Reviewerly/exHarmony huggingface dataset:
DQN Algorithm Design for Fast Efficient Shortest Path System

Reinforcement Learning is an algorithm with decision-making based on the Markov Decision Process (MDP). MDP uses actions that are assessed by rewards to achieve the goal depending on the environment. An action is taken by a policy. In this work, we consider a maze-based shortest path problem. The maze problem is solved based on the value of Q from the chosen action. The ordinary Q-learning algorithm uses these value-based states in two modes; explore and exploit. However, the continuous states require initialization. The problem appears in very large states. Thus, in this work, deep learning in the form of neural-network is embedded to determine the Q-value called by Deep Q-Network (DQN). The neural network architecture has a different number of layers and neurons for each implementation. Moreover, the implementation of agents on edges has limited resources. Thus, it requires an optimal architecture to achieve the goal. This study uses an environment maze with a size of 20 × 20 with four actions; up, down, left, and right to reach the destination state or goal. In this paper, we study how to get the optimum processing elements (PE) to be implemented in limited resources of edge computing. Experimental results show that the minimum number of layers of three layers with 14 hidden neurons, and 200 deep replay memory from DQN architecture have been able to complete the shortest path on a maze with a size of 20 × 20. The finding effectively minimizes the amount of computing and potential for edge computing applications.
Unbiased Decision-Making Framework in Long-Video Macro &amp; Micro-Expression Spotting

Spotting macro and micro expressions proves more demanding than subsequent tasks such as recognizing or classifying expressions. This intricate process entails pinpointing the precise commencement, conclusion, and pinnacle frames of brief facial expressions within video footage. Our study delves into the susceptibility of expression spotting to biases ingrained in deep learning models, often stemming from a lack of diversity in the training data. These biases manifest diversely, such as an overemphasis on specific expression categories, genders, or racial backgrounds, leading to subpar performance on other samples. These spurious biases in deep learning models adversely affect classification accuracy. Our work aims to unearth common biases in micro and macro expression spotting and suggests methods to detect them. In addressing this challenge, we introduce a causal graph that elucidates the connections among a trained deep learning model, the feature space it acquires, and the resulting outcomes. This causal graph plays a pivotal role in pinpointing potential origins of bias during the training phase. We utilise counterfactual inputs to evaluate model biases, providing advantages in terms of computational efficiency and interpretability when contrasted with in-training debiasing approaches. This makes counterfactual debiasing a promising avenue for addressing biases in machine learning models. Experimental results underscore the effectiveness of this approach in successfully mitigating biases in several state-of-the-art macro and micro-expression spotting methods.
Identifying Candidate Pages for Shortcuts in a Website for Site Personalization and Adaptation

The most peculiar characteristic of websites is that they tend to grow in their structure and become more complex in their paths for accessing needed pages. As a result, issues dealing with website efficiency, maintenance, adaptation, personalization, and link -structure optimization require close attention. A heuristic analysis framework for website adaptation and optimization is presented. A second experimental analysis of the framework is presented. The evaluation follows the same methodology of the first experiment in which a large academic website was used. The framework attempts to identify candidate shortcuts to popular pages on popular paths. The main goal is threefold (1) make a website more adapted to user needs, (2) reduce the navigational burden on the users, and (3) assist the developers in identifying hot spots of the website for efficient restructuring.
Incorporating Data Preparation and Clustering Techniques for Workload Segmentation in Large-Scale Cloud Data Centers

The interconnected dependencies of various cloud-hosted services and applications make managing cloud resources more difficult. Observing fully operational virtual computing instances based on task profiles can aid in identifying workload characteristics. Scheduling and managing workloads may be optimized by tailoring selection and decision-making processes in response to workload segmentation. Cloud data center managers use models that group operations and tasks with similar structures, allowing for a more straightforward comparison of outcomes and improved cloud service performance and availability. However, conventional clustering algorithms can cluster the cloud workload into a single data grouping pattern. Since cloud workload data is open to diverse interpretations, several legitimate categories are hiding in the various data outlooks. Considering high-dimensional data, where several attribute profiles define each job, we provide a strategy for grouping cloud workloads at the task scheduling level. The proposed model is applied to a real-world data center workload, which includes a trace of virtual instances derived from the Microsoft Azure public dataset. Several data clustering methods and pipelines are examined and contrasted. The results show that different feature engineering methods used in data pipelines lead to various valid clustering schemes that can be used to improve the performance of workload segmentation in big cloud data centers.
Time Sensitive Hash and Adaptive Image Recovery based Self-embedding Fragile Watermarking Scheme in Encrypted Images

To protect the integrity and privacy of cloud storage images, this paper proposed a self-embedding fragile watermarking scheme in encrypted images. An original image is divided into the seven least significant bit-planes (7LSBs) and the most significant bit-plane (MSB). 7LSBs plane is encrypted using pixel XOR and block scrambling. MSB plane is predicted and compressed for encoding to vacate space for reversible embedding of watermark. A time sensitive hash (TSH) method is designed to resist the collage attack. The TSH enables the hash function to generate a unique hash value under the same input, which greatly reduces the probability of missed detection under collage attack. The adaptive image recovery (AIR) method is also proposed to improve the quality of image recovery. The effectiveness of AIR is achieved by adaptively selecting the recovery method based on the estimated tamper attack type. Experimental results show that the proposed scheme effectively protects the confidentiality of image content and exhibits a strong recovery effect against various tamper attacks, even achieving lossless recovery without tamper. Compared with the existing schemes, the proposed scheme has certain effectiveness and superiority.
GAFormer: Wearable IMU-Based Human Activity Recognition with Gramian Angular Field and Transformer

Recognizing human activities (HAR) from wearable motion sensors has widely practical applications due to its low cost, convenient use, and scalability. Plenty of works utilize 1D CNN or RNN to capture temporal information from time series, while others take advantage of 2D CNN architectures that can effectively handle spatial correlation, and distinctive recognition features, which are really useful for recognition tasks. This paper proposes GAFormer, a method, which exploits the latter, for human activity recognition from wearable motion sensors. GAFormer transforms the raw IMU data into a Gramian Angular Difference Field (GADF) image, which encodes the pair-wise angles between different sensor readings to capture the temporal dynamics and relationships among the sensor measurements. Next, a transformer model is employed to extract visual features from GADF images effectively. In addition, we adapted a state-of-the-art transformer CoAtNet as the backbone of GAFormer. GAFormer is evaluated over two published datasets C-MHAD and GesHome. With the accuracies of 98% on C-MHAD and 95.5% on GesHome’s subset, GAFormer demonstrates that combining the GADF technique and a transformer model could be feasible and promising for motion sensor-based action recognition.
Combining multiple end-to-end speech recognition models based on density ratio approach

Automatic speech recognition (ASR) systems which involve the use of multiple ASR models, known as "system combination" approaches, have been confirmed by many studies to be effective for improving speech recognition performance. In this study, we propose a system combination method using multiple end-to-end (E2E) ASR models trained under different conditions (i.e., using data from different source domains), in order to perform ASR on an unknown target domain without the need for additional, expensive model training. In order to efficiently adapt existing ASR models to the target domain, we exploit a density ratio approach (DRA), and experimentally demonstrate that by combining ASRs trained with multiple types of acoustic information with an external language model for the target domain, i.e., the standard DRA, our method can more robustly perform ASR for an unknown target domain than a single ASR model trained under one domain condition.
Retinex-based Low-Light Image Enhancement

Image enhancement in low-light conditions is not solely a matter of adjusting the illumination. Due to the limited capabilities of capturing devices, there are also issues of hidden noise bursts and color distortion in dark environments. The Retinex model, which decouples observed image space into different subspaces for image enhancement, has been extensively validated as effective. Inspired by this model, this paper proposes a low-light image enhancement method based on Retinex model decomposition, guided by deep neural networks.The proposed approach decomposes the image into the illumination map responsible for light adjustment and the reflectance map responsible for removing degradation. A light enhancement network is trained using image pairs captured with different exposures to achieve user-friendly adjustable light intensity enhancement. To alleviate the issue of color distortion, a reflectance restoration network incorporates illumination guidance information. Additionally, a diffusion model is employed as a noise detector to eliminate unnecessary noise and other distortions in the brightened low-light images by fitting the conditional distribution between different images. Experimental results demonstrate that the proposed method outperforms in terms of color preservation in low-light images with varying light intensities. It effectively removes noise while preserving image details.
Question Answering System Based on Pre-Training Model and Retrieval Reranking for Industry 4.0

By providing enhanced knowledge retrieval capabilities, real-time decision support, and efficient information exchange, question answering (QA) systems play a crucial role in driving productivity, efficiency, and innovation in Industry 4.0. Today's most reliable knowledge-based QA systems require a large knowledge base, which tends to consume more reasoning time. In order to improve the inference speed and response accuracy of the system, this paper adds a Reranker between the Retriever and Reader of the traditional two-stage mechanism. This study uses pretrained Roberta to perform system retrieval and improve data processing and training methods. Experiments on Chinese Wikipedia show that the proposed system significantly reduces the system response time and improves the accuracy and scope of the response.
Trajectory-Based Active Inverse Reinforcement Learning for Learning from Demonstration

Inverse Reinforcement Learning (IRL) requires a large number of demonstrations for robust learning, especially in the context of Learning from Demonstrations (LfD). Previous Active IRL algorithms have focused on stateaction pair demonstrations and heuristic query strategies. This study introduces Trajectory-based Active Learning for IRL, utilizing acquisition functions derived from Uncertainty sampling in Active Learning. Our novel acquisition function systematically assesses the informativeness of trajectory demonstrations originating from a specific state. Experimental evaluation in the Object World environment shows a significant performance improvement over baselines. By selectively querying informative starting states, we achieve substantial progress in learning from demonstrations.
Recognition of Specific Parts of Plastic Bottles Using Improved DeepLab v3+

In this paper, we focus on the manpower shortage in factories and conduct an experiment to try to automate the process. Factory automation has many advantages, such as reducing labor costs and improving production efficiency. Among them, we focused on the sorting of plastic bottles at a waste disposal plant. Currently, garbage is sent to the landfill without being sorted, and it is sorted by hand. Unlike cans, plastic bottles cannot be easily automated because they cannot be separated using magnets. Therefore, we propose an image processing technique to recognize where plastic bottles are located, and to use a robot arm to sort them, thereby realizing automation. In this paper, we focus on image processing technique based on deep learning. As basic research, we conducted an experiment to see how well the robot can identify a single plastic bottle in an image. We attempted to use semantic segmentation methods for detection, using DeepLabv3+ as the basic model, and improved it. scSE Block and fine-tuning were introduced to improve the accuracy significantly compared to previous studies.
A Reconfigurable Hardware Architecture for Graph Convolution Network in Action Recognition

Graph convolution neural network (GCN) has gained more attention in recent years. Data types like human skeleton joints which are in non-Euclidean space are suitable for GCN. However, due to the high computational complexity and data sparsity of GCN, it is common to have high latency or low power efficiency in CPU or GPU platforms. Therefore, dedicated hardware accelerators are critical for such tasks. In this paper, we proposed a highly parallelized and flexible architecture for the GCN layers of the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model, which is a classic model and widely used in human action recognition. The accelerator also has high scalability due to our proposed method. Compared with the hardware implementation on ST-GCN, the proposed method reduces the latency by up to 39.5% and improves 1.46x on power efficiency.
Robust Watermark Imaging via Graph-signal Optimization

The topic of robust watermarking has long been a subject of interest in the field of information hiding, and in these days, the robustness against cross-media attacks, such as screen-capture and printer-capture attacks, have become an increasingly pressing concern. Previous work in the field, such as StegaStamp, one of the most popular open-source screen-shoot resilient watermark scheme, has achieved promising results in terms of decoding accuracy. However, the visual quality of some watermarked images by this scheme is still not inadequate for practical purposes. In this paper, we propose an optimization that could significantly enhances the visual quality of watermarked images. We use a graph-based method to build graph from image blocks, compute a loss designed with inspiration from GLR, and add it to the existing loss function of the neural network during training. As demonstrated by the experiment results, we have achieved substantial improvements in the quality of watermarked images, in both visual quality and metrics, while still maintaining strong robustness and decoding capability.
Investigating the Effectiveness of Speaker Embeddings for Shout Intensity Prediction

The automatic detection of shouted speeches has attracted much research attention as a core technology of audio surveillance systems. A common strategy in the past literature has been to train a binary classifier using labels of shouted or normal speeches. Although it is known that the acoustic properties of shouted speech usually differ among speakers, especially male and female groups, the conventional methods did not pay attention to encoding such personal and gender-related style information. There are recent findings that speaker embeddings, which are produced by a model trained for speaker identification, can improve other tasks such as speech emotion classification. Thus, this paper investigates the effectiveness of such speaker embeddings for a shouted speech detection problem. Specifically, we verify whether x-vector embeddings can work as effective auxiliary features for the target problem compared with a simple gender label of an input speech whose ground truth is generally unavailable in real situations. Our experiments on predicting the shout intensity beyond the traditional binary classification demonstrated the x-vector embeddings achieved performance improvement over the single use of speech features.
Data Selection Based on Phoneme Affinity Matrix for Electrolarynx Speech Recognition

Electrolarynx (EL) is a communicative aid for the patient after laryngectomy to generate communicable speech. Since EL speech exhibits low speech intelligibility and produces loud noise, understanding the content of the speech remains challenging for listeners, even if the patient is proficient in using the EL device. Accordingly, it is important to develop the tools that offer additional communication methods. Automatic speech recognition (ASR) of EL speech emerges as a method worth considering in this regard. However, the problem of under-resourced data dramatically degrades the recognition performance of EL speech. Data augmentation is one of the viable solutions for addressing the issue of under-resourced speech data. However, even with an increased health training corpus, the improvement in EL speech recognition may not be satisfactory. Because the characteristics of the EL speech still differ significantly from those of health speech. This paper proposes a data selection method using the phoneme affinity matrix to prioritize the selection of health speech that closely resembles EL speech for data augmentation. The affinity between two phonemes is defined as the similarity of the Phone Posteriorgrams(PPGs) of the two phonemes, considering the phoneme models. The experimental results demonstrate that the approach utilizing data selection based on the phoneme affinity matrix yields superior results compared to both the baseline and the method employing random sampling to select the augmented health speech corpus.
Interpretable Image Recognition in Hyperbolic Space

The lack of human-level interpretability in the decision-making process of computer vision is one of the obstacles to further its application and performance improvement. This issue is investigated by eXplainable Artificial Intelligence (XAI), and a range of interpretable XAI networks has been proposed to mitigate the black box issue. Most of them attempt to implement case-based reasoning in image recognition that is similar to humans. This cognitive process includes dissecting the image and identifying prototypical parts that distinct one class from another. However, only a few works take into consideration the hierarchical principle of this process and visual data. In contrast to previously introduced XAI approaches that utilize Euclidean space to design a network, hyperbolic space with its ability to embed hierarchical structures is exploited in this work. The proposed prototype-based hyperbolic network, namely ProtoHyp, first learns prototypes that play key roles in identifying classes. Then, using the similarity between them and an image, the human-understandable classification of an image is conducted in hyperbolic space. Experimental results on two image recognition datasets, Birds CUB-200-2011 and Stanford Dogs, have demonstrated the improvement of state-of-the-art interpretable image recognition in terms of both accuracy and interpretability.
A Futuristic Proposed Solution to a Global Challenge: The Promise of AI for Social Distancing Compliance

Infectious disease is a terrible pandemic around the world, the economy and even people’s lifestyles are affected while implementing a lot of standard operating procedures (SOPs) to control the spread of the virus. One of the most effective ways is to control the physical distance between people. Therefore, a Social Distancing Monitoring (SDM) system can be used to track the safe distance between people. In this study, a comparison between algorithms will be made to identify the most efficient algorithm based on human detection results. As a result, the OpenPose algorithm has outstanding human detection results compared with YOLOv4 and YOLOv4-pedestrian. Furthermore, a distinctive bounding box method for human detection has been proposed, which is defined from the human skeleton using the OpenPose algorithm. Hence, SDM will be efficient after a perspective transformation to calculate the distance between people, since most of the people in the frames have been detected. Finally, this study will discuss how the power of AI enhances the SDM system in detecting humans and calculating the distance between people detected.
Deep Unfolded Underwater Image Enhancement Based on Extreme Channels Prior

We propose a deep unrolling approach for underwater image enhancement using extreme channels prior. First, we formulate underwater image enhancement as a joint optimization problem that incorporates an underwater-related extreme channels prior and implicit regularization functions. Then, we solve the optimization problem iteratively and develop an unfolded deep neural network, where each block of the network represents an iteration in which the optimization variables and regularizers are updated using closed-form solutions and learned proximal operators, respectively. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art underwater image enhancement algorithms in both quantitative and qualitative comparisons.
Quick Response (QR) codes embedding in VVC using Quantisation Parameter Manipulation

This study presents an innovative approach for concealing textual content within Versatile Video Coding (VVC), H.266 by utilising the Quantisation Parameter (QP) and modifying the QP index within the existing quantisation framework. The primary objective is to seamlessly integrate concealed textual content into video streams while minimising any noticeable impact on visual fidelity and compression efficiency. Additionally, the study investigates the capability of the proposed method to embed Quick Response (QR) code into the video to enhance interactivity and provide viewers with quick access to supplementary information. The proposed method focuses on the encoder aspect, precisely adjusting the QP index to incorporate the concealed data. By strategically manipulating the index values, the encoded video frames maintain their visual fidelity while accommodating the concealed textual content. The embedding process seamlessly integrates into the pre-existing quantisation framework, providing an effective and efficient solution for hiding information in video streams.