Reviewerly/exHarmony huggingface dataset:
FedTour: Participatory Federated Learning of Tourism Object Recognition Models with Minimal Parameter Exchanges between User Devices

In this paper, we propose FedTour, a federated learning-based method for training tourism object recognition models, which utilizes short-distance direct communication between user devices and maximizes the model performance within a limited number of updates. In FedTour, whenever two user devices are within range, they first exchange metadata including the learning degree (e.g., recognition accuracy) of their models, and determine whether it is effective to integrate the peer model by using a regressor trained with various pairs of models with different accuracy to predict the accuracy of the merged model. Once it is deemed effective, the model parameters are exchanged and the model is updated using FedAvg (averaging weights of two models of user devices). By carefully setting the threshold of whether FedAvg is applied or not, model performance is improved within a limited number of model parameter exchanges resulting in lower power consumption of user devices. We conducted a simulation using mobile phone trace data of actual users in a real sightseeing area and evaluated the improvement in accuracy of a CNN model that recognizes 10 objects while limiting the number of model parameter exchanges to only 40. Results show FedTour increased the initial model accuracy by 112%, while the baseline gossip-based method achieved 69%.
Preliminary Results on Sensitive Data Leakage in Federated Human Activity Recognition

Sensor-based Human Activity Recognition (HAR) has been a hot topic in pervasive computing for many years, as an enabling technology for several context-aware applications. However, the deployment of HAR in real-world scenarios is limited by some major challenges. Among those issues, privacy is particularly relevant, since activity patterns may reveal sensitive information about the users (e.g., personal habits, medical conditions). HAR solutions based on Federated Learning (FL) have been recently proposed to mitigate this problem. In FL, each user shares with a cloud server only the parameters of a locally trained model, while personal data are kept private. The cloud server is in charge of building a global model by aggregating the received parameters. Even though FL avoids the release of labelled sensor data, researchers have found that the parameters of deep learning models may still reveal sensitive information through specifically designed attacks. In this paper, we propose a first contribution in this line of research by introducing a novel framework to quantitatively evaluate the effectiveness of the Membership Inference Attack (MIA) for FL-based HAR. Our preliminary results on a public HAR dataset show how the global activity model may actually reveal sensitive information about the participating users and provide hints for future work on countering such attacks.
Sampling diversity driven exploration with state difference guidance

Exploration is one of the key issues of deep reinforcement learning, especially in the environments with sparse or deceptive rewards. Exploration based on intrinsic rewards can handle these environments. However, these methods cannot take both global interaction dynamics and local environment changes into account simultaneously. In this paper, we propose a novel intrinsic reward for off-policy learning, which not only encourages the agent to take actions not fully learned from a global perspective, but also instructs the agent to trigger remarkable changes in the environment from a local perspective. Meanwhile, we propose the double-actors–double-critics framework to combine intrinsic rewards with extrinsic rewards to avoid the inappropriate combination of intrinsic and extrinsic rewards in previous methods. This framework can be applied to off-policy learning algorithms based on the actor–critic method. We provide a comprehensive evaluation of our approach on the MuJoCo benchmark environments. The results demonstrate that our method can perform effective exploration in the environments with dense, deceptive and sparse rewards. Besides, we conduct sufficient ablation and quantitative analyses to intrinsic rewards. Furthermore, we also verify the superiority and rationality of our double-actors–double-critics framework through comparative experiments.
Pedestrian re-identification based on multi-stream modal

The goal of PReID (Person re-identification, PReID) is to maximize the function of distinguishing pedestrians in order to deal with the influence of uncertain background. The multi-layer learning method based on the neural network is applied to the background of extracting the feature information of the image, according to the composition method of the pedestrian structure, the problem of pedestrian rematching is considered in detail. Designed and proposed a multi-flow pedestrian re-identification algorithm, based on the neural network framework of global and local feature extraction, combined with multiple loss functions to optimize the pedestrian re-identification process, and achieve the purpose of pedestrian re-matching. Use long and short-term memory (LSTM) networks to model pedestrian images, focusing on the combination of information between global and local features. Using triple network multi-channel feature extraction method to expand the breadth of image extraction. In addition, the introduction of multiple losses to optimize the fusion of local information reduces the adverse effects of this part on network discrimination, realizes the complementarity of local body features and global features, and the complementarity of recognition tasks and ranking tasks to achieve higher feature learning accuracy. Experimental results show that the proposed method is much better than the latest method on three challenging data sets (including Market-1501, CUHK03 and DukeMTMC-reID).
The Role of Open Access in Enhancing Equitable Curricula and Research Outputs

When educators have difficulty accessing peer-reviewed research, it is inequitable to expect them to compete with educators who have access to a plethora of resources. Inequities have been a historically-identified educational problem; however, the forced online learning that occurred during COVID-19 restrictions amplified discrepancies experienced by tertiary educators. Scholars who were forced to work without strong information communication technologies infrastructure and who experienced limited access to online resources struggled more than those that had 24-hour uninhibited access. Education came to a near standstill for those that could not easily move their activities online. Prior to the pandemic, individuals working with curricula were already feeling handicapped by the lack of access. When physical libraries were closed, it became nearly impossible for many to move forward. This chapter explores the changing publishing paradigms, particularly the role of OA and how increasing open dissemination of scholarly outputs can reduce inequities in curricula and research activities.
A comparative analysis of techniques for extracting features from the object in image processing

Visual informations is very rich to control and pursue mobile robots, like wheeled mobile robots, underwater robots and aerospace mobile robots, etc. These are considered as mobile robots that move in different spaces, their main problem in this case is navigation especially in unknown environments. In effect, this navigation is possible only by the localisation and orientation of the robot by using different embedded sensors. We have the camera which is a necessary sensor in this work, it is an embedded instrument that gives very rich visual information as a sensor complementing the other sensors. In this context, the recognition of objects from visual informations is a main function among the functions very useful in image processing tasks due to its varied applications in the field of robotics. Based on the analysis of this informations and the determination of image features like color or shape or object primitives (points, lines, edges, etc.) or some other features. What interest us in this paper, various feature extraction techniques and classification of point and edge detection are discussed which are required for object recognition showing advantages and disadvantages of the selected algorithms. So, points and edge detection refers to the process of identifying and detecting sharp discontinuities in an image. In this work, we try to develop a novel algorithm using the work on the existing to create a novel detector.
Differentiable Network Pruning via Polarization of Probabilistic Channelwise Soft Masks

Channel pruning has been demonstrated as a highly effective approach to compress large convolutional neural networks. Existing differentiable channel pruning methods usually use deterministic soft masks to scale the channelwise outputs and explore an appropriate threshold on the masks to remove unimportant channels, which sometimes causes unexpected damage to the network accuracy when there are no sweet spots that clearly separate important channels from redundant ones. In this article, we introduce a new differentiable channel pruning method based on polarization of probabilistic channelwise soft masks (PPSMs). We use variational inference to approximate the posterior distributions of the masks and simultaneously exploit a polarization regularization to push the probabilistic masks towards either 0 or 1; thus, the channels with near-zero masks can be safely eliminated with little hurt on network accuracy. Our method significantly relieves the difficulty faced by the existing methods to find an appropriate threshold on the masks. The joint inference and polarization of probabilistic soft masks enable PPSM to yield better pruning results than the state of the arts. For instance, our method prunes 65.91% FLOPs of ResNet50 on the ImageNet dataset with only 0.7% model accuracy degradation.
PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models

We propose a ParametRIc MAnifold Learning (PRIMAL) algorithm for Gaussian mixtures models (GMM), assuming that GMMs lie on or near to a manifold of probability distributions that is generated from a low-dimensional hierarchical latent space through parametric mappings. Inspired by principal component analysis (PCA), the generative processes for priors, means and covariance matrices are modeled by their respective latent space and parametric mapping. Then, the dependencies between latent spaces are captured by a hierarchical latent space by a linear or kernelized mapping. The function parameters and hierarchical latent space are learned by minimizing the reconstruction error between ground-truth GMMs and manifold-generated GMMs, measured by Kullback-Leibler Divergence (KLD). Variational approximation is employed to handle the intractable KLD between GMMs and a variational EM algorithm is derived to optimize the objective function. Experiments on synthetic data, flow cytometry analysis, eye-fixation analysis and topic models show that PRIMAL learns a continuous and interpretable manifold of GMM distributions and achieves a minimum reconstruction error.
Exploiting Japanese–Chinese Cognates with Shared Private Representations for NMT

Neural machine translation has achieved remarkable progress over the past several years; however, little attention has been paid to machine translation (MT) between Japanese and Chinese, which share a large proportion of cognate words that can be utilized as additional linguistic knowledge to enhance translation performance. In this article, we seek to strengthen the semantic correlation between Japanese and Chinese by leveraging cognate words that share common Chinese characters. Specifically, we experiment with three strategies: (1) a shared vocabulary with cognate lexicon induction, which models the commonality between source and target cognates; (2) a shared private representation with a dynamic gating mechanism, which models the language-specific features on the source side; and (3) an embedding shortcut, which enables the decoder to access the shared private representation with shortest distance and aids the training process. The experiments and analysis presented in this article demonstrate that our proposed approaches can significantly improve the performance of both Japanese-to-Chinese and Chinese-to-Japanese translations and verify the effectiveness of exploiting Japanese–Chinese cognates for MT.
Cross-silo federated learning based decision trees

Most research and application in the field of Machine Learning focus on training a model for a particular task such as churn prediction by using training data present on one machine or in a data center. Nowadays, in many organizations and industries, the training data exists in different (isolated) locations. In order to protect data privacy and security, it is not feasible to gather all the training data to one location and perform a centralized training of machine learning models. Federated Learning (FL) is a form of machine learning technique where the goal is to learn a high-quality model trained across multiple clients (such as mobile devices) or data centers without ever exchanging their training data. Most of the existing research on FL focuses on two directions: (a) training parametric models such as neural networks and (b) mainly focusing on an FL setup containing millions of clients. However, in this work, we focus on non-parametric models such as decision trees, and more specifically, we build decision trees using federated learning and train random forest model. Our work aims at involving corporate companies instead of mobile devices in the federated learning process. We consider a setting where a small number of organizations or industry companies collaboratively build machine learning models without exchanging their privately held large data sets. We designed a federated decision tree-based random forest algorithm using FL and conducted our experiments using different datasets. Our results demonstrate that each participating corporate company have benefit in improving their model's performance from federated learning. We also introduce how to incorporate differential privacy into our decision tree-based random forest algorithm.
Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.
Computer Deep Learning Network Security Vulnerability Detection Based on Virtual Reality Technology

In order to detect the computer network security technology vulnerabilities due to various factors, the normal operation of the computer network must be ensured, the user’s confidential information must be protected, and it is proposed that the analysis and research on security vulnerability detection must be strengthened. This study introduces the working principle of the network security monitoring system, analyzes the key technologies involved in the system development process and network programming technology, gives the overall architecture of the system, and designs the processing flow of the monitoring function. The test and analysis of the system show that the design of the system has achieved the expected design goal. The design of the system has achieved the expected design goal. The five test points can meet the standard time specified in the demand analysis process. The time difference of all module test points in the test is less than 3 s. The system can realize the remote acquisition and real-time monitoring of the network access, file system operation, system operation status, and other information of the controlled computer. Through the test and analysis of the system, it is shown that the system has achieved the expected design goal, the working state problem, and can meet the functional requirements of internal network security monitoring. It can be applied to enterprises, institutions, and departments that have higher requirements for intranet information security.
Photovoltaic Energy Production Forecasting and Operational Analytics: A Real-World Study

Photovoltaic energy production is one of the major parts for the shift of our societies away from fossil fuels. Their irregular nature however poses a challenge for electricity grid managers. PV energy production forecasting and monitoring is extremely important to guarantee that the grid will be able to supply consumers at all times and without any unexpected interruptions. Our goal is to provide build a system that can effectively monitor all aspects of the operation of a PV power station and provide owners, facility managers and power grid administrators with high quality forecasts and operational insights. To better understand and evaluate our work, we test our system in a real-world case study from Arta, Greece, and benchmark the effectiveness of both mathematical models and machine learning based solutions with regards to both precision, consistency and trustworthiness.
Elicitation of adaptive requirements using creativity triggers

Adaptive systems react to changes in their environment by changing their behavior. Identifying the needed adaptations is central to requirements elicitation for adaptive systems. As the necessary or potential adaptations are usually not obvious to the stakeholders, the problem is how to effectively elicit adaptation-relevant information. One approach is to use creativity techniques to support the systematic identification and elicitation of adaptation requirements. In particular, here, we analyze a set of creativity triggers defined for systematic exploration of potential adaptation requirements. We compare these triggers with brainstorming as a baseline in a controlled experiment with 85 master students. The results indicate that the proposed triggers are suitable for the efficient elicitation of adaptive requirements and that the 15 trigger questions produce significantly more requirements fragments than solo brainstorming.
WSN-Driven Posture Recognition and Correction Towards Basketball Exercise

In order to enhance the daily training for basketball, this paper establishes a human posture estimation framework by using monocular camera and wireless sensor network. First, the daily basketball training images are collected by monocular camera and transmitted through wireless sensor network. Second, the collected images are processed by an observation and reasoning model which is based on component and graph reasoning. The basketball player's posture is depicted by the rotation invariant features of edge field. The extracted features are used to learn a boosting classifier as the observation model. The experimental results show that the posture recognition rate can achieve more than 88% for basketball player's action.
Vehicle detection based on improved YOLOv4

With the development of deep learning, the performance of vehicle detection algorithms based on deep learning is constantly improved, which plays an important role in the construction of intelligent transportation. Single-stage target detection model is widely used in vehicle real-time detection because of its advantages of detection speed. In view of the low detection rate of small objects in images, this article proposes a vehicle object detection method based on the improved YOLOv4 algorithm, using k-means clustering algorithm to re-create a anchors suitable for the UA-Detrac dataset and improve the PANet. Compared with other target detection methods, the improved algorithm can effectively detect small targets and improve the Precision, Recall and mAP of vehicle targets.
An Energy Efficient and Elastic Edge-Cloud for Computational Sensing in Smart Geriatric Homes

One of the recent transformations in edge computing realm is an emerging computing model known as "edge-cloud computing" which mimics the cloud computing behavior on edge platforms. These hybrid platforms are in demand as they provide privacy preserving computation for always-on sensor nodes at the edge. Conventionally, they have been implemented using off-the-shelf cloud management frameworks in micro data-centers, but at the cost of increased physical volume, power consumption and portability. Alternatively, Single Board Computers (SBCs) have evolved significantly in terms of computing capability and form factor, and consequently, SBC-clusters are now being exploited for the purpose. This paper presents an energy efficient edge-cloud platform that leverages the scalability of low-cost slave clusters along with an architecture tailored for computational sensing in ambient care facilities. For instance, monitoring activities of daily living in a smart elderly care home presents challenges such as variation in occupancy rate and increased demand for time-critical processing/actuation, like detecting falls. This results in several challenges such as sudden increase in data traffic as well as increased energy consumption on the worker nodes. The proposed architecture mitigates this by virtue of computational sensing and efficient task allocation among the slave-clusters. The computing capability of the platform has been demonstrated using an ensemble action recognition classifier. The monthly power budget for the same has been estimated using a behavior model derived from an existing human occupancy data-set. The results illustrate the potential of this platform to aid computational sensing workloads at-scale.
On fast implementations of elliptic curve point multiplication

Elliptic curve point multiplication (ECPM) is the most frequently used as well as the most time consuming operation used in an elliptic curve cryptosystem. An implementation of this operation over a prime field can be orders of magnitude slower than an implementation over other finite fields and can even be a disadvantage in architectures with restricted bit size. For this reason it is useful to find other finite field alternatives that have suitable properties for facilitating fast arithmetic. In this work we propose a new FPGA implementation of elliptic curve point multiplication over an optimal extension field defined by an irreducible binomial of degree three and the operations on the ground field are modulo a pseudo Mersenne prime. The implementation was synthesized and simulated in the Virtex-4, Virtex-5 and Virtex-6 families of FPGAs. By comparing the execution times with the best times currently found in the literature, we found that ECPM times are up to 10, 43 and 36 times faster, respectively, on each of the previous platforms.