Reviewerly/exHarmony huggingface dataset:
Deep Learning Obstacle Detection and Avoidance for Powered Wheelchair

Depth sensors like RGB-D cameras, LiDARs and laser scanners are widely investigated in research for Smart Wheelchair (SW) to carry out navigation, localization and ob-stacle detection and avoidance tasks. These sensors are costly compared to monocular camera sensor. A single off-the-shelf camera can be an economically efficient sensor to achieve obstacle detection and avoidance. We present in this paper a single camera based obstacle detection and avoidance method without using any 3D information. It is a novel vision-only system for wheelchair obstacle detection and avoidance that uses a Raspberry Pi along with Raspberry Pi camera. The obstacles are detected using a deep learning model built on MobileNetV2 SSD. The model is retrained using a dedicated dataset that was built for this purpose. Bounding boxes are used to mark detected obstacles; and feed them as features to the image space obstacle avoidance module. Figure 1 depicts internal view of what does the system see and an abstract description of our system's functionality.
Research on medical data security sharing scheme based on homomorphic encryption

&lt;abstract&gt; &lt;p&gt;With the deep integration of "AI + medicine", AI-assisted technology has been of great help to human beings in the medical field, especially in the area of predicting and diagnosing diseases based on big data, because it is faster and more accurate. However, concerns about data security seriously hinder data sharing among medical institutions. To fully exploit the value of medical data and realize data collaborative sharing, we developed a medical data security sharing scheme based on the C/S communication mode and constructed a federated learning architecture that uses homomorphic encryption technology to protect training parameters. Here, we chose the Paillier algorithm to realize the additive homomorphism to protect the training parameters. Clients do not need to share local data, but only upload the trained model parameters to the server. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible for issuing training commands and weights, aggregating the local model parameters from the clients and predicting the joint diagnostic results. The client mainly uses the stochastic gradient descent algorithm for gradient trimming, updating and transmitting the trained model parameters back to the server. In order to test the performance of this scheme, a series of experiments was conducted. From the simulation results, we can know that the model prediction accuracy is related to the global training rounds, learning rate, batch size, privacy budget parameters etc. The results show that this scheme realizes data sharing while protecting data privacy, completes the accurate prediction of diseases and has a good performance.&lt;/p&gt; &lt;/abstract&gt;
How Much Noise Suffices for Privacy of Multiagent Systems?

As multiagent systems always involve a large number of nodes and connections, it is crucial to study the privacy-preserving problem with minimal “corrupted” noise in the framework of differential privacy or noise-perturbation schemes. We first show that a moderate amount of noise is sufficient to ensure privacy as long as the minimal observability subspace of the considered system is blurred by noise. Based on this, it is shown that blurring more than half of the sensors can provide a desirable level of privacy protection using a node-based privacy-preserving mechanism. By formulating the problem of the minimal amount of noise injected into an optimization framework, we give conditions on the tradeoff between privacy preservation and the amount of the noise. To further reduce the amount of the injected noise, an edge-based privacy-preserving mechanism is devised. It is found that less than half of the sensors blurred by noise still enable us to solve the privacy-preserving problem if more constraints on the communication topology are imposed. Finally, some discussions and comparisons are conducted to demonstrate the effectiveness of our proposed privacy-preserving strategies.
A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions

Been trapped by local minimums is an important problem in no-linear optimization problems, which is blocking evolutionary algorithms to find the global optimum. Normally, to increase the optimization accuracy, evolutionary algorithms implement search around the best individual. However, overuse of information from a single individual can lead to a rapid diversity losing of the population, and thus reduce the search ability. To overcome this problem, a twinning memory bare-bones particle swarm optimization (TMBPSO) algorithm is presented in this work. The TMBPSO contains a twining memory storage mechanism (TMSM) and a multiple memory retrieval strategy (MMRS). The TMSM enables an extra storage space to extend the search ability of the particle swarm and the MMRS enhances the local minimum escaping ability of the particle swarm. The particle swarm is endowed with the ability of self-rectification by the cooperation of the TMSM and the MMRS. To verify the search ability of the TMBPSO, the CEC2017 benchmark functions and five state-of-the-art population-based optimization algorithms are selected in experiments. Finally, experimental results confirmed that the TMBPSO can obtain high accurate results for no-linear functions.
A Highly Stealthy Adaptive Decay Attack Against Speaker Recognition

Speaker recognition based on deep learning is currently the most advanced and mainstream technology in the industry.Adversarial attacks, an emerging and powerful attack against neural network models, were first applied in the image domain and gradually expanded to other domains, also posing serious security problems for speaker recognition.Common gradient-based attack methods such as FGSM, PGD, and MI-FGSM can deceive speaker recognition models with high confidence, yet their carefully crafted adversarial examples suffer from poor stealthiness and are easily perceived by the human ear.To improve the stealthiness of the adversarial examples, this paper proposes a new attack method called the Adaptive Decay Attack (ADA), which is applied to three different scenarios in speaker recognition.The method takes the set number of iterations as the termination condition, automatically adjusts the size of the maximum perturbation according to whether the attack is successful or not, and then uses the decay methods in learning rates such as exponential decay and cosine annealing to continuously reduce the step size.The experimental results show that under the two speaker recognition models x-vector, and i-vector, the proposed attack method improves the stealthiness metrics such as SNR and PESQ by at least 30% and 39%, respectively, compared with the best PGD attack under speaker identification of untargeted attacks.For the speaker identification task with targeted attacks, the average improvement is at least 20% and 25% compared to PGD.For the speaker verification task, the improvement is at least 29.5% and 33.4% compared to PGD.In addition, we also use this attack method for adversarial training to enhance the robustness of the model.Experimental results show that ADA-based adversarial training takes 28.31% less time than PGD-based adversarial training, and its improved robustness is generally superior to PGD-based adversarial training.Specifically, the attack success rate of PGD and ADA methods decreased from 50.88% to 36.47% and 64.74% to 45.82%, respectively.
FedCos: A Scene-Adaptive Enhancement for Federated Learning

Federated learning (FL) training global machine learning models over distributed edge devices has attracted sustained attentions. However, the heterogeneity of client data severely degrades the performance of FL compared with that in centralized training. On the one hand, it slows down or even stalls global updates, leading to inefficient communication. On the other hand, it enlarges the distances between local models, resulting in an aggregated global model with poor performance. Fortunately, these shortcomings can be mitigated by reducing the angle between the directions in which a local model move. Based on this observation, we propose FedCos, which reduces the directional inconsistency of local models by introducing a cosine-similarity penalty. It promotes local model iterations toward an auxiliary global direction. Moreover, our approach is auto-adapted to various non-identically and independently distributed (IID) settings without an elaborate selection of hyperparameters. Experimental results on both vision and language tasks with a variety of models (including CNN, ResNet, LSTM, etc.) show that FedCos outperforms the well-known baselines and can enhance them under a variety of FL scenes, including varying degrees of data heterogeneity, different number of participants, and cross-silo and cross-device settings. Besides, FedCos improves the communication efficiency by 2–5 times. With the help of FedCos, multiple FL methods require significantly fewer communication rounds than before to obtain a comparable model.
UV Grid Generation on 3D Freeform Surfaces for Constrained Robotic Coverage Path Planning

There are many industrial robotic applications which require a manipulator’s end-effector to fully cover a 3D surface region in a constrained motion, such as painting, spray coating, abrasive blasting, polishing, shotcreting, etc. The manipulator must satisfy surface task constraints imposed on the end-effector while maintaining manipulator joint constraints. Coverage path planning (CPP) in this context generally involves placing commonly used coverage patterns (such as raster, spiral, or dual-spiral) onto the surface. There is substantial research for CPP on 2D surfaces, however, the problem of generating surface task constraints and evenly spaced coverage paths becomes particularly difficult when considering 3D freeform surfaces. Previous research concerning CPP on 3D surfaces consider parametric surfaces with limited surface curvature or produce unevenly spaced coverage paths. In this paper, we introduce a novel method to generate a uv grid on a 3D freeform surface (represented as a 3D polygon mesh) to facilitate feasibility checking for constrained coverage motion under task and manipulator constraints and to significantly ease the creation of more evenly spaced coverage paths for optimal application of task requirements. We applied our method to example 3D freeform surfaces to demonstrate its effectiveness.
A Group Feature Ranking and Selection Method Based on Dimension Reduction Technique in High-Dimensional Data

Group feature selection methods select the important group features by removing the irrelevant group features for reducing the complexity of the model.There are a few group feature selection methods that used ranking techniques.Ranking methods provide the relative importance of each group.For this purpose, we developed a sparse group feature ranking method based on the dimension reduction technique for high dimensional data.Firstly, we applied relief to each group to remove irrelevant features.Secondly, we extract the new feature that represents each feature group.To this end, we reduce the dimension of the group feature into a single dimension by applying Fisher linear discriminant analysis (FDA) for each feature group.At last, we estimate the relative importance of the new feature by applying random forest and selecting those important features that have higher importance compared with other ones.In the end, different machinelearning algorithms were used to train and test the models.For the experiment, we compared the proposed with supervised group lasso (SGL) methods by using real-life high-dimensional datasets.Results show that the proposed method selects a few important group features just like the existing group feature selection method and provides the ranking and relative importance of all group features.SGL slightly performs better on logistic regression whereas the proposed method performs better on support vector machine, random forest, and gradient boosting in terms of classification performance metrics.
Fast and Effective Superpixel Segmentation Using Accurate Saliency Estimation

Superpixels through Iterative CLEarcutting (SICLE) is a recently proposed framework for superpixel segmentation. SICLE consists of three steps: (i) seed oversampling; (ii) superpixel generation; and (iii) seed removal; such that, after step (i), steps (ii) and (iii) are repeated until a desired number of superpixels is obtained. Such pipeline showed effective and efficient multiscale superpixel segmentation. Furthermore, if an object is desired, it is possible to improve delineation by providing its probable location, often called saliency. While classical methods estimate object saliency by contrast-based criteria, recent ones use deep-learning strategies for accurate estimation. SICLE shows robustness for low-quality saliency estimations, but it struggles to effectively take advantage of the high-quality ones. In this work, we propose a generalization of its path-cost function and seed removal criterion (steps (ii) and (iii), respectively), adapting SICLE to a given saliency map. By choice of a binary parameter, SICLE can take advantage of low- and high-quality saliency maps for better segmentation. Results show that, by exploiting the accurate information of the saliency map, our improved SICLE version surpasses state-of-the-art methods in traditional delineation metrics while requiring only two iterations for segmentation, being significantly faster than its predecessor and SLIC.
Boosting Online Feature Transfer via Separable Feature Fusion

Feature distillation is a widely used training method to transfer feature information from a teacher to a student network. Current methods seek to minimize the reconstruction error of hidden feature maps between teacher-student models by explicitly optimizing distillation loss. However, some feature loss methods require complex transformations, which are not easy to optimize. In this paper, we propose a novel and effective feature distillation method, which learns to transfer knowledge by applying feature fusion as an alternative to distillation loss. Specifically, we fuse the intermediate feature of the student model to the attention teacher network, which has better representation and relatively less training cost. During training, this separable feature fusion can effectively transfer feature knowledge and is easy to optimize without complex transformation. After training, the feature fusion and the teacher network can be discarded, and the student network can be used separately in inference. Equipped with auxiliary classifier for ensemble logits distillation, our Separable Feature Knowledge Distillation (SFKD) obtains state-of-the-art performance. In experiments, SFKD achieves 4% performance improvement on CIFAR-100 and 2% on ImageNet for ResNet models, which substantially outperforms other feature distillation methods.
The mediation role of organizational culture between employee turnover intention and job satisfaction

The present research aimed to investigate the mediation role of employee turnover factors including (employee overworked, lack of feedback, lack of growth, lack of recognition, lack of reward) with job satisfaction. The current research attempted to measure five research hypotheses developed earlier in this study. For this reason, the study employed quantitative method to examine the mediation role. An online questionnaire was prepared by the researchers through developing and adapting survey from different sources. The study was able to gather 78 filled questionnaires. The findings showed that a Sobel test revealed that organizational culture strongly mediates the association between employee turnover factors (employee overworked, lack of feedback, lack of growth, lack of recognition, lack of reward) and job satisfaction in selected private businesses in Iraq's Kurdistan.
Extracting Requirements Models from Natural-Language Document for Embedded Systems

Most of the requirements of embedded systems are written in natural language by users or customers. When the size of the document is large, it is not easy for developers to understand and analyze these requirements. Requirements modeling has been widely used and proven to be helpful to understand and analyze requirements. Manual analysis of these natural language requirements and extracting models are time-consuming and error-prone. Therefore, in this paper, we present a framework to extract model elements and semi-automatically generate requirements models from the NL requirements document for embedded systems. This leads to considerably simplify and accelerate the requirements development for embedded systems.
MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects

Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background. However, it is challenging for existing bokeh rendering methods to simulate realistic partial occlusion effects due to the missing information of the occluded area in an all-in-focus image. Inspired by the learnable 3D scene representation, Multiplane Image (MPI), we attempt to address the partial occlusion by introducing a novel MPI-based high-resolution bokeh rendering framework, termed MPIB. To this end, we first present an analysis on how to apply the MPI representation to bokeh rendering. Based on this analysis, we propose an MPI representation module combined with a background inpainting module to implement high-resolution scene representation. This representation can then be reused to render various bokeh effects according to the controlling parameters. To train and test our model, we also design a ray-tracing-based bokeh generator for data generation. Extensive experiments on synthesized and real-world images validate the effectiveness and flexibility of this framework.
PRNU-Net: a Deep Learning Approach for Source Camera Model Identification based on Videos Taken with Smartphone

Recent advances in digital imaging have meant that every smartphone has a video camera that can record highquality video for free and without restrictions.In addition, rapidly developing Internet technology has contributed significantly to the widespread distribution of digital video via web-based multimedia systems and mobile applications such as YouTube, Facebook, Twitter, WhatsApp, etc.However, as the recording and distribution of digital video has become affordable nowadays, security issues have become threatening and have spread worldwide.One of the security issues is the identification of source cameras on videos.Generally, two common categories of methods are used in this area, namely Photo Response Non-Uniformity (PRNU) and Machine Learning approaches.To exploit the power of both approaches, this work adds a new PRNU-based layer to a convolutional neural network (CNN) called PRNU-Net.To explore the new layer, the main structure of the CNN is based on the MISLnet, which has been used in several studies to identify the source camera.The experimental results show that the PRNU-Net is more successful than the MISLnet and that the PRNU extracted by the layer from low features, namely edges or textures, is more useful than high and mid-level features, namely parts and objects, in classifying source camera models.On average, the network improves the results in a new database by about 4%.
MS-KARD: A Benchmark for Multimodal Karate Action Recognition

Classifying complex human motion sequences is a major research challenge in the domain of human activity recognition. Currently, most popular datasets lack a specialized set of classes pertaining to similar action sequences (in terms of spatial trajectories). To recognize such complex action sequences with high inter-class similarity, such as those in karate, multiple streams are required. To fulfill this need, we propose MS-KARD, a Multi-Stream Karate Action Recognition Dataset that uses multiple vision perspectives, as well as sensor data - accelerometer and gyroscope. It includes 1518 video clips along with their corresponding sensor data. Each video was shot at 30fps and lasts around one minute, equating to a total of 2,814,930 frames and 5,623,734 sensor data samples. The dataset has been collected for 23 classes like Jodan Zuki, Oi Zuki, etc. The data acquisition setting involves the combination of 2 orthogonal web cameras and 3 wearable inertial sensors recording both vision and inertial data respectively. The aim of this dataset is to aid research that deals with recognizing human actions that have similar spatial trajectories. The paper describes statistics of the dataset, acquisition setting, and provides baseline performance figures using popular action recognizers. We propose an ensemble-based method, KarateNet, that performs decision-level fusion on the two input modalities (vision and sensor data) to classify actions. For the first stream, the RGB frames are extracted from the videos and passed into action recognition networks like Temporal Segment Network (TSN) and Temporal Shift Module (TSM). For the second stream, the sensor data is converted into a 2-D image and fed into a Convolutional Neural Network (CNN). The results reported were obtained on performing a fusion of the 2 streams. We also report results on ablations that use fusion with various input settings. The dataset and code will be made publicly available.
Conflict-Based Search for Explainable Multi-Agent Path Finding

The goal of the Multi-Agent Path Finding (MAPF) problem is to find non-colliding paths for agents in an environment, such that each agent reaches its goal from its initial location. In safety-critical applications, a human supervisor may want to verify that the plan is indeed collision-free. To this end, a recent work introduces a notion of explainability for MAPF based on a visualization of the plan as a short sequence of images representing time segments, where in each time segment the trajectories of the agents are disjoint. Then, the problem of Explainable MAPF via Segmentation asks for a set of non-colliding paths that admit a short-enough explanation. Explainable MAPF adds a new difficulty to MAPF, in that it is NP-hard with respect to the size of the environment, and not just the number of agents. Thus, traditional MAPF algorithms are not equipped to directly handle Explainable MAPF. In this work, we adapt Conflict Based Search (CBS), a well-studied algorithm for MAPF, to handle Explainable MAPF. We show how to add explainability constraints on top of the standard CBS tree and its underlying A* search. We examine the usefulness of this approach and, in particular, the trade-off between planning time and explainability.
MAC-Layer Traffic Shaping Defense Against WiFi Device Fingerprinting Attacks

WiFi networks are vulnerable to statistical traffic analysis attacks, even when a WiFi network is securely encrypted and the attacker is unable to join the network. Many defenses proposed in the literature are inefficient to deal with profiling attacks against WiFi-based Internet-of-Things (IoT) devices, because they burden the Internet traffic with high bandwidth overhead and pose deliberate delay on packet transmission. In this paper, we propose a new MAC-layer packet injection technique where injected dummy packets only exist within the WiFi link between IoT devices and their connected WiFi access point. This traffic shaping defense is effective against data-link device profiling attacks without adding any Internet-side overhead or time delay in legitimate traffic. We evaluated our approach on four WiFi-based IoT devices against a recent privacy attack, and showed the reduction of attack classification accuracy from the original 100% to 54%, close to random guessing..