Reviewerly/exHarmony huggingface dataset:
Image Encryption Using Quantum 3D Mobius Scrambling and 3D Hyper-Chaotic Henon Map

In encryption technology, image scrambling is a common processing operation. This paper proposes a quantum version of the 3D Mobius scrambling transform based on the QRCI model, which changes not only the position of pixels but also the gray values. The corresponding quantum circuits are devised. Furthermore, an encryption scheme combining the quantum 3D Mobius transform with the 3D hyper-chaotic Henon map is suggested to protect the security of image information. To facilitate subsequent processing, the RGB color image is first represented with QRCI. Then, to achieve the pixel-level permutation effect, the quantum 3D Mobius transform is applied to scramble bit-planes and pixel positions. Ultimately, to increase the diffusion effect, the scrambled image is XORed with a key image created by the 3D hyper-chaotic Henon map to produce the encrypted image. Numerical simulations and result analyses indicate that our designed encryption scheme is secure and reliable. It offers better performance in the aspect of key space, histogram variance, and correlation coefficient than some of the latest algorithms.
Improving Text-Independent Forced Alignment to Support Speech-Language Pathologists with Phonetic Transcription

Problem: Phonetic transcription is crucial in diagnosing speech sound disorders (SSDs) but is susceptible to transcriber experience and perceptual bias. Current forced alignment (FA) tools, which annotate audio files to determine spoken content and its placement, often require manual transcription, limiting their effectiveness. Method: We introduce a novel, text-independent forced alignment model that autonomously recognises individual phonemes and their boundaries, addressing these limitations. Our approach leverages an advanced, pre-trained wav2vec 2.0 model to segment speech into tokens and recognise them automatically. To accurately identify phoneme boundaries, we utilise an unsupervised segmentation tool, UnsupSeg. Labelling of segments employs nearest-neighbour classification with wav2vec 2.0 labels, before connectionist temporal classification (CTC) collapse, determining class labels based on maximum overlap. Additional post-processing, including overfitting cleaning and voice activity detection, is implemented to enhance segmentation. Results: We benchmarked our model against existing methods using the TIMIT dataset for normal speakers and, for the first time, evaluated its performance on the TORGO dataset containing SSD speakers. Our model demonstrated competitive performance, achieving a harmonic mean score of 76.88% on TIMIT and 70.31% on TORGO. Implications: This research presents a significant advancement in the assessment and diagnosis of SSDs, offering a more objective and less biased approach than traditional methods. Our model's effectiveness, particularly with SSD speakers, opens new avenues for research and clinical application in speech pathology.
Application of Morphological Operations and Hough Transform for Recognizing Objects in an Image when High Intensity Glare Occurs

The paper considers the problem of recognizing in a photo image a large number of similar objects, the illumination of which is characterized by glare of high intensity (with a complex transmission coefficient of the optical system). Examples of such objects are glass bottles or test tubes. High-intensity glare blurs the boundaries of objects up to their visual merging. The authors chose the Hough Transform as the main methods of recognition. With properly chosen parameters, the transform recognizes both circles (disks) and circles equally well. However, for the second case, as practice shows, circles must be continuous. To fulfill this condition, the image must be subjected to preprocessing (Gaussian filter, Radon transform, etc.). In particular, the paper considers the application of morphological operations (dilatation, erosion) for image preprocessing. After the Hough transform the obtained list of circles is subjected to classification by radius and mutual location, which allows to increase the reliability of recognition
Predicting the Efficiency of Adversarial Instancesfor Language of Graphs

A method for predicting adversarial instances in a language includes producing graphs for each sentence that capture the structure. The graph comprises nodes and edges, a node label represents a perturbed word, and the edge labeled notes possible typos of perturbations. Language labels are predicted for the unlabeled movie review which involves propagating labels through the graph. The adversarial instances appear nearly identical to the given input, but their equivalent prediction can be misinterpreted. In the past few years, assessing adversarial instances has become a decorum to estimate the robustness of deep learning models. While most of the literature emphasizes continuous features (i.e., image). However, some deep learning models also became successful on discrete features, and thus demand a model to understand underlying vulnerability and robustness. In this study, all the one-edit perturbations of a specific word in a review sentence of the Stanford Sentiment Treebank textual dataset which swaps the sentiment from positive to negative are analysed. Classifier's accuracy is also observed by inducing different typos and validated results indicate that graph structure alone is sufficient to predict whether an instance can fool the classifier or not.
Time-optimal trajectory planning for industrial robots with end-effector acceleration constraints

The implementation of manipulator robots has become indispensable in various industries, enabling efficient performance of complex tasks. Swift and efficient movement of these robots is essential to maximize productivity. However, the grip limitations of the tool restrict rapid movement in scenarios where the manipulator's load varies. In this research, we propose a novel method for generating minimum-time trajectories while considering acceleration constraints on the tool. The algorithm incorporates two trajectory generation methods in the joint space: a simple polynomial approach and a composite approach. To validate the proposed method, we apply it to a UR10 robot model equipped with a vacuum gripper, specifically designed for handling boxes with diverse characteristics. By testing the algorithm on different trajectories, we successfully identify satisfactory solutions. The outcomes of this study significantly contribute to the advancement of manipulator robots by addressing the challenges posed by variable payload conditions. Our method enables efficient and rapid movement in real-world applications, enhancing overall productivity and expanding the scope of manipulator robot utilization.
Physical reservoir computing toward edge AI hardware

Recently, machine-learning based electronics for data driven computing, so-called “AI electronics”, has been greatly progressed to continuously bring about positive impacts on society and way of life. This movement was triggered by the great success in the computing algorithm with deep neural networks on software, thanks to rapid progress of versatile hardware technology, such as CMOS circuits and GPU accelerators. After a little while, various hardware technologies specialized for AI electronics, such as neuromorphic chip and computing in memory (CiM), have emerged to achieve high-performance, low-latency, and energy-efficient computing, and nowadays it pioneers a new field in electronics. The most of such technologies focus on the synaptic function, i.e., the weighted sum of many neuron output values (the function is sometimes referred as to Multiply Accumulate: MAC) and thresholding with a preset value, because it is one of the bottlenecks when algorithms with large-scaled neural networks are properly realized. Nevertheless, even when energy-efficient computing is further advanced in this direction, a massive number of neurons and synapse, complicated circuits, computing algorithm with time-varying sequential processing, and iterative learning algorithm are still issues for edge AI hardware onto terminal devices where computational resources, such as power supply and memory storage, are severely limited. Physical reservoir computing (RC) developed from recurrent neural networks has high potential to revolve technologies for edge AI hardware by overcoming those issues with a novel route, and a major advantage lies in its high applicability to temporal information processing of time-series data that are frequently detected by various sensors. The computing system utilizes nonlinear physical dynamics without adjustable weights, called “reservoir”, and a one-layer linear classifier with a small number of adjustable weights. Those features enable the uses of a feasible reservoir device and a deterministic learning algorithm with a linear regression, such as Ridge regression. Owing to a small number of hyper parameters (adjustable weights), physical RC can demonstrate high-performance, low-latency, and energy-efficient computing for a specific task, while leveraging nonlinear physical dynamics. The presentation reviews physical RC with emphasis on electronics: the basics including the historical background and computing scheme, recent advances with various electronic devices, such as analog circuits, memristors, ferroelectric devices, and spintronic devices, and current issues and perspective for edge AI hardware.
An Advanced Cloud Security and Load Balancing in Health Care Systems

Healthcare systems increasingly use cloud computing technologies to improve operational efficiency and patient care. However, due to the sensitive nature of healthcare data and the importance of uninterrupted service, cloud-based healthcare environments require robust security measures and effective load-balancing mechanisms. This paper provides advanced load balancing (ALB) with high-level security (HLS), which offers advanced cloud security and load-balancing techniques for healthcare systems. This abstract focuses on three critical aspects of cloud security: data protection, access control, and threat detection and response. Cutting-edge encryption algorithms and secure transmission protocols protect patient records, medical images, and other sensitive data. Access control mechanisms such as role-based access control and multi-factor authentication are implemented to ensure authorized access to healthcare resources and prevent unauthorized breaches. Load balancing is critical in healthcare systems to ensure high availability and optimal resource utilization. This abstract discusses load-balancing strategies that are designed explicitly for healthcare workloads. Incoming requests are distributed across multiple servers using dynamic load-balancing algorithms, preventing any single node from overloading and ensuring efficient resource utilization. In addition, the abstract emphasizes the incorporation of cloud security and load-balancing mechanisms in healthcare systems. By combining these two factors, healthcare organizations can create a secure and scalable infrastructure that can provide continuous services to patients, healthcare professionals, and other stakeholders. The abstract emphasizes the importance of constant monitoring, proactive maintenance, and regular updates to ensure the effectiveness of security and load-balancing measures in an evolving cloud environment.
An Advanced Real-Time Job Recommendation System and Resume Analyser

The increasing complexity and abundance of information in resumes have created confusion and challenges for both candidates and recruiters. An effective resume screening is crucial for students looking for job opportunities as they can demonstrate their skills and qualifications to potential employers and is essential for the recruiter. This study focuses on implementing a resume screening and job recommendation system using Graph Neural Network based on domain adaptation approach and Natural language processing. The goal is to extract latent features from job posts and resumes using GNN and NLP techniques, including Named Entity Recognition (NER). Furthermore, the system leverages the GloVe word embeddings for tokenizing and encoding textual information, enhancing semantic understanding. In addition, the system evaluates the resume score based on the job description using advanced techniques such as cosine similarity and TF-IDF to assess the relevance of keywords and phrases between the resume and job description. By considering the job roles matched to the resume it also recommends the latest jobs posted on different websites using web scraping. In conclusion, this study demonstrates the effectiveness of the GNN-based domain adaptation method can make more accurate in resume screening and job recommendations.
Application of CNN and Recurrent Neural Network Method for Osteosarcoma Bone Cancer Detection

The outlook for people with metastatic osteosarcoma at an advanced stage is poor. Osteosarcoma is the most frequent form of bone cancer in children and young adults. There is an urgent need for both advances in treatment tactics and the identification of novel therapeutic targets for osteosarcoma since the disease typically develops resistance to existing treatments. Cancer stem cells, also known as tumor stem cells, have been linked to the development and spread of cancer at multiple points in the disease's progression. Cancer stem cells are linked to treatment resistance and carcinogenesis, and recent studies have demonstrated that osteosarcoma shares these properties. The proposed methodology rests on the three pillars of preprocessing, feature extraction, and model training. During preprocessing, that the proposed approach eliminated isolated highlights to help us zero in on the trustworthy region. They use the wavelet transform and the gray level co-occurrence matrix to extract features. A CNN-RNN technique is used to evaluate the models. In terms of output quality, the proposed technique is superior to both CNN and RNN.
Automated Identification and Prioritization of Self-Admitted Technical Debt Using NLP Word Embeddings

Managing technical debt is critical for software quality and maintainability. Self-Admitted Technical Debt (SATD) is a type of debt explicitly reported by developers in various software artifacts. This study proposes an automated approach to identify and prioritize SATD using Natural Language Processing (NLP) word embeddings. Proposed objectives include designing feature extraction and selection methods for debt classification, creating a model for SATD identification in issue tracking systems, and establishing a priority model for debt classification and refactoring probability. In this study, bag-of-words model is integrated into NLP word embeddings to enhance accuracy. Through a comprehensive analysis, the study compares proposed approach with existing models, demonstrating superior performance. Furthermore, a literature review on AI-powered tools for technical debt management, highlighting their potential and limitations are discussed. This study addresses the gap in SATD identification from issue tracking systems and showcase proposed approach's effectiveness through experiments on open-source projects. Proposed work provides valuable insights and a practical framework for software development teams to mitigate technical debt efficiently and effectively.
Enhancement Techniques on Deep Learning-based Mammography Classification for Breast Cancer Detection

Breast cancer is a significant health concern worldwide, and early detection is crucial in improving patient outcomes. In this study, we aimed to develop an advanced mammography classification system using deep learning (DL) techniques and image processing methods to enhance the accuracy of breast cancer detection. Several DL models, including InceptionResNetV2, InceptionV3, DenseNet169, MobileNetV2, VGG16, ResNet101, VGG19, and ResNet50, were evaluated, along with image processing methods such as Adaptive Contrast Smoothing, gamma sharpening, and Adaptive Gamma Sharpening. Our research demonstrated that implementing image processing methods, particularly Adaptive Gamma Sharpening, significantly improved the classification accuracy across multiple models. Notably, when combined with the Adaptive Gamma Sharpening technique, the VGG16 model achieved the highest accuracy of 98.07%. The evaluation using the Area Under the Receiver Operating Characteristic curve (AUC-ROC) metric revealed that the Adaptive Gamma Sharpening method effectively discriminated between cancerous and non-cancerous cases, with the highest AUC-ROC of 9S.06% observed in the VGG16 model. By leveraging the combination of DL techniques and image processing methods, this research successfully enhanced the performance of mammography classification models in detecting breast cancer. These findings support the potential of image processing methods, particularly Adaptive Gamma Sharpening, in improving the accuracy and reliability of breast cancer detection.
Anomaly Detection in Raw Audio Using Extreme Learning Machine

Anomaly detection is a crucial problem that has garnered attention across various research areas and application domains. Numerous anomaly detection techniques have been developed, with specific focus on domains such as sound or speech recognition. Sound recognition applications are commonly employed in health system monitoring and control, making them particularly relevant in this study. The objective of this research is to detect anomalies in sound, enabling informed decision-making regarding the handling of such sounds. For this study, the TUT Rare Sound Events 2017 dataset is utilized, comprising 2987 audio files. These files encompass isolated sound events for each target class with the noise background noise from everyday acoustic scenes. The dataset is divided into a training and testing set with the split of $80:20$. The Extreme Learning Machine (ELM) method is employed for the learning process. The accuracy and performance of the ELM method are evaluated through calculations. The results reveal that the ELM method demonstrates promising capabilities in detecting anomalies within raw audio data, achieving an accuracy of 93.98%. Notably, the highest accuracy of 92.13% is achieved when detecting the anomaly of baby cry. These findings highlight the effectiveness of the ELM method in anomaly detection within sound data.
Versatile Resource-shared Cryptographic Accelerator for Multi-Domain Applications

SHA256, BLAKE256, BLAKE2s, Salsa20, and Chacha20 are crucial cryptographic algorithms used for data security in multi-domain applications. Consequently, there is a strong need for a flexible cryptographic hardware architecture that can efficiently handle various algorithms and provide excellent performance. Regrettably, existing cryptographic architectures do not meet the requirements for desired flexibility and high performance in these applications. Therefore, this paper introduces a novel resource-shared crypto accelerator (RCA) to achieve high flexibility and maximize hardware efficiency. The RCA applied two optimizations a register-sharing approach with multi-mode digest routers and an adder-sharing approach in flexible ALUs. Theoretical evaluation reveals that the RCA achieves 72% register sharing (104 out of 136) and 78.6% adder sharing (44 out of 56). Verification of RCA on Xilinx ZCU102 FPGA at the system-on-chip level is conducted to demonstrate its accuracy and efficacy. Furthermore, the RCA experimental results on multiple FPGAs show its remarkable flexibility and hardware efficiency. It outperforms existing works in terms of throughput (1.36-28.9 times) and area efficiency (1.14-2.45 times).
Homonym and Polysemy Approaches in Term Weighting for Indonesian-English Machine Translation

Many previous studies have discussed extracting ambiguous sentences in the Indonesian language to increase text extraction performance, especially for improving the accuracy of Indonesian-English Machine Translation (MT). One of the factors that can influence the presence of ambiguous sentences is the word features of homonym and polysemy; however, not many extraction methods have been developed to solve these issues. This research proposes word feature extraction of homonyms and polysemy in Indonesian to improve Indonesian-English MT accuracy. First, POS tagging is used to obtain the word types in the sentences. To measure the word similarity for extracting homonyms and polysemy within uni-gram and bi-gram features, Word2vec and Synonym-based term expansion are used. The terms indicated as homonym and polysemy are compiled in dictionaries 1 and 2. Then, Semantic similarity is used to calculate word similarity for extracting the highest similarity value as the updated terms between existing terms in the sentence and extracted synonym terms. Neural Machine Translation (NMT) is used for the translation process, which has two translation steps as the proposed MT: NMT and NMT with proposed word feature extraction. The proposed MT modifies the translation result to obtain a more accurate translation result according to the two translation results. Finally, the proposed MT performance is evaluated. The evaluation results based on precision, recall, f-1 measure, and accuracy, respectively, are 0.7791, 0.8428, 0.8097, and 0.7975.
A Systematic Literature Review of Genetic Algorithm-Based Approaches for Cloud Task Scheduling

Cloud computing offers a promising approach to efficiently distribute tasks and workflows among virtual resources. Optimal scheduling of these resources is crucial to maximize the efficiency of cloud environments. Various nature-inspired metaheuristic solutions have been suggested to tackle this challenge, focusing on Genetic Algorithm (GA)-based techniques. GA has the capability to effectively manage the intricate and ever-changing characteristics of cloud task scheduling, hence enhancing the allocation of resources in accordance with present requirements. In this study, we comprehensively analyze GA-based techniques for cloud task scheduling by performing a Systematic Literature Review (SLR). Our review involves selecting relevant studies from online electronic databases based on predefined research questions (RQs) and criteria. We narrowed the selection from 385 articles to a final set of 20 articles that provide valuable insights into our research inquiries and form the foundation of our analysis. We explore different aspects of the literature, such as its properties, benefits, drawbacks, datasets, simulation tools, performance evaluations, and function objectives. Based on this analysis, we propose a classification framework incorporating a modified and hybrid GA method for scheduling tasks and workflows. Furthermore, we outline future research directions in this field. The overall efficacy of GA-modified and GA-Hybrid algorithms is very commendable. However, it is crucial to consider the intricacy and the potential for being confined to local optima in order to get more optimal outcomes in task scheduling.