Reviewerly/exHarmony huggingface dataset:
About didactic materials creation for the discipline "engineering graphics" based on augmented reality technology

The main subject of research in this article is augmented reality (AR) technology. The object of the research is the method of application of the augmented reality technology in the educational process in the discipline "Engineering Graphics". Augmented reality technologies (AR-technologies) are promising technologies for visualization of spatial objects. In this regard, it is advisable to use these technologies in the educational process in disciplines, the assimilation of which requires a developed spatial representation. These disciplines include engineering graphics, descriptive geometry. The authors proposed a variant of creating a real Android application for practical use as an element of a didactic system for the discipline "Engineering Graphics". The application was tested when teaching the discipline "Engineering Graphics" at the Moscow Aviation Institute (National Research University) and showed positive results. According to the results of a survey conducted among students who used the application, 87% of students began to better perceive text material when studying the discipline.
YOLObin: Non-Decomposable Garbage Identification and Classification Based on YOLOv7

In order to safeguard the biological framework on which human creatures depend for presence and make society feasible improvement, it is becoming increasingly important to classify garbage.In any case, individuals are not recognizable with the classification strategy, so it is troublesome for individuals to accurately get the classification of each kind of rubbish.A proper waste management system is a primary task in building a smart and healthy city.In arrange to direct individuals to classify garbage accurately, this paper proposes a strategy of rubbish classification and acknowledgment based on YOLOv7 which is a cutting edge real time object detector.Performance of this model is compared along with two other object detectors where Mask-RCNN achieved f-measurement of 85%, YOLOv5 achieved f-measurement of 95.1% and YO-LOv7 achieved f-measurement 95.9%.We have used non-decomposable multiclass garbage images which entails messy backgrounds with unwanted images as well.Four classes of non-decomposable garbage data namely chips packet, plastic bottle, polythene and image with multiclass garbage with 1000 images are prepared for our dataset.Our experimental models performed well in classifying garbage images with cluttered backgrounds.We compared our test results to previous studies in which the majority of the models were tested and trained using laboratory images.The test comes about illustrates that the classification framework features a sensible degree of accuracy and the segmentation recognition impact is way better within the case of pointby-point picture, which can proficiently and helpfully total the rubbish classification errand.
Transformer-Based Device-Type Identification in Heterogeneous IoT Traffic

Due to the heterogeneity of Internet of Things (IoT) devices and the diversity of IoT communication protocols, it is challenging to model the communication behaviors of IoT devices to facilitate attack defense. Considering the complex correlation between the IoT device types and the patterns of their communication behaviors, one possible solution is to cluster IoT devices into different types based on the characteristics of their communication behaviors and deal with each type, respectively. However, IoT traffic includes a significant proportion of abnormal traffic, such as attack traffic sourcing from compromised devices, which cannot reflect the behavioral characteristics of the source device. In this article, we propose a Transformer-based IoT device-type identification method to address the above challenges. Specifically, our approach consists of three main components. First, we classify the traffic data from IoT devices into normal and abnormal types by a Transformer-based traffic diagnosis model. Next, another Transformer-based model is adopted on the normal traffic to identify the IoT device type. Finally, considering the immutability of IoT device types, a results-ensemble algorithm is designed to improve the accuracy of IoT device-type identification. Experimental results verify the effectiveness of our method, which brings a noticeable improvement in terms of both accuracy and macro <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score compared to other methods. Moreover, by applying the results-ensemble algorithm in the test phase, we can achieve 100% accuracy under certain conditions.
A Blockchain-Based Efficient Data Integrity Verification Scheme in Multi-Cloud Storage

The cloud storage service provides the storage and access function for massive data, reducing the management cost for large amounts of data.The data integrity verification scheme in cloud storage can be employed to help users confirm the integrity of outsourced data.Although public data integrity verification schemes allow users to outsource data integrity verification to third-party auditor (TPA), there are still many problems with centralized TPA in terms of security and efficiency.In recent years, researchers have tried to apply blockchain technology to solve the centralization problem of traditional methods, but these schemes do not pay attention to the problem of efficiency degradation caused by the use of blockchain technology.This paper proposes an efficient data integrity verification scheme for multicloud storage services by using blockchain technology.The overall verification can verify the integrity of multiple CSPs, which solves the problems of low computational efficiency.Local verification can trace the source to the specific damaged CSP, which is more secure and reliable.In addition, this paper puts the data verification process directly in the blockchain for public execution and provides data integrity verification services without the assistance of any third-party audit platform, avoiding the security problems caused by untrusted TPA.Theoretical analysis and experiments verify the safety and effectiveness of the scheme.
A Simple and Robust Correlation Filtering Method for Text-Based Person Search

Text-based person search aims to associate pedestrian images with natural language descriptions. In this task, extracting differentiated representations and aligning them among identities and descriptions is an essential yet challenging problem. Most of the previous methods depend on additional language parsers or vision techniques to select the relevant regions or words from noise inputs. But there exists heavy computation cost and inevitable error accumulation. Meanwhile, simply using horizontal segmentation images to obtain local-level features would harm the reliability of models as well. In this paper, we present a novel end-to-end Simple and Robust Correlation Filtering (SRCF) method which can effectively extract key clues and adaptively align the discriminative features. Different from previous works, our framework focuses on computing the similarity between templates and inputs. In particular, we design two different types of filtering modules (i.e., denoising filters and dictionary filters) to extract crucial features and establish multi-modal mappings. Extensive experiments have shown that our method improves the robustness of the model and achieves better performance on the two text-based person search datasets. Source code is available at https://github.com/Suo-Wei/SRCF .
Batch Evaluation Metrics in Information Retrieval: Measures, Scales, and Meaning

A sequence of recent papers, including in this journal, has considered the role of measurement scales in information retrieval (IR) experimentation, and presented the argument that (only) uniform-step interval scales should be used. Hence, it has been argued, well-known metrics such as reciprocal rank, expected reciprocal rank, normalized discounted cumulative gain, and average precision, should be either discarded as measurement tools, or adapted so that their metric values lie at uniformly-spaced points on the number line. These papers paint a rather bleak picture of past decades of IR evaluation, at odds with the IR community’s overall emphasis on practical experimentation and measurable improvement. Our purpose in this work is to challenge that pessimistic assessment. In particular, we argue that mappings from categorical and ordinal data to sets of points on the number line are valid provided there is an external reason for each target point to have been selected. We first consider the general role of measurement scales, and of categorical, ordinal, interval, ratio, and absolute data collections. In connection with the first two of those categories we also provide examples of the knowledge that is captured and represented by numeric mappings to the real number line. Focusing then on information retrieval, we argue that document rankings are categorical data, and that the role of an effectiveness metric is to provide a single value that summarizes the usefulness to a user or population of users of any given ranking, with usefulness able to be represented as a continuous variable on a ratio scale. That is, we argue that most current IR metrics are well-founded, and, moreover, that those metrics are more meaningful in their current form than in the proposed “intervalized” versions.
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission

Federated learning (FL) is a popular collaborative distributed machine learning paradigm across mobile devices. However, practical FL over resource constrained mobile devices confronts multiple challenges, e.g., the local on-device training and model updates in FL are power hungry and radio resource intensive for mobile devices. To address these challenges, in this paper, we attempt to take FL into the design of future wireless networks and develop a novel joint design of wireless transmission and weight quantization for energy efficient FL over mobile devices. Specifically, we develop flexible weight quantization schemes to facilitate on-device local training over heterogeneous mobile devices. Based on the observation that the energy consumption of local computing is comparable to that of model updates, we formulate the energy efficient FL problem into a mixed-integer programming problem where the quantization and spectrum resource allocation strategies are jointly determined for heterogeneous mobile devices to minimize the overall FL energy consumption (computation + transmissions) while guaranteeing model performance and training latency. Since the optimization variables of the problem are strongly coupled, an efficient iterative algorithm is proposed, where the bandwidth allocation and weight quantization levels are derived. Extensive simulations are conducted to verify the effectiveness of the proposed scheme.
A Deep Neural Network for Cervical Cell Classification Based on Cytology Images

Cervical cancer is one of the most common cancers among women. Fortunately, cervical cancer is treatable if it is diagnosed timely and administered appropriately. The death rate of cervical cancer has been greatly reduced since Pap smear test was applied. However, Pap smear test is a time-consuming and error-prone process. Moreover, classifying cervical cells into different categories is clinically meaningful but also challenging in the field of cervical cancer detection. To address these concerns, computer-aided diagnosis systems with deep learning need to be designed to automatically analyze cervical cytology images. In this study, we construct a deep convolutional neural network with feature representations learned via multiple kernels with different sizes to automatically classify cervical cytology images, named DeepCELL. Firstly, we design three different basic modules of DeepCELL to capture feature information via multiple kernels with different sizes. Then, we stack several such basic modules to form the cervical cell classification model. Finally, we perform a series of experiments to evaluate the proposed method on two cervical cytology datasets: Herlev and SIPaKMeD. Our method achieves the accuracy of 95.628%, precision of 95.685%, recall of 95.647% and F-score of 95.636% on SIPaKMeD dataset, which are the highest among all competing methods. Similarly, our method also achieves satisfactory result on Herlev dataset. In summary, extensive experimental results demonstrate that our proposed method has a promising performance in cervical cell image classification.
Hypergraph Similarity Measures

In this paper we present a novel framework for hypergraph similarity measures (HSMs) for hypergraph comparison. Hypergraphs are generalizations of graphs in which edges may connect any number of vertices, thereby representing multi-way relationships which are ubiquitous in many real-world networks including neuroscience, social networks, and bioinformatics. We propose two approaches for developing HSMs. The first approach is based on transforming the hypergraph into a graph representation, e.g., clique and star expansion, and then invoking the standard graph similarity measures. The second approach relies on a tensor-based representation of hypergraphs which intrinsically captures multi-way relations, and define similarity measures using tensor algebraic notions. Within each approach we present a collection of measures which either assess hypergraph similarity at a specific scale e.g., local, mesoscopic or global, or provide a more holistic multi-scale comparison. We discuss the advantages and disadvantages of the two proposed approaches, and demonstrate their performance on synthetic hypergraphs and hypergraphs derived from experimental biological datasets.
An Efficient Revocable and Searchable MA-ABE Scheme With Blockchain Assistance for C-IoT

Internet of Things (IoT) devices usually stores data on clouds for computational overhead offloading and easy data sharing. The data owners, as a result, usually have concerns about the security and privacy of their data stored in such cloud-assisted IoT (C-IoT) systems. Traditional encryption and search primitives, including attribute-based encryption (ABE) and public-key encryption with keyword search (PEKS), however, suffer from high overheads in decryption and revocation, and privacy leakage in search. To address these issues, we propose an efficient revocable and searchable multiauthority ABE (MA-ABE) scheme named ERS-ABE, which utilizes blockchain (BC) technology to implement keyword-based search and dynamic user management. ERS-ABE also adopts cloud-assisted decryption to improve the efficiency of IoT devices. It has been proven to be secure against the selective replayable chosen-ciphertext attacks and the chosen-keyword attacks under the random oracle model. The feasibility and efficiency of ERS-ABE have been evaluated through theoretical analysis and extensive simulation studies. The results indicate that EAR-ABE performs better over the state-of-the-art in both storage and computational overheads. Particularly, the operations that are usually done by a central server but taken by a BC in EAR-ABE cost only a few seconds.
Anomalous Sound Detection Based on Machine Activity Detection

We have developed an unsupervised anomalous sound detection method for machine condition monitoring that utilizes an auxiliary task - detecting when the target machine is active. First, we train a model that detects machine activity by using normal data with machine activity labels and then use the activity-detection error as the anomaly score for a given sound clip if we have access to the ground-truth activity labels in the inference phase. If these labels are not available, the anomaly score is calculated through outlier detection on the embedding vectors obtained by the activity-detection model. Solving this auxiliary task enables the model to learn the difference between the target machine sounds and similar background noise, which makes it possible to identify small deviations in the target sounds. Experimental results showed that the proposed method achieves a better performance than the conventional method particularly when the environmental noise contains sounds similar to the tar-get machine sound. In addition, the proposed method improved the anomaly-detection performance of the conventional method complementarily by means of an ensemble.
Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion Dynamics

Unsupervised monocular depth and ego-motion estimation has drawn extensive research attention in recent years. Although current methods have reached a high up-to-scale accuracy, they usually fail to learn the true scale metric due to the inherent scale ambiguity from training with monocular sequences. In this work, we tackle this problem and propose DynaDepth, a novel scale-aware framework that integrates information from vision and IMU motion dynamics. Specifically, we first propose an IMU photometric loss and a cross-sensor photometric consistency loss to provide dense supervision and absolute scales. To fully exploit the complementary information from both sensors, we further drive a differentiable camera-centric extended Kalman filter (EKF) to update the IMU preintegrated motions when observing visual measurements. In addition, the EKF formulation enables learning an ego-motion uncertainty measure, which is non-trivial for unsupervised methods. By leveraging IMU during training, DynaDepth not only learns an absolute scale, but also provides a better generalization ability and robustness against vision degradation such as illumination change and moving objects. We validate the effectiveness of DynaDepth by conducting extensive experiments and simulations on the KITTI and Make3D datasets (Code https://github.com/SenZHANG-GitHub/ekf-imu-depth ).
Arbitrary-Scale Image Synthesis

What we train with (b) What we train for (c) What we can do without explicit training Figure 1.(a) We train with our scale-consistent positional encodings and modified generator architecture that enables (b) synthesis of arbitrary scales and multi-scale consistency.(c) We showcase our results for novel configurations not encountered during training such as generation at never-seen-before scales, extrapolation and editing using spatial transformations such warping and stretching.
Lightweight Deep Neural Networks for Ship Target Detection in SAR Imagery

In recent years, deep convolutional neural networks (DCNNs) have been widely used in the task of ship target detection in synthetic aperture radar (SAR) imagery. However, the vast storage and computational cost of DCNN limits its application to spaceborne or airborne onboard devices with limited resources. In this paper, a set of lightweight detection networks for SAR ship target detection are proposed. To obtain these lightweight networks, this paper designs a network structure optimization algorithm based on the multi-objective firefly algorithm (termed NOFA). In our design, the NOFA algorithm encodes the filters of a well-performing ship target detection network into a list of probabilities, which will determine whether the lightweight network will inherit the corresponding filter structure and parameters. After that, the multi-objective firefly optimization algorithm (MFA) continuously optimizes the probability list and finally outputs a set of lightweight network encodings that can meet the different needs of the trade-off between detection network precision and size. Finally, the network pruning technology transforms the encoding that meets the task requirements into a lightweight ship target detection network. The experiments on SSDD and SDCD datasets prove that the method proposed in this paper can provide more flexible and lighter detection networks than traditional detection networks.
Learning Pseudo Front Depth for 2D Forward-Looking Sonar-based Multi-view Stereo

Retrieving the missing dimension information in acoustic images from 2D forward-looking sonar is a well-known problem in the field of underwater robotics. There are works attempting to retrieve 3D information from a single image which allows the robot to generate 3D maps with fly-through motion. However, owing to the unique image formulation principle, estimating 3D information from a single image faces severe ambiguity problems. Classical methods of multi-view stereo can avoid the ambiguity problems, but may require a large number of viewpoints to generate an accurate model. In this work, we propose a novel learning-based multi-view stereo method to estimate 3D information. To better utilize the information from multiple frames, an elevation plane sweeping method is proposed to generate the depth-azimuth-elevation cost volume. The volume after regularization can be considered as a probabilistic volumetric representation of the target. Instead of performing regression on the elevation angles, we use pseudo front depth from the cost volume to represent the 3D information which can avoid the 2D-3D problem in acoustic imaging. High-accuracy results can be generated with only two or three images. Synthetic datasets were generated to simulate various underwater targets. We also built the first real dataset with accurate ground truth in a large scale water tank. Experimental results demonstrate the superiority of our method, compared to other state-of-the-art methods.
Cross Attention Based Style Distribution for Controllable Person Image Synthesis

Controllable person image synthesis task enables a wide range of applications through explicit control over body pose and appearance. In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer. The module intentionally selects the style represented by each semantic and distributes them according to the target pose. The attention matrix in cross attention expresses the dynamic similarities between the target pose and the source styles for all semantics. Therefore, it can be utilized to route the color and texture from the source image, and is further constrained by the target parsing map to achieve a clearer objective. At the same time, to encode the source appearance accurately, the self attention among different semantic styles is also added. The effectiveness of our model is validated quantitatively and qualitatively on pose transfer and virtual try-on tasks. Codes are available at https://github.com/xyzhouo/CASD .