Reviewerly/exHarmony huggingface dataset:
Crime Analysis and Prediction using Machine Learning Algorithms

Machine Learning Algorithms are used widely nowadays in the majority of the data analysis field. Various fields of data analytics are now depending on the most advanced Machine Learning concepts and Algorithms like; K-Means, Random Forests so on and so forth. When comparing the data analysis based on society, in this research, we offer a new method for predicting and analytically classifying crimes by making use of data mining algorithms, which are K-Nearest Neighbor, Logistic Regression and Support Vector Machine algorithms. Utilizing these three commonly nsed techniques we classified the data sets along with pre-processing and the results of prediction obtained are highly accurate and excel the current models.
Avoiding Tissue Overlap in 2D Images: Single-Slice DBT Classification Using Convolutional Neural Networks

Breast cancer was the most diagnosed cancer around the world in 2020. Screening programs, based on mammography, aim to achieve early diagnosis which is of extreme importance when it comes to cancer. There are several flaws associated with mammography, with one of the most important being tissue overlapping that can result in both lesion masking and fake-lesion appearance. To overcome this, digital breast tomosynthesis takes images (slices) at different angles that are later reconstructed into a 3D image. Having in mind that the slices are planar images where tissue overlapping does not occur, the goal of the work done here was to develop a deep learning model that could, based on the said slices, classify lesions as benign or malignant. The developed model was based on the work done by Muduli et. al, with a slight change in the fully connected layers and in the regularization done. In total, 77 DBT volumes—39 benign and 38 malignant—were available. From each volume, nine slices were taken, one where the lesion was most visible and four above/below. To increase the quantity and the variability of the data, common data augmentation techniques (rotation, translation, mirroring) were applied to the original images three times. Therefore, 2772 images were used for training. Data augmentation techniques were then applied two more times—one set used for validation and one set used for testing. Our model achieved, on the testing set, an accuracy of 93.2% while the values of sensitivity, specificity, precision, F1-score, and Cohen’s kappa were 92%, 94%, 94%, 94%, and 0.86, respectively. Given these results, the work done here suggests that the use of single-slice DBT can compare to state-of-the-art studies and gives a hint that with more data, better augmentation techniques and the use of transfer learning might overcome the use of mammograms in this type of studies.
Attacking Windows Hello for Business: Is It What We Were Promised?

Traditional password authentication methods have raised many issues in the past, including insecure practices, so it comes as no surprise that the evolution of authentication should arrive in the form of password-less solutions. This research aims to explore the problems that password authentication and password policies present and aims to deploy Windows Hello for Business (WHFB) on-premises. This includes creating three virtual machines (VMs) and evaluating WHFB as a password-less solution and showing how an attacker with privileged access may retrieve the end user’s domain password from the computer’s memory using Mimikatz and describing the possible results. The conducted research tests are in the form of two attack methods. This was feasible by the creation of three VMs operating in the following way. The first VM will act as a domain controller (DC) and certificate authority server (CA server). The second VM will act as an Active Directory Federation Service (ADFS). The third VM will act as the end-user device. The test findings research summarized that password-less authentication is far more secure than the traditional authentication method; this is evidenced throughout the author’s tests. Within the first test, it was possible to retrieve the password from an enrolled device for WHFB while it was still in the second phase of the deployment. The second test was a brute-force attack on the PIN of WHFB; since WHFB has measures to prevent such attacks, the attack was unsuccessful. However, even though the retrieval of the password was successful, there are several obstacles to achieving this outcome. It was concluded that many organizations still use password authentication as their primary authentication method for accessing devices and applications. Larger organizations such as Microsoft and Google support the adoption of password-less authentication for end-users, and the current usage of password-less authentication shared by both organizations is encouraged. This usually leads organizations to adopt this new solution for their IT infrastructure. This is because it has been used and tested by millions of people and has proven to be safe. This supports the findings of increased usage and the need for password-less authentication by today’s users.
Camera Self-Calibration Using Human Faces

Despite recent advancements in depth estimation and face alignment, it remains difficult to predict the distance to a human face in arbitrary videos due to the lack of camera calibration. A typical pipeline is to perform calibration with a checkerboard before the video capture, but this is inconvenient to users or impossible for unknown cameras. This work proposes to use the human face as the calibration object to estimate metric depth information and camera intrinsics. Our novel approach alternates between optimizing the 3D face and the camera intrinsics parameterized by a neural network. Compared to prior work, our method performs camera calibration on a larger variety of videos captured by unknown cameras. Further, due to the face prior, our method is more robust to noise in 2D observations compared to previous self-calibration methods. We show that our method improves calibration and depth prediction accuracy over prior works on both synthetic and real data. Code will be available at https://github.com/yhu9/FaceCalibration.
Comparison of CNN Architecture for White Blood Cells Image Classification

Deep learning is a development of machine learning that has obtained good performance in image segmentation, prediction, classification, and machine translation. CNN has a variety of architectures that have undergone various changes and improvements. One area of medicine that requires automatic classification is white blood cell classification. The traditional blood cell classification process has many shortcomings, one of which is inefficient and takes a long time. In addition, some conventional methods can also damage the blood cell sample. This study aims to perform a comparative analysis of the performance of CNN architectures such as AlexNet, VGG19, VGG16, and ResNet50, improve accuracy performance and classify white blood cells using blood cell images. The research process involves creating and training models for each architecture using the same input data. The results showed that the model built with the ResNet50 architecture required the longest training time of 3391 seconds but produced the best accuracy in white blood cell classification of 99%, with recall, precision, and 99% f1-score values. Meanwhile, models with AlexNet, VGG19, and VGG16 architectures only obtained an accuracy of 98%, 94%, and 95%, respectively.
Requirements Elicitation for Virtual Reality Products - A Mapping Study

Software practitioners use various requirement elicitation methods to produce a well-defined product. These methods impact the software product’s eventual traits and target a particular audience segment. Virtual Reality(VR) products are no different from this influence. With the notable rise in product offerings across various domains, VR has become an essential technology for the future. Nevertheless, the type of methods practiced for requirement elicitation still has not been thoroughly studied. This paper presents a mapping study on requirement elicitation methods practiced by VR practitioners in academia and industry. We consolidated our observations based on their popularity in the practitioner community. Further, we present our insights on the necessary and sufficient conditions to conduct VR requirement elicitation using the identified methods to benefit the VR practitioner community.
Calculable lower bounds on the efficiency of universal sets of quantum gates

Abstract Currently available quantum computers, so called Noisy Intermediate-Scale Quantum devices, are characterized by relatively low number of qubits and moderate gate fidelities. In such scenario, the implementation of quantum error correction is impossible and the performance of those devices is quite modest. In particular, the depth of circuits implementable with reasonably high fidelity is limited, and the minimization of circuit depth is required. Such depths depend on the efficiency of the universal set of gates <?CDATA $\mathcal{S}$?> used in computation, and can be bounded using the Solovay–Kitaev theorem. However, it is known that much better, asymptotically tight bounds of the form <?CDATA $\mathcal{O}(\mathrm{log}(\epsilon^{-1}))$?> , can be obtained for specific <?CDATA $\mathcal{S}$?> . Those bounds are controlled by so called spectral gap, denoted <?CDATA $\mathrm{gap}(\mathcal{S})$?> . Yet, the computation of <?CDATA $\mathrm{gap}(\mathcal{S})$?> is not possible for general <?CDATA $\mathcal{S}$?> and in practice one considers spectral gap at a certain scale <?CDATA $r(\epsilon)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>r</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>ϵ</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> , denoted <?CDATA $\mathrm{gap}_r(\mathcal{S})$?> . This turns out to be sufficient to bound the efficiency of <?CDATA $\mathcal{S}$?> provided that one is interested in a physically feasible case, in which an error ϵ is bounded from below. In this paper we derive lower bounds on <?CDATA $\mathrm{gap}_r(\mathcal{S})$?> and, as a consequence, on the efficiency of universal sets of d -dimensional quantum gates <?CDATA $\mathcal{S}$?> satisfying an additional condition. The condition is naturally met for generic quantum gates, such as e.g. Haar random gates. Our bounds are explicit in the sense that all parameters can be determined by numerical calculations on existing computers, at least for small d . This is in contrast with known lower bounds on <?CDATA $\mathrm{gap}_r(\mathcal{S})$?> which involve parameters with ambiguous values.
GAN-based Detailed Clothing Generation System

The implementation of Generative Adversarial Networks (GAN) in the fashion domain has been researched for various applications such as virtual-try-on, fashion item recommendation, and design generation. In this paper, we propose a GAN-based fashion design generation system that reduces the workload of the labor-intensive design creation task. Our system consists of two generative models: one that produces images of fashion items without any clothing patterns using conditioned StyleGAN2-ADA, and one that is a style transfer model reflecting the fine texture of the fashion item. The system also allows users to edit images of garments by manipulating the latent code of the generator. We demonstrate through qualitative and quantitative experiments that the proposed system trained on a dataset of real clothing inventory images can generate realistic and diverse images that reflect the input conditions in detail.
FPGA modeling of a novel fully-synthesizable and secure TRNG based on key-dependent s-box

In this study, the Field Programmable Gate Array (FPGA) implementation of a True Random Number Generator (TRNG) using dynamic key-based s-box architecture as a post-processing technique is presented. In the proposed architecture, the post-processing method of TRNG consists of two different functions, in a total of 32x32 (32-bit input & 32-bit output), containing four independent 8x8 (8-bit input & 8-bit output) s-boxes. Raw random numbers are subjected to three-stage substitution operations in these functions, and their statistical properties are cryptographically improved. The post-processing unit of TRNG is compressionless as the s-boxes are 8 x 8. Thanks to this distinctive feature of the proposed method, TRNG can reach a final output bit rate of 250 Mbps with an entropy of 0.99. With the same output bit rate, TRNG passes the National Institute of Standard and Technology (NIST) Special Publication (SP) 800-22 statistical randomness evaluation tests with success above the minimum confidence interval of 0.9805. In addition to the NIST tests, bias, entropy, correlation, chi-square, and true randomness analyses are also given for TRNG's cryptographic verification. Besides to the statistical requirements, cost and performance analyses for TRNG are also presented in the study. The estimated dynamic power consumption for TRNG needing a low chip area of 2% (3221 LEs) for hardware implementation, is measured as 10.2 mW. The final results show that TRNG has high security and efficiency standards and can be used successfully in embedded cryptographic applications.
PERBANDINGAN SMOTE DAN ADASYN PADA DATA IMBALANCE UNTUK KLASIFIKASI RUMAH TANGGA MISKIN DI KABUPATEN TEMANGGUNG DENGAN ALGORITMA K-NEAREST NEIGHBOR

Poverty is a global problem that has occurred in various countries with various impacts. Poverty conditions are characterized by the inability of a person or household to meet the basic needs of life. Socio-economic problems, such as poverty, can be handled using machine learning, one of which is classification. The classification of households based on poverty criteria is expected to assist the government in preparing programs that are right on target. K-Nearest Neighbor is one of the easy-to-use classification algorithms. this classification is based on the closest neighborliness. The problem that can be experienced when classifying is if the data used is imbalanced. The data imbalance will causing the classification process to focus more on the majority class. SMOTE and ADASYN are used to solve the problem of imbalanced data. This study resulted in the addition of SMOTE and ADASYN to imbalanced data can improve classification performance, especially on the G-mean value. G-mean is a performance measure that is widely used in the case of imbalanced data. The result of this study is that SMOTE can increase the G-mean value to 58.5%, while ADASYN is 57.3%. Therefore, it can be concluded that SMOTE-KNN is the best classification model for household poverty classification.
Fringe pattern analysis to evaluate light sources and sensors in digital photoelasticity

When experimental photoelasticity images are acquired, the spectral interaction between the light source and the sensor used affect the visual information of the fringe patterns in the produced images. Such interaction can lead to fringe patterns with an overall high quality, but also can lead to images with indistinguishable fringes, and bad stress field reconstruction. We introduce a strategy to evaluate such interaction that relies on measuring the value of four handcrafted descriptors: contrast, an image descriptor that accounts simultaneously for blur and noise, a Fourier-based descriptor to measure image quality, and image entropy. The utility of the proposed strategy was validated by measuring the selected descriptors on computational photoelasticity images, and the fringe orders achieved when evaluating the stress field, from 240 spectral configurations: 24 light sources and 10 sensors. We found that high values of the selected descriptors can be related to spectral configurations that lead to better stress field reconstruction. Overall, the results show that the selected descriptors can be useful to identify bad and good spectral interactions, which could help to design better protocols for acquiring photoelasticity images.
Convolutional Neural Network and Deep One-Class Support Vector Machine with Imbalanced Dataset for Anomaly Network Traffic Detection

Anomaly detection of network traffic is important for real-time network monitoring and management. The research challenges for anomaly network traffic detection (ANTD) are attributable to the nature of highly imbalanced dataset of abnormal samples and poor generalization. Generating additional training data or undersampling does not perform well with highly imbalanced dataset. The drives to the common formulation of ANTD as an one-class classification problem. Convolution neural network is utilized for feature extraction. This is followed by a deep one-class support vector machine classifier with customized kernel via multiple kernel learning to address the issue of generalization and overfitting. The deep architecture leverages the performance of traditional support vector machine to a large extent. Performance evaluation reveals that the proposed algorithm achieves accuracy of 97.5%. Ablation studies show that our algorithm enhances the accuracy by 2.52–15.2%. Compared with existing work, our algorithm enhances the accuracy by 3.07%. Several future research directions are discussed for further exploration and analysis.
Scalable Graph Convolutional Network Training on Distributed-Memory Systems

Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the convolution operation on graphs induces irregular memory access patterns, designing a memory- and communication-efficient parallel algorithm for GCN training poses unique challenges. We propose a highly parallel training algorithm that scales to large processor counts. In our solution, the large adjacency and vertex-feature matrices are partitioned among processors. We exploit the vertex-partitioning of the graph to use non-blocking point-to-point communication operations between processors for better scalability. To further minimize the parallelization overheads, we introduce a sparse matrix partitioning scheme based on a hypergraph partitioning model for full-batch training. We also propose a novel stochastic hypergraph model to encode the expected communication volume in mini-batch training. We show the merits of the hypergraph model, previously unexplored for GCN training, over the standard graph partitioning model which does not accurately encode the communication costs. Experiments performed on real-world graph datasets demonstrate that the proposed algorithms achieve considerable speedups over alternative solutions. The optimizations achieved on communication costs become even more pronounced at high scalability with many processors. The performance benefits are preserved in deeper GCNs having more layers as well as on billion-scale graphs.
Trajectory Planning of Autonomous Underwater Vehicles Based on Gauss Pseudospectral Method

This paper aims to address the obstacle avoidance problem of autonomous underwater vehicles (AUVs) in complex environments by proposing a trajectory planning method based on the Gauss pseudospectral method (GPM). According to the kinematics and dynamics constraints, and the obstacle avoidance requirement in AUV navigation, a multi-constraint trajectory planning model is established. The model takes energy consumption and sailing time as optimization objectives. The optimal control problem is transformed into a nonlinear programming problem by the GPM. The trajectory satisfying the optimization objective can be obtained by solving the problem with a sequential quadratic programming (SQP) algorithm. For the optimization of calculation parameters, the cubic spline interpolation method is proposed to generate initial value. Finally, through comparison with the linear fitting method, the rapidity of the solution of the cubic spline interpolation method is verified. The simulation results show that the cubic spline interpolation method improves the operation performance by 49.35% compared with the linear fitting method, which verifies the effectiveness of the cubic spline interpolation method in solving the optimal control problem.
Cloud Security: Using Advance Encryption Standard Algorithm to Secure Cloud Data at Client Side and Taking Measures to Protect its Secrecy

Cloud computing brings a lot of advantages especially in ubiquitous services where everyone can access computer services through the internet. In spite of this, there are key security loop holes that is making users anxious about the safety of migrating to the cloud. This project uses the Advance Standard Encryption Standard Algorithm to encrypt user data at client side before transferring the data to the cloud thereby making encryption key available to only owners of the data. The project also uses Message Digest Algorithm to check for data integrity at client side to know if client’s data has been tempered. The project also employs key management technique and backup design to keep encryption keys safety at client side.
Human Eye Pupil Detection Technique Using Circular Hough Transform

Eye tracking refers to measure gaze positions and movement to reveal what individuals are looking at. Thanks to the advances of eye tracking technology, there are growing numbers of research focus in using eye tracking to study human behavior. In order to improve the accuracy of the eye gaze tracking technology, this paper presents a novel pupil detection algorithm based on intensity level with canny edge detection technique. Field programmable logic array (FPGA) based hardware implementation of the proposed technique is presented, which can be used in iris localization system on FPGA based platforms for iris recognition application.Threshold based pupil detection algorithm was found to be most efficient method to detect human eye. An implementation of a real-time system on an FPGA board to detect and track a human’s eye is the main motive to obtain from proposed work. The Pupil detection algorithm involved thresholding and image filtering. The Pupil location was identified by computing the center value of the detected region.
Sufficient Statistics and Split Idempotents in Discrete Probability Theory

A sufficient statistic is a deterministic function that captures an essential property of a probabilistic function (channel, kernel). Being a sufficient statistic can be expressed nicely in terms of string diagrams, as Tobias Fritz showed recently, in adjoint form. This reformulation highlights the role of split idempotents, in the Fisher-Neyman factorisation theorem. Examples of a sufficient statistic occur in the literature, but mostly in continuous probability. This paper demonstrates that there are also several fundamental examples of a sufficient statistic in discrete probability. They emerge after some combinatorial groundwork that reveals the relevant dagger split idempotents and shows that a sufficient statistic is a deterministic dagger epi.
Design and Development of Navigation System for an Autonomous Vehicle

Soon, autonomous vehicles will become a reality. Many tech companies and automakers are competing to develop their prototypes with the expectation of becoming the first company that could launch a fully autonomous car. To demonstrate a self-driving car, this work presents a design and development of an autonomous vehicle modified from an electric golf cart and demonstrates the lateral tracking control based on a PID controller. Having level 1 autonomy, our autonomous golf cart can control steering, speed, and braking under a take-over control action requested by the driver. To reach level 2 autonomy, the autonomous golf cart gives a demonstration for testing navigation and tracking control. GNSS positioning data obtained from the RTK modules are used to manually create the desired path while driving the vehicle. For the automated driving test, a PID controller is used in the lateral tracking control system to control the golf cart to follow two straight paths in the experiment. The results show that the vehicle can smoothly track the desired path.