piracy, rampant piracy, and difficulty in copyright monitoring, protection and sharing. Digital verification technology, supervision nodes, Oracle service and smart contract are introduced to provide certification depository, flow tracking, rights protection and sharing services for digital content. A system performance test is carried out for the registration service and query service, and the results show that the throughput of the two services was 195 and 366 times/second respectively with zero failure rate. The blockchain-based copyright depository system has been developed into a software as a service (SaaS) platform-blockchain copyright depository platform and provides services for dozens of institutions and enterprises, such as Zhijiang Notary Office, Xinhua Zhiyun, Himalaya, etc.
Driver Behavior Extraction from Videos in Naturalistic Driving Datasets with 3D ConvNets

Naturalistic driving data (NDD) is an important source of information to understand crash causation and human factors and to further develop crash avoidance countermeasures. Videos recorded while driving are often included in such datasets. While there is a large amount of video data in NDD, only a small portion of it can be annotated by human coders and used for research. In this paper, we explored a computer vision method to automatically annotate behaviors in videos. More specifically, we developed a 3D ConvNet algorithm to automatically extract cell phone behaviors from videos. The experiments show that our method can extract chunks from videos, most of which (∼ 85%) contain the automatically labeled cell phone behaviors. Importantly, we discuss and evaluate two use cases: (1) using algorithm labels without subsequent human review, and (2) using algorithm labels with subsequent human review. We find that even a 99% accurate algorithm will produce statistics that are appreciably biased towards the null, relative to ground truth, when labels are used without review. Thus, while the algorithm is not accurate enough to support the direct use of its labels in analysis, in conjunction with a human review of the extracted chunks, this approach can find cell phone behaviors much more efficiently than simply viewing a video.
Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?

Longitudinal biomedical data are often characterized by a sparse time grid and individual-specific development patterns. Specifically, in epidemiological cohort studies and clinical registries we are facing the question of what can be learned from the data in an early phase of the study, when only a baseline characterization and one follow-up measurement are available. Inspired by recent advances that allow to combine deep learning with dynamic modeling, we investigate whether such approaches can be useful for uncovering complex structure, in particular for an extreme small data setting with only two observations time points for each individual. Irregular spacing in time could then be used to gain more information on individual dynamics by leveraging similarity of individuals. We provide a brief overview of how variational autoencoders (VAEs), as a deep learning approach, can be linked to ordinary differential equations (ODEs) for dynamic modeling, and then specifically investigate the feasibility of such an approach that infers individual-specific latent trajectories by including regularity assumptions and individuals' similarity. We also provide a description of this deep learning approach as a filtering task to give a statistical perspective. Using simulated data, we show to what extent the approach can recover individual trajectories from ODE systems with two and four unknown parameters and infer groups of individuals with similar trajectories, and where it breaks down. The results show that such dynamic deep learning approaches can be useful even in extreme small data settings, but need to be carefully adapted.
Common Kernels and Convolutions in Binary- and Ternary-Weight Neural Networks

A new algorithm for extracting common kernels and convolutions to maximally eliminate the redundant operations among the convolutions in binary- and ternary-weight convolutional neural networks is presented. Precisely, we propose (1) a new algorithm of common kernel extraction to overcome the local and limited exploration of common kernel candidates by the existing method, and subsequently apply (2) a new concept of common convolution extraction to maximally eliminate the redundancy in the convolution operations. In addition, our algorithm is able to (3) tune in minimizing the number of resulting kernels for convolutions, thereby saving the total memory access latency for kernels. Experimental results on ternary-weight VGG-16 demonstrate that our convolution optimization algorithm is very effective, reducing the total number of operations for all convolutions by [Formula: see text], thereby reducing the total number of execution cycles on hardware platform by 22.4% while using [Formula: see text] fewer kernels over that of the convolution utilizing the common kernels extracted by the state-of-the-art algorithm.
Particle Swarm Optimization and Genetic Algorithm PID for DC motor position controllers

Due to the increasing number of applications, researchers have developed several methodologies to control Direct Current (DC) motor. To decide for an optimal position control, it is fundamental to analyse the performance of the system in terms of rise time (t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> ), settling time (t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ), overshoot percentage (M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> %), torque load rejection, and steady state error (E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ss</sub> ). Proportional-Integral-Derivative (PID) controller offers a stable response by tuning their coefficients u nder a s pecific me thodology. In this paper, we propose a comparative study of PID controller tuned by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The former is an optimizer that simulates the natural selection of species and is used to tune the PID gains. The latter is an algorithm that follows the social behavior of bird flocking or fish and is implemented to tune the PID coefficients. The simulations suggest that the use of PSO results in a low overshoot percentage and better system response for torque load disturbance.
Modulation Recognition Based on Lightweight Neural Networks

In order to solve the problems of complex networks, large amount of calculation and high equipment requirements in the current deep learning method to complete the modulation recognition process, this paper proposes a modulation recognition algorithm based on lightweight neural networks. First, map the common 8 kinds of modulated signals to constellation diagrams to make image data sets. In the process of retaining the original signals, make full use of the performance of the neural network, build a representative the MobileNet neural network in the neural network to complete the training of the data set, use the test samples Verify the effectiveness of the lightweight neural networks used. Simulation experiment results show that the overall recognition rate of modulation reaches 98% when the SNR is greater than 2dB, but the training speed is greatly improved.
Research on Vehicle Detection Technology of Blind Spots at Night Based on CAdaBoost Algorithm

Abstract Aiming at the problem of vehicle video detection in blind areas at night, a method of vehicle detection in blind areas at night based on CAdaBoost algorithm is proposed. Since night vehicles and daytime vehicles have different illumination, visibility, vehicle edge characteristics, vehicle contours, etc., the test image is first grayed out and ROI is determined. Then, because the features of the car head and wheels at night are more obvious than other features, a parallel method is used to train multiple weak classifiers offline at the same time, mainly to train the Haar-like features of the car head and wheels of the test image. In the meantime, in order to improve the accuracy of detection, the weighted parameter is introduced to determine the role of the weak classifier in the final strong classifier. Then, the offline trained model is used to match the Haar-like features of the test vehicle’s car head and wheels respectively, and finally realize the real-time detection of vehicles in the blind area. The results show that: for vehicle detection in blind spots at night, the algorithm has a high detection accuracy and low detection time.
Success Factors of IT Projects: Undergraduate Students Perspective

Information Technology (IT) related projects are born and die but their underlying reasons remain. The success rates of IT projects are still low. A number of studies have been carried out to understand the key success factors and hindrances of IT related projects but most of them are focused to the software industry and only a few focused to the IT related undergraduate students projects. Thus, the objective of this research was to reveal the underlying reasons of making success in IT projects from the undergraduate students perspective. To attain this objective, an empirical study was carried out in an engineering institution in Bangladesh. The study was replicated with the 95 undergraduate students major in Computer Science and Engineering (CSE) program. The study data was collected using the questionnaire approach and analysis using the content analysis and the affinity diagram. The study revealed a total of ten success factors for the successful completion of IT-related students' projects. The outcomes will help all parties (teachers, students and administration) of an academic institution to improve the awareness and to diagnose problems at the beginning of pursuing any IT related projects, which in turn will contribute to improve the overall success of IT-related students' projects. The paper also explored the possible implications of the revealed factors for teachers, students, administrators and policy makers in an academic institution.