Reviewerly/exHarmony huggingface dataset:
Two fast vector-wise update algorithms for orthogonal nonnegative matrix factorization with sparsity constraint

Recently, orthogonal nonnegative matrix factorization (ONMF) has been introduced and shown to work remarkably well for clustering tasks. Because of the nonnegativity and the orthogonality constraints, the orthogonal factor matrix of ONMF is naturally sparse. Based on this fact, by introducing sparsity constraints on the orthogonal coefficient matrix, we propose two vector-wise algorithms based on Hierarchical Alternating Least Squares (HALS) and Block Prox-linear (BPL) methods to the approximately sparse orthogonal nonnegative matrix factorization (SONMF). Some global convergence results are established under the mild conditions. Numerical results including synthetic and real-world datasets are given to illustrate that the proposed algorithms compute highly accurate values and perform better than the other testing ONMF methods.
Adversarial Examples to Fool Iris Recognition Systems

Adversarial examples have recently proven to be able to fool deep learning methods by adding carefully crafted small perturbation to the input space image. In this paper, we study the possibility of generating adversarial examples for code-based iris recognition systems. Since generating adversarial examples requires back-propagation of the adversarial loss, conventional filter bank-based iris-code generation frameworks cannot be employed in such a setup. Therefore, to compensate for this shortcoming, we propose to train a deep auto-encoder surrogate network to mimic the conventional iris code generation procedure. This trained surrogate network is then deployed to generate the adversarial examples using the iterative gradient sign method algorithm [15]. We consider non-targeted and targeted attacks through three attack scenarios. Considering these attacks, we study the possibility of fooling an iris recognition system in white-box and black-box frameworks.
LARAVEL: A PHP Framework for E-Commerce Website

In the modern era, as technologies are getting advance, the work related to them are also getting advance. Today's business is totally dependent on the internet and to run a business we need the help of the internet so that it can grow at bulkier expanse. Everyday humongous amount of data and personal information has been transmitted and retrieved. With the brisk development of internet technology in contemporary years, online business is gradually rampant. Web users are very demanding and they foresee web services to be easily and quickly accessible from other places around the world all the time, whenever they need it. Web users always need quick and rapid responses. With the broadening trend of the internet, it turns out to be an essential part of our life. Website development is a process that takes time and if done by the basic language it takes more time which is not compatible with this fast and developing world of technologies. So to overcome these problems related to web development frameworks are developed. Frameworks are used so that we can create websites conveniently. This paper generalizes the concept of e-commerce website with a framework. What parameters we have to look upon so that we can prepare a website without any Hassel. This research paper also concludes what advantages did Laravel had on other frameworks, and how it is different to work on Framework. Electronic commerce as the name suggests electronic means through digital instruments or through the World Wide Web and commerce suggest the business so it combined means business through the World Wide Web which is on gravitate. LARAVEL is a free open source PHP framework. Frameworks are on go, as there is no need to write whole code.
Unveiling the Dynamic Behavior of Fuzzy Cognitive Maps

Fuzzy cognitive maps (FCMs) are recurrent neural networks comprised of well-defined concepts and causal relations. While the literature about real-world FCM applications is prolific, the studies devoted to understanding the foundations behind these neural networks are rather scant. In this article, we introduce several definitions and theorems that unveil the dynamic behavior of FCM-based models equipped with transfer F-functions. These analytical expressions allow estimating bounds for the activation value of each neuron and analyzing the covering and proximity of feasible activation spaces. The main theoretical findings suggest that the state space of any FCM model equipped with transfer F-functions shrinks infinitely with no guarantee for the FCM to converge to a fixed point but to its limit state space. This result in conjunction with the covering and proximity values of FCM-based models helps understand their poor performance when solving complex simulation problems.
A Blockchain-Based distributed network for Secure Credit Scoring

Credit scoring is a method that is used by banks to estimate the risk of lending money to an individual. It determines the ability of that person to repay the debts. Higher the credit score, higher is the probability of getting the loan. Before approving the loan, in addition to calculating the credit score, banks also need to digitally verify the client's credentials. This is a very tedious task involving a lot of paperwork. It becomes even more cumbersome when the person is underprivileged i.e. from rural areas or does not hold any bank account. This paper presents a solution to the above problem by introducing a framework that will create a secure digital identity and store the past transactions made to calculate the credit score using blockchain. Blockchain is a digital ledger that is resistant to modifications. It creates a distributed network of users where details of each transaction are transparent, thereby enhancing the level of trust in the network. The aim of this paper is twofold: (1) providing fair and unbiased access to credit to people who are deprived of the basic financial amenities and (2) efficient digital verification.
Identification and Classification of Cybercrimes using Text Mining Technique

Cyber-crimes involve all the crimes where internet is used as an access medium and committed through some electronic device such as computers and mobile phones. Unavailability of datasets, hidden identity of predators and the privacy of the victims are the main factors for limiting the past research in cyberbullying detection. Considering these factors, an effective text mining approach using machine learning algorithms is proposed to proactively detect bullying text. The dataset collected from myspace.com and Preverted-Justice.com has been used to evaluate the system's performance. Three types of feature namely textual, behavioral and demographic features are extracted from the dataset as compared to earlier study over the same dataset where only textual features were considered. Textual features include certain bullying words that if exists within the text may lead to a true outcome for cyberbullying. Personality trait features are extracted for the user if it is involving once in bullying may bully in future too. While demographic features extracted from dataset include age, gender and location. The system is evaluated through different performance measures for both classifiers used and the performance of Support Vector Machine classifier is found better than the Bernoulli NB with an overall 87.14% of classification accuracy.
PlaidML-HE: Acceleration of Deep Learning Kernels to Compute on Encrypted Data

Machine Learning as a Service (MLaaS) is becoming a popular practice where Service Consumers, e.g., end-users, send their data to a ML Service and receive the prediction outputs. However, the emerging usage of MLaaS has raised severe privacy concerns about users' proprietary data. PrivacyPreserving Machine Learning (PPML) techniques aim to incorporate cryptographic primitives such as Homomorphic Encryption (HE) and Multi-Party Computation (MPC) into ML services to address privacy concerns from a technology standpoint. Existing PPML solutions have not been widely adopted in practice due to their assumed high overhead and integration difficulty within various ML front-end frameworks as well as hardware backends. In this work, we propose PlaidML-HE, the first end-toend HE compiler for PPML inference. Leveraging the capability of Domain-Specific Languages, PlaidML-HE enables automated generation of HE kernels across diverse types of devices. We evaluate the performance of PlaidML-HE on different ML kernels and demonstrate that PlaidML-HE greatly reduces the overhead of the HE primitive compared to the existing implementations.
Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System

In this paper, a joint adaptive sampling interval and power allocation (JASIPA) scheme based on chance-constraint programming (CCP) is proposed for maneuvering target tracking (MTT) in a multiple opportunistic array radar (OAR) system. In order to conveniently predict the maneuvering target state of the next sampling instant, the best-fitting Gaussian (BFG) approximation is introduced and used to replace the multimodal prior target probability density function (PDF) at each time step. Since the mean and covariance of the BFG approximation can be computed by a recursive formula, we can utilize an existing Riccati-like recursion to accomplish effective resource allocation. The prior Cramér-Rao lower boundary (prior CRLB-like) is compared with the upper boundary of the desired tracking error range to determine the adaptive sampling interval, and the Bayesian CRLB-like (BCRLB-like) gives a criterion used for measuring power allocation. In addition, considering the randomness of target radar cross section (RCS), we adopt the CCP to package the deterministic resource management model, which minimizes the total transmitted power by effective resource allocation. Lastly, the stochastic simulation is embedded into a genetic algorithm (GA) to produce a hybrid intelligent optimization algorithm (HIOA) to solve the CCP optimization problem. Simulation results show that the global performance of the radar system can be improved effectively by the resource allocation scheme.
Robust Segmentation of Building Planar Features From Unorganized Point Cloud

This paper presents a novel method for segmentation of planar feature from unorganized point cloud based on 2D Hough Transform and octree. Given the input point cloud, three steps are performed to segment planar features. Firstly, the original point cloud is sampled and projected to the X-Y plane, and an extended 2D Hough transform algorithm is employed to extract the line segments. The selecting weight iteration method is used to calculate line equations and endpoint coordinates of those line segments. The space geometric equations of the vertical planes are then determined. Secondly, the octree structure of the original point cloud is established, and then the exact endpoint coordinates of the line segment are used to design a cube perpendicular to the X-Y plane and all points held by the cube are extracted. The distance from each of the extracted points inside the cube to its corresponding facade is calculated, if the distance is less than the predefined threshold, the point is regarded as a point inside the facade. Finally, all the facade points are removed from the original point cloud, and remaining point cloud is sampled and projected to the X-Z plane. The above process is repeated to extract horizontal planes. Evaluation experiments are performed by analyzing the performance of our method in four different scenes. The experimental results indicate that the proposed algorithm is suitable for segmentation of building planar features in different scenes. A comparison with competing techniques shows that our approach is considerably faster and scales significantly better than previous ones.
BERT Feature Based Model for Predicting the Helpfulness Scores of Online Customers Reviews

Online product reviews help consumers make purchase decisions when shopping online. As such, many computational models have been constructed to automatically evaluate the helpfulness of customer product reviews. However, many existing models are based on simple explanatory variables, including those extracted from low quality reviews that can be misleading and lead to confusion. Quality feature selection is essential for predicting the helpfulness of online customer reviews. The Bidirectional Encoder Representations from Transformers (BERT) is a very recently developed language representation model which can attain state-of-the-art results on many natural language processing tasks. In this study, a predictive model for determining helpfulness scores of customer reviews based on incorporation of BERT features with deep learning techniques is proposed. The application analyzes the Amazon product reviews dataset, and uses a BERT features based algorithm expected to be useful in help consumers to make a better purchase decisions.
Crafting Critical Heritage Discourses into Interactive Exhibition Design

This paper argues how a more reflective design practice that embraces critical discourses can transform interactive exhibition design and therefore the museum visiting experience. Four framing arguments underpin our exhibition design making: the value of materiality, visiting as an aesthetic experience, challenging the authorized voice, and heritage as a process. These arguments were embodied through design, art and craft practice into one interactive exhibition at a house museum. We draw from our design process discussing the implications that adopting an approach informed by critical heritage debates has on exhibition design and suggest three sensitizing concepts (polyvocal narratives, dialogical interaction, interweaving time and space) bridging the practice of interactive exhibition design and critical heritage theory.
Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification

This paper proposes a set of rotation invariant features based on three dimensional Gaussian Markov Random Fields (3D-GMRF) for volumetric texture image classification. In the method proposed here, the mathematical notion of spherical harmonics is employed to produce a set of features which are used to construct the rotation invariant descriptor. Our proposed method is evaluated and compared with other method in the literature for datasets containing synthetic textures as well as medical images. The results of our experiments demonstrate excellent classification performance for our proposed method compared with state-of-the-art methods. Furthermore, our method is evaluated using a clinical dataset and show good performance in discriminating between healthy individuals and COPD patients. Our method also performs well in classifying lung nodules in the LIDC-IDRI dataset. Our results indicate that our 3D-GMRF-based method enjoys more superior performance compared with other methods in the literature.
Human Face Identification using LBP and Haar-like Features for Real Time Attendance Monitoring

Real time attendance monitoring is the essential requirement in modern era to improve the work efficiency for all private and public organization. There happens to be a number of methods to monitor the attendance of student or employee signature based method and biometric based methods like fingerprint, palm scanning, iris and voice recognition etc. One same of those, these biometrics are not much effective for person identification due to several reasons as they involves one's physical interaction during registration and testing process. Many times wet or oily skins and contact lens can therefore be the responsible cause for no detection or false/fake detection. This paper suggest a robust technique of attendance monitoring using face identification. The method proposed here for person identification in real time involves face detection approach through Haar-like features with cascade classifier. The face recognition is done using Local binary pattern histogram. Results derived for face identification of each individual are found to have 81.6 percent accuracy.
Multi-density map fusion network for crowd counting

In crowed scene, its hard to get the exact number of people due to the distorted perspectives, complex backgrounds, and scale changes. People in different locations have different sizes and dimensions in an image. To deal with this problem, we propose a new multi-density map fusion method to learn the mapping from the input image to the density map. Different form previous methods, our method mainly focuses on fusing different density maps information instead of fusing multi-scale feature of the same images. The major contributions are three paralleled branches and dynamic weighting strategy. First, our network employs the first ten layers of VGG16, and the network is combined with three paralleled branches. Each branch of our network extracts image information at different scales and each branch outputs a density map. Second, to ensure the quality of the final density map, we employ learnable relative weights to fuse the three density maps. Our method has been proved more robust than many state-of-art methods. Lots of experiments have been done in the ShanghaiTech, WorldExpo10, UCSD and UCF_CC_50 dataset to show the effectiveness of our proposed method.
Educational Resource Sharing Platform Based on Blockchain Network

The educational resource sharing among higher educational institutions is always an important element for the improvement of the educational level. With the development of digital and network construction among universities and colleges, the sharing and use of high-quality educational resources can reach a new level. This paper proposes a method to build a global platform for educational resource sharing using blockchain. Learners all over the world can get access to high-quality education easier through the proposed platform which can improve educational equality. Meanwhile, the credit recognition system can eliminate the trust barriers between higher educational institutions and social institutions and can well protect personal privacy. This article explains architectural components and preliminary solutions for implementation.
Novel clustering-based pruning algorithms

Abstract One of the crucial problems of designing a classifier ensemble is the proper choice of the base classifier line-up. Basically, such an ensemble is formed on the basis of individual classifiers, which are trained in such a way to ensure their high diversity or they are chosen on the basis of pruning which reduces the number of predictive models in order to improve efficiency and predictive performance of the ensemble. This work is focusing on clustering-based ensemble pruning, which looks for the group of similar classifiers which are replaced by their representatives. We propose a novel pruning criterion based on well-known diversity measures and describe three algorithms using classifier clustering. The first method selects the model with the best predictive performance from each cluster to form the final ensemble, the second one employs the multistage organization, where instead of removing the classifiers from the ensemble each classifier cluster makes the decision independently, while the third proposition combines multistage organization and sampling with replacement. The proposed approaches were evaluated using 30 datasets with different characteristics. Experimentation results validated through statistical tests confirmed the usefulness of the proposed approaches.
Energy Aware Next Fit Allocation Approach for Placement of VMs in Cloud Computing Environment

Cloud computing enables the IT giants to outsource their infrastructure, by providing a sharable pool of computing sources. These sources consume a huge amount of energy that not only increase the running expenses but also produce CO2 emission in the environment. Therefore, the main issue is to manage and optimize the available resources for saving the energy. It can best be done by dividing the physical machines into virtual machines and maintaining the number of active machines according to the dynamic workload. This process of server consolidation includes finding the overloaded hosts, selection of VMs from the hosts with excess or under load and, finally, placing them all over the available physical hosts dynamically. In this context, a novel approach for placing virtual machines has been proposed that aims to reduce energy consumption and SLA violation. Inspired from the bin packing problem, Next fit allocation policy is tested for placing a VM over the available hosts. Suitability of hosts is defined primarily on the basis of minimum energy consumption by a VM on a host while placement. However, searching for the hosts is optimized using next-fit policy. Experiments are performed in the cloudsim simulator tool and results are compared with the existing policy of best-fit. Proposed approach has identified better results for various performance matrices considered during the experiments.
Randomness Analyses of the Secure Hash Algorithms, SHA-1, SHA-2 and Modified SHA

This paper introduces a security analysis scheme for the most famous secure hash algorithms SHA-1 and SHA-2. Both algorithms follow Merkle Damgård structure to compute the corresponding hash function. The randomness of the output hash reflects the strength and security of the generated hash. Therefore, the randomness of the internal rounds of the SHA-1 and SHA-2 hash functions is analyzed using Bayesian and odd ratio tests. Moreover, a proper replacement for both algorithms is proposed, which produces a hash output with more randomness level. The experiments were conducted using a high performance computing testbed and CUDA parallel computing platform.
A Hybrid Update Strategy for I/O-Efficient Out-of-Core Graph Processing

In recent years, a number of out-of-core graph processing systems have been proposed to process graphs with billions of edges on just one commodity computer, due to their high cost efficiency. To obtain a better performance, these systems adopt a full I/O model that scans all edges during the computation to avoid the inefficiency of random I/Os. Although this model ensures good I/O access locality, it leads to a large number of useless edges to be loaded when running graph algorithms that only access a small portion of edges in each iteration. An intuitive method to solve this I/O inefficiency problem is the on-demand I/O model that only accesses the active edges. However, this method only works well for the graph algorithms with very few active edges, since the I/O cost will grow rapidly as the number of active edges increases due to the increasing amount of random I/Os. In this article, we present HUS-Graph, an efficient out-of-core graph processing system to address the above I/O issues and achieve a good balance between I/O traffic and I/O access locality. HUS-Graph adopts a hybrid update strategy including two update models, Row-oriented Push (ROP) and Column-oriented Pull (COP). It supports switching between ROP and COP adaptively, for the graph algorithms that have different computation and I/O features. For traversal-based algorithms, HUS-Graph also provides an immediate propagation-based vertex update scheme to accelerate the vertex state propagation and convergence speed. Furthermore, HUS-Graph adopts a locality-optimized dual-block representation to organize graph data and an I/O-based performance prediction method to enable the system to dynamically select the optimal update model between ROP and COP. To save the disk space and further reduce I/O traffic, HUS-Graph implements a space-efficient storage format by combining several graph compression methods. Extensive experimental results show that HUS-Graph outperforms two existing out-of-core systems GraphChi and GridGraph by 1.2x-52.8x.