Reviewerly/exHarmony huggingface dataset:
SSA3D: Semantic Segmentation Assisted One-Stage Three-Dimensional Vehicle Object Detection

One-stage 3D object detection using mobile light detection and ranging (LiDAR) has developed rapidly in recent years. Specifically, one-stage methods have attracted attention because of their high efficiency and light weight compared with two-stage methods. Inspired by this, we present the semantic segmentation assisted one-stage three-dimensional vehicle object detection (SSA3D), a network for the rapid detection of objects that keeps the advantages of the semantic segmentation module in the two-stage methods without increasing redundant computational load. First, we modified the sampling of the farthest point to improve the quality of the sampling points. This helps to reduce sampling outlier points and bad points that are difficult to perceive in the spatial structure information surrounding the point. Second, a neighbor attention group module is devoted to selectively add extra weight to neighbor points because of the different importance of neighbor points for the corresponding sampling point. Correctly increasing the weight is helpful to obtain richer spatial structure information. Finally, a delicate box generation module is included as a voted center point layer based on the generalized Hoff vote method and an anchor-free regression. We used the feature aggregation module as the backbone and the feature propagation module as the auxiliary network to achieve efficiency. At the same time, the auxiliary network retains the ability to extract point-wise features from the state-of-the-art semantic segmentation network. In experiments, we evaluated and tested the SSA3D on a common KITTI dataset and achieved improved performance in the class of car accuracy.
Path planning in unmanned aerial vehicles: An optimistic overview

Summary With the development in the technology, a rapid increase in the use of unmanned aerial vehicle (UAV) is observed. UAVs are executing tasks that were previously performed by humans or manned aircraft, thus reducing the man workload and saving time. Path planning of UAVs is one of the most researched topics these days. Researchers are investigating more about UAVs and path planning to make it more feasible and economical. The major issue faced while planning a feasible path is to detect and avoid the obstacles encountered during a mission. This paper presents a detailed analysis of path planning in UAVs. Important aspects of path planning, that is, environment, dimensions, obstacles, and type and number of UAVs used in path planning, are also being discussed. Obstacle scenarios which UAV may come across during a mission and constraints which UAV has to follow for successful mission are briefly mentioned. Optimization techniques used to optimize the UAV path are also presented. In addition, future challenges and issues are also discussed.
A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.
Detection of alteration zones using the Dirichlet process Stick-Breaking model-based clustering algorithm to hyperion data: the case study of Kuh-Panj porphyry copper deposits, Southern Iran

Detection of hydrothermal alteration zones (HAZs) associated with porphyry copper systems using remote sensing imagery is a crucial stage for discovering high potential zone of ore mineralization. Statistical model-based clustering methods have great potential for automatic and accurate detection of hydrothermal alteration minerals using hyperspectral remote sensing imagery. In this research, the Dirichlet Process based on Stick-Breaking (DPSB) model-based clustering algorithm was implemented to hyperion remote sensing imagery to discriminate HAZs associated with the Kuh-Panj porphyry copper deposit, south, Iran. The DPSB clustering algorithm was implemented and subsequently compared with the k-means algorithm, CLARA clustering, hierarchical clustering, Gaussian finite mixture model (GFMM), Gaussian model for high-dimensional (GMHD) and spectral clustering as well as spectral angle mapping (SAM). Results derived from the DPSB model-based clustering algorithm show 88.6% accuracy in distinguishing propylitic, argillic, advanced argillic, propylitic-argillic and phyllic alteration zones. The DPSB algorithm can be broadly implemented to hyperspectral remote sensing imagery for detecting alteration zones associated with porphyry systems.
Attention-based federated incremental learning for traffic classification in the Internet of Things

The Internet of Things (IoT) traffic follows non-independent and identical distribution (non-IID). Traditional machine learning classification methods will cause low classification accuracy, high communication costs, and privacy leakage issues. Federated Learning enables several clients to train a deep learning model collaboratively without requiring any of the clients to share their local data with a centralized server. In this paper, we propose a novel attention-based federated incremental learning algorithm: Fed-SOINN. We introduce the attention mechanism to improve the weight of parameters uploaded by clients which are beneficial to the global model, where instead of the full gradient, only a small subset of important gradients is communicated. Meanwhile, we improve the sparsity of the model by upgrading the online optimization function in Fed-SOINN, it also brings faster convergence speed and higher accuracy in the changeable network environment. Results reveal that Fed-SOINN has improved detection accuracy by 3.1% compared with benchmark methods and can reduce the number of communications rounds up to 73%. When facing new traffic categories, the incremental learning mechanism in Fed-SOINN also effectively identify unknown traffic categories.
Private Blockchain-Based AI-Envisioned Home Monitoring Framework in IoMT-Enabled COVID-19 Environment

Coronavirus disease-2019 (COVID-19) is a very serious health concern to the human life throughout the world. The Internet of Medical Things (IoMT) allows us to deploy several wearable Internet of Things-enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients’ home monitoring purpose. We first discuss various phases related to development of a new fog-based private blockchain-enabled home monitoring framework. Next, we discuss how artificial intelligence-enabled big data analytics helps in analyzing and tracking the patients’ information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.
Effects of a collaborative <scp>AR</scp>‐enhanced learning environment on learning gains and technology implementation beliefs: Evidence from a graduate teacher training course

Abstract Background The unimaginable power of emerging technologies has transformed education in terms of content creation and instructional design. Due to high value of AR/VR in education, teachers of the 21st century are expected to empower themselves professionally to be able to utilize these technologies in their instructional practices. Despite detailed examination of teachers' readiness for using AR in terms of perceptions, attitudes, and beliefs, very few studies have incorporated AR in teaching theoretical courses of graduate teacher education programs by utilizing constructivist pedagogical strategies. Objectives The current study investigated the impact of a collaborative AR‐enhanced learning environment on learning gains and technology implementation beliefs in a graduate teacher training course. Methods Thirty‐one in‐service teachers participated in the study as the experimental ( n =15) and control ( n =16) groups. An AR app was developed for teaching 20 key concepts of the course Language Curriculum Design. The experimental group was instructed to work collaboratively with the app to learn the content, while the control group learned the concepts using the app in a lecture‐based class. Learning gains were assessed by midterm and final exams and a term project. Results and Conclusions The results showed that the experimental group outperformed the control group in learning the content and completing the project; and they had more positive technology implementation beliefs at the end of the experiment. Also, the participants were found to have positive attitudes towards the educational and pedagogical values of AR; and if the curricular and situational constraints are removed, they are highly motivated to use AR in their classes. Implications The findings demonstrated the feasibility of incorporating AR technology into teaching abstract concepts in Art and humanities disciplines; and how teacher educators can benefit from integrating a collaborative approach with AR technology to teach advanced courses.
Artificial Intelligence in Practice: Implications for Information Systems Research, Case Study UAE Companies

In this paper, the researcher tried to provide a systematic review and synthesis of practice-based literature on AI, highlighting what leading industry entities and experts understand by AI in United Arab Emirates (UAE). I use these findings to propose an (AI) adoption, use and impact classification framework for information systems (IS) research and propose a corresponding research agenda. Artificial intelligence (AI) has the potential to enhance every component of information system (IS) at the individual, organizational and societal level. However, (AI) technologies are being developed and commercialized at an unprecedented speed making it hard for (IS) researchers and practitioners to keep up with these technologies and how they can enhance IS. The technologies have evolved so fast in the last 15 years that many companies have tried and failed to implement AI without truly understanding what it is. Therefore, understanding (AI) from the perspective of the leading developers of related technologies is crucial for its adoption, use and impact on IS.
Qi2He: A co-design framework inspired by eastern epistemology

The rapid development of rural societies mixed with the infrastructural transformation of emerging economies bring both challenges and opportunities to Human-Computer Interaction (HCI) design as illustrated through the emergence of the field of HCI for Development (HCI4D). A key challenge for HCI4D is how local knowledge, expertise, and culture can be constructively combined with global trends in digital innovation and socioeconomic development. Co-design and participatory design practices in HCI offer opportunities to engage diverse communities in design activities which embrace both transition and tradition in constructive ways. We present our co-design framework, Qi2He, which supports designers and local communities engaging in co-design activities. Qi2He is inspired by traditional Chinese epistemology and contributes (i) methods to support cross-cultural co-design engagement, and (ii) post-hoc critique of co-design participation. We illustrate the use of Qi2He through three case studies of HCI design over four years in rural China where local culture and traditions are in a state of flux from waves of migration to cities whilst also being an integral part of the broader national and global transformation. The first case study examines how local rural knowledge can be shared and acquired to create a design system for ethnic brocade production. The second case study explores how the creation of an interactive drama can be used as a driver for rural community engagement. The third case study focusses on the iterative design of cross-cultural interactive product innovation. We conclude by reflecting on lessons we learnt when structuring and restructuring our co-design process and offer suggestions for how our Qi2He framework could be used by others and in different cultural settings.
Live Streaming Speech Recognition Using Deep Bidirectional LSTM Acoustic Models and Interpolated Language Models

Although Long-Short Term Memory (LSTM) networks and deep Transformers are now extensively used in offline ASR, it is unclear how best offline systems can be adapted to work with them under the streaming setup. After gaining considerable experience on this regard in recent years, in this paper we show how an optimized, low-latency streaming decoder can be built in which bidirectional LSTM acoustic models, together with general interpolated language models, can be nicely integrated with minimal perfomance degradation. In brief, our streaming decoder consists of a one-pass, real-time search engine relying on a limited-duration window sliding over time and a number of ad hoc acoustic and language model pruning techniques. Extensive empirical assessment is provided on truly streaming tasks derived from the well-known LibriSpeech and TED talks datasets, as well as from TV shows on a main Spanish broadcasting station.
The Effect of Multisensory Pseudo-Haptic Feedback on Perception of Virtual Weight

Providing realistic haptic feedback of virtual objects is critical for immersive VR experience, and there have been many approaches to simulate haptic properties. Most of them, however, are limited to a narrow modulation range of simulated perception. To overcome this limitation, the current paper examines the effect of multisensory pseudo-haptic feedback that combines control-to-display (C/D) ratio manipulation and electrical muscle stimulation (EMS) on simulated weight perception. In two experiments, we independently manipulated the C/D ratio and EMS status and observed the effects on the absolute and difference thresholds of simulated weight perception. From the absolute thresholds results, we specify the effective range of C/D ratio that can successfully induce weight perception and show that the range can be more than twice widened by multisensory pseudo-haptic feedback. Furthermore, we demonstrate that the sensitivity to weight difference increases as the standard C/D ratio decreases from the difference thresholds results, which provides practical design guidelines for assigning multiple levels of weight to virtual objects. This study contributes to understanding the psychological effects of multisensory pseudo-haptic feedback on simulated weight perception in virtual reality.
Cross-modal distillation for RGB-depth person re-identification

Person re-identification is a key challenge for surveillance across multiple sensors.Prompted by the advent of powerful deep learning models for visual recognition, and inexpensive RGB-D cameras and sensor-rich mobile robotic platforms, e.g.self-driving vehicles, we investigate the relatively unexplored problem of crossmodal re-identification of persons between RGB (color) and depth images.The considerable divergence in data distributions across different sensor modalities introduces additional challenges to the typical difficulties like distinct viewpoints, occlusions, and pose and illumination variation.While some work has investigated re-identification across RGB and infrared, we take inspiration from successes in transfer learning from RGB to depth in object detection tasks.Our main contribution is a novel method for cross-modal distillation for robust person re-identification, which learns a shared feature representation space of person's appearance in both RGB and depth images.In addition, we propose a cross-modal attention mechanism where the gating signal from one modality can dynamically activate the most discriminant CNN filters of the other modality.The proposed distillation method is compared to conventional and deep learning approaches proposed for other cross-domain re-identification tasks.Results obtained on the public BIWI and RobotPKU datasets indicate that the proposed method can significantly outperform the state-of-the-art approaches by up to 16.1% in mean Average Precision (mAP), demonstrating the benefit of the distillation paradigm.The experimental results also indicate that using cross-modal attention allows to improve recognition accuracy considerably with respect to the proposed distillation method and relevant state-of-the-art approaches.
Sentiment analysis and spam filtering using the YAC2 clustering algorithm with transferability

Two notable applications of text classification are sentiment analysis and spam filtering. Traditional machine learning approaches to text classification are often complex, non-transferrable, and require supervision. This paper introduces an unsupervised approach to text classification which is relatively simple and transfers between problem domains, while providing accuracy comparable or better than established alternatives. We present an integrated solution which combines a new clustering algorithm, Yet Another Clustering Algorithm (YAC2), with a domain transferrable feature engineering approach for Twitter sentiment analysis and spam filtering of YouTube comments. We evaluate the effectiveness of this integrated solution for Twitter sentiment analysis using three datasets: Starbucks, Verizon, and Southwest Airlines. YouTube spam filtering is evaluated using four datasets: Psy, LMFAO, Shakira, and Katy Perry. We compare the results with established clustering solutions: KNN, Spectral, and DBSCAN. Our integrated solution performs better than all the alternatives for sentiment analysis. For spam filtering, YAC2 and KNN perform within 1% of each other and far better than Spectral and DBSCAN for all datasets. Additionally, our feature engineering approach improves accuracy compared to using a traditional method, while significantly reducing model dimensionality, matrix sparsity and providing transferability across the datasets tested.
Real-Time Perception-Limited Motion Planning Using Sampling-Based MPC

Motion planning with visual perception is a hot topic for autonomous flight of micro aerial vehicles (MAVs). However, many existing works fail to be implemented in realistic scenarios in real time due to practical constraints, such as the limited field of view (FOV) of the onboard camera and the limited computational capability. Compared to the existing methods, the proposed approach solves the optimization of motion and perception at the same time. A sampling-based model-predictive control framework is explored as a local planner to generate trajectories, which are <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamically feasible</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collision-free</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">limited perception</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> . The sampling-based local planning framework is extended to two independent scenarios for MAVs: 1) planning safe trajectories with limited FOV constraint and 2) planning trajectories with effective perception of the point of interest. The effectiveness of the proposed method is demonstrated through both simulation and real-flight experiments.
Strategic roles of IT modernization and cloud migration in reducing cybersecurity risks of organizations: The case of U.S. federal government

• We empirically examine a “security-by-antiquity” argument in information security. • We find that a federal agency with a larger stock of legacy systems suffers from more frequent security breaches. • A 1%-point increase in spending in IT modernization is associated with a 5.6% decrease in security incidents. • Migration to the cloud is associated with fewer security incidents as well. Many organizations run their core business operations on decades-old legacy IT systems. Some security professionals argue that legacy IT systems significantly increase security risks because they are not designed to address contemporary cybersecurity risks. Others counter that the legacy systems might be “secure by antiquity” and argue that due to lack of adequate documentation on the systems, it is very difficult for potential attackers to discover and exploit security vulnerabilities. There is a shortage of empirical evidence on either argument. Routine activity theory (RAT) argues that an organization’s guardianship is critical for reducing security incidents. However, RAT does not well explain how organizations might guard against security risks of legacy IT systems. We theorize that organizations can enhance their guardianship by either modernizing their legacy IT systems in-house or by outsourcing them to cloud vendors. With datasets from the U.S. federal agencies, we find that agencies that have more legacy IT systems experience more frequent security incidents than others with more modern IT systems. A 1%-point increase in the proportion of IT budgets spent on IT modernization is associated with a 5.6% decrease in the number of security incidents. Furthermore, migration of the legacy systems to the cloud is negatively associated with the number of security incidents. The findings advance the literature on strategic information systems by extending RAT to explain why the “security by antiquity” argument is not valid and how organizations can reduce the security risks of legacy IT systems through modernization and migration to the cloud.