Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Test Pattern Generation for Double Stuck-at Faults Based on Test Patterns of Single Faults

Multiple faults are more likely to occur in the fabricated circuits since they have been becoming larger and denser in the past decades. There have been researches that propose the ATPG method to deal with double faults by quickly selecting all the undetected faults by the test patterns for the single fault, and then generating the additional test patterns for those undetected faults. However, the generated test patterns may not be complete to cover all the double faults, and the test generation process cannot be completed in an acceptable runtime. In order to solve those problems, we propose an improved ATPG method which can detect all the double faults, covering all the cases overlooked by the previous research. Moreover, the execution time is significantly reduced by adequately integrating all the external tools and programs, as well as employing strategies such as circuit partitioning and bit parallel processing to accelerate the speed of fault simulation and the SAT process. To fully understand the performance of the proposed method, we comprehensively analyze the experimental results in several different aspects, which demonstrate that our method can quickly generate compact test patterns for double stuck-at faults on the circuits whose single stuck-at faults can be generated. Since the proposed method efficiently generates test patterns for the remaining double faults that cannot be detected given the test patterns for single faults, it can be inductively extended to generate test patterns for triple faults, quadruple faults, …, to cover all the multiple faults.
Identification and Quantitative Detection of Pesticide Residues Using Overcoupled Metamaterial Absorber

The overcoupled (OC) metamaterial absorber (MA) platform can maintain high light-matter coupling over a broadband range, and a single device enhances and extracts mid-infrared molecular fingerprint vibration peaks, which makes OC-MA platform a new method for pesticide residue detection in the mid-infrared band. We have proven through experiments that OC-MA platform can detect pesticide molecules at levels from 25 to 500 ppm and can detect the mixing ratio of two mixed pesticide molecules. Unlike traditional detection methods, our platform can achieve low cost and easy operation. The advantage of not requiring pretreatment is that it provides new insights into pesticide residue detection.
A Compact Energy-Saving D-Band Frequency Quadrupler with -1 dB Conversion Gain in 22 nm FDSOI

This work presents a compact and energy-saving D-band frequency multiplier by 4 (quadrupler) integrated into an advanced 22 nm fully-depleted silicon-on-insulator (FDSOI) technology. The circuit consists of a multi-phase driven two-stage push-push doubler architecture achieving a conversion gain of −1 dB and covers a bandwidth of 23 GHz centered at 133 GHz. At 0.8 V supply voltage, the quadrupler attains a maximum output power of −3.4 dBm and an efficiency of 1.71% while consuming only 27 mW of DC power. A harmonic rejection greater than 26 dBc is reached over the entire bandwidth. The core cell of the circuit occupies a compact chip area of 325 × 250 μm2.
Evaluating alcohol and marijuana use among emerging adult males via analysis of text messages

Abstract Background: Advances in social technologies offer new tools for large scale data collection and analysis of peer influence and social networks on substance use attitudes and behaviors. Objective: The objective of this study was to determine if text message content can predict alcohol and marijuana use attitudes and behaviors. Methods: Text messages from 91 males ages 18–25 were monitored over a period of 6 months and examined for content related to alcohol and marijuana. Self-report data indicating alcohol and marijuana use attitudes and behaviors were used to determine relationships between text message content, social network structure, and substance use attitudes and behaviors. Results: In total, 23,173 text messages were analyzed with 166 text messages including alcohol related terms and 195 text messages including drug related terms. Individuals who sent text messages related to alcohol use were more likely to have problem alcohol use and positive attitudes toward alcohol use, and individuals who sent text messages related to marijuana use reported higher frequency of marijuana use and more positive attitudes toward marijuana use. Individuals with multiple daily marijuana use were in positions that had less control over network structure. Conclusions: The results of this study indicate that monitoring text message content and social network structure among emerging adult males can potentially predict alcohol and marijuana use attitudes and behaviors. Text message content analysis is a novel technique increasing our understanding of the role of peer influence and social network on substance use attitudes and behaviors.
Group Activity Recognition in Visual Data Using Deep Learning Framework

Group activity recognition, a subset of humanactivity recognition, has garnered significant research focus due to its crucial role in comprehending videos. Particularly, group activity recognition (GAR) involves identifying activities performed by a collective of individuals. The primary hurdle in this domain lies in grasping the interplay amongst a group within a scene and how it evolves over time. GAR aims to identify the overall activity of a group, holding substantial potential across applications like sports/surveillance video analysis, social activity comprehension, and video search and retrieval. Effectively comprehending scenes with multiple individuals demands a model that not only characterizes each actor's individual actions in context but also deduces their collective activities. While tackling a video clip, this challenge entails not only recognizing individual actions but also uncovering scene information and the collaborative dynamics among people. Recent advancements have witnessed the emergence of deep network-based techniques and graphical models for addressing group activity recognition. The present study critically evaluates state-of-the-art (SOTA) techniques in group activity recognition, categorizing existing literature into distinct sub-domains. Furthermore, the paper identifies available datasets and highlights prevailing research challenges within the realm of group activity recognition.
Clinical and epidemiological features of West Nile virus equine encephalitis in New South Wales, Australia, 2011

BACKGROUND Between February and June 2011, more than 300 horses with unexplained neurological disease were observed in New South Wales, Australia. A virulent strain of West Nile virus (WNVNSW2011 ), of Australian origin, was shown to be the cause of many of these cases. METHODS We reviewed the clinical descriptions provided by veterinary practitioners and the associated laboratory results. Although there was a range of clinical signs described, ataxia was the only sign that was consistently described in laboratory-confirmed cases. RESULTS WNV was detected in brain samples by real-time reverse transcription PCR assay and virus isolation. For serological confirmation of clinical cases, an equine IgM ELISA specific for WNV was shown to be the most effective tool. CONCLUSION A state-wide serological survey undertaken after the outbreak indicated that, contrary to expectation, although infection had been widespread, the seroprevalence of antibodies to WNV was very low, suggesting that there could be a significant risk of future disease outbreaks.
Application of Example-Based Explainable Artificial Intelligence (XAI) for Analysis and Interpretation of Medical Imaging: A Systematic Review

Explainable Artificial Intelligence (XAI) is an area of growing interest, particularly in medical imaging, where example-based techniques show great potential. This paper is a systematic review of recent example-based XAI techniques, a promising approach that remains relatively unexplored in clinical practice and medical image analysis. A selection and analysis of recent studies using example-based XAI techniques for interpreting medical images was carried out. Several approaches were examined, highlighting how each contributes to increasing accuracy, transparency, and usability in medical applications. These techniques were compared and discussed in detail, considering their advantages and limitations in the context of medical imaging, with a focus on improving the integration of these technologies into clinical practice and medical decision-making. The review also pointed out gaps in current research, suggesting directions for future investigations. The need to develop XAI methods that are not only technically efficient but also ethically responsible and adaptable to the needs of healthcare professionals was emphasised. Thus, the paper sought to establish a solid foundation for understanding and advancing example-based XAI techniques in medical imaging, promoting a more integrated and patient-centred approach to medicine.
Providing Scalability to Data Layer Using a Novel Polyglot Persistence Approach

Efficiency of a system is totally dependent on how fast data is accessed from the data layer. It is not necessary that the data layer has to be a single type store. With the advancement in computer engineering nowadays we have many flavors of data store, each paradigm having its unique advantages and drawbacks. The challenge in reaping benefits from these diverse technologies is to integrate these various data stores together to have a hybrid data layer and map appropriate data to relevant data store such that scalability is not affected. In this paper, we have proposed a novel data mapping approach based on polyglot persistence. We have demonstrated this hybridization concept for e-commerce domain to achieve better performance than its traditional standalone counterparts.
Image Inpainting Techniques Applicable To Depth Image Based Rendering: A Review

Depth Image Based Rendering (DIBR) is a common access regarding 3D image. The DIBR algorithm can be applied in 3-D video applications to synthesize a number of different aspects of the same image. In DIBR, the information enclosed in a gray-level depth map is used to move the pixels in the 2D image to accomplish a new image as if it were conquered from a new angle. Larger the shift, the large is the perceived depth of the generated stereoscopic pair. The primary issue with DIBR is that the relocated pixels now conquer the new area and leave areas that they formerly occupied “empty”. These disocclusions needs to be filled appropriately, otherwise they can deteriorate image quality. To fill these disocclusions, inpainting techniques are applied. This paper focuses on a survey of various image inpainting strategies used for solving the disocclusions in Depth Image Based Rendering.
Selective Convolutional Features based Generalized-mean Pooling for Fine-grained Image Retrieval

Image retrieval with convolutional neural network (CNN) has obtained a lot of attention. In this paper, we focus on a more challenging task: fine-grained image retrieval. We propose a simple and effective feature aggregation method using generalized-mean pooling (GeM pooling), which can make better use of information from the output tensor of the convolutional layer. In addition, we propose a simple feature selection scheme to remove noise and background. Experimental results demonstrate that our aggregation method not only outperformed state-of-the-art aggregation methods for general image retrieval, but also reach up to the same level of existing aggregation method for fine-grained image retrieval, with more compact representation and less memory cost.
Toward an automatic exploration of algorithm space to speed up image annotation for applications in scientific image understanding

This project demonstrates a new image analysis approach to help researchers navigate the large search space of image annotation algorithms and their hyperparameters. A prototype of this approach (called SEE-Segment) demonstrates the exploration of image segmentation using a single training example, although it is expected to have overfitting and generalization problems. SEE-Segment implements a solution to the Combined Algorithm Selection and Hyperparameter (CASH) Optimization problem and uses a genetic algorithm to search an "algorithm space" for solutions that may work for the provided problem. The output generated by SEE-Segment is standalone code that is not dependent on SEE-Segment and can easily be copied and pasted into researchers’ existing data processing workflows. Thus, SEE-Segment can also be seen as an educational tool that keeps the researchers "in the loop" and helps them to understand and use the segmentation algorithms that are available to them. This work will present preliminary results of running SEE-Segment on a variety of scientific image datasets and outline procedures to extend the approach to other annotation workflows by building an Image Annotation Grammar.
Effective Digital Image Watermark Detection Technique based on Tamura Texture Features

One technique for limiting unauthorized changes to digital images is image watermarking. However, there are a variety of challenges with these techniques, including a poor detection accuracy rate, high processing costs, and resource usage. Due to these challenges, finding watermark in the images is important issue. Therefore, we proposed an effective watermark detection technique by combining the best texture features (Tamura) and classifier (Canonical Correlation Analysis (CCA)). The proposed watermark detection method consists of three steps: image conversion, feature extraction, and classification. The RGB images are first transformed into grayscale versions. In the feature extraction step, the Tamura is extracted from gray-scale images. Finally, Canonical Correlation Analysis (CCA), Random Forest (RF), and Adaboost perform the classification. We use the publicly available benchmark dataset VOC2012 to evaluate the performance of the proposed technique. The results of the experiment show that the Tamura texture feature is the optimal texture feature to utilize in detecting watermarks because it had the best accuracy across all classifiers. It also demonstrates that the CCA classifier fared better in terms of accuracy than the other two. The second-best classifier belongs to Adaboost. However, among the classifiers, the RF classifier exhibited the lowest accuracy. The proposed Tamura based CCA method (Tamura-CCA) is compared to other state-of-the-art techniques in terms of feature dimension and detection accuracy. The proposed method performs better than other existing techniques. The Tamura-CCA has an accuracy rate of 98%. The proposed method has the benefits of the ability to locate watermarked images in every database and not needing a particular type or procedure for the watermark embedding.
Peritubal Infiltration of Fentanyl Compared to Dexmedetomidine with Ropivacaine in Percutaneous Nephrolithotomy: A Randomized Comparative Analysis

Context: Dexmedetomidine has been found as an effective adjuvant in various nerve blocks. Despite several studies on dexmedetomidine with ropivacaine, there is no study on comparing with fentanyl in peritubal infiltration in percutaneous nephrolithotomy (PCNL). Aims: The aim of this study was to compare the effect of the addition of dexmedetomidine or fentanyl in peritubal local anesthetic infiltration on pain scores and analgesic consumption in patients who underwent PCNL. Settings and Design: This was a prospective, randomized, double-blind, tertiary care center-based study. Subjects and Methods: A total of 60 American Society of Anesthesiologists Class I, II, and III patients were selected and randomly divided into two groups: Group RF ropivacaine and fentanyl (n = 30) and Group RD ropivacaine and dexmedetomidine (n = 30). Balanced general anesthesia was given. After completion of the surgery, peritubal infiltration was given at 6 and 12 O'clock positions under fluoroscopic guidance. Postoperative pain was assessed using the visual analog scale and dynamic visual analog scale rating 0–10 for initial 48 h. Postoperative sedation was assessed using five-point sedation score. Time to first rescue analgesic, number of doses of tramadol, and total consumption of tramadol required in 48 h were noted. Statistical Analysis Used: Descriptive data were expressed in mean and standard deviation for between-group comparisons; the Chi-square and Fisher's exact tests were used for categorical variables, whereas t-test and Mann–Whitney U-test were used to compare continuous variables between two groups. Results: Duration of analgesia in group RD (12.87 ± 3.85) is more prolonged than group RF (8.13 ± 3.28) h. Total dose of rescue analgesia required in 48 h in group RF was higher as compared to group RD. Conclusions: Addition of dexmedetomidine to ropivacaine is more effective than fentanyl in terms of prolongation of analgesic efficacy of local anesthetic in peritubal block along with short-lived mild sedation.
3D-SIFT Point Cloud Registration Method Integrating Curvature Information

Aiming at the issues that point cloud registration is limited by original pose low registration efficiency, and poor robustness, a 3D-SIFT point cloud registration method that integrates curvature information is proposed. First, the Gaussian curvature of the point cloud is calculated, and the scale space of the point cloud is weighted based on the Gaussian curvature. A Gaussian pyramid is constructed, and stable extreme points are detected through the difference of Gaussian (DoG). The main direction of the extreme points is determined to get feature points. Next, the feature points are described using the Fast Point Feature Histogram (FPFH) method improved with adaptive radius. The Random Sample Consensus (RANSAC) algorithm is used to preliminarily estimate the transformation matrix of the point cloud. Finally, the improved Iterative Closest Point (ICP) algorithm is used for fine registration to obtain an accurate transformation matrix. Experiments on public point cloud datasets show that compared with the point cloud registration algorithm based on 3D-SIFT and the point cloud registration method based on the improved ISS algorithm, the proposed method has higher registration accuracy while maintaining algorithmic speed.
Face Detection and Classification using Convolutional Neural Network with Channel Attention Module

Automatic face detection is one of the most challenging in computer vision, FER is a wide range of application in human- computer interaction, behavior, and human expression. However, most of these related researches use general image classification network, which lead inadaptability while applying to face detection. This paper proposed a deep learning using Convolution Neural Network (CNN) with Channel Attention Module. The FER 2013 dataset is utilized in this research for effective classification of face detection. The Data Augmentation is used in this experiment for data pre-processing and feature extraction using high feature generation pyramid (HFGP) and low feature generation pyramid (LFGP). Face detector using Single-Short multi biox detector (SSD) and ResNet 10 based face detection. Then, face detector are given imput to channel attention module classifer which utlized Deep Learning (DL) for effective classification. The obtained result show that the proosed DL using CNN model achieves better accuracy of 99.90% on FER 2013 dataset which ensure accurate classification compared to other existing methods like Engagement Index, Deep Neural Net and Zoning based model.
The Study of RNN Enhanced Convolutional Neural Network for Fast Object Detection Based on the Spatial Context Multi-Fusion Features

In object detection, the spatial context information of the actual scene can improve the accuracy of detection while current object detection methods based on deep learning usually relies on CNNs to combine the local geometric and texture features without using these context information. In this paper, we proposal a method inspired by RNN that can extract the spatial context features and design a lighter, faster and more accurate network model for object detection that can fuse these features into multi-scale feature maps from CNNs to generate more comprehensive multi-information fusion features as inputs of localization and classification tasks subnetwork. The spatial context features extractor can be trained by joining in the BP process of the entire model, and we give the detailed optimized derivation procedures. Then we also demonstrate the validity of our model by conducting the contrastive experiments on KITTI dataset.
Tracklet Siamese Network with Constrained Clustering for Multiple Object Tracking

Multiple object tracking (MOT) is an important yet challenging task in video understanding and analysis. Basically, MOT aims to associate detected objects into trajectories based on their temporal relationships. The occlusion among moving objects poses a major challenge towards robust modeling of these relationships. In this paper, we propose a novel Tracklet Siamese Network (TSN) for learning similarities between track-lets characterized by appearance information, achieving superior performance on two MOTChallenge benchmark datasets. Our framework constructs short tracklets from highly-related object detections by excluding inaccurate object detections. We also adopt a constrained clustering technique to piece tracklets together into long trajectories, thus recovering many missing detections caused by original detector or the detection removing in the previous step. Comparisons against state-of-the-art methods were reported while ablation studies further substantiate the viability of components in our approach.
Examining Electronic Word-Of-Mouth Communication in Social Media Context

Consumers’ lifestyle has been greatly shifted due to the massive development of electronic word of mouth (eWOM) and brought unprecedented opportunities and challenges for business. Understanding of consumers’ eWOM behavior could better inform companies on how to increase consumer-generated eWOM. However, none of the past studies has examined the integrated model of eWOM, including its determinants and consequences in social media context. This study helps explaining the determinants and consequences of eWOM in social media context and its relationship with eWOM dimension of consumption, contribution, and creation. To this end, an empirical study applying partial least square (PLS) was conducted. Employing 316 of sample size, the results reveal that social influence and enjoyment are crucial determinants of eWOM consumption which subsequently contribute and create eWOM activities. Further, this study clarifies personal identity, social interaction and integration, and entertainment as the consequences from contributing and creating eWOM. This research has a potential impact in providing renewed insights for researchers into the determinants and consequences of eWOM behavior and advance the eWOM theory.