Reviewerly/exHarmony huggingface dataset:
Investigating Attention Mechanism in 3D Point Cloud Object Detection

Object detection in three-dimensional (3D) space attracts much interest from academia and industry since it is an essential task in AI-driven applications such as robotics, autonomous driving, and augmented reality. As the basic format of 3D data, the point cloud can provide detailed geometric information about the objects in the original 3D space. However, due to 3D data's sparsity and unorderedness, specially designed networks and modules are needed to process this type of data. Attention mechanism has achieved impressive performance in diverse computer vision tasks; however, it is unclear how attention modules would affect the performance of 3D point cloud object detection and what sort of attention modules could fit with the inherent properties of 3D data. This work investigates the role of the attention mechanism in 3D point cloud object detection and provides insights into the potential of different attention modules. To achieve that, we comprehensively investigate classical 2D attentions, novel 3D attentions, including the latest point cloud transformers on SUN RGB-D and ScanNetV2 datasets. Based on the detailed experiments and analysis, we conclude the effects of different attention modules. This paper is expected to serve as a reference source for benefiting attention-embedded 3D point cloud object detection. The code and trained models are available at: https://github.com/ShiQiu0419/attentions_in_3D_detection.
Sub-trajectory Similarity Join with Obfuscation

User trajectory data is becoming increasingly accessible due to the prevalence of GPS-equipped devices such as smartphones. Many existing studies focus on querying trajectories that are similar to each other in their entirety. We observe that trajectories partially similar to each other contain useful information about users’ travel patterns which should not be ignored. Such partially similar trajectories are critical in applications such as epidemic contact tracing. We thus propose to query trajectories that are within a given distance range from each other for a given period of time. We formulate this problem as a sub-trajectory similarity join query named as the STS-Join. We further propose a distributed index structure and a query algorithm for STS-Join, where users retain their raw location data and only send obfuscated trajectories to a server for query processing. This helps preserve user location privacy which is vital when dealing with such data. Theoretical analysis and experiments on real data confirm the effectiveness and the efficiency of our proposed index structure and query algorithm.
Abnormal Event Detection in Videos Based on Deep Neural Networks

Abnormal event detection has attracted widespread attention due to its importance in video surveillance scenarios. The lack of abnormally labeled samples makes this problem more difficult to solve. A partially supervised learning method only using normal samples to train the detection model for video abnormal event detection and location is proposed. Assuming that the distribution of all normal samples complies to the Gaussian distribution, the abnormal sample will appear with a lower probability in this Gaussian distribution. The method is developed based on the variational autoencoder (VAE), through end-to-end deep learning technology, which constrains the hidden layer representation of the normal sample to a Gaussian distribution. Given the test sample, its hidden layer representation is obtained through the variational autoencoder, which represents the probability of belonging to the Gaussian distribution. It is judged abnormal or not according to the detection threshold. Based on two publicly available datasets, i.e., UCSD dataset and Avenue dataset, the experimental are conducted. The results show that the proposed method achieves 92.3% and 82.1% frame-level AUC at a speed of 571 frames per second on average, which demonstrate the effectiveness and efficiency of our framework compared with other state-of-the-art approaches.
A Reinforcement Learning Model for Virtual Machines Consolidation in Cloud Data Center

Energy consumption in data center is currently the main focus of many large-scale enterprises and cloud service providers. Dynamic virtual machine (VM) consolidation technologies are widely used to improve resource utilization of data centers and reduce energy. They try to identify the poorly utilized physical hosts and make the most of the resources. Then idle hosts can be switched to sleep or active mode, meanwhile considering the real-time fluctuation of service workload. In this paper, we propose a Reinforcement Learning (RL) based Virtual Machine Consolidation (RL-VMC) framework with an application to the cloud data center operation. The RL-VMC method uses an agent corresponding to a VM planner to interact with the environment, which encapsulates the data center running state, and learns the optimal policy to determine the migration mapping from VMs to physical hosts. Specifically, we adopt the on-policy method State-Action-Reward-State-Action (SARSA) in RL-VMC, which learns from experience to get the optimal VMs migration strategy and manage the host power mode as well. Additionally, we use the PBRS technology to speed up the convergence of the RL-VMC. Experimental results show that the RL-VMC method can adapt to the dynamic workload maintaining an improved balance between energy consumption and Service Level Agreements (SLA).
De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective

In this work, we propose a novel problem formulation for de-identification of unstructured clinical text. We formulate the de-identification problem as a sequence to sequence learning problem instead of a token classification problem. Our approach is inspired by the recent state-of -the-art performance of sequence to sequence learning models for named entity recognition. Early experimentation of our proposed approach achieved 98.91% recall rate on i2b2 dataset. This performance is comparable to current state-of-the-art models for unstructured clinical text de-identification.
Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.
MPI: Multi‐receptive and parallel integration for salient object detection

The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection. In this paper, a novel method called MPI is proposed for salient object detection. Firstly, a multi-receptive enhancement module (MRE) is designed to effectively expand the receptive fields of features from different layers and generate features with different receptive fields. MRE can enhance the semantic representation and improve the model's perception of the image context, which enables the model to locate the salient object accurately. Secondly, in order to reduce the reuse of redundant information in the complex top-down fusion method and weaken the differences between semantic features, a relatively simple but effective parallel fusion strategy (PFS) is proposed. It allows multi-scale features to better interact with each other, thus improving the overall performance of the model. Experimental results on multiple datasets demonstrate that the proposed method outperforms state-of-the-art methods under different evaluation metrics.
Application of the Modified PLUM Method to a Dense Seismic Intensity Network of a Local Government in Japan: A Case Study on Tottori Prefecture

An application of the PLUM (Propagation of Local Undamped Motion) method to real-time seismic intensity distribution from a dense seismic intensity network maintained by a local government in Japan is demonstrated. The JMA (Japan Meteorological Agency) has employed the PLUM method from March 2018 as a supplement to the traditional EEW (Earthquake Early Warning) which requires hypocenter determination. A dense observation network is desirable for upgrading accuracy and immediateness of EEW including the PLUM method. Seismic intensity meters in Tottori Prefecture, Japan, are suitable for the purpose because they have been improved to broadcast packets with peak ground acceleration and JMA seismic intensity at every 1 s. Also, 34 seismic intensity meters are installed in the target area while only six observation sites are used for the EEW by the JMA. The packet data are received at Tottori University, and the modified PLUM method considers wave propagation with damping from not only the observation points but also all evaluating grid points at a 1 km mesh. Additionally, P-wave amplitudes in preliminary tremors are also introduced to estimate seismic intensities from an empirical relationship. Applying the methodology to past earthquake data, more detailed and rapid evaluations of seismic intensity distribution are achieved. We expect the system will be utilized for earthquake disaster mitigation measures of local governments.
Nonnegative Bayesian nonparametric factor models with completely random measures

Abstract We present a Bayesian nonparametric Poisson factorization model for modeling dense network data with an unknown and potentially growing number of overlapping communities. The construction is based on completely random measures and allows the number of communities to either increase with the number of nodes at a specified logarithmic or polynomial rate, or be bounded. We develop asymptotics for the number and size of the communities of the network and derive a Markov chain Monte Carlo algorithm for targeting the exact posterior distribution for this model. The usefulness of the approach is illustrated on various real networks.
Hybrid high-order semantic graph representation learning for recommendations

Abstract The amount of Internet data is increasing day by day with the rapid development of information technology. To process massive amounts of data and solve information overload, researchers proposed recommender systems. Traditional recommendation methods are mainly based on collaborative filtering algorithms, which have data sparsity problems. At present, most model-based collaborative filtering recommendation algorithms can only capture first-order semantic information and cannot process high-order semantic information. To solve the above issues, in this paper, we propose a hybrid graph neural network model based on heterogeneous graphs with high-order semantic information extraction, which models users and items respectively by learning low-dimensional representations for them. We introduced an attention mechanism to allow the model to understand the corresponding edge weights adaptively. Simultaneously, the model also integrates social information in the data to learn more abundant information. We performed some experiments on related datasets. Our method achieved better results than some current advanced models, which verified the proposed model’s effectiveness.
Nori: Concealing the Concealed Identifier in 5G

IMSI catchers have been a long standing and serious privacy problem in pre-5G mobile networks. To tackle this, 3GPP introduced the Subscription Concealed Identifier (SUCI) and other countermeasures in 5G. In this paper, we analyze the new SUCI mechanism and discover that it provides very poor anonymity when used with the variable length Network Specific Identifiers (NSI), which are part of the 5G standard. When applied to real-world name length data, we see that SUCI only provides 1-anonymity, meaning that individual subscribers can easily be identified and tracked. We strongly recommend 3GPP and GSMA to standardize and recommend the use of a padding mechanism for SUCI before variable length identifiers get more commonly used. We further show that the padding schemes, commonly used for network traffic, are not optimal for padding of identifiers based on real names. We propose a new improved padding scheme that achieves much less message expansion for a given k-anonymity.
Identifying Duplicate Bug Records Using Word2Vec Prediction with Software Risk Analysis

Reporting duplicated bugs in bug reports have serious productivity consequences on software projects.The fewer reporting of duplicated bugs, the better software maturity processes are set between the internal software stakeholders.Automated identification of the duplicated category through bug reports could enhance risk identification approaches during the software life cycle.In this paper, we propose two different similarity measures to identify duplicated bugs using the word-embedding (Word2Vec) natural language processing technique through Tensorflow tool.We conduct a comparison experiment on two related bug records descriptions from eight different software components from the Mozilla Core dataset.We choose different sentence types through the duplicated bug category records to compare and discuss each component's accuracy results and identify whether the proposed module will be able to detect the related records.Using an earlier work, this paper calculates software risk values from duplication records and from bug-fix time prediction for the components that have not been identified as duplicated by the Word2Vec approach.The study results show maximum precision accuracy of 99.89% for the components that have been identified correctly as duplicated by the used approach.Additionally, we found that 66% of the software components that were excluded from the bug duplication proposed module showed an increase in software risk values.
Trade-off between Squashed Entanglement and Concurrence in Bipartite Quantum States

In this article we investigate the unitary dynamics of squashed entanglement and concurrence measures in Werner state and maximally entangled mixed states (MEMS) under two different Hamiltonians. The aim of the present study is two fold. The first part of the study deals with the dynamics under Heisenberg Hamiltonian and the second part deals under bi-linear bi-quadratic Hamiltonian which is the extension of first Hamiltonian. In both the parts we investigate the dynamical trade off and balancing points for squashed entanglement and concurrence. During the study, we also found the results of entanglement sudden death (ESD) with Heisenberg Hamiltonian in Werner state under concurrence measure. In second part, we investigate the special result for bi-linear bi-quadratic Hamiltonian; it does not disturb squashed entanglement and concurrence in both the states and exhibit the robust character for both of the states.
Grid-VLP: Revisiting Grid Features for Vision-Language Pre-training

Existing approaches to vision-language pre-training (VLP) heavily rely on an object detector based on bounding boxes (regions), where salient objects are first detected from images and then a Transformer-based model is used for cross-modal fusion. Despite their superior performance, these approaches are bounded by the capability of the object detector in terms of both effectiveness and efficiency. Besides, the presence of object detection imposes unnecessary constraints on model designs and makes it difficult to support end-to-end training. In this paper, we revisit grid-based convolutional features for vision-language pre-training, skipping the expensive region-related steps. We propose a simple yet effective grid-based VLP method that works surprisingly well with the grid features. By pre-training only with in-domain datasets, the proposed Grid-VLP method can outperform most competitive region-based VLP methods on three examined vision-language understanding tasks. We hope that our findings help to further advance the state of the art of vision-language pre-training, and provide a new direction towards effective and efficient VLP.
Transformation of the Assessment of the Quality of Educational Activities and Training of Future Science Teachers in the Context of the Pandemic

The article proposes the concept of transformation of the assessment of the quality of educational activities and the training of future science teachers, the main ideas of which are the objectivity of the assessment; its complex nature; the technological nature of the assessment procedures; their openness; the unity of normative and “non-normative” criteria. It is established that the transformation of the assessment of the quality of educational activities and the training of future science teachers necessitate a comprehensive transformation of its content, instrumental and technological characteristics. The technology of transformation of the assessment of the quality of educational activities and the training of future science teachers in the context of the pandemic has been developed, which involves the implementation of three interrelated stages – preparatory, main and final. Its implementation will ensure the adaptation of the assessment of the quality of educational activities to the changed conditions of education; the formation of the subject position of the student in the conditions of informatization of educational activities; improving the quality of professional education by individualizing the content of the assessment of the educational activities of future science teachers, as well as the variability of the assessment procedures used in its implementation. On the basis of expert assessments, proposals for the transformation of the assessment of the quality of educational activities and the training of future science teachers are formulated. Their implementation will allow not only to quickly identify problems in the content and procedural aspects of professional education of future science teachers, but also to timely determine the directions for improving its conditions.
DETECTING CREDIT CARD FRAUD USING MACHINE LEARNING ALGORITHMS

Today the banking sector offers its clients many different financial services such as ATM cards, Internet banking, Debit card, and Credit card, which allows attracting a large number of new customers. This article proposes an information system for detecting credit card fraud using a machine learning algorithm. Usually, credit cards are used by the customer around the clock, so the bank's server can track all transactions using machine learning algorithms. It must find or predict fraud detection. The dataset contains characteristics for each transaction and fraudulent transactions need to be classified and detected. For these purposes, the work proposes the use of the Random Forest algorithm.
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data

Generative Adversarial Networks (GANs) are typically trained to synthesize data, from images and more recently tabular data, under the assumption of directly accessible training data. Recently, federated learning (FL) is an emerging paradigm that features decentralized learning on client's local data with a privacy-preserving capability. And, while learning GANs to synthesize images on FL systems has just been demonstrated, it is unknown if GANs for tabular data can be learned from decentralized data sources. Moreover, it remains unclear which distributed architecture suits them best. Different from image GANs, state-of-the-art tabular GANs require prior knowledge on the data distribution of each (discrete and continuous) column to agree on a common encoding -- risking privacy guarantees. In this paper, we propose Fed-TGAN, the first Federated learning framework for Tabular GANs. To effectively learn a complex tabular GAN on non-identical participants, Fed-TGAN designs two novel features: (i) a privacy-preserving multi-source feature encoding for model initialization; and (ii) table similarity aware weighting strategies to aggregate local models for countering data skew. We extensively evaluate the proposed Fed-TGAN against variants of decentralized learning architectures on four widely used datasets. Results show that Fed-TGAN accelerates training time per epoch up to 200% compared to the alternative architectures, for both IID and Non-IID data. Overall, Fed-TGAN not only stabilizes the training loss, but also achieves better similarity between generated and original data.
Educational innovations in the context of forced digitalization in society during the pandemic

The paper analyzes the current realities that force governments and societies to turn to digital technologies, responding to the crisis in the short term, eliminating the socio-economic consequences in the medium term, and rethinking the existing system and its tools in the long term. To overcome this difficult stage, most educational institutions are forced to use digital services and communication channels. Digital technologies allowed society to avoid complete stagnation during the COVID-19 crisis as well as prompted to accelerate the process of introducing artificial intelligence into all everyday realities. The implemented research methods allowed us to obtain results that indicate that at the moment we are at a turning point in the digital transformation of all spheres of life, including education, and the success that population is able to adapt and overcome technological gaps will determine the shape of future economies, education and society in general after the COVID era. More and more users, both newly joined and permanent, are expanding the scope of Internet services and resources use at times, pursuing more and more previously irrelevant tasks, thereby significantly increasing the time of their stay on the Internet. While some believe that an unplanned and rapid transition to online learning with no training, insufficient bandwidth, and insufficient training will lead to a poor user experience that will not lead to sustainable growth, others believe that a new hybrid education model with significant benefits will emerge. Many schools and universities have already made successful transitions. However, some students without reliable internet access and / or technology are struggling to participate in digital learning and we are going to bridge this gap.