Reviewerly/exHarmony huggingface dataset:
Various Patient classification model using ABR data

Auditory brain response (ABR) is an auditory test developed in the 1970s that enables objective testing regardless of age. However, ABR has the disadvantage that analysis and reading are essential because it is complicated to inspect and requires trained inspectors. In this paper, we present a deep learning Keras sequential model and a VGG16 model that distinguish between deaf patients and normal people using ABR data and compare the performance and accuracy of the model. It is expected that a deep learning model that automatically distinguishes deaf patients from normal people will be applied in the future using the proposed model.
Research on Violence Detection Algorithm based on Multi-UAV

Based on the 30-meter UAV aerial violence data, a multi-UAV violence detection algorithm based on target detection is proposed. In this paper, YOLOX algorithm is improved. Because the target pixel is small, the UFocus module is designed to upsample the feature image, and the channel attention module is cascaded after the UFocus. The DAM module is designed to improve spatial attention. The shallow features are extracted and cascaded with DAM modules, and multi-scale features are fused with the deep semantic features. A dual-threshold single-machine violence detection algorithm is proposed. When the number of violent actions detected in a violent video within three seconds meets the dual-threshold constraint, it is determined that violence has occurred. A violence detection algorithm based on multi-UAV information fusion is proposed. The confidence value of UAV is dynamically allocated, and the confidence and probability are fused to obtain the comprehensive results. The self-built violence detection data set is tested and trained. The experiment shows that the accuracy of the improved YOLOX algorithm is 1.56% higher than the original algorithm. The dual threshold detection violence detection algorithm can complete the detection task, with an average accuracy of 71%. Taking three UAVs as an example, the weighted fusion of multiple UAV information is carried out. When the probability threshold is 0.6, the accuracy rate reaches 89%, which is 25.4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> higher than that of a single UAV.
Semantic-visual Guided Transformer for Few-shot Class-incremental Learning

Few-shot class-incremental learning (FSCIL) has recently attracted extensive attention in various areas. Existing FSCIL methods highly depend on the robustness of the feature backbone pre-trained on base classes. In recent years, different Transformer variants have obtained significant processes in the feature representation learning of massive fields. Nevertheless, the progress of the Transformer in FSCIL scenarios has not achieved the potential promised in other fields so far. In this paper, we develop a semantic-visual guided Transformer (SV-T) to enhance the feature extracting capacity of the pre-trained feature backbone on incremental classes. Specifically, we first utilize the visual (image) labels provided by the base classes to supervise the optimization of the Transformer. And then, a text encoder is introduced to automatically generate the corresponding semantic (text) labels for each image from the base classes. Finally, the constructed semantic labels are further applied to the Transformer for guiding its hyperparameters updating. Our SV-T can take full advantage of more supervision information from base classes and further enhance the training robustness of the feature backbone. More importantly, our SV-T is an independent method, which can directly apply to the existing FSCIL architectures for acquiring embeddings of various incremental classes. Extensive experiments on three benchmarks, two FSCIL architectures, and two Transformer variants show that our proposed SV-T obtains a significant improvement in comparison to the existing state-of-the-art FSCIL methods.
Triplet Spatiotemporal Aggregation Network for Video Saliency Detection

The effective aggregation of spatiotemporal information to accommodate real-world complex scenes is a fundamental issue in video saliency detection. In this paper, we propose a Triplet Spatiotemporal Aggregation Network (TSAN) to address it from the aggregation of spatiotemporal interaction, spatiotemporal information distribution, and multi-level spatiotemporal features. Firstly, we propose an interactive aggregation gate (IAG) module to model spatial and temporal global context information and perform inter-modal information transfer. Secondly, we employ an information distribution consistency (IDC) module to enhance the consistency of spatiotemporal representation by maximizing the correlation of spatiotemporal high-level features. Finally, we design a multi-level spatiotemporal feature aggregation (MSF) framework to merge cross-level and cross-modal features. These three modules are combined into a unified framework to jointly optimize spatiotemporal information for more precise results. Experimental results on five prevailing datasets show that TSAN outperforms previous competitors.
Automating File Operations via Python

Python is a computer programming language which is used for many purposes, including automating tasks. To reduce cost and increase the level of operating efficiency, network engineers made this possible by developing network automation techniques to automate recurring tasks, such as file backup. File backups are a necessity for maintaining data in local storage. Using Python and a collection of modules and functions, such as shutil to copy data from one file to another, and os and sys to obtain the file path allows a beginner to develop this automation. Up until now, legacy methods used by network engineers were not only time-consuming, but they required relevant knowledge about the related protocols and technologies. We are now lucky to have support from most major network companies, resulting in a large open-source community where you can find key information to use the power of automation within many different applications. In this paper, we will focus on task automation, such as automating file backups, the different ways it can be implemented through techniques and technologies, and why it is essential for personal and business use.
Design and Development of Real-Time Automatic Tracking and Decision System for RoboMaster Competition Robot Using Convolutional Neural Networks

This paper presents the design and development of an automatic tracking and decision system for the Robo-Master competition robot using convolutional neural network algorithms. The system can track the target automatically and make decisions based on the target's position, improving the robot's performance in the competition. The system is robust and can handle complex lighting conditions. The paper also discusses improvements made to object detection and motion prediction, which increase the system's accuracy and efficiency. The proposed system runs in real-time and achieves a high hit rate, making it a valuable tool for RoboMaster participants.
Few-Shot Learning on Histopathology Image Classification

Cancer detection in histopathology slides is not easy even today. CNN (Convolutional Neural Network) based Object identification and segmentation algorithms work very well. A large dataset of medical images is required for classification which may not be available especially for rare diseases. Therefore, deep learning and machine learning may not be effective for rare disease classification. If CNN architecture is trained on one dataset then it performs well but the same architecture may not achieve good accuracy on other datasets. So, generalization is one of main issues. This paper proposes FSL (Few-Shot Learning) to solve generalization and size of dataset. This paper uses Prototypical networks and MAML (Model Agnostic Meta Learning) simultaneously on four different datasets. Along with this, it has also been checked whether these two networks meet the concept of generalization or not. The paper also finds accuracy of both networks in 2-way, 3-way, and 5-way modes. Simulation results show that MAML achieves accuracy of 84.56% in the 2-way 2-shot 2 query mode. Further, simulation results show that Prototypical Network achieves accuracy of 74.575%, 61.9889% and 45.762% in 2-way 2-shot 2 query mode, 3-way 3-shot 3-query mode and 5-way 5-shot 5-query mode, respectively.
Reconstructing Word Representations from Pre-trained Subword Embeddings

Subword embeddings are integral to transformers such as Generative Pre-trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which are utilized in various natural language processing tasks. A subword is a subset of a word and a word can be decomposed into one or more subwords and characters. One challenge with subword tokenization is determining the optimal tokens for representing a word. This research proposes a refinement to the GPT tokenizer based on an analysis of subword tokens from GPT. The proposed approach reconstructs the word with the objective being minimization and refinement of subword tokens in the reconstruction of the word. The evaluation of the proposed approach is done using popular analogy datasets on semantic and syntactic tasks. This research shows the morphological emphasis on reconstructed word embeddings from pre-trained subword embeddings generated from GPT. The proposed approach reports an overall improvement of approximately ten percent for the refined GPT tokenizer on syntactic tasks.
Multibox Sample Selection for Active Object Detection

Deep object detection has achieved state-of-the-art performance but highly relies on large-scale labeled datasets. Active Object Detection (AOD) presents criteria to select the most informative samples to annotate, thereby maximizing the performance with limited labeled data. But current AOD methods mostly aggregate criterion values calculated from every single box without the MultiBox prediction structure of object detection, making them easily overwhelmed by uninformative boxes (e.g. boundary and background boxes). This paper presents a MultiBox Sample Selection (MSS) criterion composed of MultiBox Uncertainty (MBU) and MultiBox Committee (MBC). MBU eliminates the influence of uninformative boxes and selects the boxes that best reflect the prediction uncertainty of the whole image. MBC consists of a committee measurement, which is based on the fact that certain but incorrect box predictions are usually inconsistent among nearby boxes. Experimental results on in-domain and cross-domain object detection datasets show the highest performance under the same labeling budget.
Residual based hierarchical feature compression for multi-task machine vision

With the remarkable success of deep learning, image/video coding for machines (VCM) has been playing an important role in facilitating intelligent vision tasks. However, the existing VCM methods suffer from either sub-optimality of using image compression standards, or generalisation issues of learning-based methods. To address these issues, this paper proposes a residual-based hierarchical feature compression (RHFC) method to achieve optimal and universal feature compression for object detection and segmentation. More specifically, we first analyse the redundancy that exists in features at multiple scales, by finding that large-scale features are surprisingly less important to the vision tasks. Thus, we propose a pair of compression and enhancement networks to extract the very basic cues from the large-scale features, which are then compressed by the VVC codec. To compensate the inevitable detail loss, we further propose the hierarchical framework to compress the residuals between the reconstructed and original features, such that the performances can be significantly improved at low bit-rate cost. Experimental results have verified our superior performances, against both the state-of-the-art learning-based and standard feature compression methods. Our RHFC method also generalises well to other scenarios without the need of any further fine-tuning.
DiST-GAN: Distillation-based Semantic Transfer for Text-Guided Face Generation

Recently, large-scale pre-training has achieved great success in multi-modal tasks and shown powerful generalization ability due to superior semantic comprehension. In the field of text-to-image synthesis, recent works induce large-scale pre-training with VQ-VAE as a discrete visual tokenizer, which can synthesize realistic images from arbitrary text inputs. However, the quality of images generated by these methods is still inferior to that of images generated by GAN-based methods, especially in some specific domains. To leverage both the superior semantic comprehension of large-scale pre-training models and the powerful ability of GAN-based models in photorealistic image generation, we propose a novel knowledge distillation framework termed DiST-GAN to transfer the semantic knowledge of large-scale visual-language pre-training models (e.g., CLIP) to GAN-based generator for text-guided face image generation. Our DiST-GAN consists of two key components: (1) A new CLIP-based adaptive contrastive loss is devised to ensure the generated images are consistent with the input texts. (2) A language-to-vision (L2V) transformation module is learned to transform token embeddings of each text into an intermediate embedding that is aligned with the image embedding extracted by CLIP. With these two novel components, the semantic knowledge contained in CLIP can thus be transferred to GAN-based generator which preserves the superior ability of photorealistic image generation in the mean time. Extensive results on the Multi-Modal CelebA-HQ dataset show that our DiST-GAN achieves significant improvements over the state-of-the-arts.
Edge-FVV: Free Viewpoint Video Streaming by Learning at the Edge

Audiences cangain an immersive experience watching videos from multiple angles (a.k.a. viewpoints). Free Viewpoint Video (FVV) is developed to enable users to choose their preferred viewpoints during the play of a video. However, users may experience a delay if video frames of the chosen viewpoint cannot be timely loaded, or synthesized from multiple video streams of neighboring viewpoints. To address this problem, we present Edge-FVV, an edge-assisted FVV system that employs edge caches to reduce the delay in streaming the requested FVV from the server to client users. We first analyze the capacity and delay at edge caches when answering FVV requests. Next, we propose two types of machine learning algorithms that allocate the users’ requests to appropriate edge caches. Our evaluation shows that two types of proposed algorithms outperform benchmarks by 4.2-7.4% and 4.6-6.8%, respectively, in reducing the delay for FVV requests.
GATE II: Visualizing Semantic Web Search

The semantic web is a mesh of information linked in a way that can be easily shared and reused to make inferences for the end user. The semantic web attempts to find and access web sites and web resources not by keywords but by descriptions of their contents and capabilities. This has been made possible by adding structure to the content of web pages and developing an environment where software agents can perform sophisticated functions for users. Semantic web customization of JMaPSS uses the Java Marker Passing Search System (JMaPSS) which applies a spreading activation search algorithm known as marker passing to significantly improve search results. This research project focuses on visualizing the semantic network and displaying marker propagation to distinctively illustrate various elements of the semantic web ontology in the form of a graph structure. The tool displays the results of marker propagation by highlighting the active nodes and the propagation path.
Improved Navigation for Social Robots Through Process Offloading

The aging population warrants new mechanisms to help care for the elderly. Robotics is one such mechanism that requires the robots to navigate to the residents. Pepper robot is used in nursing homes to navigate to the residents. The sensors that help Pepper navigate are less than par in helping Pepper navigate to the residents. To help Pepper map the nursing home for navigation we equipped it with a LiDAR detector and an NVIDIA Nano. This paper discusses the design and the enhancements to Pepper to help navigate. Limitations and struggles will also be shared along with some use cases.
Data Engineering to Support Intelligence for Precision Medicine in Intensive Care

this paper aimed to present the unique data engineering work for dealing with fragmented and infrequent data collection and to integrate data from Intensive Care (ICU) with other resources. The outcome of this data processing supports the development of an Intelligent Decision Support System for Precision Medicine (IDDS4PM) by providing the possibility to analyze all the clinical events in one platform from the date/time of admission. Thus, to obtain the precise treatment, whole clinical data will be considered regardless of the diversity of data sources and frequency of data creation.
Ada-Thres: An Adaptive Thresholding Method To Mitigate The False Alarms

In a wide variety of domains, the advanced intrusion detection system consists of a learning-based detection method and a signature-based analysis approach. Such a system scans the incoming data, performs the analytics on it by using an anomaly detection algorithm, and finally transfers the report of suspicious activity for further analysis if found. The major problem of such a current system is the high false-positive rate (FPR), specifically in the case of a highly complex system with a large dataset. Such high FPRs, which are non-crucial, can easily overwhelm the user of the system and can further increase the likelihood of ignoring such indications. Therefore, mitigation approaches aim to develop a technique to reduce high FPR without losing any potential harmful threats. Thus, in this study, we develop an adaptive thresholding algorithm that can mitigate the issue of high FPR. The proposed algorithm applies three scoring mechanisms. They are Anomaly Pruning, Sequence Scoring, and Adaptive Thresholding. The model is trained on sequential data. Anomaly Pruning gives a score to an individual data point. It either rejects or accepts the data points to be considered for Sequence Scoring. This Sequence Scoring will give a score to an individual sequence. Finally, an Adaptive Thresholding is applied to the cumulative score of all the sequences to detect the anomalous nature of the analyzed data. Multiple experiments have been conducted using various optimizers to access our proposed approach. Using the proposed approach, we train a deep learning-based LSTM algorithm widely adopted for sequential data. Furthermore, we validate it with three different datasets of various sizes. From the experimental results, we infer that the proposed approach allows the model to train faster and reach the minimum loss at a faster rate.
SQT: Debiased Visual Question Answering via Shuffling Question Types

Visual Question Answering (VQA) aims to obtain answers through image-question pairs. Nowadays, the VQA model tends to get answers only through questions, ignoring the information in the images. This phenomenon is caused by bias. As indicated by previous studies, the bias in VQA mainly comes from text modality. Our analysis of bias suggests that the question type is a crucial factor in bias formation. To interrupt the shortcut from question type to answer for de-biasing, we propose a self-supervised method for Shuffling Question Types (SQT) to reduce bias from text modality, which overcomes the prior language problem by mitigating the question-to-answer bias without introducing external annotations. Moreover, we propose a new objective function for negative samples. Experimental results show that our approach can achieve 61.76% accuracy on the VQA-CP v2 dataset, which outperforms the state-of-the-art in both self-supervised and supervised methods.
Hierarchical Attention Learning for Multimodal Classification

Multimodal learning aims to integrate complementary information from different modalities for more reliable decisions. However, existing multimodal classification methods simply integrate the learned local features, which ignore the underlying structure of each modality and the higher-order correlation across modalities. In this paper, we propose a novel Hierarchical Attention Learning Network (HALNet) for multimodal classification. Specifically, HALNet has three merits: 1) A hierarchical feature fusion module is proposed to learn multilevel features, aggregating multi-level features for a global feature representation with the attention mechanism and progressive fusion tactics. 2) A cross-modal higher-order fusion module is introduced to capture the prospective cross-modal correlations at label space. 3) A dual prediction pattern is designed to generate credible decisions. Extensive experiments on three real-world multimodal datasets demonstrate that HALNet achieves competitive performance compared to the state-of-the-art.
Machine Learning Algorithms for Natural Language Processing Tasks: A Case of COVID-19 Twitter data (Thailand)

This paper presents the use of natural language processing for the problem of information extraction and sentiment analysis. The dataset is from Twitter that has the information of people mentioning about COVID-19, this study has two tasks: (i) classification approach for information extraction task and (ii) deep learning approach for sentiment analysis task. In information extraction task, the data was gathered from twitter that related to COVID-19 information, and the sequence labelling method applied to classify text before giving it to classification algorithms (K-NN, Naïve Bayes, Decision Tree, Random Forest, and SVM). In sentiment analysis task, data was classified by convert the word into index and using word embedding, then to process deep learning algorithm (Bi-directional GRU). The accuracy of two tasks are 98% and 79% respectively.
Measuring Influencing Factors of API Usability

In this paper, we identify the influencing factors of API usability by extending a comprehensive framework for measuring API usability we proposed earlier, through a systematic examination of entities involved and artifacts produced in API development and usage. Existing research in software metrics are adapted to define metrics capturing relevant metrics dimensions and at the appropriate levels. Our current set of identified influencing factors includes metrics defined on API documentation and other entities/artifacts, in addition to the code metrics. Some preliminary results from actual measurement of such influencing factors are included to demonstrate the viability of our approach.