Reviewerly/exHarmony huggingface dataset:
Research on Micro-Expression Spotting Method Based on Optical Flow Features

This paper aims to propose an automatic micro-expression spotting method of high accuracy and high robustness. Due to the characteristics of small amplitude and short duration, how to accurately capture the subtle movements of micro-expression is a complex problem. The optical flow method is applied to estimate the motion trend of the facial regions. Because the head shaking is an essential reason for the high false-positive rate of micro-expression spotting, a reliable face alignment method becomes crucial. According to the optical flow of the nose tip region, the cutting box was adjusted several times to optimize the relative position between the face and the cutting box stable. On this basis, the optical flow features from the 14 regions of interest on the face are used to build a feature matrix, and a wave peak location technology is proposed to accurately locate the moment when the micro-expression occurs on the time-domain curve of the features. The experimental results on the CAS(ME)2-cropped and the SAMM Long Videos datasets show that our method performs significantly better than the baseline method and has a high application value in various application scenarios.
Variational quantum algorithm for molecular geometry optimization

Classical algorithms for predicting the equilibrium geometry of strongly correlated molecules require expensive wave function methods that become impractical already for few-atom systems. In this work, we introduce a variational quantum algorithm for finding the most stable structure of a molecule by explicitly considering the parametric dependence of the electronic Hamiltonian on the nuclear coordinates. The equilibrium geometry of the molecule is obtained by minimizing a more general cost function that depends on both the quantum circuit and the Hamiltonian parameters, which are simultaneously optimized at each step. The algorithm is applied to find the equilibrium geometries of the ${\mathrm{H}}_{2},$ ${\mathrm{H}}_{3}{}^{+},$ ${\mathrm{BeH}}_{2},$ and ${\mathrm{H}}_{2}\mathrm{O}$ molecules. The quantum circuits used to prepare the electronic ground state for each molecule were designed using an adaptive algorithm where excitation gates in the form of Givens rotations are selected according to the norm of their gradient. All quantum simulations are performed using the PennyLane library for quantum differentiable programming. The optimized geometrical parameters for the simulated molecules show an excellent agreement with their counterparts computed using classical quantum chemistry methods.
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder

Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space.Autoencoder-based language models are appealing in dense retrieval as they train the encoder to output highquality embedding that can reconstruct the input texts.However, in this paper, we provide theoretical analyses and show empirically that an autoencoder language model with a low reconstruction loss may not provide good sequence representations because the decoder may take shortcuts by exploiting language patterns.To address this, we propose a new selflearning method that pre-trains the autoencoder using a weak decoder, with restricted capacity and attention flexibility to push the encoder to provide better text representations.Our experiments on web search, news recommendation, and open domain question answering show that our pre-trained model significantly boosts the effectiveness and few-shot ability of dense retrieval models.
High-speed secure key distribution using local polarization modulation driven by optical chaos in reciprocal fiber channel

We propose a secure key distribution (SKD) based on local polarization modulation driven by optical chaos in a reciprocal fiber link. A robust error-free SKD with a key generation rate of 4.3 Gbit/s over transmission of 10-km standard single-mode fiber is experimentally demonstrated. A chaotic laser system shared by legitimate users serves as an external wideband entropy source. The polarization reciprocity of the fiber channel provides fundamental safety against eavesdropping. The robustness of SKD resulted from local chaotic polarization modulation is also theoretically analyzed and then verified by practical performance. The proposed scheme is an alternative SKD strategy with high speed and strong security.
Technology Leadership in Malaysian Schools

Education 4.0 is the answer to the global needs for the advanced integration of humans and technology. Leading school’s technology utilization can be the way forward to support education 4.0 realization. This study aims to investigate the effects and roles of principals’ technology leadership towards teachers’ ICT utilization and students’ academic performance in secondary schools in Selangor, Malaysia. This empirical study uses a set of questionnaires to gather information from respondents who are in the teaching profession. A total of 310 questionnaires were completed and analyzed. The findings have shown significant positive impacts between the effects of the technology leadership roles of principals on teachers’ effective ICT utilization and students’ academic performance. The integration of ICT and technological tools in schools has a great challenge towards the new era of the Education 4.0 system. This suggests that principals who embrace technology will effectively lead their schools to acquire educational resources to enhance student engagement and learning.
From Banks to DeFi: the Evolution of the Lending Market

The Internet of Value (IoV) with its distributed ledger technology (DLT) underpinning has created new forms of lending markets. As an integral part of the decentralised finance (DeFi) ecosystem, lending protocols are gaining tremendous traction, holding an aggregate liquidity supply of over $40 billion at the time of writing. In this paper, we enumerate the challenges of traditional money markets led by banks and lending platforms and present advantageous characteristics of DeFi lending protocols that might help resolve deep-rooted issues in the conventional lending environment. With the examples of Maker, Compound and Aave, we describe in detail the mechanism of DeFi lending protocols. We discuss the persisting reliance of DeFi lending on the traditional financial system and conclude with the outlook of the lending market in the IoV era.
Lessons From a Second Life &gt; Before Meta, Philip Rosedale Created an Online Universe

Presents an interview conducted with Philip Rosedale who runs the start up company, High Fidelity. Reports on the challenges involved in building a virtual world and discusses the future for metaverse applications. High Fidelity produces spatial audio technology.
An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

Nowadays, recommendation systems offer a method of facilitating the user’s desire. It is useful for recommending items from a variety of areas such as in the e-commerce, medical, education, tourism, and industry domains. The e-commerce area represents the most active research we found, which assists users in locating the things they want. A recommender system can also provide users with helpful knowledge about things that could be of interest. Sometimes, the user gets bored with recommendations which are similar to their profiles, which leads to the over-specialization problem. Over-specialization is caused by limited content data, under which content-based recommendation algorithms suggest goods directly related to the customer profile rather than new things. In this study, we are particularly interested in recommending surprising, new, and unexpected items that may likely be enjoyed by users and will mitigate this limited content. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved using genetic algorithms that brings diversity to recommendations being made. This paper describes a Revolutionary Recommender System using a Genetic Algorithm called RRSGA which improves the fitness functions for recommending optimal results. The proposed approach employs a genetic algorithm to address the over-specialization issue of content-based filtering. The proposed method aims to incorporate genetic algorithms that bring variety to recommendations and efficiently adjust and suggest unpredictable and innovative things to the user. Experiments objectively demonstrate that our technology can recommend additional products that every consumer is likely to appreciate. The results of RRSGA have been compared against recommendation results from the content-based filtering approach. The results indicate the effectiveness of RRSGA and its capacity to make more accurate predictions than alternative approaches.
Blockchain for government organizations: past, present and future

Purpose Blockchain is widely applied in e-voting, shared economy areas and other government functioning. Fragmented findings and distributed literature need consolidation for a holistic view of the research domain. The purpose of this study is to comprehensively reviews the blockchain applications for government organizations and presents the past, present and future trends of blockchain applications for government organizations. Design/methodology/approach Systematic review protocol instrumentalized the systematic review of research articles published from 2013 to 2021. Science mapping discerns scientific actors’ trends and performance analysis like most influential authors, documents and sources. Content analysis of selected data set unfolds the past, present and future of blockchain applications for government organizations. Findings Blockchain technology offers enormous potential for the transformation of government organizations and public services. The primary areas are cryptocurrency, e-voting, shared economy, smart contracts, financial and health services, tourism, logistics and water sustainability. Research limitations/implications This study reviewed only published research in journals and conference proceedings and excluded book reviews, book chapters and editorials from the review set. This study persuades governments and policymakers to invest in blockchain technology for transforming government organizations and public services. Practical implications This study highlights the importance of blockchain in government-controlled public departments, enhancing transparency and efficiency in public life. Social implications Blockchain technology enhances transparency, traceability and accountability of public records. Originality/value This study pioneers in chronologically highlighting the importance of blockchain in government-controlled public departments.
Unstructured clinical notes within the 24 hours since admission predict short, mid &amp; long-term mortality in adult ICU patients

Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of electronic health records (EHR) has enabled data-driven predictive modeling using machine learning. However, very few studies rely solely on unstructured clinical notes from the EHR for mortality prediction. In this work, we propose a framework to predict short, mid, and long-term mortality in adult ICU patients using unstructured clinical notes from the MIMIC III database, natural language processing (NLP), and machine learning (ML) models. Depending on the statistical description of the patients' length of stay, we define the short-term as 48-hour and 4-day period, the mid-term as 7-day and 10-day period, and the long-term as 15-day and 30-day period after admission. We found that by only using clinical notes within the 24 hours of admission, our framework can achieve a high area under the receiver operating characteristics (AU-ROC) score for short, mid and long-term mortality prediction tasks. The test AU-ROC scores are 0.87, 0.83, 0.83, 0.82, 0.82, and 0.82 for 48-hour, 4-day, 7-day, 10-day, 15-day, and 30-day period mortality prediction, respectively. We also provide a comparative study among three types of feature extraction techniques from NLP: frequency-based technique, fixed embedding-based technique, and dynamic embedding-based technique. Lastly, we provide an interpretation of the NLP-based predictive models using feature-importance scores.
Layer-Based Communication-Efficient Federated Learning with Privacy Preservation

In recent years, federated learning has attracted more and more attention as it could collaboratively train a global model without gathering the users' raw data. It has brought many challenges. In this paper, we proposed layer-based federated learning system with privacy preservation. We successfully reduced the communication cost by selecting several layers of the model to upload for global averaging and enhanced the privacy protection by applying local differential privacy. We evaluated our system in non independently and identically distributed scenario on three datasets. Compared with existing works, our solution achieved better performance in both model accuracy and training time.
Survey: Transformer based video-language pre-training

Inspired by the success of transformer-based pre-training methods on natural language tasks and further computer vision tasks, researchers have started to apply transformer to video processing. This survey aims to provide a comprehensive overview of transformer-based pre-training methods for Video-Language learning. We first briefly introduce the transformer structure as the background knowledge, including attention mechanism, position encoding etc. We then describe the typical paradigm of pre-training & fine-tuning on Video-Language processing in terms of proxy tasks, downstream tasks and commonly used video datasets. Next, we categorize transformer models into Single-Stream and Multi-Stream structures, highlight their innovations and compare their performances. Finally, we analyze and discuss the current challenges and possible future research directions for Video-Language pre-training.
DRONET: Multi-Tasking Framework for Real-Time Industrial Facility Aerial Surveillance and Safety

The security of key and critical infrastructures is crucial for uninterrupted industrial process flow needed in strategic management as these facilities are major targets of invaders. The emergence of non-military use of drones especially for logistics comes with the challenge of redefining the anti-drone approach in determining a drone’s harmful status in the airspace based on certain metrics before countering it. In this work, a vision-based multi-tasking anti-drone framework is proposed to detect drones, identifies the airborne objects, determines its harmful status through perceived threat analysis, and checks its proximity in real-time prior to taking an action. The model is validated using manually generated 5460 drone samples from six (6) drone models under sunny, cloudy, and evening scenarios and 1709 airborne objects samples of seven (7) classes under different environments, scenarios (blur, scales, low illumination), and heights. The proposed model was compared with seven (7) other object detection models in terms of accuracy, sensitivity, F1-score, latency, throughput, reliability, and efficiency. The simulation result reveals that, overall, the proposed model achieved superior multi-drone detection accuracy of 99.6%, attached object identification of sensitivity of 99.80%, and F1-score of 99.69%, with minimal error, low latency, and less computational complexity needed for effective industrial facility aerial surveillance. A benchmark dataset is also provided for subsequent performance evaluation of other object detection models.
Solar park detection from publicly available satellite imagery

The rapid increase in large-scale photovoltaic installations, or solar parks, causes a need to monitor their amount and allocation, and assess their impacts. While their spectral signature suggests that solar parks can be identified among other land covers, this detection is challenged by their low occurrence. Here, we develop an object-based random forest (RF) classification approach, using publicly available satellite imagery, which has the advantage of requiring relatively little training data and being easily extendable to large spatial extents and new areas. First, we segmented Sentinel-2 imagery into homogenous objects using a Simple Non-Iterative Clustering algorithm in Google Earth Engine. Thereafter, we calculated for each object the mean, standard deviation, and median for all 10- and 20-meter resolution bands of Sentinel-1 and Sentinel-2. These features are subsequently used to train and validate a range of RF models to select the most promising model setup. The training datasets consisted of subsampled presence/absence data, oversampled presence/absence data, and multiple land-cover categories. The best-performing model used an oversampled dataset trained on all 10- and 20- meter resolution spectral bands and the radar backscatter properties of one period. Independent test results show an overall classification accuracy of 99.97% (Kappa: 0.90). For this result, the producer accuracy was 85.86% for solar park objects and 99.999% for non-solar park objects. The user accuracy was 92.39% for solar park objects and 99.999% for non-solar park objects. These high classification accuracies indicate that our approach is suitable for transfer learning and is able to detect solar parks in new study areas.
3D chaotic map-cosine transformation based approach to video encryption and decryption

Abstract Data security is vital for multimedia communication. A number of cryptographic algorithms have been developed for the secure transmission of text and image data. Very few contributions have been made in the area of video encryption because of the large input data size and time constraints. However, due to the massive increase in digital media transfer within networks, the security of video data has become one of the most important features of network reliability. Block encryption techniques and 1D-chaotic maps have been previously used for the process of video encryption. Although the results obtained by using 1D-chaotic maps were quite satisfactory, the approach had many limitations as these maps have less dynamic behavior. To overcome these drawbacks, this article proposes an Intertwining Logistic Map (ILM)-Cosine transformation-based video encryption technique. The first step involved segmenting the input video into multiple frames based on the frames per second (FPS) value and the length of the video. Next, each frame was selected, and the correlation among the pixels was reduced by a process called permutation/scrambling. In addition, each frame was rotated by 90° in the anticlockwise direction to induce more randomness into the encryption process. Furthermore, by using an approach called the random order substitution technique, changes were made in each of the images, row-wise and column-wise. Finally, all the encrypted frames were jumbled according to a frame selection key and were joined to generate an encrypted video, which was the output delivered to the user. The efficiency of this method was tested based on the state of various parameters like Entropy, Unified Average Change in Intensity (UACI), and correlation coefficient (CC). The presented approach also decrypts the encrypted video, and the decryption quality was checked using parameters such as mean square error (MSE) and peak signal-to-noise ratio (PSNR).
Quantum activation functions for quantum neural networks

Abstract The field of artificial neural networks is expected to strongly benefit from recent developments of quantum computers. In particular, quantum machine learning, a class of quantum algorithms which exploit qubits for creating trainable neural networks, will provide more power to solve problems such as pattern recognition, clustering and machine learning in general. The building block of feed-forward neural networks consists of one layer of neurons connected to an output neuron that is activated according to an arbitrary activation function. The corresponding learning algorithm goes under the name of Rosenblatt perceptron. Quantum perceptrons with specific activation functions are known, but a general method to realize arbitrary activation functions on a quantum computer is still lacking. Here, we fill this gap with a quantum algorithm which is capable to approximate any analytic activation functions to any given order of its power series. Unlike previous proposals providing irreversible measurement–based and simplified activation functions, here we show how to approximate any analytic function to any required accuracy without the need to measure the states encoding the information. Thanks to the generality of this construction, any feed-forward neural network may acquire the universal approximation properties according to Hornik’s theorem. Our results recast the science of artificial neural networks in the architecture of gate-model quantum computers.
A flying Schrödinger’s cat in multipartite entangled states

Schrödinger's cat originates from the famous thought experiment querying the counterintuitive quantum superposition of macroscopic objects. As a natural extension, several "cats" (quasi-classical objects) can be prepared into coherent quantum superposition states, which is known as multipartite cat states demonstrating quantum entanglement among macroscopically distinct objects. Here, we present a highly scalable approach to deterministically create flying multipartite Schrödinger's cat states by reflecting coherent-state photons from a microwave cavity containing a superconducting qubit. We perform full quantum state tomography on the cat states with up to four photonic modes and confirm the existence of quantum entanglement among them. We also witness the hybrid entanglement between discrete-variable states (the qubit) and continuous-variable states (the flying multipartite cat) through a joint quantum state tomography. Our work provides an enabling step for implementing a series of quantum metrology and quantum information processing protocols based on cat states.