Reviewerly/exHarmony huggingface dataset:
TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos

Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. However, existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices. In this work, we propose a simple yet effective framework, TransVisDrone, that provides an end-to-end solution with higher computational efficiency. We utilize CSPDarkNet-53 network to learn object-related spatial features and VideoSwin model to improve drone detection in challenging scenarios by learning spatio-temporal dependencies of drone motion. Our method achieves state-of-the-art performance on three challenging real-world datasets (Average Precision@0.5IOU): NPS 0.95, FLDrones 0.75, and AOT 0.80, and a higher throughput than previous methods. We also demonstrate its deployment capability on edge devices and its usefulness in detecting drone-collision (encounter). Project: https://tusharsangam.github.io/TransVisDrone-project-page/
Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding

General scene understanding for robotics requires flexible semantic representation, so that novel objects and structures which may not have been known at training time can be identified, segmented and grouped. We present an algorithm which fuses general learned features from a standard pre-trained network into a highly efficient 3D geometric neural field representation during real-time SLAM. The fused 3D feature maps inherit the coherence of the neural field's geometry representation. This means that tiny amounts of human labelling interacting at runtime enable objects or even parts of objects to be robustly and accurately segmented in an open set manner. Project page: https://makezur.github.io/FeatureRealisticFusion/
Priority-Driven Differentiated Performance for NoSQL Database-as-a-Service

Designing data stores for native Cloud Computing services brings a number of challenges, especially if the Cloud Provider wants to offer database services capable of controlling the response time for specific customers.These requests may come from heterogeneous data-driven applications with conflicting responsiveness requirements.For instance, a batch processing workload does not require the same level of responsiveness as a time-sensitive one.Their coexistence may interfere with the responsiveness of the time-sensitive workload, such as online video gaming, virtual reality, and cloud-based machine learning.This paper presents a modification to the popular MongoDB NoSQL database to enable differentiated per-user/request performance on a priority basis by leveraging CPU scheduling and synchronization mechanisms available within the Operating System.This is achieved with minimally invasive changes to the source code and without affecting the performance and behavior of the database when the new feature is not in use.The proposed extension has been integrated with the access-control model of MongoDB for secure and controlled access to the new capability.Extensive experimentation with realistic workloads demonstrates how the proposed solution is able to reduce the response times for high-priority users/requests, with respect to lower-priority ones, in scenarios with mixed-priority clients accessing the data store.
Distilling Segmenters From CNNs and Transformers for Remote Sensing Images’ Semantic Segmentation

Semantic segmentation is a crucial task in remote sensing and has been predominantly performed using convolutional neural networks (CNNs) for the past decade. Recently, transformers with self-attention mechanisms have demonstrated superior performance compared to CNNs. However, due to the locality of CNN and the high computational complexity and massive data resource requirements of transformer, neither of them can be well applied in resource-constrained practical remote sensing scenarios. Motivated by the limitations of using either convolutional neural networks (CNNs) or transformers alone in the task of semantic segmentation of remote sensing images, a novel cross-model knowledge distillation framework, named distilling segmenters from CNNs and transformers (DSCT), is proposed in this paper to harness the complementary advantages of both models. The framework utilizes a channel-weighted attention-guided feature distillation (CAFD) module to condense the feature from the teacher model and enhance the student model’s focus on the teacher-focused regions. Additionally, a target-nontarget knowledge distillation (TNKD) module is proposed that decouples logit distillation into target and nontarget knowledge distillation to guide the student model in learning the underlying representations and decision boundaries from the teacher model. By learning the complementary knowledge from the teacher, our proposed DSCT framework improves the student’s segmentation performance without adding trainable parameters. Experiments on four available remote sensing datasets (ISPRS Potsdam, Vaihingen, GID and LoveDA) indicate that the proposed DSCT outperforms the state-of-the-art knowledge distillation methods and demonstrates its effectiveness and robustness.
Multi-Robot Mission Planning in Dynamic Semantic Environments

This paper addresses a new semantic multi-robot planning problem in uncertain and dynamic environments. Particularly, the environment is occupied with mobile and uncertain semantic targets. These targets are governed by stochastic dynamics while their current and future positions as well as their semantic labels are uncertain. Our goal is to control mobile sensing robots so that they can accomplish collaborative semantic tasks defined over the uncertain current/future positions and semantic labels of these targets. We express these tasks using Linear Temporal Logic (LTL). We propose a sampling-based approach that explores the robot motion space, the mission specification space, as well as the future configurations of the semantic targets to design optimal paths. These paths are revised online to adapt to uncertain perceptual feedback. To the best of our knowledge, this is the first work that addresses semantic mission planning problems in uncertain and dynamic semantic environments. We provide extensive experiments that demonstrate the efficiency of the proposed method.
Impact of digital communications on project efficiency through ease of use and top management support

The pandemic has made the world witness an overnight transition from traditional to digital space owning to a lockdown, affecting everyday life. During the lockdown, the business community and people switched to online communication tools for survival. The behaviour towards adopting online communication tools during the pandemic has opened a new direction for researchers to study. This study aims to understand how during the covid-19 lockdown, digital communication tools affect project efficiency. It also explored to what extent project performance (PP) has been affected by digital communication during the lockdown. The study has used a cross-sectional quantitative-based approach. Moreover, a convenience sampling technique was adopted to collect. The target respondents are the project staff and employees of humanitarian organisations comprising international non-profit organisations (INGOs), UN agencies, and local non-profit organisations (NGOs) based in Pakistan. The survey was shared with 400 employees, and only 302 respondents responded. The results show that the user behaviour towards adopting digital communication tools is high; digital communication tools have an impact on project performance, while ease of use significantly moderates the relationship between digital communications tools and project performance compared to top management support. The research helps study employees' struggles in adopting change and advising organisations to devise practical digital communication strategies and business continuity plans to cope with crises.
Teleportation goes to Hertz rate

Quantum teleportation has been developed to simultaneously realize the Hertz rate and the 64-km distance through fiber channels, which is essential to real-world application of quantum network.
An accessible strong-motion dataset (PGA, PGV, and site vS30) of 2022 MS6.8 Luding, China Earthquake

A MS6.8 earthquake occurred on 5th September 2022 in Luding county, Sichuan, China, at 12: 52 Beijing Time (4:52 UTC). We complied a dataset of PGA, PGV, and site vS30 of 73 accelerometers and 791 Micro-Electro-Mechanical System (MEMS) sensors within 300 km of the epicenter. The inferred vS30 of 820 recording sites were validated. The study results show that: (1) The maximum horizontal PGA and PGV reaches 634.1 Gal and 71.1 cm/s respectively. (2) Over 80% of records are from soil sites. (3) The vS30 proxy model of Zhou J et al. is superior than that of Wald and Allen and performs well in the study area. The dataset was compiled in a flat file that consists the information of strong-motion instruments, the strong-motion records, and the vS30 of the recording sites. The dataset is available at https://www.seismisite.net.
Incremental event detection via an improved knowledge distillation based model

Event detection (ED) is of fundamental importance for many information extraction tasks. But conventional ED models require a fixed set of pre-defined event types. When an event trigger that cannot be categorized as any of these types occurs, they have to be re-trained from scratch with data from both old and new classes, so they are impractical in real life. In this paper, we propose a more realistic incremental event detection (IED) model that incrementally learns new event types at different times while not catastrophically forgetting the learned old classes. Although a knowledge distillation-based (KD-based) approach can be used to solve catastrophic forgetting, we find that conflicts arise when applying a general KD method to the IED task. Specifically, such conflicts mainly occur in two common scenarios: the first is that the triggers are labeled as a non-trigger word class in the current step, but at the same time, predicted as an old event type by the previous model (None-old class label confusion). The second is that the triggers are labeled as a new event type in the current step, but at the same time, predicted as a non-trigger word class by the previous model (None-new class label confusion). To solve the conflicts in each scenario, we generate pseudo labels and modify the distillation loss to improve the prediction accuracy on old and new classes, respectively. Comparative experiments demonstrate the effectiveness of our method, outperforming the state-of-the-art model.
Meet me in VR! Can VR space help remote teams connect: A seven-week study with Horizon Workrooms

In 2020, office life switched abruptly towards a remote work model. Office meetings have since moved to virtual spaces. Unfortunately, remote video meetings have become associated with declines in engagement, collaboration, and learning, as well as ‘Zoom fatigue’. In this article, we study the potential of virtual reality (VR) technology as a solution to these problems and as a medium to enrich remote work environments. For seven weeks, we collected data on the meetings of a team of eighteen knowledge workers. For five of those weeks, the meetings were organized in VR. We used a mix of qualitative and quantitative methods to gather feedback on those sessions and to compare them with a regular video conferencing baseline. Our results demonstrate that both technologies have applications in the work environment—albeit in different situations. We provide best-practice guidelines for the use of VR by remote teams and present design implications for VR collaborative spaces.
Fake review detection system for online E-commerce platforms: A supervised general mixed probability approach

Online consumer reviews play an important role in helping consumers judge the quality and authenticity of products on e-commerce platforms. However, the constant presence of fake reviews on these platforms has significantly impacted the operation and development of e-commerce platforms. In this study, we develop a novel supervised probabilistic method to detect fake reviews by utilizing the difference in the distribution of non-fraudulent reviews and that of fake reviews. Specifically, we first derive the univariate distributions of several unique features (linguistic, behavioral, and interrelationship features). We then integrate these distributions into two mixed distributions according to their labels to represent the overall difference between non-fraudulent reviews and fake reviews. Next, we randomly generate synthetic review data points with different labels from the above mixed distributions. Finally, we train a Multilayer Perceptron model by using these synthetic review data to obtain a classifier. We conducted several experiments to test the model using several original real-world review datasets. Numerical results indicated that the proposed supervised method outperformed some well-known sampling models and fake review detection methods, in terms of classification accuracy. Moreover, we extend the proposed method to handle the scenarios with small samples of raw review data. This study contributes to the literature by exploiting the difference in the distribution of non-fraudulent reviews and that of fraudulent reviews, which can improve the accuracy of fake review detection for online platforms.
A systematic literature review on source code similarity measurement and clone detection: Techniques, applications, and challenges

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10,000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.
Deep quantum neural networks on a superconducting processor

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report an experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. We experimentally perform the forward process of the backpropagation algorithm and classically simulate the backward process. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results indicate that the number of coherent qubits required to maintain does not scale with the depth of the deep quantum neural network, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices.
Static-dynamic global graph representation for pedestrian trajectory prediction

Effectively understanding social interactions among pedestrians plays a significant role in accurate pedestrian trajectory prediction. Previous works used either distance-based or data-driven methods to model interactions. However, the distance-based method has difficulty modeling complex interactions and ignores interactive pedestrians that are beyond a certain distance. The data-driven method models interactions among all pedestrians in a scene and introduces noninteractive pedestrians into the model due to the lack of proper supervision. To overcome these limitations, we first propose a novel global graph representation, which considers the spatial distance (from near to far) and the motion state (from static to dynamic), to explicitly model the social interactions among pedestrians. The global graph representation consists of two subgraphs: the static and the dynamic graph representations, where the static graph considers only the nearby pedestrians within a certain distance threshold, and the dynamic graph considers the interactive pedestrians that will likely collide soon. The proposed graph representation explicitly models the interaction by incorporating both the static (location) and dynamic states (velocity) in a distance-based manner. Then, based on the global graph representation, a novel data driven graph encoding network is proposed to extract the interaction features. It adopts two independent LSTMs and an attention module to encode the interaction feature from the perspective of the ego-pedestrian. Finally, the proposed prediction method is evaluated on two benchmark pedestrian trajectory prediction datasets, and comparisons are made with the state-of-the-arts. Experimental results demonstrate the effectiveness of the proposed method.
Revealing the complexity of users’ intention to adopt healthcare chatbots: A mixed-method analysis of antecedent condition configurations

Healthcare chatbots provide a professional, immediate, and low-cost tool for advising people on health information. Unlike previous studies on chatbot adoption that focus on the one-way net effect of components, this study reveals the complex antecedent configurations behind online healthcare chatbot adoption through an asymmetric approach. This study attempts to explain the causal configuration of user adoption in terms of three dimensions: functional, social, and user motivation features. A sequential mixed-method approach was chosen and a two-stage study, fuzzy set comparison analysis (fsQCA), and semi-structured interviews were conducted to deepen users' understanding, perceptions, and attitudes toward online healthcare chatbots, expressing more fine-grained insights into variable relationships. Five configurations were found to explain the high willingness of users to adopt healthcare chatbots. Perceived social presence was a core condition for each configuration. However, the social features of chatbots can only lead to high levels of trust and satisfaction when combined with functionality. The findings of this paper's mixed-method design and complexity study contribute to the adoption and theoretical literature on intelligent health services and chatbots. It guides tailoring chatbot functionality to individual user needs and provides practical guidance for the development of chatbots for health services through diverse combinations of machine chatbot features and user-motivated features.
Extraction of Facial Features using an Efficient Convolution Siamese Network on Customized Dataset

In recent years, one of the best applications of visual analysis and understanding that has drawn a lot of attention is face recognition. Due to its numerous uses in areas including safety, medical marketing, identity verification, surveillance, security etc., it has caught the interest of many researchers. In this research, a network has been proposed for face recognition. The work has been performed on a customized dataset with training and testing images. 1500 photos altogether constitute the dataset. The simulation was run using hyper parameters such as the batch-size with a value of 128. optimizer as Adam. The proposed network has outperformed with the value of accuracy as 76.48%
COMA-Net: Towards generalized medical image segmentation using complementary attention guided bipolar refinement modules

Precise medical image segmentation is a crucial step for proper isolation of target regions, such as an organ or lesion for accurate medical diagnosis, prognosis and certain medical procedures. Taking advantage of the available annotated medical image datasets, many CNN-based approaches have been proposed for segmentation over the years. These conventional approaches lack appropriate supervised means to enhance the foreground/target regions relative to background at the feature level for improving the generalizability of these methods to obtain better performance across diverse imaging modalities. In this work, we introduce COMA-Net (COMplementary Attention guided bipolar refinement-based Network), which employs a complementary attention scheme between a pair of positive and negative refinement modules placed on top of two encoder structures for generating refined feature references for the decoder stage in a supervised manner. A four-way feature shifting operation is introduced in conjunction with a set of dilated convolutional layers so that it considers the spatial relationships across a wider footprint leading to better contextual feature extraction. We also formulate a novel Foreground-to-Background Ratio (FBR) index to highlight the differences in signal power between target region and background due to the refinement. Experimental results on five different publicly available medical image segmentation datasets, including BUSI, GLAS, ISIC-2018, MoNuSeg and CVC-ClinicDB reveal that on average, the proposed method can achieve an additional mean F1, IoU, precision, and recall score of +0.97%, +1.25%, +1.11%, and +0.22%, respectively over the state-of-the-art segmentation methods, suggesting its great potential for application on real-world patient image data.
Immersive technologies and consumer behavior: A systematic review of two decades of research

Immersive technologies, including augmented reality (AR), virtual reality (VR), mixed reality (MR), and three-dimensional (3D) views, are digitally expanding consumers’ reality, enabling never-before-seen experiences. As these technologies transform from a quirky novelty into a ubiquitous utility for consumers, we analyze the advances of the past two decades of research that elaborate on the influence of immersive technologies on consumer behavior. While the past reviews in this area have focused on a limited number of immersive technologies or their application in a sole industry, we holistically analyze and compare the influence of AR, VR, MR, and 3D on consumer behavior across various sectors. We adopt an integrated TCM (Theories, Contexts, and Methods)-ADO (Antecedents, Decisions, and Outcomes) framework to systematically review 129 studies from high-quality academic journals. The findings from this analysis present implications for future research, theory, and practice. JEL Classification: M31 Marketing