Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Spectral Fusion using Generative Adversarial Networks for UAV Detection of Wild Fires

Wild fires are now increasingly responsible for immense ecological damage. Unmanned aerials vehicles (UAVs) are being used for monitoring and early-detection of wild fires. Recently, significant research has been conducted for using Deep Learning (DL) vision models for fire and smoke segmentation. Such models predominantly use images from the visible spectrum, which are operationally prone to large false-positive rates and sub-optimal performance across environmental conditions. In comparison, fire detection using infrared (IR) images has shown to be robust to lighting and environmental variations, but long range IR sensors remain expensive. There is an increasing interest in the fusion of visible and IR images since a fused representation would combine the visual as well as thermal information of the image. This yields significant benefits especially towards reducing false positive scenarios and increasing robustness of the model. However, the impact of fusion of the two spectrum on the performance of fire segmentation has not been extensively investigated. In this paper, we assess multiple image fusion techniques and evaluate the performance of a U-Net based segmentation model on each of the three image representations - visible, IR and fused. We also identify subsets of fire classes that are observed to have better results using the fused representation.
Towards Dynamic Realtime Object Labeling in Augmented Reality

The applicability of augmented reality (AR) is stunted by the current limitations of localization systems. In various forms, simultaneous localization and mapping (SLAM) has become a common framework for providing device localization in AR systems; however, outside of camera localization data, SLAM systems typically fail to provide additional information about the environment to consumer applications. This limits the domain of potential AR applications, as many applications will require some degree of interaction between the real and virtual worlds. One such application is object labeling for moving objects. In this work, we implement an AR moving object labeling system by utilizing LUMO-SLAM, a SLAM system that registers and localizes unknown moving objects in the environment. Test runs of the system show that moving object information provided by LUMO-SLAM is sufficient for implementing a useful moving object labeling system and potentially other real-world applications of AR.
Comparing Deep and Machine Learning Models for Sentiment and Emotion Classification from Vaccine #sideffects

The accelerated development of Covid-19 vaccines offered tremendous promise and hope, yet stirred significant trepidation and fear. These conflicting emotions motivated many to turn to social media to share their experiences and side effects during the process of getting vaccinated. This paper analyzes sentiment and emotions from tweets collected using the hashtag #sideffects during the early roll out of the Covid-19 vaccine. Each tweet was labeled according to its sentiment polarity (positive vs. negative), and was assigned one of four emotion labels (joy, gratitude, apprehension, and sadness). Exploratory analysis of the tweets through word cloud visualizations revealed that the negativity of emotions intensified with the severity of side effects. Word and numerical features extracted from the text of the tweets and metadata were used to train conventional machine learning and deep learning models. These models resulted in an accuracy of 81% for binary sentiment classification, and 71 % for multi-label emotion identification. The proposed framework, which yielded competitive performance, may be employed to gain insights into people's thoughts and feelings from vaccine-related conversations. These insights can be helpful in devising communication and education strategies to mitigate vaccine hesitancy.
$5\times 5$ MIMO Antennas for Future 5G mm-Wave Communication

A suggested MIMO antenna's goal is to function in one of the Federal Communication Commission's designated 5G spectral bands (FCC). Due to its propensity to handle both many inputs and numerous outputs, MIMO technology may effectively address issues with large amounts of transportation and high data rates. The overall dimension of a single-element antenna is 10 x 10 mm2, The proposed MIMO antenna design consists of twenty-five elements and the resonance frequency of each antenna element is 37 GHz. The maximum gain and directivity of an antenna are greater than 6 dB. For the designing and simulation of the proposed twenty-five element MIMO antennas is CST Studio Suite software. The proposed antenna will be a candidate for future mm-Wave communication applications in terms of compactness.
Design and Implementation of SFC Monitoring and Alerting Based on YANG Data Model

Service Function Chaining (SFC) is an emerging technology that provides flexible service management and traffic control through an ordered set of service functions (SF). However, the variable deployment of service functions makes network architecture more complex and introduces uncertainty in the distribution of available resources. Additionally, the likelihood of service interruption rises with network configuration complexity. So, it is essential to monitor the SFC and alert the network operator when SFC problems are encountered. The SFC monitoring and alerting method based on the YANG data model is proposed. Each SF /SFF on the service function chaining is monitored, mainly for CPU utilization, memory utilization, bandwidth utilization, latency, average load and other SF metrics, and throughput and latency of SFF. Thresholds are set for each indicator. When a component exceeds the preset threshold, an abnormal information alarm will be triggered, and then the monitoring and alerting module will be modeled using the YANG data modeling language. Finally, the monitoring and alerting module in OpenDaylight (ODL) is implemented, and the experimental verification results are presented.
HoPP: Hardware-Software Co-Designed Page Prefetching for Disaggregated Memory

Memory disaggregation is a promising direction to mitigate memory contention in datacenters. To make memory disaggregation practical, prior efforts expose remote memory to applications transparently via virtual memory subsystem’s swapping interface. However, due to the semantic gap between OS and applications – OS cannot know the memory accessing sequences of an application but via page faults. This approach has two limitations. First, it learns little from page faults’ access history, which leads to sub-optimal prefetching predictions. Second, a page fault can still occur even if there is a prefetch-hit which leads to a large kernel overhead.To address such limitations, our key insight is to decouple the address capturing from page faults by collecting full memory access traces in the memory controller. Using this idea, we build HoPP – a hardware-software co-designed prefetching framework. HoPP adds hardware modules to the memory controller to feed sufficient hot pages to OS in real-time, which has three benefits in HoPP’s software design: 1) it improves existing prefetching algorithms with simple revamps, also offers more insights to build better policies; 2) the prefetch algorithm can run as a separate data path alongside the normal remote data path via page faults, potentially hiding the swap latency from applications, and enabling fine-grained control over prefetching behaviors; 3) the prefetch-hit overhead can be eliminated by early page table entry (PTE) injection, i.e., inject PTE for the prefetched page as soon as it returns. We implemented a proof-of-concept prototype using commodity servers along with a hardware-based memory tracking tool called HMTT to emulate a modified memory controller. Results show that compared to Fastswap and Leap, HoPP-optimized prefetching algorithm achieves over 90% accuracy and coverage, which leads to up to 59% completion time improvement for various datacenter applications.
Patch Array With High Mutual Coupling Reduction Using $\pi$-Shaped Structure and Coplanar Ground

In this letter, an effective scheme to mitigating mutual coupling in patch array utilizing a $\pi$ -shaped structure and coplanar ground is proposed. By properly adjusting the decoupling structure, the mutual coupling among adjacent patches can be reduced to a very low level without the need of increased profile and decreased radiation performance. The coupling strength of the patch antenna array after decoupling is lower than -40 dB across the entire impedance matching frequency bandwidth. Afterward, the gain of the decoupled antenna increases 0.8 dBi compared with the conventional patch antenna.
Low-Profile Complementary Dipole Antenna Using Semicircular Sector Electric Dipoles

A novel slot-combined complementary antenna composed of semicircular sector electric dipoles is proposed. Two pairs of semicircular sector electric dipoles are properly incorporated to yield a low-profile, complementary dipole antenna. The advanced antenna is measured with a relatively thin profile of 0.042-wavelength and the maximum front-to-back ratio (FBR) of the fabricated antenna can reach up to 12.7 dB. Measured results show that the mode of the slot element and the mode of the semicircular sector electric dipoles can be incorporated and the impedance bandwidth is 2.38~3.11 GHz (i.e., 25.0% in fraction) with an available quasi-unidirectional radiation bandwidth of 24.6%.
Multi-Objective Influence Maximization Under Varying-Size Solutions and Constraints

Identification of a set of influential spreaders in a network, called the Influence Maximization (IM) problem, has gained much popularity due to its immense practicality. In real-life applications, not only the influence spread size, but also some other criteria such as the selection cost and the size of the seed set play an important role in selecting the optimal solution. However, majority of the existing works have treated this issue as a single-objective optimization problem, where decision-makers are forced to make their choices regarding other variables in advance despite having a thorough understanding of them. This research formulates a multi-objective version of the IM problem (referred to as MOIMP), which considers three competing objectives while subject to certain practical restrictions. Theoretical analysis reveals that the influence spreading function under the suggested MOIMP framework is no longer monotone, but submodular. We also considered three well-established multi-objective evolutionary algorithms to solve the proposed MOIMP. Since the proposed MOIMP addresses varying-size seeds, all the considered algorithms are significantly modified to fit into it. Experimental results on four real-life datasets, evaluating and comparing the performance of the considered algorithms, demonstrate the effectiveness of the proposed MOIMP.
A Nonvolatile Al-Edge Processor with 4MB SLC-MLC Hybrid-Mode ReRAM Compute-in-Memory Macro and 51.4-251TOPS/W

Low-power Al edge devices should provide short-latency $(\mathsf{T}_{\mathsf{WK-RP}})$ and low-energy $(\mathsf{E}_{\mathsf{WK-RP}})$ wakeup responses from power-off mode to handle event-triggered computing tasks with high inference accuracy (IA), which requires high-capacity nonvolatile memory (NVM) to store high-precision weight data in power-off and high bit-precision multiply and accumulate (MAC) operations with high energy efficiency. SRAM computing-in-memory (CIM) and digital processors suffer large $\mathsf{E}_{\mathsf{WK-RP}}$ and long $\mathsf{T}_{\mathsf{WK-RP}}$ due to the movement of weight data from off-chip NVM to the on-chip buffer and processing unit after wakeup. Thus, on-chip nonvolatile CIM (nvCIM) is preferred for Al-edge processors by combining NVM storage and computing tasks on the same macro. Among nvCIM structures, in-memory compute (IMC) [1] provides short computing latency and high energy efficiency, but suffers from low computing yield. Near-memory compute (NMC) [2–4] provides high computing yield, but suffers long computing latency and low energy efficiency.
A Detailed Study on Obstacle Detection and Avoidance Techniques for On-road Vehicles

Obstacle detection and avoidance are the important components of any autonomous land vehicle. The ability of an autonomous system to safely maneuver from an obstacle must be unerring to prevent any injuries to the driver or to a pedestrian. Hence it becomes cardinal to evaluate all the methodologies available to safely navigate a vehicle to safety. This paper performs a detailed study on the various obstacle detection methodologies and also on the various ways to avoid collision with the obstacle. This study provides a clear mapping of the domains and subdomains used for obstacle detection and obstacle avoidance.
Pallet Detection and Distance Estimation with YOLO and Fiducial Marker Algorithm in Industrial Forklift Robot

Utilising technology such as artificial intelligence and robotics potentially improves E-Commerce in efficiency. In this trends, the usage of autonomous forklifts in the warehouse to lift and arrange things should be implemented. The picking system in the warehouse needs pallet detection and tracking to carry out the things. This research will find the best performance of the YOLOv5 model and correct the distance estimation model to the fiducial marker. In this paper, we used the ArUco fiducial marker to mark the pallet target and estimate the pose and distance in real time. The insertion points of the pallet were also detected using the YOLOv5 algorithm to validate the pallet and get the coordinate variables of the holes. The YOLOv5n gives the best performance at 24 fps in real-time detection. Distance measurement from the marker detection had an average error of 2.28 cm with linear regression.
Minimizing the Importance Inequality of Nodes in a Social Network Graph

Network graphs are widely used to model a variety of real-world interactions. In such graphs, nodes do not have the same importance in the graph structure as a result of the graph's topological properties. This may have various implications concerning a network's behaviour as, for example, how different nodes operate (even a node's failure) may not have the same impact for the whole network. The differences in the structural properties of the nodes imply that each node has different importance, which, in turn, gives rise to the notion of importance inequality in a graph. This paper defines and addresses the problem of importance inequality minimization, which may be useful to achieve certain properties in a network. Given a network graph and an integer $k$, the problem aims to identify $k$ edges to connect non-adjacent nodes, in a way that minimizes the importance inequality of the graph. The paper provides a formal definition of the problem and proves its NP-hardness. Then, a naive greedy method is proposed, which is enhanced by heuristics that make its use practical. Experiments using 8 real-world networks are conducted to evaluate the proposed methods in terms of effectiveness and efficiency.
Active Damping Control of Cascaded DC Converter in DC Microgrids Based on Optimized Parallel Virtual Resistance

In dc microgrid, the cascaded dc system has been extensively employed. However, the negative impedance characteristics of the converter may induce system instability. This article proposes an optimized parallel virtual resistance (PVR) based active damping control to improve the stability of the cascade dc system in the dc microgrid. The advantage of this method is that it not only meets the closed-loop dynamic performance of the source converter (SC), but also meets the stability requirements of matching the input impedance of the load converter. The stability range of PVR under full load and its influence on the output impedance of SC in cascade system are determined. Additionally, a large-signal model is constructed by using state-space averaging method to investigate the open-loop stability in the cascaded dc system. The suggested control stability requirements are investigated by using the Lyapunov indirect approach, and a matching PVR is then adopted to ensure system stability. Finally, the performance of the control strategy is verified by establishing a MATLAB simulation and experiment platform.
Bi-directional Visual Geo-localization-based Cross-Domain Matching between Digital Twin and Real World

Recently, the digital twin has been developing rapidly with the advancement of key enabling technologies such as artificial intelligence and the Internet of Things. To implement the digital twin, cross-domain image matching is required for bi-directional mapping between the real world and the virtual world. Cross-domain image matching enables the use of various image resources by matching the image across different domains. Researchers have contributed different mechanisms to address the cross-domain matching issues. In this study, we investigated and analyzed the performance of the state-of-the-art visual geo-localization approaches for cross-domain matching between the digital twin and the real world. We applied the visual geo-localization technologies to the datasets that we collected from the Berlin Smart City and Bing Map. In the experiment, the visual geo-localization technology, used for cross-view image-based matching between satellite (aerial) view and ground view, performed the best when applied to cross-domain matching between the digital twin and the real world. The evaluation result confirmed the possibility of utilizing the existing visual geo-localization approaches for the cross-domain matching between the digital twin and the real world, and vice versa.
CNN-n-GRU: end-to-end speech emotion recognition from raw waveform signal using CNNs and gated recurrent unit networks

We present CNN-n-GRU, a new end-to-end (E2E) architecture built of an n-layer convolutional neural network (CNN) followed sequentially by an n-layer Gated Recurrent Unit (GRU) for speech emotion recognition. CNNs and RNNs both exhibited promising outcomes when fed raw waveform voice inputs. This inspired our idea to combine them into a single model to maximise their potential. Instead of using handcrafted features or spectrograms, we train CNNs to recognise low-level speech representations from raw waveform, which allows the network to capture relevant narrow-band emotion characteristics. On the other hand, RNNs (GRUs in our case) can learn temporal characteristics, allowing the network to better capture the signal’s time-distributed features. Because a CNN can generate multiple levels of representation abstraction, we exploit early layers to extract high-level features, then to supply the appropriate input to subsequent RNN layers in order to aggregate long-term dependencies. By taking advantage of both CNNs and GRUs in a single model, the proposed architecture has important advantages over other models from the literature. The proposed model was evaluated using the TESS dataset and compared to state-of-the-art methods. Our experimental results demonstrate that the proposed model is more accurate than traditional classification approaches for speech emotion recognition.
Network Anomaly Detection Using a Graph Neural Network

Contrary to the many traditional network security approaches that focus on volume-based threats, the Activity and Event Network (AEN) is a new approach built on a graph model, which addresses both volumetric attacks and long-term threats that traditional security tools cannot deal with. The AEN graph structural foundation can serve as a basis to construct a graph to be used in Graph Neural Network (GNN) for anomaly and threat detection purposes. In this paper, an AEN-based supervised Graph Convolutional Network (GCN) model is proposed, then evaluated using two labelled datasets, namely, the distributed denial of service (DDoS) and the TOR-nonTOR datasets, yielding an accuracy score of 76% with the DDoS dataset and 88% with the TOR-nonTOR dataset, respectively.
Chains of Rare Events

The negative binomial distribution of Greenwood and Yule is generalized and modified in order to obtain distribution curves which could be used in many concrete cases of chains of rare events. Assuming that the numbers of single, double, triple, and so on, events are distributed according to Poisson's law with parameters $\lambda_1, \lambda_2, \lambda_3 \cdots$ respectively, and that $\lambda_s$ is given by $\lambda_s = \lambda_1 \frac{a^{s - 1}}{s! }$, the probability of obtaining $M$ successful events is studied. In the considered relation $\lambda_s$, for convenient values of $a$, first increases with $s$ and after a certain saturation value of $s$ starts to decrease. A relation of this type is very suitable for studying the distribution of score in a match between two first class billiard players, the probability of accidents on a highway of dense traffic, etc. The general methods of finding the distribution curves for arbitrary relations between the $\lambda$'s are indicated. The method of steepest descent is applied to find an acceptable approximation of the distribution function; and the advantage of this method is pointed out for other similar cases, in addition to the concrete one which was developed, in which the method of direct expansion into power series becomes inapplicable.
Investigation of Lateral Gallium Oxide MOSFET with Discrete Field Plate by Calibrated Numerical Simulation

The main barrier preventing the simulation design and optimization of wide bandgap semiconductor power devices is poor numerical convergence. Currently, the uncalibrated two-dimensional electric field distribution is the main foundation for the breakdown voltage optimization of gallium oxide power devices. This work achieves proper calibration of the current-voltage (I-V) characteristics as well as the breakdown voltage evaluation by enhancing the numerical method. An optimal set of structural parameters for a lateral gallium oxide MOSFET with a discrete field plate is designed using the suggested method. Thanks to the improved electric field profile, the breakdown voltage is increased by 19.7%, and on-state resistance did not degrade. This work resolves the wide-bandgap semiconductors' poor convergence problem and offers a workable strategy for the subsequent theoretical simulation and optimization of gallium oxide power devices.
A Comparative Study of China's Foreign Ministry Spokesperson's Use of Weibo and Twitter

Governments around the world are embracing social networks to promote their agendas, and China is no exception. Although Twitter is blocked in China, many diplomats own Twitter accounts and actively post content. Particularly, Zhao Lijian (China's Ministry of Foreign Affairs Spokesperson) is prolific on Twitter as well as its Chinese counterpart, Weibo. This paper examines the entities mentioned in and the sentiment of Zhao's posts, delivered or not delivered to people within China, to study the similarity and differences between his use of Weibo and Twitter. This paper also compares the users' engagement with Zhao on both platforms, exploring the possible factors influencing users' engagement on both platforms.