Reviewerly/exHarmony huggingface dataset:
A contour-aware feature-merged network for liver segmentation based on shape prior knowledge

One primary challenge in liver segmentation is the fuzzy edge contour. Recently, fully convolutional neural networks (FCNs) have been widely used in liver segmentation because of their adequate feature extraction. Nevertheless, the context among liver slices is still ignored by FCN. To address this issue, we first propose a bidirectional convolutional long short-term memory (BiConvLSTM) to explore contextual information. Meanwhile, the attention gate (AG) is utilized to fuse high-dimensional information from BiConvLSTM to remove irrelevant features. Besides, Shape-Net network is proposed to extend the liver shape pattern by latent space information, which will contribute to reduce the interference of fuzzy boundaries. Finally, the improved active contour loss function with L2 norm acts as a feature constraint. Experimental results on public benchmark datasets show that the proposed method slightly outperforms other newly published methods and achieves good performance for liver segmentation.
Software Implementation and Comparison of ZUC-256, SNOW-V, and AES-256 on RISC-V Platform

As candidates for cryptographic primitives in 5G communication, ZUC-256, SNOW-V, and AES-256 offer the 256-bit security in terms of confidentiality and integrity. There have been some studies on software implementations on different platforms for them. In this paper, ZUC-256, SNOW-V, and AES-256 are implemented in the standard C language on RISC-V CPU without AES new instructions. Their implementation performances are measured and compared from two aspects including keystream generation and message authentication code generation, and the results suggest that ZUC-256 is faster than SNOW-V and AES-256 algorithms on RISC-V platform.
Effect Monitoring of Physical Education Curriculum Based on Wearable Sensor in Intelligent Environment

Abstract Reasonable amount of exercise is the premise of physical education teaching. On this premise, in the process of students’ physical education teaching, the appropriate degree of actual exercise amount will affect the effect of motor skill learning and physical exercise to a great extent. Through monitoring the amount of exercise and then evaluating the quality of physical education teaching is the focus of the implementation of physical education. Looking for simple and effective monitoring equipment has important practical significance. In this paper, through the analysis of the current situation of wearable sensor in the development of physical education teaching, put forward the effect monitoring of sports curriculum based on wearable sensor in intelligent environment. In this paper, 258 freshmen and sophomores who use wearable devices are selected for monitoring and analyzing, and the pertinence, accuracy and data of wearable sensor physical education course and traditional physical education course are compared and analyzed, and the results are discussed and analyzed. It is concluded that wearable sensor can promote physical education curriculum, not only can improve the teaching pertinence, strengthen the accuracy, but also can In order to improve the scientificity of physical education curriculum effect monitoring. The research in this paper has important practical significance for monitoring the effect of wearable sensors in physical education curriculum.
Consistency Graph Modeling for Semantic Correspondence

To establish robust semantic correspondence between images covering different objects belonging to the same category, there are three important types of information including inter-image relationship, intra-image relationship and cycle consistency. Most existing methods only exploit one or two types of the above information and cannot make them enhance and complement each other. Different from existing methods, we propose a novel end-to-end Consistency Graph Modeling Network (CGMNet) for semantic correspondence by modeling inter-image relationship, intra-image relationship and cycle consistency jointly in a unified deep model. The proposed CGMNet enjoys several merits. First, to the best of our knowledge, this is the first work to jointly model the three kinds of information in a deep model for semantic correspondence. Second, our model has designed three effective modules including cross-graph module, intra-graph module and cycle consistency module, which can jointly learn more discriminative feature representations robust to local ambiguities and background clutter for semantic correspondence. Extensive experimental results show that our algorithm performs favorably against state-of-the-art methods on four challenging datasets including PF-PASCAL, PF-WILLOW, Caltech-101 and TSS.
Network Capacity as Common Pool Resource

Congestion control mechanisms, by which network users share constrained capacity on Internet links, are heavily studied in computer science. Such mechanisms are traditionally automated, assuming that users do not wish to be involved in addressing congestion. However, in community-owned and operated networks, users have control over daily operational choices. We explore the design of community-based congestion policies and mechanisms, through the lens of network capacity as a Common Pool Resource (CPR). Through a series of workshops and interviews in a rural community in Oaxaca, Mexico, we encounter design opportunities for new types of tools supporting communal network management. Participants expressed desires for preserving individual privacy while collecting longitudinal data to track the network's impact on the community, prioritization of high-value applications, equal link sharing between users, and human-mediated congestion management in lieu of automated enforcement. We report qualitative insights and offer design directions for future systems to address network resources in a manner compatible with Ostrom's principles for CPR governance.
A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor

Collision-avoidance is a crucial research topic in robotics. Designing a collision-avoidance algorithm is still a challenging and open task, because of the requirements for navigating in unstructured and dynamic environments using limited payload and computing resources on board micro aerial vehicles. This article presents a novel depth-based collision-avoidance method for aerial robots, enabling high-speed flights in dynamic environments. First of all, a depth-based Euclidean distance field mapping algorithm is generated. Then, the proposed Euclidean distance field mapping strategy is integrated with a rapid-exploration random tree to construct a collision-avoidance system. The experimental results show that the proposed collision-avoidance algorithm has a robust performance at high flight speeds in challenging dynamic environments. The experimental results show that the proposed collision-avoidance algorithm can perform faster collision-avoidance maneuvers when compared to the state-of-art algorithms (the average computing time of the collision maneuver is 25.4 ms, while the minimum computing time is 10.4 ms). The average computing time is six times faster than one baseline algorithm. Additionally, fully autonomous flight experiments are also conducted for validating the presented collision-avoidance approach.
Unbounded Multi-party Computation from Learning with Errors

We consider the problem of round-optimal unbounded MPC: in the first round, parties publish a message that depends only on their input. In the second round, any subset of parties can jointly and securely compute any function f over their inputs in a single round of broadcast. We do not impose any a-priori bound on the number of parties nor on the size of the functions that can be computed. Our main result is a semi-honest two-round protocol for unbounded MPC in the plain model from the hardness of the standard learning with errors (LWE) problem. Prior work in the same setting assumes the hardness of problems over bilinear maps. Thus, our protocol is the first example of unbounded MPC that is post-quantum secure. The central ingredient of our protocol is a new scheme of attribute-based secure function evaluation (AB-SFE) with public decryption. Our construction combines techniques from the realm of homomorphic commitments with delegation of lattice basis. We believe that such a scheme may find further applications in the future.
State of the Art in Open Platforms for Collaborative Urban Design and Sharing of Resources in Districts and Cities

This work discusses recent developments in sharing economy concepts and collaborative co-design technology platforms applied in districts and cities. These developments are being driven both by new technological advances and by increased environmental awareness. The paper begins by outlining the state of the art in smart technology platforms for collaborative urban design, highlighting a number of recent examples. The case of peer-to-peer trading platforms applied in the energy sector is then used to illustrate how sharing economy concepts and their enabling technologies can accelerate efforts towards more sustainable urban environments. It was found that smart technology platforms can encourage peer-to-peer and collaborative activity, and may have a profound influence on the future development of cities. Many of the research and development projects in this area to date have focused on demonstrations at the building, neighbourhood, and local community scales. Scaling these sharing economy platforms up to the city scale and beyond has the potential to provide a number of positive environment impacts. However, significant technical and regulatory barriers to wider implementation exist, and realising this potential will require radical new approaches to the ownership and governance of urban infrastructure. This paper provides a concise overview of the state of the art in this emerging field, with the aim of identifying the most promising areas for further research.
Mapping based Residual Convolution Neural Network for Non-embedding and Blind Image Watermarking

Traditional image watermarking algorithms directly modify the host image by watermark embedding, which is hard to balance the contradiction between the robustness and imperceptibility. Inspired by the human brain's associative memory, this paper proposes a non-embedding and blind image watermarking algorithm via mapping based Residual Convolution Neural Network (Mapping-based RCNN). For preprocessing, median filter is applied on the host image to enhance the robustness of the algorithm to against various attacks. After that, Discrete Cosine Transform and Singular Value Decomposition are adopted to extract the corresponding image information matrix. To obtain the mapping relationship between host image and watermark image, the information matrix is input into the designed Mapping-based RCNN structure for network training. The Mapping-based RCNN is a non-embedding watermarking algorithm, which not only overcomes the imperceptibility shortcoming but also wins good robustness compared with traditional watermarking algorithms. Experimental results show that the proposed algorithm can successfully extract the watermark images under various attacks, and is more robust than existing watermarking algorithms.
Deep Learning-Based Object Detection Improvement for Fine-Grained Birds

When the object detection algorithm is applied to the bird protection project, there are many problems like large model parameters, high similarity between bird species and single sample scene.In order to further improve the detection accuracy and stability of the object detection model, a multi-object detection algorithm for fine-grained birds is proposed.Firstly, the algorithm introduces Depthwise separable convolution into the feature extraction layer of YOLOv3 algorithm.The convolution process is divided into two parts: deep convolution and point-by-point convolution.The separation between intra-channel convolution and inter-channel convolution is realized.On the basis of high detection accuracy, the number of algorithm model parameters and calculation amount are greatly reduced.Finally, Focal loss was added to the loss function to solve the serious imbalance of positive and negative samples.By reducing the weight of the large number of simple background classes, the algorithm was more focused on detecting foreground classes.The experimental results show that, in the bird data set, the average precision mean (mAP) index of this algorithm is 2.71% higher than YOLOv3 algorithm, the number of parameters is 79.88% lower than YOLOv3 basic model, and the number of frames per second (FPS) is 19.98% higher than YOLOv3 algorithm.This algorithm not only greatly reduces the number of model parameters and computation, but also improves the detection speed and mAP.
A novel computation method of hybrid capacity constrained centroidal power diagram

Power diagrams, as a powerful extension of Voronoi diagrams, have been utilized in a wide range of applications in various fields. By imposing the capacity constraint and the centroid constraint to the ordinary power diagram, capacity-constrained power diagram and centroidal capacity-constrained power diagram can be obtained respectively, in which, all the capacity constraints are fixed values. However, some practical applications require a special kind of power diagrams, called hybrid capacity-constrained centroidal power diagrams, where not all capacity constraints are fixed values, and instead there are some capacities of sites constrained to intervals. To this end, we propose an iterative computation method for the power diagrams with hybrid capacity constraints. On the one hand, a weight evaluated method is introduced to update the weights of interval capacity-constrained sites, and Newton's method is applied to optimize the weights of fixed-value capacity-constrained sites. On the other hand, Lloyd's method is employed to move the sites to their respective mass centers. Experimental results prove that the proposed method can effectively compute the centroidal power diagram with hybrid capacity constraints.
Heat Behind the Meter: A Hidden Threat of Thermal Attacks in Edge Colocation Data Centers

The widespread adoption of Internet of Things and latency-critical applications has fueled the burgeoning development of edge colocation data centers (a.k. a., edge colocation) — small-scale data centers in distributed locations. In an edge colocation, multiple entities/tenants house their own physical servers together, sharing the power and cooling infrastructures for cost efficiency and scalability. In this paper, we discover that the sharing of cooling systems also exposes edge colocations’ potential vulnerabilities to cooling load injection attacks (called thermal attacks) by an attacker which, if left at large, may create thermal emergencies and even trigger system outages. Importantly, thermal attacks can be launched by leveraging the emerging architecture of built-in batteries integrated with servers that can conceal the attacker’s actual server power (or cooling load). We consider both one-shot attacks (which aim at creating system outages) and repeated attacks (which aim at causing frequent thermal emergencies). For repeated attacks, we present a foresighted attack strategy which, using reinforcement learning, learns on the fly a good timing for attacks based on the battery state and benign tenants’ load. We also combine prototype experiments with simulations to validate our attacks and show that, for a small 8kW edge colocation, an attacker can potentially cause significant losses. Finally, we suggest effective countermeasures to the potential threat of thermal attacks.
EventGraD: Event-Triggered Communication in Parallel Stochastic Gradient Descent

Communication in parallel systems consumes significant amount of time and energy which often turns out to be a bottleneck in distributed machine learning. In this paper, we present EventGraD - an algorithm with event-triggered communication in parallel stochastic gradient descent. The main idea of this algorithm is to modify the requirement of communication at every epoch to communicating only in certain epochs when necessary. In particular, the parameters are communicated only in the event when the change in their values exceed a threshold. The threshold for a parameter is chosen adaptively based on the rate of change of the parameter. The adaptive threshold ensures that the algorithm can be applied to different models on different datasets without any change. We focus on data-parallel training of a popular convolutional neural network used for training the MNIST dataset and show that EventGraD can reduce the communication load by up to 70% while retaining the same level of accuracy.
Meta-VOS: Learning to Adapt Online Target-Specific Segmentation

The task of video object segmentation is a fundamental but challenging problem in the field of computer vision. To deal with large variations in target objects and background clutter, we propose an online adaptive video object segmentation (VOS) framework, named Meta-VOS, that learns to adapt the target-specific segmentation. Meta-VOS builds an online adaptive learning process by exploiting cumulative expertise after searching for confidence patterns across different videos/frames, and then dynamically improves the model learning from two aspects: Meta-seg learner (i.e., module updating) and Meta-seg criterion (i.e., rule of expertise). As our goal is to rapidly determine which patterns best represent the essential characteristics of specific targets in a video, Meta-seg learner is introduced to adaptively learn to update the parameters and hyperparameters of segmentation network in very few gradient descent steps. Furthermore, a Meta-seg criterion of learned expertise, which is constructed to evaluate the Meta-seg learner for the online adaptation of the segmentation network, can confidently online update positive/negative patterns under the guidance of motion cues, object appearances and learned knowledge. Comprehensive evaluations on several benchmark datasets demonstrate the superiority of our proposed Meta-VOS when compared with other state-of-the-art methods applied to the VOS problem.
Ensemble Machine Learning for P2P Traffic Identification

Network traffic identification and classification in the current scenario are not only required for traffic management but in designing a future protocol for user-specific services and improve user experiences.This fundamental step of network management is perceived by the researcher long back and started developing techniques for the same.The traditional techniques for traffic identification and classification include port and payload based.The current large and complex network poses many challenges to the researchers in designing approaches for traffic classification by using dynamic ports, encryption, and masquerading techniques.The complexity is further enhanced due to increased dependence on the Internet and diverse applications to enable network administrators including ISPs to manage the network intelligently and efficiently.As traditional techniques are not effective to address the current challenges, a hybrid solution is explored.The hybrid approaches make use of statistics or behavioral-based, heuristic-based, machine learning-based along with feature selection techniques.In this paper, apart from developing enhanced hybrid approaches for identifying the P2P traffic, an extensive real dataset of size 924 GB is constructed to analyze the effectiveness of the proposed approaches.A number of hybrid approaches are designed by using feature selection techniques and machine learning (ML) algorithms.Extensive analysis of proposed hybrid approaches along with the comparative study reveals that Chi-Square and Random Forest outperform other state-of-art approaches yielding an accuracy rate of 99.46%.
Vector-Quantization-Based Topic Modeling

With the purpose of learning and utilizing explicit and dense topic embeddings, we propose three variations of novel vector-quantization-based topic models (VQ-TMs): (1) Hard VQ-TM, (2) Soft VQ-TM, and (3) Multi-View Soft VQ-TM. The model family capitalize on vector quantization techniques, embedded input documents, and viewing words as mixtures of topics. Guided by a comprehensive set of evaluation metrics, we conduct systematic quantitative and qualitative empirical studies, and demonstrate the superior performance of VQ-TMs compared to important baseline models. Through a unique case study on code generation from natural language descriptions, we further illustrate the power of VQ-TMs in downstream tasks.
Graph-based motion prediction for abnormal action detection

Abnormal action detection is the most noteworthy part of anomaly detection, which tries to identify unusual human behaviors in videos. Previous methods typically utilize future frame prediction to detect frames deviating from the normal scenario. While this strategy enjoys success in the accuracy of anomaly detection, critical information such as the cause and location of the abnormality is unable to be acquired. This paper proposes human motion prediction for abnormal action detection. We employ sequence of human poses to represent human motion, and detect irregular behavior by comparing the predicted pose with the actual pose detected in the frame. Hence the proposed method is able to explain why the action is regarded as irregularity and locate where the anomaly happens. Moreover, pose sequence is robust to noise, complex background and small targets in videos. Since posture information is non-Euclidean data, graph convolutional network is adopted for future pose prediction, which not only leads to greater expressive power but also stronger generalization capability.
An online guided tuning approach to run CNN pipelines on edge devices

Modern edge and mobile devices are equipped with powerful computing resources. These are often organized as heterogeneous multi-cores, featuring performance-asymmetric core clusters. This raises the question on how to effectively execute the inference pass of convolutional neural networks (CNN) on such devices. Existing CNN implementations on edge devices leverage offline profiling data to determine a better schedule for CNN applications. This approach requires a time consuming phase of generating a performance profile for each type of representative kernel on various core configurations available on the device, coupled with a search space exploration. We propose an online tuning technique which utilizes compile time hints and online profiling data to generate high throughput CNN pipelines. We explore core heterogeneity and compatible core-layer configurations through an online guided search. Unlike exhaustive search, we adopt an evolutionary approach with a guided starting point in order to find the solution. We show that by pruning and navigating through the complex search space using compile time hints, 79% of the tested configurations turn out to be near-optimal candidates for a throughput maximizing pipeline on NVIDIA Jetson TX2 platform.
Classification of images by using TensorFlow

In the project of image classification, one model was built to sort images. By using the model which was built in the project, pictures can be classified effectively and quickly. At the beginning of the project, an appropriate data set was chosen. Then, the model was created by using TensorFlow. Next, the model would be trained to get the parameters with good fitting. Finally, in order to evaluate the model effectively, several graphs of validation accuracy were created. In the process of completing this project, the members of Team have mastered the ability to construct convolutional neural network models using python. What's more, the members of the team also develop a good ability of data analysis.