Reviewerly/exHarmony huggingface dataset:
Edge-assisted Gossiping Learning: Leveraging V2V Communications Between Connected Vehicles

Intelligent vehicles are quickly becoming mobile, powerful computers, able to collect, exchange, and process sensed data. They are therefore expected not just to consume ITS services, but also to actively contribute to the implementation of relevant ITS applications. With an increasing role of machine learning (ML) approaches, vehicles are called to put into use their computing capabilities and sensed data for the training of ML models. This can be enacted through distributed learning approaches, which however may lead to significant communication overhead or to learners converging to different models. In this work, we envision a new distributed learning scheme, named EAGLE, that, with the assistance of the network edge, aims at exploiting the vehicles' data and computing capabilities, while enabling an efficient learning process. To this end, EAGLE combines the advantages of two existing schemes, namely, federated learning and gossiping learning, yielding a distributed paradigm that ensures both scalability and model consistency. Our results, obtained using two different real-world data sets, show that EAGLE can improve learning accuracy by 20%, while reducing the communication overhead by 45%.
Performance Evaluation of Optimizers for Deformable-DETR in Natural Disaster Damage Assessment

Global natural disasters are becoming more frequent and severe as a result of climate change. Recent advances in computer vision, particularly deep learning-based techniques and unmanned aerial vehicle (UAV) remote sensing, can aid disaster response teams in assessing the damage. Prior methods appear to be ineffective or were designed with inductive biases, making them difficult to conduct during the disaster damage assessment. In this paper, we investigate deep-learning-based methods capable of rapidly assessing building damage that follows natural disasters. Furthermore, we examine Deformable DETR, which is an improvement upon DETR, an object detection method based on the Transformer architecture, in terms of efficiency and convergence time, while inheriting DETR’s simple implementation and adaptable architecture, making it suitable for the task of damage detection. We also experimented and analyzed the performance of several optimizers to improve the performance of Deformable DETR.
Treebeard: An Optimizing Compiler for Decision Tree Based ML Inference

Decision tree ensembles are among the most commonly used machine learning models. These models are used in a wide range of applications and are deployed at scale. Decision tree ensemble inference is usually performed with libraries such as XGBoost, LightGBM, and Sklearn. These libraries incorporate a fixed set of optimizations for the hardware targets they support. However, maintaining these optimizations is prohibitively expensive with the evolution of hardware. Further, they do not specialize the inference code to the model being used, leaving significant performance on the table. This paper presents TREEBEARD, an optimizing compiler that progressively lowers the inference computation to optimized CPU code through multiple intermediate abstractions. By applying model-specific optimizations at the higher levels, tree walk optimizations at the middle level, and machine-specific optimizations lower down, TREEBEARD can specialize inference code for each model on each supported CPU target. TREEBEARD combines several novel optimizations at various abstraction levels to mitigate architectural bottlenecks and enable SIMD vectorization of tree walks. We implement TREEBEARD using the MLIR compiler infrastructure and demonstrate its utility by evaluating it on a diverse set of benchmarks. TREEBEARD is significantly faster than state-of-the-art systems, XGBoost, Treelite and Hummingbird, by 2.6×, 4.7× and 5.4× respectively in a single-core execution setting, and by 2.3×, 2.7× and 14× respectively in multi-core settings.
Making both first-person and third-person perspectives available in real world services

In virtual worlds such as those realized through virtual reality technologies, the participants usually use the third-person perspective to see a wider area because the third-person perspective provides a variety of benefits. In this study, we propose an approach using both the first-person and third-person perspectives in real world applications, such as in smart city services, and evaluate the potential opportunities to offer more advanced digital services. We collect use cases and opinions for designing the prototype system's video presentation methods. Then, we implement prototype systems that enable the users to see from both the first-person and third-person perspectives by using webcams and a head-mounted display. Our approach makes it possible to develop new types of game-inspired smart city services that offer more opportunities through the multiple perspectives.
Salat Activity Recognition on Smartphones using Convolutional Network

Salat, the penta-quotidian form of worship, is an obligation on all Muslims. Vital to Salat is a set of poses and gestures that must be performed in a specific sequence. It is possible for a person to forget their current state in the sequence and skip some of the steps perhaps because the person is new to worship. In this paper, we develop a smartphone-based solution that guides the person through all gestures and poses in the correct sequence via visual and audio feedback. We formulate the problem as human action recognition using per-pose recognition using convolutional neural networks embedded within a deterministic finite automaton of pose transitions. With this approach that uses video frames instead of volumes, and therefore with a considerably smaller model, we achieve better test accuracy on the benchmark dataset, i.e. 83% vs. 78% of the nearest competitor. We also create a larger and finer-grained proprietary dataset with eight classes, and report a test accuracy of 88%.
Multi-Swarm-based Parallel Spider Monkey Optimization Algorithm

Particle swarm optimization (PSO) algorithms face performance challenges, which lean on improving solutions quality, speed-up, dealing with large-scale problems, and exploitation of computational resources. Parallelism represents a suitable paradigm for overcoming the PSO challenges. Spider monkey optimization (SMO) algorithm is a recent PSO algorithm. SMO is based on the principle of dividing the swarm into subgroups, which may decrease its speedup. In this paper, a multi-swarm-based parallel spider monkey optimization (PSMO- MS) is proposed for dealing with large-scale problems based on the multi-swarm mechanism. PSMO-MS is implemented using a synchronous master/slave parallel model. The performance of the proposed PSMO-MS is tested on two 2-dimensional problems (Dekkers and Aarts problem and Camel Back-6 Six Hump problem). The results show that PSMO-MS outperforms SMO in terms of execution time and produces comparable and better solution quality with a large-scale problem, as well as a high solutions’ density.
Design and Implementation of Fall Detection Equipment for the Elderly Based on NB-IoT

In today’s society, the phenomenon of elderly people getting lost and accidentally falling often occurs, and the death caused by falling without timely assistance has become a public concern. In order to solve this problem, a low-power fall detection wearable device for the elderly based on Narrowband Internet of Things (NB-IoT) technology is designed and developed. The device uses STM32 MCU as the control core, and collects position information and motion status through ATK1218-BD positioning module and MPU6050 acceleration sensor respectively. When an elderly person falls, the device will immediately upload the fall alarm signal and location information to the OneNET cloud platform through the M5310A 4G wireless transmission module. The cloud platform can notify the elderly’s family by SMS through the mobile phone APP, so that the family can provide timely assistance to the elderly. Compared with traditional fall detection devices, the device is not only much smaller in size and power consumption, but also can achieve richer functions such as remote positioning and tracking, fall detection, and one-touch call for help. The experimental results prove that this device provides more reliable security for the health monitoring of the elderly.
Features Denoising for Learned Image Coding

In recent years the advancements in the neural networks field have fostered the advent of end-to-end learned coding schemes capable of efficient image representations that reduce the required storage space and transmission time.In general, the features produced by these encoders are entropy-efficient and permit reconstructing the coded image with low distortion. However, whenever they are applied to a generic image, its latent representation might not be the optimal one in the feature space since the coding network parameters were trained to generalize on a wide set of images. This implies that reducing the distance between the optimal features and those generated by the network allows for better rate-distortion tradeoffs.This work proposes a feature denoising approach that permits improving the coding performance over five different learned image coding paradigms with minimal hyperparameters tuning. The proposed solution proved to be quite general and versatile since the decoding architecture and time are not affected by such optimization.
Pairwise Constraints Multidimensional Scaling for Discriminative Feature Learning

As an important data analysis method in the field of machine learning and data mining, feature learning has a wide range of applications in various industries. The traditional multidimensional scaling (MDS) maintains the topology of data points in the low-dimensional embeddings obtained during feature learning, but ignores the discriminative nature between classes of low-dimensional embedded data. Thus, the discriminative multidimensional scaling based on pairwise constraints for feature learning (pcDMDS) model is proposed in this paper. The model enhances the discriminativeness from two aspects. The first aspect is to increase the compactness of the new data representation in the same cluster through fuzzy k-means. The second aspect is to obtain more extended pairwise constraint information between samples. In the whole feature learning process, the model considers both the topology of samples in the original space and the cluster structure in the new space. It also incorporates the extended pairwise constraint information in the samples, which further improves the model’s ability to obtain discriminative features. Finally, the experimental results on twelve datasets show that pcDMDS performs 10.31% and 8.31% higher than PMDS model in terms of accuracy and purity.
Unsupervised Video Segmentation Algorithms Based On Flexibly Regularized Mixture Models

We propose a family of probabilistic segmentation algorithms for videos that rely on a generative model capturing static and dynamic natural image statistics. Our framework adopts flexibly regularized mixture models (FlexMM) [1], an efficient method to combine mixture distributions across different data sources. FlexMMs of Student-t distributions successfully segment static natural images, through uncertainty-based information sharing between hidden layers of CNNs. We further extend this approach to videos and exploit FlexMM to propagate segment labels across space and time. We show that temporal propagation improves temporal consistency of segmentation, reproducing qualitatively a key aspect of human perceptual grouping. Besides, Student-t distributions can capture statistics of optical flows of natural movies, which represent apparent motion in the video. Integrating these motion cues in our temporal FlexMM further enhances the segmentation of each frame of natural movies. Our probabilistic dynamic segmentation algorithms thus provide a new framework to study uncertainty in human dynamic perceptual segmentation.
Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly Detection in Videos

Anomaly detection in videos is a challenging task as anomalies in different videos are of different kinds. Therefore, a promising way to approach video anomaly detection is by learning the non-anomalous nature of the video at hand. To this end, we propose a one-class few-shot learning driven transformer based approach for anomaly detection in videos that is self-context aware. Features from the first few consecutive non-anomalous frames in a video are used to train the transformer in predicting the non-anomalous feature of the subsequent frame. This takes place under the attention of a self-context learned from the input features themselves. After the learning, given a few previous frames, the video-specific transformer is used to infer if a frame is anomalous or not by comparing the feature predicted by it with the actual. The effectiveness of the proposed method with respect to the state-of-the-art is demonstrated through qualitative and quantitative results on different standard datasets. We also study the positive effect of the self-context used in our approach.
Hybrid Model-Based / Data-Driven Graph Transform for Image Coding

Transform coding to sparsify signal representations remains crucial in an image compression pipeline. While the Karhunen-Loève transform (KLT) computed from an empirical covariance matrix ${\mathbf{\bar C}}$ is theoretically optimal for a stationary process, in practice, collecting sufficient statistics from a non-stationary image to reliably estimate ${\mathbf{\bar C}}$ can be difficult. In this paper, to encode an intra-prediction residual block, we pursue a hybrid model-based / data-driven approach: the first K eigenvectors of a transform matrix are derived from a statistical model, e.g., the asymmetric discrete sine transform (ADST), for stability, while the remaining N −K are computed from ${\mathbf{\bar C}}$ for data adaptivity. The transform computation is posed as a graph learning problem, where we seek a graph Laplacian matrix minimizing a graphical lasso objective inside a convex cone sharing the first K eigenvectors in a Hilbert space of real symmetric matrices. We efficiently solve the problem via augmented Lagrangian relaxation and proximal gradient (PG). Using open-source WebP as a baseline image codec, experimental results show that our hybrid graph transform achieved better coding performance than discrete cosine transform (DCT), ADST and KLT, and better stability than KLT.
Analog Over-the-Air Federated Learning With Real-World Data

Federated edge learning enables intelligence services to be deployed at the edge of future wireless network. To address the limited spectral resource and constrained scalability, analog over-the-air federated learning is recently proposed to achieve fast aggregations with enhanced spectral efficiency, privacy and concurrent reduced access latency, via exploiting the superposition property of wireless waveforms. From a practical aspect of real-world data, noisy label quality commonly exists in local data of diverse clients in a heterogeneous wireless network, which would possibly result in performance degradation. In this work, we are the first to explore the overall effects of the interference in the random channel and the noisy labels in local datasets. A comprehensive framework to generate distributed data with discrepancies in data heterogeneity and quality is introduced. We then illustrate the overall effects of both noisy data and channel interference. The numerical results indicate that the two-sided effect also exists in this real-world system. More importantly, the potential positive effect brought by the uncertainty and randomness, could be further investigated for better utilization of the channel interference to improve the robustness of analog over-the-air federated edge learning with real-world data.
A fast method for discovering suitable number of clusters for fuzzy clustering

One main problem of Fuzzy c-Means (FCM) is deciding on an appropriate number of clusters. Although methods have been proposed to address this, they all require clustering algorithms to be executed several times before the right number is chosen. The aim of this study was to develop a method for determining cluster numbers without repeated execution. We propose a new method that combines FCM and singular value decomposition. Based on the percentage of variance, this method can calculate the appropriate number of clusters. The proposed method was applied to several well-known datasets to demonstrate its effectiveness.
Vein-Based Coalitions for Multi-Agent Pattern Formation Tasks

This letter explores the vein trait of the structural formation task and extracts four typical parallel-straight vein structures for generating coalitions. Then, the Vein-Based Multi-Agent Pattern Formation (VB-MAPF) method is proposed to resolve task and path planning on coalitions. Besides, the presented rectifying operation guarantees the optimal coalition structure, and the vein-based pruned conflict-based search reduces the searching space for collision-free path planning. Compared with typical solvers, the inter-performance and the intra-performance orders show the efficiency and scalability of the VB-MAPF method in terms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">makespan</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">average distance</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maximum distance</i> on dozens of coalitions and thousands of agents.
RAPID MAGNITUDE ESTIMATION OF THE AUGUST 5, 2018, LOMBOK EARTHQUAKE USING HIGH-RATE GNSS DATA

The rapid estimation of the magnitude of an earthquake is an essential part of an earthquake early warning system (EEWS), which is used to respond to earthquake shaking and the possible arrival of a tsunami.This research is the first in Indonesia to use Global Navigation Satellite System (GNSS) as part of an EEWS.Three high-rate GNSS datasets were processed using the precise point positioning post-processing method, which resulted in a peak ground displacement (PGD) and peak ground velocity (PGV) value due to the Lombok earthquake on August 5, 2018.The results showed that the magnitude of the PGD from the station closest to the earthquake source (KDOO) was Mw 6.6 within eight seconds of the original time, while that at the farthest location (CMAT) reached Mw 6.9 within 18 seconds of the original time.At the CMAT station, we also estimated the magnitude using PGV, which showed results consistent with the PGD data.These results confirm the potential use of the PGD and PGV data obtained from high-rate GNSS to estimate earthquake magnitudes rapidly as part of an EEWS.
Algorithms for Planning Smoothed Individual Trajectories of Ground Robots

The article is devoted to the development of an algorithm for constructing the trajectory of a robotic platform moving in an environment with obstacles. This algorithm is based on the application of a special local optimization procedure at each planning step and allows us to obtain feasible program trajectories without increasing the computational complexity of algorithms compared to existing methods. The algorithm is based on the application of the improved method of potential fields and subsequent smoothing of the resulting trajectory. The improving of the potential field method consists in a new way of detecting and avoiding local minima. When a local minimum is detected, it is added to the map as an additional obstacle, which makes it possible to avoid it during further trajectory planning. To circumvent obstacles that can be approximated by polygons, the method of the effective point to the obstacle is proposed, which is the equivalent of the latter in relation to the current location of the moving robotic platform when using this planning method. A two-stage technique for smoothing piecewise linear trajectories is proposed. It is assumed that there is some initial suboptimal curve found by any planning method. This curve is optimized using a functional that includes the length of the trajectory and the deviation of the optimized curve from the original curve. At the second stage, the linear segments of the planned straight line are conjugated with second-order curves. As a result, the planned trajectory of motion is a quadratic-linear curve with a smooth function of the trajectory velocity. At the same time, the proposed method of coupling rectilinear sections of the trajectory does not require sudden changes in speed when passing turns. Simulation results confirming the effectiveness of the proposed method of planning the trajectories of robots are considered and discussed.
Adversarial Malicious Encrypted Traffic Detection Based on Refined Session Analysis

The detection of malicious encrypted traffic is an important part of modern network security research. The producers of the current malware do not pay attention to the fact that malicious encrypted traffic can also be detected; they do not construct further adversarial malicious encrypted traffic to deceive existing malicious encrypted traffic detection methods. However, with the increasing confrontation between attack and defense, adversarial malicious encrypted traffic samples will appear gradually, which will make the existing malicious encrypted traffic detection methods obsolete. In this paper, an adversarial malicious encrypted traffic detection method based on refined session analysis (ADRSA) is proposed. The key ideas of this method are: (1) interpretability analysis is used to extract malicious traffic features that are not easily affected by encryption, (2) restoration technology is used to further improve traffic separability, and (3) a deep neural network is used to identify adversarial malicious encrypted traffic. In experimental tests, the ADRSA method could accurately detect malicious encrypted traffic, particularly adversarial malicious encrypted traffic, and the detection rate is more than 95%. However, the detection rate of other malicious encrypted traffic detection methods is almost zero when facing adversarial malicious encrypted traffic. The detection performance of ADRSA exceeds that of the most popular detection methods.