Reviewerly/exHarmony huggingface dataset:
CGSA optimized LSTM auto encoder for outlier detection

AbstractIn recent years, outlier detection has attained great attention with machine learning techniques due to its wide range of applications. By considering the input data's distributive nature and large dimensionality, outlier detection becomes a challenging issue. Robust outlier detection systems are crucial for data pattern prediction without labeled data. This research develops a novel approach based on stacking auto encoders over Long-Short Term Memory (LSTM) for outlier prediction. The detection accuracy of outlier detection is improved with the hyperparameters optimized with the Chaotic Gravitational Search Algorithm (CGSA). CGSA minimizes the training loss with enhanced detection accuracy in the proposed outlier detection process. The auto encoder in outlier detection transforms the input into a latent space representation to generate the original input sequence. The involvement of learning parameters computes and minimizes the errors between input and generated sequences. The proposed work is experimented and compared with state-of-the-art approaches of recent research. Using the proposed approach, the performance of outlier prediction is improved with an accuracy of98.6%, sensitivity of 96.1%, specificity of 97.8%, G-mean of 96%, Area Under Curve (AUC) of 0.935, Hit rate of 92.3%. Also, the outlier detection errors are minimized, showing the proposed approach's efficiency.KEYWORDS: Outlier detectionLSTM auto encoderhyperparameterloss functionheuristic optimizationchaotic gravitation search algorithm Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing not applicable to this article.Additional informationNotes on contributorsChigurupati Ravi SwaroopChigurupati Ravi Swaroop is Research Scholar at Annamalai University, Faculty of Engineering and Technology, Department of Information Technology, Chidambaram, Tamilnadu, India. He Received B.Tech degree in Sagi Ramakrishnam Raju Engineering College, Department of Information Technology in 2012, He Holds a M.Tech degree in Sagi Ramakrishnam Raju Engineering College, Department of Information Technology in 2018. His research areas are Image Processing, Bioinformatics, Machine Learning, Deep Learning, and Data Mining. He can be contacted at email: raviswaroop.chigurupati@gmail.com.K. RajaDr. K. Raja is Assistant Professor at Department of Information Technology, Faculty of Engineering and Technology, Annamalai University, Tamilnadu, India. He Received B.E degree at Annamalai University, in 2006, He Holds a M.E degree at Annamalai University ,in 2010. He Received Ph.D degree at Annamalai University, in 2018, His research areas are Wireless Sensor Networks, Image Processing, Machine Learning, Deep Learning, and Data Mining. He can be contacted at email: rajakapit@gmail.com.
Iterative-in-iterative super-resolution biomedical imaging using one real image

Deep learning-based super-resolution models have the potential to revolutionize biomedical imaging and diagnoses by effectively tackling various challenges associated with early detection, personalized medicine, and clinical automation. However, the requirement of an extensive collection of high-resolution images presents limitations for widespread adoption in clinical practice. In our experiment, we proposed an approach to effectively train the deep learning-based super-resolution models using only one real image by leveraging self-generated high resolution images. We employed a mixed metric of image screening to automatically select images with a distribution similar to ground truth, creating an incrementally curated training data set that encourages the model to generate improved images over time. After five training iterations, the proposed deep learning-based super-resolution model experienced a 7.5% and 5.49% improvement in structural similarity and peak-signal-to-noise ratio, respectively. Significantly, the model consistently produces visually enhanced results for training, improving its performance while preserving the characteristics of original biomedical images. These findings indicate a potential way to train a deep neural network in a self-revolution manner independent of real-world human data.
Active learning of quantum system Hamiltonians yields query advantage

Hamiltonian learning is an important procedure in quantum system identification, calibration, and successful operation of quantum computers. Through queries to the quantum system, this procedure seeks to obtain the parameters of a given Hamiltonian model and description of noise sources. Standard techniques for Hamiltonian learning require careful design of queries and $O({\ensuremath{\epsilon}}^{\ensuremath{-}2})$ queries in achieving learning error $\ensuremath{\epsilon}$ due to the standard quantum limit. With the goal of efficiently and accurately estimating the Hamiltonian parameters within learning error $\ensuremath{\epsilon}$ through minimal queries, we introduce an active learner that is given an initial set of training examples and the ability to interactively query the quantum system to generate new training data. To ensure applicability on near-term quantum hardware, the active learner operates in batch mode as opposed to sequentially, proposing batches of queries to be made during learning. We formally specify and experimentally assess the performance of this Hamiltonian active learning (HAL) algorithm for learning the six parameters of a two-qubit cross-resonance Hamiltonian on four different superconducting IBM quantum devices. Compared with standard techniques for the same problem and a specified learning error, HAL achieves more than a $95%$ reduction in queries required, and upwards of $33%$ reduction over a sequential active learner. Moreover, with access to prior information on a subset of Hamiltonian parameters and given the ability to select queries with linearly (or exponentially) longer system interaction times during learning, HAL can exceed the standard quantum limit and achieve Heisenberg (or super-Heisenberg) limited convergence rates during learning.
Research on a GDK-means based release privacy information protection algorithm for poor students

In the process of funding poor students, in order to ensure openness and fairness, colleges and universities will obtain many key privacy data of relevant students, such as poverty causes, student sources, family income, family members, school consumption and other sensitive privacy data. At the same time, the results of the funding also require disclosure to ensure the fairness of the management process. Aiming at the contradiction between disclosure and privacy protection in the data release of poor students, a privacy protection method based on GDK-Means is proposed. Under this algorithm, sensitive data can be generalized within the cluster on the basis of K-Means clustering, so as to de-privacy the published data, so as to achieve the purpose of user privacy protection, and quantify the degree of information loss caused by processing. Through theoretical analysis and experiments, it is verified that the GDK-Means algorithm can achieve better privacy protection in data publishing under the premise of ensuring data availability.
Script event prediction by meta-memory low resource network

Script event prediction involves building a model using recent event sequence records to capture the correlation between events and predict future ones. However, existing methods require a large amount of training data to perform well. In many real-world scenarios, labeled data is scarce and data distribution is uneven, making it challenging to achieve satisfactory performance. Additionally, the learning process of many models demands significant computational resources, which can render them unsuitable for low-resource settings. In order to fill the task blank of event prediction in low resource scenarios, a general framework called Meta-memory low resource network learning model(MLN) is proposed. Firstly, MLN captures the memory of events that occurred in the history through trend learner, learns the context-implied representation between events, and can predict future events based on the implied representation. Secondly, a Meta-memory predictor is proposed, which can further aggregate similar contextual implicit representations to form meta-memory representations, To realize the long-term memory learning process under the condition of few-shot. Finally, a gated network combination method combines the trend learner with the Meta-memory predictor to improve the effect of event prediction. We conducted experiments on four real data sets, and the results showed the effectiveness of MLN.
A comprehensive evaluation of heteroscedastic support vector regression via Monte-Carlo and Sieve simulation

The heteroscedasticity in training Machine Learning (ML) methods is vital for its prediction performance. To provide practical insights about this challenge, we modify the objective function to incorporate heterogeneous variance, which enhances ML training and improves the accuracy of prediction and evaluate the performance of the proposed heteroscedastic Support Vector Regression (HSVR) method using the Sieve simulation and Monte Carlo techniques. The results demonstrate that the performance of the HSVR method continually improves as the sample size increases, with a progressively decreased root mean squared error from 0.129 to 0.028. Our findings suggest that incorporating heterogeneous variance in the objective function significantly enhances predictive performance and provides a valuable contribution to the field of ML. The proposed method can be extended to other ML models that assume a fixed variance in the noise model.
Vector relation acquisition and scene knowledge for solving arithmetic word problems

This paper presents a vector relation-centric algorithm for solving arithmetic word problems (AWPs), which uses vector relation acquisition and scene knowledge to ensure the performances of problem understanding and symbolic solver correspondingly. The vector relation acquisition procedure builds on the synergy of the vector syntax-semantics method and the deep neural miner. Compared with the syntax-semantics method, the vector syntax-semantics method decreases not only the number of models but also semantic ambiguities and computational costs. For the scene knowledge, this paper proposes a scene-aware symbolic solver which infers relations obeying scene rules to decrease the occurrences of unwanted operations. Experimental results show that the proposed algorithm is superior to the high-performance baseline algorithm in both accuracy and computational cost. In accuracy, the proposed algorithm increased the accuracy by 3.9% on the sum of the three scene subsets due to the use of the scene knowledge and vector computing; as a result, it increased the accuracy by 0.5% on the sum of six authoritative datasets. In computational cost, the proposed algorithm decreased the computing cost by more than 50%. Thus, this paper makes a significant contribution to developing instruments for solving AWPs.
Real-time path planning of controllable UAV by subgoals using goal-conditioned reinforcement learning

The conventional path planning problem for an unmanned aerial vehicle (UAV) typically involves a pre-defined environment and mission, with the objective of reaching a single target point. However, in order to perform different missions, the agent must be trained from scratch. In this paper, we propose a new path planning algorithm for UAVs by training them to be controlled by subgoals, which enhances their degree of freedom to perform various maneuvers. The subgoals can be defined by the user and given to the agent in real-time, allowing the UAV to perform diverse flight missions without prior knowledge of the environment. To achieve this, we utilize goal-conditioned reinforcement learning to train the UAV agent to reach various goals by learning different flight maneuvers. In experiments, we designed specific scenarios to test the UAV agent’s ability to perform concrete missions, such as high-flying, low-flying, penetrating, and bypassing. The experimental results show that the same UAV agent trained in a simple environment can accomplish difficult missions in various scenarios. The pre-trained UAV agent can be utilized in other environments as it can be controlled by the subgoals.
Anti-occlusion tracker for infrared ground targets based on improved trajectory prediction network

Aiming at the problem of infrared ground target tracking drift or even failure caused by background occlusion, we propose an anti-occlusion tracker for infrared ground targets based on improved trajectory prediction network. First, the occlusion judgment criterion is proposed to accurately determine the occlusion of the infrared ground target. Then, based on the BiTraP trajectory prediction network, on the one hand, by introducing velocity information through the Siamese network structure and using one-way prediction, the SiamUniTraP trajectory prediction network is proposed, which improves the precision of trajectory prediction. On the other hand, by improving the training method and application method, it can predict the trajectory of ground target more accurately. For short-term occlusions, the SiamUniTraP network is employed to predict the bounding box for target occlusion based on temporal context information. For long-term occlusions, the search expansion strategy is proposed to handle the accumulation of prediction errors caused by the lack of real target information. Finally, the “second determination” criterion is proposed to achieve accurate recapture of the target and return to normal tracking. Infrared image sequence tests are conducted under occlusion conditions. Compared with the SuperDiMP tracker, the proposed tracker improves the success rate by 5.2% and the accuracy by 5.9% under the OPE evaluation index. This indicates that the proposed tracker has good robustness for infrared ground target tracking in occlusion situations, especially in full occlusion situations.
Filters uncovered: Investigating the impact of AR face filters and self-view on videoconference fatigue and affect

The rise of videoconferencing amidst the COVID-19 pandemic has brought about a new phenomenon, videoconference fatigue (VF), which refers to the emotional and physical exhaustion felt after videoconference meetings. Features of videoconference platforms, such as the self-view function and small screen size, increases self-awareness and cognitive load, resulting in increased negative affect and VF. However, AR face filters can soften facial expressions to reduce self-awareness and increase positive affect. Drawing from the theory of objective self-awareness, this study thus assesses the influence of AR face filters and self-view on users’ affect and perceived VF, through a 2 × 2 dyadic between-subjects experiment (N = 154). Our findings do not support the theory of objective self-awareness. Using AR face filters led to higher VF, but neither AR face filters nor self-view was significantly associated with affect. An alternative theory such as the expectancy violations theory may explain such results. Theoretical and practical implications are discussed.
Robust information filtering for distributed networks using mixed Gaussian distribution

This paper proposes a robust distributed state estimation algorithm for sensor networks. In a distributed sensor network, sensor nodes may be affected by outliers and thus influence the information fusion results. In this respect, the measurement likelihood and one-step state prediction are modeled by a mixture of Gaussian distributions. In this Gaussian mixture, one Gaussian uses nominal covariance, while the other uses adaptive covariance with larger values and lower probabilities. We build a centralized multi-sensor fusion model in the variational Bayesian framework based on the aforementioned mixture of Gaussian distributions. Then, a consensus strategy is employed to convert the centralized method into a distributed one. Each node completes estimation tasks separately based on the information from itself and nearby nodes. In addition, the weight of sensor information will adaptively update according to the Gaussian distribution that mainly affects the current sensor measurement noise. Finally, simulation experiments demonstrate the effectiveness of the proposed method in handling outlier effects.
Adaptive consensus reaching process with dynamic weights and minimum adjustments for group interactive portfolio optimization

The mean–variance model (MV) can help companies in an economic downturn identify portfolios that increase revenue while reducing risk. However, the MV optimization model derives a set of optimal portfolios. The limited rationality and experience of the company's manager, as well as the conflict of interest with the shareholders, require several experts to help the manager make investment decisions. Moreover, experts' unique preferences require negotiation through interaction to reach a consensus, which is not considered in existing group interactive optimization algorithms. The social network group decision-making (SNGDM) method is an effective approach for reaching consensus. Notably, in addition to the trust relationships, experts' intrinsic risk preferences and rational levels affect investment decisions and change with the decision-making process. Another important problem is reaching consensus with finite interaction costs. Although dynamic opinion is considered by current minimum cost models, the impact of dynamic trust on consensus is neglected. This research aims to develop a group interactive portfolio optimization method based on SNGDM (SNGDM-PO) to address the mentioned limitations. First, we introduce consensus into group interactive optimization algorithms and develop an adaptive learning model that simulates group preferences. Then, we combine dynamic intrinsic influence and extrinsic trust to generate experts' dynamic weights. To reduce interaction consensus costs, we propose an adaptive consensus feedback mechanism that minimally adjusts for dynamic opinion and trust with a time constraint. Finally, empirical study and comparison findings demonstrate that the SNGDM-PO can efficiently obtain consensus with minimum adjustments and low interaction costs and produce high group satisfaction.
Global Entropy Pooling layer for Convolutional Neural Networks

We propose a novel Global Entropy Pooling (GEP) layer for Convolutional Neural Networks (CNNs). This is the first approach that uses the Entropy value directly for pooling rather than creating a weighting mechanism for feature maps obtained via convolution. This way, we use the ”amount” of information (expressed by Entropy) to describe the feature maps in contrast to the most commonly used averaging (Global Average Pooling, GAP). We use our method in a rather unusual way. We replace GAP with GEP and use the same feature extractor and classifier weights for the prediction — with no additional training. Our layer can be easily integrated (instead of the GAP layer) into various pre-trained CNN models. From a technical perspective, it is possible to integrate GEP without additional training, because it does not contain any parameters that have to be trained. Also, its input and output have the same format as the GAP’s one. We examine how the replacement impacts the model behavior. Despite a completely different form of pooling, utilization of the GEP layer in the examined pre-trained models (InceptionV3, Xception and InceptionResNetV2) allowed us to obtain some accuracy improvements in comparison to the original networks on the subsets of the ImageNet dataset. Moreover, networks modified with our layer exhibit an interesting property — they are more ”confident” in the predictions they return. It manifests itself through higher values of the probabilities assigned to correctly predicted classes. This opens a possibility to use not only the initial layers of the pretrained network, but also to modify network’s middle parts and reuse its classifier trained on an original model as well.
A novel lossy image compression algorithm using multi-models stacked AutoEncoders

The extensive use of images in many fields increased the demand for image compression algorithms to overcome the transfer bandwidth and storage limitations. With image compression, disk space, and transmission speed can be efficiently reduced. Some of the traditional techniques used for image compression are the JPEG and ZIP formats. The compression rate (CR) in JPEG can be high but to the detriment of the quality factor of the image. ZIP has a low compression rate, where the quality remains almost unaffected. Machine learning (ML) is considered an essential technique for image compression using different algorithms. The most widely used algorithm is Deep Learning (DL), which represents the features of the image at different scales by using different types of layers. In this research, an AutoEncoder (AE) deep learning-based compression algorithm is proposed for lossy image compression and experimented with using three standard dataset types: MNIST, Grayscale, and Color images datasets. A Stacked AE (SAE) for image compression and a binarized content-based image filter are used with a high compression rate while keeping the quality above 85% using structural similarity index metric (SSIM) compared to traditional techniques. In addition, a convolutional neural network (CNN) classification model has been utilized as SAEs compression model selector for each image class. Experimental results demonstrate that the proposed SAE image compression algorithm outperforms the JPEG-encoded algorithm in terms of compression rate (CR) and image quality. The CR that the proposed model achieved with an acceptable reconstruction accuracy was about 85%, which is almost 20% higher than the standard JPEG’s compression rate, with an accuracy of 94.63% SSIM score.
Discrete optimization and optimization of ethanol preparation problem

The experimental evaluation and design model of C4 olefin preparation based on high dimensional discrete space optimization was established in this paper. First, the relationship between ethanol conversion, C4 olefin selectivity and temperature was studied by fitting. Then, we took catalyst combination and temperature as independent variables, ethanol conversion and C4 olefin selectivity as dependent variables, conducted partial correlation analysis on the latter to observe their correlation degree, further used multivariate analysis of variance, and conducted inter-agent effect test. The effects of catalyst combination and temperature on ethanol conversion and C4 olefin selectivity were evaluated by the sum of type III squares. Subsequently, we used the control value of the aforementioned dependent variable to generate a high-dimensional discrete space, conducted stepwise regression of the aforementioned dependent variable and independent variable, and then tested its accuracy according to the objective function of maximizing the yield of C4 olefin. Combining with the constraints of discrete variables, the method of ergodic and genetic algorithm is used to search for the optimal value in high dimensional discrete space, and the corresponding results are obtained. Finally, as an innovative supplement, we used Pareto optimization to explore the method of producing more C4 olefin by ethanol coupling experiment under the same amount of ethanol substance as possible.