Reviewerly/exHarmony huggingface dataset:
The role of Chatbots in Industry 4.0

Conversational artificial intelligence (CAI) more often denoted as a chatbot is an instantaneous approach used for depicting answers to those humans wanting to solicit queries via this medium. A tiny pop icon and or imagery often located at the bottom of a window (i.e., nearer the rightmost corner) of most webpages, used for asking questions whilst shopping online and in some instances also making payments. Chatbots are considered revolutionary owing to the user interface design specificity of each. Henceforth, they do ensure that human and computational interaction are primarily interactive. Because of this, they are equipped to navigate all associated digital platforms. The versatile nature of said chatbots has allowed for businesses operating online to exploit its core functionalities and in essence has created a hotpot for handling many tasks. Whatever we can envision, a chatbot can in principle be used to aid humans and has grown predominantly in the context of social media, logistics and scheduling, application functionality and even in human resources. Core algorithms seamlessly simulate, automate, and streamline customer service tasks in a quick yet convenient manner. All of which is done via access to servicing. Industry 4.0 is not an area of research that has been research in greater depth. This paper is geared towards demonstrating the viability of and stages of its usage will in industrial settings.
Design and Evaluation of M-Term Non-Homogeneous Hybrid Karatsuba Polynomial Multiplier

Finite-field multipliers progressively plays a crucial role in modern cryptography systems. While much attention has been given to the development of area-efficient Karatsuba multipliers as a means of bolstering encryption capabilities, there remains a vast and untapped realm of design space yet to be explored. An innovative technique that has emerged in this area involves the implementation of a Composite M-Term Karatsuba-like Multiplier, which integrates a schoolbook multiplier (SBM) at lower bounds to enhance performance. However, the approach of breaking down operand bit-widths homogeneously along the stages may not result in optimal hardware characteristics, and further improvement can be achieved by configuring the recursive stages to non-homogeneous ‘M’ values. This paper attempts to perform an exhaustive design-space exploration of Karatsuba-like multipliers for different bit-widths and presents a methodology for designing different possible sequences for M-Term non-homogeneous hybrid Karatsuba multiplier (MNHKA). Few MNHKA designs among several sequences achieve high performance while minimizing area requirements. This study evaluates the area, delay, and area-delay-product (ADP) characteristics of pure M-Term Karatsuba multiplier (MKA), Composite M-Term Karatsuba with SBM (CMKA), and a novel MNHKA that are configured as finite field multipliers for different popular bitwidths. In addition, this study also introduces a novel Matlab-based framework that enables the generation of an optimized hardware design code for MNHKA design with customizable sequence and operand sizes. The proposed MNHKA design was implemented and verified on ZYNQ ZCU-104 FPGA Board and also synthesized using 45 nm technology library on Cadence-Genus tool. The implemented FPGA results with LUTs utilization and delay metrics clearly indicate that the proposed category of MNHKA polynomial multiplier outperforms SOTA designs for various bit-widths. Specifically, the proposed design achieves an ADP improvement of 12.33% for a bit-width of 64, and greater gains of 21.15%, 27.74%, and 23.045% for higher order bits of 409, 1350, and 2500, respectively, when compared to CMKA(STOA) multiplier. The experimental results of ASIC flow resulted in an impressive maximum footprint saving of 47.61% as well as significant ADP gains of 45.72% for the 1350-bit design, and also achieved ADP improvement of 16.42%, 15.56%, and 22.59% for bit widths of 64, 409, and 2500, respectively, when compared to CMKA design. All the designs are made freely available for further adoption to the researchers and the designers community.
CellFlow: Automated Standard Cell Design Flow

The existing flow for creating standard cell library is industry protected, with minimum options to tweak and improve the cell properties. Open-Source ASIC tools are constantly emerging. However, the existing flow does not offer any options for the realization of custom cells. Hence a novel and reliable custom standard cell library flow integrated with a robust optimization scheme referred to as CellFlow is introduced to establish custom cell design. Cartesian genetic programming (CGP), an evolutionary algorithm, was employed to generate optimized and hardware efficient transistor level designs to incorporate in the standard cell flow. The CGP algorithm is configured to render the fewest count transistor custom standard cell designs, which are then characterized for hardware metrics, including power, delay, area, and layout. Further particle-swarm-optimization (PSO) method was adopted to optimize the spice netlist evolved from the CGP synthesized transistor-level design. The CellFlow was employed to develop custom standard cells, including compressors, full-adders, multipliers, and multiplexers designs. The developed custom standard cell library was validated for 4$\times$ 4 Systolic array architecture and PICO-RV32 RISCV core design, showing expected synthesized results.
FastNTT: Design and Evaluation of Modular-Reduction Based Fast NTT Design on FPGA

Polynomial multiplication is typically employed to establish secured systems, which is becoming inevitable for many financial and data transaction services. However, the challenge for most cryptosystems is in attaining a computing speed similar to unsecured systems. For a critical transactional activity, the encrypting process lag may allow cyber miscreants to leak information and have irreparable consequences. Hence faster cryptosystems are in demand to serve the purpose of providing security at no computational cost. The Number theoretic transform (NTT) allows compute-latency enhancements by multiplying higher-order polynomials in the transformed domain and achieve the necessary output. The hardware NTT design further tightens the security aspect and additionally offers power-performance benefits to the system. In this paper, Longa-Naehrig reduction technique accompanied with the benefits of Montgomery and Barett reduction was adopted at different stages of NTT sequence generation, to design FPGA based hardware accelerator NTT design referred to as FastNTT, and the same design was further evaluated the same with state-of-the-art (SOTA) NTT implementation on FPGA and in CPU system.The FastNTT scheme for generating 1024 sequence, when designed on FPGA offered performance improvement of 87.30% and 98% with respect to corresponding SOTA implementation on FPGA, and on CPU respectively. Hardware resource savings of 93.75% and 88.57% for flipflops and BRAM respectively was noted by the hardware designed FastNTT technique when compared with its corresponding SOTA designed on FPGA. A power savings of 83.5%, and an improvement in compute-latency of 87.30% is achieved in FastNTT design over SOTA design when implemented on FPGA systems. The hardware design was also synthesized for ASIC flow using 130 nm skywater technology library, which showcased comparable hardware metrics such as critical path delay, and design footprint, however with reduced number of clock cycles for generating NTT sequence, the proposed fast NTT scheme is preferred over other methods. The proposed NTT hardware accelerator is a performance and power-efficient design and is expected to be heavily adopted for polynomial multiplier module design towards designing secured systems in the future.
Design Space Exploration for CNN Offloading to FPGAs at the Edge

AI-based IoT applications relying on heavy-load deep learning algorithms like CNNs challenge IoT devices that are restricted in energy or processing capabilities. Edge computing offers an alternative by allowing the data to get offloaded to so-called edge servers with hardware more powerful than IoT devices and physically closer than the cloud. However, the increasing complexity of data and algorithms and diverse conditions make even powerful devices, such as those equipped with FPGAs, insufficient to cope with the current demands. In this case, optimizations in the algorithms, like pruning and early-exit, are mandatory to reduce the CNNs computational burden and speed up inference processing. With that in mind, we propose ExpOL, which combines the pruning and early-exit CNN optimizations in a system-level FPGA-based IoT-Edge design space exploration. Based on a user-defined multi-target optimization, ExpOL delivers designs tailored to specific application environments and user needs. When evaluated against state-of-the-art FPGA-based accelerators (either local or offloaded), designs produced by ExpOL are more power-efficient (by up to 2$\times$) and process inferences at higher user quality of experience (by up to 12.5%).
FSCS with Error Sharing based on Alpha Weights for Improving Accuracy of Reconstructed Images

Automatic layer decomposition, primarily in the field of image editing, has garnered substantial interest. The prevalent technique is soft color segmentation. Fast Soft Color Segmentation (FSCS), a novel neural network-based method, has been proposed to accelerate the processing time by learning the iterative optimization process responsible for the slow processing time of traditional methods. However, the reconstructed image–obtained by reconstructing the decomposed layers–does not match the original image in terms of saturation and coloring. Therefore, we introduced post-processing involving error sharing based on alpha weights to FSCS (FSCS-ESAW) to improve the agreement between reconstructed and original images. We define the “alpha weight” as the ratio of each alpha layer value corresponding to each color layer to the total value of each alpha layer. FSCS-ESAW shares the reconstruction error–the error that occurs between the reconstructed image and the original image–with each color layer based on alpha weights, thereby improving the accuracy of each decomposed layer. FSCS-ESAW is characterized by its complete independence from FSCS itself and enables getting more accurate images by adding a simple and lowcost post-processing step to FSCS. Experimental results validated the efficacy of FSCS-ESAW, demonstrating superior agreement between the original and reconstructed images compared to FSCS.
Efficient Credential Revocation Using Cryptographic Accumulators

Credential revocation is a critical component of identity management. Credentials must be revoked on time to avoid legal issues, information misuse, and potential economic damage. A cryptographic accumulator is the most efficient data structure to record whether a credential is valid. However, credential holders must keep their verification proof, or witness, whenever a credential is revoked in the system. The witness update operation is expensive and may not be feasible for applications with limited computational power, such as mobile phones. This paper proposes a credential revocation management framework that removes the need for witness updates and yet still utilises the beneficial properties of the cryptographic accumulator. Our solution involves three accumulators, the notion of an epoch, and the replication of accumulator transition from one state to another for verification. The evaluation shows our method is efficient and suitable for credential holders with limited computing resources.
Construction of English Translation System Based on Machine Learning Algorithm

With the progress of science and technology, artificial intelligence technology has been applied more, and machine learning (ML), as its core content, has always been the focus of attention machine learning, the core of artificial intelligence, is the study of how computers can mimic human learning and reorganize existing knowledge structures to improve their own functions.By analyzing the BP neural network (NN) algorithm and deep learning algorithm among the classical algorithms of ML, this paper expounds the important application of classical algorithms in ML, and discusses the future vision of ML. At the same time, this paper discusses the specific problems and solutions of the machine algorithm, and then discusses how the machine algorithm can improve and build the English translation system. Through experiments and calculations, the accuracy rate of the specific research feature translation can reach 60% or more, up to 90%. This shows that the use of ML algorithms can play a very significant role in the construction of English translation systems.
Unsupervised Contradiction Detection using Sentence Transformations

Contradiction detection (CD) is a subfield of Natural Language Inference (NLI) that is relevant to many domains where contradictory statements in texts should be avoided (e.g., in financial or regulatory documents). With the advent of large annotated NLI datasets, there has been an increased focus on supervised deep-learning approaches in this research area. However, since this training data does not necessarily reflect the characteristic properties of the application data, unsupervised CD approaches are still relevant for certain domains or languages. In this paper, we therefore take up a recently published unsupervised NLI approach, reproduce parts of the proposed sentence transformations, extend it with various modifications, and evaluate it for the sole task of contradiction detection. The results show that under the exclusion of certain transformations types, an accuracy of 71.42 can be achieved on the SNLI test dataset.
Center-free intuitionistic fuzzy c-means clustering algorithm based on similarity of hybrid spatial membership for image segmentation

In order to address the issue that the center-free fuzzy c-means (CFFCM) clustering algorithm does not consider the texture features and spatial information of pixels, and the time complexity is too high, a center-free intuitionistic fuzzy c-means clustering algorithm based on similarity of hybrid spatial membership for image segmentation is proposed. In the proposed algorithm, the voting model is used to generate intuitionistic fuzzy sets (IFS), and the generated hesitation degree and membership degree are combined with spatial information to design a spatial intuitionistic membership degree similarity model. This model can deal with the similarity between pixels and classes in gray information, so the segmentation efficiency is improved. At the same time, the intuitionistic fuzzy local binary pattern (IFLBP) operator is used to extract the image texture information and introduce it into the objective function. Spatial membership similarity model is used to process texture information and improve the segmentation accuracy of the algorithm. The results of simulation experiment show that the proposed has advantages in both visual effect and evaluation index.
Alignment Offset Based Adaptive Training for Simultaneous Machine Translation

Given incomplete source sentences as inputs, it is generally difficult for Simultaneous Machine Translation (SiMT) models to generate a target token once its aligned source tokens are absent. How to measure such difficulty and further conduct adaptive training for SiMT models are not sufficiently studied. In this paper, we propose a new metric named alignment offset (AO) to quantify the learning difficulty of target tokens for SiMT models. Given a target token, its AO is calculated by the offset between its aligned source tokens and the already received source tokens. Furthermore, we design two AO-based adaptive training methods to improve the training of SiMT models. Firstly, we introduce token-level curriculum learning based on AO, which progressively switches the training process from easy target tokens to difficult ones. Secondly, we assign an appropriate weight to the training loss of each target token according to its AO. Experimental results on four datasets demonstrate that our methods significantly and consistently outperform all the strong baselines.
Optimization of Saleman Travelling Problem Using Genetic Algorithm with Combination of Order and Random Crossover

Traveling salesman problem (TSP) is a problem of determining the shortest path for a salesman to take to visit all cities. Although a small number of cities is easy to solve, as the number of cities increases, it’s not possible to solve in polynomial time as it was a combinatorial nondeterministic polynomial (NP-hard) problem. Hence, this project is implementing a genetic algorithm (GA) to solve TSP using Python programming. The focus of this paper is to analyze the GA using order crossover (OX) and random crossover (RX) and propose a combination mechanism, direct combination (OX-RX) and Dynamic Linear combination (OXRX-Linear) to optimize TSP. We test GA for OX and RX in a random set of cities, up to 75 total cities. Then compare the result of the proposed combination OX-RX and OX-RXLinear. The result shows that both proposed combined mechanisms OX-RX and OX-RX-Linear improve the performance of GA in solving TSP.
A Comparative Performance of Genetic Algorithm and Bayesian Optimization for Hyperparameter Tuning for Mammogram Classification

An accurate breast cancer classification utilizing Convolutional Neural Network (CNN) requires the best option of hyperparameter selection to create a robust and adaptive algorithm based on different datasets. Standard optimization algorithms are subjected to nondeterministic and restricted to integer-valued parameters that cause a restricted optimization process on a highly non-linear dataset such as mammogram images. In this study, hyperparameter tuning through two optimization methods, Genetic Algorithm optimization (GAO) and Bayesian optimization (BO), are compared based on the evaluation for breast mass classification of benign and malignant on a publicly available mammogram image of the INbreast dataset. The best model shows an increase in testing accuracy at 90.05% and balancing of sensitivity to the specificity of 0.803 to 0.9481, improving its true positive rate when optimized using the GAO method. The optimization process allows for the combination of genetic mutations of the parent and fusion improves the creation of a population for the best-trained network.
Reservoir Computing System Based on Polarization Dynamics of a Semiconductor Optical Amplifier Fiber Ring Laser

All-optical reservoir computing (RC) based on polarization dynamics of a unidirectional semiconductor optical amplifier (SOA) fiber ring laser is proposed and demonstrated experimentally. The SOA is used as a nonlinear node. The dynamical response in polarization of the ring laser is detected by a polarizer and a photodetector. The prediction error can be as low as 0.0762 for the Santa Fe time series prediction task, and the recognition accuracy can be as high as 100% for the signal classification task. The results show that the proposed RC has good prediction performance and strong classification ability. The polarization dynamics RC provides a new idea for the research of optical RC neural network and can be expected to process two tasks in parallel when the polarization dynamics and the intensity dynamics are used at the same time.
Method and Application of Analyzing Multi-class Data by Artificial Intelligence Model Group Based on Machine Learning

With the continuous in-depth development of science and technology in China, the field of artificial intelligence has also made great progress [1]. As a comprehensive cross-cutting research field of computer science, information science and mathematics, artificial intelligence mainly refers to the development of intelligent technology realized by computer technology. Firstly, this paper analyzes the basic concepts of artificial intelligence and machine learning, and then studies the influencing factors and construction methods of machine learning based on artificial intelligence technology [2]. The purpose is to expand our thinking for the development of artificial intelligence technology in China, and make contributions to the field of artificial intelligence for the research of machine learning in China, so as to better serve the economic development of China [3]. In order to bring the value of artificial intelligence into full play, it is necessary to study the intrinsic meaning, research purpose and machine learning methods of machine learning [4]. Based on big data technology, this paper expounds the accurate judgment of educational data in different regions, the problems of artificial intelligence integration into the educational process, and the fact that machine learning meets people's demand for education in the process of modernization. It also discusses the basic concept and application of machine learning, so as to promote the development of artificial intelligence technology [5].
Fusing Frontal Face Recognition Using Multi View Cameras

The recognition of faces in videos has recently gained considerable attention, but the recognition process executed on a single camera has limitations, especially when dealing with uncooperative subjects, changes in body posture, or self-occlusion. These challenges are particularly relevant in the context of studying facial analysis in children with Autism Spectrum Disorder (ASD). Therefore, the use of multiple cameras in a face recognition system is proposed to overcome these limitations. Facial image realignment was employed in the automatic face recognition process. To achieve this, the Kanade-Lucas-Tomasi (KLT) algorithm was used to track facial features, and the RANSAC algorithm was utilized to estimate the homography transformation for realigning the multi-view input images. To assess and compare the similarity of the fused image, the normalized cross-correlation (NCC) was employed. The resulting fused image was obtained based on the extracted pose of the face. The results demonstrate the efficacy of the method, achieving an accuracy of 94.5% for typically developed children and 87.3% for ASD children.
An Φ-OTDR event recognition method based on Transformer

In recent years, CNN models have become a mainstream deep learning framework and have been widely used in research on improving the accuracy of $\Phi$-OTDR event recognition systems. However, a new deep learning framework, the Transformer model, has recently been proposed to replace CNN. In this article, an $\Phi$-OTDR event recognition method based on the transformer model is proposed. It has been compared with the CNN model, and experiments have shown that the transformer model performs better than CNN on pretrained models, with an accuracy rate of 99.64%. When the model is pruned to only 2 layers, its accuracy can still be saved, and the model size is reduced to 1.49M.
Multi-constraint Coupling Optimization for Salient Object Detection

In this paper, we propose a lightweight salient object detection framework called Multi-Constraint Coupling optimization Network (MCONet) to address the conflict between model scale and inference ability, which can learn more knowledge with fewer parameters through embedding feature priors. Specifically, we build a lightweight encoder as the backbone network to represent the image, and then use two parallel decoders to infer salient mask features and salient edge features respectively. Besides, we fuse the output features of different decoders by a convolutional block attention module (CBAM) module. In addition, we adopt a multi-constraint coupling optimization strategy to increase the soft constraints in the training phase, and improve the prior guidance of the edge to the inference results. Experimental results on 5 public benchmark datasets show that the proposed MCONet can reach comparable even better performance of state-of-the-art lightweight salient object detection models.