Reviewerly/exHarmony huggingface dataset:
Search-Time Efficient Device Constraints-Aware Neural Architecture Search

Edge computing aims to enable edge devices, such as IoT devices, to process data locally instead of relying on the cloud. However, deep learning techniques like computer vision and natural language processing can be computationally expensive and memory-intensive. Creating manual architectures specialized for each device is infeasible due to their varying memory and computational constraints. To address these concerns, we automate the construction of task-specific deep learning architectures optimized for device constraints through Neural Architecture Search (NAS). We present DCA-NAS, a principled method of fast neural network architecture search that incorporates edge-device constraints such as model size and floating-point operations. It incorporates weight sharing and channel bottleneck techniques to speed up the search time. Based on our experiments, we see that DCA-NAS outperforms manual architectures for similar sized models and is comparable to popular mobile architectures on various image classification datasets like CIFAR-10, CIFAR-100, and Imagenet-1k. Experiments with search spaces—DARTS and NAS-Bench-201 show the generalization capabilities of DCA-NAS. On further evaluating our approach on Hardware-NAS-Bench, device-specific architectures with low inference latency and state-of-the-art performance were discovered.
Gödel Number Based Encoding Technique for Effective Clustering

In this paper, a Gödel number-based encoding technique is proposed to encode each object of a dataset before applying any clustering algorithm. This encoding technique converts the objects into a decimal string while maintaining the properties of the features. The results of all standard existing clustering algorithms after applying this encoding are evaluated based on benchmark metrics like, Silhouette Score, Davis Bouldin, Calinski Harabasz and Dunn Index. In comparison to the existing clustering algorithms if one uses Gödel number-based encoding over the dataset, it gives better performance.
Conditioning Covert Geo-Location (CGL) Detection on Semantic Class Information

To incorporate knowledge about non-object image regions (hideouts, turns, and other obscured regions) in machines, recently, a task for identification of potential hideouts termed Covert Geo-Location (CGL) detection was proposed by Saha et al. [14]. It involves identification of image regions which have the potential to either cause an imminent threat or appear as target zones to be accessed for further investigation to identify any occluded objects. Only certain occluding items belonging to certain semantic classes can give rise to CGLs. This fact was overlooked by Saha et al. and no attempts were made to utilize semantic class information, which is crucial for CGL detection. Thus in this paper, we propose a multitask-learning-based approach to achieve two goals - 1) extraction of features having semantic class information; 2) robust training of the common encoder, exploiting large standard annotated datasets as training set for the auxiliary task (semantic segmentation). To explicitly incorporate class information in the features extracted by the encoder, we have further employed attention mechanism in a novel manner. In this work, we have also proposed a better evaluation metric for CGL detection that gives more weightage to recognition rather than precise localization. Experimental evaluations performed on the CGL dataset, demonstrate a significant increase in performance of about 3% to 14% mIoU and 3% to 16% DaR on split 1 and $$\approx $$ 1% mIoU and 1% to 2% DaR on split 2 over SOTA, serving as a testimony to the superiority of our approach. ACK: IMPRINT (MHRD/DRDO) GoI, for support
Forensic Accounting and Fraud Control in Auditing Firms in Kigali, Rwanda

The primary goal of this study was to determine how forensic accounting contributes to fraud prevention and control within the Auditing Firms in Kigali, Rwanda. More specifically, the stud y sought to examine how internal controls, knowledge of forensic accounting and skills and management tools influence fraud control in Auditing Firms in Kigali, Rwanda. The study was anchored on Fraud Triangle Theory and Fraud Diamond Model. Descriptive research design was adopted to help determine patterns and trends in order to make conclusions from the data collected. The target population for this study was a total of 76 staff members thereby employing a census approach. The researcher was collected by primary data by use of closed-ended questionnaires to the respondents. For these, the study used simple random sampling to select all the respondents. Primary data was collected by use of closed-ended questionnaires to the respondents. Secondary data was collected using data collection sheet. Secondary Data involved the use of financial reports, regulatory documents, and academic literature will be reviewed to gain insights into the prevailing financial fraud trends in Rwanda. Validity and reliability were tested to ensure the sanctity of the research tool and thus facilitate piloting which was done to selected auditing firms Rusumo. Data analysis was conducting using both descriptive (Frequencies, mean, standard deviation) and inferential (regression and correlation) statistical methods using the Statistical Package for Social Sciences (SPSS version24). Keywords: Forensic Accounting, Fraud Prevention, Auditing Firms, Internal Controls, Kigali, Rwanda
The Effects of Virtual Reality on Student Learning in Civil Engineering Program: Case of a Developing Country

In conventional teaching environment, complex civil engineering problems are taught with little visual cues given to the students mostly in the form of 2D drawings or textbooks and PowerPoint slides. The importance of visual aids cannot be downplayed in an educational environment as they increase student understanding of the course content. In this paper, the effects of desktop-based virtual reality systems on student learning in a civil engineering education environment have been investigated. The students considered as a case in this study belong to civil engineering department of a top-rated university of Pakistan. This research used interactable computer animations as a complementary tool to traditional teaching methods to enhance student learning of complex structures. The considered participants were split between the two groups, i.e., Group A (a simulation group) and Group B (a control group). Both groups were taught the same course content by the same full-time instructor. The control group studied the same topic in a conventional learning environment with textbooks and PowerPoint slides being taught, and the simulation group was taught with an interactive animation (addition of desktop virtual reality system) in addition to the traditional classroom teaching techniques. Student performance between the two groups was then compared through a knowledge assessment test. The knowledge assessment test result showed a mean value of 6.51 for Group A and 5.25 for Group B with a mean difference of 1.26. A t-test, using the Statistical Package for the Social Sciences, was further performed between the two groups’ results to determine if the results were significantly different. The test was found to be statistically significant at 5% with a t value of 3.54, thus indicating that the simulation group benefited from the desktop VR program. Hence, the assessment showed statistically significant improvement in understanding and learning of Group A as compared to Group B.
“Camping” or abortion? Polyvarience and coded language on social media

In the context of hashtag activism and hashtag feminism, this article examines the use of “camping” as a code for abortion on Twitter following the 2022 Supreme Court’s overturning of Roe v. Wade. Utilizing a critical discourse analysis approach, the analysis assesses the discursive practices of using “camping” as a symbol while considering the affordances and limitations of social media spaces in conveying messages that influence social and political life, such as abortion access advocacy online. The findings revealed the discursive practices created by the “camping” code and the impact of other users’ concerns around this iteration of hashtag activism that resulted in the following four themes: adoption, disidentification, rejection or caution, and redirection toward advocacy. This study presents the affordances of constitutive effects online and the limitations of using symbols within social media spaces to claim oneself as a “safe” person to trust in lieu of existing activist networks.
Fault Diagnosis of Analog Circuits Based on The Sailfish Algorithm Optimized SKELM

Abstract In view of the long training time for the optimization of the network model parameters of the SELM and the uncertainty of the model generalization ability, this paper proposes an analog circuit fault diagnosis model based on the sailfish algorithm to optimize the stacked kernel extreme learning machine(SKELM). This model introduces a kernel function to build a multi-layer KELM, which can improve the generalization ability and learning speed of the feedforward neural network. The weights of each layer of SKELM are obtained through the automatic encoder training based on the KELM. Since KELM-AE does not need to set initial parameters, the training speed is improved. However, the kernel parameters and regularization coefficients of KELM-AE are set manually, so the sailfish optimizer (SFO) is used to optimize these two parameters, and then the optimal SKELM model is built through layer by layer training. Finally, the Leap frog filter circuit is used as the simulation experiment circuit, and further compared with the optimized SELM. The results show that KELM-AE has strong generalization ability, and it can map fault features to high-dimensional feature space through nonlinear mapping without extracting fault features separately, thus improving the classification accuracy.
English Pronunciation Quality Evaluation System Based on Continuous Speech Recognition Technology for Multi-Terminal

Abstract This paper presents a novel approach for evaluating the pronunciation quality of English speech using continuous speech recognition technology. The research focuses on the application of artificial intelligence in speech recognition, utilizing web browsers on various terminal devices such as computers, mobile phones, and tablets to allow users to read the provided text aloud. The web program captures audio input from the microphone, records it in MP3 format, and uploads it to the server. The server employs the Whisper model to transcribe the audio into semantic text, which is then compared with the displayed text. By calculating the semantic distance and assessing the accuracy of pronunciation, the system provides an evaluation of pronunciation quality, marking correct and incorrect words. To achieve real-time processing, the compact tiny model is employed, and further optimization is performed using Ctranslate 2, resulting in significant performance improvements.
Prior Knowledge-guided Hierarchical Action Quality Assessment with 3D Convolution and Attention Mechanism

Abstract Recently, there has been a growing interest in the field of computer vision and deep learning regarding a newly emerging problem known as action quality assessment (AQA). However, most researchers still rely on the traditional approach of using models from the video action recognition field. Unfortunately, this approach overlooks crucial features in AQA, such as movement fluency and degree of completion. Alternatively, some researchers have employed the transformer paradigm to capture action details and overall action integrity, but the high computational cost associated with transformers makes them impractical for real-time tasks. Due to the diversity of action types, it is challenging to rely solely on a shared model for quality assessment of various types of actions. To address these issues, we propose a novel network structure for AQA, which is the first to integrate multi-model capabilities through a classification model. Specifically, we utilize a pre-trained I3D model equipped with a self-attention block for classification. This allows us to evaluate various categories of actions using just one model. Furthermore, we introduce self-attention mechanisms and multi-head attention into the traditional convolutional neural network. By systematically replacing the last few layers of the conventional convolutional network, our model gains a greater ability to sense the global coordination of different actions. We have verified the effectiveness of our approach on the AQA-7 dataset. In comparison to other popular models, our model achieves satisfactory performance while maintaining a low computational cost.
Research on Human Pose Estimation Based on High-resolution Network

Abstract The problem addressed in this paper is the increase in the parameter quantity of existing human body pose estimation network models when the depth of the network is scaled up to improve forecast precision. To tackle this issue, an optimization network model of the new human body pose estimation called BDENet is proposed, inspired by high-resolution detection networks. This model incorporates a bottleneck structure and dilated convolution to reduce parameters and incorporates the ECA lightweight attention mechanism to enhance precision. Compared with HRNet, the proposed model achieves a 21.4% reduction in parameter quantity on the MSCOCO dataset while scaling up precision by 1.4%. The experimental findings strongly suggest that the modified network significantly improves network precision along with lowering the number of parameters.
Insulator Defect Detection Method upon Fused Attention Mechanism and Bidirectional Feature Fusion

Abstract Insulators are important components for achieving electrical insulation and mechanical support, but they are prone to various defects in harsh operating environments, which can damage their mechanical strength and insulation performance. This article proposes the Shuffle YOLOv7 model based on the YOLOv7 algorithm for insulator defect detection, aiming to solve the weakness of low precision in traditional object detection algorithms when facing complex backgrounds and small-sized defects. To address the issue of low attention to flashover faults in traditional algorithms, the ShuffleAttention fusion attention mechanism is supplied to concentrate on both intra-channel and inter-channel deep features, and the original PANet structure is replaced with a pyramid which has a bidirectional feature fusion structure to enhance the network’s feature extraction ability. The Focal-EIOU LOSS optimization method focuses on high-quality prior boxes to improve model accuracy, and the effectiveness of the optimization method is verified through ablation experiments. These results of the experiment show that the proposed algorithm achieves varying degrees of performance improvement in terms of precision, recall, average precision, and overall loss compared to mainstream object detection algorithms in detecting insulator damage and flashover.
An airborne object detection and location system based on deep inference

Abstract In recent years, with the development of sensors, communication networks, and deep learning, drones have been widely used in the field of object detection, tracking, and positioning. However, there are inefficient task execution and some complex algorithms still need to rely on large servers, which is intolerable in rescue and traffic scheduling tasks. Designing fast algorithms that can run on the airborne computer can effectively solve the problem. In this paper, an object detection and location system for drones is proposed. We combine the improved object detection algorithm ST-YOLO based on YOLOX and Swin Transformer with the visual positioning algorithm and deploy it on the airborne end by using TensorRT to realize the detection and location of objects during the flight of the drone. Field experiments show that the established system and algorithm are effective.
RGB and LiDAR Fusion-based 3D Semantic Segmentation for Autonomous Driving

Abstract Projection-based multimodal 3D semantic segmentation methods suffer from information loss during the point cloud projection process. This issue becomes more prominent for small objects. Moreover, the alignment of sparse target features with the corresponding object features in the camera image during the fusion process is inaccurate, leading to low segmentation accuracy for small objects. Therefore, we propose an attention-based multimodal feature alignment and fusion network module. This module aggregates features in spatial directions and generates attention matrices. Through this transformation, the module could capture remote dependencies of features in one spatial direction. This helps our network precisely locate objects and establish relationships between similar features. It enables the adaptive alignment of sparse target features with the corresponding object features in the camera image, resulting in a better fusion of the two modalities. We validate our method on the nuScenes-lidar seg dataset. Our CAFNet achieves an improvement in segmentation accuracy for small objects with fewer points compared to the baseline network, such as bicycles (6% improvement), pedestrians (2.1% improvement), and traffic cones (0.9% improvement).
Complex Query in Process Knowledge Graph Based on Box Embedding

Abstract To make use of past practical experience and expertise effectively and accurately, and reduce the waste of resources due to repetitive labor, this paper conducts intricate multi-hop logical deduction by using the information obtained from the process knowledge graph (PKG). Here, BoxE is introduced in this context, which is a box embedding framework designed to address diverse First-Order Logic queries (FOL) across Process Knowledge Graphs (PKG). A key insight of BoxE is that entities and queries can be embedded as boxes and geometric operations can be used iteratively for logical reasoning to answer the complex logical queries. Our demonstration showcases BoxE’s ability to effectively respond to various First-Order Logic queries involving technology, process, procedure, etc. on PKG and shows that BoxE has higher accuracy on each query structure, which can generalize unseen query structures and answer complex query structures scalably and accurately.
Face and Background Deepfake Detection Algorithm

Abstract The paper proposes the Face and Background Deepfake Detection (FBDD) algorithm to detect both face forgery and background forgery. Multiple key frames of the video are extracted as the input of the FBDD Transformer, and the inter-frame information is also considered to obtain four feature vectors of the person, background, head, and face to improve the detection efficiency and accuracy. FBDD distance is proposed to determine the type of video forgery. Constructing a rich dataset of video forgery methods containing face forgery and background forgery improves the accuracy of the model on different datasets. The FBDD algorithm was experimented on several commonly used and article-constructed datasets and has higher accuracy and stronger image degradation generalization compared to frontier detection algorithms.
A power constant logic circuit based on mask control

Abstract As the structure of existing anti-power attack circuits has certain security problems, this paper proposes a new mask-based control constant power logic circuit based on the existing masking technology. By integrating OR/NOR and AND/NAND circuits into a dual-rail circuit, different circuit logic functions can be controlled by inputting different masks. By introducing two parameters, Normalised Energy Deviation (NED) and Normalised Standard Deviation (NSD), the structure proposed in this paper improves the level of resistance of the circuit to power attacks to a certain extent, as well as reduces the cost of the circuit compared to other power attack resistant circuits.
Research and Implementation of 3D Object Detection Based on Autonomous Driving Scenarios

Abstract Perception function, as an important part of autonomous driving, ensures the safety and intelligence of driving. We collect the surrounding environment of driving through hardware sensors, providing a basis for subsequent decision-making of autonomous driving. In recent years, deep learning has made breakthrough progress in object detection. Based on this, this paper uses lidar point cloud data, combined with deep learning theory and method, to carry out three-dimensional target detection tasks around lidar point cloud data, and carries out theoretical analysis, method verification, and result analysis. A 3D object detection method based on LiDAR point cloud data is proposed. The backbone network of the network model corresponding to the method is VoxelNet’s backbone network. After outputting the feature matrix, sparse point clouds are supplemented with point clouds, and then the feature matrix is decoded to generate candidate boxes. The model used in this paper on the KITTI data set can effectively solve the problem of 3D target detection in the process of autonomous driving and has good performance.
Multi-pedestrian Tracking Method Fusing Two-stage Matching

Abstract Multi-pedestrian tracking is one of the hot topics in computer vision. For an intelligent mobile robot, multi-pedestrian tracking from a first-person perspective can provide information for navigating through a crowd and ensure safety. Most of the existing methods cannot deal with occlusion and trajectory overlap well. In this paper, a multi-pedestrian tracking method fusing two-stage matching is proposed. Firstly, the detection and the corresponding feature values of the pedestrians are obtained by a multi-task learning network based on CenterNet. Then the detected pedestrians are matched with feature values by greedy strategy. When dealing with the reappearance of pedestrians caused by occlusion or trajectory overlap, the sample database is established to update the samples in real time. The color histogram and HOG feature are calculated for each sample. When the pedestrian disappears, the direction of disappearance and the last position is recorded for the selection of trajectory. Finally, the KM algorithm is used for cross-frame matching. Our method is compared with some recent methods on MOT data sets. The result shows that our method has a significant improvement in the main evaluation index MOTA.