Reviewerly/exHarmony huggingface dataset:
Bloomerang webinar gives tips on crafting 2024 strategic calendar

Nonprofit Business AdvisorVolume 2024, Issue 412 p. 9-9 Resources Bloomerang webinar gives tips on crafting 2024 strategic calendar First published: 11 December 2023 https://doi.org/10.1002/nba.31624Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2024, Issue412January 2024Pages 9-9 RelatedInformation
Rethinking DABNet: Light-weight Network for Real-time Semantic Segmentation of Road Scenes

Recent advancements in autonomous driving and mobile devices have led to the development of real-time and lightweight semantic image segmentation models. However, these algorithms readily suffer from inherent accuracy loss compared to large networks. DABNet [1] presented a highly efficient method to balance the accuracy-model size tradeoff. Nevertheless, the bottleneck structure and single-scale receptive field of its building block have limited performance for the given network size. To further improve the segmentation score and reduce the number of parameters, the basic block is redesigned using an inverted-residual and dilation pyramid structure (IRDP). The IRDP module can efficiently learn contextual features at multiple dilations within the block. Using the inverted-residual structure with an expansion layer prevents information loss due to the dimensionality reduction of the feature space. The IRDP block is utilized to rebuild the DABNet structure, working in real-time for resource-constrained devices. In addition, a fast and lightweight decoder-FLD is also proposed to improve the segmentation accuracy of the network. Experiments performed on Cityscapes and CamVid datasets demonstrate the effectiveness of the proposed approach. On Cityscapes, IRDPNet can achieve an mIOU of 75.62%. At the same time, the lighter version gets a mIoU of 71.32% with only 0.32 Million parameters, which is similar to the DABNet accuracy with half the number of parameters.
A Rearrangement and Restore Mixer Model for Target-Oriented Multimodal Sentiment Classification

With the development of fine-grained multimodal sentiment analysis tasks, target-oriented multimodal sentiment analysis has received more attention, which aims to classify the sentiment of target with the help of textual and associated image features. Existing methods focus on exploring fine-grained image features and incorporate transformer-based complex fusion strategies, while ignoring the heavy computational burden. Recently, some lightweight MLP-based methods have been successfully applied to multimodal sentiment classification tasks. In this paper, we propose an effective rearrangement and restore mixer model (RR-Mixer) for target-oriented multimodal sentiment classification (TMSC), which dedicates the interaction of image, text, and targets along the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">modal-axis</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sequential-axis</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature channel-axis</i> through rearrangement and restore operations. Specifically, we take Vision Transformer (ViT) and Robustly optimized BERT (RoBERTa) pre-trained models to extract image and textual features respectively. Further, we adopt cosine similarity to select the most semantically relevant image features. Then, an RR-Mixer Module is designed for mixed multimodal features, with the core technology consisting of rolling, grouping rearrangement and restore operations. Moreover, we introduce MLP Unit to learn the information of different modalities for inter-modal interaction. The results show that our model achieves superior performance on two benchmark multimodal datasets, TWITTER-15 and TWITTER-17, with a significant improvement of 4.66%, 1.26% in terms of macro-F1.
VERF: Runtime Monitoring of Pose Estimation with Neural Radiance Fields

We present VERF, a collection of two methods (VERF-PnP and VERF-Light) for providing runtime assurance on the correctness of a camera pose estimate of a monocular camera without relying on direct depth measurements. We leverage the ability of NeRF (Neural Radiance Fields) to render novel RGB perspectives of a scene. We only require as input the camera image whose pose is being estimated, an estimate of the camera pose we want to monitor, and a NeRF model containing the scene pictured by the camera. We can then predict if the pose estimate is within a desired distance from the ground truth and justify our prediction with a level of assurance. VERF-Light does this by rendering a viewpoint with NeRF at the estimated pose and estimating its relative offset to the sensor image up to scale. Since scene scale is unknown, the approach renders another auxiliary image and reasons over the consistency of the optical flows across the three images. VERF-PnP takes a different approach by rendering a stereo pair of images with NeRF and utilizing the Perspective-n-Point (PnP) algorithm. We evaluate both methods on the LLFF dataset, on data from a Unitree A1 quadruped robot, and on data collected from Blue Origin's sub-orbital New Shepard rocket to demonstrate the effectiveness of the proposed pose monitoring method across a range of scene scales. We also show monitoring can be completed in under half a second on a 3090 GPU.
Multilingual Program Code Classification Using $n$-Layered Bi-LSTM Model With Optimized Hyperparameters

Programmers are allowed to solve problems using multiple programming languages, resulting in the accumulation of a huge number of multilingual solution codes. Consequently, identifying codes from this vast archive of multilingual codes is a challenging and non-trivial task. Considering the codes' complexity compared to natural languages, conventional language models have had limited success. Deep neural network models have achieved state-of-the-art performance in programming-related tasks. However, the multilingual code classification based on the problem name or algorithm remains an open problem. This paper presents a novel multilingual program code classification model for the code classification task based on algorithms and problem names. First, a layered bidirectional long short-term memory model is designed to better understand the complex code context. Second, preprocessing, tokenization, and encoding processes are performed on real-life datasets. Next, clean and trainable formatted data are prepared. Finally, experiments are conducted on real-life datasets (e.g., sorting, searching, graphs and trees, numerical computations, basic data structures, and their combinations) with optimized hyperparameter settings. The results show that the proposed model can effectively improve the code classification accuracy compared to other baseline models.
Reliable Cross-camera Learning in Random Camera Person Re-identification

Most existing person re-identification (Re-ID) methods rely on high-cost manual annotations. To overcome the applicable issue, we focus on a novel semi-supervised Re-ID without cross-camera annotations, which we call random camera supervised person Re-ID (RCS). It is beneficial to real-world application, since a short-time and cheap annotation is conductive to rapid deployment of person re-ID. But only a small proportion of identities are annotated under a random camera, which is extremely challenging for Re-ID cross-camera pairing without cross-camera labeling or a labeled image for each identity. Towards reliable cross-camera learning, we propose a random camera guided framework (RCG) that can fully make use of the few labeled images with promising performance. RCG has two components: 1) Different from other complex methods to improve the clustering accuracy, Random camera guided clustering is adopted to mine cross-camera images of each identity, where the few labeled data helps to guide the simple but effective cluster split and combination. 2) Network learning under RCS is conducted with cluster-wise and camera-wise contrastive learning, where we deal with the camera variance in subgroup unit innovatively and further emphasis the importance of the labeled images. Extensive experiments on three large-scale Re-ID datasets show that our proposed approach not only outperforms state-of-the-art methods by a large margin, but achieves better performance with less annotation and more flexible RCS setting.
One for All: A Unified Generative Framework for Image Emotion Classification

Image Emotion Classification (IEC) is an essential research area, offering valuable insights into user emotional states for a wide range of applications, including opinion mining, recommendation systems, and mental health treatment. The challenges associated with IEC are mainly attributed to the complexity and ambiguity of human emotions, the lack of a universally accepted emotion model, and excessive dependence on prior knowledge. To address these challenges, we propose a novel Unified Generative framework for Image Emotion Classification (UGRIE), which is capable of simultaneously modeling various emotion models and capturing intricate semantic relationships between emotion labels. Our approach employs a flexible natural language template, converting the IEC task into a template-filling process that can be easily adapted to accommodate a diverse range of IEC tasks. To further enhance the performance, we devise a mapping mechanism to seamlessly integrate the multimodal pre-training model CLIP with the text generation pre-training model BART, thus leveraging the strengths of both models. A comprehensive set of experiments conducted on multiple public datasets demonstrates that our proposed method consistently outperforms existing approaches to a large margin in supervised settings, exhibits remarkable performance in low-resource scenarios, and unifies distinct emotion models within a single, versatile framework.
Stability of Aggregation Graph Neural Networks

In this paper we study the stability properties of aggregation graph neural networks (Agg-GNNs) considering perturbations of the underlying graph. An Agg-GNN is a hybrid architecture where information is defined on the nodes of a graph, but it is processed block-wise by Euclidean CNNs on the nodes after several diffusions on the graph shift operator. We derive stability bounds for the mapping operator associated to a generic Agg-GNN, and we specify conditions under which such operators can be stable to deformations. We prove that the stability bounds are defined by the properties of the filters in the first layer of the CNN that acts on each node. Additionally, we show that there is a close relationship between the number of aggregations, the filter's selectivity, and the size of the stability constants. We also conclude that in Agg-GNNs the selectivity of the mapping operators can be limited by the stability restrictions imposed on the first layer of the CNN stage, but this is compensated by the pointwise nonlinearities and filters in subsequent layers which are not subject to any restriction. This shows a substantial difference with respect to the stability properties of selection GNNs, where the selectivity of the filters in all layers is constrained by their stability. We provide numerical evidence corroborating the results derived, testing the behavior of Agg-GNNs in real life application scenarios considering perturbations of different magnitude.
DSJA: Distributed Server-Driven Joint Route Scheduling and Streaming Adaptation for Multi-Party Realtime Video Streaming

The widespread availability of convenient wireless network connection and video capture have fueled the development of multi-party realtime video streaming (MRVS) services, such as Zoom or Microsoft Teams. These services have transformed the generation and distribution of realtime streaming content and offer a new way of online communication, striving to provide high Quality-of-Experience (QoE) for individuals. However, delivering high QoE in MRVS is more challenging than in traditional video scenarios due to the stringent delay requirements and complex multi-party interactive architectures. In this paper, we propose DSJA, a distributed server-driven multi-party realtime video streaming framework that conquers the challenges. We first design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We then model the QoE maximization problem in MRVS as a route scheduling and streaming adaptation problem. Afterward, we design DSJA which seamlessly integrates multiple selective forwarding units (SFU) architecture and server-driven approaches based on a two-step solution of route scheduling and streaming adaptation. DSJA first determines the most suitable SFU and streaming routes for each video session based on SFUs' job queuing delay and path latency. Then, the server conducts joint loss and bitrate adaptation decisions to optimize the streaming configuration of all clients, considering network conditions and QoE preferences. Our evaluations show that our framework outperforms state-of-the-art solutions by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$23.1\% \sim 41.7\%$</tex-math></inline-formula> from the perspective of QoE, and reduces the backbone network transmission by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$14.0\% \sim 36.6\%$</tex-math></inline-formula> .
Online Optimization for Over-the-Air Federated Learning with Energy Harvesting

Federated learning (FL) is recognized as a promising privacy-preserving distributed machine learning paradigm, given its potential to enable collaborative model training among distributed devices without sharing their raw data. However, supporting FL over wireless networks confronts the critical challenges of periodically executing power-hungry training tasks on energy-constrained devices and transmitting high-dimensional model updates over spectrum-limited channels. In this paper, we reap the benefits of both energy harvesting (EH) and over-the-air computation (AirComp) to alleviate the battery limitation by harvesting ambient energy to support both the training and transmission of local models, and to achieve low-latency model aggregation by concurrently transmitting local gradients via AirComp. We characterize the convergence of the proposed FL by deriving an upper bound of the expected optimality gap, revealing that the convergence depends on the accumulated errors due to partial device participation and model distortion, both of which further depend on dynamic energy levels. To accelerate the convergence, we formulate a joint AirComp transceiver design and device scheduling problem, which is then tackled by developing an efficient Lyapunov-based online optimization algorithm. Simulations demonstrate that, by appropriately scheduling devices and allocating energy across multiple communication rounds, our proposed algorithm achieves a much better learning performance than benchmarks.
Joint URLLC Traffic Scheduling and Resource Allocation for Semantic Communication Systems

Recently, deep learning (DL) based semantic communication systems have shown great potential to improve transmission efficiency in various tasks. However, the coexisting mechanism between semantic communications and other services remains unexplored, which limits the application of semantic communications in practical communication systems. In this paper, we propose a dynamic multiplexing and co-scheduling scheme for the semantic and ultra-reliable low-latency communication (URLLC) traffic coexisting systems. In particular, a joint resource allocation and model training problem is formulated, which aims at maximizing the utility of semantic service while satisfying the latency requirement of URLLC traffic. To reduce the computational complexity, the original problem is simplified and decoupled into a joint resource allocation and model selection problem and a robust model training problem. In the resource allocation and model selection phase, the original problem is decomposed into three subproblems and an alternating optimization algorithm is then proposed to obtain the optimal resource allocation result. In the model training phase, a two-stage semantic communication network is designed, which can efficiently mitigate the impact of feature erasure brought by the random arrival of URLLC traffic. Simulation results show that the proposed method can effectively improve the quality of semantic service while satisfying the latency requirement of URLLC traffic.
Authorship Attribution in Astroturfing Detection and the Impact of Google Translate on Cross-lingual Text Analysis

This paper investigates the impact of using Google Translate to perform authorship attribution analysis on Norwegian texts translated into English, with the goal of detecting astroturfing activities in online public discourse. The study compares the performance of various n-gram-based attribution techniques on original Norwegian texts and their machine-translated English counterparts. Results show that the best performance was achieved without translation or pre-processing of the original Norwegian texts, reaching an accuracy of 0.84. Further research is needed to explore alternative methods and pre-processing techniques to enhance the accuracy of authorship on translated texts in this context.
Using a honeypot to improve student cybersecurity awareness

This article focuses on the evaluation of the effectiveness of honeypot activity as a tool to understand cybersecurity principles by undergraduate computer science students. It examines to what extent and in what ways this activity contributed to the acquisition of knowledge and technical skills related to honeypots and cybersecurity. By analyzing students' responses to a number of questions that focused on different aspects of the activity, their experiences, knowledge, and attitudes toward the topic were evaluated. The results are presented with respect to their improved understanding of the concept of a honeypot, their perspectives on the value of this activity in teaching cybersecurity and their opinions on the suitability of including this topic in the course curriculum.
Serious games: The Possibilities of Data Mining and Data Analysis through Neuro and Biofeedback

Serious games have emerged as valuable tools for training in various domains, making them an essential component of training and coaching programs aimed at enhancing skills and knowledge. This study aims to explore the potential of SGs in collecting and analyzing data using neuro and biofeedback techniques. Specifically, we focus on selected methods for data analysis, including eye-tracking, electroencephalography (EEG), heart rate variability (HRV), and electro-dermal activity (EDA). The objective is to investigate how playing SGs can facilitate the collection and analysis of these types of data.
A delay system reservoir based on a nano-ionic Solid Electrolyte FET<sup>*</sup>

Reservoir computing using delay systems took off when it was demonstrated that a single volatile memristor can be effectively utilized to reduce the burden of training recurrent neural networks, without the need for any interconnected reservoir nodes. Here we demonstrate that the maximum impact on the learning efficiency of a $\mathrm{Z}\mathrm{n}\mathrm{O}/\mathrm{T}\mathrm{a}_{2}\mathrm{O}_{5}$ SEFET based reservoir is derived from (i) training the output after every pulse, (ii) device variability, and (iii) scanning the input image data horizontally and vertically.
Virtual Reality in Engineering Education: A Virtual Introduction to Unit Operations and Process Scaling

Over the past few decades, digital technologies have completely transformed the world around us. There are studies that claim that the next great digital transformation of the way we live, communicate, work, trade and learn will be driven by technologies such as Virtual Reality (VR), Augmented Reality (AR) and Artificial Intelligence (AI), among others [1]. Virtual reality (VR) technology has evolved dramatically over the years as its applications have grown. The interest in students is due to the immersive experience and the feeling of presence, of being transported into the virtual environment and losing connection with the physical world. The increasingly frequent use by our students of technological devices and applications in their daily lives, also makes daily the use of technology for students to test, reinforce and practice the theoretical knowledge acquired in the classroom. This paper presents the students' perception of their learning by performing a virtual reality activity of the course Prototyping. The paper presents the impact on students' perception of the stimulus of a virtual digital activity on their learning. The results of this work show that students value the activity, demonstrating an increased understanding of the subject topics in addition to showing a high level of engagement in the subject compared to traditional learning environments.
Research on UAV Aerial Image Object Detection Method Based on Improved YOLOv5

Due to the wide variety of target types, complex scenes, and diverse scales in aerial images, this paper proposes an improved YOLOv5s algorithm for detecting small target objects accurately. The algorithm can quickly and accurately identify targets in aerial images with limited computing resources onboard UAVs. In this method, the CIOU loss function is replaced by a proposed EIOU loss function to solve the problem of large errors in the horizontal and vertical directions. The Efficient Channel Attention (ECA) mechanism is also added to the backbone network to concentrate on the required features. Finally, detection experiments were conducted on our dataset captured by a built UAV system and the the NUPW-10 remote sensing public dataset. According to the experimental results, the improved algorithm average accuracy (mAP) is increased by 1.3% compared to the classical YOLOv5s algorithm, and the channel attention mechanism significantly improved performance.
VesselNet: A Large-Scale Dataset and Efficient Mixed Attention Network for Vessel Re-identification

Compared to the rapid development of pedestrian and vehicle re-identification in urban safety management, many challenging issues of vessel re-identification have not been fully investigated. Such as more dramatic viewpoint changes, more extensive lighting conditions (reflected light from the sea), and more complex navigational backgrounds. To facilitate research on vessel Re-ID at sea, we have collected a new dataset called VeRiS, which has the following salient features: 1) the original images of ships are captured by professional photographers from different times and places around the world via airborne or land-based cameras; 2) the images have been processed with fine detection to remove redundant and complex backgrounds; 3) the dataset is huge in size and has a large number of ship types. VeRiS contains 150,623 images of 2,904 ship IDs. Furthermore, we propose a novel vessel Re-ID network, named VesselNet, that places greater emphasis on the ships in the images. Specifically, we incorporate an improved hybrid attention module into the network, which alters the original pooling method to enhance the differentiation of distinct features. Experiments indicate that our VesselNet performs favorably on both the proposed VeRiS and another vehicle dataset.