Reviewerly/exHarmony huggingface dataset:
Digital Transformation: AI-Powered Bot Solutions and Automation for Customer Services

Recent years have witnessed a growth in the adoption of AI-Powered bot solutions in many industries, including healthcare, education, manufacturing, and energy. The drive behind this adoption is to increase productivity and improve customer engagement by enhancing the communication between customers and customer service support. These solutions are at the heart of Digital Transformation, which is focused on introducing new ways for value creation, automation, interaction, transparency, and control. Furthermore, delivers customer benefits: convenience, relevance, experience, empowerment, and savings. This drive has led the IT industry to deliver digital systems for providing customer services aimed at increased customer satisfaction. Virtual Assistant agents are used by organizations to fulfill different tasks related to customer services for solving problems and providing recommendations. Among AI-Powered bot solutions, an EmailBot is an NLP-based tool used to automate routine Email conversations and enhance positive interaction with customers. This paper explores the ability to implement an EmailBot solution on a private cloud environment to automate frequently performed tasks such as application installation and upgrades, how-to, obtaining application links, permission requests, and inquiries related to request statuses. An evaluation conducted compared the automated solution capabilities of infrastructure high-availability and with traditional customer service support that relied wholly on humans for defined tasks. According to resulting measures, the automated model outperformed the traditional model in terms of response time and accuracy, with drastic improvement in the former and slight improvement in the latter.
SE-yolov5 inland waterway vessel target recognition based on CGAN image enhancement

With the continuous development of deep learning technology, ship target recognition based on video images has emerged as a viable approach for water supervision. However, traditional self-built datasets suffer from insufficient feature sampling and large target recognition errors in ship target detection. In order to enhance the accuracy of ship target detection in complex environments, this paper proposes an improved yolov5 ship target recognition method based on CGAN image enhancement. The method leverages CGAN network to augment the original dataset, thereby completing data enhancement. Furthermore, the SE (Squeeze-and-Excitation) attention mechanism module is integrated into the yolov5 network to train and evaluate the ship image dataset. Experimental results demonstrate that the CGAN network augmentation image outperforms the GAN basic network augmentation image in the dataset, generating type condition constraints. The accuracy of the SE-YOLOV5 network for bulk carriers, container ships, official ships, and beacons is 70%, 97%, 90%, and 60% respectively, showcasing improvements compared to the basic yolov5 network and Faster-RCNN network. The findings indicate that the improved yolov5 ship target recognition method, incorporating CGAN image enhancement, effectively addresses the challenge of low target detection accuracy in multi-target and inland river ships with insufficient lighting conditions under small data sets. It significantly enhances the overall performance of ship detection and recognition.
A Traffic Sign Recognition Algorithm for ADAS based on CNN for Complex Scenarios

The Traffic Sign Recognition (TSR) system is an essential component of Advanced Driver Assistance Systems (ADAS), which assists drivers in detecting and understanding traffic signs. However, recognizing traffic signs in complex situations, such as low or high lighting, adverse weather conditions, and background colour, remains a challenging task. While numerous researchers have achieved high accuracy rates, they have encountered difficulties in attaining a balance between recognition accuracy and processing speed. To address these problem, this paper introduces a TSR algorithm that uses Convolutional Neural Networks (CNN) with enhanced padding functions to increase recognition accuracy. The algorithm initially preprocesses the input image by performing contrast enhancement and normalization, and then trains the CNN model on GTSRB, a vast dataset of traffic signs to extract relevant features and classify them into respective categories. The padding function expands the input image dimensions, allowing the model to extract intricate features of background scenarios, increasing recognition precision. The proposed algorithm was evaluated on traffic sign images from real-world scenarios, including low or high illumination and adverse weather conditions, demonstrating its ability to achieve a balance between recognition accuracy and processing time, potentially enhancing the safety and efficiency of ADAS systems by providing timely and accurate information to drivers.
Complete and Separate: Conditional Separation with Missing Target Source Attribute Completion

Recent approaches in source separation leverage semantic information about their input mixtures and constituent sources that when used in conditional separation models can achieve impressive performance. Most approaches along these lines have focused on simple descriptions, which are not always useful for varying types of input mixtures. In this work, we present an approach in which a model, given an input mixture and partial semantic information about a target source, is trained to extract additional semantic data. We then leverage this pre-trained model to improve the separation performance of an uncoupled multi-conditional separation network. Our experiments demonstrate that the separation performance of this multi-conditional model is significantly improved, approaching the performance of an oracle model with complete semantic information. Furthermore, our approach achieves performance levels that are comparable to those of the best performing specialized single conditional models, thus providing an easier to use alternative.
Lightning Talk: The Incredible Shrinking Black Box Model

A black box model is an assumption on the implementation of a cryptographic primitive to limit the capabilities of the attacker. Black boxes are a useful component in a proof of protocol correctness, but it is not obvious how to securely implement one in hardware. The current state of the art in tamper from open literature shows impressive efficiency and precision in prying open these black boxes. Secure hardware designers are compelled to shrink the black boxes with every new device generation, while making a careful assessment of the need to load, store and handle secrets in hardware.
A path planning method for unmanned autonomous vehicles based on Improved A<sup>*</sup> algorithm

Different kinds of unmanned autonomous vehicles need to travel at different speeds in different operations. In order to accomplish this task, a path planning algorithm is improved in this study, which can be applied to unmanned autonomous vehicles with different speed conditions This algorithm is an improvement on the traditional $\text{A}^{\star}$ path planning algorithm. Firstly, the traditional four-direction search is changed into a multi-direction search. Secondly, the restriction condition of maximum Angle is added in the process of searching adjacent points. Finally, we get A more suitable $\text{A}^{\star}$ search algorithm. Using MATLAB, the improved $\text{A}^{\star}$ algorithm can solve the path planning problem under the requirements of different speeds $(30\text{k}\text{m}/\text{h}, 45\text{k}\text{m}/\text{h},60\text{k}\text{m}/\text{h})$.
Invited: A Scalable Formal Approach for Correctness-Assured Hardware Design

Correctness must be a first principle in hardware/-software co-design, especially for security and safety critical applications. We will give an overview of our scalable approach for correctness-assured hardware/software design at behavioral level, based on formalizing microarchitecture features as program transformations in an incremental compiler design and microprocessor correctness as a refined notation of compiler correctness. We will show how our approach is applied to designing a formally verified FHE (Fully Homomorphic Encryption) accelerator.
Processor Vulnerability Discovery

Processor security vulnerability discovery has drawn increasing attention since the disclosure of Meltdown, Spectre and other vulnerabilities. This paper presents a concise roadmap of this emerging research direction from the simple manual discovery to automated discovery methodologies, as well as the major challenges along the roadmap.
ChaosINTC: A Secure Interrupt Management Mechanism against Interrupt-based Attacks on TEE

For Trusted Execution Environment (TEE), interrupt-based side-channel attacks are becoming significant threats. Malicious supervisors use interrupts to perform single-step side-channel attacks or to improve the accuracy of existing side-channel attacks. This paper proposes a secure interrupt handle mechanism dedicated to TEE, named ChaosINTC. (1) To prevent frequent interrupts, a dynamic interrupt response delay mechanism delays the interrupt delivery with a variable time. (2) To prevent maliciously modifying ISRs, an interrupt handler protecting mechanism performs isolation and integrity checking. We deployed ChaosINTC on an open-source RISC-V core and evaluated its performance via FPGA. Our design provides strong security with marginal hardware and performance costs.
Learning Human Anatomy Using Augmented Reality Mobile Application

In this current era of technological advancement, the development of Augmented Reality (AR) technology already has a major influence on the education domain. Therefore, more efforts to create educational applications for learners to understand diverse topics using AR. This paper aims to share the experience of developing a mobile application for learning human anatomy using AR. We selected the marker-based technique as the AR technique and Android platform for mobile application development. We follow the Mobile Application Development Life Cycle (MADLC) for the development process. During the design phase, we define the system architecture, use case diagram, algorithm design, and user interface design. Then, we utilize Unity3D, Vuforia, Visual Studio Code, Blender, and AR Core during the development. The Unity’s features in this AR application include importing the Vuforia database, image targets, camera conversion, 3D modeling of human anatomy and organs, canvas, and panel, and the lean touch tool kit for scaling and rotating 3D models. As a result, we successfully developed a mobile application for learning human anatomy using AR. This application helps the learner understand as well as enhances the learning experience in human anatomy.
PertNAS: Architectural Perturbations for Memory-Efficient Neural Architecture Search

Differentiable Neural Architecture Search (NAS) relies on aggressive weight-sharing to reduce its search cost. This leads to GPU-memory bottlenecks that hamper the algorithm’s scalability. To resolve these bottlenecks, we propose a perturbations-based evolutionary approach that significantly reduces the memory cost while largely maintaining the efficiency benefits of weight-sharing. Our approach makes minute changes to compact neural architectures and measures their impact on performance. In this way, it extracts high-quality motifs from the search space. We utilize these perturbations to perform NAS in compact models evolving over time to traverse the search space. Our method disentangles GPU-memory consumption from search space size, offering exceptional scalability to large search spaces. Results show competitive accuracy on multiple benchmarks, including CIFAR10, ImageNet2012, and NASBench-301. Specifically, our approach improves accuracy on ImageNet and NASBench-301 by 0.3% and 0.87%, respectively. Furthermore, the memory consumption of search is reduced by roughly 80% against state-of-the-art weight-shared differentiable NAS works while achieving a search time of only 6 GPU hours.
NavPES: Augmented Reality Redefining Indoor Navigation in the Digital Era

Over the years, navigation has become an essential part of our daily lives. The emergence of Augmented Reality (AR) technology has created new opportunities for improving the navigation experience. The current work focuses on the development of NavPES, a mobile navigation application designed to provide users with seamless and efficient navigation throughout the PES University campus, both indoors and outdoors. The application makes use of cutting-edge tools such as the ARway software development kit (SDK), Vuforia SDK, and Azure Spatial Anchors to enable accurate indoor localization and navigation using AR. NavPES features an interactive and self-explanatory Graphical User Interface (GUI) for users, allowing them to navigate around the campus with ease. The use of AR technology in navigation applications like NavPES contributes to the development of smart digital campuses.
WPBFT:An improved consensus algorithm based on the HotStuff algorithm

This paper first analyzes the consensus algorithm that currently exists in blockchains, and then mainly discusses the PBFT consensus algorithm widely used in consortium chains and the improved HotStuff algorithm based on it. However, they also have some disadvantages, such as low throughput, especially when nodes continue to increase, throughput will decline rapidly. Although HotStuff has improved the masternode election mechanism, the signature encryption algorithm it uses is insecure and inefficient. Finally, based on these problems, we propose a consensus algorithm WPBFT based on HotStuff’s improvement. Our consensus algorithm is improved in the following ways. First, the new consensus mechanism will not need to elect a masternode, but will use random selection of masternodes, so we will change the time complexity of HotStuff from O(n) to O(1), greatly reducing network communication. Secondly, this paper adopts the Ed25519 encryption algorithm, and improves its foundation, introducing a large number compression algorithm, which effectively improves the security and encryption efficiency when the message type is relatively large. Finally, it adopts a chronologically-based chain structure at the nodes. Through experiments, we show that the proposed algorithm has been significantly improved compared with PBFT and HotStuff algorithms in terms of data throughput and communication delay.
COSA:Co-Operative Systolic Arrays for Multi-head Attention Mechanism in Neural Network using Hybrid Data Reuse and Fusion Methodologies

Attention mechanism acceleration is becoming increasingly vital to achieve superior performance in deep learning tasks. Existing accelerators are commonly devised dedicatedly by exploring the potential sparsity in neural network (NN) models, which suffer from complicated training, tuning processes, and accuracy degradation. By systematically analyzing the inherent dataflow characteristics of attention mechanism, we propose the Co-Operative Systolic Array (COSA) to pursue higher computational efficiency for its acceleration. In COSA, two systolic arrays that can be dynamically configured into weight or output stationary modes are cascaded to enable efficient attention operation. Thus, hybrid dataflows are simultaneously supported in COSA. Furthermore, various fusion methodologies and an advanced softmax unit are designed. Experimental results show that the COSA-based accelerator can achieve 2.95-28.82× speedup compared with the existing designs, with up to 97.4% PE utilization rate and less memory access.
Attention-based Bilateral LSTM-CNN for the Sentiment Analysis of Code-mixed Filipino-English Social Media Texts

The prevalence of code-mixed texts in social networks presents a challenge in Sentiment Analysis. Most existing methods are generally trained on monolingual samples, which can’t be used to analyze sentiments in a code-mixed text. Also, most existing models use classical machine learning and lexicon-based approaches, producing low-accuracy results. This study examines the sentiment classification of code-mixed Filipino-English texts from social networking sites and synthetic data. The deep-learning approach was used to build a pioneering bilingual sentiment analysis model implementing Convolutional Neural Network and Bi-LSTM with an attention mechanism. In this study, the researchers initially gathered and developed two datasets (datasets from Higher Education Institutions (HEIs) stakeholders’ feedback and synthetic data). Synthetic data was used to address language resource scarcity. This technique is commonly used in Natural Language Processing to train models that can understand and artificially generate code-mixed texts. The code-mixed data is then preprocessed, and a word embedding procedure takes place. Lastly, the researchers employ sentiment classification in the code-mixed corpus using Attention-based bilateral Long Short-Term Memory CNN (A-BLSTM-CNN). The result shows that the suggested method outperforms the alternatives in classifying the sentiment of code-mixed Filipino-English texts, with an accuracy rate of 92% (2 classes) and 80.24% (3 classes).
An Automated SAR-based Method for Ship Detection in Maritime Surveillance System

Synthetic Aperture Radar (SAR) is an active Earth observation system that can obtain high-resolution ocean images all day and all weather. It can be used for monitoring the marine environment, maritime traffic, and oil spills at sea. SAR provides accurate and real-time ocean information, which can ensure water traffic safety and ecological environment. Therefore, it plays an irreplaceable role in maritime surveillance. At present, deep-learning based object detection methods have achieved certain results in the application of ship detection in SAR images. Ship targets in SAR images have strong angular reflection characteristics, causing some parts of them to have large grayscale values, appearing as scattering points. At the same time, the heading of ship usually has a certain angle with the horizontal direction. Traditional deep-learning models do not have sufficient generalization ability for these situations. Horizontal bounding boxes also contain a large amount of background features, which affects the accuracy of the model. In response to these characteristics, first use convolutional neural network to extract ship features; Secondly, generate oriented proposals to reduce the impact of background features; Thirdly, using Canny and Harris to obtain features of ship edges and scattering points as attention mechanisms to enhance bounding box regression and classification; Finally, optimize the loss function, which will consider the influence of angle. Comparative experiments were conducted on the SSDD dataset, and the experimental results showed that compared to existing methods, the improved model has better ship detection performance and can achieve accurate detection of small ship targets in complex scene SAR images.
NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants

Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices. However, it is still challenging to unleash tiny deep learning’s full potential on both large-scale datasets and downstream tasks due to the under-fitting issues caused by the limited model capacity of tiny neural networks (TNNs). To this end, we propose a framework called NetBooster to empower tiny deep learning by augmenting the architectures of TNNs via an expansion-then-contraction strategy. Extensive experiments show that NetBooster consistently outperforms state-of-the-art tiny deep learning solutions.
Visual-based Fall Detection Characterization Using YOLOv3 Algorithm

Falls are considered the leading cause of injuries and hospitalizations, especially in elderly. Rapid detection of falls and timely medical intervention can significantly reduce the risk of serious injuries and complications. This study aims to optimize the capability of the YOLOv3 algorithm to detect prone and supine fall events based on different lighting conditions such as 25 lux, 225 lux, and 360 lux. The proposed methodology utilized a custom dataset of 800 images containing both prone and supine positions; thus, the dataset was subsequently pre-processed and augmented. The model obtained an mAP of 95.4% with a precision of 85.8% and a recall of 94.2%. Given the three levels of background illuminations, the 25-lux illumination performed poorly with a 33.33% accuracy score and a 66.67% false detection rate. The brightest lighting condition at 360 lux functioned satisfactorily with a 73.33% accuracy score and a 26.67% false detection rate. Thus, it can be concluded that illumination does indeed affect the system’s detection ability, with higher illumination producing higher accuracy.
RQ-DNN: Reliable Quantization for Fault-tolerant Deep Neural Networks

Deep Neural Networks (DNNs) are deployed in many real-time and safety-critical applications such as autonomous vehicles and medical diagnosis. In such applications, quantization is used to compress the model for storage and computation reduction. However, recent research has shown that faults in memory can cause a significant drop in DNN accuracy and conventional quantization methods focus only on model compression. This paper proposes a novel method that performs model quantization while remarkably improving the fault-tolerance of the model. It can be incorporated with other hardware approaches such as Error Correcting Code to further improve fault-tolerance. The proposed method reduces possible error patterns that negatively impact classification accuracy by modifying weight distributions and applying a novel masking-based clipping function. Experimental results show that the proposed method enhances the fault-tolerance of the quantized DNN, which can tolerate 1803× higher bit error rates than the conventional method.
Invited: Automated Code generation for Information Technology Tasks in YAML through Large Language Models

The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages. Domain specific languages, such as the ones used for IT Automation, received far less attention, despite involving many active developers and being an essential component of modern cloud platforms. This work focuses on the generation of Ansible YAML, a widely used markup language for IT Automation. We present Ansible Wisdom, a natural-language to Ansible YAML code generation tool, aimed at improving IT automation productivity. Results show that Ansible Wisdom can accurately generate Ansible script from natural language prompts with performance comparable or better than existing state of the art code generation models.