Reviewerly/exHarmony huggingface dataset:
BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning

The rapid and accurate identification of bot accounts in online social networks is an ongoing challenge. In this paper, we propose BotTriNet, a unified embedding framework that leverages the textual content posted by accounts to detect bots. Our approach is based on the premise that account personalities and habits can be revealed through their contextual content. To achieve this, we designed a triplet network that refines raw embeddings using metric learning techniques. The framework produces word, content, and account embeddings, which we evaluate on a real-world dataset consisting of three bot account categories and five bot sample sets. Our approach achieves state-of-the-art performance on two content-intensive bot sets, with an average accuracy of 98.34% and f1score of 97.99%. Moreover, our method makes a significant breakthrough on four contentless bot sets, with an average accuracy improvement of 11.52% and an average f1score increase of 16.70%. Our contribution is twofold: First, we propose a unified and effective framework that combines various embeddings for bot detection. Second, we demonstrate that metric learning techniques can be applied in this context to refine raw embeddings and improve classification performance. Our approach outperforms prior works and sets a new standard for bot detection in social networks.
PETNet: A YOLO-based prior enhanced transformer network for aerial image detection

Unmanned aerial vehicles (UAVs) have been applied to inspect in various scenarios due to their high efficiency, low cost, and excellent mobility. However, the objects in aerial images are much smaller and denser than general objects, causing it difficult for current object detection methods to achieve the expected results. To solve this issue, a prior enhanced Transformer network (PETNet) based on YOLO is proposed in this paper. Specifically, a novel prior enhanced Transformer (PET) module and a one-to-many feature fusion (OMFF) mechanism are proposed to embed into the network. Two additional detection heads are added to the shallow feature maps. In this work, PET is used to capture enhanced global information to improve the expressive ability of the network. The OMFF aims to fuse multi-type features to minimize the information loss of small objects. In addition, the added detection heads provide more possibility of detecting smaller-scale objects, and the extended multi-head parallel detection is more suitable for the multi-scale transformation of objects in aerial images. On the VisDrone-2021 and UAVDT databases, the proposed PETNet achieves state-of-the-art results with average precision (AP) of 35.3 and 21.5, respectively, which indicates that the proposed network is more suitable for aerial image detection and is of a great reference value.
An adaptive auto-scaling framework for cloud resource provisioning

Cloud computing emerged as a technology that offers scalable access to computing resources in conjunction with low maintenance costs. In this domain, cloud users utilize virtualized resources to benefit from on-demand and long-term pricing strategies. Although the latter consists of a more cost-efficient solution, it requires accurate estimations of future workload demands, which is a challenging task. Furthermore, clouds offer threshold-based auto-scaling rules that need to be manually controlled by the users according to application requirements. Still, tuning scaling parameters is not trivial, since it is mainly based on static scaling rules that may lead to unreasonable costs and quality of service violations. In this work we introduce ADA-RP, an adaptive auto-scaling framework for reliable resource provisioning in the cloud. ADA-RP uses historical time series data for training K-means and convolutional neural networks (CNN) to categorize future workload demands as High, Medium or Low based on CPU utilization. We auto-scale cloud resources in real-time based on the predicted workload demand to reduce costs and improve application performance. The experimental analysis is based on TPC-C runs on MySQL containers deployed on the Google Cloud Platform. Experimental results are prosperous, demonstrating the ability of ADA-RP (i) to reduce MySQL deployment costs by 48% in a single-tenant environment, and (ii) to double the executed queries per second in a multi-tenant environment considering user’s budget requirements.
T-GAE: A Timespan-aware Graph Attention-based Embedding Model for Temporal Knowledge Graph Completion

Temporal knowledge graphs (TKGs) often suffer from incompleteness, leading to an important research issue: Temporal Knowledge Graph Completion (TKGC). Knowledge Graph Embedding (KGE) methods have proven to be effective in solving this issue. However, most of them handle triples independently and do not capture complex information embedded in the neighborhood topology of central entities. To this end, we propose a Timespan-aware Graph Attention-based Embedding Model named T-GAE to tackle the TKGC task. To the best of our knowledge, T-GAE is the first KGE model in which Graph-Attention-Networks (GATs) and Long Short-Term Memory (LSTM) Networks are simultaneously applied to the TKGC task. In essence, our model is an Encoder-Decoder architecture, where the encoder consists of an LSTM network and a GAT network. Firstly, we employ LSTM layers to learn new time-aware relational embeddings to incorporate time information. Then, we utilize these time-aware relational embedding and GATs considered as neighborhood aggregators to learn the entity and relational features of the central entity neighborhoods. Thus, T-GAE can capture the interaction features between multi-relational facts and the abundant temporal information in TKGs. As for the decoder, we choose the ConvKB model, which is essentially a scoring function. Our experiments demonstrate that T-GAE significantly outperforms most of the existing baseline methods for TKGC in terms of MRR and Hit@1/3/10.
Multipath Planning Acceleration Method With Double Deep R-Learning Based on a Genetic Algorithm

Autonomous navigation is a well-studied field in robotics requiring high standards of efficiency and reliability. Many studies focus on applying AI techniques to obtain a high-quality map, a precise localization, or improve the proposed trajectory to be followed by the agent. As traditional planning methods need a high-quality map to obtain optimal trajectories, this paper addresses the problem of multipath map-less planning, and proposes a novel multipath planning algorithm (Double Deep Reinforcement Learning - Enhanced Genetic (DDRL-EG)) for mobile robots in an unknown environment. It combines Double Deep Reinforcement Learning (DDRL) with Heuristic Knowledge (HK), Experience Replay (ER), Genetic Algorithm (GA), and Dynamic Programming (DP), allowing the agent to reach its target successfully without maps. In addition, it optimizes the training time and the chosen path in terms of time and distance to the target. A hybrid method is also used in which Semi-Uniform Distributed Exploration (SUDE) is employed to determine the probability that the action is decided based on directed knowledge, hybrid knowledge, or autonomous knowledge. The performance of DDRL-EG is compared with two other algorithms in two different environments. The results show that DDRL-EG is a more robust and powerful algorithm since with less training, it can provide much smoother and shorter trajectories to the target.
ECDX: Energy consumption prediction model based on distance correlation and XGBoost for edge data center

The rapid development of artificial intelligence (AI) and edge computing technology has promoted the rapid growth of the number of data centers, but also caused large energy consumption. How to accurately predict the energy consumption of servers is crucial to optimize data centers. Thus, a real-time server energy consumption prediction model that integrates distance correlation and extreme gradient boosting (XGBoost) named ECDX is proposed. First, the distance correlation coefficient method is used to filter essential feature parameters and remove redundant features. Second, the cross-validation method is employed to optimize the hyperparameters. Thereafter, a data center server energy consumption prediction model is constructed using the XGBoost algorithm. Numerous experiments have been conducted to compare the performance of the ECDX model with that of benchmark models. The results show that ECDX can adapt to changes in workload, the prediction accuracy is significantly improved, and the average relative error is reduced by 4.698%.
Simultaneous local clustering and unsupervised feature selection via strong space constraint

Clustering is a fashion method applied in machine learning tasks. However, high dimensional data brings many obstacles for clustering approaches. To address such a problem, the unsupervised feature selection (UFS) method can be incorporated into clustering to reduce dimensionality. In general, most of the UFS methods adopt ℓ2,1-norm for subspace sparsity learning. However, its sparsity highly relies on the setting of trade-off parameter, which may lead to instability of ranking results and the difficulty in obtaining the optimal solution of projection matrix. In this paper, we propose to directly learn an absolutely row-sparsity subspace via the ℓ2,0-norm constraint, called Sparse constraint and Local learning for Unsupervised Feature Selection (SLUFS). It is an ideal sparse subspace constraint which can overcome the drawbacks of the ℓ2,1-norm. However, optimizing the ℓ2,0-norm constraint is an NP-hard problem, and at present, only some approximate solutions can be given, but the convergence can not be guaranteed. To tackle this challenge, we design a novel alternative iterative algorithm to directly optimize the ℓ2,0-norm based model. Most importantly, our strategy can obtain a closed-form solution with strict convergence guarantee. Comprehensive experiments are conducted on several real-world datasets to evaluate the performance of SLUFS with comparison to several related state-of-the-art methods.
Multi-Objective Combinatorial Optimization Using the Cell Mapping Algorithm for Mobile Robots Trajectory Planning

The use of optimal control theory for motion planning is a challenging task. Cell mapping offers a way to formulate combinatorial optimization problems, allowing the inclusion of complex cost functions as well as multi-objective optimization problems. This paper presents a suboptimal solution for a trajectory planning problem in a workspace with obstacles, for a differential drive mobile robot. This method relies on the use of any linearization technique that allows the regularization of the combinatorial optimization problem. We explore some classical problems in optimal control, i.e., distance, control effort, and navigation time), as well as the multi-objective optimization problem (MOP). We also performed a comparison with two classical path planning algorithms, namely A∗ and RRT∗, to validate the proposed method when the multi-objective optimization problem includes distance in the cost function, achieving a compromise of less than 2% for the worst-case scenario for our case study.
Defending Against Backdoor Attacks by Layer-wise Feature Analysis

Training deep neural networks (DNNs) usually requires massive training data and computational resources. Users who cannot afford this may prefer to outsource training to a third party or resort to publicly available pre-trained models. Unfortunately, doing so facilitates a new training-time attack (i.e., backdoor attack) against DNNs. This attack aims to induce misclassification of input samples containing adversary-specified trigger patterns. In this paper, we first conduct a layer-wise feature analysis of poisoned and benign samples from the target class. We find out that the feature difference between benign and poisoned samples tends to be maximum at a critical layer, which is not always the one typically used in existing defenses, namely the layer before fully-connected layers. We also demonstrate how to locate this critical layer based on the behaviors of benign samples. We then propose a simple yet effective method to filter poisoned samples by analyzing the feature differences between suspicious and benign samples at the critical layer. We conduct extensive experiments on two benchmark datasets, which confirm the effectiveness of our defense.
Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices

The global expansion of the Visual Internet of Things (VIoT)'s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.
Memecry: tracing the repetition-with-variation of formulas on 4chan/pol/

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A blockchain-based evaluation approach to analyse customer satisfaction using AI techniques

Due to technological advancements and consumer demands, online shopping creates new features and adapts to new standards. A robust customer satisfaction prediction model concerning trust and privacy platforms can encourage an organization to make better decisions about its service and quality. This study presented an approach to predict consumer satisfaction using the blockchain-based framework combining the Multi-Dimensional Naive Bayes-K Nearest Neighbor (MDNB-KNN) and the Multi-Objective Logistic Particle Swarm Optimization Algorithm (MOL-PSOA). A regression model is employed to quantify the impact of various production factors on customer satisfaction. The proposed method yields better levels of measurement for customer satisfaction (98%), accuracy (95%), necessary time (60%), precision (95%), and recall (95%) compared to existing studies. Measuring consumer satisfaction with a trustworthy platform facilitates to development of the conceptual and practical distinctions influencing customers' purchasing decisions.
Hybrid Vulture-Coordinated Multi-Robot Exploration: A Novel Algorithm for Optimization of Multi-Robot Exploration

Exploring unknown environments using multiple robots has numerous applications in various fields but remains a challenging task. This study proposes a novel hybrid optimization method called Hybrid Vulture-Coordinated Multi-Robot Exploration (HVCME), which combines Coordinated Multi-Robot Exploration (CME) and African Vultures Optimization Algorithm (AVOA) to optimize the construction of a finite map in multi-robot exploration. We compared HVCME with four other similar algorithms using three performance measures: run time, percentage of the explored area, and the number of times the method failed to complete a run. The experimental results show that HVCME outperforms the other four methods, demonstrating its effectiveness in optimizing the construction of a finite map in an unknown indoor environment.
Robust Hashing via Global and Local Invariant Features for Image Copy Detection

Robust hashing is a powerful technique for processing large-scale images. Currently, many reported image hashing schemes do not perform well in balancing the performances of discrimination and robustness, and thus they cannot efficiently detect image copies, especially the image copies with multiple distortions. To address this, we exploit global and local invariant features to develop a novel robust hashing for image copy detection. A critical contribution is the global feature calculation by gray level co-occurrence moment learned from the saliency map determined by the phase spectrum of quaternion Fourier transform, which can significantly enhance discrimination without reducing robustness. Another essential contribution is the local invariant feature computation via Kernel Principal Component Analysis (KPCA) and vector distances. As KPCA can maintain the geometric relationships within image, the local invariant features learned with KPCA and vector distances can guarantee discrimination and compactness. Moreover, the global and local invariant features are encrypted to ensure security. Finally, the hash is produced via the ordinal measures of the encrypted features for making a short length of hash. Numerous experiments are conducted to show efficiency of our scheme. Compared with some well-known hashing schemes, our scheme demonstrates a preferable classification performance of discrimination and robustness. The experiments of detecting image copies with multiple distortions are tested and the results illustrate the effectiveness of our scheme.
Automated pattern generation for swarm robots using constrained multi-objective genetic programming

Swarm robotic systems (SRSs), which are widely used in many fields, such as search and rescue, usually comprise a number of robots with relatively simple mechanisms collaborating to accomplish complex tasks. A challenging task for SRSs is to design local interaction rules for self-organization of robots that can generate adaptive patterns to entrap moving targets. Biologically inspired approaches such as gene regulatory network (GRN) models provide a promising solution to this problem. However, the design of GRN models for generating entrapping patterns relies on the expertise of designers. As a result, the design of the GRN models is often a laborious and tedious trial-and-error process. In this study, we propose a modular design automation framework for GRN models that can generate entrapping patterns. The framework employs basic network motifs to construct GRN models automatically without requiring expertise. To this end, a constrained multi-objective genetic programming is utilized to simultaneously optimize the structures and parameters of the GRN models. A multi-criteria decision-making approach is adopted to choose the preferred GRN model for generating the entrapping pattern. Comprehensive simulation results demonstrate that the proposed framework can obtain novel GRN models with simpler structures than those designed by human experts yet better performance in complex and dynamic environments. Proof-of-concept experiments using e-puck robots confirmed the feasibility and effectiveness of the proposed GRN models.
An active contour model for image segmentation using morphology and nonlinear Poisson’s equation

Active contour model (ACM) is considered as a feasible tool to handle image segmentation problems via an unsupervised learning approach. This paper compensates the shortcomings of well-known ACMs to introduce erosion and dilation operations in mathematical morphology to fit the intensity differential functions of contours on both sides of the image. A nonlinear Poisson’s equation is developed to deal with the interference of nonuniform noise within the image, and finally build a new model. This paper develops a new image processing approach using morphology and nonlinear Poisson’s equation, which yields a fast and efficient algorithm among known similar image segmentation methods, and has a high level of accuracy in processing image segmentation.
Cross-domain few-shot action recognition with unlabeled videos

Current few-shot action recognition approaches have achieved impressive performance using only a few labeled examples. However, they usually assume the base (train) and target (test) videos typically come from the same domain, which may limit their further applications. In this paper, we introduce a new practical task, termed as cross-domain few-shot action recognition, and hypothesize there is a domain shift between the base and target videos and the unlabeled target videos are available. To address this task, we further propose a Self-supervised learning Enhanced tEmporal Network (SEEN), which incorporates temporal modeling and self-supervised learning techniques to learn more transferable representations. Concretely, the temporal modeling mechanism aims to learn long-range temporal semantics from the features output by the backbone, and the self-supervised learning focuses on exploring the underlying data patterns to reduce domain shifts under the few-shot setting, which can help to improve the generalization ability. Therefore, the proposed SEEN can capture broader variations of the feature distributions and is more appropriate for the cross-domain few-shot action recognition task. Extensive experiments on multiple cross-domain benchmarks show that our SEEN consistently outperforms several strong baseline methods by a convincing margin.
Review of Studies on User Research Based on EEG and Eye Tracking

Under the development of interdisciplinary fusion, user research has been greatly influenced by technology-driven neuroscience and sensory science, in terms of thinking and methodology. The use of technical methods, such as EEG and eye-tracking, has gradually become a research trend and hotspot in this field, in order to explore the deep cognitive states behind users’ objective behaviors. This review outlines the applications of EEG and eye-tracking technology in the field of user research, with the aim of promoting future research and proposing reliable reference indicators and a research scope. It provides important reference information for other researchers in the field. The article summarizes the key reference indicators and research paradigms of EEG and eye-tracking in current user research, focusing on the user research situation in industrial products, digital interfaces and spatial environments. The limitations and research trends in current technological applications are also discussed. The feasibility of experimental equipment in outdoor environments, the long preparation time of EEG experimental equipment, and the accuracy error of physiological signal acquisition are currently existing problems. In the future, research on multi-sensory and behavioral interactions and universal studies of multiple technology fusions will be the next stage of research topics. The measurement of different user differentiation needs can be explored by integrating various physiological measurements such as EEG signals and eye-tracking signals, skin electrical signals, respiration, and heart rate.