Reviewerly/exHarmony huggingface dataset:
EVALUATING THE ROBUSTNESS OF YOLO OBJECT DETECTION ALGORITHM IN TERMS OF DETECTING OBJECTS IN NOISY ENVIRONMENT

Our daily lives are impacted by object detection in many ways, such as automobile driving, traffic control, medical fields, etc. Over the past few years, deep learning techniques have been widely used for object detection. Several powerful models have been developed over the past decade for this purpose. The YOLO architecture is one of the most important cutting-edge approaches to object detection. Researchers have used YOLO in their object detection tasks and obtained promising results. Since the YOLO algorithm can be used as an object detector in critical domains, it should provide a quite high accuracy both in noisy and noise-free environments. Consequently, in this study, we aim to carry out an experimental study to test the robustness of the YOLO v5 object detection algorithm when applied to noisy environments. To this end, four case studies have been conducted to evaluate this algorithm's ability to detect objects in noisy images. Specifically, four datasets have been created by injecting an original quality image dataset with different ratios of Gaussian noise. The YOLO v5 algorithm has been trained and tested using the original high-quality dataset. Then, the trained YOLO algorithm has been tested using the created noisy image datasets to monitor the changes in its performance in proportion to the injected Gaussian noise ratio. To our knowledge, this type of performance evaluation study did not conduct before in the literature. Furthermore, there are no such noisy image datasets have been shared before for conducting these types of studies. The obtained results showed that the YOLO algorithm failed to handle the noisy images efficiently besides degrading its performance in proportion to noise rates.
Investigating and acquiring motor expertise using virtual reality

After just months of simulated training, on January 19, 2019 a 23-year-old E-sports pro-gamer, Enzo Bonito, took to the racetrack and beat Lucas di Grassi, a Formula E and ex-Formula 1 driver with decades of real-world racing experience. This event raised the possibility that practicing in virtual reality can be surprisingly effective for acquiring motor expertise in real-world tasks. Here, we evaluate the potential of virtual reality to serve as a space for training to expert levels in highly complex real-world tasks in time windows much shorter than those required in the real world and at much lower financial cost without the hazards of the real world. We also discuss how VR can also serve as an experimental platform for exploring the science of expertise more generally.
Impact of Evaluation of Tumour Grade by Core Needle Biopsy on Clinical Risk Assessment and Patient Selection for Adjuvant Systemic Treatment in Breast Cancer

Introduction: Breast cancer is one of the most common cancers affecting women worldwide. The prognosis and treatment of breast cancer depend largely on various prognostic factors, including tumour grade, hormone receptor status, and HER2/neu overexpression. Objectives: The main objective of the study is to find the impact of evaluation of tumour grade by core needle biopsy on clinical risk assessment and patient selection for adjuvant systemic treatment in breast cancer Material and Methods: This retrospective cohort study was conducted in Services Hospital, Lahore during January 2020 to January 2021. The study participants were women with breast cancer who underwent core needle biopsy for tumour grade evaluation at a single institution during the study period. Results: Of the 70 patients included in the study, the mean age was 58 years (range, 32-85 years), and the majority were postmenopausal (60%). Most patients had invasive ductal carcinoma (IDC) (85%), and the remainder had invasive lobular carcinoma (ILC) (15%). Most patients had stage II (45%) or stage III (35%) breast cancer at the time of diagnosis. All patients underwent core needle biopsy for tumour grade evaluation. Conclusion: In conclusion, the study supports the use of core needle biopsy as a reliable method for evaluating tumour grade in breast cancer patients. Further research is needed to evaluate the long-term outcomes of patients who are treated based on tumour grade assessed by core needle biopsy.
Machine Learning Models for Alzheimer’s Disease Detection Using Medical Images

Human brain is an exclusive, sophisticated, and intricate structure. Neuro-degeneration is the death of neurons which is the ultimate cause of brain atrophy resulting in multiple neurodegenerative diseases. Neuro-imaging is the most critical method for the detection Alzheimer’s and quantification of brain atrophy. Magnetic resonance imaging (MRI), computed tomography (CT), single-photo emission computes tomography (SPECT), and positron emission tomography (PET) are the widely used neuroimaging techniques to image/estimate altered brain tissue and to assess neurodegeneration associated with Alzheimer’s. Traditionally, neuro-radiologists incorporate clinically useful information and medical imaging data from various sources to interrelate the structural changes, reduction in brain volume, or changes in patterns of brain activity. In recent years, machine learning and artificial intelligence-based approaches continue to garner substantial interest in neurobiology domains and have emerged as powerful tools for the efficient prediction of neurological and psychiatric disorder-related outcomes. Traditional machine learning algorithms show limitations in terms of the data size and image feature extractions. To address such concerns, Deep Learning Algorithms relying on Deep Convolution Networks (DCN) and Recurrent Neural-inspired Networks (RNN) have advanced to more powerful paradigms to solve the complexity of multistate brain imaging data and to provide extensive solutions in the better understanding of mechanistic details of the progression of brain atrophy in Alzheimer’s disease. The rationale of this study is to provide an in-sight to role of Machine learning for AD detection using neuroimaging data.
Secure Enrollment Token Delivery Mechanism for Zero Trust Networks Using Blockchain

Zero Trust Networking (ZTN) is a security model where no default trust is given to entities in a network infrastructure. The first bastion of security for achieving ZTN is strong identity verification. Several standard methods for assuring a robust identity exist (E.g., OAuth2.0, OpenID Connect). These standards employ JSON Web Tokens (JWT) during the authentication process. However, the use of JWT for One Time Token (OTT) enrollment has a latent security issue. A third party can intercept a JWT, and the payload information can be exposed, revealing the details of the enrollment server. Furthermore, an intercepted JWT could be used for enrollment by an impersonator as long as the JWT remains active. Our proposed mechanism aims to secure the ownership of the OTT by including the JWT as encrypted metadata into a Non-Fungible Token (NFT). The mechanism uses the blockchain Public Key of the intended owner for encrypting the JWT. The blockchain assures the JWT ownership by mapping it to the intended owner's blockchain public address. Our proposed mechanism is applied to an emerging Zero Trust framework (OpenZiti) alongside a permissioned Ethereum blockchain using Hyperledger Besu. The Zero Trust Framework provides enrollment functionality. At the same time, our proposed mechanism based on blockchain and NFT assures the secure distribution of OTTs that is used for the enrollment of identities.
Search-Based Regular Expression Inference on a GPU

Regular expression inference (REI) is a supervised machine learning and program synthesis problem that takes a cost metric for regular expressions, and positive and negative examples of strings as input. It outputs a regular expression that is precise (i.e., accepts all positive and rejects all negative examples), and minimal w.r.t. to the cost metric. We present a novel algorithm for REI over arbitrary alphabets that is enumerative and trades off time for space. Our main algorithmic idea is to implement the search space of regular expressions succinctly as a contiguous matrix of bitvectors. Collectively, the bitvectors represent, as characteristic sequences, all sub-languages of the infix-closure of the union of positive and negative examples. Mathematically, this is a semiring of (a variant of) formal power series. Infix-closure enables bottom-up compositional construction of larger from smaller regular expressions using the operations of our semiring. This minimises data movement and data-dependent branching, hence maximises data-parallelism. In addition, the infix-closure remains unchanged during the search, hence search can be staged: first pre-compute various expensive operations, and then run the compute intensive search process. We provide two C++ implementations, one for general purpose CPUs and one for Nvidia GPUs (using CUDA). We benchmark both on Google Colab Pro: the GPU implementation is on average over 1000x faster than the CPU implementation on the hardest benchmarks.
Unified Approach to Secret Sharing and Symmetric Private Information Retrieval With Colluding Servers in Quantum Systems

This paper unifiedly addresses two kinds of key quantum secure tasks, i.e., quantum versions of secret sharing (SS) and symmetric private information retrieval (SPIR) by using multi-target monotone span program (MMSP), which characterizes the classical linear protocols of SS and SPIR. SS has two quantum extensions; One is the classical-quantum (CQ) setting, in which the secret to be sent is classical information and the shares are quantum systems. The other is the quantum-quantum (QQ) setting, in which the secret to be sent is a quantum state and the shares are quantum systems. The relation between these quantum protocols and MMSP has not been studied sufficiently. We newly introduce the third setting, i.e., the entanglement-assisted (EA) setting, which is defined by modifying the CQ setting with allowing prior entanglement between the dealer and the end-user who recovers the secret by collecting the shares. Showing that the linear version of SS with the EA setting is directly linked to MMSP, we characterize linear quantum versions of SS with the CQ ad QQ settings via MMSP. Further, we introduce the EA setting of SPIR, which is shown to link to MMSP. In addition, we discuss the quantum version of maximum distance separable codes.
On conjunctive complex fuzzification of Lagrange's theorem of &lt;i&gt;ξ&lt;/i&gt;−CFSG

&lt;abstract&gt;&lt;p&gt;The application of a complex fuzzy logic system based on a linear conjunctive operator represents a significant advancement in the field of data analysis and modeling, particularly for studying physical scenarios with multiple options. This approach is highly effective in situations where the data involved is complex, imprecise and uncertain. The linear conjunctive operator is a key component of the fuzzy logic system used in this method. This operator allows for the combination of multiple input variables in a systematic way, generating a rule base that captures the behavior of the system being studied. The effectiveness of this method is particularly notable in the study of phenomena in the actual world that exhibit periodic behavior. The foremost aim of this paper is to contribute to the field of fuzzy algebra by introducing and exploring new concepts and their properties in the context of conjunctive complex fuzzy environment. In this paper, the conjunctive complex fuzzy order of an element belonging to a conjunctive complex fuzzy subgroup of a finite group is introduced. Several algebraic properties of this concept are established and a formula is developed to calculate the conjunctive complex fuzzy order of any of its powers in this study. Moreover, an important condition is investigated that determines the relationship between the membership values of any two elements and the membership value of the identity element in the conjunctive complex fuzzy subgroup of a group. In addition, the concepts of the conjunctive complex fuzzy order and index of a conjunctive complex fuzzy subgroup of a group are also presented in this article and their various fundamental algebraic attributes are explored structural. Finally, the conjunctive complex fuzzification of Lagrange's theorem for conjunctive complex fuzzy subgroups of a group is demonstrated.&lt;/p&gt;&lt;/abstract&gt;
Empirical modeling of optimum tilt angle for flat solar collectors and PV panels

A new model has been developed to determine the optimal tilt angle for PV panels and solar collectors on a yearly, seasonal, and monthly basis. The model estimates the diffusion component of solar radiation using Orgill and Holland’s model, which relates the diffusion fraction of solar radiation to the sky clearness index. Empirical data on the clearness index is used to derive the relationship between the diffusion and direct components of solar radiation at any global latitude on any given day of the year. By maximizing the total amount of diffused and direct radiation, the optimal tilt angle for each month, season, and year is determined relative to the latitude angle. The model is programmed in MATLAB and is freely available for download from the MATLAB file exchange website. The model shows that small deviations from the optimal inclination angle have only a minor effect on overall system yield. The monthly optimal tilt angles predicted by the model are consistent with experimental data and other published model predictions for various locations around the globe. Importantly, unlike some other models, the present model does not predict negative optimal inclination angles for small northern hemisphere latitudes or vice versa.
Hybrid attention generative adversarial network: texture inpainting algorithm for iris defects with excellent repair performance and generalization

Iris defect texture inpainting is a challenging problem that is not only limited by a lack of research but also by requiring a higher degree of texture refinement than other types of images. In the field of image inpainting, most recent research has focused on designing improved encoder-decoder models. Solving the image blurring problem caused by autoencoder models has become a key factor in judging their merits. Generative adversarial networks were designed based on a hybrid attention generative adversarial network (Hybrid A-GAN) mechanism to repair missing iris textures. The generator is based on the encoder-decoder structure and introduces two attention mechanisms to enable obtaining the correlation between channels in the feature map and pixel importance in space, which enhances the network’s ability to utilize feature information. Moreover, the improved jump connection effectively fuses the high-level features with the low-level features after weighting, which prevents the information loss caused by the downsampling process and enhances the image generation capability. In addition, the joint Wasserstein generative adversarial network-gradient penalty and L1 loss jointly guide network training, which further enhances network generation performance and ensures global consistency of the generated images. Extensive repair experiments and recognition experiments conducted on three publicly available datasets demonstrated that hybrid A-GAN has excellent repair capability and generalization performance.
Make your next item recommendation model time sensitive

Recommender systems have become increasingly popular for providing personalized recommendations to users. Recent studies have shown that transformer-based approaches can enhance the performance of these systems. However, these models usually consider the sequence of past user interactions and do not take into account the time of prediction. In this paper, we address this issue by proposing a simple yet effective method in the form of adapter to make next-item recommenders time-aware. Specifically, we introduce a novel approach that incorporates time information into the modeling process. We conduct extensive experiments on two commonly used sequential recommenders, GRU4Rec and TiSASRec, using four real-world datasets. Our results demonstrate that our approach increases the quality of existing methods and improves the accuracy of recommendations. Our approach is easy to implement and can be applied to a wide range of next-item recommendation systems. It provides a structured framework for incorporating time information into the modeling process, making it easier for researchers to replicate and build upon our findings. Overall, our work contributes to the development of more accurate and efficient recommendation systems, with potential applications in various domains such as e-commerce, social media, and online content delivery. Code is available at GitHub repo1.
Displacement based Simulation and Material Calibration based on Digital Image Correlation Part II - Application

Abstract An alternative approach to calibrate material models is proposed. The entire displacement field of a specimen measured with a DIC system was converted into boundary conditions by the Envyo® mapping software. These boundary conditions were imposed on all nodes in the corresponding LS-DYNA® simulation model. In this way a digital twin of the specimen geometry and the experimental kinematics was obtained. Considering the local stress equilibrium over the entire specimen and the equilibrium of global force integrated over many hypothetical cross sections, LS-OPT® was used to iteratively determine the isotropic model parameters of the tested material. A new tensile specimen geometry is introduced to get the most out of a single test. The findings suggest that, at present, convergence in iterative parameter optimization cannot be attained without prior filtering and smoothing of the DIC input data in time and space, despite the utilization of the chosen material model.
When Students Take the Lead on Freedom of Expression: Free Speech Clubs Coming to a School Near You

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Low energy split-cluster wireless acceleration sensors for motion and posture change recognition in sports

The rise of the national fitness trend has led more and more people to join the ranks of physical fitness, and the current physical education resources and human resources have not been able to keep pace with the development of sports. Due to the lack of guidance, many beginners of sports are not standardized, and their movements are not effective and even cause injuries. Sports action recognition technology can make up for this lack and realize the action recognition and guidance for sports players. In this paper, we build a sports movement and posture recognition model based on acceleration sensors, introduce Selectable Mode Vocoder (SMV) algorithm to optimize the performance, and ensure the effectiveness of sensor data transmission through a low energy multi-layer clustered wireless sensor network. The experiment mainly compares the generation results of the first energy depleted node in the entire network with the number of rounds with energy depleted nodes reaching 10 %. The experimental results show that the low-energy multi-layer clustered wireless sensor network effectively shortens the data transmission path, reduces the energy consumption of nodes, extends the network life time, ensures the data transmission effectiveness, and provides network guarantees for sports action recognition. In addition, in the comparison experiments, the action recognition model in this paper shows better recognition effect and efficiency with obvious performance advantages.
Optimization of fuzzy rules using neural network to control mobile robot in non-structured environment

It is still a challenge for all authors to control an autonomous mobile robot in an unstructured environment. The purpose of this paper is to propose a new control method for mobile robots in unstructured environments using neuro fuzzy technique. The proposed algorithm reduces the processing time of the fuzzy logic controller (FLC) inference engine. The neural network (NN) will therefore select the optimum rule(s) directly from the inference engine. This means that all of the rules of the inference engine do not need to be processed. As a result, the inference engine process speed will decrease, and the fuzzy logic response will increase. An actual mobile robot with three distance sensors and one virtual orientation angle is used to test the proposed algorithm. Based on the results, the mobile robot is capable of avoiding all obstacles and reaching the target point accurately.
Identification and Detection of Credit Card Frauds Using CNN

Phishing, vishing, spamming, ransomware, cybersquatting, and other forms of financial frauds have been committed on credulous people for years now. With this lockdown which pushed millions of people to make online transactions through credit cards with less provision for security, fraudsters found a big population of preys to target. This COVID-19 pandemic made the world population to stay home and purchase their basic needs in e-commerce sites through their cards in simple steps. This ease of transactions made by technological development also makes it easy for fraudsters attempting to abuse the card. To restrict the fraudster’s actions, the main step is the verification of credit card transactions and identifying between the fraud and the genuine transactions. Traditional methods employ rule-based expert systems to identify fraud transactions without considering the diverse scenarios and extremely unbalanced feature samples. Here, we propose the credit card fraud detector using CNN with smart matrix algorithm suitable for large-sized real-time datasets. The pre-processing of dataset is performed using random under sampling for effective training of the model. This pre-processed dataset is normalized for acquiring standardized input. The feature sequencing for feature selection is performed by the smart matrix algorithm. When compared to other machine learning methods such as Naive Bayes and K-nearest neighbor, the three-layered CNN model performs better. The performance is evaluated using parameters such as confusion matrix, false alarm rate, sensitivity, Matthew’s correlation coefficient, balanced classification rate, and F1 score.