Reviewerly/exHarmony huggingface dataset:
Zero-shot Video Retrieval using CLIP with Temporally Ordered Multi-query Scoring

In this work, we present a new method for video retrieval using OpenAI's CLIP and Temporally Ordered Multi-query Scoring (TOMS). Our approach extends CLIP with a scoring function for matching multiple ordered queries, which enables fast, accurate video search while retaining its zero-shot capability. This allows effective video retrieval on any dataset without the cost of data annotation and model fine-tuning, both of which can be expensive if not unaffordable in Vietnam. An extensive benchmark against using CLIP alone shows superior performance in video searching. Furthermore, we also present our solution for Ho Chi Minh City AI Challenge 2023, which is built upon this method and achieved competitive results.
Integrating Technology: Engaging With VR: Where Will You Take Your Students?

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsChristopher LongChristopher Long (Chris.Long@unt.edu) is an assistant professor and Lauren Eutsler (Lauren.Eutsler@unt.edu) is an assistant professor, both in the Department of Teacher Education and Administration at the University of North Texas College of Education in Denton, Texas.Lauren EutslerChristopher Long (Chris.Long@unt.edu) is an assistant professor and Lauren Eutsler (Lauren.Eutsler@unt.edu) is an assistant professor, both in the Department of Teacher Education and Administration at the University of North Texas College of Education in Denton, Texas.
A Robust and Practical Solution to ADS-B Security Against Denial-of-Service Attacks

Automatic dependent surveillance-broadcast (ADS-B) has been widely deployed on aircraft to facilitate aviation information exchange and improve air traffic safety. However, its broadcast nature and lack of security considerations like encryption and authentication have caused the counterfeit of ADS-B messages to be straightforward. Flooding forged messages to legitimate aircraft, denial-of-service (DoS) attacks threaten flight security severely. In this paper, we propose a practical security solution against DoS attacks on ADS-B based on high-precision timestamp and position information. The solution achieves high feasibility and reliability by accommodating measurement errors of physical quantities. Besides, it preserves ADS-B frame size and ensures efficient computation in frame generation and verification. Comprehensive security analyses demonstrate robust filtrations of the proposed solution on malicious messages from DoS adversaries with different capabilities. Further simulations on real-world aviation data exhibit significant defensive performance realized by the DoS-immune ADS-B security solution. Whether adversaries can only intercept ciphertext, or they have opportunities to acquire decrypted messages, all DoS attacks on ADS-B are successfully thwarted by the solution. Even for adversaries with victim aircraft location estimation capacity, the solution resists all DoS attacks transmitting less than 50 forged messages per second.
Enhancing Twitter Sentiment Analysis using Attention-based BiLSTM and BERT Embedding

Sentiment analysis is a rapidly expanding field with a broad spectrum of uses. Researchers and industrialists alike have found analyzing sentiments on social media platforms like Twitter, to be of particular interest due to the influx of opinionated data. In this paper, we propose an Attention-based BiLSTM sentiment model for Twitter data that is integrated with BERT embedding. The BERT pre-trained language model represents each word as a vector, while the Bi-Directional Long Short Term Memory (BiLSTM) extracts word information from both directions. To enhance prediction accuracy, the attention mechanism determines how much each word contributes to the final score. We conducted experiments using the Sentiment140 dataset and evaluated the results based on ac-curacy, recall, precision, and Fl-Score. The empirical results reveal that the pro-posed model outperforms the baseline model. Our model effectively analyzes and interpret the vast amount of opinionated data on Twitter providing valuable in-sights for researchers and businesses alike.
Multimodal Locomotion: Next Generation Aerial–Terrestrial Mobile Robotics

Mobile robots have revolutionized the public and private sectors for transportation, exploration, and search and rescue. Efficient energy consumption and robust environmental interaction needed for complex tasks can be achieved in aerial–terrestrial robots by combining advantages of each locomotion mode. This review surveys over two decades of development in multimodal robots that move on the ground and in air. Multimodality can be achieved by leveraging three main design approaches: adding morphological features, adapting forms for locomotion transitions, and integrating multiple vehicle platforms. Each classification is thoroughly examined and synthesized, encompassing both qualitative and quantitative aspects. The authors delved into the intricacies of these approaches and explored the challenges and opportunities that lie ahead in pursuit of the next generation of mobile robots. This review aims to advance future deployment of multimodal robots in the real world for challenging operations in dangerous, unstructured, contact‐prone, cluttered and subterranean environments.
The Promise of the Fourth Industrial Revolution: Unleashing the Potential of Virtual Reality in the Teaching of History Curriculum

The growing economic viablity of virtual reality has made it possible for educational institutions to use the technology in their teaching. The growth of mobile networks and the spread of digital gadgets, which are more widely available to teachers and students, support this. Notwithstanding the known critical role of virtual reality in the teaching of engineering and medical education, its use and application in history curriculum stays unchartered terroitory. This paper interrogates the affordances of virtual reality in education with a slant towards its use in History teaching. Understanding how the adoption of the fourth industrial revolution and the affordances of virtual reality in education could unlock new avenues in the teaching of history curriculum. Thus by exploring the potentialities of virtual reality in the teaching of history, the paper hopes to lay a foundation on which social science researchers can further explore how the affordances of virtual reality improve the teaching fraternity especially so in developing countries. The paper advances the arguement that virtual reality is not only useful in engineering and medical education but also to social sciences in general and history in particular. This study provides insights about this underexplored territory through assessing the functional implications of virtual reality in the teaching of history curriculum.
News Event Retrieval from Large Video Collection in Ho Chi Minh City AI Challenge 2023

Event retrieval from large collections of TV news videos is crucial for efficient information access, enabling researchers, journalists, and the general public to quickly locate and analyze relevant content amidst the vast sea of news coverage, facilitating informed decision-making and a comprehensive understanding of significant events. This paper presents an overview of the AI-driven video retrieval task in Ho Chi Minh City AI Challenge 2023. The competition draws inspiration from internationally recognized competitions, namely the Video Browser Showdown (VBS) and the Lifelog Search Challenge (LSC). Participants are tasked with developing AI models to retrieve specific video segments from a diverse dataset from reputable news channels. The dataset comprises a vast collection of videos, keyframes, object detections, CLIP features, and metadata. It is divided into three packs with a total of 1,270 videos, spanning approximately 360 hours of content. The challenge comprises two groups. Group A is open to students, researchers, and practitioners in artificial intelligence and information retrieval, emphasizing substantial knowledge and experience. Group B is tailored for high school students, focusing on nurturing interest, learning, and engagement among the next generation of AI enthusiasts. The wide variation in the content of queries challenged participants to demonstrate their adaptability and creativity in effectively retrieving diverse events from the extensive TV news video dataset. The winning teams showcased promising solutions by effectively harnessing artificial intelligence and information retrieval techniques to excel in event retrieval from a vast collection of TV news videos.
MetaCluster: An open-source Python library for metaheuristic-based clustering problems

<h2>Abstract</h2> Clustering, based on metaheuristic algorithms, is a rapidly developing field. Its goal is to use these methods to reframe clustering issues as optimization problems. In this study, we propose an open-source library named MetaCluster. This library leverages the latest metaheuristic algorithms to tackle partitional clustering challenges. MetaCluster has two primary goals: to be open-source and to provide a user-friendly interface with comprehensive documentation. This design aims to facilitate usage for individuals with varying experience levels across diverse applications and domains. The current version of MetaCluster is easy to install and deploy. It offers access to 48 available datasets, over 40 performance metrics, more than 40 evaluation measurement methods, and a wide array of 200 metaheuristic algorithms.
Noisy gates for simulating quantum computers

We present a novel method for simulating the noisy behavior of quantum computers, which allows to efficiently incorporate environmental effects in the driven evolution implementing the gates acting on the qubits. We show how to modify the noiseless gate executed by the computer to include any Markovian noise, hence resulting in what we will call a noisy gate. We compare our method with the IBM qiskit simulator, and show that it follows more closely both the analytical solution of the Lindblad equation as well as the behavior of a real quantum computer, where we ran algorithms involving up to 18 qubits; as such, our protocol offers a more accurate simulator for NISQ devices. The method is flexible enough to potentially describe any noise, including non-Markovian ones. The noise simulator based on this work is available as a python package at the link, https://pypi.org/project/quantum-gates.
Prediction of the neutron drip line in oxygen isotopes using quantum computation

In the noisy intermediate-scale quantum era, variational algorithms have become a standard approach to solving quantum many-body problems. Here, we present variational quantum eigensolver results of selected oxygen isotopes within the shell-model description. The aim of the present work is to locate the neutron drip line of the oxygen chain using unitary coupled cluster type Ans\"atze with different microscopic interactions (DJ16, JISP16, and N3LO), in addition to a phenomenological USDB interaction. While initially infeasible to execute on contemporary quantum hardware, the size of the problem is reduced significantly using qubit tapering techniques in conjunction with custom circuit design and optimization. The optimal values of Ansatz parameters from classical simulation are taken for the DJ16 interaction, and the tapered circuits are run on IonQ's Aria, a trapped-ion quantum computer. After applying gate error mitigation for three isotopes, we reproduced exact ground-state energies within a few percent error. The postprocessed results from hardware also clearly show $^{24}\mathrm{O}$ as the drip line nucleus of the oxygen chain. Future improvements in quantum hardware could make it possible to locate drip lines of heavier nuclei.
Nonergodic Measurements of Qubit Frequency Noise

Slow fluctuations of a qubit frequency are one of the major problems faced by quantum computers. To understand their origin it is necessary to go beyond the analysis of their spectra. We show that characteristic features of the fluctuations can be revealed using comparatively short sequences of periodically repeated Ramsey measurements, with the sequence duration smaller than needed for the noise to approach the ergodic limit. The outcomes distribution and its dependence on the sequence duration are sensitive to the nature of the noise. The time needed for quantum measurements to display quasiergodic behavior can strongly depend on the measurement parameters.
Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization

Bayesian model reduction provides an efficient approach for comparing the performance of all nested sub-models of a model, without re-evaluating any of these sub-models.Until now, Bayesian model reduction has been applied mainly in the computational neuroscience community on simple models.In this paper, we formulate and apply Bayesian model reduction to perform principled pruning of Bayesian neural networks, based on variational free energy minimization.Direct application of Bayesian model reduction, however, gives rise to approximation errors.Therefore, a novel iterative pruning algorithm is presented to alleviate the problems arising with naive Bayesian model reduction, as supported experimentally on the publicly available UCI datasets for different inference algorithms.This novel parameter pruning scheme solves the shortcomings of current state-of-the-art pruning methods that are used by the signal processing community.The proposed approach has a clear stopping criterion and minimizes the same objective that is used during training.Next to these benefits, our experiments indicate better model performance in comparison to state-of-the-art pruning schemes.
Teaching Artificial Intelligence Through the Arts in Beijing

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsBarnas MonteithBarnas Monteith (barnas@tumblehomelearning.com) and Pendred Noyce are cofounders of Tumblehome, Boston, MA. Pei Zhang is Executive Director of the US-China Scitech Education Promotion Association, Andover, MA.Pendred NoyceBarnas Monteith (barnas@tumblehomelearning.com) and Pendred Noyce are cofounders of Tumblehome, Boston, MA. Pei Zhang is Executive Director of the US-China Scitech Education Promotion Association, Andover, MA.Pei ZhangBarnas Monteith (barnas@tumblehomelearning.com) and Pendred Noyce are cofounders of Tumblehome, Boston, MA. Pei Zhang is Executive Director of the US-China Scitech Education Promotion Association, Andover, MA.
Towards Implementation of Detection and Tracking of Wild Animals

The conflicts between human and animal in regions closer to forest areas leading to destruction of resources, loss of human life and domestic animals etc. have become a subject of great concern. Advanced algorithms in object detection and visual object tracking can assist in developing proper monitoring systems that can help in reducing the causalities by providing warnings while preserving animal life. A tracking-by-detection approach is presented in this paper, detection of animals done by Yolov4 which is trained using custom image dataset for 9 specific classes. The trained model is used as descriptor in DeepSORT tracker to track the movement of animals along video frames. The detector has an mAP accuracy of 98% and the performance evaluation of the tracker is done using the benchmark dataset AnimalTrack.
Dynamic acceleration: Service robots in retail

Robotics and automation are of the most prominent technologies that will probably define how retail operates in the future. This statement is especially relevant in today’s industry conditions characterized by rising wages, severe increases in energy costs and smaller consumer spending across the world. Digitalization accelerated by the pandemic also forces retailers to turn towards technology to keep pace with their competitors. These businesses build on their dynamic capabilities to succeed. The purpose of this article is to analyse successful implementation of robotics in retail in the context of these dynamic capabilities. A key proposition we lay the foundation upon is that investing in dynamic capabilities has an overall positive impact on the implementation of robotic technologies to sustain the digital competitiveness of retail companies. Our document analysis focuses on 190 retail-based news articles about the application of service robots at retail companies from the complete news archives. Findings highlight the importance of enhancing customer experience, regardless of front-end or back-end activities all work towards this one outcome. The implementation examples also reveal that retailers rely heavily on robots in their effort to harmonize digital and physical environment. Looking at geographical differences, Asia is way ahead in front-end application of robots that contribute more directly to customer experience. Retailers in Europe and the United States follow a more balanced approach with many backend service robot applications. Having adopted a global research view, our findings are not only applicable for the whole sector but can be interpreted in a local context.
Robust Speech Processing with Fuzzy Logic-Driven Anti-Spoofing Techniques

Speech processing applications, such as speaker verification and voice authentication systems, face the challenge of spoof attacks, where adversaries attempt to deceive the system using fake speech signals. This research study proposes a robust speech processing approach using fuzzy logic-driven anti-spoofing techniques. The study explores the adaptability of fuzzy logic in modeling speech patterns and detecting spoof attempts effectively. Experimental evaluations demonstrate the superiority of the proposed method in countering spoof attacks, providing enhanced security and accuracy in speech-based authentication systems.
Accuracy assessment of satellite-based and reanalysis solar irradiance data for solar PV output forecasting using SARIMAX

Forecasting models are often constrained by data availability, and in forecasting solar photovoltaic (PV) output, the literature suggests that solar irradiance contributes the most to solar PV output. The objective of this study is to identify which between the satellite-based and reanalysis solar irradiance data, namely, short wave radiation (SWR) and surface solar radiation downward (SSRD), respectively, is a better alternative to in situ solar irradiance in forecasting solar PV output should the latter become unavailable. Nine seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models were presented in this study to assess the forecasting performance of each solar irradiance data together with weather parameters. Using only historical data to forecast solar PV output, three seasonal autoregressive integrated moving average (SARIMA) models were run to forecast solar PV output and to compare and validate the efficacy of the SARIMAX models. The analysis was divided into seasons as defined by the Philippine Atmospheric, Geophysical and Astronomical Services Administration: hot dry, rainy, and cool dry. Results show that the use of SSRD is a better alternative than SWR when forecasting solar PV output for the hot dry season and cool dry season. For the hot dry season, SSRD has an root mean square error (RMSE) value of 0.411 kW while SWR has 0.416 kW. For the cool dry season, SSRD has an RMSE value of 0.457 kW while SWR has 0.471 kW. Meanwhile, SWR outperforms SSRD when forecasting solar PV output during the rainy season, with RMSE values at 0.375 and 0.401 kW, respectively.
Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System

With the advancements in utilizing Artificial Intelligence (AI) in embedded safety-critical systems based on Field-Programmable Gate Arrays (FPGAs), assuring that these systems meet their safety requirements is of paramount importance for their revenue service. Based on this context, this paper has two main objectives. The first of them is to present the Safety ArtISt method, developed by the authors to guide the lifecycle of AI-based safety-critical systems, and emphasize its FPGA-oriented tasks and recommended practice towards safety assurance. The second one is to illustrate the application of Safety ArtISt with an FPGA-based braking control system for autonomous vehicles relying on explainable AI generated with High-Level Synthesis. The results indicate that Safety ArtISt played four main roles in the safety lifecycle of AI-based systems for FPGAs. Firstly, it provided guidance in identifying the safety-critical role of activities such as sensitivity analyses for numeric representation and FPGA dimensioning to achieve safety. Furthermore, it allowed building qualitative and quantitative safety arguments from analyses and physical experimentation with actual FPGAs. It also allowed the early detection of safety issues—thus reducing project costs—and, ultimately, it uncovered relevant challenges not discussed in detail when designing safety-critical, explainable AI for FPGAs.
An Efficient Breast Cancer Segmentation System based on Deep Learning Techniques

Breast cancer is one of the major threats that attack women around the world. Its detection and diagnosis in the early stages can greatly improve care efficiency and reduce mortality rate. Early detection of breast cancer allows medical professionals to use less intrusive treatments, such as lumpectomies or targeted medicines, improving survival rates and lowering morbidity. This study developed a breast cancer segmentation system based on an improved version of the U-Net 3+ neural network. Various optimizations were applied to this architecture to improve the localization and segmentation performance. An evaluation of different state-of-the-art networks was performed to improve the performance of the proposed breast cancer diagnosis system. Various experiments were carried out on the INbreast Full-Field Digital Mammographic dataset (INbreast FFDM). The results obtained demonstrated that the proposed model achieved a dice score of 98.47%, which is a new state-of-the-art segmentation finding, showcasing its efficiency in detecting breast cancer from mammography images with the possibility of implementation for real applications.
SPARSESAT-NERF: DENSE DEPTH SUPERVISED NEURAL RADIANCE FIELDS FOR SPARSE SATELLITE IMAGES

Abstract. Digital surface model generation using traditional multi-view stereo matching (MVS) performs poorly over non-Lambertian surfaces, with asynchronous acquisitions, or at discontinuities. Neural radiance fields (NeRF) offer a new paradigm for reconstructing surface geometries using continuous volumetric representation. NeRF is self-supervised, does not require ground truth geometry for training, and provides an elegant way to include in its representation physical parameters about the scene, thus potentially remedying the challenging scenarios where MVS fails. However, NeRF and its variants require many views to produce convincing scene’s geometries which in earth observation satellite imaging is rare. In this paper we present SparseSat-NeRF (SpS-NeRF) – an extension of Sat-NeRF adapted to sparse satellite views. SpS-NeRF employs dense depth supervision guided by crosscorrelation similarity metric provided by traditional semi-global MVS matching. We demonstrate the effectiveness of our approach on stereo and tri-stereo Pléiades 1B/WorldView-3 images, and compare against NeRF and Sat-NeRF. The code is available at https://github.com/LulinZhang/SpS-NeRF