Reviewerly/exHarmony huggingface dataset:
TEA: Time-aware Entity Alignment in Knowledge Graphs

Entity alignment (EA) aims to identify equivalent entities between knowledge graphs (KGs), which is a key technique to improve the coverage of existing KGs. Current EA models largely ignore the importance of time information contained in KGs and treat relational facts or attribute values of entities as time-invariant. However, real-world entities could evolve over time, making the knowledge of the aligned entities very different in multiple KGs. This may cause incorrect matching between KGs if such entity dynamics is ignored. In this paper, we propose a time-aware entity alignment (TEA) model that discovers the entity evolving behaviour by exploring the time contexts in KGs and aggregates various contextual information to make the alignment decision. In particular, we address two main challenges in the TEA model: 1) How to identify highly-correlated temporal facts; 2) How to capture entity dynamics and incorporate it to learn a more informative entity representation for the alignment task. Experiments on real-world datasets1 verify the superiority of our TEA model over state-of-the-art entity aligners.
Variational quantum simulation of the critical Ising model with symmetry averaging

Here we investigate the use of deep multiscale entanglement renormalization ansatz (DMERA) circuits as a variational ansatz. We use the exactly solvable one-dimensional critical transverse-field Ising model as a test bed. Numerically exact simulation of the quantum circuit ansatz can in this case be carried out to hundreds of qubits by exploiting efficient classical algorithms for simulating matchgate circuits. We find that, for this system, the DMERA strongly outperforms a standard quantum approximate optimization algorithm (QAOA)--style ansatz, and that a major source of systematic error in correlation functions approximated using the DMERA is the breaking of the translational and Kramers-Wannier symmetries of the transverse-field Ising model. We are able to reduce this error by up to four orders of magnitude by symmetry averaging, without incurring additional cost in qubits or circuit depth. We propose that this technique for mitigating systematic error could be applied to noisy intermediate-scale quantum (NISQ) simulations of physical systems with other symmetries.
Multi-Modal Fusion Based on Depth Adaptive Mechanism for 3D Object Detection

Lidars and cameras are critical sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, accurate and robust fusion methods are still under exploration due to non-homogenous representations. In this paper, we find that the complementary roles of point clouds and images vary with depth. An important reason is that the point cloud appearance changes significantly with increasing distance from the Lidar, while the image's edge, color, and texture information are not sensitive to depth. To address this, we propose a fusion module based on the Depth Attention Mechanism (DAM), which mainly consists of two operations: gated feature generation and point cloud division. The former adaptively learns the importance of bimodal features without additional annotations, while the latter divides point clouds to achieve differential fusion of multi-modal features at different depths. This fusion module can enhance the representation ability of original features for different point sets and provide more comprehensive features by using the dual splicing strategy of concatenation and index connection. Additionally, considering point density as a feature and its negative correlation with depth, we build an Adaptive Threshold Generation Network (ATGN) to generate the depth threshold by extracting density information, which can divide point clouds more reasonably. Experiments on the KITTI dataset demonstrate the effectiveness and competitiveness of our proposed models.
Features for Hate? Using the Delphi Method to Explore Digital Determinants for Online Hate Perpetration and Possibilities for Intervention

Online hate speech on social media platforms causes harm to those who are victimized as well as society at large. The prevalence of hateful content has, thus, prompted numerous calls for improved countermeasures and prevention. For such interventions to be effective, it is necessary to gain a nuanced understanding of influences that facilitate the spread of hate speech. This study does so by investigating what are relevant digital determinants for online hate perpetration. Moreover, the study explores possibilities of different technology-driven interventions for prevention. Thereby, the study specifically considers the digital environments in which online hate speech is most often produced and disseminated, namely social media platforms. We apply frameworks related to the concept of digital affordances to focus on the role that technological features of these platforms play in the context of online hate speech. Data were collected using the Delphi method in which a selected sample of experts from both research and practice answered multiple rounds of surveys with the goal of reaching a group consensus. The study encompassed an open-ended collection of initial ideas, followed by a multiple-choice questionnaire to identify, and rate the most relevant determinants. Usefulness of the suggested intervention ideas was assessed through the three lenses of human-centered design. The results of both thematic analysis and non-parametric statistics yield insights on how features of social media platforms can be both determinants that facilitate online hate perpetration as well as crucial mechanisms of preventive interventions. Implications of these findings for future intervention development are discussed.
Detecting while accessing: A semi-supervised learning-based approach for malicious traffic detection in Internet of Things

In the upcoming large-scale Internet of Things (IoT), it is increasingly challenging to defend against malicious traffic, due to the heterogeneity of IoT devices and the diversity of IoT communication protocols. In this paper, we propose a semi-supervised learning-based approach to detect malicious traffic at the access side. It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side, and also is free of labeled traffic data in model training. Specifically, we design a coarse-grained behavior model of IoT devices by self-supervised learning with unlabeled traffic data. Then, we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data. Experimental results show that our method can achieve the accuracy of 99.52% and the F1-score of 99.52% with only 1% of the labeled training data based on the CICDDoS2019 dataset. Moreover, our method outperforms the state-of-the-art supervised learning-based methods in terms of accuracy, precision, recall and F1-score with 1% of the training data.
Research on path planning based on improved ant colony algorithm

Aiming at the problems of blindness, slow convergence and easy to fall into local optimal solution in the early stage of traditional ant colony algorithm search, an improved ant colony algorithm was proposed. The algorithm first introduces the gravitational potential field function, constructs new heuristic information, and improves the convergence speed. Secondly, learn from the wolf pack update rules, improve the pheromone update function, and give full play to the feedback function of pheromone. Finally, the path is optimized locally to remove redundant nodes. The comparison experiment with the traditional ant colony algorithm proves that the improved algorithm shortens the running time, the convergence speed and the global search ability are better than the traditional ant colony algorithm, and it is not easy to fall into the local optimal solution, which verifies the effectiveness of the algorithm.
Research and Practice on Discrete Mathematics Using Blended Learning Model and "Rain Classroom" Teaching

A significant area of contemporary mathematics is Discrete Mathematics, which is the field of mathematics that examines the structure of discrete quantities and their interactions. The study of the structure and relationships based on discrete quantities, whose objects are typically finite or countable elements, is known as discrete quantity theory. Discrete means various connected elements. A preparatory course in discrete mathematics is also required for many professional computer and big data courses, including those in data structure, artificial intelligence, databases, algorithm design and analysis, etc. We adopt a hybrid teaching approach and "rain classroom" teaching method based on the improvement of students’ comprehensive ability, to train students to apply relevant knowledge and master the descriptive tools and methods for dealing with discrete structures, and to reform for this course based on the improvement of students’ comprehensive ability. This will help to set the stage for the learning of subsequent courses. The goal of the course is to build a strong foundation for future involvement in cutting-edge research and development activities by enhancing students’ abilities in rigorous logical reasoning and abstract thought.
TML: A Temporal-aware Multitask Learning Framework for Time-sensitive Question Answering

Many facts change over time, Time-sensitive Question Answering(TSQA) answers questions about time evolution facts to test the model’s ability in the dimension of the time. The existing methods obtain the representations of questions and documents and then compute their similarity to find the answer spans. These methods perform well in simple moment questions, but they are difficult to solve hard duration problems that need temporal relations and temporal numeric comparisons. In this paper, we propose Temporal-aware Multitask Learning (TML) with three auxiliary tasks to tackle with them. First, we propose a temporal-aware sequence labeling task to help the model distinguish the temporal expressions by detecting temporal types of tokens in the document. Then a temporal-aware masked language modeling task is used to capture the temporal relation between events based on the context. Furthermore, temporal-aware order learning is proposed to inject the ability of numeric comparison into the model. We carried out comprehensive experiments on the TimeQA benchmark, aiming to evaluate the performance of our proposed methodology in handling temporal question answering. TML significantly outperforms the baselines by a relative 10% on the two splits of the dataset.
An Approach for Mining Top-k High Utility Item Sets (HUI)

Itemsets have been extracted by utilising high utility item (HUI) mining, which provides more benefits to the consumer. This could be one of the significant domains in data mining and be resourceful for several real-time implementations. Even though modern HUI mining algorithms may identify item sets that meet the minimum utility threshold, However, fixing the minimum threshold utility value has not been a simple task, and often it is intricate for the consumers when we keep the minimum utility value low. It might generate a massive amount of itemsets, and when the value is at its maximum, it might provide a smaller amount of itemsets. To avoid these issues, top-k HUI mining, where k represents the number of HUIs to be identified, has been proposed. Further, in this manuscript, the authors projected an algorithm called the top-k exact utility (TKEU) algorithm, which works without computing and comparing transaction weighted utilisation (TWU) values and deliberates the individual utility item values for deriving the top-k HUI. The datasets are pre-processed by the proposed algorithm to lessen the system memory space and to provide optimal outcomes for condensed datasets.
Machine Learning Approach for Comparative Analysis of De-Noising Techniques in Ultrasound Images of Ovarian Tumors

Ovarian abnormalities such ovarian cysts, tumors, and polycystic ovaries are one of the serious disorders affecting women's health. In ultrasound imaging of ovarian abnormalities, noise during capturing of the image and its transmission process frequently corrupts the image. In order to make the best judgments possible at the appropriate moment, ovarian cysts in females must be accurately detected. In computer aided diagnosis of ovarian tumors, preprocessing is a very important step. In preprocessing, de-noising of medical images is a particularly a difficult task since it must be done while maintaining image features that are essential for diagnosis. In this research work we are using various denoising filters on ultrasound images of ovarian tumors. For different noise denoising techniques, performance measures like MSE, PSNR, SSIM, and UQI etc. are calculated. According to experimental findings, Block matching 3-D filter outperforms all other methods. Radiologists can better diagnose the condition with the use of this computer-assisted system.
Parallel Suffix Sorting for Large String Analytics

The suffix array is a fundamental data structure to support string analysis efficiently. It took about 26 years for the sequential suffix array construction algorithm to achieve $$\mathcal {O}(n)$$ time complexity and in-place sorting. In this paper, we develop the D-Limited Parallel Induce (DLPI) algorithm, the first $$\mathcal {O}(\frac{n}{p})$$ time parallel suffix array construction algorithm. The basic idea of DLPI includes two aspects: dividing the $$\mathcal {O}(n)$$ size problem into p reduced sub-problems with size $$\mathcal {O}(\frac{n}{p})$$ so we can handle them on p processors in parallel; developing an efficient parallel induce sorting method to achieve correct order for all the reduced sub-problems. The complete algorithm description is given to show the implementation method of the proposed idea. The time and space complexity analysis and proof are also given to show the correctness and efficiency of the proposed algorithm. The proposed DLPI algorithm can handle large strings with scalable performance.
Development of Experimental Materials for Cooperative IoT Devices

Recently, industries related to IoT technologies have been flourishing, and IoT education in higher education institutions has become more important accordingly. According to the "Courses of Study" released by the Ministry of Education in Japan, Culture, Sports, Science and Technology at the end of March 2017, programming education from elementary education become compulsory from 2020, and the groundwork is being prepared for the education of advanced IoT engineers. In the future, with the increase in communication speed through 5G and the advancement of AI and databases, an era in which each device is distributed and cooperates with each other is expected to arrive. In this paper, we will propose a practical engineer education program to develop cooperative and distributed IoT systems (IoT systems that help each other) for the next generation IoT, developed IoT experiment as a basic experiment, and conducted a demonstration experiment as an experiment for third-year students in a technical college.
Unsupervised Process Anomaly Detection Under Industry Constraints in Cyber-Physical Systems Using Convolutional Autoencoder

To realize complex industrial processes, the application of cyber-physical systems (CPS) in modern manufacturing is rising. Thereby, CPS perform repetitive and recurrent process steps, e.g., in production, packaging, and transportation. With the growing complexity of these processes, the relevance of each process step to realize the desired outcome is increasing. A failure in one step can result in an entire faulty process that can interrupt the current operation of the CPS or even the whole production line. Therefore, it is essential for modern and safe CPS to monitor each single process step and detect potential failures as soon as possible. With the help of a detected anomaly, the emergence of a failure could be prevented, or an occurred failure could be solved as quickly as possible. The following paper introduces a concept for unsupervised process anomaly detection in CPS under industry constraints. We focus on repetitive process tasks with a fixed duration and industry constraints like near-time requirements, prediction quality, configurable design, data-driven limitations, processing limitations, and communication interface constraints. The concept is evaluated using an industrial application, which simulates a transportation process. The test result confirms that the presented concept can be successfully applied to detect anomalies in the investigated process. Therefore, the concept is a promising approach for anomaly detection in repetitive process tasks for CPS.
Trajectory privacy data publishing scheme based on local optimisation and R-tree

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DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space

DeepFake is a concern for celebrities and everyone because it is simple to create.DeepFake images, especially high-quality ones, are difficult to detect using people, local descriptors, and current approaches.On the other hand, video manipulation detection is more accessible than an image, which many state-of-the-art systems offer.Moreover, the detection of video manipulation depends entirely on its detection through images.Many worked on DeepFake detection in images, but they had complex mathematical calculations in preprocessing steps, and many limitations, including that the face must be in front, the eyes have to be open, and the mouth should be open with the appearance of teeth, etc.Also, the accuracy of their counterfeit detection in all previous studies was less than what this paper achieved, especially with the benchmark Flickr faces high-quality dataset (FFHQ).This study proposed, a new, simple, but powerful method called image Re-representation by combining the local binary pattern of multiple-channel (IR-CLBP-MC) color space as an image re-representation technique improved DeepFake detection accuracy.The IR-CLBP-MC is produced using the fundamental concept of the multiple-channel of the local binary pattern (MCLBP), an extension of the original LBP.The primary distinction is that in our method, the LBP decimal value is calculated in each local patch channel, merging them to re-represent the image and producing a new image with three color channels.A pretrained convolutional neural network (CNN) was utilized to extract the deep textural features from twelve sets of a dataset of IR-CLBP-MC images made from different color spaces: RGB, XYZ, HLS, HSV, YCbCr, and LAB.Other than that, the experimental results by applying the overlap and non-overlap techniques showed that the first technique was better with the IR-CLBP-MC, and the YCbCr image color space is the most accurate when used with the model and for both datasets.Extensive experimentation is done, and the high accuracy obtained are 99.4% in the FFHQ and 99.8% in the CelebFaces Attributes dataset (Celeb-A).
Optimizing Image Rectangular Boundaries with Precision: A Genetic Algorithm-Based Approach with Deep Stitching

This paper presents a novel method for optimizing rectangular boundaries in stitched images using a combination of deep learning and genetic algorithms.The proposed method utilizes an enhanced structure of convolutional neural network (CNN) in order to extract features for residual regression and a genetic algorithm to tune CNN parameters.The performance of the proposed method was evaluated using various metrics, including mean squared error, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID).The outcomes from the conducted experiment demonstrated that the suggested method achieved high accuracy in rectangular boundary optimization, even for images with complex shapes and orientations.The method was also tested with different parameters, and results indicated that increasing the number of generations and population size led to improved performance.Also, the combination of deep learning algorithm specifically (CNN) and optimization algorithm specifically (genetic algorithm (GA)) led to an increase in the accuracy of the rectified images where the average PSNR reached 23.98, SSIM is 0.8066, and FID is 18.72.The proposed method has potential applications in various areas of study, such as image cropping, object detection, and image segmentation.
Rules Determination Based on Time-Series Data to Classify Unsupervised Cases Based on Fuzzy Expert System

Classification of uncertain conditions requires computational modeling to obtain exact non-vague results for making the right decision, such as opening and closing school cases during a pandemic.We cannot rely solely on normative and textual government regulations because of numerous constraints and uncertainty in implementation.Unsupervised classification techniques can deal with such issues without needing prior references that contain definitive hesitancy.This motivates us to use a fuzzy system based on knowledge-based composition rules for complex problems such as the dynamics of COVID-19 because of its ability to adapt to changes and uncertainties.Therefore, we construct rules based on knowledge about COVID-19 to the issue of opening/closing schools using three fuzzy approaches: conventional fuzzy, intuitionistic fuzzy system (IFS), and fuzzy c-means (FCM).We can demonstrate a correlation between the number of school openings and the COVID-19 dynamics by utilizing the fuzzy approach to reduce the degree of hesitance.Experiments on available public time-series datasets demonstrate that the IFS is more efficient in forming rigidly distinct two classes.The results indicate that the accuracy of IFS is 99.47%, FCM is 91.28, and conventional FS is 84.33%, including the IFS silhouette score, which is higher than the others, at 0.91 or closer to 1, indicating excellent classification results.IFS is less superior in running time, while FCM is the fastest.This is because there are multiple stages in the IFS by considering non-membership functions.
Spiral Search Ant Colony Optimization Based AlexNet Model for Emotion Recognition

Emotion recognition is useful in understanding human recognition and is necessary for a variety of applications.Existing methods involve applying deep learning (DL) based techniques in emotion recognition and have the drawbacks of overfitting.This research involves applying the spiral search -Ant colony optimization (SS-ACO) for the optimal learning rate of the AlexNet model.The SS-ACO-AlexNet model is applied in the input face image for feature extraction.The extracted features are applied to long short term memory (LSTM) model for emotion classification.The spiral search technique is applied to increase the exploitation of the model and helps to escape the local optima trap.The SS-ACO method selects optimal learning rate and selective dropout to overcome overfitting problems in classification.On YALE datasets, the existing stochastic gradient descent linear collaborative discriminant regression classification (SGD-LCDRC) has 90.07%accuracy, which are lower when compared to proposed SS-ACO method, which has 99.2% accuracy.