Reviewerly/exHarmony huggingface dataset:
Preparing Future Designers for Human-AI Collaboration in Persona Creation

This paper presents findings from an exploratory study investigating the use of AI text-generation tools to support novice designers in persona creation. We conducted a workshop with 22 undergraduate students enrolled in an introductory human-computer interaction course, who were instructed to use GPT-3 in the creation of personas. These novice designers were able to use GPT-3 to iterate to produce satisfactory personas, particularly when providing detailed prompts. Our findings suggest that personas created with GPT-3 assistance were mostly comparable to those created manually but rated lower on some evaluation dimensions. The study also reveals merits and concerns of using GPT-3 for persona creation. Based on our findings, we propose recommendations for novice designers on how to use text-generative AIs to create personas effectively and responsibly.
Personal Informatics at the Office: User-Driven, Situated Sensor Kits in the Workplace

Workplaces are increasingly leveraging data-driven technological interventions to optimize employee productivity, health, and wellbeing. Yet employees are rarely involved in designing these initiatives, nor have access to the data collected to act upon it. Building on approaches from personal informatics, we investigate the use of user-driven, open-ended sensor kits in an office context. We conducted a 3-week field study, deploying a research probe at a workplace (N=5). Findings show that users explored aspects meaningful to them yet highlight discrepancies between the envisioned self-tracking goals and participants’ practices. Regarding sensors’ open-endedness, a balance between the burden of data collection and the value derived from it appeared critical. We contribute new insights into how an open-ended sensor kit can be designed to support self-tracking practices in the underexplored context of office work. We discuss implications for the use of personal informatics at the office and highlight opportunities for future research.
Butterfly Recognition and Detection Method Based on Improved YOLOv5 Model

As one of the most advanced models in computer vision, the model of the YOLO series has excellent accuracy and efficiency. However, YOLOv5 has achieved great success in object detection. Even since YOLOv5 has a function of image classification, we can still modify and optimize Yolov5 based on model structure, data augmentation, loss function and optimizer, and attention mechanism. Based on the above problems, we replaced the backbone of YOLOv5 with RepVGG as the backbone network of our model. We also used methods such as Cutout, HSV, etc., for image augmentation. We also introduced CBAM (Convolutional Block Attention Module) based on the attention mechanism to improve the model's performance. Besides, we also replaced the GIOU (Generalized Intersection over Union) loss function with the CIOU (Complete-IOU). At the same time, some suitable activation function is selected according to the model, which improves the robustness of the model. Finally, we use the 'Pink Butterfly' dataset to test this model. We were pleasantly surprised to find out about the promising performance. The precision was improved by 4.451 % (from 91.950% to 96.401%). The recall declined 0.413% (from 80.198 % to 79.785%). The [email protected] was improved 2.466 %(from 88.826 % to 91.292%). The [email protected]:0.95 was improved by 2.466 % (from 68.975 % to 73.017 %). Moreover, the model has better robustness.
Inner Wall Abnormality Detection of Rotating Shaft Based on Deep Learning

To address the challenges of inconsistent detection standards, high labor intensity, and low efficiency in existing manual detection methods, a method based on an improved YOLOv5 algorithm is proposed for detecting abnormalities in the inner wall of rotating shafts. An industrial endoscope is used to illuminate and capture images of the inner wall of the rotating shaft, and the YOLOv5 6.x version model is trained using different optimizers to detect abnormalities in the inner wall. The model is trained on a dataset of images captured from an industrial site, and experimental results show that the YOLOv5 model optimized with AdamW achieves an average mAP improvement of 4.4% compared to the model optimized with SGD, enabling effective detection of inner wall abnormalities in rotating shafts.
Federated Learning with ResNet-18 for Medical Image Diagnosis

Deep learning has shown promise in accurate medical image analysis, but challenges remain. Data privacy concerns hinder the availability of large, high-quality medical datasets. Traditional deep learning approaches are computationally intensive and lack efficiency. This paper proposes the use of Federated Learning (FL) with ResNet-18, a deep neural network architecture. ResNet-18 addresses gradient issues using residual blocks and skip connections. FL enables collaborative training while preserving data privacy. The training process utilizes stochastic gradient descent and techniques such as data augmentation and regularization for improved model performance.
Research on Poetry Generation Based on Bi-LSTM Networks and Attention Mechanism

Poetry is a common language for studying automatic text generation by neural network. Using neural network model to realize automatic writing of poetry is meaningful to the study of automatic text generation and the inheritance of traditional Chinese culture. This topic designs and trains a deep learning model based on bidirectional long short-term memory network (Bi-LSTM) and attention mechanism (Attention). The function of generating acrostic with customizable poet's style and artistic conception are realized. Under the Encoder-Decoder framework, the model improves the traditional encoder based on recurrent neural network (RNN) and makes it based on Bi-LSTM. It can better capture the semantic dependencies of the context, and effectively overcome gradient disappearance and gradient explosion in RNN. The decoder is improved based on the long short-term memory network (LSTM) and the attention mechanism, so that the model can better solve the long-term dependence problem in the traditional decoder. The addition of the attention mechanism enables the model to better connect contextual information, grasp the central semantics, and achieve better generation effects. This subject uses the Tang poetry corpus to train the above model. It achieves a good effect of generating acrostic, but the acrostics also have problems such as word repetition. The follow-up research direction of the subject is how to use more advanced models, such as ERNIE-GEN, to solve the above problems and achieve better generation effects.
A New Database of Houma Alliance Book Ancient Handwritten Characters and Its Baseline Algorithm

The Houma Alliance Book is one of the national treasures of the Museum in Shanxi Museum Town in China. It has great historical significance in researching ancient history. To date, the research on the Houma Alliance Book has been staying in the identification of paper documents, which is inefficient to identify and difficult to display, study and publicize. Therefore, the digitization of the recognized ancient characters of Houma League can effectively improve the efficiency of recognizing ancient characters and provide more reliable technical support and text data. This paper proposes a new database of Houma Alliance Book ancient handwritten characters and a multi-modal fusion method to recognize ancient handwritten characters. In the database, 297 classes and 3,547 samples of Houma Alliance ancient handwritten characters are collected from the original book collection and by human imitative writing. Furthermore, the decision-level classifier fusion strategy is applied to fuse three well-known deep neural network architectures for ancient handwritten character recognition. Experiments are performed on our new database. The experimental results first provide the baseline result of the new database to the research community and then demonstrate the efficiency of our proposed method.
A Data Hiding Scheme for JPEG Images Based on EMD with Coefficient Selection Strategy

Joint photographic experts group (JPEG) is a widely used image compression format due to its high compression ratio and relatively good image quality. Embedding data into JPEG images is useful for many applications, such as digital watermarking, copyright protection, and privacy preservation. In response to this demand, this paper proposes a new lossy data hiding scheme that can be applied to JPEG images. In 2021, Xie et al. proposed a lossy data hiding scheme based on the exploiting modification direction (EMD) embedding technology, which embeds data into the alternative current (AC) coefficients of the image after DCT transformation. However, their scheme causes significant errors in coefficient selection, leading to a reduction in image quality. Thus, we propose a new coefficient selection strategy to improve the quality of embedded images. Experimental results show that the proposed method improves Lena image quality by 1.15 dB compared to Xie et al.'s method at a quality factor of 90.
Research on Intelligent Construction of Bilingual Translation Corpus Based on Differential Evolution Algorithm

In the construction stage of bilingual translation corpus, due to the lack of analysis of the relationship between the basic corpora, the translation quality is low. Therefore, a research on intelligent construction of bilingual translation corpus based on differential evolution algorithm is proposed. The differential evolution algorithm is used to optimize the reverse learning of bilingual translation corpus, improve the quality of natural language corpus, and expand the search area of corpus to complement the global exploration ability of bilingual translation corpus. The method of random neighborhood variation is used to extract bilingual sentence pairs. From the perspective of diversity to ensure the quality of natural language corpus, the method of adaptive updating is used to establish the relationship between the basic corpus through differentiated neighborhood size and scaling factors, and to extract bilingual sentence pairs.In this test, the translation quality of the designed bilingual translation corpus pair and the sentence level and text level test data is at a high level.
Piedmontese Poetry Today: Main Trends, Main Challenges

Abstract This study surveys the poetry written in Piedmontese today. It identifies the main trends as well as the principal centers of production and provides a profile for some of the most significant voices. It concentrates on the work of poets Remigio Bertolino and Giovanni Tesio, the latter a scholar of Piedmontese and Italian literature who recently turned writer.
Deep Learning Technology

In this chapter, we will mainly introduce basic concepts of neural networks to lay a good foundation for a better understanding of the content of the rest chapters.
CFAB: An Online Data Augmentation to Alleviate the Spuriousness of Classification on Medical Ultrasound Images

Convolutional neural networks (CNNs) may learn spurious correlations between bias features (e.g., background) and labels in image classification. The spuriousness in CNNs usually occurs in building connections between the background of images and labels. Such spurious correlation limits the generalizability of CNNs in classification tasks. Changing backgrounds and foregrounds of original samples can reduce the spuriousness in natural image classification. However, generating annotations for foreground on medical image datasets is time-consuming and labor-intensive. To solve this problem, we propose an online data augmentation method named Combining Foreground And Background (CFAB), which makes CNNs focus on key causal features without foreground annotations and breaks the correlation between backgrounds and labels by changing different backgrounds for one sample. Furthermore, we propose a framework for collaborative augmenting samples using CFAB and training CNNs. Comprehensive experiments indicate that the proposed method weakens the spuriousness, improves the generalizability of the model, and achieves state-of-the-art results in medical ultrasound dataset classification.
A Hardware-Aware Sampling Parameter Search for Efficient Probabilistic Object Detection

Recent advancements in Deep Neural Networks (DNNs) have led to remarkable achievements in object detection, making them increasingly relevant for safety-critical domains like autonomous driving. However, a significant challenge for deploying DNN-based object detection in safety-critical applications remains the inability of the models to estimate their own uncertainty. To address this issue, Probabilistic Object Detection has emerged as a solution, allowing for the assessment of both semantic and spatial uncertainty. Monte-Carlo sampling methods, such as Dropout and its variants, are commonly used to generate the necessary probability distributions. Nonetheless, determining the appropriate Dropout variant, sample size, and drop probability for a specific probabilistic model remains a complex task, especially when considering the importance of balancing algorithmic accuracy and hardware efficiency. Ensuring hardware efficiency is particularly crucial for deploying these models in embedded systems. To tackle this challenge, we treat it as an optimization problem and employ an evolutionary multi-objective search to identify the best-fitting sampling parameters. In our evaluation using the YOLOv5 model, we demonstrate that Gaussian Dropout outperforms other Dropout variants. Notably, we achieve a doubling of the PDQ score with no retraining and an $$mAP_{50-95}$$ loss of only 1% on the COCO dataset. Additionally, our study unveils the non-intuitive trade-offs considering hardware performance.
TrackAgent: 6D Object Tracking via Reinforcement Learning

Tracking an object’s 6D pose, while either the object itself or the observing camera is moving, is important for many robotics and augmented reality applications. While exploiting temporal priors eases this problem, object-specific knowledge is required to recover when tracking is lost. Under the tight time constraints of the tracking task, RGB(D)-based methods are often conceptionally complex or rely on heuristic motion models. In comparison, we propose to simplify object tracking to a reinforced point cloud (depth only) alignment task. This allows us to train a streamlined approach from scratch with limited amounts of sparse 3D point clouds, compared to the large datasets of diverse RGBD sequences required in previous works. We incorporate temporal frame-to-frame registration with object-based recovery by frame-to-model refinement using a reinforcement learning (RL) agent that jointly solves for both objectives. We also show that the RL agent’s uncertainty and a rendering-based mask propagation are effective reinitialization triggers.
Data-Free Model Extraction Attacks in the Context of Object Detection

A significant number of machine learning models are vulnerable to model extraction attacks, which focus on stealing the models by using specially curated queries against the target model. This task is well accomplished by using part of the training data or a surrogate dataset to train a new model that mimics a target model in a white-box environment. In pragmatic situations, however, the target models are trained on private datasets that are inaccessible to the adversary. The data-free model extraction technique replaces this problem when it comes to using queries artificially curated by a generator similar to that used in Generative Adversarial Nets. We propose for the first time, to the best of our knowledge, an adversary black box attack extending to a regression problem for predicting bounding box coordinates in object detection. As part of our study, we found that defining a loss function and using a novel generator setup is one of the key aspects in extracting the target model. We find that the proposed model extraction method achieves significant results by using reasonable queries. The discovery of this object detection vulnerability will support future prospects for securing such models.
The influence of schematic design document on outcome of design and build project of public buildings

Observing the advantages of integrated solutions for public buildings, the government has accommodated the use of integrated methods through the Minister of PUPR Regulation No. 25 of 2020. The current phenomenon, the minimal initial design is still considered a major weakness. For this reason, it is necessary to explore the level of influence of the schematic design document on the success of the outcome. This research objective was to analyse the influence of the schematic design document on the success of the outcome based on the time variable. The selection of case studies is aimed at public buildings since there are still weak points in the smoothness of the articulation of schematic design documents from service users to design development by service providers. This research approach focuses on intensive and in-depth analysis through observation of project case studies in 3 cities and statistical tests. The results of this exploration indicate that the schematic design document influences the success of receiving the outcome, with the highest influence coefficient of 4.244 on the variable of landscape documents. Hopefully, this study can serve as a performance tool for service user requirements for construction service actors with the Integrated Design and Build Method.
CT Image Super-Resolution: A Perspective on Tissue-Specific Performance Analysis

The potential of various image super-resolution (SR) techniques has been widely discussed and analyzed for medical imaging. Different body tissue segments in an image have different characteristics and levels of importance in evaluating the image for diagnosis. Therefore, evaluating the performance of the SR methods for different tissues is essential to assess the usability of the approach. In the present work, we evaluated SR methods performance for various tissues or organs, e.g., liver, bladder, lung, kidney, and bone, in CT images. We used bicubic interpolation and modified-ESPCN deep learning model as SR methods and peak signal-to-noise ratio (PSNR) and similarity index measure (SSIM) as performance measures. We observed significantly (p < 0.01, paired t-test) higher performance ratings for performance metrics computed over a full CT image than computed over body tissue region. PSNR and SSIM (primarily structural component) values were significantly (p < 0.01, paired t-test) lower for lung and bone tissues compared to liver, bladder, and kidney. Therefore, tissue-specific performance measure computation is essential for evaluating the SR methods for a medical imaging application. It would be good practice for researchers to report tissue-specific performance analysis as opposed to performance metrics computed over the entire images, to avoid overestimating the performance capabilities of new methods.
A Quantitative Study on the FaceNet System

Face recognition is the science of identifying and recognizing human faces in various situations, keeping the constraints like pose variation and occlusion in mind. Due to its impactful applications in safety and security systems, face recognition is becoming extremely popular and is researched extensively even today. The FaceNet method is among the most tested approaches in Deep learning face identification. This method uses a deep convolution neural network for training the data. The face embedding generated can be used to train a face identification system. This study aims to comprehend the FaceNet system, evaluate its performance, and test its accuracy on seven standard datasets. The study also tries to compare how well the FaceNet method works compared to other popular holistic and hybrid methods. From the outcomes of this study, we can conclude that FaceNet showed outstanding results and was better than the other methods. The FaceNet system reached a minimum of 90% accuracy on all standard datasets used on both the pre-trained models, which is a significant number for any face recognition method.
DIVS: A Real-Time Video Stabilization Framework for Consumer Camera

Video stabilization is one of the most interesting areas of research that can be implemented in consumer camera technology. Various works have been proposed to overcome this problem. However, there are many practical difficulties in deploying them in real time. Many frames are taken as an input to generate a stabilized output, which leads to high computation of the image sensing hardware. A novel approach to solve these challenges is proposed in this paper using the Hybrid Deep Neural Network Model (HDNNM) that uses gyroscopic sensor data and the optical flow analysis. This network is based on the deep unsupervised learning techniques, which require less computation and low buffer memory. The workflow of the proposed model is based on the integration of the optical flow with the joint motion representation to maintain a correspondence between the frames and the poses of scene objects. The LSTM module in the network wraps the grid and stabilizes the video frames. The relative motion along with the multi-stage training strategies was adopted to make the model unsupervised. The HDNNM is validated using the ablative dataset collected from various scenes, and the results of the model surpass the existing model’s performance metrics. To the best of the authors’ knowledge, this is the first unsupervised approach to overcome the challenges in video stabilization.
Saliency-Driven ‘Evidence CNN’ for Fine-Grained Recognition of Twins

The extremely subtle differences between a pair of twins make their individual recognition an interesting fine-grained classification challenge. When the images are clicked in the wild, the confidence score of predictions is generally low. Most of the previous work in this domain focuses on improving the accuracy of the model. There is no check on whether the correct evidence was considered while making a prediction. Without such a check, the predictions cannot be trusted, especially when the confidence score is low. The objective of our research is to use visual attribution techniques like image saliency to understand, guide and improve a model’s recognition. We propose a saliency-driven fine-grained classifier that learns the most discriminative evidence for each twin during training, and uses it to train an ‘Evidence CNN’. At test-time, the Evidence CNN validates the predictions, by matching the coherence of the evidence used to make the prediction, with the evidence detected for correct predictions of the same class during training. We used the Guided Grad-CAM method for generating saliency evidence, and provide the theoretical justification for selecting this technique. We tested our model on a dataset of 700 public images of the celebrity Olsen twins, ‘Ashley’ and ‘Mary-Kate’. With saliency-driven prediction refinement, our classifier showed a significant improvement in the test accuracy. Since our predictions are grounded on explainable evidence that is cross-checked at test-time, our model can be considered trustworthy.
Research Study on Leukemia Detection Using Deep Learning Techniques

Leukemia is a type of cancer found in blood, which commonly starts within the bone marrow and impacts the capacity of the body to combat infection. In a country like India, less than 1000 Lakh instances having leukemia are stated each time. Several of the not uncommon place signs and symptoms are pores and membrane outbreaks, hemorrhage, sensing tiredness, infection, and accelerated hazard of infections. It could be extremely critical to come across leukemia in the initial phases. Conventional techniques, along with limited studies on plasma stains, for detecting Leukemia are no longer cost-effective and rely heavily on skilled scientific personnel. To overcome these limitations, this study proposes the implementation of automated algorithms using image processing and the MATLAB software for the recognition and categorization of Leukemia.