Reviewerly/exHarmony huggingface dataset:
Proposed Machine Learning Classifier Algorithm for Sentiment Analysis

Text Mining has emerged as an active domain in the field of NLP (Natural Language Processing) and due to availability of large data sets of reviews, it has become easy to do sentiment analysis and extract the result from it, but Now-a-days the objectives are expressed in different ways making the data massive and difficult to understand for machines. In this research work, machines are first trained (Supervised learning) with the help of the predefined data (or more clearly reviews) and then tested with the reviews available. This Research work will show you the working of a system that uses the supervised training which classifies a product review as positive or negative using various classifier algorithms like KNN, Logistic Regression and Support Vector Machines. The model which will give the more accuracy will be considered as the best model.
A Custom Implementation of the Velocity Threshold Algorithm for Fixation Identification

Identifying fixations and saccades is an essential component of eye movement data analysis. There exist many algorithms that are employed to find fixations and saccades. In this paper we study the Velocity-Threshold Identification algorithm and implement it as an open source code for a specific dataset. A modified version of the algorithm was implemented and the number of fixations were compared with those reported by running a commercial software application. It was observed that the results are similar with an average difference of only 6 fixations per stimulus. The open source implementation also has a module to identify the centroids of fixation groups. This research aims to provide the open source community the ability to analyse an eye-tracking dataset without the use of any commercial software application.
Usability Evaluation using NAU Method on Web Design Technique for Web Portal Development in STT Nurul Fikri

Usability is essential for the existence of the web so that users could use and visit. One method for improving web usability is through a series of tests. The Usability Nielsen (NAU) model can be used to test whether a web design has a high level of usability. In this study, the development of a web portal on the STT- NF campus uses a design that refers to the standard usability NAU principle, user interface, and material design. A series of tests with the NAU questionnaire was carried out on the STT-NF web portal prototype. The test consists of the principles of learnability, efficiency, memorability, errors, and satisfaction. From this research, it is hoped that the web portal design will be attractive and has a high level of usability so that the impact of the STT-NF campus academic community will remain faithful in using the web portal.
Multi-frame motion deblurring using coded exposure imaging with complementary fluttering sequences

Motion blurring is one of the most common but unpleasant phenomenons along with the camera imaging process, resulting in image resolution degradation. Coded exposure technique is an effective computational imaging approach to handle with this problem by introducing extra shutter control within the exposure time and help the deblurring problem become well-posed. In this paper, a multi-frame motion deblurring framework is presented using coded exposure imaging with complementary fluttering sequences. A set of complementary sequences are well designed to compensate for the spatial losses due to motion blurring in each frame based on the complementary theory of Golay matrix pair. Hadamard matrix is served as the Golay sequence pair to generate complementary fluttering sequences to capture multi-frame images for the following deblurring processing. The joint modulation transfer function (MTF) of the complementary code sequences has a more flat spectrum curve with smaller variance value and higher MTF value. For multi-frame deblurring implementation, a unified multi-image deblurring framework is performed by introducing Total Variation (TV) regularization prior with each selected code sequence in the complementary set integrated in the energy function. A real imaging system is built and various groups of synthetic and real-world imaging experiments are carried out to verify the effectiveness of the method. Experimental results demonstrate that the deblurrring images obtained by the proposed approach are of high quality with improved objective image evaluation values and better negative artifacts suppression.
Improved CRT-RSA Secret Key Recovery Method from Sliding Window Leakage

In this paper, we discuss side-channel attacks on the CRT-RSA scheme (RSA scheme with Chinese Remainder Theorem) implemented by the left-to-right sliding window method. This method calculates exponentiations by repeating squaring and multiplication. In CHES 2017, Bernstein et al. proposed side-channel attacks on the CRT-RSA signature scheme implemented by the left-to-right sliding window method. We can obtain square-and-multiply sequences by their side-channel attacks, but cannot calculate CRT-RSA secret keys because there are multiple candidates of multiplications. Then, Bernstein et al. calculated CRT-RSA secret keys by using two methods. First, they recovered CRT-RSA secret keys partially and calculated all secret key bits by using the Heninger–Shacham method. Second, they applied the Heninger–Shacham method to square-and-multiply sequences directly. They showed that we can calculate CRT-RSA secret keys more efficiently when we use square-and-multiply sequences directly. They also showed that we can recover CRT-RSA secret keys in polynomial time when $$w \le 4$$. Moreover, they experimentally showed that we can recover secret keys of 2048-bit CRT-RSA scheme when $$w=5$$. However, their latter method is simple and has room for improvement. Here, we study bit recovery more profoundly to improve their method. First, we calculate the exact rate of all knowable bits. Next, we propose a new method for calculating the proportion of each bit 0 or 1 in each nonrecovery bit. Finally, we propose a new method for calculating CRT-RSA secret key using this bit information. In our proposed algorithm, we extend Bernstein et al.’s method in combination with Kunihiro et al.’s method. We calculate more secret keys when $$w=5$$ by our proposed method compared to Bernstein et al.’s method.
Efficient Sparse Neural Networks Using Regularized Multi Block Sparsity Pattern on a GPU

A large portion of the computation in sparse neural networks comprises of multiplying a sparse matrix with a dense matrix, denoted SDMM in this paper. The SDMM operation with an unstructured sparsity pattern cannot be efficiently processed on modern architectures such as GPUs due to irregularity in compute and memory accesses. However, efficient parallel algorithms on a GPU can be designed for SDMM when the sparsity pattern is more structured. Thus, the run time performance of sparse neural networks on a GPU is dependent on the sparsity pattern present in the underlying matrix. In sparse neural networks obtained using pruning based approaches, the choice of sparsity pattern not only effects the run time, but also effects the accuracy of the task for which the neural network is trained for. Sparsity patterns which have a good run time performance on a GPU may not have a good accuracy and vice-versa. The real challenge then is to given a target architecture, identify a sparsity pattern, a storage format, and an algorithm for SDMM that leads to sparse neural networks which are efficient in both run time and accuracy. In this work, we propose a novel, structured, flexible, and generic sparsity pattern called the RMB (Regularized Multi Block) sparsity pattern, and an efficient storage format (CRMB), and a fast GPU algorithm for processing RMBMM (SDMM with the multiplicand having RMB sparsity pattern). Using the RMB sparsity pattern, we achieve better trade-offs between the accuracy, and the run time performance of sparse neural networks on a GPU when compared to commonly used sparsity patterns like unstructured, and block sparsity patterns.
Hardware Acceleration of Multilayer Perceptron Based on Inter-Layer Optimization

Multilayer Perceptron (MLP) is used in a broad range of applications. Hardware acceleration of MLP is one most promising way to provide better performance-energy efficiency. Previous works focused on the intra-layer optimization and layer-after-layer processing, while leaving the inter-layer optimization never studied. In this paper, we propose hardware acceleration of MLPs based on inter-layer optimization which allows us to overlap the execution of MLP layers. First we describe the inter-layer optimization from software and mathematical perspectives. Then, a reference Two-Neuron architecture which is efficient to support the inter-layer optimization is proposed and implemented. Discussions about area cost, performance and energy consumption are carried out to explore the scalability of the Two-Neuron architecture. Results show that the proposed MLP design optimized across layers achieves better performance and energy efficiency than the conventional intra-layer optimized designs. As such, the inter-layer optimization provides another possible direction other than the intra-layer optimization to gain further performance and energy improvements for the hardware acceleration of MLPs.
Velocity Planning for Astronaut Virtual Training Robot with High-Order Dynamic Constraints

SUMMARY In order to improve the training efficiency and establish a multi-person cooperative training simulation system, including “virtual human,” in the process of virtual reality-based astronaut training, it is necessary to plan the velocity at which astronauts carry the target object. A velocity planning algorithm, combining a traditional six-stage acceleration/deceleration algorithm, based on a time-discrete model with high-order dynamic constraints, considering the elastic damping torque of the space suit, is proposed. The described algorithm is verified on MATLAB to prove its feasibility. Compared to other algorithms, the planning time of the proposed algorithm is significantly reduced.
Various Data Skewness Methods in the Hadoop Environment

Hadoop provides an environment for efficient storage and processing of data. Time for completion of a BigData job depends on the slowest mapper or slowest reducer. So for an efficient job there has to be an efficient division of data at both mapper side and reducer side which would effectively decrease the time taken by the job to complete. But under many conditions, Hadoop MapReduce fails to implicitly divide the data in an efficient way between mappers and reducers. This gives rise to data skewness or load imbalance. From the study, it is revealed that data skewness can occur at both the mapper side and reducer side, such as the page rank algorithm or cloudburst. Techniques like LEEN, PTSH, SkewTune, Skew Reduce are implemented to tackle the problem of data skewness. Each technique has its own set of advantage and disadvantage. LIBRA and DREAMS are the most preferred and popular techniques against data skewness.
A Multi-Objective Optimization Method based on Dimensionality Reduction Mapping for Path Planning of a HALE UAV

The high-altitude long-endurance (HALE) unmanned aerial vehicles (UAV) have long flight distance and wide range of path coverage, mainly used to perform reconnaissance and strike missions. The distances of such UAVs need to be considered in calculation of the deviation caused by the curvature of the Earth. To avoid negative effects of the distances to path planning, in this paper, we proposed a path planning method based on dimensionality reduction mapping. First, we use Gauss positive projection to project the route data and the threat information from the geodetic coordinates to the Gauss plane coordinates to perform dimensionality reduction mapping. Then, we use multi-objective optimization Dijkstra algorithm to obtain the optimal path with minimum total cost. Finally, Gauss inverse projection is applied to restore the optimal path at the geodetic coordinate system, providing the navigation information for HALE UAV. The simulation results have demonstrated that the method is suitable for calculating a path with large latitude and longitude range, and can be effectively used for path planning of a HALE UAV.
Improving artificial algae algorithm performance by predicting candidate solution quality

The success of optimization algorithms is most of the time directly proportional to the number of fitness evaluations. However, not all fitness evaluations lead to successful fitness updates. Besides, the maximum number of fitness evaluations is limited and also balance of exploration and exploitation is still challenging. Best possible solution should be found in a reasonable time. Surely it can be said more fitness evaluation takes more time. Since methods are tested under fixed numbers of maximum fitness evaluation and the duration of each fitness evaluation of a problem may vary depending on the characteristic of the problem, finding best result with fewer fitness evaluations is challenging in optimization algorithms. For that reason in this study, we proposed a new method that predicts the quality of a candidate solution before evaluation of its fitness employing Gaussian-based Naïve Bayes probabilistic model. If the candidate solution is predicted to generate good result then that solution is evaluated by the objective function. Otherwise new candidate solution is created as usual. The primary purpose of the proposed method is improving the performance of AAA and at the same time preventing unnecessary fitness evaluation. The proposed method is evaluated using standard benchmark functions and CEC’05 test suite. The obtained results suggests that the new method outperformed the basic AAA and other state-of-the-art meta-heuristic algorithms with fewer fitness evaluations. Thus, the new method can be extended to cost sensitive industrial problems.
Mitigating Application Diversity for Allocating a Unified ACC-Rich Platform

Heterogeneous accelerator-rich (ACC-rich) platforms combining general-purpose cores and specialized HW accelerators (ACCs) promise high-performance and low-power streaming application (app) deployments, e.g. for video analytics, software-defined radio, and radar. In order to recover NRE, a unified platform for a set of applications (apps) is desirable. When apps have functional and structural similarities, they can benefit from common ACCs. Identifying the most beneficial set of common ACCs is challenging. However, current allocation strategies mostly focus on one app in isolation. Automatically allocating a unified platform requires simultaneously considering many apps, an efficient design space traversal and a fair evaluation across diverse apps. This paper introduces a Unified ACC-rich Platform Allocation (UPA) methodology for sets of data flow apps. Key contributions are: (1) a genetic algorithm (GA) guided by a fair and efficient evaluation to allocate one unified platform for many apps, (2) defining relative efficiency for fair comparison across diverse apps, and (3) defining metrics to quantify many app platform efficiency. This paper demonstrates UPA's benefits using OpenVX apps. A 12-ACCs-UPA improves average efficiency 4.59x over app-dedicated platforms. The UPA platform enables more apps (55% of OpenVX apps) to be efficiently deployed (≥ 60% of optimal app-dedicated platform). The benefits increase even further with increasing ACC budget.
Multi-temporal remote sensing image registration based on multi-layer feature fusion of deep residual network

Image registration is a key technology in remote sensing image processing and application. In the registration of multi-temporal remote sensing images, due to differences in imageing conditions, there are two types of typical anomalies in the image change and relative parallax shift between images, which will affect the registration accuracy. Therefore, this paper proposes an algorithm for multi-temporal remote sensing image registration based on depth residual network. In feature extraction stage, multi-scale descriptors are generated from the advanced convolution information of the trained ResNet50 network layer to improve the quantity and quality of feature point extraction. In the registration stage of point set, the difference of feature is calculated by Bhattacharyya distance, and the mismatched point pairs are eliminated by Random Sampling Consistency Algorithms (RANSAC). Finally, the transformation model of the point set is calculated by using the coordinates of the matching point pairs to achieve accurate registration of multitemporal remote sensing images. The experiment uses image data obtained from Google Earth and Lansat 8 satellites and Baidu Map to test the proposed algorithm, and compares it with two feature-based algorithms (PSO-SIFT and CNN). The experimental results show that the proposed algorithm achieves better multi-temporal remote sensing image registration results.
Activity Monitoring System to Support Elderly Independent Living

The desire to live independently may conflict with safety and practical needs as old age progresses toward a state of frail elderly. With old age the risk of debilitating events rises. Having someone notice such a debilitating state and call for help becomes increasingly important. Thus, many elderly people move out of their independent residence and into a group setting, such as a nursing home, but with associated risk of depression, decline in self-confidence or self-determination. An activity monitoring system with sensors both on-body and in-home, which would detect possible debilitating events and send alerts for help, could allow some elderly people to maintain independent living. This project developed prototype modules to track room location, heart rate, and fall detection through sensors connected to a hub microcontroller system. A prototype system was developed and tested. The system shows promise but needs further development and testing before deployment.
A New Approach Towards Fully Homomorphic Encryption Over Geometric Algebra

The ability to compute on encrypted data in a meaningful way is a subject of increasing interest in both academia and industry. The type of encryption that allows any function to be evaluated on encrypted data is called fully homomorphic encryption, or FHE, and is one promising way to achieve secure computation. The problem was first stated in 1978 by Rivest et al. and first realized by Gentry in 2009, but remains an open problem since an FHE scheme that is both efficient and secure is yet to be presented. Most of the prominent FHE schemes follow Gentry's blueprint which concentrates the efforts of researchers on very similar algebraic structures and noise management techniques. The intrinsic complexity of these schemes results in the similar shortfalls that they share in efficiency. We introduce the application of Geometric Algebra (GA) to encryption in conjunction with p-adic arithmetic and a modified version of the Chinese Remainder Theorem and we demonstrate an efficient, noise-free, symmetric-key FHE scheme. We focus the security analysis on demonstrating that our FHE scheme is not linearly decryptable. Further, we discuss a practical approach for generalizing different types of algebraic structures in the geometric product space of two dimensions, which allows us to export GA operations to other algebras and vice-versa. Our construction supports a variety of applications, from homomorphic obfuscation to general purpose FHE computations.
An Overview into the Aspects of Fake Product Reviews, its Manipulation, and its Effects and Monitoring

If you are on Amazon, you might have noticed that it is sometimes difficult to know which products you should purchase. Sometimes they all look alike. There are Half a billion products in one location, and they all compete for your attention. But the fact is that most buyers may just click on some, existing at the top of the list. For one of those spots, some sellers are willing to do anything. This article focuses on all the aspects of reviews on Amazon, like their manipulation, malpractices, effects and monitoring which might have a huge role on which product the customer finally purchases.
A possibility of artificial neural networks to be applied in the predictive test: A systematic literature review and study case

Abstract Artificial Neural Network (ANN’s) was developed continuously and applied to each smart technology. The developing smart tests are continuous with ANN’s possibility applied to increase efficiencies and time of decides. The Predictive Test was developed using the Smart Algorithm with the possibility of ANN to apply. Assessments of ANN implementation have been carried out based on a study in past time. The systematic literature review was used to found possibility implementation ANN’s in the predictive test and combined with the study case. The result showed as ANN’s could implement in the predictive test that used big data inputs. In addition, ANN’s can be implantation on the predictive test such as education predictive test, talent and aptitude test or psychological test.
Affective Interaction based Hybrid Approach for Emotion Detection using Machine Learning

Affective communication is a fresh region of research interest emerging in the interaction designers. This research will explore our hybrid approach's potential depends on both, Lexical and machine learning methods to detect the six emotional categories of Ekman in the text of the user. Initial results of the presentation evaluation of the suggested hybrid method is promoting and similar to relate the research. A demonstrative mobile application that uses the suggested strategy has been created to engage the users in a dialog that requests their views on multiple daily occurrences and offers appropriate affective answers. The novelty of this strategy is the selection of characteristics and the generation for classification of a single feature vector. Our primary objective is to improve the precision of voice classification of emotions by taking into account audio characteristics. Standard techniques like Natural Language Processing, Support Vector Machines, WordNet Affect and SentiWordNet are used in this job.
A family of planar binomials in characteristic 2

Planar polynomials of type fa,b(x)=ax22m+1+bx2m+1, a,b∈F23m⁎ are investigated. In particular, all the possible pairs (a,b)∈(F23m⁎)2 for which fa,b(x) is planar are determined.
Research and design of travel insurance system based on blockchain

From the perspective of travel insurance, this paper combines travel insurance and blockchain technology to design a travel insurance system based on blockchain technology. This paper first analyzes the existing problems related to travel insurance, and then further studies the core technologies of blockchain applied in this system, such as equality network, Byzantine consensus mechanism, asymmetric encryption and intelligent contract. Then the model of travel insurance system based on blockchain is established, and the architecture, function and process design of the system are carried out. Finally, the system is evaluated and extended from the single travel insurance to the whole insurance industry, and the combination of block chain technology and insurance industry innovation is summarized and prospected.