Reviewerly/exHarmony huggingface dataset:
Obstacle Avoidance and Path Planning for UGVs Using Improved RRT Algorithm

An improved Rapidly-exploring random trees (RRT) algorithm based on the contour of obstacles is proposed to solve the problems of excessive search blindness and easy to fall into local dead zones in the path planning of unmanned ground vehicles (UGVs) in complex environments. The algorithm firstly reduces the rejection rate of new nodes by limiting the sampling range, optimizes the pathfinding direction by combining node expansion with target points, then speeds up the planning speed by combining obstacle contour, and finally reduces the path distance by multi-layer optimization of the planned path. Simulation results show that this algorithm can effectively improve the speed of path planning, shorten the path distance, and is widely applicable to different environments.
Construction of English Writing Hybrid Teaching Model Based on Machine Learning Automatic Composition Scoring System

With the continuous development and advancement of modern computer technology, educational information technology has also touched on English composition evaluation. In China, automatic composition evaluation system has also been applied to foreign language writing teaching. Writing is an important comprehensive skill for language learners. The traditional writing feedback is not timely and other problems easily lead to students' lack of writing motivation. At present, there are many advanced automatic composition scoring systems, such as Bingguo intelligent composition scoring. This paper studies the method of applying the automatic composition grading system to college English writing teaching. First, some advantages and disadvantages of the automatic scoring system are illustrated by practical examples in application. Secondly, how to effectively use the automatic scoring system in College English writing teaching is proposed, and then a better teaching model is constructed. From the writing teaching experiment, we can know that the new writing teaching mode urges students to write, maintains the frequency of writing, stimulates students' interest in writing, cultivates students' autonomous writing ability, and improves students' English writing level.
AnimalDraw: Drawing Animal Cardboard Toys Design for Children’s Art Education and Entertainment

In this paper, we present AnimalDraw: a drawing animal cardboard toys design interface for children’s art education and entertainment. Through learning AnimalDraw, children can be taught to draw curves and planes in 3D so that they can design their favorite animals’ figures in 3D easily. Once their designs are finished, AnimalDraw can print out those figures of 3D animal toy designs on cardboard. After cutting and assembling that cardboard, children can create their own animal cardboard toys designed by themselves. Through this interface of AnimalDraw, it becomes possible to connect art education and entertainment seamlessly.
A proactive method of the webshell detection and prevention based on deep traffic analysis

The popularity of today's web application has led to web servers frequently the objects of webshell attacks. In this paper, we propose a new deep inspection method that is composed of a deep learning algorithm and signature-based technique for webshell detection, namely DLWSD. Moreover, to avoid bottlenecks, DLWSD built-in DeepInspector inspects in real-time the large-scale traffic flows with a strategy of periodic sampling at a defined frequency and interval for only flows that do not satisfy any signature. DeepInspector can create/update rules from webshell attacking alert results to prevent in future. We also proposed a mechanism using the cross-entropy loss function to regulate the training imbalanced dataset. Our experiments allow validating the performance of DLWSD using a popular dataset CSE-CIC-IDS2018 with the metrics accuracy, F1-score, FPR of 99.99%, 99.98%, and 0.01% respectively. It is also better compared with other studies using the same dataset.
Fuzzy Isolation Forest for Anomaly Detection

Anomaly detection is nowadays a key data mining task. Anomaly detection methods generally look for patterns of "normal" profile and then identify data points that do not match that profile. One outstanding method, Isolation Forest, showed high detection performance and computational efficiency. This method does not profile normal instances. Instead, it seeks to separate "abnormal" data points that are "few and different", from the others. This method builds a forest of trees to define a model for classifying data points as normal or abnormal. Building trees involve recursive random partitioning of data. Instances resulting in shorter path lengths are considered anomalies because they are located in sparse regions whereas instances ending deeper in tree path are normal points. Most outlier detection approaches, including Isolation Forest, have been used on precise, crisp data. However, in real life, data may be imprecise, incomplete, ambiguous, or uncertain. In this paper, we propose a new approach, called Fuzzy Isolation Forest, which adapts the Isolation Forest method to fuzzy data to handle the "vagueness" of data. We use a fuzzy partitioning strategy based on the alpha-cut approach in which data instances will be assigned to tree nodes by comparing their membership value to an alpha-cut threshold. Fuzzy Isolation Forest is as efficient as Isolation Forest in detecting anomalies on fuzzy data without significantly increasing processing time.
QUIC Protocol with Post-quantum Authentication

Post-quantum ciphers (PQC) are designed to replace the current public-key ciphers which are vulnerable against the quantum-equipped adversaries, e.g., RSA. We study the incorporation of the PQC algorithms into the QUIC and TCP/TLS networking protocols and analyze the performances and overheads in authentication and connection establishment. To distinguish from previous research, we focus on the newer QUIC networking protocol while comparing it with TCP/TLS. The QUIC protocol builds on UDP and its superiority over TCP/TLS is highlighted by the quicker and lower-overhead connection establishments. QUIC is thus gaining wider deployment, including its planned standardization for HTTP/3. We implement and experiment in local networking environment which provides greater analyzability and control. We compare QUIC vs. TCP/TLS when using PQC and measure the handshake overhead in time duration while varying both the PQC security strength and the networking conditions. Our results show that the PQC overhead increases with the PQC cipher security strength (the key and signature sizes) and as the network condition worsens (greater occurrences of packet dropping). Comparing between the PQC and the classical cipher with comparable security strengths, the PQC ciphers outperform RSA in the handshake time duration; both Dilithium 2 and Falcon 512 handshakes are quicker than RSA 3072.
Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach

Missing sensor data is a common problem associated with the Internet of Things (IoT) ecosystems, which affect the accuracy of the associated services such as adequate medical intervention for older adults living at home. This problem is caused by many factors, power down is one of them, communication failure and sensor failure are another two reasons. Multiple missing data imputation methods have been developed to solve this issue. However, irregular temporal missing data locations is challenging to handle, due to lack of knowledge of their occurrence probability and their random temporal location. In this paper, we propose a Bayesian Gaussian Process based imputation technique that accounts for temporal forcing to fill in the missing sensor data. Our approach; Bayesian Gaussian Process (BGaP); can impute missing data efficiently at any missing rate and for any temporal location using prior knowledge gathered on past observations. We illustrated how our approach performs using real data collected from sensors deployed in the residence of 10 older adults over a two-year period. Using our novel approach, we were able to impute all the missing data which allowed us to observe long-term behavior changes that we would not have been able to observe otherwise.
RBWCI: Robust and Blind Watermarking Framework for Cultural Images

Heritage multimedia, which comprises images, audio, and videos, are precious artifacts of a region. In addition to enabling a better understanding of earlier generations, heritage media provides information about their creative approach, style of living, and diversity of historical and archaeological ideologies. Heritage is an important resource that boosts the local economy, generates sustainable communities, and improves tourism and business sectors. Once heritage images are dissipated to consumers, they may be transmitted through wire/wireless systems, where the data may be accessed by both authorized and unauthorized consumers. With the rapid advancement of technology and 5G networks, establishing an approach that protects cultural heritage media from unauthorized consumers is necessary. This study presents a robust and blind watermarking-framework for cultural images (RBWCI) utilizing the discrete cosine transform domain for ownership verification and copyright protection. The embedding scenario of our technique relies on the difference between coefficients obtained from the two channels of the YCbCr color model. The watermark bit is embedded by taking the difference between two preselected mid-frequency coefficients (Cb-Cr). Moreover, chaotic and deoxyribonucleic acid encryption are employed to ensure double-layer watermark security. The proposed algorithm demonstrates a peak signal-to-noise ratio between 42-43 dB and structural similarity index metric value of approximately 1 for different test images when not under attack. The robustness of the RBWCI was revealed by comparing it with various state-of-the-art schemes, making it suitable for different consumer applications.
Neural Joint Space Implicit Signed Distance Functions for Reactive Robot Manipulator Control

In this letter, we present an approach for learning a neural implicit signed distance function expressed in joint space coordinates, that efficiently computes distance-to-collisions for arbitrary robotic manipulator configurations. Computing such distances is a long standing problem in robotics as approximate representations of the robot and environment geometry can lead to overly conservative constraints, numerical instabilities and expensive computations – limiting real-time reactive control and task success. Leveraging GPU parallelization and the differentiable nature of the proposed distance function allows for fast calculation of gradients with respect to the neural network inputs, providing a continuous repulsive vector field directly in joint space. We show that the learned high-resolution collision representation can be used to achieve real-time reactive control by i) formulating it as a collision-avoidance constraint for a quadratic programming (QP) inverse kinematics (IK), and ii) introducing it as a collision cost in a sampling-based joint space model predictive controller (MPC). For a reaching benchmark task with a 7DoF robot and dynamic obstacles intentionally obstructing the robot's path we achieve average 250 Hz control frequency with QP-IK and 92 Hz with MPC, showing an accelerated performance of 15% for QP-IK and 40% for MPC over baseline distance computation techniques.
Machine Learning Face Recognition Model for Employee Tracking and Attendance System

An attendance system is widely implemented to monitor someone's presence in the office, schools, or events. Several technologies can be implemented as the attendance system. Face recognition is a natural, inexpensive, and easy way to be implemented as an attendance system. In this pandemic and post-pandemic era, face recognition can be the best alternative to be implemented as the attendance system. This research aims to propose a real-time attendance system using face recognition with consistently high accuracy. Moreover, the system is able to update the attendee's face periodically to tackle the changes in the face over time. The attendee can take their attendance using a camera. The camera captures the face and detects the face using the Multi-Task Cascaded Convolutional Neural Network (MTCNN). In addition, The Face Alignment N etwork (FAN) is applied to the image to extract the facial landmark in the image. The next step is to extract information from the face by using FaceN et. Finally, the face embedding extracted from Face Detection System is classified. The best classifier accuracy achieved by the model was 100% and 99.90% for training and validation, respectively.
Semi-supervised Monocular 3D Object Detection by Multi-view Consistency

The success of monocular 3D object detection highly relies on considerable labeled data, which is costly to obtain. To alleviate the annotation effort, we propose MVC-MonoDet, the first semi-supervised training framework that improves Monocular 3D object detection by enforcing multi-view consistency. In particular, a box-level regularization and an object-level regularization are designed to enforce the consistency of 3D bounding box predictions of the detection model across unlabeled multi-view data (stereo or video). The box-level regularizer requires the model to consistently estimate 3D boxes in different views so that the model can learn cross-view invariant features for 3D detection. The object-level regularizer employs an object-wise photometric consistency loss that mitigates 3D box estimation error through structure-from-motion (SFM). A key innovation in our approach to effectively utilize these consistency losses from multi-view data is a novel relative depth module that replaces the standard depth module in vanilla SFM. This technique allows the depth estimation to be coupled with the estimated 3D bounding boxes, so that the derivative of consistency regularization can be used to directly optimize the estimated 3D bounding boxes using unlabeled data. We show that the proposed semi-supervised learning techniques effectively improve the performance of 3D detection on the KITTI and nuScenes datasets. We also demonstrate that the framework is flexible and can be adapted to both stereo and video data.
Data sharing in plant phenotyping research: Perceptions, practices, enablers, barriers and implications for science policy on data management

Abstract The application of new technologies in scientific research, particularly automated sensing of plant phenotypic performance, has resulted in a deluge of data and raised the question of how these data can be efficiently managed and shared. Many studies have examined the benefits and constraints of data sharing in different disciplines. We focus on plant phenotyping due to the increasing volume of digital data generated in multi‐disciplinary plant phenotyping research. Data sharing and reuse practices in plant phenotyping research have not been widely explored. Study results show that data sharing in plant phenotyping research occurs mostly through direct personal requests based on trust relationships and technical supplements (appendices) to publications, and researchers are willing to share data if incentives and policies are aligned to overcome the barriers. This paper provides empirical evidence to guide the establishment of incentive systems and policy frameworks that support FAIR (findability, accessibility, interoperability, and reusability) data, promote behavioral change, and enhance data sharing for the advancement of science and innovation by research communities, institutions, policymakers, and funders.
Features-Based IoT Security Authentication Framework Using Statistical Aggregation, Entropy, and MOORA Approaches

The Internet of Thing (IoT) is one of the most imperative technology for all organizations that's play a vital role in many operations, using communication networks, for exchange of data in order to perform a useful task.However, security of IoT devices and data is a major concern.This research work prioritizes the alternatives of security authentication features from studied articles.The multi-objective optimization method based on the ratio analysis (MOORA) is useful in multi-criteria decision making (MCDM) for ranking the alternatives.The statistical aggregation (SA) method has been used to assign weights to security authentication features in comparison to entropy method.In this paper, we identify weights for authentication features using the proposed SA method.Moreover, we evaluate the accuracy rates of the proposed model using entropy method.Finally, we ranked out the alternatives of authentication features using the MOORA approach.In fact, the entropy weight values came against the initial value of the objects in which the accuracy was 15% which is not suitable to this problem while the accuracy of the SA is 85%.Hence, the accuracy improvement is approximately 70 % using the SA method.This method is applicable for finding the weights of the objects based on initial values by MCDM approaches.We study the key security authentication which is the preserving of confidentiality, integrity, and availability that are the prime objectives for security of an IoT device.Furthermore, challenges are preserving the selected attributes through any approach, as discussed in the literature, adds to the complexity of IoT device security.We identify the future challenges to improve the security of IoT devices.
Paint2Pix: Interactive Painting Based Progressive Image Synthesis and Editing

Controllable image synthesis with user scribbles is a topic of keen interest in the computer vision community. In this paper, for the first time we study the problem of photorealistic image synthesis from incomplete and primitive human paintings. In particular, we propose a novel approach paint2pix, which learns to predict (and adapt) "what a user wants to draw" from rudimentary brushstroke inputs, by learning a mapping from the manifold of incomplete human paintings to their realistic renderings. When used in conjunction with recent works in autonomous painting agents, we show that paint2pix can be used for progressive image synthesis from scratch. During this process, paint2pix allows a novice user to progressively synthesize the desired image output, while requiring just few coarse user scribbles to accurately steer the trajectory of the synthesis process. Furthermore, we find that our approach also forms a surprisingly convenient approach for real image editing, and allows the user to perform a diverse range of custom fine-grained edits through the addition of only a few well-placed brushstrokes. Source code and demo is available at https://github.com/1jsingh/paint2pix .
Self-supervised learning of smart contract representations

Learning smart contract representations can greatly facilitate the development of smart contracts in many tasks such as bug detection and clone detection. Existing approaches for learning program representations are difficult to apply to smart contracts which have insufficient data and significant homogenization. To overcome these challenges, in this paper, we propose SRCL, a novel, self-supervised approach for learning smart contract representations. Unlike existing supervised methods, which are tied on task-specific data labels, SRCL leverages large-scale unlabeled data by self-supervised learning of both local and global information of smart contracts. It automatically extracts structural sequences from abstract syntax trees (ASTs). Then, two discriminators are designed to guide the Transformer encoder to learn local and global semantic features of smart contracts. We evaluate SRCL on a dataset of 75,006 smart contracts collected from Etherscan. Experimental results show that SRCL considerably outperforms the state-of-the-art code representation models on three downstream tasks.
An Efficient Hardware Accelerator for Sparse Transformer Neural Networks

Transformers have been an indispensable staple in deep learning. However, it is challenging to realize efficient deployment for Transformer-based model due to their substantial computation and memory demands. To address this issue, we present an efficient sparse Transformer accelerator on FPGA, namely STA, by exploiting N:M fine-grained structured sparsity. Our design features not only a unified computing engine capable of performing both sparse and dense matrix multiplications with high computational efficiency, but also a scalable softmax module eliminating the latency from intermediate off-chip data communication. Experimental results show that our implementation achieves the lowest latency compared to CPU, GPU, and prior FPGA-based accelerators. Moreover, compared with the state-of the-art FPGA-based accelerators, it can achieve up to $12.28\times$ and $51.00\times$ improvement on energy efficiency and MAC efficiency, respectively.
Hunting Group Clues with Transformers for Social Group Activity Recognition

This paper presents a novel framework for social group activity recognition. As an expanded task of group activity recognition, social group activity recognition requires recognizing multiple sub-group activities and identifying group members. Most existing methods tackle both tasks by refining region features and then summarizing them into activity features. Such heuristic feature design renders the effectiveness of features susceptible to incomplete person localization and disregards the importance of scene contexts. Furthermore, region features are sub-optimal to identify group members because the features may be dominated by those of people in the regions and have different semantics. To overcome these drawbacks, we propose to leverage attention modules in transformers to generate effective social group features. Our method is designed in such a way that the attention modules identify and then aggregate features relevant to social group activities, generating an effective feature for each social group. Group member information is embedded into the features and thus accessed by feed-forward networks. The outputs of feed-forward networks represent groups so concisely that group members can be identified with simple Hungarian matching between groups and individuals. Experimental results show that our method outperforms state-of-the-art methods on the Volleyball and Collective Activity datasets.
Comparing Correspondences: Video Prediction with Correspondence-wise Losses

Image prediction methods often struggle on tasks that require changing the positions of objects, such as video prediction, producing blurry images that average over the many positions that objects might occupy. In this paper, we propose a simple change to existing image similarity metrics that makes them more robust to positional errors: we match the images using optical flow, then measure the visual similarity of corresponding pixels. This change leads to crisper and more perceptually accurate predictions, and does not require modifications to the image prediction network. We apply our method to a variety of video prediction tasks, where it obtains strong performance with simple network architectures, and to the closely related task of video interpolation. Code and results are available at our webpage: https://dangeng.github.io/CorrWiseLosses